Search Results (22)

Amid China’s push for new urbanization and refined urban governance, urban villages function as key transitional spaces in the process of rural–urban spatial restructuring. Their internal differentiation and typological governance approaches warrant systematic exploration. This study examines 140 urban villages located in the core and peripheral areas of Kunming as empirical cases. By innovatively integrating polycentric urban theory with spatial accessibility theory, we construct a dual-dimensional classification framework. Employing the Analytic Hierarchy Process (AHP), we develop a comprehensive evaluation system encompassing ecological, spatial, social, and economic dimensions. Our findings reveal the following: (1) Urban villages with different levels of accessibility within the same region tend to exhibit broadly similar characteristics across most evaluation dimensions. However, outlier cases demonstrate distinct development trajectories that transcend spatial constraints, driven by unique mechanisms underlying their atypical evolution. (2) Cross-regional comparisons highlight systematic disparities across several dimensions, most notably in ecological quality, spatial efficiency, and economic vitality. Based on spatial differentiation, we propose five governance models tailored to varied urban village types. The proposed typological governance framework provides a replicable methodology for addressing urban-rural transition challenges in diverse contexts. By emphasizing the spatial heterogeneity of informal settlements and advocating for place-specific strategies based on geographic endowments, this model enables policymakers to move beyond one-size-fits-all approaches. For Chinese cities, it offers a systematic toolkit to classify urban villages according to their regional roles and developmental potentials, informing tailored regeneration plans. Globally, the framework’s emphasis on context-sensitive typology and multidimensional evaluation can guide the upgrading of informal settlements in rapidly urbanizing regions, particularly where rural-urban interfaces face similar fragmentation pressures. Full article

Under the “double security” goal of achieving both food security and ecological protection, this study explores the green and low-carbon utilization efficiency of cultivated land (GLCUECL) in the Huaihe River Ecological Economic Belt (HREEB). This study identifies the spatiotemporal evolution characteristics and trends, promoting the green, low-carbon, and sustainable utilization of arable land resources in the HREEB, thus contributing to regional and national food and ecological security. Using a global super-efficiency EBM framework that accounts for undesirable outputs, as well as the GML index, the researchers measured and decomposed the GLCUECL in 25 prefecture-level cities of the HREEB from 2005 to 2021. The Theil index and kernel density estimation were applied to analyze regional disparities and changing developmental traits. Spatial convergence and divergence were assessed using the coefficient of variation and spatial convergence models. Key findings include the following: (1) Over time, the GLCUECL in the HREEB exhibited an overall upward trend and a non-equilibrium characteristic, namely the “East Sea-river-lake Linkage Area (ESLA) > Midwest Inland Rising Area (MIRA) > Huaihe River Ecological Economic Belt (HREEB) > North Huaihai Economic Zone (NHEZ)”. The increase in the GML index of the GLCUECL is mainly attributable to a technical progress change. (2) The overall difference in the GLCUECL tends to decline, which is mainly attributable to the intra-regional differences. (3) The overall kernel density curves for the HREEB and its three sub-regions exhibited a “rightward shift” trend. Except for the expansion and polarization of the absolute difference in the GLCUECL in the NHEZ, the absolute difference in GLCUECL in other regions, such as the HREEB, ESLA, and MIRA, exhibited a decreasing trend. (4) Spatial convergence analysis revealed that only the NHEZ lacks σ-convergence, whereas all regions exhibited β-convergence. Moreover, factors such as rural economic development level, cultivated land resource endowment, agricultural subsidy policy, crop planting structure, and technological input exerted a heterogeneous effect on the change in the GLCUECL. Based on these findings, this study offers recommendations for improving GLCUECL in the HREEB. Our recommendations include the implementation of the concept of green new development, optimization of the institution supply, establishing a regional cooperation mechanism for green and low-carbon utilization of cultivated land, and formulation of differentiated paths for improving the green and low-carbon utilization efficiency of cultivated land according to local conditions. Full article

The objective of this article is to conduct a review to analyze global trends in the use of air pollution models under the influence of climate variability (CV) over urban areas. Five scientific databases were used (2013–2024): Scopus, ScienceDirect, SpringerLink, Web of Science, and Google Scholar. The frequency of citations of the variables of interest in the selected scientific databases was analyzed by means of an index using quartiles (Q). The results showed a hierarchy in the use of models: regional climate models/RCMs (Q3) > statistical models/SMs (Q3) > chemical transport models/CTMs (Q4) > machine learning models/MLMs (Q4) > atmospheric dispersion models/ADMs (Q4). RCMs, such as WRF, were essential for generating high-resolution projections of air pollution, crucial for local impact assessments. SMs, such as GAM, excelled in modeling nonlinear relationships between air pollutants and climate variables. CTMs, such as WRF-Chem, simulated detailed atmospheric chemical processes vital for understanding pollutant formation and transport. MLMs, such as ANNs, improved the accuracy of predictions and uncovered complex patterns. ADMs, such as HYSPLIT, evaluated air pollutant dispersion, informing regulatory strategies. The most studied pollutants globally were O₃ (Q3) > PM (Q3) > VOCs (Q4) > NOx (Q4) > SO₂ (Q4), with models adapting to their specific characteristics. Temperature emerged as the dominant climate variable, followed by wind, precipitation, humidity, and solar radiation. There was a clear differentiation in the selection of models and variables between high- and low-income countries. CTMs predominated in high-income countries, driven by their ability to simulate complex physicochemical processes, while SMs were preferred in low-income countries, due to their simplicity and lower resource requirements. Temperature was the main climate variable, and precipitation stood out in low-income countries for its impact on PM removal. VOCs were the most studied pollutant in high-income countries, and NOx in low-income countries, reflecting priorities and technical capabilities. The coupling between regional atmospheric models and city-scale air quality models was vital; future efforts should emphasize intra-urban models for finer urban pollution resolution. This study highlights how national resources and priorities influence air pollution research over cities under the influence of CV. Full article

Drawing on the longitudinal dataset from 262 cities at the provincial tier and higher across China between 2011 and 2022, this research employs the production model to formulate the China Urban Industrial Land Mismatch Index, quantifying the extent of industrial land misalignment across China. It also analyzes its spatiotemporal evolution characteristics and regional differentiation characteristics, and explores the influence of China’s urban industrial land discordance on the advancement of urban ecological modernization. The key insights are outlined below. Firstly, across the entire spectrum of Chinese urban centers, cities from the eastern, central, and western zones, as well as those situated along the Yangtze River and the Yellow River basins, exhibit comparable patterns in industrial land misalignment. The extent of industrial land discordance has diminished, regional disparities have lessened to some degree, and there is an absence of polarization or the Matthew effect. Secondly, the variation in industrial land discordance within cities in the eastern region is the most pronounced, followed by the central region, with the western region showing the least disparity. The greatest contrast in the urban industrial land mismatch is found between the eastern and central regions. The primary driver of the discrepancy in industrial land misalignment across the eastern, central, and western regions is predominantly the ultra-variable density, followed by intra-regional disparities, with inter-regional differences contributing the least. Furthermore, the variation in the industrial land mismatch within cities in the Yangtze River Basin surpasses that within cities in the Yellow River Basin. The disparity in industrial land misalignment between the two follows a pattern of initially increasing, then decreasing, and subsequently rising again. The primary origin of this discrepancy lies within regional variations, followed by ultra-variable density, with inter-regional differences contributing the least. Thirdly, the regression analysis reveals that the discordance in industrial land use across Chinese cities exerts a substantial negative influence on urban ecological evolution. This effect operates through technological innovation and the employment levels in the secondary sector. Fourthly, industrial land discordance significantly hampers urban ecological advancement in the eastern region, shows a negative but statistically insignificant impact in the central region, and has a positive yet inconsequential effect in the western region. Moreover, the misalignment of industrial land exerts a notable suppressive influence on the ecological modernization of cities within the Yangtze River Basin, while it plays a significant role in fostering the ecological modernization of cities in the Yellow River Basin. Fifth, the mismatch of urban industrial land has produced significant negative spatial spillover effects on urban ecological modernization. Full article

Porcine astrovirus (PoAstV), porcine sapovirus (PoSaV), porcine norovirus (PoNoV), and porcine rotavirus A (PoRVA) are newly discovered important porcine diarrhea viruses with a wide range of hosts and zoonotic potential, and their co-infections are often found in pig herds. In this study, the specific primers and probes were designed targeting the ORF1 gene of PoAstV, PoSaV, and PoNoV, and the VP6 gene of PoRVA. The recombinant standard plasmids were constructed, the reaction conditions (concentration of primers and probes, annealing temperature, and reaction cycle) were optimized, and the specificity, sensitivity, and reproducibility were analyzed to establish a quadruplex real-time quantitative RT-PCR (RT-qPCR) assay for the detection of these four diarrheal viruses. The results demonstrated that the assay effectively tested PoAstV, PoSaV, PoNoV, and PoRVA without cross-reactivity with other swine viruses, and had limits of detection (LODs) of 138.001, 135.167, 140.732, and 132.199 (copies/reaction) for PoAstV, PoSaV, PoNoV, and PoRVA, respectively, exhibiting high specificity and sensitivity. Additionally, it displayed good reproducibility, with coefficients of variation (CVs) of 0.09–1.24% for intra-assay and 0.08–1.03% for inter-assay. The 1578 clinical fecal samples from 14 cities in Guangxi Province, China, were analyzed via the developed assay. The results indicated that the clinical samples from Guangxi Province exhibited the prevalence of PoAstV (35.93%, 567/1578), PoSaV (8.37%, 132/1578), PoNoV (2.98%, 47/1578), and PoRVA (14.32%, 226/1578), and had a notable incidence of mixed infections of 18.31% (289/1578). Simultaneously, the 1578 clinical samples were analyzed with the previously established assays, and the coincidence rates of these two approaches exceeded 99.43%. This study developed an efficient and precise diagnostic method for the detection and differentiation of PoAstV, PoSaV, PoNoV, and PoRVA, enabling the successful diagnosis of these four diseases. Full article

Unveiling the spatial and temporal distribution of urban ecological resilience and analyzing the configuration paths for enhancing its levels are crucial for promoting sustainable development in China. Our study integrates the DPSIR and EES models, considering the causal relationships between systems affecting urban ecological resilience while also examining their internal structures. Based on this, we construct an evaluation system for urban ecological resilience indicators. Utilizing the entropy-TOPSIS method, we assess the ecological resilience index (ERI) across 280 Chinese cities from 2011 to 2021, and the kernel density estimation and Markov chain are used to study the evolution process while the magnitude and source of spatial–regional differences are examined by the Dagum Gini coefficient decomposition method. Additionally, we empirically investigate the driving mechanisms toward high ERI with the focused stepwise quantitative case analysis (fsQCA) method based on the technology–organization–environment (TOE) framework. The results find that the ERI in China shows a tendency of moderate growth in variability, with an obvious gradient distribution: higher levels in the eastern and southern and lower levels in the western and northern regions. Also, ERI exhibits evolutionary features of increasing polarization and inter-regional differentiation. Spatial disparities gradually increase with fluctuations, driven primarily by transvariation density and intra-regional differences, contributing to a dual non-equilibrium state of east–west and north–south directions. Achieving a high ERI is influenced by various antecedent variables interacting with each other, and there are three predominant driving paths among these variables, with the level of informatization playing a central role in each pathway. Full article

Europe has been experiencing an increasing number of sweltering heat waves in recent years. This run of hot extremes induces a significant impact on the human environment, especially in terms of excess mortality, highlighting the urgent need for improved heat–health action planning. This is particularly true in countries situated in the eastern Mediterranean, which is considered a climate change hot spot. To increase preparedness and response to overheating risks, heat–health warning systems (HHWSs) are of vital importance. In this direction, the principal aim of the HEAT-ALARM research project is to provide a novel scientific and technological framework for the development of efficient HHWSs, employing Greece as a testbed. Going beyond the simple notion that outdoor meteorological conditions alone can adequately describe the heat–health nexus, a sophisticated human-biometeorological index, the modified physiologically equivalent temperature (mPET), is used. Advanced statistical models and tools are employed in order to establish a clear link between mPET and excess mortality at regional-unit administrative level. Moreover, urban climate factors produced by combining remote sensing and geographical information system techniques are incorporated into the HHWS via a state-of-the-art numerical weather prediction model. The latter includes a scheme that combines the parameterization and modeling of building effects and energy, respectively, in order to account for the urban indoor thermal conditions and the intra-urban differential heat exposure within the five highest populated cities of Greece (Athens, Thessaloniki, Patras, Heraklion and Larissa). Further, the human body’s acclimatization ability is considered, as well as the physiological characteristics of different vulnerable groups of people, including the elderly, women and outdoor workers. The current paper describes the scientific background of HEAT-ALARM and provides preliminary results associated with the project’s realization. Full article

The current global situation is complex and volatile. Thus, promoting the construction of a resilient digital innovation ecosystem has become an important issue for regional innovation development. Based on panel data from 30 Chinese provinces and cities, this study empirically investigated the current situation and resilience level of China’s regional digital innovation ecosystem development using a global entropy method, a technique for order performance by similarity to the ideal solution (TOPSIS), Moran’s I, obstacles, and panel models. The results indicate that the resilience level of China’s regional digital innovation ecosystem will grow from 2011 to 2021. Spatially, it showed a spatial distribution of “coastal high–central flat–northwest depression,” with inter-regional “low coupling” and intra-regional “high cohesion” characteristics. The global Moran’s I was greater than zero, decreasing by 43.860% from 2011 to 2021, and the scope of the “high-high” quadrant and the “low-high” quadrant has been expanding and narrowing, respectively. The state and response indicators were the main obstacles to the resilience development of the regional digital innovation ecosystem, and the obstacle degree of the pressure indicators increased during the survey period. Industrial structure, regional urbanization, human capital, and digital industrialization have made significant positive contributions to regional digital innovation ecosystem resilience overall, and the drivers were regionally heterogeneous in space. Finally, this study proposes strategies for improving the resilience of regional digital innovation ecosystems, including strengthening top-level design, differentiated development, and lowering obstacles. Full article

This study used a two-stage network data envelopment analysis model to measure the water use efficiency of 108 cities in the Yangtze River Economic Belt in the initial water use and wastewater treatment phases from 2009 to 2019. We divided the urban water use efficiency of six significant urban clusters in the Yangtze River Economic Belt using the Dagum Gini coefficient. We also tested the convergence characteristics of urban water use efficiency in six significant urban clusters in the Yangtze River Economic Belt using convergence and convergence kinds. According to this report, the Yangtze River Economic Zone’s cities often have low levels of water use efficiency, which is primarily due to ineffective wastewater treatment. The 108 cities in the Yangtze River Economic Zone are divided into four types based on the average values of water use efficiency in the initial use and wastewater treatment phases; the highest number of cities are in the double-low category, with low average values of water use efficiency in the initial use and wastewater treatment phases. During the study period, spatial differences in urban water use efficiency in the Yangtze River Economic Zone narrowed, with the differences stemming mainly from hyperdensity, followed by intra- and inter-regional differences. Meanwhile, there is convergence in urban water use efficiency in the Yangtze River Economic Belt, significant β convergence in the urban agglomerations of the Yangtze River Delta, Jianghuai, middle reaches of the Yangtze River, Chengdu–Chongqing, and Central Yunnan, and insignificant β convergence in the Central Qian urban agglomeration. After considering control factors, such as industrial structure, financial development level, environmental regulation, economic development level, and science and education development level, the water use efficiency of the six major urban clusters in the Yangtze River Economic Belt converges faster, but the influence of these control factors on the water use efficiency of each urban cluster is heterogeneous. Research results have reference value for the development of improvement strategies on differentiated urban water use efficiency in the Yangtze River Economic Belt. By measuring the regional differences in water use efficiency of urban agglomerations in the Yangtze River Economic Belt and clarifying their convergence mechanism, it provides a basis for analyzing the spatial pattern of water use efficiency in urban agglomerations and has reference value for formulating differentiated urban water use efficiency improvement strategies in the Yangtze River Economic Belt. Full article

Examining and assessing the characteristics of innovation networks among science and technology firms at the city level is essential for comprehending the innovation patterns of cities and improving their competitiveness. Nevertheless, the majority of studies in this field solely rely on patent and paper data, neglecting the analysis of networks across diverse scales and dimensions. Websites offer a novel platform for companies to exhibit their products and services, and the utilization of hyperlink data better captures the dynamics of innovative cooperation. Thus, to attain a more realistic and precise comprehension of China’s technology enterprise cooperation networks, enhance the understanding of intra-city and cross-border cooperation within innovation networks, and offer more scientific guidance to cities in enhancing their innovation capabilities by investigating the factors influencing innovation scenarios and the mechanisms of their interactions, this study constructs an innovation network based on the hyperlink data extracted from Chinese science and technology enterprises’ websites in 2022. It explores the network’s inherent characteristics and spatial patterns across multiple dimensions and scales. Additionally, it employs GeoDetector to analyze the driving factors behind the heterogeneity of city quadrants across each dimension. The findings suggest the following: (1) Evident polarization of innovation capability exists, with a more pronounced differentiation of cities between high capability zones. (2) Contrary to the conventional notion of geographical proximity, cross-region website cooperation prevails, with cross-provincial cooperation being more prevalent than intra-provincial cross-city cooperation. (3) Enterprise cooperation tends to align with partners of similar scale, and small and medium-sized enterprises primarily engage in internal cooperation, primarily concentrated in second and third-tier cities. (4) Cities with high degree centrality and structure holes are primarily located in the construction areas of Chinese urban agglomerations, while those with low degree centrality and structure holes are situated near double-high cities. (5) The spatial heterogeneity of innovation networks across the four dimensions is primarily influenced by STI, while cooperation intensity and innovation capacity dimensions are strongly influenced by traffic capacity. The intra- and inter-city cooperation intensity dimensions are significantly impacted by administrative grade, and the enterprise scale and network location dimensions are most affected by the level of digital infrastructure. Full article

The mobility plan and the road infrastructure works implemented, together with the Bus Rapid Transit (BRT) connected bus system in its first two phases, generated optimistic expectations about the reduction of lethal crashes in the city. This research studies the relationship between investments in transportation infrastructure in the city and the distribution of traffic fatalities. Although it is not strictly speaking an impact assessment, the approach we propose performs geostatistical contrasts between intervened and non-intervened areas, using a geographically weighted model that attempts to model the spatial variability of the factors associated with the intra-urban road traffic crash rate, controlling for infrastructure interventions and some proxy indicators of urban structure. The findings reveal that fatalities decreased in areas both with and without intervention. Despite the expectation of reducing fatal injuries, the differential effects of the interventions were relatively small. The risk of road traffic crashes was even increased in critical points of the city with recurrent lethal crashes. The effects of road interventions on fatal road traffic crashes in Cali did not correspond to the high social and economic costs involved in the BRT system and the work plan. Full article

The spatiotemporal patterns of urban expansion have attracted worldwide attention and have been generalized into several prevalent hypotheses, such as the diffusion–coalescence hypothesis and the three-growth-type hypothesis. Although many studies have examined the applicability of these hypotheses, long-term research and evidence are still lacking. This study incorporated a compiled dataset of multisource remote sensing images and historical maps covering nine snapshots of the urban built-up area from 1914 to 2018 to monitor the urban expansion process in Hangzhou, China. A fractal analysis of the area–radius relationship was employed for areal demarcation to explore the heterogenous patterns across different intra-city spatial extents. The results show that (1) Hangzhou has experienced a turbulent period of primitive urbanization in the pre-reform era and a consecutive period of market-oriented urbanization after the economic reform; (2) the urban expansion pattern characterized by landscape metrics demonstrates the existence of multiple alternations between diffusion and coalescence phases with peculiarities across different intra-city spatial extents; (3) the analysis of urban growth types documents a consistent predominance of edge-expansion with wax and wane between infilling and leapfrogging; and (4) institutional reform, industrial development, and administrative division adjustment are the main drivers of urban expansion in Hangzhou. Our findings suggest that effective planning policies need to be raised to curb urban sprawl. Differentiated planning strategies should be proposed to accommodate unique conditions in different urban subregions. The integrated-analysis approach based on multisource remote sensing images and historical maps establishes a feasible pathway for long-term urban research. Full article

The new development mode represented by the digital economy has provided new ideas for sustainable rural development. To comprehensively understand the status of digital rural development and propose scientific measures of rural revitalization in the Yellow River Basin (YRB), this study used counties as the research unit and data from 2020 to analyze the spatial differentiation characteristics and influencing factors by employing the Theil index, spatial autocorrelation analysis, and a geodetector model. The results showed that the digital rural development index in the YRB is slightly higher than it is in China overall, but the sub-index for the digital economy is lagging. The levels of digital rural development in the different reaches were lower reaches > middle reaches > upper reaches. Additionally, municipal districts and county-level cities have higher statuses than t general counties. Moreover, the decomposition of the Theil index shows that the intra-group differences in the upper reaches and general counties are the most important cause of the total differences. Moreover, the levels of digital rural development demonstrate spatial differences, with high and low levels in the east and west, respectively. An obvious reliable spatial correlation exists, and the spatial agglomeration featured with a similar level is significant. Finally, the influencing factors of spatial heterogeneity of digital rural development in the YRB and different reaches were different, with government expenditure being the main leading factor in the YRB and its upper reaches, while educational attainment and industrial structure are the leading factors in the middle reaches and lower reaches, respectively. The explanatory power of the interactions between the factors far exceeds that of a single factor, as shown through double-factor and nonlinear enhancement. This study provides a scientific reference for facilitating more targeted policy measures to achieving the goal of digital China and rural revitalization. Full article

This research project investigates the direct and indirect influences of the digital economy in promoting the green high-quality development of industry. We analyze the baseline regression, spatial spillover effect, intermediary effect, and threshold effect of the digital economy and its technological innovation processes on the green high-quality development of industry in 31 provinces and cities in China during the period between 2012–2020. The results reveal that the digital economy generates a positive impact and nonlinear incremental character on the green high-quality development of industry, and technological innovation is the mediating factor of both. Digital economy and technological innovation have a threshold effect. When they cross the threshold value, the force will be strengthened. The econometric analysis of spatial spillover shows the green high-quality development of intra-regional and inter-regional industries is promoted by the digital economy. The dimension of regional differences in the analysis shows that the effect of the digital economy is not evident in the western region of China, while the impact is a lot more prominent in the eastern than in the middle area of the country. Study results provide measures of the green high-quality development of industry, such as the application of green scenarios, differential development, and collaborative development in the digital economy. Full article

Since the ecological protection and high-quality development of the Yellow River Basin (YRB) in China have become a primary national strategy, the low-carbon economy is crucial. To formulate effective emission mitigation policies for the YRB, we need to comprehensively understand the characteristics of the spatial agglomeration of the carbon emissions intensity in the YRB and its regional heterogeneity. Therefore, based on the relevant data from 2005 to 2017, we first scientifically measure the carbon emissions intensity of 57 cities along the YRB. Then, we analyze the spatial agglomeration characteristics and long-term transfer trends of carbon emission intensity using exploratory spatial data analysis methods and Markov chains. Finally, the Dagum Gini coefficient and the variation coefficient method are used to study the regional differences and differential evolution convergence of the carbon emissions intensity in the YRB. The results show that the carbon emissions intensity of the YRB has dropped significantly with the spatial distribution characteristics “high in the west and low in the east”, and there is a significant spatial autocorrelation phenomenon. In addition, the probability of a shift in urban carbon intensity is low, leading to a “club convergence” and a “Matthew effect” in general and across regions. Inter-regional differences have always been the primary source of spatial differences in carbon emissions intensity in the YRB, and the intra-regional differences in carbon emissions intensity in the lower YRB show a significant convergence phenomenon. The research results may provide a reference for the regional coordinated development of a low-carbon economy in the YRB, and serve to guide the win-win development model of ecological environment protection and economic growth in the YRB. Full article