This paper therefore aims to address the question of whether and how green bonds promote energy efficiency quantified as energy intensity using panel data from 22 countries which are pio
INTRODUCTION
Overview
Over the past two decades, the global environmental landscape has become a pressing issue, drawing the attention of policymakers, activists, and multinational corporations Climate change and global warming, primarily caused by the heavy reliance on polluting energy sources, pose serious threats to the well-being of all living beings The rise in global temperatures and its adverse effects on climate patterns led to the formation of key international agreements, including the United Nations Framework Convention on Climate Change (UNFCCC) in 1992, the Kyoto Protocol in 1997, and the Paris Agreement in 2015 Despite these commitments, progress in reducing dependence on nonrenewable energy and advancing renewable energy initiatives remains slow.
Energy efficiency is a critical element in reducing pollution and combating global warming, alongside fossil fuel consumption Research by prominent scientists, including Vieira et al (2018) in Brazil, Adua et al (2021) in the USA, and Peng et al (2015) in China, highlights its importance Improving energy efficiency can lead to lower energy consumption and reduced CO2 emissions, thereby enhancing environmental protection and energy security—essential for achieving the sustainable development goals established by the United Nations in 2015 Additionally, energy efficiency contributes significantly to sustainability development across nations through various mechanisms (Ayres et al 2007; Zakari et al 2022).
Energy efficiency projects often face significant challenges, especially in countries with limited capital for optimal financing (Irfan et al., 2022) To overcome this capital shortage, it is essential to develop innovative financing mechanisms that can enhance these projects One effective approach is the adoption of green finance, which Klein et al (2019) define as "financing of investments that provide environmental benefits." A notable instrument in this domain is green bonds, introduced by the International Capital Markets Association (ICMA), which are designed to fund or refinance eligible green projects, ensuring that the proceeds are allocated exclusively for environmental initiatives.
Green bonds play a crucial role in directing investments towards clean energy and sustainable projects, helping to lower CO2 emissions and achieve growth targets However, capital providers often lack confidence in how the proceeds from these bonds are used To address these concerns, Blockchain technology can effectively monitor and verify the allocation of funds raised, providing assurance that capital is used as intended and protecting against greenwashing practices.
Research Rationale
The growing importance of green bonds necessitates a thorough investigation into their impact and implications Research findings on green bonds are diverse and stem from multiple global contexts While current reviews of green finance tend to offer limited perspectives on green bonds (Jones et al., 2020; Schiederig et al., 2012) or simply compile relevant literature (Zhang et al.), a deeper analysis is essential for understanding their significance in the financial landscape.
This paper addresses the gap in existing reviews by exploring the impact of green bond issuance on energy efficiency projects across 22 nations, highlighting areas that require further research.
Research Objectives
This study aims to address the limitations found in previous research on energy efficiency by introducing and assessing an integration model for green bonds By doing so, it seeks to demonstrate the positive impact of green bonds on energy efficiency, ultimately fostering greater confidence among capital providers in their investments in this sustainable financial instrument.
Research Subjects
This research examines 22 countries that have consistently issued a significant volume of green bonds from 2016 to 2022, analyzing their impact on national energy efficiency.
Research Methods
This study employed a quantitative approach to explore the relationship between energy efficiency and green bonds The research team organized the collected data into tables for analysis, utilizing STATA 16.0 software to compute descriptive statistics and gain insights into the study sample Various quantitative methods, including Pooled Ordinary Least Squares (POLS), Fixed-Effect Model (FEM), and Random-Effect Model (REM), were applied, along with diagnostic tests to identify the most appropriate model Following the assessment of potential diagnostic issues, the research team implemented Feasible Generalized Least Squares (FGLS) and Two-Stage System Generalized Method of Moments (GMM) for further analysis.
Research Scope
Spatial range: the 22 nations that issue green bonds.
To ensure the sufficiency of the data, the research does not take account of countries that issue green bonds in a low volume or inconsistently.
Time range: the duration from the year 2016 to 2022.
LITERATURE REVIEW AND THEORETICAL FRAMEWORK
Literature review
Green bonds represent a unique asset class, similar to traditional corporate and government bonds in terms of financial structure, pricing mechanisms, and ratings However, their key distinction lies in their purpose: the funds raised from green bonds are specifically allocated by issuers for environmentally beneficial projects, as noted by Reboredo in 2018 These bonds come in various forms, such as use-of-proceeds bonds, project bonds, and securitized bonds (ABS), each influencing the legal recourse available in the event of issuer default The introduction of the Green Bond Principles has further shaped the landscape of green financing.
2014, this first internationally recognized standard served as a crucial catalyst for the subsequent growth of the market and laid the foundation for numerous existing green labels (Ehlers and Packer 2017).
Energy efficiency plays a crucial role in combating climate change while offering benefits such as improved comfort, increased productivity, resource conservation, and decreased reliance on foreign energy It is part of the broader concept of energy services, which encompass functions performed using energy to achieve desired outcomes These energy services, enabled by general-purpose technologies, are essential to nearly all economic activities.
Energy efficiency refers to the ability of a technology to reduce the energy input, such as kilowatt-hours (kWh) or megajoules (MJ), needed to deliver a particular level of energy service, which can include outputs like lumens, temperature, or passenger kilometers.
2.1.3 Green bonds and energy efficiency:
Green bonds are essential for financing green projects and have attracted significant scholarly attention due to the challenges of insufficient funding for such initiatives, which hinder countries' efforts to reduce greenhouse gas emissions Increased private sector involvement in financing sustainable projects has been advocated, yet the lower returns on these investments compared to nonrenewable energy create financial disparities Consequently, financial challenges hinder global progress toward a sustainable economy However, green bonds are crucial for promoting sustainable project growth, facilitating government efforts to engage the private sector in environmentally friendly developments, and reducing CO2 emissions Research indicates that issuing green bonds positively impacts investments in sustainable projects, helping to mitigate pollution from greenhouse emissions This relationship underscores the importance of a sound financial sector in addressing global warming, as green bonds support sustainable initiatives and decrease pollution levels Additionally, green bonds advance low-carbon transitions by improving energy efficiency and encouraging the use of green energy resources across various sectors of national energy consumption.
Recent urbanization trends have raised concerns about environmental degradation, yet few studies examine its effects on energy efficiency Research spanning 38 OECD countries from 1990 to 2015 found that urbanization increases energy intensity (Zhu, J et al, 2021) In China, a study from 1995 to 2022 revealed that while urbanization directly raises energy intensity, it also has an indirect effect that reduces it (Elliott, R J et al, 2017) Additionally, an analysis of 10 Asian countries between 1990 and 2014 showed a positive impact of urbanization on energy intensity in both the short and long term (Bilgili, F et al, 2017) Furthermore, research from 1980 to 2010 confirmed a positive correlation between urbanization and energy intensity using various methodologies (Rafiq, S et al, 2016).
In a separate study, the research team employed the System-GMM model to explore the relationship between urbanization and energy intensity across African nations from
Research conducted from 1980 to 2015 revealed a negative correlation between urbanization and energy intensity in various nations (Aboagye et al., 2016) Furthermore, a study examining 99 countries from 1975 to 2005, using the STIRPAT model, found that urbanization decreased energy intensity in low-income countries while increasing it in middle-income countries (Poumanyvong et al., 2010).
2.1.5 Carbon emissions and energy efficiency
CO2 emissions play a crucial role in environmental efficiency analyses, prompting an exploration of their relationship with energy efficiency While many studies highlight that improving energy efficiency enhances carbon productivity and contributes to carbon emissions reduction (Cai et al., 2019; Faria and Blok, 2000; Ma et al., 2020; Zhao et al., 2021), some researchers argue that increased energy efficiency could lead to higher overall energy consumption, potentially raising carbon emissions (Court and Sorrell, 2020; Kunkel and Tyfield, 2021) This rebound effect indicates that the relationship between energy efficiency and carbon emissions is complex, suggesting that reducing carbon emissions may also influence energy efficiency in dual ways.
2.1.6 Trade openness and energy efficiency
Numerous studies have explored the relationship between energy intensity and trade openness, yielding consistent findings (Benli, M 2019; Wang, R et al, 2023) One notable investigation focused on China from 1986 to 2020, demonstrating a negative correlation between trade openness and energy intensity levels (Wang, R et al, 2023) Additionally, research examining BRICS economies from 1990 to 2019 utilized various econometric methods, including FMOLS, GMM, and dynamic OLS (DOLS), and similarly found a negative association between energy intensity and trade openness (Liu, F et al, 2022).
A study examining the relationship between trade openness and energy intensity in the economies of OPEC from 1990 to 2016, utilizing panel Autoregressive Distributed Lag (ARDL) and Cross-Sectional ARDL methods, revealed that trade openness negatively impacts energy intensity (Samargandi, N 2019) In contrast, an analysis of 59 countries involved in the Belt and Road Initiative (BRI) from 1996 to 2015, employing panel smooth transition regression, identified a positive correlation between trade openness and energy intensity (Qi, S Z et al, 2019).
A study examining trade openness and its impact on energy intensity from 1992 to 2014 using system GMM found that increased trade openness leads to higher energy intensity in high-income and middle-income countries (Benli, M 2019) Conversely, research focused on selected African economies from 1970 to 2011 employed a bound co-integration test and identified a positive relationship between trade openness and energy intensity levels (Yaya et al 2016).
Population size significantly impacts energy efficiency, with high-density nations often developing energy-efficient infrastructure to meet increased demands However, if infrastructure cannot keep up with rapid population growth, energy efficiency may decline, resulting in greater facility usage and transportation congestion (Moshiri and Duah 2016).
Changes in demographics, particularly population size, significantly influence energy consumption and greenhouse gas emissions (Adorn et al., 2018) Growing populations can lead to a rise in the use of low-energy transportation methods such as bicycles, walking, and motorcycles This increased population density enhances energy efficiency, as more individuals share limited space (Gangopadhyay and Shakar, 2016).
Theoretical framework
This research introduces a model comprising six key variables: Energy Intensity (EI), Green Bond (GB), Carbon Emission (CO), Urbanization (URB), Population (POP), and Trade Openness (TO) The study utilizes various frameworks, including the STIRPAT model, and integrates Financial Literacy as an additional variable to enhance the analysis.
The STIRPAT model, which stands for STochastic Impacts by Regression on Population, Affluence, and Technology, is a framework designed to assess human impacts on the environment It focuses on three critical components: population (P), affluence (A), and technology (T) The model's development is rooted in foundational research by Ehrlich and Holdren (1971) and Dietz and Rosa (1997), and it has been further explored by numerous scholars, including Hasanov and Mikayilov (2017) and Ghazali and All.
In recent studies by Ferrat et al (2019), Ferrat et al (2021), and Nasr Esfahani et al (2022), a robust theoretical framework has been employed to analyze various environmental issues This model is applied in our research to explore the relationship between green bonds and energy efficiency Additionally, as described by Battese and Coelli (1995), a set of explanatory variables can clarify the inefficiency component, represented as uct To effectively understand the correlation between green bonds, energy efficiency, and relevant control variables, we define the dynamic panel function accordingly.
El = po + pi *GB + /12* URB + p3* POP +/Ì4 * TO +/35 * co + V
In selecting control variables for our study, we draw on prior research by Hanif (2018), Phuc Nguyen et al (2020), and Pham et al (2020), which have explored the relationships among CO2 emissions, urbanization, trade openness, and population dynamics The error term is denoted as p(i,t), while p represents the associated vector to be estimated.
RESEARCH DATA AND RESEARCH METHODS
Research data
Our study examines countries that have consistently issued green bonds from 2016 to 2022, utilizing a dataset from reputable open data sources including the World Bank Open Data, International Monetary Fund, Our World In Data, Green Bond Guide, and World Development Indicators These sources are well-regarded in academic research (Cortellini et al., 2021; Wang et al., 2023; Khan et al., 2022) We have chosen panel data analysis for its advantages, such as a larger sample size that minimizes collinearity issues and enhances the degrees of freedom for more robust analysis.
The green bond market has only recently emerged, beginning around 2013, which has made it challenging for many countries to compile comprehensive data from 2016 to 2022 To address this issue, we carefully screened and selected data from 22 countries that possess complete datasets for our analysis The countries included in our dataset are Australia, Austria, Brazil, Canada, China, Finland, France, Germany, Italy, Japan, Luxembourg, and Mexico.
Netherlands, New Zealand, Norway, Philippines, South Korea, Spain, Switzerland, Sweden, United Kingdom, and the United Slates.
Table 1 provides an overview of the variables, their respective abbreviations used in the study, and the methods employed for their measurement Details about variable description will be provided below.
EI Natural logarithm of energy intensity
Green bond GB Natural logarithm of green bond issued
CO Natural logarithm of carbon emission per capita tonnes Our World In
TO Natural logarithm of merchandise trade as to GDP
Population POP Natural logarithm of population people International
Urbanization URB Natural logarithm of urban population as to total population
Variable description
Energy efficiency is defined as the ratio of output—such as performance, goods, or services—to the energy consumed in their production Essentially, it means achieving the same results with less energy, thereby reducing losses Economically, various indicators exist to evaluate energy efficiency, which can be categorized into two main types: partial or single-factor energy efficiency (PFEE) measures and total factor energy efficiency (TFEE) measures PFEE, often known as energy intensity, is the more frequently utilized metric in assessing energy efficiency.
It specifically evaluates the relationship between the input of energy and its output, with energy typically being the sole factor considered in the generating process (Jebali, Essid,
Energy intensity serves as a key indicator of energy efficiency, playing a crucial role in various studies that examine its relationship with green financing (Quang, P.T et al., 2022) and the functionality of green bonds (Anh Tu, c et al., 2022).
Green bonds are financial instruments whose proceeds are specifically allocated by issuers for projects that yield environmental benefits (Reboredo, 2018) Research on green bonds often focuses on the volume issued by countries, organizations, or corporations, highlighting their significant role in promoting sustainable initiatives and combating air pollution caused by greenhouse gases (Hanif et al., 2019) Consequently, numerous studies examine the correlation between the issuance of green bonds and energy efficiency (Anh Tu et al., 2022; Zhao).
L et al, 2022), stock market reaction (Baulkaran, V., 2019, Wang, J et al, 2020), and cost of capital (Zhang, R et al, 2021).
Urbanization has a complex relationship with energy intensity, with some studies indicating that it increases energy demand and consumption (Wang et al., 2022; Zhao et al., 2018) On the other hand, well-planned urbanization can enhance energy efficiency by optimizing energy usage (Wang et al., 2022) Additionally, factors such as population agglomeration and improved public transportation infrastructure have been shown to positively influence energy efficiency and promote energy conservation (Sadorsky, 2013) Therefore, the urban population size serves as a key indicator of urbanization's impact on energy dynamics.
Carbon emissions are influenced by various factors, including technology level, energy structure, economic structure, and population composition, which vary for each individual Consequently, per capita CO2 emissions serve as a useful indicator to examine the connections between carbon dioxide emissions, energy consumption (Luo, G et al, 2023), environmental policy (Padilla Rosa, E et al, 2023), and demographic factors such as population aging and unemployment rates (Wang, Q et al, 2021).
Population is an important factor since the impact of urbanization on energy demand and pollutant emissions depends on the associated population density (Ruhul Salim,
2019) Consequently, the proxy of population is the volume of people in one country.
Trade openness serves as a key indicator for assessing globalization's impact on energy outcomes (Shahbaz et al., 2017) Research indicates that increased trade fosters energy intensity in Southern Asia (Pan et al., 2019) and raises energy intensity levels in the Middle East, both in the short and long term (Sadorsky, 2011) However, excessive specialization in competitive advantages without addressing environmental concerns can negatively impact the environment (Canh et al., 2019) Consequently, the volume of merchandise trade relative to GDP is utilized as a proxy for this variable, as it has been effectively employed in studies examining its relationship with environmental consequences (Pham et al., 2020).
Research methodology
The study begins by assessing the variables through descriptive statistics and a correlation matrix To determine the most suitable statistical model for the analyzed variables, the Breusch-Pagan Lagrange multiplier test and the Hausman test are utilized, comparing the Pooled Ordinary Least Squares (POLS), Fixed-Effect Model (FEM), and Random-Effect Model (REM) The findings suggest that the Fixed-Effect Model is the most appropriate baseline model for this analysis Numerous economic studies have successfully employed this model to explore relationships between variables.
To assess potential diagnostic issues, we utilize the Variance Inflation Factor (VIF), the Wooldridge test for autocorrelation, and the modified Wald test for groupwise heteroskedasticity To address any identified issues, we apply Feasible Generalized Least Squares (FGLS), which has been recognized in various studies, including Quang et al (2022), as a reliable method for analyzing the relationship between green bonds and energy intensity.
Concerns about endogeneity effects on FGLS estimates necessitate the use of Durbin and Wu-Hausman tests to ensure the reliability of the coefficient estimates In cases of endogeneity, the two-stage system generalized method of moments (GMM) is employed as a robust alternative, addressing the limitations of the one-step system GMM This approach has been validated by numerous studies, confirming its reliability and applicability Additionally, the quality of the estimations is assessed using the Hansen J-test, Differences-in-Hansen test, and Arellano-Bond test for second-order autocorrelation.
EMPIRICAL RESULTS AND DISCUSSION
Descriptive statistics
Table 2 reports the descriptive statistics of all variables used in this study This sample includes 154 yearly observations of 22 countries in 7 years from 2016 to 2022.
Variable Obs Mean Std Dev Min Max
Notes: EI, GB, co, TO, POP and URB stand for energy intensity, issued green bonds, carbon emission, trade opennes, population and urbanization
According to Table 2, the mean value of energy intensity (EI) is 1.439149 with a range from 2483726 to 2.419039 The minimal and maximal value of green bonds variable (GB) is 17.5175 and 25.2904, respectively.
Between 2016 and 2022, the average CO2 emissions (CO) for countries was 1.898593, with a standard deviation of 0.6202714 The trade openness (TO) variable exhibited the highest value at 5.219086, while the lowest recorded was 2.895153 Additionally, the population distribution (POP) among these countries was unequal, indicated by a standard deviation of 1.678881, with maximum and minimum values of 13.27425 and 21.06853, respectively.
(URB) ranges from a low of 3.838915 to a peak of 4.531373 with a standard deviation of.1608612.
Correlation matrix
EI GB CO TO POP URB
Notes: EỈ, GB, co, TO, POP and URB stand for energy intensity, issued green bonds, carbon emission, trade opennes, population and urbanization
The correlation coefficients presented in Table 3 indicate a range from -0.4316 to 0.5416 Specifically, carbon emissions (CO) and energy intensity (EI) exhibit a weak negative correlation of -0.0015, while the strongest positive correlation is observed between population (POP) and energy intensity (EI) with a coefficient of 0.5416 Since all coefficients are below 0.8, the likelihood of multicollinearity among these variables in the regression model remains low.
Regression results and postestimation
Pooled Ordinary Least Squares, Fixed-effect Model and Random-effect Model are sequentially performed and the regression results are illustrated in Table 4.
Table 4: POLS, FEM and REM regression results
Notes: *, * and ** denote 1%, 5%, and 10% significance levels, respectively EI, GB,
CO, TO, POP and URB stand for energy intensity, issued green bonds, carbon emission, trade opennes, population and urbanization
The F-test results in Table 4 demonstrate that the Fixed Effects Model (FEM) outperforms Ordinary Least Squares (OLS) for the analyzed variables Additionally, the Breusch and Pagan Lagrangian test (1980) and the Hausman test (1978) were conducted, yielding significant test statistics.
The analysis revealed a p-value of 0.0000, indicating that the Finite Element Method (FEM) is the most reliable model among the three evaluated Consequently, FEM was selected as the baseline research model for this study, as detailed in Tables 5 and 6.
Table 5: Breusch and Pagan Lagrangian multiplier test for random effects
Note: The lower p-values reject the null hypothesis.
Ho: difference in coefficients not systematic
Note: The lower p-values reject the null hypothesis.
Notes: EI, GB, co, TO, POP and URB stand for energy intensity’, issued green bonds, carbon emission, trade opennes, population and urbanization
The variance inflation factor (VIF) analysis is conducted to assess multicollinearity among the sample variables With all VIF coefficients below 5 and a mean VIF of 1.44, the results indicate that there is no significant multicollinearity issue among the variables (Gujarati, 2011).
The fixed-effect regression analysis reveals potential diagnostic issues, including heteroskedasticity and autocorrelation The Wooldridge test for autocorrelation returned a p-value of 0.000, while the modified Wald test for groupwise heteroskedasticity also indicated a p-value of 0.000 These results, presented in Table 8 and Table 9, respectively, confirm that the fixed-effect estimator exhibits significant diagnostic problems.
Table 8: Wooldridge test for autocorrelation
HO: no first order autocorrelation
Table 9: Modified Wald test for groupwise heteroskedasticity
HO: sigma(i)A2 = sigmaA2 for all i chi2 (22) 262.72 Prob > chi2 ().()()()()
In section 3.3, FGLS Regression is utilized to address heteroskedasticity and autocorrelation, enabling a thorough empirical analysis of the impact of green bonds on energy efficiency in specific economies, with findings presented in Table 10.
Notes: *, * and ** denote I %, 5%, and 10% significance levels, respectively Eỉ, GB,
CO, TO, POP and URB stand for energy intensity, issued green bonds, carbon emission, trade opennes, population and urbanization
After addressing the assumption violations of the fixed-effect model using the FGLS model, we will utilize these findings for our analysis As shown in Table 12, green bonds (GB) significantly contribute to reducing energy intensity, thereby enhancing energy efficiency in the selected economies Specifically, a 1% increase in the volume of issued green bonds leads to a nearly 0.024% decrease in energy intensity.
Carbon emissions (CO) significantly impede advancements in energy efficiency, with a 1% rise in emissions resulting in a 0.1% increase in energy intensity Additionally, population (POP) is positively correlated with energy intensity, exhibiting a coefficient of 0.14% While the variables of trade openness (TO) and urbanization (URB) are positively related to energy intensity, their impact is less substantial, as indicated by the high p-values in the empirical results.
To ensure the reliability of regression results, it is crucial to address the potential risk of endogeneity We conducted the Durbin-Wu-Hausman test to assess endogeneity, with the findings detailed in Table 11.
The Durbin-Wu-Hausman test indicates that a low p-value ( z = 0.342 Prob > chi2 = 0.127 Prob > chi2 = 0.666 Prob > chi2 = 0.519
Notes: * * and ** denote 1%, 5%, and /0% significance levels, respectively El, GB,
CO, TO, POP and URB stand for energy intensity, issued green bonds, carbon emission, trade opennes, population and urbanization AR(2) is Arellano-Bond test for second-order serial autocorrelation
The consistency of all coefficient signs with the FGLS model validates the robustness of the results Table 12 indicates that E/(t-l) has a positive and significant coefficient, suggesting a positive relationship between current and previous period EI Furthermore, there are 13 instruments, which is fewer than the 22 groups The quality of these instruments was assessed using the Sargan test of overidentification, Hansen J-test, Differences-in-Hansen test, and the Arellano test.
The Bond test for second-order autocorrelation indicates that the AR(1) and AR(2) values, reported in Table 12, demonstrate acceptable limits with AR(1) at 0.002 and AR(2) at 0.720, reflecting weak serial correlation in the model's residuals Additionally, the Sargan test yields a p-value of 0.188, which exceeds the 0.05 threshold, confirming the validity of the instruments used in the GMM estimation Consequently, the findings presented in Table 12 are both validated and well-specified.
This study demonstrates that green bonds can effectively enhance energy efficiency by channeling capital into green and energy efficiency projects The analysis reveals that the variable green bonds (GB) have a negative beta coefficient with a high statistical significance of 1%, indicating that an increase in the issuance of green bonds correlates with reduced energy intensity Specifically, a 1% rise in the volume of issued green bonds leads to an approximate 0.1% decrease in energy intensity These results are consistent with prior research by Sadorsky (2013), Sun et al (2020), Mensi et al (2021), and Naeem et al (2021) Furthermore, McInerney and Bunn (2019) highlighted the critical impact of green bonds on promoting green projects and attracting private investor participation, as noted by Tang and Zhang.
The green bond market has the potential to improve energy efficiency in countries, as noted in studies from 2020 However, research by Chen and Zhao (2021), Pineiro-Chousa et al (2021), and Let et al (2021) argues that this market may not effectively enhance energy efficiency due to several constraints, including inconsistent standards and inadequate government involvement.
A positive coefficient of 0.15 for CO2 emissions indicates that a 1% decrease in per capita CO2 emissions leads to a 0.15% reduction in energy intensity (EI), statistically significant at 1% This correlation arises as countries implement policies aimed at reducing carbon emissions, which promotes energy efficiency The increasing trend of CO2 emissions poses significant risks to natural ecosystems, prompting nations and international organizations to develop various strategies and plans to mitigate emissions, enhance energy efficiency, and advance green initiatives.
Trade openness (TO) positively influences energy intensity (EI), with a percentage increase in TO resulting in a less than proportional decrease in EI (0.16%) and a simultaneous decline in energy efficiency This contrasts with Rahman et al (2023), who argue that trade facilitates the transfer of energy-efficient technologies from industrialized nations, promoting better production methods among trading partners However, it aligns with findings from Keho (2016), Dogan et al (2020), and Shahzad et al (2021), which support the notion that increased trade openness correlates with higher energy intensity Despite the potential for technology absorption through spillover effects, greater openness leads to significant energy resource waste in production across sampled countries Additionally, global trade often includes the export of resource-intensive goods, contributing to elevated energy consumption and emissions, as highlighted by Dogan et al (2020) Furthermore, many products, even those not directly traded as energy, still demand substantial energy resources (Shahzad et al., 2021) Collectively, these factors elucidate the positive relationship between trade openness and energy intensity.
CONCLUSION
This study evaluates the impact of green bonds on energy efficiency across twenty-two economies from 2016 to 2022, utilizing energy intensity as a proxy for energy efficiency—where lower intensity indicates higher efficiency Key explanatory variables, including carbon emissions, trade openness, population, and urbanization, were selected based on existing literature The analysis employs the Hausman and Breusch-Pagan Lagrangian tests to establish a Fixed-effect model as the baseline Findings reveal that green bonds significantly enhance energy efficiency However, tests such as the Wald and Wooldridge indicate notable assumption violations in the Fixed-effect settings, prompting the use of FGLS regression to address these issues Additionally, GMM is applied as an alternative robustness check, yielding consistent results.
Our research indicates that green bonds effectively attract private investment in energy efficiency projects, leading to improved energy efficiency and reduced greenhouse gas emissions for sustainable growth Interestingly, urbanization appears to have little impact on energy efficiency levels The significant correlation between CO2 emissions and energy efficiency suggests that countries should enhance their support for green initiatives to boost energy efficiency Additionally, trade openness negatively affects energy efficiency, highlighting the need for trade policies that facilitate the exchange of information and technology related to energy efficiency and renewable energy Furthermore, as population growth contributes to decreased energy efficiency, governments, particularly in rapidly growing countries, should implement policies that promote energy efficiency to ensure sustainable development.
IMPLICATION
The research highlights the importance of implementing effective policies to enhance energy efficiency, particularly through green financing mechanisms like Green Bonds (GBs), which have shown positive results in the analyzed countries To maximize the benefits of green financing, governments should develop comprehensive long-term strategies that include a robust monitoring system to track the progress of green financing initiatives and address barriers that may limit their long-term market impact.
Research indicates a positive correlation between population size and increased energy intensity in the analyzed nations To enhance energy efficiency, it is essential for policies to focus on reducing population growth Furthermore, implementing widespread marketing campaigns across various media channels can significantly raise awareness about population policies, thereby improving their effectiveness.
The study reveals a positive correlation between trade openness and energy intensity, suggesting that policymakers should not impose tariff barriers or avoid free trade agreements This relationship highlights significant energy resource wastage in production processes To address this, policymakers should promote the adoption of advanced technologies among domestic companies to improve energy efficiency Additionally, they should enforce the three Rs of waste management—Reduce, Reuse, Recycle—encouraging production enterprises to optimize resource utilization and ultimately decrease energy intensity.
The study reveals a positive correlation between CO2 emissions and energy intensity, indicating that a reduction in per capita CO2 emissions can enhance energy efficiency Policymakers should focus on encouraging emission reductions in high-energy-consuming industries Additionally, governments can effectively lower CO2 emissions by participating in international agreements like the Paris Agreement and launching community campaigns aimed at minimizing CO2 release, ultimately improving energy efficiency.
LIMITATION AND FUTURE RESEARCH DIRECTION
This study offers valuable insights into the role of green bonds in controlling urbanization, carbon emissions, population growth, and trade openness in relation to energy efficiency However, it is important to recognize certain limitations The focus on 22 countries that consistently issue green bonds may restrict our understanding of the broader context, as different nations possess unique economic conditions and indices To enhance the study's relevance and persuasiveness, expanding the research to include a more diverse range of countries could yield additional valuable insights.
The survey participants were limited to the period from 2016 to 2022, which may affect the generalizability of the findings Due to restricted resources, the research team was unable to access more recent data, resulting in outdated information Future studies should consider utilizing a broader time frame and more current data to improve the accuracy and applicability of findings related to energy efficiency.
The current research is limited by its neglect of contextual factors, including economic globalization, energy prices, and green energy supply, which may affect green bond issuance and its impact on energy efficiency Future studies should systematically explore how these variables moderate the proposed relationships Utilizing multi-site experimental designs and qualitative methods across various contexts will offer a deeper understanding of these dynamics By examining these nuances, researchers can formulate more targeted policies that effectively boost the role of green bonds in enhancing energy efficiency.
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