The impact of policy stringency on green innovation and environmental performance: A system GMM analysis of 25 OECD countries
-
Author(s)Walid BelazregLink to ORCID Index: https://orcid.org/0000-0002-3297-0031
,
Kais MtarLink to ORCID Index: https://orcid.org/0000-0001-9700-5657
,
Aymen SmondelLink to ORCID Index: https://orcid.org/0000-0001-9819-1869
-
DOIhttp://dx.doi.org/10.21511/ee.17(2).2026.07
-
Article InfoVolume 17 2026, Issue #2, pp. 81-95
- 9 Views
-
0 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
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.
- Keywords
-
JEL Classification (Paper profile tab)Q54, Q58, O31, C33
-
References47
-
Tables6
-
Figures1
-
- Figure 1. The marginal effects of green innovation depending on the level of market-based (MEPS) and non-market (NMEPS) environmental policy instruments as well as the aggregate environmental policy level, with 95% confidence bound
-
- Table 1. Definition and source of variables
- Table 2. Cross-sectional dependency tests
- Table 3. Slope heterogeneity tests
- Table 4. Unit root test results
- Table 5. Effects of market and non-market-based instruments on CO2 emissions and ecological footprint
- Table 6. The aggregate policy effect on CO2 emissions and ecological footprint
-
- Albrizio, S., Kozluk, T., & Zipperer, V. (2017). Environmental policies and productivity growth: Evidence across industries and firms. Journal of Environmental Economics and Management, 81, 209-226.
- Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277-297.
- Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29-51.
- Binsaeed, R. H., Khan, Z., Dogan, E., & Rahim, S. (2025). The role of energy efficiency, renewable resources, green innovation, and fiscal decentralization in sustainable development: Evidence from OECD countries. Utilities Policy, 95, Article 101915.
- Borowiec, J., Papież, M., & Śmiech, S. (2024). The impact of environmental regulations on carbon emissions in countries with different levels of emissions. Environmental Science and Pollution Research, 31(59), 66759-66779.
- Botta, E., & Kozluk, T. (2014). Measuring environmental policy stringency in OECD countries – A composite index approach (OECD Economics Department Working Paper, No. 1177). Paris: OECD Publishing.
- Breusch, T. S., & Pagan, A. R. (1980). The Lagrange multiplier test and its applications to model specification in econometrics. The Review of Economic Studies, 47(1), 239-253.
- Carrión-Flores, C. E., & Innes, R. (2010). Environmental innovation and environmental performance. Journal of Environmental Economics and Management, 59(1), 27-42.
- Chang, K., Liu, L., Luo, D., & Xing, K. (2023). The impact of green technology innovation on carbon dioxide emissions: The role of local environmental regulations. Journal of Environmental Management, 340, Article 117990.
- Cheng, C., Ren, X., Dong, K., Dong, X., & Wang, Z. (2021). How does technological innovation mitigate CO2 emissions in OECD countries? Heterogeneous analysis using panel quantile regression. Journal of Environmental Management, 280, Article 111818.
- Du, K., Cheng, Y., & Yao, X. (2021). Environmental regulation, green technology innovation, and industrial structure upgrading: The road to the green transformation of Chinese cities. Energy Economics, 98, Article 105247.
- Du, K., Li, P., & Yan, Z. (2019). Do green technology innovations contribute to carbon dioxide emission reduction? Empirical evidence from patent data. Technological Forecasting and Social Change, 146, 297-303.
- Fabrizi, A., Guarini, G., & Meliciani, V. (2018). Green patents, regulatory policies and research network policies. Research Policy, 47(6), 1018-1031.
- Ghisetti, C., & Quatraro, F. (2017). Green technologies and environmental productivity: A cross-sectoral analysis of direct and indirect effects in Italian regions. Ecological Economics, 132, 1-13.
- Guo, X., Fu, L., & Sun, X. (2021). Can environmental regulations promote greenhouse gas abatement in OECD countries? Command-and-control vs. market-based policies. Sustainability, 13(12), Article 6913.
- Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. Econometrica: Journal of the Econometric Society, 50(4), 1029-1054.
- Hassan, M., Kouzez, M., Lee, J. Y., Msolli, B., & Rjiba, H. (2024). Does increasing environmental policy stringency enhance renewable energy consumption in OECD countries? Energy Economics, 129, Article 107198.
- He, D., Ma, H., Hu, D., Wang, X., Dong, Z., & Zhu, B. (2024). Biochar for sustainable agriculture: Improved soil carbon storage and reduced emissions on cropland. Journal of Environmental Management, 371, Article 123147.
- Hou, S., Yu, K., & Fei, R. (2023). How does environmental regulation affect carbon productivity? The role of green technology progress and pollution transfer. Journal of Environmental Management, 345, Article 118587.
- Ji, Z. (2020). Does factor market distortion affect industrial pollution intensity? Evidence from China. Journal of Cleaner Production, 267, Article 122136.
- Langinier, C., Martínez-Zarzoso, I., & RayChaudhuri, A. (2025). Environmental regulations and green innovation: The role of trade and technology transfer. Energy Economics, 150, Article 108755.
- Lin, B., & Ma, R. (2022). Green technology innovations, urban innovation environment and CO2 emission reduction in China: Fresh evidence from a partially linear functional-coefficient panel model. Technological Forecasting and Social Change, 176, Article 121434.
- Liu, S., Li, S., & Liu, Y. (2025). The impacts of heterogeneous environmental regulations on atmospheric environmental total factor productivity: From an internal structural perspective. SSRN Electronic Journal.
- Lutfi, A., Alqudah, H., Alrawad, M., Alshira’h, A. F., Alshirah, M. H., Almaiah, M. A., Alsyouf, A., & Hassan, M. F. (2023). Green environmental management system to support environmental performance: What factors influence SMEs to adopt green innovations? Sustainability, 15(13), Article 10645.
- Mahmood, N., Zhao, Y., Lou, Q., & Geng, J. (2022). Role of environmental regulations and eco-innovation in energy structure transition for green growth: Evidence from OECD. Technological Forecasting and Social Change, 183, Article 121890.
- Martínez-Zarzoso, I., Bengochea-Morancho, A., & Morales-Loge, R. (2019). Does environmental policy stringency foster innovation and productivity in OECD countries? Energy Policy, 134, Article 110982.
- Murad, S. M. W., Rahman, A., & Mohsin, A. K. M. (2025). From policy to progress: Environmental taxation to mitigate air pollution in OECD countries. Journal of Environmental Management, 374, Article 124143.
- Peñalosa, P., & Kleine-Rueschkamp, L. (2024). The geography of green innovation hubs in OECD regions (OECD Local Economic and Employment Development (LEED) Papers No. 2024(09)). Paris: OECD Publishing.
- Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265-312.
- Pesaran, M. H., & Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of Econometrics, 142(1), 50-93.
- Pesaran, M. H., Ullah, A., & Yamagata, T. (2008). A bias-adjusted LM test of error cross-section independence. The Econometrics Journal, 11(1), 105-127.
- Porter, M. E., & van der Linde, C. (1995). Toward a new conception of the environment-competitiveness relationship. The Journal of Economic Perspectives, 9(4), 97-118.
- Ramanathan, R., He, Q., Black, A., Ghobadian, A., & Gallear, D. (2017). Environmental regulations, innovation and firm performance: A revisit of the Porter hypothesis? Journal of Cleaner Production, 155(2), 79-92.
- Rubashkina, Y., Galeotti, M., & Verdolini, E. (2015). Environmental regulation and competitiveness: Empirical evidence on the Porter Hypothesis from European manufacturing sectors. Energy Policy, 83, 288-300.
- Shahbaz, M., Ali Nasir, M., & Roubaud, D. (2018). Environmental degradation in France: The effects of FDI, financial development, and energy innovations. Energy Economics, 74, 843-857.
- Sohag, K., Husain, S., & Soytas, U. (2024). Environmental policy stringency and ecological footprint linkage: Mitigation measures of renewable energy and innovation. Energy Economics, 136, Article 107721.
- Tao, M., Tiwari, A. K., Poletti, S., Roubaud, D., & Silva, E. (2025). The credibility of environmental policy stringency: Implications for sustainability in OECD countries. Energy Economics, 146, Article 108492.
- Wang, X., Chai, Y., Wu, W., & Khurshid, A. (2023). The empirical analysis of environmental regulation’s spatial spillover effects on green technology innovation in China. International Journal of Environmental Research and Public Health, 20(2), Article 1069.
- Wang, Y., Shi, M., Zhao, Z., Liu, J., & Zhang, S. (2025). How does green finance improve the total factor energy efficiency? Capturing the mediating role of green management innovation and embodied technological progress. Energy Economics, 142, Article 108157.
- Wei, H., & Joo, Y. C. (2025). Comparing the dynamic impact of environmental policies on greenhouse gas emission: Evidence from OECD countries. Journal of Environmental Management, 394, Article 127226.
- Weina, D., Gilli, M., Mazzanti, M., & Nicolli, F. (2016). Green inventions and greenhouse gas emission dynamics: A close examination of provincial Italian data. Environmental Economics and Policy Studies, 18(2), 247-263.
- Wu, T., Yang, S., & Tan, J. (2020). Impacts of government R&D subsidies on venture capital and renewable energy investment – An empirical study in China. Resources Policy, 68, Article 101715.
- Xu, L., Fan, M., Yang, L., & Shao, S. (2021). Heterogeneous green innovations and carbon emission performance: Evidence at China’s city level. Energy Economics, 99, Article 105269.
- Zhang, J., Kang, L., Li, H., Ballesteros-Pérez, P., Skitmore, M., & Zuo, J. (2020). The impact of environmental regulations on urban green innovation efficiency: The case of Xi’an. Sustainable Cities and Society, 57, Article 102123.
- Zhong, S., Xiong, Y., & Xiang, G. (2021). Environmental regulation benefits for whom? Heterogeneous effects of the intensity of environmental regulation on employment in China. Journal of Environmental Management, 281, Article 111877.
- Zhou, H., Pervaiz, A., Ather Bukhari, W. A., Ather Bukhari, A. A., & Sadiq, S. Z. (2025). Green technology and stringent environmental policy interactions in a spatial context: Assessing domestic and cross border CO2 emission reductions in border sharing OECD economies. Journal of Environmental Management, 391, Article 126435.
- Zhu, X., Zuo, X., & Li, H. (2021). The dual effects of heterogeneous environmental regulation on the technological innovation of Chinese steel enterprises – Based on a high dimensional fixed effects model. Ecological Economics, 188, Article 107113.
-
Relationships between economic growth, CO2 emissions, and innovation for nations with the highest patent applications
Mahmoud Tnani
doi: http://dx.doi.org/10.21511/ee.09(2).2018.04
Environmental Economics Volume 9, 2018 Issue #2 pp. 47-69 Views: 2433 Downloads: 652 TO CITE АНОТАЦІЯThis study aims to provide insight on the nexus between innovation, economic growth and CO2 emissions. In order to achieve this, data on potential factors such as innovation, environmental taxes, research and development (R&D) spending, electricity production, population size, high-technology exports and prices of photovoltaic systems are collected for the sample of the leading innovative countries over the period from 1990 to 2014. Based on a cointegrated panel methodology and a vector error correction model, the long-run, as well as the short-run dynamics of all possible combinations between the variables under study, are estimated. The results reveal that except for China, economic growth is mainly driven by electricity production, population size, CO2 emissions and R&D spending. However, innovation was found to have lesser effect on economic growth. In addition to that, the authors found evidence in favor of CO2 emissions being affected positively by population size and prices of photovoltaic systems and negatively by environmental taxes, high-technology exports, R&D spending and innovation. Moreover, on the contrary to population size, well-being is positively affected by CO2 emission and R&D spending.
-
Information and communication technologies, road freight transport, and environmental sustainability
Walid Chatti 
doi: http://dx.doi.org/10.21511/ee.11(1).2020.11
Environmental Economics Volume 11, 2020 Issue #1 pp. 124-132 Views: 2067 Downloads: 675 TO CITE АНОТАЦІЯDespite progress in reducing air pollutants in several countries, freight transport continues to have undesirable effects on environmental quality, human health, and the economy. Road freight transport, in particular, is associated with various negative externalities, including environmental and health damages, and the overexploitation of non-renewable natural resources. This paper investigates how ICTs interact with road freight transport to affect environmental quality regarding reducing CO2 emissions. The empirical strategy is focused on the yearly dataset from 2002 to 2014 in 43 countries. Using the two-step GMM techniques, the findings suggest that ICTs can decrease road freight transport’s negative impacts on environmental sustainability. Besides, the interactions of mobile phone and fixed telephone technologies with road freight transport are more efficient in reducing pollution than using internet networks. This paper underlines the importance of using ICTs to dampen road freight transport’s negative effects on environmental sustainability.
-
Digital and economic transformations for sustainable development promotion: A case of OECD countries
Leonid Melnyk ,
Oleksandr Kubatko ,
Vladyslav Piven ,
Kyrylo Klymenko ,
Larysa Rybina
doi: http://dx.doi.org/10.21511/ee.12(1).2021.12
Environmental Economics Volume 12, 2021 Issue #1 pp. 140-148 Views: 1876 Downloads: 615 TO CITE АНОТАЦІЯDigitalization, dematerialization of production and consumption, and structural shifts in the direction of service economy forming do promote to reduction of material use and sustainable development. The paper aims to investigate the role of digital, structural, economic, and social factors in sustainable development promotion in OECD countries. The paper uses the data on digital achievements, social and economic development of OECD member states from World Bank data sources for the period 2007–2018. The random-effects GLS regression model is used, and empirical regression models to estimate the influence of key factors related to digital transformation on GDP per capita and CO2 emissions per capita are constructed. The results of the regression analysis show that using the number of Internet users as an indicator for achievement in digitalization has a positive and statistically significant influence on GDP per capita due to lower transaction costs and higher share service economy. An increase in urbanization rates (as an indicator of capital concentrations and labor specialization) by one percent promotes a GDP per capita increase of 299 USD. Also, an increase in Gini coefficient by one percentage point correlates with decrease in GDP per capita on 196 USD and the reduction of CO2 per capita by 0.12 tones due to the structural shifts in aggregate demand. Still, improvements in digital transformations have no significant environmental effect in OECD members, while processes related to urbanization, income inequality, and share of industrial output are important drivers for CO2 per capita reduction.
Acknowledgments
The paper contains the results of a study conducted within the framework of research projects: “Sustainable development and resource security: from disruptive technologies to digital transformation of Ukrainian economy” (No. 0121U100470); “Fundamental bases of the phase transition to an additive economy: from disruptive technologies to institutional sociologization of decisions” (No. 0121U109557).
