The impact of regional policy implementation on the decoupling of carbon emissions and economic development.

The contradiction between economic growth demands and the achievement of the "dual-carbon" goals at the regional level is a pressing issue in China. As a significant economic and cultural center in the western region of China, the Guanzhong Plain urban agglomeration has experienced rapid development and urbanization, making it one of the key areas for national development. Therefore, greater attention should be given to carbon emission reduction in this region. This study focuses on the dataset from 2010 to 2019 in the Guanzhong Plain urban agglomeration, utilizing an input-output table to construct a carbon dioxide emission inventory. The research investigates the impact of regional classification on carbon emission levels within the Guanzhong Plain urban agglomeration. Furthermore, the Tapio decoupling analysis method is employed to assess the decoupling coefficient between regional economic development and carbon emissions. Additionally, the Theil index inequality analysis method is utilized to measure the disparities in per capita carbon emissions among cities within the region. Research findings indicate the following: 1) The regional classification of the Guanzhong Plain urban agglomeration is an effective policy for reducing regional carbon emissions and promoting carbon emissions reduction. 2) There exist variations in energy and industrial structures among cities within the urban agglomeration, necessitating tailored measures for low-carbon transition based on the specific circumstances of each city. 3) The regional classification of the urban agglomeration significantly influences the degree of decoupling between economic development and carbon emissions, with a trend towards stronger decoupling. The study suggests that cities within the Guanzhong Plain urban agglomeration should adopt measures aligned with their natural conditions and economic characteristics to achieve a low-carbon transition. Leveraging the regional cooperation capacity of the urban agglomeration is crucial to decouple economic development from carbon emissions, thereby promoting sustainable economic growth and environmental protection in a mutually beneficial manner.

Decoupling economic growth from construction waste generation: Comparative analysis between the EU and China.

The reduction and management of construction waste is crucial for the sustainable development of the construction industry. This research aims to explore a comparative analysis on decoupling relationship between economic growth and construction waste generation on European Union (EU) and Chi et al., 2020 to 2020 in the construction industry, through an integrated method framework of "Tapio + Kaya + LMDI". The research results indicate that there are significant differences in construction waste generation among different countries. The growth rates of construction waste in the EU and China from 2004 to 2020 were 2.47 % and 10.5 %, respectively, showing an upward trend. The economic growth of the construction industry in most EU countries is in a decoupling and negative decoupling state with significant regional differences in decoupling status. The construction waste generation in China is mainly in a weak decoupling state. Economic and demographic factors are the main factors promoting the increase in construction waste generation, while technological factors are the main factors inhibiting construction waste generation in EU and China. However, the impact of each factor on construction was generation varies from EU countries. The research reveals the decoupling effect mechanism between construction waste generation and economic growth, and improves the theory of construction waste management, promotes sustainable development. These findings have feasible inspiration for construction waste management in developing countries with different economic growth levels.

Impact of global value chain embedding on decoupling between China's CO2 emissions and economic growth: Based on Tapio decoupling and structural decomposition.

With the increasing fragmentation of global production, China's participation in cross-border production sharing activities has had a considerable impact on the nation's economy and carbon dioxide (CO2) emissions. This study applied the Tapio model to quantitatively evaluate the decoupling between CO2 emissions and economic growth in China, dividing the decoupling index based on global value chains (GVCs) and domestic production within the IO framework, and introducing structural decomposition analysis (SDA) to analyze the GVC-related factors to the decoupling. The relevant research results are fourfold. (1) From 2000 to 2018, China achieved weak decoupling between emissions and economic growth. Domestic and GVC effects each had a negative impact on the decoupling; however, after 2008, the GVC effect had a promotional effect and the negative domestic effect declined. (2) Emission intensity was the primary factor promoting decoupling through domestic and GVC effects, while the scale of final demand was the main hindrance. And the negative effects of GVC-related factors declined following the economic crisis. (3) The regional and sectoral structures of GVC production (58.44 % and 56.08 %) had promotional roles in the changes in GVC effects, while GVC production linkages (-20.19 %) had hindering effects. Various factors contributed to the hindering effect from the 2008 to 2011 index, whereas from the 2011 to 2018 index, all factors contributed to the promotional effect. (4) From 2000 to 2018, the average annual global value chain effect promoted the low-carbon development of China's labor-intensive and knowledge-based manufacturing. In order for GVCs to play a positive role in decoupling, China should promote trade facilitation through international platforms, support the advancement of production technology, reasonably guide China's industries to participate in the regional and industrial links of GVCs, and develop strategic emerging industries.

Carbon ecological security assessment based on the decoupling relationship between carbon balance pressure and ecological quality in Xuzhou City, China.

Building a carbon ecological security (CES) framework helps to scientifically evaluate and manage the regional carbon cycle and eco-environment and support regional ecological security patterns. This paper adopted the pressure-state-response-immune (PSRI) model and the carbon balance index method to evaluate the ecological quality and carbon balance pressure. Then, based on the decoupling model and the improved four-quadrant model, the CES framework was constructed to evaluate the changing trend of the CES of Xuzhou City from 2005 to 2020. The results showed that the carbon balance pressure of Xuzhou City showed a pattern of "low-high-low" from east to west, and most areas tended to have a carbon balance and surplus in 2020. The ecological quality showed an overall upward trend during the study period. Protection and restoration drove the response and immune index growth from 2010 to 2020. In the Thirteenth Five-Year Plan stage, the nine districts of Xuzhou City were in a stable decoupling state, and the overall decoupling process was ideal. The CES of districts showed individual differences in the general upward trend. The carbon balance pressure of Gulou and Quanshan Districts was the main factor restricting the districts' CES. Therefore, based on the empirical results, this research proposes relevant suggestions to enhance carbon ecological security to achieve regional green and low-carbon development.

Research on the evolution mechanism and decoupling effect of China's carbon emissions from the perspective of green credit: based on system dynamics model and Tapio model.

Green credit is an important topic in the study of green finance system, but it has not been combined with China's carbon emission reduction effect and industrial carbon emissions. This study takes different industries in China as research objects to explore the relationship between green credit and industrial carbon emissions. First, the LMDI decomposition model was used to decompose the driving factors of industrial carbon emissions, and the effects of green credit efficiency and scale on carbon emissions were obtained. Secondly, on this basis, a system dynamics model was established to predict the changing trend of carbon emissions in different industries. By setting different scenarios of green credit, the development and evolution trend of carbon emission system was simulated when parameters changed, and the Tapio decoupling model was further established to analyze the decoupling effect of green credit and carbon emissions under different scenarios. Finally, the research results show that the increase in the scale of green credit can effectively inhibit carbon emissions and has the greatest effect on carbon emissions of the secondary industry. The incentive policy of green credit can effectively encourage industrial upgrading and development. With the growth of the balance of green credit, green credit and carbon emissions gradually reach the best decoupling state. This study provides empirical evidence for the objective evaluation of the implementation effect of China's green credit policy and has important reference value for the improvement and development of future policies.

Driving factors and decoupling effects of agricultural carbon emissions in Jiangxi Province based on time-varying parameter C-D production function.

The reduction of agricultural emission plays an important role in realizing the dual-carbon goals. It is thus of great significance to examine the characteristics and drivers of regional agricultural carbon emission. We measured agricultural carbon emission in Jiangxi Province from the perspective of input-output and production processes, and explored the drivers and decoupling dynamics of agricultural carbon emission by using the LMDI decomposition method together with the Tapio decoupling model modified by time-varying parameter C-D production function. The results showed that agricultural carbon emission in Jiangxi increased by 26.4% from 2010 to 2021, and the carbon emission intensity decreased year by year with an average annual rate of 4.9%. Factors such as agricultural carbon intensity, labor input, and capital stock collectively reduced carbon emission by a total of 61.05 Mt, with a contribution of 27.0%, 44.5% and 28.5%, respectively. Level of agricultural economic development, agricultural structure, and technological progress had strong driving effects, which accounted for 75.7%, 5.6% and 18.8%, respectively. Agricultural carbon emission in Jiangxi was weakly decoupled from economic development, capital stock, and technological progress factors, but was negatively decoupled from labor input. Moreover, the decoupling state was more desirable in the later period than in the earlier period. Our results suggested that the application of the time-varying parameter C-D production function is innovative and applicable by incorporating technology, labor, and capital factors in the examination of carbon emission drivers and decoupling effects.

Exploring the factors effecting on carbon emissions in each province in China: A comprehensive study based on symbolic regression, LMDI and Tapio models.

Carbon emission (CE) has led to increasingly severe climate problems. The key to reducing CE is to identify the dominant influencing factors and explore their influence degree. The CE data of 30 provinces from 1997 to 2020 in China were calculated by IPCC method. Based on this, the importance order of six factors included GDP, Industrial Structure (IS), Total Population (TP), Population Structure (PS), Energy Intensity (EI) and Energy Structure (ES) affecting the CE of China's provinces were obtained by using symbolic regression, then the LMDI and the Tapio models were established to deeply explore the influence degree of different factors on CE. The results showed that the 30 provinces were divided into five categories according to the primary factor, GDP was the most important factor, followed by ES and EI, then IS, and the least TP and PS. The growth of per capita GDP promoted the increase of CE, while reduced EI inhibited the increase of CE. The increase of ES promoted CE in some provinces but inhibited in others. The increase of TP weakly promoted the increase of CE. These results can provide some references for governments to formulate relevant CE reduction policies under dual carbon goal.

The relationship between agricultural and animal husbandry economic development and carbon emissions in Henan Province, the analysis of factors affecting carbon emissions, and carbon emissions prediction.

This study aims to investigate the relationship between agricultural and animal husbandry economic development and carbon emissions and the influencing factors on carbon emissions. Here, we combine the Tapio decoupling model with the STIRPAT model by using the panel data of Henan province from 2000 to 2020 for it. Our results reveal that (i) the main relationship between agricultural and animal husbandry economic development and carbon emissions is strong decoupling and weak decoupling; (ii) the intensity of carbon emissions and labor effects can optimize their relationship; (iii) the urbanization rate and per capita consumption expenditure in rural areas have a negative impact on carbon emissions, while the carbon emission intensity and total power of agricultural machinery are opposite. Therefore, Henan province needs to optimize its industrial structure, improve the economic level of rural areas, and reduce the use of fertilizers.

Evaluation of linkage relationships between carbon emissions and economic development based on the decoupling model and the VAR model: a case study of Shanxi Province (China).

Economic development is the core driver of carbon emissions. It is of great significance to clarify the linkage relationship between economic development and carbon emissions. Therefore, the static and dynamic relationship between carbon emissions and economic development in Shanxi Province is analyzed by using the VAR model and decoupling model combined with data from 2001 to 2020. The results show that economic development and carbon emissions in Shanxi Province have mainly presented a weak decoupling state in the past 20 years, but the decoupling state is gradually increasing. Meanwhile, carbon emissions and economic development constitute a bidirectional cycle dynamic system. The impact of economic development on itself and carbon emissions account for 60% and 40%, respectively, while the impact of carbon emissions on itself and economic development accounts for 71% and 29%, respectively. This study provides a relevant theoretical basis for solving the problem of excessive dependence on energy consumption in economic development.

Research on Rural Population/Arable Land/Rural Settlements Association Model and Coordinated Development Path: A Case Analysis of the Yellow River Basin (Henan Section).

The countryside is a complex regional system with population and land as the core elements, and it is of great significance to study the coordination of the rural human-land relationship for promoting rural ecological protection and high-quality development. The Yellow River Basin (Henan section) is an important grain-producing area with dense population, fertile soil, and rich water resources. Based on the rate of change index and Tapio decoupling model, this study took county-level administrative region as the evaluation unit to explore the characteristics of the spatio-temporal correlation model of rural population/arable land/rural settlements in the Yellow River Basin (Henan section) from 2009 to 2018 and the optimal path of coordinated development. The results show the following: (1) The decrease of rural population, the increase of arable land in a relatively large part of non-central cities, the decrease of arable land in central cities, and the general increase in the area of rural settlements are the most important characteristics of the Yellow River Basin (Henan section) for the change of rural population/arable land/rural settlements. (2) There are spatial agglomeration characteristics of rural population changes, arable land changes, and rural settlements changes. Areas with a high degree of change in arable land have a certain degree of spatial consistency with areas with a high degree of change in rural settlements. (3) The type of T3 (rural population and arable land)/T3 (rural population and rural settlement) is the most important temporal and spatial combination mode, and rural population outflow is serious. In general, the spatio-temporal correlation model of rural population/arable land/rural settlements in the eastern and western sections of the Yellow River Basin (Henan section) is better than that in the middle section. The research results are helpful to deeply understand the relationship between rural population and land in the period of rapid urbanization and can provide reference for the classification and sub-standard policies of rural revitalization. It is urgent to establish sustainable rural development strategies for improving the human-land relationship, narrowing the rural-urban disparity, innovating rural residential land area policies, and revitalizing the rural area.

Decoupling relationship between carbon emissions and economic development and prediction of carbon emissions in Henan Province: based on Tapio method and STIRPAT model.

In order to cope with global warming, China has put forward the "30 · 60" plan. We take Henan Province as an example to explore the accessibility of the plan. Tapio decoupling model is used to discuss the relationship between carbon emissions and economy in Henan Province. The influence factors of carbon emissions in Henan Province were studied by using STIRPAT extended model and ridge regression method, and the carbon emission prediction equation was obtained. On this basis, the standard development scenario, low-carbon development scenario, and high-speed development scenario are set according to the economic development model to analyze and predict the carbon emissions of Henan Province from 2020 to 2040. The results show that energy intensity effect and energy structure effect can promote the optimization of the relationship between economy and carbon emissions in Henan Province. Energy structure and carbon emission intensity have a significant negative impact on carbon emissions, while industrial structure has a significant positive impact on carbon emissions. Henan Province can achieve the "carbon peak" goal by 2030 years under the standard and low-carbon development scenario, but it cannot achieve this goal under the high-speed development scenario. Therefore, in order to achieve the goals of "carbon peaking" and "carbon neutralization" as scheduled, Henan Province must adjust its industrial structure, optimize its energy consumption structure, improve energy efficiency, and reduce energy intensity.

Study on the Decoupling and Interaction Effect between Industrial Structure Upgrading and Carbon Emissions under Dual Carbon Targets.

The issue of climate and environment has been paid more and more attention by countries all over the world, especially regarding carbon emissions. Many national policies and scholars' research contents have focused on this issue, which has become a hot topic in today's society. As the world's largest carbon emitter, it is vital for China to achieve green development, upgrade its industrial structure and explore the relationship between industrial structure upgrading and carbon emissions. To explore the decoupling and interactive effects of industrial structure upgrading and carbon emissions, this paper divides industrial structure upgrading into two aspects: rationalization of industrial structure and upgrading of industrial structure. Indicators related to industrial structure upgrading and carbon emissions are selected and the decoupling model of carbon emissions and industrial structure upgrading is constructed using panel data from 30 regions from 1997 to 2019. The core density function is used to analyze the decoupling distribution characteristics, and then the Gini coefficient decomposition method is used to analyze the carbon emissions decoupling index, revealing the regional differences and sources of carbon emissions decoupling index. Finally, spatial factors are included in the regression model to verify the spatial synergy effect of industrial structure upgrading on carbon emissions. The overall and local Moran indexes are used to reveal the spatial internal structure and agglomeration characteristics of industrial structure upgrading and carbon emissions, and, based on the research results, policy recommendations are put forward to promote sustainable and stable development of industrial structure upgrading in China. This provides a new perspective for understanding the relationship between industrial structure upgrading and carbon emissions and also provides a decision-making reference for promoting decoupling of industrial structure upgrading and carbon emissions under high-quality economic development and forcing low-carbon transformation of the industrial structure.

Analysis of carbon emission drivers of secondary industries in Energy "Golden Triangle" area based on LMDI and two-dimensional decoupling model.

As an essential energy and chemical base in China, carbon reduction in the Energy "Golden Triangle" (EGT) area is significant. This paper used the logarithmic mean Divisia index (LMDI) method to analyze the drivers of carbon emissions from secondary industry energy consumption (CESEC) in EGT from 2005 to 2019 and then used the GM (1,1) method to simulate carbon emissions in 2030. Meanwhile, the decoupling relationship between carbon emissions and economic development was also analyzed using the two-dimensional decoupling model to test the effectiveness of carbon reduction by the region's government. This paper showed the following: (1) CESEC in the EGT area increased from 1.89×108t to 2.617×108 t; (2) the economic output effect is the main factor influencing carbon emissions in the EGT area, followed by population effect and energy structure effect, while energy intensity effect mitigates carbon emissions; and (3) CESEC will peak at 12.362×108t in 2030, leaving an arduous task on carbon reduction. The two-dimensional decoupling condition between carbon emissions and economic growth in the EGT area is low level-weak decoupling (WD-LE) for 2005-2019. The decoupling condition in Yulin and Ningdong is concentrated in low level-expansion connection (EC-LE) and low level-weak decoupling (WD-LE). Furthermore, Erdos reached high level-expansion negative decoupling (END-HE) condition during 2015-2019. Based on the above findings, a low-carbon development strategy for EGT should consider improving emission reduction technologies for high-carbon energy sources like coal, adjusting the energy consumption structure and seeking government policy support for carbon reduction.

Two-Dimensional Decoupling and Decomposition Analysis of CO2 Emissions from Economic Growth: A Case Study of 57 Cities in the Yellow River Basin.

Precise decoupling of CO2 emission and economic development holds promise for the sustainability of China in a post-industrialization era. This paper measures the energy-related CO2 emissions of 57 cities in the Yellow River Basin (YRB) during 2006-2019 and analyzes their decoupling states and dynamic evolution paths based on the derived general analytical framework of two-dimensional decoupling states to decompose their decoupling index using the LMDI method. The results show that (1) from 2006 to 2019, the economic growth and CO2 emissions of cities along the YRB are dominated by weak decoupling at an average contribution of 53.2%. Their dynamic evolution paths show fluctuations of "decoupling-recoupling" states, while the evolution trend is relatively ideal. (2) The factors of economic output, energy intensity and population scale inhibit the decoupling in most cities, which contribute 39.44%, 19.34%, and 2.75%, respectively, while the factors of industrial structure, carbon emission coefficient, and energy structure promote the decoupling in most cities in the YRB, with average contributions of -12.63%, -8.36%, and -0.67%, respectively. (3) The significant increase in the contribution of energy intensity is the main reason for the "Worse" path of cities, while the industrial structure and energy structure factors promote to the "Better" path of cities. This work satisfies the urgent need for the ecological protection of the YRB and opens new avenues for its high-quality development.

CO2 emissions in the Middle East: Decoupling and decomposition analysis of carbon emissions, and projection of its future trajectory.

Despite their acute climatic susceptibility, the hot, arid Middle Eastern countries are among the world's largest energy consumers and emitters of greenhouse gases, particularly carbon dioxide (CO2). Nonetheless, no study has been conducted to decompose regionally the influential primary factors of the Middle East's carbon emissions. This study utilized the logarithmic mean Divisia index (LMDI) method to fill this knowledge gap and investigate the driving forces of CO2 emissions in 12 Middle Eastern countries, namely, Bahrain, Iran, Iraq, Jordan, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, Syria, the United Arab Emirates, and Yemen (1990-2020). The research confirmed that, with a contribution rate of 53.89 %, population growth is the primary driver of CO2 emissions in the Middle East, followed by energy intensity (31.97 %) and economic growth (18.42 %); and the most straightforward approach to reduce emissions, are boosting energy efficiency and reforming energy subsidies. It also concluded that the West Asian economy is gradually decoupling from CO2 due to the effective decarbonization of countries, such as Saudi Arabia and Kuwait, based on the Tapio decoupling model results. Furthermore, each country's future emissions (2020-2026) were projected using a novel group method of data handling (GMDH) approach based on the main identified factors. The countries' decoupling status confirms the accuracy of the projected data on CO2 emissions growth. The region's CO2 emissions are expected to rise 13.28 % by 2026, with Syria and Yemen experiencing the most significant increases (129.45 % and 112.14 %, respectively) due to post-civil war economic growth. Other aspects of regional conflicts and migration impacts on the CO2 emission influencing factors were also explored. Indeed, besides providing a comprehensive analysis of the current and future status of CO2 emissions in the Middle East, the effects of military conflicts on CO2 emissions have been investigated using this regional case study for global application.

Nonlinear forces in urban thermal environment using Bayesian optimization-based ensemble learning.

Urbanization witnessed unprecedented development globally, which causes citizens and urban temperature to become increasingly intertwined. Although researchers were interested in the field, most studies focused on holistic linear links between the characteristics of the urban built-up environment and temperature. The study used Bayesian optimization ensemble learning and Shapley value to decouple the urban thermal environment by Landsat satellite data. This work's novelties reveal the specific driving effect of different value ranges of urban features in the overall process on the urban thermal environment and advancing an optimum observation buffer zone of the urban surface temperature. The study's results were only for daytime and Beijing scope. The following are the main findings: (1) The 2 km observation buffer zone is best to analyze the urban thermal environment for this dataset. (2) The ecological environment factors have a more significant effect on the urban temperature than the urban morphology factors. (3) In summer, when the vegetation coverage exceeds 58.1%, every 10% increase could reduce the temperature by 0.84 °C. In contrast to summer, when vegetation coverage exceeds 64.7% and 73.2%, respectively, in spring and fall, there will be a significant marginal utility. (4) The effect of the building height has seasonal variations. It has the greatest cooling effect in the spring when the height is between 18 m and 75 m, and the daytime surface temperature at the time of Landsat overpass will drop by 1.25 °C. These findings will aid in understanding how building construction influences urban surface temperature and provide statistical support for planners.

An investigation of the relationship among economic growth, agricultural expansion and chemical pollution in Iran through decoupling index analysis.

Due to the significant role of agricultural chemicals in increasing agricultural production and ensuring food security, the excessive use of chemical fertilizers and pesticides has been intensified in Iran. These chemical inputs are important environmental pollutants that threaten human health. In the recent years, in agricultural sector, the balance between the growth of agricultural economy and the spread of pollution in Iran has been one of the major challenges. In this regard, the use of decoupling index to decouple the link between agricultural production and pollution caused by the consumption of chemical inputs, such as fertilizers and pesticides, has been emphasized; Therefore, in the present study, the decoupling index first is calculated in relation to the emission of pollution caused by the use of chemical inputs in the process of agricultural production during the period of 1991-2016 in Iran. Then, by reviewing the existing literature systematically, the factors affecting the decoupling index in the agricultural sector of Iran are evaluated using the autoregressive distributed lag (ARDL) model. The results showed that in the recent years, pollution indicators in relation to chemical inputs have not had ideal trends, and despite the further growth of agricultural production, the quality of the environment has experienced a declining trend. The results of the decoupling index related to the use of chemical pesticides and fertilizers in Iran show that during a period of 26-year, only 5 and 4 years of using these inputs have had a sustainable state compared to the production growth; besides, a strong negative decoupling state occurred as the most unsustainable state in relation to chemical fertilizer for 7 years. Moreover, among the factors affecting the decoupling index, the value-added variable of the agricultural sector has had the most positive effect on this index, and thus, in the long run, it increases the level of pollution in the agricultural sector. The variables of gross domestic product (GDP) per capita and the area under cereal cultivation in the agricultural sector would also increase the decoupling index. Accordingly, adopting effective strategies to improve resource efficiency, planning for the implementation of biotechnological methods, and doing investment for creating green infrastructure in the agricultural sector can be effective in the ideal decoupling of pollution and agricultural economy growth in Iran.

Trend analysis and obstacle factor of inter provincial water resources carrying capacity in China: from the perspective of decoupling pressure and support capacity.

The high distribution of water resources among provinces in China considerably impacts the development of society and economy in each region. Thus, it is of great practical significance to examine the water resources carrying capacity (WRCC) of each Chinese province. This paper constructs a comprehensive evaluation index system for the WRCC from two aspects: pressure and support. First, it analyzes dynamic changes in the WRCC of 31 Chinese provinces in China by using the decoupling model (DM). Second, it analyzes the key factors that hinder the improvement of WRCC by using the obstacle degree model (ODM). The study found that there are significant inter-provincial differences in China's WRCC. Provinces with greater natural water resources have a higher WRCC. Under the condition of similar natural water resources, WRCC in economically developed provinces is higher. From 2008 to 2015, China's overall WRCC has been increasing. Moreover, three-fifth of China's provinces can be classified as Upward-type (Upward I, Upward II, and Upward III) provinces and their WRCC is in a good state by considering the decoupling type and trend of WRCC in two periods together. The main obstacle factors hindering the improvement of the WRCC are total water resources ([Formula: see text]), water supply per capita ([Formula: see text]), total water supply ([Formula: see text]), forest cover rate ([Formula: see text]), soil erosion control area ([Formula: see text]), water consumption saving ([Formula: see text]), and water usage penetration rate ([Formula: see text]). This study can provide a scientific basis for understanding change trend of WRCC in Chinese provinces and improve their WRCC.

Exploration on the coordinated development of urbanization and the eco-environmental system in central China.

Scientific evaluation of the interaction between urbanization and the eco-environmental system in Central China is of great significance. To optimizing the quality of urbanization and improving the eco-environmental state. As a case study of Central China, this research attempts to build a set of analytical systems to realize the integrated deconstruction from analyzing comprehensive evaluation indexes, quantification of coupling coordination, spatiotemporal evolution traits, decoupling path exploration to influential factor analysis. It tries to clarify the differences between cities, identify problematic areas, and propose targeted improvement measures. The outcomes show that the urbanization level of the cities in Central China has been improved significantly. In contrast, their eco-environmental levels are fluctuating, with the growth rate lower than that of urbanization. The coordination level between the two systems is rising, changing from primary dysfunction to intermediate coordination. The coordination level is characterized by obvious spatial association dominated by Types H-H and L-L and ever-increasing agglomeration. The decoupling between the two systems only falls into two types: strong decoupling and relative decoupling with expansion, indicating a negative effect between them. There is a problem regarding negative urbanization development. The factors including energy consumption, investment in fixed assets, opening to the outside world, technological progress, and government management capabilities all have an impact on the coordination of the two with divergent significances.

Resources and Environment Carrying Capacity, Social Development and Their Decoupling Relationship: A Case Study of Hubei Province, China.

With the rapid urbanization in recent decades, resource shortage and environmental damage have hindered the process of urban sustainable development (SD). As a yardstick of sustainable development, the evaluation of resources and environment carrying capacity (RECC) and its decoupling relationship with social comprehensive development index (SCDI) are of great significance. In this paper, RECC and SCDI are taken as research objects to establish resource and environment system evaluation index system and social comprehensive development level evaluation index system, respectively. Then, the RECC and SCDI of 17 cities in Hubei province during 2009-2018 are calculated by the projection pursuit model based on genetic algorithm, and their spatial-temporal variance characteristics are analyzed. On this basis, the RECC-SCDI Tapio decoupling model is constructed to explore the decoupling relationship between RECC and SCDI. The result shows that: (1) The RECC of Hubei shows a V-shaped development trend during 2009-2018. The SCDI of Hubei rose steadily during 2009-2018. (2) RECC in western and eastern Hubei Province is higher than that in central Hubei Province. SCDI in eastern and central Hubei Province is higher than that in the west. (3) 11 of the 17 cities in Hubei Province have got rid of excessive dependence on resources environment for social development. The study could contribute to scientific and effective policies be formulated by government to promote urban sustainable development.