Is There a Synchronisation Between Government Environmental Strictness Policy and Firm-Level Environmental Investment? A European Evidence from the Disaggregated Data

Is There a Synchronisation Between Government Environmental Strictness Policy and Firm-Level Environmental Investment? A European Evidence from the Disaggregated Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Is There a Synchronisation Between Government Environmental Strictness Policy and Firm-Level Environmental Investment? A European Evidence from the Disaggregated Data Alper Ozun, Emre Atilgan, Hasan Murat Ertugrul, Omer Tugsal Doruk This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9415906/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract This study examines the synchronization between government environmental strictness policy and firm-level environmental investment across 19 European countries from 2005-2020. Using disaggregated firm-level data from Thomson Reuters Refinitiv database and the OECD Environmental Policy Stringency Index, we employ local projections methodology and bias-corrected panel logit models to capture dynamic policy-firm interactions. Our findings reveal robust evidence of positive synchronization between environmental policy stringency and corporate environmental investment decisions. The environmental stringency indicates economically significant effects, with larger firms showing stronger responsiveness. Local projections analysis demonstrates sustained positive effects over a three-year horizon, suggesting genuine behavioral changes rather than temporary compliance. Results remain consistent across multiple methodological approaches, supporting the view that European environmental policies successfully translate macro-level regulatory stringency into micro-level firm investment behavior. This study contributes to the macro-micro alignment literature in environmental economics and provides evidence for policymakers that environmental strictness policies can effectively drive corporate environmental investment without creating unsustainable compliance burdens. JEL Codes: Q58; G31; C23 Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Environmental policy environmental investment firm-level data local projections methodology panel bias corrected logit model Figures Figure 1 Figure 2 1. Introduction With global warming intensifying environmental challenges, the alignment between macro-level government policies and micro-level firm behaviors has emerged as a critical determinant of environmental policy effectiveness (WorldBank, 2024 ). While environmental policies are designed to drive sustainable practices and reduce ecological footprints, their success fundamentally depends on the extent to which firms synchronize their investment decisions with policy objectives. This study examines the encouraging or possible wedge effects of government environmental strictness policy on firm-level environmental investment, with particular focus on the synchronization between public environmental policy and corporate environmental behavior in the European context. The relationship between environmental policy stringency and firm-level investment decisions represents a complex interplay of regulatory pressures, financial incentives, and strategic responses that has received insufficient attention in the existing literature. While substantial research has examined environmental policy effectiveness at the macro level and firm environmental performance at the micro level, the critical linkage between these two levels—the macro-micro alignment—remains understudied (Ning & Shen, 2024 ). This gap is particularly pronounced in understanding how government environmental strictness policies translate into tangible firm-level environmental investments, and whether such policies create genuine synchronization or merely symbolic compliance. The theoretical foundation for expecting synchronization between environmental strictness policy and firm behavior draws from multiple complementary frameworks. Institutional theory suggests that firms respond to coercive, mimetic, and normative pressures from their external environment, with regulatory coercion serving as a significant driver of substantive environmental management behavior (Ma et al., 2022 ). a Regulatory compliance theory emphasizes how environmental regulations create pressure that fosters financial gains by encouraging firms to proactively invest in sustainable practices for long-term profitability (Ning & Shen, 2024 ). Furthermore, stakeholder theory explains firm responses through the lens of diverse stakeholder expectations, where environmental policies serve as external pressures that shape corporate environmental strategies (Vazquez-Brust et al., 2010 ). The European context provides a particularly compelling setting for examining policy-firm synchronization due to its comprehensive and integrative environmental regulatory framework. The European Union has established robust environmental policies through initiatives such as the European Green Deal and the Emissions Trading System, which combine environmental protection with economic and social objectives while aiming to transform Europe into a climate-neutral continent by 2050 (Tosun, 2023 ). These policies not only set ambitious environmental targets but also create market-based incentives and regulatory pressures that directly influence firm behavior, making Europe an ideal laboratory for studying how environmental strictness policies encourage firm-level environmental investment. The significance of understanding this synchronization extends beyond academic inquiry to critical policy implications. Environmental policies that fail to generate corresponding firm-level investments represent inefficient allocation of regulatory resources and missed opportunities for achieving sustainability goals. Conversely, policies that successfully encourage firm environmental investment can create positive feedback loops, where regulatory stringency drives innovation, competitiveness, and further voluntary environmental initiatives (Fabrizi et al., 2024 ). The dynamic relationship between policy stringency and firm investment also has important implications for economic competitiveness, as environmental regulations can either burden firms with compliance costs or stimulate innovation that enhances long-term competitive advantage. Despite the theoretical importance of policy-firm synchronization, existing environmental policy effectiveness studies suffer from several methodological and conceptual limitations that hinder comprehensive understanding of this relationship. Many studies analyze policy design and implementation in isolation, failing to capture the dynamic interactions between regulatory frameworks and firm strategic responses (Steinebach, 2019 ). Additionally, conventional methodological approaches often treat environmental policies as static interventions rather than dynamic processes that evolve with firm behavior and market conditions over time. This study makes several important contributions to the environmental economics literature. First, it addresses the critical research gap regarding macro-micro alignment in environmental policy effectiveness by providing empirical evidence of synchronization between government environmental strictness policies and firm-level environmental investment decisions. Second, it employs innovative methodological approaches—specifically local projections methodology and bias-corrected panel logit models—to capture the dynamic nature of policy-firm interactions and examine both short-term and long-term effects of environmental strictness on corporate investment behavior. Third, by focusing on European firms and utilizing disaggregated firm-level data spanning 19 countries over the 2005–2020 period, the study provides crucial insights into how environmental policy stringency operates across diverse institutional and economic contexts. The findings of this research have significant implications for policy design and corporate strategy. For policymakers, understanding the mechanisms through which environmental strictness policies influence firm investment decisions is essential for designing effective regulations that achieve environmental goals while maintaining economic competitiveness. For corporate managers, insights into the dynamic relationship between regulatory stringency and investment decisions can inform strategic planning and help firms anticipate and adapt to evolving environmental policy landscapes. The remainder of this paper is organized as follows. The second section reviews the existing literature on environmental policy effectiveness and firm environmental investment, developing hypotheses about the synchronization between policy stringency and corporate behavior. The third section presents the data sources, variable construction, and empirical methodology, including detailed explanation of the local projections approach and bias-corrected panel logit models. The fourth section reports the empirical findings, including both static and dynamic analyses of the relationship between environmental strictness policy and firm-level environmental investment. The final section discusses the implications of the findings and concludes with suggestions for future research and policy applications. 2. Literature Review and Hypothesis Development The literature examining environmental policy effectiveness reveals complex findings highlighting both potential and limitations of regulatory interventions. Empirical evidence generally supports positive influence of environmental regulations on firm behavior, though effects vary significantly depending on policy design, firm characteristics, and implementation contexts (P. Li et al., 2024 ; Y. Li et al., 2024 ; Ning & Shen, 2024 ). Studies examining the relationship between environmental regulation and corporate environmental investment demonstrate that environmental policies can drive firms to invest in sustainable practices through multiple channels. Wang et al. ( 2022 ) find China's Central Environmental Inspection policy positively influences corporate environmental investment, particularly in non-state-owned enterprises and regions with poor environmental performance. Similarly, Yu et al. ( 2023 ) show local government environmental concerns significantly boost corporate investments in regions with high regulatory intensity. These findings suggest regulatory pressure serves as a critical mechanism translating policy intentions into firm-level actions. However, the relationship between environmental policies and firm investment is not uniformly positive. Cheng et al. ( 2022 ) demonstrate that environmental protection taxes in China significantly reduce corporate environmental investment due to financing constraints and substitution effects with innovation investment. Jacob and Zerwer ( 2024 ) find that emission taxes in European countries lead to declines in firm investments, particularly affecting firms with low pricing power and financial flexibility. These conflicting findings underscore the importance of policy design and firm-specific factors in determining environmental policy effectiveness. The heterogeneity in firm responses to environmental regulations reflects underlying differences in firm characteristics, including size, ownership structure, and industry type. Larger firms tend to respond more positively to environmental regulations due to greater resource availability and capability to absorb compliance costs (Balasubramanian et al., 2020 ). Foreign-owned firms often demonstrate more proactive environmental behavior compared to domestic firms, possibly due to stricter international standards and reputational concerns (Wang & Jin, 2002). Industry characteristics also play a crucial role, with high-polluting industries facing more stringent regulatory pressures but also experiencing greater potential for strategic use of environmental investments (Vormedal & Skjærseth, 2019 ). The theoretical literature identifies several mechanisms through which environmental regulations influence firm investment decisions. The Porter Hypothesis provides a foundational framework suggesting that well-designed environmental regulations can stimulate innovation within firms, potentially leading to enhanced competitiveness and profitability (Lanoie et al., 2011 ). Empirical support for this hypothesis varies, with He and Wang ( 2023 ) finding that China's Ambient Air Quality Standard improved green innovation among firms through increased R&D and environmental protection investments, while Su ( 2024 ) shows that Total Energy Consumption Target policies encouraged firms in pollution-intensive industries to prioritize green inventions . Regulatory compliance theory emphasizes how environmental regulations create financial incentives for firms to invest in sustainable practices. Environmental regulations can foster financial gains by encouraging firms to proactively invest in environmental initiatives for long-term profitability, particularly when combined with mechanisms that enhance access to funding such as transparency in environmental and social responsibility reporting (Ning & Shen, 2024 ). The effectiveness of this mechanism depends critically on the balance between compliance costs and innovation incentives, with market-based regulations generally providing more flexibility for firms to choose cost-effective compliance strategies (Malcolm et al., 2006 ). Institutional theory offers another lens for understanding policy-firm alignment, emphasizing how firms respond to coercive, mimetic, and normative pressures from their institutional environment. Ma et al. ( 2022 ) demonstrate that coercive pressure from regulatory bodies serves as the strongest facilitator of substantive environmental management behavior, while mimetic and normative pressures contribute to both substantive and symbolic environmental actions. The availability of slack resources within firms moderates these institutional pressures, with resource-rich firms better positioned to respond positively to regulatory demands. Recent research increasingly recognizes the dynamic nature of environmental policy effects on firm behavior, moving beyond static analyses to examine how policy-firm relationships evolve over time. P. Li et al. ( 2024 ) find that the combination of environmental punishment and subsidies significantly enhances firms' environmental performance, with the effectiveness of these policies influenced by timing and magnitude. The dynamic effects are evident as subsidies implemented around the time of penalties can either enhance or diminish the effectiveness of standalone penalty policies, highlighting the importance of policy coordination and timing. Studies examining lag effects in environmental policy-firm investment relationships reveal that firms often exhibit delayed responses to policy changes due to uncertainty and adjustment costs. Gulen and Ion ( 2015 ) show that policy uncertainty can lead to precautionary delays in firm investment, particularly for firms with high investment irreversibility. However, the duration and magnitude of these lag effects vary significantly across industries and firm characteristics, with pollution-intensive industries often experiencing longer adjustment periods due to the complexity and cost of compliance (Deng & Hao, 2024 ). The temporal dimension of environmental policy effectiveness also encompasses the evolution of firm strategies in response to regulatory changes. Yu and Liu ( 2024 ) demonstrate that environmental regulations initially boost green innovation and later promote digital transformation, showing how firms adapt their innovation focus over time. This dynamic adaptation suggests that environmental policies may have different effects across various time horizons, necessitating analytical approaches that can capture these temporal variations. The role of financial constraints in moderating the relationship between environmental policy and firm investment represents a critical area of research with significant policy implications. Financial constraints can significantly impact firms' ability to respond to environmental regulations, with effects varying depending on firm size, access to capital, and industry characteristics. Bouchmel et al. ( 2024 ) find that firms with better access to internal finance are more likely to invest in green initiatives, while high leverage and financial constraints negatively impact green investments. The interaction between financial constraints and environmental regulations can create both barriers and opportunities for firm investment. On one hand, stringent environmental policies can increase financing constraints by imposing additional compliance costs and operational requirements (Y. Li et al., 2024 ). On the other hand, environmental regulations can drive financial gains by creating incentives for sustainable practices that enhance long-term profitability and access to green financing (Ning & Shen, 2024 ). The heterogeneity in financial constraints across firm types leads to differential responses to environmental policies. Sun et al. ( 2022 ) show that while some financially constrained small and medium-sized enterprises exit the market following environmental policy implementation, those that survive tend to decrease pollution, suggesting that financial pressure can drive environmental improvements among viable firms. This finding highlights the complex relationship between financial constraints, environmental policy, and firm survival. The European environmental policy framework provides a unique context for examining policy-firm synchronization due to its comprehensive and integrated approach to environmental regulation. The European Union's environmental policies are embedded in multiannual Environment Action Programmes that integrate climate and environmental objectives with economic and social goals, exemplified by the European Green Deal's aim to transform Europe into a climate-neutral continent by 2050 (Tosun, 2023 ). The EU's approach to environmental regulation increasingly adopts impact-based regulatory strategies that emphasize biophysical sciences in designing and implementing regulations (Soininen et al., 2023 ). This approach, while enhancing the scientific basis of environmental law, also creates more complex compliance requirements that may differentially affect firms based on their technological capabilities and resources. The harmonization efforts across different regulatory frameworks, such as the REACH regulation for chemicals, aim to ensure consistent identification and management of environmental risks, though implementation effectiveness varies across member states (Bondarouk & Mastenbroek, 2017 ). European environmental policies have demonstrated resilience to geopolitical and economic crises, continuing to propose and implement policies aligned with long-term sustainability goals despite short-term disruptions (Tosun, 2023 ). This policy stability provides a favorable environment for studying long-term policy-firm relationships, as firms can form expectations about future regulatory developments and plan their investment strategies accordingly. Based on the theoretical foundations and empirical evidence reviewed above, this study develops the following hypothesis regarding the synchronization between environmental strictness policy and firm-level environmental investment: H1: Environmental strictness policy positively influences firm-level environmental investment, with the relationship being stronger for larger firms and those with better access to financial resources. This hypothesis draws from institutional theory's prediction that regulatory coercion drives substantive environmental behavior, regulatory compliance theory's emphasis on financial incentives for sustainable investment, and empirical evidence showing differential firm responses based on resource availability. The dynamic nature of this relationship suggests that the effects may vary over time, with initial compliance-driven investments potentially evolving into more strategic environmental initiatives as firms adapt to regulatory requirements and discover competitive advantages from environmental investments. The European context provides an ideal setting for testing this hypothesis due to the comprehensive environmental policy framework, diverse firm characteristics across member states, and availability of detailed firm-level data spanning multiple countries and time periods. The expected positive relationship between environmental strictness and firm investment reflects the alignment of regulatory pressures with economic incentives for sustainable practices, though the magnitude and timing of these effects may vary based on firm-specific and institutional factors. 3. Dataset and Empirical Strategy The data set used in the study was obtained from two separate sources. All firm-level variables were obtained from the Thomson Reuters Refinitiv database. The firm-level environmental investment expenditures (ENVINV) variable and control variables were obtained from this database. First, the ENVINV variable is a binary variable that takes the value 1 if the firm made an environmental investment in that year; otherwise, it takes the value 0. Firm-level control variables were also obtained from Thomson Reuters Refinitiv. The environmental stringency index variable, indexed from 1 to 6, obtained from the OECD ( 2025 ) green growth database is used as a macro variable. As can be seen in Fig. 1 , environmental policy stringency and firm-level environmental investment expenditures for 19 countries are considered in this study. The time period between 2005 and 2020 was selected as the sample period for this study, as the environmental policy stringency index variable used in the study was matched with firm-level data and was suitable for this period (the analysis was cut off at this year as 2020 is the latest date for which the environmental policy stringency index is available, and no data/information on environmental investment expenditures is available for the period before 2005, so 2005 was selected as the starting year for the sample). and data/information on environmental investment expenditures prior to 2005 are not available, so 2005 was selected as the starting year for the sample). To identify both the short- and long-term effects of public environmental policies in a panel context, this study employs the local projections method (Jordà, 2005 ), suitable for dynamic analysis with firm-level fixed effects. Given the cross-country, firm-level panel dataset and the binary outcome for environmental investment, the local projections method enables estimation of dynamic impulse-responses using a panel fixed effects logit model. Equation ( 1 ) presents the main model specification: $$\:Pr\left(ENVIN{V}_{i,t}\right)={\beta\:}_{0}+{\beta\:}_{n}{X}_{i,t}+{\beta\:}_{1}ENVST{R}_{i,t}+\gamma\:+{\epsilon\:}_{i,t}$$ 1 where \(\:ENVIN{V}_{i,t}\) is a binary indicator for whether firm i invests in environmental expenditures at time t; \(\:ENVSTR\text{ᵢ},\text{ₜ}\) denotes the environmental policy stringency; \(\:{X}_{i,t}\) is a vector of firm controls; \(\:\gamma\:\) are fixed effects; and \(\:{\epsilon\:}_{i,t}\) is the error term. Adapting this to the local projections framework we get Eq. ( 2 ): $$\:{\varDelta\:}^{h}\left(ENVIN{V}_{i,t+n}\right)={\beta\:}_{1}ENVST{R}_{i,t-1}+\sum\:_{j=1}^{n}{\lambda\:}_{j}^{h}{X}_{i,t-j}+\gamma\:+{\epsilon\:}_{i,t}$$ 2 Here, \(\:{\varDelta\:}^{h}\left(ENVIN{V}_{i,t+n}\right)\) represents the change in the probability of environmental investment over horizon h ; \(\:\beta\:\) is the parameter of interest, reflecting the average impulse effect of policy stringency; \(\:{X}_{i,t-j}\) are lagged control matrix variables and other terms are as previously defined. The control matrix X in Eq. 2 consists of the variables RoA, GSALES, LEV, and SIZE. RoA: net operating income to total assets, GSALES: sales growth rate, which represents the difference between the logarithmic sales of the previous period and the logarithmic sales of the current period, SIZE: natural logarithm of total assets, and LEV: financial leverage, which is expressed as total liabilities to total assets. All these variables were trimmed from the sample using the trimming method to remove outliers, i.e., observations below 1% and above 99%. As a robustness check, the Analytical Bias Corrected Panel Logit Model with Fixed Effects method is also employed. Analytical Bias Corrected Panel Logit Model with Fixed Effects As robustness check, we employ analytical bias-corrected panel logit model with fixed effects, addressing incidental parameter problems arising in binary choice models when N > T (Cruz-Gonzalez et al., 2017 ). This novel methodology differs from classic probit/logit models by reducing bias through average partial effects (APE) calculated based on asymptotic distribution of true parameter values. The bias-corrected model is expressed as Eq. 3 : $$\:Pr\left(ENVINVᵢ,ₜ=1|Xᵢ,ₜ,\alpha\:ᵢ,ₜ,\gamma\:ᵢ,ₜ\right)=F\left({X}^{{\prime\:}}ᵢ,ₜ\beta\:+\alpha\:ᵢ,ₜ+\gamma\:ᵢ,ₜ\right)$$ 3 Where X is explanatory variables matrix, α denotes unobserved firm effects, \(\:\gamma\:\) represents unobserved year effects, and \(\:F\left(.\right)\) is standard normal cumulative distribution function. Results from this model are presented in Table 3 , with additional robustness checks using conditional panel fixed effects logit model shown in Table 4 . All models estimated with heteroscedasticity and autocorrelation robust standard errors. Descriptive statistics for the variables used are presented in Table 1 . According to the explanatory statistics in Table 1 , due to the cross-country nature of the data set and the fact that the firm level covers 19 EU countries, it is a highly heterogeneous data set, and with this heterogeneity, environmental strictness policy is also heterogeneous at the macro level. It has been determined that a comprehensive and heterogeneous data set at both the macro and micro levels was used in the analyses of this study. Table 1 Descriptive Statistics Variable Mean Std. dev. Min Max Observations ENVINV i,t 0.566174 0.495676 0 1 N = 3317 n = 242 T-bar = 13.7066 P/A i,t 0.072489 0.07468 -1.13938 0.379223 N = 3317 n = 242 T-bar = 13.7066 SIZE i,t 22.856 1.458537 18.64146 26.93209 N = 3317 n = 242 T-bar = 13.7066 GSALES i,t 0.026281 0.192572 -1.39409 1.670109 N = 3317 n = 242 T-bar = 13.7066 LEV i,t 0.622917 0.176696 0.026051 1.799699 N = 3317 n = 242 T-bar = 13.7066 ENVSTR t 3.217615 0.717089 0.67 4.89 N = 3317 n = 242 T-bar = 13.7066 Interpretation of the correlation matrix in Table 2 indicates the absence of multicollinearity, as none of the independent variables are highly correlated. Preliminary results further suggest that firm profitability and sales growth rate are negatively and significantly associated with environmental investment, except for financial leverage, which does not show a statistically significant effect. In contrast, stricter government policy exhibits a positive and statistically significant association with environmental investment. Table 2 Correlation Matrix Variables (1) (2) (3) (4) (5) (6) (1) ENVINV i,t 1.000 (2) P/A i,t -0.081* 1.000 (3) SIZE i,t 0.192* -0.071* 1.000 (4) GSALES i,t -0.072* 0.286* -0.013 1.000 (5) LEV i,t -0.007 -0.313* 0.155* -0.080* 1.000 (6) ENVSTR t 0.158* -0.091* 0.071* -0.070* -0.083* 1.000 Note: *** p < 0.01, ** p < 0.05, * p < 0.1. 4. Findings The findings obtained using the local projections method are presented in Fig. 2. The results in Fig. 2 indicate that environmental strictness increases firms' environmental investment. More precisely, it has been determined that the government's environmental strictness policies are a driving factor for firms to make environmental investments and that this effect is valid for an average period of three years and has positive effects. The results of another panel model used as a robustness check in the study, the bias-corrected panel fixed effects logit model, are also presented in Table 3. Please note that the econometric model findings are presented in two parts in Table 3. The first part of Table 3 presents the results of the classic logit model, while the second part presents the bias-corrected average partial effects results by variable. According to these calculation results, it was determined that firm profitability has a positive effect on environmental investment, while firm size, growth rate of sales, and financial leverage have a negative effect; however, these effects are not statistically significant at the 5% statistical significance level. It has been determined that an increase in public environmental strictness policy increases environmental investment, and this finding is statistically significant. This finding is consistent with the findings obtained using the local projections method, although the local projections method also shows the long-term dynamics of this relationship. It has also been determined that environmental investment increases as firm size increases, and this effect is also statistically significant. According to the APE results of the analytical bias corrected panel fixed effects model in Table 3, it has been determined that environmental investment expenditures are positively and statistically significantly affected by firm size and government environmental strictness policy. The model is also significant as a whole. Table 3 Bias Corrected Panel Fixed Effects Logit Model (1) ENVINV i,t P/A i,t -0.291 (-0.35) SIZE i,t 0.372 * (2.49) GSALES i,t -0.388 (-1.62) LEV i,t -0.0690 (-0.13) ENVSTR t 2.102 *** (16.00) APE P/A i,t -0.024 (-0.44) SIZE i,t 0.031 * (2.42) GSALES i,t -0.032 + (-1.76) LEV i,t -0.0058 (-0.17) ENVSTR t 0.177 *** (3.68) N Pseudo R-square F stat., p-val. 3317 0.36 0.00 Note: t statistics in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001. Analytical standard errors are used. For the final robustness checks of the study, a classic panel conditional fixed effects logit model was used, and the findings of this model are also presented in Table 4. The findings in Table 4 are again consistent with our local projections model and the results of the Analytical Bias Corrected Panel Logit Model with Fixed Effects. Consequently, it has been determined that government strictness policy, which is the main exploration topic of this study, motivates firm-level environmental investment and is synchronized with it. Table 4 Conditional panel fixed effects logit model results (1) ENVINV i,t P/A i,t -0.287 (-0.36) SIZE i,t 0.370 ** (2.59) GSALES i,t -0.418 (-1.83) LEV i,t -0.0744 (-0.15) ENVSTR t 2.099 *** (16.81) N 3901 LR Chi-square Stats, p-val. 0.00 Note: t statistics in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001. 5. Discussion The findings of this study provide compelling evidence for the synchronization between government environmental strictness policy and firm-level environmental investment, contributing to the growing literature on macro-micro alignment in environmental policy effectiveness. The results from both the local projections methodology and the bias-corrected panel logit models consistently demonstrate that environmental stringency policies serve as significant drivers of firm environmental investment decisions across 19 European countries, supporting our main hypothesis and extending our understanding of policy-firm dynamics in the European context. The positive and statistically significant relationship between environmental stringency policy and firm environmental investment, as demonstrated across all our methodological approaches, provides robust evidence of genuine synchronization rather than mere symbolic compliance. The environmental stringency index coefficient of 2.102 (t-statistic: 16.00) in our bias-corrected panel logit model and the corresponding average partial effect of 0.177 indicate economically meaningful impacts. This finding aligns with and extends Wang et al. ( 2022 ), who found that China's Central Environmental Inspection policy positively influenced corporate environmental investment, and Yu et al. ( 2023 ) who demonstrated that local government environmental concerns significantly boost corporate environmental investments. However, our study advances this literature by providing cross-country European evidence using advanced dynamic methodological approaches that capture the temporal evolution of policy effects. The three-year duration of positive effects identified through our local projections analysis (Fig. 2 ) provides crucial insights into the temporal dimension of policy-firm synchronization. The coefficients remain positive and significant across all horizons (h0 to h5), with values ranging from 1.173 at h0 to 0.829 at h5, demonstrating sustained rather than transitory effects. This temporal pattern resonates with Y. Li et al. ( 2024 ) who found that the timing and magnitude of environmental policies significantly influence their effectiveness, but extends their findings by showing that European environmental strictness policies create sustained behavioral changes in firm investment patterns over multiple years. The robustness of our findings across different model specifications—from the local projections approach to the bias-corrected logit model and the conditional fixed effects logit model—addresses methodological concerns raised in the environmental policy effectiveness literature. The consistency of the environmental stringency coefficients across these models (2.102 in the bias-corrected model and 2.099 in the conditional logit model) provides strong evidence for the reliability of our core finding. Our findings provide empirical support for multiple theoretical mechanisms linking environmental policy to firm investment decisions. The strong positive relationship we observe (coefficient of 2.102 with t-statistic of 16.00) supports regulatory compliance theory's prediction that environmental regulations create powerful incentives for sustainable investment (Ning & Shen, 2024 ). The magnitude of this effect is substantially larger than typically reported in single-country studies, suggesting that the European environmental policy framework may be particularly effective in driving firm-level responses. The relationship we document provides strong support for the "weak" version of the Porter Hypothesis, which suggests that environmental regulations stimulate environmental innovations (Lanoie et al., 2011 ). Our cross-country European evidence extends the findings of He and Wang ( 2023 ), who showed that China's Ambient Air Quality Standard improved green innovation among firms, and Su ( 2024 ), who demonstrated that Total Energy Consumption Target policies encouraged green inventions. The strength and persistence of the relationship we observe across diverse European institutional environments suggests that the Porter Hypothesis may be more broadly applicable than previously documented. The institutional theory framework receives strong empirical support from our findings, particularly the prediction that regulatory coercion serves as a significant driver of substantive environmental management behavior (Ma et al., 2022 ). The coefficient magnitude and statistical significance across all model specifications suggest that European environmental stringency policies create powerful coercive pressures that translate into measurable firm-level investment responses. The positive and significant effect of firm size on environmental investment (coefficient of 0.372 with t-statistic of 2.49 in the bias-corrected model) confirms theoretical predictions about resource availability and environmental responsiveness. This finding strongly aligns with Balasubramanian et al. ( 2020 ), who found that larger firms implement environmental practices more extensively due to greater resource availability and innovation capacity. The average partial effect of 0.031 for firm size indicates that larger firms have a substantially higher probability of making environmental investments, supporting the view that resource constraints limit smaller firms' ability to respond to environmental policies. The insignificant effects of profitability (ROA coefficient of -0.291, t-statistic of -0.35) and financial leverage (coefficient of -0.0690, t-statistic of -0.13) present interesting contrasts with some existing literature. While Bouchmel et al. ( 2024 ) found that internal finance positively affects green investment, our results suggest that in the European context with strong environmental policies, traditional financial metrics may be less important determinants of environmental investment decisions than policy pressures and firm scale. This finding implies that environmental stringency policies may be effective in driving investment decisions across firms with varying financial profiles. The negative coefficient for sales growth (-0.388 with t-statistic of -1.62), though not statistically significant at conventional levels, provides some support for the crowding-out hypothesis suggested by Weche ( 2018 ). The direction of this effect suggests potential trade-offs between pursuing growth opportunities and investing in environmental initiatives, though the lack of statistical significance indicates this relationship is not robust in our European sample. The dynamic pattern revealed by our local projections analysis provides novel insights into the temporal evolution of environmental policy effects. The persistence of positive coefficients across all time horizons (from 1.173 at h0 to 0.829 at h5) demonstrates that environmental stringency policies create lasting rather than temporary changes in firm investment behavior. This finding contrasts with concerns about short-term compliance responses and supports the view that environmental policies can fundamentally alter firm investment strategies. The detailed results in our Appendix Table A1 reveal interesting temporal patterns in the policy effects. The immediate impact (h0) shows a coefficient of 1.173, which initially declines at h1 (0.862) and h2 (0.638) before recovering at h3 (0.967) and h4 (1.140), then declining again at h5 (0.829). This non-monotonic pattern suggests that firms may experience initial adjustment costs before fully realizing the benefits of environmental investments, supporting dynamic theories of environmental policy adjustment. Our methodological approach addresses several limitations identified in existing environmental policy effectiveness studies. The use of local projections methodology specifically responds to criticisms by Steinebach ( 2019 ) regarding the isolation of policy design and implementation analysis. By capturing dynamic interactions between environmental stringency and firm investment over multiple time horizons, our approach provides a more comprehensive understanding of policy effectiveness than static analytical frameworks. The bias-corrected panel logit methodology addresses incidental parameter problems noted by Cruz-Gonzalez et al. ( 2017 ) in short panel binary choice models. The consistency of results between our bias-corrected approach and conventional fixed effects models provides confidence in the robustness of our findings while demonstrating the value of advanced methodological approaches in environmental policy research. Our findings provide strong empirical support for the effectiveness of European environmental policy frameworks in driving firm-level environmental investment. The high correlation between environmental stringency index and firm environmental investment (0.158 as shown in Table 2 ) combined with the strong regression coefficients suggests that European environmental policies successfully translate macro-level policy stringency into micro-level firm behavior. The strength of our results supports the characterization of European environmental policies as comprehensive and integrative approaches that effectively combine environmental protection with economic incentives (Tosun, 2023 ). The EU's approach of integrating climate and environmental objectives with economic and social goals appears to create powerful incentives for firm-level environmental investment that persist across different economic conditions and policy cycles. Our sample period (2005–2020) encompasses various economic conditions, including the 2008 financial crisis and subsequent recovery, yet the relationship between environmental stringency and firm investment remains robust throughout this period. This stability supports the resilience of European environmental policy frameworks documented by Tosun ( 2023 ) and suggests that environmental policies can maintain their effectiveness even during economic downturns. The evidence for strong synchronization between environmental strictness policy and firm environmental investment has important implications for policy design and implementation. The large magnitude of policy effects (coefficient of 2.102) suggests that environmental stringency policies can achieve their intended objectives without creating unsustainable compliance burdens. This finding supports continued strengthening of environmental policy frameworks while providing confidence that such policies will generate corresponding firm-level responses. The significant role of firm size in determining environmental investment responses (coefficient of 0.372) suggests that policymakers should consider differentiated approaches that account for firm heterogeneity. Smaller firms may require additional support mechanisms to fully participate in policy-driven environmental transitions, while larger firms appear well-positioned to respond to policy signals independently. The sustained nature of policy effects revealed through our dynamic analysis suggests that environmental stringency policies represent reliable tools for driving long-term changes in firm investment behavior. This persistence supports the integration of environmental objectives into broader economic policy frameworks and suggests that environmental investments driven by policy stringency contribute to sustainable economic transformation rather than temporary compliance responses. While our study provides significant insights into policy-firm synchronization, several limitations should be acknowledged. The binary nature of our environmental investment variable captures the extensive margin of investment decisions but does not measure investment intensity or quality. Future research incorporating continuous measures of environmental investment could provide additional insights into the magnitude and efficiency of policy-driven investments. Our focus on European countries provides valuable insights into developed economy contexts but limits generalizability to developing countries with different institutional frameworks and firm capabilities. The strong institutional characteristics that facilitate policy-firm synchronization in Europe may not be present in other contexts, suggesting the need for comparative studies across different institutional environments. The sample period ending in 2020 precedes the implementation of the European Green Deal's most ambitious targets. Future research examining more recent policy developments could provide insights into whether the synchronization patterns we observe continue under even more stringent environmental policy frameworks and in the context of post-pandemic economic recovery. 6. Conclusion This study investigated synchronization between public environmental strictness policy and firm-level environmental investment across 19 European countries (covering 2005-2020 sample) by employing advanced panel data methodologies. Findings reveal robust positive synchronization, with environmental stringency coefficient of 2.102 indicating economically significant effects. Local projections analysis demonstrates sustained three-year positive effects, while bias-corrected panel logit models confirm robustness across methodological approaches. Results support theoretical predictions from institutional theory, regulatory compliance theory, and Porter Hypothesis, demonstrating European environmental policies successfully translate macro-level regulatory stringency into micro-level firm behavior. The evidence suggests environmental strictness policies achieve intended objectives without creating unsustainable compliance burdens, supporting continued strengthening of environmental frameworks while highlighting importance of differentiated approaches accounting for firm heterogeneity. Declarations and Statements Data Statement The data that support the findings of this study are available from two sources. Firm-level data were obtained from Thomson Reuters Refinitiv database (2005-2020), which requires institutional subscription and is subject to licensing agreements that restrict public sharing. Environmental policy data were sourced from the publicly accessible OECD Green Growth Indicators database (https://doi.org/10.1787/data-00665-en). Due to commercial licensing restrictions, the Thomson Reuters Refinitiv data cannot be publicly shared. However, aggregated summary statistics and econometric codes are available from the corresponding author upon reasonable request. The OECD environmental policy data are freely accessible to all researchers. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work the author(s) used AI for language editing. All research content, analyses, interpretations, and conclusions were solely developed by the authors. The use of AI technology was limited to improving language quality, ensuring that the academic integrity and originality of the study remain intact. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article. Ethical Approval This article does not contain any studies with human participants performed by any of the authors. Informed Consent This article does not contain any studies with human participants performed by any of the authors. References Balasubramanian, S., Shukla, V., Mangla, S., & Chanchaichujit, J. (2020). Do firm characteristics affect environmental sustainability? A literature review‐based assessment. Business Strategy and the Environment , 30 (2), 1389-1416. https://doi.org/10.1002/bse.2692 Bondarouk, E., & Mastenbroek, E. (2017). Reconsidering EU Compliance: Implementation performance in the field of environmental policy. 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Journal of Environmental Planning and Management , 66 (12), 2513-2535. https://doi.org/10.1080/09640568.2022.2079077 Malcolm, A., Zhang, L., & Linninger, A. A. (2006). Design of environmental regulatory policies for sustainable emission reduction. AIChE Journal , 52 (8), 2792-2804. https://doi.org/10.1002/aic.10861 Ning, Y., & Shen, B. (2024). Environmental regulations, finance, and firm environmental investments: an empirical exploration. Total Quality Management & Business Excellence , 35 (7-8), 713-738. https://doi.org/10.1080/14783363.2024.2329691 OECD. (2025). Green growth indicators database (https://doi.org/https://doi.org/10.1787/data-00665-en Soininen, N., Romppanen, S., Nieminen, M., & Soimakallio, S. (2023). The Impact-based Regulatory Strategy in Environmental Law: Hallmark of Effectiveness or Pitfall for Legitimacy? Journal of Environmental Law , 35 (2), 185-206. https://doi.org/10.1093/jel/eqad013 Steinebach, Y. (2019). Instrument choice, implementation structures, and the effectiveness of environmental policies: A cross‐national analysis. Regulation & Governance , 16 (1), 225-242. https://doi.org/10.1111/rego.12297 Su, L. (2024). Environmental regulation and corporate green innovation: evidence from the implementation of the total energy consumption target in China. Journal of Business Economics . https://doi.org/10.1007/s11573-024-01207-6 Sun, J., Wang, F., Yin, H., & Zhao, R. (2022). Death or rebirth? How small‐ and medium‐sized enterprises respond to responsible investment. Business Strategy and the Environment , 31 (4), 1749-1762. https://doi.org/10.1002/bse.2981 Tosun, J. (2023). The European Union's Climate and Environmental Policy in Times of Geopolitical Crisis. JCMS: Journal of Common Market Studies , 61 (S1), 147-156. https://doi.org/10.1111/jcms.13530 Vazquez-Brust, D. A., Liston-Heyes, C., Plaza-Úbeda, J. A., & Burgos-Jiménez, J. (2010). Stakeholders Pressures and Strategic Prioritisation: An Empirical Analysis of Environmental Responses in Argentinean Firms. Journal of Business Ethics , 91 (S2), 171-192. https://doi.org/10.1007/s10551-010-0612-0 Vormedal, I., & Skjærseth, J. B. (2019). The good, the bad, or the ugly? Corporate strategies, size, and environmental regulation in the fish-farming industry. Business and Politics , 22 (3), 510-538. https://doi.org/10.1017/bap.2019.30 Wang, J., Dong, H., & Xiao, R. (2022). Central environmental inspection and corporate environmental investment: evidence from Chinese listed companies. Environmental science and pollution research international , 29 (37), 56419-56429. https://doi.org/10.1007/s11356-022-19538-8 Weche, J. P. (2018). Does green corporate investment crowd out other business investment? Industrial and Corporate Change . https://doi.org/10.1093/icc/dty056 WorldBank. (2024). World Development Report 2024: The Middle-Income Trap (9781464820786). http://dx.doi.org/10.1596/978-1-4648-2078-6 Yu, D., Hu, K., & Hao, Y. (2023). The Effect of Local Government Environmental Concern on Corporate Environmental Investment: Evidence from China. Sustainability , 15 (15), 11604. https://doi.org/10.3390/su151511604 Yu, Y., & Liu, Y. (2024). Environmental regulations and firms’ trade-offs between innovations: Empirical evidence from the quasi-experiment in China. Journal of Environmental Management , 370 , 122819. https://doi.org/10.1016/j.jenvman.2024.122819 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9415906","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":641483737,"identity":"e26ea578-f08c-4cdb-b4d0-4dea1686f1e5","order_by":0,"name":"Alper Ozun","email":"data:image/png;base64,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","orcid":"","institution":"Alanya Hamdullah Emin Pasa University","correspondingAuthor":true,"prefix":"","firstName":"Alper","middleName":"","lastName":"Ozun","suffix":""},{"id":641483738,"identity":"1b627f00-6046-4a6e-88ae-c4a0d7d45306","order_by":1,"name":"Emre Atilgan","email":"","orcid":"","institution":"Trakya University","correspondingAuthor":false,"prefix":"","firstName":"Emre","middleName":"","lastName":"Atilgan","suffix":""},{"id":641483739,"identity":"1bb67def-8529-4b3f-a5d1-274609a0f69b","order_by":2,"name":"Hasan Murat Ertugrul","email":"","orcid":"","institution":"Anadolu University","correspondingAuthor":false,"prefix":"","firstName":"Hasan","middleName":"Murat","lastName":"Ertugrul","suffix":""},{"id":641483741,"identity":"4a0e6c95-9918-4fd2-aad6-6c35aae99bb3","order_by":3,"name":"Omer Tugsal Doruk","email":"","orcid":"","institution":"Adana Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Omer","middleName":"Tugsal","lastName":"Doruk","suffix":""}],"badges":[],"createdAt":"2026-04-14 13:10:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9415906/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9415906/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109760190,"identity":"4f174d99-3602-4c1b-84e0-370de299c0d7","added_by":"auto","created_at":"2026-05-22 07:28:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":175552,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eEnvironmental Stringency Index, 2005-2020\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource:\u003c/strong\u003e Compiled by the authors using Thomson Reuters/LSEG database.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9415906/v1/cf3228d5785853ce4e8f3ecc.png"},{"id":109437504,"identity":"71075121-537b-45d5-9f5b-2983c5c66118","added_by":"auto","created_at":"2026-05-18 06:37:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47666,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eEffect of Environmental Strictness Policy on firms' environmental investment\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: \u003c/strong\u003eCalculated by the authors using Thomson Reuters/LSEG data.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9415906/v1/c6df22f249d6ebeffc5a2c3f.png"},{"id":109765126,"identity":"d4836236-f6f6-4cfa-881c-35f6e0b2834f","added_by":"auto","created_at":"2026-05-22 07:39:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":534427,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9415906/v1/708cfd86-e0e0-43d7-b24f-d1e5d35cd188.pdf"},{"id":109437502,"identity":"e4119717-13e9-4564-9b15-4fa37589865e","added_by":"auto","created_at":"2026-05-18 06:37:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24835,"visible":true,"origin":"","legend":"","description":"","filename":"Appendi.docx","url":"https://assets-eu.researchsquare.com/files/rs-9415906/v1/d3a581030f1adbb22f27b76d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Is There a Synchronisation Between Government Environmental Strictness Policy and Firm-Level Environmental Investment? A European Evidence from the Disaggregated Data","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWith global warming intensifying environmental challenges, the alignment between macro-level government policies and micro-level firm behaviors has emerged as a critical determinant of environmental policy effectiveness (WorldBank, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While environmental policies are designed to drive sustainable practices and reduce ecological footprints, their success fundamentally depends on the extent to which firms synchronize their investment decisions with policy objectives. This study examines the encouraging or possible wedge effects of government environmental strictness policy on firm-level environmental investment, with particular focus on the synchronization between public environmental policy and corporate environmental behavior in the European context.\u003c/p\u003e \u003cp\u003eThe relationship between environmental policy stringency and firm-level investment decisions represents a complex interplay of regulatory pressures, financial incentives, and strategic responses that has received insufficient attention in the existing literature. While substantial research has examined environmental policy effectiveness at the macro level and firm environmental performance at the micro level, the critical linkage between these two levels\u0026mdash;the macro-micro alignment\u0026mdash;remains understudied (Ning \u0026amp; Shen, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This gap is particularly pronounced in understanding how government environmental strictness policies translate into tangible firm-level environmental investments, and whether such policies create genuine synchronization or merely symbolic compliance.\u003c/p\u003e \u003cp\u003eThe theoretical foundation for expecting synchronization between environmental strictness policy and firm behavior draws from multiple complementary frameworks. Institutional theory suggests that firms respond to coercive, mimetic, and normative pressures from their external environment, with regulatory coercion serving as a significant driver of substantive environmental management behavior (Ma et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). a Regulatory compliance theory emphasizes how environmental regulations create pressure that fosters financial gains by encouraging firms to proactively invest in sustainable practices for long-term profitability (Ning \u0026amp; Shen, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, stakeholder theory explains firm responses through the lens of diverse stakeholder expectations, where environmental policies serve as external pressures that shape corporate environmental strategies (Vazquez-Brust et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe European context provides a particularly compelling setting for examining policy-firm synchronization due to its comprehensive and integrative environmental regulatory framework. The European Union has established robust environmental policies through initiatives such as the European Green Deal and the Emissions Trading System, which combine environmental protection with economic and social objectives while aiming to transform Europe into a climate-neutral continent by 2050 (Tosun, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These policies not only set ambitious environmental targets but also create market-based incentives and regulatory pressures that directly influence firm behavior, making Europe an ideal laboratory for studying how environmental strictness policies encourage firm-level environmental investment.\u003c/p\u003e \u003cp\u003eThe significance of understanding this synchronization extends beyond academic inquiry to critical policy implications. Environmental policies that fail to generate corresponding firm-level investments represent inefficient allocation of regulatory resources and missed opportunities for achieving sustainability goals. Conversely, policies that successfully encourage firm environmental investment can create positive feedback loops, where regulatory stringency drives innovation, competitiveness, and further voluntary environmental initiatives (Fabrizi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The dynamic relationship between policy stringency and firm investment also has important implications for economic competitiveness, as environmental regulations can either burden firms with compliance costs or stimulate innovation that enhances long-term competitive advantage.\u003c/p\u003e \u003cp\u003eDespite the theoretical importance of policy-firm synchronization, existing environmental policy effectiveness studies suffer from several methodological and conceptual limitations that hinder comprehensive understanding of this relationship. Many studies analyze policy design and implementation in isolation, failing to capture the dynamic interactions between regulatory frameworks and firm strategic responses (Steinebach, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, conventional methodological approaches often treat environmental policies as static interventions rather than dynamic processes that evolve with firm behavior and market conditions over time.\u003c/p\u003e \u003cp\u003eThis study makes several important contributions to the environmental economics literature. First, it addresses the critical research gap regarding macro-micro alignment in environmental policy effectiveness by providing empirical evidence of synchronization between government environmental strictness policies and firm-level environmental investment decisions. Second, it employs innovative methodological approaches\u0026mdash;specifically local projections methodology and bias-corrected panel logit models\u0026mdash;to capture the dynamic nature of policy-firm interactions and examine both short-term and long-term effects of environmental strictness on corporate investment behavior. Third, by focusing on European firms and utilizing disaggregated firm-level data spanning 19 countries over the 2005\u0026ndash;2020 period, the study provides crucial insights into how environmental policy stringency operates across diverse institutional and economic contexts.\u003c/p\u003e \u003cp\u003eThe findings of this research have significant implications for policy design and corporate strategy. For policymakers, understanding the mechanisms through which environmental strictness policies influence firm investment decisions is essential for designing effective regulations that achieve environmental goals while maintaining economic competitiveness. For corporate managers, insights into the dynamic relationship between regulatory stringency and investment decisions can inform strategic planning and help firms anticipate and adapt to evolving environmental policy landscapes.\u003c/p\u003e \u003cp\u003eThe remainder of this paper is organized as follows. The second section reviews the existing literature on environmental policy effectiveness and firm environmental investment, developing hypotheses about the synchronization between policy stringency and corporate behavior. The third section presents the data sources, variable construction, and empirical methodology, including detailed explanation of the local projections approach and bias-corrected panel logit models. The fourth section reports the empirical findings, including both static and dynamic analyses of the relationship between environmental strictness policy and firm-level environmental investment. The final section discusses the implications of the findings and concludes with suggestions for future research and policy applications.\u003c/p\u003e"},{"header":"2. Literature Review and Hypothesis Development","content":"\u003cp\u003eThe literature examining environmental policy effectiveness reveals complex findings highlighting both potential and limitations of regulatory interventions. Empirical evidence generally supports positive influence of environmental regulations on firm behavior, though effects vary significantly depending on policy design, firm characteristics, and implementation contexts (P. Li et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Y. Li et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ning \u0026amp; Shen, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eStudies examining the relationship between environmental regulation and corporate environmental investment demonstrate that environmental policies can drive firms to invest in sustainable practices through multiple channels. Wang et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) find China\u0026apos;s Central Environmental Inspection policy positively influences corporate environmental investment, particularly in non-state-owned enterprises and regions with poor environmental performance. Similarly, Yu et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) show local government environmental concerns significantly boost corporate investments in regions with high regulatory intensity. These findings suggest regulatory pressure serves as a critical mechanism translating policy intentions into firm-level actions.\u003c/p\u003e\n\u003cp\u003eHowever, the relationship between environmental policies and firm investment is not uniformly positive. Cheng et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) demonstrate that environmental protection taxes in China significantly reduce corporate environmental investment due to financing constraints and substitution effects with innovation investment. Jacob and Zerwer (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) find that emission taxes in European countries lead to declines in firm investments, particularly affecting firms with low pricing power and financial flexibility. These conflicting findings underscore the importance of policy design and firm-specific factors in determining environmental policy effectiveness.\u003c/p\u003e\n\u003cp\u003eThe heterogeneity in firm responses to environmental regulations reflects underlying differences in firm characteristics, including size, ownership structure, and industry type. Larger firms tend to respond more positively to environmental regulations due to greater resource availability and capability to absorb compliance costs (Balasubramanian et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Foreign-owned firms often demonstrate more proactive environmental behavior compared to domestic firms, possibly due to stricter international standards and reputational concerns (Wang \u0026amp; Jin, 2002). Industry characteristics also play a crucial role, with high-polluting industries facing more stringent regulatory pressures but also experiencing greater potential for strategic use of environmental investments (Vormedal \u0026amp; Skj\u0026aelig;rseth, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe theoretical literature identifies several mechanisms through which environmental regulations influence firm investment decisions. The Porter Hypothesis provides a foundational framework suggesting that well-designed environmental regulations can stimulate innovation within firms, potentially leading to enhanced competitiveness and profitability (Lanoie et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). Empirical support for this hypothesis varies, with He and Wang (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) finding that China\u0026apos;s Ambient Air Quality Standard improved green innovation among firms through increased R\u0026amp;D and environmental protection investments, while Su (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) shows that Total Energy Consumption Target policies encouraged firms in pollution-intensive industries to prioritize green inventions .\u003c/p\u003e\n\u003cp\u003eRegulatory compliance theory emphasizes how environmental regulations create financial incentives for firms to invest in sustainable practices. Environmental regulations can foster financial gains by encouraging firms to proactively invest in environmental initiatives for long-term profitability, particularly when combined with mechanisms that enhance access to funding such as transparency in environmental and social responsibility reporting (Ning \u0026amp; Shen, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The effectiveness of this mechanism depends critically on the balance between compliance costs and innovation incentives, with market-based regulations generally providing more flexibility for firms to choose cost-effective compliance strategies (Malcolm et al., \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eInstitutional theory offers another lens for understanding policy-firm alignment, emphasizing how firms respond to coercive, mimetic, and normative pressures from their institutional environment. Ma et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) demonstrate that coercive pressure from regulatory bodies serves as the strongest facilitator of substantive environmental management behavior, while mimetic and normative pressures contribute to both substantive and symbolic environmental actions. The availability of slack resources within firms moderates these institutional pressures, with resource-rich firms better positioned to respond positively to regulatory demands.\u003c/p\u003e\n\u003cp\u003eRecent research increasingly recognizes the dynamic nature of environmental policy effects on firm behavior, moving beyond static analyses to examine how policy-firm relationships evolve over time. P. Li et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) find that the combination of environmental punishment and subsidies significantly enhances firms\u0026apos; environmental performance, with the effectiveness of these policies influenced by timing and magnitude. The dynamic effects are evident as subsidies implemented around the time of penalties can either enhance or diminish the effectiveness of standalone penalty policies, highlighting the importance of policy coordination and timing.\u003c/p\u003e\n\u003cp\u003eStudies examining lag effects in environmental policy-firm investment relationships reveal that firms often exhibit delayed responses to policy changes due to uncertainty and adjustment costs. Gulen and Ion (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) show that policy uncertainty can lead to precautionary delays in firm investment, particularly for firms with high investment irreversibility. However, the duration and magnitude of these lag effects vary significantly across industries and firm characteristics, with pollution-intensive industries often experiencing longer adjustment periods due to the complexity and cost of compliance (Deng \u0026amp; Hao, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe temporal dimension of environmental policy effectiveness also encompasses the evolution of firm strategies in response to regulatory changes. Yu and Liu (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) demonstrate that environmental regulations initially boost green innovation and later promote digital transformation, showing how firms adapt their innovation focus over time. This dynamic adaptation suggests that environmental policies may have different effects across various time horizons, necessitating analytical approaches that can capture these temporal variations.\u003c/p\u003e\n\u003cp\u003eThe role of financial constraints in moderating the relationship between environmental policy and firm investment represents a critical area of research with significant policy implications. Financial constraints can significantly impact firms\u0026apos; ability to respond to environmental regulations, with effects varying depending on firm size, access to capital, and industry characteristics. Bouchmel et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) find that firms with better access to internal finance are more likely to invest in green initiatives, while high leverage and financial constraints negatively impact green investments.\u003c/p\u003e\n\u003cp\u003eThe interaction between financial constraints and environmental regulations can create both barriers and opportunities for firm investment. On one hand, stringent environmental policies can increase financing constraints by imposing additional compliance costs and operational requirements (Y. Li et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). On the other hand, environmental regulations can drive financial gains by creating incentives for sustainable practices that enhance long-term profitability and access to green financing (Ning \u0026amp; Shen, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe heterogeneity in financial constraints across firm types leads to differential responses to environmental policies. Sun et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) show that while some financially constrained small and medium-sized enterprises exit the market following environmental policy implementation, those that survive tend to decrease pollution, suggesting that financial pressure can drive environmental improvements among viable firms. This finding highlights the complex relationship between financial constraints, environmental policy, and firm survival.\u003c/p\u003e\n\u003cp\u003eThe European environmental policy framework provides a unique context for examining policy-firm synchronization due to its comprehensive and integrated approach to environmental regulation. The European Union\u0026apos;s environmental policies are embedded in multiannual Environment Action Programmes that integrate climate and environmental objectives with economic and social goals, exemplified by the European Green Deal\u0026apos;s aim to transform Europe into a climate-neutral continent by 2050 (Tosun, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe EU\u0026apos;s approach to environmental regulation increasingly adopts impact-based regulatory strategies that emphasize biophysical sciences in designing and implementing regulations (Soininen et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). This approach, while enhancing the scientific basis of environmental law, also creates more complex compliance requirements that may differentially affect firms based on their technological capabilities and resources. The harmonization efforts across different regulatory frameworks, such as the REACH regulation for chemicals, aim to ensure consistent identification and management of environmental risks, though implementation effectiveness varies across member states (Bondarouk \u0026amp; Mastenbroek, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eEuropean environmental policies have demonstrated resilience to geopolitical and economic crises, continuing to propose and implement policies aligned with long-term sustainability goals despite short-term disruptions (Tosun, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). This policy stability provides a favorable environment for studying long-term policy-firm relationships, as firms can form expectations about future regulatory developments and plan their investment strategies accordingly.\u003c/p\u003e\n\u003cp\u003eBased on the theoretical foundations and empirical evidence reviewed above, this study develops the following hypothesis regarding the synchronization between environmental strictness policy and firm-level environmental investment:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1: Environmental strictness policy positively influences firm-level environmental investment, with the relationship being stronger for larger firms and those with better access to financial resources.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis hypothesis draws from institutional theory\u0026apos;s prediction that regulatory coercion drives substantive environmental behavior, regulatory compliance theory\u0026apos;s emphasis on financial incentives for sustainable investment, and empirical evidence showing differential firm responses based on resource availability. The dynamic nature of this relationship suggests that the effects may vary over time, with initial compliance-driven investments potentially evolving into more strategic environmental initiatives as firms adapt to regulatory requirements and discover competitive advantages from environmental investments.\u003c/p\u003e\n\u003cp\u003eThe European context provides an ideal setting for testing this hypothesis due to the comprehensive environmental policy framework, diverse firm characteristics across member states, and availability of detailed firm-level data spanning multiple countries and time periods. The expected positive relationship between environmental strictness and firm investment reflects the alignment of regulatory pressures with economic incentives for sustainable practices, though the magnitude and timing of these effects may vary based on firm-specific and institutional factors.\u003c/p\u003e"},{"header":"3. Dataset and Empirical Strategy","content":"\u003cp\u003eThe data set used in the study was obtained from two separate sources. All firm-level variables were obtained from the Thomson Reuters Refinitiv database. The firm-level environmental investment expenditures (ENVINV) variable and control variables were obtained from this database. First, the ENVINV variable is a binary variable that takes the value 1 if the firm made an environmental investment in that year; otherwise, it takes the value 0. Firm-level control variables were also obtained from Thomson Reuters Refinitiv. The environmental stringency index variable, indexed from 1 to 6, obtained from the OECD (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) green growth database is used as a macro variable. As can be seen in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, environmental policy stringency and firm-level environmental investment expenditures for 19 countries are considered in this study. The time period between 2005 and 2020 was selected as the sample period for this study, as the environmental policy stringency index variable used in the study was matched with firm-level data and was suitable for this period (the analysis was cut off at this year as 2020 is the latest date for which the environmental policy stringency index is available, and no data/information on environmental investment expenditures is available for the period before 2005, so 2005 was selected as the starting year for the sample). and data/information on environmental investment expenditures prior to 2005 are not available, so 2005 was selected as the starting year for the sample).\u003c/p\u003e\n\u003cp\u003eTo identify both the short- and long-term effects of public environmental policies in a panel context, this study employs the local projections method (Jord\u0026agrave;, \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e), suitable for dynamic analysis with firm-level fixed effects. Given the cross-country, firm-level panel dataset and the binary outcome for environmental investment, the local projections method enables estimation of dynamic impulse-responses using a panel fixed effects logit model.\u003c/p\u003e\n\u003cp\u003eEquation (\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) presents the main model specification:\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:Pr\\left(ENVIN{V}_{i,t}\\right)={\\beta\\:}_{0}+{\\beta\\:}_{n}{X}_{i,t}+{\\beta\\:}_{1}ENVST{R}_{i,t}+\\gamma\\:+{\\epsilon\\:}_{i,t}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ENVIN{V}_{i,t}\\)\u003c/span\u003e\u003c/span\u003e is a binary indicator for whether firm i invests in environmental expenditures at time t; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ENVSTR\\text{ᵢ},\\text{ₜ}\\)\u003c/span\u003e\u003c/span\u003e denotes the environmental policy stringency; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i,t}\\)\u003c/span\u003e\u003c/span\u003e is a vector of firm controls; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\gamma\\:\\)\u003c/span\u003e\u003c/span\u003e are fixed effects; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{i,t}\\)\u003c/span\u003e\u003c/span\u003eis the error term.\u003c/p\u003e\n\u003cp\u003eAdapting this to the local projections framework we get Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003c/p\u003e\n\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\:{\\varDelta\\:}^{h}\\left(ENVIN{V}_{i,t+n}\\right)={\\beta\\:}_{1}ENVST{R}_{i,t-1}+\\sum\\:_{j=1}^{n}{\\lambda\\:}_{j}^{h}{X}_{i,t-j}+\\gamma\\:+{\\epsilon\\:}_{i,t}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eHere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varDelta\\:}^{h}\\left(ENVIN{V}_{i,t+n}\\right)\\)\u003c/span\u003e\u003c/span\u003e represents the change in the probability of environmental investment over horizon \u003cem\u003eh\u003c/em\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e is the parameter of interest, reflecting the average impulse effect of policy stringency; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i,t-j}\\)\u003c/span\u003e\u003c/span\u003e are lagged control matrix variables and other terms are as previously defined. The control matrix X in Eq. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e consists of the variables RoA, GSALES, LEV, and SIZE. RoA: net operating income to total assets, GSALES: sales growth rate, which represents the difference between the logarithmic sales of the previous period and the logarithmic sales of the current period, SIZE: natural logarithm of total assets, and LEV: financial leverage, which is expressed as total liabilities to total assets. All these variables were trimmed from the sample using the trimming method to remove outliers, i.e., observations below 1% and above 99%. As a robustness check, the Analytical Bias Corrected Panel Logit Model with Fixed Effects method is also employed.\u003c/p\u003e\n\u003ch3\u003eAnalytical Bias Corrected Panel Logit Model with Fixed Effects\u003c/h3\u003e\n\u003cp\u003eAs robustness check, we employ analytical bias-corrected panel logit model with fixed effects, addressing incidental parameter problems arising in binary choice models when N\u0026thinsp;\u0026gt;\u0026thinsp;T (Cruz-Gonzalez et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). This novel methodology differs from classic probit/logit models by reducing bias through average partial effects (APE) calculated based on asymptotic distribution of true parameter values.\u003c/p\u003e\n\u003cp\u003eThe bias-corrected model is expressed as Eq. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e:\u003c/p\u003e\n\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$\\:Pr\\left(ENVINVᵢ,ₜ=1|Xᵢ,ₜ,\\alpha\\:ᵢ,ₜ,\\gamma\\:ᵢ,ₜ\\right)=F\\left({X}^{{\\prime\\:}}ᵢ,ₜ\\beta\\:+\\alpha\\:ᵢ,ₜ+\\gamma\\:ᵢ,ₜ\\right)$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere X is explanatory variables matrix, \u0026alpha; denotes unobserved firm effects, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\gamma\\:\\)\u003c/span\u003e\u003c/span\u003e represents unobserved year effects, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:F\\left(.\\right)\\)\u003c/span\u003e\u003c/span\u003e is standard normal cumulative distribution function. Results from this model are presented in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, with additional robustness checks using conditional panel fixed effects logit model shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. All models estimated with heteroscedasticity and autocorrelation robust standard errors.\u003c/p\u003e\n\u003cp\u003eDescriptive statistics for the variables used are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. According to the explanatory statistics in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, due to the cross-country nature of the data set and the fact that the firm level covers 19 EU countries, it is a highly heterogeneous data set, and with this heterogeneity, environmental strictness policy is also heterogeneous at the macro level. It has been determined that a comprehensive and heterogeneous data set at both the macro and micro levels was used in the analyses of this study.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive Statistics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. dev.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENVINV\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.566174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.495676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN = 3317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en = 242\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT-bar =\u0026thinsp;13.7066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP/A\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.072489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.13938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.379223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN = 3317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en = 242\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT-bar =\u0026thinsp;13.7066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIZE\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.458537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.64146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.93209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN = 3317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en = 242\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT-bar =\u0026thinsp;13.7066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSALES\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.026281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.192572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.39409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.670109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN = 3317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en = 242\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT-bar =\u0026thinsp;13.7066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLEV\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.622917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.176696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.799699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN = 3317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en = 242\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT-bar =\u0026thinsp;13.7066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENVSTR\u003csub\u003et\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.217615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.717089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN = 3317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en = 242\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT-bar =\u0026thinsp;13.7066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eInterpretation of the correlation matrix in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e indicates the absence of multicollinearity, as none of the independent variables are highly correlated. Preliminary results further suggest that firm profitability and sales growth rate are negatively and significantly associated with environmental investment, except for financial leverage, which does not show a statistically significant effect. In contrast, stricter government policy exhibits a positive and statistically significant association with environmental investment.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation Matrix\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1) ENVINV\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2) P/A\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.081*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3) SIZE\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.192*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.071*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4) GSALES\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.072*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.286*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5) LEV\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.313*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.155*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.080*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(6) ENVSTR\u003csub\u003et\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.158*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.091*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.071*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.070*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.083*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cem\u003eNote: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"4. Findings","content":"\u003cp\u003eThe findings obtained using the local projections method are presented in Fig. 2. The results in Fig. 2 indicate that environmental strictness increases firms\u0026apos; environmental investment. More precisely, it has been determined that the government\u0026apos;s environmental strictness policies are a driving factor for firms to make environmental investments and that this effect is valid for an average period of three years and has positive effects.\u003c/p\u003e\n\u003cp\u003eThe results of another panel model used as a robustness check in the study, the bias-corrected panel fixed effects logit model, are also presented in Table 3. Please note that the econometric model findings are presented in two parts in Table 3. The first part of Table 3 presents the results of the classic logit model, while the second part presents the bias-corrected average partial effects results by variable. According to these calculation results, it was determined that firm profitability has a positive effect on environmental investment, while firm size, growth rate of sales, and financial leverage have a negative effect; however, these effects are not statistically significant at the 5% statistical significance level. It has been determined that an increase in public environmental strictness policy increases environmental investment, and this finding is statistically significant. This finding is consistent with the findings obtained using the local projections method, although the local projections method also shows the long-term dynamics of this relationship. It has also been determined that environmental investment increases as firm size increases, and this effect is also statistically significant. According to the APE results of the analytical bias corrected panel fixed effects model in Table 3, it has been determined that environmental investment expenditures are positively and statistically significantly affected by firm size and government environmental strictness policy. The model is also significant as a whole.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBias Corrected Panel Fixed Effects Logit Model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eENVINV\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP/A\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIZE\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.372\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSALES\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLEV\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0690\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENVSTR\u003csub\u003et\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.102\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(16.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP/A\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIZE\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSALES\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.032\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLEV\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENVSTR\u003csub\u003et\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.177\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ePseudo R-square\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eF stat., p-val.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3317\u003c/p\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eNote: t\u003c/em\u003e statistics in parentheses. \u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Analytical standard errors are used.\u003c/p\u003e\n\u003cp\u003eFor the final robustness checks of the study, a classic panel conditional fixed effects logit model was used, and the findings of this model are also presented in Table 4. The findings in Table 4 are again consistent with our local projections model and the results of the Analytical Bias Corrected Panel Logit Model with Fixed Effects. Consequently, it has been determined that government strictness policy, which is the main exploration topic of this study, motivates firm-level environmental investment and is synchronized with it.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eConditional panel fixed effects logit model results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eENVINV\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP/A\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIZE\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.370\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSALES\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLEV\u003csub\u003ei,t\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0744\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENVSTR\u003csub\u003et\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.099\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(16.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3901\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLR Chi-square Stats, p-val.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eNote: t\u003c/em\u003e statistics in parentheses. \u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe findings of this study provide compelling evidence for the synchronization between government environmental strictness policy and firm-level environmental investment, contributing to the growing literature on macro-micro alignment in environmental policy effectiveness. The results from both the local projections methodology and the bias-corrected panel logit models consistently demonstrate that environmental stringency policies serve as significant drivers of firm environmental investment decisions across 19 European countries, supporting our main hypothesis and extending our understanding of policy-firm dynamics in the European context.\u003c/p\u003e \u003cp\u003eThe positive and statistically significant relationship between environmental stringency policy and firm environmental investment, as demonstrated across all our methodological approaches, provides robust evidence of genuine synchronization rather than mere symbolic compliance. The environmental stringency index coefficient of 2.102 (t-statistic: 16.00) in our bias-corrected panel logit model and the corresponding average partial effect of 0.177 indicate economically meaningful impacts. This finding aligns with and extends Wang et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who found that China's Central Environmental Inspection policy positively influenced corporate environmental investment, and Yu et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) who demonstrated that local government environmental concerns significantly boost corporate environmental investments. However, our study advances this literature by providing cross-country European evidence using advanced dynamic methodological approaches that capture the temporal evolution of policy effects.\u003c/p\u003e \u003cp\u003eThe three-year duration of positive effects identified through our local projections analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) provides crucial insights into the temporal dimension of policy-firm synchronization. The coefficients remain positive and significant across all horizons (h0 to h5), with values ranging from 1.173 at h0 to 0.829 at h5, demonstrating sustained rather than transitory effects. This temporal pattern resonates with Y. Li et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) who found that the timing and magnitude of environmental policies significantly influence their effectiveness, but extends their findings by showing that European environmental strictness policies create sustained behavioral changes in firm investment patterns over multiple years.\u003c/p\u003e \u003cp\u003eThe robustness of our findings across different model specifications\u0026mdash;from the local projections approach to the bias-corrected logit model and the conditional fixed effects logit model\u0026mdash;addresses methodological concerns raised in the environmental policy effectiveness literature. The consistency of the environmental stringency coefficients across these models (2.102 in the bias-corrected model and 2.099 in the conditional logit model) provides strong evidence for the reliability of our core finding.\u003c/p\u003e \u003cp\u003eOur findings provide empirical support for multiple theoretical mechanisms linking environmental policy to firm investment decisions. The strong positive relationship we observe (coefficient of 2.102 with t-statistic of 16.00) supports regulatory compliance theory's prediction that environmental regulations create powerful incentives for sustainable investment (Ning \u0026amp; Shen, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The magnitude of this effect is substantially larger than typically reported in single-country studies, suggesting that the European environmental policy framework may be particularly effective in driving firm-level responses.\u003c/p\u003e \u003cp\u003eThe relationship we document provides strong support for the \"weak\" version of the Porter Hypothesis, which suggests that environmental regulations stimulate environmental innovations (Lanoie et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Our cross-country European evidence extends the findings of He and Wang (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who showed that China's Ambient Air Quality Standard improved green innovation among firms, and Su (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who demonstrated that Total Energy Consumption Target policies encouraged green inventions. The strength and persistence of the relationship we observe across diverse European institutional environments suggests that the Porter Hypothesis may be more broadly applicable than previously documented.\u003c/p\u003e \u003cp\u003eThe institutional theory framework receives strong empirical support from our findings, particularly the prediction that regulatory coercion serves as a significant driver of substantive environmental management behavior (Ma et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The coefficient magnitude and statistical significance across all model specifications suggest that European environmental stringency policies create powerful coercive pressures that translate into measurable firm-level investment responses.\u003c/p\u003e \u003cp\u003eThe positive and significant effect of firm size on environmental investment (coefficient of 0.372 with t-statistic of 2.49 in the bias-corrected model) confirms theoretical predictions about resource availability and environmental responsiveness. This finding strongly aligns with Balasubramanian et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who found that larger firms implement environmental practices more extensively due to greater resource availability and innovation capacity. The average partial effect of 0.031 for firm size indicates that larger firms have a substantially higher probability of making environmental investments, supporting the view that resource constraints limit smaller firms' ability to respond to environmental policies.\u003c/p\u003e \u003cp\u003eThe insignificant effects of profitability (ROA coefficient of -0.291, t-statistic of -0.35) and financial leverage (coefficient of -0.0690, t-statistic of -0.13) present interesting contrasts with some existing literature. While Bouchmel et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that internal finance positively affects green investment, our results suggest that in the European context with strong environmental policies, traditional financial metrics may be less important determinants of environmental investment decisions than policy pressures and firm scale. This finding implies that environmental stringency policies may be effective in driving investment decisions across firms with varying financial profiles.\u003c/p\u003e \u003cp\u003eThe negative coefficient for sales growth (-0.388 with t-statistic of -1.62), though not statistically significant at conventional levels, provides some support for the crowding-out hypothesis suggested by Weche (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The direction of this effect suggests potential trade-offs between pursuing growth opportunities and investing in environmental initiatives, though the lack of statistical significance indicates this relationship is not robust in our European sample.\u003c/p\u003e \u003cp\u003eThe dynamic pattern revealed by our local projections analysis provides novel insights into the temporal evolution of environmental policy effects. The persistence of positive coefficients across all time horizons (from 1.173 at h0 to 0.829 at h5) demonstrates that environmental stringency policies create lasting rather than temporary changes in firm investment behavior. This finding contrasts with concerns about short-term compliance responses and supports the view that environmental policies can fundamentally alter firm investment strategies.\u003c/p\u003e \u003cp\u003eThe detailed results in our Appendix Table A1 reveal interesting temporal patterns in the policy effects. The immediate impact (h0) shows a coefficient of 1.173, which initially declines at h1 (0.862) and h2 (0.638) before recovering at h3 (0.967) and h4 (1.140), then declining again at h5 (0.829). This non-monotonic pattern suggests that firms may experience initial adjustment costs before fully realizing the benefits of environmental investments, supporting dynamic theories of environmental policy adjustment.\u003c/p\u003e \u003cp\u003eOur methodological approach addresses several limitations identified in existing environmental policy effectiveness studies. The use of local projections methodology specifically responds to criticisms by Steinebach (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) regarding the isolation of policy design and implementation analysis. By capturing dynamic interactions between environmental stringency and firm investment over multiple time horizons, our approach provides a more comprehensive understanding of policy effectiveness than static analytical frameworks.\u003c/p\u003e \u003cp\u003eThe bias-corrected panel logit methodology addresses incidental parameter problems noted by Cruz-Gonzalez et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in short panel binary choice models. The consistency of results between our bias-corrected approach and conventional fixed effects models provides confidence in the robustness of our findings while demonstrating the value of advanced methodological approaches in environmental policy research.\u003c/p\u003e \u003cp\u003eOur findings provide strong empirical support for the effectiveness of European environmental policy frameworks in driving firm-level environmental investment. The high correlation between environmental stringency index and firm environmental investment (0.158 as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) combined with the strong regression coefficients suggests that European environmental policies successfully translate macro-level policy stringency into micro-level firm behavior.\u003c/p\u003e \u003cp\u003eThe strength of our results supports the characterization of European environmental policies as comprehensive and integrative approaches that effectively combine environmental protection with economic incentives (Tosun, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The EU's approach of integrating climate and environmental objectives with economic and social goals appears to create powerful incentives for firm-level environmental investment that persist across different economic conditions and policy cycles.\u003c/p\u003e \u003cp\u003eOur sample period (2005\u0026ndash;2020) encompasses various economic conditions, including the 2008 financial crisis and subsequent recovery, yet the relationship between environmental stringency and firm investment remains robust throughout this period. This stability supports the resilience of European environmental policy frameworks documented by Tosun (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and suggests that environmental policies can maintain their effectiveness even during economic downturns.\u003c/p\u003e \u003cp\u003eThe evidence for strong synchronization between environmental strictness policy and firm environmental investment has important implications for policy design and implementation. The large magnitude of policy effects (coefficient of 2.102) suggests that environmental stringency policies can achieve their intended objectives without creating unsustainable compliance burdens. This finding supports continued strengthening of environmental policy frameworks while providing confidence that such policies will generate corresponding firm-level responses.\u003c/p\u003e \u003cp\u003eThe significant role of firm size in determining environmental investment responses (coefficient of 0.372) suggests that policymakers should consider differentiated approaches that account for firm heterogeneity. Smaller firms may require additional support mechanisms to fully participate in policy-driven environmental transitions, while larger firms appear well-positioned to respond to policy signals independently.\u003c/p\u003e \u003cp\u003eThe sustained nature of policy effects revealed through our dynamic analysis suggests that environmental stringency policies represent reliable tools for driving long-term changes in firm investment behavior. This persistence supports the integration of environmental objectives into broader economic policy frameworks and suggests that environmental investments driven by policy stringency contribute to sustainable economic transformation rather than temporary compliance responses.\u003c/p\u003e \u003cp\u003eWhile our study provides significant insights into policy-firm synchronization, several limitations should be acknowledged. The binary nature of our environmental investment variable captures the extensive margin of investment decisions but does not measure investment intensity or quality. Future research incorporating continuous measures of environmental investment could provide additional insights into the magnitude and efficiency of policy-driven investments.\u003c/p\u003e \u003cp\u003eOur focus on European countries provides valuable insights into developed economy contexts but limits generalizability to developing countries with different institutional frameworks and firm capabilities. The strong institutional characteristics that facilitate policy-firm synchronization in Europe may not be present in other contexts, suggesting the need for comparative studies across different institutional environments.\u003c/p\u003e \u003cp\u003eThe sample period ending in 2020 precedes the implementation of the European Green Deal's most ambitious targets. Future research examining more recent policy developments could provide insights into whether the synchronization patterns we observe continue under even more stringent environmental policy frameworks and in the context of post-pandemic economic recovery.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study investigated synchronization between public environmental strictness policy and firm-level environmental investment across 19 European countries (covering 2005-2020 sample) by employing advanced panel data methodologies. Findings reveal robust positive synchronization, with environmental stringency coefficient of 2.102 indicating economically significant effects. Local projections analysis demonstrates sustained three-year positive effects, while bias-corrected panel logit models confirm robustness across methodological approaches.\u003c/p\u003e\n\u003cp\u003eResults support theoretical predictions from institutional theory, regulatory compliance theory, and Porter Hypothesis, demonstrating European environmental policies successfully translate macro-level regulatory stringency into micro-level firm behavior. The evidence suggests environmental strictness policies achieve intended objectives without creating unsustainable compliance burdens, supporting continued strengthening of environmental frameworks while highlighting importance of differentiated approaches accounting for firm heterogeneity.\u003c/p\u003e"},{"header":"Declarations and Statements","content":"\u003cp\u003e\u003cstrong\u003eData Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from two sources. Firm-level data were obtained from Thomson Reuters Refinitiv database (2005-2020), which requires institutional subscription and is subject to licensing agreements that restrict public sharing. Environmental policy data were sourced from the publicly accessible OECD Green Growth Indicators database (https://doi.org/10.1787/data-00665-en). Due to commercial licensing restrictions, the Thomson Reuters Refinitiv data cannot be publicly shared. However, aggregated summary statistics and econometric codes are available from the corresponding author upon reasonable request. The OECD environmental policy data are freely accessible to all researchers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the author(s) used AI for language editing. All research content, analyses, interpretations, and conclusions were solely developed by the authors. The use of AI technology was limited to improving language quality, ensuring that the academic integrity and originality of the study remain intact. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBalasubramanian, S., Shukla, V., Mangla, S., \u0026amp; Chanchaichujit, J. (2020). 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Environmental regulations and firms\u0026rsquo; trade-offs between innovations: Empirical evidence from the quasi-experiment in China. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e,\u003cem\u003e\u0026nbsp;370\u003c/em\u003e, 122819. https://doi.org/10.1016/j.jenvman.2024.122819\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Environmental policy, environmental investment, firm-level data, local projections methodology, panel bias corrected logit model","lastPublishedDoi":"10.21203/rs.3.rs-9415906/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9415906/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the synchronization between government environmental strictness policy and firm-level environmental investment across 19 European countries from 2005-2020. Using disaggregated firm-level data from Thomson Reuters Refinitiv database and the OECD Environmental Policy Stringency Index, we employ local projections methodology and bias-corrected panel logit models to capture dynamic policy-firm interactions. Our findings reveal robust evidence of positive synchronization between environmental policy stringency and corporate environmental investment decisions. The environmental stringency indicates economically significant effects, with larger firms showing stronger responsiveness. Local projections analysis demonstrates sustained positive effects over a three-year horizon, suggesting genuine behavioral changes rather than temporary compliance. Results remain consistent across multiple methodological approaches, supporting the view that European environmental policies successfully translate macro-level regulatory stringency into micro-level firm investment behavior. This study contributes to the macro-micro alignment literature in environmental economics and provides evidence for policymakers that environmental strictness policies can effectively drive corporate environmental investment without creating unsustainable compliance burdens.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Codes: \u003c/strong\u003eQ58; G31; C23\u003c/p\u003e","manuscriptTitle":"Is There a Synchronisation Between Government Environmental Strictness Policy and Firm-Level Environmental Investment? 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