Prefectural Cities Research Articles

Characteristics of the Spatial Structure of Traditional Villages in the Xinjiang Uygur Autonomous Region in China and Their Influence Mechanisms

Traditional villages are one of the basic types of rural revitalisation and one of the important carriers of cultural inheritance. This research is based on the data of 53 traditional villages in the Xinjiang Uyghur Autonomous Region with the aid of the ArcGIS10.8.1 spatial analysis platform. The study identifies the spatial evolution characteristics from the spatial distribution type, distribution direction, distribution density, distribution balance, etc., and explores their influence mechanisms. The study shows that 1. the spatial structure of traditional villages in Xinjiang was analysed as a cohesive structure type by using the nearest neighbour index method, which shows the evolutionary characteristics of the agglomerative tendency to increase gradually. Among them, Changji Hui Autonomous Prefecture and Turpan City have the highest degree of concentration. 2. The establishment and development of traditional villages in Xinjiang is mainly influenced by natural factors such as geographical features and hydrography. Social factors such as population distribution, transport conditions and economic progression have very important implications for the preservation and continuation of traditional villages.

The impact of pilot policies for low carbon city construction on the development of urban circular economy multiple time-point difference-in-differences model

IntroductionAs a means of environmental regulation, the low-carbon city pilot policy aims to address climate change and alleviate environmental resource pressures in economic development. The development of low-carbon cities often requires changes to the existing industrial and energy structures, which will have a significant impact on the regional economy.MethodsThe study is based on panel data from 273 prefecture level cities in China from 2012 to 2022, and uses the Analytic Hierarchy Process and Entropy Comprehensive Evaluation Method to construct an evaluation index system for urban circular benefits. Finally, the multiple time point differences model and mediation effect model were used to evaluate the impact and influencing factors of policies on urban economy.ResultsThe results indicate that at the 1% level, the explanatory variable coefficient significantly improves the level of urban circular economy development after adding control variables. The construction of low-carbon pilot cities has improved the performance of circular economy development by about 6.42%. The mediating effect of industrial structure rationalization is significantly positive at the 1% significance level, indicating that the construction of low-carbon pilot cities has greatly promoted the coordinated development of industrial relations within China’s low-carbon development regions.DiscussionIt can be seen that the low-carbon city pilot policy has a significant promoting effect on China’s economic development and the development of urban circular economy. Therefore, it is recommended to further expand the pilot scope of low-carbon cities and fully leverage their promoting role.

Digital financial inclusion role to promote renewable energy technology innovation in Chinese Prefectural cities: Moderating role of environmental governance

Renewable energy technology innovation is not only an important technical component of Chinese construction of low-carbon energy systems but also a key way to realize long-term, sustainable development. Digital inclusive finance plays an indispensable role in this process. Using panel data of 275 Chinese cities from 2011 to 2022, a two-step systematic methodology is used to assess the impacts and mechanisms of digital financial inclusion and the moderating role of different environmental governance models on qualitative and quantitative renewable energy innovation. The results indicate that digital inclusive finance has an incentive effect on both qualitative and quantitative renewable energy innovations, with a stronger incentive effect on qualitative behavior. This effect can act on renewable energy technology innovation by reducing financing constraints and incentivizing information disclosure mechanisms. Moreover, different forms of environmental governance have a positive, moderating effect, while the effect of top-down environmental governance is more manifested in quantitative renewable energy innovation. Furthermore, heterogeneity analyses show that the incentive effect of digital financial inclusion is more pronounced in the PV sector and the eastern regions. The findings provide a theoretical basis and practical insights that enable policy-makers to formulate relevant renewable energy technology innovation policies and promote low-carbon energy transition.

The Impact of Pilot Policies for Smart Cities on Carbon Emissions in Cities along the Eastern Coast of China

Based on panel data from 111 prefecture level cities in the eastern coastal region of China from 2006 to 2021, this article regards the three batches of smart city pilot policies implemented since 2012 as a "quasi natural experiment", uses the asymptotic difference in differencesAA (DID) method to explore their impact on carbon emissions in the eastern coastal region of China, and verifies the scientific and credibility of the research results through multiple robustness tests. Then, heterogeneity analysis and mechanism analysis are conducted to explore the impact mechanism of smart cities on carbon reduction according to policies. The research results indicate that, firstly, the pilot policy of smart cities is beneficial for reducing carbon emissions in the eastern coastal areas of China, and this result remains robust after robustness tests such as PSM-DID (propensity score matching), placebo test, and exclusion of other policy interferences. Secondly, the pilot policies of smart cities have different carbon emission reduction effects in cities with different characteristics, and have more significant carbon emission reduction effects on small cities, non resource-based cities, and high GDP cities. Thirdly, the pilot policy of smart cities promotes the green and low-carbon development of cities in the eastern coastal areas of China by enhancing technological level and optimizing industrial structure.

How can agricultural water use efficiency be promoted in the North China Plain?

Water resources are reliable guarantees for high quality development of agriculture in the North China Plain. Improving agricultural water use efficiency is one of the essential ways to solve agricultural water resources shortage. This study measured agricultural water use efficiency (AWUE) using a stochastic frontier analysis (SFA) model and estimated agricultural water-saving potential (AWP) for 74 prefecture-level cities in the North China Plain. The main factors that influenced AWUE were further identified by developing spatial econometric models. The results showed that: (1) From 2000 to 2020, the AWUE of the study area ranged from 0.701 to 0.755 and first rose then fell, and then leveled off. (2) Spatially, the best AWUE was in the central part, followed by the north, and the worst was in the south, and nearly 65% of prefectural cities had annual average AWUE below 0.8. (3) Significant gaps in AWUE and AWP were observed in different regions. Shijiazhuang, Anyang, Hebi and Xinxiang should be considered as key targets to improve AWUE. (4) The effective irrigation degree, per sown area annual precipitation, and annual average temperature had positive effect on AWUE. Increasing the corn sowing area appropriately and reducing the input of per sown area machinery power could also improve AWUE. Therefore, adopting deep plowing measures, developing semi-arid agriculture, and advancing water-saving irrigation technology were feasible ways to improve AWUE and contribute to sustainable agriculture in the North China Plain. These findings can provide a reference for the formulation of high-water efficiency agricultural management strategies in other similar major grain-producing areas.

Spatio-Temporal Diversification of per Capita Carbon Emissions in China: 2000–2020

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.

The potential synergy between air pollution control and employment growth

Public service sectors play crucial roles in maintaining societal functioning through the provision of essential services. However, amidst the backdrop of air pollution, their developmental trajectories may veer off course from their intended goals. Quantifying the impact of air pollution on public service sectors, particularly from an employment perspective, offers valuable insights for policymakers aiming to foster the sustainable growth of these sectors and expand the analysis framework on air pollution externalities. Drawing on monthly data spanning from January 2015 to December 2019 across 269 prefecture cities, this paper therefore explores the impact of air pollution on public service sector employment using a cross-lagged panel model with fixed effects. I find that air pollution negatively affects the employment scales in public service sectors. This negative employment shock is also unevenly distributed across areas and sub-sectors and intensifies over time. Cities belong to key areas for air pollution prevention and control, small and medium-sized cities, and secondary-industry dominated cities, suffer disproportionately negative employment shocks in their public service sectors. These findings offer actionable insights for policymakers to implement targeted measures supporting the continued growth of public service sectors and deepen understanding of the economic ramifications of air pollution.

Analysis of the Impact of Urban Infrastructure on Urbanization Processes at Different Levels from a Spatiotemporal Perspective

A comprehensive understanding of the heterogeneity of urbanization development at different levels and its influencing factors is crucial for promoting global urbanization and advancing China’s new urbanization. Using indicators related to urbanization development, a multidimensional index system was constructed based on five dimensions: population, economy, space, society, and ecology. Employing methods such as the Mann–Kendall test, Sen’s trend analysis, multiple linear regression, and spatial autocorrelation analysis, the spatiotemporal evolution characteristics of urbanization from 2000 to 2019 were analyzed comprehensively at national, economic zone, provincial, and prefectural city scales. The results indicate the following. (1) From 2000 to 2019, urbanization levels at all levels showed an overall upward trend, with the national urbanization rate increasing most rapidly at 5.39%. (2) Trend analysis reveals rapid and significant growth trends in urbanization at the national and economic zone scales, while urban-level changes exhibit greater diversity and spatiotemporal heterogeneity. (3) Spatial distribution patterns show that urbanization levels in the eastern coastal economic zones are significantly higher than those in the northeastern economic zones, highlighting pronounced regional disparities in development and agglomeration effects in economically advanced regions and provinces. (4) Regression analysis demonstrates that spatial urbanization significantly influences urbanization development in China, with urban infrastructure playing a crucial role across different levels.

Estimating GDP by Fusing Nighttime Light and Land Cover Data

Accurate information on gross domestic product (GDP) is essential for better understanding the dynamics of regional economies and urbanization processes. Satellite based nighttime light datasets can well track GDP in urban areas, however, they are difficult to be used in suburban, rural and sparsely populated areas. Thus, this study explored the potential of GlobeLand30 and relief degree for improving the ability of VIIRS Nighttime Light data of GDP estimation. Firstly, we calculated the Moran’s Index (Moran’s I) to measure spatial auto-correlation of GDP. At provincial level, Moran’s I Index of GDP is 0.14, Z value is 2.29. While at the prefecture city level, it is 0.11 and 14.54, respectively. Then, we compared the results derived from geographically weighted regression (GWR) and OLS models (i.e., R2, root mean square error, corrected Akaike information criteria and residuals). Both models suggest that land cover information can significantly improve GDP estimation performance, and total nighttime light (TNL) is the most important economic indicator for estimating GDP. The coefficients of the GWR model for TNL at the provincial and prefecture levels are 1.75 and 1.19, respectively, which are significantly larger than the coefficients for other factors such as land cover and terrain undulation. In addition, the GWR model performed better than OLS model in GDP estimation at both provincial and prefecture levels, and prefecture-level models can better depict the spatial variation in detail. In provincial-level models, GWR could account for 93% of economic development, while OLS could only reflect 82%. Likewise, in prefecture-level models, R2 of GWR model improved almost 50% compared with that of OLS model.

Veto and versatile: understanding the local implementation of environmental policies in China

ABSTRACT Whether local governments effectively implement environmental policies is crucial for successfully addressing global environmental challenges. However, scholars are still unclear about when and why local implementation of environmental policies fails, especially in developing countries where high priority is often given to economic development and where local officials are held accountable upwards. In this study, we investigate how prefectural city governments implement environmental performance assessment policy (EPAP) in China. Based on a sample of prefecture-level cities from 2005 to 2015 and a difference-in-differences estimator, we find that the EPAP has effectively reduced pollution emissions. However, the pollution abatement effect only emerges in assessment years but not in non-assessment years, indicating an opportunistic implementation of environmental policy at the city level. The opportunistic implementation behaviour is more evident in cities where emission reduction task is higher and where city mayors lack environmental work experience or professional expertise.

How rising housing prices affect labor costs in China: Heterogeneity and lagging

AbstractThe impact of house prices rising on urban labor costs has typical heterogeneity and lag effects, but there is little literature on it. Based on the panel data of 283 prefecture level cities from 2001 to 2017, we constructed panel model considering lag effect and threshold effect. The results show that lag one period of housing price has significant positive impact on urban labor costs, with the increase of housing prices, the effect of pushing up the current labor costs is gradually increasing. So, this impact has obvious lag effect and threshold effect. Through grouped regression to test heterogeneity effect, at the regional level, the heterogeneity of the impact is more significant. The more developed the economy is, the greater the pushing effect of house prices on labor costs.

Enhanced large-scale building extraction evaluation: developing a two-level framework using proxy data and building matching

ABSTRACT Deep learning-based building extraction methods have widespread applications in diverse fields. However, the evaluation of large-scale extraction results remains challenging, due to traditional evaluation metrics rely on manually created ground-truth samples and the lack of comprehensive reference-building data for developing countries. To address these problems, we proposed a two-level framework for evaluating large-scale footprint extraction. First, we utilised global open-source population and land use data as the proxy data, to assess grid-level completeness for the areas with insufficient reference data. Second, we introduced an improved two-way area-overlapping method to match the extracted footprints with the reference buildings, thereby enabling a comprehensive evaluation of the study region. Tested in Hyogo Prefecture and Numazu City, Japan, the results demonstrated a 2.6-% improvement in grid classification accuracy and an increase of 0.53 in the completeness correlation, compared with the results obtained using a single proxy indicator. Moreover, the optimised matching method achieved an outstanding semantic matching accuracy of 99%, with high efficiency and robustness in multi-scale matching. Therefore, the proposed approach can effectively evaluate large-scale footprint extraction results and interpret their semantic relationship with actual buildings, applicable globally regardless of the availability of reference building datasets.

Diversification in birds is promoted by plant diversity, topographic heterogeneity and stable paleoclimate

AbstractAimsLong‐term climate stability, contemporary climate and environmental heterogeneity have been linked to bird diversity patterns through their direct impacts on diversification rate, as well as through their indirect effects on plant species richness, which could also directly and indirectly affect bird diversity. This study aimed to quantitatively assess whether these potential drivers could indirectly affect bird species richness through their direct effects on the diversification rate in birds in eastern Asia.LocationMainland China.TaxonBirds.MethodsUsing the distribution data of 1127 bird species across 320 prefecture cities in China and a phylogenetic tree of these bird species, we analysed the distribution patterns of bird species richness and diversification rate of all birds, passerine birds and non‐passerine birds, respectively. We also investigated their relationships with long‐term climate stability, contemporary climate, elevation range and plant diversity using ordinary least squares regression model and simultaneous autoregressive model. In addition, structural equation model was used to analyse whether these drivers could indirectly affect bird species richness through their direct effects on bird diversification rate.ResultsThe results showed that bird species richness and diversification rate were highest in southwestern China. Additionally, diversification rate for passerine birds was higher than non‐passerine birds and all birds. Regression analyses revealed that plant species richness was the variable most associated with bird species richness and diversification rate across the three groups. Notably, results from structural equation model indicated that plant species richness, elevation range and glacial–interglacial climate change can indirectly influence bird species richness by directly affecting diversification rates.Main ConclusionsThese findings emphasize the importance of considering diversification rate when understanding the geographic distribution patterns of bird diversity. The direct and indirect effects of plant species richness, elevation range and glacial–interglacial climate change on bird diversity highlight the crucial role of mountain regions with higher plant diversity and stable paleoclimate in forming and maintaining biodiversity.

Evaluating the Spatial-Temporal Dynamics of Urbanization in Prefecture Cities of China Using SNPP-VIIRS Nighttime Light Remote Sensing Data

Ensuring the well-being of urban communities hinges on sustainable urban planning strategies informed by current data, particularly in China since urbanization has been one of the most significant demographic shifts in recent decades. Therefore, our research aimed to evaluate the spatio-temporal dynamics of urbanization and sub urbanization across prefecture and provincial levels in China by utilizing consistent SNPP-VIIRS-like and NPP-VIIRS nighttime data spanning the years 2000 to 2020. The k-means method was applied to derive urban and sub urban features from above datasets. The findings uncovered a significant expansion of urban entities at the prefecture level, escalating from 16,209 km2 to 89,631 km2 over the specified period showing a 5% growth. Among five main urban agglomerations, the Yangtze River Delta stands out with the highest urbanization rate, witnessing a remarkable expansion of urban entities from 2,684 km2 to 41,465 km2. This growth reflects an average growth rate of 72.2% per annum. The analysis revealed that the overall area of suburbs expanded from 59,151 km2 to 120,339 km2 between 2012 and 2020 indicating a proportional growth rate ranging from 0.4% to 1.9%. The peak growth rate of suburbs was recorded between 2012 and 2014, reaching 18%. Guizhou, Hunan, and Hubei provinces have exhibited growth rates of 334%, 258%, and 246% respectively while Beijing, Guangdong, Tianjin, and Shanghai have experienced relatively low growth rates of 50%, 56%, 46%, and 17%. The analysis of urban growth with GDP, population, and electricity consumption revealed an inverse relationship during the specified period. Therefore, the findings of this research can provide immense support to sustainable urban planning initiatives at both the provincial and prefecture-level cities in China. The findings can assist city planning authorities in making informed decisions regarding optimizing resource distribution, all while prioritizing the preservation of ecological footprint within urban environments. Also, the limitations addressed in our study must be taken into account in future research works aimed at deriving reliable urban extraction results using nighttime light remote sensing data.

Has the smart city construction reduced energy consumption? Evidence from China

PurposeThis study investigates the impact of smart city construction (SCC) on urban energy consumption.Design/methodology/approachThe focus is on a panel of 285 prefecture level cities in China from 2010 to 2021. The empirical evidence is based on the difference-in-difference (DID) method. We uses per capita coal consumption as a proxy variable to measure urban energy consumption energy consumption. We set the SCC as a policy dummy variable, with pilot cities set to 1 and non-pilot cities set to 0. We also selected a series of control variables that affect urban energy consumption, such as urbanization rate, labor force, road density, number of college students per 10000 people, regional economic development level and so on.Findings(1) SCC significantly reduces urban energy consumption, and the conclusion still holds after conducting robustness testing; (2) SCC reduces urban energy consumption is mainly effective in those cities with larger scale, stronger human capital, larger financial services and better information infrastructure construction; (3) The technological innovation and industrial structure upgrading are the main mechanisms for the SSC policy to reduce urban energy consumption.Originality/valueThe results in this study provide evidence for achieving an environmentally friendly society.

Can the natural resource balance sheet system promote ecological civilization? --Empirical evidence from panel data of prefecture-level cities in China

As a crucial carrier of natural resource and environmental information, the natural resources balance sheet is important in enhancing ecological governance capabilities and advancing ecological civilization construction. Utilizing panel data from Chinese prefecture cities spanning 2012 to 2018, we employ a Difference-in-Differences (DID) model to examine the policy effects of the natural resources balance sheet system. The findings indicate that compiling the natural resources balance sheet significantly promotes local ecological civilization construction, and local government environmental governance efficiency partially mediates this relationship. Further analysis indicates that the outgoing audit of natural resources and public environmental concern positively moderates the relationship between the two. Drawing on the principal-agent theory, we provide in-depth analysis and elucidation of the mechanisms through which the natural resources balance sheet promotes ecological civilization construction, offering theoretical guidance and empirical data support for strengthening the compilation and application of the natural resources balance sheet.

Will the Low-carbon City Pilot Policy Achieve Regional Emission Reduction?

In order to address climate change and promote green and low-carbon development, the Chinese government has implemented a pilot policy for low-carbon cities since 2010 and has continuously expanded the scope of the pilots. Taking the second batch of low-carbon pilot policies as an example, this paper uses difference in difference method to study the impact of low-carbon pilot policies on reducing urban carbon emission intensity based on the panel data of prefecture level cities from 2006 to 2019. It is found that the construction of low-carbon cities has a positive effect on reducing carbon emissions,and this conclusion is still valid under the robustness test. Based on this conclusion,this paper argues that China should further promote low-carbon pilot policies and actively explore the development model of low-carbon cities, especially to improve the livable and green urban environment.

Dynamic indirect impacts of urbanization on vegetation growth in Chinese cities

Numerous studies have examined the potential impacts of urban expansion on vegetation growth. However, the impacts of urbanization on vegetation growth (Urbi) across urban-rural gradients and its driving forces remain poorly understood. In this study, we used satellite observations of MODIS Enhanced Vegetation Index (EVI) from 2001 to 2019 to quantify the spatial–temporal dynamics of indirect urbanization impacts on vegetation growth and analysed their driving forces in 60 prefectural cities across China. The relationship between the Urbi and urban intensity (β) was S-shaped, with a turning point where the indirect impact changed from negative to positive. The left-moving trend of the turning point in the southeastern cities indicated the expansion of areas with positive Urbi during the last two decades. We further investigated the spatially heterogeneous effects of two natural and three anthropogenic driving factors on the Urbi across the urban-rural gradient in different climate zones. The results showed that in temperate cities, as urban intensity increased, the presence of turning points was probably attributed to population increase. In cold cities, the change in the Urbi was most likely induced by the enlarging population and increasing economic level from rural to urban areas. However, in arid cities, it was mainly determined by water supply and the cool island effect in urban cores rather than socioeconomic factors.

Study on the Evolution of Spatial and Temporal Patterns of Carbon Emissions and Influencing Factors in China

Abstract: Industrialization has increased global carbon emissions, necessitating effective climate change mitigation measures. China, the most populous developing nation, faces the challenge of strategizing emissions to meet national carbon neutrality objectives. However, research on specific regions’ carbon emissions drivers and causal factors is limited, particularly across prefectural-level cities. This study estimates the spatial and temporal patterns of carbon emissions across China’s prefectural cities and utilizes both OLS regression and stepwise regression models to analyze the impact of various factors influencing carbon emissions in these cities. Results reveal the following: (1) The country’s overall 20-year carbon emissions continue to grow from 3020.29 Mt in 2001 to 9169.74 Mt in 2020, with an average annual growth rate of 5.71%; the eastern region has seen a gradual deceleration in emissions, whereas the western region continues to experience an increase. Carbon emissions in cities within each subregion consistently rise. (2) Carbon emissions in Chinese prefectural-level cities exhibit strong spatial autocorrelation and clustering (Z > 1.96, p < 0.05), with hot spots primarily in the eastern coastal areas and cold spots in the northwest to southwest regions. (3) Economic and demographic factors significantly increase carbon emissions, while climate and urbanization effects are more complex and variable. Economic growth and population increase are the most significant influencing factors, but regional variances exist in carbon emissions determinants in subregional prefectural cities. These insights provide valuable insights into national emission dynamics at the prefectural level, providing a theoretical basis for enhancing carbon emission strategies across various jurisdictions.

Can government digitalization promote the urban–rural equalization of basic public services? Evidence from double machine learning

ABSTRACT Information technology, as an important tool for realizing the modernization of national governance system and capacity, has brought new opportunities for improving the government’s capacity to deliver basic public services (BPS). Taking the information benefiting people pilot policy as an exogenous shock, we use the double machine learning model to examine the impact of government digitization on urban – rural BPS equalization based on panel data of prefectural cities in China from 2006 to 2021, which overcomes the limitations of the linear setting of traditional causal inference models and maintains estimation accuracy under high-dimensional control variables. The findings suggest that government digitization can significantly contribute to urban – rural BPS equalization, and this conclusion holds after a series of robustness tests. Mechanism test reveals that government digitization promotes urban – rural BPS equalization by improving the efficiency of BPS supply and the efficiency of fiscal transfer payment. Moreover, for cities with low fiscal pressure, high fiscal transparency and low economic growth pressure, government digitization will have a stronger driving effect on urban – rural BPS equalization. This study provides valuable theoretical insights and policy ideas for promoting BPS equalization through digital governance.