Search Results (41)

Background: Synergistic development of the coupled and coordinated relationship of medical service capacity, residents’ health level, and regional economic development (MRR) is crucial for realizing the “Healthy China 2030” strategy. This study aims to analyze the spatiotemporal features of the coupling coordination degree (CCD) of MRR. Methods: Based on panel data from 2012 to 2022, we constructed the MRR evaluation system. The entropy method was adopted to calculate the comprehensive index of each subsystem, and the coupling coordination degree model (CCDM) was used to evaluate the CCD of MRR during the study period. Furthermore, we applied exploratory spatial data analysis, the Dagum Gini coefficient decomposition, extreme gradient boosting (XGBoost) and Shapley Additive Explanations (SHAP) to explore spatiotemporal dynamic characteristics and factors associated with CCD. Results: (1) Throughout the study period, the overall value of the MRR composite system increased from 0.3798 to 0.4097, with an average annual growth rate of 0.54%. (2) CCD remained generally low, dominated by the basic coordination type. Both global and local spatial autocorrelation analyses revealed significant differentiated spatial clusters of high and low CCD, with the overall spatial distribution fitting a “high in the east, low in the west” pattern. Inter-regional gaps are the main reason for the overall differences. (3) In the full sample, Population density (PD)was the dominant external predictive contributor, with a mean absolute SHAP value of 0.028, followed by Internet broadband access ports (IBAP)and Government health expenditure (GHE). The key predictive factors differed among regions. Conclusions: This study clarifies spatiotemporal evolution and predictive contributors of CCD. These findings provide valuable insights for decision-makers to promote coordinated MRR development. Full article

Coordinating ecological protection with economic development has become essential for regional sustainability amid intensifying climate-related risks and growing economic uncertainty. This study investigates the coupling coordination between ecological resilience (EcR) and economic resilience (EnR) across 108 cities in the Yangtze River Economic Belt (YREB) and 76 cities in the Yellow River Basin (YRB) from 2005 to 2022. By combining the entropy weight method, coupling coordination degree (CCD) model, Dagum Gini coefficient, and XGBoost-SHAP framework, this study examines the temporal dynamics, spatial inequality, and explanatory factors of EcR–EnR coordination. The results show that CCD improved continuously in both regions. The YREB gradually moved into the moderate coordination stage, whereas the YRB remained at a low coordination level despite sustained progress. Spatially, both regions exhibited a downstream–midstream–upstream gradient, but the YREB showed more pronounced spatial differentiation, while the YRB displayed a more balanced improvement pattern, with higher-level coordination gradually shifting from downstream to midstream areas. Overall disparities in CCD narrowed in both regions. Decomposition results reveal that inter-regional differences were the main source of disparity in the YREB, whereas transvariation intensity contributed most to disparity in the YRB. Regarding explanatory factors, green technological innovation made the largest SHAP-based contribution to CCD in the YREB, while digital infrastructure development contributed most in the YRB, with substantial regional variation in factor contributions. By examining EcR–EnR coordination as a dual-resilience coordination process, this study provides basin-based empirical evidence on ecological–economic resilience coordination in two major Chinese river-basin regions. The findings further translate basin-scale coordination patterns into city-level planning and governance implications for land-use regulation, infrastructure layout, urban form, service distribution, and differentiated regional sustainability strategies. Full article

Energy-efficiency retrofit assessment for old residential buildings commonly relies on energy benchmarks, but such benchmarks cannot reveal household-level disparities in energy use. This study integrates energy-consumption benchmarks with distributional-fairness indicators to support retrofit prioritization. Monitored electricity data from 1024 households in four representative old residential building types in Chongqing were analyzed using the Dagum Gini coefficient decomposition method. The results show clear seasonal and typological differences in energy-use imbalance. The annual Gini coefficients for Types A–D were 0.34, 0.42, 0.45, and 0.40, respectively, while the overall level of imbalance generally followed the order winter > summer > transition seasons > annual average. Median energy use intensity (EUI) did not correspond directly to distributional fairness. Type B had the highest annual median EUI (3.89 kWh/m²) but not the highest Gini coefficient, whereas Type C had the lowest median EUI (3.28 kWh/m²) and the highest Gini coefficient (0.45). These findings show that benchmark-based assessment alone may misidentify retrofit priorities. A dual-benchmark diagnostic framework is therefore proposed to integrate energy-use level and distributional fairness, supporting more precise retrofit prioritization, fairer resource allocation, and sustainable renewal of old residential communities. Full article

Amid the compounded impacts of climate change, economic volatility, and sudden shocks, small and medium-sized cities (SMSCs) have become a critical yet frequently overlooked weak link in urban resilience research. Objective assessment of resilience in SMSCs is essential for improving the design and effectiveness of resilience-building policies. Following China’s official city-size classification criteria—i.e., using urban-district resident population as the statistical basis and defining SMSCs as cities with less than 1 million urban-district residents—this study examines 510 Chinese SMSCs from 2012 to 2023. An entropy-weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is employed to construct an urban resilience index. Subsequently, spatiotemporal disparities are characterized using nonparametric kernel density estimation and Dagum Gini coefficient decomposition. The results indicate that: (1) from 2012 to 2023, the resilience index of Chinese SMSCs rose from 0.1108 to 0.1121, with an average annual growth rate of 0.1067%. Overall resilience remains low, and the increase is modest, showing a clear regional gradient of Eastern > Central > Western > Northeastern China. (2) Spatiotemporal differentiation reveals the fastest growth in the Eastern region, a similar trajectory in the Central region, slower growth in the Western region, and a decline in the Northeastern region, reinforcing a pattern of gradient divergence. Within-region disparities generally converged, whereas between-region disparities expanded markedly; nonetheless, the transvariation (overlapping) component remained the primary contributor to overall inequality. (3) The resilience of Chinese SMSCs may face a potential Matthew-effect risk driven by the interaction of scale disadvantage and regional disadvantage. These findings provide evidence to support policies aimed at enhancing resilience in SMSCs and inform the development of differentiated resilience-building strategies across regions. Full article

Against the backdrop of China’s “carbon peaking” and “carbon neutrality” goals, as well as the advancement of new industrialization, the oil and gas industry is undergoing a critical transformation from resource-dependent growth toward innovation-driven, low-carbon, and high-quality development. The integrated advancement of high-end, intelligent, and green transformation—collectively referred to as the “Three Modernizations”—has become a vital pathway for promoting industrial upgrading and sustainable growth. Based on panel data from 30 Chinese provinces from 2009 to 2023, this study constructs a comprehensive evaluation index system covering 19 secondary indicators across three dimensions: high-end, intelligent, and green development. Using the entropy-weighted TOPSIS method, kernel density estimation, Dagum Gini coefficient decomposition, and σ–β convergence models, the study examines the spatiotemporal evolution, regional disparities, and convergence characteristics of HIG integration, and further explores its driving mechanisms through a two-way fixed effects model and mediation effect analysis. The results show that (1) the overall HIG integration index rose from 0.34 in 2009 to 0.46 in 2023, forming a spatial pattern of “high in the east, low in the west, stable in the center, and fluctuating in the northeast”; (2) regional disparities narrowed significantly, with the Gini coefficient declining from 0.093 to 0.058 and σ decreasing from 7.114 to 6.350; and (3) oil and gas resource endowment, policy support, technological innovation, and carbon emission constraints all positively promote integration, with regression coefficients of 0.152, 0.349, 0.263, and 0.118, respectively. Heterogeneity analysis reveals an increasing integration level from upstream to downstream, with eastern regions leading in innovation-driven development. Based on these findings, the study recommends strengthening policy and institutional support, accelerating technological innovation, improving intelligent infrastructure, deepening green and low-carbon transformation, promoting regional coordination, and establishing a long-term monitoring mechanism to advance the integrated high-quality development of China’s oil and gas industry. Overall, this study deepens the understanding of the internal logic and spatial dynamics of the “Three Modernizations” integration in China’s oil and gas industry, providing empirical evidence and policy insights for accelerating the construction of a low-carbon, secure, and efficient modern energy system. Full article

Rural revitalization relies on both industrial upgrading and cultural reinvigoration. Tea-culture–tourism integration has been regarded as a potentially effective route for advancing rural revitalization, yet its interactive relationship and dynamic evolution remain insufficiently understood. Based on provincial-level panel data from 18 major tea-producing provinces in China between 2013 and 2022, this study constructs two comprehensive evaluation index systems for tea–culture–tourism integration and rural revitalization. It employs the entropy weight method, the coupling coordination degree model, the Dagum Gini coefficient decomposition, and spatial econometric models to measure their coupling coordination degree and analyze its spatiotemporal pattern, regional disparities, and driving factors. The main findings are as follows: (1) Both tea–culture–tourism integration and rural revitalization exhibited upward trends, with the latter being consistently higher. (2) The coupling coordination degree transitioned from dissonance to coordination, spatially forming a gradient progression pattern of “coastal-river-inland”. (3) Regional disparities were primarily dominated by transvariation density and inter-regional differences. (4) Results of spatial econometric models indicated that fiscal conditions exerted a significant positive direct effect, while urban–rural social development, employment structure, and human capital generated significant positive direct and indirect effects. Full article

Understanding the multidimensional sources and key drivers of differences in green transition performance (GTP) among resource-based cities is vital for accomplishing national sustainable development objectives and facilitating regional coordination. This study proposes a “Carbon Reduction–Pollution Control–Greening–Growth” evaluation framework and utilizes the entropy method to assess the GTP of China’s resource-based cities from 2013 to 2022. The Dagum Gini coefficient and variance decomposition methods are employed to investigate the GTP differences, and the Optimal Parameters-Based Geographical Detector and the Geographically and Temporally Weighted Regression model are applied to identify the driving factors. The results indicate the following trends: (1) GTP exhibits a fluctuating upward trend, accompanied by pronounced regional imbalances. A pattern of “club convergence” is observed, with cities showing a tendency to shift positively toward adjacent types. (2) Spatial differences in GTP have widened over time, with transvariation density emerging as the dominant contributor. (3) Greening differences represent the primary structural source, with an average annual contribution exceeding 60%. (4) The impact of digital economy, the level of financial development, the degree of openness, industrial structure, and urbanization level on GTP differences declines sequentially. These factors exhibit notable spatiotemporal heterogeneity, and their interactions display nonlinear enhancement effects. Full article

Understanding the coupled and coordinated development of China’s urban and rural basic education systems is crucial for fostering their interaction and synergistic growth. Using China’s provincial panel data from 2011 to 2023, this study measures the coupled and coordinated development level of urban–rural basic education (CCD-URBE) via the entropy weight method, G1-method and coupling coordination degree model. On this basis, the Dagum Gini coefficient decomposition method, traditional and spatial Markov chain models, as well as convergence test models are employed for empirical research. The results show that: (1) During the study period, the CCD-URBE across the nation and the four major regions improves significantly. Both intra-regional and inter-regional disparities show a consistent downward trend. Inter-regional disparities are the main source of the overall disparities, and the contribution rate of transvariation density to the overall disparities exhibits the most significant increase. (2) The CCD-URBE demonstrates strong stability, as most regions tend to maintain their original CCD-URBE grades. Meanwhile, neighborhood grades moderate the local transition probability significantly. Neighborhoods with high CCD-URBE promote the upward improvement of the local CCD-URBE, while those with low CCD-URBE inhibit it. (3) The CCD-URBE across the nation and the four major regions shows obvious trends of σ-convergence, absolute β-convergence, and conditional β-convergence. The central region, which has lower CCD-URBE, exhibits higher convergence speed. Based on these findings, targeted policy implications are derived. Full article

Agriculture is fundamental to food security and environmental sustainability. Advancing its holistic ecological transformation can stimulate socioeconomic progress while fostering human–nature harmony. Utilizing provincial data from mainland China (2013–2022), this research establishes a multidimensional evaluation framework across four pillars: agricultural ecology, low-carbon practices, modernization, and productivity enhancement. Through comprehensive assessment, we quantify China’s low-carbon green agriculture (LGA) development trajectory and conduct comparative regional analysis across eastern, central, and western zones. As for methods, this study employs multiple econometric approaches: LGA was quantified using the TOPSIS entropy weight method at the first step. Moreover, multidimensional spatial–temporal patterns were characterized through ArcGIS spatial analysis, Dagum Gini coefficient decomposition, Kernel density estimation, and Markov chain techniques, revealing regional disparities, evolutionary trajectories, and state transition dynamics. Last but not least, Tobit regression modeling identified driving mechanisms, informing improvement strategies derived from empirical evidence. The key findings reveal the following: 1. From 2013 to 2022, LGA in China fluctuated significantly. However, the current growth rate is basically maintained between 0% and 10%. Meanwhile, LGA in the vast majority of provinces exceeds 0.3705, indicating that LGA in China is currently in a stable growth period. 2. After 2016, the growth momentum in the central and western regions continued. The growth rate peaked in 2020, with some provinces having a growth rate exceeding 20%. Then the growth rate slowed down, and the intra-regional differences in all regions remained stable at around 0.11. 3. Inter-regional differences are the main factor causing the differences in national LGA, with contribution rates ranging from 67.14% to 74.86%. 4. LGA has the characteristic of polarization. Some regions have developed rapidly, while others have lagged behind. At the end of our ten-year study period, LGA in Yunnan, Guizhou and Shanxi was still below 0.2430, remaining in the low-level range. 5. In the long term, the possibility of improvement in LGA in various regions of China is relatively high, but there is a possibility of maintaining the status quo or “deteriorating”. Even provinces with a high level of LGA may be downgraded, with possibilities ranging from 1.69% to 4.55%. 6. The analysis of driving factors indicates that the level of economic development has a significant positive impact on the level of urban development, while the influences of urbanization, agricultural scale operation, technological input, and industrialization level on the level of urban development show significant regional heterogeneity. In summary, during the period from 2013 to 2022, although China’s LGA showed polarization and experienced ups and downs, it generally entered a period of stable growth. Among them, the inter-regional differences were the main cause of the unbalanced development across the country, but there was also a risk of stagnation and decline. Economic development was the general driving force, while other driving factors showed significant regional heterogeneity. Finally, suggestions such as differentiated development strategies, regional cooperation and resource sharing, and coordinated policy allocation were put forward for the development of LGA. This research is conducive to providing references for future LGA, offering policy inspirations for LGA in other countries and regions, and also providing new empirical results for the academic community. Full article

Exploring the regional disparities in and spatiotemporal evolution of the new energy vehicle promotion level (NEVPL) is essential for facilitating low-carbon and environmentally sustainable development. This study employs a multidimensional index system to assess the NEVPL across 31 Chinese provinces from 2017 to 2023, utilizing data on NEV ownership, annual NEV sales, the number of public charging piles, and the vehicle-to-pile ratio. The NEVPL scores were estimated using the Entropy-TOPSIS method. Spatial patterns and the mechanisms of regional disparities were examined using a suite of spatial analytical techniques, including the standard deviation ellipse (SDE), gravity center analysis, Dagum Gini coefficient decomposition, and kernel density estimation. The results reveal three principal findings. First, NEVPL exhibited a sustained upward trend nationwide. The eastern region consistently maintained a leading position, the central and western regions demonstrated steady growth, and the northeastern region remained underdeveloped, leading to an expanding regional gap. Second, spatial distribution transitioned from an early dispersed pattern to a structure characterized by both agglomeration and differentiation. The promotional center gradually shifted toward the southeast, and high-value regions became increasingly isolated, forming island-like clusters. Third, spatial inequality was mainly driven by inter-regional differences, which contributed to over 70 percent of the total variance. The rising intra-regional disparity within the eastern region emerged as the predominant factor widening the national gap. These findings offer empirical evidence to support the refinement of new energy vehicle (NEV) policy frameworks and the promotion of balanced regional development. Full article

The sustainable development of higher education exhibits a strong and measurable association with the level of regional innovation capacity. Drawing on panel data covering 31 provincial-level administrative regions in China from 2010 to 2022, we construct evaluation frameworks for both higher education and regional innovation capacity using the entropy weight method. These are complemented by kernel density estimation, spatial autocorrelation analysis, Dagum Gini coefficient decomposition, and the Obstacle Degree Model. Together, these tools enable a comprehensive investigation into the spatiotemporal evolution and driving mechanisms of coupling coordination dynamics between the two systems. The results indicate the following: (1) Both higher education and regional innovation capacity indices exhibit steady growth, accompanied by a clear temporal gradient differentiation. (2) The coupling coordination degree shows an overall upward trend, with significant inter-regional disparities, notably “higher in the east and low in the west”. (3) The spatial distribution of the coupling coordination degree reveals positive spatial autocorrelation, with provinces exhibiting similar levels tending to form spatial clusters, most commonly of the low–low or high–high type. (4) The spatial heterogeneity is pronounced, with inter-regional differences driving overall imbalance. (5) Key obstacles hindering regional innovation include inadequate R&D investment, limited trade openness, and weak technological development. In higher education sectors, limitations stem from insufficient social service benefits and efficiency of flow. This study recommends promoting the synchronized advancement of higher education and regional innovation through region-specific development strategies, strengthening institutional infrastructure, and accurately identifying and addressing key barriers. These efforts are essential to fostering high-quality, coordinated regional development. Full article

By constructing a comprehensive evaluation index system for the development level of Agricultural Machinery Socialization Services (AMSS) in China’s hilly and mountainous areas, the article adopts the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) entropy weight method to carry out a comprehensive evaluation of the development level of AMSS in China’s 17 major hilly mountainous provinces, and utilizes the obstacle degree model and the Dagum Gini coefficient decomposition method to deeply explore the developmental constraints and regional differences in characteristics. The results of the study show that the development level of AMSS in all provinces is generally on the rise, and the overall development level of the Southwest region is relatively lagging behind, with significant differences from other regions. The obstacle degree model shows that industrial development, Government funding, and farmland construction are the main factors constraining AMSS in hilly and mountainous areas, specifically, the degree of coverage of AMSS, the percentage of agricultural machinery professional cooperatives, the degree of land fragmentation, and the level of agricultural machinery extension inputs have a greater impact on the level of development of AMSS. Dagum Gini coefficient calculations show that the overall relative differences in development levels have a tendency to decrease, but the level of development of agricultural machinery socialization in the southwestern hilly and mountainous second-maturity areas is still low, with an imbalance in development within the region and a more significant gap with the development levels of other hilly and mountainous regions. Full article

The digital economy, as an emerging productive force, has become a critical catalyst for economic sustainable progress. This study examines the regional disparities and origins of the digital economy, as well as its spatial and temporal growth patterns, which are essential for bridging the financial gap among regions and promoting synchronized economic progress. We developed a multidimensional index system to evaluate the level of digital economic development across 30 Chinese provinces, encompassing aspects such as the Internet’s evolution, digital infrastructure, capacity for digital innovation, and the application of digital technologies. The regional imbalances and origins of the digital economy were scrutinized using Dagum Gini coefficient decomposition. Furthermore, the study investigated the spatiotemporal dynamics of digital economy progression in China, employing methods like kernel density estimation, migration models of the gravity center, and an analysis via the standard deviation ellipse. The results reveal the following: (1) Digital economic development is more advanced in Eastern China compared to the western regions, with variations primarily driven by inter-regional differences. (2) The absolute gap in national digital economic development levels is widening, and relative disparities within the eastern region are increasing. Significant polarization is observed in the development of the digital economy across the central and western regions, while disparities in the northeastern region are comparatively minimal. (3) The center of gravity for China’s digital economy has overall shifted southward; the standard deviation ellipse has extended both northward and southward and become more oblate; and the dispersion within China’s digital economy has intensified. Full article

In the context of global efforts to address climate change and pursue sustainable development, green finance (GF) and energy efficiency (EE) have become key issues of focus for academics and policymakers. This study explores the spatiotemporal coupling characteristics and driving factors of China’s green finance and energy efficiency from 2011 to 2022, aiming to help China achieve its dual carbon goals. This study used a three-dimensional framework to assess 30 provinces, considering factor inputs, expected outputs, and undesirable outputs. The study employed the global benchmark super-efficiency EBM model, entropy method, coupling coordination model (CCD), Dagum Gini coefficient decomposition, and spatiotemporal geographic weighted regression model (GTWR). Key findings include a “high in the east, low in the west” gradient distribution of both green finance and energy efficiency, expanding regional disparities, and a strong synergistic effect between technological innovation and energy regulation. Based on the findings, this paper proposes a three-tier governance framework: regional adaptation, digital integration, and institutional compensation. This study contributes to a deeper understanding of the coupling theory of environmental financial systems and provides empirical support for optimizing global carbon neutrality pathways. Full article

Exploring the law and evolution mechanism of coupling and coordination between agricultural carbon emission efficiency (ACE) and agricultural economic growth (AEG) can provide a reference basis for agricultural low-carbon transformation. This study takes 11 cities in Jiangxi Province as the research object; measures the level of ACE based on the panel data from 2008 to 2022; and analyzes the development and influencing factors of the coupling and coordination between ACE and AEG by using the coupling coordination degree model, the Dagum Gini coefficient decomposition method, and the Tobit regression model. The results reveal the following: (1) The overall ACE in Jiangxi Province displays a significant upward trend, with the average efficiency value increasing from 0.172 to 0.624, reflecting an average annual growth rate of 72.43%. Nonetheless, there remains clear regional heterogeneity, characterized by lower efficiencies in Central and Southern Jiangxi compared to the higher efficiencies found in Northern and Western Jiangxi. (2) Despite gradual improvements in regional coordination, the Central and Southern Jiangxi regions still lag Northern and Western Jiangxi in terms of the linked coordination between ACE and AEG, symptoms of which had been previously misaligned. (3) The results of Dagum’s Gini coefficient decomposition show that inter-regional disparities are the main source of overall disparities, with a contribution of 37.43%, which is higher than the synergistic effect of intra-regional disparities and hyper-variable densities, corroborating the core contradiction of uneven development across regions. (4) The Tobit model reveals that government investment, industrial structure optimization, urbanization, and educational attainment exert a significant positive influence on promoting coupling coordination. To establish a scientific basis for achieving a low-carbon agricultural transformation and equitable AEG in Jiangxi Province, this research recommends bolstering regional cooperation, fostering innovations in agricultural science and technology, optimizing the industrial structure, and enhancing farmers’ awareness of low-carbon practices. This study expands the theoretical system of agricultural low-carbon transition in terms of research methods and scales to provide a scientific basis for agricultural provinces to realize agricultural low-carbon transition and balanced economic development. Full article