The Impact of Climate Change on Regional Government Expenditures: Evidence from Russia

被引:0
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作者
Simo Leppänen
Laura Solanko
Riitta Kosonen
机构
[1] Aalto University School of Business,Center for Markets in Transition (CEMAT)
[2] Bank of Finland,The Bank of Finland Institute for Economies in Transition (BOFIT)
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关键词
Climate change; Public expenditures; Adaptation; Non-linearity; Russia; Q54; Q58; H72; R59; C50; P20;
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摘要
This paper explores an almost untouched topic in the fast-growing climate econometrics literature—the implications of climate change for government expenditures. Using a rich sub-national dataset for Russia covering 1995–2009, we estimate the impacts of changes in climatic conditions through short-term variation and medium-term changes in average regional temperatures and precipitation. We show a strong and robust negative (but non-linear) relation between regional budget expenditures and population-weighted temperature. The results indicate that an increase in temperature results in a decrease in public expenditures and that the magnitude of this effect diminishes the warmer the region. Further, our results suggest that the benefits from warming accumulate and that adaptation measures could help leverage those benefits. The estimated decreases in regional government expenditure are, however, quite small. In mild warming scenarios, according to our estimates Russia saves roughly USD 3–4 billion in regional government expenditures between 2000 and the 2020s without undertaking adaptation measures, depending on the scenario. It should be noted that our results are estimated for a scenario of mild temperature increase (1–2 ∘\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}C). Larger temperature increases are likely to have dramatic consequences e.g. from loss of permafrost and methane release that are impossible to predict with available historical data.
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页码:67 / 92
页数:25
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