green economics research paper

• Open access
• Published: 22 June 2020

The green economy transition: the challenges of technological change for sustainability

• Patrik Söderholm   ORCID: orcid.org/0000-0003-2264-7043 1  

Sustainable Earth volume  3 , Article number:  6 ( 2020 ) Cite this article

118k Accesses

147 Citations

66 Altmetric

Metrics details

The Green Economy is an alternative vision for growth and development; one that can generate economic development and improvements in people’s lives in ways consistent with advancing also environmental and social well-being. One significant component of a green economy strategy is to promote the development and adoption of sustainable technologies. The overall objective of this article is to discuss a number of challenges encountered when pursuing sustainable technological change, and that need to be properly understood by policy makers and professionals at different levels in society. We also identify some avenues for future research. The discussions center on five challenges: (a) dealing with diffuse – and ever more global – environmental risks; (b) achieving radical and not just incremental sustainable technological change; (c) green capitalism and the uncertain business-as-usual scenario; (d) the role of the state and designing appropriate policy mixes; and (e) dealing with distributional concerns and impacts. The article argues that sustainable technological change will require a re-assessment of the roles of the private industry and the state, respectively, and that future research should increasingly address the challenges of identifying and implementing novel policy instrument combinations in various institutional contexts.

The green economy transition and sustainable technological change

Over the last decade, a frequent claim has been that the traditional economic models need to be reformed in order to address climate change, biodiversity losses, water scarcity, etc., while at the same time addressing key social and economic challenges. The global financial crisis in 2008–2009 spurred this debate [ 4 ], and these concerns have been translated into the vision of a ‘green economy’ (e.g., [ 31 , 33 , 48 , 54 , 55 ]). Furthermore, in 2015, countries world-wide adopted the so-called 2030 Agenda for Sustainable Development and its 17 Sustainable Development Goals. These goals recognize that ending world poverty must go hand-in-hand with strategies that build economic growth but also address a range of various social needs including education, health, social protection, and job creation, while at the same time tackling environmental pollution and climate change. The sustainable development goals thus also establish a real link between the ecological system and the economic system. They also reinforce the need for a transition to a green economy, i.e., a fundamental transformation towards more sustainable modes of production and consumption.

In this article, we focus on a particularly important component of such a transition, namely the development of sustainable technological change, i.e., production and consumption patterns implying profoundly less negative impacts on the natural environment, including the global climate. Specifically, the article addresses a number of key challenges in supporting – and overcoming barriers to – sustainable technological change. These challenges are presented with the ambition to communicate important lessons from academic research to policy makers and professionals as well as the general public.

Addressing climate and environmental challenges, clearly requires natural scientific knowledge as well as engineering expertise concerning the various technical solutions that can be adopted to mitigate the negative impacts (e.g., carbon-free energy technologies). However, pursuing sustainable technological change is also a societal, organizational, political, and economic endeavor that involves several non-technical challenges. For instance, the so-called transitions literature recognizes that many sectors, such as energy generation, water supply etc., can be conceptualized as socio-technical systems and/or innovation systems [ 24 , 40 ]. These systems consist of networks of actors (individuals, private firms, research institutes, government authorities, etc.), the knowledge that these actors possess as well as the relevant institutions (legal rules, codes of conduct, etc.). In other words, the development of, for instance, new carbon-free technologies may often require the establishment of new value chains hosting actors that have not necessarily interacted in the past; this necessitates a relatively long process that can alter society in several ways, e.g., through legal amendments, changed consumer behavior, distributional effects, infrastructure development and novel business models.

In other words, beyond technological progress, economic and societal adjustment is necessary to achieve sustainable technological change. In fact, history is full of examples that illustrate the need to address the organizational and institutional challenges associated with technological change and innovation. In hindsight, the societal impacts of electricity in terms of productivity gains were tremendous during the twentieth century. Still, while electrical energy was discovered in the late 1870s, in the year 1900, less than 5% of mechanical power in American factories was supplied by electric motors and it took yet another 20 years before their productivity soared [ 14 ]. An important reason for the slow diffusion of electric power was that in order to take full advantage of the new technology, existing factories had to change the entire systems of operation, i.e., the production process, the architecture, the logistics as well as the ways in which workers were recruited, trained and paid. Footnote 1 A similar story emerges when considering the impact of computers on total productivity during the second half of the twentieth century. For long, many companies invested in computers for little or no reward. Also in this case, however, the new technology required systemic changes in order for companies to be able to take advantage of the computer. This meant, for instance, decentralizing, outsourcing, and streamlining supply chains as well as offering more choices to consumers [ 9 ].

This key argument that the adoption of new technology has to be accompanied by systemic changes, applies both to the company as well as the societal level. Any novel solutions being developed must take into account the complexity of the interdependencies between different types of actors with various backgrounds, overall market dynamics, as well as the need for knowledge development and institutional reforms. In fact, the need for systemic changes may be particularly relevant in the case of green technologies, such as zero-carbon processes in the energy-intensive industries (see further below).

Against this background, the issue of how to promote sustainable technological change has received increasing attention in the policy arena and in academic research. The main objective of this article is therefore to discuss some of the most significant societal challenges in pursuing such change, and outline key insights for policy makers as well as important avenues for future research. In doing this, we draw on several strands of the academic literature. The article centers on the following five overall challenges:

Dealing with diffuse – and ever more global – environmental risks

Achieving radical – and not just incremental – sustainable technological change;

The advent of green capitalism: the uncertain business-as-usual scenario

The role of the state: designing appropriate policy mixes, dealing with distributional concerns and impacts.

The first two challenges address the various types of structural tasks that are required to pursue sustainable technological change, and the barriers that have to be overcome when pursuing these tasks. The remaining points concern the role and the responsibility of different key actors in the transition process, not least private firms and government authorities. Each of these five challenges in turn involves more specific challenges, and these are identified and elaborated under each heading. We also provide hints about how to address and manage these challenges, but specific solutions will likely differ depending on the national or regional contexts. The paper concludes by briefly outlining some key avenues for future research, and with an emphasis on research that can assist a green socio-technical transition. Footnote 2

With the advent of modern environmental policy in the 1960s, stringent regulations were imposed on emissions into air and water. However, the focus was more or less exclusively on stationary pollution sources (i.e., industrial plants), which were relatively easy to monitor and regulate, e.g., through plant-specific emission standards. In addition, during this early era there was a strong emphasis on local environmental impacts, e.g., emissions into nearby river basins causing negative effects on other industries and/or on households in the same community.

Over the years, though, the environmental challenges have increasingly been about targeting various types of diffuse emissions. These stem from scattered sources such as road transport, shipping, aviation, and agriculture. Pollution from diffuse sources takes place over large areas and individually they may not be of concern, but in combination with other diffuse sources they can cause serious overall impacts. The growing importance of global environmental challenges such as climate change in combination with globalization and more international trade in consumer products, adds to this challenge. Managing these issues often requires international negotiations and burden-sharing, which in itself have proved difficult [ 12 ]. The difficulties in reaching a stringent-enough global climate agreement illustrate this difficulty.

Diffuse emissions are typically difficult to monitor and therefore also to regulate. For instance, environmental authorities may wish to penalize improper disposal of a waste product since this would help reduce various chemical risks, but such behavior is typically clandestine and difficult to detect. Plastic waste is an apt example; it stems from millions of consumer products, is carried around the world by the currents and winds, and builds up microplastics, particularly in the sea. Many dangerous substances, including chemicals such as solvents and phthalates, are embedded in consumer products, out of which many are imported. Monitoring the potential spread of these substances to humans and the natural environment remains difficult as well. Technological innovation that permits better tracing and tracking of materials should therefore be a priority (see also [ 21 ]).

In order to address these diffuse environmental impacts, society has to find alternative – yet more indirect – ways of monitoring and regulating them. This could translate into attempts to close material cycles and promote a circular economy, i.e., an economy in which the value of products, materials and resources are maintained as long as possible [ 19 ]. In practice, this implies an increased focus on reduction, recycling and re-use of virgin materials [ 30 ], material and energy efficiency, as well as sharing of resources (often with the help of various digital platforms such as Uber and Airbnb). In other words, rather than regulating emissions as close to damage done as possible, the authorities may instead support specific activities (e.g., material recycling) and/or technologies (e.g., low-carbon production processes) that can be assumed to correlate with reduced environmental load.

Addressing diffuse emissions in such indirect ways, though, is not straightforward. In several countries, national waste management strategies adhere to the so-called waste hierarchy (see also the EU Waste Framework Directive). This sets priorities for which types of action should be taken, and postulates that waste prevention should be given the highest priority followed by re-use of waste, material recycling, recovery of waste and landfill (in that order). Even though research has shown that this hierarchy is a reasonable rule of thumb from an environmental point of view [ 42 ], it is only a rule of thumb! Deviations from the hierarchy can be motivated in several cases and must therefore be considered (e.g., [ 58 ]). Footnote 3

One important way of encouraging recycling and reuse of products is to support product designs that factor in the reparability and reusability of products. Improved recyclability can also benefit from a modular product structure (e.g., [ 20 ]). However, this also comes with challenges. Often companies manufacture products in such ways that increase the costs of recycling for downstream processors, but for institutional reasons, there may be no means by which the waste recovery facility can provide the manufacturer with any incentives to change the product design [ 11 , 46 ]. One example is the use of multi-layer plastics for food packaging, which could often be incompatible with mechanical recycling.

While the promotion of material and energy efficiency measures also can be used to address the problem of diffuse environmental impacts, it may be a mixed blessing. Such measures imply that the economy can produce the same amount of goods and services but with less material and energy inputs, but they also lead to a so-called rebound effect [ 27 ]. Along with productivity improvements, resources are freed and can be used to increase the production and consumption of other goods. In other words, the efficiency gains may at least partially be cancelled out by increased consumption elsewhere in the economy. For instance, if consumers choose to buy fuel-efficient cars, they are able to travel more or spend the money saved by lower fuel use on other products, which in turn will exploit resources and lead to emissions.

Finally, an increased focus on circular economy solutions will imply that the different sectors of the economy need to become more interdependent. This interdependency is indeed what makes the sought-after efficiency gains possible in the first place. This in turn requires new forms of collaborative models among companies, including novel business models. In some cases, though, this may be difficult to achieve. One example is the use of excess heat from various process industries; it can be employed for supplying energy to residential heating or greenhouses. Such bilateral energy cooperation is already quite common (e.g., in Sweden), but pushing this even further may be hard and/or too costly. Investments in such cooperation are relation-specific [ 60 ], i.e., their returns will depend on the continuation of the relationships. The involved companies may be too heterogeneous in terms of goals, business practices, planning horizons etc., therefore making long-term commitment difficult. Moreover, the excess heat is in an economic sense a byproduct, implying that its supply will be constrained by the production of the main product. Of course, this is valid for many other types of waste products as well, e.g., manure digested to generate biogas, secondary aluminum from scrapped cars.

In brief, the growing importance of addressing diffuse emissions into the natural environment implies that environmental protection has to build on indirect pollution abatement strategies. Pursuing each of these strategies (e.g., promoting recycling and material efficiency), though, imply challenges; they may face important barriers (e.g., for product design, and byproduct use) and could have negative side-effects (e.g., rebound effects). Moreover, a focus on recycling and resource efficiency must not distract from the need to improve the tracing and tracking of hazardous substances and materials as well as provide stronger incentives for product design. Both technological and organizational innovations are needed.

Achieving radical – and not just incremental – sustainable technological change

Incremental innovations, e.g., increased material and energy efficiency in existing production processes, are key elements for the transition to a green economy. However, more profound – and even radical – technological innovation is also needed. For instance, replacing fossil fuels in the transport sector as well as in iron and steel production requires fundamental technological shifts and not just incremental efficiency improvements (e.g., [ 1 ]). There are, however, a number of factors that will make radical innovation inherently difficult. Below, we highlight three important obstacles.

First , one obstacle is the risk facing firms that invest in technological development (e.g., basic R&D, pilot tests etc.) in combination with the limited ability of the capital market to handle the issue of long-term risk-taking. These markets may fail to provide risk management instruments for immature technology due to a lack of historical data to assess risks. There are also concerns that the deregulation of the global financial markets has implied that private financial investors take a more short-term view [ 44 ]. In fact, research also suggests that due to agency problems within private firms, their decision-making may be biased towards short-term payoffs, thus resulting in myopic behavior also in the presence of fully efficient capital markets [ 53 ].

Second , private investors may often have weak incentives to pursue investments in long-term technological development. The economics literature has noted the risks for the under-provision of public goods such as the knowledge generated from R&D efforts and learning-by-doing (e.g., [ 38 ]). Thus, private companies will be able to appropriate only a fraction of the total rate-of-return on such investment, this since large benefits will also accrue to other companies (e.g., through reverse engineering). Due to the presence of such knowledge spillovers, investments in long-term technological development will become inefficient and too modest.

Third , new green technologies often face unfair competition with incumbent technologies. The incumbents, which may be close substitutes to their greener competitors, will be at a relative competitive advantage since they have been allowed to expand during periods of less stringent environmental policies as well as more or less tailor-made institutions and infrastructures. This creates path-dependencies, i.e. where the economy tends to be locked-in to certain technological pathways [ 2 ]. In general, companies typically employ accumulated technology-specific knowledge when developing new products and processes, and technology choices tend to be particularly self-reinforcing if the investments are characterized by high upfront costs and increasing returns from adoption (such as scale, learning and network economies). Existing institutions, e.g., laws, codes of conduct, etc., could also contribute to path dependence since these often favor the incumbent (e.g., fossil-fuel based) technologies [ 57 ].

The above three factors tend to inhibit all sorts of long-run technological development in the private sector, but there is reason to believe that they could be particularly troublesome in the case of green technologies. First, empirical research suggests that green technologies (e.g., in energy and transport) generate large knowledge spillovers than the dirtier technologies they replace [ 15 , 49 ]. Moreover, while the protection of property rights represents one way to limit such spillovers, the patenting system is subject to limitations. For instance, Neuhoff [ 43 ] remarks that many sustainable technologies:

“consist of a large set of components and require the expertise of several firms to improve the system. A consortium will face difficulties in sharing the costs of ‘learning investment’, as it is difficult to negotiate and fix the allocation of future profits,” (p. 98).

These are generally not favorable conditions for effective patenting. Process innovations, e.g., in industry, are particularly important for sustainable technology development, but firms are often more likely to employ patents to protect new products rather than new processes [ 39 ]. Footnote 4

Furthermore, one of the key socio-technical systems in the green economy transition, the energy system, is still today dominated by incumbent technologies such as nuclear energy and fossil-fueled power, and exhibits several characteristics that will lead to path dependent behavior. Investments are often large-scale and exhibit increasing returns. Path dependencies are also aggravated by the fact that the outputs from different energy sources – and regardless of environmental performance – are more or less perfect substitutes. In other words, the emerging and carbon-free technologies can only compete on price with the incumbents, and they therefore offer little scope for product differentiation. In addition, the energy sectors are typically highly regulated, thus implying that existing technological patterns are embedded in and enforced by a complex set of institutions as well as infrastructure.

In brief, technological change for sustainability requires more radical technological shifts, and such shifts are characterized by long and risky development periods during which new systemic structures – i.e., actor networks, value chains, knowledge, and institutions – need to be put in place and aligned with the emerging technologies. Overall, the private sector cannot alone be expected to generate these structures, and for this reason, some kind of policy support is needed. Nevertheless, in order for any policy instrument or policy mix to be efficient, it has to build on a proper understanding of the underlying obstacles for long-run technological development. As different technologies tend to face context-specific learning processes, patenting prospects, risk profiles etc., technology-specific support may be needed (see also below).

At least since the advent of the modern environmental debate during the 1960s, economic and environmental goals have been perceived to be in conflict with each other. Business decisions, it has been argued, build on pursuing profit-maximization; attempts to address environmental concerns simultaneously will therefore imply lower profits and reduced productivity. However, along with increased concerns about the environmental footprints of the global economy and the growth of organic products and labels, material waste recycling, climate compensation schemes etc., sustainability issues have begun to move into the mainstream business activities. In fact, many large companies often no longer distinguish between environmental innovation and innovation in general; the environmental footprints of the business operations are almost always taken into consideration during the innovation process (e.g., [ 47 ]).

Some even puts this in Schumpeterian terms, and argues that sustainable technological change implies a “new wave of creative destruction with the potential to change fundamentally the competitive dynamics in many markets and industries,” ([ 37 ], p. 315). The literature has recognized the potentially important roles that so-called sustainability entrepreneurs can play in bringing about a shift to a green economy; these types of entrepreneurs seek to combine traditional business practices with sustainable development initiatives (e.g., [ 25 ]). They could disrupt established business models, cultures and consumer preferences, as well as help reshape existing institutions. Just as conventional entrepreneurs, they are agents of change and offer lessons for policy makers. However, the research in this field has also been criticized for providing a too strong focus on individual success stories, while, for instance, the institutional and political factors that are deemed to also shape the priorities made by these individuals tend to be neglected (e.g., [ 13 ]).

Ultimately, it remains very difficult to anticipate how far voluntary, market-driven initiatives will take us along the long and winding road to the green economy. In addition to a range of incremental developments, such as increased energy and material efficiency following the adoption of increased digitalization, industrial firms and sustainability entrepreneurs are likely to help develop new and/or refined business models (e.g., to allow for increased sharing and recycling of resources) as well as adopt innovations commercially. In the future, businesses are also likely to devote greater attention to avoiding future environmental liabilities, such as the potential costs of contaminated land clean-up or flood risks following climate change. Far from surprising, large insurance companies were among the first to view climate change as a risk to their viability. One response was the development of new financial instruments such as ‘weather derivatives’ and ‘catastrophe bonds’ [ 35 ].

In other words, there is an increasing demand for businesses that work across two logics that in the past have been perceived as incompatible: the commercial and the environmental. There are however huge uncertainties about the scope and the depth of green capitalism in this respect. Moreover, the answer to the question of how far the market-driven sustainability transition will take us, will probably vary depending on business sector and on factors such as the availability of funding in these sectors. Footnote 5

As indicated above, there are reasons to assume that in the absence of direct policy support, businesses will not be well-equipped to invest in long-term green technology development. Green product innovations may often be easier to develop and nurture since firms then may charge price premiums to consumers. In fact, many high-profile sustainability entrepreneurs in the world (e.g., Anita Roddick of The Body Shop) have been product innovators. In contrast, green process innovation is more difficult to pursue. It is hard to get consumers to pay premiums for such innovations. For instance, major efforts are needed to develop a carbon-free blast furnace process in modern iron and steel plants (e.g., [ 1 ]). And even if this is achieved, it remains unclear whether the consumers will be willing to pay a price premium on their car purchases purely based on the knowledge that the underlying production process is less carbon-intense than it used to be. Moreover, taking results from basic R&D, which appear promising on the laboratory scale, through “the valley of death” into commercial application is a long and risky journey. Process innovations typically require gradual up-scaling and optimization of the production technologies (e.g., [ 29 ]). For small- and medium-sized firms in particular, this may be a major hurdle.

In brief, the above suggests that it is difficult to anticipate what a baseline scenario of the global economy – i.e., a scenario involving no new policies – would look like from a sustainability perspective. Still, overall it is likely that green capitalism and sustainability entrepreneurship alone may have problems delivering the green economy transition in (at least) two respects. First, due to the presence of knowledge spillovers and the need for long-term risk-taking, the baseline scenario may involve too few radical technology shifts (e.g., in process industries). Second, the baseline scenario is very likely to involve plenty of digitalization and automation, in turn considerably increasing the potential for material and energy efficiency increases. Nevertheless, due to rebound effects, the efficiency gains resulting from new technologies alone may likely not be enough to address the sustainability challenge. This therefore also opens up the field for additional policy support, and – potentially – a rethinking of the role of the state in promoting sustainable technological change.

An important task for government policy is to set the appropriate “framework conditions” for the economy. This refers primarily to the legal framework, e.g., immaterial rights, licensing procedures, as well as contract law, which need to be predictable and transparent. Traditional environmental policy that regulates emissions either through taxes or performance standards will remain important, as will the removal of environmentally harmful subsidies (where such exist). The role of such policies is to make sure that the external costs of environmental pollution are internalized in firms’ and households’ decision-making (e.g., [ 7 ]). Still, in the light of the challenges discussed above – i.e., controlling diffuse emissions, the need for more fundamental sustainable technological change, as well as the private sector’s inability to adequately tackle these two challenges – the role of the state must often go beyond providing such framework conditions. In fact, there are several arguments for implementing a broader mix of policy instruments in the green economy.

In the waste management field, policy mixes may be needed for several reasons. For instance, previous research shows that in cases where diffuse emissions cannot be directly controlled and monitored, a combined output tax and recycling subsidy (equivalent to a deposit-refund system) can be an efficient second-best policy instrument mix (e.g., [ 59 ]). This would reduce the amount of materials entering the waste stream, while the subsidy encourages substitution of recycled materials for virgin materials. Footnote 6 An extended waste management policy mix could also be motivated by the limited incentives for manufacturers of products to consider product design and recyclability, which would decrease the costs of downstream recycling by other firms. This is, though, an issue that often cannot be addressed by traditional policies such as taxes and standards; it should benefit from technological and organizational innovation. Finally, the establishment of efficient markets for recycled materials can also be hampered by different types of information-related obstacles, including byers’ inability to assess the quality of mixed waste streams. In such a case, information-based policies based on, for instance, screening requirements at the waste sites could be implemented (e.g., [ 46 ]).

At a general level, fostering green technological development, not least radical innovation, must also build on a mix of policies. The literature has proposed an innovation policy mix based on three broad categories of instruments (see also [ 36 , 51 , 52 ]):

Technology-push instruments that support the provision of basic and applied knowledge inputs, e.g., through R&D grants, patent protection, tax breaks etc.

Demand-pull instruments that encourage the formation of new markets, e.g., through deployment policies such as public procurement, feed-in tariffs, quotas, etc.

Systemic instruments that support various functions operating at the innovation system level, such as providing infrastructure, facilitating alignment among stakeholders, and stimulating the development of goals and various organizational solutions.

A key role for a green innovation policy is to support the development of generic technologies that entrepreneurial firms can build upon [ 50 ]. Public R&D support and co-funding of pilot and demonstration plants help create variation and permit new inventions to be verified, optimized and up-scaled. As noted above, there is empirical support for public R&D funding of green technology development, as underinvestment due to knowledge spillovers might be particularly high for these technologies.

As the technology matures, though, it must be tested in a (niche) market with real customers, and the state will often have to create the conditions for private firms to raise long-term funding in areas where established financial organizations are not yet willing to provide sufficient funds. For instance, in the renewable energy field, this has been achieved by introducing feed-in tariffs or quota schemes for, for instance, wind power and solar PV technology (e.g., [ 16 ]). Finally, well-designed systemic instruments will have positive impacts on the functioning of the other instruments in the policy mix; while technology-push and demand-pull instruments are the engines of the innovation policy mix, the systemic instruments will help that engine run faster and more efficiently.

The implementation of the above policy mixes will be associated with several challenges, such as gaining political acceptability, identifying the specific designs of the policy instruments, and determining how these instruments can be evaluated. All these issues deserve attention in future research. Still, here we highlight in particular the need for policies that are technology-specific; i.e., in contrast to, for instance, pollution taxes or generic R&D subsidies they promote selected technological fields and/or sectors. Based on the above discussions one can point out two motives for relying on technology-specific instruments in promoting sustainable technological change: (a) the regulations of diffuse emissions can often not target diffuse emissions directly – at least not without incurring excessively high monitoring costs; and (b) the need to promote more radical environmental innovations.

The innovation systems surrounding green energy technology tend to be technology-specific. Different technologies are exposed to unique and multi-dimensional growth processes, e.g., in terms of bottlenecks, learning processes, and the dynamics of the capital goods industries [ 34 ]. The nature of the knowledge spillovers and the long-term risks will also differ as will the likelihood that green technologies suffer from technological lock-in associated with incumbent technology (e.g., [ 38 ]). For instance, the technological development process for wind power has been driven by turbine manufacturers and strong home markets, while equipment suppliers and manufacturers that own their own equipment have dominated solar PV development [ 32 ].

Clearly, technology-specific policies are difficult to design and implement; regulators typically face significant information constraints and their decisions may also be influenced by politico-economic considerations such as bureaucratic motives, and lobby group interests. Moreover, the prospects for efficient green technology-specific policies may likely also differ across jurisdictions; some countries will be more likely to be able to implement policies that can live up to key governing principles such as accountability, discipline and building on arms-length interactions with the private sector. As noted by Rodrik [ 50 ], “government agencies need to be embedded in, but not in bed with, business,” (p. 485).

The above begs the question whether the governance processes at the national and the supra-national levels (e.g., the EU) are in place to live up to a more proactive and transformative role for the state. Newell and Paterson [ 45 ] argue that such a state needs to balance two principles that have for long been seen as opposed to one another. These are, one the one hand, the empowerment of the state to actively determine priorities and, on the other, “providing citizens with more extensive opportunities to have a voice, to get more involved in decision-making processes, and to take on a more active role in politics,” (p. 209). The latter issue is further addressed also in the next section.

In brief, the climate and environmental challenges facing society today require a mix of policy instruments, not least because the barriers facing new sustainable technology are multi-faceted and often heterogeneous across technologies. Supporting green innovation should build on the use of technology-specific policies as complements to traditional environmental policies. This in itself poses a challenge to policy-making, and requires in-depth understanding of how various policy instruments interact as well as increased knowledge about the institutional contexts in which these instruments are implemented.

The transition to a green economy, including technological change, affects the whole of society. It is therefore necessary to not only optimize the performance of the new technologies and identify efficient policies; the most significant distributional impacts of technological change must also be understood and addressed. All societal changes involve winners and losers, and unless this is recognized and dealt with, the sought-after green transition may lack in legitimacy across various key groups in society. Bek et al. [ 6 ] provide an example of a green economy initiative in South Africa – the so-called Working for Water (WfW) program – that has failed to fully recognize the social aspects of the program goals.

This challenge concerns different dimensions of distributional impacts. One such dimension is how households with different income levels are affected. Economics research has shown that environmental policies in developed countries, not least taxes on pollution and energy use, tend to have regressive effects [ 22 ], thus implying that the lowest-income households are generally most negatively affected in relative terms. Such outcomes may in fact prevail also in the presence of policies that build on direct support to certain technological pathways. For instance, high-income households are likely to benefit the most from subsidies to solar cells and electric cars, this since these households are more likely to own their own house as well as to be more frequent car buyers. Of course, technological change (e.g., digitalization, automation etc.), including that taking place in green technology, may also have profound distributional impacts in more indirect ways, not the least through its impacts on the labor market (e.g., wages. Work conditions) (e.g., [ 3 ]).

The regional dimension of sustainable development is also important (e.g., [ 26 ]). One challenge in this case is that people increasingly expect that any green investments taking place in their own community (e.g., in wind power) should promote regional growth, employment and various social goals. The increased emphasis on the distributional effects at the regional level can also be attributed to the growing assertion of the rights of people (e.g., indigenous rights), and increased demands for direct participation in the relevant decision-making processes. However, new green technology may fail to generate substantial positive income and employment impacts at the local and regional level. For instance, one factor altering the renewable energy sector’s relationship with the economy has been technological change. A combination of scale economies and increased capital intensity has profoundly increased the investment capital requirements of facilities such as wind mill parks and biofuel production facilities. The inputs into modern green energy projects increasingly also have to satisfy high standards in terms of know-how, and these can therefore not always be supplied by local firms (e.g., [ 18 ]). Indeed, with the implementation of digital technology, the monitoring of, say, entire wind farms can today be done by skilled labor residing in other parts of the country (or even abroad).

Ignoring the distributional effects of sustainable technological change creates social tensions, thereby increasing the business risks for companies and sustainability entrepreneurs. Such risks may come in many forms. For instance, reliability in supply has become increasingly important, and customers will generally not be very forgiving in the presence of disruptions following the emergence of tense community relations. Furthermore, customers, fund managers, banks and prospective employees do not only care about the industry’s output, but increasingly also about how the products have been produced.

In fact, while the economies of the world are becoming more integrated, political trends are pointing towards a stronger focus on the nation state and even on regional independence. If anything, this will further complicate the green economy transition. Specifically, it will need to recognize the difficult trade-offs between efficiency, which typically do require international coordination (e.g., in terms of policy design, and R&D cooperation), and a fair distribution of benefits and costs, which instead tends to demand a stronger regional and local perspective.

In brief, the various distributional effects of sustainable technological change deserve increased attention in both scholarly research and the policy domain in order to ensure that this change emerges in ways that can help reduce poverty and ensure equity. These effects may call for an even broader palette of policies (e.g., benefit-sharing instruments, such as regional or local natural resource funds, compensation schemes, or earmarked tax revenues), but they also call for difficult compromises between efficiency and fairness.

Conclusions and avenues for future research

The scope and the nature the societal challenges that arise as a consequence of the climate and environmental hazards are complex and multi-faceted, and in this article we have focused on five important challenges to sustainable technological change. These challenges are generic, and should be a concern for most countries and regions, even though the specific solutions may differ depending on context. In this final section, we conclude by briefly discussing a number of implications and avenues for future research endeavors. Footnote 7 These knowledge gaps may provide important insights for both the research community as well as for policy makers and officials.

It should be clear that understanding the nature of – as well as managing – socio-technical transitions represents a multi-disciplinary research undertaking. Collaborations between natural scientists and engineers on the one hand and social scientists on the other are of course needed to translate environmental and technical challenges into societal challenges and action. In such collaborative efforts, however, it needs to be recognized that technological change is not a linear process; it entails phases such as concept development, pilot and demonstration projects, market formation and diffusion of technology, but also with important iterations (i.e., feedback loops) among all of these phases. It should be considered how bridges between different technical and social science disciplines can be built, this in order to gain a more in-depth understanding of how technology-specific engineering inventions can be commercialized in various institutional contexts. Transition studies, innovation and environmental economics, as well the innovation system and the innovation management literatures, among others, could help provide such bridges. Other types of systems studies, e.g., energy system optimization modeling, will also be important.

In addition to the above, there should also be an expanded role for cross-fertilization among different social sciences, e.g., between the economics, management and political science fields and between the research on sustainability entrepreneurs and transition studies (see also [ 26 ]). This could help improve the micro-foundations of, for instance, innovation system studies, i.e., better understanding of companies’ incentives, drivers etc., but also stress the need for considering socio-technical systems in the management research. For instance, the focus on individual heroes that pervades much of the entrepreneurship literature may lead to a neglect of the multiple factors at work and the role of framework conditions such as institutions (e.g., legal rules, norms) and infrastructure at the national and local scales. Better integration of various conceptual perspectives on green business and innovation could generate less uncertain business-as-usual scenarios.

The discussions in this article also suggest that green innovation in the public sector should be devoted more attention in future research. This could, of course, focus on various institutional and organizational innovations in the form of new and/or revised policy instrument design. The challenges involved in designing and implementing technology-specific sustainability policies, typically referred to as green industrial policies [ 50 ], tend to require such innovation (e.g., to increase transparency, and avoid regulatory capture). These policies are essentially processes of discovery, both by the state and the industry, rather than a list of specific policy instruments. This implies learning continuously about where the constraints and opportunities lie, and then responding to these.

The risk associated with regulatory capture is one issue that deserves increased attention in future research, including how to overcome such risks. Comparisons of green industrial policies across countries and technological fields – as well as historical comparative studies – could prove useful (e.g., [ 8 ]). How different policies interact as well as what the appropriate level of decision-making power is, are also important questions to be addressed. Of course, given the context-specificity of these types of policies, such research must also address the issue of how transferable innovation and sustainable practices are from one socio-technical and political context to another.

Moreover, the growing importance of diffuse emissions also requires green innovation in the public sector. Specifically, implementing environmental regulations that are close to damages demand specific monitoring technologies that can measure pollution levels. The development of new technologies – which, for instance, facilitates cheap monitoring of emissions – ought to be promoted, but it is quite unclear who has the incentive to promote and undertake such R&D activities. Similar concerns can be raised about the innovations that permit consumers to better assess the environmental footprints of different products and services (e.g., [ 21 ]). Private firms cannot be expected to pursue these types of green innovations intensively. Nevertheless, governments often spend substantial amounts on funding R&D on pollution abatement technology, but less frequently we view government programs funding research on technologies that can facilitate policy enforcement and environmental monitoring.

Finally, the green economy transition should also benefit from research that involves various impact evaluations, including methodological innovation in evaluation studies. This concerns evaluations of the impacts of important baseline trends, e.g., digitalization and automation, globalization versus nationalization, etc., on environmental and distributional outcomes but also on the prospects for green innovation collaborations and various circular economy-inspired business models. Such evaluations could be particularly relevant for understanding possible future pathways for the greening – and de-carbonization – of key process industries. Clearly, there is also need for improved evaluations of policy instruments and combinations of policies. With an increased emphasis on the role of technology-specific policies, such evaluations are far from straightforward. They must consider the different policies’ roles in the innovation systems, and address important interaction effects; any evaluation must also acknowledge the policy learning taking place over time.

Availability of data and materials

Not applicable.

For instance, in the new system, workers had more autonomy and flexibility (e.g., [ 28 ]).

Clearly, given the focus on sustainable technological change, this article does not address all dimensions of the transition to a green economy. Heshmati [ 31 ] provides a recent review of the green economy concept, its theoretical foundations, political background and developmental strategies towards sustainable development. See also Megwai et al. [ 41 ] for an account of various green economy initiatives with a specific focus on developing countries, and Bartelmus [ 5 ] for a critical discussion of the link between the green economy and sustainable development.

For instance, it is typically less negative for the environment to landfill a substantial share of mining waste such as hard rock compared to recycling. Hard rock typically causes little environmental damage, except aesthetically, unless such waste interacts with surface or ground water [ 17 ].

In fact, patents protecting intellectual property rights could even slow down the diffusion of green technologies offering deep emission reductions by creating a bias towards development of close-to-commercial technologies. For instance, Budish et al. [ 10 ] shows that while patents award innovating companies a certain period of market exclusivity, the effective time period may be much shorter since some companies choose to file patents at the time of discovery rather than at first sale. One consequence of this is that the patent system may provide weak incentives for companies to engage in knowledge generation and learning about technologies that face a long time between invention and commercialization.

The UNFCCC [ 56 ] reports substantial increases in climate-related global finance flows, but these flows are still relatively small in the context of wider trends in global investment. They are even judged to be insufficient to meet the additional financing needs required for adaptation to the climate change that cannot be avoided.

If the tax is assessed per pound of intermediate material produced, it will also give producers the incentive to supply lighter-weight products.

See also Future Earth [ 23 ] for a comprehensive list of priorities for a global research sustainability agenda.

Åhman M, Nilsson LJ, Johansson B. Global climate policy and deep de-carbonization of energy-intensive industries. Clim Pol. 2017;17(5):634–49.

Google Scholar  

Arthur WB. Competing technologies, increasing returns, and lock-in by historical small events. Econ J. 1989;99:116–31.

Avent R. The wealth of humans: work, power, and status in the twenty-first century. London: St. Martin’s Press; 2016.

Barbier E. How is the global green new deal going? Nature. 2010;464:832–3.

CAS   Google Scholar  

Bartelmus P. The future we want: green growth or sustainable development? Environ Dev. 2013;7:165–70.

Bek D, Nel E, Binns T. Jobs, water or conservation? Deconstructing the green economy in South Africa’s working for water programme. Environ Dev. 2017;24:136–45.

Bennear LS, Stavins RN. Second-best theory and the use of multiple policy instruments. Environ Resour Econ. 2007;37:111–29.

Bergquist AK, Söderholm K, Kinneryd H, Lindmark M, Söderholm P. Command-and-control revisited: environmental compliance and technological change in Swedish industry 1970–1990. Ecol Econ. 2013;85:6–19.

Brynjolfsson E, Hitt LM. Computation: information technology, organizational trans-formation and business performance. J Econ Perspect. 2000;14:23–48.

Budish E, Roin BN, Williams H. Do firms underinvest in long-term research? Evidence from cancer clinical trials. Am Econ Rev. 2015;105(7):2044–85.

Calcott P, Walls M. Waste, recycling, and ‘design for environment’: roles for markets and policy instruments. Resour Energy Econ. 2005;27(4):287–305.

Ciscar J-C, Saveyn B, Soria A, Szabo L, Van Regemorter D, Van Ierland T. A comparability analysis of global burden sharing GHG reduction scenarios. Energy Policy. 2013;55:73–81.

Cohen B. Sustainable valley entrepreneurial ecosystems. Bus Strateg Environ. 2006;15(1):1–14.

David P. The computer and the dynamo: an historical perspective. Am Econ Rev. 1990;80:355–61.

Dechezleprêtre A, Glachant M, Mohnen M. Knowledge spillovers from clean and dirty technologies: a patent citation analysis. Working paper no. 135. London: Grantham Research Institute on Climate Change and the Environment; 2017.

Del Rio P, Bleda M. Comparing the innovation effects of support schemes for renewable electricity technologies: a function of innovation approach. Energy Policy. 2012;50:272–82.

Eggert RG, editor. Mining and the environment: international perspectives on public policy. Washington, DC: Resources for the Future; 1994.

Ejdemo T, Söderholm P. Wind power, regional development and benefit-sharing: the case of northern Sweden. Renew Sust Energ Rev. 2015;47:476–85.

European Commission. Closing the loop – an EU action plan for the circular economy. Brussels: COM (2015) 614 final; 2015.

European Environment Agency (EEA). Circular by design. Copenhagen: Products in the Circular Economy; 2017.

Federal Ministry of Education and Research. Green economy research agenda. Bonn: German Government; 2015.

Fullerton D. Six distributional effects of environmental policy. CESifo working paper 3299. Germany: Ifo Institute – Leibniz Institute for Economic Research at the University of Munich; 2010.

Future Earth. Strategic research agenda 2014. Priorities for a global sustainability research strategy. Paris: International Council for Science (ICSU); 2014.

Geels FW. From sectoral systems of innovation to socio-technical systems: insights about dynamics and change from sociology and institutional theory. Res Policy. 2004;33:897–920.

Gibbs D. Sustainability entrepreneurs, Ecopreneurs and the development of a sustainable economy. Greener Manag Int. 2009;51:63–78.

Gibbs D, O’Neill K. Future green economies and regional development: a research agenda. Reg Stud. 2017;51(1):161–73.

Greening LA, Greene DL, Difiglio C. Energy efficiency and consumption – the rebound effect – a survey. Energy Policy. 2000;28(6–7):389–401.

Harford T. Fifty things that made the modern economy. London: Little, Brown; 2017.

Hellsmark H, Frishammar J, Söderholm P, Ylinenpää H. The role of pilot and demonstration plants in technology development and innovation policy. Res Policy. 2016;45:1743–61.

Heshmati A. A review of the circular economy and its implementation. Int J Green Econ. 2017;11(3/4):251–88.

Heshmati A. An empirical survey of the ramifications of a green economy. Int J Green Econ. 2018;12(1):53–85.

Huenteler J, Schmidt T, Ossenbrink J, Hoffman V. Technology life-cycles in the energy sector – technological characteristics and the role of deployment for innovation. Technol Forecast Soc Chang. 2016;104:102–21.

International Energy Agency (IEA). Ensuring green growth in a time of economic crisis: the role of energy technology. Siracusa: IEA; 2009.

Jacobsson S, Bergek A. Innovation system analyses and sustainability transitions: contributions and suggestions for research. Environ Innov Soc Transit. 2011;1:41–57.

Kirby P, O’Mahony T. The political economy of the low-carbon transition. Pathways beyond techno-optimism. London: Palgrave McMillan; 2018.

Kivimaa P, Kern F. Creative destruction or mere niche support? Innovation policy mixes for sustainability transitions. Res Policy. 2016;45(1):205–17.

Larson AL. Sustainable innovation through an entrepreneurship lens. Bus Strateg Environ. 2000;9:304–17.

Lehmann P, Söderholm P. Can technology-specific deployment policies be cost-effective? The case of renewable energy support schemes. Environ Resour Econ. 2018;71:475–505.

Levin RC, Klevoriek AK, Nelson RR, Winter SG. Appropriating the returns from industrial research and development. Brook Pap Econ Act. 1987;3:783–820.

Markard J, Raven R, Truffer B. Sustainability transitions: an emerging field of research and its prospects. Res Policy. 2012;41:955–67.

Megwai G, Njie NI, Richards T. Exploring green economy strategies and policies in developing countries. Int J Green Econ. 2016;10(3/4):338–57.

Moberg Å, Finnveden G, Johansson J, Lind P. Life cycle assessment of energy from solid waste – part 2: landfilling compared to other treatment methods. J Clean Prod. 2005;13(3):231–40.

Neuhoff K. Large-scale deployment of renewables for electricity generation. Oxf Rev Econ Policy. 2005;21(1):88–110.

Newell P. Globalization and the environment: capitalism, ecology and power. Cambridge: Cambridge University Press; 2012.

Newell P, Paterson M. Climate capitalism: global warming and the transformation of the global economy. Cambridge: Cambridge University Press; 2010.

Nicolli F, Johnstone N, Söderholm P. Resolving failures in recycling markets: the role of technological innovation. Environ Econ Policy Stud. 2012;14(3):261–88.

Organization of Economic Cooperation and Development (OECD). Sustainable manufacturing and eco-innovation. Framework, practices and measurement. Paris: OECD; 2009.

Organisation for Economic Cooperation and Development (OECD). Interim report of the green growth strategy: implementing our commitment for a sustainable future. Paris: OECD; 2010.

Popp D, Newell R. Where does energy R&D come from? Examining crowding out from energy R&D? Energy Econ. 2012;34(4):980–91.

Rodrik D. Green industrial policy. Oxford Rev Econ Policy. 2014;30(3):469–91.

Rogge KS, Reichardt K. Policy mixes for sustainability transitions: an extended concept and framework for analysis. Res Policy. 2016;45(8):1620–35.

Söderholm P, Hellsmark H, Frishammar J, Hansson J, Mossberg J, Sandström A. Technological development for sustainability: the role of network management in the innovation policy mix. Technol Forecast Soc Chang. 2019;138:309–23.

Stein JC. Efficient capital markets, inefficient firms: a model of myopic corporate behavior. Q J Econ. 1989;104:655–69.

United Nations Environment Programme (UNEP). Towards a green economy: pathways to sustainable development and poverty eradication. Nairobi: United Nations; 2011.

United Nations Environment Programme (UNEP). A guidance manual for green economy policy assessment. New York: United Nations; 2014.

United Nations Framework Convention on Climate Change (UNFCCC). 2016 biennial assess-ment and overview of climate finance flows report. Bonn: United Nations; 2016.

Unruh GC. Understanding carbon lock-in. Energy Policy. 2000;28(12):817–30.

Van Ewijk S, Stegemann JA. Limitations of the waste hierarchy for achieving absolute reductions in material throughput. J Clean Prod. 2016;132:122–8.

Walls M, Palmer K. Upstream pollution, downstream waste disposal, and the design of comprehensive environmental policies. J Environ Econ Manag. 2001;41:94–108.

Williamson O. Credible commitments: using hostages to support exchange. Am Econ Rev. 1983;73:519–40.

Download references

Acknowledgements

Financial support from Nordforsk (the NOWAGG project) is gratefully acknowledged, as are valuable comments on earlier versions of the manuscript from Åsa Ericson, Johan Frishammar, Jamil Khan, Annica Kronsell, one anonymous reviewer and the Editor. Any remaining errors, however, reside solely with the author.

Financial support from Nordforsk and the NOWAGG project on Nordic green growth strategies is gratefully acknowledged. Open access funding provided by Lulea University of Technology.

Author information

Authors and affiliations.

Economics Unit, Luleå University of Technology, Luleå, Sweden

Patrik Söderholm

You can also search for this author in PubMed   Google Scholar

Contributions

Single-authored article. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Patrik Söderholm .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Söderholm, P. The green economy transition: the challenges of technological change for sustainability. Sustain Earth 3 , 6 (2020). https://doi.org/10.1186/s42055-020-00029-y

Download citation

Received : 24 July 2019

Accepted : 05 January 2020

Published : 22 June 2020

DOI : https://doi.org/10.1186/s42055-020-00029-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Green economy
• Technological development
• Radical innovation, green capitalism
• Environmental policy
• Policy mixes
• Distributional effects

Sustainable Earth Reviews

ISSN: 2520-8748

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]

SYSTEMATIC REVIEW article

Green economy studies amongst the global climate change challenge between 2016 and 2022: a bibliometric review.

• 1 Belt and Road International School, Guilin Tourism University, Guilin, Guangxi, China
• 2 Faculty of Arts and Social Sciences, University of Sydney, Sydney, NSW, Australia
• 3 Office of Post Graduate Studies, National University of Laos, Vientiane, Laos
• 4 Faculty of Tourism Department, Trisakti Institute of Tourism, Jakarta, Indonesia
• 5 School of Foreign Studies, Guilin Tourism University, Guilin, Guangxi, China

Practical and theoretical advancements have not caught pace with rising scientific researches in the rapidly emerging economy undertaking a shift to a more sustainable and particularly green model. After the UN adopted the 2030 Agenda for Sustainable Development, there has been a surge in interest in the green economy among academics around the world, and the literature on the issue is proliferating. This paper adopts the methodology of bibliometric review and thematic analysis to summarize the relevant literature from 2016 to 2022 on areas related to the theme of green economy. The literature was obtained from the Web of Science database with a total of 1,022 articles. Furthermore, the literature was analyzed using VOSviewer as well as the R language to couple the literature by keywords, country, affiliation, author, and publication. The findings of the current paper show that the green economy has received more academic attention from scholars since 2016. Asia and Europe are leaders in green economy studies. In the context of climate change, future research is anticipated to concentrate on establishing a green economy for global economic growth. This paper makes a substantial contribution to future research on the green economy.

Introduction

The twenty-first century is defined by rising environmental degradation and depletion of resources, as well as the need to achieve strategic goals for sustainable development, of which the green economy is a crucial component in advancing global economic growth ( Jin et al., 2022 ). This is a unique opportunity to reset national and corporate agendas in the wake of the Environmental, Social, and Governance (ESG) investment boom and the imperative for economic recovery and sustainable growth ( Government of Dubai, 2022 ). Many sustainable development indicators, such as health ( Seshaiyer and McNeely, 2020 ), inequality ( Barbier and Burgess, 2020 ), and education ( Anholon et al., 2020 ), are influenced by the global COVID-19 pandemic ( Naidoo and Fisher, 2020 ). In recent years, the worldwide environment became more devastating, revealing the volatility of the green economy ( Gunay et al., 2022 ), which will have a significant influence on the achievement of sustainable development objectives. In the post-pandemic era, it will be crucial to determine how to designate appropriate legislation and regulations, modify the government's transformation and upgrading, support economic growth, and energetically develop the green economy in order to accomplish UN sustainable development goals ( Campbell, 2017 ; Kronenberg and Fuchs, 2021 ). This must be driven by a synthesis of academic researches, technologies, and policies ( Lee et al., 2022 ; Metawa et al., 2022 ). Governments and organizations, under the leadership of the United Nations, have taken action and adopted a variety of policies to achieve the Sustainable Development Goals ( Rosati and Faria, 2019 ). Previous literature studies in the topic of economics tended to summarize and discuss particular green economy concerns ( Ferguson, 2015 ). In numerous past research works, it is argued that the current weak articulation of the green economy agenda does not necessarily imply a future transition to a post-growth society, but Ferguson summarizes and proposes a strategy for reformulating the green economy agenda in a post-growth direction ( Bina and La Camera, 2011 ). The green economy has the potential to achieve what sustainable development cannot and can in some way address the limits of traditional economic growth. Although green economy development is now to some extent similar to the past, it has the potential to m move toward a post-growth society. In the face of global climate change challenges now, where economic development is influenced by environmental factors, only the emergence of a green economy community of practice can truly develop the development potential of a green economy. New technologies such as artificial intelligence, big data, the Internet of Things, and blockchain radically alter how industrial companies capture, generate, and distribute corporate value ( Hristov Kalin, 2017 ; Arenal et al., 2020 ). Currently, nations throughout the globe may energetically advance the application of the fourth industrial revolution's technology group in the sphere of business innovation and green economy ( Wang et al., 2022 ). In reality, many businesses struggle to properly incorporate the green economy into their operational business models ( Sjödin et al., 2021 ). In the era of digital intelligence, when the function of digital technology is rising, the significance and urgency of this issue are intensifying ( Linde et al., 2021 ). Consequently, it is essential to systematically evaluate and research the relationship between the green economy and business innovation in detail, as well as to thoroughly discuss the mechanism and process of the new generation of green technology that impacts the innovation strategy of global enterprises.

Over the course of the past few years, a large number of scholars have authored academic papers ( Loiseau et al., 2016 ; Georgeson et al., 2017 ; Mikhno et al., 2021 ), in an attempt to grasp the impacts of green economy on various global industries and the potential of these industries to reflect the emergence of green digitalization. Most of these articles, however, have only summarized previous research in a somewhat categorized manner, without applying bibliometric-related techniques to it and without considering the issue in a worldwide context. In order to fill in this gap, this study, unlike those previous literature review papers, utilizes a bibliometric analysis technique, which is not influenced by the author's subjective considerations, to analyse the present trends and research tendency of the issue of green economy. Consider that the advancement of technology has been a major contributor to green economy model, the current paper hence particularly attempts to assess the significant themes of prior researches on the subjects of green economy and green finance in order to contribute to the debate involving how major corporations are adopting digital resources to redevelop the operational construct concerning the latest digital advancement. In particular, the purpose of this investigation is to grasp an overall bibliometric understanding to the existing research advancement of green economy and to establish a future research agenda. Some of the following are instances of questions that are pertinent to the bibliometric analysis of green economy: What part has the technology advanced the development of the sustainable and environment-friendly green economy? What does the research agenda for the green economy look like?

In this study, a synthesis of the results of previous research on green economy was first conducted and then the constraints caused by environmental repercussions accordingly. These were the two subjects that generated the greatest conversation in relation to a comprehensive understanding of the green economy and the concerns surrounding environmental preservation. This study drew on the prior work of a combined amount of 1,022 articles about the topic of green economy on environmental issues using a comprehensive selected database. The paper selection procedure as well as the inclusion criteria were further expounded upon throughout the subsequent sections on the research methodology as well as the literature evaluation. Specifically, the organization of this paper is broken down into the following sections. First, the article begins with a summary of the current academic background, which gives an overview of the green economy as well as relevant national policies. In the second part of research methodology, the criteria utilized for selecting the relevant prior literature as well as our full research methodology were explained. The finding section presents the outcomes of this study. Last but not least, the study concludes with a discussion of its theoretical and practical contributions, as well as its limitations and suggestions for further research.

Literature review

According to scholars and a large number of international organizations, the green economy may be characterized as low in carbon emissions, resource-efficient, and socially inclusive ( UNEP, 2011 ). For a significant number of years, one of the primary focal points for economic sustainability has been the application of green economy. A green economy strives to minimize resource depletion and environmental damage, “to generate sustainable, long-term economic growth without causing major environmental damage” ( Jacobs, 2012 ). Meanwhile, the green economy focuses on change, particularly health-improving change. This form of economy prioritizes using renewable energy sources, sustainable transportation, and adequate water, land, and waste management to achieve its goals. Although it is argued such a transition, which emphasizes low-carbon resources, could negatively affect the environment and the local population ( Sovacool et al., 2019 ), businesses everywhere are contemplating changing to adapt to the new paradigm, given the importance attached to the green economy worldwide. The green economy is assumed to have various benefits and therefore vital for creating a sustainable economy and is closely tied to the notion of green growth.

In retrospect, the green economy was originally implemented worldwide in reaction to the global financial crisis and to promote economic recovery ( Bina and La Camera, 2011 ). It has been crucial in attaining the low-carbon transition and sustainable development objectives. As of today, the green economy has had some effect on global policymaking, with Europe and Asia being the most quickly expanding regions ( Kaur et al., 2018 ). For instance, there are a lot of studies that concentrate upon such geographies, such as China ( Zhang et al., 2021 ) and the United Kingdom ( Gainsborough, 2018 ), as well as other emerging nations like Laos ( Luukkanen et al., 2019 ), India ( Reddy, 2016 ), and Cambodia ( Vuola et al., 2020 ).

As was discussed in the preceding section, the primary goal of a green economy is to gradually shift away from the use of traditional energy that are the sources of devastating pollution. Renewable energy sources, such as solar and wind, can help establish a new standard of energy efficiency if they follow the new guiding principles that they have developed ( Chenari et al., 2016 ). It is a terrible thing when certain markets reject the new green economic model and fail to adhere to environmental protection standards for people, animals, and the planet. A substantial portion of expenses are incurred outside of the local market or nation.

Environmental and resource conservation are vital to the development of a green economy. Businesses are supposed to ensure that their economic activities are consistent with the concepts of sustainable development by reducing their environmental effect. In other words, the economic growth does not threaten ecological sustainability. The green economy incorporates precautions to prevent environmental harm from normal financial transactions ( Kasayanond, 2019 ). To connect their operations with sustainable development objectives, enterprises must minimize their ecological impact. This may include establishing sustainable resource management and decreasing air and water pollution. Moreover, corporations must ensure that their actions have no negative impact on the environment, which includes refraining from behaviors that could cause pollution or deplete natural resources.

In addition, there are several benefits of the green economy development. Firstly, the green economy promotes the development of new product markets and the more efficient use of natural resources. It can also solve the energy problem to a certain extent ( UNEP, 2012 ). Currently, many developing countries relying on the import of fossil fuel are heavily influenced by the international situation and pollute the environment. The development of a green economy can reduce the impact of these problems by replacing fossil fuels with green energy ( Policy Advisor, 2016 ). What is more, a green economy aims to achieve sustainable development through the rational use of resources and the regulation of policies that will lead to sustainable development ( Smith et al., 2007 ). All these studies demonstrate that the green economy and environmental protection (for dealing with global climate change) concepts are inextricably intertwined. Green economics apply protections to minimize environmental damage from economic processes, and environmental protection is integral to green economies through fostering efficient and sustainable resource management.

Research methodology

The authors began by conducting an exhaustive research for pertinent publications indexed in Web of Science Index, for instance, the Science Citation Index Expanded (SCI-EXPANDED) and Social Sciences Citation Index (SSCI). Consequently, through searching the Web of Science data, the authors searched the database with the terms of green economy, sustainable development, policy, goal, as well as a review of prior researches. The results included journal articles and proceeding from a variety of conferences. Additionally, we scanned the bibliographies of pertinent review papers. The following criteria were used to screen the papers.

1. Topic = (Green Economy OR Sustainable Development) AND Topic = (Policy) AND Topic = (Goal).

2. Research domains: Sustainability Science; Economics; Environmental Sciences; Environmental Sciences; Ecology; Business Economics.

3. Document Types: Peer-reviewed articles and conference proceedings written in English, which were indexed in Science Citation Index Expanded (SCI-EXPANDED) or Social Sciences Citation Index (SSCI).

4. Web of Science Categories: Environmental Sciences; Green Sustainable Science Technology; Environmental Studies; Economics.

In this article, the research objects utilized to generate the mapping are important indicators linked to the topic of green economy, such as keywords, number of publications, number of citations, nations, and authors of literature. For each article, the authors examined the title, abstract, introduction, or them together to determine that the investigations are pertinent to the current study. Inter-coder dependability was examined throughout such encoding procedure to increase the accuracy and dependability ( Clarke and Visser, 2019 ; Baek et al., 2021 ). As a result, a total of 1,022 articles were selected after the initial categorization procedure. Specifically, the particular steps can then be broken down into the following phases. First, the target literature was screened from the Web of Science database, with the previously mentioned criteria as the specific research indicator. Second, country analysis was conducted for the published literature. Third, the affiliation of the existing literature was analyzed. Fourth, the journals to which the literature belongs were classified and summarized. Fifth, the number of publications of researchers in the field of green economy was counted. Sixth, using the correlation method of literature coupling, the keyword network mapping was constructed, and the correlation between the countries, authors and references of the literature design was analyzed in detail. Last but not least, the authors used the R language analysis technique to identify the summative analysis to the existing literature. For the acquired literature, this paper adopts a bibliometric-related approach for quantitative research ( Farrukh et al., 2020 ). This method is a combination of three fields: literature, statistics and mathematics, and it analyses the correlation between specific indicators of published scientific results, such as disciplines, journals, regions and countries ( Bonilla et al., 2015 ; Amiguet et al., 2017 ; Martínez-López et al., 2018 ).

More specifically, to analyze the existing data, the authors use VOSviewer and R language, two software programs widely used in various fields for their simplicity and efficiency. For literature analysis, coupling analysis is often used, which simply means that the relevance of a research topic is determined by analyzing the citations among published articles ( Mora-Valentin et al., 2022 ). Another alternative to this is to analyze the citations of the existing articles to determine the relevance of the themes' co-relationship ( Wang et al., 2013 ). Both of these two methods are applied in this study to achieve precision with reliability and credibility in the analysis of the acquired literature.

Bibliometric analysis process

Analysis of the published literature.

To locate relevant scholarly materials, this article searched the Web of Science database. This section describes the methods used to obtain data from published sources. These particular strategies contain data obtained by two independent reviewers, and we proceed to further investigation to the obtained data. In addition, we explained the Web of Science in terms of the automation technologies used in the procedure. Keyword searches to ensure that no relevant literature on green economy-related policy research is excluded. According to the findings of an information retrieval study conducted using Web of Science, there were 1,022 papers on green economy. A framework analysis of the articles indicates that the number of papers has increased exponentially over the past 9 years, especially from 2016 to 2022. This substantial increase suggests that the study of green economy is gaining increasing growth momentum (see Figure 1 ).

Figure 1 . The increasing number of articles published on green economy development goals and policies.

To be specific, as shown in Figure 2 , 1,022 papers were published between 2016 and 2022 with a quick annual growth. In 2016, there were only 22 papers while by the end of 2022, 322 articles will have been published, a 10-fold increase of 2016. It is assumed that this growth trend will continue.

Figure 2 . Annual publication analysis of green economy issues in published literature.

Analysis of the study area of green economy

Besides the growth in numbers shown before, the study on the green economy has also experienced a geographic expansion worldwide in recent years, as indicated in Table 1 . The top 10 nations were selected in terms of the number of papers published worldwide by its scholars from 2016 to 2022. With 353 publications and 5,597 citations, China is far ahead of other countries. This signifies that Chinese scholars have gradually shifted their focus to the green economy, an active response to the green transition policy by the Chinese government Second to China is the United Kingdom. The United States ranks third with 107 articles, subsequently followed by Germany, three Asian countries, Turkey, and Pakistan, India, and three European countries, Italy, Spain, and Netherlands.

Table 1 . Country analysis.

Figure 3 depicts the outcomes of the subsequent coupling analysis, which was conducted with VOSviewer analysis software. The quantity of publications is proportional to the diameter of the circle. China, the United Kingdom, and the United States are the top three countries. This conclusion is consistent with the results presented in Table 1 .

Figure 3 . Country coupling analysis.

Since many studies are conducted internationally, it was appropriate to consider the collaboration between scholars in each country. The outcomes of this investigation are demonstrated in Figure 4 . China continues to lead the list of countries most inclined to collaborate in research on the trending topic of green economy. It is anticipated that Chinese academics would participate significantly in future studies on the green economy.

Figure 4 . National co-authorship analysis.

Authors' affiliation analysis

The attributing affiliations of the authors of the publication are also an essential component of the bibliometric analysis. Table 2 shows the findings of the authors' affiliation via the VOSviewer analysis. With 32 papers, the Chinese Academy of Sciences was the most prolific institution. With 29 and 28 articles, respectively, Istanbul Gelisim University from Turkey and the University of London from the United Kingdom scored second and third in the list. The significance of Asian research institutions in the study of the green economy is accurately depicted.

Table 2 . Article affiliation analysis.

Analysis of the volume of publications in relevant journals

Journal analysis is also an integral aspect of our investigation. As shown in Table 3 , the authors selected a total of 10 journals that publish high-profile in-depth research on topics connected to the green economy between 2016 and 2022. Sustainability is one of the most highly ranked journals on the list. The Journal of Cleaner Production , and Environmental Science and Pollution Research ranked second and third, respectively, with 124 and 115 articles. Science of the Total Environment is the most highly-cited journal, with an average of 57.56 citations per article.

Table 3 . Journal analysis.

For journal citation research, literature co-citation analysis is frequently employed. Figure 5 illustrates the findings of this analysis. The Journal of Cleaner Production is the journal with the highest co-citation frequency, followed by Sustainability and Environmental Science and Pollution Research , where the high co-citation rate is attributable to a large number of related references.

Figure 5 . Journal co-citation analysis.

Analysis of the researchers focusing on the field of green economy

This section contains data pertaining to the green economy researchers with the most publications. Sinha A. is in international spotlight with 21 published articles, as shown in Table 4 . With 1,737 citations, Alola A.A. ranked first on the list in terms of citations. With an average of 97.77 citations per article, Bekun F.V. topped the list. Each of the remaining academics has authored a minimum of seven articles.

Table 4 . Author analysis.

Literature citation analysis

The number of citations in the selected literature is also a significant indicator when performing a literature review and a crucial criterion for evaluating the quality of a publication. In this part, the top 10 most-cited papers from the Web of Science database were selected based on the search parameters established previously and listed in Table 5 . The results suggest that Toward a sustainable environment: Nexus between CO 2 emissions, resource rent, renewable and non-renewable energy in 16-EU countries is ranked in the first place, which shows its academic significance. The remaining publications have been mentioned at least 156 times, which demonstrates in part their high reference value in the subject of green economy research.

Table 5 . Literature citation analysis.

Three-field plot analysis

Among numerous different ways of analysis, the Three-Field Plot Analysis is frequently used to determine the researcher's area of study. This section applies this methodology to the study of the green economy. Figure 6 illustrates the findings of this analysis. On the far left are the names of the researchers, in the center are the most often used terms, and on the right are the countries of the authors. Thus, it is straightforward to associate the researcher with his field of study and nationality. For instance, Liu Y.'s primary research keywords are sustainable development and CO 2 emissions, indicating that he is primarily concerned with sustainable development as a result of CO 2 emissions.

Figure 6 . Three-field plot analysis.

Keyword analysis

This component utilizes the VOSviewer program to perform keyword analysis on the selected documents; the results are displayed in Figure 7 . The importance of sustainable development, economic growth, and climate change is evident. This technique is used to map the frequency of keywords in published works. Therefore, future study on the green economy is expected to concentrate on these three terms, which reflect a trend in the field.

Figure 7 . Keyword analysis.

Although the keyword analysis references the knowledge content of big data to some extent, it can be utilized to determine future research topics. However, research on the green economy is frequently influenced by a number of uncontrollable circumstances, thus the results of the analysis can serve as a benchmark for particular measurements. Nevertheless, we anticipate that these topics will continue to evolve in the current global situation.

Thematic map analysis

This part presents a structured analysis of the selected keywords in the green economy-related literature, drawing a Thematic Map using the bibliophagy data package in R language. The first quadrant of the four-quadrant diagram indicates research fields that are both significant and well-developed. The second quadrant consists of well-developed but less significant research directions. The third quadrant represents insignificant research content, whereas the fourth quadrant represents significant but underdeveloped study areas ( Tennekes, 2018 ). The fourth quadrant represents research fields that are vital but underdeveloped. Figure 8 demonstrates the outcomes. The first quadrant contains the current topical issues, including renewable energy, economic growth, sustainable development, energy transition and other key words.

Figure 8 . Thematic map.

Niche Themes focuses on the more contemporary and well-established fields of study, including public policy, food shortages, and sustainability factors. The most crucial are the transitions to sustainable development and the shadow economy. The keywords environmental Kuznets curve, environmental regulation, green innovation, poverty alleviation, energy security, sustainable development, and climate change dominate the third quadrant. As a result of climate change, it is evident that research on sustainable development is becoming more centralized and of a broader study interest. The fourth quadrant represents research directions that are not well-developed at present but have a greater scientific value in the future. Green economy, sustainability, circular economy and other contemporary hot topics are included in this quadrant, reflecting the importance of green economy and sustainable development research in the coming period.

Trends in topic selection

Adopting the bibliophagy data package in the R language to construct time windows with literature keywords reveals future research trends. As depicted in Figure 9 , energy security is a leading research topic from 2016 to 2018. From 2018 to 2019, the study field is gradually changing toward the establishment of a low-carbon economy to achieve economic growth. Life cycle assessment have been reintroduced and examined bioeconomic issues by 2020. From 2021 onward, policy research on the formation of a green economy to achieve sustainable development goals became an important topic, paving the path for future studies.

Figure 9 . Trend topics advancement over the time.

The development of a green economy can be distinguished into four different forms: green growth, green transformation, green resilience, and green revolution ( Death, 2015 ). Among them, green growth is the most common green economy model in the global context and belongs to a high-quality development model that focuses on the rational use of resources and the reduction of damage to nature ( Li et al., 2022 ). Green resilience is more technical in nature and places greater emphasis on sustainability ( Rizzo, 2020 ). Green transformation and green revolution Green Transformation and Green Revolution are national government policies to promote economic development ( Thenkabail, 2010 ; Lee and Woo, 2020 ). The green economy as a whole is a series of policies that are designed to promote economic development. As a whole, the green economy is the sum of a number of concepts that encompass the world economy, energy issues, national policies and more. Du et al. (2019) assert that the output of carbon dioxide into the atmosphere, a contributor to climate change, is reduced by using green technologies such as electric vehicles. In addition, reducing waste and pollution through implementing efficient production and consumption processes adds to environmental protection. Environmental safeguards are also essential for the long-term management of resources. This involves protecting biodiversity, vital to ecosystem health and human quality of life. Moreover, because water is a finite resource, preserving its quality through conservation is essential for maintaining a healthy ecosystem. By protecting the environment, a green economy may ensure that resources are managed responsibly, which is advantageous for both the environment and the economy.

Promoting environmentally friendly ways of transportation is essential to the green economy. Sustainable is any mode of transportation that considers the needs of society, the environment, and the climate, as well as the effects that transportation has on these factors ( Björklund, 2011 ). Since transportation contributes significantly to carbon dioxide emissions and consumes more than 25 per cent of global energy, its environmental effects cannot be overstated ( Barceló, 2010 ). UN Environment Programme (UNEP) asserts that if persons switched to a better and safer mode of transportation, outdoor air pollution-related premature mortality might be reduced ( Mahmood, 2011 ; Levy and Patz, 2015 ). Electric vehicles are increasingly popular in countries such as the United States and Germany because they reduce air pollution and improve the environment. Due to the increasing number of incentives offered by nations with advanced economies, it is easier for businesses and municipalities to manage the rise in electric vehicle usage.

Environmental protection and a green economy are required for sustainable development. A strategy for sustainable development ensures that present and future generations have access to the resources they need to live happy and productive lives. It considers the needs of the economy, environment, and society. The objective of a green economy is to maximize economic output while decreasing environmental hazards and resource shortages, which include reducing pollution and waste; boosting energy efficiency; promoting renewable energy sources and safeguarding natural resources. In addition, it seeks to promote environmentally responsible economic growth. Renewable energy, energy efficiency, sustainable agriculture, and environmentally friendly transportation are green economic projects.

The development of the green economy plays a decisive role in solving the problem of carbon emissions. In the past, governments favored fossil fuels over the green economy, partly because the green economy sector then was relatively underdeveloped, and partly because of the higher risks and lower returns ( Tarkhanova et al., 2020 ). To achieve the goal of sustainable development, a series of policies must be developed to raise funds for the green economy to thrive.

Theoretical implications

Different from the conventional literature reviews, the current research analyzes a broader number of papers, from various aspects, including geographies, publishing organizations, high-profile journals, authorship, citation frequency, and several other variables. Based on this, the future research trend is predicted, which would be conducive for researchers to define the direction of future research and to facilitate research institutions in conducting research and cooperating with their intended academia. In addition, the current research would be beneficial for national governments to precisely identify countries that are in the forefront of green economy research and then to change their policies accordingly. With the acceleration of global digital and intelligent transformation, the corporate environment is in a state of perpetual flux. It is now necessary for the survival and success of businesses to encourage the integration of new technologies and business to drive business innovation. As green digital technologies are increasingly used in corporate management practices nowadays, green economy, a kind of advanced technology, plays a growing key role in facilitating the business innovation process. The present fast growth of green financing strengthens the prospect of incorporating it into the corporate innovation process. The expansion of sustainable finance has created favorable conditions for an industrial ecosystem that embraces the logic of digital services. Green improvements in product service processes and corporate strategies generate opportunities. Simultaneously, facilitating and supporting enterprise company innovation become more approachable, simple, and collaborative.

Limitations

Despite the fact that this paper has made some contributions, it still has certain limitations. The literature is obtained from the Web of Science database, and there are inevitably important publications that were not counted, which has an influence on the correctness of the analysis. Second, the analytical period for this article is the period between 2016 and 2022, when the green economy is thriving. Therefore, we propose that future research investigate the evolution of the green economy in different eras by comparing the results of studies conducted at different time intervals.

Future research agenda

Future research and analysis on the digital transformation of businesses merits more examination. Topics for research may include the existing and future effects of digital technology, such as artificial intelligence, on digital transformation and business innovation inside enterprises. Based on the theoretical exploration and empirical research conducted, relevant academic achievements can provide practical and effective theoretical guidance and strategies for enterprises to implement digital transformation in the context of the era of digital intelligence enlightenment. In addition, future study subjects may include the role and influence of the COVID-19 pandemic. On the basis of the concepts of digital empowerment and innovation, theoretical debate and empirical study can be conducted on business remodeling, transformation, and upgrading. The relevant academic accomplishments may give useful theoretical advice for conventional firms to implement green innovation using new technologies in the context of the digital economy.

Data availability statement

The original contributions presented in the study are included in the article/ Supplementary material , further inquiries can be directed to the corresponding author.

Author contributions

JZ, XS, and RZ: conceptualization and validation. JZ and XS: data curation, investigation, and writing—review and editing. JZ and RZ: formal analysis, methodology, resources, software, visualization, and writing—original draft. JZ: funding acquisition, project administration, and supervision. All authors contributed to the article and approved the submitted version.

This article is part of academic achievements of First-Class Universities and Disciplines in Tourism Management Discipline (Project) in Guangxi, China. XS has also been participating in research projects supported by Guilin Tourism University-China ASEAN Research Center. This paper is part of the academic achievements of the Translation and Language Testing Center of Guilin Tourism University, China.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fevo.2023.1168437/full#supplementary-material

Amiguet, L., Gil Lafuente, A. M., Kydland, F. E., and Merigó Lindahl, J. M. (2017). One hundred twenty-five years of the journal of political economy: a bibliometric overview. J. Pol. Econ . 125. Available online at: https://recercat.cat//handle/2072/330181

Google Scholar

Anholon, R., Rampasso, I. S., Silva, D. A. L., Filho, W. L., and Goncalves Quelhas, O. L. (2020). The COVID-19 pandemic and the growing need to train engineers aligned to the sustainable development goals. Int. J. Sustainabil. High. Educ . 21, 1269–1275. doi: 10.1108/IJSHE-06-2020-0217

CrossRef Full Text | Google Scholar

Arenal, A., ArmuÃa, C., Feijoo, C., Ramos, S., Xu, Z., and Moreno, A. (2020). Innovation ecosystems theory revisited: The case of artificial intelligence in China. Artif. Intell. Econ. Soc . 44, 101960. doi: 10.1016/j.telpol.2020.101960

Baek, S., Jung, W., and Han, S. H. (2021). A critical review of text-based research in construction: Data source, analysis method, and implications. Automat. Constr . 132, 103915. doi: 10.1016/j.autcon.2021.103915

PubMed Abstract | CrossRef Full Text | Google Scholar

Barbier, E. B., and Burgess, J. C. (2020). Sustainability and development after COVID-19. World Dev . 135, 105082. doi: 10.1016/j.worlddev.2020.105082

Barceló, J. (2010). Fundamentals of Traffic Simulation (? 145) . Berlin: Springer.

Bina, O., and La Camera, F. (2011). Promise and shortcomings of a green turn in recent policy responses to the “double crisis.” Ecol. Econ . 70, 2308–2316. doi: 10.1016/j.ecolecon.2011.06.021

Björklund, M. (2011). Influence from the business environment on environmental purchasing—Drivers and hinders of purchasing green transportation services. J. Purchasing Supply Manag . 17, 11–22. doi: 10.1016/j.pursup.2010.04.002

Bonilla, C. A., Merigó, J. M., and Torres-Abad, C. (2015). Economics in Latin America: A bibliometric analysis. Scientometrics 105, 1239–1252. doi: 10.1007/s11192-015-1747-7

Campbell, D. A. (2017). An update on the United Nations millennium development goals. J. Obstetr. Gynecol. Neonatal Nurs . 46, e48–e55. doi: 10.1016/j.jogn.2016.11.010

Chenari, B., Dias Carrilho, J., and Gameiro da Silva, M. (2016). Towards sustainable, energy-efficient and healthy ventilation strategies in buildings: A review. Renew. Sustain. Energy Rev . 59, 1426–1447. doi: 10.1016/j.rser.2016.01.074

Clarke, E., and Visser, J. (2019). Pragmatic research methodology in education: Possibilities and pitfalls. Int. J. Res. Method Educ . 42, 455–469. doi: 10.1080/1743727X.2018.1524866

Death, C. (2015). Four discourses of the green economy in the global South. Third World Quart . 36, 2207–2224. doi: 10.1080/01436597.2015.1068110

Du, K., Li, P., and Yan, Z. (2019). Do green technology innovations contribute to carbon dioxide emission reduction? Empirical evidence from patent data. Technol. Forecast. Soc. Change 146, 297–303. doi: 10.1016/j.techfore.2019.06.010

Farrukh, M., Meng, F., Raza, A., and Tahir, M. S. (2020). Twenty-seven years of Sustainable Development Journal: A bibliometric analysis. Sustain. Dev . 28, 1725–1737. doi: 10.1002/sd.2120

Ferguson, P. (2015). The green economy agenda: Business as usual or transformational discourse? Environ. Polit . 24, 17–37. doi: 10.1080/09644016.2014.919748

Gainsborough, M. (2018). Transitioning to a green economy? Conflicting visions, critical opportunities and new ways forward: review essay: Transitioning to a green economy? Dev. Change 49, 223–237. doi: 10.1111/dech.12364

Georgeson, L., Maslin, M., and Poessinouw, M. (2017). The global green economy: A review of concepts, definitions, measurement methodologies and their interactions. Geo Geogr. Environ . 4, e00036. doi: 10.1002/geo2.36

Government of Dubai (2022). 8th World Green Economy Summit starts Wednesday With the Participation of Prominent Climate Action Leaders, Officials and Experts From Around the World . Available online at: https://mediaoffice.ae/en/news/2022/September/27-09/World-Green-Economy-Summit (accessed January 18, 2022).

Gunay, S., Muhammed, S., and Elkanj, N. (2022). Risk transmissions between regional green economy indices: Evidence from the US, Europe and Asia. J. Clean. Product . 379, 134752. doi: 10.1016/j.jclepro.2022.134752

Hristov Kalin. (2017). Internet plus policy: A study on how China can achieve economic growth through the internet of things. J. Sci. Technol. Pol. Manag . 8, 375–386. doi: 10.1108/JSTPM-03-2017-0007

Jacobs, M. (2012). Green Growth: Economic Theory and Political Discourse (? 108) . London: Grantham Research Institute on Climate Change and the Environment London.

Jin, Y., Tang, Y. M., Chau, K. Y., and Abbas, M. (2022). How government expenditure Mitigates emissions: A step towards sustainable green economy in belt and road initiatives project. J. Environ. Manag . 303, 113967. doi: 10.1016/j.jenvman.2021.113967

Kasayanond, A. (2019). Environmental sustainability and its growth in Malaysia by elaborating the green economy and environmental efficiency. Int. J. Energy Econ. Pol . 9, 465–473. doi: 10.32479/ijeep.8310

Kaur, G., Uisan, K., Ong, K. L., and Lin, C. S. K. (2018). Recent trends in green and sustainable chemistry & waste valorisation: Rethinking plastics in a circular economy. Curr. Opin. Green Sustain. Chem . 9, 30–39. doi: 10.1016/j.cogsc.2017.11.003

Kronenberg, K., and Fuchs, M. (2021). Aligning tourism's socio-economic impact with the United Nations' sustainable development goals. Tour. Manag. Perspect . 39, 100831. doi: 10.1016/j.tmp.2021.100831

Lee, C.-C., Wang, C.-W., and Ho, S.-J. (2022). The dimension of green economy: Culture viewpoint. Econ. Anal. Pol . 74, 122–138. doi: 10.1016/j.eap.2022.01.015

Lee, J.-H., and Woo, J. (2020). Green new deal policy of South Korea: Policy innovation for a sustainability transition. Sustainability 12, 10191. doi: 10.3390/su122310191

Levy, B. S., and Patz, J. A. (2015). Climate change, human rights, and social justice. Ann. Glob. Health 81, 310–322. doi: 10.1016/j.aogh.2015.08.008

Li, J., Dong, K., and Dong, X. (2022). Green energy as a new determinant of green growth in China: The role of green technological innovation. Energy Econ . 114, 106260. doi: 10.1016/j.eneco.2022.106260

Linde, L., SjÃdin, D., Parida, V., and Gebauer, H. (2021). Evaluation of digital business model opportunities. Res. Technol. Manag . 64, 43–53. doi: 10.1080/08956308.2021.1842664

Loiseau, E., Saikku, L., Antikainen, R., Droste, N., Hansjürgens, B., Pitkänen, K., et al. (2016). Green economy and related concepts: An overview. J. Clean. Prod . 139, 361–371. doi: 10.1016/j.jclepro.2016.08.024

Luukkanen, J., Kaivo-oja, J., Vahakari, N., O'Mahony, T., Korkeakoski, M., Panula-Ontto, J., et al. (2019). Green economic development in Lao PDR: A sustainability window analysis of Green Growth Productivity and the Efficiency Gap. J. Clean. Prod . 211, 818–829. doi: 10.1016/j.jclepro.2018.11.149

Mahmood, S. A. I. (2011). Air pollution kills 15,000 Bangladeshis each year: The role of public administration and government's integrity. J. Public Admin. Pol. Res . 3, 129.

Martínez-López, F. J., Merigó, J. M., Valenzuela-Fernández, L., and Nicolás, C. (2018). Fifty years of the European Journal of Marketing: A bibliometric analysis. Eur. J. Market . 52, 439–468. doi: 10.1108/EJM-11-2017-0853

Metawa, N., Dogan, E., and Taskin, D. (2022). Analyzing the nexus of green economy, clean and financial technology. Econ. Anal. Pol . 76, 385–396. doi: 10.1016/j.eap.2022.08.023

Mikhno, I., Koval, V., Shvets, G., Garmatiuk, O., and Tamošiuniené, R. (2021). Green economy in sustainable development and improvement of resource efficiency. Central Eur. Bus. Rev . 10, 99–113. doi: 10.18267/j.cebr.252

Mora-Valentin, E.-M., Najera-Sanchez, J.-J., and Ortiz-de-Urbina-Criado, M. (2022). Assessment of success in university-industry cooperation literature: A bibliographic coupling analysis. Sci. Publ. Pol . 49, 686–698. doi: 10.1093/scipol/scac019

Naidoo, R., and Fisher, B. (2020). Reset sustainable development goals for a pandemic world. Nature 583, 198–201. doi: 10.1038/d41586-020-01999-x

Policy Advisor (2016). Benefits of a Green Economy Transformation in Sub-Saharan Africa. Green Growth Knowledge Partnership . Available online at: https://www.greengrowthknowledge.org/blog/benefits-green-economy-transformation-sub-saharan-africa (accessed January 18, 2022).

Reddy, B. S. (2016). India's energy system transition-Survival of the greenest. Renew. Energy 92, 293–302. doi: 10.1016/j.renene.2016.02.027

Rizzo, A. (2020). Megaprojects and the limits of “green resilience” in the global South: Two cases from Malaysia and Qatar. Urban Stud . 57, 1520–1535. doi: 10.1177/0042098018812009

Rosati, F., and Faria, L. G. D. (2019). Addressing the SDGs in sustainability reports: The relationship with institutional factors. J. Clean. Prod . 215, 1312–1326. doi: 10.1016/j.jclepro.2018.12.107

Seshaiyer, P., and McNeely, C. L. (2020). Challenges and opportunities from COVID-19 for global sustainable development. World Med. Health Pol . 12, 443–453. doi: 10.1002/wmh3.380

Sjödin, D., Parida, V., Palmié, M., and Wincent, J. (2021). How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops. J. Bus. Res . 134, 574–587. doi: 10.1016/j.jbusres.2021.05.009

Smith, K. R., Dutta, K., Chengappa, C., Gusain, P. P. S., and Berrueta, O. M. V.. (2007). Monitoring and evaluation of improved biomass cookstove programs for indoor air quality and stove performance: Conclusions from the Household Energy and Health Project. Energy Sustain. Dev . 11, 5–18. doi: 10.1016/S0973-0826(08)60396-8

Sovacool, B. K., Martiskainen, M., Hook, A., and Baker, L. (2019). Decarbonization and its discontents: A critical energy justice perspective on four low-carbon transitions. Climat. Change 155, 581–619. doi: 10.1007/s10584-019-02521-7

Tarkhanova, E. A., Chizhevskaya, E. L., Fricler, A., Baburina, N. A., and Firtseva, S. (2020). Green economy in Russia: The investments' review, indicators of growth and development prospects. Entrepreneur. Sustainabil. Iss . 8, 649–661. doi: 10.9770/jesi.2020.8.2(39)

Tennekes, M. (2018). tmap: Thematic Maps in R. J. Stat. Softw . 84, 1–39. doi: 10.18637/jss.v084.i06

Thenkabail, P. S. (2010). Global croplands and their importance for water and food security in the twenty-first century: Towards an ever green revolution that combines a second green revolution with a blue revolution. Remote Sensing 2, 2305–2312. doi: 10.3390/rs2092305

UNEP (2011). Towards a Green Economy: Pathways to Sustainable Development and Poverty Eradication. Department of Economic and Social Affairs . Available online at: https://sdgs.un.org/publications/towards-green-economy-pathways-sustainable-development-and-poverty-eradication-17007 (accessed January 18, 2022).

UNEP (2012). Green Economy Sectoral Study: A Catalyst for Transitioning to a Green Economy in Namibia. African Wildlife Economy Institute . Available online at: https://www0.sun.ac.za/awei/publications/green-economy-sectoral-study-catalyst-transitioning-green-economy-namibia (accessed January 18, 2022).

Vuola, M., Korkeakoski, M., Vähäkari, N., Dwyer, M. B., Hogarth, N. J., Kaivo-oja, J., et al. (2020). What is a green economy? Review of national-level green economy policies in Cambodia and Lao PDR. Sustainability 12, 6664. doi: 10.3390/su12166664

Wang, X., Xu, Z., Qin, Y., and Skare, M. (2022). Innovation, the knowledge economy, and green growth: Is knowledge-intensive growth really environmentally friendly? Energy Econ . 115, 106331. doi: 10.1016/j.eneco.2022.106331

Wang, X., Zhao, Y., Liu, R., and Zhang, J. (2013). Knowledge-transfer analysis based on co-citation clustering. Scientometrics 97, 859–869. doi: 10.1007/s11192-013-1077-6

Zhang, J., Lu, G., Skitmore, M., and Ballesteros-Perez, P. (2021). A critical review of the current research mainstreams and the influencing factors of green total factor productivity. Environ. Sci. Pollut. Res . 28, 35392–35405. doi: 10.1007/s11356-021-14467-4

Keywords: green economy, systematic review, policy, sustainability, recycling economy, bibliometric

Citation: Zhu JJ, Zhang R, Kanhalikham K, Liu Z and Shen X (2023) Green economy studies amongst the global climate change challenge between 2016 and 2022: a bibliometric review. Front. Ecol. Evol. 11:1168437. doi: 10.3389/fevo.2023.1168437

Received: 17 February 2023; Accepted: 18 May 2023; Published: 26 June 2023.

Reviewed by:

Copyright © 2023 Zhu, Zhang, Kanhalikham, Liu and Shen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Xiaoping Shen, 352966933@qq.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

International Journal of Green Economics

IJGE addresses all aspects of Green Economics, aiming to encourage economic change and positioning Green Economics at the centre of the Economics disciplines. Green Economic theories, policies, tools, instruments and metrics are developed to facilitate a change to the current economic models for the benefit of the widest number of people and the planet as a whole. IJGE focuses particularly on resource management, meeting peoples'needs and the impact and effects of international trends and how to increase social justice.

Topics covered include

• Theories/concepts; critique of corporate activity, economic discourse/disciplines
• Environmental/welfare/development economics, fair-trade, aid, FDI, trickle down
• Neo-classical/Marxist/colonial, eco socialism/feminism, women's economics
• Costing resources, patriarchy and accumulation, resource allocation
• Government, freedom, democracy, privatisation, human happiness/needs hierarchy
• Bretton Woods, EU, UN, IMF, World Bank, WTO, GATTS
• Buying politics: war, arms trade, oil, war on terrorism; multinationals, globalisation
• Trading blocks, new protectionism, international governance, Tobin tax
• Offshoring, outsourcing, tariff barriers, new economic indicators, consumerism
• Green solutions, eco taxes, eco-labelling, environmental management as an industry
• Resource management, zero waste, site here to sell here, reuse, recycle, repair
• Quality of life, QoL indicators, consumerism, co-ops, land values, resource valuation
• New paradigms of the economy, grass roots activism, surplus reduction
• Social/environmental justice, participatory practices, polluter pays, triple bottom line
• Indigenous rights, less-developed countries, subsistence economies, poverty, wealth
• Economic discourse and disciplines, compared, critiqued and contrasted in order to position Green Economics - including the following subjects
• Environmental economics
• Welfare economics
• Development economics
• Costing resources
• Neo classical
• Neo Marxist
• Neo colonial
• Eco socialism
• Eco feminism and women’s economics
• Patriarchy and accumulation: problems in resource allocation
• Foreign direct investment
• Trickle down theories
• Game theory
• New theories of human needs hierarchy vs. supply and demand
• Human happiness
• Implementation problems and issues in government
• Bretton Woods
• Democracy and privatisation
• Buying politics - war, the arms trade, oil and the war on terrorism
• Trading blocks
• New protectionism
• International governance
• Off shoring
• Outsourcing
• Multinationals and tariff barriers
• Green solutions
• Resource management
• New economic indicators
• Site here to sell here
• Recycle, repair
• Quality of life and consumerism
• Land value issues
• Resource valuation
• New paradigms of the economy: nature, colonies, biosphere, non human species, women minorities
• Questions of 10,000 years of agriculture and patriarchy dominating the economy
• Post agricultural social and economic requirements and organisation
• Planning to reduce surplus and not to harness it for power over others
• Inclusive, participatory practices
• Polluter pays
• Triple bottom line
• Stakeholder perspectives
• Roles and activity within multinationals
• Consumerism
• Procurement
• Processes of globalisation at a practical level
• Corporate governance
• Environmental management as an industry
• Eco-labelling
• Limiting the power of the multinationals
• Shareholding as a means of gaining influence on the board
• Grass roots activism
• Civil society and attitudes to acceptable economic activity
• Indigenous rights
• Less developed countries
• Subsistence economies
• Purchasing power parity
• Balance of rich and poor in different societies
• Poverty and its effects
• Poverty and its definitions
• Quality of life indicators
• Tools for ensuring that policies involve justice
• Environmental justice and consequences of a lack of it

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

•  We're Hiring!
•  Help Center

Green Economics

• Most Cited Papers
• Most Downloaded Papers
• Newest Papers
• Last »
• Ecological Economics Follow Following
• Environmental Economics Follow Following
• Green Politics Follow Following
• Sumber Daya Alam Follow Following
• Lingkungan Hidup Follow Following
• Environmental Justice Follow Following
• Bioeconomics Follow Following
• Welfare Economics Follow Following
• Socialist Economics Follow Following
• Energy Economics Follow Following

Enter the email address you signed up with and we'll email you a reset link.

• Academia.edu Journals
•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

Green Finance Research Around the World: A Review of Literature

International Journal of Green Economics, Forthcoming

31 Pages Posted: 25 Apr 2022 Last revised: 13 Jun 2022

Peterson K Ozili

Central Bank of Nigeria

Date Written: 2022

This paper reviews the existing research on green finance. It identifies the important themes in the green finance literature, particularly, the strategies to increase green financing; efforts to make green investment profitable; promoting green financing using technology and policy, the role of regulators and financial institutions in the green finance agenda, and the challenges of green financing. Several cross-country observations about the challenges of green finance and solutions to green finance issues are documented. The findings show that green finance has the potential to make a significant difference in the environment, society and for climate change mitigation, but many challenges abound such as the lack of awareness about green finance, inconsistent definitions of green finance, lack of policy coordination for green financing, inconsistent policies, and lack of profitable incentives to investors and financial institutions who are willing to invest in climate change mitigation.

Keywords: literature review, green finance, green investment, climate change, sustainable finance, green bonds, green banks, sustainable development goals, climate finance, environment, green loan, climate change mitigation. Paris Agreement, COP26.

JEL Classification: F64, F65, G21, Q01, Q56.

Suggested Citation: Suggested Citation

Peterson K Ozili (Contact Author)

Central bank of nigeria ( email ).

Abuja Abuja, 09 Nigeria

Do you have a job opening that you would like to promote on SSRN?

Paper statistics, related ejournals, european finance ejournal.

Subscribe to this fee journal for more curated articles on this topic

International Finance eJournal

Corporate social responsibility (csr) ejournal, investment & social responsibility ejournal, environmental economics ejournal, socially responsible investment ejournal, environmental, social & governance (esg) research hub ejournal.

Subscribe to this free journal for more curated articles on this topic

Political Economy - Development: Environment eJournal

Environmental education ejournal.

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

Can green credit policies improve the digital transformation of heavily polluting enterprises: A quasi-natural experiment based on difference-in-differences

Roles Conceptualization, Writing – review & editing

Affiliation School of Economics and Management, North University of China, Taiyuan, Shanxi, China

Roles Writing – original draft

* E-mail: [email protected]

Affiliation School of Economics and Management, North University of China, Guiyang, Guizhou, China

Roles Conceptualization

Affiliation School of Economics and Management, North University of China, Xingtai, Hebei Province, China

• Xuan Zhou, 
• Dejia Yuan, 
• Zhengwei Geng

• Published: August 29, 2024
• https://doi.org/10.1371/journal.pone.0307722
• Peer Review
• Reader Comments

The digital transformation of the manufacturing industry is closely linked to green credit policies, which jointly promote the development of the manufacturing industry towards a more environmentally friendly, efficient and sustainable development. Based on the research sample of China’s manufacturing A-share listed companies from 2008 to 2022, this paper uses the difference-in- differences (DID) method to analyze the impact of green credit policies on the digital transformation of heavily polluting enterprises. The results show that green credit policies significantly inhibit the digital transformation of heavily polluting enterprises. In terms of the adjustment mechanism, the R&D investment of enterprises and the financial background of senior executives have weakened the inhibitory effect of green credit policies on the digital transformation of heavily polluting enterprises. When the R&D investment is low, the inhibitory effect of the policy is more significant, but with the increase of R&D investment, the inhibitory effect of the policy gradually weakens, indicating that there is a substitution relationship between the two. Enterprises with senior financial expertise have a deeper understanding of financial feasibility and benefit analysis, and are more receptive to the high-risk investment of digital transformation, while their financial network resources can help broaden financing channels, reduce financing constraints, and further reduce the financial difficulty of digital transformation. In addition, the green credit policy has a stronger inhibitory effect on the digital transformation of non-state-owned enterprises and enterprises that do not hold bank shares. The conclusions of this paper are expected to provide some policy implications for the subsequent green credit policies in promoting the digital transformation of the manufacturing industry.

Citation: Zhou X, Yuan D, Geng Z (2024) Can green credit policies improve the digital transformation of heavily polluting enterprises: A quasi-natural experiment based on difference-in-differences. PLoS ONE 19(8): e0307722. https://doi.org/10.1371/journal.pone.0307722

Editor: Juan E. Trinidad-Segovia, University of Almeria: Universidad de Almeria, SPAIN

Received: March 29, 2024; Accepted: July 10, 2024; Published: August 29, 2024

Copyright: © 2024 Zhou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Today, as the world experiences rapid digital technology and rising environmental issues, the challenges facing businesses are more complex and urgent. The frontier of digital technology has not only changed the business landscape, but also redefined the position of enterprises in global competition. At the same time, global environmental problems, such as climate change and resource depletion, are threatening the sustainable development of enterprises. As a result, digital transformation and environmental protection, as the two major themes that will lead the future development, are gradually becoming the core elements shaping the corporate strategy. On the one hand, driven by the current wave of digitization, the manufacturing industry is undergoing a profound change, and digital transformation has become a strategic choice for enterprises in meeting future challenges and seizing opportunities, as a strategic initiative integrating advanced technology and innovative thinking, which is leading the manufacturing industry into a new era. The digitalization of the manufacturing industry is a product of internal and external environmental factors [ 1 ], which has a significant impact on the production process and management process of enterprises, which not only changes the traditional production methods, but also leads to a major transformation of enterprise management, marketing, product innovation and other levels. On the other hand, environmental protection is of great importance in today’s global economy. Manufacturing companies must comply with increasingly stringent environmental regulations and standards, which is not only a social responsibility for enterprises, but also an important way to achieve sustainable development. Environmental requirements are driving companies to innovate in technology and business models, and to explore new development opportunities. Through R&D and application of environmental protection technologies, enterprises can develop new products and services, and open up new markets, which can not only enable the manufacturing industry to meet the regulatory requirements of the green environment and social expectations, but also improve resource utilization efficiency, reduce operational risks, enhance market competitiveness, and explore innovation and development opportunities. Driven by digitalization and environmental protection, manufacturing enterprises should integrate environmental protection into their strategic planning, promote green transformation, and achieve a win-win situation of economic and environmental benefits.

As a financial instrument to encourage environmental initiatives, green credit policies provide a new source of funding for companies, and by rewarding environmental measures, they may play a key role in driving companies to participate more actively in the process of digital transformation. From the perspective of capital, the digital transformation of the manufacturing industry requires huge financial support, and this policy may provide enterprises with a way of sustainable financing, which is expected to alleviate the huge financial pressure they may face in the digital transformation. From the perspective of incentive mechanism, green credit policies may also become a driving force for enterprises to take the initiative to move towards digital transformation. With the emphasis of the government and society on environmental responsibility, enterprises are expected to obtain more favorable green credit terms by adopting digital technologies to improve the efficiency of production processes, reduce resource waste, and reduce environmental emissions.

However, while green credit policies may encourage firms to invest more in environmentally friendly technologies in the short term, their specific impact on long-term technological innovation by firms, especially digital transformation, which requires a significant investment of capital and time to bear fruit, remains an area of challenge and unanswered questions. Firstly, the complexity of digital transformation is reflected in the fact that it is not just a technological update, but a comprehensive organizational change. It involves adjustments to company culture, employee training, and the integration of new technologies on a number of levels, all of which take time and effort to change. While short-term green credit policies incentives may push companies to make initial investments in environmentally friendly technologies, to achieve digital transformation in the true sense of the word, companies need longer-term plans and commitments. Second, investments in digital transformation are not permanent and require a continuous injection of capital at different stages. The incentives provided by green credit policies in the short term may not be able to meet the funding needs for the entire transformation cycle. Enterprises may receive some financial support in the initial stage, but the scale and frequency of financial investment may gradually increase as the project deepens and expands. In summary, enterprises must explore the relationship between green finance and digital transformation more actively while pursuing sustainable development. Especially for those heavily polluting enterprises, digital transformation is not only a need to enhance their competitiveness, but also an urgent requirement to fulfill their social responsibility. In this context, whether green credit policy can become a catalyst to promote the accelerated digital transformation of heavily polluting enterprises is a question that deserves in-depth exploration.

Currently, academics have conducted a lot of research on manufacturing digital transformation and green credit policies respectively. On the one hand, studies have shown that digital transformation helps alleviate the information asymmetry between investors and enterprises, and between enterprises and product supply and demand markets, enabling investors to more accurately assess the value and potential of enterprises [ 2 ]. At the same time, the information asymmetry between enterprises and product supply and demand markets has also been alleviated to a certain extent, which leads to more efficient operation of the market. Enterprise digital transformation through digital technology, enterprises can more easily access to financing channels and financing information, improve the flexibility and efficiency of financing, can ease the enterprise financing constraints and reduce the cost of financing, which provides a wider range of financial support for the development and expansion of enterprises, and helps to promote the innovation and upgrading of the manufacturing industry [ 3 ]. In addition, digital transformation can significantly improve the innovation efficiency of enterprises, especially green innovation [ 4 ]. Digital knowledge management (KM) has a significant positive impact on technological innovation, mainly through absorptive capacity, adaptive capacity and innovative capacity [ 5 ]. Meanwhile, the digital transformation of high-tech industries has a positive effect on both technological innovation and achievement transformation [ 6 ]. On the other hand, in terms of green credit policies, the introduction of the Green Credit Guidelines in 2012 marked the official implementation of green credit policies, which is the core of China’s green credit policies system and an important perspective for many scholars to study [ 7 ]. However, most current studies show that the implementation effect of green credit policies is not satisfactory [ 8 ]. On the one hand, green credit policies will inhibit bank loans and long-term financing of heavy polluting enterprises through financing constraint theory and financing cost theory [ 9 ], and significantly reduce long-term bank loans of heavy polluting enterprises [ 10 ]. On the other hand, the green credit policy significantly inhibits the level of technological innovation of heavy polluters [ 11 ]. Maybe the policy will improve the sustainable development of enterprises in the short term, but it has no long-term effect [ 12 ] and promotes poorly managed zombie enterprises [ 13 ].

In summary, digital transformation and green credit policies are key factors in the process of high-quality development of the manufacturing industry in terms of technological innovation, transformation and upgrading. At present, there is a large number of literatures on the digital transformation of the manufacturing industry and green credit policies, but few studies combine the two to explore the relationship between green credit policies and the digital transformation of the manufacturing industry. Therefore, the marginal contributions of this paper may be: Firstly, the uniqueness of the research: This paper may be the first time to deeply explore the relationship between digital transformation in the manufacturing industry and green credit policies, combining these two key areas for research. This research is unique in that it connects the two key themes of digital transformation and environmental policies, filling a gap in the existing literature and providing a new research perspective for the academic community. Secondly, the importance of research to academia and practice: This paper fills the gap in the academic understanding of the relationship between digital transformation and green development in the manufacturing industry, and provides new ideas and methods for solving problems in this field. At the same time, the research results of this paper are of great significance for practice, which can provide useful reference suggestions for China’s green credit policies formulation and digital transformation of the manufacturing industry, promote the sustainable development of the manufacturing industry, and promote the development of China’s economy in a greener and more innovative direction. Thirdly, the theoretical and empirical contributions of the research: By exploring the impact mechanism of green credit policies on the digital transformation of the manufacturing industry, this paper expands the existing theoretical framework and provides new ideas and perspectives for theoretical research. Besides, this paper provides new empirical evidence based on empirical data, deepens the understanding of the mechanism of green credit policies in the process of digital transformation, and provides strong support for practice in related fields. Fourthly, the potential impact of the research: The research results of this paper are expected to have a profound impact on policy-making and practice. By proposing more effective green credit policies to promote the sustainable development of the manufacturing industry, this paper will help guide the government and enterprises to better formulate policies and strategies, promote the development of China’s manufacturing industry in a more digital, green and sustainable direction, and contribute to the realization of high-quality economic development.

Materials and methods

Theoretical analysis and research hypothesis.

Digital transformation typically requires large-scale capital investments to meet the costs of building information technology infrastructure, procuring innovative technologies and training employees. Such investment is necessary to drive enterprises to achieve business process optimization, improve productivity, expand market share, and enhance innovation. However, the introduction of “the Green Credit Guidelines” tends to exacerbate the financing constraints of heavy polluters [ 14 ], which in turn may hinder their active participation in digital transformation. Firstly, from a financial perspective, the financial requirements for digital transformation are usually large, including but not limited to the updating of IT infrastructure, the construction of big data analytics platforms, the introduction of artificial intelligence technologies and related training costs. Heavily polluting enterprises usually face higher environmental risks, and from the "principal-agent cost theory" and "modern contract theory", it can be seen that the principal-agent cost between the bank, as a creditor, and the enterprise will increase with the increase in project risks, including the costs of identification, monitoring, management and auditing. The cost of identification, monitoring, management and auditing, etc. will lead banks to adopt a more conservative strategy when considering costs and benefits. Meanwhile, according to the "risk compensation theory", in order to compensate for the potential environmental risks and possible default risks in the future, banks and financial institutions may require heavy polluting enterprises to pay higher financing costs or put forward more stringent lending conditions [ 15 ], such as higher interest rates or additional collateral, in order to obtain the price of risk-bearing compensation. This will lead to higher financing costs for heavy polluters [ 16 ]. This means a tighter financial situation for heavy polluting enterprises who are already under pressure to make environmental improvements, reducing their ability to invest in digital transformation.

Secondly, from the perspective of environmental protection and governance costs, the environmental regulatory effect brought about by “the Green Credit Guidelines” will increase the rectification efforts of heavy polluting enterprises to reduce pollution and emissions, which will to some extent reduce the priority and capital investment in digital transformation projects, thus slowing down the process of digital transformation. On the one hand, heavy polluting enterprises may need to reallocate resources in order to comply with the requirements of “the Green Credit Guidelines”, which means that enterprises may need to invest more R&D funds and human resources into the end-of-pollution treatment [ 17 ], reducing the allocation of funds and resources in digital transformation. This not only makes digital transformation projects significantly less economically attractive within enterprises, but also further inhibits the pace of transformation in the digital field for heavily polluting enterprises. On the other hand, the process of environmental protection management may involve changes such as re-planning of production lines, optimization of production processes, and upgrading of environmental protection facilities. This not only requires the investment of a large amount of resources, but also may lead to disruptions and uncertainties in the production process, bringing additional disturbances to the normal operation of the enterprise. Accordingly, the author proposes the following hypothesis:

H1: “The Green Credit Guidelines” significantly inhibit the digital transformation of heavy polluters.

The amount and quality of an enterprise’s R&D investment is directly related to its innovative capacity and future development potential. In today’s competitive market, firms that are able to increase their R&D investment on a sustained basis are usually more likely to be able to adapt to market changes and meet future challenges. High levels of R&D investment may play a key role in the digital transformation of heavily polluting firms in weakening the disincentive effect of green credit policies. Firstly, increased R&D investment can make firms more technologically innovative [ 18 ], accelerate their digital transformation process, and promote the adoption of more advanced digital technologies. This not only improves productivity and product quality, but also helps to reduce environmental emissions, thus meeting the expectations of green credit policies on environmental requirements. Technological innovation makes enterprises more flexible in digital transformation and allows them to better respond to the environmental standards of the policy, thus weakening the inhibiting effect of the policy on digital transformation. Secondly, a high level of R&D investment helps to improve the productivity of enterprises, and through the application of digital technology, enterprises are able to manage and utilize resources more effectively. Initiatives such as optimizing the supply chain and implementing smart manufacturing can reduce the waste of energy and raw materials and lessen the burden on the environment. This efficient use of resources makes it easier for firms to adapt to the environmental requirements of the policy, diminishing the constraints of green credit policies on digital transformation. Once again, increased investment in R&D demonstrates a firm’s commitment to innovation and sustainability. This strategic shift makes firms more inclined to adopt digital technologies to improve productivity and reduce environmental impacts. For heavily polluting firms, digital transformation is not only a technological upgrade, but also a necessary tool to comply with the SDGs. Investments in research and development lead companies towards a digitalization path that is consistent with green credit policies, slowing down the disincentive effect of the policies.

At the same time, investment in R&D is not only about technical aspects, but also includes investment in training and culture. By improving employees’ understanding and ability to apply digital technologies, companies can better adapt to the level of technology required for digital transformation and more easily comply with green credit policies. Building green awareness and a culture of sustainability can help firms better integrate digital technologies and mitigate the disincentive effect of policies on digital transformation. In addition, the relationship between R&D investment intensity and enterprise survivability shows a "U" non-linear relationship, i.e., R&D investment intensity can greatly improve the survivability of enterprises after reaching a certain level [ 19 ]. This implies that a moderate increase in R&D investment by enterprises in the process of digital transformation can improve their competitive position in the market while increasing their innovation ability, and mitigate the potential inhibitory effect of green credit policy on their digital transformation. Overall, corporate R&D investment may affect corporate digital transformation on multiple levels by driving technological innovation, improving productivity, promoting sustainable development, and fostering corporate culture. Efforts in all these areas can help weaken the inhibitory effect of green credit policies on the digital transformation of heavy polluting enterprises and enable them to carry out their digital transformation more smoothly. Accordingly, the author proposes the following research hypothesis:

H2: Firms’ R&D investment weakens the dampening effect of “the Green Credit Guidelines” on the digital transformation of heavily polluting firms.

The digital transformation of an enterprise is inherently a high-risk business investment, as it involves huge capital investment in new technologies, systems, training and human resources, and such high-cost, resource-intensive investment poses a greater financial challenge to the enterprise. Importantly, digital transformation is usually characterized by greater uncertainty, with technology risk being a key consideration. The introduction of new technologies may lead to technology integration issues and additional costs, and the results and rewards of digital transformation usually take longer to become apparent. In addition, digital transformation requires a cultural shift within the organization, including employee training and adaptation to new ways of working, and this cultural change can be a complex and time-consuming process. Top echelon theory suggests that executives with a financial background typically have a greater tolerance for risk. This trait may have a significant impact in the project decision-making process, making executives more willing to take risks and thus increasing the likelihood that firms will choose riskier projects [ 20 ]. Because executives with a financial background typically have a deeper understanding of national policies, market volatility, and financial instruments, they may be more responsive to financial incentives in green credit policies. Compared to their counterparts with non-financial backgrounds, they may be able to utilize green credit resources more effectively in digital transformation and reduce the cost of corporate finance, which in turn will make them more confident in dealing with potential risks, and thus more willing to choose higher-risk investments in corporate projects, leading to a smooth digital technology transition.

At the same time, as executives with a financial background usually have profound financial knowledge and risk management skills, they have a deeper understanding of financial feasibility and benefit analysis. Therefore, they pay more attention to the financial feasibility of enterprise digital transformation in the decision-making process, which helps to establish a more efficient financial review and decision-making process [ 21 ], and can more accurately assess the positive impact of green credit policies on the enterprise’s financial position compared to others. This financial sensitivity makes them more capable of reducing potential uncertainties through rational financial strategies, and more able to increase enterprises’ acceptance of digital transformation, thus more actively promoting enterprises to follow the path of green transformation. Additionally, executives with financial background can use their own financial network resources to establish bank-enterprise contacts, broaden financing channels, reduce the information asymmetry between the enterprise and the bank, so that the enterprise can obtain more funds to alleviate the degree of enterprise financing constraints [ 22 ], and further reduce the financial difficulty of digital transformation. Based on the above analysis, the author puts forward the following research hypotheses:

H3: Executive financial background weakens the dampening effect of “The Green Credit Guidelines” on digital transformation of heavily polluting firms.

Research design

Model building..

“The Green Credit Guidelines” issued in 2012 provide a good quasi-natural experiment to study the impact of green credit policies on the digital transformation of manufacturing industries. According to the characteristics of this policy, heavily polluting firms should be affected firstly because they face higher environmental risks. Therefore, this paper includes heavily polluting enterprises in the experimental group and non-heavily polluting enterprises in the control group.

Data sources.

This paper takes listed companies in China’s manufacturing industry from 2008 to 2022 as the initial sample, and in order to improve the data quality and ensure the validity of the empirical analysis, the initial sample [ 23 ] is screened in accordance with the following criteria: (1) exclude companies with financial anomalies during the sample period, such as ST,* ST, and PT; (2) exclude companies that change their industries between heavy polluting enterprises and non- heavy polluting enterprises during the sample period; (3) exclude key data companies with serious missing data; (4) to avoid extreme values interfering with the findings of this paper, all continuous variables are subjected to the upper and lower 1% shrinkage. Through the above screening, the final sample includes 660 companies with a total of 9,345 observations, of which heavy polluting enterprises contain 220 companies and non- heavy polluting enterprises contain 440 companies; the data used in the study come from the CSMAR database, the iFind database, the Wind database, the National Bureau of Statistics, and MarkData.com , among others.

Variable selection.

Explained variable . The explained variable in this paper is the level of digital transformation of the enterprise, referring to the research results of Chen et al. (2021) [ 24 ]: Based on the statistics of 99 digital-related word frequencies in four dimensions: digital technology application, Internet business model, intelligent manufacturing, and modern information system, the digital transformation index of manufacturing enterprises was constructed by using text analysis method and expert scoring method. First, use text analytics to construct Digit_text variables. The first step is to collect the annual reports of listed companies in the manufacturing industry from 2008 to 2022 and convert them into text format, and then extract the text of the business analysis part through Python. The second step is to extract a certain number of samples of enterprises that have been successful in digital transformation through manual judgment. In the third step, the selected samples were processed by word segmentation and word frequency statistics to screen out high-frequency words related to digital transformation, which can be divided into four dimensions: digital technology application, Internet business model, intelligent manufacturing and modern information system, which suggests that we can construct the digital transformation index of enterprises from four dimensions (see Table 1 ). In the fourth step, based on the words formed in the third step, the text before and after is extracted from the total sample of listed companies, and the text combinations with high frequency are found. The fifth step is to supplement the keywords on the basis of the existing literature to form the final word segmentation dictionary. In the sixth step, based on the self-built word segmentation dictionary, the Jieba function is used to segment all samples, and the number of keyword disclosures is counted from four aspects: digital technology application, Internet business model, intelligent manufacturing and modern information system, so as to reflect the degree of transformation of the enterprise in all aspects. On this basis, the word frequency data was standardized, and the entropy method was used to determine the weight of each index, and finally the Digit text index was obtained.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0307722.t001

Secondly, according to the description of the above keywords in the annual report, the number of disclosures, and the production and operation of the enterprise, the expert scoring method is used to judge the degree of digital transformation of each company. Specifically, if "digitalization" is the main investment direction of the enterprise in the year, or "digitalization" has been integrated into the main business of the enterprise (including production, operation, R&D, sales and management, etc.), the Digit_score variable is scored with 3 points; If the enterprise’s relevant investment involves "digitalization", but "digitalization" is not the main investment direction at this stage, or the company’s main business has not yet achieved deep integration with "digitalization", 2 points will be scored for the Digit_score variable; If the company only touches on a small aspect of "digitalization", or only mentions it in its development strategy and business plan, the Digit_score is set at 1; If there is no mention of "digitalization" in the company’s annual report, or if the annual report reflects that the company has not implemented digital transformation, the Digit_score score is 0.

Finally, on the basis of the obtained Digit_text and Digit_score, the final total index Digit is synthesized according to the weight of 50% each, so as to fully reflect the degree of digital transformation of manufacturing enterprises.

Explanatory variable . Based on the principle of DID model, the explanatory variable is the interaction “Post*Treat” (DID) of the policy dummy variable (Post) and the industry dummy variable (Treat). Since “The Green Credit Guidelines” came into effect on 24 February 2012, 2012 is used as a time dummy variable in this article, and for 2012 and subsequent years, Post is equal to 1, otherwise it is equal to 0. Referring to previous studies [ 25 ], this paper selects the Catalogue of Classified Management Industries for Environmental Protection Verification of Listed Companies issued by the Ministry of Environmental Protection in 2008 to identify heavy polluting enterprises, and if they belong to the heavy polluting industries mentioned in the 2008 Ministry of Environmental Protection Notice, they are defined as heavy polluting enterprises. Treat is a grouping dummy variable, with 1 for heavily polluting enterprises and 0 for non-heavily polluting enterprises.

Control variables . In order to avoid the estimation bias caused by omitted variables, this paper refers to the results of previous research [ 26 ], and selects the following variables as the control variables in the empirical process: (1) Size, (2) Lev, (3) ROE, (4) Tobin Q, (5) Liquid, (6) Cashflow, (7) Loss, (8) Dual.

In summary, the specific definitions of the variables are shown in Table 2 .

https://doi.org/10.1371/journal.pone.0307722.t002

Descriptive statistics and analysis.

After the data in this paper were analyzed by descriptive statistics, the results are shown in Table 3 . It can be seen that the level of digital transformation (Digit) of China’s heavy polluting enterprises has a maximum value of 757, a minimum value of 0, and a standard deviation of 42.2630, indicating that there is a large difference in the degree of digital transformation among enterprises. The current ratio (Liquid) has a maximum value of 204.7421, a minimum value of 0.1065, and a standard deviation of 4.4500, indicating that there are also large differences in current ratios among firms. A higher liquidity ratio may indicate a more flexible operation and liquidity, while a lower liquidity ratio may indicate that a company is facing a shortage of funds or assets that cannot be liquidated quickly. Taken together, the descriptive statistics of both the level of digital transformation and the current ratio reveal that there are large differences in the operational management of China’s heavy polluters, and that these differences may have an important impact on the competitiveness and long-term development of the enterprises.

https://doi.org/10.1371/journal.pone.0307722.t003

Results and discussion

Benchmark regression.

Table 4 shows the empirical results of the impact of green credit policies on the digital transformation of heavy polluting enterprises, columns (1) and (2) are the cases of regression alone and adding control variables and fixing the year and individual, respectively. It can be concluded that the DID coefficients are all significantly negative, and the implementation of green credit policies significantly inhibits the digital transformation of heavily polluting enterprises, and hypothesis 1 is verified. The possible explanation is that at present, bank credit is the main financing method for most enterprises in China, and the introduction of the “The Green Credit Guidelines” will make banks more inclined to provide financial support to environmental protection enterprises, while heavy polluting enterprises are difficult to obtain financial support from banks due to serious environmental risks, which will eventually lead to a lack of funds for heavy polluting enterprises, thereby inhibiting a series of technological research and development activities such as digital transformation.

https://doi.org/10.1371/journal.pone.0307722.t004

Robustness check

Parallel trend test..

To ensure that the results of this paper are not affected by other policies and events, referring to the study of Zhang and Hu (2022) [ 27 ], the event study method is used to introduce multiple time dummy variables to construct early and lagged policy variables, and regressions are added while keeping the control variables unchanged. The results of the four coefficients before the promulgation of the policy and the coefficients in the last nine periods are shown in Table 5 , and the parallel trend test chart is shown in Fig 1 , the DID coefficients in the first four periods of the policy are not significant, while the coefficients in the nine periods after the promulgation of the policy are significantly negative. Therefore, the experimental group and the control group are comparable before the implementation of the policy in 2012, and the difference-in-difference regression model in this paper conforms to the parallel trend hypothesis, indicating that the original regression results are robust.

https://doi.org/10.1371/journal.pone.0307722.g001

https://doi.org/10.1371/journal.pone.0307722.t005

Placebo testing.

In order to ensure that the impact of “the Green Credit Guidelines” on the digital transformation of heavy polluting enterprises can truly reflect the effect of the policy without being influenced by other factors, drawing on the research results of Guo and Yin (2023) [ 17 ], an experimental group is randomly generated to simulate a situation that is not affected by the green credit policy, in order to compare the differences between the experimental group and the control group before the implementation of the policy. This is done by randomly, year-by-year and no-putback sampling 2008–2022 enterprises as the experimental group and the rest of the enterprises as the control group, and substituting them into model (1) for regression respectively. The probability density distribution of the coefficient estimates in the placebo test was obtained after 500 random draws and regression tests (see Fig 2 ). As can be seen from Fig 2 , the coefficient estimates from the placebo test are mainly distributed around zero, indicating that the original regression results are robust.

https://doi.org/10.1371/journal.pone.0307722.g002

In order to eliminate the endogeneity problem caused by potential selection bias, ensure the robustness of the research results, and improve the comparability of the experimental and control groups in terms of digital transformation, the propensity score matching method was used to conduct the robustness test, drawing on the study of Li (2023) [ 28 ]. All control variables in model (1) are selected as matching indicators in the propensity score matching model, and a Logit model is selected to estimate the propensity score, and then nearest-neighbor matching is used to re-match the experimental and control groups to ensure that there is no difference in other factors between the matched experimental and control groups except for the policy differences, and then subsequently re-estimate the model (1). Fig 3 shows that there is a significant difference between the experimental and control groups before matching, and Fig 4 shows the same trend after matching; the DID coefficient is still significantly negative at the 1% level from column (1) of Table 6 , which further validates the robustness of the findings of this paper.

https://doi.org/10.1371/journal.pone.0307722.g003

https://doi.org/10.1371/journal.pone.0307722.g004

https://doi.org/10.1371/journal.pone.0307722.t006

Replacement of core explanatory variables.

This paper replaces the explanatory variables with reference to the research results of Wu et al. (2021) [ 29 ], which are statistically derived from a total of 76 digitization-related word frequencies in five dimensions, namely, artificial intelligence technology, big data technology, cloud computing technology, blockchain technology, and the use of digital technology. The regression results are shown in column (2) of Table 6 , and the coefficient of DID is still significantly negative, which again verifies the robustness of the findings of this paper.

Treatment of endogenous problems.

Lagging the core explanatory variables by one period helps to alleviate the endogeneity problem and improves the model’s ability to explain time correlation and long-term causality. In this paper, by regressing the core explanatory variable DID with one period lag, the results are shown in column (3) of Table 6 , and the DID coefficient of is still significantly negative, which indicates that the findings of this paper are still robust after taking into account the time lag effect.

Mechanism of action tests

Moderating effects of r&d investment..

The results of the moderating effect test for R&D inputs reported in column (1) of Table 7 show a significantly negative coefficient for R&D inputs and a significantly positive coefficient for the interaction term between R&D inputs and green credit policies. This reflects that overall, enterprise R&D investment weakens the inhibitory effect of the Guidelines on the digital transformation of heavily polluting enterprises, and the inhibitory effect exerted by the policy is more obvious when R&D investment is low, but the inhibitory effect brought about by the policy gradually decreases with the increase of enterprise R&D investment, which suggests that there is a significant substitution relationship between R&D investment and the “the Green Credit Guidelines” in influencing the digital transformation of heavily polluting enterprises, and Hypothesis 2 can be verified. Firstly, the reason why R&D investment can attenuate the inhibitory effect of the green credit policy on the digital transformation of heavy polluting enterprises may be that by strengthening R&D investment, enterprises are more likely to improve their technological level, adopt more environmentally friendly technologies and production methods, and receive more support under the green credit policy, thus alleviating the policy’s restriction on the funds required for digital transformation. At the same time, it may indicate that policymakers recognize and encourage firms that meet their environmental goals through independent R&D, as these firms are more likely to succeed in digital transformation; second, the disincentive effect of the policy is relatively more pronounced when R&D inputs are low, which may be due to the fact that the policy puts more emphasis on promoting the digital transformation of firms through financial support, whereas, in the case of low R&D inputs, firms may be more rely on the green credit support provided by the government; finally, the inhibitory effect brought by the policy gradually decreases as the R&D investment of enterprises increases, which suggests that there is an obvious substitution relationship between the R&D investment and the green credit policy in influencing the digital transformation of heavily polluting enterprises, and the possible explanation is that enterprises may prefer to choose to meet the environmental protection requirements through independent R&D, instead of overly relying on the government’s green credit policies.

https://doi.org/10.1371/journal.pone.0307722.t007

Moderating effects of executive financial background.

The test results of the moderating effect of executive financial background reported in column (2) of Table 7 show that the coefficient of executive financial background is negative but insignificant and the coefficient of its interaction term with green credit policies is significantly positive, which suggests that executive financial background weakens the inhibitory effect of “the Green Credit Guidelines” on the digital transformation of heavily polluting firms, and Hypothesis 3 is verified. The possible reasons for this are as follows, the advantage of executive financial background lies in its greater tolerance to the high-risk nature of digital transformation. This is mainly reflected in the fact that financial expertise makes them more sensitive to the financial incentives of green credit policies and more effective in utilizing green credit resources, thus reducing the cost of corporate financing and increasing the acceptance of digital transformation as a high-risk investment. At the same time, gold executives with financial backgrounds have a deeper understanding of financial feasibility and benefit analysis, which reduces uncertainty through rational financial strategies and pushes enterprises to follow the green transformation path more actively. In addition, their financial contacts help broaden financing channels and reduce financing constraints, further easing the financial difficulty of digital transformation.

Heterogeneity analysis

Whether the enterprise is a state-owned enterprise..

In this paper, state-owned enterprises (SOEs) and non-state-owned enterprises (NSOEs) are regressed separately, and the results, as shown in columns (1) and (2) of Table 8 , indicate that the inhibitory effect of green credit policy on digital transformation is significantly higher for NSOEs than for SOEs. The possible explanations are as follows: firstly, SOEs and non-SOEs play different roles in China’s economic environment, with SOEs usually having easier access to government support and financing, while non-SOEs may be more dependent on indirect financing such as bank loans. Green credit policies may lead banks to be more prudent in approving loans and may place greater constraints on the financing needs of non-SOEs, thus inhibiting their digital transformation process; secondly, green credit policies usually require companies to take more steps in environmental compliance to qualify for loans. Non-state-owned enterprises may need more time and resources to meet these requirements, and thus may face greater resistance in the digital transformation process; finally, state-owned enterprises may enjoy market monopolies or more government support in some cases, which may make them more able to bear the costs of digital transformation. In contrast, non-State-owned enterprises may operate in more competitive market environments and be more vulnerable to green credit policies, as digital transformation requires greater capital investment.

https://doi.org/10.1371/journal.pone.0307722.t008

Whether the enterprise holds shares in the bank.

In this paper, firms holding bank shares and firms not holding bank shares are regressed separately. The results show that the inhibition effect of green credit policies on the digital transformation of non-state-owned enterprises is significantly higher than that of state-owned enterprises. The possible explanations are as follows: firstly, that the green credit policy may impose stricter environmental requirements on heavily polluting firms that do not hold bank shares by strengthening loan approval criteria, thereby limiting their access to funds for digital transformation. In contrast, firms that hold bank shares may be more likely to fulfill the conditions of green credit policies due to closer relationships with financial institutions such as banks. Secondly, firms with different shareholding structures may adopt different strategies in responding to green credit policies. Firms that do not hold bank shares may be more inclined to adopt a strategy of directly confronting environmental requirements by adapting their production and management practices to reduce environmental impacts, while relatively slowing down the pace of digital transformation. In contrast, firms with bank holdings may be more likely to obtain funding through green credits and thus invest more aggressively in digital transformation in order to adapt to environmental trends.

Conclusions and policy recommendations

Based on “the Green Credit Guidelines” issued in 2012, this paper selects China’s manufacturing A-share listed companies from 2008 to 2022 as the research sample. Based on the existing research, this paper uses the DID method to investigate and evaluate the impact of green credit policies on the digital transformation of heavily polluting enterprises. The research results show that: Firstly, the green credit policy, represented by “the Green Credit Guidelines”, has a significant inhibitory effect on the digital transformation of heavily polluting enterprises. Secondly, from the perspective of the adjustment mechanism, the R&D investment and the financial background of senior executives weaken the inhibition effect of “the Green Credit Guidelines” on the digital transformation of heavily polluting enterprises, and when the R&D investment is low, the inhibitory effect of the policy is more obvious, but with the increase of enterprise R&D investment, the inhibitory effect of the policy gradually decreases, that is, the R&D investment of enterprises and the Guidelines have an obvious substitution relationship in affecting the digital transformation of heavily polluting enterprises. Thirdly, “the Green Credit Guidelines” has a significantly stronger inhibitory effect on the digital transformation of non-SOE heavy polluting enterprises than that of SOEs; it has a significant inhibitory effect on the digital transformation of heavy polluting enterprises that do not hold shares in a bank, while the effect on heavy polluting enterprises that hold shares in a bank is insignificant.

Based on the above conclusions, this paper puts forward the following policy recommendations from the perspectives of government and enterprises.

On the one hand, the government should launch a special digital transformation loan program to provide heavily polluting enterprises with preferential conditions such as low interest rates and extended repayment periods, so as to ensure that they receive adequate financial support in the process of digital transformation. At the same time, the government should encourage enterprises to increase R&D investment, such as through tax incentives and scientific research funding support, to encourage enterprises to increase R&D investment in the field of digitalization. Flexibly adjust the green credit conditions according to the level of enterprise R&D investment, and provide more flexible credit support for enterprises with low R&D investment. In addition, the government should implement differentiated green credit policies. Formulate differentiated policies according to the nature and shareholding of enterprises, and promote close cooperation between non-state-owned enterprises and non-bank shares and financial institutions to ensure that these enterprises can obtain favorable financial support. On the other hand, enterprises should actively apply for the government’s digital transformation loan program to take advantage of low interest rates and flexible repayment terms to reduce financing pressure and ensure the funds needed for digital upgrading. At the same time, enterprises should increase R&D investment and increase digital technology R&D and innovation activities to improve their competitiveness. In addition, enterprises should pay attention to financial literacy training such as digital literacy of senior executives, and encourage enterprises to participate in training programs to enhance their understanding and support for digital transformation. Finally, companies should optimize their financing structures and strengthen financial cooperation. Specifically, non-state-owned enterprises should explore flexible financing methods and establish close cooperation with financial institutions to obtain favorable financial support. Companies with bank stakes should optimize their financing structures and leverage their banking relationships to obtain better financing conditions to support digital transformation.

Supporting information

https://doi.org/10.1371/journal.pone.0307722.s001

Acknowledgments

We would like to express my sincere thanks to the editors and reviewers of the magazine. Thank you for your meticulous review of my manuscript and your valuable comments during your busy schedule. Your professional insights and constructive suggestions have greatly improved the quality and scientific of this paper, and provided important guidance for the refinement and improvement of this study. We have benefited greatly from your hard work and patience in the course of my research. We know that your valuable time and energy play an important role in advancing academic research and knowledge. Therefore, we would like to express my heartfelt respect and gratitude to you for your selfless dedication.

Thank you again for your attention and support to my manuscript, and look forward to your continued guidance and help in the future.

• View Article
• Google Scholar
• PubMed/NCBI

Environmental Economics Research Paper Topics

This comprehensive guide to environmental economics research paper topics is designed to assist students and researchers in selecting a subject for their study. Environmental economics, a field at the intersection of economics and environmental science, offers a wide array of topics that explore the economic aspects of environmental issues. From policy and natural resource economics to sustainability and climate change, this guide provides a diverse list of topics to inspire your research journey. Additionally, it offers expert advice on choosing a topic and writing a research paper in environmental economics. The guide also introduces iResearchNet’s writing services, which offer custom research papers on any topic in environmental economics, ensuring high-quality, in-depth research, and timely delivery.

Environmental economics is a fascinating field that combines the principles of economics with the study of environmental issues. It seeks to understand the economic impacts of environmental policies and to develop solutions that can balance economic growth with environmental sustainability. Here is a comprehensive list of environmental economics research paper topics that you can explore:

Academic Writing, Editing, Proofreading, And Problem Solving Services

Get 10% off with 24start discount code.

Environmental Policies and Economic Growth

• The impact of environmental regulations on economic growth.
• The role of green taxes in promoting sustainable development.
• The economic benefits of renewable energy policies.
• The cost-effectiveness of carbon pricing mechanisms.
• The impact of environmental policies on job creation.
• The role of government subsidies in promoting green technologies.
• The economic implications of the Paris Agreement.
• The effect of environmental policies on industrial competitiveness.
• The role of environmental policies in shaping market behavior.
• The economic feasibility of transitioning to a circular economy.

Natural Resource Economics

• The economic valuation of natural resources.
• The role of property rights in natural resource management.
• The economic implications of overfishing.
• The impact of mineral extraction on local economies.
• The economics of water scarcity.
• The role of economic incentives in promoting sustainable forestry.
• The impact of climate change on agricultural economics.
• The economic costs and benefits of biodiversity conservation.
• The role of natural resources in economic development.
• The economic implications of soil degradation.

Environmental Cost-Benefit Analysis

• The use of cost-benefit analysis in environmental decision making.
• The challenges of monetizing environmental benefits.
• The role of discount rates in environmental cost-benefit analysis.
• The use of cost-effectiveness analysis in environmental policy.
• The limitations of cost-benefit analysis in addressing environmental justice issues.
• The role of cost-benefit analysis in climate change mitigation strategies.
• The use of multi-criteria analysis in environmental decision making.
• The role of risk analysis in environmental cost-benefit analysis.
• The impact of uncertainty on environmental cost-benefit analysis.
• The use of cost-benefit analysis in ecosystem service valuation.

Economics of Climate Change

• The economic impacts of climate change.
• The role of carbon markets in mitigating climate change.
• The economic feasibility of climate change adaptation strategies.
• The impact of climate change on global trade.
• The role of climate finance in promoting low-carbon development.
• The economic implications of sea level rise.
• The impact of climate change on agricultural productivity.
• The role of economic modeling in climate change projections.
• The economic costs and benefits of geoengineering.
• The impact of climate change on energy economics.

Environmental Justice and Economics

• The economic dimensions of environmental justice.
• The role of economic inequality in environmental degradation.
• The impact of environmental policies on marginalized communities.
• The role of green jobs in promoting environmental justice.
• The economic implications of environmental racism.
• The impact of environmental displacement on economic wellbeing.
• The role of economic empowerment in promoting environmental justice.
• The economic costs of environmental health disparities.
• The impact of environmental gentrification on urban economies.
• The role of economic policy in addressing environmental justice issues.

Green Economy and Sustainable Development

• The economic benefits of transitioning to a green economy.
• The role of green jobs in sustainable development.
• The economic implications of sustainable consumption and production.
• The impact of green growth strategies on economic competitiveness.
• The role of green finance in promoting sustainable development.
• The economic implications of the circular economy.
• The impact of green innovation on economic growth.
• The role of sustainable tourism in the green economy.
• The economic feasibility of green infrastructure projects.

Environmental Economics and Policy

• The economic impacts of environmental regulations.
• The role of economic incentives in environmental policy.
• The impact of environmental taxes on economic behavior.
• The role of trade policy in environmental protection.
• The economic implications of the polluter pays principle.
• The impact of environmental subsidies on market behavior.
• The role of economic instruments in biodiversity conservation.
• The economic feasibility of ecosystem service payments.
• The impact of environmental policy on economic competitiveness.
• The role of economic analysis in environmental policy making.

Economics of Energy and Environment

• The economic impacts of renewable energy policies.
• The role of energy economics in environmental sustainability.
• The impact of fossil fuel subsidies on the environment.
• The role of energy efficiency in economic growth.
• The economic implications of the energy transition.
• The impact of energy prices on environmental quality.
• The role of energy policy in climate change mitigation.
• The economic feasibility of carbon capture and storage.
• The impact of energy security on environmental sustainability.
• The role of energy markets in environmental protection.

Environmental Economics and Agriculture

• The economic impacts of agricultural pollution.
• The role of agricultural economics in environmental sustainability.
• The impact of agricultural subsidies on the environment.
• The role of sustainable agriculture in economic development.
• The economic implications of organic farming.
• The impact of agricultural trade on the environment.
• The role of agricultural policy in environmental protection.
• The economic feasibility of agroecology.
• The impact of agricultural innovation on environmental sustainability.
• The role of agricultural markets in environmental protection.

Environmental Economics and Urbanization

• The economic impacts of urban pollution.
• The role of urban economics in environmental sustainability.
• The impact of urban sprawl on the environment.
• The role of sustainable urban development in economic growth.
• The economic implications of urban green spaces.
• The impact of urban transportation on environmental quality.
• The role of urban planning in environmental protection.
• The economic feasibility of green buildings.
• The impact of urbanization on biodiversity.
• The role of urban infrastructure in environmental protection.

These environmental economics research paper topics cover a wide range of issues in the field of environmental economics, from policy and law to energy and agriculture. They provide a starting point for your research and can be tailored to fit your specific interests and the requirements of your assignment. Remember, choosing the right topic is the first step in writing a successful research paper. So take your time, explore these topics, and choose one that you find interesting and meaningful.

Environmental Economics Research Guide

Environmental economics is a vital field that examines the interplay between economic systems and the environment. As our world faces increasingly complex environmental challenges, understanding the economic dimensions of these issues becomes crucial for developing effective solutions. This page aims to provide a comprehensive resource for students studying environmental science and seeking research paper topics in the field of environmental economics.

Environmental economics focuses on analyzing the costs and benefits associated with environmental policies, natural resource management, pollution control, and sustainable development. It explores the ways in which economic activities impact the environment and how environmental factors influence economic decision-making. By studying environmental economics, students gain insights into the intricate relationship between human activities and the natural world, enabling them to propose informed strategies for sustainable development.

For students pursuing a degree in environmental science, conducting research in environmental economics offers a unique perspective on addressing environmental challenges. It provides a framework to assess the economic implications of environmental issues and develop innovative solutions that balance ecological sustainability and economic prosperity. Research papers in environmental economics not only contribute to the academic discourse but also equip students with the knowledge and skills necessary to effect positive change in their future careers.

The purpose of this page is to serve as a valuable resource for students seeking inspiration and guidance for their research papers in environmental economics. We aim to provide a curated list of diverse research paper topics, expert advice on topic selection, and practical tips on writing an effective environmental economics research paper. Additionally, we introduce the writing services offered by iResearchNet, providing students the opportunity to order custom research papers tailored to their specific needs.

By exploring the topics and advice presented on this page, students will be equipped with the tools and insights necessary to delve into the fascinating field of environmental economics. Whether you are interested in studying the economic impact of climate change, analyzing environmental policies, or exploring sustainable development strategies, this page will help you navigate the vast landscape of environmental economics research.

We invite you to embark on this exciting journey of exploring environmental economics research paper topics and discovering the potential to make a meaningful impact on the environmental challenges of our time.

Choosing an Environmental Economics Topic

Choosing the right research paper topic is a crucial step in the process of writing an impactful and successful environmental economics research paper. With the vast scope of environmental economics, it is essential to select a topic that is not only interesting but also relevant, feasible, and has the potential to contribute to the field. Here are ten expert tips to help you navigate the process of choosing environmental economics research paper topics:

• Identify your Interests : Start by identifying your personal interests within the field of environmental economics. Think about the environmental issues that resonate with you, such as climate change, natural resource management, pollution control, or sustainable development. Choosing a topic that genuinely interests you will make the research process more enjoyable and motivating.
• Stay Updated with Current Issues : Keep yourself informed about the latest environmental issues and developments in environmental economics. Follow reputable news sources, academic journals, and research publications to stay abreast of emerging trends, debates, and areas of active research. This will enable you to select topics that are timely and relevant.
• Conduct Preliminary Research : Before finalizing a research topic, conduct preliminary research to gain a broad understanding of the existing literature in the field. Review academic papers, books, and reports related to environmental economics to identify gaps in knowledge and potential research areas that warrant further exploration.
• Narrow Down the Scope : Environmental economics is a vast field, and it is essential to narrow down the scope of your research topic. Focus on a specific aspect, problem, or geographic region that you can realistically address within the scope of your research paper. Narrowing down the topic will allow you to delve deeper and provide a more comprehensive analysis.
• Consult with Your Advisor : Seek guidance from your academic advisor or faculty members specializing in environmental economics. They can provide valuable insights, suggest potential research topics, and offer guidance on selecting a topic that aligns with your academic goals and interests.
• Consider the Research Gap : Look for areas in environmental economics where there is a research gap or limited literature available. Identifying gaps in the existing body of knowledge will allow you to contribute to the field by conducting original research and generating new insights.
• Balance Practicality and Significance : When choosing a research topic, consider the practicality of data collection and analysis. Ensure that you have access to relevant data sources and research methods required to investigate the topic effectively. Additionally, evaluate the potential significance of the research topic in addressing real-world environmental challenges.
• Engage in Discussions and Seminars : Participate in discussions, seminars, and conferences related to environmental economics. Engaging with peers and experts in the field will expose you to diverse perspectives and help you discover potential research topics and areas of interest that you may not have considered before.
• Seek Interdisciplinary Approaches : Environmental economics often intersects with other disciplines such as ecology, policy studies, sociology, and public health. Consider incorporating interdisciplinary approaches into your research topic to explore the interconnectedness between environmental and social factors, which can provide a more holistic understanding of the issues at hand.
• Reflect on Practical Applications : Reflect on the practical applications of your research topic. Consider how your findings and analysis can contribute to policy development, inform decision-making processes, or propose sustainable solutions. Topics that have practical implications and can make a positive impact in real-world contexts tend to be more compelling and meaningful.

By following these expert tips, you will be well-equipped to choose a compelling and relevant environmental economics research paper topic that aligns with your interests, addresses knowledge gaps, and has the potential to contribute to the field. Remember to consult with your academic advisor throughout the process to ensure that your chosen topic aligns with the requirements and objectives of your research paper.

How to Write an Environmental Economics Research Paper

Writing an environmental economics research paper requires a systematic and well-structured approach to effectively communicate your research findings and contribute to the field. Here are ten essential tips to help you navigate the process of writing an impactful environmental economics research paper:

• Understand the Research Question : Start by clearly understanding the research question or objective of your paper. Identify the specific problem or issue you aim to address and formulate a concise and focused research question. This will serve as the guiding principle throughout your research and writing process.
• Conduct In-depth Literature Review : Before delving into your own research, conduct a comprehensive literature review to familiarize yourself with the existing body of knowledge. Identify key theories, concepts, methodologies, and empirical studies relevant to your research question. This will help you establish the context for your research and identify research gaps to address.
• Develop a Solid Research Methodology : Choose an appropriate research methodology that aligns with your research question and objectives. Whether it’s quantitative, qualitative, or a combination of both, ensure that your chosen methodology allows you to collect and analyze data effectively to answer your research question.
• Collect and Analyze Data : Depending on your research question, collect relevant data from credible sources. This may include primary data collected through surveys, interviews, or experiments, or secondary data from academic journals, government reports, or databases. Analyze the data using appropriate statistical or qualitative analysis techniques to derive meaningful insights.
• Organize Your Paper : Structure your research paper in a logical and coherent manner. Start with an introduction that provides background information, states the research question, and outlines the paper’s structure. Follow this with a literature review, methodology section, presentation and analysis of findings, discussion of results, and a conclusion that summarizes your key findings and their implications.
• Provide Clear and Concise Writing : Ensure that your writing is clear, concise, and accessible to your target audience. Use plain language and avoid unnecessary jargon. Clearly articulate your arguments, provide sufficient evidence, and use appropriate citations to support your claims. Use subheadings, bullet points, and paragraph breaks to improve readability.
• Support Your Arguments with Evidence : Back up your claims and arguments with reliable evidence. Use empirical data, case studies, academic research, and real-world examples to support your analysis. Be critical of your sources and ensure they are reputable and peer-reviewed.
• Engage in Critical Analysis : Demonstrate critical thinking skills by analyzing and interpreting your findings in the context of existing literature and theories. Identify strengths, limitations, and potential biases in your research. Discuss alternative explanations and consider counterarguments to strengthen the validity of your research.
• Discuss Policy Implications : Environmental economics research often has policy implications. Discuss the implications of your research findings for environmental policies, regulations, or economic decision-making. Highlight the potential benefits and challenges of implementing your research outcomes in real-world scenarios.
• Revise and Edit : Revise and edit your research paper thoroughly before final submission. Pay attention to clarity, grammar, spelling, and formatting. Read your paper multiple times, seek feedback from peers or advisors, and make necessary revisions to improve the overall quality and coherence of your work.

By following these tips, you can effectively write an environmental economics research paper that is well-structured, evidence-based, and contributes to the understanding of environmental issues from an economic perspective. Remember to maintain a systematic approach, stay focused on your research question, and continuously refine your writing through feedback and revisions.

Custom Research Paper Writing Services

When it comes to writing a custom environmental economics research paper, iResearchNet is here to assist you. Our writing services offer a range of features that ensure the highest quality and professionalism in delivering customized research papers. By ordering a custom paper from us, you can benefit from the following:

• Expert Degree-Holding Writers : We have a team of experienced writers with advanced degrees in environmental economics and related fields. They possess the expertise and knowledge to write high-quality research papers tailored to your specific requirements.
• Custom Written Works : Each research paper we deliver is 100% original and tailored to your unique needs. Our writers follow your instructions and conduct thorough research to provide you with a custom-written paper that meets your academic standards.
• In-depth Research : Our writers are skilled in conducting in-depth research on environmental economics topics. They access reputable academic sources, scholarly journals, and up-to-date research databases to ensure your paper is well-informed and based on the latest findings in the field.
• Custom Formatting : We understand the importance of adhering to specific formatting styles. Whether it’s APA, MLA, Chicago/Turabian, Harvard, or any other style, we ensure that your research paper is formatted correctly, including citations, references, and bibliography.
• Top Quality : Our commitment to quality is unwavering. We strive to deliver research papers that meet the highest standards of excellence. Our rigorous quality control measures ensure that your paper is well-written, well-structured, and free from errors.
• Customized Solutions : We recognize that every research paper is unique. Our writers work closely with you to understand your specific requirements and tailor the paper accordingly. We take into account your research question, objectives, and any specific guidelines provided by your institution.
• Flexible Pricing : We offer competitive and flexible pricing options to accommodate your budget. Our pricing structure considers factors such as the complexity of the research paper, deadline, and additional services required. We strive to provide affordable solutions without compromising on quality.
• Short Deadlines : We understand that deadlines can be tight. That’s why we offer short deadlines of up to 3 hours for urgent research paper requests. Our dedicated team works efficiently to ensure timely delivery, even for tight schedules.
• Timely Delivery : We value your time and strive to deliver your research paper within the agreed-upon deadline. Our writers and support team work collaboratively to ensure your paper is completed and delivered to you on time.
• 24/7 Support : Our customer support team is available round-the-clock to assist you with any queries or concerns you may have. Whether it’s assistance with placing an order, tracking your paper’s progress, or addressing any post-delivery concerns, we are here to support you.
• Absolute Privacy : We prioritize the privacy and confidentiality of our customers. All personal information and details of your order are treated with the utmost confidentiality. We never share your information with third parties, and all transactions are secured.
• Easy Order Tracking : We provide a user-friendly platform that allows you to easily track the progress of your order. You can communicate directly with your assigned writer, monitor the writing process, and receive updates on the status of your paper.
• Money Back Guarantee : We stand behind the quality of our work. If, for any reason, you are not satisfied with the final research paper, we offer a money back guarantee. Your satisfaction is our top priority, and we are committed to ensuring your academic success.

By choosing our writing services, you can rest assured that your environmental economics research paper will be handled by professionals who are dedicated to delivering customized and high-quality work. Our extensive features and commitment to excellence make us the ideal choice for students seeking reliable and professional assistance in their research paper endeavors.

Unlock Your Academic Success with iResearchNet!

Are you ready to take the next step towards achieving your academic goals in environmental economics? Don’t let the challenges of writing a research paper hold you back. Take advantage of our professional writing services at iResearchNet and order your custom environmental economics research paper today.

Why settle for generic research papers when you can have a paper tailored specifically to your requirements? Our team of expert degree-holding writers is ready to assist you in crafting a research paper that showcases your understanding of environmental economics concepts and explores your chosen topic in-depth. Whether you need a paper on pollution pricing mechanisms, environmental policy analysis, or sustainable resource management, we have the expertise to deliver outstanding results.

With our user-friendly platform and 24/7 support, the process of ordering your custom research paper is simple and hassle-free. Just provide us with the details of your assignment, including the topic, formatting requirements, and any specific guidelines. Our writers will meticulously research and develop your paper, ensuring that it meets your academic standards and reflects your unique perspective.

Take the leap and order your custom environmental economics research paper today. Experience the benefits of working with a trusted writing service and unlock your full potential in the field of environmental science. Let us assist you in making a significant impact through your research and contribute to the sustainable future of our planet.

Order now and embark on a rewarding academic journey with iResearchNet!

ORDER HIGH QUALITY CUSTOM PAPER

Credit: Yaorusheng/Moment/Getty

Green Energy Research: Collaboration and Tools for a Sustainable Future

Science Article | Green Energy | 6 Sep 2024

The Urgency of Green Energy Innovation

The recent Climate Change 2023 synthesis report emphasizes the consequences of delayed emission reductions: fewer effective adaptation options for a warming planet 2 . Geopolitical factors like the Russia-Ukraine conflict further underscore the need for a green energy transition, with Europe’s energy security concerns highlighting the reliance on imported fossil fuels.

The Green Energy Research Landscape

Against this backdrop, green energy development has become a critical area of research, reflected in a more than 10-fold increase in related publications from 2010 (1,105) to 2023 (11,346), according to Digital Science’s Dimensions database. Researchers around the world are striving to improve green energy technology and society’s ability to harness renewable energy sources more efficiently.

According to data analysed by Nature Navigator , which uses artificial intelligence to generate comprehensive summaries of research topics, ‘renewable energy systems and technologies’ is the field’s most frequently mentioned subtopic (Fig.1). At a research concept level, wind power generation, grid optimization and resource management all feature as common underlying themes.

Figure 1: Topic anatomy of green energy research First-level nodes denote the research subtopic (highest prevalence themes emerging from green energy research). Second-level nodes denote the research concepts associated with these research subtopics. Note: only the research concepts mentioned in the highest count of outputs within each subtopic are presented here. Credit: Nature Research Intelligence

Of the primary green energy research subtopics presented by Nature Navigator , it is telling that ‘materials for energy storage and conversion’ is the fastest-growing, with a compound annual growth rate (CAGR) of 30.2% over the last five years. This may reflect a growing consensus among researchers and industry that a lack of options to efficiently store electricity generated by intermittent renewable sources for later use is a key bottleneck preventing the greater penetration of these sources into the grid.

Real-World Example: Accelerating Heat Pump Innovation

Changmo Sung, a prominent green energy researcher at Korea University, leveraged Nature Navigator to identify trends, key areas, and potential breakthroughs in heat pump technology. This facilitated a collaborative project with LG Electronics, accelerating their research efforts.

“It also enabled the rapid discovery of researchers and institutions outside Korea working on similar or complementary projects related to heat pumps” Sung says.

• International Energy Agency, Global Energy Review 2021 (2021).
• Intergovernmental Panel on Climate Change, Climate Change 2023 (2023).

Ut enim ad minima veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Life cycle sustainability assessment: an index system for building energy retrofit projects.

1. Introduction

2. literature review, 2.1. building energy retrofit (ber), 2.2. sustainability assessment methodology and indicator system, 3. research methodology, 3.1. life cycle assessment (lca), 3.2. the delphi method, 3.3. analytic hierarchy process (ahp), 4. assessment framework development and results, 4.1. sustainability indicator identification, 4.2. judgment matrix, 4.3. weight vector determination, 4.4. consistency check, 4.5. results, 5. discussion, 5.1. design phase, 5.2. construction phase, 5.3. use phase, 5.4. end-of-life phase, 5.5. practical suggestions for the ber market, 6. conclusions, author contributions, data availability statement, acknowledgments, conflicts of interest.

• Li, X.; Zhou, Y.; Yu, S.; Jia, G.; Li, H.; Li, W. Urban heat island impacts on building energy consumption: A review of approaches and findings. Energy 2019 , 174 , 407–419. [ Google Scholar ] [ CrossRef ]
• Guo, S.; Yan, D.; Hu, S.; Zhang, Y. Modelling building energy consumption in China under different future scenarios. Energy 2021 , 214 , 119063. [ Google Scholar ] [ CrossRef ]
• Yu, S.; Zhang, Q.; Hao, J.L.; Ma, W.; Sun, Y.; Wang, X.; Song, Y. Development of an extended STIRPAT model to assess the driving factors of household carbon dioxide emissions in China. J. Environ. Manag. 2023 , 325 , 116502. [ Google Scholar ] [ CrossRef ]
• Song, K.; Ahn, Y.; Ahn, J.; Kwon, N. Development of an energy saving strategy model for retrofitting existing buildings: A Korean case study. Energies 2019 , 12 , 1626. [ Google Scholar ] [ CrossRef ]
• Ma, W.; Hao, J.L.; Zhang, C.; Di Sarno, L.; Mannis, A. Evaluating carbon emissions of China’s waste management strategies for building refurbishment projects: Contributing to a circular economy. Environ. Sci. Pollut. Res. 2023 , 30 , 8657–8671. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Almomani, A.; Almeida, R.M.; Vicente, R.; Barreira, E. Critical Review on the Energy Retrofitting Trends in Residential Buildings of Arab Mashreq and Maghreb Countries. Buildings 2024 , 14 , 338. [ Google Scholar ] [ CrossRef ]
• Toosi, H.A.; Lavagna, M.; Leonforte, F.; Del Pero, C.; Aste, N. Life cycle sustainability assessment in building energy retrofitting; A review. Sustain. Cities Soc. 2020 , 60 , 102248. [ Google Scholar ] [ CrossRef ]
• Fallah Shayan, N.; Mohabbati-Kalejahi, N.; Alavi, S.; Zahed, M.A. Sustainable development goals (SDGs) as a framework for corporate social responsibility (CSR). Sustainability 2022 , 14 , 1222. [ Google Scholar ] [ CrossRef ]
• Liu, Z.; Deng, Z.; He, G.; Wang, H.; Zhang, X.; Lin, J.; Qi, Y.; Liang, X. Challenges and opportunities for carbon neutrality in China. Nat. Rev. Earth Environ. 2022 , 3 , 141–155. [ Google Scholar ] [ CrossRef ]
• Zhao, W.; Hao, J.L.; Gong, G.; Ma, W.; Zuo, J.; Di Sarno, L. Decarbonizing prefabricated building waste: Scenario simulation of policies in China. J. Cleaner Prod. 2024 , 458 , 142529. [ Google Scholar ] [ CrossRef ]
• Hao, J.L.; Zhao, W.; Gong, G.; Ma, W.; Li, L.; Zhang, Y. Catalyzing sustainability through prefabrication: Integrating BIM-LCA for assessing embodied carbon in timber formwork waste. Sustain. Chem. Pharm. 2024 , 41 , 101698. [ Google Scholar ] [ CrossRef ]
• Bian, J.; Liu, C.; Zuo, C.; Hao, J.; Ma, W.; Duan, B.; Chen, C.; Liu, J. Reducing Carbon Emissions from Prefabricated Decoration: A Case Study of Residential Buildings in China. Buildings 2024 , 14 , 550. [ Google Scholar ] [ CrossRef ]
• Afshari, A.; Nikolopoulou, C.; Martin, M. Life-cycle analysis of building retrofits at the urban scale—A case study in United Arab Emirates. Sustainability 2014 , 6 , 453–473. [ Google Scholar ] [ CrossRef ]
• Marrero, M.; Rivero-Camacho, C.; Alba-Rodríguez, M.D. What are we discarding during the life cycle of a building? Case studies of social housing in Andalusia, Spain. Waste Manag. 2020 , 102 , 391–403. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Sharif, S.A.; Hammad, A. Simulation-based multi-objective optimization of institutional building renovation considering energy consumption, life-cycle cost and life-cycle assessment. J. Build. Eng. 2019 , 21 , 429–445. [ Google Scholar ] [ CrossRef ]
• Zhang, H.; Hewage, K.; Karunathilake, H.; Feng, H.; Sadiq, R. Research on policy strategies for implementing energy retrofits in the residential buildings. J. Build. Eng. 2021 , 43 , 103161. [ Google Scholar ] [ CrossRef ]
• Hao, J.L.; Ma, W. Evaluating carbon emissions of construction and demolition waste in building energy retrofit projects. Energy 2023 , 281 , 128201. [ Google Scholar ] [ CrossRef ]
• Liu, G.; Li, X.; Tan, Y.; Zhang, G. Building green retrofit in China: Policies, barriers and recommendations. Energy Policy 2020 , 139 , 111356. [ Google Scholar ] [ CrossRef ]
• Liu, G.; Tan, Y.; Li, X. China’s policies of building green retrofit: A state-of-the-art overview. Build. Environ. 2020 , 169 , 106554. [ Google Scholar ] [ CrossRef ]
• Shen, P.; Braham, W.; Yi, Y. The feasibility and importance of considering climate change impacts in building retrofit analysis. Appl. Energy 2019 , 233 , 254–270. [ Google Scholar ] [ CrossRef ]
• Mukhtar, M.; Ameyaw, B.; Yimen, N.; Zhang, Q.; Bamisile, O.; Adun, H.; Dagbasi, M. Building retrofit and energy conservation/efficiency review: A techno-environ-economic assessment of heat pump system retrofit in housing stock. Sustainability 2021 , 13 , 983. [ Google Scholar ] [ CrossRef ]
• Rodrigues, C.; Freire, F. Environmental impacts and costs of residential building retrofits–What matters? Sustain. Cities Soc. 2021 , 67 , 102733. [ Google Scholar ] [ CrossRef ]
• Streicher, K.N.; Berger, M.; Panos, E.; Narula, K.; Soini, M.C.; Patel, M.K. Optimal building retrofit pathways considering stock dynamics and climate change impacts. Energy Policy 2021 , 152 , 112220. [ Google Scholar ] [ CrossRef ]
• Ongpeng, J.M.C.; Rabe, B.I.B.; Razon, L.F.; Aviso, K.B.; Tan, R.R. A multi-criterion decision analysis framework for sustainable energy retrofit in buildings. Energy 2022 , 239 , 122315. [ Google Scholar ] [ CrossRef ]
• Mirzabeigi, S.; Homaei, S.; Razkenari, M.; Hamdy, M. The Impact of Building Retrofitting on Thermal Resilience Against Power Failure: A Case of air-Conditioned House. In Proceedings of the International Conference on Building Energy and Environment, Dalian, China, 25–29 July 2022. [ Google Scholar ]
• Elsharkawy, H.; Zahiri, S. The significance of occupancy profiles in determining post retrofit indoor thermal comfort, overheating risk and building energy performance. Build. Environ. 2020 , 172 , 106676. [ Google Scholar ] [ CrossRef ]
• Li, Q.; Zhang, L.; Zhang, L.; Wu, X. Optimizing energy efficiency and thermal comfort in building green retrofit. Energy 2021 , 237 , 121509. [ Google Scholar ] [ CrossRef ]
• Kaur, H.; Garg, P. Urban sustainability assessment tools: A review. J. Clean. Prod. 2019 , 210 , 146–158. [ Google Scholar ] [ CrossRef ]
• Carlson, K.; Pressnail, K.D. Value impacts of energy efficiency retrofits on commercial office buildings in Toronto, Canada. Energy Build. 2018 , 162 , 154–162. [ Google Scholar ] [ CrossRef ]
• Sharifi, A. Urban sustainability assessment: An overview and bibliometric analysis. Ecol. Indic. 2021 , 121 , 107102. [ Google Scholar ] [ CrossRef ]
• Kempeneer, S.; Peeters, M.; Compernolle, T. Bringing the user Back in the building: An analysis of ESG in real estate and a behavioral framework to guide future research. Sustainability 2021 , 13 , 3239. [ Google Scholar ] [ CrossRef ]
• Lee, S.; Park, S.; Kang, B.; Choi, M.-i.; Jang, H.; Shmilovitz, D.; Park, S. Enhancing zero-energy building operations for ESG: Accurate solar power prediction through automatic machine learning. Buildings 2023 , 13 , 2050. [ Google Scholar ] [ CrossRef ]
• Redlein, A.; Baretschneider, C.; Thrainer, L. ESG monitoring and optimisation solutions and their return on investment: Results of several case studies. IOP Conf. Ser. Earth Environ. Sci. 2023 , 1176 , 012029. [ Google Scholar ] [ CrossRef ]
• Li, R.Y.M.; Li, B.; Zhu, X.; Zhao, J.; Pu, R.; Song, L. Modularity clustering of economic development and ESG attributes in prefabricated building research. Front. Environ. Sci. 2022 , 10 , 977887. [ Google Scholar ] [ CrossRef ]
• Kim, S.; Chang, S. Construction project level-based environmental, social, and governance (C-ESG): A review. EPiC Ser. Built Environ. 2022 , 3 , 236–244. [ Google Scholar ]
• Daszyńska-Żygadło, K.; Fijałkowska, J.; Hadro, D. Communicating on ESG impacts: The case of the largest European construction industry companies. In Corporate Social Responsibility and Sustainability ; Routledge: London, UK, 2022; pp. 199–213. [ Google Scholar ]
• Finkbeiner, M.; Schau, E.M.; Lehmann, A.; Traverso, M. Towards life cycle sustainability assessment. Sustainability 2010 , 2 , 3309–3322. [ Google Scholar ] [ CrossRef ]
• Zubair, M.U.; Ali, M.; Khan, M.A.; Khan, A.; Hassan, M.U.; Tanoli, W.A. BIM-and GIS-Based Life-Cycle-Assessment Framework for Enhancing Eco Efficiency and Sustainability in the Construction Sector. Buildings 2024 , 14 , 360. [ Google Scholar ] [ CrossRef ]
• Hao, J.L.; Cheng, B.; Lu, W.; Xu, J.; Wang, J.; Bu, W.; Guo, Z. Carbon emission reduction in prefabrication construction during materialization stage: A BIM-based life-cycle assessment approach. Sci. Total Environ. 2020 , 723 , 137870. [ Google Scholar ] [ CrossRef ]
• Kapica, B.; Targowski, W.; Kulowski, A. Is the Concept of Zero Waste Possible to Implement in Construction? Buildings 2024 , 14 , 428. [ Google Scholar ] [ CrossRef ]
• Fathullah, M.A.; Subbarao, A.; Muthaiyah, S. Methodological investigation: Traditional and systematic reviews as preliminary findings for Delphi technique. Int. J. Qual. Methods 2023 , 22 , 16094069231190747. [ Google Scholar ] [ CrossRef ]
• Smarandache, F.; Ricardo, J.E.; Caballero, E.G.; Vázquez, M.Y.L.; Hernández, N.B. Delphi Method for Evaluating Scientific Research Proposals in a Neutrosophic Environment; Infinite Study. 2020. Available online: https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Delphi+Method+for+Evaluating+Scientific+Research+Proposals+in+a+Neutrosophic+Environment&btnG= (accessed on 12 March 2024).
• Von Der Gracht, H.A. Consensus measurement in Delphi studies: Review and implications for future quality assurance. Technol. Forecast. Social Chang. 2012 , 79 , 1525–1536. [ Google Scholar ] [ CrossRef ]
• Büyüközkan, G.; Kahraman, C.; Ruan, D. A fuzzy multi-criteria decision approach for software development strategy selection. Int. J. Gen. Syst. 2004 , 33 , 259–280. [ Google Scholar ] [ CrossRef ]
• Ahmed, F.; Kilic, K. Fuzzy Analytic Hierarchy Process: A performance analysis of various algorithms. Fuzzy Sets Syst. 2019 , 362 , 110–128. [ Google Scholar ] [ CrossRef ]
• Ayyildiz, E.; Taskin Gumus, A. Pythagorean fuzzy AHP based risk assessment methodology for hazardous material transportation: An application in Istanbul. Environ. Sci. Pollut. Res. 2021 , 28 , 35798–35810. [ Google Scholar ] [ CrossRef ]
• Tsai, H.C.; Lee, A.S.; Lee, H.N.; Chen, C.N.; Liu, Y.C. An application of the fuzzy Delphi method and fuzzy AHP on the discussion of training indicators for the regional competition, Taiwan national skills competition, in the trade of joinery. Sustainability 2020 , 12 , 4290. [ Google Scholar ] [ CrossRef ]
• Pathak, S.K.; Sharma, V.; Chougule, S.S.; Goel, V. Prioritization of barriers to the development of renewable energy technologies in India using integrated Modified Delphi and AHP method. Sustain. Energy Technol. Assess. 2022 , 50 , 101818. [ Google Scholar ] [ CrossRef ]
• Marzouk, M.; Sabbah, M. AHP-TOPSIS social sustainability approach for selecting supplier in construction supply chain. Clean. Environ. Syst. 2021 , 2 , 100034. [ Google Scholar ] [ CrossRef ]
• Alyamani, R.; Long, S. The application of fuzzy Analytic Hierarchy Process in sustainable project selection. Sustainability 2020 , 12 , 8314. [ Google Scholar ] [ CrossRef ]
• Abadi, M.; Moore, D.R. Selection of circular proposals in building projects: An MCDM model for lifecycle circularity assessments using AHP. Buildings 2022 , 12 , 1110. [ Google Scholar ] [ CrossRef ]
• Al-Husain, R.; Khorramshahgol, R. Incorporating analytical hierarchy process and goal programming to design responsive and efficient supply chains. Oper. Res. Perspect. 2020 , 7 , 100149. [ Google Scholar ] [ CrossRef ]
• Yang, J.; Jia, J.; Zhao, T. Research on Evaluation Elements of Design Works for Design Education. In Proceedings of the International Conference on Human-Computer Interaction, Copenhagen, Denmark, 23–28 July 2023. [ Google Scholar ]
• Gu, H.; Wang, J.; Yu, J.; Wang, D.; Li, B.; He, X.; Yin, X. Towards virtual machine scheduling research based on multi-decision AHP method in the cloud computing platform. PeerJ Comput. Sci. 2023 , 9 , e1675. [ Google Scholar ] [ CrossRef ]
• Abadi, S.; Huda, M.; Basiron, B.; Ihwani, S.S.; Jasmi, K.A.; Hehsan, A.; Safar, J.; Mohamed, A.K.; Embong, W.H.W.; Mohamad, A.M.; et al. Implementation of fuzzy analytical hierarchy process on notebook selection. Int. J. Eng. Technol. 2018 , 7 , 238–243. [ Google Scholar ] [ CrossRef ]
• Cai, S.; Han, T.; Wang, Y.; Zhang, H. Study of blockchain’s consensus mechanism based on score: An improved consensus mechanism. IET Blockchain 2021 , 1 , 41–55. [ Google Scholar ] [ CrossRef ]
• Ikram, M.; Sroufe, R.; Zhang, Q. Prioritizing and overcoming barriers to integrated management system (IMS) implementation using AHP and G-TOPSIS. J. Clean. Prod. 2020 , 254 , 120121. [ Google Scholar ] [ CrossRef ]
• Ramírez-Villegas, R.; Eriksson, O.; Olofsson, T. Life cycle assessment of building renovation measures–trade-off between building materials and energy. Energies 2019 , 12 , 344. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

Song, P.; Wu, L.; Zhao, W.; Ma, W.; Hao, J. Life Cycle Sustainability Assessment: An Index System for Building Energy Retrofit Projects. Buildings 2024 , 14 , 2817. https://doi.org/10.3390/buildings14092817

Song P, Wu L, Zhao W, Ma W, Hao J. Life Cycle Sustainability Assessment: An Index System for Building Energy Retrofit Projects. Buildings . 2024; 14(9):2817. https://doi.org/10.3390/buildings14092817

Song, Pei, Lingyu Wu, Wenbo Zhao, Wenting Ma, and Jianli Hao. 2024. "Life Cycle Sustainability Assessment: An Index System for Building Energy Retrofit Projects" Buildings 14, no. 9: 2817. https://doi.org/10.3390/buildings14092817

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

An Adaptive Differential Evolution Algorithm Based on Data Preprocessing Method and a New Mutation Strategy to Solve Dynamic Economic Dispatch Considering Generator Constraints

• Published: 08 September 2024

Cite this article

• Ruxin Zhao   ORCID: orcid.org/0000-0002-6810-2631 1 ,
• Wei Wang 1 ,
• Tingting Zhang 1 ,
• Chang Liu 2 ,
• Lixiang Fu 1 ,
• Jiajie Kang 1 ,
• Hongtan Zhang 1 ,
• Yang Shi 1 &
• Chao Jiang 1  

Differential evolution (DE) algorithm is a classical natural-inspired optimization algorithm which has a good. However, with the deepening of research, some researchers found that the quality of the candidate solution of the population in the differential evolution algorithm is poor and its global search ability is not enough when solving the global optimization problem. Therefore, in order to solve the above problems, we proposed an adaptive differential evolution algorithm based on the data processing method and a new mutation strategy (ADEDPMS). In this paper, the data preprocessing method is implemented by k -means clustering algorithm, which is used to divide the initial population into multiple clusters according to the average value of fitness, and select candidate solutions in each cluster according to different proportions. This method improves the quality of candidate solutions of the population to a certain extent. In addition, in order to solve the problem of insufficient global search ability in differential evolution algorithm, we also proposed a new mutation strategy, which is called “DE/current-to- \({p}_{1}\) best& \({p}_{2}\) best”. This strategy guides the search direction of the differential evolution algorithm by selecting individuals with good fitness, so that its search range is in the most promising candidate solution region, and indirectly increases the population diversity of the algorithm. We also proposed an adaptive parameter control method, which can effectively balance the relationship between the exploration process and the exploitation process to achieve the best performance. In order to verify the effectiveness of the proposed algorithm, the ADEDPMS is compared with five optimization algorithms of the same type in the past three years, which are AAGSA, DFPSO, HGASSO, HHO and VAGWO. In the simulation experiment, 6 benchmark test functions and 4 engineering example problems are used, and the convergence accuracy, convergence speed and stability are fully compared. We used ADEDPMS to solve the dynamic economic dispatch (ED) problem with generator constraints. It is compared with the optimization algorithms used to solve the ED problem in the last three years which are AEFA, AVOA, OOA, SCA and TLBO. The experimental results show that compared with the five latest optimization algorithms proposed in the past three years to solve benchmark functions, engineering example problems and the ED problem, the proposed algorithm has strong competitiveness in each test index.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price excludes VAT (USA) Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Explore related subjects

• Artificial Intelligence

Data availability

The materials and associated data will be made available on rational request.

Al-Betar, M. A., Awadallah, M. A., Zitar, R. A., & Assaleh, K. (2023). Economic load dispatch using memetic sine cosine algorithm. Journal of Ambient Intelligence and Humanized Computing, 14 (9), 11685–11713.

Article   Google Scholar  

Andrei, N. (2008). An unconstrained optimization test functions collection. Advanced Modeling Optimization, 10 (1), 147–161.

Google Scholar  

Anita, Yadav, A., & Kumar, N. (2021). Application of artificial electric field algorithm for economic load dispatch problem. In: Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019) 11. Springer International Publishing. pp. 71–79

Awad, N. H., Ali, M. Z., Liang, J. J., Qu, B. Y., & Suganthan, P. N. (2016). Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective bound constrained real-parameter numerical optimization. In: Technical report Singapore: Nanyang Technological University Singapore pp. 1-34

Bai, Y., Wu, X., & Xia, A. (2021). An enhanced multi-objective differential evolution algorithm for dynamic environmental economic dispatch of power system with wind power. Energy Science & Engineering, 9 (3), 316–329.

Chandrasekar P, Qian K. (2016). The impact of data preprocessing on the performance of a naive bayes classifier. In: IEEE 40th Annual Computer Software and Applications Conference (COMPSAC), Conference on IEEE 2, 618-619

Abdel-Rahman Hedar. (2013). Global Optimization Test Problems. http://www-optima.amp.i.kyoto-u.ac.jp/member/student/hedar/Hedar_files/TestGO.htm . Retrieved June.

Heidari, A. A., Mirjalili, S., Faris, H., et al. (2019). Harris hawks optimization: algorithm and applications. Future Generation Computer Systems, 97 , 849–872.

Ismaeel, A. A., Houssein, E. H., Khafaga, D. S., Abdullah Aldakheel, E., AbdElrazek, A. S., & Said, M. (2023). Performance of osprey optimization algorithm for solving economic load dispatch problem. Mathematics, 11 (19), 4107.

Kamran, S., Safavi, H. R., Golmohammadi, M. H., et al. (2022). Maximizing sustainability in reservoir operation under climate change using a novel adaptive accelerated gravitational search algorithm. Water, 14 (6), 905.

Li, X., Zhang, H., & Lu, Z. (2019). A differential evolution algorithm based on multi-population for economic dispatch problems with valve-point effects. IEEE Access, 7 , 95585–95609.

Li, M., Zou, D., & Ouyang, H. (2023). Differential evolution algorithm with improved crossover operation for combined heat and power economic dynamic dispatch problem with wind power. Journal of Computational Design and Engineering, 10 (4), 1821–1837.

Liu, C., Wan, Z., Liu, Y., et al. (2021). Trust-region based adaptive radial basis function algorithm for global optimization of expensive constrained black-box problems. Applied Soft Computing, 105 , 107233.

Luo, W., Yu, X., & Wei, Y. (2023). Solving combined economic and emission dispatch problems using reinforcement learning-based adaptive differential evolution algorithm. Engineering Applications of Artificial Intelligence, 126 , 107002.

Mandal, P., Sanimul, S., Mandal, B., & Roy, P. K. (2023). Economic Load Dispatch Problem Using African Vulture Optimization Algorithm (AVOA) in Thermal Power Plant with Wind Energy. In: International Conference on Computational Intelligence in Communications and Business Analytics. Cham: Springer Nature Switzerland. pp. 107–120

Mishra, P., Biancolillo, A., Roger, J. M., et al. (2020). New data preprocessing trends based on ensemble of multiple preprocessing techniques. TrAC Trends in Analytical Chemistry, 132 , 116045.

Moré, J. J., Garbow, B. S., & Hillstrom, K. E. (1981). Testing unconstrained optimization software. ACM Transactions on Mathematical Software (TOMS), 7 (1), 17–41.

Niu, X., & Wang, J. (2019). A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting. Applied Energy, 241 , 519–539.

Oh, S. K., Kim, W. D., & Pedrycz, W. (2016). Design of radial basis function neural network classifier realized with the aid of data preprocessing techniques: Design and analysis. International Journal of General Systems, 45 (4), 434–454.

Pothiya, S., Ngamroo, I., & Kongprawechnon, W. (2008). Application of multiple tabu search algorithm to solve dynamic economic dispatch considering generator constraints. Energy Conversion and Management, 49 (4), 506–516.

Ramírez-Gallego, S., Krawczyk, B., García, S., et al. (2017). A survey on data preprocessing for data stream mining: current status and future directions. Neurocomputing, 239 , 39–57.

Rezaei, F., Safavi, H. R., Abd Elaziz, M., et al. (2022). An enhanced grey wolf optimizer with a velocity-aided global search mechanism. Mathematics, 10 (3), 351.

Shehadeh, H. A., Mustafa, H. M. J., & Tubishat, M. (2022). A hybrid genetic algorithm and sperm swarm optimization (HGASSO) for multimodal functions. International Journal of Applied Metaheuristic Computing (IJAMC), 13 (1), 1–33.

Srinivas, C., Senthilkumar, A., Kumar, I. K., & Rao, Y. B. K. (2021, March). Optimal Solution of Economic Load Dispatch using Teaching Learning Algorithm. In: 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). pp. 828–833

Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11 (4), 341–359.

Tanabe R, Fukunaga A. (2013). Success-history based parameter adaptation for differential evolution. IEEE congress on evolutionary computation, Conference on IEEE, 71–78.

Tian, C., Hao, Y., & Hu, J. (2018). A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization. Applied Energy, 231 , 301–319.

Wang, J., Li, J., & Li, Z. (2022). Prediction of air pollution interval based on data preprocessing and multi-objective dragonfly optimization algorithm. Frontiers in Ecology and Evolution, 10 , 855606.

Yao, X., Liu, Y., & Lin, G. (1999). Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation, 3 (2), 82–102.

Zhang, J., & Sanderson, A. C. (2009). JADE: adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation, 13 (5), 945–958.

Zhang, Q., Zou, D., Duan, N., & Shen, X. (2019). An adaptive differential evolutionary algorithm incorporating multiple mutation strategies for the economic load dispatch problem. Applied Soft Computing, 78 , 641–669.

Zhu, C., & Gao, D. (2016). Influence of data preprocessing. Journal of Computing Science and Engineering, 10 (2), 51–57.

Download references

Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions.

This work is supported by the National Natural Science Foundation of China (with number 61906164), by the Natural Science Foundation of Jiangsu Province of China (with number BK20190875).

Author information

Authors and affiliations.

School of Information Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China

Ruxin Zhao, Wei Wang, Tingting Zhang, Lixiang Fu, Jiajie Kang, Hongtan Zhang, Yang Shi & Chao Jiang

School of Intelligent Manufacturing, Yangzhou Polytechnic Institute, Yangzhou, 225127, Jiangsu, China

You can also search for this author in PubMed   Google Scholar

Contributions

Ruxin Zhao, Wei Wang and Tingting Zhang wrote the main manuscript text. Chang Liu, Jiajie Kang and Lixiang Fu prepared figures and tables. Hongtan Zhang, Shi Yang and Chao Jiang were responsible for editing. All authors reviewed the manuscript.

Corresponding author

Correspondence to Ruxin Zhao .

Ethics declarations

Conflict of interest.

The authors declared that they have no conflicts of interest to this work.

Informed consent

For all the above contents and statements, all authors in this manuscript have informed consent.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Zhao, R., Wang, W., Zhang, T. et al. An Adaptive Differential Evolution Algorithm Based on Data Preprocessing Method and a New Mutation Strategy to Solve Dynamic Economic Dispatch Considering Generator Constraints. Comput Econ (2024). https://doi.org/10.1007/s10614-024-10705-2

Download citation

Accepted : 20 August 2024

Published : 08 September 2024

DOI : https://doi.org/10.1007/s10614-024-10705-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Differential evolution algorithm
• Data preprocessing method
• DE/current-to- \({p}_{1}\) best& \({p}_{2}\) best
• Adaptive parameter control method
• Dynamic economic dispatch problem
• Find a journal
• Publish with us
• Track your research

Mental Health, Substance Use, and Child Maltreatment

Child maltreatment is a pressing concern in the United States, with more than four million children referred to child protective services in 2022. Reducing child maltreatment is a national health objective given the substantial, negative consequences for children who experience maltreatment, both in the short- and long-term. Parental mental health and substance use disorders are strongly associated with child maltreatment. In this study, we use administrative data over the period 2004 to 2021 to study the relationship between the number of mental health and substance use treatment centers per county and child maltreatment reports. Our findings provide evidence that better access to mental health and substance use treatment reduces child maltreatment reports. In particular, an 8% increase in the supply of treatment would reduce maltreatment reports by 1%. These findings suggest that recent and ongoing efforts by the federal government to expand mental health and substance use treatment availability may lead to reduced child maltreatment.

All authors contributed equally to this study. Authors are listed in alphabetical order. Research reported in this publication was supported by the National Institute on Mental Health of the National Institutes of Health under Award Number 1R01MH132552 (PI: Johanna Catherine Maclean). Dr. Meinhofer acknowledges support from the Foundation for Opioid Response Efforts GR00015582 and the National Institute on Drug Abuse K01DA051777. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Institutes of Health or the Foundation for Opioid Response Efforts. We thank Douglas Webber and Jiaxin Wei for excellent comments. All errors are our own. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

MARC RIS BibTeΧ

Download Citation Data

More from NBER

In addition to working papers , the NBER disseminates affiliates’ latest findings through a range of free periodicals — the NBER Reporter , the NBER Digest , the Bulletin on Retirement and Disability , the Bulletin on Health , and the Bulletin on Entrepreneurship  — as well as online conference reports , video lectures , and interviews .