Carbon Emissions

Research on the Evolution Trend of Carbon Emissions under Exogenous Shocks: Evidences from Russia

  • WAN Yongkun ,
  • ZHAO Xiaoliang , * ,
  • HAI Ruxin
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  • Lanzhou University of Finance and Economics, Lanzhou 730030, China
*ZHAO Xiaoliang, E-mail:

WAN Yongkun, E-mail:

Received date: 2021-12-18

  Accepted date: 2022-09-07

  Online published: 2023-02-21

Supported by

The Ministry of Science & Technology Basic Resources Survey Project(2017FY101304)

The Gansu Provincial Department of Education Project(GSSYLXM-06)

The Gansu Provincial Social Science Fund Project(2021YB074)

The Lanzhou University of Finance and Economics Scientific Research Innovation Team Project(2020TD08)

Abstract

At present, carbon intensity in the economy has become a realistic problem faced by many countries. Decarbonization and green development have gradually become one of the main trends in the world, and major countries around the world have put forward carbon neutrality targets. Russia is one of the largest greenhouse gas emitters in the world. Therefore, under the current international situation of The Russia-Ukraine war and the exogenous impact on Russia’s economy and finance, it is of great significance to study Russia’s carbon emissions for Russia’s economic development, environmental protection and global green development. In recent years, Russia’s carbon intensity has remained high, which may be caused by several factors, such as the decline in actual investment level, single industrial structure, excessive dependence on oil and gas industry, external shocks to the Russian economy in 2014 and other macroeconomic factors. The purpose of this study is to identify trends in carbon intensity during the period of exogenous shocks to the Russian economy and financial sector from 2014 to 2018, and to explain the causes. First, the synthetic control method is used to examine the changes in Russia’s carbon intensity since 2014, and the results show that since 2014, Russia’s carbon intensity has increased significantly; Secondly, using the mediation effect analysis model to test the impact mechanism, it is found that since 2014, the Russian industrial structure has not been actively improved, but instead increased its dependence on the resource industry, thereby increasing carbon emissions. Combined with the tail effect analysis, Russia has faced significant economic pressure, and its carbon intensity is unlikely to return to the state before 2014 in the short term.

Cite this article

WAN Yongkun , ZHAO Xiaoliang , HAI Ruxin . Research on the Evolution Trend of Carbon Emissions under Exogenous Shocks: Evidences from Russia[J]. Journal of Resources and Ecology, 2023 , 14(3) : 454 -467 . DOI: 10.5814/j.issn.1674-764x.2023.03.003

1 Introduction

At present, carbon intensity in the economy (carbon dioxide emissions per unit of GDP) has become a realistic problem faced by many countries. Decarbonization and green development have gradually become one of the major trends in the world, and major countries have put forward carbon neutrality targets. Russia is one of the world’s largest emitters of greenhouse gases, accounting for about 5 percent of global emissions, leading to increasing international scrutiny of its emissions reduction goals and policies. The transition to a low-carbon economy has been particularly difficult for Russia, a large resource economy, which is one reason why its timetable for achieving carbon neutrality has been difficult. However, Russia’s low-carbon development is inevitable: on the one hand, for political reasons, the global climate governance issue has evolved into an international discourse, and countries are playing the “climate politics” card. With the intensification of the current Russia-Ukraine conflict, Russia has become a key area affecting the wind direction of the world, and low-carbon development is a new challenge for Russia in the international public opinion. On the other hand, based on economic considerations, if the carbon footprint of Russian products does not meet the standard, Russian exporters of aluminum products, ferrous metals, natural gas, chemicals and cement will face huge losses. Therefore, the study of Russian carbon emissions is of great significance to Russia’s economic development, environmental protection and global green development.
Russia plays an important role in the construction of the China-Mongolia-Russia Economic Corridor, which is mainly reflected in the fact that, in terms of energy, 60% of China’s imported oil currently passes through the Strait of Malacca. In confrontation or conflict, the Strait of Malacca has undoubtedly become a powerful weapon for the external power to suppress China, while Russia is a large country with oil and gas resources across Europe and Asia, and is currently China’s largest source of oil imports, playing an important role in reducing its dependence on the Strait of Malacca; In terms of trade, strengthening trade exchanges and development cooperation between China and Russia will not only benefit the economic development of the two countries, but also bring benefits to neighboring countries and regions; From an environmental perspective, Russia is currently the fourth largest contributor to global carbon dioxide emissions, and research on Russia’s carbon emissions will play an important role in global environmental protection and green development. Russia stated in the 2021 “Russian National Security Strategy” that sanctions by Western countries are one of the major external challenges facing Russia. Therefore, the exogenous impact in this article mainly refers to the economic and financial sanctions imposed by Western countries on Russia in 2014. Therefore, this paper aims to study the changes in Russia’s economic growth, industrial structure and environmental changes during the period from 2014 to 2018 when Russia’s economic and financial sectors were under the pressure of sanctions by Western countries, and then to China’s response to external restrictions and sanctions. The transformation of industrial policies and other aspects has an important enlightening role.
Russia has adhered to international agreements on climate change and environmental protection for many years (Zakharova et al., 2021). However, since 2014, Russia’s economic situation has deteriorated sharply: economic recession, soaring inflation, rising unemployment rate, intensified environmental pollution, etc. (Andermo and Kragh, 2021; Nusratullin et al., 2021). This has led to a loss of 280 billion USD for the Russian economy and reduced its GDP by 2.4% (Ulyukaev and Mau, 2015; Christie, 2016). Although Russia’s carbon dioxide emissions declined in 2013, they have been rising year by year since 2014, and reached 1.7 billion tons in 2018 to make it the fourth largest contributor to global carbon dioxide emissions. This increase in emissions has occurred because the energy industry is the backbone of the Russian economy and the main target of economic sanctions (Aalto, 2016; Van de Graaf and Colgan, 2017). The depletion of deposits in the Siberian oil and gas fields, the high cost of extraction of oil and gas from the Arctic Circle (Shestak et al., 2020; Chanysheva and Ilinova, 2021; Skuf et al., 2021), and sanctions have made it difficult for Russia to introduce clean technologies to transform its outdated production processes, because of which it is facing tremendous pressure to reduce carbon dioxide emissions. For this reason, Therefore, it is of great significance to study the impact of changes in Russia’s industrial structure and degree of openness on carbon emissions after Russia was under pressure from sanctions in 2014 to promote global green development.
In recent years, many studies have examined changes in carbon emissions due to various policies. Chishti et al. (2021) studied the impact of fiscal and monetary policies on the carbon emissions of the BRICS countries (Brazil, Russia, India, China and South Africa), and noted that their expansionary fiscal and monetary policies had increased CO2 emissions while contractionary fiscal and monetary policies had the opposite effect. Tawiah et al. (2021) analyzed the impact of the China-Africa partnership on carbon emissions, and noted that China’s construction activities and exports from African countries will increase carbon emissions, but foreign investment and imports from China have reduced carbon emissions in Africa. Kirikkaleli and Adebayo (2021) discussed the impact of renewable energy consumption and public-private partnership-based energy investment on India’s consumption-based carbon dioxide emissions in the first quarter of 1990 and the fourth quarter of 2015. They pointed out that renewable energy consumption and public-private energy-related investment were beneficial for reducing CO2 emissions due to consumption. By taking China as an example, Shen et al. (2021) studied the impact of green investment, financial development, and natural resource rents on carbon emissions, and noted that natural resource rents and financial development increase carbon emissions while green investment reduces them. Cao et al. (2021) studied the impact of the environmental protection inspection system on China’s air quality based on the double difference model, and found that after the implementation of the environmental protection inspection system, China’s annual average PM2.5 concentration has dropped significantly.
A number of scholars have researched Russia’s carbon emissions from different perspectives. Masyagina and Menyailo (2020) studied the influence of permafrost on carbon dioxide emissions in Siberia, and found that the flux in CO2 in the soil in the non-frozen area of western Siberia had increased, whereas the flux in CO2 soil in the non-frozen area of central Siberia had decreased. Sun et al. (2019) studied the CO2 emissions implied in Russia’s international trade and found that carbon was transferred from the upstream resource sector to the downstream manufacturing sector and Russia’s service industry. In addition, Russia is a net exporter of carbon dioxide emissions, and the basic resources and energy sectors are exports. The main source of carbon dioxide emissions, the impact of modern technology industries on imported implied carbon dioxide emissions is increasing. Adedoyin et al. (2020) studied the relationship among coal rent, economic growth, and carbon dioxide emissions in the BRICS countries, and showed that coal rent has a significant but negative impact on carbon dioxide emissions. Sustained development thus requires more stringent environmental policies.
Since carbon emissions reduction is a systematic project, the study of the change trend of carbon emission in Russia after sanctions cannot only focus on a certain link (some) of economic operation. At present, most of the existing literatures only study the impact of a certain policy on carbon emissions, and most of them focus on empirical research on the relationship between the two. Few literatures explore the mechanism of the impact of sanctions on Carbon emissions in Russia from the perspectives of industrial structure change and openness. In view of this, this paper firstly analyzed and predicted the change trend of Russia’s carbon emissions after the sanctions were imposed in 2014 through the synthetic control method, and analyzed its internal mechanism by using the mediation effect model, and explored the tail effect of Russia’s carbon emissions since 2022. The possible marginal contributions of this paper are as follows: first, the paper comprehensively and deeply analyzes the changes of Russia’s carbon emissions and their mechanism of action after the pressure of sanctions, revealing the mechanism of action of Russia’s industrial structure, resource substitution industries and the level of opening to the outside world; Second, the empirical analysis of the changes in Russia’s carbon emissions after sanctions, while the synthetic control method determines the optimal weight of the control group, reduces the error of subjective selection, but also effectively alleviates the endogenous problem, which can provide a more reliable policy basis for Russia’s environmental protection and global green development.

2 Materials and methods

2.1 Carbon emissions measurement

The method of measuring carbon emissions in this paper is based on the methodology given by the UN Intergovernmental Panel on Climate Change (IPCC), which uses CO2 emissions intensity to measure carbon emissions based on the fact that Russia’s economy has been in recession since the sanctions, but CO2 emissions are not low but high. Energy-related CO2 emissions are calculated for each country (region) by using energy consumption, average heat generation and carbon emission factors, and the CO2 intensity of each output is measured as follows:
$C{{E}_{jt}}=\underset{i}{\mathop \sum }\,C{{E}_{ijt}}=\underset{i}{\mathop \sum }\,E{{C}_{ijt}}E{{F}_{i}}{{E}_{si}}\times $ 293
In the above, the subscript j represents the country, subscript i represents various fossil fuels, and t is time in years. CEjt represents the CO2 emissions of the j-th country (region) in the t-th year. CEijt represent the CO2 emissions of the j-th country or region based on fuel type i in year t (100 million t), ECijt is the total energy consumption of the j-th country (region) based on fuel type i in year t, Esi×293 represents the average calorific value of the i-th fuel, and the constant 293 is the average calorific value of the coal equivalent. Esi is the conversion factor of the i-th fuel converted into its coal equivalent, and EFi represents the CO2 emission coefficient of the i-th fuel designated by the IPCC. The intensity of carbon dioxide as measured by its emissions for each output is as follows,
${{C}_{jt}}$=$C{{E}_{jt}}$/$GD{{P}_{jt}}$
where ${{C}_{jt~}}$ represents the intensity of emissions of carbon dioxide in the j-th region in year t, and $GD{{P}_{jt}}$ represents the actual GDP of the j-th area in year t.

2.2 Analysis of the action mechanism of Russia’s carbon emission changes under exogenous shockse

As analyzed above, since coming under the pressure of sanctions in 2014, Russia has strengthened its reliance on the resource industry, thereby affecting Russia’s carbon emissions through various channels. To sum up, there are several mechanisms of action.
First, after exogenous shocks, Russia’s industrial structure has changed, increasing its dependence on the resource industry and making carbon emission reduction more difficult. Russia’s economic development, tax revenue, and trade are heavily dependent on oil (Tabata, 2002). Since 2014,the United States and Europe have barred all economic entities in the country from making new investments and providing key equipment and technology for Arctic oil exploration, deep-sea drilling, and shale oil and gas development in the Russian oil sector (Shapovalova et al., 2020). Coupled with the lack of private investment in Russia (Uskova and Razgzulina, 2015), the cost of oil and gas exploration is much higher than before the sanctions. To increase government revenue, develop the economy, and maintain military expenditure, Russia has to develop its oil and gas industry, and thus its dependence on resources is increasing. According to data released by the Russian Federal State Statistics Service, the proportion of production of oil and gas in the economy has risen from 34.3% in 2010 to 38.9% in 2018. The share of other types of production activities has fallen. For instance, the share of the manufacturing industry has declined from 53.2% to 50.7%, and the economic structure has gradually become fragile. Although Russia’s oil and gas exports decreased after the sanctions, production has increased year by year. From 2014 to 2019, Russian oil production increased by 6.17%, from 535.1 million tons to 568.1 million tons. Its natural gas production increased by 14.85%, from 591.2 billion m3 to 679 billion m3. According to the 2020 budget of the Russian Federation and the fiscal plan for 2021-2022, Oil and gas revenue account for 36.7% of Russia’s gross national income. As many of the newly developed oil and gas fields are located in regions with permafrost development is difficult, the technical requirements are stringent, and energy consumption is high (Gautier et al., 2011; Egorov et al., 2021). This has significantly increased CO2 emissions compared with before. Therefore, the following hypothesis is proposed:
Hypothesis 1: Since exogenous shocks in 2014, Russia has not optimized its industrial structure in a timely manner. On the contrary, its economy relies heavily on resources, making it more difficult to reduce carbon emissions, and thus its carbon emissions have increased significantly.
Second, excessive reliance on the oil and gas industry will produce a “crowding out effect”, making it difficult to develop alternative industries (Orazalin and Mahmood, 2018) and increasing Russia’s carbon emissions. The development of the oil and gas industry has squeezed the share of development of other emerging industries, which is also a reason for the increase in carbon dioxide emissions. Emerging industries have significant advantages in terms of reducing energy consumption, improving efficiency, and promoting economic structural transformation. They are an important starting point for achieving green development and reducing carbon dioxide emissions (Gu et al., 2020; Wang and Feng, 2021). However, Russia’s long-term investment in the oil and gas industry has driven its exports and GDP growth, which has resulted in a simplification of capital flows. The oil and gas industry will squeeze development funds from other emerging industries, resulting in insufficient motivation for their development. In Russia, the income of workers in the oil and gas industry is four times the average social income, which promotes the flow of talent to this industry. The resulting loss of human resources in other social industries will affect social development. Although in recent years, in response to sanctions by Western countries, Russia has sought a developmental path to rid itself of its “oil addiction” and develop its emerging industries (Zemtsov et al., 2016; Pasholikov and Dudakov, 2019), this development has been slow owing to insufficient investment funds. Compared with China, Russia’s high-tech output accounted for 21.1% of its GDP in 2015, and was only 21.8% in 2019, with a slight decline in 2018. By contrast, China’s multi-faceted economic growth is clear. The share of high-tech output in China’s GDP has increased from 27.5% in 2015 to 32.7% in 2019, an average annual growth of 1.03%. Therefore, this article proposes the following hypothesis:
Hypothesis 2: Since exogenous shocks in 2014, Russia’s emerging industries have not developed rapidly, and alternative industries have been difficult to develop, thus increasing Russia’s carbon emissions.
Third, since exogenous shocks in 2014, Russia’s level of openness has been significantly reduced. Since Western countries imposed sanctions in 2014, many foreign investors have withdrawn from the Russian energy industry (Zagashvili, 2016). According to the World Bank, the average net inflow of foreign direct investment in Russia from 2005 to 2013 was 534 million USD, compared with only 168 million U.S. dollars in 2014. The average net investment inflow from 2014 to 2018 was only 127 million USD, a decrease of 3.21 percentage points over the previous 9 years. The resulting technological outflow, reduced efficiency of production, and blocked exports have made it more difficult for Russia to introduce advanced technologies for clean production from abroad. It is difficult to control pollutant emissions from production processes such as geophysical prospecting, drilling, down hole operations, and oil production in the context of oilfield development. Increasing the cost of extraction of oil and gas has also increased the burden on the environment through higher carbon emissions, making Russia’s “source governance and process control” more difficult. This article thus proposes the following hypotheses:
Hypothesis 3: Since exogenous shocks in 2014, Russia’s level of openness has been significantly reduced, triggering a sharp rise in Russia’s carbon emissions.

2.3 Empirical model

This article uses the synthetic control method to estimate the impact of Western sanctions on Russia’s carbon emissions. Russia, sanctioned by Western countries, is used as the treatment group, and countries that are not sanctioned by Western countries are used as the control group. We calculate the optimal weights of the control units, and synthesize a virtual “synthetic Russia” with characteristics of change most similar to those of the “real Russia”. If “synthetic Russia” and “real Russia” had similar characteristics of carbon emissions before the imposition of Western sanctions in 2014, we can determine the effectiveness of these sanctions on Russia’s carbon emissions by comparing the difference between the output variables of “real Russia” and “synthetic Russia” before and after the sanctions came into effect.
Suppose that there are (1+J) countries or regions have observed the increase in carbon emissions during t (t=1,...,T) periods. Under the framework of counterfactual analysis, Cit represents the carbon emissions of the i-th country or region in period t, $\text{ }\!\!~\!\!\text{ }C_{it}^{N}$ represents the growth in emissions by country or region i that was not sanctioned by the sanctions during t, and $C_{it}^{I}\text{ }\!\!~\!\!\text{ }$ represents the country or region i that was sanctioned by the sanctions during period t. Assuming that country or region i begins to be sanctioned by the sanctions at t=T0, the country’s carbon emissions will not be affected by the sanctions during the period [1, T0], then $C_{it}^{N}$=$C_{it}^{I}$. After being sanctioned, that is, during the [T0, T] period, let ${{\alpha }_{it}}$=$C_{it}^{I}$-$C_{it}^{N}$ to represent the increase in carbon emissions brought by the sanctions to the i-th country or region at time t. The goal of this article is to estimate ${{\alpha }_{it}}$, using the factor model proposed by Abadie et al. (2010). The model is as follows:
$~C_{it}^{N}$=${{\vartheta }_{t}}$+${{\delta }_{t}}{{X}_{i}}$+${{\tau }_{t}}{{\mu }_{i}}$+${{\varepsilon }_{it}}$
where$\text{ }\!\!~\!\!\text{ }C_{it}^{N}$ represents the growth in emissions by country or region i that was not sanctioned by the sanctions during, ${{\vartheta }_{t}}$ is the fixed time effect, Xi represents a control variable,${{\delta }_{t}}$ is parameter vector to be estimated, $~{{\mu }_{i}}$ is unobservable fixed effect vector, ${{\tau }_{t~}}~$ is unobservable common factor vector, and ${{\varepsilon }_{it}}$ is an unobservable temporary shock with mean value zero.
Consider constructing the weight vector W=(w2, w3, …, wJ+1)′ of synthetic control, each eigenvalue of the weight vector W represents a combination of synthetic control, all weights are non-negative, and the sum of weights is equal to 1. That is, the specific weights of the carbon emissions of j countries or regions can be synthesized, and the resulting variable of the synthetic control country can be written as:
$\underset{j=2}{\overset{J+1}{\mathop \sum }}\,{{w}_{j}}{{C}_{jt}}$=${{\vartheta }_{t}}$+${{\delta }_{t}}\underset{j=2}{\overset{J+1}{\mathop \sum }}\,{{w}_{j}}{{X}_{j}}$+$\underset{j=2}{\overset{J+1}{\mathop \sum }}\,{{w}_{j}}{{\mu }_{j}}$+$\underset{j=2}{\overset{J+1}{\mathop \sum }}\,{{w}_{j}}{{\varepsilon }_{jt}}$
Where ${{C}_{jt}}$ represents the carbon emissions of the j-th country in period t,${{w}_{j}}$ is the weight vector, ${{\vartheta }_{t}}$ is the fixed time effect, Xj represents a control variable that is observable and not affected by sanctions,${{\mu }_{j}}~$ represents unobservable country or region fixed effects vector, ${{\varepsilon }_{jt}}$ is an unobservable temporary shock with mean value zero.
Suppose there is a vector$~{{W}^{*}}$= ($w_{2}^{*}$, $w_{3}^{*}$,,$w_{J+1}^{*}$) that satisfies $\underset{j=1}{\overset{J+1}{\mathop \sum }}\,w_{j}^{*}{{X}_{j}}$=${{X}_{1}}$, and, for any t $\in $ 1,${{T}_{0}}$]], satisfies $\underset{j=1}{\overset{J+1}{\mathop \sum }}\,w_{j}^{*}{{C}_{jt}}$=${{C}_{1t}}$. If $\underset{t=1}{\overset{{{T}_{0}}}{\mathop \sum }}\,{{{\tau }'}_{t}}{{\tau }_{t}}$ is non-singular, then it follows the proof of Abadie et al. (2010) that $C_{1t}^{N}$-$\underset{j=1}{\overset{J+1}{\mathop \sum }}\,w_{j}^{*}{{X}_{jt}}$ will converge to 0. Therefore, when t $~>{{T}_{0}},$ the unobservable $C_{1t}^{N}$ can be replaced by the observed $\underset{j=1}{\overset{J+1}{\mathop \sum }}\,w_{j}^{*}{{C}_{jt}}$, and an unbiased estimator ${{\hat{\alpha }}_{it}}$ of the change in carbon emissions after sanctions can then be obtained:
${{\hat{\alpha }}_{it}}$=${{C}_{1t}}$-$\underset{j=1}{\overset{J+1}{\mathop \sum }}\,w_{j}^{*}{{C}_{jt}}$
where C1t represents the carbon emissions of the first country or region in period t, Cjt represents the carbon emissions of the j-th country in period t. In equation (5), the difficulty in estimating ${{\hat{\alpha }}_{it~}}$ lies in finding the weight vector $~{{W}^{*}}$=($w_{2}^{*}$, $w_{3}^{*}$,,$w_{J+1}^{*}$), However, in fact, it is difficult to find solutions that make the system of equations strictly true in the data, this requires an approximate solution to determine the weight vector $~{{W}^{*}}$ of the synthetic control, by minimizing the distance function between ${{X}_{0}}$ and X1to determine W, this article draws on the distance function ${{W}^{\text{*}}}$ used by Abadie et al. (2015), namely, =$\sqrt{({{X}_{1}}{{X}_{0}}W{)}'V({{X}_{1}}{{X}_{0}}W)}$, where V is a (P×P)-dimensional positive semi-definite symmetric matrix, while assigning weights to ${{X}_{0}}~\text{and}\ {{X}_{1}}$, and getting the most minimized mean prediction error. The weighted “synthetic Russia” carbon emissions simulate the assumption that “real Russia” was not under pressure from sanctions. The difference in carbon emissions between “real Russia” and “synthetic Russia” is the quantitative change in Russia’s carbon emissions after sanctions.

2.4 Variable selection

2.4.1 Explained variable

Carbon emissions constituted the explained variable, which was obtained by dividing the total volume of carbon dioxide emissions by the actual gross domestic product (t (100 USD)-1).

2.4.2 Control variables

The law of synthetic control requires that the determinants of carbon emissions of “synthetic Russia” and Russia be as consistent as possible. The variables of predictive control represent control variables that can be observed, and are not affected by sanctions pressure. This article selected the following variables as control variables: 1) The proportion of exported goods (%), as expressed by the percentage of exports of goods and services as part of the GDP. The greater the proportion of exported goods is, the greater is the industrial pollution and environmental degradation. This variable is therefore positively correlated with carbon dioxide emissions. 2) Energy structure, as expressed by the proportion of fossil energy consumption (%), Expressed by the proportion of fossil energy consumption (%), the greater the proportion of fossil energy consumption, the higher the carbon dioxide emissions. 3) Energy intensity (%), as expressed by the proportion of the total rent of natural resources in GDP. The contribution of natural resources to the GDP is closely related to carbon emissions. 4) The degree of industrial deepening, as expressed by the ratio of the added value of the service industry to that of all industry. An increase in industrial deepening reduces carbon dioxide emissions. 5) Technological progress (%), as expressed by the expenditure on research and experimental development (R&D). This is calculated by the ratio of expenditure on R&D to the GDP. Technological advances can improve energy efficiency and reduce carbon dioxide emissions.

2.4.3 Intermediary variables

These were as follows, 1) The percentage of the output of the secondary industry (%) and total energy output of the primary industry (quadrillion Btu) (reflecting changes in industrial structure and dependence on resource industries); 2) Technological progress (%) (reflecting alternative resource industries); 3) Foreign direct investment in terms of GDP, which is the proportion (%) (reflecting the level of opening to the outside world) used as an intermediate variable for mechanism testing.

2.5 Sample selection and data sources

With reference to the practices of Zhang et al. (2018) and Tao and Gao (2020), this article screened out 25 countries (regions) with similar development levels as Russia as the control group, specifically the Philippines, South Korea, Thailand, Malaysia, India, Vietnam, Indonesia, Saudi Arabia, the Czech Republic, and Macedonia, Estonia, Poland, Slovakia, Slovenia, Lithuania, Hungary, Croatia, Bulgaria, Romania, Latvia, Chile, Colombia, Mexico, Morocco, Peru. Based on the panel data of these 25 countries (regions) from 2009 to 2018, the synthetic control method is used to analyze the increase in Russian carbon emissions after sanctions pressure.
The data were taken from the World Energy Statistical Yearbook, the United Nations ESD database, International Monetary Fund database, the OECD OLIS database, and the World Bank-World Economic Development Database, and compiled by EPS DATA. To eliminate the influence of price-related factors, this article used the GDP deflator, total reserves, fiscal deficit variables, and data on the net capital account (previous year=100). We also used CPI (Consumer Price Index) to deflate net foreign direct investment and per capita income with 2010 as the base period. Owing to a lack of national data for some years, this paper used the average growth rate to fill in the missing data. We finally formed a balanced panel dataset covering 26 countries (regions) over 10 years.

3 Results

3.1 Selection of weight coefficient of synthetic control group

We subjected the chosen set of control countries (regions) and predictive variables to the synthetic control method to form a “synthetic Russia” as a counterfactual to compare with the Russia. The weight composition of “synthetic Russia” is shown in Table 1. Colombia had the highest weight of 0.438; followed by Saudi Arabia, Thailand, Macedonia, India, and Chile, with weights of 0.239, 0.225, 0.081, 0.016, and 0.001, respectively; the other countries (regions) in the control group had weights of zero.
Table 1 The weight composition of “synthetic Russia”
Country ( region) India Colombia Chile Saudi Arabia Thailand Macedonia
Weights 0.016 0.438 0.001 0.239 0.225 0.081
Table 2 shows a comparison of related variables between Russia and “synthetic Russia”, composed of the six countries (regions) of India, Colombia, Chile, Saudi Arabia, Thailand, and Macedonia, through synthetic control methods from 2009 to 2013. It shows that the difference between variables representing Russia and “synthetic Russia” was small. Except for the proportion of exported goods and energy intensity, the other three variables were smaller than the average for each of the corresponding variables for Russia. This shows that “synthetic Russia” adequately fitted the situation of Russia from 2009 to 2013.
Table 2 Fitting of related variables from 2009 to 2013
Variable Russia Synthetic Russia Average of other countries
Proportion of exported goods 27.562 39.032 51.530
Energy structure 0.887 0.854 1.465
Energy intensity 15.857 14.659 4.873
Degree of industrial deepening 2.133 1.439 2.056
Skill improvement 1.089 0.379 0.896

Note: The other countries are the 25 countries selected above, including the Philippines, South Korea, Thailand, Malaysia, India, Vietnam, Indonesia, Saudi Arabia, Czech Republic, Macedonia, Estonia, Poland, Slovakia, Slovenia, Lithuania, Hungary, Croatia, Bulgaria, Romania, Latvia, Chile, Colombia, Mexico, Morocco, Peru.

3.2 Analyzing effects of carbon emission changes in Russia after exogenous shocks

The trajectories of changes in carbon emissions in “synthetic Russia” and Russia are drawn in Fig. 1 from 2009 to 2018, The abscissa represents carbon emissions, the horizontal axis is the year, the vertical dotted line in the figure shows the impact time of sanctions in 2014. The left side of the dotted line is the carbon emission curve of Russia and “synthetic Russia” and before sanctions, and the right side is the carbon emission curve of Russia and “synthetic Russia” after sanctions.and show that they were close from 2009 to 2013. In 2014, when Russia was under sanctions pressure, a significant difference was noted in the paths of change between them, indicating that the significant changes after the pressure of sanctions in Russia’s carbon emissions.
Fig. 1 Carbon emissions in “real Russia” and “synthetic Russia”
The vertical dashed line in Fig. 2 indicates the time point 2014, the time of the sanctions impact, the abscissa is the year, and the ordinate is the difference between the “real Russia” and the “synthetic Russia’s” carbon emissions, that is, the “real Russia” minus the “synthetic Russia’s” carbon emissions. To more intuitively the change of Russia’s carbon emissions after sanctions, we calculated the difference between the carbon emissions of “synthetic Russia” and Russia, as shown by the solid line in Fig. 2, the horizontal dashed line represents the situation when the difference in carbon emissions between “synthetic Russia” and “real Russia” is 0. Figure 2 shows that the difference fluctuated around zero during 2009-2013.After the pressure of sanctions, the carbon intensities of “synthetic Russia” and Russia began to diverge, and this effect is continued to increase. It is clear that Russia’s carbon emissions have increased significantly following sanctions, and this effect has become larger over time. During 2014-2018, except for a small decline in 2017, the intensity of carbon emissions increased significantly, rising by as much as 60%.
Fig. 2 The gap in carbon emissions between “real Russia” and “synthetic Russia”

3.3 Test of model robustness

3.3.1 SCM-DID inspection

We used the SCM-based DID method to test the robustness of the trend of Russia’s carbon emissions after the pressure of sanctions.,
C i t= β 0 + β 1 P o s t i t T r e a t i t+   β 2 C o u n t r y i t + f i + f t+   ε i t
where
C i t
is the measured value of intensity of carbon emissions.
P o s t i t T r e a t i t
represents the state of sanctions on the relevant countries or regions in 2014, that is, if country i is a sanctioned country, then
P o s t i t T r e a t i t
=1; if country i is not sanctioned, then
P o s t i t T r e a t i t
=0.
C o u n t r y i t
represents control factors at the national level, including the proportion of exported goods, energy structure, energy intensity, industrial deepening, and technological progress.
f i
is the fixed national effect that controls national characteristics that do not change with time,
  f t
is a fixed time effect to control macroeconomic shocks in time, and
  β 0
represents the constant term,
β 1
,
β 2  
represent the coefficients,
  ε i t
are error terms.
Table 3 shows that
P o s t i t T r e a t i t  
was the core explanatory variable, and was significantly positive at the 1% significance level. The results show that after the pressure of sanctions, Russia's carbon emissions have increased significantly. This is because Russia is under pressure from sanctions. To continue to develop its economy, Russia increase the exploitation of fossil energy to boost economic growth. The control groups in columns (1), (2), and (3) represent countries (regions) with positive, matching weight coefficients in the SCM method, and those in column (4) are the other 25 countries (areas) except Russia. Columns (1) and (2) do not contain control variables, and columns (3) and (4) do. The results of regression of the core explanatory variables did not change. For example, once the time and country had been fixed, the coefficient of
P o s t i t T r e a t i t
in column (3) was 4.014, and the coefficient of column (4) was 4.212; both were significant at the 1% level, indicating that the model setting was reasonable.
Table 3 Benchmark regression results for Russia’s carbon intensity after exogenous shocks (DID method)
Variable No control variable added Added control variable
(1) (2) (3) (4)
P o s t i t T r e a t i t 3.368**
(2.27)
4.341***
(5.33)
4.472***
(4.52)
4.461***
(5.13)
Proportion of exported goods - - -0.068***
(-3.01)
0.012
(1.18)
Energy structure - - 8.964*
(1.83)
-0.173**
(-2.08)
Energy intensity - - 0.106***
(3.84)
-0.030
(-1.57)
Degree of industrial deepening - - 0.847
(1.65)
0.343
(0.90)
Skill improvement - - -1.493**
(-2.67)
-0.220
(-1.24)
Fixed time effect No Yes Yes Yes
Fixed country effect No Yes Yes Yes
Control groups Colombia, Saudi Arabia, Thailand, Macedonia, India,Chile Colombia, Saudi Arabia,India,Chile Colombia, Saudi Arabia, Thailand, Macedonia, India, Chile Other 25 countries
(areas) except Russia
Observations 70 70 70 260
R2 0.070 0.945 0.960 0.961

Note: *, **, and *** are significant at levels of 0.1, 0.05, and 0.01, respectively.

3.3.2 Disposition group transformation

The basic idea of this placebo test is to assume that any control country other than Russia is the treatment group, and then use the same synthetic method to estimate the change in carbon emissions in that country. We compare the growth trajectories of these countries with their composite counterparts to assess whether the Russian carbon emissions gap is likely random. In other words, if the disparity in countries not subject to sanctions is found to be quantitatively similar to that in Russia, it cannot be explained as a result of West ern economic and financial sanctions. To this end, this paper will use the treatment group transformation to conduct a placebo test. The specific method is to select Thailand①(Among the 6 countries in the “synthetic Russia”, Colombia has the largest proportion, followed by Thailand and Saudi Arabia. Due to the randomness of the placebo test, we choose Thailand, which is the second largest, for the test.), which has a larger weight in the control group, to do a placebo test. Figure 3 shows the result of the test. It is clear that before 2014, the trends of carbon emissions of “real Thailand” and “synthetic Thailand” were significantly different, and after 2014, there was no significant difference in the carbon emission trends of the two.
Fig. 3 Trends of carbon emissions (Placebo test) in “real Thailand” and “synthetic Thailand”

3.3.3 Sorting test

In order to further test the robustness of the model, this paper uses the ranking test method to evaluate whether the sanction effect is robust. The specific approach is to make a placebo test for 25 countries except Russia. For the countries (regions) in the control group, the carbon emission trajectories of some countries (regions) cannot pass the weighted combination of other countries (regions). Good fit, may produce errors in the results. This paper refers to the method of Abadie et al. (2010), to calculate the root mean square prediction error (Root Mean Square Prediction Error, RMSPE) to measure the degree of difference in carbon emissions between Russia and its synthetic control group (25 countries (regions) except Russia). The formula is shown in formula (7):
RMSPE= 1 T t = 1 T C 1 t j = 2 J + 1 W j C j t 2
where RMSPE represents root mean square prediction error,
C 1 t
represents the carbon emissions of the first country (“real Russia”) in period t,
W j
is the weight vector of the j-th country or region,
C j t
is the carbon emission of the j-th country or region in the t period.
Taking 2014 as the dividing point, first calculate the RMSPE value before the implementation of sanctions, then compare the RMSPE values of the hypothetical sanctions countries (25 countries (regions) except Russia) and Russia, and finally remove the hypothetical sanctions whose RMSPE value is ten times greater than Russia Countries (regions), a total of 19 countries (excluding India, Bulgaria, Colombia, Estonia, Vietnam and Macedonia) were screened out for ranking test. If the carbon emissions of the selected 19 countries in the synthetic control group before the imposition of sanctions by Western countries (2014) can not fit well with the carbon emissions of real countries, it cannot be guaranteed that the difference in carbon emissions after the imposition of sanctions is due to sanctions. Figure 4 shows the difference in carbon emissions between real countries and synthetic countries, where the black solid line represents Russia, the black dashed line represents 19 other countries, and the left side of the vertical dashed line is the real countries (regions) and their composites in the years before 2014. The difference in carbon emissions of countries (regions), the right side of the vertical dotted line is the difference after 2014.
As can be seen from Fig. 4, before 2014, the carbon emission difference between real countries and synthetic countries fluctuated within a certain range, and there was no obvious difference, but after 2014, the carbon emission difference in Russia expanded significantly, which was significantly higher than that of other countries. This shows that the economic and financial sanctions of Western countries have a certain impact on Russia’s carbon emissions. There is about 5% (1/19) probability that there is a large gap between “real Russia” and “synthetic Russia” carbon emissions. Therefore, it can be considered that Russia’s emission- increasing effect is significant at the 10% level.
Fig. 4 Distribution of carbon emissions difference between Russia and 19 other countries

Note: RMSPE≤0.240994. Since the focus of this paper is on the impact of Western sanctions on Russia’s carbon emissions, due to space limitations, the 19 other countries represented by each dotted line in Fig. 4 will not be listed in the figure for lack of space. The 19 other countries are Philippines, South Korea, Thailand, Malaysia, Indonesia, Saudi Arabia, Czech Republic, Poland, Slovakia, Slovenia, Lithuania, Hungary, Croatia, Romania, Latvia, Chile, Mexico, Morocco, Peru, respectively.

The above robustness test confirm that beginning in 2014, the intensity of carbon emissions of Russia became increasingly different from that of “synthetic Russia”, indicating that the Russia’s carbon emissions has indeed increased after being pressured by sanctions. Although there was a short-term decline in carbon emissions after 2017, they quickly picked up again.

4 Discussion

4.1 Research on mechanism of influence

Mediation effect analysis, proposed by Baron and Kenny (1986), was used along with the double difference method to test the mechanism of the increase of Russia’s carbon emissions after the sanctions by western countries. The regression models shown in equations (8) and (9),
M i t= β 0 + β 1 P o s t i t T r e a t i t + β 2 C o u n t r y i t + f i + f t + ε i t
C i t = α 0 + α 1 P o s t i t T r e a t i t + α 2 M i t +   α 3 C o u n t r y i t + f i + f t + δ i t
where the mediation effect
M i t
included the proportion of output of the secondary industry, the total primary energy output, the proportion of investment in R&D, and the proportion of foreign direct investment.
M i t  
indicates the proportion of output of the secondary industry, the total primary energy output, the proportion of investment in R&D, and the proportion of foreign direct investment of the i-th country or region in the t-th period. where
C i t
is the measured value of intensity of carbon emissions.
P o s t i t T r e a t i t
represents the state of sanctions on the relevant countries or regions in 2014, that is, if country i is a sanctioned country, then
P o s t i t T r e a t i t
=1; if country i is not sanctioned, then
P o s t i t T r e a t i t
=0.
C o u n t r y i t
represents control factors at the national level, including the proportion of exported goods, energy structure, energy intensity, industrial deepening, and technological progress.
f i
is the fixed national effect that controls national characteristics that do not change with time,
  f t
is a fixed time effect to control macroeconomic shocks in time,
β 0
and
α 0
represents the constant term,
β 1
,
β 2
,
α 1  
,
α 2  and α 3
represent the coefficients,
  ε i t
and
δ i t  
are error terms.

4.1.1 Percentage of output of secondary industry and total primary energy output

The secondary industry includes mining, manufacturing, electricity, gas and water production and supply, and construction. Excessive dependence on industry, especially extractive industries, can lead to resource misallocation in a country, and is accompanied by such negative effects as over-exploitation and environmental damage. The results of columns (1) and (2) in Table 4 show that after being pressured by sanctions, Russia’s secondary industry output proportion and total primary energy output increased significantly, indicating that Russia’s industrial structure has changed, increasing its reliance on the resource industry. The estimated coefficients of carbon emissions in columns (1) and (2) in Table 5 are 4.236 and 3.629, respectively, and are significantly positive at the 1% significance level, indicating that the changes in Russia’s industrial structure and its dependence on resource industries have increased carbon emissions, so that Hypothesis 1 is verified and its conclusion is correct.
Table 4 Effect of sanctions pressure on mediating variables in Western countries
Variable Percentage of secondary
industry output (1)
Total primary energy production (2) Skill improvement
(3)
Proportion of foreign direct investment in GDP (4)
P o s t i t T r e a t i t 2.561***
(3.10)
4.564***
(3.38)
-0.150*
(-1.68)
-2.099**
(-2.13)
Proportion of exported goods 0.008
(0.41)
-0.042***
(-3.78)
0.001
(0.47)
0.028
(0.53)
Energy structure -0.007
(-0.03)
0.129
(1.25)
0.020
(0.91)
-0.127
(-0.19)
Energy intensity 0.595***
(6.12)
-0.021
(-0.87)
-0.001
(-0.31)
0.0410
(0.42)
Degree of industrial deepening -7.202***
(-5.27)
-0.329
(-0.82)
-0.128*
(-1.94)
-3.344
(-1.38)
Skill improvement 0.153
(0.46)
-0.130
(-0.32)
-2.974
(-1.43)
Fixed time effect Yes Yes Yes Yes
Fixed country effect Yes Yes Yes Yes
Observations 260 260 260 260
R2 0.985 0.997 0.966 0.149

Note: The numbers in parentheses are the t-values of robust clustering; *, **, and *** are significant at levels of 0.1, 0.05, and 0.01, respectively.

Table 5 Effect of mediating variables on carbon emissions in Russia
Variable Carbon emissions
(1) (2) (3) (4)
P o s t i t × T r e a t i t 4.236***
(4.69)
3.629***
(3.13)
4.461***
(5.13)
4.462***
(5.11)
Percentage of secondary industry output 0.088*
(1.85)
Total primary energy production 0.182*
(1.72)
Skill improvement -0.220
(-1.24)
Proportion of foreign direct investment in GDP 0.001
(0.13)
Proportion of exported goods 0.012
(1.11)
0.020*
(1.82)
0.012
(1.18)
0.012
(1.18)
Energy structure -0.172**
(-2.21)
-0.196**
(-2.19)
-0.173**
(-2.08)
-0.173**
(-2.07)
Energy intensity -0.082**
(-2.23)
-0.026
(-1.34)
-0.030
(-1.57)
-0.030
(-1.57)
Degree of industrial deepening 0.975**
(2.36)
0.403
(1.12)
0.343
(0.90)
0.345
(0.90)
Skill improvement -0.234
(-1.29)
-0.197
(-1.11)
-0.219
(-1.22)
Time fixed effect Yes Yes Yes Yes
Country fixed effect Yes Yes Yes Yes
Observations 260 260 260 260
R2 0.962 0.963 0.961 0.961

Note: The numbers in parentheses are the t-values of robust clustering; *, **, and *** are significant at levels of 0.1, 0.05, and 0.01, respectively.

4.1.2 Technological progress

With the increasing difficulty of development of the Russian energy industry, its singular industrial structure, and environmental pollution, there is an urgent need to cultivate and develop new industries, and guide Russia to rid itself of the status quo of over-reliance on resources. Since the sanctions, Russia has developed its emerging industries to achieve “import substitution”. The increase in R&D investment is conducive to promoting high-tech innovation, speeding-up the development of emerging industries, and reducing carbon emissions. Therefore, technological progress is used to reflect the impact of the development of resource substitution industries on carbon emissions. Emerging industries have the advantages of high technological content, high added value, low resource consumption, and low pollution. They are ideal alternatives to energy-based industries in these regions. However, from the estimated results in column (3) of Table 4, we can see that the regression coefficient of technological progress is negative 0.150, indicating that the sanctions of Western countries have led to a decrease in the proportion of Russia’s R&D investment. This may be because the oil and gas industry has crowded out emerging industries. In addition, as shown in column (3) of Table 5, the mediating effect of technological progress on carbon emissions was not significant, the regression coefficient is -0.220. This shows that Russia’s “import substitution” was not effective in emerging industries with a long production cycle and high technological content. That is, it was not clear that Russia to develop emerging industries to achieve “import substitution” after the pressure of sanctions. That is, Hypothesis 2 does not hold.

4.1.3 The proportion of foreign direct investment in GDP

Foreign investment flowed out of Russia in 2014 after pressure from sanctions. On the one hand, this makes it difficult to introduce advanced, foreign, and clean technologies; on the other hand, most foreign capital is invested in Russian resource-based industries. Under the influence of sanctions, domestic capital fills the gap created by the outflow of foreign capital, making it difficult for low-carbon industries to develop. These two aspects have contributed to the continual rise in Russia’s carbon emissions. The estimation results in column (4) of Table 4 show that the retrospective coefficient of the proportion of foreign direct investment in GDP is 2.099, and it is significantly negative at the 5% significant level, indicating that under the pressure of sanctions, the proportion of foreign direct investment decreased significantly. However, the estimated coefficient of carbon emissions in column (4) in Table 5 is 0.001, which is not significant, which means that the mediating effect of foreign direct investment on carbon emissions is not significant, which may be related to the increase in expenditure on environmental protection in Russia in recent years. This suggests the correctness of Hypothesis 3.That is, under the pressure of sanctions, The openness of Russia decreases and the outflow of foreign investment intensifies, but its impact on Russia’s carbon emissions is not significant.

4.2 Tail effect analysis

Since 2016, Russia’s anti-sanction measures have begun to show results, and the economic operation has developed on a normal track, rewriting the reality of the recession since the sanctioned economy in 2014. However, affected by the Russian-Ukrainian war, Russia’s economic outlook is not optimistic.

4.2.1 Impact on the economy

In terms of growth, according to data released by the Russian Statistical Yearbook and the International Monetary Fund(The data in this section come from the Russian Statistical Yearbook and the International Monetary Fund before 2022, and the 2022 and beyond data are from the International Monetary Fund.), Russia’s real GDP growth rate turned from negative to positive in 2016. In 2018, it reached a peak of 2.8%. In 2020, it turned from positive to negative and dropped to -2.7%. In the first and second quarters of 2022, Russia’s real GDP growth rate was about -8.0%. Russia is currently facing a huge economic crisis. In 2024 Russia may emerge from a recession marked by negative growth, but growth will be limited until 2027. The average annual inflation rate in Russia in 2016 was 7.04%, and it continued to decline in the following years. It dropped to 3.4% in 2020, but jumped to 21.3% in the first and second quarters of 2022. Russia is currently facing huge inflationary pressure. The unemployment rate, which was 5.2% in 2014, rose to 5.6% in 2015, but continued to decline in the following years, below 6.0%, and peaked at 9.3% in the first and second quarters of 2022. The current account balance has been on an upward trend since 2016, peaking at 115.68 billion USD in 2018, and will reach another higher peak of 227.466 billion USD in 2022. Government debt as a share of GDP was 15.2% in 2015, peaked at 19.2% in 2020, and will reach 16.8% in 2022. The proportion of income to GDP was 33.87% in 2014, dropped to 31.89% in 2015, and has been on the rise since then, until it dropped to 34.92% in 2022. From the above information, it can be seen that after being sanctioned by Western countries in 2014, after several years of development, Russia’s macro economy has recovered somewhat compared to before 2014. However, with the outbreak of the Russian-Ukrainian war in 2022, Russia has experienced a series of economic and social problems such as rapid currency depreciation, rising unemployment, increasing government debt and declining income, and the Russian economic outlook is not optimistic.

4.2.2 Impact on carbon dioxide emissions

The impact of Western sanctions has accelerated the emergence of the “reverse mechanism,” and Russia has changed its raw material-based model of economic development. However, because the raw material economy is closely related to economic growth (its contribution is usually more than a third of the total), it is also closely related to the national fiscal revenue (contributes about half) (Cheng, 2017) and to export trade (contributes two-thirds). Moreover, it influences the development of social undertakings, such as pensions, support for mothers, and unemployment benefits. In summary, carbon dioxide emissions brought about by Russia’s raw material economy are inevitable, and it is almost impossible for Russia to return to its pre-crisis state in the short term.

5 Conclusions

In the context of global efforts to protect the environment and reduce carbon emissions, this article used panel data from 26 countries (regions) from 2009 to 2018 to discuss from the perspective of empirical analysis that Russia was under the pressure of economic and financial sanctions from Western countries in the 2014 Changes in post-carbon emissions. The main conclusions are as follows.
Changes in Russia’s industrial structure and its reliance on resource industries have increased its carbon emissions after sanctions pressure.
The results of mechanism testing showed that since the exogenous shock of sanctions in 2014, Russia has not optimized its industrial structure in a timely manner, but has increased its dependence on the resource industry, and its openness has also decreased. The analysis of intermediary effects showed that changes in Russia’s industrial structure and its dependence on the resource industry have increased its carbon emissions. The tail effect analysis showed that since the pressure of sanctions in 2014, the Russian economy has been in a state of negative growth. Until 2016, Russia’s anti-sanction measures showed initial results, and the economic operation was developing on a normal track. However, affected by the Russian-Ukrainian war, Russia’s economic development may stagnate in 2022. In addition, the carbon dioxide emissions from Russia’s raw material economy are inevitable, and a return to the level before sanctions were imposed in 2014 is almost impossible in the shortterm.
Since Russia was under the pressure of sanctions in 2014, its industrial structure has not been optimized in time, and Russia has long been committed to vigorously developing the resource-based economy, Russia’s economic structure has gradually become fragile and simplistic; The “crowding out effect” caused by over-reliance on the oil and gas industry makes it difficult for Russia to develop alternative industries; the technology blockade of the United States and Europe against Russia has reduced the scale of Russia’s application of foreign capital and increased the difficulty of Russia’s introduction of foreign advanced clean production technology. Therefore, if Russia wants to get out of the economic cycle and reduce environmental pollution, first of all, it should speed up the upgrading of its industrial structure, get rid of the “curse of resources”, especially the “curse of oil and gas”, and accelerate the transformation of its economic growth mode from “oil and gas dependence” to “innovative” change; secondly, Russia should increase investment in innovation and encourage independent innovation. If Russia does not strengthen the motivation of independent innovation, it will be more difficult to achieve “import substitution” and break through the technological blockade; Thirdly, investment should be opened up and free trade should be implemented. If Russia can open up the investment market more liberally, create a better investment environment and maintain a stable foreign investment policy, then a large number of foreign-funded enterprises will enter Russia, so that foreign capital, technical equipment, human capital and modern management methods can be transferred. The advantages are truly combined with Russia’s resource advantages to improve Russia’s highly resource-dependent industrial structure; finally, Russia should implement an independent, pragmatic and responsible foreign policy and create a favorable external environment. Continue to strengthen comprehensive cooperation with neighboring friendly countries such as China and Mongolia, learn from each other’s strengths and complement each other’s weaknesses, and achieve a win-win situation.

We thank the reviewers for their valuable comments on earlier drafts of this manuscript that helped us improve its quality.

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Outlines

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