Abstract
Increasing the net carbon sequestration of forests is the only way for Russia to achieve carbon neutrality by 2060. In this context, along with measures to increase the area and quality of forest stands, ways to reduce carbon emissions from human activities and natural disturbances have become important. Using regression models of panel data, the article analyzes the spatial heterogeneity of carbon emissions in Russian forests in 2009–2021, measured by tools of the Global Forest Watch project, as a function of economic (logging volumes, government spending on forestry, forest protection and forest fire activities) and natural (the scale of forest fires and outbreaks of mass reproduction of insect pests) factors. Logging and forest fires are expected to have the greatest impact on carbon losses from forests, while the costs of government functions in forestry affairs have almost no response in reducing carbon emissions. Thus, in fact, the goal of forest conservation through public investment in relevant activities has not yet been achieved. The resulting set of regression models can be used to predict the dynamics of regional effects of carbon losses by forests with changes in logging volumes and various trajectories of forest fire activity dynamics. Such an analysis will be critical to formulate regional plans for reducing greenhouse gas emissions, taking into account the maximum use of the potential for increasing the net carbon sequestration of forests.
Similar content being viewed by others
Notes
National Report on the Inventory of Anthropogenic Emissions by Sources and Removals by Greenhouse Gas Sinks Not Regulated by the Montreal Protocol for 1990–2020, Part 1, Moscow, 2022.
If They Don’t Do It, At Least They’ll Warm, Kommersant, November 1, 2022. https://www.kommersant.ru/doc/5060885. Accessed November 5, 2022.
Forest Monitoring, Land Use & Deforestation Trends. Global Forest Watch. https://www.globalforestwatch.org/map/?modalMeta=tree_cover_losses. Accessed November 12, 2022.
National Report on the Inventory of Anthropogenic Emissions by Sources and Removals by Greenhouse Gas Sinks Not Regulated by the Montreal Protocol for 1990–2020, Part 1, Moscow, 2022.
Unified Interdepartmental Information and Reference System. https://www.fedstat.ru. Accessed September 30, 2022.
Wickham, H., Henry, L., Pedersen, T.L., Jake Luciani, T., Decorde, M., Lise, V. svglite: An 'SVG' Graphics Device. R package version 2.1.0. 2022. https://CRAN.R-project.org/package=svglite. Accessed November 30, 2022.
Hlavac, M., stargazer: Well-Formatted Regression and Summary Statistics Tables. R package version 5.2.1. 2018. https://CRAN.R-project.org/package=stargazer. Accessed November 30, 2022.
REFERENCES
Arellano, M. and Bond, S., Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations, Rev. Econ. Stud., 1991, vol. 58, no. 2, pp. 1–277. https://doi.org/10.2307/2297968
Bartalev, S.A. and Stytsenko, F.V., Satellite assessment of the death of forest stands from fires according to the data on the seasonal distribution of the area covered by fire, Lesovedenie, 2021, no. 2, pp. 115–122. https://doi.org/10.31857/S0024114821020029
Croissant, Y. and Millo, G., Panel data econometrics in R: The plm package, J. Stat. Software, 2008, vol. 27, no. 2, pp. 1–43.
Filipchuk, A.N., Moiseev, B.N., and Malysheva, N.V., New aspects of assessing the absorption of greenhouse gases by Russian forests in the context of the Paris Agreement on Climate Change, Lesokhoz. Inf., 2017, no. 1, pp. 88–98.
Filipchuk, A., Moiseev, B., Malysheva, N., et al., Russian forests: A new approach to the assessment of carbon stocks and sequestration capacity, Environ. Dev., 2018, vol. 26, pp. 68–75. https://doi.org/10.1016/j.envdev.2018.03.002
Hansen, M.C., Potapov, P.V., Moore, R., et al., High-resolution global maps of 21st-century forest cover change, Science, 2013, vol. 342, no. 6160, pp. 850–853.
Harris, N.L., Gibbs, D.A., Baccini, A., Birdsey, R.A., et al., Global maps of twenty-first century forest carbon fluxes, Nat. Clim. Change, 2021, vol. 11, pp. 234–240. https://doi.org/10.1038/s41558-020-00976-6
Kharuk, V.I., Ponomarev, E.I., Ivanova, G.A., et al., Wildfires in the Siberian taiga, Ambio, 2021, vol. 50, pp. 1953–1974. https://doi.org/10.1007/s13280-020-01490-x
Pan, Y., Birdsey, R.A., Fang, J., Houghton, R., Kauppi, P.E., Kurz, W.A., Phillips, O.L., et al., A large and persistent carbon sink in the world’s forests, Science, 2011, vol. 333, no. 6045, pp. 988–993. https://doi.org/10.1126/science.1201609
Porfir’ev, B.N., Shirov, A.A., Semikashev, V.V., and Kolpakov, A.Yu., Economic risks in the context of policy development with low greenhouse gas emissions in Russia, Energ. Politika, 2020, vol. 147, no. 5, pp. 92–103.
Pyzhev, A.I., No one canceled the climate agenda: Why is it important for the Russian economy, EKO, 2022, vol. 577, no. 7, pp. 31–50. https://doi.org/10.30680/ECO0131-7652-2022-7-31-50
Pyzhev, A.I., Gordeev, R.V., and Vaganov, E.A., Reliability and integrity of forest sector statistics – a major constraint to effective forest policy in Russia, Sustainability, 2021, vol. 1, no. 13, pp. 1–86. https://doi.org/10.3390/su13010086
Rogelj, J., Geden, O., Cowie, A., and Reisinger, A., Net-zero emissions targets are vague: Three ways to fix, Nature, 2021, vol. 591, no. 7850, pp. 365–368. https://doi.org/10.1038/d41586-021-00662-3
Romanov, A.A., Tamarovskaya, A.N., Gloor, E., et al., Reassessment of carbon emissions from fires and a new estimate of net carbon uptake in Russian forests in 2001–2021, Sci. Total Environ., 2022, vol. 846, p. 157322.
Romanovskaya, A.A., Trunov, A.A., Korotkov, V.N., and Karaban’, R.T., The problem of taking into account the absorption capacity of Russian forests in the Paris Agreement, Lesovedenie, 2018, no. 5, pp. 323–334.
Schepaschenko, D., Moltchanova, E., Fedorov, S., et al., Russian forest sequesters substantially more carbon than previously reported, Sci. Rep., 2021, vol. 11, no. 1, p. 12825.
Shimizu, K., Ota, T., and Mizoue, N., Accuracy assessments of local and global forest change data to estimate annual disturbances in temperate forests, Remote Sens., 2020, vol. 15, no. 12, p. 2438. https://doi.org/10.3390/rs12152438
Shvarts, E.A. and Ptichnikov, A.V Low-carbon development strategy and the role of forests in its implementation, Nauchn. Tr. Vol’nogo Ekon. O-va Ross., 2022, vol. 236, pp. 399–426.
Shvidenko, A. and Shchepashchenko, D., Carbon budget of Russian forests, Sib. Lesn. Zh., 2014, no. 1, pp. 69–92.
Tennekes, M., tmap: Thematic maps in R, J. Stat. Software, 2018, vol. 84, no. 6. pp. 1–39.
Vaganov, E.A., Porfiryev, B.N., Shirov, A.A., et al., Evaluation of the contribution of Russian forests to reducing the risks of climate change, Ekon. Reg., 2021, vol. 17, no. 4, pp. 1096–1109. https://doi.org/10.17059/EKON.REG.2021-4-4
Wickham, H., Averick, M., Bryan, J., et al., Welcome to the Tidyverse, J. Stat. Software, 2019, vol. 4, no. 43, p. 1686. https://doi.org/10.21105/joss.01686
Zamolodchikov, D.G., Grabovskii, V.I., and Kaganov, V.V., Ecosystem services and spatial distribution of protective forests of the Russian Federation, Lesovedenie, 2021, no. 6, pp. 581–592.
Zhang, D., Wang, H., Wang, X., and Lü, Z., Accuracy assessment of the Global Forest Watch Tree Cover 2000 in China, Int. J. Appl. Earth Obs. Geoinf., 2020, no. 87, p. 102033. https://doi.org/10.1016/j.jag.2019.102033
ACKNOWLEDGMENTS
The author is grateful to D.G. Zamolodchikov and A.A. Romanovskaya for their attention to the study and their valuable comments in its first stage. Detailed feedback from two anonymous reviewers on the original manuscript helped to significantly improve the article. Excellent working conditions were provided by E.A. Vaganov and E.V. Zander.
Funding
The study was supported by the Russian Science Foundation (grant no. 19-77-30015). The data set used for modeling was compiled with the support of the state task of the Ministry of Science and Higher Education of the Russian Federation to Siberian Federal University (project no. FSRZ-2021-0011). Map imaging was partially supported by the state task to the Center for Forest Ecology and Productivity RAS (project no. AAAA-A18-118052400130-7).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
The author declares that he has no conflicts of interest.
Rights and permissions
About this article
Cite this article
Pyzhev, A.I. Economic and Natural Factors of Spatial Heterogeneity of Carbon Emissions in Russian Forests in the 2010s. Reg. Res. Russ. 13, 622–630 (2023). https://doi.org/10.1134/S2079970523701010
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S2079970523701010