Correlates of forest-cover change in European Russia, 1989–2012

• Uvsh, Delgerjargal ^[1] ; Gehlbach, Scott ^[2] ; Potapov, Peter V. ^[3] ; Munteanu, Catalina ^[4] ; Bragina, Eugenia V. ^[5] ; Radeloff, Volker C. ^[1]
  1. [1] University of Wisconsin–Madison

     University of Wisconsin–Madison

     City of Madison, Estados Unidos

     
  2. [2] University of Chicago

     University of Chicago

     City of Chicago, Estados Unidos

     
  3. [3] University of Maryland, College Park

     University of Maryland, College Park

     Estados Unidos

     
  4. [4] Humboldt University of Berlin

     Humboldt University of Berlin

     Berlin, Stadt, Alemania

     
  5. [5] Russian Program, Wildlife Conservation Society, USA

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• Localización: Land use policy: The International Journal Covering All Aspects of Land Use, ISSN 0264-8377, ISSN-e 1873-5754, Nº. 96, 2020
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • European Russia rapidly transitioned after the collapse of the Soviet Union from state socialism to a market economy. How did this political and economic transformation interact with ecological conditions to determine forest loss and gain? We explore this question with a study of European Russia in the two decades following the collapse of the Soviet Union. We identify three sets of potential determinants of forest-cover change—supply-side (environmental), demand-side (economic), and political/administrative factors. Using new satellite data for three distinct types of forest-cover change—logging, forest fires, and forest gain—we quantify the relative importance of these variables in province-level regression models during periods of a) state collapse (1990s), and b) state growth (2000s). The three sets of covariates jointly explain considerable variation in the outcomes we examine, with size of forest bureaucracy, autonomous status of the region, and prevalence of evergreen forests emerging as robust predictors of forest-cover change. Overall, economic and administrative variables are significantly associated with rates of logging and reforestation, while environmental variables have high explanatory power for patterns of forest fire loss.