The impact of green climate fund portfolio structure on green finance: Empirical evidence from EU countries

Mohsin, Muhammad; Dilanchiev, Azer; Umair, Muhammad Article The impact of green climate fund portfolio structure on green finance: Empirical evidence from EU countries Ekonomika Provided in Cooperation with: Vilnius University Press Suggested Citation: Mohsin, Muhammad; Dilanchiev, Azer; Umair, Muhammad (2023) : The impact of green climate fund portfolio structure on green finance: Empirical evidence from EU countries, Ekonomika, ISSN 2424-6166, Vilnius University Press, Vilnius, Vol. 102, Iss. 2, pp. 130-144, https://doi.org/10.15388/Ekon.2023.102.2.7 This Version is available at: https://hdl.handle.net/10419/323135 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ 130 Ekonomika ISSN 1392-1258 eISSN 2424-6166 2023, vol. 102(2), pp. 130–144 DOI: https://doi.org/10.15388/Ekon.2023.102.2.7 The Impact of Green Climate Fund Portfolio Structure on Green Finance: Empirical Evidence from EU Countries Muhammad Mohsin School of Finance and Economics, Jiangsu University, China https://orcid.org/0000-0002-4723-4184 [email protected] Azer Dilanchiev School of Business, International Black Sea University, Georgia https://orcid.org/0000-0002-9899-6621 [email protected] Muhammad Umair School of Economics and Management, Chongqing Jiaotong University, China https://orcid.org/0000-0001-8159-0054 [email protected] Abstract. The financing sector drives the Future of Environmental Funds to achieve climate financing. In this study, we have employed panel regression analysis and the generalized two-step moment method (GMM) for the 25 EU countries from 2000 to 2021 to explore the relationship between green financing and the portfolio structure of green climate funds. According to the findings of this research, green financing significantly impacts quality economic growth. The GCFs enhance the capacity to channel public and private funding while contributing to de-risking more conventional forms of funding, increasing climate financing, and boosting the GCFs. In addition, the study concluded that Global Climate Support might fund nonbankable components of more significant “almost bankable projects” by analyzing the portfolio’s policies and methods. Keywords: Financing Sector; Green Climate Funds (GCF); Green financing; EU countries 1. Introduction Developing nations have consistently undervalued the importance of combating global warming. Nevertheless, as Chen et al. (2021)have shown, the effects of global warming and climatic variability are directly linked to political destabilization and food emergencies (Bashir et al., 2021), and climatic change volatility plays a crucial role in both domestic and global migration (X. Liu et al., 2021). Received: 14/10/2022. Revised: 26/03/2023. Accepted: 21/06/2023 Copyright © 2023 Muhammad Mohsin, Azer Dilanchiev, Muhammad Umair. Published by Vilnius University Press This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Contents lists available at Vilnius University Press Muhammad Mohsin et al. The Impact of Green Climate Fund Portfolio Structure on Green Finance: Empirical Evidence from EU Countries 131 In contrast, analysis of EU countries found that industrialized nations could only handle $81 billion of the US$101 billion objectives by 2020. World temperature must be limited to no more than 1.5 degrees Celsius to avoid disastrous environmental alteration, according to the International Panel on Environmental Change’s most recent assessment (International Panel on Climate Change [IPCC], 2018). Coal must be phased out if the global temperature is kept at 1.6 degrees Celsius. While EU countries have previously restricted the funding of coal-fired power facilities in other countries, the protection and rehabilitation of trees, marshland, and other standard carbon drops may also help mitigate biodiversity loss and climate change (Grossman & Krueger, 1995). According to the United Nations Framework Convention on Climate Change (UNFCCC), until 2040, green funding of US$1.5 trillion per year will be required to devise the Sorbonne Accord and reach this environmental goal fully. Since present and future generations have a firm conviction in environmental ethics and religious views, they want their expenditures to reflect that view (Gouveia et al., 2019). For emerging financial systems, the issue of long-term viability is critical (Zihan Li et al., 2019). Investing in maintainable resources, the constraints of such savings, and a comparison of responsible investing with their traditional equivalents are all critical threads in the research on responsible finance, according to (X. Song et al., 2019). The initial line of attack focuses on making investments in long-term solutions. Nielsen et al. (2014) point out that green bonds may be used as a hedge, particularly during times of crisis (such as a pandemic), while (M. Song et al., 2021) argue that clean energy share benchmarks in Europe and throughout the world are more efficient than those in the United States. The second strand highlights some difficulties and inadequacies in implementing renewable expenditures. According to (Nabeeh et al., 2021), processing expenses and workable deficiencies in Nepal hinder the expansion of green finance in the country. One set of studies found no variation in economic returns between renewable investment options and their comparable traditional substitutes (Yang et al., 2021). According to an alternate set of studies, an economic crisis would need traders to accept more risk and poorer profits. Green finance has also been the subject of a handful of journal papers. Space technology is used to describe the state quo, and growth patterns in green finance (Churchill et al., 2020) brief compendium gives an overview of the wide-ranging area of green finance. There is a focus on the causes and potential advantages of company involvement in ecologically acceptable initiatives in green bonds and green loans following the fast development of green finance (Bouzarovski & Tirado Herrero, 2017). Green finance knowledge and its translation from theory to practice is the focus of this study, which adopts an integrated method using patent citation and subjective assessment of sample papers. It shows the present status of the study and its development trends. In the context of environmental transformation, climate finance refers to the funding of reduction and adaptation initiatives derived from public and private sources, as well as substitute sources of lending (Karásek & Pojar, 2018). Under the UNFCCC, wealthy nations are obligated to help poor counties that are less well-off and more susceptible to the effects of climate change (Herrero, 2017). Evaluation of the impact of green climate funds on money mobilization in EU contires is presented in this study, which adds to the body of knowledge on the subject. Analyzing ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2023, vol. 102(2) 132 their portfolio structure and strategy empirically, we examine the green climate funds in structuring and scaling up climate funding. The green climate funds may successfully fund nonbankable sections of more significant “almost bankable projects,” as opposed to a Fund that focuses primarily on financing nonbankable projects. Analysis of the green climate funds portfolio of sponsored projects compared to WRI provides new study pathways on how private money might assist adaptation and mitigation strategies often supported mainly by state funds. Green credit, green securities, green insurance, green investment, and carbon finance are all included in this study’s construction of a green finance development index using the global principal component analysis (GPCA). The objective of the paper is to investigate the relationship between green financing and the portfolio structure of green climate funds in the 25 EU countries from 2000 to 2021 using panel regression analysis and the generalized two-step moment method (GMM) and to determine the impact of green financing on quality economic growth and the role of GCFs in channelling public and private funding for climate financing. The paper’s research question is, how does green financing impact the quality of economic growth and the portfolio structure of green climate funds in the 25 EU countries from 2000 to 2021? The rest of the paper is structured in the following way. Related literature is discussed in Section 2wo. Methodology and data are discussed in Section 3. Empirical results and discussion is presented in Section 4. The last Section 5 contains conclusions and policy implications. 2. Literature Review Financial instruments that positively impact the environment are called “green finance” (IFC, 2017). Financial firms immediately consider environmental regulation when making investment and funding choices, which drives cash toward the green economy. Therefore, the traditional economic market largely ignores the ecological impacts in favor of the investing project’s performance. Accordingly, green financing encourages the switch between high-emission and energy-intensive investments to those that enhance energy efficiency and ecological protection (D’Orazio & Popayan, 2019). After carefully examining the climate finance literature, they discover that study in this area is still in its early stages, with little development. Early studies mainly emphasized how green finance will affect society and its associated policies. Hafner et al. (2020) examined significant impediments to private investment in infrastructure supporting energy from renewable sources and potential governmental solutions. They advise developing a long-term involvement founded on a systems approach since their study reveals that volatility and brief in the finance industry are two essential investment impediments. Mazzucato & Semieniuk (2018) focused on the role of public players in overall financing and looked at asset portfolios of various clean energy technologies that various financial stakeholders funded with variable risks. In order to combat climate change and advance green financing, D’Orazio & Popoyan (2019) looked at the green economy concept. The influence of policies on two substantial investor Muhammad Mohsin et al. The Impact of Green Climate Fund Portfolio Structure on Green Finance: Empirical Evidence from EU Countries 133 choice measures – risk and return – was analyzed by Polzin et al. (2019) to determine how well policies mobilize private capital. Research also highlighted how green financing had improved many businesses’ effectiveness. For instance, Jin & Han (2018) investigated how sustainable finance affected the production bases and found a link involving green funding sources, especially in automotive. They also pointed out the prospect of green financing to play an essential part in China’s shift to an economy that is both innovative and ecological. Economic assets are critical to controlling ecological degradation in industrialized countries, but actual data is sparse. Because of this, researchers are working to fill the gap (Bohr & McCreery, 2020). There was little in the way of GHG releases and deposits in the economy. Switching to a more environmentally friendly type of energy use is the only way to reduce the pace of ecological degradation. As noted (Bouzarovski 2014), governments must drastically decrease their reliance on fossil fuels and significantly increase their investment in financial technology to lower their carbon footprints. According to those who lauded the need to generate and promote renewable energy, renewable electricity usage was also criticized. Items and Hotaling also stressed the need to shift to Financial technology sources, greener swiftly or much less harmful, while fostering economic progress (Aristondo & Onaindia, 2018). However, the long-term viability of green financing depends on private sector assets’ confidence in their capacity to achieve the intended result (Castaño-Rosa et al., 2020). To put it another way: If an asset’s current price is based on its previous data, then more data will quickly boost the price, putting the investment’s real value closer to its market appraisal. Investors, economic advisors, and management are in danger when financial statistics are not connected. As a result, green finance’s capacity to direct global economic growth toward financial technology might be diminished. To boost their business brand, corporations have sought to publicly accept financial technology issues without providing solid material to support such “green” pronouncements regarding ecological preservation (Bouzarovski & Petrova, 2015). All three impacts are accounted for sustainable growth: businesses, decision-makers, and end-users. Regarding financial reporting and justifications, shareholders are affected by the quality of the information presented. It has been suggested that certain firms are participating in “green finance” by exaggerating their “agenda statements” regarding effectiveness assessment via their disclosure methods. 3. Empirical strategy and data analysis 3.1. Theoretical framework Most worldwide financial and monetary processes were hampered by the current Covid-19 pandemic, with EU countries being the first nation diseased and the most badly afflicted, being the most affected. The research results and the survey’s stated objectives agree with one another. EU countries, the world’s second-largest CO2 emitter, is also a significant energy exporter and a major CO2 emitter (Muhammad et al., 2016). There has been a great deal of research into green finance’s role in Green climate funds. In a two-way interplay known as the “Sustainable Energy Performance and Green climate funds,” green finance and process breakthroughs are connected. In past research, Green Finance was overesti- ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2023, vol. 102(2) 134 mated as a measure of financial growth, which did not consider its impact (Castaño-Rosa et al., 2019). Others have claimed that green finance may affect Green climate funds rates by influencing savings prices, expenditure decisions, and global climate change. There are numerous other studies. Scientific measures with a high probability of winning can quickly determine the most encouraging new advances in monetary services. Additional benefits include improved resource allocation and technological technology due to the market assistance company’s ability to collect cost-saving savings and make it easier to use those funds. Irrespective of the nation’s bank or sharemarket framework, green finance positively impacts financial growth. On the other hand, developing countries are at a disadvantage because they may be unable to benefit from innovation transmissions that could aid their growth. Green finance is expected to positively impact renewable growth because it is thought to spur innovation. Based on prior studies, this impact is obtained through various mechanisms (Shahbaz et al., 2018). Since nonfinancial technology consumption causes uncontrollable environmental degradation and a decrease in expected wealth, argues that it is untenable, green finance may be utilized to create green climate funds. Furthermore, in the study, the degree of green funding is assessed using the global principal component analysis (GPCA) approach, a statistical technique that combines many indices into a single index. In order to arrive at a total score of the level of green financing, the GPCA approach analyzes five separate indicators, including the human capital index, the economic innovation index, the technical innovation index, the energy sector index, and the resource management index. This method contributes to a comprehensive understanding of the level of investment in environmentally friendly programs and its influence on lowering the adverse environmental effects of resource consumption. 3.2. Model specification The following regression model is used to assess the influence of green finance and innovation on green climate funds indices. 𝐺𝐺𝐺𝐺𝐺𝐺𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛼𝛼0+ 𝛼𝛼1𝐺𝐺𝐺𝐺𝑖𝑖𝑖𝑖 + 𝛼𝛼2𝐺𝐺𝐹𝐹𝑛𝑛𝑖𝑖𝑖𝑖 + 𝛼𝛼3𝐺𝐺𝐺𝐺𝑖𝑖𝑖𝑖 ∗𝐺𝐺𝐹𝐹𝑛𝑛𝑖𝑖𝑖𝑖 + 𝛼𝛼4𝐹𝐹𝑙𝑙𝑙𝑙𝑙𝑙 + 𝛼𝛼5npuequ +  + 𝛼𝛼6iopen +𝛼𝛼7Private + 𝛼𝛼8𝑍𝑍𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 In this equation, variables are defined as: a) GCF: The dependent variable, representing the “Green Climate Fund” index. b) GF_it: The independent variable, “green finance,” represents the influence of green finance on the GCF index. c) Fin_it: The independent variable, “innovation,” represents the influence of innovation on the GCF index. d) GF_it*Fin_it: The interaction term between green finance and innovation, representing the joint influence of both variables on the GCF index. e) i_loa: The independent variable, “international loan,” represents the amount of international loans received by the EU countries. f) npu_equ: The independent variable “national public equity,” representing the amount of national public equity received by the EU countries. Muhammad Mohsin et al. The Impact of Green Climate Fund Portfolio Structure on Green Finance: Empirical Evidence from EU Countries 135 g) i_open: The independent variable, “international open market,” represents the amount of international open market funding received by the EU countries. h) Private: The independent variable, “private project,” represents the amount of private funding received by the EU countries for projects. i) Z_it: The independent variable “control variable,” representing any additional control variables that might affect the GCF index. j) ε_it: The error term representing any unmeasured factors that might influence the GCF index. Note: The index “i” represents the EU country, and “t” represents the time period. 3.3. Data sources The EPS database (https://www.epsnet.com.cn/index.html) provided information on environmental factors, green investments, green insurance, and carbon financing. The EU countries’ Statistical Yearbook was used to gather economic efficiency and structure information. Financial technology credits and securities were purchased from the Wind database (https://www.wind.com/en/edb.html). 4. Empirical results 4.1. Summary of descriptive statistics In Table 1, the descriptive analysis of the variables is represented. It signifies that the sample has an average amount of dispersion when values fluctuate. Table 1. Descriptive statistics. Variables Mean Standard deviation Minimum Max Ecological environment (EN) 0.54 0.19 0.14 0.96 Economic efficiency (EF) 0.31 0.14 0.05 0.90 Economic structure (ES) 0.21 0.12 0.04 0.68 Green finance (GF) 2.58e-11 0.73 -1.66 3.04 Fintech (FIN) 155.57 521.94 06331 Energy consumption of last year (CONSM) 6.19 0.57 4.83 7.90 GDP per capita (GDPPC) 11.55 0.57 7.97 13.01 Urbanization (URB) 0.06 0.03 0.03 0.11 The scale of local fiscal expenditure (FISC) 0.31 0.12 0.11 0.80 International loan (i_loa) 0.29 0.11 6.87 0.78 National public equity (npu_loa) 0.07 0.05 0.04 0.76 International open market (i_open) 4.21 0.59 3.90 2.03 Private project (Private) 2.11 0.41 0.08 0.07 ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2023, vol. 102(2) 136 4.2. Stationary test results As shown in Table 2, the data were tested for stationarity using an enhanced HT test. There is a p-value of less than 1 percent and a static test at a certain level in the data, whereas the EN indicates a stationary test decision at the first significance levels. Therefore, the null hypothesis (Ha) is a viable option. It signifies that the dynamic nature model is acceptable for use and enhances the outcomes of the models. Table 2. Stationarity test results. Variable Test method Stationarity CADF test P-value CIPS test P value EN 0.421 0.000 -1.29 0.088 Stationary EF 0.550 0.000 1.53 0.094 Stationary ES 0.403 0.000 -2.72 0.000 Stationary GF 0.569 0.000 -3.898 0.000 Stationary FIN 0.996 1.000 1.577 0.943 Nonstationary CONSM 0.950 0.004 -3.39 0.006 Stationary GDPPC 0.904 0.997 -0.58 0.276 Nonstationary URB 0.570 0.000 -1.39 0.078 Stationary FISC 0.731 0.059 -1.43 0.79 Stationary i_loa 0.543 0.061 -2.45 0.78 Stationary i_open 0.653 0.000 -3.47 0.73 Nonstationary Private 0.321 1.000 -1.48 0.72 Stationary CSD-related issues must be considered when predicting a series’s integration/standing order. CSD cannot be detected using first-generation approaches because they presuppose cross-sectional independence. The CADF and CIPS panel unit root estimate methods of Pesaran were thus used. As shown in Table 4, the results of the CADF unit root test indicate for EU countries that the variables EN, GF, FIN, and CONSM are stationary at their current levels because the correlating assumed statistical tests reject the existence of the unit root process at 1% and 5% significance levels. For central and southern regions of EU countries, on the other hand, the variables EN, GF, FIN, and CONSM a are stationary at their current levels, while the other variables EN, GF, FIN, and CONSM are stationary at their first difference in the level of significance. Furthermore, across the whole sample, the variables EN, GF, FIN, and CONSM are stable at levels, but the other series have no unit root at their initial difference. There is a diverse order of implementations among the variables in this sample of emerging market economies in developing nations from three regions. Furthermore, the conclusions of the unit root are shown to be consistent across several estimating methodologies. Both the CADF and CIPS tests show that the variables under consideration in this research are integrated at their first difference and none at their second difference, which is perfect for the GMM analysis to be carried out. Muhammad Mohsin et al. The Impact of Green Climate Fund Portfolio Structure on Green Finance: Empirical Evidence from EU Countries 137 4.3. Outcomes of cointegration tests The Pedroni (2004) and Kao tests (1998) show that the model panels represented in Table 3 are cointegrated. Furthermore, the Pedroni and Kao tests show that the panel is cointegrated. The results show that the “Ha of the alternative hypothesis” is supported by the data. Table 3 . Cointegration test results. Variables Kao Pedroni ADF Modified Phillips–Perron t Phillips–Perron t Augmented Dickey–Fuller t EN -4.241 (0.000) -8.329 (0.000) 8.221 (0.000) -7.922 (0.000) EF 1.670 (0.051) -14.641 (0.000) -12.761 (0.000) -11.302 (0.000) ES 3.531 (0.005) -12.713 (0.000) -12.142 (0.000) -7.704 (0.000) 4.4 Regression analysis Green climate fund numbers show a positive and statistically significant, at a 5% significance level, influence on green finance usage in EU countries. A one percent rise in the amount of FIN and GF tends to raise the Green Climate Fund statistics by 0.0014 percent if the model is used. An explanation for this result might be found in the fact that many EU countries have a wealth of Financial technology and make good use of them to fulfil the continent’s energy needs. Significant amounts of crude oil are mined in EU countries and processed to make hydrocarbons, making many countries gasoline and transferring states. Since crude oil extraction and use will likely impact the environment, it may be inferred that this supports the constructive nexus between green finance and the Green climate fund. (Shaktawat & Vadhera, 2022) concluded that this result is consistent for developing economies. Moreover, it has been shown that the flexibility factor of innovations has a negative and statistically significant influence on the Green climate fund at a 10% significance level. There is a 0.0224 percent reduction in the green climate fund if there is a 1% increase in the number of patent applications. It suggests that EU countries may use cutting-edge technology to slow the growth of their ecological footprints, limiting environmental destruction. As a result, environmental performance in EU countries technological advancements. There is a possibility that technical innovation might help lessen the EU countries’ dependence on fossil fuels, which could lead to the Financial technology industry in these economies via the use of new technologies. Because of this, African countries’ adoption of Financial technology is expected to minimize their ecological footprints. In EU countries, this result is consistent with previous findings by (K. H. Kabir et al., 2022); however, in EU countries, this finding contradicts (Z. Kabir, 2022) conclusions. Contrary to (Rohr et al., 2022), who looked at APEC countries, which included some of the world’s most established economies, this research focused on developing African states. The findings from the research of Zhenghui Li et al. (2018) and Antal et al. (2017) suggest that the financial policies implemented by African and EU countries are partially ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2023, vol. 102(2) 144 Liu, X., Zhao, T., Chang, C.-T., & Fu, C. J. (2021). EU countries ’s Financial technology strategy and industrial adjustment policy. Renewable Energy, 170, 1382–1395. https://doi.org/https://doi.org/10.1016/j. renene.2021.02.045 Muhammad, Javid, Fatima, & Sharif. (2016). Environmental Kuznets curve and financial development in Pakistan. Renewable & Sustainable Energy Reviews, 54, 406–414. Nabeeh, N. A., Abdel-Basset, M., & Soliman, G. (2021). A model for evaluating green credit rating and its impact on sustainability performance. Journal of Cleaner Production, 280, 124299. Nielsen, M., Ravensbeck, L., & Nielsen, R. (2014). Green growth in fisheries. Marine Policy, 46, 43–52. https://doi.org/10.1016/j.marpol.2014.01.003 Rohr, V., Blakley, J., & Loring, P. (2022). Food Security Assessment: An Exploration of Canadian Offshore Petroleum SEA Practice. Journal of Environmental Assessment Policy and Management, 2250016. https:// doi.org/10.1142/S1464333222500168 Shahbaz, M., Nasir, M. A., & Roubaud, D. (2018). Environmental Degradation in France: The Effects of FDI, Financial Development, and Energy Innovations. MPRA Paper, 74, 843–857. Shaktawat, A., & Vadhera, S. (2022). Sustainability Assessment of Financial technology Technologies in Context to India Using Multicriteria Analysis with and without Incorporating Risk Analysis. Journal of Environmental Assessment Policy and Management, 2250020. https://doi.org/10.1142/S146433322250020X Song, M., Xie, Q., & Shen, Z. (2021). Impact of green credit on high-efficiency utilization of energy in EU countries considering environmental constraints. Energy Policy, 153. https://doi.org/10.1016/j. enpol.2021.112267 Song, X., Zhou, Y., & Jia, W. (2019). How do Economic Openness and R&D Investment Affect Green Economic Growth?—Evidence from EU countries . Resources, Conservation and Recycling, 146, 405–415. https://doi.org/10.1016/j.resconrec.2019.03.050 Yang, X., Wu, H., Ren, S., Ran, Q., & Zhang, J. (2021). Does the development of the internet contribute to air pollution control in EU countries ? Mechanism discussion and empirical test. Structural Change and Economic Dynamics, 56, 207–224. https://doi.org/10.1016/j.strueco.2020.12.001