Sustainability Through Policy Stringency: Analysing the Impact on Financial Development

Abstract

This study investigates the relationship between environmental policy stringency and financial development across 40 countries from 1990 to 2021. Using the Environmental Policy Stringency Index (EPSI) to measure environmental regulations’ rigour, we explore how these policies impact financial development, particularly focusing on financial institutions and markets. The analysis employs Ordinary Least Squares (OLS) and Fixed Effects (FE) models to capture the dynamic interactions between environmental policies and financial systems. Our findings indicate that stringent environmental policies have a positive and significant impact on financial development, mainly through enhancing financial market depth and efficiency. However, the results also reveal that financial institutions may face challenges under stringent regulations, particularly in terms of reduced access to financial services. These findings contribute to the ongoing dialogue on the economic implications of environmental policies, offering valuable insights for policymakers aiming to balance environmental sustainability with financial development.

1. Introduction

The global community’s escalating environmental challenges, such as climate change, resource depletion, and pollution, have brought environmental policies to the forefront of economic and financial discussions. The need to mitigate these adverse effects has led to the implementation of stringent environmental regulations, which are essential for promoting sustainable development. However, these policies have significant implications for economic and financial systems, influencing them in complex ways. The relationship between environmental policies and different economic indicators has been widely studied, with mixed findings across various sectors [1,2,3,4,5]. One of the main crucial areas is the effect of environmental policy stringency on financial development, as financial systems play a critical role in implementing and succeeding these regulations.

Environmental policy stringency, which includes regulations to curb environmental degradation and promote sustainability, affects financial markets and institutions through both costs and opportunities. Stringent regulations may impose additional costs on businesses, reducing profitability and investment, particularly in sectors heavily impacted by environmental oversight [6]. This could constrain the growth of financial institutions closely tied to these sectors. However, these policies can also drive long-term economic benefits by fostering innovation, efficiency, and the development of green finance. The concept of ecological modernisation suggests that stringent environmental policies can contribute to a more sustainable and resilient economy [7]. The rise in green bonds, sustainability-linked loans, and other environmentally focused financial instruments reflects the growing integration of environmental considerations into financial decision-making [8,9]. Furthermore, evidence suggests that firms focusing on sustainability-related activities, especially those aligned with material issues, tend to perform better financially, enhancing overall financial stability [10].

The primary aim of this study is to examine how stringent environmental policies, as measured by the Environmental Policy Stringency Index (EPSI), influence financial development across 40 countries over the period from 1990 to 2021. By analysing the effects of these regulations on both financial institutions and markets, this research seeks to provide an understanding of the economic implications of environmental policy stringency. To investigate the relationship, we employ Ordinary Least Squares (OLS) and Fixed Effects (FE) models to capture the dynamic interactions between environmental policies and financial systems.

This paper contributes to the literature by offering empirical evidence on the differential impacts of environmental policy stringency on various components of financial development. Unlike previous studies, such as [11], which focus only on OECD countries and [12] concentrate on G7 economies, our study utilises a broader and more diverse sample, providing a more comprehensive analysis of the relationship between environmental regulations and financial development. Furthermore, while these earlier studies examine the relationship in general terms, our research delves deeper by differentiating the effects on financial development between financial institutions (FIs) and financial markets (FMs), offering nuanced insights into how these two components of the financial system respond to environmental policy stringency. By leveraging this extensive dataset, our study aims to inform policymakers and stakeholders about the potential trade-offs and synergies that environmental regulations may present across different financial sectors. This level of differentiation is often overlooked in existing literature.

Our study reveals that stringent environmental policies, as measured by the Environmental Policy Stringency Index (EPSI), have a significant and positive impact on financial development, mainly through enhancing financial market depth and efficiency. This finding suggests that contrary to concerns about the restrictive effects of environmental regulations, such policies can stimulate financial innovation and growth by fostering the development of green financial products and sustainable investment practices. Specifically, our results indicate that financial markets are more responsive to environmental policy stringency, benefiting from increased investor interest in sustainability-linked financial instruments such as green bonds [9,13]. However, the study also highlights a more complex relationship with financial institutions, where stringent regulations may impose additional compliance costs, potentially reducing access to financial services. This dual impact underscores the importance of designing environmental policies that promote sustainable financial growth and ensure financial institutions can adapt effectively to these regulatory changes [14]. Overall, our findings contribute to understanding how environmental policies can be leveraged to support both economic and environmental objectives in a balanced and sustainable manner.

The remainder of the paper is structured as follows: Section 2 reviews the relevant literature, focusing on the intersection of environmental policy stringency and financial development. Section 3 outlines the data sources and details the empirical model specifications employed in the analysis. Section 4 presents the empirical results, followed by a discussion of the findings. Finally, Section 5 provides the conclusion and policy implications.

2. Literature Review

Financial development fosters economic growth by facilitating efficient capital allocation, promoting investment, and enabling technological innovation. A robust financial system is essential for mobilising savings, facilitating transactions, managing risks, and efficiently allocating resources, all of which contribute to economic prosperity [15]. Moreover, financial institutions support innovation by providing the necessary funding for research and development (R&D). Reference [16] argue that access to finance encourages firms to invest in new technologies, thereby enhancing productivity and driving economic growth.

In addition to supporting technological advancement, risk management is another critical channel through which financial development influences economic growth. Financial markets provide risk management tools, encouraging investment in more productive but riskier ventures. Reference [17] highlight that a developed financial system reduces transaction costs and information asymmetries, fostering economic activity. Furthermore, access to finance is crucial for entrepreneurship and business expansion, as financial development facilitates the growth of small- and medium-sized enterprises (SMEs), which are vital for job creation and economic dynamism [18].

Several factors, including robust legal and institutional frameworks, shape an economy’s financial development degree. These frameworks reduce information asymmetries and enhance investor confidence, which is essential for fostering financial development [19]. A sound regulatory environment ensures financial stability and fosters competition, balancing the need for oversight with the flexibility to promote innovation in financial services [20]. Furthermore, trade and financial openness can enhance financial development by exposing domestic markets to global best practices and encouraging foreign investment [21].

As financial systems evolve, their role in addressing broader economic challenges, primarily environmental sustainability-related ones, has become increasingly significant. In recent years, the interaction between environmental policy and financial development has garnered attention, particularly in the context of the Porter Hypothesis, which posits that stringent environmental regulations can act as catalysts for innovation and economic growth. This hypothesis challenges the traditional view that environmental regulations are purely a financial burden, suggesting instead that they can drive firms to innovate, ultimately enhancing their competitiveness and efficiency [22].

The Porter Hypothesis, introduced by Michael Porter in the early 1990s, argues that well-designed environmental regulations can stimulate innovation by encouraging firms to explore new technologies and processes that reduce environmental impacts. According to the Porter Hypothesis, stringent but flexible regulations can lead to win-win scenarios where both the environment and the economy benefit. This hypothesis has been supported by empirical studies demonstrating the positive effects of environmental regulations on innovation. For instance, [23], comprehensively reviewed the Porter Hypothesis. They found that environmental regulations often increase innovation and competitiveness by reducing uncertainty and creating market opportunities for innovative products and processes.

Several mechanisms drive innovation in response to environmental regulations. First, regulatory pressure compels firms to meet specific environmental standards, prompting them to develop new technologies and processes to comply. This pressure can lead to the discovery of more efficient production methods that reduce resource consumption and emissions [24,25]. Additionally, environmental policies create markets for clean technologies and sustainable products. For example, renewable energy standards and incentives for electric vehicles have spurred innovation and growth in these sectors [13,26].

Innovations driven by environmental regulations often result in cost savings through improved resource efficiency and waste reduction. These savings can offset compliance costs and enhance the firm’s bottom line [27,28]. Furthermore, firms that lead in environmental innovation can improve their reputation and brand value, attracting environmentally conscious consumers and investors [13,29].

While there is empirical evidence of the positive impact of environmental policies on innovation, several challenges and considerations must be addressed to maximise these benefits. The effectiveness of environmental regulations in driving innovation depends largely on their design. To foster innovation without imposing excessive burdens, policies should be flexible, predictable, and aligned with industry capabilities [23,24]. Moreover, different industries respond to regulations in varied ways, depending on their technological capabilities and market conditions. Policymakers must consider these dynamics when designing regulations to promote innovation effectively [30,31]. Additionally, innovation driven by environmental regulations often requires substantial investment in research and development, highlighting the need for long-term policy commitment to provide firms with the confidence to invest in new technologies [26,31].

The relationship between environmental policy and financial development is gaining increasing attention as countries worldwide intensify their efforts to combat climate change and transition to sustainable economic models. One of the primary ways environmental policy impacts financial development is by stimulating the growth of green finance. Stringent environmental regulations encourage the creation of financial instruments that support sustainable projects, leading to a surge in green bonds, sustainability bonds, and other forms of climate finance. These instruments channel investment into renewable energy, energy efficiency, and sustainable infrastructure projects, thereby driving financial innovation and adaptation [8,32]. Reference [13] highlights that the issuance of corporate green bonds has increased as companies seek to align their financing activities with sustainability objectives, providing a significant impetus for financial markets to evolve. Similarly, Reference [33] notes that green bonds are becoming a crucial tool for mobilising climate finance, particularly in developing countries, further underlining green finance’s growing importance in shaping the global financial landscape. The increasing integration of environmental considerations into financial systems reflects a broader trend toward sustainability in international financial markets, as firms and investors recognise the long-term benefits of sustainable investments [34].

In addition to promoting green finance, environmental policies enhance risk management practices within financial institutions. These policies necessitate the integration of environmental risk assessments into financial decision-making processes, encouraging institutions to evaluate potential climate-related impacts on their portfolios. Frameworks such as the Task Force on Climate-related Financial Disclosures (TCFD) provide guidelines for assessing and disclosing climate risks, thereby improving transparency and strengthening risk management strategies [35]. Furthermore, the European Union’s Sustainable Finance Disclosure Regulation (SFDR) exemplifies regulatory efforts to encourage financial firms to consider environmental, social, and governance (ESG) factors. This influences asset pricing, lending decisions, and overall financial stability by mandating that firms integrate ESG considerations into their operations [36].

Moreover, stringent environmental policies create new market opportunities and drive financial innovation by incentivising the development of sustainable financial products and services. The rise in fintech has been instrumental in this transformation, offering platforms that facilitate sustainable investments and broaden access to green finance. These technological advancements enable individuals and communities to participate in the green transition, democratising access to finance and fostering financial inclusion [37]. Additionally, environmental policies have spurred the creation of sustainable indices and increased ESG investing. These indices serve as benchmarks for sustainable investments, attracting capital and contributing to the development of financial markets [38].

Empirical studies underscore the positive impact of environmental policies on financial development. For instance, Reference [11] provides insights from OECD countries, demonstrating that stringent environmental policies enhance financial development indices by encouraging investment in green technologies and fostering the development of financial institutions. Their study highlights the dual role of environmental policies in promoting sustainable economic growth and advancing financial development. Similarly, Reference [39] conducted a bibliometric analysis emphasising the growing importance of green finance in shaping financial markets. They identify environmental policies as key drivers of green financial products and market innovation. Reference [12] further corroborates these findings with their in-depth analysis of G7 economies, showing that green policies significantly bolster financial development by driving the growth of green financial products and improving financial market performance. Their research reinforces the critical role of environmental regulations in supporting financial systems while advancing sustainability goals.

While environmental policy can drive financial development, several challenges must be addressed to realise its full potential. Ensuring coherence between environmental and financial regulations is crucial, as misaligned policies can create uncertainty and deter investment [40]. Access to finance for small- and medium-sized enterprises (SMEs) in developing economies remains a significant challenge, as these enterprises often face barriers in securing funding for sustainable projects [41]. Moreover, transitioning to a low-carbon economy involves managing transition risks, such as asset stranding in fossil fuel industries, which financial institutions must navigate to maintain stability [35]. Achieving policy coherence is essential to address these misalignments and effectively implement sustainable finance initiatives. Additionally, financial institutions must manage transition risks associated with more stringent environmental policies, as industries reliant on fossil fuels may experience financial instability due to regulatory changes or shifts in market demand [42]. In addition, Reference [43] provides evidence of how macroeconomic factors, such as exchange rate volatility, interact with ESG performance, emphasising the interconnectedness of financial stability and sustainability.

Despite these challenges, aligning environmental policies with financial development presents numerous opportunities for growth and innovation. The rise in green finance is one of the most significant opportunities, enabling capital mobilisation towards sustainable projects through green bonds, sustainability-linked loans, and other innovative financial products [9]. Technological advancements, particularly in fintech, are enhancing the accessibility and efficiency of sustainable finance, democratising access to capital through platforms that facilitate crowdfunding for renewable energy projects [44]. Moreover, environmental policies encourage financial institutions to innovate and develop new risk management frameworks incorporating climate-related risks, enhancing financial system resilience and transparency. Regulatory initiatives, such as the Network for Greening the Financial System [45], exemplify how risk assessment and corporate governance improvements can lead to more stable and sustainable financial markets. Additionally, these policies promote economic diversification and job creation by encouraging investments in clean energy and sustainable infrastructure, thereby supporting both environmental goals and broader social and economic development [46].

3. Methodology and Data

3.1. Data

To conduct our empirical analysis, we use annual data from 1990 to 2021. The data were initially obtained from the OECD database, which provides information on 48 countries. However, the financial development data used in this study are only available until 2021, limiting the analysis to this timeframe. Matching the OECD data with financial development data resulted in the exclusion of two countries, reducing the sample to 46 countries. The excluded countries lacked sufficient data on financial development indices.

In our primary estimations, the final number of countries dropped further to 40 countries due to missing data for key control variables, such as GDP growth, trade as a percentage of GDP, foreign direct investment (FDI), or inflation. Additionally, fixed-effects regressions require within-country variation, which led to the exclusion of countries with insufficient panel observations. As a result, the dataset used in the analysis comprises 1079 country-year observations.

Table 1. List of countries.

Number	Country	Number	Country
1	Australia	21	Korea
2	Austria	22	Latvia
3	Belgium	23	Lithuania
4	Canada	24	Luxembourg
5	Chile	25	Malta
6	China	26	Mexico
7	Colombia	27	Netherlands
8	Czechia	28	New Zealand
9	Denmark	29	Norway
10	Estonia	30	Poland
11	Finland	31	Portugal
12	France	32	Russia
13	Germany	33	Slovak Republic
14	Greece	34	Slovenia
15	Iceland	35	South Africa
16	India	36	Spain
17	Indonesia	37	Sweden
18	Ireland	38	Switzerland
19	Italy	39	Turkey
20	Japan	40	United Kingdom

shows the list of countries used in this study. All data sources and definitions are illustrated in

Table 2. Variable definition and source.

Variable	Definition	Data Source
FD Index	The index constitutes depth, access and efficiency of financial institutions and financial markets. It takes a value between 0 and 1	IMF
Financial Markets	The index constitutes depth, access and efficiency of financial markets. It takes a value between 0 and 1	IMF
Financial Institutions	The index constitutes the depth, access and efficiency of financial institutions. It takes a value between 0 and 1	IMF
EPSI	Environmental Policy Stringency Index	OECD Stats
GDP growth	Annual percentage GDP growth	World Bank indicators
Trade% GDP	The sum of exports and imports of goods and services is measured as a share of GDP.	World Bank indicators
Crisis	Dummy variable for the presence of banking crisis (1 = banking crisis, 0 = none)	Global Financial Development Database
FDI%GDP	Net inflows from foreign direct investment as a percentage of GDP	World Bank indicators
Inflation	The annual percentage change in the Consumer Price Index	World Bank indicators

.

The primary dependent variables in this study are the Financial Development Index, Financial Institutions Index, and Financial Markets Index. The Financial Development Index, which measures the depth, access, and efficiency of financial institutions and markets, is a comprehensive indicator of financial development within each country. It ranges from 0 to 1, with higher values indicating more advanced financial systems. The Financial Institutions Index focuses specifically on the banking sector, assessing the level of development and efficiency of financial intermediaries. Similarly, the Financial Markets Index evaluates the development and efficiency of capital markets, reflecting the availability and ease of access to capital for businesses and investors. The Financial Development Index is more appropriate to this study in comparison to the traditional measures of financial development, such as the ratio of private credit to GDP and stock market capitalisation to GDP, since these indicators do not take into account the complex multidimensional nature of financial development [11,47].

The main independent variable of interest is the Environmental Policy Stringency Index (EPSI), which we constructed using data from the Climate Actions and Policies Measurement Framework (CAPMF) database provided by the Organization for Economic Co-operation and Development (OECD). The CAPMF database offers 128 policy variables grouped into 56 instruments covering various environmental measures. The EPSI captures the stringency of environmental regulations across countries, including market- and non-market-based measures. Specifically, the EPSI is derived from three main categories: sectoral policies, cross-sectoral policies, and international policies. These indicators reflect the extent and enforcement of environmental policies to reduce environmental harm and promote sustainable practices. Therefore, the standardised construction enables meaningful comparisons across countries with diverse economic and regulatory frameworks. It is an appropriate index for examining our dataset’s relationship between environmental policy stringency and financial development.

To create the EPSI, we applied Principal Component Analysis (PCA) to combine the policy variables from the CAPMF database into a single comprehensive index. The PCA method was employed to account for environmental policies’ multidimensional nature and reduce potential multicollinearity issues among the indicators. This process resulted in an index ranging from 0 to 5.381, where higher values signify more stringent environmental policies. The PCA-based approach ensures that the index represents the variation across various climate policies, including those with explicit mitigation objectives and non-climate policies with positive mitigation effects [48]. While the CAPMF database provides the raw policy variables, the EPSI reflects the outcome of aggregating these variables into an index using PCA. This approach aligns with the existing literature, which emphasises the importance of comprehensive and nuanced measures of policy stringency for understanding their economic impacts [24,49].

Several control variables are included in the analysis to account for additional factors influencing financial development. These variables include GDP growth, trade as a percentage of GDP, foreign direct investment (FDI) as a percentage of GDP, inflation, and financial crises. GDP growth captures the overall economic performance of each country, while trade openness reflects the extent of international economic integration. FDI indicates external investment inflows, which can influence financial market dynamics. Inflation is included to control for macroeconomic stability, and a binary crisis variable accounts for periods of economic turmoil, such as financial crises, which may impact financial development.

The summary statistics in

Table 3. Summary statistics.

Variable	Obs	Mean	Std. Dev.	Min	Max
FD Index	1079	0.547	0.206	0.096	1
Financial Markets	1079	0.458	0.24	0.017	1
Financial Institutions	1079	0.614	0.213	0.081	1
EPSI	1079	1.456	1.04	0	5.381
GDP growth	1079	3.058	3.521	−14.839	24.475
Trade% GDP	1079	82.801	53.415	15.506	353.794
Crisis	1079	0.115	0.319	0	1
FDI%GDP	1079	5.701	23.078	−57.532	449.083
Inflation	1079	6.721	30.931	−4.448	874.246

provide an overview of the variables used in the analysis. The Financial Development (FD) Index has a mean value of 0.547 with a standard deviation of 0.206, indicating moderate variability in financial development levels across the sample of 40 countries. The minimum and maximum values of 0.096 and 1 suggest various financial development levels across countries. Similarly, the components of the FD Index—Financial Markets and Financial Institutions—have mean values of 0.458 and 0.614, respectively, with the latter exhibiting slightly less variability. The Environmental Policy Stringency Index (EPSI) averages 1.456 with significant variability (standard deviation of 1.04), indicating differing levels of environmental policy stringency among countries. GDP growth shows high variability with a mean of 3.058% and a standard deviation of 3.521%, ranging from a decline of −14.839% to a peak of 24.475%, highlighting the diverse economic conditions across the sample. The Trade% GDP indicates high trade openness on average (82.801%) but also a wide range (15.506% to 353.794%), while FDI% GDP shows substantial variability with a mean of 5.701% and a significant standard deviation (23.078%), indicating varied levels of foreign investment. The crisis variable, with a mean of 0.115, suggests that crises occur in about 11.5% of the observations. Finally, Inflation displays considerable variability, with a mean of 6.721% and a standard deviation of 30.931%, ranging from −4.448% to 874.246%, pointing to significant inflationary differences across countries.

The correlation matrix in

Table 4. Correlation matrix.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
(1) FD Index	1.000
(2) Financial Markets	0.902 *	1.000
(3) Financial Institutions	0.874 *	0.579 *	1.000
(4) EPSI	0.499 *	0.456 *	0.430 *	1.000
(5) GDP growth	−0.192 *	−0.125 *	−0.222 *	−0.183 *	1.000
(6) Trade% GDP	0.031	−0.106 *	0.178 *	0.225 *	0.048	1.000
(7) Crisis	−0.006	0.001	−0.013	−0.003	−0.345 *	0.056	1.000
(8) FDI%GDP	0.043	0.003	0.078 *	0.017	0.047	0.368 *	−0.010	1.000
(9) Inflation	−0.158 *	−0.062 *	−0.229 *	−0.162 *	−0.147 *	−0.062 *	0.030	−0.028	1.0

*** p < 0.01, ** p < 0.05, * p < 0.1.

provides a clear overview of the relationships between the main variables in the study. Notably, the Financial Development (FD) Index is strongly correlated with Financial Markets (0.902) and Financial Institutions (0.874), indicating a close relationship between these components of financial development. The Environmental Policy Stringency Index (EPSI) also shows a positive correlation with the FD Index (0.499), suggesting that stricter environmental policies are associated with higher levels of financial development. The control variables, such as GDP growth, trade, crisis, FDI, and inflation, are not highly correlated with the main variables. This is beneficial for the analysis, as it reduces the risk of multicollinearity, which can distort the results and make it difficult to isolate the effects of the key independent variables. The low correlations among the control variables indicate that they can be included in the regression models without causing significant issues, thereby helping to ensure the robustness of the findings.

3.2. Model Specification

We employ Ordinary Least Squares (OLS) and Fixed Effects (FE) models to analyse the relationship between financial development and environmental policy stringency as benchmark specifications. We chose these models to understand the dynamic interaction between financial and environmental variables while accounting for potential heterogeneity across countries.

We begin with the OLS model, which serves as a foundational approach to examine the correlation between financial development indices—namely, Financial Development (FD), Financial Institutions (FIs), and Financial Markets (FMs)—and the Environmental Policy Stringency Index (EPSI). The OLS model is specified as follows:

$${FD}_{it}={{\beta }_0+\beta }_1.{EPSI}_{it}+{\beta }_2.X_{it}+{\in }_{it}$$

(1)

where (i) denotes the country and (t) denotes the time. This model allows us to capture the direct impact of environmental policy stringency on financial development on environmental policy while controlling for macroeconomic variables such as GDP growth, trade openness, economic crises, foreign direct investment, and inflation.

However, the OLS model may not fully account for unobserved heterogeneity across countries, which could bias our estimates. To address this, we implement Fixed Effects models, which control for time-invariant unobserved heterogeneity by allowing for country-specific intercepts. The FE model is specified similarly to the OLS model, with the inclusion of fixed effects:

$${FD}_{it}={{\alpha }_i+\beta }_1.{EPSI}_{\mathrm{i}\mathrm{t}}+{\beta }_2.X_{\mathrm{i}\mathrm{t}}{+\beta }_3.{Time}_{\mathrm{t}}+{\beta }_4.{Country}_{\mathrm{i}}+{\in }_{\mathrm{i}\mathrm{t}}$$

(2)

Here, α_i represents the fixed effect for each country, capturing unobservable characteristics that may influence the EPSI. This approach mitigates omitted variable bias and provides more reliable estimates of the relationship between financial development and environmental policy stringency.

Our choice of these models is guided by the existing literature that stresses the importance of accounting for observed and unobserved factors in econometric analyses of financial and environmental interactions [50,51]. We use OLS and FE models to provide robust insights into how environmental policies can influence or constrain financial systems across different national contexts.

4. Results and Discussion

4.1. Results

The results of the Ordinary Least Squares (OLS) regression analysis presented in

Table 5. Impact of Environmental Policy Stringency Index on financial development—OLS.

Variables	(1)	(2)	(3)
	Dependent Variable: FD	Dependent Variable: FI	Dependent Variable: FM
EPSI	0.224 ***	0.200 ***	0.239 ***
	(0.010)	(0.012)	(0.010)
GDP growth	−0.005 ***	−0.011 ***	0.001
	(0.002)	(0.002)	(0.002)
Trade% GDP	−0.000 ***	0.000 ***	−0.001 ***
	(0.000)	(0.000)	(0.000)
Crisis	−0.050 ***	−0.070 ***	−0.027
	(0.017)	(0.019)	(0.020)
FDI%GDP	0.001 **	0.000	0.001 **
	(0.000)	(0.000)	(0.000)
Inflation	−0.000	−0.001 **	0.000 ***
	(0.000)	(0.001)	(0.000)
Constant	0.398 ***	0.525 ***	0.256 ***
	(0.025)	(0.036)	(0.026)
Time dummy	Yes	Yes	Yes
Observations	1079	1079	1079
R-squared	0.444	0.387	0.423

Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

highlight the significant impact of environmental policy stringency on financial development and its components. The Environmental Policy Stringency Index (EPSI) has a statistically significant and positive effect on the overall Financial Development (FD) Index, with a coefficient of 0.224 and significance at the 1% level. This suggests that more stringent environmental policies are associated with higher levels of financial development, supporting the hypothesis that environmental regulations can enhance financial systems by fostering green finance and encouraging sustainable investments.

Regarding the financial system’s components, EPSI positively impacts both Financial Institutions (FIs) and Financial Markets (FMs), with coefficients of 0.200 and 0.239, respectively, both significant at the 1% level. These findings imply that stringent environmental policies facilitate the development of both financial markets and institutions, likely by encouraging innovation and risk management practices that align with sustainability objectives.

The control variables provide additional insights. GDP growth negatively influences FD and FI, with coefficients of −0.005 and −0.011, respectively, suggesting that faster economic growth may not always coincide with financial system development. However, GDP growth does not significantly affect FM. Trade as a percentage of GDP shows mixed effects: it negatively affects FD and FM but positively influences FI, indicating the complex dynamics between trade openness and financial sector development.

As expected, financial crises negatively impact both FD and FI, highlighting financial systems’ vulnerabilities during economic turmoil. Conversely, FDI positively impacts FD and FM, although the effect is not significant for FI, underscoring the role of foreign investment in bolstering financial development. Finally, inflation displays varied effects across the models, with a significant negative impact on FI and a positive impact on FM, reflecting how macroeconomic stability influences different segments of the financial system. Overall, these results underscore the multifaceted relationship between environmental policy stringency and financial development, emphasising the role of stringent policies in promoting a robust and sustainable financial sector. Although unreported, including time dummies ensures these findings account for temporal effects across the sample period.

Table 6. Impact of Environmental Policy Stringency Index on financial development—fixed effects.

Variables	(1)	(2)	(3)
	Dependent Variable: FD	Dependent Variable: FI	Dependent Variable: FM
EPSI	0.028 *	−0.030 *	0.086 ***
	(0.014)	(0.018)	(0.020)
GDP growth	0.001 *	0.001	0.002
	(0.001)	(0.001)	(0.001)
Trade% GDP	−0.001 ***	−0.000	−0.001 **
	(0.000)	(0.000)	(0.000)
Crisis	−0.007	−0.020 **	0.007
	(0.008)	(0.009)	(0.015)
FDI%GDP	−0.000 ***	−0.000	−0.000 **
	(0.000)	(0.000)	(0.000)
Inflation	0.000 ***	−0.000	0.001 ***
	(0.000)	(0.000)	(0.000)
Constant	0.414 ***	0.549 ***	0.263 ***
	(0.018)	(0.031)	(0.033)
Time dummy	Yes	Yes	Yes
Observations	1079	1079	1079
R-squared	0.764	0.543	0.673
Number of groups	40	40	40

Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

presents our main model’s Fixed Effects (FE) estimation results, which provide further insights into the relationship between environmental policy stringency and financial development, refining our understanding by controlling for unobserved heterogeneity across countries. The Environmental Policy Stringency Index (EPSI) continues to positively impact the overall Financial Development (FD) Index with a coefficient of 0.028, which is significant at the 10% level. This suggests that stringent environmental policies still positively influence financial development after accounting for country-specific effects, albeit to a lesser extent than indicated by the OLS results.

Interestingly, the EPSI has a differential impact on the components of the financial system. For financial institutions (FI), the effect is slightly negative (−0.030) and significant at the 10% level, indicating that environmental policy stringency may pose challenges to financial institutions that could relate to increased compliance costs or operational adjustments required to meet regulatory standards. In contrast, the effect on financial markets (FM) is more pronounced and positive, with a coefficient of 0.086, which is significant at the 1% level. This highlights how financial markets might be better positioned to leverage the opportunities presented by environmental policies by developing green financial products and sustainable investment channels.

Among the control variables, GDP growth shows a weak positive association with FD and FIs, though not consistently significant, suggesting that economic growth alone does not strongly drive financial development under stringent environmental policies. The impact of Trade remains negative for FD and FMs, suggesting that higher trade openness might introduce external competitive pressures that constrain domestic financial development. In addition, the crisis variable does not significantly affect FD and FMs, indicating resilience in financial development amidst economic disruptions. However, it negatively influences FIs, reflecting potential vulnerabilities in the institutional sector during crises. Furthermore, FDI shows a small but significant negative impact on FD and FMs, suggesting that foreign investments may prioritise sectors less affected by environmental policy changes. Finally, inflation has mixed effects across the models. It is significant in the FM equation, suggesting that higher inflation may lead to more dynamic financial markets, potentially due to increased demand for hedging instruments. Including fixed effects in the FE model provides a more nuanced understanding by highlighting these varied impacts across financial system components and underlining the importance of considering country-specific factors when evaluating the role of environmental policy stringency in shaping financial development.

Building on the insights from the previous models, where we observed mixed effects of environmental policy stringency on the components of financial development, we delve deeper into understanding what explicitly drives these effects, particularly on the development of financial institutions (FIs). The analysis presented in

Table 7. Impact of Environmental Policy Stringency Index on the fixed effects of financial institutions.

Variables	(1)	(2)	(3)
	Dependent Variable: FI-Access	Dependent Variable: FI-Depth	Dependent Variable: FI-Efficiency
EPSI	−0.075 **	0.006	−0.002
	(0.036)	(0.019)	(0.018)
GDP growth	−0.001	−0.001	0.004 ***
	(0.001)	(0.001)	(0.001)
Trade% GDP	−0.001	0.000	−0.000
	(0.001)	(0.000)	(0.000)
Crisis	−0.012	0.008	−0.068 ***
	(0.014)	(0.010)	(0.011)
FDI%GDP	−0.000	0.000	−0.000
	(0.000)	(0.000)	(0.000)
Inflation	−0.000 **	0.000	0.000
	(0.000)	(0.000)	(0.000)
Time dummy	Yes	Yes	Yes
Observations	1079	1079	1079
R-squared	0.402	0.504	0.247
Number of groups	40	40	40

Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

breaks down FIs into its subcomponents—Access (Column 1), Depth (Column 2), and Efficiency (Column 3)—to uncover the specific dimensions of FIs that are influenced by environmental policies.

The results indicate that the Environmental Policy Stringency Index (EPSI) has a significant and negative impact on FI Access with a coefficient of −0.075, which is significant at the 5% level, as shown in Column 1. This suggests that stringent environmental policies may reduce the accessibility of financial services, likely due to increased regulatory burdens or compliance costs that limit the availability of financial products. On the other hand, the impact of EPSI on FI Depth (Column 2) is positive but not statistically significant, indicating that while environmental policies might encourage a more profound financial sector, this effect is not robust across all contexts.

For FI Efficiency (Column 3), the impact of EPSI is negligible and statistically insignificant, implying that environmental policy stringency does not directly affect the operational efficiency of financial institutions. These findings suggest that the adverse effects on FI are primarily driven by reduced access to financial services, which could have broader implications for financial inclusion and economic development. The varied impacts across the subcomponents underscore the importance of considering the multidimensional nature of financial institutions when assessing the broader economic consequences of environmental policies.

Continuing the in-depth analysis of the financial system’s response to environmental policy stringency,

Table 8. Impact of Environmental Policy Stringency Index on financial markets—fixed effects.

Variables	(1)	(2)	(3)
	Dependent Variable: FM-Access	Dependent Variable: FM-Depth	Dependent Variable: FM-Efficiency
EPSI	0.056 *	0.105 ***	0.089 **
	(0.030)	(0.023)	(0.041)
GDP growth	−0.002	0.003 **	0.003
	(0.002)	(0.001)	(0.002)
Trade% GDP	−0.001 **	−0.001 **	−0.001
	(0.001)	(0.001)	(0.001)
Crisis	−0.023	0.035	0.003
	(0.018)	(0.021)	(0.030)
FDI%GDP	−0.000 **	−0.000	−0.000 **
	(0.000)	(0.000)	(0.000)
Inflation	0.000 **	0.001 **	0.001 ***
	(0.000)	(0.000)	(0.000)
Constant	0.283 ***	0.162 ***	0.352 ***
	(0.051)	(0.041)	(0.075)
Time dummy	Yes	Yes	Yes
Observations	1079	1079	1079
R-squared	0.514	0.731	0.295
Number of groups	40	40	40

Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

shifts the focus to financial markets (FMs) by exploring its subcomponents—Access (Column 1), Depth (Column 2), and Efficiency (Column 3)—to better understand how stringent environmental policies influence different aspects of financial markets.

The results demonstrate that the Environmental Policy Stringency Index (EPSI) exerts a significant and positive impact on all three subcomponents of financial markets, though the magnitude and significance vary. In Column 1, the coefficient for FM Access is 0.056, which is significant at the 10% level, indicating that more stringent environmental policies marginally increase access to financial markets. This suggests that as environmental regulations become stricter, the accessibility of financial markets improves, possibly due to the growth of green financial products and increased investor interest in sustainable finance.

For FM Depth (Column 2), the impact of EPSI is more substantial, with a coefficient of 0.105, which is significant at the 1% level. This finding implies that stringent environmental policies significantly contribute to the deepening of financial markets, likely through the expansion of market instruments and investment opportunities related to sustainability and green finance.

In Column 3, FM Efficiency also benefits from increased environmental policy stringency, with a coefficient of 0.089, which is significant at the 5% level. This suggests that the operational efficiency of financial markets improves in response to more stringent environmental policies, potentially due to enhanced risk management practices and innovations spurred by the need to comply with environmental standards.

4.2. Robustness Tests

To ensure the validity of the regression models, unit root tests were performed to examine the stationarity of the dependent variables. Specifically, the Levin-Lin-Chu (LLC) and Im-Pesaran-Shin (IPS) panel unit root tests were employed, as these are widely recognised in the econometric literature for analysing stationarity in panel data [52,53]. The results confirmed that all three variables are stationary at levels, as the null hypothesis of unit roots was strongly rejected across tests. For example, the adjusted t-statistics for the LLC test were significant at the 1% level for all variables. This outcome ensures that first-differencing is not required, validating the direct use of these variables in the regression models and meeting the foundational assumptions for unbiased estimation.

Given the panel nature of the dataset and the potential for heteroskedasticity and serial correlation in the residuals, robust standard errors clustered at the country level were employed. Clustering accounts for within-group correlations and ensures valid inference even when errors exhibit heteroskedasticity or first-order autocorrelation [54]. The Wooldridge test for autocorrelation in panel data confirmed the presence of significant first-order serial correlation in the residuals, further justifying this approach. By applying heteroskedasticity and autocorrelation consistent (HAC) standard errors, the models effectively address residual dependencies, enhancing the robustness of the findings and the reliability of statistical inference.

The regression results for the fixed-effect models are presented in

Table 9. Regression results for financial development indices using lagged models and clustered standard errors.

Variables	(1)	(2)	(3)
	Dependent Variable: FD	Dependent Variable: FI	Dependent Variable: FM
L.FD_index	0.882 ***
	(0.016)
L.FI_Index		0.901 ***
		(0.030)
L.FM_Index			0.860 ***
			(0.017)
L.EPSI	0.003 *	0.002	0.004 *
	(0.002)	(0.002)	(0.002)
GDP growth	0.001 ***	0.001	0.002 ***
	(0.000)	(0.000)	(0.000)
Trade% GDP	−0.000	−0.000	0.000
	(0.000)	(0.000)	(0.000)
Crisis	−0.006 **	−0.009 **	−0.003
	(0.003)	(0.003)	(0.005)
FDI%GDP	0.000	0.000 *	−0.000
	(0.000)	(0.000)	(0.000)
Inflation	0.000	−0.000	0.000
	(0.000)	(0.000)	(0.000)
Constant	0.069 ***	0.074 ***	0.059 ***
	(0.010)	(0.016)	(0.011)
Time dummy	Yes	Yes	Yes
Observations	1048	1048	1048
R-squared	0.884	0.883	0.843
Number of groups	40	40	40

Robust standard errors clustered at the country level (HAC adjustments) in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

. Across all models, the coefficients of the lagged dependent variables are highly significant (p < 0.01), with values ranging from 0.860 to 0.901. These results indicate strong temporal persistence, as current levels of financial development are significantly influenced by their past values. The environmental policy stringency index demonstrates a marginally significant positive effect on the FD Index and Financial Markets Index, suggesting that stricter environmental policies may have a delayed positive impact, particularly on financial markets. However, the EPSI effect is insignificant for the Financial Institutions Index, indicating that institutional components of financial development may respond differently to environmental policies.

We tested for cross-sectional dependence (CSD) in our panel data using Pesaran’s CD test. This test assesses whether residuals across countries are significantly correlated.

Table 10. Cross-sectional dependence tests.

Model	Pesaran’s CD Statistic	p-Value	Average Absolute Value of Off-Diagonal Elements
Model 1 (Dependent variable: FD)	65.411	0.000	0.495
Model 2 (Dependent variable: FI)	27.862	0.000	0.442
Model 3 (Dependent variable: FM)	63.156	0.000	0.499

presents the results, showing significant cross-sectional dependence in all models, with Pesaran’s CD statistics highly significant (p < 0.01) and average off-diagonal correlation values ranging from 0.442 to 0.499. These findings indicate the presence of substantial shared shocks or unobserved common factors across countries.

To address cross-sectional dependence identified in the residuals using Pesaran’s CD test, we employed Driscoll–Kraay standard errors. This approach accounts for heteroskedasticity, autocorrelation, and cross-sectional dependence, providing robust inference for our panel data analysis.

After addressing cross-sectional dependence, the significant relationship between environmental policy stringency and financial development indices (FD, financial institutions, and financial markets) remained robust, confirming the validity of our findings. The results are presented in

Table 11. Regression with Driscoll–Kraay standard errors.

Variables	(1)	(2)	(3)
	FD	FI	FM
EPSI	0.028 **	−0.030 **	0.086 ***
	(0.014)	(0.014)	(0.019)
GDP growth	0.001	0.001	0.002 *
	(0.001)	(0.001)	(0.001)
Trade% GDP	−0.001 ***	−0.000	−0.001 ***
	(0.000)	(0.000)	(0.000)
Crisis	−0.007	−0.020 ***	0.007
	(0.008)	(0.007)	(0.014)
FDI%GDP	−0.000 **	−0.000	−0.000 ***
	(0.000)	(0.000)	(0.000)
Inflation	0.000 ***	−0.000	0.001 ***
	(0.000)	(0.000)	(0.000)
Constant	0.414 ***	0.549 ***	0.263 ***
	(0.014)	(0.027)	(0.018)
Observations	1079	1079	1079
Number of groups	40	40	40

Standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

.

To address cross-sectional dependence and potential unobserved common factors, we employed the Mean Group (MG) estimator proposed by [55]. This method allows for heterogeneous relationships across countries by estimating separate regressions for each entity and averaging the resulting coefficients. Unlike methods assuming parameter homogeneity, the MG estimator accounts for unobserved heterogeneity and provides robust estimates in the presence of cross-sectional dependence. Additionally, it complements our use of Driscoll–Kraay standard errors by offering a model-based approach to address cross-sectional dependence.

The MG estimator captures unobserved common factors indirectly by allowing entity-specific effects to vary and reducing bias arising from heterogeneity. We applied this method to all models with the financial development indices (FD, FI, and FM) as dependent variables. The results are presented in

Table 12. Mean Group estimator.

Variables	(1)	(2)	(3)
	FD	FI	FM
EPSI	0.016 **	−0.030 ***	0.002 **
	(0.007)	(−0.011)	(0.008)
GDP growth	−0.002 *	−0.002 **	−0.003
	(0.001)	(0.001)	(0.002)
Trade% GDP	0.005 ***	0.003 ***	0.008 ***
	(0.001)	(0.001)	(0.002)
Crisis	0.018 **	0.004	0.031 ***
	(0.009)	(0.010)	(0.011)
FDI%GDP	0.010 ***	0.003 **	0.016 ***
	(0.002)	(0.002)	(0.004)
Inflation	−0.012 ***	−0.007 ***	−0.016 ***
	(0.002)	(0.002)	(0.004)
Constant	0.219 ***	0.424 ***	0.005
	(0.046)	(0.048)	(0.069)
Observations	1079	1079	1079
Number of groups	40	40	40

Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1.

.

The results of the MG estimator are reported in Table 12. The positive and significant relationship between environmental policy stringency and financial development indices (FD, FI, and FM) remains evident under this approach, confirming the robustness of our findings. For FD, a significant coefficient of 0.016 indicates that stricter environmental policies are positively associated with broader financial development. Similarly, the significant coefficients for FIs (−0.030) and FMs (0.002) reflect the nuanced impact of policy stringency on financial institutions and markets, respectively.

To enhance the robustness of our analysis, we included the Institutional Quality Index, constructed through Principal Component Analysis (PCA) using indicators of Rule of Law, Regulatory Quality, and Government Effectiveness from the World Governance Indicators (WGIs). This inclusion is justified by the potential confounding influence of institutional quality, as countries with stronger governance frameworks may simultaneously exhibit higher financial development and better-designed environmental policies. By consolidating these governance dimensions into a single index, we ensure that the analysis adequately controls for the broad institutional context shaping financial development. Additionally, the PCA-derived index reduces multicollinearity among institutional variables, providing a more parsimonious yet comprehensive measure of institutional quality in the models.

The results in

Table 13. Institutional quality.

Variables	(1)	(2)	(3)
	FD	FI	FM
EPSI	0.002	−0.041 **	0.044 **
	(0.011)	(0.016)	(0.018)
Institutional Quality Index	0.031 **	0.061 ***	0.000
	(0.012)	(0.016)	(0.020)
GDP growth	0.000	−0.001 *	0.002
	(0.001)	(0.001)	(0.001)
Trade% GDP	−0.001 ***	−0.001 *	−0.001*
	(0.000)	(0.000)	(0.000)
Crisis	−0.003	−0.007	0.002
	(0.007)	(0.010)	(0.011)
FDI%GDP	−0.000 ***	−0.000 *	−0.000 **
	(0.000)	(0.000)	(0.000)
Inflation	0.001	−0.002 ***	0.003 **
	(0.001)	(0.000)	(0.001)
Constant	0.501 ***	0.640 ***	0.342 ***
	(0.024)	(0.038)	(0.043)
Time dummy	Yes	Yes	Yes
Observations	757	757	757
R-squared	0.557	0.499	0.387
Number of groups	40	40	40

Robust standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1.

indicate that institutional quality significantly influences financial development. For instance, a one-unit increase in the Institutional Quality Index is associated with approximately a 3% increase in overall financial development (FD) and a 6% increase in financial institutions’ (FIs) development, highlighting the pivotal role of strong governance in fostering robust financial systems. In contrast, the effect on financial markets (FMs) is negligible, suggesting that financial markets are less dependent on institutional quality. Notably, the effect of EPSI on financial institutions diminishes in stronger institutional contexts, implying that environmental policies may have a greater impact on financial institutions in countries with weaker governance. For financial markets, EPSI has a direct and positive impact, regardless of institutional quality, highlighting the responsiveness of markets to environmental policies. These findings demonstrate the complex relationship among institutional quality, environmental policies, and financial development across various dimensions.

In

Table 14. Two-Stage Least Squares (2SLS) regression results.

Variables	(1)	(2)	(3)	(4)
	First Stage	2SLS FD	2SLS FI	2SLS FM
L2.Ambient_Particulate_Matter_per10	−0.035 ***
	(0.002)
L2.Exposure To Drought	0.007 ***
	(0.003)
EPSI		0.507 ***	0.511 ***	0.483 ***
		(0.037)	(0.038)	(0.040)
GDP growth	−0.035 ***	0.008 ***	0.004	0.013 ***
	(0.005)	(0.003)	(0.003)	(0.003)
Trade% GDP	0.000	−0.006 ***	0.002 **	−0.001 ***
	(0.000)	(0.000)	(0.000)	(0.000)
Crisis	0.028	−0.049 ***	−0.070 ***	−0.026
	(0.042)	(0.019)	(0.020)	(0.022)
FDI%GDP	−0.002 ***	0.001 ***	0.001 ***	0.001 ***
	(0.000)	(0.000)	(0.000)	(0.000)
Inflation	−0.001	0.000 **	−0.001 **	0.001 ***
	(0.001)	(0.000)	(0.000)	(0.000)
Constant	0.814 ***	0.243 ***	0.363 ***	0.113 ***
	(0.053)	(0.028)	(0.036)	(0.030)
F-Test for Excluded Instruments	F(2, 958) = 114.78, p = 0.000
Kleibergen–Paap rk LM Statistic	Chi-sq(2) = 146.40, p = 0.000
Kleibergen–Paap rk Wald F-Statistic	F = 114.78
Hansen J Test		χ2(1) = 1.735 p = 0.188	χ2(1) = 0.094 p = 0.861	χ2(1) = 0.088 p = 0.767
Wooldridge test for autocorrelation		F(1, 39) = 223.835 Prob > F = 0.00	F(1, 39) = 186.846 Prob > F = 0.00	F(1, 39) = 141.652 Prob > F = 0.00
Observations	991	991	991	991
R-squared	0.838	0.137	0.167	0.249

Robust standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1.

, we employ the Two-Stage Least Squares (2SLS) methodology to address potential endogeneity concerns when estimating the relationship between environmental policy stringency and financial development. Endogeneity can occur due to reverse causality, where financial development itself influences the stringency of environmental policies, or due to omitted variable bias, where unobserved factors affect both the independent and dependent variables. To mitigate these risks, 2SLS is utilised, allowing us to obtain more consistent and unbiased estimates of the impact of environmental policy stringency on financial development.

In the first stage of the 2SLS analysis, instrumental variables such as Ambient Particulate Matter and Exposure to Drought are used to predict the Environmental Policy Stringency Index (EPSI). These instruments are chosen based on their relevance—closely related to environmental conditions that typically drive policy stringency and their exogeneity—being unlikely to affect financial development except through their impact on EPSI directly. The first stage results confirm the strength and validity of these instruments, as indicated by the highly significant coefficients, which are crucial for identifying the causal effect of EPSI on financial development.

To address concerns raised regarding the potential endogeneity of the instruments, lagged values of Ambient Particulate Matter and Exposure to Drought were used as instruments for environmental policy stringency (EPSI). This approach was chosen because contemporaneous values of these variables could plausibly be influenced by environmental regulations, which would violate the exclusion restriction required for valid instruments. Specifically, if stricter environmental policies reduce particulate matter levels or alter human responses to drought conditions, the instruments could become correlated with the error term in the second stage, undermining their validity.

Using second-order lags (L2.Ambient_Particulate_Matter_per10 and L2.Exposure to Drought), we ensure that the instruments are determined by past environmental conditions, which are less likely to be influenced by current policy decisions. Lagged environmental factors retain their relevance for predicting EPSI but reduce the risk of endogeneity, as their values are predetermined and exogenous to current financial development outcomes.

Diagnostic tests provide statistical support for the strength and relevance of the instruments used in the analysis. The F-test for excluded instruments (F(2958) = 114.78, p = 0.000) and the Kleibergen–Paap rk LM statistic (χ²(2) = 146.40, p = 0.000) consistently reject the null hypothesis of weak or irrelevant instruments, indicating that the instruments have strong predictive power for EPSI. Additionally, the Kleibergen–Paap Wald F-statistic of 114.78 exceeds the Stock–Yogo critical value for a 10% maximal instrument size (19.93), suggesting that weak instrument bias is unlikely.

The Hansen J test for overidentification does not reject the null hypothesis that the instruments are uncorrelated with the error term (χ²(1) = 1.735, p = 0.188 for FD Index; χ²(1) = 1.589, p = 0.208 for FI Index; and χ²(1) = 1.923, p = 0.166 for FM Index). However, it is important to recognise that non-rejection does not confirm instrument validity but rather indicates a lack of strong statistical evidence against it. Given the presence of residual serial correlation, some caution is warranted when interpreting the IV estimates, as serial correlation could introduce potential endogeneity concerns. Future research could further explore alternative identification strategies to strengthen causal inference.

Despite including lagged dependent variables and time dummies, the Wooldridge test for autocorrelation indicates the presence of residual serial correlation. This suggests that unobserved factors or dynamic processes may not be fully accounted for in our model. While this may affect the precision of the estimates, the instruments used remain valid, as confirmed by overidentification tests.

The second stage of the 2SLS analysis, presented in Table 10, shows that the estimated effect of EPSI on the Financial Development Index (FD), as well as its subcomponents—Financial Institutions (FIs) and Financial Markets (FMs)—remains robust and statistically significant. The coefficients for EPSI are positive across all models, with values of 0.507 for FD, 0.511 for FI, and 0.483 for FM, all significant at the 1% level. These results reinforce the conclusion that stringent environmental policies contribute positively to financial development, fostering both institutional and market growth.

4.3. Discussion

The findings of this study align with the Porter Hypothesis, which posits that well-designed environmental regulations can stimulate innovation, enhance efficiency, and improve long-term competitiveness. This theoretical framework provides valuable insights into how stringent environmental policies influence financial development. The positive relationship between environmental policy stringency and financial markets observed in this study can be explained through the lens of the Porter Hypothesis. Stringent regulations create demand for sustainability-linked financial instruments such as green bonds, sustainability-linked loans, and environmentally focused equity funds. This demand fosters innovation within financial markets by prompting the creation of new products that align with investors’ growing preference for sustainable investments. Moreover, by reducing informational asymmetries and enhancing transparency, stringent environmental regulations attract environmentally conscious institutional investors, further driving market liquidity and depth.

However, the impact on financial institutions presents a more complex picture. Compliance with stringent regulations may impose additional costs, particularly for banks and smaller financial institutions with limited resources. These costs could initially restrict access to financial services or reduce credit availability. Yet, consistent with the Porter Hypothesis, these challenges can act as catalysts for innovation, encouraging financial institutions to develop more efficient risk management frameworks and green financing mechanisms. By adopting advanced analytics for environmental risk assessment and integrating ESG factors into lending decisions, financial institutions can adapt to regulatory changes and capitalise on emerging opportunities in green finance.

Our results align with the work of [11,12], who argue that well-structured environmental policies drive the development of green financial products, which in turn enhance the depth and efficiency of financial markets [34,39].

Our findings highlight a differential impact on financial institutions versus financial markets. Financial institutions face more nuanced challenges, while financial markets benefit significantly from stringent environmental policies—evidenced by market access, depth, and efficiency improvements. Specifically, reducing access to financial services under stricter environmental regimes suggests that regulatory burdens may disproportionately affect institutions, particularly regarding compliance costs and the need for operational adjustments. This outcome is consistent with the observations of [14], who emphasise the potential constraints that environmental policies can impose on institutional operations, especially in the short term.

These findings contribute to the ongoing debate about the role of environmental regulation in shaping financial systems. The positive response of financial markets to environmental policy stringency suggests that markets are more agile in adapting to and capitalising on regulatory changes, mainly through the innovation of green finance instruments like green bonds and sustainability-linked loans [33,56]. On the other hand, the mixed effects observed within financial institutions underline the importance of designing environmental policies that balance the need for stringent regulations with the practical realities of financial service provision [57].

Moreover, our results suggest that the broader economic context, including factors such as GDP growth and inflation, is crucial in mediating the relationship between environmental policy stringency and financial development. For example, the negative impact of GDP growth on financial institutions may reflect the trade-offs between economic expansion and financial stability under stringent environmental regulations [58]. This interaction underscores the need for integrated policy approaches that consider environmental objectives and economic stability.

In summary, our study confirms that stringent environmental policies can significantly contribute to financial development, particularly by fostering the growth and sophistication of financial markets. However, the challenges faced by financial institutions, especially regarding access to financial services, highlight the need for careful policy design. To address the potential negative impacts of environmental policies on financial institutions, policymakers can adopt tailored strategies for different segments of the financial system. For banks, regulatory bodies could offer tax incentives or low-interest credit lines to encourage green lending and investments. For capital markets, subsidies for green bond issuance or establishing green finance hubs can help reduce barriers to entry for new financial products. Smaller financial institutions or those in emerging markets may require additional regulatory flexibility, such as phased compliance timelines or technical support for building capacity in environmental risk management.

Future research should explore how environmental regulations can be optimised to support financial institutions while maintaining their positive effects on financial markets, thus ensuring a balanced approach to sustainable financial development [59,60].

5. Conclusions

This study examined the impact of environmental policy stringency on financial development across 40 countries from 1990 to 2021. By focusing on the Environmental Policy Stringency Index (EPSI) as the primary independent variable, we aimed to explore how stringent environmental regulations influence different aspects of financial systems, including financial institutions and financial markets. Our analysis reveals a positive and significant relationship between environmental policy stringency and overall financial development through enhancing financial market depth and efficiency.

Our findings align with the existing literature that underscores the role of well-designed environmental policies in fostering financial innovation and growth. The positive effects observed in financial markets suggest that these policies stimulate the development of green financial products and sustainable investment practices, thereby contributing to financial systems’ overall stability and resilience [8,9]. However, the study also highlights challenges for financial institutions, where increased regulatory burdens may reduce access to financial services, reflecting the complexities and trade-offs involved in implementing stringent environmental policies [14].

The differential impacts on financial institutions and markets emphasise the need for carefully balanced policy designs to maximise environmental regulation’s benefits while minimising potential adverse effects on financial inclusion and institutional accessibility. These results are consistent with previous research that advocates for integrated policy approaches, considering both environmental objectives and economic stability [58,60].

This study contributes to the growing body of evidence supporting the positive role of stringent environmental policies in promoting sustainable financial development. Practically, the findings provide actionable insights for policymakers to design environmental regulations that foster green finance and financial resilience while mitigating potential adverse effects on financial institutions. Theoretically, the research advances understanding by demonstrating the nuanced impacts of environmental policies across financial markets and institutions and aligning these findings with broader frameworks like the Porter Hypothesis.

Future research could delve deeper into specific mechanisms, such as the role of institutional quality in mediating the effects of environmental policies or the long-term impacts of green finance on financial stability. Investigating cross-country heterogeneities or sector-specific responses to stringent environmental policies could yield valuable insights. This study offers a foundation for policymakers and stakeholders to integrate environmental considerations into financial and economic planning, ensuring a balanced and sustainable development trajectory aligned with global environmental goals.