Market-based climate-policy stringency and European machinery production after Russia’s full-scale invasion of Ukraine: Energy-market stress and energy vulnerability

As the global community intensifies efforts to transition toward sustainable energy systems, the role of institutional and fiscal arrangements in fostering renewable energy has gained increasing attention. This study aims to assess whether fiscal decentralization contributes to the expansion of renewable electricity generation in OECD countries by analyzing panel data and identifying the direction and significance of this relationship. Utilizing a panel dataset of 34 countries spanning 2000–2023, the analysis employs a fixed-effects regression model with Driscoll-Kraay standard errors. It includes a one-year lag of fiscal variables to ensure robustness. The findings reveal a statistically significant but modest negative relationship between the share of subnational revenues in GDP and the share of renewables in electricity generation, suggesting that greater fiscal decentralization may not automatically incentivize renewable energy adoption. More specifically, the fixed-effects model corrected for heteroskedasticity and autocorrelation indicates that the coefficient for lagged subnational revenue (as a percentage of GDP) is negative and marginally significant (p ≈ 0.057), hinting at a potential delayed inhibitory effect. Additionally, country-level fixed effects demonstrate substantial heterogeneity, with nations like Iceland, Norway, and Canada showing systematically higher renewable electricity shares, regardless of fiscal structure. These results underscore the importance of complementary institutional frameworks and national coordination mechanisms to ensure that decentralization effectively supports climate policy goals.

Acknowledgment
This study was carried out within the framework of a research grant awarded by the Swiss National Science Foundation (grant no. IZURZ1_224119/1) and funded by the European Union grant “NextGenerationEU through the Recovery and Resilience Plan for Slovakia” (No. 09I03-03-V01-00130) and project VEGA – 1/0392/23 “Changes in the approach to the creation of companies’ distribution management concepts influenced by the effects of social and economic crises caused by the global pandemic and increased security risks”.

Type of the article: Research Article

Abstract
Considering the increasing environmental pressures associated with economic growth and industrial expansion, improving environmental quality has become a central challenge for advanced economies. Green innovation is crucial for sustainable development, but its success largely depends on the regulatory context. This paper examines the relationship between green innovation and environmental degradation and assesses how market-based and non-market environmental policy instruments shape this relationship in 25 OECD countries over the period 2000–2020. Building on existing theoretical and empirical frameworks linking environmental regulation, technological innovation, and environmental performance, the study employs a dynamic panel modeling approach using a two-step System-GMM estimator to address endogeneity, heterogeneity, and the persistence of environmental indicators. Empirical results indicate that a 1% increase in green patent activity reduces CO2 emissions by 0.34% and the ecological footprint by 0.33%. Market-based instruments significantly decrease CO2 emissions and the ecological footprint with coefficients of –0.679 and –0.068, respectively, whereas non-market instruments are associated with increases in CO2 emissions and the ecological footprint with coefficients of +0.045 and +0.038, respectively. The interaction between market-based instruments and green innovation further reduces CO2 emissions and the ecological footprint, with coefficients of –0.00004 and –0.0006, respectively, while the interaction with non-market instruments also yields negative effects (–0.00001 and –0.00002). The aggregate environmental policy stringency index lowers CO2 emissions by 0.159 and the ecological footprint by 0.041, strengthening the innovation effect. Overall, the findings suggest that well-designed and coherent policy mixes are essential for maximizing green innovation’s environmental benefits in advanced economies.

Type of the article: Research Article

Abstract
Sustainable agrifood transformation requires clearer evidence on whether stricter environmental policy frameworks are associated with measurable changes in organic agricultural land use. This study examines whether environmental policy stringency is associated with the development of organic agriculture across OECD member countries, accession candidates, and key partner economies during 2004–2023. The analysis uses three unbalanced panel datasets covering total organic agricultural land, organic cropland, and organic permanent meadows and pastures, with 951 observations for 49 countries, 461 observations for 35 countries, and 457 observations for 35 countries, respectively; the methodology applies a set of panel-data specifications with robustness checks based on alternative specifications and individual policy-component models. The results show that international policy stringency has the strongest association with total organic agricultural land. In the preferred two-way fixed-effects model, a one-point increase in lagged international policy stringency is associated with a 0.721 percentage-point increase in the organic agricultural land share. In comparison, cross-sectoral policy stringency is associated with a 0.155 percentage-point increase. For organic cropland, both policy dimensions are positive and statistically significant, with coefficients of 0.216 and 0.354, respectively. For organic permanent meadows and pastures, policy coefficients indicate weaker responsiveness to broad environmental policy frameworks. Supplementary checks provide more cautious evidence: the first-difference result for international policy stringency is positive but only weakly significant, while the quadratic specification indicates a U-shaped pattern with an estimated turning point near 2.00 on the 0–10 scale. The findings should be interpreted as conditional associations rather than causal effects.