Management of Regional Natural Resources based on the Variability of Environmental and Economic Indicators
DOI:
https://doi.org/10.17059/ekon.reg.2021-2-12Keywords:
balanced environmental management, natural resource region, temporal variability, atmospheric resources, water resources, forest resources, land resources, time series data, ecological and economic indicators, environmental managementAbstract
Major ecological problems encourage regional authorities to find a balance between the consumption of natural resources and the preservation of the environment. To this end, we assessed environmental management in three Russian regions (the Ural, Siberian and Far Eastern Federal Districts) in the period 1990-2018 using temporal variability analysis of ecological and economic indicators. Based on the spatial and temporal variability and time series analysis, we developed a methodology for examining the use of natural resources and occurring violations. Temporal variability of environmental and economic indicators was visualised for each type of natural resources (atmospheric, water, forest and land). Additionally, the proposed method allowed us to identify a trend towards balanced environmental management and restoration of regional natural resources over a long period. The variability of environmental and economic indicators of 27 constituent entities of the Russian Federation was analysed based on graphic material. Further, four main groups of these indicators (stable, unstable, homogeneous, and heterogeneous ones) were identified. This typology can be used to determine the leading and outsider regions in terms of the balanced environmental management, indicating its general trend (positive or negative one). Overall, satisfactory and positive environmental management dominate in the examined districts. Simultaneously, negative environmental management (predominance of resource use over their restoration) leads to the adoption of drastic measures to remedy the situation. The obtained results may be useful for developing a methodology to assess environmental and economic indicators of balanced environmental management in regions.References
Semyachkov, A. I. (Ed.). (2013). Ekologo-ekonomicheskie aspekty deyatelnosti predpriyatiy gornoy promyshlennosti [Environmental and Economic Aspects of the Activities of Mining Enterprises]. Ekaterinburg: IE UB RAS, 252. (In Russ.)
Hou, W. (2016). Environmental Economics. Beijing: Peking University Press, 266. (In Chin.) 候伟丽 (2016). 环境经济学. 北京：北京大学出版社, 266.
Xie, G. (2004). Political Economy. Yulin: Yulin University, 202. (In Chin.) 谢国忠 (2004). 政治经济. 榆林：榆林学院, 202.
Baturin, L. A., Ignatov, V. G. & Kokin, A. V. (1998). Sbalansirovannoe prirodopolzovanie [Balanced Use of Natural Resources]. Rostov-on-don: Rostizdat. 335. (In Russ.)
Golubetskaya, N. P. (2000). Sbalansirovannoe prirodopolzovanie v usloviyakh perekhodnoy ekonomiki [Balanced nature management in a transitional economy]. M.: NIA-Priroda, 168. (In Russ.)
Loginov, V .G. & Balashenko, V. V. (2019). Sustainable natural resource use: approaches to evaluartion. Izvestiya UGGU [News of the Ural State Mining University], 1(53), 156–161. DOI: 10.21440 / 2307–2091–2019–1-156–161(In Russ.)
Semyachkov, A. I. (Ed.). (2017). Institutsionalnye aspekty sbalansirovannogo prirodopolzovaniya [Institutional aspects of sustainable natural resource management]. Ekaterinburg: IE UB RAS, 134. (In Russ.)
Semyachkov, A. I. (Ed.). (2019). Teoretiko-metodologicheskie osnovy sbalansirovannogo prirodopolzovaniya [Theoretical and methodological foundations for optimal use of natural resources]. Ekaterinburg: IE UB RAS, 260. (In Russ.)
Akhatov, A. G. (1995). Ekologiya. Entsiklopedicheskiy slovar [Ecology. Encyclopedic Dictionary]. Kazan: TKI Ecopolis, 368. (In Russ.)
Su, I. & Wang, H. (2016). The system of natural resources management in Russia and its understanding. China Water Potential, 55–60. (In Chin.) 苏颖 & 王海平 (2016). 俄罗斯的自然资源管理制度及其认识. 中国水势, 55–60.
Zhang, R. (2005). Theory and application of spatial variability. Beijing: Contemporary Outstanding Youth Science Library, 628. (In Chin.) 张仁铎 (2005). 空间变异性的理论与应用. 北京：当代杰出青年科学图书馆, 628.
Xu, B. (2016). Applied research of the method of time series analysis in processing deformation data. Journal of Geomatics, 64–69. DOI: 10.14188/j.2095–6045.2016.01.016 (In Chin.) 徐北海 (2016). 时间序列分析方法在变形数据处理中的应用研究. 测绘地理信息, 64–69.
Cheng, Z. (2002). Time series analysis: historical review and future prospects. Statistics and Decision, 45–46. DOI: 10.13546/j.cnki.tjyjc.2002.09.035 (In Chin.) 程振源 (2002). 时间序列分析：历史回顾与未来展望. 统计与决策, 45–46.
Wang, Y. (2005). Application of time series analysis. Beijing: Renmin University of China, 274. (In Chin.) 王洋 (2005). 时间序列分析的应用. 北京：中国人民大学, 274.
Hong, Y. (2017). Econometrics. Scientific Observation, 38–40. DOI: 10.15978/j.cnki.1673–5668.201705008 (In Chin.) 洪永淼 (2017). 计量经济学. 科学观察, 38–40.
Wang, Z. & Hu, Y. (2007). Application of time series analysis. Beijing: Chinese Academy of Sciences, 246. (In Chin.) 王振龙 & 胡永红 (2007). 时间序列分析的应用. 北京：中国科学院, 246.
Li, D. (2020). Lecture Notes on Financial Time Series Analysis. Shanghai: Shanghai University of Finance and Economics, 770. (In Chin.) 李东风 (2020). 金融时间序列分析的讲义. 上海：上海财经大学, 770.
Wang, J. (2000). Temporal and spatial variability of soil moisture in the small watershed of a Loess hilly area. Acta Geographica Sinica, 70–80. (In Chin.) 王军 (2000). 黄土丘陵区小流域土壤水分的时空变化. 地理学报, 70–80.
Fu, Q. & Jiang, Q. (2008). Research progress in the application of the theory of spatial variability to the analysis of soil characteristics. Soil and Water Conservation Research, 250–253. (In Chin.) 傅强 & 姜秋香 (2008). 空间变异性理论在土壤特征分析中的研究进展. 水土保持研究, 250–253.
Cheng, L. & Niu, J. (2017). Entropy-Based Investigation on the Precipitation Variability over the Hexi Corridor in China. Entropy, 101–111. DOI: 10.3390/e19120660 (In Chin.) 程亮 & 牛俊 (2017). 中国河西走廊降水时空变化研究. 熵, 101–111.
Zhao, L. (2007). Study of the method of periodic analysis of hydrological time series. Nanjing: He Hai University, 75. (In Chin.) 赵立宏 (2007). 水文时间序列周期性分析方法的研究. 南京：河海大学, 75.
Bondarik, G. K. (1971). Osnovy teorii izmenchivosti inzhenerno-geologicheskikh svoystv gornykh porod [Fundamentals of the Theory of Geotechnical Characteristic Variations in Rocks]. Moscow: «Nedra», 272. (In Russ.)
Wan, M. (2011). Time series and spatial dimension studies. Beijing: Capital University of Economics, 148. (In Chin.) 王玛妮 (2011). 时间序列和空间维度研究. 北京：首都经济大学, 148.
Lin, H. (2005). Investigation of a method for modeling non-stationary time series. Wuhan: Wuhan University of Technology, 72. (In Chin.) 林慧 (2005). 对非平稳时间序列建模方法的研究. 武汉：武汉理工大学, 72.
Yu, C. (2014). A Study on Estimating the Parameters of a Time Series Model Based on a Sustainable Distribution. Harbin: Harbin University of Science and Technology, 56. (In Chin.) 于成秀 (2014). 基于稳定分布的时间序列模型参数估计的研究. 哈尔滨：哈尔滨科技大学, 56. (In Chin.)
Zhu, J. (2010). Distribution Characteristics and Time Series Forecasting. Nanjing: Nanjing University of Information Technology, 52. 朱佳 (2010). 时间序列的分布特征和预测. 南京：南京信息工程大学, 52.
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Copyright (c) 2021 Aleksandr I. Semyachkov, Rong Gao, Elena A. Atamanova
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