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Environmental Research: Ecology is a multidisciplinary, open access journal devoted to addressing important macroscale challenges at the interface of ecology, biodiversity and conservation. The journal bridges scientific progress and methodological advances with assessments of environmental change impacts on ecosystems, and the responses of those ecosystems to change, including resilience, vulnerability and adaptation. For detailed information about subject coverage see the About the journal section.

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Qiuyan Yu et al 2024 Environ. Res.: Ecology 3 025001

The height of woody plants is a defining characteristic of forest and shrubland ecosystems because height responds to climate, soil and disturbance history. Orbiting LiDAR instruments, Ice, Cloud and land Elevation Satellite-2 (ICESat-2) and Global Ecosystem Dynamics Investigation LiDAR (GEDI), can provide near-global datasets of plant height at plot-level resolution. We evaluate canopy height measurements from ICESat-2 and GEDI with high resolution airborne LiDAR in six study sites in different biomes from dryland shrub to tall forests, with mean canopy height across sites of 0.5–40 m. ICESat-2 and GEDI provide reliable estimates for the relative height with RMSE and mean absolute error (MAE) of 7.49 and 4.64 m (all measurements ICESat-2) and 6.52 and 4.08 m (all measurements GEDI) for 98th percentile relative heights. Both datasets slightly overestimate the height of short shrubs (1–2 m at 5 m reference height), underestimate that of tall trees (by 6–7 m at 40 m reference height) and are highly biased (>3 m) for reference height <5 m, perhaps because of the difficulty of distinguishing canopy from ground signals. Both ICESat-2 and GEDI height estimates were only weakly sensitive to canopy cover and terrain slope ( R 2 < 0.06) and had lower error for night compared to day samples (ICESat-2 RMSE night: 5.57 m, day: 6.82 m; GEDI RMSE night: 5.94 m, day: 7.03 m). For GEDI, the day versus night differences varied with differences in mean sample heights for the day and night samples and had little effect on bias. Accuracy of ICESat-2 and GEDI canopy heights varies among biomes, and the highest MAE was observed in the tallest, densest forest (GEDI: 7.85 m; ICESat-2: 7.84 m (night) and 12.83 m (day)). Improvements in canopy height estimation would come from better discrimination of canopy photons from background noise for ICESat-2 and improvements in the algorithm for decomposing ground and canopy returns for GEDI. Both would benefit from methods to distinguish outlier samples.

C R Hakkenberg et al 2023 Environ. Res.: Ecology 2 035005

Biodiversity-structure relationships (BSRs), which describe the correlation between biodiversity and three-dimensional forest structure, have been used to map spatial patterns in biodiversity based on forest structural attributes derived from lidar. However, with the advent of spaceborne lidar like the Global Ecosystem Dynamics Investigation (GEDI), investigators are confronted with how to predict biodiversity from discrete GEDI footprints, sampled discontinuously across the Earth surface and often spatially offset from where diversity was measured in the field. In this study, we used National Ecological Observation Network data in a hierarchical modeling framework to assess how spatially-coincident BSRs (where field-observed taxonomic diversity measurements and structural data from airborne lidar coincide at a single plot) compare with BSRs based on statistical aggregates of proximate, but spatially-dispersed GEDI samples of structure. Despite substantial ecoregional variation, results confirm cross-biome consistency in the relationship between plant/tree alpha diversity and spatially-coincident lidar data, including structural data from outside the field plot where diversity was measured. Moreover, we found that generalized forest structural profiles derived from GEDI footprint aggregates were consistently related to tree alpha diversity, as well as cross-biome patterns in beta and gamma diversity. These findings suggest that characteristic forest structural profiles generated from aggregated GEDI footprints are effective for BSR diversity prediction without incorporation of more standard predictors of biodiversity like climate, topography, or optical reflectance. Cross-scale comparisons between airborne- and GEDI-derived structural profiles provide guidance for balancing scale-dependent trade-offs between spatial proximity and sample size for BSR-based prediction with GEDI gridded products. This study fills a critical gap in our understanding of how generalized forest structural attributes can be used to infer specific field-observed biodiversity patterns, including those not directly observable from remote sensing instruments. Moreover, it bolsters the empirical basis for global-scale biodiversity prediction with GEDI spaceborne lidar.

Morgan S Tassone et al 2024 Environ. Res.: Ecology 3 015003

The direction and magnitude of tundra vegetation productivity trends inferred from the normalized difference vegetation index (NDVI) have exhibited spatiotemporal heterogeneity over recent decades. This study examined the spatial and temporal drivers of Moderate Resolution Imaging Spectroradiometer Max NDVI (a proxy for peak growing season aboveground biomass) and time-integrated (TI)-NDVI (a proxy for total growing season productivity) on the Yamal Peninsula, Siberia, Russia between 2001 and 2018. A suite of remotely-sensed environmental drivers and machine learning methods were employed to analyze this region with varying climatological conditions, landscapes, and vegetation communities to provide insight into the heterogeneity observed across the Arctic. Summer warmth index, the timing of snowmelt, and physiognomic vegetation unit best explained the spatial distribution of Max and TI-NDVI on the Yamal Peninsula, with the highest mean Max and TI-NDVI occurring where summer temperatures were higher, snowmelt occurred earlier, and erect shrub and wetland vegetation communities were dominant. Max and TI-NDVI temporal trends were positive across the majority of the Peninsula (57.4% [5.0% significant] and 97.6% [13.9% significant], respectively) between 2001 and 2018. Max and TI-NDVI trends had variable relationships with environmental drivers and were primarily influenced by coastal-inland gradients in summer warmth and soil moisture. Both Max and TI-NDVI were negatively impacted by human modification, highlighting how human disturbances are becoming an increasingly important driver of Arctic vegetation dynamics. These findings provide insight into the potential future of Arctic regions experiencing warming, moisture regime shifts, and human modification, and demonstrate the usefulness of considering multiple NDVI metrics to disentangle the effects of individual drivers across heterogeneous landscapes. Further, the spatial heterogeneity in the direction and magnitude of interannual covariation between Max NDVI, TI-NDVI, and climatic drivers highlights the difficulty in generalizing the effects of individual drivers on Arctic vegetation productivity across large regions.

Louise Mercer et al 2023 Environ. Res.: Ecology 2 045001

Community-based monitoring (CBM) is increasingly cited as a means of collecting valuable baseline data that can contribute to our understanding of environmental change whilst supporting Indigenous governance and self-determination in research. However, current environmental CBM models have specific limitations that impact program effectiveness and the progression of research stages beyond data collection. Here, we highlight key aspects that limit the progression of Arctic CBM programs which include funding constraints, organisational structures, and operational processes. Exemplars from collaborative environmental research conducted in the acutely climate change impacted Hamlet of Tuktoyaktuk, Inuvialuit Settlement Region (ISR), Canada, are used to identify co-developed solutions to address these challenges. These learnings from experience-based collaborations feed into a new solution-orientated model of environmental community-based research (CBR) that emphasises continuity between and community ownership in all research stages to enable a more complete research workflow. Clear recommendations are provided to develop a more coherent approach to achieving this model, which can be adapted to guide the development of successful environmental CBR programs in different research and place-based contexts.

Yu Nan et al 2023 Environ. Res.: Ecology 2 015003

The modern economic growth paradigm relies heavily on natural endowments. Renewable energy as a permanent energy source has the potential to reduce the ecological footprint (EF). We adopt the Vector Autoregressive model to examine the impact of renewable energy consumption on the energy EF and use the quantile regression method to test the heterogeneity and asymmetry between energy EF and photovoltaic, wind energy, and biomass energy. The results show that renewable energy has a long-term negative impact on the EF, and for every 1% increase in renewable energy consumption, the energy EF will decrease by 2.91%. The contribution of renewable energy consumption to reducing the EF is 1.34% on average. There is no two-way Granger causality between renewable energy consumption and energy EF. The reduction effect of wind energy consumption on the energy EF varies the most, followed by biomass energy and photovoltaic. In addition, under different energy EF distribution conditions, the impact of photovoltaic or wind energy or biomass energy consumption on the energy EF is different.

Christopher E Doughty et al 2023 Environ. Res.: Ecology 2 035002

The stratified nature of tropical forest structure had been noted by early explorers, but until recent use of satellite-based LiDAR (GEDI, or Global Ecosystems Dynamics Investigation LiDAR), it was not possible to quantify stratification across all tropical forests. Understanding stratification is important because by some estimates, a majority of the world's species inhabit tropical forest canopies. Stratification can modify vertical microenvironment, and thus can affect a species' susceptibility to anthropogenic climate change. Here we find that, based on analyzing each GEDI 25 m diameter footprint in tropical forests (after screening for human impact), most footprints (60%–90%) do not have multiple layers of vegetation. The most common forest structure has a minimum plant area index (PAI) at ∼40 m followed by an increase in PAI until ∼15 m followed by a decline in PAI to the ground layer (described hereafter as a one peak footprint). There are large geographic patterns to forest structure within the Amazon basin (ranging between 60% and 90% one peak) and between the Amazon (79 ± 9% sd) and SE Asia or Africa (72 ± 14% v 73 ± 11%). The number of canopy layers is significantly correlated with tree height ( r 2 = 0.12) and forest biomass ( r 2 = 0.14). Environmental variables such as maximum temperature ( T max ) ( r 2 = 0.05), vapor pressure deficit (VPD) ( r 2 = 0.03) and soil fertility proxies (e.g. total cation exchange capacity − r 2 = 0.01) were also statistically significant but less strongly correlated given the complex and heterogeneous local structural to regional climatic interactions. Certain boundaries, like the Pebas Formation and Ecoregions, clearly delineate continental scale structural changes. More broadly, deviation from more ideal conditions (e.g. lower fertility or higher temperatures) leads to shorter, less stratified forests with lower biomass.

K Best et al 2023 Environ. Res.: Ecology 2 045003

Significant uncertainties persist concerning how Arctic soil tundra carbon emission responds to environmental changes. In this study, 24 cores were sampled from drier (high centre polygons and rims) and wetter (low centre polygons and troughs) permafrost tundra ecosystems. We examined how soil CO 2 and CH 4 fluxes responded to laboratory-based manipulations of soil temperature (and associated thaw depth) and water table depth, representing current and projected conditions in the Arctic. Similar soil CO 2 respiration rates occurred in both the drier and the wetter sites, suggesting that a significant proportion of soil CO 2 emission occurs via anaerobic respiration under water-saturated conditions in these Arctic tundra ecosystems. In the absence of vegetation, soil CO 2 respiration rates decreased sharply within the first 7 weeks of the experiment, while CH 4 emissions remained stable for the entire 26 weeks of the experiment. These patterns suggest that soil CO 2 emission is more related to plant input than CH 4 production and emission. The stable and substantial CH 4 emission observed over the entire course of the experiment suggests that temperature limitations, rather than labile carbon limitations, play a predominant role in CH 4 production in deeper soil layers. This is likely due to the presence of a substantial source of labile carbon in these carbon-rich soils. The small soil temperature difference (a median difference of 1 °C) and a more substantial thaw depth difference (a median difference of 6 cm) between the high and low temperature treatments resulted in a non-significant difference between soil CO 2 and CH 4 emissions. Although hydrology continued to be the primary factor influencing CH 4 emissions, these emissions remained low in the drier ecosystem, even with a water table at the surface. This result suggests the potential absence of a methanogenic microbial community in high-centre polygon and rim ecosystems. Overall, our results suggest that the temperature increases reported for these Arctic regions are not responsible for increases in carbon losses. Instead, it is the changes in hydrology that exert significant control over soil CO 2 and CH 4 emissions.

M M Seeley et al 2024 Environ. Res.: Ecology 3 011001

Vegetation species mapping using airborne imaging spectroscopy yields accurate results and is important for advancing conservation objectives and biogeographic studies. As these data become more readily available owing to the upcoming launch of spaceborne imaging spectrometers, it is necessary to understand how these data can be used to consistently classify species across large geographic scales. However, few studies have attempted to map species across multiple ecosystems; therefore, little is known regarding the effect of intra-specific variation on the mapping of a single species across a wide range of environments and among varying backgrounds of other non-target species. To explore this effect, we developed and tested species classification models for Metrosideros polymorpha , a highly polymorphic canopy species endemic to Hawai'i, which is found in a diverse array of ecosystems. We compared the accuracies of support vector machine (SVM) and random forest models trained on canopy reflectance data from each of eight distinct ecosystems (ecosystem-specific) and a universal model trained on data from all ecosystems. When applied to ecosystem-specific test datasets, the ecosystem-specific models outperformed the universal model; however, the universal model retained high (>81%) accuracies across all ecosystems. Additionally, we found that models from ecosystems with broad variation in M. polymorpha canopy traits, as estimated using chemometric equations applied to canopy spectra, accurately predicted M. polymorpha in other ecosystems. While species classifications across ecosystems can yield accurate results, these results will require sampling procedures that capture the intra-specific variation of the target species.

Mei-Ling E Feng et al 2022 Environ. Res.: Ecology 1 011004

Animal-related outages (AROs) are a prevalent form of outages in electrical distribution systems. Animal-infrastructure interactions vary across species and regions, underlining the need to study the animal-outage relationship in more species and diverse systems. Animal activity has been an indicator of reliability in the electrical grid system by describing temporal patterns in AROs. However, these ARO models have been limited by a lack of available species activity data, instead approximating activity based on seasonal patterns and weather dependency in ARO records and characteristics of broad taxonomic groups, e.g. squirrels. We highlight available resources to fill the ecological data gap limiting joint analyses between ecology and energy sectors. Species distribution modeling (SDM), a common technique to model the distribution of a species across geographic space and time, paired with community science data, provided us with species-specific estimates of activity to analyze alongside spatio-temporal patterns of ARO severity. We use SDM estimates of activity for multiple outage-prone bird species to examine whether diverse animal activity patterns were important predictors of ARO severity by capturing existing variation within animal-outage relationships. Low dimensional representation and single patterns of bird activity were important predictors of ARO severity in Massachusetts. However, both patterns of summer migrants and overwintering species showed some degree of importance, indicating that multiple biological patterns could be considered in future models of grid reliability. Making the best available resources from quantitative ecology known to outside disciplines can allow for more interdisciplinary data analyses between ecological and non-ecological systems. This can result in further opportunities to examine and validate the relationships between animal activity and grid reliability in diverse systems.

Maria Magdalena Warter et al 2023 Environ. Res.: Ecology 2 025001

In dryland ecosystems, vegetation within different plant functional groups exhibits distinct seasonal phenologies that are affected by the prevailing hydroclimatic forcing. The seasonal variability of precipitation, atmospheric evaporative demand, and streamflow influences root-zone water availability to plants in water-limited environments. Increasing interannual variations in climate forcing of the local water balance and uncertainty regarding climate change projections have raised the potential for phenological shifts and changes to vegetation dynamics. This poses significant risks to plant functional types across large areas, especially in drylands and within riparian ecosystems. Due to the complex interactions between climate, water availability, and seasonal plant water use, the timing and amplitude of phenological responses to specific hydroclimate forcing cannot be determined a priori , thus limiting efforts to dynamically predict vegetation greenness under future climate change. Here, we analyze two decades (1994–2021) of remote sensing data (soil adjusted vegetation index (SAVI)) as well as contemporaneous hydroclimate data (precipitation, potential evapotranspiration, depth to groundwater, and air temperature), to identify and quantify the key hydroclimatic controls on the timing and amplitude of seasonal greenness. We focus on key phenological events across four different plant functional groups occupying distinct locations and rooting depths in dryland SE Arizona: semi-arid grasses and shrubs, xeric riparian terrace and hydric riparian floodplain trees. We find that key phenological events such as spring and summer greenness peaks in grass and shrubs are strongly driven by contributions from antecedent spring and monsoonal precipitation, respectively. Meanwhile seasonal canopy greenness in floodplain and terrace vegetation showed strong response to groundwater depth as well as antecedent available precipitation (aaP = P − PET) throughout reaches of perennial and intermediate streamflow permanence. The timings of spring green-up and autumn senescence were driven by seasonal changes in air temperature for all plant functional groups. Based on these findings, we develop and test a simple, empirical phenology model, that predicts the timing and amplitude of greenness based on hydroclimate forcing. We demonstrate the feasibility of the model by exploring simple, plausible climate change scenarios, which may inform our understanding of phenological shifts in dryland plant communities and may ultimately improve our predictive capability of investigating and predicting climate-phenology interactions in the future.

Latest articles

Manette E Sandor et al 2024 Environ. Res.: Ecology 3 015002

How species richness scales spatially is a foundational concept of community ecology, but how biotic interactions scale spatially is poorly known. Previous studies have proposed interactions-area relationships (IARs) based on two competing relationships for how the number of interactions scale with the number of species, the 'link-species scaling law' and the 'constant connectance hypothesis.' The link-species scaling law posits that the number of interactions per species remains constant as the size of the network increases. The constant connectance hypothesis says that the proportion of realized interactions remains constant with network size. While few tests of these IARs exist, evidence for the original interactions-species relationships are mixed. We propose a novel IAR and test it against the two existing IARs. We first present a general theory for how interactions scale spatially and the mathematical relationship between the IAR and the species richness-area curve. We then provide a new mathematical formulation of the IAR, accounting for connectance varying with area. Employing data from three mutualistic networks (i.e. a network which specifies interconnected and mutually-beneficial interactions between two groups of species), we evaluate three competing models of how interactions scale spatially: two previously published IAR models and our proposed IAR. We find the new IAR described by our theory-based equation fits the empirical datasets equally as well as the previously proposed IAR based on the link-species scaling law in one out of three cases and better than the previously-proposed models in two out of three cases. Our novel IAR improves upon previous models and quantifies mutualist interactions across space, which is paramount to understanding biodiversity and preventing its loss.

Ezrah Natumanya et al 2024 Environ. Res.: Ecology 3 015001

Riparian vegetation usually gets less focus in biodiversity assessments and yet species diversity is important knowledge when applying patch specific conservation value in the Niassa Special Reserve (NSR). This study assessed the composition and conservation status of riparian species in an exposed river basin downstream location. Purposive sampling was used in the selection of sites and respondents to maximize data collection. The study found 19 species belonging to 15 families with 52.63% of them having a frequency of ⩾50% in sampling plots. There were 10 species that are endemic to the sub-Sharan Africa Region. Fabaceae was the dominant family with 5 species. The species with the highest population was Flacourtia indica (Burm. f.) Merr. Species richness ranged from 0.35 to 0.98 with a mean of 0.66 ± 0.22. The IVI ranged from 34.70 ( F. indica (Burm. f.) Merr) to 4.43 ( Tribulus cistoides L.) with a mean of 15.79 ± 7.79. Threats of species loss and ecosystem disturbance were agriculture, infrastructure development and plant harvests. There was a reported decline in species availability over the previous 10 years by 18.7% of the respondents. The results added to existing studies and records of vegetation species of conservation value in areas exposed to loss in the NSR. This study advances research on vegetation range dynamics in the NSR and presents a need to mitigate human land use impacts on riparian vegetation species composition.

Review articles

Davide Vione 2023 Environ. Res.: Ecology 2 012001

Reactions induced by sunlight (direct photolysis and indirect photochemistry) are important ecosystem services that aid freshwater bodies in removing contaminants, although they may also exacerbate pollution in some cases. Without photoinduced reactions, pollution problems would be considerably worse overall. The photochemical reaction rates depend on seasonality, depth, water chemistry (which also significantly affects the reaction pathways), and pollutant photoreactivity. Photochemical reactions are also deeply impacted by less studied factors, including hydrology, water dynamics, and precipitation regimes, which are key to understanding the main impacts of climate change on surface-water photochemistry. Climate change is expected in many cases to both exacerbate freshwater pollution, and enhance photochemical decontamination. Therefore, photochemical knowledge will be essential to understand the future evolution of freshwater environments.

Accepted manuscripts

Stanley et al 

There is an increasing disconnect between people and nature as we become more urbanised. Intensification in cities often results in a reduction of natural areas, more homogenised and manicured green spaces, and loss of biota. Compared to people in rural areas, urban dwellers are less likely visit natural areas and recognise and value biota. Reconnecting people with nature in the city not only benefits human mental and physical wellbeing but can also have positive effects on how people value biodiversity and act on conservation issues. However, in some contexts, the push to reconnect people with nature may have unintended negative outcomes on biodiversity, particularly if place-specific nature is not used in urban greening. In the current biodiversity crisis, using vegetation and green space design that is not reflective of the environmental context of a city can further disconnect residents, particularly Indigenous people, from their local environment and species, and further entrench extinction of experience and loss of environmental values. This disconnect can result in residents applying wildlife gardening practices, such as bird feeding, that are not specific to place, and benefit introduced species over indigenous species. Furthermore, cities are gateways for invasive species, and using species in greening projects that are not locally sourced has already left cities and their surrounding regions with a large weed legacy. Using place-specific nature and green space in cities can be less resource intensive, highly beneficial for biodiversity and give residents a unique sense of place. Rather than simply adding 'more nature' in cities, the messaging should be more complex, emphasising the need for urban greening to be context specific to avoid negative impacts on biodiversity and ecological and cultural services.

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• 2022-present Environmental Research: Ecology doi: 10.1088/issn.2752-664X Online ISSN: 2752-664X

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• Environmental Science (miscellaneous)

Zhongguo huan jing ke xue chu pan she

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Environmental and earth sciences research journal.

• ISSN:  2369-5668 (print); 2369-5676 (online)
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Environmental and Earth Sciences Research Journal (EESRJ) is a top-rated international quarterly reporting the most outstanding discoveries in both basic and applied research of environmental and earth science. Our mission is to build an open-access platform covering all aspects of environmental and earth science, and develop into a premier hub of quality research around the world. All submissions to the journal will be evaluated for novelty and usefulness to the scientific community and society at large, before being sent to review or reject. The editorial board welcomes original papers on environmental and earth sciences, especially those selected for presentation at international conferences or academic forums, and promises to conduct rigorous review of the selected papers. The journal is published quarterly by the IIETA.

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ORIGINAL RESEARCH article

The influence of digital platform on the implementation of corporate social responsibility: from the perspective of environmental science development to explore its potential role in public health.

• 1 School of Management, Guangzhou University, Guangzhou, China
• 2 Guangzhou Xinhua University, Dongguan, China
• 3 School of Public Administration, Guangzhou University, Guangzhou, China
• 4 School of Economics and Statistics, Guangzhou University, Guangzhou, China
• 5 School of Journalism and Communication, Guangzhou University, Guangzhou, China

Introduction: This paper aims to explore the intersection of corporate social responsibility (CSR) and public health within the context of digital platforms. Specifically, the paper explores the impact of digital platforms on the sustainable development practices of enterprises, seeking to comprehend how these platforms influence the implementation of environmental protection policies, resource management, and social responsibility initiatives.

Methods: To assess the impact of digital platforms on corporate environmental behavior, we conducted a questionnaire survey targeting employees in private enterprises. This survey aimed to evaluate the relationship between the adoption of digital platforms and the implementation of environmental protection policies and practices.

Results: Analysis of the survey responses revealed a significant positive correlation between the use of digital platforms and the environmental protection behavior of enterprises ( r = 0.523 ; p < 0.001 ), Moreover, the presence of innovative environmental protection technologies on these platforms was found to positively influence the enforcement of environmental policies, with a calculated impact ratio of ( a ∗ b / c = 55.31 % ). An intermediary analysis highlighted that environmental innovation technology plays a mediating role in this process. Additionally, adjustment analysis showed that enterprises of various sizes and industries respond differently to digital platforms, indicating the need for tailored environmental policies

Discussion: These findings underscore the pivotal role of digital platforms in enhancing CSR efforts and public health by fostering improved environmental practices among corporations. The mediating effect of environmental innovation technologies suggests that digital platforms not only facilitate direct environmental actions but also enhance the efficiency and effectiveness of such initiatives through technological advances. The variability in response by different enterprises points to the importance of customizable strategies in policy formulation. By offering empirical evidence of digital platforms’ potential to advance CSR and public health through environmental initiatives, this paper contributes to the ongoing dialogue on sustainable development goals. It provides practical insights for enterprises and policy implications for governments striving to craft more effective environmental policies and strategies.

1 Introduction

Global environmental issues have gained prominence in today’s society, raising a great deal of concern. Environmental challenges such as climate change, resource depletion and ecosystem destruction threaten the sustainable development of the earth and the survival of mankind ( 1 , 2 ). In this context, enterprises not only need to find a balance between economic interests and environmental protection, but also need to hypothesize social responsibilities and contribute to sustainable development ( 3 ). As a tool for information dissemination, cooperation and interaction, and resource integration, digital platform is regarded as an emerging force that may have a far-reaching impact on corporate environmental protection behavior and social responsibility ( 4 ). In the past decades, corporate social responsibility (CSR) has become an important part of business practice. Enterprises no longer only pay attention to economic performance, but link their economic activities with social and environmental issues to ensure sustainable development ( 5 – 7 ). Meanwhile, the rise of digital platform has changed the interaction between enterprises and their stakeholders, providing enterprises with more opportunities to disseminate environmental information, cooperate to solve environmental problems, and supervise their environmental protection behavior ( 8 ). However, despite these potential opportunities, there are still many unknown factors about the actual impact of digital platforms on corporate environmental behavior and social responsibility ( 9 ).

In recent years, with the rapid development of digital technology, digital platform has become an important force to promote social change. Especially in corporate social responsibility and public health, the role of digital platform has become increasingly prominent. Early studies such as Wang et al. ( 10 ) have pointed out that digital transformation can promote enterprises to implement environmental protection policies and social responsibility plans more efficiently. However, there is still a lack of existing literature on how the digital platform affects the sustainable practice of enterprises under the guidance of the development of environmental science, especially the contribution to public health. At present, digital platform plays a vital role in the practice of CSR. Through digital means, enterprises can manage resources more effectively, improve energy efficiency, reduce carbon emissions and other environmental protection behaviors. Taking an energy company as an example, the company uses digital platform to implement intelligent energy management system, monitor energy usage, and optimize energy distribution, thus reducing energy waste and improving energy utilization efficiency. Through digital monitoring and data analysis, enterprises can know the energy consumption in real time, adjust production plans in time to reduce carbon emissions, and realize green production. These measures not only help enterprises to comply with environmental laws and regulations and fulfill their social responsibilities, but also bring them economic benefits and brand reputation. Looking forward to the future, the potential of digital platform lies in promoting enterprises to achieve sustainable development goals and promoting environmental protection behavior and social responsibility practice to a higher level. The continuous innovation and application of digital technology will provide more environmental protection solutions and tools for enterprises and support the realization of environmentally friendly production. However, the digital platform also faces some challenges, such as data privacy protection and information security risks, which need to be effectively controlled. Meanwhile, in the process of digital transformation, enterprises may face challenges in technology upgrading and talent training, and it is necessary to strengthen their understanding and application ability of digital technology. Considering the development perspective of environmental science, the relationship between digital platform and CSR is very important. Through the application of digital platform, enterprises can better practice environmental protection behavior, promote sustainable development, and integrate social responsibility into all aspects of business operations. The in-depth discussion of this relationship fills the gap in the existing research and provides new ideas and viewpoints for the related influence in the field of public health. By combining the concepts of digital platform, environmental science and CSR, future research will help to better explore the potential role of digital platform in CSR and public health, and promote the development of enterprises in a more sustainable and socially responsible direction. Therefore, this paper attempts to fill this knowledge gap and explore the subject through empirical research. Specifically, this paper uses the methods of descriptive statistical analysis, correlation analysis and hypothesis test analysis to evaluate the relationship between the use of digital platforms and corporate environmental behavior, investigates the impact of digital platforms on CSR policies and practices, explores the intermediary variables and moderating variables between digital platforms and corporate environmental behavior, and compares the differences in the impact of digital platforms on corporate environmental behavior and social responsibility between different industries and geographical regions. This paper deeply discusses the important role of digital platform in enterprise operation and the possible positive impact of corporate social responsibility on public health and environmental protection. With the acceleration of digital transformation, enterprises increasingly rely on digital platforms to optimize their operational efficiency and market competitiveness, which provides new opportunities and challenges for enterprises to fulfill their social and environmental responsibilities. By revealing how the digital platform can help enterprises to better implement CSR strategy, and then have a positive impact on environmental protection, this paper aims to provide policy makers and business managers with empirical insights and suggestions to promote the realization of sustainable development goals.

In order to achieve the above research objectives, this paper adopts various research methods, including quantitative questionnaire survey, to collect relevant data of enterprises and digital platforms. Then, descriptive statistical analysis is used to summarize the basic characteristics of the data, correlation analysis is used to test the relationship between variables, and hypothesis testing analysis is used to verify the research hypothesis. In addition, intermediary analysis and adjustment analysis are used to deeply understand the influence mechanism of digital platform on corporate environmental behavior and social responsibility. This paper fills the knowledge gap of the influence of digital platform on corporate environmental behavior and social responsibility, and provides practical and policy enlightenment. By deeply understanding the relationship between digital platform and sustainable development of enterprises, it can provide strong support for enterprises and governments to formulate more effective environmental protection policies and strategies.

There are three innovations in this paper. First, from the perspective of environmental science development, the influence mechanism of digital platform on corporate environmental behavior and social responsibility is deeply explored. The second is to put forward the application strategy of digital platform in corporate environmental behavior and social responsibility to provide guidance for corporate practice. Thirdly, by means of questionnaire survey, descriptive statistical analysis, correlation analysis and hypothesis test analysis, the influence mechanism of digital platform on corporate environmental behavior and social responsibility is comprehensively studied.

2 Literature review

Scholars have carried out extensive research in the field of corporate environmental behavior and CSR. They paid attention to the motivation, influencing factors and effects of corporate environmental protection behavior, and discussed the influence of CSR on corporate performance and sustainable development from different dimensions. Afsar and Umrani ( 11 ) investigated the influence of perceived CSR on employees’ environmental behavior. The results showed that perceived CSR had a significant and positive impact on environmental commitment. Raza et al. ( 12 ) investigated hotel employees’ views on CSR activities and their influence on employees’ voluntary environmental protection behavior based on the theory of social exchange and identity. The results showed that CSR had a direct impact on employees’ voluntary environmental protection behavior. Latif et al. ( 13 ) analyzed the relationship between CSR and employees’ environmental behavior from the perspective of sustainable development, and found that employees’ perceived CSR actively promoted employees’ environmental behavior. Deng et al. ( 14 ) studied the relationship between CSR initiatives in hospitals and employees’ environmental behavior, and found that CSR directly and indirectly affected employees’ environmental behavior through environment-specific transformational leadership. Guan et al. ( 15 ) proposed that CSR was mainly related to the environmental performance and economic performance of enterprises. Nowadays, people can improve the environmental performance and economic performance of enterprises by promoting employees’ environmental behavior and altruistic values, and realize CSR. Giacalone et al. ( 16 ) believed that CSR involved the aim of having a positive impact on the community operated by the analyzed company. International organizations and government agencies had also issued a series of environmental science guidelines to encourage enterprises to adopt sustainable development practices, reduce carbon emissions and protect ecosystems. The Global Environment Outlook report provided a comprehensive assessment of the global environmental situation, and called on governments, enterprises and all sectors of society to take actions to reduce carbon emissions, protect ecosystems and promote sustainable development. The report included detailed analysis and suggestions on many environmental problems such as climate change, biodiversity loss and land degradation, and encourages enterprises to take environmental protection measures to promote the realization of global sustainable development goals. It shows that the environmental protection behavior of enterprises has a far-reaching impact on their economic performance and social reputation. Environmental protection behavior not only helps to reduce the environmental footprint of enterprises, but also improves the trust of consumers and investors in enterprises. However, the environmental protection behavior of enterprises is influenced by many factors, including laws and regulations, market pressure and social expectations. Therefore, it has become an important topic to study how to promote enterprises to participate in environmental protection activities more actively.

The emergence of digital platform provides a new perspective for studying corporate environmental behavior and CSR. Among them, technologies and algorithms play a key role in the digital platform, which can be used for data analysis, user behavior prediction and information dissemination. The participation of artificial intelligence (AI) can effectively interact with experts and non-experts in different social places to promote the wise judgment of opaque artificial intelligence systems and realize their democratic governance ( 17 ). Li ( 18 ) believed that big data analysis played an important role in green governance and CSR. Kong and Liu ( 19 ) thought that digital transformation has greatly promoted CSR, and it was helpful to improve pollution control ability and internal control efficiency in enterprises with low financing constraints and high regulatory pressure, thus improving CSR performance. Li ( 20 ) evaluated the financial investment environment of enterprises based on blockchain and cloud computing, and found that cloud computing technology and blockchain technology expanded the construction performance of financial investment data from 5.98 to 9.27. The computing performance was improved by 3.29. Based on two-stage structural equation modeling-artificial neural network (ANN) method, Najmi et al. ( 21 ) discussed the role of consumers in the recycling plan of scrapped mobile phones. Yan et al. ( 22 ) used two-stage structural equation modeling and ANN to analyze the impact of the adoption of financial technology on the sustainable development performance of banking institutions. The research results showed that green finance and green innovation fully mediate the relationship between the application of financial technology and the sustainable development performance of banking institutions ( 22 ). Diaz and Nguyen ( 23 ) predicted the minimum prediction error of CSR index through gray correlation analysis and gray correlation analysis, and found that BPN model had the smallest prediction error, which was better than recurrent neural network (RNN) and radial basis function neural network model. Ezzi et al. ( 24 ) analyzed the important role of blockchain technology in explaining CSR performance, and the results showed that the implementation of blockchain technology had a significant and positive impact on CSR performance.

Wang et al. ( 25 ) constructed a recommendation and resource optimization model by using neural network algorithm from the perspective of cultural and creative industries to promote enterprise project decision-making and resource optimization. The research showed that the entrepreneurial project recommendation and resource optimization model can significantly improve the recognition accuracy, reduce the prediction error, and contribute to the sustainable development of social economy and the optimization of entrepreneurial resources. Combined with the research content of this paper, these research results can provide effective decision-making reference for enterprises and promote the realization of sustainable development goals. Wang et al. ( 26 ) used blockchain technology to build an intelligent contract, established a risk management system for online public opinion, and tracked public opinion through risk correlation tree technology, thus improving the accuracy of risk prediction and credibility detection. The research results showed that with the support of blockchain technology, the three experimental schemes designed can reasonably predict the risk and detect the credibility of NPO. This work was helpful to optimize the control measures of network environment and provide an important reference for improving the management level of network public opinion. Deng et al. ( 27 ) promoted the mechanism of public participation and enhanced the vitality of the economic market of resource-based cities by increasing policy intervention. This study had important reference value for promoting urban resource management and economic efficiency. Li et al. ( 28 ) paid attention to the influence of the pilot policy of low-carbon cities on urban entrepreneurial activities and its role in promoting green development. The results showed that the pilot policy of low-carbon cities generally inhibits entrepreneurial activities, but the level of green innovation can alleviate this inhibitory effect. In addition, the pilot policy of low-carbon cities inhibited the entrepreneurial activities of high-carbon industries, while encouraging the entrepreneurial activities of emerging industries, which led to the changes and upgrading of industrial structure. Li et al. ( 29 ) discussed the development path of clean energy and related issues of sustainable development of mining projects in the ecological environment driven by big data. Through this study, it was hoped to provide empirical support and decision-making reference for mining projects in the development of clean energy, promote the sustainable development of mining industry and realize a win-win situation of economic and ecological benefits. This was of great significance for protecting the ecological environment and realizing the sustainable utilization of resources. Li et al. ( 30 ) investigated the influence of regional digital finance development on corporate financing constraints. It was found that digital finance can significantly alleviate the financing constraints of enterprises, and the impact on small and medium-sized enterprises and private enterprises was more significant. Li et al. ( 31 ) discussed the impact of climate change on corporate environmental, social and governance performance. According to the empirical results, the environmental, social, and governance (ESG) performance of enterprises was significantly inhibited by climate change. It was also found that eliminating the mismatch between internal and external resources would help to alleviate the adverse impact of climate change on ESG performance.

The above literature review provides a comprehensive overview of the relevant research status and scholars’ views on corporate environmental behavior, CSR and digital platform. The research shows that scholars have carried out extensive research in the fields of corporate environmental behavior and CSR, and paid attention to different aspects of these fields, including environmental commitment, environmental behavior of employees, and sustainable development performance. Their research reveals the profound influence of environmental protection behavior of enterprises on their economic performance and social reputation, and the direct influence of CSR on employees’ voluntary environmental protection behavior. In addition, as a new technology and tool, digital platform has attracted the interest of research circles. Technologies and algorithms play a key role in the digital platform, which can be used for data analysis, user behavior prediction and information dissemination, thus affecting the environmental protection behavior and CSR of enterprises. Many studies have shown that AI, big data analysis, blockchain and other technologies have a positive impact on CSR performance and environmental protection behavior ( 32 – 35 ). However, these studies also have some limitations, such as differences in research methods, limitations in sample selection and heterogeneity between different fields. Therefore, this paper aims to explore the influence mechanism of digital platform on corporate environmental behavior and social responsibility, adopt various research methods, and pay attention to the differences between different industries and geographical regions. This will help to fill the knowledge gap in existing research and provide more specific guidance for enterprises and policy makers to promote the realization of sustainable development goals.

The design of this paper focuses on the interaction between digital platform and corporate social responsibility and its influence on environmental protection behavior, which reflects the complexity and scientific value of the study. Based on the theoretical framework and previous empirical research, this paper investigates how the digital platform affects the environmental protection behavior by promoting the practice of corporate social responsibility. This not only deepens the understanding of the role of digital platform in the field of corporate social responsibility, but also provides a new perspective on how to use digital technology to promote environmentally friendly behavior of enterprises, thus filling the gaps in the existing literature.

3 Research methodology

3.1 cross-influence of csr and development of environmental science.

CSR and environmental science development are two interrelated fields, and their cross-influence is very important for understanding the mechanism behind corporate environmental protection behavior. This section deeply discusses the relationship between CSR and the development of environmental science, and establish the theoretical basis of the research. In this section, the guiding principles of environmental science development, including environmental protection and sustainable development policy documents issued by international organizations such as the United Nations Environment Programme and government agencies, are shown in Table 1 .

Table 1 . Guidance document for the development of environmental science.

In Table 1 , the common goal of core policies and plans is to encourage enterprises to adopt sustainable development practices, reduce carbon emissions and protect ecosystems, thus promoting global sustainable development. Enterprises can fulfill their social and environmental responsibilities by actively participating in these initiatives and complying with relevant policies. Meanwhile, they can gain economic and reputation benefits in terms of sustainability. These policies and plans provide a framework and guidance for enterprises to play an active role in environmental protection behavior ( 36 , 37 ).

CSR covers the social and environmental impacts of enterprises in their business activities, and emphasizes the active obligations of enterprises in fulfilling their social responsibilities ( 38 ). Figure 1 shows the cross influence of CSR and the development of environmental science.

Figure 1 . Cross-influence of CSR and the development of environmental science.

In Figure 1 , in the cross-influence between CSR and environmental science, the core principles and active obligations of CSR play a key role. The core principles of CSR, such as social responsibility, transparency and sustainability, guide enterprises to actively consider social and environmental factors in their business activities. Meanwhile, CSR, as an active obligation, requires enterprises not only to fulfill their legal obligations, but also to actively participate in solving social and environmental problems. These behaviors are the concrete application of CSR in the environmental field, which shows how enterprises actively fulfill their social and environmental responsibilities and promote the practice of sustainable development.

3.2 Potential mechanism of digital platform in enterprise environmental protection behavior

Digital platform refers to a platform based on digital technology and Internet, which connects different participants and provides various services and solutions through online interaction and data sharing ( 39 ). Figure 2 shows the technical architecture of digital platform.

Figure 2 . Digital platform technology architecture.

The technical architecture of Figure 2 digital platform includes infrastructure, middleware, service layer, data layer, application layer and user interface. The user interface is the part where users interact with the digital platform, which provides the functions of users to operate and manage the platform. The application layer is responsible for handling business logic and functions. The data layer is responsible for data storage, reading, updating and deleting, and provides data access interfaces for the application layer. The service layer is a part that provides various services, and provides a series of interfaces and functions for the application layer to call and use. Middleware is a part that connects various components and levels, provides a mechanism for data exchange and communication, and ensures the coordination and interaction between various parts. Infrastructure provides computing resources and storage space to ensure the stability and reliability of the digital platform ( 40 , 41 ). Figure 3 shows the potential mechanism of digital platform in enterprise environmental protection behavior.

Figure 3 . Potential mechanism of digital platform in enterprise environmental protection behavior.

In Figure 3 , digital platform plays an important role in corporate environmental behavior. Through data analysis, resource management, carbon management, environmental protection innovation and other mechanisms, the digital platform helps enterprises to better fulfill their social and environmental responsibilities, promote the practice of sustainable development, improve the environmental performance of enterprises, create economic value for enterprises, and promote the positive relationship between environmental protection and sustainability ( 42 ).

3.3 Research hypothesis

The research hypothesis is a speculative statement about the relationship among different variables. The research hypothesis of this paper focus on the influence of digital platform on corporate environmental behavior and social responsibility.

Hypothesis 1 : There is a positive correlation between the widespread application of digital platforms and corporate environmental protection behavior.

Hypothesis 2 : There is a positive correlation between the environmental protection innovation technology of digital platform and the implementation of environmental protection policies of enterprises.

Hypothesis 3 : There is a positive correlation between social responsibility tools of digital platform and CSR activities.

Hypothesis 4 : There is a positive correlation between enterprise scale and industry type on digital platform and enterprise environmental behavior.

3.4 Method of data capture

In this study, the questionnaire design is to explore the influence of digital platform on corporate social responsibility practice by investigating employees in private enterprises. In order to ensure that the questionnaire can accurately reflect the actual digital actions and CSR activities of enterprises, a series of measures have been taken to enhance the reliability and validity of the questionnaire. Firstly, before designing the problem, the relationship between CSR and the development of environmental science is deeply studied, and the cross influence of CSR and environmental science is clarified. With reference to the policy documents on environmental protection and sustainable development issued by international organizations such as the United Nations Environment Programme, the theoretical basis of the research is constructed. This helps to ensure that the questionnaire design is closely related to the research objectives. Secondly, in the process of questionnaire design, 20 professionals with relevant backgrounds are invited to fill in the first edition of the questionnaire, and the expression and order of questions are adjusted according to their feedback to improve the clarity and logic of the questionnaire. This step is helpful to optimize the questionnaire design, ensure that the questions are accurate and clear, and capture the required information effectively. In addition, referring to the published related research, a measurement tool is constructed based on the indicators used in these studies to ensure the relevance and effectiveness of the questionnaire. In order to further improve the reliability and representativeness of the questionnaire, the online survey platform is used to distribute the questionnaire, and a reminder mechanism is set up to increase the response rate. Meanwhile, small rewards are provided for participants who completed the questionnaire to ensure the data quality. Cronbach’s α coefficient and exploratory factor analysis are used to verify the internal consistency test of sample data to evaluate the consistency and reliability of the questionnaire results. In addition, Pearson correlation coefficient is used to evaluate the correlation among different variables to ensure the accuracy and reliability of data analysis. In the questionnaire design, the respondents of private enterprises are divided into three categories: managers, team members and ordinary employees to ensure that employees with different positions and responsibilities are covered to fully understand the digital actions and CSR activities of enterprises. Through the questionnaire collection and analysis of employees in different positions, people can better understand the views and practices of digital platforms and environmental protection behaviors at all levels within the enterprise, and thus draw more objective research conclusions. The comprehensive application of the above measures makes it possible to explore the influence of digital platform on corporate social responsibility practice more comprehensively and accurately, and ensure that the obtained data has high credibility and representativeness, thus providing a solid foundation for subsequent analysis and conclusions. The specific questionnaire design and collection contents are as follows:

The choice of questionnaire survey in this paper is mainly based on its ability to effectively collect a wide range of data, while ensuring anonymity and authenticity. Compared with other data collection methods, questionnaire survey can cover a wider audience and get direct feedback on their opinions and behaviors, which is very important for exploring the role of digital platform in corporate environmental protection behavior.

In this paper, the data of environmental behavior and environmental science development released by the United Nations Environment Programme are used as the control data set of questionnaire survey results. Questionnaire survey is the main means to obtain information about environmental behavior and social responsibility of participating enterprises. Siyal et al. ( 43 ) used questionnaires to analyze how inclusive leaders cultivate employees’ innovative work behavior and creativity, and the results showed that inclusive leadership had a positive impact on innovative work behavior and creativity. In this paper, the respondents of private enterprises are divided into three categories: managers (M) who are related to environmental protection behavior and social responsibility activities of enterprises, team members (T) who are responsible for social responsibility, and ordinary employees (N). The sample size is determined based on Cochran formula. Considering the expected effect, α level and statistical power, it is estimated that at least 250 questionnaires are needed to ensure the reliability and representativeness of the research results. Finally, 256 valid questionnaires are collected, which meets the demand of sample size. After the preliminary design of the questionnaire, 20 professionals with relevant backgrounds are invited to fill it out, and the expression and order of the questions are adjusted based on their feedback to improve the clarity and logic of the questionnaire.

In order to ensure the validity and reliability of the questionnaire, this paper refers to the published related research and builds a measurement tool based on the indicators used in these studies. By using the online survey platform to distribute questionnaires and setting up a reminder mechanism, the response rate is improved, and small rewards are provided to participants who complete the questionnaires to ensure the data quality. In order to verify the consistency and reliability of data, Cronbach’s α coefficient and exploratory factor analysis are used for internal consistency test, and Pearson correlation coefficient is also used to evaluate the correlation among variables. The questionnaire is distributed to 297 respondents by e-mail or online survey platform. Two hundred and fifty six valid questionnaires are collected.

The questionnaire is divided into six sections. The first section is basic information statistics, including gender, working years, education level and occupation. The second section is the development level of environmental science, which mainly focuses on the degree of attention paid by enterprises to the development of environmental science and whether enterprises are developing or applying related technologies of environmental science. The third section is the application level of digital platform, knowing the application of digital platform in the enterprise where the interviewee works, including: the experience of using digital platform, whether the enterprise widely uses digital platform to support business operations, and whether the enterprise uses digital platform to monitor and manage data related to environmental protection and social responsibility. The fourth section is the environmental behavior of enterprises, mainly including whether enterprises have taken measures to reduce carbon emissions and whether enterprises actively participate in resource management and sustainable practice. The fifth section investigates the respondents’ questions about CSR activities, and whether they hold positions related to environmental protection or social responsibility, including: whether enterprises actively participate in social responsibility activities, such as charitable donations and community support. Whether the enterprise has social responsibility report or traceable social responsibility record. The sixth section is the intermediary role of digital platform in environmental behavior and social responsibility, mainly including whether enterprises use digital platform to monitor and report environmental behavior and social responsibility activities. In the definition of variables and the construction of measurement scale, this paper defines “corporate social responsibility” as that enterprises voluntarily assume social and environmental responsibilities while pursuing economic benefits. “Digital platform usage” refers to the degree to which enterprises integrate and use digital technology platforms in their operations and management. “Environmental protection behavior” covers all practical actions taken by enterprises to reduce environmental impact and promote sustainable development. The measurement of these variables is based on the previous literature review, combined with expert opinions and pretest results, forming a set of scales containing multiple items, aiming at comprehensively and accurately capturing the core content of each variable. Table 2 shows the definition and selection basis of research variables:

Table 2 . Study the definition and selection basis of variables.

According to the intermediary effect analysis method mentioned by Alfons et al. ( 44 ), Pearson correlation coefficient and Bootstrap method are used in this paper to evaluate the relationship among digital platform usage, CSR policy implementation and corporate environmental behavior. This method is widely recognized and used in social science research, and has been recognized by academic circles for its robustness and applicability. Pearson correlation coefficient is used to analyze the correlation among different variables, and the calculation is shown in Equation (1) :

In Equation (1) , r represents the correlation coefficient. x and y represent two variables respectively, and n represents the sample size. Using Baron and Kenny’s mediation effect analysis method, Equations (2–4) shows the calculation of intermediary effect:

In the above equations, a stands for total effect, b stands for direct effect, c ′ stands for indirect effect, X stands for intermediary variable (application level of digital platform), M stands for the influence of intermediary variable on dependent variable, and Y stands for dependent variable (environmental protection behavior or social responsibility activities of enterprises).

4 Results and discussion

4.1 the results of reliability and validity test and descriptive statistical analysis of the questionnaire.

The reliability and validity of the questionnaire are shown in Figure 4 . It shows that each factor has a high reliability coefficient (greater than 0.84), the factor load (greater than 0.75) indicates that there is a correlation between the problem and each factor, and the KMO value shows that the data is applicable in factor analysis.

Figure 4 . Results of reliability and validity test of questionnaire.

Figure 5 shows the descriptive statistical analysis results of the questionnaire. According to the descriptive statistical results, the respondents’ average scores on policy pressure, market pressure, CSR, environmental performance, and enterprise digital platform level are 4.07, 3.49, 4.27, 3.93, and 4.1, respectively. The evaluation results are relatively consistent. However, there are great differences in the evaluation of public opinion pressure and corporate environmental protection behavior.

Figure 5 . Descriptive statistical analysis results of the questionnaire.

4.2 The correlation between the usage of digital platform and the environmental protection behavior of enterprises

Figure 6 shows the results of correlation analysis between the usage of digital platform and the environmental protection behavior of enterprises. Pearson correlation coefficient shows that there is a moderate positive correlation between the use of digital platforms and corporate environmental behavior (correlation coefficient is 0.523). The Sig. value of correlation analysis is 0.001 (<0.05), which indicates that this correlation is significant. The correlation between the usage of digital platform and enterprise’s environmental behavior is 5.367, Sig. = 0.000 ( p  < 0.05), which verifies hypothesis 1.

Figure 6 . The results of correlation analysis between the use of digital platform and the environmental protection behavior of enterprises.

Figure 7 shows the intermediary analysis of the usage of digital platform. The intermediary analysis shows that the intermediary effect ratio (a * b/c) is 55.31%, and the 95% Bootstrap CI range does not include 0, which indicates that the usage of digital platform plays a significant intermediary role between digital platform and corporate environmental protection behavior.

Figure 7 . Intermediary analysis of the usage degree of digital platform.

4.3 The influence of digital platform on CSR policy and practice

Figure 8 shows the results of correlation analysis between digital platform and CSR. Pearson correlation coefficient shows that there is a moderate positive correlation between the use of digital platforms and CSR policies and practices (correlation coefficient is 0.481). The Sig. value of correlation analysis is 0.003, which is less than the significance level of 0.05, indicating that this correlation is significant. The correlation T between digital platform and CSR is 4.825, Sig. = 0.000 ( p  < 0.05), which shows that there is a positive correlation between digital platform’s social responsibility tools and CSR activities, and supports hypothesis 3.

Figure 8 . Correlation analysis results between digital platform and CSR.

Mediating analysis shows that the mediating effect ratio (a * b/c) is 52.40%, and the 95% Bootstrap CI range does not include 0, indicating that the usage of digital platforms plays a significant mediating role between digital platforms and CSR policies and practices. Figure 9 shows the intermediary analysis of digital platform on CSR policy and practice.

Figure 9 . Intermediary analysis of digital platform on CSR policy and practice.

4.4 Mediating and regulating functions of digital platform and enterprise’s environmental protection behavior

Figure 10 shows the analysis results of the intermediary role and regulatory role of digital platform on enterprise environmental protection behavior. The total effect (a) of digital platform on corporate environmental behavior is 0.627, the total effect (b) of intermediary variable CSR policy implementation is 0.452, and the total effect (b) of intermediary variable environmental innovation technology is 0.313. The mediating effect and 95% confidence interval calculated by Bootstrap method show that the mediating variable CSR policy implementation and environmental protection innovation technology significantly mediate the influence of digital platform on corporate environmental protection behavior, because their confidence intervals do not include 0. T -value and Sig. value also support the significance of these mediating effects. The moderating effect of moderating variable enterprise scale is 0.284, and that of moderating variable industry type is 0.179. The t -value and Sig. value of the regulatory effect show that both the scale of enterprises and the types of industries have a significant regulatory effect on the impact of digital platforms on corporate environmental behavior.

Figure 10 . The analysis results of the mediating and regulating effects of digital platform on enterprise’s environmental protection behavior [ (A) the mediating effect; (B) for regulatory purposes].

In order to further explore the potential causal relationship between the use of digital platforms and the environmental behavior of enterprises, Structural Equation Modeling (SEM) is introduced for analysis. In addition, through the analysis of mediating and moderating effects, it further analyzes how the digital platform affects the CSR practice and environmental behavior of enterprises through different mediating variables (environmental innovation technology) and moderating variables (enterprise scale and industry type). Firstly, a structural equation model is established to evaluate the direct and indirect relationship between digital platform use (independent variable) and enterprise environmental behavior (dependent variable). As a part of indirect relationship, two intermediary variables are considered: CSR policy implementation and environmental innovation technology. Meanwhile, enterprise scale and industry type are regarded as moderating variables to test whether they will change the correlation between the main variables. The hypothesis is tested by multiple regression analysis. This analysis helps to verify the correlation between the use of digital platform, the implementation of CSR policy, environmental innovation technology and corporate environmental behavior, and also examines the regulatory role of enterprise scale and industry type. Table 3 shows the results of multiple regression analysis, which is used to test the direct impact of the use of digital platforms on corporate environmental behavior and its indirect impact through intermediary variables.

Table 3 . Results of SEM and multiple regression analysis.

In Table 3 , the use of digital platform has a significant positive impact on corporate environmental behavior (β = 0.623, p  < 0.001), and CSR policy implementation and environmental innovation technology both show significant positive effects as intermediary variables. In addition, as moderating variables, enterprise scale and industry type have a significant moderating effect on the relationship between the main variables. Through the structural equation model and the results of multiple regression analysis, it is confirmed that there is a significant positive relationship between the use of digital platforms and corporate environmental behavior. Environmental innovation technology and the implementation of CSR policy have played an important intermediary role in this relationship. In addition, the analysis also reveals the moderating role of enterprise scale and industry type in the relationship between digital platform use and enterprise environmental behavior. This emphasizes the need to consider the specific background and characteristics of enterprises when encouraging enterprises to take digital measures to improve their environmental performance. The above findings have important implications for decision makers and policy makers. They emphasize the necessity of supporting enterprises to adopt digital technology to improve environmental protection behavior and CSR practice, and suggest the importance of considering enterprise scale and industry characteristics when designing relevant policies and interventions.

The findings of this paper provide valuable insights for decision makers and policy makers. Firstly, the paper emphasizes the core role of digital platform in promoting corporate environmental behavior and social responsibility practice. The application of digital technology can help enterprises to manage resources more efficiently and formulate environmental protection strategies, thus promoting sustainable development. It is suggested that policy makers should support and encourage enterprises to adopt digital technology to improve their environmental friendliness and social responsibility practice. Secondly, future policy planning needs to take into account the differences in the influence of enterprise scale and industry type on digital platforms. Enterprises of different scales and industries may face different challenges and opportunities in digital transformation, so customized guidelines are needed to guide them to make rational use of digital platforms. Policymakers can formulate targeted policies and measures according to the characteristics of different enterprises to promote the combination of digitalization and sustainable development. Finally, it is suggested that further research should pay attention to the differences in the impact of digital platforms on corporate social responsibility and public health in different regions and cultural backgrounds. Different regions and cultures may have different degrees of acceptance and practice of digitalization, which will have different degrees of impact on corporate social responsibility and public health. In-depth study of the mechanism of digital platforms in different contexts will help to better guide enterprises and policy makers in their decision-making and practice in different environments. Through these suggestions and research directions, people can better promote the goals of corporate social responsibility and sustainable development with the help of digital platforms.

5 Conclusion

The purpose of this paper is to explore the influence of digital platform on corporate environmental behavior and social responsibility, and to deeply understand how digital platform shapes the sustainable development practice of enterprises. Through comprehensive analysis of questionnaire survey data and various research methods, it is found that digital platform plays an active role in the sustainable development of enterprises. There is a positive correlation between the wide application of digital platform and corporate environmental behavior and social responsibility, which shows that digital platform helps enterprises to participate in environmental protection and social responsibility activities more actively and promote sustainable development. Secondly, the environmental protection innovation technology of digital platform has a positive impact on the implementation of environmental protection policies of enterprises. Environmental protection innovation technology plays an intermediary role between digital platform and enterprise environmental protection behavior, which strengthens the influence of digital platform on enterprise environmental protection behavior. In addition, the scale of enterprises and the types of industries plays a regulatory role in the influence mechanism of digital platforms. Enterprises of different scales and industries have different responses to digital platforms, which requires individualized consideration when formulating environmental protection policies and strategies. However, there are some shortcomings in this paper. The research sample has limitations and may not fully represent enterprises of other industries and scales. Future research can expand the sample range, deeply analyze the relationship between digital platform and sustainable development of enterprises, and consider more regulatory factors.

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

MW: Conceptualization, Data curation, Validation, Writing – review & editing. RY: Conceptualization, Formal analysis, Writing – original draft. XG: Investigation, Methodology, Writing – original draft. ZW: Formal analysis, Methodology, Visualization, Writing – review & editing. YZ: Investigation, Software, Writing – review & editing. TL: Funding acquisition, Project administration, Resources, Software, Supervision, Writing – original draft.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the 2022 Philosophy and Social Science Foundation of Guangdong Province of China (GD22XXW05) entitled “Study on niche selection of Guangdong mainstream media in Guangdong-Hong Kong-Macao Greater Bay Area”, 2018 Social Science Foundation of Guangzhou city of China (2018GZMZYB39) entitled “Research on Guangzhou city brand building and communication strategy under UGC production paradigm” and 2013 Philosophy and Social Science Foundation of Guangdong Province of China (GD13XXW03) entitled “Research on the Reporting Framework of important Health Issues in Guangdong Newspaper Industry.”

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.

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Keywords: digital platform, corporate environmental protection behavior, corporate social responsibility, sustainable development, intermediary analysis

Citation: Wang M, Yuan R, Guan X, Wang Z, Zeng Y and Liu T (2024) The influence of digital platform on the implementation of corporate social responsibility: from the perspective of environmental science development to explore its potential role in public health. Front. Public Health . 12:1343546. doi: 10.3389/fpubh.2024.1343546

Received: 23 November 2023; Accepted: 03 April 2024; Published: 22 April 2024.

Reviewed by:

Copyright © 2024 Wang, Yuan, Guan, Wang, Zeng and Liu. 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: Tao Liu, [email protected]

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.

Scientists trigger mini-earthquakes in the lab

Earthquakes and landslides are famously difficult to predict and prepare for. By studying a miniature version of the ground in the lab, scientists at the UvA Institute of Physics have demonstrated how these events can be triggered by a small external shock wave. Bring a flotation device: it involves the ground briefly turning into a liquid!

Unlike a true solid, the ground we stand on is generally made of granules such as sand grains or pieces of rock. Deeper down in Earth's crust, the same holds for the fault lines where two tectonic plates meet. These types of disordered granular materials are never fully stable. And when they fail, it can have catastrophic effects for us, living on Earth's surface.

The trouble is: it is not easy to predict or control when exactly the friction forces resisting a landslide or earthquake will stop being enough to keep the ground in place. Thankfully, the physics works exactly the same in smaller systems that you can study in the lab. To reproduce an earthquake, physicists Kasra Farain and Daniel Bonn of the University of Amsterdam used a 1-mm thick layer of tiny spheres that are each the width of a human hair.

Their experimental setup allowed them to keep precise track of the granules' response to external forces. To simulate the forces that would be present on a steep mountain slope or at a tectonic fault, they pressed a disc on the surface and slowly rotated it with a constant speed. By subsequently bouncing a ball next to the experimental setup, triggering a small seismic wave, they saw how all the granules rapidly shifted in response: they had triggered a miniature earthquake!

"We found that a very small perturbation, a small seismic wave, is capable of causing a granular material to completely restructure itself," explains Farain. Further examination revealed that for a brief moment, the granules behave like a liquid rather than a solid. After the triggering wave has passed, friction takes over once more and the granules get jammed again, in a new configuration.

The same happens in real seismic events. "Earthquakes and tectonic phenomena follow scale-invariant laws, so findings from our laboratory-scale frictional setup are relevant for understanding remote earthquake triggering by seismic waves in much larger-scale faults in the Earth's crust," says Farain.

The researchers show that the mathematical model they deduced from their experiments quantitatively explains how the 1992 Landers earthquake in Southern California remotely triggered a second seismic event, 415 km to the north. In addition, they show that their model accurately describes the rise in fluid pressure observed in the Nankai subduction zone near Japan after a series of small earthquakes in 2003.

Inspired by a shaky table Interestingly, this entire research project might not have come to fruition if it weren't for Farain's colleagues: "Initially, my experimental setup was just on a regular table, lacking all the fancy vibration isolation needed for precise measurements. Soon enough, I realised that simple things like someone walking by or the door closing could affect the experiment. I must have been a bit of a bother to my colleagues, always asking for quieter footsteps or gentler door closures."

Inspired by how his colleagues' movements disrupted his setup, Farain began to investigate the physics at work: "After some time, I upgraded to a proper optical table for the setup, and people could jump, or do whatever they wanted without disrupting my work. But, true to my troublemaking tendencies, that wasn't the end of it. A little while later, I returned to the lab with a loudspeaker to generate noise and see the effects of controlled perturbations!"

• Earthquakes
• Natural Disasters
• Earth Science
• Environmental Issues
• Near-Earth Object Impacts
• Seismometer
• Elastic-rebound theory of earthquakes
• Inversion (meteorology)
• Making existing structures earthquake resistant

Story Source:

Materials provided by Universiteit van Amsterdam . Note: Content may be edited for style and length.

Journal Reference :

• Kasra Farain, Daniel Bonn. Perturbation-induced granular fluidization as a model for remote earthquake triggering . Science Advances , 2024; 10 (16) DOI: 10.1126/sciadv.adi7302

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• Perspective
• Published: 26 June 2023

GREENER principles for environmentally sustainable computational science

• Loïc Lannelongue   ORCID: orcid.org/0000-0002-9135-1345 1 , 2 , 3 , 4 ,
• Hans-Erik G. Aronson   ORCID: orcid.org/0000-0002-1702-1671 5 ,
• Alex Bateman 6 ,
• Ewan Birney 6 ,
• Talia Caplan   ORCID: orcid.org/0000-0001-8990-1435 7 ,
• Martin Juckes   ORCID: orcid.org/0000-0003-1770-2132 8 ,
• Johanna McEntyre 6 ,
• Andrew D. Morris 5 ,
• Gerry Reilly 5 &
• Michael Inouye 1 , 2 , 3 , 4 , 9 , 10 , 11  

Nature Computational Science volume  3 ,  pages 514–521 ( 2023 ) Cite this article

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The carbon footprint of scientific computing is substantial, but environmentally sustainable computational science (ESCS) is a nascent field with many opportunities to thrive. To realize the immense green opportunities and continued, yet sustainable, growth of computer science, we must take a coordinated approach to our current challenges, including greater awareness and transparency, improved estimation and wider reporting of environmental impacts. Here, we present a snapshot of where ESCS stands today and introduce the GREENER set of principles, as well as guidance for best practices moving forward.

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Scientific research and development have transformed and immeasurably improved the human condition, whether by building instruments to unveil the mysteries of the universe, developing treatments to fight cancer or improving our understanding of the human genome. Yet, science can, and frequently does, impact the environment, and the magnitude of these impacts is not always well understood. Given the connection between climate change and human health, it is becoming increasingly apparent to biomedical researchers in particular, as well as their funders, that the environmental effects of research should be taken into account 1 , 2 , 3 , 4 , 5 .

Recent studies have begun to elucidate the environmental impacts of scientific research, with an initial focus on scientific conferences and experimental laboratories 6 . The 2019 Fall Meeting of the American Geophysical Union was estimated to emit 80,000 metric tonnes of CO 2 equivalent (tCO 2 e), equivalent to the average weekly emissions of the city of Edinburgh, UK 7 (CO 2 e, or CO 2 -equivalent, summarizes the global warming impacts of a range of greenhouse gases (GHGs) and is the standard metric for carbon footprints, although its accuracy is sometimes debated 8 ) The annual meeting of the Society for Neuroscience was estimated to emit 22,000 tCO 2 e, approximately the annual carbon footprint of 1,000 medium-sized laboratories 9 . The life-cycle impact (including construction and usage) of university buildings has been estimated at ~0.125 tCO 2 e m −2  yr −1 (ref. 10 ), and the yearly carbon footprint of a typical life-science laboratory at ~20 tCO 2 e (ref. 9 ). The Laboratory Efficiency Assessment Framework (LEAF) is a widely adopted standard to monitor and reduce the carbon footprint of laboratory-based research 11 . Other recent frameworks can help to raise awareness: GES 1point5 12 provides an open-source tool to estimate the carbon footprint of research laboratories and covers buildings, procurement, commuting and travel, and the Environmental Responsibility 5-R Framework provides guidelines for ecologically conscious research 13 .

With the increasing scale of high-performance and cloud computing, the computational sciences are susceptible to having silent and unintended environmental impacts. The sector of information and communication technologies (ICT) was responsible for between 1.8% and 2.8% of global GHG emissions in 2020 14 —more than aviation (1.9% 15 )—and, if unchecked, the ICT carbon footprint could grow exponentially in coming years 14 . Although the environmental impact of experimental ‘wet’ laboratories is more immediately obvious, with their large pieces of equipment and high plastic and reagent usage, the impact of algorithms is less clear and often underestimated. The risks of seeking performance at any cost and the importance of considering energy usage and sustainability when developing new hardware for high-performance computing (HPC) was raised as early as 2007 16 . Since then, continuous improvements have been made by developing new hardware, building lower-energy data centers and implementing more efficient HPC systems 17 , 18 . However, it is only in the past five years that these concerns have reached HPC users, in particular researchers. Notably, the field of artificial intelligence (AI) has first taken note of its environmental impacts, in particular those of the very large language models developed 19 , 20 , 21 , 22 , 23 . It is unclear, however, to what extent this has led the field towards more sustainable research practices. A small number of studies have also been performed in other fields, including bioinformatics 24 , astronomy and astrophysics 25 , 26 , 27 , 28 , particle physics 29 , neuroscience 30 and computational social sciences 31 . Health data science is starting to address the subject, but a recent systematic review found only 25 publications in the field over the past 12 years 32 . In addition to the environmental effects of electricity usage, manufacturing and disposal of hardware, there are also concerns around data centers’ water usage and land footprint 33 . Notably, computational science, in particular AI, has the potential to help fight climate change, for example, by improving the efficiency of wind farms, by facilitating low-carbon urban mobility and by better understanding and anticipating severe weather events 34 .

In this Perspective we highlight the nascent field of environmentally sustainable computational science (ESCS)—what we have learned from the research so far, and what scientists can do to mitigate their environmental impacts. In doing so, we present GREENER (Governance, Responsibility, Estimation, Energy and embodied impacts, New collaborations, Education and Research; Fig. 1 ), a set of principles for how the computational science community could lead the way in sustainable research practices, maximizing computational science’s benefit to both humanity and the environment.

The GREENER principles enable cultural change (blue arrows), which in turn facilitates their implementation (green arrows) and triggers a virtuous circle.

Environmental impacts of the computational sciences

The past three years have seen increased concerns regarding the carbon footprint of computations, and only recently have tools 21 , 35 , 36 , 37 and guidelines 38 been widely available to computational scientists to allow them to estimate their carbon footprint and be more environmentally sustainable.

Most calculators that estimate the carbon footprint of computations are targeted at machine learning tasks and so are primarily suited to Python pipelines, graphics processing units (GPUs) and/or cloud computing 36 , 37 , 39 , 40 . Python libraries have the benefit of integrating well into machine learning pipelines or online calculators for cloud GPUs 21 , 41 . Recently, a flexible online tool, the Green Algorithms calculator 35 , enabled the estimation of the carbon footprint for nearly any computational task, empowering sustainability metrics across fields, hardware, computing platforms and locations.

Some publications, such as ref. 38 , have listed simple actions that computational scientists can take regarding their environmental impact, including estimating the carbon footprint of running algorithms, both a posteriori to acknowledge the impact of a project and before starting as part of a cost–benefit analysis. A 2020 report from The Royal Society formalizes this with the notion of ‘energy proportionality’, meaning the environmental impacts of an innovation must be outweighed by its environmental or societal benefits 34 . It is also important to minimize electronic waste by keeping devices for longer and using second-hand hardware when possible. A 2021 report by the World Health Organization 42 warns of the dramatic effect of e-waste on population health, particularly children. The unregulated informal recycling industry, which handles more than 80% of the 53 million tonnes of e-waste, causes a high level of water, soil and air pollution, often in low- and middle-income countries 43 . The up to 56 million informal waste workers are also exposed to hazardous chemicals such as heavy metals and persistent organic pollutants 42 . Scientists can also choose energy-efficient hardware and computing facilities, while favoring those powered by green energy. Writing efficient code can substantially reduce the carbon footprint as well, and this can be done alongside making hardware requirements and carbon footprints clear when releasing new software. The Green Software Foundation ( https://greensoftware.foundation ) promotes carbon-aware coding to reduce the operational carbon footprint of the softwares used in all aspects of society. There is, however, a rebound effect to making algorithms and hardware more efficient: instead of reducing computing usage, increased efficiency encourages more analyses to be performed, which leads to a revaluation of the cost–benefit but often results in increased carbon footprints. The rebound effect is a key example of why research practice should adapt to technological advances so that they lead to carbon footprint reductions.

GREENER computational science

ESCS is an emerging field, but one that is of rapidly increasing importance given the climate crisis. In the following, our proposed set of principles (Fig. 1 ) outlines the main axes where progress is needed, where opportunities lie and where we believe efforts should be concentrated.

Governance and responsibility

Everyone involved in computational science has a role to play in making the field more sustainable, and many do already, from grassroots movements to large institutions. Individual and institutional responsibility is a necessary step to ensure transparency and reduction of GHG emission. Here we highlight key stakeholders alongside existing initiatives and future opportunities for involvement.

Grassroots initiatives led by graduate students, early career researchers and laboratory technicians have shown great success in tackling the carbon footprint of laboratory work, including Green Labs Netherlands 44 , the Nottingham Technical Sustainability Working Group or the Digital Humanities Climate Coalition 45 . International coalitions such as the Sustainable Research (SuRe) Symposium, initially set up for wet laboratories, have started to address the impact of computing as well. IT teams in HPC centers are naturally key, both in terms of training and ensuring that the appropriate information is logged so that scientists can follow the carbon footprints of their work. Principal investigators can encourage their teams to think about this issue and provide access to suitable training when needed.

Simultaneously, top–down approaches are needed, with funding bodies and journals occupying key positions in both incentivizing carbon-footprint reduction and in promoting transparency. Funding bodies can directly influence the researchers they fund and those applying for funding via their funding policies. They can require estimates of carbon footprints to be included in funding applications as part of ‘environmental impacts statements’. Many funding bodies include sustainability in their guidelines already; see, for example, the UK’s NIHR carbon reduction guidelines 1 , the brief mention of the environment in UKRI’s terms and conditions 46 , and the Wellcome Trust’s carbon-offsetting travel policy 47 .

Although these are important first steps, bolder action is needed to meet the urgency of climate change. For example, UKRI’s digital research infrastructure scoping project 48 , which seeks to provide a roadmap to net zero for its digital infrastructure, sends a clear message that sustainable research includes minimizing the GHG emissions from computation. The project not only raises awareness but will hopefully result in reductions in GHG emissions.

Large research institutes are key to managing and expanding centralized data infrastructures and trusted research environments (TREs). For example, EMBL’s European Bioinformatics Institute manages more than 40 data resources 49 , including AlphaFold DB 50 , which contains over 200,000,000 predicted protein structures that can be searched, browsed and retrieved according to the FAIR principles (findable, accessible, interoperable, reusable) 51 . As a consequence, researchers do not need to run the carbon-intensive AlphaFold algorithm for themselves and instead can just query the database. AlphaFold DB was queried programmatically over 700 million times and the web page was accessed 2.4 million times between August 2021 and October 2022. Institutions also have a role in making procurement decisions carefully, taking into account both the manufacturing and operational footprint of hardware purchases. This is critical, as the lifetime footprint of a computational facility is largely determined by the date it is purchased. Facilities could also better balance investment decisions, with a focus on attracting staff based on sustainable and efficient working environments, rather than high-powered hardware 52 .

However, increases in the efficiencies of digital technology alone are unlikely to prove sufficient in ensuring sustainable resource use 53 . Alongside these investments, funding bodies should support a shift towards more positive, inclusive and green research cultures, recognizing that more data or bigger models do not always translate into greater insights and that a ‘fit for purpose’ approach can ultimately be more efficient. Organizations such as Health Data Research UK and the UK Health Data Research Alliance have a key convening role in ensuring that awareness is raised around the climate impact of both infrastructure investment and computational methods.

Journals may incentivize authors to acknowledge and indeed estimate the carbon footprint of the work presented. Some authors already do this voluntarily (for example, refs. 54 , 55 , 56 , 57 , 58 , 59 ), mostly in bioinformatics and machine learning so far, but there is potential to expand it to other areas of computational science. In some instances, showing that a new tool is greener can be an argument in support of a new method 60 .

International societies in charge of organizing annual conferences may help scientists reduce the carbon footprint of presenting their work by offering hybrid options. The COVID-19 pandemic boosted virtual and hybrid meetings, which have a lower carbon footprint while increasing access and diversity 7 , 61 . Burtscher and colleagues found that running the annual meeting of the European Astronomical Society online emitted >3,000-fold less CO 2 e than the in-person meeting (0.582 tCO 2 e compared to 1,855 tCO 2 e) 25 . Institutions are starting to tackle this; for example, the University of Cambridge has released new travel guidelines encouraging virtual meetings whenever feasible and restricting flights to essential travel, while also acknowledging that different career stages have different needs 62 .

Industry partners will also need to be part of the discussion. Acknowledging and reducing computing environmental impact comes with added challenges in industry, such as shareholder interests and/or public relations. While the EU has backed some initiatives helping ICT-reliant companies to address their carbon footprint, such as ICTfootprint.eu, other major stakeholders have expressed skepticism regarding the environmental issues of machine learning models 63 , 64 . Although challenging, tech industry engagement and inclusion is nevertheless essential for tackling GHG emissions.

Estimate and report the energy consumption of algorithms

Estimating and monitoring the carbon footprint of computations is an essential step towards sustainable research as it identifies inefficiencies and opportunities for improvement. User-level metrics are crucial to understanding environmental impacts and promoting personal responsibility. In some HPC situations, particularly in academia, the financial cost of running computations is negligible and scientists may have the impression of unlimited and inconsequential computing capacity. Quantifying the carbon footprint of individual projects helps raise awareness of the true costs of research.

Although progress has been made in estimating energy usage and carbon footprints over the past few years, there are still barriers that prevent the routine estimation of environmental impacts. From task-agnostic, general-purpose calculators 35 and task-specific packages 36 , 37 , 65 to server-side softwares 66 , 67 , each estimation tool is a trade-off between ease of use and accuracy. A recent primer 68 discusses these different options in more detail and provides recommendations as to which approach fits a particular need.

Regardless of the calculator used, for these tools to work effectively and for scientists to have an accurate representation of their energy consumption, it is important to understand the power management for different components. For example, the power usage of processing cores such as central processing units (CPUs) and GPUs is not a readily available metric; instead, thermal design power (meaning, how much heat the chip can be expected to dissipate in a normal setting) is used. Although an acceptable approximation, it has also been shown to substantially underestimate power usage in some situations 69 . The efficiency of data centers is measured by the power usage effectiveness (PUE), which quantifies how much energy is needed for non-computing tasks, mainly cooling (efficient data centers have PUEs close to 1). This metric is widely used, with large cloud providers reporting low PUEs (for example, 1.11 for Google 70 compared to a global average of 1.57 71 ), but discrepancies in how it is calculated can limit PUE interpretation and thus its impact 72 , 73 , 74 . A standard from the International Organization for Standardization is trying to address this 75 . Unfortunately, the PUE of a particular data center, whether cloud or institutional, is rarely publicly documented. Thus, an important step is the data science and infrastructure community making both hardware and data centers’ energy consumption metrics available to their users and the public. Ultimately, tackling unnecessary carbon footprints will require transparency 34 .

Tackling energy and embodied impacts through new collaborations

Minimizing carbon intensity (meaning the carbon footprint of producing electricity) is one of the most immediately impactful ways to reduce GHG emissions. Carbon intensities depend largely on geographical location, with up to three orders of magnitude between the top and bottom performing high-income countries in terms of low carbon energies (from 0.10 gCO 2 e kWh −1 in Iceland to 770 gCO 2 e kWh −1 in Australia 76 ). Changing the carbon intensity of a local state or national government is nearly always impractical as it would necessitate protracted campaigns to change energy policies. An alternative is to relocate computations to low-carbon settings and countries, but, depending on the type of facility or the sensitivity of the data, this may not always be possible. New inter-institutional cooperation may open up opportunities to enable access to low-carbon data centers in real time.

It is, however, essential to recognize and account for inequalities between countries in terms of access to green energy sources. International cooperation is key to providing scientists from low- and middle-income countries (LMICs), who frequently only have high-carbon-intensity options available to them, access to low-carbon computing infrastructures for their work. In the longer term, international partnerships between organizations and nations can help build low-carbon computing capacity in LMICs.

Furthermore, the footprint of user devices should not be forgotten. In one estimate, the energy footprint of streaming a video to a laptop is mainly on the laptop (72%), with 23% used in transmission and a mere 5% at the data center 77 . Zero clients (user devices with no compute or storage capacity) can be used in some research use cases and drastically reduce the client-side footprint 78 .

It can be tempting to reduce the environmental impacts of computing to electricity needs, as these are the easiest ones to estimate. However, water usage, ecological impacts and embodied carbon footprints from manufacturing should also be addressed. For example, for personal hardware, such as laptops, 70–80% of the life-cycle impact of these devices comes from manufacturing only 79 , as it involves mining raw materials and assembling the different components, which require water and energy. Moreover, manufacturing often takes place in countries that have a higher carbon intensity for power generation and a slower transition to zero-carbon power 80 . Currently, hardware renewal policies, either for work computers or servers in data centers, are often closely dependent on warranties and financial costs, with environmental costs rarely considered. For hardware used in data centers, regular updates may be both financially and environmentally friendly, as efficiency gains may offset manufacturing impacts. Estimating these environmental impacts will allow HPC teams to know for sure. Reconditioned and remanufactured laptops and servers are available, but growth of this sector is currently limited by negative consumer perception 81 . Major suppliers of hardware are making substantial commitments, such as 100% renewable energy supply by 2030 82 or net zero by 2050 83 .

Another key consideration is data storage. Scientific datasets are now measured in petabytes (PB). In genomics, the popular UK Biobank cohort 84 is expected to reach 15 PB by 2025 85 , and the first image of a black hole required the collection of 5 PB of data 86 . The carbon footprint of storing data depends on numerous factors, but based on some manufacturers’ estimations, the order of magnitude of the life-cycle footprint of storing 1 TB of data for a year is ~10 kg CO 2 e (refs. 87 , 88 ). This issue is exacerbated by the duplication of such datasets in order for each institution, and sometimes each research group, to have a copy. Centralized and collaborative computing resources (such as TREs) holding both data and computing hardware may help alleviate redundant resources. TRE efforts in the UK span both health (for example, NHS Digital 89 ) and administrative data (for example, the SAIL databank on the UK Secure Research Platform 90 and the Office for National Statistics Secure Research Service 91 ). Large (hyperscale) data centers are expected to be more energy-efficient 92 , but they may also encourage unnecessary increases in the scale of computing (rebound effect).

The importance of dedicated education and research efforts for ESCS

Education is essential to raise awareness with different stakeholders. In lieu of incorporating some aspects into more formal undergraduate programs, integrating sustainability into computational training courses is a tangible first step toward reducing carbon footprints. An example is the ‘Green Computing’ Workshop on Education at the 2022 conference on Intelligent Systems for Molecular Biology.

Investing in research that will catalyze innovation in the field of ESCS is a crucial role for funders and institutions to play. Although global data centers’ workloads have increased more than sixfold between 2010 and 2018, their total electricity usage has been approximately stable due to the use of power-efficient hardware 93 , but environmentally sustainable investments will be needed to perpetuate this trend. Initiatives like Wellcome’s Research Sustainability project 94 , which look to highlight key gaps where investment could deliver the next generation of ESCS tools and technology, are key to ensuring that growth in energy demand beyond current efficiency trends can be managed in a sustainable way. Similarly, the UKRI Data and Analytics Research Environments UK program (DARE UK) needs to ensure that sustainability is a key evaluation criterion for funding and infrastructure investments for the next generation of TREs.

Recent studies found that the most widely used programming languages in research, such as R and Python 95 , tend to be the least energy-efficient ones 96 , 97 , and, although it is unlikely that forcing the community to switch to more efficient languages would benefit the environment in the short term (due to inefficient coding for example), this highlights the importance of having trained research software engineers within research groups to ensure that the algorithms used are efficiently implemented. There is also scope to use current tools more efficiently by better understanding and monitoring how coding choices impact carbon footprints. Algorithms also come with high memory requirements, sometimes using more energy than processors 98 . Unfortunately, memory power usage remains poorly optimized, as speed of access is almost always favored over energy efficiency 99 . Providing users and software engineers with the flexibility to opt for energy efficiency would present an opportunity for a reduction in GHG emissions 100 , 101 .

Cultural change

In parallel to the technological reductions in energy usage and carbon footprints, research practices will also need to change to avoid rebound effects 38 . Similar to the aviation industry, there is a tendency to count on technology to solve sustainability concerns without having to change usage 102 (that is, waiting on computing to become zero-carbon rather than acting on how we use it). Cultural change in the computing community to reconsider how we think about computing costs will be necessary. Research strategies at all levels will need to consider environmental impacts and corresponding approaches to carbon footprint minimization. The upcoming extension of the LEAF standard for computational laboratories will provide researchers with tangible tools to do so. Day to day, there is a need to solve trade-offs between the speed of computation, accuracy and GHG emissions, keeping in mind the goal of GHG reduction. These changes in scientific practices are challenging, but, importantly, there are synergies between open computational science and green computing 103 . For example, making code, data and models FAIR so that other scientists avoid unnecessary computations can increase the reach and impact of a project. FAIR practices can result in highly efficient code implementations, reduce the need to retrain models, and reduce unnecessary data generation/storage, thus reducing the overall carbon footprint. As a result, green computing and FAIR practices may both stimulate innovation and reduce financial costs.

Moreover, computational science has downstream effects on carbon footprints in other areas. In the biomedical sciences, developments in machine learning and computer vision impact the speed and scale of medical imaging processing. Discoveries in health data science make their way to clinicians and patients through, for example, connected devices. In each of these cases and many others, environmental impacts propagate through the whole digital health sector 32 . Yet, here too synergies exist. In many cases, such as telemedicine, there may be a net benefit in terms of both carbon and patient care, provided that all impacts have been carefully accounted for. These questions are beginning to be tackled in medicine, such as assessments of the environmental impact of telehealth 104 or studies into ways to sustainably handle large volumes of medical imaging data 105 . For the latter, NHS Digital (the UK’s national provider of information, data and IT systems for health and social care) has released guidelines to this effect 106 . Outside the biomedical field, there are immense but, so far, unrealized opportunities for similar efforts.

The computational sciences have an opportunity to lead the way in sustainability, which may be achieved through the GREENER principles for ESCS (Fig. 1 ): Governance, Responsibility, Estimation, Energy and embodied impacts, New collaborations, Education and Research. This will require more transparency on environmental impacts. Although some tools already exist to estimate carbon footprints, more specialized ones will be needed alongside a clearer understanding of the carbon footprint of hardware and facilities, as well as more systematic monitoring and acknowledgment of carbon footprints. Measurement is a first step, followed by a reduction in GHG emissions. This can be achieved with better training and sensible policies for renewing hardware and storing data. Cooperation, open science and equitable access to low-carbon computing facilities will also be crucial 107 . Computing practices will need to adapt to include carbon footprints in cost–benefit analyses, as well as consider the environmental impacts of downstream applications. The development of sustainable solutions will need particularly careful consideration, as they frequently have the least benefit for populations, often in LMICs, who suffer the most from climate change 22 , 108 . All stakeholders have a role to play, from funding bodies, journals and institutions to HPC teams and early career researchers. There is now a window of time and an immense opportunity to transform computational science into an exemplar of broad societal impact and sustainability.

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Acknowledgements

L.L. was supported by the University of Cambridge MRC DTP (MR/S502443/1) and the BHF program grant (RG/18/13/33946). M.I. was supported by the Munz Chair of Cardiovascular Prediction and Prevention and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014; NIHR203312). M.I. was also supported by the UK Economic and Social Research 878 Council (ES/T013192/1). This work was supported by core funding from the British Heart Foundation (RG/13/13/30194; RG/18/13/33946) and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014; NIHR203312). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This work was also supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland) and the British Heart Foundation and Wellcome.

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Lannelongue, L., Aronson, HE.G., Bateman, A. et al. GREENER principles for environmentally sustainable computational science. Nat Comput Sci 3 , 514–521 (2023). https://doi.org/10.1038/s43588-023-00461-y

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Geophysical and hydro physicochemical investigation and pollution potential evaluation of an urban landfill in southern Nigeria

• Original Paper
• Published: 21 April 2024

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• F. B. Akiang   ORCID: orcid.org/0000-0002-5325-371X 1 ,
• S. O. Onyekuru 1 ,
• E. B. Ulem 2 ,
• C. C. Agoha 1 ,
• M. C. Umeh 3 ,
• S. I. Ibeneme 1 &
• M. Ohakwere-Eze 4  

The study was conducted in an old landfill in the Calabar South local government area of Cross River State. The essence of the research work is to investigate groundwater quality within the area and evaluate the level of environmental pollution due to radioelement exposure to landfill waste. Ten profile (traverse) lines were created for the survey, and twenty vertical electrical soundings were probed with a maximum half-current electrode (AB/2) of 200 m. The result revealed four to five geoelectric layers. The rock strata and soil layers with a resistivity of less than 15 Ωm were interpreted as zones of contamination. Also, samples of groundwater were collected from four boreholes and six hand-dug wells for water quality and physicochemical analysis. The analysis shows that iron (Fe), total dissolved solids, Sulfate ( \(SO_{4}\) ) and turbidity exceeded the recommended thresholds for drinking water. Consequently, the water quality index result showed that 2% of the samples produced good water, 1% produced water of poor quality, 20% of the samples produced very poor water, and 50% produced water unsuitable for human consumption. Subsequently, a radiation spectrometer was deployed to measure the radioelement concentration to determine the radio hazard and environmental pollution status of the area. Measurements of radiation due to man-made sources were undertaken in the ten profiles. The mean concentration and mean specific activity were determined for all the survey lines. While the mean concentration of \({}^{40}K\) and radiation dose rate were recorded below UNSCEAR’s ( 2000 ) proposed limits. \({}^{238}U\) and \({}^{232}Th\) exceeded the recommended threshold for profiles seven to ten. The annual effective dose equivalent (AEDE) in the area was also calculated to be \(40.98\,\mu \,{\text{Sv/year}}\) . The research provides a multi-geophysical approach to evaluating and understanding hydro-geophysical and geo-environmental conditions related to landfill contamination.

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Acknowledgements

The authors are thankful to the Department of Geology, Federal University of Technology, Owerri, and the Department of Physics, University of Port Harcourt, for providing the equipment used for the data acquisition and for quality supervision of this research work.

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Department of Geology, Federal University of Technology, Owerri, Imo State, Nigeria

F. B. Akiang, S. O. Onyekuru, C. C. Agoha & S. I. Ibeneme

Department of Physics, University of Calabar, Calabar, Nigeria

Department of Applied Geophysics, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria

Department of Physics, Federal University of Kashere, Kashere, Gombe State, Nigeria

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All the authors contributed to the study conceptualization and design. The first draft of the manuscript was written by Francis Begianpuye Akiang. Samuel Okechukwu Onyekuru supervised the work. Eric Bekongshelhe Ulum, Chidiebere Charles Agoha and Michael Ohakwere-Eze acquired the data and did the analysis. Maureen Chioma Umeh provided software support, while Sabinus Ikechukwu Ibeneme reviewed and edited the final draft. Every step was carried out with the collective decision and consent of all the authors.

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Correspondence to F. B. Akiang .

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Akiang, F.B., Onyekuru, S.O., Ulem, E.B. et al. Geophysical and hydro physicochemical investigation and pollution potential evaluation of an urban landfill in southern Nigeria. Int. J. Environ. Sci. Technol. (2024). https://doi.org/10.1007/s13762-024-05580-1

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Received : 16 July 2022

Revised : 23 December 2023

Accepted : 06 March 2024

Published : 21 April 2024

DOI : https://doi.org/10.1007/s13762-024-05580-1

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