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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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The expansion of Xi'an City has caused the consumption of energy and land resources, leading to serious environmental pollution problems. For this purpose, this study was carried out to measure the carbon carrying capacity, net carbon footprint and net carbon footprint pressure index of Xi'an City, and to characterize the carbon sequestration capacity of Xi'an ecosystem, thereby laying a foundation for developing comprehensive and reasonable low-carbon development measures. This study expects to provide a reference for China to develop a low-carbon economy through Tapio decoupling principle. The decoupling relationship between CO2 and driving factors was explored through Tapio decoupling model. The time-series data was used to calculate the carbon footprint. The auto-encoder in deep learning technology was combined with the parallel algorithm in cloud computing. A general multilayer perceptron neural network realized by a parallel BP learning algorithm was proposed based on Map-Reduce on a cloud computing cluster. A partial least squares (PLS) regression model was constructed to analyze driving factors. The results show that in terms of city size, the variable importance in projection (VIP) output of the urbanization rate has a strong inhibitory effect on carbon footprint growth, and the VIP value of permanent population ranks the last; in terms of economic development, the impact of fixed asset investment and added value of the secondary industry on carbon footprint ranks third and fourth. As a result, the marginal effect of carbon footprint is greater than that of economic growth after economic growth reaches a certain stage, revealing that the driving forces and mechanisms can promote the growth of urban space.

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This study utilizes a threefold empirical analysis in order to examine the relationship between income and water equity in Bulgaria, the fastest shrinking country in the world in terms of population, and the most unequal member of the EU in terms of regional income distribution. First, in line with this goal, the income elasticity of regional water demand in the country is calculated using the Tapio decoupling model. Second, the WPAT equation which measures the weighted impacts of population, per capita income and intensity on water demand, is utilized. And finally, using the local dissimilarity index, spatial differences in access to water by region are measured. Underscoring the essence of regional level analyses based on specific decomposed sub-groups, findings are thus compatible with the economic and demographic characteristics of the regions handled in the study. In the northern regions where income and population levels are relatively low, during periods when income and water demand both increase, the rise in water demand is usually higher than the increase in income; conversely, when income decreases, the water demand also falls. The effect of population dominates the changes in water demand in the northern regions, with the exception of the Northwestern Region, where water intensity is the leading factor, possibly because the Balkans' largest nuclear power plant is located in the region. In the southern regions where income is higher, income has been the most important determinant of water demand, particularly in recent years.

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A comprehensive understanding of the relationships between greenhouse gas (GHG) emissions and industrial structure and economic growth holds great significance for China to realize the development of a green economy. This paper calculates GHG emissions based on China's energy consumption, divides the industrial structure in detail, and uses the extended Stochastic Impacts by Regression on Population, Affluence, and Technology model that is realized by PLS method and Tapio decoupling model to study the relationship of GHG emissions to industrial structure and economic growth. The results show that (1) China's total GHG emissions showed a year-on-year growth trend from 2000 to 2017. For CO2, CH4, and N2O, only N2O emission showed a significant downward trend, while CO2 and CH4 emissions showed a slow growth trend. (2) The proportions of added value of industry and construction are positively correlated with GHG emissions, while those of farming, forestry, animal husbandry, and fishery; wholesale and retail trade; transport; and accommodation and catering are negatively correlated with GHG emissions. (3) China's GHG emissions and overall economic growth are in a decoupling state, but in the energy field, N2O emission reduction control has the best effect. Additionally, the overall economic growth of China's industrial sector and GHG emissions have experienced the process of decoupling-link-negative decoupling-link-decoupling. Graphical abstract.

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In order to analyze and control air pollutant emissions effectively, on the basis of comprehensive consideration of three different pollution sources of industrial sulfur dioxide, industrial nitrogen oxides, and industrial smoke and dust, the Tapio decoupling model and LMDI decomposition model with six decomposition variables are constructed to compare the effects of socioeconomic factors on industrial air pollutant emissions in 11 cities in Zhejiang Province during 2006-2017. Then, a decoupling effort model is developed to analyze the effectiveness of the decoupling efforts taken at city level. This study found that (1) during the period of 2006-2017, the air pollutant emission reduction work in Zhejiang Province achieved remarkable results. More specifically, economic scale effect and population effect are the main factors for the increase of air pollutant emissions. And, the energy emission intensity effect and technological progress are the main driving forces for the reduction of three atmospheric pollutants, followed by the reduction effect of industrial structure and energy structure. (2) The environmental pollution problems of different air pollution sources in different cities are heterogeneous. (3) Eleven cities in Zhejiang Province have made significant decoupling efforts on the emission of three kinds of air pollutants, but there are some differences in the trend of the decoupling effort index of different pollution sources in different cities. In the future, illustrating by the example of Zhejiang, we should implement a "common but different" emission reduction strategy and emphasize pollutant emissions control during energy use in the efforts of further promoting the reduction of air pollutants.

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Quantitative analysis on decoupling between economic output, carbon emission, and the driving factors behind decoupling states can serve to make the economy grow without increasing carbon emission in China's transport sector. In this work, we investigate the decoupling states and driving factors of decoupling states in the transport sector of China's four municipalities (Beijing, Shanghai, Tianjin, and Chongqing) through combining the Tapio decoupling approach with the decomposition technique. The results show that (i) the decoupling state of Beijing, Shanghai, and Tianjin improved; Beijing stabilized in weak decoupling; Shanghai and Tianjin appeared to have strong decoupling, but the decoupling state of Chongqing deteriorated from decoupling to negative decoupling. (ii) The energy-saving effect was the primary contributor to decoupling in these four municipalities, promoting transport's economic growth strongly decouple from carbon emission. The economic scale effect was not optimized enough in Chongqing, facilitating expansive coupling, and expansive negative decoupling emerged. But it had a rather positive impact on decoupling process in Beijing, Shanghai and Tianjin, promoting economic growth to weakly decouple from carbon emission. (iii) The carbon-reduction effect promoted strong decoupling, which emerged in Shanghai's transport sector, more so than in the other three municipalities, in which weak decoupling emerged. Finally, several relevant policy recommendations were offered to promote the decoupling of carbon emission from economic growth and low-carbon transport.

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