Comprehensive assessment of the coupling coordination degree between urbanization and ecological environment in the Siberian and Far East Federal Districts, Russia from 2005 to 2017

The urbanization growth in the 20th and 21st centuries has led to a series of unprecedented problems in the ecological environment. Based on constructing an integrated urbanization-ecological environment index system, this article conducts a comprehensive evaluation of the coupling coordination degree between urbanization and the ecological environment and uncovers its spatiotemporal variation characteristics in the Siberian and Far East Federal Districts, Russia from 2005 to 2017. The coupling coordination of urbanization and the ecological environment in the Siberian and Far East Federal Districts improve from slightly unbalanced development stage to barely balanced development stage from 2005 to 2017. In 2017, more than half regions achieved the barely balanced development of urbanization and the ecological environment. However, the most desirable development stage, the superior balanced development stage, is never achieved in the Siberian and Far East Federal Districts during the study period. The spatial pattern of the coupling coordination degree of urbanization and the ecological environment in the Siberian and Far East Federal District gradually changes from “dumbbell” to “high-north low-south”. The south part of the Siberian and Far East Federal Districts should be paid more attention in the future urban development process. This research will provide support in the future coordination of urban development in the Siberian and Far East Federal Districts.


INTRODUCTION
Urbanization is a process that comprehensively shifts from rural to urban, transforming their population, land, economies, and social properties (Bekhet & Othman, 2017). Since 2008, the proportion of the global population living in urban areas is over 50% (United Nations, 2019). During the 20th and 21st centuries, urbanization in developing countries has occurred at unprecedented rates. While producing enormous benefits in employment, infrastructure, service, welfare and innovation (Ochoa et al., 2018), urbanization has also caused a series of ecological and environmental problems (Zhang, 2016), including PM2.5-dominated air pollution (Fang et al., 2015;Du et al., 2019), water pollution (Ma, Chou & Wang, 2016), climate change (Gurney et al., 2015) and health challenges (Gong et al., 2012). Therefore, to achieve sustainable urban development, it is necessary to explore the relationship between urbanization and the ecological environment.
Many studies have provided important insights on the relationship between urbanization and the ecological environment. There are various methods being applied to investigate the relationship between urbanization and the ecological environment: Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model (Lin et al., 2017), Kaya Identity (Zhang et al., 2017b), Logarithmic Mean Divisia Index (LMDI) method (Ding & Li, 2017), Ordinary Least Square (OLS) method (Yu et al., 2018), Geographically Weighted Regression (GWR) model (Liang, Wang & Li, 2019), panel data regression (Yao et al., 2018;Du & Xia, 2018), Grey Correlation Analysis (Zhang et al., 2017a), Environment Kuznets Curve (EKC) approach (Xu, Dong & Yang, 2018), Granger Causality Test (Bekhet & Othman, 2017;Ahmed, Wang & Ali, 2019) and Coupling Coordination Degree Method (CCDM) (Song et al., 2018;Geng et al., 2020). However, the STIRPAT model, Kaya Identity, LMDI method, OLS method, GWR model, and panel data regression can only reveal the unidirectional influences of urbanization on the ecological environment. Grey Correlation Analysis, EKC approach, Granger Causality Test and CCDM can enrich the understanding of the bidirectional relationship between urbanization and the ecological environment. The Grey Correlation Analysis can reveal the synchronization degree of the variation of urbanization and the ecological environment and does not require a large-size sample. The EKC approach can express the relationship between urbanization and the ecological environment clearly and it is easy to use. The Granger Causality Test can reveal the influence of urbanization on the ecological environment, and it can also unravel the response of the ecological environment to urbanization. However, the Grey correlation analysis, EKC approach and Granger Causality Test cannot reveal the spatiotemporal variation characteristics of the relationship between urbanization and the ecological environment. From the perspective of coordinated development, the CCDM can assess the coordination level between urbanization and the ecological environment and reveal the spatiotemporal variation characteristics of the coordination development of urbanization and the ecological environment. Fang, Liu & Li (2016) proposed that the coupling relationship between urbanization and the eco-environment will become an important international research frontier over the next 10 years. Recently, the CCDM method was widely applied to investigate the relationship between urbanization and the ecological environment (Guo et al., 2015;Liu et al., 2018;Xu & Hou, 2019). China and observed obvious regional disparity. However, most existing research was conducted in China. Only Dong et al. (2019) conducted a quantitative assessment of the coupling coordination degree of urbanization and the eco-environment in Mongolia at the provincial level, and Zhao et al. (2017) investigated the relation of 2019 countries and regions all over the world in 2014.
Russia is an important country in the China-Mongolia-Russia Economic Corridor. To promote the national and international influences of the Siberian and Far East region, the Russian federal government proposed the Strategy of Socioeconomic Development of the Far East and the Baikal Region until 2025. In 2018, Russia and China jointly signed the China-Russia Cooperation and Development Plan in the Far East (2018)(2019)(2020)(2021)(2022)(2023)(2024) to prompt the development of the Far East. The Siberian and Far East Federal Districts are expected to develop rapidly and face more challenges in their ecological environment caused by more severe anthropogenic activities over the next decade. An assessment of the coupling coordination degree of urbanization and the ecological environment in the Siberian Federal District and Far East Federal District is urgently needed. However, due to data limitations, the related research is still somewhat insufficient in this region. Chu et al. (2018) measured urbanization in the Siberian and Far East Federal Districts in Russia from the perspectives of population, economy and society, and revealed the spatial heterogeneity of urbanization development. Zhu et al. (2018) investigated the influences of urbanization and income on CO 2 emissions in the BRICS (Brazil, Russia, India, China and South Africa) and observed a significantly negative influence of urbanization on CO 2 emissions. Fan et al. (2018) explored the relationship between urbanization and sustainability in Asian Russia from 1990 to 2014 and found a generally positive trend of urbanization during the study period.
To rectify the above shortcomings, this study establishes a comprehensive index system for the urbanization-ecological environment system, quantitatively evaluates the coupling coordination degree of urbanization and the ecological environment, and reveals the spatiotemporal variations of the coupling coordination degree in the Siberian and Far East Federal Districts in Russia during 2005-2017. It will provide scientific support and insights into coordinating urbanization development and ecological environment protection in the Siberian and Far East Federal Districts in Russia.

Study area and data collection
This study conducts a comprehensive evaluation of the coupling coordination degree of urbanization and ecological environment in the Siberian Federal District and the Far East Federal District (Fig. 1)

Comprehensive urbanization-ecological environment index system
Urbanization is an integrated development that not only reflects an increase in urban population but also an expansion of urban land, economy and lifestyle to a rural area (Weng, 2007;Bekhet & Othman, 2017). Considering the comprehensiveness of the process of urbanization and the data limitation of spatial urbanization in the Siberian and Far East Federal Districts, three first grade indicators, including demographic urbanization, economic urbanization and social urbanization, are selected into the urbanization subsystem. Referred to the previous research, which build the urbanization index system and investigate the relation between urbanization and ecological environment (Zhao, Wang & Zhou, 2016;He et al., 2017;Wang, Gao & Li, 2020), the most cited and as comprehensive as possible basic indicators are collected. Therefore, the urbanization index subsystem consists of three primary indicators and 12 basic indicators (Table 1). According to the environmental pressure-state-response framework, widely used in the ecological environment (Hughey et al., 2004;Liu et al., 2018), we establish an ecological environment index subsystem containing three first grade indicators (ecological environment pressure, ecological environment state, and ecological environment response). Regarding the basic indicators of the ecological environment pressure and response, the solid, liquid and air pollutants are covered as much as possible. Since the crops and forests are both essential ecological and resource elements, forest area per capita and sown area of all crops per capita are selected as the basic indicators of the ecological environment state. We employed the modified entropy method to determine the weight of each indicator in the comprehensive urbanization-ecological environment index system. The detailed steps of the entropy model are as follows: Normalization pre-processing. All indicators were normalized by Eqs.
(1) and (2) to remove the effects of dimension, magnitude and orientation: Positive indicator (favorable condition): Negative indicator (unfavorable condition): where i denotes the year, j denotes the indicator, r ij denotes the normalized value, X ij denotes the original value, max(X j ) denotes the maximum value of the indicator j during the study period, and min(X ij ) denotes the minimum value of the indicator j during the study period. Information entropy of the indicator j (e j ): (3) where X ij denotes the proportion of the indicator j in year i, n is the number of years (2005-2017, n = 13). Entropy redundancy (g j ): Weight of the indicator j (w j ): where m denotes the number of indicators (m = 11 for the urbanization index subsystem, m = 6 for the ecological environment subsystem). Evaluation of the indicator (Y ij ): Comprehensive level in year i (Y i ): The weight of each indicator in the urbanization-ecological environment index system is shown in Table 1.

Coupling coordination degree model
Coupling is the interactive influence between two or more systems. Urbanization may cause a series of ecological and environmental problems (Chen et al., 2006), and conversely, a deteriorated and fragile ecological environment will limit urbanization development (Li et al., 2012). The modified coupling coordination degree model is as follows (He et al., 2017): where C denotes the coupling degree of urbanization and ecological environment, f (U) denotes the urbanization subsystem, and g(E) denotes the ecological environment subsystem.
The level of influence of urbanization and ecological environment: where T denotes the level of influence of urbanization and ecological environment, a denotes the contribution of urbanization to the comprehensive system, and β denotes the ecological environment's contribution to the comprehensive system. The value of a and β are also determined by entropy method (a = 0.467, β = 0.533).
The coupling coordination degree of urbanization and ecological environment: Table 2 shows the typology of the developmental stages of the coupling coordination degree of urbanization and the ecological environment. It can be divided into four primary stages (balanced development, transitional development, slightly unbalanced development, and seriously unbalanced development) and 12 basic stages.

RESULTS
Each indicator's weight in the urbanization index subsystem and the ecological environment index subsystem is assessed by the entropy method separately (Table 1). The weights of the basic indicators of the urbanization index subsystem are: population density (22.19%) > gross regional product per capita (14.54%) > number of sports facilities per 10,000 population (14.53%) > average income per capita (8.88%) > per capita monetary expenses and savings (7.15%) > the volume of communication services per capita (6.09%) > number of high education institutions per 10,000 population (5.99%) > number of doctors per 10,000 population (5.62%) > percentage of economic activity population (3.62%) > unemployment rate (1.50%). Three basic indicators, population density, gross regional product per capita and number of sports facilities per 10,000 population, account for more than half of the total impact in relation to the    The comprehensive ecological environment of the Siberian and Far East Federal Districts shows a "high-north low-south" spatial pattern (Fig. 5). The comprehensive ecological environment scores are relatively higher in the north region, where are high latitude and cold. The population of the high latitude and cold region in the north part of the Siberian and Far East Federal Districts are scarce. Richer per capita natural resources and weaker destruction from anthropogenic activities on the ecological environment cause the comprehensive ecological environment scores higher in the north region of the Siberian and Far East Federal Districts. However, the ecological environment of the high latitude and cold region is fragile. It's difficult to recover since the fragile ecological environment is destroyed. Therefore, the north region of the Siberian and Far East Federal Districts should be paid attention during the urbanization process though the north region have relatively higher comprehensive ecological environment scores.

Development stages and spatiotemporal patterns of the coupling coordination degree between urbanization and the ecological environment
We calculated the coupling coordination degree between urbanization and the ecological environment in the Siberian Federal District and the Far East Federal District using the CCDM model on the basis of the comprehensive urbanization-ecological environment   The spatial pattern of the coupling coordination degree of urbanization and the ecological environment in the Siberian and Far East Federal Districts gradually changes from "dumbbell" pattern to "high-north low-south" pattern. At the early stage of the period 2005-2017, the coupling coordination of urbanization and the ecological environment development stages showed a "dumbbell" spatial pattern. The coupling coordination degree is higher in the east and west regions of the Siberian and Far East Federal Districts, and lower in the central region. At the late stage of the study period, the coupling coordination of urbanization and the ecological environment development stages showed a "high-north low-south" spatial pattern. Only some regions in the south part of the Siberian and Far East Federal Districts have not achieved the balanced development stage. The

CONCLUSIONS AND DISCUSSION
Based on the comprehensive assessment of the urbanization and the ecological environment, this paper conducts an evaluation of the coupling coordination degree of urbanization and the ecological environment in the Siberian and Far East Federal Districts at regional scale from 2005 to 2017. The temporal variation trend and the spatial pattern of the comprehensive urbanization level, the ecological environment status and the coupling coordination degree are revealed to provide scientific support to achieve the coordinating development of urbanization and the ecological environment in the Siberian and Far East Federal Districts. Under the background of the Belt and Road Initiative, the results are also meaningful for the green construction of the China-Mongolia-Russia Economic Corridor.
From 2005 to 2017, the comprehensive urbanization level of the Siberian Federal District and the Far East Federal District both keep stable and continuous improvement. The comprehensive urbanization growing rate is higher in the Far East Federal District (6.57%) than the Siberian Federal District (4.12%). Since 2009, the comprehensive urbanization level of the Far East Federal District has surpassed the Siberian Federal District and the gap of the comprehensive urbanization level between the two federal districts become larger. It indicates that the policies of advanced development in the Far East Federal District have important impacts on the socio-economic development of this region. The comprehensive ecological environment level of the Siberian and Far East Federal Districts are similar and keep a relative stable level of 0.20-0.25 during the period 2005-2017.
The coupling coordination of urbanization and the ecological environment in the Siberian and Far East Federal Districts improve from slightly unbalanced development stage to barely balanced development stage from 2005 to 2017. In 2017, more than half regions, including Republic of Khakassia, Omsk Oblast, Novosibirsk Oblast and Krasnoyarsk Kray in the Siberian Federal District and Republic of Sakha, Amursk Oblast, Magadansk Oblast, Chukotska Autonomous Oblast, Khabarovskiy Kray, Primorskiy Kray and Kamchatskiy Kray in the Far East Federal District, achieve the barely balanced development of urbanization and the ecological environment. However, the most desirable development stage, the superior balanced development stage, is never achieved in the Siberian and Far East Federal Districts during the study period. More efforts should be made to achieve the superior balanced development of urbanization and the ecological environment in the study area.
The spatial pattern of the coupling coordination degree of urbanization and the ecological environment in the Siberian and Far East Federal District gradually changes from "dumbbell" to "high-north low-south". At the early stage of the period 2005-2017, the coupling coordination degree of urbanization and the ecological environment is higher in the east and west regions and lower in the central region of the Siberian and Far East Federal Districts. At the late stage, the coupling coordination degree is higher in the north regions and lower in the south regions. In 2017, only some regions in the south part of the Siberian and Far East Federal Districts have not achieved the balanced stage. The south part of the Siberian and Far East Federal Districts should be paid more attention in the future urban development process.
Since the composition differences of the integrated urbanization-ecological environment index system due to the inconsistence of the indicators related to urbanization and the ecological environment in different countries and regions, the comparability of the coupling coordination degree evaluating results among the existing research is limited. Under the background of the Belt and Road Initiative and the construction of the China-Mongolia-Russia Economic Corridor, an integrated evaluation of the coupling coordination degree of urbanization and the ecological environment along the corridor at the regional scale will be important research topics in the future. Due to the lack of spatial urbanization data, the spatial urbanization indicators are not introduced into the urbanization index subsystem in this paper. Since the ecological environmental data are not available before 2005, this study only evaluates the coupling coordination degree of urbanization and the ecological environment from 2005 to 2017.