Environmental outlook of ASEAN-5 through the lens of green bonds,environmental technologies and financialization

Abstract

Sustainable development goals call for urgent action with an aim to spur socio-economic growth along with the protection of the environment and resources. However, these goals are difficult to be achieved when finances are not mobilized to mitigate harmful resources. In return, numerous green bond manifestations appeared in the market, which are viewed as a bridge to have successful implementation of sustainable development goals. Therefore, the study is aimed to explore the relevance of green bonds, financialization, and sustainable environmental change mitigation technologies for environmental change in ASEAN economies. The initial outcomes confirm the long-run cointegration association between study variables and also explore the slope heterogeneity and cross-sectional dependencies. To explore the causal connections between the study variables, the current study employs CS-ARDL technique. The study finds significant contributions of sustainable environmental change mitigation technologies and green bonds in the reduction of pollution, hence improving climate quality. The short-run infers similar results with relatively lower magnitudes of coefficients as compared to the long-run. Negative error correction terms are used to confirm the stability of the model, which implies sustained convergence towards “steady-state equilibrium” in case of expected deviations. The results offer practical implications and recommendations.

Keywords

Sustainable environmental change mitigation technologies financialization green bonds pollution environmental sustainability

Introduction

Sustainable environmental change mitigation technologies are projected to diminish the adverse influence of manufacturing processes on climate quality. From technology and economic aspects, innovative technologies enhance products, services, production methods/processes, and management of businesses.^1,2 Green technologies mitigate climate threats and are gaining attention because novel technologies like biomass processing, carbon mitigation technologies, climate management technologies, waste recycling technologies, and energy-saving technologies enhance the natural environment.^3,4 Additionally, green technologies improve everyday life, human development and enhance climate quality.^5–7 Climate-related technologies, clean technologies and socially responsible investments⁸ address business and social demands without deteriorating the natural environment.^9,10 Research and development in environmental technologies forces businesses to utilize economical and sustainable energy sources to facilitate the environment.^11,12 In this lieu, renewable energy consumption reduces carbon emissions, meanwhile, green innovations and related technologies are efficient sources of energy as compared to traditional sources of energy.^13,14 Carbon neutrality is the most popular goal among the SDGs, which focuses on attaining net zero emissions and has gained the attention of policymakers, governments, researchers, policymakers, and academicians^15,16 and forces to use sustainable, green, and clean technologies to enhance the climate quality.^14,17 Countries are putting a wide range of efforts to promote green technologies, e.g., GTB (green technology bank) of China promotes the green development of information technologies.^18,19 Green Technologies promote the natural ecology and reduce pollution in emerging and emerged countries.²⁰ Registered patents for Sustainable environmental change mitigation technologies in ASEAN countries show mixed results (Figure 1) over the last two decades. Five countries are selected from ASEAN economies i.e., Indonesia, Malaysia, Cambodia, Singapore, Philippines because of the availability of data¹ for registered patents for Sustainable environmental change mitigation technologies. Secondly, ASEAN-5 sampling is being drawn because a pattern of economic activity can be observed in these particular economies which is sufficient to be recognized in order to scrutinize the impact on environmental quality.²¹ The advancement of industrialization in these economies also results in high energy consumption which is the ultimate reason of high emissions and the ASEAN region has received limited attention.^22,23 Singapore shows the highest trends for environmental technologies and followed by Malaysia. The rest of the ASEAN economies exhibit similar trends in climate technologies. The ASEAN economies are very small in the development of environmental change mitigation technologies as compared to the entire world (Figure 2). The selected ASEAN economies show the highest carbon emission for Indonesia while Singapore and Cambodia show the lowest trends of carbon emissions (Figure 3). Figures 1 and 2 show, as the selected ASEAN economies exhibit a decline in the development of sustainable environmental change mitigation technologies, the carbon emission increase in these economies. However, ASEAN countries have tremendously reduced industrial carbon emissions. Apart from environmental technologies, the connection between financial advancements and climate issues receives the attention of academicians. However, few studies infer that financial advancement is the main cause of enhanced climate mitigation.^24–26 A well-functioning financial ecosystem improves the performance and efficiency of the financial industry and improves economic development.^27,28 However, improved economic growth results in enhanced levels of energy consumption and carbon emissions. Moreover, the advancement of financial and stock markets offers funds for new projects which cause a rise in energy demands and environmental pollution.^29,30

Figure 1.

Sustainable environmental change mitigation technologies patents (%) for selected ASEAN economies.

Figure 2.

Sustainable environmental change mitigation technologies patents (%) Worldwide.

Figure 3.

CO₂ emissions for selected ASEAN economies in metric tons per capita.

Further to a discussion, the most sadden fact of present-day economy is low-rate investment. Before 2008 financial crisis, high-income economies’ growth was shoved by spending on consumption, education and housing.³¹ However, due to the crisis, such spendings declined and those investment which should have made a recovery, never got an opportunity to be materialized. Thus, it demands change. After the crisis period, major global central banks made an attempt to revitalize spendings as well as employment with the help of slashed interest rates. Interestingly, the strategy proved to be effective to some extent. Moreover, policymakers also schooled investors to bid up stock and bond prices through capital market flooding via liquidity and interest rate, and foreign direct investment.^32,33 This effort established financial wealth and stimuli consumption via initial public offerings in the form of investment. However, the said policy is not cost-effective and has reached to its limitations. Besides, monetary policy was also pushed to limits; hence, there is an absence of long-term investments along with infrastructure financing. In most economies, especially emerging ones, public sector is not in position to fill the gap, meanwhile, no interest can be seen from private sector. This is due to low return rate and risk factors. Currently, three challenges have been surfaced which need a proper strategy: (1) right project identification; (2) development of complex plans that integrate private and public both sectors (including multiple countries); (3) structured financing. However, this needs long-term planning, sleek budgeting and effective implementation of project. At the global level, there is a need of massive investment in green energy systems in order to sharpen the reduction of carbon emissions. Emerging economies especially are in need of substantial investments in green projects to attain sustainable development.^13,34

Although, several studies explored the causes of carbon emission through financial advancement, but these studies considered a single proxy for financial advancement; however, the current study uses financial markets’ efficiency and access to financial institutions to proxy financial advancement. Similarly, the rapid growth of the green finance industry has increased the flow of funds for both private and public sector sustainable development projects. The proceeds of green bonds issued by the Worldbank have increased over 16 billion and are issued in twenty-three different denominations since 2008.³⁵ Muganyi, Yan³⁶ argue that green bonds help to achieve environmental quality goals in China. A green financing ecosystem ensures a reduction in environmental pollution where France, China, and the US are the top issuers of green bonds.³⁷ Green bonds enhance the sustainable utilization of natural resources, conserve biodiversity, control pollution, and enhance renewable energy efficiency.^37,38 Green bonds reduce environmental issues and carbon emissions; however, empirical evidence of the connectedness of green bonds and environmental change is still limited from the ASEAN economies perspective.

Based on the above-mentioned arguments, the current research investigates the impact of sustainable environmental change mitigation technologies, sustainable finance, and financial advancements on environmental sustainability in ASEAN economies. The study employs advanced panel econometric models to explore slope heterogeneity, CSD (cross-section dependence), and cointegration using panel data over the period from 2011 to 2020. More specifically, the study integrates the impact of financial advancement and green bonds on low-carbon economies. The outcomes of the study exhibit that sustainable green bonds and sustainable environmental change mitigation technologies promote low carbon emissions. Similarly, access to financial institutions also improves environmental quality but the efficiency of financial markets is incapable to cope with environmental deterioration. The research work is presented in five sections: after the introduction part, the next segment explores the literature to find a reasonable gap and construct a theoretical framework. The variables, empirical tools, and data sources are debated in segment 3, while the following segment discusses the empirical outcomes, which is further followed by conclusion.

Review of relevant literature

Environmental change mitigation technology and climate issues

Discussing about environmental change mitigation technology and climate issues, the seminal work of Sikhar, Gaurav³⁹ is needed to be highlighted. The authors proposed the model which was developed through the implementation of various working assumptions. One prominent assumption is “full greening or full penalty of pollution inducing activity.” The theoretical assumption may be effective 100; however, in practical scenario, partial greening of an activity could be achievable. On the basis of economic viability, technological scope and legislative targets stationed by government and agencies, a firm is capable of taking a decision to opt for partial greening activities in their production phase where unavoidable and viable pollutants can be controlled and meddled. This establishes a situation where it is up to producer to take the decision to bear partial penalty payment for not implementing green norms and procedures. The partial penalty also covers the disengagement of organizations in eco-friendly mechanisms. Thereby, the maximum benefit of a supply chain can be gauged in such a scenario is possible through an “intricate function” that has a reliance on green cost and green financial burden, which would be implied together on a particular activity.^21,40

It is also argued that the surroundings where manufacturing mode could be defined via data explosion and globalization. These certain characteristics lead to agile and malleable manufacturing in the last few years, however, they come up with challenges in the present environment. The first and foremost challenge is having the complexity in data integration because of various formats such as structured, semi-structured or unstructured.^41–43 The integration of data is a crucial step as most of the organizations believe in the concept synergetic manufacturing. Another challenge is linked to the resilience of manufacturing processes at the global level. In the present environment, it is more difficult to achieve as social and political norms have made the system vulnerable to a great extent. Hence, the only solution to overcome such challenges is to introduce sustainable technologies which help economies to ease the burden of environmental pollution.^17,19,44

The connection between environmental technology and climate quality is also explored by different researchers in different countries. Shan, Genç¹⁴ argue that innovative green technologies help to attain SDGs without environmental deterioration. By employing the STIRPART model, the study explores the connectedness between green technology, per-capita income, energy consumption, and climate change mitigation in Turkey. Meirun, Mihardjo⁴⁵ employ BARDL (bootstrap autoregressive distributive lag) model and explore that green innovations are vital in the attainment of economic excellence without deteriorating the environment. The authors further argue though Singapore has attained magnificent economic growth, but still, the country is facing a climate mitigation problem. Hence, environmental change mitigation technologies provide a better solution for climate sustainability. From emerging and emerging economies, Shi and Lai⁴⁶ investigate the trends of green innovations and argue that North American and Western Europe regions are advanced in green technological development as compared to other emerging nations. Over the period from 1996 to 2017, Razzaq, Wang² investigate the asymmetric linkages between CO2 emissions and climate-related technological innovations in BRICS economies. The authors argue that environmental technologies mitigate the emissions of greenhouse gasses and carbon dioxides. Gu, Wang⁴⁷ argue the transfer of low-carbon technologies reduces the warming effects. Chien, Anwar⁴⁸ observe the EKC framework and argue that ICT (information and communication technology) reduces climate degradation. In the recent past, many studies endorse causal and dynamic associations between environmental technologies and climate issues^49,50 and argue that environmental technologies reduce pollution issues. Iyer, Hultman⁵¹ examine the implications of sustainable climate technologies and claim that these technologies are eco-friendly but the cost of implementation of these technologies hinders these implications. Lin and Ma⁵² observe the heterogeneous impact of sustainable climate technologies on carbon emissions in different cities. From 35 OECD economies, Lu, Mahalik⁵³ investigate the influence of technology adoption and democracy on carbon emissions and find that democracy reduces pollution while trade liberalization enhances carbon emissions.

Financialization and climate issues

The connection between financialization and climate issues is explored by different researchers with mixed findings. Few authors claim financial growth enhances climate degradation, while few researchers reject it. One pillar of the relationship explores that financial growth enhances a well-organized financial ecosystem that sheds more financial resources to facilitate many schemes, consumption patterns, and production processes, which results in higher energy utilization.^54,55 Therefore, environmental deterioration is evident from rapid financialization or vice versa. But, the other pillar claims that financialization promotes climate protection via efficient energy utilization technology.^56,57 Prevailing research work justifies both destructive and constructive impacts of financial growth on environmental pollution. From China, Zhang⁵⁸ investigates the effect of financial growth on the emission of CO₂. The authors find that financial growth increases carbon emissions. Khan, Khan⁵⁹ explore 192 economies by inspecting the heterogeneity of consumption of renewable energy, financial advancement, and carbon emissions. The authors find that financial growth enhances carbon emissions. Bouraima, Stević⁶⁰ and Haseeb, Xia⁶¹ investigate the effect of globalization, financial growth, and renewable energy consumption on CO₂ emissions under the umbrella of EKC. Their study not only confirms the cointegration but also infers the prevalence of “cross-sectional dependence and heterogeneity of slope parameters”. The authors also infer that financial growth results in higher carbon emissions. Sheraz, Deyi⁶² explore the period from 1986 to 2018 and examine the connection between financial advancements and emissions of carbon dioxide in the G20 economies. The authors find that financial growth offers financial resources for the adoption of new industrial technologies, hence, leading to reduced carbon emissions. From Turkey, Rjoub, Odugbesan⁶³ examine financial advancements and climate deterioration. The authors find a strong moderating effect of financial advancement. From the agriculture sector of China, Koondhar, Shahbaz⁶⁴ investigate financial advancement and climate quality. The authors find that agriculture’s financial advancement is negatively associated with environmental quality over a period from 1998 to 2018. Using a longer data set comprising four decades, Li and Wei⁶⁵ investigate the carbon emission trends for 30 different provinces of China and explore that financial advancement is associated with carbon emissions.

Green financing (green bonds) and climate challenges

Investment opportunities and green bonds positively influence climate quality, therefore getting the attention of researchers. The implication of green loans and bonds is explored by Gilchrist, Yu,⁶⁶ and the authors infer that corporate environmentally responsible attitude enhances the natural ecosystem. Tuhkanen and Vulturius⁶⁷ claim that green bonds are a significant form of sustainable and green finance, yet there is a gap in the literature regarding the influence of green bonds on carbon neutrality. Fatica and Panzica⁶⁸ argued that after the Paris Agreement, green bonds have significantly reduced carbon emissions. Similarly, it claims that rapid industrial and economic growth has adversely affected the environmental quality while green bonds and green projects can enhance the climate quality.^69,70 Wang and Zhi⁷¹ advocate that green bonds are beneficial for economic advancements and climate protection. The authors further suggest that biodiversity funds, environmental funds, weather derivative funds, green investment funds, nature-linked securities, and debt for environmental swaps are important sources of green financing for climate change mitigation technologies. Muganyi, Yan³⁶ and Zhang and Qi⁷² claim that fintech development and finance-related policies significantly reduce carbon emissions and urban economic development. The current study infers that sustainable climate change mitigation technologies, green bonds, and financialization disrupt the drifts of CO₂ emissions.^1,73

Data description and methodology

Panel designs are normally used to address the accuracy issue through data collection process. The present study used panel cointegration due to higher number of T compared to N. Hence, in this scenario, conventional approaches such as random or fixed effect model cannot be used due to inappropriate measures. Since, ASEAN economies are grouped due to similar characteristics such as ecological or biological Thus, CSD, slope heterogeneity and stationarity issues exist in such economies that is observed in the nature of panel data. CSD test is employed due to widespread coordination and cooperation within ASEAN. Also, ASEAN economies do have somewhat similar economic growth rate but their relative strength varies. Therefore, the slope homogeneity test is used, which is followed by unit roots and cointegration tests. The current research starts with the detection of CSD (cross-section dependence), which may arise from unexpected volatility, interdependencies of residuals, and due to global shocks in several macroeconomic pillars. If any study fails to address the CSD issues, the analysis and findings would be considered inappropriate and biased.^77,78 For this reason, the CSD test introduced by Pesaran⁷⁹ is employed. After exploring the CSD properties of data series, the study evaluates the stationarity of the variable, which is a common characteristic in panel data. The stationarity problems have been classified into three different generations. In this regard, Levin, Lin⁸⁰ assume unit root issues in panel data (homogenous in nature), however, Im, Pesaran⁸¹ consider unit root issues in panel data (heterogenous in nature). As the study initially assumes the likelihood of CSD existence, hence, the study primarily focuses on the robust arguments of Pesaran⁸² and Bai and Carrion-I-Silvestre.⁸³

After exploring the stationarity of the variables, the study detects the existence of slope heterogeneity using the econometric approach introduced by,⁸⁴ which is a modern version of Swamy.⁸⁵ The null hypothesis infers the existence of slope homogeneity. Without adequate investigation of slope heterogeneity, the coefficients, estimation, and findings of the study cannot be considered reliable and credible. After investigating the slope heterogeneity of coefficients, the next step is to explore the long-run cointegration between variables of the model. Therefore, we employ the cointegration test introduced by Westerlund and Edgerton⁸⁶ and Banerjee and Carrion-i-Silvestre,⁸⁷ and these panel cointegration tests can deal with correlated errors, slope heterogeneity, and CSD. Once the cointegration is confirmed, we employ the CS-AARDL model, which captures slope heterogeneity, structural breaks, and CSD.²⁵ Since panel estimations produce unreliable outcomes due to CSD existence and slope heterogeneity issues. Therefore, such issues are handled by CS-ARDL in an efficient manner. Interestingly, techniques such as FMOLS and DOLS do not cater to such issues, hence, CS-ARDL has benefits over others. Besides, compared to panel ARDL approaches, CS-ARDL is robust to address various problems such as “misspecification biases, serial error correlation, cross-sectional dependence, non-stationarity and the endogeneity bias.” CS-ARDL also has the potential to address arbitrary integration. Thereby, the study utilizes CS-ARDL approach to measures long and short-run coefficient values since the approach is appropriate to handle heterogeneity and CD problems.⁸⁸

Thus, by following the aforementioned criteria, researchers are allowed to fulfil the objectives in order to verify “the validity of the assumption of constant slopes.” Moreover, the study is able to identify any sign of CSD in panel data which is a necessary step to be taken. After following these steps, the study is able to analyze the proposed relationship between variables with the accurate and appropriate econometric approach and estimation method that would provide more accurate outcomes. This study used per-capita CO₂ emission as the dependent construct, while Environmental Patent technologies (EPTech), green credit (measured through green bonds: GBonds), and Financial Advancement (measured through access to financial institutions; AFinI and financial market efficiency; FinME) as explanatory variables. The model is presented through equation (1):

P C {C O}_{2, i, t} = f ({E P T e c h}_{i, t}, {G B o n d s}_{i, t}, {F i n M E}_{i, t}, {A F i n I}_{i, t},)

(1)

Cross sections (ASEAN-5 countries) are presented by symbol i, while t indicates the period. Moreover, regression from equation (1) is given as:

{P C C O}_{2, i, t} = β_{1 i t} + β_{2 i t} {E P T}_{i t} + β_{3 i t} {G B o n d s}_{i t} + β_{4 i t} {F i n M E}_{i t} + β_{4 i t} {A F i n I}_{i t} + α_{i} + δ_{i}

(2)

W_{i, t} = \sum_{i = 0}^{p w} φ_{i, t} W_{i, t - 1} + \sum_{i = 0}^{p z} γ_{i, t} Z_{i, t - 1} + ε_{i, t}

(3)

Where the ARDL model is covered in equation (3), and further extended for cross-section in equation (4):

W_{i t} = \sum_{i = 0}^{p w} φ_{i, t} W_{i, t - 1} + \sum_{i = 0}^{p Z} γ_{i, t} Z_{i, t - 1} + \sum_{i = 0}^{p X} a_{i} {\bar{X}}_{t - 1} + ε_{i, t}

(4)

where PCCO₂ is expressed with a title like Wit which is the main outcome variable, while all the explanatory variables (CETech, GBonds, AFIns; FinME) are presented in term Z_{i, t− 1.} Moreover, dependent and independent variables average is given by $\bar{X}$ _{t− 1} which mitigates the CSD spillover effects. Where lagged value for each variable is given by P_w, P_z, and P_x. The next equation presents the long-run coefficients and mean group estimators:

{\hat{π}}_{C D - A R D L, i} = \frac{1}{1 = Σ_{I} = 0} \sum_{I = 0}^{p z} {\hat{γ}}_{I i} {\hat{φ}}_{I, t}

(5)

{\hat{\bar{π}}}_{M G} = \frac{1}{N} \sum_{i = 1}^{N} {\hat{π}}_{i}

(6)

While the short run estimators are given as:

{∆ W}_{i t} = ϑ_{i} [W_{i, t - 1} - π_{i} Z_{i, t - 1}] - \sum_{i = 0}^{p w - 1} φ_{i, t} ∆_{i} W_{i, t - 1} + \sum_{i = 0}^{p z} γ_{i, t} ∆_{i} Z_{i, t - 1} + \sum_{i = 0}^{p x} α_{i} {\bar{X}}_{t} + ε_{i, t}

(7)

where in aforesaid expression: Δi = t − (t − 1)

{\hat{τ}}_{i} = - (1 - \sum_{i = 0}^{p w} {\hat{φ}}_{i, t})

(8)

{\hat{π}}_{i} = \frac{1}{{\hat{τ}}_{i}} \sum_{i = 0}^{p z} {\hat{γ}}_{i, t}

(9)

{\hat{\bar{π}}}_{M G} = \frac{1}{N} \sum_{i = 1}^{N} {\hat{π}}_{i}

(10)

For robustness checks, the study employs AMG model proposed by Teal and Eberhardt⁸⁹ and CCEMG estimator proposed by Pesaran.⁹⁰ These two estimators infer robust findings and better capture the structural breaks, slope heterogeneity, and CSD. The description, abbreviations, measurement, and sources of data for each variable are presented in Table 1. Over the sample period, the ASEAN economies adopted dramatic trends of green financing, financialization, and engineering technologies for climate quality (Table 2).

Table 1.

Measurements, abbreviations, and data source for the variables.

Variables	Measurement	Abbreviations	Data-source
Climate quality	CO₂ emission MTPC	PCCO₂	WDI
Green credit	Index of green bond	GBonds	S&P Index
Environmental technologies	Environmental patents % of total patents	SEPTech	OECD.stat
Financial advancement	Financial institution access index	AFinI	IMF.org
	+	+
	Financial market efficiency	FinME

Note. MTPC stands for “Metric tons per capita”. The selection of dependent variable is motivated by the study of Cosmas, Chitedze,⁷⁴ while the selection of Green credit, environmental technologies, and financial advancement is motivated by Hammoudeh, Ajmi,⁷⁵ Cheng, Ren⁷⁶ and Amin, Dogan,⁵⁴ respectively.

Table 2.

CSD outputs.

Variables	t-Statistics
PCCO₂	30.375***
SEPTech	22.092***
GBonds	29.187***
FinME	24.292***
AFinI	30.184***

Note. *** indicates the level of significance with 1 % confidence interval.

Results and discussion

Firstly, cross-sectional dependence was tested for the variables of the study. The null hypothesis assumes for CSD states that “CSD does not exist in the data”. It means an alternate hypothesis is the opposite of it.⁷⁹ Table 3 indicates that there is a presence of CSD, hence, null hypothesis is rejected for all the variables at 1% significance level. The study mainly emphasizes on tests to evaluate stationarity properties recommended by Pesaran⁸² and Bai and Carrion-I-Silvestre.⁸³ These tests are prominent in the literature when compared to traditional unit root techniques. This is because of a certain range of benefits and inclusion of CSD. These tests also consider heterogeneity and structural breaks in data and slope coefficient. Pesaran unit root model presented in Table 3 indicates that all of the constructs are stationary at level and first difference, but the model does not explore the structural breaks in data. However, Bai and Carrion-I-Silvestre⁸³ cointegration test rejects the variables’ stationarity at levels, which confirm a unit root in the series while integrating the structural changes as presented by P and Pm statistics in Table 3. In the next step, we investigate the heterogeneity of slope parameters with the application of the model proposed by Swamy⁸⁵ and modified by Pesaran and Yamagata.⁸⁴ Alam, Miah⁹¹ argue that the estimation of slope heterogeneity is very important in panel data analysis. For this purpose, the null hypothesis is proposed i.e., “homogeneity in the slope coefficient”, with an alternative hypothesis being the presence of slope heterogeneity. To justify both hypotheses, the findings of Pesaran and Yamagata⁸⁴ test are exhibited via Table 4. The outcomes reveal that t-statistics for slope heterogeneity are statistically significant at a 1% level of confidence; hence, the alternative hypothesis is accepted because coefficients exhibit significant slope heterogeneity.

Table 3.

Outcomes of unit-root test with and with no structural-breaks.⁸²

Variables	I(0)		I(1)
Variables	CIPS	MCIPS	CIPS	MCIPS
Panel A: Pesaran (2007) tests for structural breaks
SEPTech	−5.745**	−8.648***	−11.184***	−12.664***
FinME	−4.102**	−5.493**	−9.104***	−11.749***
AFinI	−4.002***	−6.194***	−16.554***	−15.840***
GBonds	−7.174***	−7.001**	−13.093***	−15.941***
PCCO₂	−6.754***	−5.193**	−15.886***	−17.749***

	I(0)			I(1)
	Z	Pm	P	Z	Pm	P
Panel B: Bia and Carrion-i-silvestre (2009)
SEPTech	0.258	0.965	20.352	−3.027***	8.526***	88.323***
FinME	0.682	0.524	19.526	−4.608***	6.211***	60.258***
AFinI	0.635	0.875	24.028	−6.245***	4.527***	72.632***
GBonds	0.752	0.517	18.504	−5.685***	7.570***	62.204***
PCCO₂	0.125	0.368	19.652	−4.826***	9.663***	71.205***

Note. *** indicates the level of significance with a 1 % confidence interval. While ** indicates the same for a 5 % confidence interval. I(0) presents the outcome at level, while I(1) is the indication for first difference.

Table 4.

Estimation for heterogeneity of slope coefficients.

DV: PCCO₂
	t-score (Sig.)
Δ-tilde	32.935***
Δ-tilde adjusted	37.436***

In the next step, the cointegration property is examined with the application of the cointegration test introduced by Westerlund and Edgerton,⁸⁶ the null hypothesis here means “no cointegration with the presence of CSD”. It is clear from Table 5 that cointegration exists as the study finds negative and significant coefficients (at a 1 % significant level) in all three scenarios: “No-Break, Mean-shift, and regime-shift”, therefore, the alternative hypothesis is accepted.

Table 5.

Outcomes of panel cointegration tests of Westerlund and Edgerton.⁸⁶

Test	No-break	Mean-shift	Regime-shift
Dependent variable: PCCO₂
Zφ(N)	−5.458***	−4.642***	−4.792***
Zτ(N)	−4.725***	−4.983***	−4.874***

Note. *** indicates the level of significance with a 1 % confidence interval.

Moreover, the current research work also employs cointegration analysis with three different standardized criteria namely, trend, constant, and no deterministic specification. Cointegration results for these three criterions are presented in Table 6 for each ASEAN country. The test statistics accept the alternative hypothesis because Table 6 is evidence of long-run significant cointegration among variables for all the countries.

Table 6.

Outcomes of cointegration tests of Banerjee and Carrion-i-Silvestre.⁸⁷

Panel	No deterministic specification	At constant	At trend
DV: PCCO₂
Full sample	−6.693***	−5.793***	−5.625***
Malaysia	−4.009***	−4.832***	−4.524***
Indonesia	−6.119***	−5.948***	−5.215***
Cambodia	−4.348***	−4.112***	−5.318***
Singapore	−6.872***	−5.009***	−5.524***
Philippines	−6.002***	−5.043***	−3.985***

Note. *** indicates the level of significance with a 1 % confidence interval.

After the confirmation of the presence of significant cointegration, the outcomes for “CS-ARDL” are shown in Table 7. Initially, we consider the long-run estimations and find that sustainable environmental change mitigation technologies (SEPTech) are negatively and significantly connected with per capita carbon emissions in selected ASEAN economies. It is clear that a rise in the SEPTech brings a 0.615 % decline in carbon emission, the connection is significant at a 1% level of confidence. The prevailing literature infers from the launch of the UNFCCC progression, all the members had assured to encourage technology usage that mitigates carbon emissions.⁹² Du, Li⁵⁵ investigate the influence of green technologies on CO₂ emissions from 71 nations over a period from 1996 to 2012. The authors divide the sample into high-income and low-income economies. The authors find that innovative green technologies do not favorably enhance the climate quality in countries where the level of income is below the threshold level. While green technologies reduce climate deterioration in economies with levels of income above the threshold point Turkish economy is investigated by Shan, Genç,¹⁴ and the authors find that green innovations and related applications mitigate carbon emissions in Turkey. He, Xue⁹³ confirm the optimistic role of green technological innovations in mitigating carbon emissions in 26 provinces in China. This study infers that climate change mitigation technologies/patents or related technologies reduce the reliance on conventional technologies and can better cope with environmental degradation, hence leading to better environmental quality. It is clear from the Table 7 that the efficiency of financial markets has a positive (β = 0.249) and significant (t-stat = 2.648) influence on CO₂ emission. Sheraz, Deyi⁶² find similar outcomes from G20 countries and argue that carbon emission enhances with financial advancements.

Table 7.

Outcomes of CS-AARDL model for long-run and short-run.

	B	t-stat
Long-run
SEPTech	−0.615***	−5.583
FinME	0.249**	2.648
AFinI	−0.092***	−3.784
GBonds	−0.269***	−3.859
CSD-stat	–	0.021
Short-run
SEPTech	−0.166**	−3.128
FinME	0.193**	3.016
AFinI	−0.224*	−2.954
GBonds	−0.195**	−3.002
ECT(−1)	−0.284**	−3.136
R-sq		0.329
Adj R-sq		0.261

Note. *** indicates the level of significance with a 1 % confidence interval. While ** indicates the same for a 5 % confidence interval.

From Nigeria, Odugbesan and Adebayo⁹⁴ investigate the stimulus of financial advancements on emissions of CO₂, and observe a positive-significant connectedness between financial advancements and carbon emissions. Taking a global sample, Jiang and Ma⁹⁵ explore that financial advancement induces positive trends in carbon emissions, hence, mitigating the environmental quality. However, the authors find an insignificant association between the variables in emerging economies. The positive linkages between the efficiency of financial markets and emission of CO₂ may be because the capital markets of ASEAN countries allow the firms to avail debt and equity financing which leads to enhanced economic development via the consumption of traditional energy. Reducing this trend of increasing carbon emissions through the efficiency of financial markets requires strict supervision of financial markets to discourage investments in traditional energy sources.

As far as the connection between access to financial institutions and carbon emissions is concerned, we observe that an upsurge of 1% in access to financial institutions reduces 0.092 % carbon emissions. A significant connectedness between access to financial institutions and carbon emission is explored by Amin, Dogan,⁵⁴ the authors observed a significant linkage between the variables. Table 7 also signifies that green bonds decrease CO₂ emission. It is clear from Table 8 that an increase of 1% in green bonds issuance reduces 0.269% emission of carbon dioxide. Green bonds play a constructive role in climate quality because the proceeds from green bonds are attributed to projects that bring no or low climate impacts. Heine, Semmler⁹⁶ also argue that proceeds of green bonds reduce environmental damage. Similarly, Yeow and Ng⁹⁷ claim that the proceeds of green bonds enhance the quality of the environment. Governments and policymakers should enhance or strengthen the accountability and transparency of climate disclosure to tighten the control over climate concerns.

Table 8.

Outcomes for robustness estimations.

Variables	AMG		CCEMG
Variables	β	t-stat	β	t-stat
SEPTech	−0.625**	−2.587	−0.882**	−2.746
FinME	0.257**	2.612	0.246*	1.830
AFinI	−0.311***	−3.715	−0.304**	−2.358
GBonds	−0.148**	−2.070	−0.156**	−2.145
Wald test	−	36.320	−	45.052

For the short run, Table 7 presents the outcomes of the CS-ARDL model. Sustainable environmental change mitigation technologies (SEPTech) are significantly and negatively connected with environmental quality. The efficiency of financial markets is extremely accountable for enhanced carbon emission, while access to financial institutions reduces environmental deterioration. Finally, the issuance of green bonds also enhances the climate quality by diminishing carbon emission in the short run.

Bekun⁹⁸ and Glomsrød and Wei⁹⁹ endorse similar findings and claim that green finance diminishes the global ingesting of coal and enhances the feasting of renewable energy, therefore mitigating global climate deterioration. Furthermore, ECT (error correction term) infers the “response magnitude” or “speed adjustment” of the outcome variable (PCCO₂) to variations in explanatory variables of the current study. The ECT coefficient is inverse and significant, which confirms the convergence towards equilibrium with an annual adjustment rate of 32%. Table 8 exhibits the robustness of findings, where the Table makes it clear that there exists an inverse and significant association between the sustainable environmental change mitigation technologies (SEPTech) and carbon emissions with AMG and CCEMG estimations. Furthermore, green bonds and access to financial institutions reduce the emission of CO_2, while the efficiency of financial markets enhances carbon emissions with both estimators,¹⁰⁰ see Table 8).

Conclusion and policy implications

For international communities, climate change threat is viewed as a topic of concern in recent years. Regardless of sequential meetings and arguments within academia and practitioners, economies are failed to get rid of the threat. Because of excessive usage of non-renewable sources, ASEAN region made huge contribution to the threat, due to which it becomes more difficult to be removed permanently. In this context, sustainable environmental change mitigation technology is vital for the enhancement of climate quality. In this regard, the UN has defined SDGs for industry, structure, innovations, and climate action with 17 other labels that trigger global sustainability. Similarly, plenty of literature investigates the impression of financial advancement on the emission of carbon dioxide on a large scale; however, less attention is paid to the impact of access to financial institutions and the efficiency of financial markets on climate change. Meanwhile, in a complex financial ecosystem, financing and investing activities are confined to conventional sources of energy, which results in enhanced greenhouse gas and carbon emissions. The complexity of the financial ecosystem enhances the need to utilize green bonds, and sustainable finance for environmental protection. The study considers a panel examination to analyze the impact of sustainable environmental change mitigation technologies, access to financial institutions, efficiency of financial markets, and green bonds over a period from 2011 to 2020 from ASEAN countries. The current research work utilizes the CS-ARDL model to estimate the connectedness between dependent and independent variables. It is clear from the results of the long-run ARDL model that sustainable environmental change mitigation technologies, green bonds, and access to financial institutions mitigate carbon emissions. However, sustainable development goals can not be achieved unless corporate firms and private investor mobilize finance for it. Practitioners are also obliged to devote themselves in green education and focus on consumer’s green preferences. Also, the increasing awareness of investors in terms of green initiative helps firms to design sustainable products and promote green mandate in society. Since, green bonds help in carbon reduction, therefore, investors’ trust is crucial for green bond investment. The study, thus, calls for green bond initiatives to be considered by corporate firms. Also, a deeper understanding of green financing help in the implementation of sustainable climate policies and offer a platform to shift the focus of resource toward zero carbon projects. Also, in order to acknowledge ecological adversities linked with financial inclusivity, it is quite obvious for emerging markets to motivate their financial markets more inclusive and eco-friendlier. In this context, it is crucial for economies to determine financial risks that are associated with environmental risks. This way proactive measures can be taken to make financial services environmentally friendly. Also, the proposition of green financial schemes should be considered by institutions to neutralize the damage inflicted by financial institutions.

Based on the findings, ASEAN economies must devise diverse policy brackets at the state and provincial levels. Policymakers in ASEAN economies should find ways to generate sustainable jobs at the macroeconomic level. This helps in creating a positive impact as a green workforce means less emission. Promoting green jobs would also increase the green consumption basket in society, which means the per capita income will increase, which will eventually enhance environmental conditions. Besides, effective development in green finance areas may extend the scope of financial support specifically for green projects, which would counterbalance the consequences of inflation on green project development. In the end, efforts will be diverted toward renewable energy consumption. Concerning the provincial level, ASEAN policymakers should constitute more incentive policies in those provinces where a sustainable development path is difficult to be maintained. Contrarily, at the regional level green subsidies and loans, pressure policies such as carbon taxes should be promoted in order to achieve sustainable development. Besides, uniform policies are essential to be counted generally because this could be an underlying issue that would make it difficult for economies to achieve sustainability in the long-run. In addition, to regulate climate control from the demand side also, the study also elucidates how the trend of low carbon and environmentally friendly goods/services has been emerged in the long-term; however, the shift can be hindered due to external shocks or critical events. This indicates that there is a need to find innovative ways and integrate the demand side with the supply side of environmental policies.

The efficiency of financial markets enhances carbon emissions. Almost similar sign of coefficients is observed in the short run with slightly different magnitudes. AMG and CCEMG estimators endorsed similar outcomes. Based on these findings, the study proposes the following policy recommendations. In the current era, the adoption of sustainable environmental technologies is a serious issue. A huge capital investment is required for replacing traditional technologies with eco-friendly technologies. ASEAN economies should propagate climate regulations to overcome hindrances in the implementation of climate patents in different sectors. In this regard, removal of industries using traditional energy sources, or imposition of tax on heavily polluting enterprises may be fruitful. Moreover, financial markets and financial institutions lack climate regulation policies; hence, the businesses result in higher carbon emissions. Therefore, the study suggests that efficient financial markets regarding climate regulation can ensure an eco-friendly environment. ASEAN countries should encourage capital markets to develop green growth-inclusive policies to trigger green financing for the development of eco-friendly technologies. In this regard, the promotion of green and sustainable financial instruments i.e., green bonds would promote environmental quality. The proceeds of green bonds would facilitate the development and implementation of green technologies for climate change mitigation. Also, in the absence of standardized and universally accepted framework of green bonds, the current market depends on “private governance regimes”. Although their brutal focus on project evaluation, fair reporting, and clean and standardized procedures enlighten transparency and disclosure, which are the two success parameters of the green bond market. However, credibility is still an issue to be addressed to make it easier for investors to opt for sustainable investment.

The future researcher may compare the impact of traditional versus environmental technologies to explore the relationship introduced in this study. The researcher may compare the ASEAN with Asian or European economies. Moreover, it would be fruitful if the future researchers divide the sample period in different subsamples or divide the sample period on the basis of different conditions or methods introduced by Shahid and Sattar¹⁰¹ and Shahid.¹⁰² Subsample analysis may provide more fruitful results, but we rest it for future researchers to incorporate the suggestions.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Ho Chi Minh City University of Economics and Finance (UEF), FPT University. This research is partly funded by University of Economics Ho Chi Minh City (UEH), Vietnam.

ORCID iD

Nguyen Tran Thai Ha

Note

References

Irfan

Razzaq

Sharif

, et al. Influence mechanism between green finance and green innovation: exploring regional policy intervention effects in China. Technol Forecast Soc Change 2022; 182: 121882.

Razzaq

Wang

Chupradit

, et al. Asymmetric inter-linkages between green technology innovation and consumption-based carbon emissions in BRICS countries using quantile-on-quantile framework. Technol Soc 2021; 66: 101656.

Nota

Peluso

Lazo

. The contribution of industry 4.0 technologies to facility management. Int J Eng Bus Manag 2021; 13: 184797902110241.

Huang

Liao

. Loaning scale and government subsidy for promoting green innovation. Technol Forecast Soc Change 2019; 144: 148–156.

Nguyen

TTH

Phan

Tran

, et al. The role of renewable energy technologies in enhancing human development: empirical evidence from selected countries. Case Stud Chem Environ Eng 2023; 8: 100496.

Pérez-Campdesuñer

García-Vidal

Sánchez-Rodríguez

, et al. Influence of the socio-economic environment on the entrepreneurs behavior. Cases of Cuba and Ecuador. Int J Eng Bus Manag 2021; 13: 184797902199450.

Guo

Nowakowska-Grunt

Gorbanyov

, et al. Green technology and sustainable development: assessment and green growth frameworks. Sustainability 2020; 12(16): 6571.

Shahid

Azmi

Ali

, et al. Uncovering risk transmission between socially responsible investments, alternative energy investments and the implied volatility of major commodities. Energy Econ 2023; 120: 106634.

Nguyen

Dau

V-H

, et al. The nexus between greenhouse gases, economic growth, energy and trade openness in Vietnam. Environ Technol Innov 2022; 28: 102912.

10.

Vuong

Bui

. The role of corporate social responsibility activities in employees’ perception of brand reputation and brand equity. Case Stud Chem Environ Eng 2023; 7: 100313.

11.

Nguyen

TTH

Bui

LTB

Tran

, et al. The toxic waste management towards corporates’ sustainable development: a causal approach in Vietnamese industry. Environ Technol Innov 2023; 31: 103186.

12.

Yap

Sankaran

Chew

, et al. Advancement of green technologies: a comprehensive review on the potential application of microalgae biomass. Chemosphere 2021; 281: 130886.

13.

Zhang

Sadiq

, et al. The impact of non-renewable energy production and energy usage on carbon emissions: evidence from China. Energy Environ. 2023; 0958305X2211504.

14.

Shan

Genç

Kamran

, et al. Role of green technology innovation and renewable energy in carbon neutrality: a sustainable investigation from Turkey. J Environ Manag 2021; 294: 113004.

15.

Razzaq

Sharif

, et al. Testing the directional predictability between carbon trading and sectoral stocks in China: new insights using cross-quantilogram and rolling window causality approaches. Technol Forecast Soc Change 2022; 182: 121846.

16.

Liu

Razzaq

Shahzad

, et al. Technological changes, financial development and ecological consequences: a comparative study of developed and developing economies. Technol Forecast Soc Change 2022; 184: 122004.

17.

Jaiswal

Chowdhury

Yadav

, et al. Renewable and sustainable clean energy development and impact on social, economic, and environmental health. Energy Nexus 2022; 7: 100118.

18.

Zhao

Huang

. Understanding the dynamic role of natural resources, green technology, economic integration and social globalization towards sustainable environment in China. Resour Pol 2022; 79: 103079.

19.

Wang

Mirza

Vasbieva

, et al.

The nexus of carbon emissions, financial development, renewable energy consumption, and technological innovation: what should be the priorities in light of COP 21 Agreements?

J Environ Manag 2020; 271: 111027.

20.

Braun

Wield

. Regulation as a means for the social control of technology. Technol Anal Strat Manag 1994; 6(3): 259–272.

21.

Tran

. The role of green trade, green energy technologies, and green finance on environmental degradation: evidence from asean countries. Int J Econ Finance Stud 2023; 15(2): 20–39.

22.

Fikru

Kisswani

. Environmental impacts of household energy use in ASEAN-5 countries: are there asymmetric effects? Energy Pol 2023; 182: 113771.

23.

Munir

Lean

Smyth

. CO2 emissions, energy consumption and economic growth in the ASEAN-5 countries: a cross-sectional dependence approach. Energy Econ 2020; 85: 104571.

24.

Wang

Rasool

Asghar

, et al. Dynamic linkages among CO2 emissions, human development, financial development, and globalization: empirical evidence based on PMG long-run panel estimation. Environ Sci Pollut Res Int 2019; 26(36): 36248–36263.

25.

Çoban

Topcu

. The nexus between financial development and energy consumption in the EU: a dynamic panel data analysis. Energy Econ 2013; 39: 81–88.

26.

Tang

Tan

. The linkages among energy consumption, economic growth, relative price, foreign direct investment, and financial development in Malaysia. Qual Quant 2012; 48(2): 781–797.

27.

Arivazhagan

Srikrishna

Kaliyan

, et al. State estimation with trusted phasor measuring unit placements of distribution system involving renewable energy sources. Int J Eng Bus Manag 2022; 14: 184797902211063.

28.

Tamazian

Chousa

Vadlamannati

. Does higher economic and financial development lead to environmental degradation: evidence from BRIC countries. Energy Pol 2009; 37(1): 246–253.

29.

. The influence of financial development on energy consumption: Worldwide evidence. Int J Environ Res Public Health 2020; 17(4): 1428.

30.

Moslehpour

Firman

Lin

C-H

, et al. The moderating impact of government support on the relationship between tourism development and growth, natural resources depletion, sociocultural degradation, economic environment, and pollution reduction: case of Indonesian economy. Environ Sci Pollut Res Int 2023; 30(19): 56863–56878.

31.

Chu

Hölscher

McCarthy

. The impact of productive and non-productive government expenditure on economic growth: an empirical analysis in high-income versus low- to middle-income economies. Empir Econ 2020; 58(5): 2403–2430.

32.

Tran

. Financial development and environmental quality: differences in renewable energy use and economic growth. Pol J Environ Stud 2023; 32: 2855–2866.

33.

Talha

Sohail

Tariq

, et al. Impact of oil prices, energy consumption and economic growth on the inflation rate in Malaysia. Cuad Econ 2021; 44(124): 26–32.

34.

Zhang

Sadiq

, et al. Impact of a sharing economy on sustainable development and energy efficiency: evidence from the top ten Asian economies. J Innov Knowl 2023; 8(1): 100320.

35.

Worldbank . World Development Indicators. Washington, DC, Worldbank, 2021.

36.

Muganyi

Yan

Sun

H-P

. Green finance, fintech and environmental protection: evidence from China. Environ Sci Ecotechnol 2021; 7: 100107.

37.

Taghizadeh-Hesary

Yoshino

Phoumin

. Analyzing the characteristics of green bond markets to facilitate green finance in the post-COVID-19 world. Sustainability 2021; 13(10): 5719.

38.

Lin

C-Y

Chau

Tran

, et al. Development of renewable energy resources by green finance, volatility and risk: empirical evidence from China. Renew Energy 2022; 201: 821–831.

39.

Sikhar

Gaurav

Zhang

, et al. A decision framework for the analysis of green supply chain contracts: an evolutionary game approach. Expert Syst Appl 2012; 39(3): 2965–2976.

40.

Chien

Hsu

C-C

Zhang

, et al. Sustainable assessment and analysis of energy consumption impact on carbon emission in G7 economies: mediating role of foreign direct investment. Sustain Energy Technol Assessments 2023; 57: 103111.

41.

Bai

Wang

K-T

Tran

, et al. Measuring China’s green economic recovery and energy environment sustainability: econometric analysis of sustainable development goals. Econ Anal Policy 2022; 75: 768–779.

42.

Chien

Chau

Sadiq

, et al. The impact of economic and non-economic determinants on the natural resources commodity prices volatility in China. Resour Pol 2022; 78: 102863.

43.

Tseng

M-L

Chang

C-H

Lin

C-W

, et al. Environmental responsibility drives board structure and financial and governance performance: a cause and effect model with qualitative information. J Clean Prod 2020; 258: 120668.

44.

Zhang

Liu

Zhao

, et al. Does green finance agglomeration improve carbon emission performance in China? A perspective of spatial spillover. Appl Energy 2024; 358: 122561.

45.

Meirun

Mihardjo

LWW

Haseeb

, et al. The dynamics effect of green technology innovation on economic growth and CO2 emission in Singapore: new evidence from bootstrap ARDL approach. Environ Sci Pollut Res Int 2021; 28(4): 4184–4194.

46.

Shi

Lai

. Identifying the underpin of green and low carbon technology innovation research: a literature review from 1994 to 2010. Technol Forecast Soc Change 2013; 80(5): 839–864.

47.

Wang

. Carbon emission reductions under global low-carbon technology transfer and its policy mix with R&D improvement. Energy 2021; 216: 119300.

48.

Chien

Anwar

Hsu

C-C

, et al. The role of information and communication technology in encountering environmental degradation: proposing an SDG framework for the BRICS countries. Technol Soc 2021; 65: 101587.

49.

Jiao

Chen

Bai

. Is green technology vertical spillovers more significant in mitigating carbon intensity? Evidence from Chinese industries. J Clean Prod 2020; 257: 120354.

50.

Yin

Zhang

. Enhancing the competitiveness of multi-agent cooperation for green manufacturing in China: an empirical study of the measure of green technology innovation capabilities and their influencing factors. Sustain Prod Consum 2020; 23: 63–76.

51.

Iyer

Hultman

Eom

, et al. Diffusion of low-carbon technologies and the feasibility of long-term climate targets. Technol Forecast Soc Change 2015; 90: 103–118.

52.

Lin

. Green technology innovations, urban innovation environment and CO2 emission reduction in China: fresh evidence from a partially linear functional-coefficient panel model. Technol Forecast Soc Change 2022; 176: 121434.

53.

Mahalik

Mallick

, et al. The moderating effects of democracy and technology adoption on the relationship between trade liberalisation and carbon emissions. Technol Forecast Soc Change 2022; 180: 121712.

54.

Amin

Dogan

Khan

. The impacts of different proxies for financialization on carbon emissions in top-ten emitter countries. Sci Total Environ 2020; 740: 140127.

55.

Yan

. Do green technology innovations contribute to carbon dioxide emission reduction? Empirical evidence from patent data. Technol Forecast Soc Change 2019; 146: 297–303.

56.

Aldulaimi

Abdeldayem

. Examining the impact of renewable energy technologies on sustainability development in the middle east and north Africa region. Int J Eng Bus Manag 2022; 14: 184797902211108.

57.

Jalil

Feridun

. The impact of growth, energy and financial development on the environment in China: a cointegration analysis. Energy Econ 2011; 33(2): 284–291.

58.

Zhang

Y-J

. The impact of financial development on carbon emissions: an empirical analysis in China. Energy Pol 2011; 39(4): 2197–2203.

59.

Khan

Binh

. The heterogeneity of renewable energy consumption, carbon emission and financial development in the globe: a panel quantile regression approach. Energy Rep 2020; 6: 859–867.

60.

Bouraima

Stević

Tanackov

, et al. Assessing the performance of Sub-Saharan African (SSA) railways based on an integrated Entropy-MARCOS approach. Oper Res Eng Sci Theor Appl 2021; 4(2): 13–35.

61.

Haseeb

Xia

Danish , et al. Financial development, globalization, and CO2 emission in the presence of EKC: evidence from BRICS countries. Environ Sci Pollut Res Int 2018; 25(31): 31283–31296.

62.

Sheraz

Deyi

Ahmed

, et al. Moderating the effect of globalization on financial development, energy consumption, human capital, and carbon emissions: evidence from G20 countries. Environ Sci Pollut Res Int 2021; 28(26): 35126–35144.

63.

Rjoub

Odugbesan

Adebayo

, et al. Sustainability of the moderating role of financial development in the determinants of environmental degradation: evidence from Turkey. Sustainability 2021; 13(4): 1844.

64.

Koondhar

Shahbaz

Ozturk

, et al. Revisiting the relationship between carbon emission, renewable energy consumption, forestry, and agricultural financial development for China. Environ Sci Pollut Res Int 2021; 28(33): 45459–45473.

65.

Wei

. Financial development, openness, innovation, carbon emissions, and economic growth in China. Energy Econ 2021; 97: 105194.

66.

Gilchrist

Zhong

. The limits of green finance: a survey of literature in the context of green bonds and green loans. Sustainability 2021; 13(2): 478.

67.

Tuhkanen

Vulturius

. Are green bonds funding the transition? Investigating the link between companies’ climate targets and green debt financing. J Sustain Finance Invest 2020; 12(4): 1194–1216.

68.

Fatica

Panzica

. Green bonds as a tool against climate change? Bus Strategy Environ 2021; 30(5): 2688–2701.

69.

Yang

Ran

, et al. Energy structure, digital economy, and carbon emissions: evidence from China. Environ Sci Pollut Res Int 2021; 28(45): 64606–64629.

70.

Vulturius

Maltais

Forsbacka

. Sustainability-linked bonds – their potential to promote issuers’ transition to net-zero emissions and future research directions. J Sustain Finance Invest 2024; 14(1): 116–127.

71.

Wang

Zhi

. The role of green finance in environmental protection: two aspects of market mechanism and policies. Energy Proc 2016; 104: 311–316.

72.

Zhang

. Transportation infrastructure, innovation capability, and urban economic development. Transform Bus Econ 2021; 20(3C): 526–545.

73.

Theeraviriya

Ruamboon

Praseeratasang

. Solving the multi-level location routing problem considering the environmental impact using a hybrid metaheuristic. Int J Eng Bus Manag 2021; 13: 184797902110173.

74.

Cosmas

Chitedze

Mourad

. An econometric analysis of the macroeconomic determinants of carbon dioxide emissions in Nigeria. Sci Total Environ 2019; 675: 313–324.

75.

Hammoudeh

Ajmi

Mokni

. Relationship between green bonds and financial and environmental variables: a novel time-varying causality. Energy Econ 2020; 92: 104941.

76.

Cheng

Ren

Wang

, et al. Heterogeneous impacts of renewable energy and environmental patents on CO2 emission - evidence from the BRIICS. Sci Total Environ 2019; 668: 1328–1338.

77.

Westerlund

. Panel cointegration tests of the fisher effect. J Appl Econ 2008; 23(2): 193–233.

78.

Salim

Yao

Chen

. Does human capital matter for energy consumption in China? Energy Econ 2017; 67: 49–59.

79.

Pesaran

. Testing weak cross-sectional dependence in large panels. Econom Rev 2014; 34(6–10): 1089–1117.

80.

Levin

Lin

James Chu

. Unit root tests in panel data: asymptotic and finite-sample properties. J Econom 2002; 108: 1–24.

81.

Pesaran

Shin

. Testing for unit roots in heterogeneous panels. J Econom 2003; 115(1): 53–74.

82.

Pesaran

. A simple panel unit root test in the presence of cross-section dependence. J Appl Econ 2007; 22(2): 265–312.

83.

Bai

Carrion-I-Silvestre

. Structural changes, common stochastic trends, and unit roots in panel data. Rev Econ Stud 2009; 76(2): 471–501.

84.

Hashem Pesaran

Yamagata

. Testing slope homogeneity in large panels. J Econom 2008; 142(1): 50–93.

85.

Swamy

PAVB

. Efficient inference in a random coefficient regression model. Econometrica 1970; 38(2): 311.

86.

Westerlund

Edgerton

. A simple test for cointegration in dependent panels with structural breaks. Oxf Bull Econ Stat 2008; 70(5): 665–704.

87.

Banerjee

Carrion-i-Silvestre

. Testing for panel cointegration using common correlated effects estimators. J Time Ser Anal 2017; 38(4): 610–636.

88.

Zeqiraj

Sohag

Soytas

. Stock market development and low-carbon economy: the role of innovation and renewable energy. Energy Econ 2020; 91: 104908.

89.

Teal

Eberhardt

. Productivity analysis in global manufacturing production. England: Department of Economics, University of Oxford, 2010.

90.

Pesaran

. Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 2006; 74(4): 967–1012.

91.

Alam

Miah

Hammoudeh

, et al. The nexus between access to electricity and labour productivity in developing countries. Energy Pol 2018; 122: 715–726.

92.

UNFCCC . United Nations climate change annual report 2021. Rio de Janeiro: United Nations Framework Convention on Climate Change, 2021.

93.

Xue

Zhao

, et al. Renewable energy technological innovation, market forces, and carbon emission efficiency. Sci Total Environ 2021; 796: 148908.

94.

Odugbesan

Adebayo

. The symmetrical and asymmetrical effects of foreign direct investment and financial development on carbon emission: evidence from Nigeria. SN Appl Sci 2020; 2(12): 1982.

95.

Jiang

. The impact of financial development on carbon emissions: a global perspective. Sustainability 2019; 11(19): 5241.

96.

Heine

Semmler

Mazzucato

, et al. Financing low-carbon transitions through carbon pricing and green bonds. Vierteljahrshefte Wirtschaftsforsch 2019; 88(2): 29–49.

97.

Yeow

S-H

. The impact of green bonds on corporate environmental and financial performance. Manag Finance 2021; 47(10): 1486–1510.

98.

Bekun

. Race to carbon neutrality in South Africa: what role does environmental technological innovation play? Appl Energy 2024; 354: 122212.

99.

Glomsrød

Wei

Goli

. Effect of wedelolactone and gallic acid on quinolinic acid-induced neurotoxicity and impaired motor function: significance to sporadic amyotrophic lateral sclerosis. Neurotoxicology 2018; 68: 1–12.

100.

Lin

Ullah

. Effectiveness of energy depletion, green growth, and technological cooperation grants on CO2 emissions in Pakistan’s perspective. Sci Total Environ 2024; 906: 167536.

101.

Shahid

Sattar

. Behavior of calendar anomalies, market conditions and adaptive market hypothesis: evidence from Pakistan stock exchange. Pak J Commer Soc Sci 2017; 11(2): 471–504.

102.

Shahid

. COVID-19 and adaptive behavior of returns: evidence from commodity markets. Humanit Soc Sci Commun 2022; 9(1): 323.