ISSN 1875-3728, Geography and Natural Resources, 2018, Vol. 39, No. 2, pp. 103-110. © Pleiades Publishing, Ltd., 2018. Original Russian Text © I.P. Glazyrina, I.A. Zabelina, 2018, published in Geografiya i Prirodnye Resursy, 2018, Vol. 39, No. 2, pp. 14-22.
Spatial Heterogeneity of Russia in the Light of the Concept of a Green Economy: The Social Context I. P. Glazyrina a, b,* and I. A. Zabelina a, b,**
a
Institute of Natural Resources, Ecology and Cryology, Siberian Branch, Russian Academy of Sciences, Chita, 672014 Russia b Transbaikal State University, Chita, 672039 Russia *е-mail:
[email protected] **е-mail:
[email protected] Received December 8, 2017
Abstract—This paper presents the results of a comparative spatial analysis of the regions of the Russian Federation in the context of the concept of a green economy with the use of two quantitative factors that characterize the well-being of the population: the payroll fund and the total wage fund and own revenues of the regional budgets per capita. The key environmental and economic indicators used in this study are the indicators of eco-intensity for regional economic systems. They show the particular negative impact on the environment “produced” by the regional economy per unit of economic result. The following characteristics of the region’s socio-ecological system are considered: the volume of atmospheric pollutant emissions in terms of 1000 rubles of the regional payroll fund and in terms of 1000 rubles of own income to regional budgets. The study revealed a high degree of heterogeneity of Russia’s regions in socio-environmental characteristics. It is shown that not only does the population of many natural resource and industrial regions live in conditions of increased anthropogenic pressure (both total and specific), but it also is not provided with additional resources of “collective well-being” through its own budget income, in spite of the unfavorable ecological and natural conditions. It is concluded that carbon regulation aimed at developing a low-carbon economy should not focus on identical quantitative indicators for the whole country. DOI: 10.1134/S1875372818020026 Keywords: eco-intensity, social and environmental factors of well-being, ecological and economic zones, comparative spatial analysis, reduction of risk factors, carbon regulation.
INTRODUCTION A green economy is defined as such an organization of the economic activities of mankind that improves the well-being of people, ensures social justice and, at the same time, substantially reduces the risks for the environment and of its degradation [1]. The task of improving the well-being occupies the first place, and it is a very important aspect of the concept. In spite of a critical attitude of specialists toward the gross domestic product (GDP) as the quantitative indicator of the level of development [2–4], in many publications it still remains the “hallmark” of the well-being. As before, the dynamics of the indicators of negative influences on natural systems is compared with the dynamics of economic growth [5, 6]. However, the growth of GDP is not always accompanied by the growth of the well-being and real incomes of the population in particular [7]. For developing the measures related to the transition to a green economy implying the growth of the well-being with a simultaneous improvement in the ecological indicators of the quality of life, it is important to make an assessment of the level of socioecological heterogeneity from the spatial perspective.
This paper seeks to carry out a comparative spatial analysis for the regions of the Russian Federation from the perspective of the concept of a green economy y using two quantitative factors characterizing the wellbeing of the population living in the regions (the symbol N is used to indicate the population size of a region): the wages fund per capita (determining the well-being of households), the factor Wwage/N, and the own per capita incomes of regional budgets (determining, to a significant extent, the level of social expenditures in the regions and used to generate public benefits, i. e. characterizing the “collective” well-being, including the prospects for the development of education, health care, environmental well-being, etc.), the factor Wtax/N. A steady decrease in all kinds of negative influence, including pollutions, constitutes an important aspect of a green growth. This study is based on using data on the volumes of pollutants emitted into the atmosphere in order to obtain characteristics of the social and environmental system of the region, quantifying the negative influence on the environment “produced” by this system per 1000 rubles of the regional wages fund and per 1000 rubles of the own revenues to regional budgets.
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METHODS AND SOURCES OF INFORMATION This paper employs the tools developed in [8] (on the basis of the conceptual scheme suggested by P. Victor in [5]), which was used for the spatial analysis of the effectiveness of forest use in the light of the concept of the green growth. Here, we present a modification of the model adapted for the socioeconomic spatial analysis. The key ecological and economic indicators as used in this study are the indicators of eco-intensity (EI) [9]: TO/tax – volume of pollutants emitted from stationary sources in terms of 1000 rubles of taxes, charges and other compulsory payments to the consolidated budget of the federal subjects of Russia (kg/1000 rubles); CO/ tax – volume of carbon dioxide emissions in terms of 1000 rubles of taxes, charges and other compulsory payments to the consolidated budget of the federal subjects of Russia (kg/1000 rubles); TO/wage – volume of pollutants emitted from stationary sources in terms of 1000 rubles of the wages fund (kg/1000 rubles), and CO/wage – volume of carbon oxide emissions in terms of 1000 rubles of the wages fund (kg/1000 rubles). Let us now introduce also the indicators TO/N and CO/N, i.e. the volume of pollutants emitted from stationary sources and of carbon oxide per capita, respectively. They are important social-ecological indicators for regional economic systems demonstrating the particular “ecological price” to be paid for ensuring the human livelihood in a given region. The general scheme of the model is presented in Fig. 1. The horizontal axis on the two-dimensional plot shows one of the EI indicators. The vertical axis shows one of the indicators characterizing the per capita well-being of a region’s population (W/N); in this case, it is the annual wages fund or the annual volume of taxes, charges and other compulsory payments to the consolidated budget of the federal subjects of Russia per capita.
Fig. 1. Ecological and economic zones in the concept of green growth.
The point I0 designates the relationship between EI and W/N, average for Russia as a whole for a particular year. The curve Г is defined by the equation p = EI*W/N = const, where p is one of the indicators, TO/N or CO/N. Thus the points lying on the curve Г are characterized by an identical state of anthropogenic load per capita, the same as at the point I0. The relationship between EI and W/N for a particular region is determined by a certain point on the plot. Hence, if for some region the point corresponding to it lies below the curve Г, then in this region the total negative influence on the environment per capita is less than Russia’s average level. Accordingly, at the points lying above the curve Г, the negative influence is larger. The vertical line corresponding to EI = EI(I0), the horizontal line corresponding to W/N = W/N(I0), and the curve Г divide the plane into six zones, each of which may be characterized in terms of the concept of green economy (Table 1), i. e. by the relationship between the ecological and social indicators versus Russia’s average level. For characterizing them we shall use the concept of the “color of the zone”, by analogy with the “color of the growth” used in [5, 8, 10, 11]. The + and – symbols correspond to zones in which the per capita indicators of well-being are, respectively, higher and lower than the Russia-averaged indicators. This study uses the following official data of the Federal State Statistics Service [12–14] and the Federal Taxation Service of Russia [15] (the cost indicators were brought to a comparable form) characterizing: the volume of pollutants emissions from stationary sources and of carbon oxide; the mean annual number of employees and the average monthly earnings of employees (for assessing the wages fund); revenues of taxes, charges and other compulsory payments to the consolidated budget of the federal subjects of Russia (tax and other revenues), and the population size. RESULTS This section presents results of calculations, based on data for 2008 and 2015. It is therefore also possible to assess the changes that occurred within this time interval. For the sake of comparability of results, the economic indicators of the year 2015 (total taxes, and the wages fund) were brought into the level of 2008. Parameters of the initial point I0 are presented in Table 2. Calculations showed that Russia’s average annual revenues of taxes, charges and other compulsory payments to the consolidated budgets of the federal subjects of Russia, per capita in 2015 that were brought to the prices of the year 2008, turned out to be 13.7% smaller than the figures for the year 2008 and the mean annual wages fund was only 5.9% higher. In
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Table 1. Ecological and economic zones in the concept of green growth Zones “Green” zone Gr+: the three indicators are better than Russia’s average indicators ”Brown” zone Br+ ”Black” zone Bl+ ”Black” zone Bl–: the three indicators are worse than Russia’s average indicators
Characteristic Higher (than Russia’s average) per capita indicators of the well-being (W/N), with lower indicators of EI as well as of anthropogenic load per capita (TO/N or CO/N) EI is below Russia’s average, with higher indicators of the well-being per capita (W/N); however, the negative influence per capita is higher (TO/N or CO/N) EI is higher than Russia’s average, with higher indicators of the well-being per capita (W/N); the negative influence per capita (TO/N or CO/N) is also higher The two ecological indicators–EI and negative influence per capita (TO/N or CO/N)–are higher, i. e. worse, while the indicators of the well-being per capita (W/N) is lower
They are characterized by lower (than Russia’s average) indicators of the well-being (W/N) and anthropogenic load per capita (TO/N or CO/N) but by higher EI. A decrease ”Green” zone Gr– in anthropogenic load per capita is attendant by an increase of the EI indicators, i. e. through a decrease of the indicators of the well-being The two ecological indicators–EI and negative influence per capita (TO/N или CO/N)– Absolutely “green” zone AGr– are lower, i. e. better than Russia’s average; however, the indicator of the well-being per capita (W/N) is also lower Note. The + and – symbols correspond to zones in which the per capita indicators of well-being are, respectively, higher and lower than the Russia-averaged indicators.
Table 2. Parameters of the initial point I0 for the factors Wwage and Wtax* Years
Substantive characteristic of parameters
2008
2015
31 082
26838
100 773
106 719
Russia-averaged eco-intensity of pollutants emissions from stationary sources according to the factor Wtax /NTO/wage: TO/tax (I0) (kg/1000 rubles)
4.6
4.5
Russia-averaged eco-intensity of carbon oxide emissions according to the factor Wtax/N: CO/tax(I0) (kg/1000 rubles)
1.4
1.3
Russia-averaged eco-intensity of pollutants emissions from stationary sources according to the factor Wwage/N: TO/wage(I0) (kg/1000 rubles)
1.4
1.1
Russia-averaged eco-intensity of carbon oxide emissions according to the factor Wwage/N: СO/wage(I0) (kg/1000 rubles)
0.43
0.31
Values of coordinates for the point I0 along the vertical axis
Russia-averaged annual revenues of taxes, charges and other compulsory payments to consolidated budgets of federal subjects of Russia in per capita terms (rubles), Wtax/N(I0)
Mean annual wages fund in RF in per capita terms (rubles), Wwage/N(I0) Values of coordinates for the point I0 along the horizontal axis
*Calculated by the authors.
the European part of the country, only in eight regions was this indicator in 2015 higher than Russia’s average: in Moscow and St. Petersburg, in Moscow, Leningrad, Ryazan and Murmansk oblasts, in the Komi Republic and in the Republic of Tatarstan as well as in Nenets Autonomous Okrug. Even in Kaluga oblast that is traditionally regarded as prosperous from the perspective of investments, its own per capita revenues to the regional budget in 2008 and 2015 were lower than Russia’s average level. The distribution of Russian regions in ecological and economic zones, presented in Table 1, is shown in GEOGRAPHY AND NATURAL RESOURCES
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Fig. 2. They demonstrate that only a very small number of regions fall into the “green” zone, i. e. the three kinds of their indicators are better than Russia’s average. This is true for the two social factors, Wwage/N and (Wtax/N). As regards all types of emissions from stationary sources, the number of regions in “green” zone Gr+ became in 2015 larger than in 2008, in terms of the factor of ensuring the public benefits in federal subjects of Russia (Wtax/N). However, for regions where the three indicators are worse than Russia’s average, nothing has changed, with Vologda and Amur oblasts added to them (see Fig. 2, a, b).
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Fig. 2. Distribution of Russian regions in ecological and economic zones in 2008 (a, c, e, g) and 2015 (b, d, f, h): volume of pollutants emitted from stationary sources in terms of 1000 rubles of taxes, charges and other compulsory payments to the consolidated budget (TO/tax) (a, b) and wages fund (TO/wage) (c, d); volume of carbon oxide emissions in terms of 1000 rubles of taxes, charges and other compulsory payments to the consolidated budget (CO/tax) (e, f) and wages fund (CO/wage) (f, g). Zones: 1 – “green” (Gr+), 2 – “brown” (Br+), 3 – “black” (Bl+), 4 – absolutely “green” (AGr–), 5 – “green” (Gr–), 6 – “black” (Bl–). GEOGRAPHY AND NATURAL RESOURCES
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The “black” zones Bl+ and Bl– now include all oil- and gas-bearing and most of the northern regions as well as the industrial territories of Ural. Most of the regions in the European and eastern (southern) parts of Russia are within (both in 2008 and in 2015) “green” zones Gr– and AGr–, where the well-being indicators are below European averages. A similar picture emerges as regards the well-being factor of households (Wwage/N). In zone Br+, a relative social-ecological wellbeing (i. e. higher per capita incomes) occurs with a higher EI, i. e. with higher (than national averages) volumes of emissions in terms of 1000 rubles of budget revenues. Thus we cannot say that these regions are more successful in the context of social-ecological modernization, it is rather reversely. In zone Agr–, the specific social EI (i. e. the indicators TO/tax and TO/ wage) are below national averages, which is a positive aspect; in this case, however, the social indicators of the well-being (Wwage/N and Wtax/N) and also lower. In addition to the social-ecological aspects, results of calculations provide insight into the distribution of the social well-being indicators themselves (Wwage/N and Wtax/N). The factor of well-being of households (Wwage/N) in 2015 was below Russia’s average in almost all regions of the European part and in the “southern belt” of the country as far as Primorie (see Fig. 2, d, e). The sole exception is provided by Moscow and Tyumen oblasts, Khabarovsk krai and Sakhalin. This category also includes almost all border regions, both in the west and in the east of the country, which is another evidence for the high level of the barrier character of Russia’s borders [16, 17]. Thus the advantages of the border location with which considerable expectations were associated in the east of the country due to the proximity of the rapidly growing economy of China, and in the west with the economic relations with the European Union, failed to give a substantial impetus to the socioeconomic development and an improvement of the well-being. For most of the northern regions the well-being factor is higher than Russia’s average, which is explained by relatively high wages and by the small population size. However, most of them are concentrated in the “black” zone with worse social-ecological indicators TO/wage and TO/N, i. e. with higher average indicators of the well-being, the ecological load per “unit” of this well-being (which is estimated in our calculations at 1000 rubles) and the per capita emissions themselves are higher than average. Considering that in the northern part of Russia with huge unpopulated spaces the populated is concentrated in territories adjacent production facilities, it may be concluded that the level of environmental discomfort is high, even with respect to the not high Russian standards. GEOGRAPHY AND NATURAL RESOURCES
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Noteworthy is the fact that the spatial distribution in the ecological-economic zones according to the factors Wwage/N and Wtax/N with respect to pollutants emissions is very similar. This suggests in particular that not only do the inhabitants of the natural-resource regions, when categorized as living in the “black” zones, live in conditions of increased anthropogenic load (both general and specific), but they also are not provided with additional resources of “collective wellbeing” on account of their own budget incomes, in spite of the unfavorable ecological and natural conditions. A number of regions with a relatively large industrial potential (Irkutsk, Kemerovo, Tomsk and Vologda oblasts, and some regions of Ural) consistently fall within the “black” zone Bl–, where both the well-being indicators and the social-ecological indicators of EI are worse than Russia’s average. In all federal subjects of Russia in the Baikal region, the indicators TO/tax and TO/wage are higher than Russia’s averages. This means that the social-ecological-economic systems of these territories per unit of negative influence produce less “resources for the well-being”. It is an expected result, in view of data reported in [18]. Perhaps surprisingly, Irkutsk oblast finds itself in a worse-off position from the social-ecological perspective when compared with the other two regions, because most of its electricity (usually, one of the pollutants emissions into the atmosphere) is generated by large hydroelectric power stations of the Angara cascade. THE “CARBON PRICE” OF WELL-BEING IN REGIONS OF RUSSIA In numerous publications [1, 4–6, 19–21] devoted to the development of a green economy, a great deal of attention is paid to greenhouse gas emissions, and a minimization of which is regarded as a necessary condition for a green growth. The problem of minimizing greenhouse gas emissions gave rise to two (as a minimum) global initiatives: the Kyoto Protocol, and the Paris Agreement [22] as well as led to the establishment of new international, regional and national institutes with the mission of resolving this challenge. Before October 2016, the Paris Agreement was signed by 190 countries and the European Union. Russia signed the Agreement in April 2016. In spite of the debatability of the issue of the influence of these processes on climate change and economic development of the country [23, 24], we think it would be important to quantify the degree and character of their inhomogeneity in the regions of Russia. Calculations also used the model described above. Results are presented in Fig. 2. Noteworthy is a general decrease in EI of carbon oxide emissions in Russia. As regards the indicator CO/tax for some of the regions, a worsening of the situation is observed. For No. 2
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instance, Krasnoyarsk krai “passed” from the “brown” to “black” Bl+, which means that in 2015 carbon oxide emissions per 1000 rubles of the own budget revenues became higher than Russia’s average, whereas in 2008 they were lower. On the contrary, the indicators for Khabarovsk krai and Ryazan, Yaroslavl and Leningrad oblasts reflected improvements (see Fig. 2, e, f). As regards the indicator CO/wage, the situation remained virtually unchanged in 2015 when compared with 2008. Only nine regions (including the cities of federal significance – Moscow and St. Petersburg) have the three indicators higher than Russia’s averages in 2015 (see Fig. 2, g, h). These findings suggest not only a high degree of spatial inhomogeneity according to the indicators СО/ tax and СО/wage. As is seen in Fig. 2, e–h, in most regions carbon oxide emissions per 1000 rubles of the well-being factors are higher than Russia’s averages, i. e. nowadays the regional social-ecological-economic systems are structured so that for generating a unit of socially meaningful benefits they have to exceed the standards of carbon emissions in the process of production. This should be taken into account when discussing the issue of introducing the carbon tax in the light of the Paris Agreement. One of its key aspects is the obligation of the countries to develop the plan of adaptation to climate change. As the instrument, it is proposed to introduce, in particular, the carbon tax in participant countries. The primary goal of this tax is not to replenish the budgets but to carry out the “carbon regulation”, i. e. to create the stimuli for the switchover to low-carbon technologies. The Government of Russia instructed the Ministry of Economic Development to prepare in 2016 the draft law of the regulation of emissions, the carbon tax and quoting. By mid-2017, the draft law had not yet been submitted to the State Duma of Russia. Discussions on the carbon tax in Russia appear to be still in their infancy. Nevertheless, the issue of the “carbon regulation” is on the agenda, and the Government of Russia is planning to develop in 2017 the strategy of low-carbon development of the country [25]. This initiative reflects primarily the tendency of the demand of an ever increasing number of end consumers for goods and services with an authoritative ecological standing. Exports will be increasingly more sensitive to the “carbon intensity” of products. Therefore, Russia cannot fall outside of these tendencies. A loss of time and rates may well led to a next economic and social loss, which would imply direct damages to the economy as well as an enhancement in migration of human assets toward a better quality of life. When development the instruments of the “carbon regulation”, it is exceptionally important to take into account the relationship between emissions and well-
being indicators as well as the inhomogeneity of their distribution among the regions. Such an inhomogeneity is revealed by calculations done in this Section. More specifically, the “carbon regulation” should not be guided by quantitative indicators which are identical for the whole country. Irrespective of whether it is realized in the form of the “carbon tax” or some other instruments will be developed, a “fine adjustment” will be required, with due regard for the regional specificity of the relationships between the factors of well-being and anthropogenic load. Considering that most of the carbon oxide emissions, especially in the eastern regions of the country [26], correspond to the electric power generating facilities having a vital significance, there is an obvious need to be guided from the outset by the fact that the transition to a low-carbon economy in different regions of the country will require different period of time, and this should be taken into account in relevant “roadmaps”. The distribution in zones for carbon oxide emissions (see Fig. 2, e–g) is in fairly good agreement with what is presented in Fig. 2, a–d. This is to some extent caused by objective factors. The northern regions require that large amounts of energy should be generated for the human livelihood and economic activities; therefore, carbon oxide emissions, like pollutants emissions per capita, are higher here than national averages. For the same reason, the ecological load in terms of a unit of the well-being factors is also higher. However it is only an explanation, and it cannot serve as a justification of the existing environmental discomfort for Russian citizens living there, and of the “social-ecological inequality” between regions. There is a need for systematic measures in order to minimize the anthropogenic influence precisely in these territories. With regards to the two kinds of negative influence, most of oil- and gas-bearing territories fall within the zone Bl+, i. e. the per capita factors of the well-being there are higher than Russia’s average. For a large number of industrially developed regions (Irkutsk, Kemerovo, Sverdlovsk, Chelyabinsk and other oblasts) these indicators are consistently (both in 2008 and in 2015) below the national averages, with the socialecological indicators of EI are worse there. This means that environmental discomfort of the life in these territories is not compensated by high incomes of households and by social benefits on account of revenues to the regional budgets [27]. CONCLUSIONS It is unlikely that the issues related to an improvement of the quality of life and the elimination of the “social-ecological inequality” largely at the federal level can be successfully dealt with because of an extremely large diversity of causes generating
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them; therefore, these problems do not have a “general” solution. On the other hand, Their solution at the regional level and the development of regional strategies of the transition to a green economy are drastically hampered by the existing system of centralized finance flows, and by the deplorable conditions of the regional budgets [28, 29]. The need to minimize the “social-ecological inequality” is another compelling case for the decentralization of regional authority as well as of resources for their realization. It should be borne in mind, however, that this condition arising from the identified spatial inhomogeneity is only a necessary but not a sufficient condition. In order to be successful in this regard, it is necessary to at least reduce the institutional barriers so that businesses could participate in processes aimed at a green growth, and to generate relevant motivations. The results of the analysis can be used in developing strategic planning documents at the regional as well as the federal level. This is especially true in regard to medium- and long-term programs of the development of the regional electric power generation industry, because in many regions it is the electric power generation facilities that are the main sources of anthropogenic load on natural systems. The inclusion of EI indicators in decision-making procedures when assessing the consequences of the implementation of large projects would become the first step toward a decrease in social-ecological inequality as well as toward reaching a green growth path in Russia’s regions. This work was done within the SB RAS Basic Research Program (XI.174.1). REFERENCES 1. Toward a Green Economy: Avenues for Sustainable Development and Poverty Eradication. URL: http:// www.sustainabledevelop-ment.ru/upload/File/Reports/ ISD_UNEP_GE_Rus.pdf (Accessed 3.26.2016) [in Russian]. 2. Daly, H.E., Beyond Growth: The Economics of Sustainable Development, Boston: Beacon Press, 1996. 3. Daly, H.E. and Farley, J., Ecological Economics: Principles and Applications, Washington: Island Press, 2003. 4. Bobylev, S.N., Zubarevich, N.V. and Solovyeva, S.V., Challenges of the Crisis: How to Measure Sustainable Development? Voprosy Ekonomiki, 2015, no. 1, pp. 147–160 [in Russian]. 5. Victor, P.A., The Kenneth E. Boulding Memorial Award 2014: Ecological Economics: A Personal Journey, Ecol. Econ., 2015, vol. 109, pp. 93–100. 6. Shang, Y., Si, Y. And Zeng, G., Black or Green? Economic Growth Patterns in China Under Low Carbon Economy Targets, JoRE, 2015, no. 6 (5), pp. 310–317. 7. Why Does GDP Grow and Why Do the Revenues of the Population Not Grow? URL: http://www.vedomosti. GEOGRAPHY AND NATURAL RESOURCES
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ru/opinion/articles/2017/07/06/711128-pochemu-rastetvvp (Accessed 8.9.2017) [in Russian]. 8. Glazyrina, I.P., Faleichik, L.M. and Yakovleva, K.A., Socoeconomic Effectiveness and “Green” Growth of Regional Forest Use, Geogr. Nat. Resour., 2015, vol. 36, issue 4, pp. 327–334. 9. Quality of Growth Indicators for Regional Economies, I.P. Glazyrina and I.M. Potravnyi, Eds., Moscow: NIAPriroda, 2005 [in Russian]. 10. Glazyrina, I.P. and Zabelina, I.A., Prospects for a “Green Growth” in the East of Russia and the New Silk Road, EKO, 2016, no. 7 (505), pp. 5–20 [in Russian]. 11. Glazyrina, I.P. and Zabelina, I.A., The Silk Road Economic Belt and Green Growth in the East of Russia, JoRE, 2016, vol. 7 (5), pp. 342–351. 12. Regions of Russia. Socioeconomic Indicators. URL: http://www.gks.ru/wps/wcm/connect/rosstat_ main/rosstat/ru/statistics/publications/catalog/ doc_1138623506156 (Accessed 3.16.2017) [in Russian]. 13. Main Indicators of the Environmental Protection. URL: http://www.gks.ru/wps/wcm/connect/rosstat_ main/rosstat/ru/statistics/publications/catalog/ doc_1140094699578 (Accessed 3.16.2017) [in Russian]. 14. Environmental Protection in Russia. URL: http:// www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ ru/statistics/publications/catalog/doc_1139919459344 (Accessed 3.16.2017) [in Russian]. 15. Consolidated Reports for the Russian Federation in General and for the Subjects of the Russian Federation in Particular. URL: https://www.nalog.ru/rn75/related_ activities/statistics_and_analytics/forms/ (Accessed 3.1.2017) [in Russian]. 16. Kolosov, V.A., Zotova, M.V. and Sebentsov, A.B., The Barrier Function of Russia’s Borders, Reg. Res. Russ., 2016, vol. 6, issue 4, pp. 387–397. 17. Glazyrina, I.P., Zabelina, I.A., Klevakina, E.A., and Bogomolova, T.Yu., The “Eastern Vector” Revisited: Labor Productivity in Border Regions of Siberia and the Far East, EKO, 2015, no. 12 (498), pp. 93–107 [in Russian]. 18. Batuev, A.R., Beshentsev, A.N., Bogdanov, V.N., Dorjgotov, D., Korytny, L.M., and Plyusnin, V.M., Ecological Atlas of the Baikal Basin: Cartographic Innovation, Geogr. Nat. Resour., 2015, vol. 36, issue 1, pp. 5–16. 19. Bobylev, S.N., Kudryavtseva, O.V. and Yakovleva, Ye.Yu., Regional Priorities of Green Economy, Ekonomika Regiona, 2015, no. 2 (42), pp. 148–159. 20. Gollier, С., Ecological Discounting, J. Econ. Theory, 2010, vol. 145, issue 2, pp. 812–829. 21. The Global Green Economy IndexTM (GGEI) 2016: Measuring National Performance in the Green Economy, Dual Citizen LLC, 5th Ed., September 2016. URL: http://dualcitizeninc.com/GGEI2016.pdf (Accessed 11.20.2017). 22. From Kyoto to Paris. URL: http://bellona.ru/ 2016/07/05/ climate-economics/ (Accessed 8.4.2017) [in Russian]. 23. In the view of RUIE, the Ratification of the Paris Agreement Will Have a Negative Influence on the No. 2
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