Search Results (91)

Rapid urbanization in China has driven the continuous expansion of urban land over many years. While most studies have examined the scale, speed, and spatial distribution of urban expansion, few have explored its temporal evolution and convergence patterns. To address this gap, the present study employs kernel density estimation (KDE) and the log t regression test to analyze urban expansion across 315 Chinese cities from 2000 to 2022. Our results reveal that the per capita urban area increased by a factor of 1.58 amid a fluctuating process on a national scale. Moreover, the log t regression test rejects convergence at national and regional levels while identifying six distinct convergence clubs. The evolutionary trajectories of these clubs have diverged over time, with inter-club gaps stabilizing and their spatial distribution exhibiting significant agglomeration. These findings indicate that urban expansion in China remains characterized by multiple equilibria, with imbalanced growth having stabilized. Consequently, effective urban planning, along with the diffusion of capital and technology, may promote a shift towards global convergence in urban expansion dynamics. Full article

Economic financialization refers to misappropriating workers’ earnings and enriching wealthy individuals through financial cycles. This process leads to an unequal distribution of wealth and income, particularly pronounced between urban and rural areas. This article examines the impact of economic financialization on the income gap between urban and rural residents by analyzing provincial-level data from China collected between 2003 and 2022. Utilizing the FE-SCC model and SDM, this study reveals that economic financialization increases the income gap between urban and rural residents, especially in eastern China and regions characterized by advanced economic development. The findings indicate that economic financialization significantly exacerbates the wage income gap between urban and rural residents but reduces the property income gap, which relates directly to the nature of work performed by urban and rural residents. The income disparity between these two groups correlates with each region’s economic financialization level. It is influenced by spillover effects from neighboring areas, evidenced by a phenomenon known as “club convergence”. Strengthening regulations on economic financialization, leveraging policy-driven financial systems, promoting regional development, and enhancing inclusive financial services could alleviate income disparity in urban–rural areas and improve the population’s overall well-being. Full article

With the global increase in disaster risks, enhancing urban resilience has become a critical strategy for risk mitigation and sustainable development. This study develops a two-dimensional indicator framework based on urban systems and resilience capacity from the perspective of the disaster management cycle and applies an improved CRITIC-TOPSIS method to evaluate the resilience levels of the Chengdu–Chongqing urban agglomeration, China. The spatiotemporal evolution of urban resilience from 2010 to 2022 is systematically examined. Furthermore, the dynamics of urban resilience transitions are investigated using a spatial Markov chain model, and the driving factors behind the spatial distribution of resilience are explored through the Geo-detector method. The results indicate the following: (1) Comprehensive resilience demonstrated a steady upward trend during the study period, with Chengdu and Chongqing, as core cities, driving regional resilience improvement and reducing disparities within the urban agglomeration. (2) Significant spatial heterogeneity was observed in the distribution of the comprehensive resilience index and the indices of individual resilience dimensions. (3) The Markov chain analysis revealed a distinct “club convergence” pattern in the dynamic transitions of resilience levels, with development trends closely tied to spatial factors. (4) The Geo-detector model analysis highlighted that infrastructure development and technological innovation exert long-term and substantial impacts on resilience improvement. These findings provide valuable insights for enhancing resilience and promoting sustainable development in the Chengdu–Chongqing region and other similar urban systems. Full article

New quality productive forces serve as a catalyst for high-quality development and act as a critical driver of Chinese-style modernization. This study evaluated the degree of new quality productive force in China’s five major urban agglomerations between 2013 and 2022 using the entropy approach. Additionally, it utilized kernel density estimation, the Dagum Gini coefficient, and Markov chain analysis to explore the spatial and temporal dynamics of these forces and their evolutionary trends. The findings revealed the following: (1) Overall, the new quality productive forces in China’s five major urban agglomerations have exhibited a steady upward trend, although the overall level remains relatively low. Among these regions, the Pearl River Delta ranks the highest, followed by the Yangtze River Delta, Beijing–Tianjin–Hebei, Chengdu–Chongqing, and the Urban Cluster in the Middle Reaches of the Yangtze River. Nevertheless, significant potential for improvement persists. (2) The traditional Markov probability transfer matrix suggests that the new quality productive forces in these urban agglomerations are relatively stable, with evidence of “club convergence”. Meanwhile, the spatial Markov transfer probability matrix indicates that transfer probabilities are influenced by neighborhood contexts. (3) Over time, the new quality productive forces in Chinese urban agglomerations show a tendency to concentrate at higher levels, reflecting gradual improvement. The developmental state and evolutionary patterns of new quality productive forces in Chinese urban agglomerations are thoroughly evaluated in this paper, along with advice for accelerating their growth to promote Chinese-style modernization. Full article

New productive forces are the new impetus for the high-quality development of the marine economy. To accurately measure the development level of marine new productive forces, this study constructs an evaluation index system from four aspects: development impetus, development structure, development mode, and development achievements. This study determines the combination weights of indicators based on relative entropy. Kernel density estimation, spatial Markov chain and Dagum Gini coefficient are used to analyze the spatiotemporal evolution, regional disparities and sources of marine new productive forces in coastal provinces of China. Finally, the decision-making trial and evaluation laboratory together with interpretative structural modeling (DEMATEL-ISM) is used to analyze the key influencing factors of marine new productive forces. Results show that the marine new productive forces have been increasing year by year, but the overall level is relatively low. There is a phenomenon of “club convergence” in the development level of marine new productive forces, and the state transfer occurs between adjacent types. The overall variation in marine new productive forces is showing a downward trend, with disparities arising mainly from inter-regional variation and hypervariable densities. The key influencing factors include investment in marine R&D, the openness of foreign investment, the openness of foreign trade, and investment in pollution control. The study conclusion provides support for designing a development path for marine new productive forces that conforms to regional characteristics. Full article

Eco-efficiency is crucial for the European rubber and plastics industry to minimize production costs through effective resource management (e.g., energy management) and reduce environmental impacts like greenhouse gases (GHGs) emissions. Circular economy (CE) solutions can support the industry’s competitiveness while aligning with sustainability goals and regulatory requirements. In the present research, we employ a hybrid window data envelopment analysis (WDEA) methodology to measure panel data eco-efficiency via the application of the moving average principle. The examination of 27 European countries as decision-making units (DMUs), in the period 2014–2022, led to the conclusion that the average eco-efficiency is 70.33%, showing that most of the DMUs can ameliorate their performance regarding pollution control. The highest eco-efficiency in 2014 can be monitored in Ireland, Switzerland, Norway, and Poland, but in 2022, only Ireland and Switzerland kept their positions, whereas Norway dropped to the 16th position and Poland plummeted to the 24th hierarchical position. Geographical disparities can be spotted, as Northern and Western Europe have greater eco-efficiency than Eastern and Southern Europe. At a second level of analysis, the convergence between the 27 European countries in the period under consideration is examined using the log t regression test and club clustering. The analysis leads to three final clubs where conditional convergence dominates. Full article

Achieving sustainable rural development in China requires effectively integrating agricultural growth with ecological balance. However, existing research on the coordination between agricultural economy and ecosystems has often focused on isolated aspects, such as economic growth or ecological sustainability, or has been limited to specific provinces or regions, lacking a comprehensive nationwide analysis. To address this gap, this study uses spatial data from 31 provincial-level regions in China from 2008 to 2022, developing a multidimensional framework that encompasses economic input, structure, efficiency, benefits, vitality, ecological conditions, and pressure. Using multi-factor econometric methods, we comprehensively evaluate the coordination between China’s agricultural economy and ecosystems, revealing regional disparities and spatiotemporal variations in their coupling coordination, and analyzing the barriers affecting this coordination. Our findings show that: First, coupling coordination has steadily improved, narrowing regional disparities. Second, regional differences are primarily driven by variations between the eastern, central, and western regions, with structural disparities shifting from interregional to hyper-variable density. Third, development exhibits a “club convergence” pattern, where upward transitions are difficult and downward mobility is a risk. Key barriers include farmland scale, land efficiency, afforestation area, and soil erosion control. Based on these findings, we recommend regional development strategies, dynamic monitoring mechanisms, optimized land use, and enhanced ecological protection. This study provides valuable insights for policymakers and practitioners to promote the coordinated and sustainable development of agricultural economies and ecosystems in China. Full article

Sustainable development (SD) is vital for the progress of Chinese provinces, especially in the face of emerging challenges. This study constructs an index system for SD based on five dimensions: economic, social, ecological, political, and cultural aspects, aligning with scientific connotations and contemporary requirements. We employ an improved entropy-weight-TOPSIS method to assess the SD of 30 provinces from 2012 to 2022. Our analysis explores the dynamic evolution, regional disparities, coupling coordination, long-term trends, and convergence of provincial SD. The findings include: (1) Provincial SD in China has shown consistent growth, but significant regional disparities remain, forming a gradient distribution from high to low in the order of “East-Central-Northeast-West.” (2) While both intra-regional and inter-regional SD differences have decreased over time, inter-regional disparities continue to be significant, serving as the primary source of regional differences. (3) Coupling and coordination across the five dimensions of SD have improved; however, an imbalance persists, with uncoordinated development remaining a prominent issue. (4) A clear “club convergence” phenomenon is observed, indicating that the SD of neighboring provinces influences one another. Higher SD in adjacent regions increases the likelihood of upward shifts, while lower SD tends to lead to downward shifts. (5) Evidence of both σ-convergence and β-convergence in provincial SD development suggests that SD is ultimately converging toward a stable state. These findings provide valuable insights for policymakers aiming to enhance sustainable development across China’s provinces. Full article

New-quality productivity (NQP) serves as a critical indicator for measuring the level of high-quality economic development. Gaining insights into the spatial and temporal patterns along with the key drivers of NQP is essential for promoting the global industrial transformation and revitalizing old cities. This study utilized spatial analysis, Dagum Gini coefficient, Markov chains, and optimal parameter geographical detectors to analyze spatial patterns and influencing factors of NQP across 271 Chinese prefecture-level cities from 2011 to 2021. Findings reveal that the average index of NQP increased from 0.045 in 2011 to 0.072 in 2021, with spatial patterns showing higher levels in coastal regions compared to inland areas. The overall disparity of NQP has diminished, although significant internal imbalances persist, particularly in the eastern region, where the pronounced gap between eastern and central areas remains the primary source of variation. Local NQP development is strongly influenced by proximity to adjacent areas, characterized by path dependence and club convergence effects. Additionally, cultural foundation, urbanization, and economic development play pivotal roles in fostering NQP, with their interactions exhibiting notable nonlinear and dual-factor enhancement effects. These findings provide valuable theoretical and practical insights for advancing NQP levels in China and globally. Full article

Finding ways to improve regional energy efficiency is important for the Chinese government to achieve its dual carbon target. This paper aims to explore ways to improve regional energy efficiency by studying the spatial–temporal dynamic evolution of energy efficiency. To scientifically study the evolution trend in regional energy efficiency in China, this study uses convergence analysis, a spatial Gini coefficient decomposition model (no spatial consideration), and a spatial Markov chain model and spatial measurement model (spatial consideration). The results show the following: from 2008 to 2019, the mean value of regional single-factor energy efficiency (RS) showed an obvious trend of continuous increase, while the mean value of regional green total-factor energy efficiency (RT) changed from a trend of continuous decline to a relatively stable trend. The overall Gini coefficient of RS showed a trend of “steady–rising–steady”, and the overall Gini coefficient of RT showed a trend of “steady–small increase–sharp increase–fall”. There was club convergence in the two types of regional energy efficiency, and both of them achieved certain “leapfrog” changes. The factors that had a significant impact on RS include human capital, industrialization, openness, urbanization, financial development, and innovation environment. The significant factors for RT included governance structure, industrialization, openness, policy support, and financial development. The limitation of this paper is that only provincial data were used. In the future, city-level data can be mined and more detailed policy suggestions can be put forward for city-level differences. The research method used in this paper to study regional energy efficiency evolution trends is also applicable to other countries. Full article

The World Bank (WB) has described the European Union (EU) as a convergence machine, and the real and institutional convergence has been achieved for a long period of time, and EU’s cohesion policy, alongside the Recovery and Resilience Facility (RRF), remains crucial for driving reforms and fostering investments that promote growth. But, in the last two decades this convergence machine has stopped working, and the convergence process has turned in the divergence. The divergence process poses a great risk for the smooth functioning of the EU, and it increases vulnerability of the EU to negative economic shocks. Productivity and institutional convergence are a necessary precondition for the smooth functioning of the EU, reducing differences in standards of living, increasing resilience, and achieving environmental sustainability. In the present paper, we will apply log t-test over the period 2003–2023 to investigate the formation of productivity and institutional convergence clusters. Our goal is to identify which countries belong to the poor productivity/institutional clubs, and to provide the necessary policy implications. Results indicate the existence of multiple steady states, which means that EU is vulnerable to external economic shocks Full article

Education is the cornerstone of rural revitalization. This study aims to comprehensively evaluate the development of rural education in China from 2006 to 2020. From a systemic perspective, this study established a multidimensional evaluation index system for rural education and used the weight-TOPSIS method for measurement. Additionally, geographic information system and spatial econometric methods were employed to explore spatial–temporal differentiation and influencing factors. The results show that (1) rural education levels in China have generally improved in recent years, with higher development in northern, northeastern, and eastern regions and lower levels in central and southwestern regions. (2) In terms of spatial differentiation, rural education development among provinces has significant spatial agglomeration. The provinces around Beijing are hot spots, while remote southwestern provinces are cold spots. (3) Regarding dynamic evolution, the disparity in rural education development among provinces has widened, with a few provinces significantly ahead. There are club convergence features, and the hierarchy of rural education development between provinces is relatively stable, with less likelihood of lagging provinces catching up. (4) Economy, finance, industry, population, and urbanization are key factors influencing rural education, with spatial spillover effects on neighboring provinces. The study provides empirical support and policy insights for advancing balanced and high-quality rural education development. Full article

Industrial mobile robots easily experience actuator loss of some effectiveness and additive bias faults due to the working scenarios, resulting in unexpected performance degradation. This article proposes a novel adaptive fault-tolerant control (FTC) strategy for nonholonomic mobile robot systems subject to simultaneous actuator lock-in-place (LIP) and partial loss-of-effectiveness (LOE) faults. First, a nominal fractional-order sliding mode controller based on the designed exponential super-twisting reaching law is investigated to reduce the reaching phase time and eliminate the chattering. To address the time-varying LIP faults and uncertainties, a novel barrier function (BF)-based gain is explored to assist the super-twisting law. An estimator is designed to estimate the lower bound of the time-varying partial LOE fault coefficients, thus without requiring the boundary information of faults that is commonly requested in traditional FTC schemes. Combined with the nominal controller clubbed with BF and estimator-based LOE fault compensation term, the fault-tolerant controller is finally constructed. The proposed FTC scheme achieves fast convergence and the sliding variables can be confined in a predetermined neighborhood of the sliding manifold under actuator faults. The results show that the proposed controller has superior tracking performance under faulty conditions compared with other state-of-the-art adaptive FTC approaches. Full article

Exploring the low-carbon transition in China can offer profound guidance for governments to develop relevant environmental policies and regulations within the context of the 2060 carbon neutrality target. Previous studies have extensively explored the promotion of low-carbon development in China, yet no studies have completely explained the mechanisms of the low-carbon transition in China from the perspective of per capita carbon emissions (PCEs). Based on the statistics and carbon emissions data of 367 prefecture level cities in China from 2000 to 2020, this study employed markov chain, kernel density analysis, hotspots analysis, and spatial regression models to reveal the spatiotemporal distribution patterns, future trends, and driving factors of PCEs in China. The results showed that China’s PCEs in 2000, 2010, and 2020 were 0.72 ton/persons, 1.72 ton/persons, and 1.91 ton/persons, respectively, exhibiting a continuous upward trend, with evident regional heterogeneity. PCEs in northern China and the eastern coastal region were higher than those of southern China and the central and southwestern regions. The PCEs in China showed obvious spatial clustering, with hot spots mainly concentrated in Inner Mongolia and Xinjiang, while cold spots were mainly in some provinces in southern China. The transition of PCEs in China exhibited a strong stability and a ‘club convergence’ phenomenon. A regression analysis revealed that the urbanization level and latitude had negative effects on PCEs, while the regional economic development level, average elevation, average slope, and longitude showed positive effects on PCEs. These findings have important implications for the promotion of the low-carbon transition and the effective achievement of the “dual carbon” goal. Full article

This paper studies the convergence of environmental sustainability and its main determinants in selected American countries. In addition, it studies the impact of economic activity, income inequality, trade openness, and innovative activity on the sustainability of these countries. Convergence tests such as unit root and club convergence are applied. Furthermore, cointegration and causality tests are used, and long-term parameters are estimated using methods robust for cross-sectional dependence. The results show evidence of stochastic convergence with the univariate unit root tests in the five indicators (energy consumption, carbon dioxide emissions, ecological footprint, energy intensity, and load capacity factor) used, while with the panel data unit root tests only in four (carbon dioxide emissions, ecological footprint, energy intensity, and load capacity factor). There is no evidence of convergence towards a single club considering the complete sample, but there is evidence of convergence towards several clubs. The variables are integrated of order one and are cointegrated. Moreover, using robust estimators in the presence of cross-sectional dependence in long-term economic activity, income inequality, trade openness, and innovative activity deteriorate sustainability, while renewable energy improves it in these countries. Full article