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グリーン構造転換:分断化する世界経済における気候変動・産業政策・開発

The Green Structural Transformation: Climate Change, Industrial Policy, and Development in a Fragmenting Global Economy (原題)

Ibad ur Rahman, Faisal Ijaz, Atoofa Kalsoom, M. Altaf, Saima Batool Saima Batool

Inverge Journal of Social Sciences📚 査読済 / ジャーナル2026-09-17#政策Origin: Global経営インパクト: 調達リスク対象セクター: cross_sector
DOI: 10.63544/ijss.v5i5.335
原典: https://invergejournals.com/index.php/ijss/article/download/335/579
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🤖 gxceed AI 要約

日本語

本論文は、地政学的分断が進む中で、新興・途上国(EMDEs)におけるグリーン産業政策と気候変動・構造転換の関係を分析する。85〜95カ国の2013〜2025年パネルデータを用い、Green Structural Transformation Index(GSTI)を構築し、System-GMMや分位点回帰などで推計。グリーン産業政策は再エネ導入や産業高度化を通じて転換を促進する一方、地政学的分断は技術移転や資金調達を阻害し制約することを示す。制度の質と地域統合が政策効果を高め、分断の悪影響を緩和することを明らかにする。

English

This study examines how green industrial policy and geoeconomic fragmentation shape green structural transformation in 85–95 emerging and developing economies (2013–2025). Using a new Green Structural Transformation Index and System-GMM, quantile regression, and FMOLS, it finds green industrial policy promotes renewable adoption and industrial upgrading, while fragmentation disrupts technology transfer and financing. Institutional quality and regional integration strengthen policy effectiveness and buffer fragmentation, with heterogeneity by income and resource dependence.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本企業にとっては、EMDEsでのグリーン産業政策・地域統合の動向が、サプライチェーン再編や移転先国の政策リスク評価に直結する。SSBJ・有報でのScope3や移行計画開示を進める上で、投資先・調達先国の制度品質や地域統合度をリスク要因として捉える視点を提供する。

In the global GX context

For global disclosure scholarship, this paper links macro-level green industrial policy and geoeconomic fragmentation to structural transformation outcomes in EMDEs—context that complements firm-level TCFD/ISSB transition-planning frameworks. It highlights how institutional quality and regional integration mediate transition finance and technology flows, relevant to ISSB/CSRD discussions on country- and sector-level transition risk.

👥 読者別の含意

🔬研究者:EMDEsのグリーン構造転換を測るGSTIと、分断・制度品質を組み込んだ実証枠組みを提供する。

🏢実務担当者:調達・投資先EMDEsの政策・制度リスクと地域統合度を、移行計画・Scope3リスク評価に反映する示唆。

🏛政策担当者:途上国支援や地域協力・制度構築を通じ、グリーン産業政策の実効性と分断耐性を高める設計の重要性を示す。

📄 Abstract(原文)

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.

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