Global geoclimatic shifts pose substantial risks to the Russian economy, including infrastructure degradation, declining agricultural productivity, and structural challenges associated with the energy transition. The study is motivated by the need to critically evaluate existing climate-economic modeling approaches and assess their applicability to Russian conditions. The objective is to propose an ensemble framework capable of addressing structural uncertainty in climate-economic projections. Method . The research employs a systematic literature review and comparative analysis of four major model classes, examining their theoretical foundations, data requirements, and limitations, with particular attention to the compatibility of Russian statistics with international databases and the treatment of region-specific climate processes. Results . The study identifies three principal sources of distortion in standard models when applied to Russia: reliance on global mean temperature as the sole climatic predictor, which fails to capture Arctic amplification; omission of permafrost feedback mechanisms; and aggregation bias from treating Russia as a single region. The analysis reveals that integrated assessment model (IAM)-based estimates for Russia exhibit extreme variation across studies, reflecting the inadequacy of globally aggregated damage functions. The proposed ensemble strategy — combining econometric estimation, computable general equilibrium (CGE) modeling, IAM calibration, and agent-based modeling (ABM) simulation — offers a pathway for explicit accounting of structural uncertainty. Contribution . The study provides: (1) a systematic comparison of four climate-economic model classes applied to the Russian economy; (2) substantiation of the need for regionally calibrated damage functions; and (3) an ensemble methodological strategy for robust climate-policy assessments under deep uncertainty, adapted to the structural realities of the Russian economy.
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Krinichansky et al. (2026) studied this question.
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