Assessing the long-term impact of macroeconomic and environment dynamics: Does sustainable energy production shape the environmental landscape of south ASIAN nations?

This study delves into the intricate interconnections among the financial sector, macroeconomic dynamics, and the advancement of sustainable energy production in South Asia. Utilizing data from 1987 to 2022 in Bangladesh, India, Pakistan, and Sri Lanka, the research employs the Structural Vector Autoregressive (SVAR) method to analyze financial market responses. The findings reveal nuanced insights into the complex relationship between economic and environmental factors, fostering a comprehensive understanding of the region's intricate nexus between financial markets and sustainable energy initiatives. In the Economic Growth model, the negative coefficient for CO2 emissions underscores environmental concerns, indicating an inverse relationship with economic growth. Conversely, positive coefficients for non-fossil energy production, Gross Fixed Capital Formation, stock exchange value, and domestic credit emphasize their positive impact on economic growth. A positive GDP coefficient in the CO2 emission model signifies heightened carbon emissions with economic growth, while a negative coefficient for non-fossil energy production suggests potential environmental benefits. The nearly unitary positive coefficient for Gross Fixed Capital Formation highlights its significant role in driving carbon emissions. The Non-Fossil Energy Production model reveals an inverse relationship between GDP and clean energy, suggesting a potential reliance on fossil fuels during economic growth. Positive coefficients for CO2 emissions, Gross Fixed Capital Formation, stock exchange value, and domestic credit highlight factors influencing non-fossil energy production. Long-run stable coefficients affirm variable significance, while a negative error correction term suggests convergence in developing countries. These models offer nuanced insights into the intricate relationships among economic, environmental, and energy variables


Introduction
The relationship between economic development, environmental sustainability, and financial dynamics has become a focal point of research in recent years (Sethi et al., 2020;Khan et al., 2022).Fossil fuel, a critical essential energy source, considerably influences economic growth and macroeconomic indicators (Husnain et al., 2023).Oil price shocks of the 1970s were characterized by high unemployment, inflation, and sluggish growth.However, since the mid-1980s, the relationship between oil prices and macroeconomics has eroded.Increases in oil prices can cause inflation and slow economic development and employment.Elevated oil prices increase manufacturing and transportation expenses, leading to higher costs for goods and services (Hashmi, 2023).The surge in oil prices has constrained the production of goods and services, exacerbating unemployment rates.This has implications for inflation, corporate profitability, investment dynamics, and productivity (Cal et al., 2024;Raihan, 2023).It also diminishes overall wealth and raises concerns about future economic conditions, affecting demand dynamics and overall wealth (Hamissou et al., 2023;O. R. Togun et al., 2023).
The existing literature extensively employs aggregate stock market indices to illustrate the adverse correlation between rising fossil fuel use and negative stock market returns.In contrast, a prevailing trend in research indicates that as the use of non-renewable fuel reduces, alternative energy stock prices tend to experience favorable returns (Sardorsky, 2008;Kumar et al., 2012;Managi and Okimoto, 2013).Nevertheless, the effects of both positive and negative fluctuations in the stock prices of clean energy companies exhibit considerable variation over short and long time frames (Kocaarslan and Soytas, 2019).Consequently, the current state of research on the interaction among green energy sources, stock market volume, and other macroeconomic factors becomes pivotal (Lopatin, 2023).Other asset groups, such as technology and investment, are also important.Various scholars underscore the importance of considering stock markets when valuing renewable energy sources (Zhang et al., 2018).
Additionally, some researchers delve into the role of commodities, such as gold and silver, as potential mitigators of risks associated with the fluctuations in clean energy stocks (Kilian, 2008).Another strand of research explores the relationship between green bonds and the dynamics of the clean energy market (Allegret et al., 2015).Gaining insights into the mechanisms governing the variability in alternative energy prices is crucial for market participants, especially environmentally conscious investors, to comprehend their exposure to price volatility and the underlying forces at play (Broadstock and Filis, 2014).Policymakers are urged to formulate regulations that address the risk of contagion stemming from the volatility of these asset types (Creti et al., 2014).This multifaceted approach aims to enhance comprehension and risk management within the evolving landscape of clean energy investments.
The dynamic relationship between macroeconomic fluctuations and environmental sustainability, particularly their impact on energy production, is multifaceted.It involves various economic, social, and environmental factors that interact and influence each other.Environmental conditions can affect macroeconomic trends, further complicating the relationship.This idea posits two fundamental queries discussed below, which pave the objectives of this research.
Corollary I: How Macroeconomic Fluctuations Affect Environment and Energy Production: Investment and Innovation: Economic expansions often lead to increased investment in research and development, promoting cleaner and sustainable energy technologies, while contractions may result in reduced funding, stifling the progress of environmentally friendly technologies during periods of economic stability.
Consumption Patterns: Economic booms can increase energy consumption due to increased industrial production and consumer demand, potentially utilizing conventional, less sustainable energy sources.Conversely, economic downturns can lead to decreased energy consumption due to reduced industrial activities, potentially promoting cleaner energy sources.
Policy and Regulation: Macroeconomic conditions significantly impact government policies and regulations, with periods of economic growth promoting environmental regulations, while economic downturns may result in regulatory rollbacks due to short-term economic recovery.Renewable Energy Investment: Economic stability boosts renewable energy investment, with growth periods offering more robust financing.However, economic downturns can create financial constraints, potentially slowing down investments in renewable energy infrastructure.
Corollary II: How Environment and Energy Production Affect Macroeconomic Fluctuations: Resource Scarcity and Price Volatility: Environmental degradation and resource scarcity can lead to energy market price volatility, with sudden disruptions affecting macroeconomic stability, inflation, and economic uncertainties, potentially causing geopolitical tensions.
Transition to Clean Energy: Transitioning to cleaner energy sources can boost economic activity by creating jobs and contributing to growth while enhancing energy security and reducing vulnerability due to dependence on exhaustible resources.
Climate Change Impacts: Extreme weather events, sea-level rise, and natural disasters can significantly impact economic productivity, disrupt supply chains, damage infrastructure, and cause substantial financial losses, affecting macroeconomic stability.
Policy and Regulatory Effects: Environmental policies reducing carbon emissions can impact industries and markets, impacting economic performance.Sustainable practices can stimulate innovation and create new opportunities in the renewable energy sector.
This study examines the relationship between financial advancement, macroeconomic fluctuations, and sustainable energy production in South Asia.It aims to evaluate existing research, identify gaps in understanding, and suggest future research directions.The study focuses on the impact of economic growth on crucial indicators like per capita GDP, CO2 emissions, electricity generation, stock market prices, gross fixed capital formation (GFCF), and credit.Integrating environmental factors with economic and financial indicators underscores the need to understand how economic activities influence the environment and how environmental factors impact economic and financial outcomes.The study also explores financial market responses, such as stock market prices and credit dynamics, to understand how economic and environmental factors manifest in the financial domain.The concept of Gross Fixed Capital Formation (GFCF) further explains the role of investment in driving economic growth and shaping environmental and financial outcomes.This comprehensive analysis is crucial for policymakers, economists, and environmentalists seeking sustainable economic development.
Against this backdrop, this study endeavors to contribute to the existing literature by employing a Structural Vector Autoregressive (SVAR) model to unravel the dynamic relationships among GDP per capita, CO2 emissions, electricity production, stock exchange prices, investment (GFCF), and credit to the private sector.By employing an empirical approach, this research aims to discern the nuanced interactions between these variables, providing a basis for informed decision-making and policy recommendations in the pursuit of a harmonious balance between economic growth, environmental sustainability, and financial stability.
After this introduction, section 2 reviews the existing literature, section 3 presents the empirical model and methodology, section 4 discusses the results and findings, and section 5 concludes.

Macroeconomic Fluctuations and Environmental Impact
The investigation into the relation between fluctuations in macroeconomic conditions, such as changes in GDP growth rates, inflation levels, and unemployment rates, and environmental factors, including climate change, pollution levels, and resource depletion, as well as energy production, has emerged as a central area of interest within the realm of academic research.This critical literature review examines vital research findings, methodologies, and gaps in understanding this multifaceted interaction.The existing body of research has explored the general correlation between oil prices and stock market activity.Many studies have utilized overall stock market indices to demonstrate that an upswing in oil prices correlates with a downturn in stock market returns (e.g., Creti et al., 2014;Broadstock and Filis, 2014;Ghosh and Kanjilal, 2016;Degiannakis et al., 2014).While there seem to be varied responses to changes in oil prices depending on the sector (Broadstock and Filis, 2014), the majority of literature indicates that escalating oil prices yield positive returns on stocks within the alternative energy sector (Sardorsky, 2008;Kumar et al., 2012;Managi and Okimoto, 2013, among others).This critical literature review synthesizes key insights from seminal works, methodological approaches, and integrated studies to unravel the complexities of this multifaceted relationship.Acemoglu et al.'s (2012) exploration of directed technical change delves into how environmental concerns influence the trajectory of technological progress.Dell et al. (2012) contribute by examining the historical interplay between climate change and economic growth.Shifting the focus to energy production within the framework of macroeconomic dynamics, Apergis and Payne's (2010) study investigates the relationship between renewable energy consumption and economic growth.Brunnermeier & Landau (2022) delve into the intricacies of aligning financial systems with climate goals.Cherp and Jewell (2014) explore the concept of energy security, emphasizing its evolution amidst changing macroeconomic conditions.
However, Kocaarslan and Soytas (2019) have demonstrated that the positive and negative shifts in oil prices on the stock prices of clean energy companies significantly differ in both short-and long-term perspectives.Consequently, the state of research on the impact of oil prices on the stock prices of clean energy firms remains inconclusive.Stern's (2007) influential Economics of Climate Change underscores the urgency of addressing climate-related challenges and highlights the economic implications of environmental shifts.Some studies, such as Doda et al.'s (2015) analysis of industrial action on energy policy outcomes and Müller and Schmidt's (2020) examination of the political economy of renewable energy production in Germany, provide nuanced perspectives.Financial development plays a crucial role in facilitating the growth of renewable energy projects.Researchers such as Ang (2019) argue that well-developed financial markets are essential for attracting investment in renewable energy initiatives.Countries with robust economic sectors, like Japan and South Korea, have witnessed significant advancements in renewable energy production in Asia.However, the relationship is complex, as highlighted by Du, Wei, and Wei (2020), who found that the impact of financial development on renewable energy varies across different Asian economies.
One key aspect is the role of banking institutions in financing renewable energy projects.Studies by Zhang et al. (2018) reveal that countries with a proactive banking sector are more likely to channel funds into renewable energy ventures.Conversely, nations with underdeveloped financial markets may struggle to attract sufficient investment, hindering the expansion of their renewable energy capacities.It is imperative to recognize these nuances when evaluating the relationship between financial development and renewable energy production in Asia.Waris et al. (2023b) investigate the dynamic interplay between vital maize crops, fertilizer consumption, and meteorological factors in Pakistan's provinces, emphasizing the agricultural sector's susceptibility to climate change.Utilizing quarterly data from 2000 to 2020, the research employs breakpoint unit root tests and the ARDL approach to unravel these complex relationships.Methodological considerations are addressed through Ang's (2004) discussion on decomposition analysis and Lütkepohl's (2006) insights into structural vector autoregressive models.Yet, Bassi and Van Sinderen's (2007) work on climate policy and renewable energy in Brazil and Miketa and Schrattenholzer's (2004) experiences with long-term energy scenarios reveal gaps and challenges in financial mechanisms and scenario development.As the literature underscores progress, it simultaneously emphasizes the need for future research to fill gaps, employ robust methodologies, and inform policy in a rapidly evolving global context.Waris et al. (2023c) explore the nexus between renewable energy patent innovation, international trade, and carbon emissions in ASEAN countries from 1990-2021.Findings affirm the KFC, highlighting the need for green product prioritization, advanced manufacturing, and policy interventions to address escalating carbon emissions in the region.Macroeconomic oscillations, characterized by economic boom and bust cycles, introduce complexity to the renewable energy landscape.During economic downturns, governments may prioritize short-term financial stability over long-term investments in renewable energy.This is evident in studies by Liu and Zhang (2017), who observed a slowdown in renewable energy projects during periods of economic recession in certain Asian countries.

Macroeconomic Oscillations and Renewable Energy
Conversely, some scholars (Nureen, Sun, et al., 2023) argue that economic downturns can stimulate interest in renewable energy as governments seek alternative sources of economic growth (Tang, 2018).The relationship between macroeconomic oscillations and renewable energy production in Asia is multifaceted, requiring a nuanced understanding of each country's specific economic, political, and social contexts (Nureen, Xin, et al., 2023).Shah et al. (2023), examining G-11 countries from 1990-2021, this study reveals that positive GDP shocks consistently increase CO2 emissions, while education has mixed effects over time.Natural resources and financial inclusion contribute to long-term emissions, emphasizing the need for sustainable policies promoting renewable energy and environmental awareness in pursuit of sustainable development goals (Nureen, Liu et al., 2023).
Interactive Dynamics Financial institutions may become risk-averse during economic uncertainty, leading to reduced funding for renewable energy projects (Fareed et al., 2022).Conversely, well-established financial markets can stabilize during economic downturns, providing a buffer for the renewable energy sector (Huang et al., 2019).Moreover, government policies are pivotal in shaping the interaction between financial development, macroeconomic oscillations, and renewable energy production.For instance, countries with supportive policy frameworks and incentives are more likely to weather economic uncertainties and attract sustained investment in renewable energy (Wang et al., 2021).Understanding the dynamic relationships between these factors is crucial for devising effective strategies to promote renewable energy in the diverse Asian economic landscape.
Studies have shown that financial development, particularly financial institutions such as banks, positively impacts renewable energy consumption from a macro perspective (Sun et al., 2023).Additionally, the depth, access, and efficiency of financial institutions and the development of financial markets also contribute to promoting renewable energy consumption (Obobisa, 2022).However, the impact of economic growth on CO2 emissions varies across regions.While financial development increases CO2 emissions in Africa, Asia-Pacific, and globally, it decreases CO2 emissions in America and Europe (Kim & Park, 2016).Furthermore, the growth of the renewable energy sector is facilitated by well-developed financial markets, which provide easier access to external financing (Amuakwa-Mensah & Näsström, 2022).Overall, a well-functioning banking sector is crucial for ensuring the necessary investments in renewable energy and achieving the goal of reducing CO2 emissions.Waris et al. (2023a) investigate the dynamic causal relationships between meteorological parameters and evapotranspiration in Lahore, Pakistan, from 2004-2020, using the ARDL Approach to Cointegration.Findings reveal significant long-run associations with geopotential height, surface air temperature, and other variables.At the same time, short-run relationships include cloud fraction and precipitation, with evapotranspiration strongly influenced by surface wind speed.
The literature review highlights the interconnected factors influencing Asia's renewable energy production, emphasizing the need for comprehensive, regionally focused, and longitudinal research to bridge existing knowledge gaps.Analysis often overlooks technological innovation's impact on financial viability and macroeconomic susceptibility to renewable energy projects (Rehman et al., 2023).This study explores the intricate relationship between technology, economic development, and macroeconomic fluctuations to understand the factors shaping Asia's renewable energy landscape.Understanding the complex relationships between finance, macroeconomics, and renewable energy production is crucial for promoting sustainable and resilient renewable energy development in Asia, contributing to academic discourse, and assisting policymakers, investors, and industry stakeholders.

Model and Methodology
To address the questions discussed in section 1, we now define a proper model to determine the relationships between macroeconomic factors and the energy and environment.The choice of the variables used with the underlined methodology is a novel feature of this study.
To comply with the cyclical innovations, let's modify the structural VAR model to include a log of GDP per capita (LY), log of total CO2 emissions (LCO), non-fossil Energy production (E), Stock exchange value (LS), Gross Fixed Capital Formation (LCF), and Domestic credit to the private sector (DC): Equation ( 1) has a unique feature: we define the variables of interest in the top three rows, whereas the given macroeconomic variables include investment, stock exchange volumes, and domestic credit (CD) to the private sector.We assume the credit variable represents the policy variables and financial innovations, while the stock exchange variable represents economic oscillations.In this model, aij are the coefficients in matrix A, capturing the contemporary impact of the variables on each other.힊i,t are the structural shocks or innovation.
We summarize Equation 1 as follows: Xt represents contemporaneous, while X(t-1) shows each variable's instantaneous behavior in this model specification.The detailed derivations of such models can be found in VAR literature (Bernanke & Mihov, 1998;Shahab & Mahmood, 2012;Ma et al., 2022).
To empirically test the model, we applied the unit root test and then used the SVAR approach to find the impulse responses of each variable.The estimated VAR residuals show a high level of volatility in some cases.The annual data of 36 years, spanning 1987-2022, is used to analyze four South Asian economies, including Bangladesh, India, Pakistan, and Sri Lanka.

Descriptive Analysis
To be in line with the objectives of this study, at first place, the data are analyzed for descriptive purposes.Table 1 shows the basic statistics about the variables of the model.The highest variation is observed in green energy production followed by domestic credit to private sector.The GDP per capita of these countries has shown the lowest variation because these countries belong to almost the same income group and have very close socio-cultural structures.Due to geographic connectedness, the CO2 emissions also show minimal standard deviations.In a nutshell, the data does not exhibit normality property, as the value of the Jarque-Bera test indicates.Then, we estimated the confidence ellipse between domestic credit to the private sector and renewable energy production, among renewable energy and environment, investment and environment, and stock market value and atmosphere.Figures 1 (a to d) show the direction of the relationship.The confidence Ellipse between Credit and Energy shows a negative correlation (but significant) between Bangladesh, India, and Sri Lanka, whereas a minimal but positive relationship exists in Pakistan.The confidence ellipse between renewable energy and pollution is high and adverse in all four economies.As indicated by Figure 1 (c), the confidence ellipse between Investment and Environment is very granular, showing a high but positive correlation between both, i.e., investment activities generate pollution.Investment in the stock market and renewable energy production has a low correlation in the case of Pakistan, whereas they are negative and medium in all other cases.

SVAR Approach
After applying the unit-root test for panel data, the mixed order of integration among the variables is assumed (to converse space, we have not shown its results here).However, the Structural VAR methodology is preferred over ARDL because the VAR model does not require the specific order of integration of certain dependent variables.
Moreover, it can help us estimate the impulse responses, variance decomposition, and long-run and short-run stable coefficients.It also shows the fluctuations we are looking for among the variables of interest.We discuss these factors turn by turn.

Impulse Responses:
Figure 2 shows that due to a positive shock in per capita GDP, the carbon emissions in South Asian economies respond positively.This response is stable over the long run.The initial response of renewable energy is approximately zero.Still, it becomes negative sooner, indicating that over the long run, a high growth trajectory reduces the chances of investing in green energy until a certain point of economic development.In these middleincome South Asian economies, this point has not yet been achieved.Credit to the private sector and investment respond positively, but the stock market over the periods shows a negative response to economic shock.
The response of GDP per capita, investment, green energy stock market, and domestic credit to the private sector following a shock in carbon emissions is shown in Figure 3.A negative response in GDP per capita indicates that an increase in carbon emissions leads to a decrease in GDP per capita.This could be because specific industries, potentially carbon-intensive ones, hamper output.A positive response of investment to a shock in carbon emissions suggests an economic incentive to invest in sectors related to carbon emissions or in technologies aimed at mitigating the impact of emissions.Green Energy Stock Market Response: An initially negative response to green energy indicates investors' delayed response to green energy production due to pollution.A delayed stock market response suggests that investors view an increase in carbon emissions as an opportunity for growth in the stock market for renewable energy assets.An increase in domestic credit to the private sector following a shock in carbon emissions may suggest that financial institutions perceive business opportunities or increased demand in industries affected by the carbon shock.Figure 4 analyzes the impulse response of GDP per capita to a shock in green energy and involves understanding how an exogenous change or shock in green energy affects the trajectory of GDP per capita over time.A negative impulse response implies that a shock in green energy is associated with a decline in GDP per capita.This negative response might occur if the costs of transitioning to green energy sources outweigh the benefits in the short term, leading to economic disruptions.It could also happen if the economy heavily relies on traditional, carbon-intensive industries that experience negative shocks due to the transition to green energy.A delay in the response could suggest that the total economic effects of the shock take time to materialize.Res pons e of LCO to LCO Res pons e of ELEC to LCO Res pons e of DCPS to LCO Response to Cholesky One S.D. (d.f.adjusted) Innovations ± 2 S.E.

Figure 3.
Response of all variables to a shock in Emissions.
On the other hand, a positive impulse response in carbon emissions implies it is associated with an increase in carbon emissions.This unexpected response would raise concerns as it contradicts the expected environmental benefits of transitioning to green energy.Possible explanations for a positive answer might include inefficiencies in adopting green technologies or unintended consequences that lead to increased emissions.A delayed and negative impulse response would imply that the shock in green energy is associated with a decrease in investment.This is also an unexpected response, suggesting that the transition to green energy may face challenges or that investors perceive uncertainties and risks associated with green technologies in developing economies of South Asia.
Figure 5 shows that in South Asian economies, a positive response of GDP and carbon emissions to investment and a negative response of green energy to buy may be interpreted in several ways.A positive response of GDP to investment suggests that increased investment contributes to economic growth in South Asian economies.Infrastructure development, increased production capacity, and job creation could drive this.The positive response of carbon emissions to investment may indicate that the investments made in South Asian economies are associated with activities that contribute to higher carbon emissions.This might be due to a reliance on carbon-intensive industries, such as manufacturing and energy production, which are common in developing economies.The negative response of green energy to investment implies that, despite overall investment, the growth or adoption of green energy practices is not keeping pace.This could be due to insufficient investment in renewable energy projects, inadequate policy support, or challenges transitioning to a more sustainable energy sector.
It's crucial to conduct a detailed empirical analysis to validate these interpretations and understand the specific factors influencing the relationships between investment, GDP, carbon emissions, and green energy in the context of South Asian economies.Additionally, considering the diverse nature of countries within the region, variations in policy frameworks and economic structures should be considered for a more nuanced interpretation.Res pons e of LSTK to ELEC   Figure 6 presents a positive response of GDP, carbon emissions, and green energy to the stock market in the context of South Asian economies.A positive response of GDP to the stock market implies that a robust and growing stock market contributes to overall economic growth.This may be due to increased investor confidence, improved corporate performance, and an influx of capital into the economy.However, caution is needed to ensure that stock market gains reflect genuine economic productivity and not speculative bubbles.Similarly, the positive response of carbon emissions to the stock market suggests that economic activities linked to stock market growth may be associated with industries that contribute to higher carbon emissions.Initiatives such as manufacturing, construction, and energy production often play a significant role in emerging economies and may contribute to increased emissions.The positive but delayed response of green energy to the stock market indicates that the stock market's growth is associated with increased investment and support for the green energy sector.This could reflect a positive trend in investor interest and confidence in environmentally sustainable practices.The simultaneous positive responses of GDP, carbon emissions, and green energy to the stock market could suggest that economic growth in South Asian economies is tied to industries with positive and negative environmental implications.This scenario emphasizes balancing economic development with sustainable and environmentally friendly practices.
In Figure 7, a delayed positive response of GDP to domestic credit to the private sector suggests that increased access to credit stimulates economic activity over time.Businesses may take time to utilize the additional credit effectively, invest in projects, and contribute to overall economic growth.Moreover, the delayed positive response of CO2 emissions to domestic credit suggests that the increased economic activity fueled by credit is associated with a subsequent rise in carbon emissions.This delay could result from the time it takes for industries to scale up production, leading to an eventual increase in energy consumption and emissions.On the other hand, the negative response of green energy to domestic credit indicates that despite increased credit availability, investments in the green energy sector may not be growing proportionally.This could suggest that businesses are not directing credit toward sustainable and environmentally friendly projects, potentially due to economic priorities or regulatory challenges.Res pons e of LCO to DCPS

Long-run Analysis
To be concise, we utilized three models to determine the long-term implications of the model presented in Table 2.
퐸푐표푛표푚푖푐 푔푟표푤푡ℎ = −0.501퐿퐶푂+ 0.017퐸 + 0.492퐿퐶퐹 + 0.039퐿푆 + 0.012퐷퐶 (3) Equation ( 3) represents a model for assessing economic growth based on several factors.A negative coefficient of -0.501 associated with CO2 emissions implies an inverse relationship, suggesting that economic growth will decrease as carbon emissions increase.This highlights a concern for the potential environmental impact of carbon emissions on economic activities.Conversely, the positive coefficient of 0.017 linked to non-fossil energy production suggests a positive relationship.As the production of clean energy increases, the model predicts a corresponding rise in economic growth, indicating a potential link between cleaner energy sources and positive financial outcomes (Esen & Bayrak, 2017).
Moreover, the model places significant importance on gross fixed capital formation, as reflected by its high positive coefficient of 0.492.This implies that increasing investments in fixed assets, such as infrastructure and equipment, are substantial drivers of economic growth.The positive coefficient of 0.039 associated with the stock exchange value indicates a positive relationship between stock market performance and economic development.A thriving stock market contributes to overall economic growth in this context (Khalid & Khan, 2017).
Furthermore, domestic credit availability to the private sector positively impacts economic growth, as suggested by the coefficient of 0.012.The model implies that as the private sector gains increased access to credit, economic growth is expected to rise, underlining the importance of credit availability for businesses and private enterprises in fostering economic growth.In summary, the model suggests that, among the factors considered, reducing carbon emissions, promoting clean energy production, encouraging investments in fixed assets, maintaining a flourishing stock market, and ensuring access to credit for the private sector are crucial elements in fostering economic growth.These results also align with the outcomes of Ren et al. (2014).
퐶푂 푒푚푖푠푠푖표푛 = 1.995퐿푌 − 0.034퐸 + 0.981퐿퐶퐹 + 0.078퐿푆 + 0.024퐷퐶 (4) The coefficient associated with GDP signifies a direct and substantial correlation with carbon dioxide emissions.As GDP experiences an increase, the model anticipates a significant upsurge in CO2 emissions, underscoring the link between economic growth and heightened carbon emissions.Conversely, the negative coefficient linked to nonfossil energy production suggests an inverse relationship.With an escalation in non-fossil energy production, the model predicts a reduction in CO2 emissions, implying a potential environmental advantage associated with adopting cleaner energy sources.The outcomes are aligned with Shah et al. (2023).
Furthermore, the nearly unitary positive coefficient for Gross Fixed Capital Formation highlights its role as a significant driver of carbon emissions in this model.This suggests that augmented investments in fixed assets, such as infrastructure and equipment, wield a substantial positive influence on CO2 emissions.The positive coefficient associated with the stock exchange value signifies a direct relationship with carbon dioxide emissions.As the stock exchange value ascends, the model foresees an increase in CO2 emissions, implying that a flourishing stock market is correlated with elevated carbon emissions.
Similarly, the positive coefficient indicating a relationship between domestic credit extended to the private sector and carbon dioxide emissions suggests that increased credit availability to the private sector is associated with higher predicted CO2 emissions.This implies that credit availability may contribute to economic activities that result in heightened emissions.In summary, the model suggests that economic growth, investments in fixed assets, a thriving stock market, and increased credit availability positively correlate with carbon dioxide emissions (Ren et al., 2014).At the same time, adopting non-fossil energy sources presents an environmentally beneficial counterbalance.
퐸푛푒푟푔푦 푝푟표푑푢푐푡푖표푛 = −58.670퐿푌+ 29.414퐿퐶푂 + 28.867퐿퐶퐹 + 2.303퐿푆 + 0.71 (5) The negative coefficient associated with GDP in Equation 5, -58.670, implies an inverse relationship with nonfossil energy production.As GDP experiences growth, the model predicts a decrease in non-fossil energy production, indicating a potential inclination towards reliance on fossil fuels during economic expansion.Conversely, the positive coefficient of 29.414 linked to carbon dioxide emissions suggests that as CO2 emissions increase, there is an expected rise in non-fossil energy production.This implies a potential motivation to invest in cleaner energy sources to respond to environmental concerns (Irfan et al., 2023).Furthermore, the positive coefficient for Gross Fixed Capital Formation, with a value of 28.86, signifies that as investments in fixed assets, including infrastructure and equipment, increase, there is a substantial positive impact on the production of non-fossil energy.The positive coefficient associated with the stock exchange value (2.303) suggests a positive relationship between a thriving stock market and non-fossil energy production.As the stock exchange value rises, the model predicts an increase in non-fossil energy production, indicating a potential link between stock market prosperity and investments in cleaner energy (Abbasi et al., 2022).Additionally, the positive coefficient of 0.71 related to domestic credit availability to the private sector suggests a positive relationship with non-fossil energy production.As the private sector receives more credit, the model predicts a corresponding increase in non-fossil energy production, implying that credit availability may contribute to investments in cleaner energy sources.Table 3 shows that the error correction term is negative for all three models, meaning a possibility of convergence in the developing countries.In the short run, the dynamics of impact change in magnitude and, in some cases, direction.In the short run, the stock market's effect on GDP and CO2 emissions is negative, contrary to what we observed in the long run.Similarly, the impact of GDP on CO2 is negative, and green energy is positive.In the short run, domestic credit reduces the production of green energy.

Conclusions and Implications
This study explores the link between financial advancement, macroeconomic fluctuations, and sustainable energy production in South Asia.It evaluates existing research, identifies gaps, and suggests future directions.Key indicators include GDP per capita, CO2 emissions, non-fossil Energy production, Stock exchange value, Gross Fixed Capital Formation, and Domestic credit to the private sector.The study emphasizes the need to understand how economic activities influence the environment and how environmental factors impact economic and financial outcomes.It also examines financial market responses to understand the manifestation of economic and environmental factors.
Understanding the dynamics of alternative energy price fluctuations is crucial for market participants, especially environmentally conscious investors, to navigate exposure and comprehend the underlying factors.Policymakers are essential to mitigating risk from volatile asset types.Negative reactions to green energy by investment suggest that investments may not be strategically directed towards cleaner, more sustainable energy sources, posing concerns for long-term environmental and energy security objectives.Variations in responses across economic indicators may indicate policy challenges or structural issues, necessitating a reassessment of government policies to incentivize sustainable investments and promote green technology adoption.
These results imply that policymakers should precisely assess the environmental ramifications of economic growth and stock market activities.This presents an opportunity to craft policies that guide investments toward cleaner technologies and industries, aligning economic growth with sustainable practices.A comprehensive empirical analysis, considering the unique circumstances of each South Asian economy, such as financial structures, policy frameworks, and environmental awareness levels, is essential.The study suggests that the relationship between the region's stock market, GDP, carbon emissions, and green energy is complex, with a negative response to domestic credit indicating potential obstacles to investment, such as policy barriers or insufficient incentives.Mixed responses highlight the need to recognize sustainable projects, suggesting policymakers and financial institutions should explore ways to incentivize green energy investments and promote sustainable economic growth.
While this study offers insights into cyclical innovations, its limitations include a reliance on a specific set of variables that may not capture all influencing factors.The complexity of economic and environmental systems introduces potential endogeneity, suggesting a need for advanced econometric techniques.The study lacks regional variations, limiting generalizability.Future research could expand variables, include case studies, and refine the model for a more nuanced understanding of cyclical innovations across diverse contexts.production of maize in Pakistan using ARDL approach.Acta Geophysica.https://doi.org/10.1007/s11600-023-01118-0Wei, Y., Zhang, J., Chen, Y., & Wang, Y. (2022)

Figure 4 .
Figure 4. Response to Green Energy Shock.

Figure 6 .
Figure 6.Response to Stock Exchange Shock.
pons e of DCPS to DCPS Response to Cholesky One S.D. (d.f.adjusted) Innovations ± 2 S.E.

Figure 7 .
Figure 7. Response to Domestic-Credit-to-Private Sector.
Note: All the parameters are statistically significant, at least 10%.

Table 3 .
Short-run Dynamics and Error Corrections.
Note: All the parameters are statistically significant, at least 10%.