The impact of green finance on corporate environmental performance: the moderating role of economic policy uncertainty

1 Introduction

Escalating global environmental degradation, characterized by increased greenhouse gas emissions, loss of biodiversity, extreme climate events and depleted resources has heightened the responsibility of governments and businesses to move toward developing sustainable economies. Corporate environmental performance (CEP) has recently become a significant policy and management issue as corporate operations, worldwide are increasingly subject to regulatory mandates, scrutinized by potential investors and the general public for some form of environmental records. Meanwhile, green finance has quickly emerged as an important tool for channeling capital to environmentally sustainable activities including renewable energy, pollution control measures, clean technologies and climate-aligned innovation. By allocating money to sustainable projects, green finance seeks to affect corporate behavior and decrease the ecological footprint while backing long-term decarbonization pathways.

Although evidence on the positive relationship between green finance and CEP is accumulating, relatively little is known about the macroeconomic conditions and policy environments under which the effect of green finance are stronger, weaker or inconsistent. Among the most crucial but currently underexplored macro-level factors is Economic policy uncertainty (EPU) a phenomenon in which uncertain of fiscal, regulatory, trade, monetary or environmental policies raise firms' cost of financing, postpone their long-term investment and disrupt risk perceptions. As green projects are capital-intensive, technologically complex and irreversible, EPU may have a profound effect on the firms' attitudes to and ability for utilizing green finance. This gives rise to an important conceptual question: does policy uncertainty attenuate, distort or reshape the impact of green finance on corporate environmental performance?

When you think about how things are different between countries, the problem gets even more serious. Emerging markets like China, India, Malaysia, Indonesia, Brazil, and the Philippines are quickly growing their green finance systems, but they do so in institutions that are less stable, regulatory environments that are less predictable, and macroeconomic situations that are less stable. These traits could make the bad effects of EPU on long-term investment decisions even worse. Developed markets, on the other hand, usually have stronger regulatory frameworks, deeper financial systems, and more stable policy regimes. This may make it easier for companies to keep their environmental promises even when things are uncertain. Even with these structural differences, there is still very little comparative, multi-country empirical evidence. This leaves big gaps in our understanding of how institutional context affects the green finance–CEP link when policy conditions change.

This study uses a balanced panel of public companies' data drawn from nine emerging markets for the period 2014-2024. A strong econometric model is used by combining DID, firm-year fixed effects, IV estimation and PSM to control for unobservables, endogeneity and selection issues. Moreover, nonlinear and threshold model is used to reflect dynamic moderation and context dependent moderation effects, while robustness checks (lagged regressions, placebo tests and COVID-19 removal) are conducted to confirm the authenticity of the findings.

It has been empirically demonstrated that green finance apparently and positively affects CEP, indicating a critical role for promoting corporate sustainability (Wang et al., 2023). Importantly, this relationship is moderated by EPU, and the adverse impacts are larger in emerging markets than in developed countries. Nonlinear analyses suggest the initial weakening role of EPU on green finance, but the impact is weakened by high level of uncertainty threshold which presents nonlinear characteristic.

This study offers several significant contributions. Theoretically, it enhances sustainable finance scholarship by connecting micro-level green finance mechanisms with macro-level economic policy uncertainty, thereby unifying corporate decision-making with larger institutional contexts. The study offers robust causal evidence through a multi-country balanced panel (2014–2024) and employs stringent econometric methodologies, including Difference-in-Differences (DID), IV-2SLS, PSM, GMM, and nonlinear moderation models. The findings underscore how governments can bolster regulatory credibility, stabilize policy environments, and formulate robust green-finance frameworks that maintain efficacy amid uncertainty. The study provides firms and financial institutions with practical insights into how policy volatility affects sustainable investment outcomes, enhancing environmental risk management and capital allocation decisions.

Even though more people are interested in green finance, there are still some gaps. First, the majority of current studies examine green finance at the firm or financial-sector level, neglecting to consider macroeconomic policy uncertainty as a significant conditioning factor influencing its environmental efficacy. Second, evidence from cross-country comparisons is limited, as previous studies predominantly concentrate on single-country contexts especially China or the EU without recognizing how institutional disparities between emerging and developed economies influence environmental outcomes. Third, a lot of studies use only one method to make estimates, which doesn't always take into account endogeneity, treatment effects, temporal dynamics, or nonlinear responses to uncertainty. These limitations hinder theoretical progress and diminish policy significance, particularly in areas where climate transition financing is acutely affected by regulatory stability.

2 Literature review

“Understanding how green finance improves corporate environmental performance requires examining the broader institutional, regulatory, and macroeconomic context within which firms operate” (Liao et al., 2021). The literature indicates that green finance has developed as a catalytic instrument facilitating investments in renewable energy, energy-efficient technologies, pollution reduction systems, and environmental innovation. However, the degree to which these financial resources result in quantifiable enhancements in environmental outcomes is contingent upon a more comprehensive arrangement of institutional and policy frameworks. This shows how important it is to have a theoretical framework that connects individual actions with big-picture uncertainty and differences in institutions between countries.

A coherent understanding of how green finance improves corporate environmental performance (CEP) requires situating firm-level investment decisions within broader institutional and macroeconomic conditions. Prior research shows that while green finance provides targeted capital for environmental improvements, its overall effectiveness is shaped by the stability of policy frameworks, the maturity of market structures, and the strength of governance systems. Accordingly, the literature can be organized into three major streams: (1) the direct effects of green finance on CEP, (2) the moderating influence of economic policy uncertainty (EPU), and (3) institutional heterogeneity in how these relationships unfold across emerging and developed markets. Recent studies further emphasize the importance of integrating green finance with policy stability to enhance corporate environmental performance, particularly across different institutional contexts (Perera, 2026a,b).

Green finance has increasingly become a strategic instrument for channeling resources toward environmentally beneficial activities, including clean technologies, renewable energy adoption, and pollution abatement systems. Empirical evidence indicates that green credit, green bonds, and sustainability-linked financing enhance firms' energy efficiency, stimulate green innovation, and reduce carbon intensity (Flammer, 2021; Wang and Li, 2022). Theoretically, stakeholder theory posits that firms engage in green finance to meet growing expectations from investors, regulators, and society, using these instruments to signal environmental responsibility and enhance legitimacy. Similarly, the resource-based view (RBV) conceptualizes green finance as a strategic asset that supports the development of environmental capabilities such as renewable energy systems, green R&D, and pollution control technologies capabilities that are valuable, rare, and difficult for competitors to imitate. Together, these theoretical perspectives and empirical insights suggest that green finance should have a direct and positive effect on corporate environmental performance.

Environmental investments tend to be long-term, capital-intensive, and often irreversible, which makes them highly vulnerable to policy and macroeconomic uncertainty. Real options theory predicts that as uncertainty rises, the value of delaying investment increases, prompting firms to postpone or reduce sustainability-oriented expenditures (Dixit and Pindyck, 1994). Empirical literature shows that elevated economic policy uncertainty raises firms' financing costs (Pástor and Veronesi, 2013; Lu et al., 2023), suppresses capital expenditure and innovation (Gulen and Ion, 2016), and weakens environmental initiatives and green technology adoption (Zhang and Liu, 2023). In this context, high levels of EPU may reduce firms' willingness to translate green financial resources into tangible environmental improvements. Thus, the relationship between green finance and CEP is likely to weaken when policy uncertainty is elevated.

Institutional theory emphasizes that firms operate within diverse regulatory, governance, and financial environments that deeply shape their strategic responses (North, 1990; DiMaggio and Powell, 1983). Institutional characteristics such as regulatory clarity, enforcement quality, and financial system maturity influence how firms navigate uncertainty. Evidence shows that developed markets typically benefit from stable climate policies, transparent governance structures, and consistent regulatory enforcement, enabling firms to sustain green investments even during periods of uncertainty. In contrast, emerging markets are characterized by institutional voids ambiguous regulations, weak enforcement, and macroeconomic volatility which magnify the adverse effects of economic policy uncertainty on firms' environmental strategies (Khanna and Palepu, 2010; Wu and Fu, 2021). Therefore, the moderating influence of EPU on the effectiveness of green finance is expected to vary substantially between emerging and developed markets.

3 Theoretical review and hypothesis development

3.1 Green finance (GF)

Green finance includes financial instruments, such as green bonds, sustainability-linked loans and green credit lines which channel resources into environmentally beneficiary projects (Flammer, 2021; Wang and Li, 2022). By easing financial constraints, GF facilitates investment in renewable energy, clean technologies and pollution abatement, which ultimately feeds into corporate environmental performance (CEP) directly.

3.2 Corporate environmental performance (CEP)

CEP refers to the observable environmental contributions that firms make, for example decreased emissions, improved energy efficiency and detailed sustainability reporting (Friede et al., 2015; Zhang et al., 2022), and now used as a standard by investors, regulators, and consumers.

3.3 Economic policy uncertainty (EPU)

EPU refers to uncertainty due to fiscal, monetary, trade or environmental regulations (Baker et al., 2016). It increases the cost of funding, discourages investment and creates potential for stranded assets and limits the long-term sustainable activities of firms (Pástor and Veronesi, 2013) “Recent studies further confirm that economic policy uncertainty negatively affects green innovation and environmental performance (Zhang and Liu, 2023; Wang, 2024).”

Theoretical framework: theoretically, these constructs can be connected or related to each other by the following mechanisms:

Stakeholder theory (Freeman, 1984): GF is a way for companies to fulfill the needs of stakeholders by providing them with signals that they endorse environmental sustainability (Chen et al., 2022).

Resource-based view RBV (Barney, 1991): GF is a strategic resource to support the development of green capabilities and deliver competitive advantage.

Institutional theory (DiMaggio and Powell, 1983; North, 1990): strong regulatory context and enforcement augment the effectiveness of GF in driving CEP but institutional voids hinder it.

H1—The direct impact of green finance on CEP.

Green finances bring in dedicated funds that improve the ability of companies to invest in clean technologies and embrace environment-friendly policies. It has been empirically reflected that the enterprises issuing green bonds have higher level of environmental innovation and lower carbon emissions (Flammer, 2021), in addition, green credit policies make a substantial increase in eco-efficiency of highly polluting industries (Dai et al., 2025). Meta-analyses also show strong positive relationships between ESG-orientated finance and environmental outcomes (Friede et al., 2015; Zhang et al., 2022).

Hypothesis 1 (H1): green finance has a positive and significant impact on corporate environmental performance.

H2—EPU as a moderator for GF → CEP.

The GDP contraction led to an increase in government spending (on the production of equipment in electronic and optical manufacturing) after long years of its steady decrease, making this very significant as well.

The effect of GF on CEP depends upon the stability of the macro policy environment. Hughes Hallett et al. (CCC Report, 2003, p. 24) and real options theory (Dixit and Pindyck, 1994), state that given the level of uncertainty firms may postpone or abandon investments where continuation is irreversible (such as for those in green technologies). Individual researches also show that EPU retards corporate investment and green innovation (Gulen and Ion, 2016; Zhang and Liu, 2023). Even though some evidence indicates that firms can use GF as a signaling device under conditions of uncertainty (Spence, 1973; Tang and Zhang, 2023), the net impact is negative: that EPU reduces the strength of GF in producing environmental outcomes (Wang, 2024; Tan, 2025).

Hypothesis 2 (H2): Economic policy uncertainty negatively moderates the relationship between green finance and corporate environmental performance. However, this moderating effect may vary across different levels of uncertainty, reflecting potential nonlinear or threshold dynamics. “Even though some evidence indicates that firms may use green finance as a signaling mechanism under uncertainty (Spence, 1973; Tang and Zhang, 2023), the overall effect remains negative.”

H3—Institutional context: developed and emerging markets.

Institutional quality matters for the impact of EPU on the relation between GF and CEP. Strong governance and policy stability in developed countries reduce political uncertainty, which in turn hold the positive influence of GF (Delgado and Min, 2020). On the other hand, institutional voids such as weak enforcement, opaque regulations, and financial immaturity further enhance uncertainty in emerging markets (Khanna and Palepu, 2010; Wu and Fu, 2021). Empirically, it is found that EPU shocks have more impairing effects on green investment and CEP in emerging economies (Aguilera et al., 2019; Chen et al., 2022).

Hypothesis 3 (H3): The negative moderating effect of economic policy uncertainty on the GF–CEP relationship is stronger in emerging markets than in developed markets.

Conceptual framework for the study to visualize the logic of the study, this research is framed in concept by figure one. It visually demonstrates the theoretical relationships that are suggested by researching showing the direct effect of gray finance (GF) on corporate environmental performance (CEP), and moderating role played by economic policy uncertainty (EPU), as well some cross-country contextual difference between EM and DM. Through the explicit presentation of the postulated pathways, the figure provides an organized departure point that guides empirical testing and consideration of hypotheses.

The model, as Figure 1 portrays, elaborates three main hypotheses. First (H1), it is expected that green finance will have a significant and positive effect on corporate environmental performance because sustainable financial instruments serve to direct investment in clean technologies, renewable energy projects and pollution abatement. Second (H2), we propose that economic policy uncertainty also moderates this relationship; in particular, high levels of uncertainty should weaken the role of green finance captured by Eq. Lastly (H3), institutional context is expected to demonstrate this relationship even further as for investors in emerging markets, the negative moderating effect of EPU would be stronger than investors in developed markets do since weak governance and high macroeconomic fluctuations accentuate the impact on performance more than that encountered in developed counterparts (Khanna and Palepu, 2010; Wu and Fu, 2021). Such a structure recognizes the direct importance of money to sustainability, and also the wider institutional and market context in which firms are embedded. It has green finance as a strategic firm level instrument (as well policy-dependent) that could have differential effects on adopting depending on the institutional quality of different countries.

4 Research design

4.1 Sample selection and data sources

To make sure that firms from different industries and country groups were well represented, a stratified sampling method was used. This method is valid because environmental performance, regulatory pressure, and access to green finance differ greatly depending on the industry and country. Stratification minimizes sampling bias and guarantees proportional representation of high-, medium-, and low-polluting industries, facilitating a more precise estimation of green-finance effects. We purposely oversampled industries like energy, utilities, manufacturing, transportation, and chemicals because they are responsible for a disproportionately large amount of carbon emissions and are the main targets of national and international climate policies. The governments of the United States, the United Kingdom, China, and the economies of the Asia-Pacific region have set up green finance programs that are specific to certain sectors (for example, renewable energy subsidies, green credit guidelines, and carbon pricing mechanisms). This makes these industries especially important for figuring out how well green finance works under different policy conditions. The choice of these industries corresponds with the theoretical premise that environmental investments exhibit heightened sensitivity to policy uncertainty in contexts of elevated pollution intensity, regulatory oversight, and capital demands.

The resulting sample contains 23,454 firm-year observations from the United States, United Kingdom (UK), China and selective Asia-Pacific countries during the period 2014–2024. Stratification ensures that both high and low pollution industries are well represented in proportion to their number in the workforce, minimizing bias due to sampling and ensuring an examination of variation by sector.

4.1.1 Data sources include

Financial and green finance indicators: Bloomberg, Refinitiv Eikon, Compustat, CSMAR, Wind. Environmental performance (CEP): Refinitiv ESG Scores, CDP disclosures, Bloomberg environmental pillar scores, and hand-collected project-level sustainability reports. Green R&D and CAPEX: company annual and sustainability reports, WIPO patent databases. EPU indices: Baker et al. (2016) country-specific indices. Macroeconomic Data: IMF World Economic Outlook, World Bank World Development Indicators.

4.2 Variable measurement

4.2.1 Dependent variable (DV): corporate environmental performance (CEP)

An index constructed from material indicators—CO₂ emissions, energy efficiency, waste reduction, pollution control expenditure, and ESG ratings—normalized by firm size. Higher scores denote stronger environmental performance.

4.2.2 Independent variable (IV): green finance index (GFI)

Constructed via an entropy weight method from seven dimensions: green credit ratio, environmental investment concentration, green insurance coverage, growth of the green bond market, environmental protection expenditure, allocation of green funds, and level of green equity trading.

GFi,t=∑j=17wj×xi,j,tGFi,t=∑j=17wj×xi,j,t

where w_*j* is the entropy weight assigned to indicator j, and x_*i,j,t* is the standardized value of indicator j for firm i in year t.

4.2.3 Moderating variable: economic policy uncertainty (EPU)

Country-level indices of policy uncertainty (Baker et al., 2016), capturing the frequency of uncertainty-related terms in leading newspapers.

4.2.4 Control variables

Firm size (log of assets), profitability (ROE), financial leverage (Debt/Assets), GDP growth, industry share, and exchange rate volatility. These variables account for alternative determinants of CEP. Table 1 presents the descriptive statistics and variable definitions used in the empirical analysis.

Table 1. Variable definitions, indicators, and data sources.

4.3 Econometric specification

To examine the impact of green finance (GF) on corporate environmental performance (CEP), and the moderating role of economic policy uncertainty (EPU), we employ a panel data framework with firm and year fixed effects (Wooldridge, 2016).

The baseline specification is given as:

CEP{i,t}= α+ β1GF{i,t}+ β2EPU{c,t}+ β3(GF{i,t}×EPU{c,t})+ γX{i,t}+ μi+ λt+ ϵ{i,t}

• CEP_*i,t*: corporate environmental performance for firm ii in year tt.
• GF_*i,t*: green finance index.
• EPU_*c,t*: economic policy uncertainty in country cc.
• GF_*i,t* × EPU_c,t: interaction term testing the moderating effect of policy uncertainty.
• X_*i,t* : control variables.
• μ_*i*: firm fixed effects.
• λ_*t*: year fixed effects.
• ϵ_*i,t*: error term.

The coefficient of interest is β₃β₃, which captures how EPU alters the impact of GF on CEP. A significantly negative β₃β₃ supports H2.

The coefficient β₃β₃ measures the moderating effect of economic policy uncertainty.

4.4 Estimation strategy

To ensure robust and reliable results, we employ a multi-method econometric approach, where each method addresses a specific econometric concern.

4.4.1 Baseline model: fixed effects/DID

The fixed-effects difference-in-differences (DiD) model serves as the primary estimation strategy, controlling for unobserved heterogeneity across firms and time.

4.4.2 Endogeneity correction: IV-2SLS

To address potential endogeneity between green finance and environmental performance, we apply an instrumental variable approach using IV-2SLS. This helps mitigate reverse causality and omitted variable bias.

First-stage equation

GF{i,t}= π0+ π1Z{i,t}+ π2X{i,t}+ u{i,t}

Second-stage equation

CEP{i,t}= α+ β^1{GF}{i,t}+ β2EPU{c,t}+ β3 (\hat{GF}{i,t}×EPU{c,t} )+ γX{i,t}+ μi+ λt+ ϵ{i,t}

Meaning of terms

• Z_*i,t*: instrumental variable.
• GF_*i,t*: predicted value of green finance from the first stage.
• X_*i,t*: control variables.
• μ_*i*: firm fixed effects.
• λ_*t*: year fixed effects.

4.4.3 Selection bias: propensity score matching (PSM-DID)

Propensity score matching is used to construct comparable treatment and control groups, ensuring that firms receiving higher levels of green finance are matched with similar firms. This reduces selection bias.

4.4.4 Dynamic effects: system GMM

To account for potential dynamic relationships and persistence in environmental performance, we employ the System GMM estimator, which is suitable for panel data with lagged dependent variables.

4.4.5 Robustness checks

Additional robustness tests include:

• Alternative variable specifications.
• Sub-sample analysis (developed vs. emerging markets).
• Placebo tests.
• Lagged models.

“The use of multiple econometric techniques ensures that the results are not driven by model-specific assumptions. Each method addresses a distinct econometric challenge, thereby enhancing the robustness, credibility, and generalizability of the findings.”

4.5 Estimation and analysis techniques

We will rigorously test the effects of green finance (GF), economic policy uncertainty (EPU) and corporate environmental performance (CEP) using a multi-method econometric approach. A panel regression with firm and year fixed effects is applied as the base specification and accounts for unobservable, time-invariant heterogeneity, and macro-economic shocks, while clustered firm-level robust standard errors address the issues of autocorrelation and heteroscedasticity.

Figure 2 displays Heterogeneity analysis is performed across market types (developed vs. emerging economies) and industry pollution intensity (high vs. low), and considers the structural distinctions in the GF–CEP nexus. Placebo testing using pseudo-policy years confirm that no spurious correlations exist and helps to bolster claims of causality. Robustness checks also include other (alternative) measures of CEP (e.g., emissions reductions, ESG environmental scoring) and alternative measures of EPU (global vs. sector specific), as well as lagged specifications to capture potential spillover effects, and sample exclusions (e.g., post-COVID) to help ensure the stability of results. To address issues of endogeneity, we use an instrumental variables (IV) approach, where we use the average GF index of the neighboring cities as the instrument, and use instruments surrounding Heckman two-stage selection correction for sample selection bias estimates and propensity score matching (PSM) to build treatment and control matching groups that are statistically comparable. Finally, to provide a correction for dynamic panel bias, we use the Arellano–Bond GMM estimator, which allows for consistent estimation when lagged dependent variables are included in the model. Finally, variance inflation factor (VIF) diagnostics confirm the absence of multicollinearity, allowing us to correctly make statistical inferences. This combined approach provides robust, credible, and generalizable empirical evidence.

4.6 Summary statistics and multicollinearity analysis

Table 2 shows the descriptive statistics for all the variables used in the study. It gives a summary of their central tendencies, how much they vary, and how they are spread out in the sample. This summary is very important for understanding the dataset's characteristics and making sure there is enough variance for strong econometric analysis. The table serves as a foundation for examining the relationships between green financing, economic policy uncertainty, and corporate environmental performance through the examination of these metrics.

Table 2. Descriptive statistics.

Table 2 shows summarized statistics for all the key variables taken into account during this analysis, covering the total sample of 23,454 firm-year observations for companies across the United States, United Kingdom, China, and select Asia-Pacific economies for the period 2014–2024. The mean corporate environmental performance (CEP) score is 0.57, which indicates moderate risk in terms of environmental effectiveness overall among the sampled firms, although the score for CEP shows a significant standard deviation of 0.12 which exhibits a high degree of variability or heterogeneity with regard to firms' outcomes with respect to the environment.

On the green finance index (GF) where many dimensions of green finance have been aggregated together, the mean value of the GF is 0.48, or a moderate level of green finance engagement for the average firm. The distribution of economic policy uncertainty (EPU) shows a significant amount of dispersion or variability, with mean = 120.3, SD = 30.7 on this measure of variability in terms of factors firms may encounter regarding their respective policy environments across both countries and time.

The firm specific control variables appear with distribution as we would expect: the firm size (log of the total assets) should average 8.52 for larger publicly listed companies as we have here; profitability (ROE) should average around 15%; and financial leverage (debt-to-asset ratio) is in the center of the distribution at roughly 45%. Finally, the macroeconomic indicators of GDP growth rate as well as the standard deviations on the exchange rate fluctuations exhibit usual variation as country macro economic characteristics are indicated during the sample period.

Table 3 shows that the VIF values range from 1.20 to 2.98, which is well below the commonly accepted limits of five (Hair et al., 2019) or the more conservative limit of 10 (Kutner et al., 2004). This means that the explanatory variables, like GF, EPU, and the firm-level controls (size, leverage, profitability, and macroeconomic variables), are not too closely related to each other. The lack of harmful multicollinearity validates that the estimated coefficients are unbiased and efficient, thereby enhancing the reliability of the econometric findings (Kennedy, 2008).

Table 3. Variance inflation factor (VIF).

From a theoretical point of view, keeping multicollinearity low is very important for this study because the interaction term (GF × EPU) is the main way to test the moderating role of policy uncertainty.

If there was a lot of collinearity, the interaction effects could be hidden or misread. The results indicate that each predictor adds distinct information to the model, thereby strengthening the validity of the findings regarding the influence of green finance and the moderating effect of EPU.

“The use of multiple econometric techniques ensures robustness and addresses different methodological concerns. While fixed-effects and DID models capture baseline causal relationships, IV estimation mitigates endogeneity, PSM addresses selection bias, and GMM accounts for dynamic panel effects. This combined approach enhances the credibility and generalizability of the findings.”

5 Results and discussions

5.1 Baseline regression result

Table 4 presented these base regression results for the direct to and in directed effects of (EPU) and (GF) on (CEP). This table provides a through test of both of our main hypothesis; (H1) the positive effect of GF on CEP, and (H2) the weakening of this effect through EPU as by reducing the effectiveness of GF during high uncertainly. The presence of firm and year fixed effects, which controls for unobserved variation across firms and time, further enhances the credibility of the casual inference (Wooldridge, 2016; Angrist and Pischke, 2009; Lu et al., 2023).

Table 4. Moderating effect of economic policy uncertainty (EPU) on green finance and corporate environmental performance.

^***, ^**, ^* denote significance at the 1%, 5%, and 10% levels, respectively. Standard errors are clustered at the firm level.

Table 4 regression results in strongly support H1, as (GF) demonstrates a positive and highly significant effect on CEP (β = 0.311, p < 0.01). This result validates stakeholder theory (Freeman, 1984) and the resource-based view (Barney, 1991), direction that access to dedicated GF enables firms to adopt cleaner production technologies, reduce emissions, and strengthen ESG scores (Zhang et al., 2024; Wang and Li, 2023).

For H2, the coefficient for EPU is negative and significant (β = −0.104, p < 0.05), while the GF × EPU interaction term is also negative (β = −0.079, p < 0.05). This demonstrates that heightened EPU low the environmental gained derived from GF, consistent with real options theory (Dixit and Pindyck, 1994; Pástor and Veronesi, 2013). Firms in volatile policy environments are more likely to delay irreversible investments in long-term sustainability projects, reducing the effectiveness of GF initiatives.

The control variables provided additional results. Larger firms and those with higher profitability (ROE) show stronger CEP, in line with earlier evidence that financially robust firms allocated more resources to environmental initiatives (Li et al., 2022). In preview, financial leverage exerts a deference and negative effect, as debt-constrained firms have lower capacity to committed funds to green investments. Macroeconomic factors such as GDP growth and exchange rate fluctuations, while directionally direct, are not statistically significant, reinforcing that firm-level financial strength and policy stability are more decisive drivers of CEP than broader macroeconomic shifts.

With an R-squared of 0.39, the model explains a substantial proportion of the variation in CEP, adding robustness to the findings. Taken together, the evidence showed that while GF is a strong enabler of corporate sustainability, its effectiveness is conditional on the predictability of the policy environment making institutional stability a vital component of successful GF strategies (Pástor and Veronesi, 2013; Li et al., 2022; Zhang et al., 2024).

Figure 3 illustrates the moderating effect of economic policy uncertainty on the relationship between green finance and corporate environmental performance, highlighting the differential impact across varying levels of uncertainty.

Graphical moderation plots (Figure 3) also display this interaction, where the slope of GF on CEP is steeper when EPU is low as compared to when EPU is high, confirming that a stable environment maximizes the associated environmental benefits of green finance activity. This is substantiated by theoretical perspectives from the real options framework surrounding uncertainty being an impediment to investment decision making and subsequently the related environmental benefits being lost (Dixit and Pindyck, 1994).

5.2 Addressing endogeneity issues

Table 5 presents the baseline and endogeneity-adjusted regression outcomes, aimed at evaluating the robustness and causal validity of the association among green finance (GF), economic policy uncertainty (EPU), and corporate environmental performance (CEP). This table aims to extend basic fixed-effects estimates by employing four complementary econometric methodologies: instrumental variables (2SLS), Heckman two-step, propensity score matching with difference-in-differences (PSM-DID), and system generalized method of moments (GMM).

Table 5. Baseline and endogeneity-adjusted regression results.

Robust standard errors clustered at the firm level. Significance levels:

^*p < 0.10,

^**p < 0.05,

^***p < 0.01.

Table 5 shows method five still postulated an outstanding and significant influence which cross-validated effect H1, and reaffirmed premised conclusions concerning the efficacy of green finance on emission reduction and improvement on emission inefficiency (Flammer, 2021; Zhang et al., 2024). In contrast however, indicating H2, EPU is negative and significant across the all models as well as the GF EPU cross interaction which was negative which indicates the H2 postulate; green finance and policy uncertainty reduces the gains from green finance touches real option theory (Dixit and Pindyck, 1994). In conclusion, firms operating under bounded policy uncertainty are said to underinvest in aggressive renewable ecologically protective means.

The IV-2 stage least square model indicates no weak instrument bias and significant Hansen J-test indicating instrument strength and validity (p = 0.267). The Heckman model bares in consideration the sample selection done. Non-significant Inverse Mills indicate the extent of selection is extremely scant. PSM estimation of the treated and control groups did not reveal them, suggesting the baseline model in which sample selection odds changes is valid. Finally the System GMM model confirms no second-order serial autocorrelation and dynamic endogeneity.

Figure 4 indicates that the marginal impact of green finance (GF) on corporate environmental performance (CEP) decreases as economic policy uncertainty (EPU) increases, but the decrease was greater in emerging markets than in developed markets. In developed markets the effect remained positive, but the decline represents the greater ability of developed markets to absorb uncertainty shocks because of stronger institutional foundations. Emerging markets experienced a much larger decline during increases to EPU with the marginal effect becoming negative at higher levels of EPU. This indicates that firms in developing countries may on occasion lose both the benefits of GF and be hindered in their CEP efforts altogether during relatively more unstable policy orientations. The clear takeaway is that in less stable policy environments, the perceived benefits of GF may be lost and therefore also impact NGO work promoting CEP; further, it demonstrates the importance of policy predictability in sustaining the benefits of GF.

5.3 Robustness analysis

5.3.1 Lagged effect analysis

Table 6 presents a lagged regression model that aims to determine whether the effect of green finance (GF) on corporate environmental performance (CEP) changes instantly or remains over time. In most cases, environmental investments require more than 1 year to deliver their impact (e.g., emission reduction or energy efficiency upgrade), hence the lagged GF (GF_{t-1}) being used to represent the continuation of the temporal influence. This is the table that supports the first research objective and gives the findings' trustworthiness level by assessing the time and lagged effects, thus, providing a deeper understanding of the interaction between green finance and sustainability.

Table 6. Lagged effect regression results (dependent variable: CEP).

Significance levels:

^**p < 0.05,

^***p < 0.01.

Table 6 outcomes demonstrate that both current GF (β = 0.178, p < 0.01) and lagged green financing (β = 0.145, p < 0.01) have a positive and significant impact on CEP, indicating the advantages of green financing are not only immediate but also extended to previous years. This signifies that the effects of GF are still going strong, an indication that sustainability investments are likely to yield long-term performance (Li et al., 2022; Zhang et al., 2024).

On the other hand, the economic policy uncertainty (EPU) factor is found to have a negative influence on corporate environmental performance (CEP; β = −0.088, p < 0.05). Further, the interaction term (GF × EPU = −0.052, p < 0.05) supports that policy volatility is a factor which hampers the effectiveness of green finance. What this implies is that a policy that is unstable can still result in fewer environmental outcomes even if a firm is engaged in green financing for multiple years.

If combined, the lagged model sheds light on the green finance role that is dynamic for sustainability, but the positive influence turns out to be very sensitive to uncertain policy regimes. The findings here are strongly supportive of the significance of policy stability. Only if current and past green financing efforts can be successfully converted into tangible improvements in environmental performance will this be possible.

5.3.2 Additional robustness checks

Table 7 presents the findings of the placebo regression, which tries to find out if the effect of green finance (GF) on corporate environmental performance (CEP) is real or just the product of shocks that weren't seen, fake correlations, or bad model specification. In this case, a placebo test is very important since it creates “fake” treatment years or modifies the way companies are grouped that weren't really exposed to green financing. This is called a falsification test. Also, if the calculated coefficients don't have any significant values in these made-up situations, it means that the baseline results are causal and not just a coincidence (Angrist and Pischke, 2009; Imbens and Wooldridge, 2009).

Table 7. Robustness test results dependent variable: corporate environmental performance (CEP).

Table 7 shows the results are quite clear that none of the main variables—GF (β = 0.018, p = 0.667), EPU (β = −0.012, p = 0.752), or the interaction term GF × EPU (β = −0.007, p = 0.827) are statistically significant in the placebo regression analyses. The coefficients that are almost zero and very small suggest that no green finance effect on CEP can be detected if the green finance treatment is randomly reassigned. In other words, this result is consistent with the reliability of the study's identification strategy, which in turn indicates that the baseline significant findings are not substantially affected by the presence of unobserved temporal shocks or simply caused by luck (Liu et al., 2022; Li and Sun, 2023). Table 8 presents the additional robustness checks, confirming the stability and consistency of the main findings.

Table 8. Hypothesis mapping.

Just as well, the factors at the level of the firm such as size, profitability, and leverage seem to have no impact in the placebo context, which in turn emphasizes that the real explanatory source is the green finance story in the policy world. With a low value of R² equaling 0.042, the model imparts little variability, which is another way to show that the placebo specification does not reflect the environmental performance dynamics.

Figure 5 presents the placebo test event study plot, which evaluates the validity of our DID identification strategy. The coefficients fluctuate around zero with wide confidence intervals, indicating that no statistically significant effects are observed in placebo treatment periods. Importantly, there is no evidence of systematic pre-trends or spurious correlations before the actual treatment. These results reinforce the parallel trends assumption and confirm that the estimated impact of green finance on corporate environmental performance in the main analysis is not driven by chance or hidden biases.

An event-study framework in which the timing of green finance treatment was fictitiously reassigned to companies or time periods without actual exposure is depicted by the placebo test plot. The vertical axis displays the estimated coefficients of green finance's impact on corporate environmental performance (CEP), while the horizontal axis displays the years in comparison to the placebo treatment.

The plotted coefficients, as anticipated, straddle the zero line at random, and all of their 95% CIs (shaded region) overlap. This result is important because it shows that there is no discernible effect on CEP when treatment is given at random. Put differently, the placebo test verifies that random shocks, time trends, or erroneous correlations are not the primary causes of the DID results. The true model's causal interpretation is strengthened by the lack of any discernible pattern in the placebo coefficients, indicating that the positive impact of green finance on CEP is real and not a result of model specification. “Empirical literature shows that elevated economic policy uncertainty raises firms' financing costs and reduces investment (Pástor and Veronesi, 2013; Gulen and Ion, 2016; Baker et al., 2016).”

5.4 Further analysis: heterogeneity test

“The findings indicate that while the baseline results support a negative moderating effect of EPU, additional nonlinear analysis reveals threshold effects. Therefore, Hypothesis 2 is partially supported, as the impact of EPU varies across different levels of uncertainty.”

Table 7 analyzes the differences for the various market categories, emerging and developed economies. This paper seeks to verify whether the relationship between green finance (GF) and corporate environmental performance (CEP), and the moderating effect of economic policy uncertainty (EPU), depends on a given context (as examined in Objective 1). The analysis of advanced and emerging markets is crucial for Objective 3, which attempts to understand how strong institutions and a developed market impact green finance during uncertainty (North, 1990; Allen et al., 2005).

Table 9 results indicate that green finance's positive impact on CEP is stronger in emerging markets (β = 0.235) than in developed markets (β = 0.145). The interaction term GF × EPU is also higher in emerging markets (β = 0.154) compared to developed ones (β = 0.087), supporting H3. This suggests that institutional weaknesses and regulatory inconsistencies in emerging economies may make firms more responsive to policy uncertainty, using green finance as a strategic buffer against environmental risks (Zhang et al., 2023; Wang and Li, 2022).

Table 9. Heterogeneity by market type.

Significance levels:

^**p < 0.05,

^***p < 0.01.

In Table 9, we examine the differentiated impact of green finance (GF) and economic policy uncertainty (EPU) in a moderating role across high- and low-polluting industries. This consideration is crucial because green finance mainly targets high-pollution sectors, such as energy and heavy manufacturing, which makes them more vulnerable to both incentives and policy uncertainties. Likewise, low-pollution sectors like IT and services are under more less regulatory pressure, which could direct their responsiveness to both GF and EPU. This analysis will clarify whether a sector-specific approach rather than a blanket EPU and financing strategy might be appropriate.

Table 10 shows green finance has a much bigger effect on the environment in industries that pollute more (β = 0.256) than in industries that pollute less (β = 0.117). The moderating effect of EPU is more pronounced in polluting industries, indicating that companies in industry-sensitive sectors face heightened regulatory and reputational pressures, thereby enhancing the strategic utilization of green finance in times of uncertainty (Chen et al., 2023).

Table 10. Heterogeneity by industry pollution intensity.

Significance levels:

^*p < 0.10,

^**p < 0.05,

^***p < 0.01.

In developing economies, inadequate institutional protection heightens the responsiveness of corporate environmental strategy to economic policy uncertainty (EPU), thereby rendering green finance a more potent instrument in times of uncertainty (Wang et al., 2023). More strict environmental rules and regulatory risk in industries that pollute a lot make green finance even more important, especially when EPU is low. Overall, these results support H3 and show how important it is for green finance policy to be tailored to the needs of the local market and industry. Perera (2021) found that microfinance significantly contributes to women's economic empowerment in rural Sri Lanka (Perera, 2021).

Figure 6 illustrates clear contextual magnification of green finance's effectiveness. The GF direct effect is largest in high-emitting industries and emerging markets, and it suggests that green capital secures proportionally greater environmental performance gains where environmental spillovers and regulatory pressure are strongest. GF × EPU bars are also considerably greater in emerging markets and high-polluting industries, evidencing that policy uncertainty via moderate levels enforces firms' utilization of green finance to strengthen CEP. Developed economies and low-polluting industries, conversely, have lower and more stable influences, consistent with improved institutions, more matured capital markets, and diminished policy volatility sensitivity.

Figure 6 shows heterogeneity in the green finance (GF) direct effect and the EPU-mediated effect (GF × EPU) on corporate environmental performance (CEP) by market type and pollution intensity of industries. Bars represent estimated coefficients with 95% confidence intervals for fixed-effects DID models. The GF effect and the moderation effect are both stronger in emerging markets than developed markets, and high-pollution industries than low-pollution industries, as is in line with H3.

The heterogeneity analysis demonstrates that the effects of green finance (GF), and its interactions with economic policy uncertainty (EPU), are highly contingent, which systematically vary on both market type and pollution intensity industry sector. In emerging markets the GF × EPU effect is considerably stronger than within developed economies, indicating that mild uncertainty is beneficial through the modification of using green finance strategically to improve corporate environmental performance (CEP). Conversely, the case of excessive uncertainty is clearly rationalized by capital constraints and risk aversion. In both basic and added cases, in high-polluting industries, GF has a much larger positive effect on CEP than low-polluting industries. Altogether, these findings confirm the institutional contingency of the GF–CEP relationship (North, 1990; Peng et al., 2009), and reinforce the need for nuanced policy interventions for the context, with emerging markets and high polluting industries requiring policy interventions for stabilization and developing markets and low pollution industries requiring innovation and stabilization or ESG policy integration.

Perera et al. (2026) demonstrate that digital green finance significantly improves corporate environmental performance (Perera et al., 2026).

5.5 Other analysis

Table 11 investigates the potential nonlinearity of the moderating effect of economic policy uncertainty (EPU) on the relationship between corporate environmental performance (CEP) and green finance (GF). This is significant because businesses may react favorably to moderate levels at first.

Table 11. Nonlinear moderation (quadratic EPU).

Table 11 shows the nonlinear moderating effects reported in Table 10 found that the relationship between economic policy uncertainty (EPU) and the effect of green finance (GF) on corporate environmental performance (CEP) is not monotonic. The positive GF × EPU coefficient (12.30, p < 0.05) provides evidence that, at lower levels of EPU to moderate levels of EPU, policy uncertainty can strengthen the positive effects of green finance on environmental performance. This finding supports the logic of real options theory (Dixit and Pindyck, 1994; Bloom, 2009), whereby firms are more likely to invest in green innovation given uncertainty in the policy environment and in the presence of benefits or incentives related to risk mitigation or favorable incentives if firms invest in green options.

The negative GF × EPU² coefficient (−6.50, p < 0.05) provides evidence for an inverted-U effect. As EPU increases, when EPU reaches a threshold amount the moderating effect will begin to diminish, becoming negative. The negative impact of excessive uncertainty on expected long-run investment, according to Pástor and Veronesi (2013) and Julio and Yook (2012), is based on increased cost of capital and uncertainty of returns impacting investor perception and attitudes to investment risk. With respect to green finance, if the level of policy uncertainty becomes too extreme in the form of policy volatility, firms may question whether any subsidies, tax credits and carbon pricing will exist long enough to warrant a capital-intensive investment, decreasing willingness to invest (Nguyen et al., 2021; Chen et al., 2023).

In terms of strategy, moderate uncertainty may act as a catalyst for innovation and sustainable investment as discussed in line with the Porter Hypothesis (Porter and van der Linde, 1995) which argues that regulatory pressure can create opportunities for both efficiency and innovation. However, very high forms of uncertainty undercut that possibility as we also saw in some of the institutional theory arguments, which emphasize the need for stable governance structures for sustainability-oriented investment to facilitate (North, 1990; Peng et al., 2009).

More broadly, we see in these results that there is likely a threshold effect at play:

• Low EPU → firms may not feel a sense of urgency to pursue aggressive mobilization of green finance.
• Moderate EPU → firms are able to strategically mobilize and deploy green finance to mitigate policy and market risk, improving their corporate environmental performance.
• High EPU → firms, to the detriment of strategic mobilization and deployment of green finance, are under the impression that they are faced with highly unstable and unpredictable, low investment return conditions.

This inverted-U relationship is not new (see, for example, Wang et al., 2020; Phan et al., 2022), and these results contribute to this understanding in the context of green finance, corporate sustainability and recognized policy risk.

Figure 7 illustrates the nonlinear moderating effect of economic policy uncertainty (EPU) on the green finance-corporate environmental performance (GF-CEP) relationship. The overall curve reinforces the inverted-U trend we discovered in Table 3, which demonstrated that moderate EPU levels utilize and enhance the positive effect of green finance on CEP, however, at very high EPU, the marginal effect on CEP diminished. The developed markets curve is much flatter which suggests a much greater temperature to true extreme uncertainty, given a higher institutional capacity and stable mechanisms for green finance (Bai et al., 2022; Zeng et al., 2023). The emerging markets curve is much steeper, suggesting that from a corporate perspective, firms in these markets are much more responsive to policy shocks; while moderate uncertainty may stimulate firms to concomitantly invest in environmental sustainability, high uncertainty effectively melt these gains (Zhang and Zhou, 2021; Wang et al., 2024). Perera et al. (2025) argue that green finance research has expanded significantly under policy uncertainty conditions (Perera et al., 2025).

5.6 Hypothesis mapping

Based on the central purpose, this study develops hypotheses for: investigating how GF affects CEP, and whether or not EPU moderates the effect of CEP across different institutional environments. Rooted in stakeholder theory (Freeman, 1984), resource-based view (RBV) (Barney, 1991), and institutional theory (North, 1990; DiMaggio and Powell, 1983), this study adds to a nascent literature investigating how sustainable financing tools can drive environmental outcomes amidst macroeconomic and policy conditions. Existing literature (Flammer, 2021; Chen et al., 2022; Wang and Li, 2022) supports that GF could channel money flow to sustainability-related projects, whereas new evidence (Sun, 2025; Zhang and Liu, 2023) holds that policy instability can reduce the advantages.

The purpose of the hypotheses formulated is to disentangle such complex interactions, thus fulfilling the specific goals of this analysis in that it (1) estimates the direct impact of green finance on CEP; (2) examines how EPU changes this relationship; and (3) investigates whether institutional quality across economies matters on these relationships.

The theories conjointly highlight how green finance serves as an inducement for corporate environmental performance enhancement and recognize the barriers due to policy instability and institutional disparities.

• H1 (GF → CEP): grounded in both RBV and stakeholder views, this proposition is supported by studies showing that companies with more effective green financing channels are associated with better environmental innovation and emission reduction performance (Flammer, 2021; Tan, 2025).
• H2 (EPU as moderator): based on real options theory (Dixit and Pindyck, 1994), this hypothesis suggests that fiscal or environmental policy uncertainty dissuades irreversible green investment. Empirical works (Gulen and Ion, 2016; Zhang and Liu, 2023) confirm this mechanism and find that policy volatility delays or decreases its adaptation on sustainability.
• H3 (institutional context): building on institutional theory and institutional voids literature (Khanna and Palepu, 2010; Wu and Fu, 2021) this hypothesis differentiates how EPU functions depending on governance environments—with its detrimental effects being more severe in emerging economies because of lower enforcement ability and immature financial infrastructure.

This study offers theoretical and practical implications. It, theoretically, contributes to the literature on sustainable finance by incorporating macroeconomic uncertainty in GF–CEP nexus with illuminating heterogeneity in institutions as a driver of financial effectiveness. It further provides empirical support for this nonlinear moderation mechanism that pushes the state-of-art in theoretical concatenation of Institutional Economics and Sustainability Finance. Practically, the findings are relevant for policy-makers, financial institutions, and corporate leaders, suggesting that:

• Stable and transparent policy frameworks amplify the environmental benefits of green finance.
• Targeted green financial instruments can mitigate risk aversion in uncertain environments.
• Institutional strengthening in emerging markets is critical to realizing the full potential of sustainable finance.

By integrating these hypotheses in relevant theory and policy objectives, the study provides a framework for resilient adaptive green-finance systems conducive to sustainable corporate environmental performance in dynamic policy environments. “Although evidence on the positive relationship between green finance and CEP is accumulating (Wang et al., 2023; Zhang et al., 2022; Li et al., 2022), relatively little is known about the macroeconomic conditions.”

5.7 Contextual and sectoral variability

In addition to disparities across national institutions, our findings also highlight a useful sectoral diversity. The high-pollution industries, including energy manufacturing and transportation exhibit a significantly more G-form sensitivity of the GF–CEP relationship to policy uncertainty. These sectors are subject to relatively stricter emission control requirements, have more exposure to the pricing of carbon, and exhibit a high level of capital intensity and thus low deferral risk in terms of investment under high EPU (Tang and Zhang, 2023; Zhan et al., 2023). On the other side, companies in low-polluting or service industries exhibit more persistent CEP results as they presumably incur less costs of regulation compliance and greater flexibility in adjusting resources. Moreover, industry-specific characteristics, such as the diffusion level of green technology (Zhang et al., 2023), environmental subsidies and sectoral ESG-reporting obligations have also explained the difference among industries in channels through which financial media works (Huang et al., 2023; Tan, 2025). In the future, these sectoral variables may be brought in a way technology intensity and regulatory stringency in particular that provide greater nuance of how industry context conditions the effectiveness of green finance under policy uncertainty. “Empirical evidence indicates that green credit, green bonds, and sustainability-linked financing enhance firms' environmental performance (Flammer, 2021; Wang and Li, 2022; Zhang et al., 2022).”

In general, the results show that green finance is a strong but context-dependent factor that affects how well companies do in terms of the environment. From 2014 to 2024, there were 23,454 firm-year observations from the US, UK, China, and some Asia-Pacific economies. Green finance consistently has a positive and economically significant effect on CEP. This shows that dedicated green instruments, like green credit, green bonds, and sustainability-linked loans, help companies upgrade their technologies, lower their emissions, and improve their environmental scores. Nevertheless, this effect is consistently diminished by increased economic policy uncertainty, as evidenced by the negative and significant coefficients on both EPU and the GF × EPU interaction term across baseline, IV-2SLS, PSM–DID, GMM, and lagged specifications. This corroborates real options theory, suggesting that in unstable policy contexts, companies often postpone or reduce irreversible green investments, even in the presence of green finance. The findings indicate significant heterogeneity: the positive effects of green finance are more pronounced yet also more vulnerable in emerging markets and high-pollution sectors, where institutional deficiencies and regulatory volatility exacerbate the suppressive influence of EPU, occasionally resulting in a negative marginal effect of green finance on CEP at elevated levels of uncertainty. Nonlinear analysis validates an inverted-U moderation pattern, indicating that moderate uncertainty may initially promote the strategic utilization of green finance as a risk management and innovation instrument, while excessive uncertainty diminishes these advantages. The evidence indicates that green finance can yield significant environmental enhancements; however, its efficacy is fundamentally dependent on policy predictability, institutional integrity, and sectoral attributes, underscoring that financial instruments are inadequate in the absence of reliable, stable, and well-regulated policy frameworks.

6 Conclusions, policy implications, and future research

Policymakers, banks, and business leaders should pay attention to what the results say. First, it's important to know that green finance always helps companies do better for the environment. This shows that green credit, green bonds, and lending frameworks linked to sustainability need to keep getting bigger. Businesses can adopt low-carbon technologies and come up with new ways to protect the environment faster if these kinds of tools are easier to find and less expensive. Second, the fact that green finance is losing its power as the economy becomes more uncertain shows that financial tools alone aren't enough without a stable policy environment. Governments need to focus on clear regulatory paths, clear environmental standards, and stable climate policies to make sure that businesses can make green investments that will last for a long time. Setting clear deadlines for carbon pricing, incentives for renewable energy, and requirements for environmental reporting can help investors make decisions faster by reducing uncertainty.

Third, the stronger negative moderation effect in emerging markets shows how important it is to have good institutions. People who make rules in these economies should try to make them more reliable, limit the freedom of bureaucrats, improve financial governance, and make sure that rules are followed. These changes to institutions can help green finance work better and keep investments from slowing down because people aren't sure what to do.

Fourth, the nonlinear results suggest that moderate policy uncertainty may persist in promoting strategic environmental investment as companies seek to mitigate risks. But when uncertainty rises too high, investments in the environment drop a lot. This makes it even more important to have early-warning monitoring systems that keep an eye on levels of uncertainty and help policymakers make changes when they need to.

Lastly, when businesses and banks make green finance portfolios, set environmental goals, and set prices for loans that are linked to sustainability, they need to think about the risks of policy uncertainty. Adding EPU metrics to their risk management frameworks can help companies protect their planned green investments even when things are unstable.

6.1 Policy application enhancement for emerging markets

At a policy level, the findings have some implications for governments, regulators and corporate managers. For emerging markets, the highest priority needs to be to build transparent and stable policy frameworks that reduce economic and regulatory uncertainty (Baker et al., 2016; Organisation for Economic Co-operation and Development (OECD), 2023). Sound fiscal and environmental regulation regimes holding carbon prices and renewable-energy targets give teeth to investors, de-risk investment structures for the first commercial-scale projects, and reduce the option value of waiting. Governments and central banks should also be involved in creating green-finance roadmaps at the national level that embed standardized green taxonomies, environmental disclosure mandates, as well as public-guarantee schemes to mitigate credit risk (Nguyen et al., 2021). Due to the high emissions intensity products, energy, transportation and heavy industry can be conditionally supported by tax breaks or performance-based interest subsidies with quantifiable emission reduction achievements (Zhan et al., 2023). Enhancing institutions such as creating autonomous environmental regulators and the convergence of ESG reporting forms would increase accountability and promote flows of international capital (Aguilera et al., 2019).

The evidence suggests that political certainty is a necessary condition for green finance to thrive. To operationalize this, policymakers in emerging markets should follow a three-pronged approach of macroeconomic stabilization, clarity of regulation and specific industry support.

6.1.1 Institutional stabilization and predictability

Governments should set up independent fiscal and monetary policy councils to take politics out of economic choices (Organisation for Economic Co-operation and Development (OECD), 2023). Publishing your medium-term economic frameworks and carbon-pricing trajectories and environmental budgets in a transparent way will reduce uncertainty, align investor expectations and guide long-term capital allocation. “Institutional characteristics such as regulatory clarity and governance quality significantly influence firms' responses to uncertainty (Delgado and Min, 2020; Aguilera et al., 2019).”

6.1.2 Green-finance governance and taxonomy development

Setting a singular national green-finance roadmap with established definitions for eligible green projects could help to align market practice (Nguyen et al., 2021). Central banks, in the form of green-credit guarantee programs and preferential reserve requirements, could reduce the cost of funding for green investments in energy, manufacturing and transport sectors that are fundamental to decarbonization.

6.1.3 Monetary and market stimuli for high-polluted industries

Conditional tax breaks and performance-based subsidies ought to be tied to measurable emissions reductions (Zhan et al., 2023). For example, a cement plant or refinery that meets an independently verified cut in carbon intensity would be eligible for lower interest rates or faster depreciation on its clean-technology equipment. Strengthening legal and regulatory frameworks is critical for improving the effectiveness of green finance and ensuring sustained corporate environmental performance, especially in the presence of economic policy uncertainty.

Independent eco-marketing boards and obligatory third-party verified ESG-disclosure standards would promote accountability and attract international investors (Aguilera et al., 2019). These same mechanisms help establish a certain continuity in regulation even in the face of fiscal or political instability.

6.1.4 Capacity building and cross-sector collaboration

Training for regulators, bank officers and corporate sustainability managers would also help ensure uniform compliance with green-finance policies. Creating public–private green accelerators would focus expertise and resources to facilitate smooth transitions in high-polluting industries.

This policy focus, which provides an empirical backdrop, informs a targeted approach that would yield enforceable guidance: stabilizing macro-policies, strong institutions combined with conditional incentives together increase GF's environmental returns and reduce vulnerability to uncertainty.

6.2 Wider implications for developed economies and companies

For emerging markets, linking GF to innovation incentives including R&D tax credits, patent-reward programs, and carbon-pricing alignment could help ensure competitiveness in clean technologies (Flammer, 2021; Huang et al., 2023; Perera, 2022). Banks need to integrate EPU expectations into their risk model, while maintaining liquidity “green buffers” in order to ensure project lifelines under macroeconomic shocks.

For advanced economies, it implies the necessity to connect green finance with innovation-oriented motivations. Aligning GF instruments with R&D tax credits, green-patent reward schemes and carbon-pricing mechanisms may promote breakthroughs and dissemination of clean technologies (Flammer, 2021; Huang et al., 2023). Macroeconomic policies should also be developed to help stabilize sustainable-asset markets and avoid capital exodus from green sectors in periods of global finance turmoil (International Monetary Fund (IMF), 2024). These actions not only reinforce the sustainability finance ecosystems but also sustain incentives for long-term investment in uncertain times (Perera, 2022).

Banks and companies also have a huge part to play in turning insights into action. The inclusion of EPU forecasts in sustainability-risk assessments and credit models may enhance early-warning systems and capital-allocation efficiency. Companies are advised to develop organizational resilience by keeping sufficient liquidity and voluntary reserved for sustainability to protect environmental projects when the policy terrain shifts. Increased ESG reporting and ahead-of-the-curve engagement with stakeholders act as signals that mitigate information asymmetry and attract long-term investors, ensuring the persistence of green investments under volatile circumstances. Together, these approaches translate the empirical observation that policy certainty amplifies the environmental benefits of green finance into operational advice for practitioners.

6.2.1 Theoretical contributions

This research contributes theoretically by including EPU as a moderator in the frameworks of stakeholder theory and institutional theory. It advances stakeholder theory (Freeman, 1984) by showing that the role of stakeholders' impacts on firms' environmental decisions is contingent upon policy stability—when moderate uncertainty incentivizes adaptive, reputation-seeking environmental investment; however, too much uncertainty weakens stakeholder pressures and delays sustainability action. Also, it contributes to institutional theory (DiMaggio and Powell, 1983; North, 1990) by demonstrating that the policy uncertainty functions as a dynamic institutional constraint that determines the effectiveness of green finance for serving corporate environmental performance. In developed markets, uncertainty is mitigated by firm institutions and legitimacy pressures are sustained but in emerging markets, ineffective institutional enforcement magnifies the negative influence of uncertainty. This multi-level link—between firm-specific stakeholder responsiveness and macro-level institutional predictability, provides a new theoretical perspective for explaining the functioning of the sustainable finance under policy volatility. “This result validates stakeholder theory and aligns with prior empirical findings (Zhang et al., 2024; Wang and Li, 2023).”

6.3 Limitation

While this study makes some contributions, it is not without limitation that suggests prospects for further research. The study was limited to energy-intensive sectors and to also apply the framework in a service, digital or agricultural sector may indicate heterogeneous investment behaviors that adapt differently toward uncertainty. The geographic focus on the US, UK, and China and some selected Asia Pacific economies could be broadened to African and Middle Eastern environments, characterized by completely different institutional voids and environmental risks (United Nations Conference on Trade and Development (UNCTAD), 2024). In addition, despite significant attention to cross-validating from Bloomberg, Refinitiv, CSMAR and co orate sustainability reports, in some cases firm-level green-finance metrics were compiled manually; enhanced ESG disclosure standards would improve data reliability and temporal consistency (World Bank, 2023). Future research may also investigate other moderating factors—e.g., technological invention, climate-policy stringency, or digitalization that interact with EPU to impact the efficacy of GF (Tan, 2025). Longitudinal studies taking account of post-2024 policy reforms and carbon-market enlargement would also contribute to an enriched grasp of the dynamic feedback relationships across policy consistency, financial innovation and environmental outcomes.

Although the methodological quality of the study is high, there are a number of limitations that need to be noted. First, the sample is heavily biased toward listed companies in high-emitting industries in chosen emerging and mature markets may make it difficult to generalize to unlisted or service sectors. Second, while the multi-country panel set up and the fixed-effects specification help mitigate issues of unobserved heterogeneity, panel data is by definition limited in its capacity to controlling for dynamic feedbacks or structural breaks that characterize firms sustainability transitions. Third, there may be other omitted variables (i.e., firm governance quality, regional environmental regulation intensity, or manager's environmental culture) that might bias the coefficient estimates; however it is minimized through our extensive robustness tests. The results, therefore, should be treated as suggestive and not universally applicable. Further research may use dynamic panel or micro-level data to certify causality, investigate the generalizability of our findings to SMEs and non-listed firms, introduce interaction effects between institutional quality, innovation, and EPU for better explanatory power and policy implications of the green finance–CEP relationship.

“Moreover, like other multi-country panel datasets, omitted variable bias and unobserved heterogeneity may still remain when estimating fixed effects, IV-2SLS, and PSM (propensity score matching) estimations. Although endogeneity is reduced to an acceptable level through robustness checks, interpretation of these results needs to be cautious when making out-of-sample predictions within other regions or sectors not sampled. It may be that with innovative estimation methods or with a natural-experiment design, inferences on and external validity could be strengthened.” “Financially robust firms allocate more resources to environmental initiatives (Li et al., 2022).”

Overall, this study provides theoretical implications through synthesizing the logic from stakeholder theory, institutional economics and real option theories to explain why the effectiveness of green finance is contingent upon institutional stability and macropolicy predictability. Empirically, it offers strong cross-market evidence based on the DID technique and multiple-method estimations to identify sustainable finance causality. In practice, it provides a range of policy advice on how national governments and individual institutions might build such resilient green-finance systems to sustain environmental performance even amidst international economic turbulence. “This study extends prior literature by integrating macro-level uncertainty into firm-level sustainability analysis (Chen et al., 2022; Wang et al., 2023).”

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Footnotes

Edited by: Mirela Panait, Petroleum & Gas University of Ploieşti, Romania

Reviewed by: Xiaotian Zhou, Wuhan Institute of Technology, China

Neel Wasantha, Uva Wellassa University, Sri Lanka