Exploring the economic and noneconomic determinants of investments in renewable energy

Uddin, Mohammed Gazi Salah; Hasan, Md. Bokhtiar; Park, Donghyun; Ali, Md. Sumon; Wadström, Christoffer Working Paper Exploring the economic and noneconomic determinants of investments in renewable energy ADB Economics Working Paper Series, No. 740 Provided in Cooperation with: Asian Development Bank (ADB), Manila Suggested Citation: Uddin, Mohammed Gazi Salah; Hasan, Md. Bokhtiar; Park, Donghyun; Ali, Md. Sumon; Wadström, Christoffer (2024) : Exploring the economic and noneconomic determinants of investments in renewable energy, ADB Economics Working Paper Series, No. 740, Asian Development Bank (ADB), Manila, https://doi.org/10.22617/WPS240406-2 This Version is available at: https://hdl.handle.net/10419/305386 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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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/3.0/igo/ ASIAN DEVELOPMENT BANK ASIAN DEVELOPMENT BANK 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org Exploring the Economic and Noneconomic Determinants of Investments in Renewable Energy This paper explores the determinants of renewable energy investments along with other economic and noneconomic variables in both developed and developing economies. The findings of this paper indicate that industrial growth, environmental taxes, social globalization, and climate vulnerability positively influence renewable energy investments in developed economies, while inflation and political instability have negative impacts. In developing economies, environmental taxes, social globalization, environmental technologies, and climate vulnerability are beneficial, while industrial growth and oil prices have adverse effects. These factors are valuable information for policymakers to create specific strategies to meet global sustainability goals. About the Asian Development Bank ADB is committed to achieving a prosperous, inclusive, resilient, and sustainable Asia and the Pacific, while sustaining its efforts to eradicate extreme poverty. Established in 1966, it is owned by 68 members —49 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. EXPLORING THE ECONOMIC AND NONECONOMIC DETERMINANTS OF INVESTMENTS IN RENEWABLE ENERGY Gazi Salah Uddin, Md. Bokhtiar Hasan, Donghyun Park, Md. Sumon Ali, and Christoffer Wadström ADB ECONOMICS WORKING PAPER SERIES NO. 740 August 2024 ASIAN DEVELOPMENT BANK The ADB Economics Working Paper Series presents research in progress to elicit comments and encourage debate on development issues in Asia and the Pacific. The views expressed are those of the authors and do not necessarily reflect the views and policies of ADB or its Board of Governors or the governments they represent. ADB Economics Working Paper Series Exploring the Economic and Noneconomic Determinants of Investments in Renewable Energy Gazi Salah Uddin, Md. Bokhtiar Hasan, Donghyun Park, Md. Sumon Ali, and Christoffer Wadström No. 740 | August 2024 Gazi Salah Uddin ([email protected]) is an associate professor and Christoffer Wadström (christoffer.[email protected]) is a lecturer at Linköping University, Sweden. Md. Bokhtiar Hasan (bokhtiar_ [email protected] ) is an associate professor at the Islamic University, Bangladesh. Donghyun Park ([email protected]) is an economic advisor at the Economic Research and Development Impact, Asian Development Bank. Md. Sumon Ali ([email protected].edu) is PhD student at the University of Texas at El Paso. Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) © 2024 Asian Development Bank 6 ADB Avenue, Mandaluyong City, 1550 Metro Manila, Philippines Tel +63 2 8632 4444; Fax +63 2 8636 2444 www.adb.org Some rights reserved. Published in 2024. ISSN 2313-6537 (print), 2313-6545 (PDF) Publication Stock No. WPS240406-2 DOI: http://dx.doi.org/10.22617/WPS240406-2 The views expressed in this publication are those of the authors and do not necessarily reflect the views and policies ofthe Asian Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use. 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If the material is attributed toanother source, please contact the copyright owner or publisher of that source for permission to reproduce it. ADB cannot be held liable for any claims that arise as a result of your use of the material. Please contact [email protected] if you have questions or comments with respect to content, or if you wish toobtain copyright permission for your intended use that does not fall within these terms, or for permission to use theADB logo. Corrigenda to ADB publications may be found at http://www.adb.org/publications/corrigenda. Notes: In this publication, “$” refers to United States dollars. ADB recognizes “China” as the People’s Republic of China. ABSTRACT Amid a shifting global energy landscape driven by concerns about climate change and fossil fuel depletion, there is a heightened need to move toward sustainable energy sources. Although there has been a significant increase in investments in renewable energy (RE) globally, there is still a considerable shortfall in achieving sustainability goals. This study is the first to explore the determinants of RE investments, considering a range of important economic and noneconomic variables. The research employs a balanced annual panel dataset covering 36 countries from 2000 to 2020. The findings indicate that, in developed economies, industrial growth, environmental taxes, social globalization, and climate vulnerability positively influence RE investments, while inflation and political instability have negative impacts. In developing economies, environmental taxes, social globalization, environmental technologies, and climate vulnerability are beneficial, while industrial growth and oil prices have adverse effects. These factors are significant for policy, providing governments and policymakers with valuable information to create specific strategies to meet global sustainability goals. Keywords: renewable energy investments, economic and noneconomic factors, developed and developing economies, panel data estimates JEL codes: C33, F64, Q50, Q42 1. Introduction Recently, the global energy landscape has undergone profound transformations, driven by a combination of factors, such as growing concerns over the negative impacts of climate change, diminishing reserves of fossil fuels, the urgent need to cut down carbon emissions, and the inherent instability in the markets for fossil energy (Kilinc-Ata and Dolmatov 2023, Omri and Jabeur 2024). In this regard, world leaders have made commitments to lessen the use of fossil energy, acknowledging its substantial contribution to global carbon emissions, which, as estimated by the United Nations (2024), account for approximately 90%. Escalating carbon emissions is a significant driver of the current climate crisis, leading to widespread and severe effects on the global ecosystem (Silva 2008). We thus find ourselves in a situation where fossil fuel reserves are continuously depleting, and the markets for fossil energy, particularly oil and gas, have encountered significant turbulence in recent years due to various geopolitical, economic, and financial uncertainties. In light of these challenges, the shift toward sustainable and renewable energy sources has become crucial on the global agenda. This transition is essential to address climate change, environmental degradation, and the pressing issues of energy crises and socioeconomic disparities. Despite substantial global renewable energy (RE) investments, currently around $2 trillion annually, there is a significant gap compared to the $5 trillion yearly investment stipulated by the International Energy Agency (IEA) until 2030, and beyond to 2050, for net-zero carbon emissions (Lenaerts, Tagliapietra, and Wolff 2021). This shortfall persists despite widespread governmental initiatives. Understanding why renewable investments lag behind the necessary levels to meet global sustainability targets is a crucial and not well-explored topic. This stimulates us to investigate the potential determinants affecting RE investments. The factors influencing RE investments are complex, involving a combination of economic and noneconomic elements. Previous studies that highlight aspects such as economic growth, government policies, subsidies, incentives, environmental taxation, and fossil fuel prices have a significant impact on additional RE capacity, as noted in research by Bourcet (2020) and Rajendran, Krishnaswamy, and Subramaniam (2023). Additionally, noneconomic factors such as globalization, environmental technologies, 2 climate vulnerability, and political instability are also found to have an impact on RE deployment (Bourcet 2020; Rajendran, Krishnaswamy, and Subramaniam 2023). Furthermore, recent studies have also indicated that noneconomic factors, although their exact impact is not unanimously agreed upon, could have a greater influence on RE capacity deployment than economic factors, as suggested in the research by Bourcet (2020) and Abban and Hasan (2021). This raises an important question that has yet to be addressed—whether these factors influence RE investments similarly as their impact on RE capacity deployment. The extent to which these factors influence investment decisions and whether they vary across different levels of economic resilience remains unclear. It is a question warranting further investigations. Furthermore, the differences in RE investments across countries are striking (Reboredo 2015, Abban and Hasan 2021). As shown in Figure 1, investments in clean energy per capita are notably higher in developed economies compared to emerging and developing economies (excluding the People’s Republic of China [PRC]). Even within this context, it is important to note that the PRC’s investments alone significantly surpass those of other emerging and developing countries. According to the IEA (2022), the PRC led global clean energy investments in 2021 with $380 billion, followed by the European Union at $260 billion and the United States at $215 billion. Hence, it appears that RE investments are growing but their distribution across countries is uneven. Substantial barriers continue to exist, especially in emerging and developing economies (Azarova and Jun 2021). Understanding these disparities and the factors behind them is essential for crafting effective policies aimed at increasing RE investments and achieving emissions reduction targets. Despite the recognized importance of both economic and noneconomic determinants of RE investments, previous research in this domain has exhibited limitations. Past literature primarily focused on RE deployment, measured by RE consumptions or supply or share in total energy or electricity, to identify influencing factors (Can Şener et al. 2018; Bourcet 2020; Rajendran, Krishnaswamy, and Subramaniam 2023). The majority of earlier studies considered economic, environmental, and energyrelated factors of RE use, with few studies addressing political, regulatory, and demographic factors (Can Şener et al. 2018; Bourcet 2020; Rajendran, Krishnaswamy, 3 and Subramaniam 2023). Moreover, the findings of these studies, particularly on noneconomic factors, failed to provide robust consensus on the determinants of renewable energy deployment (Bourcet 2020, Abban and Hasan 2021). Figure 1: Per-capita Clean Energy Investment in Selected Regions, 2020-2022 PRC = People’s Republic of China. Source: International Energy Agency. 2022. World Energy Investment 2022 . Furthermore, previous studies mainly focused on the national level, with an emphasis on developed or emerging economies like the PRC, Germany, United States, and European nations, while often neglecting developing economies (Bourcet 2020). Only a limited number of studies have focused on RE from an investment perspective, even though RE investments, measured by installed capacity, are deemed a more appropriate measure for RE development (Abban and Hasan 2021). Although informative, these studies lack comprehensiveness, often examining only a limited number of factors and employing conventional methodologies. For example, Abban and Hasan (2021) explored the influence of government systems (presidential or parliamentary) on renewable energy investments (measured by installed capacity) across 60 countries, revealing significant government nature effects. Similarly, using panel data from 34 Organisation for Economic Co-operation and Development (OECD) countries and the 5 BRICS (Brazil, the Russian Federation, India, the PRC, and South Africa) countries, Kilinc-Ata and Dolmatov (2023) employed installed capacity as a measure of RE investments, indicating a favorable 412 491 525 264 269 298 40 42 44 0 100 200 300 400 500 600 2020 2021 2022 $ billion Advanced economies China Emerging market and developing economies PRC 4 impact from economic growth, renewable policy, and research and development expenditures. Given the background outlined here and considering the identified gaps in the existing literature, this study aims to thoroughly investigate both economic and noneconomic factors that may influence RE investments. This study considers a balanced annual panel dataset encompassing 36 countries for 21 years spanning from 2000 to 2020, further divided into two categories: 21 developed economies and 15 emerging and developing economies. This division is used to examine whether the impact of sample factors differs between these two groups. Several econometric tools are employed: Panel Corrected Standard Errors (PCSE), Feasible Generalized Least Squares (FGLS), and Panel Quantile Regression (QR). The PCSE analysis reveals that in developed economies, factors like industrial growth, environmental tax revenue, social globalization, and climate vulnerability positively affect RE investments, while inflation and political instability negatively impact them. Conversely, in developing countries, environmental tax revenue, social globalization, environmental-related technologies, and climate vulnerability positively influence RE investments, but industrial growth and oil prices have adverse effects. These findings suggest RE investments are shaped by a combination of both economic and noneconomic factors, with clear disparities between developed and developing countries. Similar findings are unearthed by FGLS model estimations. Therefore, these findings are robust as further validated by QR estimates but evidence a noticeable variation in results across quantiles. Our study contributes significantly to the extant literature in several key aspects. Firstly, it addresses a critical gap by focusing on the determinants of RE investments, bridging a substantial void left by prior studies that predominantly concentrated on RE deployment. By utilizing installed capacity as a proxy for RE investments, this study aligns with the forward-looking aspects of investment decisions, offering a more accurate measure of RE development. Secondly, unlike earlier research, the study also advances the understanding of RE investments by examining a broader set of noneconomic factors, such as social globalization, environmental technologies, climate vulnerability, and political instability, alongside traditional economic variables. Moreover, it extends beyond the conventional focus on developed and emerging economies by including developing 11 distributions with possible outliers, indicating a deviation from normal distribution. Significant Jarque-Bera statistics further confirm this non-normality. Table 2: Results of Summary Statistics Variable lnREI lnIND lnTR lnIN lnOP lnGLO lnERT InVUL PI Entire s ample Mean 3.957 25.669 9.007 0.803 4.022 4.243 4.654 -1.029 0.147 Maxi. 10.878 29.383 11.951 4.965 4.591 4.520 9.264 -0.608 1.760 Mini. -4.906 20.613 4.209 -5.323 3.228 3.163 -1.609 -1.410 -2.810 S.D. 3.182 1.499 1.592 1.119 0.435 0.261 2.355 0.175 0.886 Skew. -0.151 -0.343 -0.376 -0.890 -0.345 -1.437 -0.143 0.408 -0.806 Kurtosis 2.251 3.894 2.385 5.910 1.955 4.864 2.479 2.683 3.155 JB 20.51*** 40.04*** 29.76*** 366.56*** 49.35*** 369.52*** 11.12*** 24.09*** 82.67*** Developed economies Mean 4.161 26.032 9.725 0.314 4.022 4.409 5.924 -1.143 0.622 Maxi. 9.830 28.941 11.951 4.965 4.591 4.520 9.264 -0.946 1.760 Mini. -4.906 24.068 6.211 -5.323 3.228 4.131 1.970 -1.410 -1.630 S.D. 2.999 1.168 1.188 1.049 0.435 0.073 1.681 0.102 0.601 Skew. -0.348 0.487 -0.481 -1.278 -0.345 -0.969 0.044 -0.058 -1.298 Kurtosis 2.277 2.605 3.346 7.340 1.955 3.644 2.677 3.040 4.879 JB 18.51*** 20.33*** 19.22*** 466.16*** 28.79*** 76.58*** 2.05 0.28 188.76*** Developing economies Mean 3.671 25.160 8.003 1.488 4.022 4.010 2.876 -0.868 -0.518 Maxi. 10.878 29.383 11.689 4.006 4.591 4.425 8.684 -0.608 1.070 Mini. -3.598 20.613 4.209 -1.671 3.228 3.163 -1.609 -1.040 -2.810 S.D. 3.406 1.747 1.544 0.815 0.435 0.251 1.985 0.123 0.787 Skew. 0.094 -0.231 0.245 -0.363 -0.345 -0.912 0.500 0.622 -0.601 Kurtosis 2.257 3.242 2.193 4.488 1.955 3.847 3.413 2.075 2.998 JB 7.70** 3.58 11.68*** 35.95*** 20.56*** 53.09*** 15.36*** 31.56*** 18.94*** Notes: ln is the natural logarithm. REI, IND, TR, IN, OP, GLO, ERT, VUL, and PI represent renewable energy investment, industrial growth, environmental tax revenue, inflation rate, oil price, social globalization, environment-related technology, climate vulnerability, and political instability, respectively. Max, Min, S.D., Skew, and JB indicate maximum, minimum, standard deviation, skewness, and Jarque-Bera ‘***,’ ‘**,’ and ‘*’ indicate the significance levels at 1%, 5%, and 10%, respectively. Source: Authors’ estimates. The correlation matrix, presented in Table 3, shows RE investments positively and significantly correlated with most variables (excluding inflation and climate vulnerability), with strong connections evident. Inflation and climate vulnerability are negatively associated with most variables, while other variable pairs display positive interrelationships. 12 Table 3: Correlation Matrix Variables lnREI lnIND lnTR lnIN lnOP lnGLO lnERT InVUL PI lnREI 1.000 lnIND 0.143*** 1.000 lnTR 0.252*** 0.691*** 1.000 lnIN -0.177*** -0.172*** -0.311*** 1.000 lnOP 0.076** 0.051*** 0.070*** 0.033*** 1.000 lnGLO 0.089** 0.169*** 0.370*** -0.500*** 0.215*** 1.000 lnERT 0.253*** 0.747*** 0.816*** -0.432*** 0.135*** 0.559*** 1.000 InVUL -0.008 -0.249*** -0.465*** 0.444*** -0.032 -0.823*** -0.536*** 1.000 PI 0.020 0.137*** 0.245*** -0.371*** -0.071* 0.632*** 0.398*** -0.575*** 1.000 Notes: ‘***,’ ‘**,’ and ‘*’ indicate the significance levels at 1%, 5%, and 10%, respectively. Source: Authors’ estimates. 4. Results and Analysis 4.1. Cross-sectional Dependence, Slope Homogeneity Test, and Panel Unit Root Analysis Initially, the dataset is evaluated for CSD, a frequent issue in panel data. CSD often arises from common shocks, increasing globalization, economic interconnections among nations at regional and global levels, and other unobserved factors (Abban and Hasan 2021, Naz 2023). Identifying CSD in the sample data is essential before conducting the main analysis, as unaddressed CSD can lead to spurious results (Abban and Hasan 2021). To address this, second-generation methods are employed: the Pesaran (2004) cross-sectional dependence (CD) test and the Breusch and Pagan (1980) Lagrange Multiplier (LM) test. These tests are particularly suitable when the number of time periods (T) is smaller than the number of cross-sectional units (N). In the dataset, T is smaller than N. The results, summarized in Table 4, show that the null hypothesis of no CSD is rejected at the 1% level of significance, hence, evidencing the presence of cross-sectional dependence in all data series across all samples, which implies that a shock in one country can spread to others. 13 Table 4: Cross-sectional Dependence Analysis Test lnREI lnIND lnTR lnIN lnOP lnGLO lnERT InVUL PI Entire sample CD-tests 30.415*** 52.95** 30.29*** 115.02*** 37.70*** 65.91*** 109.48*** 57.58*** 15.15*** p-value 0.000 0.023 0.000 0.000 0.000 0.000 0.000 0.000 0.000 BreuschPagan LM 5498.39*** 7501.34*** 2467.29*** 13230.00*** 6989.12*** 5991.28*** 12009.98*** 7441.69*** 2504.23*** p-value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Developed economies CD-tests 31.728*** 19.95*** 36.70*** 25.76*** 66.40*** 62.84*** 47.25*** 25.82*** 11.25*** p-value 0.000 0.023 0.000 0.000 0.000 0.000 0.000 0.000 0.000 BreuschPagan LM 2006.78*** 2063.38*** 2565.19*** 1117.11*** 4410.00*** 3958.32*** 2432.74*** 2327.05*** 830.14*** p-value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 De veloping economies CD-tests 5.95*** 40.83*** 4.46*** 5.42*** 46.95*** 45.27*** 18.89*** 31.79*** 3.63*** p-value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 BreuschPagan LM 816.79*** 1688.98*** 1079.21*** 312.14*** 2205.00*** 2051.29*** 754.40*** 1349.67*** 426.43*** p-value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Note: ‘***,’ and ‘**,’ indicate the significance levels at 1% and 5%, respectively. Source: Authors’ estimates. 14 Table 5 presents the slope homogeneity test results. The outcomes reveal that slope homogeneity tests are highly statistically significant at a 1% level for each of the three models. Therefore, the null hypothesis of slope homogeneity will be turned down and it is concluded that the slope coefficients are heterogenous. Table 5: The Estimates of Slope Homogeneity Test Test statistics Entire s ample Developed e conomies Developing e conomies Delta 14.106*** (0.000) 11.461*** (0.000) 2.782*** (0.005) Adjusted Delta 19.490*** (0.000) 15.836*** (0.000) 3.844*** (0.000) Note: ‘***,’ ‘**,’ and ‘*’ indicate the significance levels at 1%, 5%, and 10%, respectively. Source: Authors’ estimates. Given the CSD issue in the panel data, two second-generation unit root tests are employed: The cross-sectional augmented Im, Pesaran, and Shin (CIPS) test and the cross-sectional augmented Dickey-Fuller (CADF) test, both developed by Pesaran (2007). These tests are selected for the robustness and consistency they offer, considering the specific characteristics of the dataset. Notably, in cases of CSD, the conventional Augmented Dickey-Fuller (ADF) test can produce unreliable results (Pesaran 2007, Naz 2023). The outcomes of both tests, as presented in Table 6, indicate that most of the data series exhibit non-stationarity at their levels for the entire and segregated samples. However, all series become stationary at the first difference at the 1% level of significance, i.e., I(1). 15 Table 6: Results of Unit Root Tests Variables CIPS CADF Level First Difference Level First Difference Entire s ample lnREI -1.608 -3.785*** -1.638 -2.907*** lnIND -1.592 -2.916*** -1.353 -1.946* lnTR -2.675*** -4.866*** -2.143*** -2.621*** lnIN -4.160 ** -4.160*** - 1.945 -2.708*** lnOP -1.580 -3.252*** -1.580 -3.252*** lnGLO -2.972*** -5.056*** -2.409*** -2.424*** lnERT -2.856*** -4.038*** -1.432 -2.027** InVUL -1.314 -4.355*** -2.308 -3.145*** PI -1.357 -3.114*** -1.724 -2.910*** Developed economies lnREI -1.482 -3.666*** -1.848 -2.447*** lnIND -1.391 -2.804*** -1.931 -2.609*** lnTR -1.587** -3.181*** -2.070* -2.416*** lnIN -2.217*** -4.925*** -2.126** -3.646 lnOP -1.580 -3.252*** -1.580 -3.252*** lnGLO -2.208*** -4.847*** -2.186** -3.476*** lnERT -1.362 -3.406*** -1.848 -2.447*** InVUL -1.136 -4.561*** -2.131** -3.154*** PI -2.058*** -4.680*** -1.838 -3.271*** Developing economies lnREI -1.152 -3.408*** -0.760 -2.839*** lnIND -1.424 -3.032*** -1.057 -3.095*** lnTR -0.951 -2.521*** -2.323*** -2.553*** lnIN -2.349*** -4.907*** -2.385*** -3.408*** lnOP -1.580 -3.252*** -1.580 -3.252*** lnGLO -1.472 -4.819*** -2.569*** -3.649*** lnERT -2.442*** -5.030*** -2.468*** -3.998*** InVUL -1.570 -4.177*** -2.085 -3.063*** PI -1.674** -4.084*** -1.957 -2.990*** CADF = cross-sectional augmented Dickey-Fuller; CIPS = cross-sectional augmented Im, Pesaran, and Shin. Note: ‘***,’ ‘**,’ and ‘*’ indicate the significance levels at 1%, 5%, and 10%, respectively. Source: Authors’ estimates. 16 4.2. Results of Cointegration Test Considering the presence of CSD and stationarity issues in the panel data, a cointegration test by Kao (1999) is employed. Although academic literature often recommends the Westerlund (2007) cointegration technique in the presence of CSD, its application is not feasible in this study due to the inclusion of more than six explanatory variables. Therefore, Kao’s (1999) test, widely acknowledged in the research community (Gengenbach, Palm, and Urbain 2005), is chosen. This test presents several advantages over traditional cointegration tests. Kao’s (1999) test is notably capable of handling both I(0) and I(1) processes and is robust even with limited sample sizes. Additionally, it effectively addresses the CSD in data (Kao 1999). The results, presented in Table 7, unanimously reject the null hypothesis of no cointegration, indicating a cointegrating relationship among the variables under investigation. Table 7: The Estimates of Panel Cointegration Test by Kao (1999) Test statistics Entire sample Developed e conomies Developing e conomies Augmented Dickey -Fuller -2.242** (0.013) -3.220*** (0.001) -1.442* (0.074) Note: ‘***,’ ‘**,’ and ‘*’ indicate the significance levels at 1%, 5%, and 10%, respectively. Source: Authors’ estimates. 4.3. Results of PCSE Estimates This study employs the PCSE model to address the inherent issues associated with panel data. This model, as recognized in the literature (e.g., Reed and Webb 2010, Millo 2017, Adeleye et al. 2023), is particularly robust in situations with limited sample size, particularly where T is less than N. The PCSE analysis results in Table 8 show a significant negative coefficient for industrial growth across the entire sample and more markedly in developing countries, suggesting that industrial growth adversely affects RE investments, especially in developing economies. This may be due to rapid industrialization and economic growth in these economies, largely driven by extensive primary energy utilization (Khan and Majeed 2023). The substantial energy demands resulting from industrial growth are not immediately met by RE sources (Cadoret and Padovano 2016), as RE projects entail substantial upfront investment costs and extended 17 implementation periods, dissuading their adoption in developing countries (World Bank 2024). Moreover, developing economies often prioritize rapid economic development, driven by industrialization, to tackle socioeconomic challenges such as poverty, unemployment, and infrastructure needs (Caglar and Askin 2023). This emphasis on immediate economic goals may result in reduced commitment to RE investments. The findings partially align with Cadoret and Padovano (2016), who observed the negative impacts of manufacturing on RE deployment, and with Chen, Pinar, and Stengos (2021), who noted economic growth’s adverse effect on RE consumption in less democratic countries. Conversely, in developed economies, the results differ: industrial growth positively correlates with RE investments. Developed economies, having achieved their economic targets, now focus more on environmental sustainability and climate change mitigation, reducing fossil fuel dependence in line with global commitments like the Paris Agreement. Moreover, these countries have the necessary technology and infrastructure for effective RE integration. Factors such as strong environmental regulations, sustainability objectives, accessible capital, advanced technologies, and increased environmental consciousness collectively push industries toward cleaner energy sources. This makes RE investments more viable and efficient. The findings concur with studies by Yang et al. (2019); Chen, Pinar, and Stengos (2021); and Kilinc-Ata and Dolmatov (2023), which all report a positive link between economic growth and RE consumption. Turning to the impact of environmental tax revenue, the study finds a significant and positive effect on RE investments at the 5% significance level across all samples, with a stronger coefficient observed in the sub-sample containing developed economies. This result is consistent with expectations, as environmental taxes internalize the external costs of environmental pollution and provide economic incentives for individuals and businesses to embrace cleaner technologies and reduce their environmental footprint (Sarpong et al. 2023). By taxing activities such as carbon emissions, pollutants, and nonrenewable resource use, environmental taxes encourage a shift toward more sustainable and environmentally friendly behavior, while generating revenue that can be reinvested in environmental conservation and sustainable initiatives (Abban and Hasan 2021, Doğan et al. 2022, Sarpong et al., 2023). The findings are also in line with Fan, Li, and Yin (2019), 18 who underscore the favorable role of environmental taxes in promoting green development. Nevertheless, the results differ from certain previous studies, such as Abban and Hasan (2021), who reported an insignificant relationship between environmental tax and RE investments, albeit with a positive direction. Furthermore, the findings challenge the results of Bashir et al. (2021), who observed a negative linkage between environmental tax and RE utilization. Conversely, inflation shows a negative impact on RE investments for both the entire sample and developed economies. Inflation’s detrimental influence operates primarily through two mechanisms. Rising inflation leads to increased interest rates, thus escalating the cost of capital (Calthrop 2022, Akan 2023). Additionally, inflationary conditions contribute to widespread increases in prices, including project costs, raising the total costs of RE projects. Secondly, inflation brings uncertainty to the business environment, deterring investment and innovation in the RE sector (Akan 2023). This reluctance may be related to the risks associated with the considerable upfront costs of RE investments. However, in developing countries, the results show no significant link between inflation and RE investments. Regarding oil prices, a significant negative link with RE investments is found exclusively in samples from developing economies. This suggests that the typical substitution effect, where rising oil prices lead to reduced oil utilization and increased RE use, does not apply in these developing countries (Salim and Rafiq 2012, Abban and Hasan 2021, Mukhtarov et al. 2022). In developing countries, rising oil prices can strain budgets, leaving fewer funds for RE projects. The high initial costs of RE infrastructure are often seen as prohibitive, especially compared to the immediate affordability of fossil fuels. Additionally, an increase in the price of one fossil fuel may result in the higher utilization of alternative fuels. For example, the rise in gas prices in 2021 led to a partial shift back to coal for electricity generation in some countries (Jaller-Makarewicz 2021, Gilly and Jørgensen 2022). These factors could account for the negative relationship between oil prices and RE investments in developing countries, aligning with findings by Abban and Hasan (2021) and Mukhtarov et al. (2022). Interestingly, the oil price coefficient lacks statistical significance for the entire and developed economies samples, suggesting that RE investments in these nations are becoming less influenced by oil 19 prices. Instead, in developed economies, RE investments are increasingly motivated by international pressure to prioritize environmental sustainability and climate change mitigation, as highlighted by Salim and Rafiq (2012) and Abban and Hasan (2021). For noneconomic determinants, social globalization significantly and positively impacts RE investments in both developed and developing countries. Social globalization acts as a catalyst for global environmental awareness and climate change recognition, encouraging individuals, businesses, and governments to prefer cleaner energy sources to reduce carbon emissions (Sinha et al. 2020, Zafar et al. 2020, Urom et al. 2022). Additionally, the interconnectedness and information exchange promoted by social globalization enhance international collaboration in RE technology and policy (Urom et al. 2022), aiding the adoption and investment in these technologies. These results partly align with Nan, Huo, and Lee (2023), who emphasized globalization’s role in advancing RE adoption. Similarly, a positive effect of climate vulnerability on RE investments is observed across all samples, highlighting the increased awareness in both developed and developing countries of their susceptibility to climate-related challenges and risks. This awareness motivates these countries to increase RE investments as a strategic response to mitigate carbon emissions and proactively address climate-related threats. These findings align with Wen et al. (2023), who also noted a positive correlation between physical vulnerability and green investments. Accordingly, environmental-related technology also demonstrates a positive association with RE investments for the overall sample and developing countries. This suggests that advancements in environmental technology are associated with increased RE investments. The underlying rationale is likely the instrumental role these technologies have in improving the viability and efficiency of RE solutions (Sinha et al. 2020, Qin et al. 2021), leading to greater energy efficiency, lower costs, and enhanced reliability. These advancements make RE projects more economically viable and sustainable (Sinha et al. 2020). These observations are somewhat in line with Nosheen, Iqbal, and Abbasi (2021), who highlighted the impact of climate change technology on green growth. In contrast, developed economies show a statistically insignificant negative relationship between environmental-related technology and RE investments. This may 20 suggest a different dynamic in these economies, perhaps due to already substantial investments in RE infrastructure and technology, indicating sectoral maturity. The focus in these economies might be on optimizing and maintaining existing RE systems, not on continued expansion, hence the lack of a statistically significant positive correlation between ongoing technological advancements and further RE investments. Lastly, it is noted that political instability significantly hampers RE investments in developed economies due to the risks and uncertainties of unstable political climates. Deviations from presumed political stability in these nations can deter investors and stakeholders, as political conflicts or policy changes add unpredictability to the business environment (Zhang et al. 2022, Wang et al. 2022). This uncertainty makes investors wary of committing to long-term, capital-intensive RE projects with extended payback periods. Wang et al. (2022) partially confirm this with their findings of a negative link between political risk and RE utilization. Conversely, in developing countries, political instability has an insignificant, yet positive, impact on RE investments. This indicates that in less stable political environments, such instability might actually encourage RE investments. This counterintuitive effect could be due to efforts in these countries to diversify energy sources and reduce fossil fuel reliance for energy security and environmental sustainability. Additionally, international aid and cooperation promoting RE in these regions may alleviate the negative impact of political instability on investments. In summary, the study’s analysis shows that both economic and noneconomic factors significantly affect RE investments, with the impact’s direction and significance varying depending on whether countries are developed or developing. For developed economies, factors like industrial growth, environmental tax revenue, social globalization, and climate vulnerability positively influence RE investments, while inflation and political instability act as deterrents. In contrast, in developing countries, positive drivers of RE investments include environmental tax revenue, social globalization, environmentalrelated technologies, and climate vulnerability, while industrial growth and oil prices negatively affect these investments. These results highlight the complex interplay of various factors on RE investments across different national contexts, enhancing understanding of the diverse dynamics in the RE sector. 27 REFERENCES Abban, Abdul Rashid, and Mohammad Zahid Hasan. 2021. “Revisiting the Determinants of Renewable Energy Investment-New Evidence from Political and Government Ideology.” Energy Policy, 151: 112184. Adeleye, Bosede Ngozi, Darlington Akam, Nasiru Inuwa, Henry Tumba James, and Denis Basila. 2023. “Does Globalization and Energy Usage Influence Carbon Emissions in South Asia? An Empirical Revisit of the Debate.” Environmental Science and Pollution Research, 30 (13): 36190–36207. Akan, Taner. 2023. “Can Renewable Energy Mitigate the Impacts of Inflation and Policy Interest on Climate Change?” Renewable Energy, 214: 255–289. Akram, Rabia, Fuzhong Chen, Fahad Khalid, Zhiwei Ye, and Muhammad Tariq Majeed. 2020. “Heterogeneous Effects of Energy Efficiency and Renewable Energy on Carbon Emissions: Evidence from Developing Countries.” Journal of Cleaner Production, 247: 119122. Alam, Md. Samsul, Sudharshan Reddy Paramati, Muhammad Shahbaz, and Mita Bhattacharya. 2015. “Dynamics of Natural Gas Consumption, Output and Trade: Empirical Evidence from the Emerging Economies.” Discussion Paper Series 21/15. Monash University, Department of Economics. Azarova, Ekaterina, and Hannah Jun. 2021. “Investigating Determinants of International Clean Energy Investments in Emerging Markets.” Sustainability, 13 (21): 11843. Bhattacharya, Mita, Sudharshan Reddy Paramati, Ilhan Ozturk, and Sankar Bhattacharya. 2016. “The Effect of Renewable Energy Consumption on Economic Growth: Evidence from Top 38 Countries.” Applied Energy, 162: 733–741. Bourcet, Clémence. 2020. “Empirical Determinants of Renewable Energy Deployment: A Systematic Literature Review.” Energy Economics, 85: 104563. Buckley, Tim, and Saurabh Trivedi. 2021. “Global Investors Are Moving Away from the Massive Climate-Related Risks Associated with Fossil Fuels.” Institute for Energy Economics & Financial Analysis. 19 July. https://ieefa.org/articles/global-investorsare-moving-away-massive-climate-related-risks-associated-fossil-fuels. Cadoret, Isabelle, and Fabio Padovano. 2016. “The Political Drivers of Renewable Energy Policies.” Energy Economics, 56: 261–269. Caglar, Abdullah E., and Bekir Emre Askin. 2023. “A Path Towards Green Revolution: How Do Competitive Industrial Performance and Renewable Energy Consumption Influence Environmental Quality Indicators?” Renewable Energy, 205: 273–280. 28 Calthrop, Edward. 2022. “Energy Crisis Makes Public Banks Even More Important.” European Investment Bank. 11 November. https://www.eib.org/en/stories/energycrisis-net-zero-transition. Camarero, Mariam, and Cecilio Tamarit. 2002. “A Panel Cointegration Approach to the Estimation of the Peseta Real Exchange Rate.” Journal of Macroeconomics, 24 (3): 371–393. Can Şener, Şerife Elif, Julia L. Sharp, and Annick Anctil. 2018. “Factors Impacting Diverging Paths of Renewable Energy: A Review.” Renewable and Sustainable Energy Reviews, 81: 2335–2342. Chen, Chaoyi, Mehmet Pinar, and Thanasis Stengos. 2021. “Determinants of Renewable Energy Consumption: Importance of Democratic Institutions.” Renewable Energy, 179: 75–83. Doğan, Buhari, Lan Khanh Chu, Sudeshna Ghosh, Huong Hoang Diep Truong, and Daniel Balsalobre-Lorente. 2022. “How Environmental Taxes and Carbon Emissions Are Related in the G7 Economies?” Renewable Energy, 187: 645–656. Fan, Xinghua, Xuxia Li, and Jiuli Yin. 2019. “Impact of Environmental Tax on Green Development: A Nonlinear Dynamical System Analysis.” PLOS ONE, 14 (9): e0221264. Galvao, Antonio F. 2011. “Quantile Regression for Dynamic Panel Data with Fixed Effects.” Journal of Econometrics, 164 (1): 142–157. Gengenbach, Christian, Franz C. Palm, and Jean-Pierre Urbain. 2005. “Panel Cointegration Testing in the Presence of Common Factors.” Department of Quantitative Economics, Universiteit Maastricht. Gilly, Patrick, and Per Jørgensen. 2022. “What Do Rising Energy Prices Mean for the Green Energy Transition?” Ramboll. 27 April. https://www.ramboll.com/insights/decarbonise-for-net-zero/what-do-risingenergy-prices-mean-for-the-green-energy-transition. Gutierrez, Luciano. 2003. “On the Power of Panel Cointegration Tests: A Monte Carlo Comparison.” Economics Letters, 80 (1): 105–111. Gygli, Savina, Florian Haelg, Niklas Potrafke, and Jan-Egbert Sturm. 2018 The KOF Globalisation Index—Revisited. CESifo Working Paper No. 7430. Center of Economic Studies. Hasan, Md. Bokhtiar, Md. Sumon Ali, Gazi Salah Uddin, Masnun Al Mahi, Yang Liu, and Donghyun Park. 2022. “Is Bangladesh on the Right Path toward Sustainable 29 Development? An Empirical Exploration of Energy Sources, Economic Growth, and CO2 Discharges Nexus.” Resources Policy, 79: 103125. International Energy Agency (IEA). 2022. World Energy Investment 2022. https://iea.blob.core.windows.net/assets/b0beda65-8a1d-46ae-87a2f95947ec2714/WorldEnergyInvestment2022.pdf. Jaller-Makarewicz, Ana Maria. 2021. “As Fossil Fuel Prices Skyrocket Globally, Renewables Grow Steadily Cheaper.” Institute for Energy Economics and Financial Analysis. 27 September. https://ieefa.org/resources/fossil-fuel-pricesskyrocket-globally-renewables-grow-steadily-cheaper. Kao, Chihwa. 1999. “Spurious Regression and Residual-Based Tests for Cointegration in Panel Data.” Journal of Econometrics, 90 (1): 1–44. Khan, Sher, and Muhammad Tariq Majeed. 2023. “Toward Economic Growth without Emissions Growth: The Role of Urbanization and Industrialization in Pakistan.” Journal of Environmental Studies and Sciences, 13 (1): 43–58. Kilinc-Ata, Nurcan, and Ilya A. Dolmatov. 2023. “Which Factors Influence the Decisions of Renewable Energy Investors? Empirical Evidence from OECD and BRICS Countries.” Environmental Science and Pollution Research, 30 (1): 1720–1736. Kim, Jeayoon, and Kwangwoo Park. 2016. “Financial Development and Deployment of Renewable Energy Technologies.” Energy Economics, 59: 238–250. Koenker, Roger. 2004. “Quantile Regression for Longitudinal Data.” Journal of Multivariate Analysis, 91 (1): 74–89. Le, Thai-Ha, and Canh Phuc Nguyen. 2019. “Is Energy Security a Driver for Economic Growth? Evidence from a Global Sample.” Energy Policy, 129: 436–451. Lenaerts, Klaas, Simone Tagliapietra, and Guntram B. Wolff. 2021. “How Much Investment Do We Need to Reach Net Zero?” Bruegel-Blogs. 25 August. https://www.bruegel.org/blog-post/how-much-investment-do-we-need-reach-netzero. Masini, Andrea, and Emanuela Menichetti. 2013. “Investment Decisions in the Renewable Energy Sector: An Analysis of Non-financial Drivers.” Technological Forecasting and Social Change, 80 (3): 510–524. Millo, Giovanni. 2017. “Robust Standard Error Estimators for Panel Models: A Unifying Approach.” Journal of Statistical Software, 82: 1–27. 30 Mukhtarov, Shahriyar, Jeyhun I. Mikayilov, Shahin Maharramov, Javid Aliyev, and Elchin Suleymanov. 2022. “Higher Oil Prices, Are They Good or Bad for Renewable Energy Consumption: The Case of Iran?” Renewable Energy, 186: 411–419. Nan, Shijing, Yuchen Huo, and Chien-Chiang Lee. 2023. “Assessing the Role of Globalization on Renewable Energy Consumption: New Evidence from a Spatial Econometric Analysis.” Renewable Energy, 215: 118974. Naz, Ayesha. 2023. “Linkages between Different Types of Globalization and SocioEconomic Variables: Panel Data Analysis for 129 Countries.” Journal of Economic Structures, 12 (1): 1–23. Nosheen, Misbah, Javed Iqbal, and Muhammad Ali Abbasi. 2021. “Do Technological Innovations Promote Green Growth in the European Union?” Environmental Science and Pollution Research, 28: 21717–21729. Omri, Anis, and Sami Ben Jabeur. 2024. “Climate Policies and Legislation for Renewable Energy Transition: The Roles of Financial Sector and Political Institutions.” Technological Forecasting and Social Change, 203: 123347. Qin, Lingui, Dervis Kirikkaleli, Yao Hou, Xu Miao, and Muhammad Tufail. 2021. “Carbon Neutrality Target for G7 Economies: Examining the Role of Environmental Policy, Green Innovation and Composite Risk Index.” Journal of Environmental Management, 295: 113119. Pesaran, M. Hashem. 2004. General Diagnostic Tests for Cross Section Dependence in Panels. Cambridge Working Paper in Economics 0435. University of Cambridge. Pesaran, M. Hashem. 2007. “A Simple Panel Unit Root Test in the Presence of CrossSection Dependence.” Journal of Applied Econometrics, 22 (2): 265–312. Rajendran, Rajitha, Jayaraman Krishnaswamy, and Nava Subramaniam. 2023. “Dynamics of Macro-Economic Factors for Energy Transition and Its Reviews—A Conceptual Framework for G7 Countries.” Renewable and Sustainable Energy Reviews, 187: 113692. Reboredo, Juan C. 2015. “Renewable Energy Contribution to the Energy Supply: Is There Convergence across Countries?” Renewable and Sustainable Energy Reviews, 45: 290–295. Reed, William Robert, and Rachel Webb. 2010. “The PCSE Estimator Is Good—Just Not as Good as You Think.” Journal of Time Series Econometrics, 2 (1). Salim, Ruhul A., and Shuddhasattwa Rafiq. 2012. “Why Do Some Emerging Economies Proactively Accelerate the Adoption of Renewable Energy?” Energy Economics, 34 (4): 1051–1057. 31 Salman, Verda, Imtiaz Ahmad, and Shahzad Alvi. 2023. “Is Globalization Driving the Use of Renewable Energy? A Global Macro Perspective.” Problemy Ekorozwoju Problems of Sustainable Development, 18 (1): 68–80. Sarpong, Kwabena Agyarko, Wanzhen Xu, Bright Akwasi Gyamfi, and Elvis Kwame Ofori. 2023. ”Can Environmental Taxes and Green-Energy Offer Carbon-Free E7 Economies? An Empirical Analysis in the Framework of COP-26.” Environmental Science and Pollution Research, 30 (18): 51726–51739. Shahbaz, Muhammad, Chinazaekpere Nwani, Festus Victor Bekun, Bright Akwasi Gyamfi, and Divine Q. Agozie. 2022. “Discerning the Role of Renewable Energy and Energy Efficiency in Finding the Path to Cleaner Consumption and Production Patterns: New Insights from Developing Economies.” Energy, 260: 124951. Silva, Carlos Eduardo Torres. 2008. “Factors Influencing the Development of Local Renewable Energy Strategies: The Cases of Lolland and Samsø Islands in Denmark.” Lund University Centre for Sustainability Studies. Sinha, Avik, Tuhin Sengupta, and Rafael Alvarado. 2020. “Interplay between Technological Innovation and Environmental Quality: Formulating the SDG Policies for Next 11 Economies.” Journal of Cleaner Production, 242: 118549. United Nations. 2024. Causes and Effects of Climate Change. https://www.un.org/en/climatechange/science/causes-effects-climate-change. Urom, Christian, Ilyes Abid, Khaled Guesmi, and Gideon Ndubuisi. 2022. “Renewable Energy Consumption, Globalization, and Economic Growth Shocks: Evidence from G7 Countries.” The Journal of International Trade & Economic Development, 31 (2): 204–232. Van Song, Nguyen, Nguyen Dang Que, Nguyen Cong Tiep, Dinh van Tien, Thai Van Ha, Pham Thi Lin Phuong, Tran Ba Uan, and Thai Thi Kim Oanh. 2023. “The Influence of Economic and Non-economic Determinants on the Sustainable Energy Consumption: Evidence from Vietnam Economy.” Environmental Science and Pollution Research, 30 (14): 42282–42295. Wang, Erhong, Giray Gozgor, Mantu Kumar Mahalik, Gupteswar Patel, and Guoheng Hu. 2022. “Effects of Institutional Quality and Political Risk on the Renewable Energy Consumption in the OECD Countries.” Resources Policy, 79: 103041. Wen, Jun, Sen Zhang, Chun-Ping Chang, Donni Fajar Anugrah, and Yoga Affandi. 2023. “Does Climate Vulnerability Promote Green Investment under Energy Supply Restriction?” Energy Economics, 124: 106790. Westerlund, Joakim. 2007. “Testing for Error Correction in Panel Data.” Oxford Bulletin of Economics and Statistics, 69 (6): 709–748. 32 World Bank. 2024. Energy. https://www.worldbank.org/en/topic/energy/overview. Yang, Xiaolei, Lingyun He, Yufei Xia, and Yufeng Chen. 2019. “Effect of Government Subsidies on Renewable Energy Investments: The Threshold Effect.” Energy Policy, 132: 156–166. Zafar, Muhammad Wasif, Muhammad Shahbaz, Avik Sinha, Tuhin Sengupta, and Quande Qin. 2020. “How Renewable Energy Consumption Contribute to Environmental Quality? The Role of Education in OECD Countries.” Journal of Cleaner Production, 268: 122149. Zhang, Wenwen, Yi-Bin Chiu, and Cody Yu-Ling Hsiao. 2022. “Effects of Country Risks and Government Subsidies on Renewable Energy Firms’ Performance: Evidence from China.” Renewable and Sustainable Energy Reviews, 158: 112164. ASIAN DEVELOPMENT BANK ASIAN DEVELOPMENT BANK 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org Exploring the Economic and Noneconomic Determinants of Investments in Renewable Energy This paper explores the determinants of renewable energy investments along with other economic and noneconomic variables in both developed and developing economies. The findings of this paper indicate that industrial growth, environmental taxes, social globalization, and climate vulnerability positively influence renewable energy investments in developed economies, while inflation and political instability have negative impacts. In developing economies, environmental taxes, social globalization, environmental technologies, and climate vulnerability are beneficial, while industrial growth and oil prices have adverse effects. These factors are valuable information for policymakers to create specific strategies to meet global sustainability goals. About the Asian Development Bank ADB is committed to achieving a prosperous, inclusive, resilient, and sustainable Asia and the Pacific, while sustaining its efforts to eradicate extreme poverty. Established in 1966, it is owned by 68 members —49 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. EXPLORING THE ECONOMIC AND NONECONOMIC DETERMINANTS OF INVESTMENTS IN RENEWABLE ENERGY Gazi Salah Uddin, Md. Bokhtiar Hasan, Donghyun Park, Md. Sumon Ali, and Christoffer Wadström ADB ECONOMICS WORKING PAPER SERIES NO. 740 August 2024