Russia as a Microcosm of Arctic Decarbonization: A Data-Driven Framework for Region- Specific Climate Policy in Federated Systems
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The emission of greenhouse gases (GHG) in the Russia’s Arctic, accelerates warming twice the current global rate, making it a crucial framework for countries with similar decarbonization policies. This study employed a dynamic approach towards identifying regional heterogeneity by integrating spatial correlation analysis, Monte Carlo simulation, and optimization of cost-benefit in climate policies. We observed a clear disparity in Moscow’s CO 2 emissions (24%) associated with aviation activities, and largest mean projection (1,300 kg CO 2 eq) predicted to have considerably higher CO 2 levels than any region. Policy scenario analysis showed that cities with a higher auto footprint total (Ufa, St. Petersburg, and Vladivostok) have a higher value (5.3–5.4%) in total CO 2 compared to cities with lower contributions such as Kaluga, Vladimir, and Tomsk (4.0-4.9%). Using Monte Carlo simulation we modeled regional heterogeneity in Russian cities, the result showed a 15% auto tax reduction in Moscow and coal-to-gas switching grants (20–24%) in Tomsk is cost-effective (benefit-cost ratio 2.1–2.8). Auto tax is the most cost-effective policy as it provides the most CO 2 reduction per unit cost. In Arctic city such as Norilsk whose Arctic Energy Poverty Risk Index (AEPRI = 0.81) has critical risk level, we recommend an immediate transition to waste-heat recycling and geothermal heating to eliminate energy poverty and encourage sustainable development. Beyond 2030, we recommend a transition to Reykjavik and Umea’s clean and resilient climate policies in high-risk regions and other federated systems to ensure equity in energy transitions.
