S no. | Sector | Number of firms |
---|---|---|
1 | Agriculture | 5 |
2 | Consumer goods | 21 |
3 | Conglomerates | 6 |
4 | Financial services | 57 |
5 | Health care | 11 |
6 | ICT | 7 |
7 | Industrial goods | 15 |
8 | Natural resources | 4 |
9 | Oil and gas | 12 |
10 | Services | 25 |
Total | 163 |
IntR | InfR | ExcR | GDPR | Firm Size | Leverage | Liquidity | |
---|---|---|---|---|---|---|---|
Mean | 12.56164 | 11.50645 | 211.9160 | 3.273973 | 24.18891 | 0.536857 | 2.405487 |
Median | 12.00000 | 10.80000 | 188.4524 | 4.300000 | 24.77322 | 0.293164 | 0.495891 |
Maximum | 14.00000 | 16.50000 | 305.5000 | 6.300000 | 27.01342 | 5.950043 | 161.7999 |
Minimum | 11.00000 | 8.047411 | 159.2632 | −1.600000 | 18.04201 | −1.020951 | −87.86760 |
SD | 1.050272 | 3.175347 | 58.61567 | 2.616975 | 2.038982 | 0.858093 | 21.45939 |
Skewness | 0.194952 | 0.490764 | 0.822211 | −0.711094 | −1.218807 | 3.351398 | 5.349119 |
Kurtosis | 1.754506 | 1.672726 | 1.873250 | 2.215223 | 4.337613 | 18.97932 | 45.02500 |
Jarque–Bera | 10.36163 | 16.57741 | 24.17328 | 16.05085 | 47.03127 | 1,826.619 | 11,440.03 |
Probability | 0.005623 | 0.000251 | 0.000006 | 0.000327 | 0.000000 | 0.000000 | 0.000000 |
Sum | 1,834.000 | 1,679.942 | 30,939.74 | 478.0000 | 3,531.581 | 78.38117 | 351.2011 |
Sum sq. dev. | 159.9452 | 1,462.010 | 498,190.6 | 993.0411 | 602.8298 | 106.7668 | 66,773.30 |
Observations | 146 | 146 | 146 | 146 | 146 | 146 | 146 |
Variable | Coefficient | SE | -Statistic | Prob. |
---|---|---|---|---|
−0.259721 | 0.204524 | −1.269880 | 0.2063 | |
IntR | −0.006350 | 0.016894 | −0.375893 | 0.7076 |
InfR | 0.009470 | 0.005263 | 1.799492 | 0.0741 |
ExcR | −0.000413 | 0.000457 | −0.902177 | 0.3685 |
GDPR | 0.015564 | 0.009098 | 1.710709 | 0.0894 |
Firm Size | 0.013125 | 0.006829 | 1.922123 | 0.0567 |
Leverage | 0.059902 | 0.017021 | 3.519396 | 0.0006 |
Liquidity | 0.000678 | 0.000200 | 3.390252 | 0.0009 |
0.278248 | Mean dependent variable | 0.366369 | ||
Adjusted | 0.241638 | SD dependent variable | 0.555485 | |
SE of regression | 0.423122 | Sum squared resid. | 24.70646 | |
-statistic | 7.600210 | Durbin–Watson stat. | 1.214496 | |
Prob. ( -statistic) | 0.000000 | |||
-squared | 0.052610 | Mean dependent variable | 0.101658 | |
Sum squared resid | 34.68503 | Durbin–Watson stat. | 1.233974 |
Source: EViews 9
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This chapter highlights the existing research works carried out in India and abroad by the scholars exploring the microeconomic, macroeconomic and industry-specific factors affecting the firm-level performance. Comprehensive review of the existing literature on the effect of these factors on the efficiency, profitability and stock prices was accomplished. The research gap in the existing literature was identified in this chapter by using Evidence Gap Map. The chapter also outlines the objectives of the study in the perspective of such research gap.
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Banking sector is a notable exception in this regard. As a matter of fact, there are many empirical studies on the banking sector where the researchers have attempted to explain the profitability of banks of different countries on the basis of the various company-specific, industry-specific and macroeconomic factors (Ali et al., 2011 ; Goddard et al., 2004 ; Kosmidou et al., 2007 ; Ramya & Mahesha, 2012 ; Sufian & Habibullah, 2010a ; Vejzagic & Zarafat, 2014 ; Vong & Chan, 2009 ; Williams, 2003 ; Zang & Daly, 2013 ). It has been observed that the profitability of banks and other firms is not only influenced by internal factors but also by the macroeconomic factors (Raza et al., 2013 ).
Some of these notable studies in this context are the studies carried out by Schumpeter ( 1912 ), Fama ( 1981 , 1990 ), Chen ( 1986 ), Hamao ( 1988 ), Poterba and Summers ( 1988 ), Macdonald and Power ( 1991 ), Thornton ( 1993 ), Kaneko and Lee ( 1995 ), Cheung and Ng ( 1998 ), Darrat and Dickens ( 1999 ), Mukhopadhyay and Sarkar ( 2003 ), Maysami et al. ( 2004 ), Vikramasinghe ( 2006 ), Agrawalla and Tuteja ( 2008 ), Asaolu and Ogunmuyiwa ( 2011 ), Sultana and Pardhasaradhi ( 2012 ), Hassan and Sangmi ( 2013 ), Chkili and Nguyen ( 2014 ), Pradhan et al. ( 2015 ), and Wu et al. ( 2016 ).
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Sumit Kumar Maji
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Maji, S.K., Laha, A., Sur, D. (2022). Factors Affecting Financial Performance of Firms: An Exploration of the Existing Research Works. In: Indian Manufacturing Sector in Post-Reform Period. Palgrave Macmillan, Singapore. https://doi.org/10.1007/978-981-19-2666-2_3
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Positive energy districts: fundamentals, assessment methodologies, modeling and research gaps.
State of the art on positive energy districts, 2. methodology.
3.2. quality-of-life indicators in positive energy districts, 3.3. technologies in positive energy districts: development, use and barriers, 3.4. positive energy districts modeling: what is further needed to model peds, 3.5. sustainability assessment of positive energy districts, 3.6. stakeholder engagement within the design process, 3.7. tools and guidelines for ped implementation, 4. conclusions, author contributions, data availability statement, acknowledgments, conflicts of interest.
Click here to enlarge figure
Question #1 | Question #2 | Question #3 | |
---|---|---|---|
| What are the essential PED DNAs? Can generic PED archetypes be created based on them? | What are the categories of quality-of-life indicators relevant for PED development? | How would you use a database tool to learn about PED development process (e.g., using static information for dynamic decision-making)? |
| Which future technologies would you expect to be adopted in PEDs and cities? | What can be the challenges and the barriers in the future (regarding e.g., control, smart solutions, modeling, technologies) to PED development and diffusion? | What is your expectation for urban and district energy modeling? How can models help to shape PEDs and cities? |
| What is the impact of stakeholders in the PED design/decision process, what are their interests and how are stakeholders likely to be involved in the overall process? | What costs do you expect to bear and what revenues do you expect to realize from the PED implementation? Which aspects should be included in the organizational/business models? | What would you prioritize in terms of energy aspects or efficiency and social implications of living in a PED? Which aspects are more relevant for you? |
| Annex 83 together with other PED initiatives is developing a database of PEDs and PED-Labs: what would be your main interest in consulting the database? | Having the outcomes from PED guidelines analysis, what information would be the most interesting for you to see? | Who can benefit from the PED research studies and Annex 83 results? Which stakeholders are interested? |
Categories | Key Characteristics |
---|---|
Facts and Figures | Physical sizes/population size |
Geographical location | |
Climate | |
Density | |
Built form | |
Land use | |
Energy demand | |
Renewable energy potential | |
Technologies | Renewable energy supplies |
Energy-efficiency measures | |
Energy distribution (e.g., co-generation, district network) | |
Energy storage | |
Mobility solutions | |
Quality of Life | User comfort |
Social-economic conditions | |
Health impacts (e.g., air pollution, noise pollution) | |
Accessibility to green space | |
Accessibility to services (e.g., bike lane, public transportation) | |
Local value/sense of community | |
Others | Regulations/Policies |
Stakeholder involvement | |
Local targets and ambitions | |
Local challenges | |
Impacts of PEDs |
Type | Quality Categories | |
---|---|---|
Tangible | Indoor and outdoor environmental quality | Physical quality and comfort of the environment |
Security and safety | ||
Level and accessibility of servicing | Public and active transport facilities including walkability, energy services (access to affordable energy including access to energy efficiency), sustainable waste management | |
Access to daily life amenities including education, culture, sports, coworking and study places, provisions for children, but even common gardens or community kitchens | ||
Aesthetic quality | ||
Functional mix | ||
Future-proofness | ||
Acceptable cost of life (affordability, inclusivity) | ||
Equity and just transition | ||
Functional links to realizing circularity and reducing emissions | ||
Citizen engagement | Involvement in decision-making | |
Social diversity in participation | ||
Access to greenery | The possibility to reconnect with nature | |
Sufficient open space | ||
Information flow | From creating awareness over enhancing knowledge and literacy up to capacity of control | |
Transparency on energy flows and information for the end prosumer | ||
Insight in applicable PED solutions and in healthy lifestyles | ||
Intangible | Sense of well-being | |
Quality of social connections | ||
Sense of personal achievement | ||
Level of self-esteem | ||
Sense of community | ||
Degree of cooperation and engagement for the common interest | ||
Time spent with friends (outdoor) | ||
Budget available at the end of the month to spend freely | ||
Not being aware or realizing of living in a PED |
Technology Groups | Solutions | |
---|---|---|
Energy efficiency | New energy-efficient buildings and building retrofitting. | |
Nature-based solutions (natural sinks) and carbon capture solutions (CCS) | ||
Efficient resource management | ||
Efficient water systems for agriculture (smart agriculture, hydroponics, agrivoltaics, etc.) | ||
Organic photovoltaics and a circular approach (second life materials, like batteries) | ||
Energy flexibility | Hardware | Storage (long-term and short-term) |
Monitoring systems (sensors, smart meters, PLCs *, energy management systems, etc.) | ||
Vehicle to grid | ||
Heat pumps | ||
Electronic devices like IoT * technologies | ||
Buildings fully automated with real time monitoring behind-the-meter and automated actions | ||
Cybersecurity, data rights and data access | ||
Demand management and remote control of devices | ||
Software | Edge computing | |
Machine learning | ||
Blockchain | ||
Digital twins | ||
5G | ||
City management platform and platforms for city planning (space, refurbishment, climate change, etc.) | ||
E-mobility | Promotion of shared vehicles over individual car use, lift sharing, and alternative ways (like micromobility) to collective transports | |
Soft mobility | Promotion of a lifestyle that require less use of cars, i.e., “soft mobility” solutions like low emission zones or banning the entrance of some type of car (e.g., Singapore and Iran have policies in place to allow only certain car groups to drive freely in certain periods) | |
E-vehicle charging stations and vehicle-to-grid solutions | ||
Low-carbon generation | Photovoltaics | |
Energy communities | ||
Electrification of heating and cooling (H&C) using heat pumps, district heating networks utilizing waste heat, or solar thermal technologies | ||
Virtual production | ||
Fusion technology |
Challenges and Barriers | Key Topics |
---|---|
Capacity building and policy issues | Political and legal barriers |
Regulatory frameworks and policy constraints | |
Tailored legislation | |
Bridging the knowledge gap | |
Inadequate data sharing practices | |
Securing sufficient financial resources | |
Lack of clear regulations defining PED classification | |
Active involvement of policymakers | |
Widespread dissemination of knowledge | |
Collaborative data-sharing efforts | |
Securing adequate funding | |
Establishing supportive policies and regulations | |
Social challenges and considerations | Cultural barriers |
Access to affordable and sustainable energy for all | |
Building social agreements and fostering collaboration | |
Energy literacy | |
Addressing personal behavior acceptance | |
Transition strategy for inclusivity | |
Social inclusion and trust-building | |
Data sharing and privacy concerns | |
Overcoming public opposition and promoting knowledge dissemination | |
Financial barriers | Long-term storage investment and space competition |
Insufficient investment | |
High upfront costs | |
Allocation of costs among stakeholders | |
Incentives for participation | |
Addressing investment challenges for different stakeholders | |
Accounting for battery costs | |
Data management | Data standardization |
Data security measures and protocols | |
Sustainability and maintenance of data infrastructure | |
Privacy regulations and data anonymization techniques | |
Sustainable business models and ownership structures | Standardization of control technologies and replication strategies |
Grid management approaches | |
Deep penetration of sustainable technologies | |
Implementation of predictive models Long-term maintenance activities and resident data collection | |
Balancing diverse requirements | |
Addressing grid operation challenges | |
Managing multiple independent energy districts | |
Inclusivity strategies for digital technology reliance | |
Managing production peaks and defining the role of buildings and districts | |
Effective management strategies for grid congestion and stability |
Categories of Innovation | Innovation Types | Possible Revenues/Advantages in PED Business Model/Governance | Possible Costs/Drawbacks in PED Business Model/Governance |
---|---|---|---|
Configuration | Profit Model | Providing thermal comfort instead of a certain amount of thermal energy to inhabitants | Misconducts or rebound effect |
Network | Inclusion of the PED into larger projects and international networks, possibility of co-financing and knowledge sharing | Misalignment or delay of the PED project to the original timeline due to constrains related to international activities and networking | |
Structure | Participation of the real estate companies/investors in the development and management of the energy infrastructure and EV mobility services as well as building management | Lack of knowledge, involvement in activities out of the usual business of investors | |
Free or almost free thermal energy supply from “waste energy” sources | Failure of the network due to unliteral decisions of a member in ceasing the provision of energy | ||
Process | Involvement of future inhabitants in the design phase of the energy community since the early stage, to share the sense of belonging and ownership | Reluctancy of inhabitants to participate in additional expenses or being involved in “entrepreneurial” activities or bored by the participation in boards and governance structures | |
Offering | Product Performance | Investors and companies involved in the PED development take profit from their role of frontrunner placing them before the competitors or entering in new market niches | Hi-tech BA and BEM systems may result costly in O&M, because of digital components, cloud and computing services, rapid aging of technology |
Product System | Including EV available for PED users may generate new incomes and reduce the need of individual cars. The integration of EV in the energy system may offer “flexibility services” | Lack of knowledge, involvement in activities out of the usual business of investors/real estate companies. Low interest of users in participating to the flexibility market, because of discomfort (unexpected empty battery of the EV) | |
Experience | Services | Provision of high tech and high-performance buildings, with outstanding energy performances (lower heating/cooling costs) and sophisticated Building Automation and Energy Management systems | Sophisticated Building Automation and Energy Management systems may result “invasive” to users, asking for continuous interaction with complicate systems, or leaving them not enough freedom to choose (e.g., opening the windows is not possible to achieve some energy performance) |
Channel | The PED is promoted as a rewarding sustainable investment, this allows the city to attract more clean investments (public funds, investment funds, donors), speeding up the energy transition | The communication of the characteristics of the PED is not done in the proper way | |
Brand | Gold class rated buildings may have an increased value on the market, resulting in higher selling and rental costs, occupancy rate. The high architectural quality is appreciated by the market | The Branding/certification of the PED is not recognized by the market as an added value. | |
The development of the PED takes longer as expected. Technology failures during the implementation or operation phase create a bad reputation and discourage future similar activities | |||
Customer Engagement | The PED is available as a digital twin, users are engaged via a dedicated app, allowing interaction, communication, reporting, monitoring of bills, etc. | The PED is perceived by users (e.g., social housing tenants) as a hassle and not responding to their needs, because they have not been involved in the identification of peculiar traits since the beginning |
Category | Beneficiaries |
---|---|
Citizens and communities | Citizens, inhabitants, residents, general public, local communities and neighborhoods, municipalities and provinces, energy communities, and socially disadvantaged groups. |
City decision-makers and planners | City decision-makers, city planners, local authorities, policy-makers, public administrations, politicians, local and national governments. |
Research | Scientists, publishers, and research organizations. |
Private companies and technology developers | Private companies of RES technologies, ICT companies, start-ups and new companies, entrepreneurs, technology developers and other companies involved in local development (tech development and evaluation). |
Energy providers | Energy providers, grid operators. |
Education stakeholders | Students and teachers. |
Non-governmental organizations (NGOs) | NGOs and other civil society groups |
Category | Comments |
---|---|
Strategies | Most comments dealt with the strategies on how to achieve PEDs, that should focus on success factors of PED initiatives, technologies and stakeholders rather than a standardized approach |
References | Useful information, special attention to Liwen Li, planning principles for integrating community empowerment into zero-carbon transformation |
Definitions | Help to reduce uncertainty |
Boundaries | Energy balance calculations, mobility, definition (of buildings) |
Finance | Financial mechanisms, support schemes |
Citizen engagement | From engagement to empowerment |
Management | Process management, organizing involvement, information provision |
Policy | Incentives, regional policies |
Flexibility/Grid interaction | Timesteps, credit system |
Form | Dissemination through video and other forms (not only written information) |
Category | Comments |
---|---|
Lessons learned | Special reference to real life implementation |
Results | Data analysis and potential research on the field |
Metadata as the useful information that can the real goal of consultation | |
Benchmarking to compare PEDs | |
Need to normalize results depending on a number of factors (size, location…) to really compare different initiatives | |
Privacy and data protection | |
Sets of technologies and solutions | - |
Economic parameters | As a way to benchmark the different PED technologies |
Citizen engagement | Energy poverty |
Prosumers | |
From engagement to empowerment | |
Definition and boundaries | Need to standardize and have a reference framework to establish the energy balance |
Contact persons | It is very valuable to have a contact address to ask more about the initiative |
Regulatory framework | Drivers and Enablers |
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
Kozlowska, A.; Guarino, F.; Volpe, R.; Bisello, A.; Gabaldòn, A.; Rezaei, A.; Albert-Seifried, V.; Alpagut, B.; Vandevyvere, H.; Reda, F.; et al. Positive Energy Districts: Fundamentals, Assessment Methodologies, Modeling and Research Gaps. Energies 2024 , 17 , 4425. https://doi.org/10.3390/en17174425
Kozlowska A, Guarino F, Volpe R, Bisello A, Gabaldòn A, Rezaei A, Albert-Seifried V, Alpagut B, Vandevyvere H, Reda F, et al. Positive Energy Districts: Fundamentals, Assessment Methodologies, Modeling and Research Gaps. Energies . 2024; 17(17):4425. https://doi.org/10.3390/en17174425
Kozlowska, Anna, Francesco Guarino, Rosaria Volpe, Adriano Bisello, Andrea Gabaldòn, Abolfazl Rezaei, Vicky Albert-Seifried, Beril Alpagut, Han Vandevyvere, Francesco Reda, and et al. 2024. "Positive Energy Districts: Fundamentals, Assessment Methodologies, Modeling and Research Gaps" Energies 17, no. 17: 4425. https://doi.org/10.3390/en17174425
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