The Green Structural Transformation: Climate Change, Industrial Policy, and Development in a Fragmenting Global Economy
Abstract
This study examines the relationship between green industrial policy, climate change, and structural transformation in emerging market and developing economies (EMDEs) amid rising geoeconomic fragmentation. Using panel data from 85–95 EMDEs for 2013–2025, it develops a Green Structural Transformation Index (GSTI) and applies System-GMM, Fixed Effects, Method of Moments Quantile Regression, and Fully Modified OLS estimations, supported by comparative case studies of Vietnam, South Africa, Chile, and Nigeria. The findings show that green industrial policy is positively associated with green structural transformation by promoting renewable energy adoption, cleaner production systems, industrial upgrading, and climate-compatible diversification. By contrast, geoeconomic fragmentation constrains transformation outcomes by disrupting technology transfer, weakening trade linkages, increasing financing uncertainty, and limiting access to green value chains. Institutional quality significantly enhances the effectiveness of green industrial policy, indicating that credible governance, administrative capacity, regulatory consistency, and coordinated public investment are essential for translating policy ambition into structural change. The adverse effects of fragmentation are also weaker in economies with stronger regional integration, suggesting that cooperative markets, shared infrastructure, and regional financing mechanisms can partially offset global disruptions. The results further reveal substantial heterogeneity across income levels and resource dependence, with lower-income and resource-based economies facing deeper constraints. The study concludes that resilient green development in EMDEs requires coherent industrial policy, strengthened institutions, regional cooperation, inclusive transition strategies, and targeted support for countries most exposed to climate risk, external shocks, and structural dependency. References Akash, M. M. R. (2026). Cardiotoxicity prediction models in cancer patients using artificial intelligence and genomics. International Journal of Drug Delivery Technology, 16, 60–73. Akram, M., Khan, W., Rasheed, M. D., Imran, M., Iqbal, M. W., Delshadi, A., & Sultana, M. (2026). Cybersecurity risk assessment model for Internet of Medical Things (IoMT) devices in healthcare systems. Spectrum of Engineering Sciences, 4(3), 312–326. Ali, N. M., Ahmed, M. E., Islam, M. F., Gomes, C. A., & Islam, M. S. (2026). Toward a circular US economy: Green and artificial intelligence innovation, renewable energy, and domestic material consumption. Energy Sources, Part B: Economics, Planning, and Policy, 21(1), Article 2685043. Altenburg, T., & Rodrik, D. (2017). Green industrial policy: Accelerating structural change towards a low-carbon economy. German Development Institute. Ameli, N., et al. (2021). The political economy of green industrial policy. Nature Energy, 6, 1005–1013. Balcioglu, Y. S., Cubukcu Cerasi, C., Kilitci Calayir, A., & Bilgen, A. (2026). Mapping global green transformation: Integrating OECD Green Growth Indicators into a composite policy-innovation index. Sustainability, 18(3), Article 1513. https://doi.org/10.3390/su18031513 Caldara, D., & Iacoviello, M. (2022). Measuring geopolitical risk. American Economic Review, 112(4), 1194–1225. https://doi.org/10.1257/aer.20191823 European Commission. (2020). The European Green Deal Industrial Plan. Brussels, Belgium. Global Policy Journal. (2026). Decarbonization and the new geography of industrial power. https://www.globalpolicyjournal.com Hausmann, R., Hwang, J., & Rodrik, D. (2007). What you export matters. Journal of Economic Growth, 12(1), 1–25. https://doi.org/10.1007/s10887-006-9009-4 Health Equity. (2023). Health equity and digital disparities in cancer screening and cardiovascular care across socioeconomic and ethnic groups: A systematic review. Vascular and Endovascular Review, 6(2), 35–44. https://doi.org/10.64149/ International Institute for Sustainable Development. (2025). Green industrial policies: Opportunities and obstacles from the global trade and investment regime. Climate Policy. https://doi.org/10.1080/14693062.2025.2591880 International Institute for Sustainable Development. (2025). Global Subsidies Initiative. Geneva, Switzerland. Islam, M. F., Ahmed, M. E., Ali, N. M., Gomes, C. A., & Faisal-E-Alam, M. (2026). US path to Industry 4.0: Reassessing supply chain digitalization and AI for industrial sustainability. Sustainable Development. Jabed, M. I. K. (2024). Stock market price prediction using machine learning techniques. American International Journal of Sciences and Engineering Research, 7(1), 1–6. Jabed, M. I. K., Ahmed, M. P., Tofa, F. M., Islam, M. F., Gomes, C. A., & Sirazy, R. M. (2026). Federated intrusion detection for Internet of Medical Things networks: Differential privacy, non-IID robustness, and cross-device generalization. Journal of Computer Science and Technology Studies, 8(8), 303–315. Jabed, M. I. K., Imran, M., Gomes, C. A., & Ponduru, P. S. (2025). Machine learning-based prediction of poor self-rated health among U.S. adults using behavioral and demographic factors. World Journal of Advanced Research and Reviews, 27(1), 2817–2829. https://doi.org/10.30574/wjarr.2025.27.1.2649 Jabed, M. I. K., Imran, M., Gomes, C. A., Ponduru, P. S., Mandal, S., & Hassan, S. (2025). Deep learning and explainable AI framework for predicting lung cancer severity. Journal of Computer Science and Technology Studies, 7(12), 573–598. Jabed, M. I. K., Imran, M., Khan, A. A., Mehedi, M., Islam, M., & Pervez, R. (2026). Explainable machine learning framework for early heart disease detection using SMOTE and SHAP. Vascular and Endovascular Review, 9(1), 316–324. Jabed, M. I. K., Manzoor, M. A., Tofa, F. M., & Khan, M. H. (2026). Interpretable ensemble learning approach for breast cancer diagnosis using SHAP-based explainable AI. Journal of Computer Science and Technology Studies, 8(8), 244–255. Jabed, M. I. K., Sirazy, M. R. M., Mandal, S., Akter, S. A., Hassan, A., & Esa, H. (2026). Developing AI-based financial forecasting and cybersecurity systems for the US digital economy. Frontiers in Computer Science and Artificial Intelligence, 5(5), 30–38. Pacific Economic Cooperation Council. (2026). State of the Region Report 2025–2026. https://www.pecc.org Ponduru, P. S. (2023). Road accident prediction using LSTM GRU neural networks [Doctoral dissertation, California State University, Northridge]. Ponduru, P. S. (2024). Decision intelligence for AI and emerging technologies: The AEGIS-DM framework for trustworthy, cost-aware, and low-latency decision making. Ponduru, P. S., Nandanavanam, P. P. V., & Ponduru, S. K. K. (2026). Ecological proportionality in generative AI: The ethics of marginal capability and environmental sufficiency. Poornima, G. (2025). Unified AI-driven cognitive ecosystem for cloud security and self-healing infrastructure. International Journal of Technology, Management and Humanities, 11(4), 132–138. Rahman, M. A., Devnath, R. K., Niloy, S. B., Mehedi, C. M., Chowdhury, T. H., & Jabed, M. I. K. (2025). A stacking ensemble framework for predicting employee turnover: Explainable AI with SHAP. In Proceedings of the 2025 IEEE 2nd International Conference on Computing, Applications and Systems (COMPAS) (pp. 1–6). Rahman, M. A., Jabed, M. I. K., Devnath, R. K., Mehedi, C. M., Begum, M., & Mahmud, T. (2026). MSRFF: An interpretable CNN-Vision Transformer framework for diabetic retinopathy detection. In Proceedings of the 2026 2nd International Conference on Computational Intelligence Approaches and Applications (ICCIAA) (pp. 1–7). Rasheed, M. D., Akram, M., Imran, M., Ahmed, R. H., & Rauf, A. (2025). Towards human-centric smart manufacturing: A digital twin enabled affective ergonomic framework for adaptive human robot collaboration. International Journal of Advances in Signal and Image Sciences, 1544–1555. Rasheed, M. D., Khan, W., Imran, M., Ahmad, N., Khan, Y., Akram, M., & Sultana, M. (2026). Leveraging artificial intelligence for advance data networking and cybersecurity. Spectrum of Engineering Sciences, 4(3), 286–298. Rodrik, D. (2014). Green industrial policy. Oxford Review of Economic Policy, 30(3), 469–491. https://doi.org/10.1093/oxrep/gru025 Shovon, M. S. S., Gomes, C. A., Reza, S. A., Bhowmik, P. K., Gomes, C. A. H., Jakir, T., & Hasan, M. S. (2025). Forecasting renewable energy trends in the USA: An AI-driven analysis of electricity production by source. Journal of Ecohumanism, 4(3), 322–345. Steffen, B. (2020). The importance of political risk for investment in renewable energy. Energy Policy, 143, Article 111520. Transnational Institute. (2026). Rethinking green industrial policy in ASEAN. Amsterdam, The Netherlands. https://www.tni.org Warwick, K. (2013). Beyond industrial policy: Emerging issues and new trends. OECD Science, Technology and Industry Policy Papers, No. 2. https://doi.org/10.1787/5k46n3w2kqmq-en World Bank. (2026). Industrial policy for development: Approaches in the 21st century. Washington, DC: World Bank. https://openknowledge.worldbank.org Zeeshan, M., Ali, U., Khan, M. S., Hassan, S. M. J., Imran, M., Ahmad, N., et al. (2025). Machine learning and deep learning approaches for strengthening cyber security in intrusion detection system. Spectrum of Engineering Sciences, 1274–1286.