Measurement and factor analysis of carbon neutrality development level in eastern China.

The Chinese government has officially announced that China's carbon dioxide emissions will reach to peak before 2030 and achieve carbon neutrality before 2060. Based on the carbon neutrality development of 12 provinces and cities in eastern China from 2010 to 2019, this paper constructs an evaluation index system, and it uses the entropy weight method and coefficient of variation method to measure the carbon neutrality development level in the eastern China. The results show that from 2010 to 2019, the changes of carbon source level in 12 provinces and cities in the eastern China are lower than the changes in carbon sink level, and the changes of carbon source and sink level in most provinces and cities show the increasing trend. Spatially, the carbon neutral development level shows the differentiation characteristics of "low in the middle, high in the north and south." The main factors affecting the carbon neutrality level of eastern provinces and cities include policy, economic development and industrial structure, energy intensity and structure, urban development, and population size ecological environment. High-value areas are mainly distributed in Heilongjiang Province, Jilin Province and Fujian Province. Low-value areas are mainly distributed in Jiangsu Province and Shandong Province. Eastern China still needs to strengthen its emphasis on low-carbon policies. For Shandong Province, Jiangsu Province, and Hebei Province, policies should be introduced to reduce carbon sources, accelerate their industrial upgrading, and optimize their energy use structure. For Beijing City, Shanghai City, Heilongjiang Province, and Jilin Province, policies should be introduced to develop carbon sinks while maintaining their low carbon source levels. For Beijing City and Shanghai City, policies related to green and low-carbon technologies should be introduced to promote the development of carbon sink capacity through low-carbon technologies in limited urban areas.

The characteristics and influencing factors of spatial network of city-based innovation correlation in China: from the perspective of high tech zones.

In the context of "space of flows", city-based innovation correlation in driving economic growth is no longer limited to the traditional hierarchical structure. It is of great significance to explore Chinese cities innovation association network from the perspective of high-tech zones which gather a large number of innovation resources. Here our report is to provide new ideas for improving the innovation capability of high-tech zones and accelerating the construction of Chinese high-quality innovation system. Here we take 142 cities with high-tech zones as research samples, and explore the characteristics and influencing factors of spatial network of city-based innovation correlation in China, through modified gravity modelsocial, network analysis and QAP analysis. The results show that city-based innovation network is not closely connected, the number of redundant connection channels is low efficiency, showing a four-level spatial pattern of "Z" shaped spindle. Among them, degree centrality of cities in eastern China is higher than that in the western region, the core cities in central China play a bridging role, and western remote cities are easily affected by related cities. Moreover, there are four innovation cohesion subgroups, including the northern hinterland subgroup, the eastern coastal subgroup, the southern subgroup and the western cooperation subgroup. Furthermore, the results of the influencing factors analysis show the differences in administrative level, economic development level, openness to the outside world, and investment in technology are conducive to the innovation association between cities, while the similarities in spatial adjacency and industrial structure will promote the strong innovation association between cities.

Financial development, technological innovation and urban-rural income gap: Time series evidence from China.

The main purpose of the paper is to investigate the relationship between technological innovation and income inequality for China based on the financial Kuznets curve (FKC) hypothesis. The study uses time-series data from 1985 to 2019. We employ the Johansen cointegration, ARDL model and VECM Granger causality techniques to analyze the links between the variables. We also use the DOLS, FMOLS and CCR mechanisms to estimate the long-run parameters. The paper finds that the FKC is valid for China's economy in the long run. Technological innovation positively affects the urban-rural income gap, while there is an inverted-U shaped between financial development and the urban-rural income gap. The relationship between financial development and the urban-rural income gap is bi-directional causality. Technological innovation and the urban-rural income gap cause each other. Empirical results suggest a twofold policy meaning: i) to further the financial system and ii) to eliminate the adverse impacts of technological innovations on income distribution.

Cross-Border Accessibility and Spatial Effects of China-Mongolia-Russia Economic Corridor under the Background of High-Speed Rail Environment.

Under the background of "the Belt and Road" and "China-Mongolia-Russia economic corridor" initiatives, we studied the urban accessibility level and regional spatial effect of the west line and east line of China-Mongolia-Russia economic corridor in the high-speed rail (HSR) environment. The results are as following. (1) The operation of China-Mongolia-Russia HSR will greatly improve the urban accessibility level, which will shorten the whole journey time to two days along China-Mongolia-Russia economic corridor. The regional space-time convergence effect will be very strong in the China-Mongolia-Russia HSR environment. (2) The accessibility level and its improvement degree of the China-Mongolia-Russia east line are stronger than those of the west line. The accessibility level of different countries differs: China > Russia > Mongolia. The accessibility improvement degree of different countries also differs: Mongolia > Russia > China. Spatially, the accessibility improvement degree of the cities, which are located in the middle of the line is stronger than those cities at the beginning and end of the line. (3) Affected by the China-Mongolia-Russia HSR environment, the spatial polarization effect of China-Mongolia-Russia HSR axial belt will be further enhanced. The internal boundary effect of the China-Mongolia-Russia HSR axial belt will disappear. New HSR economic growth poles will occur, promoting the formation of point-axis system. China-Mongolia-Russia cross-border trade creation and transfer effects will be deepened.

Spatiotemporal evolution characteristics of coordinated development of urbanization and ecological environment in eastern Russia-Perspectives from the 3D global trend and 2D plane analysis.

Under the background of "the Belt and Road" and "the economic corridor of China, Mongolia and Russia" initiatives, it has great value to study the temporal and spatial evolution characteristics of the coordinated development between the urbanization and ecological environment in eastern Russia (the Siberian Federal District and the Far East Federal District). In this paper, we studied the urbanization development level, eco-environment development level, and their coupling coordinated development degree during 2005-2018 in the eastern Russia from the perspectives of the 3D global trend and 2D plane analysis. First, combining with the Population-Economic-Sociology and Pressure-State-Response models, the urbanization development level and eco-environment development level were calculated by the comprehensive weighting method of entropy weight and variation coefficient for eastern Russia. Second, the coupling coordinated development degree of the urbanization development level and eco-environment development level was measured by the coupling coordination model for eastern Russia. Finally, the spatial differentiation of the urbanization development level, the eco-environment development level and their coupling coordinated development degree was performed respectively by the 3D global trend and 2D plane analysis using ArcGIS. The results are as following. First, the comprehensive urbanization development level of eastern Russia has increased from 2005 to 2018, and the economic urbanization is the main factor that affects the urbanization development in eastern Russia. The comprehensive eco-environment development level of eastern Russia has decreased from 2005 to 2018, and the eco-environment pressure is the main factor that affects the eco-environment development in eastern Russia. The coupling coordination degree of the urbanization development and eco-environment development has increased from 2005 to 2018. However, it is still in the uncoordinated stage. Second, from 2005 to 2018, the urbanization development level of the Siberian Federal District is higher than that of the Far East Federal District. The eco-environment development level of the Siberian Federal District is balanced to that of the Far East Federal District. The coupling coordination degree of the Siberian Federal District is higher than that of the Far East Federal District. Among the Siberian and Far East Federal Districts, most of the federal subjects belong to the uncoordinated stage of the urbanization development and the eco-environment development. Third, the urbanization development level, the eco-environment development level, and their coupling coordinated development level are all spatially imbalanced in the eastern Russia, which show the "High West, Low East" and "High Center, Low North and Low South" spatial pattern from the perspectives of the 3D global trend and 2D plane analysis. The areas with high levels are concentrated in the Novosibirsk Region, Altay Territory, Kemerovo Region, Krasnoyarsk Territory, and Irkutsk Region. The areas with low ones are mostly in the Republic of Altay and Chukotka Autonomous Area. Finally, we suggest policies and strategies that can boost the growth and development of the urbanization and the eco-environment in the Sino-Russian border areas.

Spatiotemporal evolution characteristics of urbanization and its coupling coordination degree in Russia - perspectives from the population, economy, society, and eco-environment.

Under the background of "the Belt and Road" and "the economic corridor of China, Mongolia and Russia" initiatives, it is of great significance to study the temporal and spatial evolution characteristics of urbanization in Russia. This paper studied the population urbanization level, economic urbanization level, social urbanization level, eco-environment urbanization level, and their coupling coordination development degree during 2005-2020 in Russia. First, combining with the Population-Economic-Sociology-Eco-environment model, the paper constructed the index systems to evaluate the urbanization development levels in Russia. Second, based on the comprehensive weighting method of entropy weight and variation coefficient, this paper calculated the population urbanization level, economic urbanization level, social urbanization level, and eco-environment urbanization level in Russia. Third, this paper used the coupling coordination model to measure the coupling coordination degree of the urbanization development levels in Russia. Finally, the spatial differentiation of the population urbanization level, economic urbanization level, social urbanization level, eco-environment urbanization level, and their coupling coupling-coordination degree was performed, respectively, by using ArcGIS. The results are as the following. First, from 2005 to 2020, the economic urbanization level and eco-urbanization level have shown the increasing trend in Russia. The population urbanization level and social urbanization level have shown the stable changing trend in Russia. The eco-environment urbanization and economic urbanization contribute larger share to the urbanization system compared with the population urbanization and social urbanization. The coupling coordination development degree of population urbanization level, economic urbanization level, social urbanization level, and eco-environment urbanization level has showed a slight increasing trend. However, the overall situation of the urbanization in Russia is still in the moderate uncoordinated recession stage. Second, the federal subjects with high urbanization development levels are mainly distributed in Moscow city, Moscow Region, Sverdlovsk Region, Tumen Region, Saint-Petersburg city, Republic of Tatarstan, Krasnoyarsk Territory, Republic of Bashkortostan, Chelyabinsk Region, Nizhny Novgorod Region, Krasnodar Territory, Rostov Region, and Khanty-Mansiysky Autonomous Area. The federal subjects with low ones are mainly located in Republic of Khakasia, Republic of Marii El, Republic of Kabardino-Balkaria, Republic of Tyva, Republic of Karachaevo-Cherkessia, Republic of Kalmykia, Republic of Altay, Jewish Autonomous Area, and Republic of Ingushetia. Third, spatially, from 2005 to 2020, the urbanization pattern of population, economy, society, eco-environment, and their coupling coordination degree in Russia all show unbalanced development characteristics. The population urbanization pattern and the social urbanization pattern have not changed significantly, showing the spatial characteristics of "high west, low east," and "high middle, low north, low south." The economic urbanization pattern has been increasing significantly, showing the spatial characteristics of "high core, low edge." The eco-environment urbanization pattern has not changed significantly, showing the spatial characteristics of "high north, low south." The coupling coordinated development degree of urbanization pattern has showed a slight increasing trend, showing the spatial characteristics of "high middle, low north, low south," "high west, low east". Finally, we suggest policies and strategies that can boost the growth and development of the urbanization in Russia.

Evolution characteristics of temporal and spatial pattern of Russian economic differences since the 21st century.

Under the background of "the Belt and Road" and "the economic corridor of China, Mongolia and Russia" initiatives, it is of great significance to study the temporal and spatial economic pattern in the Russian Federation. Based on the economic development difference index, regional economic grade index, global trend analysis tool and spatial autocorrelation model, this paper analyzes the temporal and spatial pattern evolution characteristics of Russian economic differences from 2002 to 2020. The results are as following. First, although the economic imbalance among various federal subjects has been decreasing, the economic polarization has been still severe between the prosperous developed regions and the stagnant backward regions during 2002-2020. Russia's economy shows a trend of changing from significant positive correlation in strong agglomeration space to positive correlation in weak agglomeration space, and then to random distribution. Second, there has been great differences of the economic development among various federal subjects. The economic grade of the Russian federal subjects presents a significant spatial differentiation pattern. The Russian Federation's economic resources are concentrated in the first-class federal subject (Moscow City), second-class federal subjects (Tumen Region, Moscow Region and Saint-Petersburg city) and a few third-class federal subjects (Yamalo-Nenetsky Autonomous Area, Khanty-Mansiysky Autonomous Area, Republic of Tatarstan, Krasnodar Territory, Sverdlovsk Region, etc). Third, the Russian Federation's economy presents "High Core, Low Periphery", "High West, Low East" and "High south, Low north" spatial differentiation pattern. The economic hot regions coincide with the high-class economic regions, which are mainly distributed in the contiguous areas of Ural Federal District and Volga Federal District, as well as the Moscow City, Moscow Region, Saint-Petersburg city, Krasnodar Territory and Rostov Region. The economic cold regions coincide with the low-class economic regions, which are mainly located in the Far East Federal District, the east of Siberian Federal District, the north of North West Federal District and the south of North-Caucasian Federal District. Finally, we suggest the recommendation for policy makers in Russia. And we propose the future research ideas.

Spatial Distribution Pattern Evolution of the Population and Economy in Russia since the 21st Century.

Under the background of "the Belt and Road" and "China, Mongolia and Russia economic corridor" initiatives, this paper studied the spatial distribution pattern evolution of population and economy in Russia since the 21st century, which could provide implications for the regional development planning, economic optimization layout, energy resource development, transportation infrastructure construction between China and Russia. Combined with the panel data of population, GDP, land area, etc., we used the gravity center analysis, geographic concentration degree, and inconsistency index to study Russia's population pattern evolution trend, economic pattern evolution trend, spatial inconsistency types of population distribution and economic development. The results and conclusions are as follows. Russia's population and economic gravity centers have migrated towards the northwest direction. Russia's population and economic distribution pattern presents the unbalanced development trend, which could be characterized by the differentiation pattern of "High West, Low East" and "High South, Low North" divided by the Ural Federal District. In the southwest areas of Russia, the population concentration degree is higher than the economic concentration degree in most federal subjects. In the northeast areas of Russia, the economic concentration degree is higher than the population concentration degree in most federal subjects.