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

Journal article

Delgerjargal Uvsh, Scott Gehlbach, Peter V. Potapov, Catalina Munteanu, Eugenia V. Bragina, Volker C. Radeloff
Land Use Policy, vol. 96, 2020, p. 104648

DOI: 10.1016/j.landusepol.2020.104648

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APA Click to copy
Uvsh, D., Gehlbach, S., Potapov, P. V., Munteanu, C., Bragina, E. V., & Radeloff, V. C. (2020). Correlates of forest-cover change in European Russia, 1989–2012. Land Use Policy, 96, 104648. https://doi.org/10.1016/j.landusepol.2020.104648

Chicago/Turabian Click to copy
Uvsh, Delgerjargal, Scott Gehlbach, Peter V. Potapov, Catalina Munteanu, Eugenia V. Bragina, and Volker C. Radeloff. “Correlates of Forest-Cover Change in European Russia, 1989–2012.” Land Use Policy 96 (2020): 104648.

MLA Click to copy
Uvsh, Delgerjargal, et al. “Correlates of Forest-Cover Change in European Russia, 1989–2012.” Land Use Policy, vol. 96, 2020, p. 104648, doi:10.1016/j.landusepol.2020.104648.

BibTeX Click to copy

@article{uvsh2020a,
  title = {Correlates of forest-cover change in European Russia, 1989–2012},
  year = {2020},
  journal = {Land Use Policy},
  pages = {104648},
  volume = {96},
  doi = {10.1016/j.landusepol.2020.104648},
  author = {Uvsh, Delgerjargal and Gehlbach, Scott and Potapov, Peter V. and Munteanu, Catalina and Bragina, Eugenia V. and Radeloff, Volker C.}
}

Abstract

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.