ECONOMIC FOUNDATIONS OF DECARBONIZATION: INTERNALIZATION OF ENVIRONMENTAL EXTERNALITIES AND IDENTIFICATION OF COLLABORATIVE DECARBONIZATION HUBS

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🔬研究者:This paper provides a conceptual framework integrating environmental economics and machine learning for decarbonization pathway analysis, offering a testable hypothesis for future empirical work.

🏢実務担当者:Corporate sustainability teams can use the taxonomy to benchmark national decarbonization profiles and identify strategic collaboration opportunities for supply chain or investment.

🏛政策担当者:Policymakers can leverage the proposed clustering to inform international climate finance allocation, technology transfer, and policy coordination among countries.

Introduction. This study considers concept of developing a hybrid economic–machine learning framework to analyze decarbonization as a multidimensional structural trans-formation process integrating environmental externalities, macro-financial dynamics, geopolitical risks and cross-country heterogeneity. The proposed approach is grounded in environmental economics theory extending it through da-ta-driven clustering techniques to identify structurally similar decarbonization pathways and collaborative transition hubs to internalize environmental externalities. Purpose. The purpose of the study is to consider the integration of green economy relevant theoretical and methodological directions into a single conceptual model for the analysis of decarbonization. Therefore, the author proposes to combine the theory of environmental externalities, principles of macroeconomic efficiency, concepts of sustainable financing and ESG investing, analysis of geopolitical and climate risks, modern methods and mechanisms of clustering and machine learning for common management of decarbonization as the basis of the proposed concept of the approach. Materials and methods. The methodological framework consists of proposed four interconnected analytical steps. Firstly, the study establishes an economic foundation based on Pigouvian externality theory, where decarbonization is interpreted as a market correction mechanism addressing the divergence between marginal private and social costs. Second step proposes the construction of a structured cross-country dataset, incorporating key indicators related to emissions intensity, renewable energy integration, ESG performance, financial system development, institutional quality and geopolitical risk exposure. Third, a latent decarbonization potential function was proposed to be specified to capture multidimensional national capabilities for low-carbon transition. Fourth, unsupervised machine learning methods, including self-organizing maps (SOM) was proposed to be applied to classify countries into homogeneous decarbonization system. Results. The empirical logic of the framework suggests that decarbonization is not a linear emissions-reduction process but a systemic transformation shaped by interactions between financial structures, institutional capacity, technological development and geopolitical constraints. The clustering results will provide a taxonomy of countries characterized by distinct transition profiles, including high-efficiency decarbonization economies, transition economies, and fossil-dependent economies. These clusters will be served as analytical foundations for identifying collaborative decarbonization hubs that enhance policy coordination, climate fi-nance efficiency and technology diffusion of countries. The study contributes to the literature by integrating economic externality theory with machine learning–based classification of national decarbonization path-ways, offering a unified analytical framework for evidence-based climate governance and sustainable economic transformation. Discussion. Further research should be aimed at quantitative testing of the proposed model, the application of dynamic forecasting and artificial intelligence-based approaches, as well as scenario analysis of long-term decarbonization trajectories taking into account financial, technological and geopolitical factors.

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