Carbon emission efficiency network formation mechanism and spatial correlation complexity analysis: Taking the Yangtze River Economic Belt as an example

Towards sustainable development: can industrial collaborative agglomeration become an accelerator for green and low-carbon transformation of resource-based cities in China?

In the context of accelerating the comprehensive green transformation of economic and social development, this study explored the mechanisms through which the collaborative agglomeration of manufacturing and productive services affected the green and low-carbon transformation of resource-based cities in China, based on Marshallian externalities. Using fixed effects and mediation effect models, this study analyzed the effects of industrial collaborative agglomeration on the green and low-carbon transformation of 114 resource-based cities in China from 2003 to 2021, as well as the regional differences therein. The results indicated that industrial collaborative agglomeration significantly promoted the green and low-carbon transformation of resource-based cities. The labor pooling effect, intermediate product sharing effect, and knowledge and technology spillover effect, as key pathways of industrial collaborative agglomeration, also contributed to this transformation. Heterogeneity analysis revealed that the positive effect of industrial collaborative agglomeration on the green and low-carbon transformation of resource-based cities was more pronounced after the implementation of the "12th Five-Year Plan for National Strategic Emerging Industries," particularly in the western regions and mature resource-based cities. These findings provided valuable insights for further promoting the green and low-carbon transformation of resource-based cities.

Spatial distribution characteristics and influencing factors of China martial arts schools based on Baidu map API

This paper examines the spatial distribution pattern and influencing factors of Martial Arts Schools (MASs) based on Baidu map data and Geographic Information System (GIS) in China. Using python to obtain the latitude and longitude data of the MASs through Baidu Map API, and with the help of ArcGIS (10.7) to coordinate information presented on the map of China. By harnessing the geographic latitude and longitude data for 492 MASs across 31 Provinces in China mainland as of May 2024, this study employs a suite of analytical tools including nearest neighbor analysis, kernel density estimation, the disequilibrium index, spatial autocorrelation, and geographically weighted regression analysis within the ArcGIS environment, to graphically delineate the spatial distribution nuances of MASs. The investigation draws upon variables such as martial arts boxings, Wushu hometowns, intangible cultural heritage boxings of Wushu, population education level, Per capita disposable income, and population density to elucidate the spatial distribution idiosyncrasies of MASs. (1) The spatial analytical endeavor unveiled a Moran's I value of 0.172, accompanied by a Z-score of 1.75 and a P-value of 0.079, signifying an uneven and clustered distribution pattern predominantly concentrated in provinces such as Shandong, Henan, Hebei, Hunan, and Sichuan. (2) The delineation of MASs exhibited a prominent high-density core centered around Shandong, flanked by secondary high-density clusters with Hunan and Sichuan at their heart. (3) Amongst the array of variables dissected to explain the spatial distribution traits, the explicative potency of 'martial arts boxings', 'Wushu hometowns', 'intangible cultural heritage boxings of Wushu', 'population education level', 'Per capita disposable income', and 'population density' exhibited a descending trajectory, whilst 'educational level of the populace' inversely correlated with the geographical dispersion of MASs. (4) The entrenched regional cultural ethos significantly impacts the spatial layout of martial arts institutions, endowing them with distinct regional characteristics.

Dynamic mechanism and evolutionary game analysis of sports industry service transformation

Service transformation plays a pivotal role in achieving the sustainable development of the sports industry. This study originates from the interactive relationships among sports enterprises, consumers, and regulatory authorities, proposing a logical framework for the service transformation of the sports industry. Furthermore, a three-party evolutionary game model is constructed to explore the strategic evolution and stability conditions under both single-agent and multi-agent scenarios. The primary findings are as follows: (1) Interactive relationships among sports enterprises, consumers, and regulatory authorities exhibit a game dilemma resembling the "prisoner's dilemma." (2) A positive promotion relationship conducive to the transformation of the sports industry towards a service model is triggered only when at least two stakeholders' strategic choices surpass a certain threshold. (3) Fiscal subsidies play a facilitating role in encouraging service transformation for sports enterprises but have limited incentives for consumers. Finally, this paper suggests the introduction of competition mechanisms and the establishment of reward and penalty systems, offering decision-making guidance for the service transformation of the sports industry.

Coupling coordination and spatial network characteristics of carbon emission efficiency and urban green innovation in the Yellow River Basin, China

Carbon emission and sustainable development have attracted global attention. Promoting urban green innovation (UGI) in the Yellow River Basin (YRB) will help in lowering the intensity of carbon emissions and improve the safety and sustainability. A SBM-DEA model was constructed to measure carbon emission efficiency (CEE) and the degree of coupling and coordination with UGI was calculated in 73 prefecture-level cities in the YRB. The spatial association network of CEE coupled with UGI is constructed by using a modified gravity model, social network analysis and the quadratic assignment procedure (QAP), to analyze spatial potential energy, network characteristics and clustering characteristics. The study found that: (1) The coupling coordination degree of CEE and UGI in the YRB shows fluctuating growth, mutual promotion and continuous coordinated development. (2) The spatial linkage between CEE and UGI is gradually close, and the potential energy of the spatial linkage increases year by year, with obvious spatial spillover effect, indicating that the radiation and influence between cities are gradually increasing. In contrast to the middle stream, the upstream and downstream regions show a higher percentage of spatial potential energy in the entire network, and their network structure is more intricate and robust. (3) The clustering patterns of the three major urban clusters are examined using the block model, exploring the positioning and functions of various cities in these urban conglomerations, which includes the net spillover, net benefit, two-way spillover and broker plate, so as to strengthen the connection and coordinated development between cities. (4) Factors such as spatial adjacency, industrial structure, population density, digital economy and urbanization level, and energy intensity significantly impact the spatial association network, along with temporal and regional heterogeneity. Therefore, tailored policies are needed in the YRB to strengthen collaboration between CEE and UGI, fostering the development of a circular economy and promoting sustainable development.

Factors influencing the coupled and coordinated development of cities in the Yangtze River Economic Belt: A focus on carbon reduction, pollution control, greening, and growth

Atmospheric pollutants PM2.5 and CO2 share similar sources and impact mechanisms. Green innovations and urban greening significantly reduce these pollutants while promoting economic growth. However, the synergies and trade-offs between carbon reduction, pollution control, green expansion, and economic growth remain understudied. This paper examines 110 cities in the Yangtze River Economic Belt (YREB), China's premier green development site, as a unified system. Using fractional-order synthesis analysis, this paper constructs an assessment indicator system and measures synergy with a coupled coordination degree model. The driving factors are explored using a system-generalized method of moments estimation. The findings indicate that most cities in the YREB are at an intermediate coordination stage. The coupling of greening with carbon reduction, pollution control, and growth has a low degree, highlighting an urgent need to strengthen greening efforts. Key drivers include the digital economy, advanced industrial structure, innovative talent aggregation, infrastructure construction, financial investment, and marketization. The digital economy significantly influences all regions of the Yangtze River. Notable heterogeneity exists in the impact of other drivers across different regions. These results offer valuable policy insights for managing carbon emissions and pollutants, contributing to sustainable urban development.

Urban synergistic carbon emissions reduction research: A perspective on spatial complexity and link prediction

Reducing urban carbon emissions (UCEs) holds paramount importance for global sustainable development. However, the complexity of interactions among urban spatial units has impeded further research on UCEs. This study investigates synergistic emission reduction between cities by analyzing the spatial complexity within the UCEs network. The future potential for synergistic carbon emissions reduction is predicted by the link prediction algorithm. A case study conducted in the Pearl River Basin of China demonstrates that the UCEs network has a complex spatial structure, and the synergistic capacity of emission reduction among cities is enhanced. The core cities in the UCEs network, including Dongguan, Shenzhen, and Guangzhou, have spillover effects that contribute to synergistic emission reduction. Community detection reveals that the common characteristics associated with UCEs become concentrated, thereby enhancing the synergy of joint efforts between cities. The link prediction algorithm indicates a high probability of strengthened carbon emission connections in the Pearl River Delta, alongside those between upstream cities, which shows potential in forecasting synergistic emission reductions. Our research framework offers a comprehensive analysis for synergistic emission reduction from the spatial complexity of UCEs network and link prediction. It acts as a worthwhile reference for developing differentiated policies on synergistic emission reduction.

Assessing and gauging the carbon emission efficiency in China in accordance with the sustainable development goals

In light of the growing urgency of climate change, carbon emissions reduction has emerged as a pivotal concern within global governance. In this paper, we take carbon emission efficiency (CEE) as the research object to characterize the relationship between economic, social, and environmental development in the context of the Sustainable Development Goals (SDGs). According to the regional division standard of eight comprehensive economic zones in China, this paper analyzed the spatial differences, evolutionary characteristics, and influencing factors of CEE in 257 Chinese cities over the period 2003-2019. The analysis conducted the Dagum Gini Coefficient, Markov Transition Probability Matrix, and geographically and temporally weighted regression model (GTWR). The results demonstrate that: (1) The CEE of Chinese cities exhibits an upward trajectory. (2) The inter-differences among the eight comprehensive economic zones represent the primary spatial source of CEE divergence. (3) The CEE of Chinese cities is a staged process of gradual enhancement with spatial spillover effects. (4) Environmental regulation, energy consumption intensity, and green finances are significant factors affecting CEE, and the direction and intensity of their influence have distinct spatial heterogeneity. Ultimately, this paper proposes measures to narrow the development gap between regions and enhance the CEE across the region. Meanwhile, implementing regional refinement management and formulating differentiated regional sustainable development planning.

Research on different modes of energy conservation and emission reduction: A differential game model based on carbon trading perspective

In recent years, due to global climate change, increasing resource scarcity, and environmental constraints, countries have prioritized energy conservation and emissions reduction. However, enterprises are primarily responsible for energy saving and emissions reduction. To encourage industrial enterprises to engage in energy conservation and emissions reduction, high-carbon enterprises must purchase carbon emission rights from low-carbon counterparts. Common modes of energy conservation and emission reduction of industrial enterprises include reducing production scale, improving energy utilization efficiency, and expanding renewable energy. This article constructs three differential game models to identify the applicable scope of various energy conservation and emission reduction strategies, comparing and analyzing the equilibrium results. The study concludes that when the cost of changing the production mode and the income obtained from the production of unit product is large, the low-carbon enterprise can obtain the maximum benefit by reducing the production scale mode. Otherwise, low carbon enterprises can be maximized through improving energy efficiency mode. For both low-carbon and high-carbon enterprises, reducing production scale is the fastest way to enhance efficiency when the costs of energy conservation and emission reduction are substantial.

Analysis on the spatial correlation network and driving factors of carbon emissions in China's logistics industry

Promoting the green development of the logistics industry has become a focal point of attention in China. This study combines an improved gravity model and social network analysis method, focusing on the nineteen provinces of the Yangtze Economic Belt and Yellow River Basin. It constructs a spatial correlation network of carbon emissions in the logistics industry from 2010 to 2021, exploring its formation mechanism and spatial evolution characteristics. The study utilizes a quadratic assignment procedure model to investigate internal driving factors. Building upon this, the study employs an improved STIRPAT model to predict the emission reduction path of the future logistics industry. Results are as follows: (1) Carbon consumption is most pronounced in Shandong, Jiangsu, and Shanghai. The carbon emission shows the characteristics of larger downstream; (2) The bridges of carbon emission-related networks gradually shift from Shandong to Shanxi, Anhui, and Sichuan. Carbon emissions in each sector and spatial spillover effects exhibit dynamic correlations and interactive influences; (3) Energy intensity, freight volume, and the spatial correlation network of the logistics industry are highly correlated; (4) The overall carbon emissions from the logistics industry show a decreasing trend in the future. Anhui and Shaanxi provinces will have high carbon emissions in 2035. The conclusions aim to provide policy suggestions for the region's low-carbon transformation.

Impact path of digital economy on carbon emission efficiency: Mediating effect based on technological innovation

The digital economy (DIE), a new economic form with digitalization at its core, has become an important driving force for promoting regional economy development. In the context of the COVID-19 pandemic, exploring the impact path of the DIE on carbon emission efficiency (CEE) is conducive to giving full play to the "carbon-reduction-and-efficiency-enhancement" role of the DIE, and to promoting the realization the "dual carbon" goal of carbon peak and carbon neutrality. In this paper, the Yellow River Basin (YRB) and the Yangtze River Economic Belt (YREB) are taken as study areas, the panel Tobit model is used to explore the impact of the DIE on CEE, and the intermediary-effect model and threshold-effect model are constructed to test the intermediary and threshold effects of technological innovation, respectively. The results show that the DIE has a U-shaped nonlinear impact on CEE in both the YRB and the YREB and that the impact has regional heterogeneity. Technological innovation can play a mediating effect between the DIE and CEE, whereas the mediating effect in the YRB is stronger than that in the YREB. Technological innovation has a threshold effect on the DIE to improve CEE, while the threshold value in the YREB is higher than that in the YRB. Furthermore, this paper proposes some suggestions to guide regional low-carbon and sustainable development.

Can industrial co-agglomeration improve carbon emission efficiency? Empirical evidence based on the eastern coastal areas of China

The goal of "carbon peak and carbon neutrality" is the key to coping with global warming and achieving high-quality development. Producer services and manufacturing co-agglomeration (Coagglo) is an important path to achieve low-carbon development. Therefore, the relationship between industrial co-agglomeration and carbon emission efficiency (CEE) needs to be discussed. Based on the panel data of 114 cities along the eastern coast of China from 2006 to 2021, this study uses a panel quantile regression model and dynamic spatial Durbin model to evaluate the impact and spatial effect of Coagglo on CEE. The results show that there is a nonlinear relationship between Coagglo and CEE. When it exceeds the 50th quantile, the degree of influence decreases slightly, but it still shows a significant positive correlation. When considering industry heterogeneity, we find that the co-agglomeration of warehousing and postal industry (TRA) and manufacturing has the most significant impact on CEE, while the co-agglomeration of leasing and commercial service industry (LEA) and manufacturing has the least impact on CEE. Regional heterogeneity shows that the Coagglo has a greater impact on carbon emission efficiency in the northern region than in the southern region. In addition, Coagglo promotes the spillover of knowledge and technology and has a positive spatial spillover effect on CEE. This conclusion provides a theoretical reference for carbon emission reduction in eastern coastal areas of China.

How does new-type urbanization affect total carbon emissions, per capita carbon emissions, and carbon emission intensity? An empirical analysis of the Yangtze River economic belt, China

The coordination of the relationship between new-type urbanization (NTU) and carbon emission reduction has become China's primary strategic goal. However, previous studies on this topic mainly examined the unidirectional impact of NTU on carbon emissions, while disregarding their potential relationship. This study establishes an evaluation system for measuring NTU and explores the bidirectional impact between NTU and total carbon emissions (TCE), per capita carbon emissions (PCE), and carbon emission intensity (CEI). The Yangtze River Economic Belt of China is selected as the study area, and the period from 2005 to 2019 is studied. The results show that: (1) The NTU levels in the upstream, midstream, and downstream regions of the Yangtze River increase from 0.148, 0.208, and 0.365 in 2005 to 0.465, 0.503, and 0.675 in 2019, suggesting that NTU levels tend to be balanced in these three reaches. (2) A positive bidirectional impact is found between NTU and TCE, as well as PCE in midstream and upstream regions, whereas in downstream regions, only a positive unidirectional effect of NTU on TCE and PCE is found. (3) Specifically, TCE plays the most significant role in promoting NTU in upstream regions, while NTU exerts the greatest pulling force on TCE and PCE in midstream regions. (4) Unlike the positive impact between NTU and TCE or PCE, there is a significant two-way inhibitory effect between NTU and CEI. (5) A three-step carbon emission reduction law is found in the process of NTU, where NTU towards low carbon development will experience NTU inhibits CEI, PCE, and TCE in sequential order. These findings provide an important reference for promoting a harmonious relationship between NTU and carbon emission reduction, helping governments formulate reasonable measures to achieve high-quality urban development and carbon emission reduction goals.

Industrial carbon emission efficiency prediction and carbon emission reduction strategies based on multi-objective particle swarm optimization-backpropagation: A perspective from regional clustering

Against the backdrop of global climate change, industrial carbon emission reduction has become an important pathway to for global low-carbon development. This study constructs a framework of geographic spatial constraints regionalization and multi-objective machine learning to predict future industrial carbon emission efficiency (ICEE) and explore strategies for carbon emission reduction. Firstly, the ICEE of 285 Chinese cities were calculated by the super-efficiency slacks-based measure. Secondly, the cities were classified into four ICEE level regions through the spatially constrained multivariate clustering. Next, the multi-objective particle swarm optimization-BP (MOPSO-BP) model was constructed to predict the future trends of ICEE in the four regions. Finally, the geographical detector and multi-scale geographically weighted regression were employed for exploring driving force and carbon emission reduction strategies in different regions. The results show that most cities had low or medium ICEE, while super efficiency cities were mainly distributed in the east coastal areas. The prediction performance of the MOPSO-BP model for the four regions was better than the ordinary particle swarm optimization-BP and traditional BP model. Except for the Agricultural Production Region, there is considerable room for improving the ICEE of other regions over the next decade. Macroeconomic and microeconomic development have a global effect in promoting regional ICEE improvement, urban construction shows a promoting or inhibiting effect in different regions, and information technology has significant spatial heterogeneity in its influence within each region. The analysis framework developed in the study is a reliable solution for managing and planning ICEE and provides constructive suggestions for future regional low-carbon development.

Spatial-temporal pattern and spatial convergence of carbon emission intensity of rural energy consumption in China

Based on the panel data of 30 provinces (municipalities and autonomous regions) in China from 2005 to 2019, this paper uses Gini coefficient decomposition and kernel density estimation to investigate the regional differences and dynamic evolution trend of rural energy carbon emission intensity in China. Then, the convergence model is used to analyze the convergence characteristics and influencing factors of carbon emission intensity. The study found the following: (1) During the observation period, the carbon emissions of coal energy and oil energy were much higher than those of gas energy. The carbon emissions of rural energy consumption experienced three stages of development, and the carbon emission intensity showed a downward trend as a whole. The spatial distribution pattern of total carbon emissions present an "adder" distribution, and the spatial agglomeration phenomenon gradually strengthens with the passage of time. (2) The Gini coefficient of China's rural energy consumption carbon emission intensity shows a trend of "Inverted N-shaped." The Gini coefficient of carbon emission intensity in the eastern and northeastern regions shows an increasing trend, while the Gini coefficient of carbon emission intensity in the western and central regions shows a downward trend. The super variable density is the main source of carbon emission intensity difference. The peak value of the main peak of the nuclear density curve of the carbon emission intensity increased significantly, the bimodal form evolved into a single peak form, and the density center moved to the left. (3) The carbon emission intensity of rural energy consumption in the whole, central, and western regions of China has the characteristic of σ convergence, while the carbon emission intensity in the eastern and northeastern regions does not have the characteristic of σ convergence. There is a significant spatial positive correlation in the carbon emission intensity, there is also a significant β convergence characteristic, the speed of conditional β convergence is significantly higher than that of absolute β convergence, and the spatial interaction will further improve the convergence speed. Industrial structure, industrial agglomeration, and energy efficiency will increase the convergence speed. In terms of sub-regions, the conditional convergence rate of carbon emission intensity in the four regions shows a decreasing trend in the northeast, central, eastern, and western regions.

Assessing the spatial spillover effects and influencing factors of carbon emission efficiency: a case of three provinces in the middle reaches of the Yangtze River, China

Studying urban carbon emission efficiency is vital for promoting city collaboration in combating climate change. Prior research relied on traditional econometric models, lacking spatial spillover effects understanding at the urban scale. To provide a more comprehensive and visually insightful representation of the evolving characteristics of carbon emission efficiency and its spatial clustering effects and to establish a comprehensive set of indicators to explore the spatial spillover pathways of urban carbon emission efficiency, we conducted an analysis focusing on 42 cities in the middle reaches of the Yangtze River. By employing the index decomposition method, the super-efficiency SBM model, spatial autocorrelation analysis, and the spatial Durbin model, the study calculates the urban carbon emission efficiency from 2011 to 2019 and analyzes the spatial spillover effects and influencing factors of urban carbon emission efficiency. The main conclusions are as follows: (1) Jiangxi Province displayed stable urban carbon emission efficiency evolution, while Hubei and Hunan showed significant internal disparities. (2) Positive spatial correlation exists in urban carbon emission efficiency, with an imbalanced distribution. (3) Various factors influence urban carbon emission efficiency. Technological innovation and economic development have positive direct and indirect impacts, whereas industrial structure, urbanization, population, and energy consumption have negative effects. Spatial spillover effects of vegetation coverage are insignificant. These methods and findings offer insights for future research and policy formulation to promote regional sustainable development and carbon emission reduction.

Spatiotemporal differences in and influencing factors of urban carbon emission efficiency in China's Yangtze River Economic Belt

Improving urban carbon emission efficiency (CEE) is vital to achieving the goal of urban carbon neutrality. However, the synergistic configurational effect of multiple influencing factors on CEE is not clear. Taking the Yangtze River Economic Belt (YREB) as an example, this paper adopts the standard deviation ellipse and Dagum Gini coefficient method to investigate the spatiotemporal differences in urban CEE in the YREB, and using the fuzzy-set qualitative comparative analysis (fsQCA) method, it explores the configurational effect of CEE influencing factors from the system perspective. The main conclusions are as follows: first, the overall level of urban CEE in the YREB is low, with a certain polarization phenomenon. Second, the relative differences in urban CEE in the YREB show a fluctuating upward trend, and the regional differences mainly originate from the overlapping part between regions. Finally, the main CEE influencing factors do not act in isolation, they constitute a complex process of synergistic interaction, with complementary substitution and causal asymmetry.

Analysis on spatial difference and spillover effect of development resilience index of sports industry: A case study of 285 cities in China

The sustainable development of the sports industry has garnered extensive attention worldwide. In this study, after a rigorous explanation of the connotation of the sports industry development resilience coefficient (SIDRC), the Topsis model and exploratory spatial data analysis were comprehensively employed to evaluate and visualize the SIDRC of 285 cities in China. Additionally, a spatial econometric model was constructed to explore the influencing factors of SIDRC. The major conclusions drawn from this study are as follow: (1) While the SIDRC has improved significantly over the study period, it still remains overall at a low level of resilience with a widening gap between cities. (2) A strong spatial imbalance exists in the distribution of SIDRC, with coastal regions demonstrating greater resilience compared to the central and western regions, and provincial capital cities faring better than other cities. (3) Policy support index, economic development level, structural diversity of the sports industry, and social participation play crucial roles in promoting SIDRC. Finally, social participation has a positive impact on SIDRC in neighboring cities by facilitating resource sharing, market expansion, and extending the industrial chain. The paper concludes by offering recommendations such as increasing the construction of sports markets and public participation, which can optimize the layout of the sports industry and enhance industrial development resilience.

Spatial correlation network structure of energy-environment efficiency and its driving factors: a case study of the Yangtze River Delta Urban Agglomeration

Improving energy-environment efficiency is not only a requirement for constructing China's ecological civilization but also inevitable for achieving sustainable economic and social development. Studies on energy-environment efficiency based on relational data and network perspectives are limited, which hinders the development of collaborative regional emission reduction activities. This study uses the SBM-Undesirable model to measure the energy-environment efficiency of the Yangtze River Delta Urban Agglomeration from 2010 to 2020, adopts a modified gravity model and social network analysis to reveal the structural characteristics of its spatial correlation network, and explores its driving factors through the QAP method. The study found (1) an overall upward trend in energy-environment efficiency but with problems of uneven development. (2) The spatial correlation of energy-environment efficiency shows a complex network structure, with increasing network correlation and strong network stability; the network can be divided into four plates: net benefit, net overflow, two-way spillover, and agent. (3) Differences in industrial structure, environmental regulation, economic development, and technological innovation significantly impact the formation of spatial correlation network of energy-environment efficiency. This study provides a reference for the construction of a cross-regional synergistic mechanism to improve energy-environment efficiency.

The future of green transportation: evaluating the impact of innovation in hybrid electric vehicles relating technologies on carbon dioxide emissions in Asia's top knowledge-based economies

The significant contribution of the transportation sector to carbon dioxide emissions (CO2e) has become a developing concern for legislators and environmental experts. Innovation in hybrid electric vehicle-related technologies (IHVRTs) has been identified as a possible strategy for reducing CO2e in the transportation industry. Even though IHVRTs have the potential to reduce CO2e, there are insufficient studies on their impact in the top three Asian knowledge-based economies (Japan, South Korea, and Japan). This study attempts to address this gap in the literature by investigating the association between innovation in IHVRTs and CO2e in the top three Asian knowledge-based economies, with independent variables gross domestic product per capita (GDPPC), economic complexity (ECC), renewable energy consumption (RNEC), and financial development (FD). The model's coefficients are estimated using the augmented mean group, which considers cross-country dependencies and country-specific effects. The empirical findings indicate that IHVRTs have a substantial negative effect on CO2e. In addition, FD has a favorable relationship with CO2e, whereas ECC has a negative relationship with CO2e. The results also demonstrated that RNEC reduces CO2e, whereas the GDPPC reduces CO2e. The policy implications of the results imply an urgent need for additional investment in IHVRTs and a transition towards more environmentally conscious and less ecologically damaging economic activity.

Green location-oriented policies and carbon efficiency: a quasi-natural experiment from National Eco-industrial Demonstration Parks in China

This paper investigates how National Eco-industrial Demonstration Parks (NEDP) in China affects carbon emission efficiency. The difference-in-differences (DID) strategy is used for analysis. This paper finds that the construction of NEDP is conducive to the improvement of carbon emission efficiency, and the findings remain robust through placebo tests and propensity score matching. Heterogeneity analysis shows NEDP construction has greater utility on carbon efficiency in non-resource-based cities as well as in environmentally friendly cities. The mechanism analysis found that green technology innovation, industrial restructuring, and the relocation of industrial enterprises are effective ways to improve carbon efficiency in NEDP. Finally, this paper finds that the construction of NEDP has obvious spatial spillover effects on carbon efficiency, which can effectively heighten the carbon efficiency level of this locality and nearby areas.

How does technological innovation affect carbon emission efficiency in the Yellow River Economic Belt: the moderating role of government support and marketization

The Yellow River Economic Belt (YREB) is a fundamental ecological protection barrier for China. Its carbon pollution issues are currently severe owing to the extensive energy consumption and unsatisfactory industrial constructions. In this context, this paper estimates carbon emission efficiency (CEE) based on the panel data from 56 cities in the YREB during the period 2006-2019 and analyzes its spatial distribution characteristics. Additionally, the spatial Durbin model (SDM) is utilized to examine the effect of technological innovation (TI) on CEE as a result of the moderating effects of government support (GS) and marketization (MA), respectively. The results indicated that (i) in the YREB, CEE exhibited significant spatial autocorrelation characteristics; (ii) TI negatively affected local CEE; (iii) the moderating effect of local GS on the relationship between TI and CEE in the local area was negative, but its spatial spillover effect was still not significant; (iv) the moderating effect of local MA on the relationship between TI and CEE in the local area was also negative, but positive in the surrounding areas. Based on the empirical analysis, a series of policy suggestions are proposed to improve the YREB's CEE.

The heterogeneous drivers of CO2 emissions in China's two major economic belts: new evidence from spatio-temporal analysis

CO2 emissions have become increasingly prominent in China, and the primary emitters are economic belts that are spread throughout China. Two major economic belts, i.e., the Yangtze River Economic Belt (YTREB) and the Yellow River Economic Belt (YREB). Combined with stochastic impacts by regression on population, affluence and technology model, the spatial Durbin model under the space-and-time fixed effect and the Geographical and Time-Weighted Regression are employed to explore the spatio-temporal distribution characteristics and heterogeneous drivers of CO2 emissions in the two economic belts. The results are as follows. First, CO2 emissions exhibit obvious spatial correlation features in the YREB, but no such obvious spatial correlation is found in the YRETB. Second, in the YREB, the magnitude of the total influencing factors on CO2 emissions follows an order where affluence (A) is the biggest driver, followed by energy intensity (EI), technology (TEC) and openness (OP), while the biggest driver in the YRETB is industrial structure supererogation (ISS), followed by population (P), energy intensity (EI), and affluence (A). Both direct and spatial spillover effects of the drivers are observed in the two economic belts. Third, the CO2 emissions show a notable temporal lag effect in the YREB, but not in the YRETB. Fourth, the effects of the CO2 emission drivers illustrate significant spatio-temporal heterogeneity in the two economic belts.

Spatio-temporal variations, spatial spillover, and driving factors of carbon emission efficiency in RCEP members under the background of carbon neutrality

Improving carbon emission efficiency (CEE) has emerged as a critical way for Regional Comprehensive Economic Partnership (RCEP) members to promote carbon reduction in the context of climate change mitigation and carbon neutrality. The super-efficiency slacks-based measure (SBM) model, which considers non-desired outputs, is adopted to comprehensively assess the current state and trend of CEE in 15 RCEP countries from a spatio-temporal dynamic perspective, and the global Malmquist-Luenberger (GML) index is coupled to quantify the spatial and temporal differences and dynamic changes. Following that, taking into account the spatial characteristics of CEE, the extended STIRPAT model and the spatial Durbin model are combined to further investigate the primary influencing factors of CEE. It is found that (1) the CEE of RCEP members is generally poor and unevenly distributed in temporal and spatial dimensions, with significant room for improvement and an overall positive spatial autocorrelation; (2) CEE varies considerably among RCEP members, with developed countries far outstripping developing countries in terms of both the current status and trend of CEE; (3) on a dynamic level, the GML index exhibits W-shaped fluctuations, with technological progress acting as the dominant force; and (4) in terms of spillover effects, affluence and economic agglomeration inhibit CEE enhancement, whereas technology level and investment capacity facilitate it. The findings will be useful in developing carbon-neutral plans for various countries as well as coordinated sustainable development for RCEP regions.

Spatiotemporal heterogeneity between agricultural carbon emission efficiency and food security in Henan, China

It is significant to investigate the coupling and coordination between agricultural carbon emission efficiency (ACEE) and food security and to achieve peak carbon dioxide emissions and carbon neutrality in agriculture as early as possible while ensuring national food security. The Super-SBM (slack-based model) and the comprehensive index method were used to measure the ACEE and food security level in Henan province from 2010 to 2020. The coupling coordination degree (CCD) and the relative state of ACEE and food security were analyzed using the coupling coordination degree model (CCDM) and the relative development degree model (RDDM). In addition, the interaction between ACEE and food security and the spatial-temporal heterogeneity were analyzed by combining with the geographically and temporally weighted regression (GTWR) model. The results showed that: Firstly, the overall level of ACEE was high, and the spatial heterogeneity of ACEE was significant. The spatial pattern of food security is relatively stable, with high levels in the south and low levels in the north. Secondly, The CCD between ACEE and food security in Henan province generally shows a decreasing trend. In the spatial dimension, the CCD between ACEE and food security in Henan province exhibits a spatial divergence characteristic of low in the center and high in the north and south, with significant regional variations. Finally, there is spatial and temporal heterogeneity between ACEE and food security. The regression coefficients differ significantly among different cities, the regression coefficients do not show a consistent positive or negative correlation, and the regression coefficients are distributed both positively and negatively. This study serves as a guide for achieving the goal of double carbon in agriculture and ensuring food security.

Spatial Association Network Evolution and Variance Decomposition of Economic Sustainability Development Efficiency in China

The economy's sustainable development has become a national strategic deployment in China. Research on the difference between the economic sustainable development efficiency (ESDE) and the spatial network will assist the government with the deployment of sustainable development strategies and the achievement of the "peak carbon dioxide emissions". This paper designs the input-output indicator system of sustainable economic development efficiency and builds an unexpected output super-EBM-Malmquist model to measure the ESDE of 30 provinces in China from 2008-2020. According to the ranking of ESDE, the 30 provinces in China are classified into four groups by applying the quartile method, and the difference in the ESDE in different regions and the temporal variation of different provinces are studied by using the Dagum Gini coefficient and Gaussian Kernel density. Moreover, the relationship between ESDE in different provinces is studied based on the revised gravity model and social network analysis method. The connections between provinces with related relations constitute the ESDE network. Results show that (1) the average ESDE in China shows an upward trend, the eastern region is in a leading position, the central and western regions are trying to catch up with the eastern region, and the development of the northeast region is lagging behind. (2) The level of ESDE in different provinces is clearly arranged from high to low, illuminating a distinct pattern. Moreover, provinces with high levels of development are much higher than provinces with low levels of development, presenting a phenomenon of polarization. (3) The regional ESDE development imbalance is prominent, and the ESDE in the eastern region is closely related, while the connection in the western region is lower. (4) Beijing-Tianjin Urban Agglomeration and the Yangtze River Delta have significant spatial spillover effects in the association network, while the northeast, northwest, southwest and central regions have significant spatial benefit relationships. These findings provide important enlightenment for promoting the sustainable and balanced development of China's economy.

Differentiation Analysis on Carbon Emission Efficiency and Its Factors at Different Industrialization Stages: Evidence from Mainland China

Industrial production is currently the main source of global carbon emissions. There are obvious differences in regional carbon emission efficiencies (CEE) at different industrial stages. We investigate CEE and explore its factors in mainland China at different industrialization stages from 2008-2020 using the super-SBM model with an undesirable output and the STIRPAT model. There is significant spatial heterogeneity in regional CEE, with gaps gradually widening. CEE's spatial heterogeneity in mid-industrialized provinces is narrowing, while in late-industrialized and post-industrialized provinces, it is widening. CEE's factors also differ in provinces at different industrialization stages. At the mid-industrialization stage, the industrial structure (IS) is the dominant factor, while population urbanization (PU) is dominant at the late-industrialization stage, and both PU and IS are dominant at the post-industrialization stage. Based on CEE's characteristics at different industrialization stages, we propose suggestions for green development.

How Big Data Affect Urban Low-Carbon Transformation-A Quasi-Natural Experiment from China

As a new factor of production, data play a key role in driving low-carbon and sustainable development relying on the digital economy. However, previous studies have ignored this point. Based on the panel data of 283 cities in China from 2007 to 2019, we investigated the construction of national big data comprehensive pilot zones (NBDCPZs) in China as a quasi-natural experiment, using the difference-in-differences (DID) model to empirically test the impact of NBDCPZ policies on urban low-carbon transformation. The following conclusions can be drawn: NBDCPZ construction significantly promotes urban low-carbon transformation, and a series of robustness analysis supports this conclusion. NBDCPZ constructions mainly promotes urban low-carbon transformation by stimulating urban green innovation and optimizing the allocation of urban resource elements. Compared with eastern cities, small and medium-sized cities, and resource-based cities, the construction of NBDCPZs can promote the low-carbon transformation of cities in central and western China, large cities, and non-resource-based cities. Further analysis shows that the construction of NBDCPZs can only improve the low-carbon transformation of local cities, with negative spatial spillover effects on the low-carbon transformation of surrounding cities. Therefore, in the future, it is vital to consider the promotion effect of the construction of NBDCPZs on the low-carbon transformation of local cities and prevent its negative impact on the low-carbon transformation of surrounding cities.

Research on Carbon Emission Efficiency Space Relations and Network Structure of the Yellow River Basin City Cluster

The Yellow River Basin serves as China's primary ecological barrier and economic belt. The achievement of the Yellow River Basin's "double carbon" objective is crucial to China's green and low-carbon development. This study examines the spatial link and network structure of city cluster carbon emission efficiency in the Yellow River Basin, as well as the complexity of the network structure. It focuses not only on the density and centrality of the carbon emission efficiency network from the standpoint of city clusters, but also on the excellent cities and concentration of the city cluster 's internal carbon emission efficiency network. The results show that: (1) The carbon emission efficiency of the Yellow River Basin has been dramatically improved, and the gap between city clusters is narrowing. However, gradient differentiation characteristics between city clusters show the Matthew effect. (2) The distribution of carbon emission efficiency in the Yellow River Basin is unbalanced, roughly showing a decreasing trend from east to west. Lower-level efficiency cities have played a significant role in the evolution of carbon emissions efficiency space. (3) The strength of the carbon emission efficiency network structure in the Yellow River Basin gradually transitions from weakly correlated dominant to weakly and averagely correlated dominant. Among them, the Shandong Peninsula city cluster has the most significant number of connected nodes in the carbon emission efficiency network. In contrast, the emission efficiency network density of the seven city clusters shows different changing trends. Finally, this study suggests recommendations to improve carbon emission efficiency by adopting differentiated governance measures from the perspective of local adaptation and using positive spatial spillover effects.

Spatial Evolutionary Characteristics and Influencing Factors of Urban Industrial Carbon Emission in China

Climate warming caused by carbon emissions is a hot topic in the international community. Research on urban industrial carbon emissions in China is of great significance for promoting the low-carbon transformation and spatial layout optimization of Chinese industry. Based on ArcGIS spatial analysis, Markov matrix and other methods, this paper calculates and analyzes the temporal and spatial evolution characteristics of industrial carbon emissions in 282 cities in China from 2003 to 2016. Based on the spatial Dubin model, the influencing factors of urban industrial carbon emissions in China and different regions are systematically analyzed. The study shows that (1) China's urban industrial carbon emissions generally show a trend of first growth and then slow decline. The trend of urban industrial carbon emissions in the western, central, northeastern and eastern regions of China is basically consistent with the overall national trend; (2) In 2003, China's urban industrial carbon emissions were dominated by low carbon emissions. In 2016, China's urban industrial carbon emissions were dominated by high carbon emissions, and the spatial trend is gradually decreasing from the eastern region to the central region to the northeast region to the western region; (3) In 2003, the evolution pattern of China's urban industrial carbon emissions was "low carbon-horizontal expansion" dominated by positive growth, and in 2016, it was "low carbon-vertical expansion" dominated by scale growth; (4) China's urban industrial carbon emissions have spatial viscosity, and the spatial viscosity decreases with the increase of industrial carbon emissions. (5) In 2004, the relationship between urban industrial carbon emissions and gross industrial output value in China is mainly weak decoupling. In 2016, various types of decoupling regions are more diversified and dispersed, and strong decoupling cities are mainly formed from weak decoupling cities in southwest China and eastern coastal areas; (6) From a national perspective, indicators that are significantly positively correlated with industrial carbon emissions are urban industrial structure, industrial agglomeration level, industrial enterprise scale and urban economic development level, in descending order. Indicators that are significantly negatively correlated with urban industrial carbon emissions are industrial structure and industrial ownership structure, in descending order. Due to the different stages of industrial development and industrial structure in different regions, the influencing factors are also different.

Industrial Carbon Emission Efficiency of Cities in the Pearl River Basin: Spatiotemporal Dynamics and Driving Forces

In the context of green and high-quality development, effectively enhancing industrial carbon emission efficiency is critical for reducing carbon emissions and achieving sustainable economic growth. This study explored this research area using three models: the super-efficient SBM model was used to measure the industrial carbon emission efficiency of 48 cities in the Pearl River Basin from 2009 to 2017; the exploratory spatiotemporal data analysis method was used to reveal the spatiotemporal interaction characteristics of industrial carbon emission efficiency; and the geographical detectors and geographically weighted regression model were employed to explore the influencing factors. The results are as follows: (1) The Pearl River Basin’s industrial carbon emission efficiency steadily increased from 2009 to 2017, with an average annual growth rate of 0.18 percent, but the industrial carbon emission efficiency of some sites remains low; (2) The local spatiotemporal pattern of industrial carbon emission efficiency is solitary and spatially dependent; (3) The spatial variation of industrial carbon emission efficiency is influenced by a number of factors, including the industrialization level, openness to the outside world, the science and technology level, energy consumption intensity, and productivity level, with the productivity level, industrialization level, and openness to the outside world being the most important. Among these factors, the productivity level, science and technology level, openness to the outside world, and industrialization level all have a positive correlation with industrial carbon emission efficiency, but energy consumption intensity has a negative correlation. This study provides an integrated framework using exploratory spatiotemporal analysis and geographically weighted regression to examine carbon emission efficiency among cities. It can serve as a technical support for carbon reduction policies in cities within the Pearl River Basin, as well as a reference for industrial carbon emission studies of other regions of the world.