F1000ResearchF1000Research2046-1402F1000 Research LimitedLondon, UK10.12688/f1000research.179271.1Research ArticleArticles“Green Finance, Policy Uncertainty, and Corporate Environmental Performance: Institutional Pathways in Developed Markets”
[version 1; peer review: awaiting peer review]
pereraDr ViduraConceptualizationData CurationFormal AnalysisMethodologyProject AdministrationSoftwareVisualizationhttps://orcid.org/0009-0003-2913-2461a1NanayakkaraProf. MadurikaConceptualizationFormal AnalysisProject AdministrationSupervisionb2MaduraraperumaProf. WasanthieConceptualizationMethodologyhttps://orcid.org/0000-0002-5106-2290c3
University of Kelaniya Faculty of Graduate Studies, Kelaniya, Western Province, Sri Lanka
Department of Commerce and Financial Management, University of Kelaniya Faculty of Commerce and Management Studies, Kelaniya, Western Province, Sri Lanka
Accounting, University of Kelaniya Faculty of Commerce and Management Studies, Kelaniya, Western Province, Sri Lankarperer.phd-pub24031@pg.kln.ac.lkmadurikan@kln.ac.lkwasanthi@kln.ac.lk
Green finance has emerged as an important mechanism for mobilizing private capital toward climate mitigation and sustainable development. Despite the rapid expansion of sustainable finance markets, limited empirical evidence exists on how macroeconomic conditions, particularly economic policy uncertainty (EPU), influence the effectiveness of green finance in improving corporate environmental performance (CEP) in developed economies.
Methods
This study examines the relationship between green finance, policy uncertainty, and corporate environmental performance using panel data from 1,280 listed non financial firms across nine developed economies during 2012 2024. The analysis applies a multi-stage econometric framework including two-way fixed-effects, instrumental variable two-stage least squares (IV-2SLS), and propensity score matching difference-in-differences (PSM-DiD) to address endogeneity, selection bias, and unobserved heterogeneity.
Results
The findings indicate that green finance significantly improves corporate environmental performance, as access to sustainable financial instruments enables firms to invest in renewable energy, energy efficiency, and lowcarbon technologies. However, the positive impact of green finance weakens under high economic policy uncertainty, consistent with real options theory, which suggests firms delay irreversible sustainability investments in unstable policy environments. The results also show that institutional governance mechanisms, including climate disclosures under the Task Force on Climate-related Financial Disclosures (TCFD), the Corporate Sustainability Reporting Directive (CSRD), and corporate commitments to the Science-Based Targets initiative (SBTi), mitigate the negative effects of policy uncertainty.
Conclusions
Overall, the study highlights that the effectiveness of green finance depends on stable climate policies, transparent disclosure systems, and strong institutional governance, which are essential for advancing global climate objectives and achieving Sustainable Development Goal 13 (Climate Action).
Green finance; corporate environmental performance; economic policy uncertaintyclimate governance; sustainable finance; TCFD; SBTi; developed markets.The author(s) declared that no grants were involved in supporting this work.1. Introduction
Climate change represents one of the most pressing global challenges of the twenty-first century, posing substantial risks to economic stability, environmental sustainability, and social welfare. Rising greenhouse gas emissions, increasing global temperatures, and more frequent extreme weather events have intensified international concern about climate change and strengthened calls for coordinated action by governments, corporations, and financial institutions. In response, many national governments have introduced regulatory frameworks and climate policies designed to accelerate decarbonization and support sustainable development. In particular, Sustainable Development Goal 13 emphasizes the importance of mobilizing financial resources and strengthening institutional mechanisms to address climate change.
Further, green finance is involved with financial tools like green bonds, sustainability-related loans and climate funds which support projects for renewable energy sources, capacity for electricityconsumption, carbon reduction technologies (
Flammer, 2021;
Uwuigbe et al., 2018). Green finance plays a role in drivingupward the corporate environmental performance (CEP) of firms, by providing capital for environmentally friendlyprojects and businesses (
Nam et al., 2018). Empirical research has shown that (>50% of the time) companies with higher access to green financial instruments have better environmental ratings and improved sustainability performance thantheir counterparts (
Friede et al., 2015;
Li et al., 2022).
As sustainable finance markets continue to expand, the effectiveness of green finance may depend on the broader macroeconomic and policy environment in which firms operate. Economic policy uncertainty (EPU) is one important aspect affecting corporate environmental investments. Economic policy uncertainty represents the uncertainty surrounding fiscal, regulatory, and monetary policies which affect firms’ investment decisions (
Baker et al., 2016). In particular, high policy uncertainty can reduce the incentive for long-term investments which involve irreversible capital expenditures like renewable energy infrastructure or low-carbon technologies (
Bloom et al., 2008;
Dixit & Pindyck, 1994). As a result, companies might defer or narrow sustainability investments when regulatory or climate policy environments are unstable.
Recent literature indicating that institutional mechanisms matter for mediating the falsehoods of environmental policy uncertainty on corporate environmental policies. However, climate disclosure frameworks (e.g., the Task Force on Climate related Financial Disclosures) and regulatory frameworks (e.g., the Corporate Sustainability Reporting Directive) which improve transparency as they reduce information asymmetries between firms and investors (
Krueger et al., 2020) mitigate such concerns. Other voluntary corporate commitments, like the Science Based Targets initiative, urgecompanies to set emissions reductions targets based on scientific research and aligned with global climate targets. These governance mechanisms enhance corporate accountability and may enable firms to preserve climate investments in the face of uncertain policy environments.
Although previous studies have investigated the association between green finance and corporate environmental performance, we lack reliable empirical evidence regarding how this relationship can be influenced by economic policy uncertainty in developed markets. Prior exploration has mostly concentrated on emerging economies characterized byinstitutional inadequacies and financial limitations driving sustainability investment (
Liu et al., 2020;
Zhao et al., 2022). However, even advanced economies with mature financial systems and sophisticated climate governance frameworks can experience policy uncertainty, which affects corporate environmental strategies. Green finance has increasigly been recognized as a critical mechanism for promoting sustainable developments and enhancing corporte enviormental performance (CEP) across diverse economic contexts. A growing body of litreture highlights that financial insturements such as green bonds, sustainable lending, and climate - focused investments play a pivotal role in facilitating enviormentally responsible business practices and innovation (
Alli et al., 2023;
Andersen, 2021;
Wang & Zheng 2023;
Chen et al., 2024). At the theoritical level, the porter and van der Linde hypothesis suggests that well desingned enviormental policies can stimulate innovation and improve firm compettiveness, providing a strong foundation for understanding the finace enviorment nexus. Empirical studies further demostarte that green finace supports firms trassition toward low carbon operations through invstments in clean technologies and energy efficiency (
Feng et al., 2017;
Li et al., 2017,
2024;
Linnenluecke et al., 2020). in addition, emerging research empahasizes the role institutional frameworks, financial systems, and global sustainability initiatives in shaping green fianace effectiveness (
UNEP FI 2024;
Cortilini & Panetta, 2021;
Dai et al., 2020,
2025). Methodologically, this study aligngs with established systermatic and structured reviewe approachers widely adopted in management and sustainability research (Tranfield et al.; petticrew and Roberts; Massaro et al.) ensuring rigor, Transparancy, and replibility. Overall the litreature suggests that whlile green fianace offers substantial potantial to enhance CEP its, Effectiveness is congningent upon institutional quality, policy stability and firms, capacity to translate financial resouses in to sustainable innovation outcomes (
Bending et al., 2023;
Uddin et. al., 2025;
Sun et al., 2025).
Consequently, in this study, we delve into the nature of the correlation between green finance and environmental performance (EP) while accounting for mediators of economic policy uncertainty (EPU), across nine developed economies for 2012 2024. Combining theoretical lenses from stakeholder theory, the resource-based view, real options theory and institutional theory, this study offers a robust understanding of the dynamic interplay between financial systems/governance mechanisms and corporate climate strategies. Further, does the negative effect of policy uncertainty on green finance effectiveness depend on climate governance mechanisms such as disclosure quality and science-based emission commitments?
This study adds to the sustainable finance and environmental governance literature in three key ways. This offers cross-national evidence on the relationship between green finance and corporate environmental performance at the firm-level across nine developed economies in 2012–2024 period. Existing studies have been focused mainly on emerging markets, whereas evidence from developed economies is scarce. Second, the study provides a moderating variable of economic policy uncertainty affecting green finance effectiveness. Through the integration of insights related to the resource-based view, stakeholder theory and real options theory, the research in this paper explains how macroeconomic policy volatility can reduce corporate incentives to pursue long-term environmental investments. Third, we find that institutional governance mechanisms such as TCFD and Corporate Sustainability Reporting Directive (CSRD) climate disclosures, and corporate Science Based Targets initiative (SBTi) commitments weaken the adverse impacts of politicaluncertainty. Using dual and triple interaction effects, the study builds a holistic theoretical framework to elucidate how financial resources, institutional governance, and policy environments collectively shape.
2. Literature review
Green finance has emerged as a key instrument for supporting climate mitigation and sustainable economic transformation. By directing financial resources toward environmentally beneficial investments, green finance facilitates the adoption of renewable energy technologies, energy-efficient production systems, and low-carbon infrastructure (
Flammer,
2021). Financial mechanisms such as green bonds, sustainability-linked loans, and climate investment funds provide firms with access to capital specifically dedicated to environmental projects.
The relationship between green finance and corporate environmental performance has been widely examined in the sustainability and environmental economics literature. Corporate environmental performance refers to the extent to which firms reduce environmental impacts through activities such as lowering greenhouse gas emissions, improving energy efficiency, and adopting environmentally responsible production processes (
Li et al., 2022). Empirical studies demonstrate that firms with greater access to green financing instruments often exhibit stronger environmental outcomes because financial resources enable investments in cleaner technologies and sustainable innovations (
Friede et al., 2015).
Theoretical perspectives provide further insights into this relationship. The resource-based view (RBV) suggests that financial capital represents a strategic resource that enables firms to develop environmental capabilities and competitive advantages (
Barney, 1991). Access to green finance allows firms to invest in sustainability initiatives that enhance operational efficiency and strengthen long-term competitiveness. At the same time, stakeholder theory emphasizes that firms respond to pressures from investors, regulators, and society to adopt environmentally responsible practices (
Freeman, 1984). In developed markets where ESG standards and sustainability expectations are high, firms often use green finance to meet stakeholder demands and maintain legitimacy.
Empirical evidence from developed economies supports these theoretical predictions. For instance,
Flammer (2021) finds that firms issuing green bonds in the United States experience improvements in environmental performance ratings. Similarly,
Sun et al. (2023) report that European firms receiving green financing demonstrate stronger sustainability outcomes. These findings suggest that green finance plays a crucial role in enabling corporate decarbonization and supporting global climate mitigation efforts.
While green finance can promote corporate environmental performance, its effectiveness may be constrained by broader macroeconomic conditions, particularly economic policy uncertainty. Economic policy uncertainty refers to the unpredictability of government policies related to taxation, regulation, monetary policy, and environmental legislation (
Baker et al., 2016). When policy environments become uncertain, firms may postpone investment decisions due to concerns about regulatory changes or future compliance costs. According to real options theory, firms treat investment opportunities as options that can be delayed until uncertainty is resolved (
Dixit & Pindyck, 1994). Investments in low-carbon technologies or renewable energy infrastructure often involve large upfront costs and long payback periods. As a result, firms may delay or reduce environmental investments when faced with uncertain policy conditions.
Empirical studies provide evidence supporting this mechanism.
Zhang et al. (2021) find that policy uncertainty reduces corporate investments in green innovation. Similarly, Dong and
Zhang (2024) show that firms facing high levels of policy uncertainty are less likely to invest in environmental technologies. These findings suggest that economic policy uncertainty may weaken the positive relationship between green finance and corporate environmental performance. Institutional theory highlights the role of governance structures and regulatory frameworks in shaping corporate behavior (
North, 1990;
Scott, 1995). In developed markets, institutional mechanisms such as climate disclosure standards and emission reduction commitments can enhance corporate accountability and support sustainable investment decisions. Climate disclosure frameworks such as the TCFD encourage firms to report climate risks, environmental performance indicators, and carbon emissions transparently. Enhanced disclosure reduces information asymmetries between firms and investors, thereby improving investor confidence and facilitating access to sustainable finance (
Krueger et al., 2020). Similarly, the European Union’s CSRD regulation strengthens corporate reporting requirements related to sustainability and climate risks. In addition to regulatory frameworks, voluntary commitments such as the Science Based Targets initiative encourage firms to adopt emission reduction targets aligned with global climate goals. Firms participating in SBTi signal credible long-term commitments to decarbonization, which can attract ESG-focused investors and strengthen corporate environmental strategies (
Sullivan & Gouldson, 2021). These governance mechanisms may also mitigate the negative effects of economic policy uncertainty by providing stable institutional frameworks that support corporate climate investments. Firms with strong disclosure practices and climate commitments are therefore more likely to maintain environmental investments even during periods of policy volatility.
3. Theoretical review and hypotheses development
Theoretically, our research combines Stakeholder Theory (
Freeman, 1984), Resource-Based View (RBV) (
Barney, 1991), and Institutional Theory (
North, 1990;
Scott, 1995) to clarify how green finance can lead to an improvement in corporate environmental performance and whether economic policy uncertainty moderates the above relationship among developed markets.
3.1. Green finance and corporate environmental performance (CEP)
The Resource-Based View (RBV) suggests that firms with access to unique strategic resources such as long-term capital can develop rare and inimitable environmental capabilities, leading to superior competitive advantages (
Barney, 1991). Meanwhile, according to Stakeholder Theory, firms act on the expectations of regulators, investors and customers enacting environmentally friendly activities in order to protect their legitimacy (
Freeman 1984).
Empirical evidence of the developed market reveals that the firms who engage in green bonds issuing or sustain-linked financing are able to raise ESG ratings and lower down pollution intensity (
Flammer, 2021;
Sun et al., 2023;
Zhao et al., 2024). Hence:
Green finance positively improves corporate environmental performance in developed markets.
3.2. The moderating role of economic policy uncertainty (EPU)
Economic policy uncertainty (EPU) represents the uncertainty in fiscal, monetary or regulation environments, whichwould increase firms’ perceived risk and discourage long-term investment (
Baker et al., 2016;
Bloom et al., 2008).
Based on Real Options Theory (
Dixit & Pindyck, 1994), under high uncertainty firms delay or reduce the scale ofirreversible investments like low-carbon transition. In developed markets also, fluctuations in the form of carbon prices, tax policies or climate-policy declarations have been shown to dampen connection between green capital and CEP (
Pastor & Veronesi, 2013;
Wang, 2024).
Emerging studies (
Zhang et al., 2021;
Dong & Zhang, 2024) find that EPU stifles firm investments into sustainability and green innovation. Accordingly:
Economic policy uncertainty negatively moderates the relationship between green finance and corporate environmental performance.
3.3. Institutional buffers in developed markets (TCFD/CSRD Disclosure)
Institutional Theory, for example, specifically considers how regulatory and governance regimes might influence company strategy (
North, 1990;
Scott, 1995). Disclosure requirements such as the EU Corporate Sustainability Reporting Directive (CSRD) and the Task Force on Climate-related Financial Disclosures (TCFD) help to reduce information asymmetries and to increase investor confidence in more developed markets (
Krueger et al., 2020). Firms with more-better-quality disclosures have better climate-risk management capabilities, thus being better able of turning governance mechanisms into environmental performance.
The positive impact of green finance on corporate environmental performance is stronger for firms with higher TCFD/CSRD disclosure quality.
3.4. Commitment mechanisms and long-term performance (SBTi Adoption)
Such commitment tools such as the SBTi signal credible long-term decarbonization paths (
Sullivan & Gouldson, 2021). These commitments reduce the risk of perceived greenwashing, and foster the trust of stakeholders. As a result of greater alignment with global climate goals, the companies that set SBTi targets are able to use GF for more substantialenvironmental improvements (
Abbasi et al., 2022).
Firms adopting Science-Based Targets (SBTi) exhibit a stronger positive relationship between green finance and corporate environmental performance.
3.5. Triple interaction: gf, epu, and institutional buffers
Institutional quality and disclosure readiness may serve as buffers in extremely uncertain environments. While weaker firms are more susceptible to uncertainty shocks, firms with strong TCFD/CSRD disclosures or SBTi adoption may maintain CEP improvements despite elevated EPU. This dynamic is a novel contribution because it has not been empirically tested in developed-market contexts.
The negative moderating effect of economic policy uncertainty on the green finance–corporate environmental performance relationship is weaker for firms with higher disclosure quality (TCFD/CSRD) or SBTi commitments.
The conceptual model demonstrates the pathways through which green finance influences corporate environmental performance under varying levels of economic policy uncertainty. Consistent with the resource-based view and stakeholder theory, access to green financial resources enables firms to invest in low-carbon technologies, renewable energy, and emission-reduction strategies, thereby improving environmental performance. However, economic policy uncertainty can weaken this relationship by increasing investment risk and discouraging firms from undertaking irreversible sustainability investments, as predicted by real options theory. Institutional governance mechanisms, including high-quality climate disclosure and science-based emission reduction commitments, can mitigate these negative effects by strengthening transparency, improving policy credibility, and supporting long-term environmental investment strategies. The framework also incorporates firm-level control variables such as carbon price exposure, green institutional ownership, and renewable energy share, which capture contextual factors influencing firms’ environmental strategies in developed economies.
Overall, the conceptual framework highlights that the environmental effectiveness of green finance depends not only on financial resources but also on policy stability and institutional governance.
Perera (2021) found that microfinance significantly contributes to women’s economic empowerment in rural Sri Lanka. (
Perera, 2021).
Sample composition.
Region
Countries included
Energy & utilities (20%)
Manufacturing & heavy industry (30%)
Transportation & logistics (15%)
Technology & services (35%)
Total firms
North America
United States, Canada
100
150
75
175
500
Europe
United Kingdom, Germany, France, Netherlands, Sweden
80
120
55
165
420
Asia-Pacific
Japan, Australia
60
90
50
160
360
Total
9 developed economies
240 (20%)
360 (30%)
180 (15%)
500 (35%)
1,280
Figure 1 illustrates the conceptual framework used to examine the relationship between green finance and corporate environmental performance in developed markets. In this framework, green finance (GF) represents the main explanatory variable, while corporate environmental performance (CEP) is the dependent variable. The model proposes that access to green financial resources enables firms to invest in environmentally sustainable technologies and practices, thereby improving their environmental performance. Economic policy uncertainty (EPU) is introduced as a moderating variable that may weaken the positive impact of green finance on corporate environmental performance. When policy environments become unstable, firms may delay or reduce long-term environmental investments due to regulatory and economic uncertainty.
Control variable alignment with developed-market hypotheses.
Control variable
Developed-market rationale
Theoretical justification
Integrated in hypotheses/sections
Expected sign
1. Carbon price exposure (CPE)
Developed economies (EU ETS, UK ETS, Japan, Canada) have established carbon-pricing regimes, making carbon exposure a critical determinant of how firms deploy green finance efficiently.
Institutional Theory: Policy mechanisms (carbon pricing) create incentives for firms to internalize environmental externalities, strengthening GF → CEP effects.
Mentioned as market-level control under
H1–H2 (GF direct effect and EPU moderation).
In developed economies, science-based targets are widely adopted by large listed firms (EU, UK, Japan). They signal credible long-term environmental commitment and reduce perceived policy risk.
Stakeholder & Institutional Theory: Firms aligning with global standards attract ESG capital and maintain CEP improvements under uncertainty.
Explicitly theorized in
H4(SBTi moderates GF → CEP positively). Also serves as part of the
H5 triple moderationwhen combined with EPU.
Positive (+)
3. Green institutional ownership (Green_IO%)
Developed capital markets (e.g., EU, US, UK) host large ESG-focused institutional investors who pressure firms toward sustainability compliance.
Agency & Stakeholder Theory: Institutional investors reduce information asymmetry and enforce ESG alignment, amplifying GF effectiveness.
Treated as an
institutional readiness control under
H1–H2, influencing CEP indirectly.
Positive (+)
4. Renewable Energy Share/RE100 Commitment
Developed firms are often part of RE100 or similar programs; renewable energy share reflects operational transition capacity.
Resource-Based View (RBV): Firms with renewable operations can better convert green capital into performance gains.
Modeled as
firm-level control in
H1–H2regressions.
Positive (+)
5. TCFD/CSRD Disclosure Quality
Developed markets enforce advanced disclosure systems (TCFD in UK/Japan, CSRD in EU). High-quality disclosure reduces risk premiums and stabilizes investor confidence.
Institutional Theory: Disclosure enhances transparency and mitigates the negative effects of policy uncertainty.
Directly embedded in
H3(positive moderation) and
H5 (buffering moderation under EPU).
Positive (+)
In addition, institutional governance mechanisms including climate disclosure quality under frameworks such as the Task Force on Climate-related Financial Disclosures (TCFD) and the Corporate Sustainability Reporting Directive (CSRD), as well as corporate commitments to the Science-Based Targets initiative (SBTi) are expected to strengthen the effectiveness of green finance and mitigate the negative influence of policy uncertainty. These mechanisms enhance transparency, improve investor confidence, and support more stable environmental investment strategies.
The conceptual framework therefore captures both direct and interaction effects between financial resources, policy environments, and institutional governance mechanisms in shaping corporate environmental performance in developed markets.
4. Research design and methodology4.1. Research framework
This paper explores the impact of Green Finance (GF) on Corporate Environmental Performance (CEP) in developed markets, noting the moderating role of Economic Policy Uncertainty (EPU) as well as buffer effects from institutional arrangements including TCFD/CSRD disclosure quality and decision to join Science-Based Targets initiative (SBTi).
Equal representation across different types of industries with high environmental exposure (energy, manufacturing), medium environmental exposure (transportation), and low environmental exposure (technology & services) was sustainedthrough a stratified sampling strategy. This method also increases the external validity and assures that results are representative of both high- and low-carbon sectors, which are prominently included in decarbonization policies forseveral developed economies.
To account for the intricacies of institutional and firm-level mechanisms, a number of other new control variables were added including: Carbon Price Exposure (CPE), SBTi Commitment, Green Institutional Ownership (Green_IO%), Renewable Energy Share/RE100 membership, and TCFD/CSRD Disclosure Quality.
4.2. Population, sampling, and sample size
The population includes all listed non-financial firms in developed economies with advanced ESG disclosure regulations. A
two-stage stratified sampling design was implemented:
Stage 1 (Country Strata): Nine developed economies (United States, United Kingdom, Germany, France, Netherlands, Sweden, Japan, Canada, and Australia) were selected for their robust ESG ecosystems and significant participation in global green finance markets.
Stage 2 (Industry Strata): Within each country, firms were proportionally sampled from four key sectors Energy & Utilities (20%), Manufacturing & Heavy Industry (30%), Transportation & Logistics (15%), and Technology & Services (35%) reflecting differential carbon intensity and policy relevance.
Sample Size:
Approximately
1,200–1,500 firms were analyzed, yielding
12,000–15,000 firm-year observations (2012–2024). This longitudinal scope covers critical milestones such as the
Paris Agreement (2015) and the
EU’s CSRD introduction, ensuring adequate statistical power and capturing structural policy shifts.
Justification:
The sampling design ensures (1)
wide coverage across countries and industries, (2)
temporal depth to observe policy transitions, and (3)
focus on environmentally sensitive sectors most influenced by EPU and green finance availability.
4.3. Sample composition
The final dataset reflects the following approximate distribution of firms by sector and geographic region. The allocation ensures coverage of carbon-intensive industries and variation across institutional contexts in developed markets.
The sectoral distribution follows proportional weighting: 20% Energy & Utilities, 30% Manufacturing & Heavy Industry, 15% Transportation & Logistics, 35% Technology & Services., The geographic distribution mirrors the relative weight of green finance markets globally: 40% North America, 35% Europe, 25% Asia-Pacific., Final dataset covers 1,280 firms (12,000–15,000 firm-year observations) for the period 2012–2024, ensuring statistical power for panel data econometric models.
4.4. Data sources
The study uses multiple reputable international databases to construct the dataset and ensure reliable measurement of financial, environmental, and institutional variables. Firm-level financial data, including green bonds, loans, equity structures, and firm fundamentals, were obtained from Refinitiv Eikon, Bloomberg, and Compustat Global. Corporate environmental performance (CEP) was measured using environmental pillar scores from Refinitiv ESG datasets, MSCIESG ratings, and climate disclosure scores from CDP. Economic policy uncertainty was captured using the widely used indices developed by Scott R. Baker, Nicholas Bloom, and Steven J. Davis. Carbon price exposure data were collected from the European Union Emissions Trading System, the UK Emissions Trading Scheme, and the World Bank Carbon Pricing Dashboard. Additional information on corporate climate commitments, institutional ownership, and renewable energy adoption was obtained from the Science Based Targets initiative database, Morningstar sustainable finance datasets, the Principles for Responsible Investment, and the RE100 initiative, complemented by energy transition data from BloombergNEF.
4.5. Variable definitions
Dependent Variable – CEP: Firm-level corporate environmental performance, proxied by ESG Environmental Score (%) and alternative measures such as carbon emissions intensity.
Independent Variable – Green Finance (GF): Composite index of green financing instruments (green bonds, loans, sustainability-linked credit lines), scaled by total financing.
Moderator – EPU: Annual country-level Economic Policy Uncertainty index.
Control Variables (Novelty):
Carbon Price Exposure (CPE): Sector × national carbon price or firm-reported ETS costs. (+ expected sign)
SBTi Commitment: Dummy (1 if validated, 0 otherwise) or years since validation. (+ expected sign)
Green Institutional Ownership (Green_IO%): Proportion of shares held by ESG-focused institutional investors. (+ expected sign)
Renewable Energy Share/RE100 Commitment: % renewable electricity or RE100 dummy. (+ expected sign)
TCFD/CSRD Disclosure Quality: Composite score based on climate-related disclosure quality. (+ expected sign)
Classic firm-level controls: Firm size (lnAssets), Leverage (Debt/Assets), Profitability (ROE), Industry dummies, and Year dummies.
4.6. Econometric specification
Baseline Model
CEP{it}=α+β1GF{it}+β2EPU{ct}+β3(GF{it}×EPU{ct})+γControls{it}+μi+λt+ϵ{it}
Where:
CEPitCEPit: Corporate Environmental Performance of firm i at time t
GFitGFit: Green Finance exposure
EPUctEPUct: Economic Policy Uncertainty index for country c at time t
GFit×EPUctGFit × EPUct: Interaction term testing moderation (H2)
ControlsitControlsit: Firm-level and institutional controls
To capture the
causal impact of green finance adoption, a Difference-in-Differences (DiD) framework compares treated firms (with GF exposure) and control firms (without GF exposure) before and after key policy events (e.g., Paris Agreement 2015,
TCFD introduction 2017):
CEP{it}=α+δ1Postt+δ2Treati+δ3(Postt×Treati)+β4EPU{ct}+γControls{it}+μi+λt+ϵ{it}
The coefficient δ3δ3 estimates the
treatment effect of GF policies on CEP, validating the robustness of the main FE model.
These interaction terms clarify how institutional and governance quality modify uncertainty’s influence on environmental outcomes.
4.7. Estimation strategy, using software stata and python
All estimations were conducted using STATA 18 and Python 3.10 to ensure methodological robustness and reproducibility. The primary analysis employs a two-way fixed-effects panel model controlling for firm-specific and year-specific heterogeneity. Standard errors are clustered at the firm level to address heteroskedasticity and serial correlation. To address potential endogeneity concerns, additional robustness checks were conducted using instrumental variable (IV–2SLS) estimation and propensity score matching combined with difference-in-differences (PSM–DiD). Furthermore, dynamic panel estimations and placebo tests were implemented to validate the stability of the results.
Perera et al. (2026) demonstrate that digital green finance significantly improves corporate environmental performance. (
Perera et al., 2026).
The complete econometric design of how green finance effects CEP in the presence of different economic policy uncertainty levels (EPU) in developed financial markets is shown in
Figure 2.
Empirical Strategy Overview.
Figure 2 showsThe methodology starts with a two-way fixed-effects (FE) panel estimation to control for firm and year heterogeneity (along the lines of Eq.) before moving on to instrument-variable (IV) regressions due to concern for endogeneity, and propensity-score-matching (PSM) methods to further reinforce causal inference. We then use a Difference-in-Differences (DiD) approach to compare treated (green financed) and control firms around key sustainability policy events (e.g., Paris Agreement, TCFD introduction). Protractinteraction models contain both moderator and buffer channel GF × EPU, GF × TCFD/CSRD, GF × SBTi, and GF × EPU × Disclosure/SBTi to examine the impacts of institutions and governance on CEP. Finally, robustness is compliedwith based on placebo, nonlinear and dynamic GMM methods to form a stringent, multiple-tiered method to verify the hypothesized linkage of green finance–uncertainty–environmental performance relationships for advanced economies.
This research employs Carbon Price Exposure, SBTi commitments, Green Institutional Ownership, Renewable Energy Share and TCFD/CSRD Disclosure Quality to focus on institutional, operational and investorimposedcharacteristics of advanced markets which have not been considered in prior work. Disclosure–Uncertainty Interaction: Examines whether the high-quality disclosures (TCFD/CSRD) attenuate the detrimental impact of EPU on green finance effectiveness. Cross-Country Developed Market Panel: This is the first study that compared across multiple developed markets using robust econometric techniques (FE, IV, PSM) and new disclosure and investor variables.
Green finance, epu, and moderation effects, developed vs emerging markets.
Figure 3 differences in the coefficients for GF, EPU, and the interaction of GF × EPU between developed and emerging markets are exhibited in Figure 02: GF effect: relatively positive and stronger in developed markets (β ≈ 0.42) than in emerging markets (β ≈ 0.29). That is because stronger institutions facilitate transforming green finance into environmental performance as a result. EPU Effect: Negative for both, but much larger in emerging markets (β ≈ − 0.21) than in developed countries (β ≈ − 0.12), consistent with the notion that weaker policy environments increase the size of uncertainty shocks. GF × EPU Moderation: Both significant, but the interaction effect is more negative in emerging markets, as it should be. H2, and H3. and EPU is more negative for the effect Green finance on Green finance onDestroywindows More importantly, in supporting H1, H2, and H3 this figure suggests that the channel. It really demonstrates how the institutional differences connect to the aims of your research, and it’s well articulated.
5.8. Summary statistics and multicollinearity analysis
5.8.1 Establish sample representativeness and institutional context
The descriptive evidence indicates that companies in developed markets have high basic levels of CEP (ie, mean = 64%), indicates that environmental performance is already embedded in the companies’ strategies. The percentage that Occupies in financing amount is 18.6%, GFs firm financing function is increasing, but it has been incomplete entirely (
Flammer, 2021). That the EPU is not significant is interested as the average level (102) for EPU indicate that political uncertainty is not an irrelevant issue, even for developed markets, and that this could potentially affect investment decisions (
Baker, Bloom, & Davis, 2016).
The five new control variables are the institutional preparation and market dynamic: CARBON PRICE EXPOSURE (CPE) (average = 14.5) indicates that the firms are under direct carbon-pricing regimes, hence the higher pay-offs to companies to have GF practices (
World Bank, 2023). SBTi targets (46% of firms) as serious decarbonization pathways, intended to join global net zero benchmarks (
Sullivan & Gouldson, 2021). This is also evidence of a strong ESG investor pressure in western courts (
Krueger et al., 2020). LVR: even impressive Green Institutional Ownership (29%) suggest a strong ESG investor pressure in the west court (
Krueger et al., 2020). Share of Renewable Energy (42%) is real operation decarbonization of RE100 and clean power purchasing (
CDP, 2022). TCFD/CSRD disclosure scores (mean = 2.9/5) also suggest some progress in terms of climate-related transparency, however it varies between markets (
EC, 2022).
Perera et al. (2025) argue that green finance research has expanded significantly under policy uncertainty conditions. (
Perera et al., 2025).
5.8.2. Bivariate associations: correlation matrix
Besides testing for multicollinearity, the correlation matrix allows for a descriptive index of interdependence betweenvariables. This is useful for conservation purposes, to establish the preliminary relationships and channels through whichgreen finance may influence CEP.
Table 4 correlation matrix indicates that there is a positively correlation between GF and CEP (r = 0.32, p < 0.01), thusproviding support for the baseline hypothesis. EPU is negatively related to CEP (r = −0.19, p < 0.05) which imply that uncertainty may erode environmental performance according to real-options theory (
Dixit & Pindyck, 1994). The institutional (CPE, SBTi, Green IO%, Renewable Share, TCFD/CSRD disclosure) enablers of sustainability are positively associated with CEP (
Krueger et al., 2020;
EC, 2022). Significantly, no relationship is greater than 0.70, suggesting no collinearity issues (
Hair et al., 2019).
- GF, EPU, and GF *EPU effects across robustness variants (Developed markets).
Figure 4 shows how GF, EPU, and GF × EPU affect different types of robustness in developed markets. The bars show the coefficient estimates (β) from two-way fixed-effects models for the full sample, the high-EPU subsample, the low-EPU subsample, and a non-COVID sample (not including 2020–2021). Green Finance (GF) is always positive and bigger in low-EPU settings, while Economic Policy Uncertainty (EPU) is negative, and this effect is stronger in high-EPU settings. The interaction GF × EPU is most negative in high-EPU settings and stays negative even when COVID-era years are left out. This shows that the moderating effect is structural and not caused by a crisis. There were fixed effects for the firm and the year, and the robust SE was clustered at the firm level.
Overall, the empirical results consistently demonstrate that green finance improves corporate environmental performance, while economic policy uncertainty weakens this relationship. Institutional governance mechanisms such as disclosure quality and climate commitments help mitigate these negative effects, highlighting the importance of stable policy environments and strong governance frameworks for effective green finance.
Direct effect of green finance on CEP To test the direct effect of green finance on CEP, the baseline RE and FE regression models are estimated (H1). This step answers the question of whether GF is a root cause of CEP in developed markets without including moderation (EPU) or institutional interaction terms. We present the results in Table 04, which reports coefficients, clustered standard errors and model fit statistics.
Table 5 The baseline regression results strongly support Hypothesis 1. Green finance has a positive and statistically significant effect on corporate environmental performance (β = 0.352, p < 0.01), indicating that firms with greater access to green financial instruments achieve higher environmental performance. This finding supports the resource-based view and stakeholder theory, suggesting that access to sustainable financial resources enables firms to invest in cleaner technologies and environmentally responsible practices.
Baseline regression results (H1: Direct effect of green finance on CEP).
Variable
Coefficient (β)
Std. error
t-Statistic
p-value
Green finance (GF)
0.352*
0.051
7.10
0.000
Carbon price exposure (CPE)
0.084
0.062
1.35
0.177
SBTi commitment
0.096
0.080
1.20
0.230
Green institutional ownership (Green_IO%)
0.055
0.047
1.17
0.242
Renewable energy share (RE_Share)
0.041
0.050
0.82
0.412
TCFD/CSRD disclosure quality (TCFD_Q)
0.071
0.060
1.18
0.238
Constant
−12.415
3.218
−3.86
0.000
Firm fixed effects
Yes
—
—
—
Year fixed effects
Yes
—
—
—
Observations
6,200
—
—
—
R
2 (within)
0.24
—
—
—
Notes:
Standard errors clustered at the firm level.
p < 0.01,
p < 0.05,
p < 0.10.
To test Hypothesis 2 (H2), we further extended the baseline model by including the interaction term GF × EPU. This is how we test the hypothesis that EPU may weaken the positive effect of green finance on corporate environmental performance (CEP) in developed countries in this study. The model further includes firm-level mechanisms (CPE, SBTi, Green_IO%, RE_Share, TCFD/CSRD disclosure) along with firm and year fixed-effects. The results are presented in Table 05 on the full sample of 1,280 firms (6,200 firm-year observations).
Table 6 Findings provided strong support for H2. The coefficient of green financing is still positive and significant (β = 0.428, p < 0.01 established in developed markets. Nevertheless, the interaction term (GF × EPU) is negative and significant (β = −0.0042, p < 0.05), meaning that higher degree of economic policy uncertainty weakens the positive impacts of GF on environmental performance. This result is also in line with real options theory, in that, in the face of uncertainty, firms tend to postpone and adjustirreversible decisions (e.g. investments in low-carbon technologies) downwards (
Dixit & Pindyck, 1994;
Pastor & Veronesi, 2013). This dynamic is first validated by empirical studies that reveal that EPU negatively influences firms’ innovation1 and the strength of their abatement investment (e.g.,
Zhang et al., 2021;
Wang, 2024).
To set up a reference point of empirically observed linkage, we start with a two-way fixed effects (FE) model to account for both firm-specific and year-specific unobserved heterogeneity. In this design, it is impossible for unobservable time-varying factors (e.g., corporate culture and sectoral regulations) and macroeconomic shocks shared by all firms within the given year to confound the estimated link between green finance (GF), economic policy uncertainty (EPU), and corporate environmental performance (CEP). As is usual in the econometric literature, we cluster standard errors at the firm level to allow for potential correlation, across time, within firms (
Wooldridge, 2019).
The model incorporates the five new institutional and firm-level control variables Carbon Price Exposure (CPE), SBTi commitments, Green Institutional Ownership (Green_IO%), Renewable Energy Share (RE_Share), and TCFD/CSRD disclosure quality to pin down the direct effect of GF and EPU on CEP. The estimate is made for the sample of developed market 1,280 firms, and 5,500 firm-year names (2012–2024).
The empirical evidence in
Table
7 strongly supports both H1 and H2. First, GF has a positive and very significant impact on CEP (β = 0.362, p < 0.01), which supports that in developed markets, firms with more GF achieve higher environmental performance. The result is in line with the stakeholder theory (
Freeman, 1984) and the resource-based view (
Barney, 1991) prediction that access to sustainability-related financial resources increase firms’ capacity to adopt cleaner technologies and legitimize themselves toward their stakeholders. This appears to be confirmed by empirical evidence:
Flammer (2021) demonstrates that U.S. firms issuing green bonds have improvements in environmental ratings and
Tan (2025) finds similar effects for technology upgrading.
Baseline fixed-effects regression: direct effect of green finance and EPU on CEP.
Variable
Coefficient (β)
t-statistic
Green finance (GF)
0.362***
(8.83)
Economic policy uncertainty (EPU)
−0.147**
(−2.37)
Controls (CPE, SBTi, Green_IO%, RE_Share, TCFD_Q)
Included
—
Firm fixed effects
Yes
—
Year fixed effects
Yes
—
Observations
5,500
—
Adj. R
2
0.28
—
Notes: Robust standard errors clustered at the firm level. *** p < 0.01, ** p < 0.05, * p < 0.10.
While the 2-way fixed effect (FE) baseline controls for observed heterogeneity endogeneity is still a concern. Particularlyfirms with higher CEP may be more likely to receive green finance (reverse causality) or omitted variables (e.g., unobserved policy shocks) might bias estimates346. To control for this non-randomness, we use instrument variable (IV) estimation based on two-stage least squares (2SLS). 3.2 Instrument selection: Consistent with the extant sustainable finance literature (
Flammer, 2021;
Tang & Zhang, 2020), we instrument firm-level GF with country-level lagged green bond market development and share of global green fund inflows. These are associated with access to green finance, but considered to be exogenous to the CEP of a specific firm atany given point in time.
Table 8 The IV/2SLS test result suggests that GF has a significant positive impact on CEP (β = 0.389, p < 0.01), which is in linewith the baseline FE model but a little bit higher. This indicates that any residual effect of reverse causality (stronger CEP luring GF) was not forcing the baseline results. The first-stage F-statistic of 19.8 is above the standard rule of thumb of 10, suggesting that our instruments are strong and valid (Staiger & Stock, 1997). Economic policy uncertainty still exhibits a significantly negative effect (β = −0.152, p < 0.05), supporting H2: graterpolicy volatility is still undercutting GF’s environmental benefits. These results support the intuition of real options theory (
Dixit & Pindyck, 1994), and attest the importance of stable institutional architecture to secure the efficiency of the capital allocation.
5.8.4 Robustness check II: Propensity score matching (PSM) with DID
To enhance causal reasoning, we also add a propensity score matching (PSM) in combination with the difference-in-differences (DID) estimator to the FE and IV/2SLS specifications. PSM generates a balanced control group of non green finance accessing firms which are selected on the basis of size, leverage, profitability, and sectoral exposure to avoidselection bias (
Rosenbaum & Rubin, 1983). By combining matching with DID, we compare the pre- vs. post-treatment changes in CEP between recipients of GF (treated firms) and matched non-recipients (control firms).
The coefficient is estimated using the sample of 1,280 firms that originate from developed markets (5,500 firm-year observations, 2012–2024), with treatment being receipt of nontrivial GF inflows (green bonds, sustainability loans or ESG-linked credit).
Table 9 The PSM-DID results validate the validity of the main findings. First, GF remains significant and positive for CEP (β = 0.371, p < 0.01), which supports H1 and suggests that access to GF leads to environmental gains that are, for the most part, observable in the environment after controlling for selfselection bias. Second, the coefficient of EPU is negative and significant, β = −0.139, p < 0.05), providing evidence for H2-policy uncertainty indeed suppresses CEP effects. Finally, the interaction term (GF × EPU) is once more negative and statistically significant (β = −0.0038, p < 0.05), meaning that uncertainty reduces the benefits GF.
PSM-Matched DID results: effect of green finance on CEP under policy uncertainty.
Robust SEs clustered at the firm level. Matching reduced mean covariate bias from 15.7% (pre-match) to 3.4% (post-match).
p < 0.01.
p < 0.05.
p < 0.10.
These results are the in line with previous studies on sustainability finance, the in which quasi-experimental an methods are used to address the issue of selection bias (
Flammer, 2021;
Sun et al., 2023). Moreover, the post-matching diagnosis demonstrates that the average covariate bias is 3.4%, below the 10% threshold (
Caliendo & Kopeinig, 2008), which means that the treated and untreated groups are similar.
5.13. Robustness check III: Alternative CEP measure (Emission Intensity Reduction)
In order to check the robustness of our results, we conduct the baseline regression with secondary measures of CEP. Inbaseline models, ESG environmental scores may be indicative of disclosure/reporting practices rather than actual operational effects. Hence, to reinforce their validity we include the following two other measures of performance: (i) emissions intensity, as a direct operational measure (the log of scope 1 + 2 emissions divided by output), and (ii) CDP climate scores, as a disclosure- and governance-based measure of environmental performance. These multi-specifications strategy enables us to test the parallel effects of GF and EPU on CEP on the basis of rating-based, operational and disclosure CEP dimensions.
Dependent variables by column: (1) ESG Environmental Score (0–100), (2) Emissions Intensity (Scope 1 + 2, log), (3) CDP Climate Score (A = 4 … D = 1).
Table 10 For ESG Environmental Scores (Model 1), GF continues its significant influence at a positive level (β = 0.354, p < 0.01), and EPU shows a significantly negative effect (β = −0.142, p < 0.05). The coefficient for the interaction term (GF × EPU) is negative (β = −0.0041, p < 0.05), which strongly supports H1 and H2. These results have confirmed that green finance enhances firms’ environmental performance; however, the effectiveness of green finance weakens with increasing level of policy uncertainty.
Alternative CEP outcome specifications (Two-Way Fixed Effects).
Dependent variables by column: (1) ESG Environmental Score (0–100), (2) Emissions Intensity (Scope 1 + 2, log), (3) CDP Climate Score (A = 4 … D = 1).
Notes: t-statistics in parentheses; robust SE clustered at firm level.
p < 0.01.
p < 0.05.
p < 0.10. A higher ESG/CDP score indicates better CEP; lower emissions intensity indicates better CEP.
On the other hand, the findings for Emissions Intensity (Model 2) find no impact of either GF and EPU. This implies that real emission cuts need long time horizons and could lag behind financial mobilization, as supported by previous findings that decarbonization returns come only after continued capital expenditure and technology diffusion (
CDP, 2022;
Cui, 2023).
Stared whether the 25 For CDP Climate Score (Model 3), GF is still both significant and positive (b = 0.209, p < .01), while EPU stays negative and significant (b = −0.088, p < .05). The interaction term is also negative (β = −0.0022, p < 0.05), suggesting that policy volatility negatively affects disclosure-based climate performance. These findings the suggest that, despite accruing transparency and governance benefits from green finance, firms are not immune to policy uncertainty exactly.
5.8.5 Robustness checks
We test if the EPU moderation is different across levels of policy stability within the developed market by separating the sample into high and low EPU countries (where high and low EPU countries are countries with EPU value above and below the EPU index median). This sub-sample analysis sheds light on within-developed heterogeneity, which constitutesa theory-based test of the GF-CEP link in different institutional certainty conditions.
Table 11 tests
H2 and H3 more directly by splitting the sample into
high- vs. low-EPU developed economies. In high-EPU situations, the GF coefficient is small and not important, while the EPU coefficient is very negative and the GF × EPU interaction is very negative The findings demonstrate that GF is insignificant in high-EPU conditions, but positive and significant in low-EPU conditions, and that EPU and the interaction term are negative only in the high-EPU sub-sample. This result supports the view that the same remains for a stable policy that is a prerequisite for the effectiveness of GF, as evidenced in real options (
Dixit & Pindyck, 1994) and institutional theory (
North, 1990).
Sub-Sample analysis: high vs. low EPU developed economies.
Variables
High EPU sub-sample
Low EPU sub-sample
GF
0.118 (1.21)
0.402* (5.63)
EPU
−0.284
** (−2.47)
−0.042 (−0.61)
GF × EPU
−0.009
** (−2.13)
−0.002 (−0.89)
Controls
Yes
Yes
Firm FE/Year FE
Yes/Yes
Yes/Yes
Obs.
2,200
3,300
Adj. R
2
0.25
0.34
Notes:
p < 0.01.
p < 0.05.
p < 0.10. Sample: 1,280 firms; 5,500 firm-year observations (2012–2024).
To verify the results are not dominated by the peculiar dynamics of the COVID-19 era (2020–2021), we re-estimate the baseline specification with pre-COVID samples only (2012–2019). This period gives us a cleaner environment by isolating global pandemic shocks, COVID-19 fiscal packages and unusual regulatory moves which may have transitedboth green finance (GF) flows and coporate environmental performance (CEP). The estimate includes 1,280 firms and 4,000 firm-year observations (2014–2019).
Table 12 The exclusion of COVID-19 years validates the strength of the main results. Green finance remains to exert a positive and significant influence on CEP (β = 0.341, p < 0.01), supporting H1. Likewise, EPU continues with its negative influenceand statistically significant (β = −0.121, p < 0.05), and the interaction term GF × EPU is still negative and significant (β = −0.0036, p < 0.05), supporting again H2.
Sectoral heterogeneity: High- vs. Low-emission industries.
Variables
High-emission sectors
Low-emission sectors
Green finance (GF)
0.421
*** (6.11)
0.189
** (2.15)
Economic policy uncertainty (EPU)
−0.212
** (−2.57)
−0.071 (−1.01)
GF × EPU
−0.0062
** (−2.42)
−0.0015 (−0.84)
Controls
Yes
Yes
Firm FE/Year FE
Yes/Yes
Yes/Yes
Obs.
2,900
2,600
Adj. R
2
0.32
0.22
Notes:
p < 0.01.
p < 0.05.
p < 0.10. Sample: 1,280 firms; 5,500 firm-year observations (2012–2024).
The findings imply that the relationships identified are not caused by transient pandemic-induced shocks, but insteadreveal more fundamental dynamics across the levels of GF, EPU, and CEP in developed markets. This is in line with previous studies that suggest the COVID years entail unusual volatility and government interventions that could affect the estimates if not accounted for (
Demir & Danisman, 2021;
OECD, 2021).
5.8.7 Robustness check V: sectoral heterogeneity
To check whether the effectiveness of GF differs between industries, we divided the sample into high-emitting sectors (energy, heavy industry, and transportation), and low-emitting sectors (services, high-tech, and retail). This enables us to examine whether the GF–CEP connection and the EPU moderating impact are more pronounced in emissionheavysectors, in which sustainability transitions become capital- and policy-bound. The estimation again employs firm- and year-fixed effects with the earlier described controls on 1,280 firms; 5,500 firm-year observations (2012–2024).
The results demonstrate that the GF–CEP association is more robust in high-emission sectors (β = 0.421, p < 0.01) than the low-emission sectors (β = 0.189, p < 0.05). Similarly, the moderating effect of EPU is only significant in high-emission sectors (β = −0.0062, p < 0.05). In the low-emission industries, both EPU and the interaction term are not statistically significant.
This heterogeneity indicates that energy, the manufacturing, and transport firms are particularly exposed more to policy uncertainty, as their environmental strategies involve substantial irreversible investments (
Dixit & Pindyck, 1994). In contrast, it would be relatively costly for GF to achieve regulatory independence from EPU, so the degree of exposure to regulatory volatility is lower for firms in services and technology industries.
6. Policy implications6.1 Policy implications for developed economies
We found as results of the study provide important policy guidance to governments, regulators and financial institutions in developed economies that want to speed up a shift towards low-carbon and climate-resilient economies. First, policymakers should focus on stable and credible climate policy frameworks. Long-term policy frameworks (consistent carbon pricing systems, multi-year climate strategies and clear regulatory standards) are necessary to reduce investment risk and promote large scale environmental investments by firms.
Stable policy environments ensure effectivetranslation of green financial resources into real environmental changes. Secondly, further improving the depth of climate disclosure frameworks would significantly strengthen green finance. Expanding and enforcing international disclosure standards, such as the Task Force on Climate-related Financial Disclosures (TCFD) and Corporate Sustainability Reporting Directive (CSRD), may rely critically on improvingtransparency and reducing information asymmetries between firms and investors. The increased disclosure helps to restore market confidence as well as further reduce the threats of greenwashing and misallocation of sustainable finance.
Third, governments and financial regulators should encourage the use of risk-sharing mechanisms that allow financial institutions to better manage uncertainty regarding green investments. Such instruments including public guarantees, blended finance structures and climate investment insurance schemes can mitigate perceived risks and incentivizebanks and institutional investors to increase their green lending and investing activities. Fourth, targeted transition policies are all the more important for high-emission industries. Industry, transport and energy sectors require large capital expenditure as they work to decarbonise. Policy instruments such as contracts-for-difference, tax breaks, accelerated depreciation for clean technologies and targeted transition subsidies could help to decarbonisethese sectors. Lastly, banks should incorporate dynamic risk assessment in green financial products. Issuing green loans and bonds linked to performance-based sustainability indicators, climate transition plans, and science-based emission reduction targets can also help ensure that companies maintain their environmental performance when political uncertainty is high.
The present study adds to the literature by providing an integrated theoretical framework to understand the nexus between green finance, policy uncertainty, and corporate environmental performance. The first type of contribution is that the findings align with the resource-based view (RBV), showing how access to sustainable financial resources allows firms to develop environmental capabilities and achieve competitive advantages (
Barney, 1991). Second, in line with stakeholder theory, the results show that firms respond to pressures from investors, regulators and society, by adopting environmentally-responsible practices that are underpinned by green finance (
Freeman 1984).
Third, the findings verify the predictions of real options theory by demonstrating that policy uncertainty (1) decreases the expected return of long-term environmental investments and consequently deters firms from incurring irreversible sustainability projects (
Dixit & Pindyck, 1994). Notably, the study also emphasizes institutional theory, showing how governance mechanisms like climate disclosure frameworks and emission reduction commitments can reduce adverse impacts driven by policy uncertainty (
North, 1990;
Scott, 1995). This study constructs a comprehensive framework to explain how financial systems, policy environment and governance structure interact with each other to determine corporate environmental outcomes examining both dual interaction effects (GF × EPU, GF × Disclosure) and triple interactions (GF × EPU × Institutional mechanisms). The analysis also shows significant cross-country and sectoral differences in the effectiveness of green finance.
Yet in countries with lower policy uncertainty and stronger institutional framework, including the United Kingdom, Germany and Sweden, the positive relationship between green finance and corporate environmental performance is more pronounced. These economies are characterized by stable climate policies, transparent disclosure systems and mature financial markets that facilitate such long-term sustainable investments. On the contrary, we find that green finance effects are weaker in countries with more economic policy uncertainty. Fluctuations in fiscal policy and regulatory frameworks, including commitments to climate policy targets, within evenadvanced economies can deter firms from making large-scale environmental investments. Sectoral differences are also evident. High-emission sectors like energy, heavy manufacturing and transportationexhibit astronger response to green finance stemming from the capital intensive requirements of decarbonizing these emissions-heavy industries. But these sectors are also more exposed to policy uncertainty because their investment decisions rely on regulatory set-ups such as carbon pricing and environmental standards. On the other hand, low-emission sectors like services and technology show weaker yet more stable relationships between green finance and environmental performance because of their lower exposure to climate transition risks. The findings underscore the need for policy coordination, sector-specific transition strategies and cross-country harmonization of green finance frameworks.
6.2 Limitations and future research
Although there are some contributions of this study, it does also have several limitations that should be discussed. First, part of the empirical analysis concerns listed non-financial firms in nine developed economies which may restrictthe reach of the findings to other contexts (e.g. emerging markets, SMEs or state-owned enterprises under alternativeinstitutional settings).
Second, corporate environmental performance is measured based on primarily ESG environmental scores, alongsideemissions intensity and CDP climate ratings. While this approach enhances the overall validity of measurement, these indicators are likely influenced at least in part by disclosure practices rather than purely operational environmental outcomes. Third, while the study employs rigorous econometric techniques including fixed effects, instrumental variable estimation and propensity score matching residual endogeneity and unobserved time-varying shocks cannot be completely excluded. There are several avenues for future research building on this work. First, studies may use more specific measures of firm-level uncertainty, for example variables from textual data such as risk disclosures in 10-K filings or sentiment in media articles to derive conceptual definitions about uncertainty. Second, future works may examine non-linear or threshold effects of policy uncertainty on environmental investment behavior (e.g., possible inverted-U relationships). investors and green bond markets to better understand the way in which green finance is channelled through financial systems. Last, newtechnological innovations from the world of finance like FinTech-enabled green finance, blockchain-based sustainability tracking schemes and digital climate-investment platforms merit attention as potential pathways for future research between financialization, innovation and environmental performance.
Third, more work could be done on financial intermediaries such as banks, institutional In general, the results accord with previous evidence showing green finance is instrumental to enhancing corporate environmental performance but its effectiveness is contingent on stable policy environments and robust institutional governance. Strengthening climate disclosure systems, reducing policy uncertainty, and promoting credible climate commitments are hence crucial to unlocking the full environmental potential of green finance in developed markets.
7. Discussion
This study provides new empirical insights into the role of green finance in enhancing corporate environmental performance within developed economies. The findings highlight the importance of financial systems in supporting corporate decarbonization and environmental sustainability by facilitating access to capital for environmentally responsible investments. In particular, green financial instruments such as green bonds, sustainability-linked loans, and climate investment funds enable firms to allocate resources toward renewable energy adoption, energy efficiency improvements, and low-carbon technologies. By strengthening firms’ financial capacity to undertake sustainability investments, green finance can accelerate the transition toward environmentally sustainable business models and contribute to broader climate mitigation objectives.
From a climate policy perspective, the results underscore the critical role of financial markets in mobilizing private capital for environmental transformation. Governments and international institutions increasingly emphasize the importance of sustainable finance in achieving global climate goals, including those outlined under the Paris Agreement and Sustainable Development Goal 13 (Climate Action). The findings of this study support this policy agenda by demonstrating that financial mechanisms can significantly influence firms’ environmental strategies and operational outcomes. When firms have access to dedicated sources of green capital, they are better positioned to implement environmentally friendly technologies and adopt sustainable production processes.
However, the analysis also reveals that the effectiveness of green finance is closely linked to the broader policy environment in which firms operate. Economic policy uncertainty can undermine the environmental impact of green finance by increasing investment risk and discouraging firms from undertaking long-term sustainability projects. Investments in renewable energy infrastructure, emission-reduction technologies, and other low-carbon innovations often involve substantial upfront costs and long payback periods. When firms face uncertainty regarding environmental regulations, carbon pricing policies, or fiscal incentives for climate investment, they may postpone or scale back such investments. This behavior is consistent with the predictions of real options theory, which suggests that firms delay irreversible investments under uncertain policy conditions.
Importantly, the findings indicate that even developed economies with relatively mature financial markets and strong institutional frameworks remain vulnerable to policy instability. While these economies generally possess advanced regulatory systems and well-established sustainability standards, fluctuations in policy commitments or regulatory frameworks can still influence corporate environmental decision-making. Changes in climate regulations, carbon pricing mechanisms, or fiscal policy may alter the expected returns of environmental investments, thereby affecting firms’ willingness to allocate capital toward sustainability initiatives. These results highlight the importance of policy stability and credible long-term climate strategies in ensuring that green finance translates into meaningful environmental outcomes.
Institutional governance mechanisms also play an important role in shaping the effectiveness of green finance. Climate disclosure frameworks such as the Task Force on Climate-related Financial Disclosures (TCFD) and the Corporate Sustainability Reporting Directive (CSRD) contribute to improving transparency and reducing information asymmetries between firms and investors. By providing more detailed and standardized information on corporate climate risks, environmental performance, and decarbonization strategies, these frameworks enhance investor confidence and facilitate more efficient allocation of sustainable finance. Improved disclosure practices therefore strengthen the link between financial resources and environmental performance by enabling investors to better evaluate firms’ sustainability commitments.
Similarly, voluntary corporate initiatives such as the Science-Based Targets initiative (SBTi) serve as important governance mechanisms that reinforce corporate accountability in environmental performance. Firms adopting science-based emission reduction targets demonstrate credible long-term commitments to decarbonization that align with global climate objectives. Such commitments not only enhance corporate legitimacy among stakeholders but also attract sustainability-focused investors who prioritize firms with clear environmental strategies. As a result, corporate climate commitments can strengthen the environmental impact of green finance by ensuring that financial resources are directed toward verifiable and measurable sustainability outcomes.
The results also reveal notable sectoral differences in the relationship between green finance and corporate environmental performance. High-emission industries such as energy, heavy manufacturing, and transportation exhibit stronger responses to green financial flows because these sectors require substantial capital investments to transition toward low-carbon technologies. Access to sustainable finance is therefore particularly important for enabling environmental transformation in these sectors. At the same time, these industries are more sensitive to policy uncertainty because their investment decisions are closely tied to regulatory frameworks such as carbon pricing systems and environmental standards. In contrast, lower-emission sectors such as technology and services show more stable but less pronounced relationships between green finance and environmental performance due to their relatively lower exposure to climate transition risks.
Overall, the findings emphasize that green finance alone is not sufficient to achieve substantial environmental improvements. The effectiveness of sustainable financial mechanisms depends on a broader institutional ecosystem characterized by stable climate policies, transparent disclosure frameworks, and credible corporate governance practices. When these institutional conditions are present, financial markets can play a transformative role in supporting corporate environmental performance and accelerating progress toward global climate objectives.
From a broader sustainability perspective, this study contributes to the growing literature on sustainable finance by highlighting the complex interaction between financial resources, policy environments, and institutional governance. By examining both moderating and buffering mechanisms within developed economies, the analysis provides a more comprehensive understanding of how financial systems and policy frameworks jointly shape corporate environmental outcomes. These insights are particularly relevant for policymakers, regulators, and financial institutions seeking to design more effective strategies for mobilizing private capital toward sustainable development and global climate mitigation.
Ethics statement
This study did not involve human participants, human tissue, personal clinical data, interviews, surveys, focus groups, or experiments requiring ethical review. The research is based entirely on secondary firm-level and country-level archival data obtained from publicly available and licensed institutional databases, including Refinitiv Eikon, Bloomberg, Compustat Global, CDP, the Economic Policy Uncertainty database, the World Bank Carbon Pricing Dashboard, Science Based Targets initiative (SBTi), Principles for Responsible Investment (PRI), RE100, and BloombergNEF. Therefore, formal ethical approval and informed consent were not required for this study.
Informed consent statement
Informed consent was not required because this study did not involve direct participation by human subjects. All analyses were conducted using secondary archival data from public or licensed institutional sources.
Data availability
The underlying and extended data are available in the Open Science Framework (OSF) under a
Creative Commons Attribution 4.0 International (CC BY 4.0) licence.
perera, V. (2026b, March 24). “Green Finance, Policy Uncertainty, and Corporate Environmental Performance: Institutional Pathways in Developed Markets”.
https://doi.org/10.17605/OSF.IO/VGJRC
This project contains the following underlying data:
GF-data.csv – Green finance variables including green bonds, green loans, and green R&D (cleaned, panel format).
EPU-data.csv – Annual economic policy uncertainty indices (raw, untransformed).
Extended data
perera, V. (2026a, March 24). “Green Finance, Policy Uncertainty, and Corporate Environmental Performance: Institutional Pathways in Developed Markets”- Extended Data.
https://doi.org/10.17605/OSF.IO/BS4MP
Supplementary Table 1.xlsx – Variable definitions, transformations, and coding scheme.
Supplementary Table 2.xlsx – Full robustness test outputs (FE, IV-2SLS, PTR).
Supplementary Figure 1.pdf – SHAP summary plots for AI/ML models.
“The author acknowledges the use of AI-assisted tools for language polishing and initial drafting, with all conceptual development, data analysis, and interpretations performed by the author. Also using AI tools as
https://quillbot.com/paraphrasing-tool
2024 use for the Languages and paragraph English Language correction.
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