Yangtze River Urban Agglomeration Research Articles

Carbon emissions path of public buildings based on LEAP model in Changsha city (China)

China is currently the world's top carbon emitter due to the acceleration of urbanization, the rapid growth of energy consumption and the rise in carbon emissions, with the construction industry leading the three energy sectors in terms of emission reduction potential. Among the various sectors, the construction industry holds immense potential for emission reduction, with public buildings playing a crucial role. In China, the expansion of public building spaces is closely linked to the pace of urbanization and contributes to a rising share of total building area. Public buildings, characterized by high energy usage, offer a great opportunity for energy conservation and emission reduction. Changsha is experiencing rapid growth and change, and the city is becoming more industrialized and urbanized, which leads to a prominent contradiction between energy supply and demand. There is also great potential for energy conservation and emission reduction in public buildings. Additionally, Changsha is an important node city in the Yangtze River urban agglomeration and the middle reaches of the Yangtze River Economic Belt. It is a typical area that has hot summer and cold winter, thus Changsha is selected as the research case to examine the carbon emission path of public buildings. The primary influencing factors of carbon emissions of public buildings in Changsha City are determined by using the LMDI factor decomposition method addition model. The scenario analysis method is adopted to set the two energy consumption forecast scenarios, which are the baseline scenario and the green scenario. Based on LEAP model and GREAT framework structure, the LEAP prediction model is constructed to predict the carbon emissions of public buildings in Changsha City from 2021 to 2035. The Baseline scenario indicates that while carbon emissions from public buildings in Changsha continue to rise, they are expected to decline starting in 2030. The peak carbon emission of 34.279 million tons of CO2 is predicted for 2032. In contrast, the Green scenario reveals a significantly slower growth rate in carbon emissions from public buildings, with the peak carbon emission projected to reach 27.054 million tons in 2030. This aligns with China's national goal. The findings of this study support the accuracy of the long-term prediction of the model, and offer recommendations for energy conservation and emission reduction in Changsha. This study also provides an example and certain guidance for other provinces and cities that have similar development models and climate conditions. Pertinent development strategies serve as a point of reference for other typical hot summer and cold winter regions.

Spatial and temporal divergence and driving mechanisms of carbon sinks in terrestrial ecosystems in the middle reaches of the Yangtze River urban agglomerations during 2008–2020

Urban cluster carbon sinks are key parts of the terrestrial carbon cycle, and play an important role in global climate change. Based on the inversion results of net ecosystem productivity (NEP) in Middle Reaches of the Yangtze River Urban Agglomeration (MRYRUA), this study considered the influence of both natural environmental and socioeconomic factors on NEP, and investigated the driving mechanism of carbon sink emphasizing spatial and temporal heterogeneity by the geographically and temporally weighted regression model (GTWR). Results demonstrated that (1) During 2008–2020, the annual average NEP was positive in MRYRUA, and the carbon sink effect of terrestrial ecosystem increased. (2) In MRYRUA, Precipitation and DEM were positively correlated with carbon sinks, opposite for temperature, percentage of water areas and population density. (3) During the study period, the influence of natural environmental factors, such as temperature and precipitation on NEP grew gradually weaker, whereas that of socioeconomic factors increased. The influence of urbanization rate, night lights index and factors of industrial structure changed from negative to positive, while percentage of built-up area had an increasing negative effect on NEP. Based on the conclusions above, this study proposed differentiated policy implications, including improving vegetation hydrothermal environment, controlling urban expansion and accelerating industrial transformation. It would provide empirical inspiration for attaining peak carbon, carbon neutrality, and sustainable development goals.

Spatiotemporal differentiation of carbon budget and balance zoning: Insights from the middle reaches of the Yangtze River Urban Agglomeration, China

Urbanization has substantially changed land use and led to local growth and an overall imbalance in carbon emissions. Scientific analysis of carbon emissions from land use (CELU) and exploration of carbon balance zoning can provide useful references for regional low-carbon land use management in urban agglomerations. This study analyzed the spatiotemporal dynamics of CELU based on land use and socioeconomic data, and utilizing the emission coefficient method. The economic contribution coefficient of carbon emissions (ECC) and Ecological Support Coefficient of carbon emissions (ESC) was used as the basis for developing carbon balance zoning, and the main function positioning of counties was used to combine with carbon balance zoning. Subsequently carbon balance partitions with determinants across 190 counties in the Middle Reaches of the Yangtze River Urban Agglomeration (MRYRUA) was obtained. We found that the net carbon emissions from land use in MRYRUA increased from 1.242 to 4.049 million tons, showing a clear upward trend. Notably, the Gini coefficients of carbon emissions and sinks of the MRYRUA were both higher than 0.4, and the pressure on the regional carbon balance intensified. In addition, based on the carbon balance analysis, MRYRUA was divided into five types: low-carbon economic zones, low-carbon optimization zones, carbon intensity control zones and high-carbon optimization zones. These zones were used to combine with the main function positioning, and finally got 11 types of zoning results. In response, this study proposes different low-carbon land-use optimization strategies based on the carbon budget and comprehensive development characteristics of different zones.

Toward dual carbon targets: Spatial correlation on comprehensive carbon emission index in urban agglomerations based on a new evaluation model

China is confronted with substantial pressure to mitigate its carbon emissions. Urban agglomerations assume a pivotal role in the attainment of carbon peaking and carbon neutrality. This study constructed a carbon emission evaluation model from the perspectives of total quantity, intensity, efficiency, population, and land. A modified gravity model and social network analysis method was used to analyze the spatial correlation characteristics of six urban agglomerations, clarified their carbon emission spillover effects, and explored their influencing factors using the quadratic assignment procedure method. The results are as follows. (1) The carbon emission correlations in the Yangtze River Delta, Pearl River Delta, and Chengdu-Chongqing urban agglomerations increased, while Beijing-Tianjin-Hebei, Central Plains, and Middle Reaches of Yangtze River urban agglomerations decreased. (2) From a macro perspective, the Yangtze River Delta and Pearl River Delta urban agglomerations were at the core of the network, and the “bridging” role of the Middle Reaches of Yangtze River urban agglomeration was evident. From a micro perspective, the cities in the middle of urban agglomerations were more likely to become network cores. (3) Spillover effects between plates were present in the correlation network, with a gradual balance between the reception and spillover relationships. Most urban agglomerations exhibited an uneven distribution of community correlation capacity. (4) Geographic adjacency and urbanization level significantly promoted carbon emission correlation, although the direction and magnitude of other factors’ influence varied. The findings contribute to the implementation of low-carbon planning, carbon reduction strategies and regional sustainable development.

Carbon emission efficiency of land use in urban agglomerations of Yangtze River Economic Belt, China: Based on three-stage SBM-DEA model

Exploring regional differences in land use carbon emission efficiency (LUCE) and the path of collaborative emission reduction in the Yangtze River Economic Belt (YREB) are of immense importance to achieve the sustainable regional development. Firstly, based on the estimation method of land use carbon emissions, this paper scientifically calculates the carbon emission of land use of the Yangtze River Delta urban agglomeration (YRD), the middle reaches of the Yangtze River urban agglomeration (MRYR), and the Chengdu-Chongqing urban agglomeration (CC) from 2010 to 2021 in the YREB. And the carbon emission of land use of each city in urban agglomerations was obtained, which was regarded as the unexpected output in the efficiency evaluation index system. Secondly, the three-stage SBM-DEA model was used to reveal the LUCE and its spatiotemporal characteristics in different regions from a static perspective. The empirical results of the second stage SFA showed that the level of economic development, urbanization rate, industrial structure, non-agricultural conversion rate of agricultural land, the level of scientific and technological innovation and government intervention had significant effects on LUCE. By comparing the efficiency values and their decompositions in the first and third stage of various urban agglomerations, it was found that after eliminating environmental and random factors, the average ranking of comprehensive technical efficiency (TE) of land use carbon emission changed from YRD > MRYR > CC to YRD > CC > MRYR due to the cross influence of pure technical efficiency (PTE) and scale efficiency (SE). Thirdly, Malmquist index model was used to analyze the change of total factor productivity index (TFPch) and its spatiotemporal pattern from a dynamic perspective. It was found that the TFPch, technological progress index (TC), pure technical efficiency index (PEC) and scale efficiency index (SEC) of land use carbon emission of the three major urban agglomerations all showed varying degrees of increase after adjustment. Finally, to further analyze the impact of technological progress, PTE and SE on LUCE, a regression model was constructed with LUCE of each city in urban agglomeration as the dependent variable, and TC, PEC and SEC as independent variables. It was concluded that technological progress was the main factor to promote the improvement of LUCE in the whole YREB. The trend of changes of LUCE in various urban agglomerations was closely related to its management level, technology level and factor structure adjustment. The above conclusions provide support for the coordinated emission reduction path of urban agglomerations in the YREB from the perspective of land use, which is conducive to actively and steadily promoting the realization of carbon peak and carbon neutrality goals.

Comparison of Different Impact Factors and Spatial Scales in PM2.5 Variation

PM2.5 particles with an aerodynamic diameter of less than 2.5 μm are receiving increasing attention in China. Understanding how complex factors affect PM2.5 particles is crucial for the prevention of air pollution. This study investigated the influence of meteorological factors and land use on the dynamics of PM2.5 concentrations in four urban agglomerations of China at different scales from 2010 to 2020, using the Durbin spatial domain model (SDM) at five different grid scales. The results showed that the average annual PM2.5 concentration in four core urban agglomerations in China generally had a downward trend, and the meteorological factors and land use types were closely related to the PM2.5 concentration. The impact of temperature on PM2.5 changed significantly with an increase in grid scale, while other factors did not lead to obvious changes. The direct and spillover effects of different factors on PM2.5 in inland and coastal urban agglomerations were not entirely consistent. The influence of wind speed on coastal urban clusters (the Pearl River urban agglomeration (PRD) and Yangtze River urban agglomeration (YRD)) was not significant among the meteorological factors, but it had a significant impact on inland urban clusters (the Beijing–Tianjin–Hebei urban agglomeration (BTH) and Chengdu–Chongqing urban agglomeration (CC)). The direct effect of land use type factors showed an obvious U-shaped change with an increase in the research scale in the YRD, and the direct effect of land use type factors was almost twice as large as the spillover effect. Among land use type factors, human factors (impermeable surfaces) were found to have a greater impact in inland urban agglomerations, while natural factors (forests) had a greater impact in coastal urban agglomerations. Therefore, targeted policies to alleviate PM2.5 should be formulated in inland and coastal urban agglomerations, combined with local climate measures such as artificial precipitation, and urban land planning should be carried out under the consideration of known impacts.

Co-agglomeration, technological innovation and haze pollution: An empirical research based on the middle reaches of the Yangtze River urban agglomeration

The issue of haze in the middle reaches of the Yangtze River urban agglomeration has significant implications for China. Concurrently, co-agglomeration and technological innovation are key factors influencing haze pollution. Using the panel data composed of PM2.5 concentration and other data from 27cities in the middle reaches of the Yangtze River urban agglomeration between 2010 and 2020, this paper empirically investigates the impact of co-agglomeration and technological innovation on haze pollution. From this rigorous analysis, several conclusions crystallize: (1)the co-agglomeration of productive services and manufacturing accentuates haze pollution, showcasing notable regional variances;(2)the haze pollution has a significant positive correlation with the co-agglomeration levels in local and neighboring regions;(3)the negative consequences of co-agglomeration on environments reduce as the agglomeration of productive services and economic development increases;(4)technological innovation significantly moderates the pollution effects of co-agglomeration.

PM2.5 estimation and analysis of BiCNN model considering spatiotemporal characteristics: a case study of the middle reaches of the Yangtze River urban agglomeration

PM2.5 estimation and analysis of BiCNN model considering spatiotemporal characteristics: a case study of the middle reaches of the Yangtze River urban agglomeration

Coupling coordination between the ecological environment and urbanization in the middle reaches of the Yangtze River urban agglomeration

The rapid development of urban agglomerations has endangered the eco-environment. It is critical to research the coupling coordination between the eco-environment and urbanization to promote regional sustainable development. The aim of this study is to investigate the coupling coordination degree (CCD) between the eco-environment and urbanization in the MRYRUA and to identify the key factors influencing the CCD. Based on the remote sensing perspective, this study used the remote sensing ecological index (RSEI) and comprehensive nighttime light index (CNLI) to measure the eco-environment and urbanization level of the middle reaches of the Yangtze River urban agglomeration (MRYRUA). Then, based on the CCD model, the relationship between the eco-environment and urbanization was evaluated. The geographical detector was utilized to analyze the key factors. The results showed the following: (1) both the eco-environment and urbanization of the MRYRUA show a fluctuating upward trend, and the change rate of urbanization is greater than that of the eco-environment. (2) The overall coupling level of the MRYRUA changes from moderate unbalanced coupling to moderate balanced coupling, and the distribution of the CCD presents spatial imbalance. The coupling level of the central region is higher than that of the peripheral cities, and the coupling level of the provincial capital cities is higher than that of other cities. (3) The explanatory power of socioeconomic factors in the CCD is higher than that of natural factors in the single-factor detection; urban green space (UGS) plays the most significant role in the interaction detection. (4) RSEI has a negative correlation with the CCD, while the CNLI has a positive correlation with the CCD. The development of urbanization has played a decisive role in the CCD. The results provide a scientific foundation on which the MRYRUA can achieve sustainable urban development.

Technological Innovation, Urban Spatial Structure, and Haze Pollution: Empirical Evidence from the Middle Reaches of the Yangtze River Urban Agglomeration

The rapid economic growth of China has caused significant adverse effects on the environment. Meanwhile, technological innovation, a fundamental driver of economic development and social progress, plays a pivotal role in mitigating haze pollution. This study comprehensively examines the influence of technological innovation on haze pollution in the Yangtze River urban agglomeration, using the STIRPAT model and analyzing research data from 2004 to 2020. Additionally, the study investigates the specific moderating role of urban spatial structure in the relationship between technological innovation and haze pollution. The findings reveal that technological innovation in the middle reaches of the Yangtze River urban agglomeration can effectively curtail haze pollution, and its impact extends to surrounding areas through spillover effect. The polycentric urban spatial structure significantly enhances the haze-reducing effect of technological innovation in the region. Notably, in cities with a strong level of environmental regulation, the urban spatial structure plays a substantial role in augmenting the haze-reducing impact of technological innovation. The policy implications of this research underscore the importance of continuous improvement in technological innovation within the middle reaches of the Yangtze River urban agglomeration. To address haze pollution in future development, the adoption of a polycentric development strategy and the establishment of sound environmental governance policies are recommended.

Spatio-temporal analysis of the impact of land urbanization on the gross primary productivity of vegetation in the Middle Reaches of the Yangtze River Urban Agglomeration: new evidence from the township scale

The urgent need to maintain ecosystem provisioning services and achieve urban sustainable development goals has led to a long-standing focus on the driving effects of land urbanization (LU) in the Middle Reaches of the Yangtze River Urban Agglomeration (MRYRU) on vegetation cover system productivity. Previous studies have lacked consideration for the long-term correlation between land urbanization (LU) and the Gross Primary Productivity (GPP) of vegetation, as well as the spatial non-stationarity of LU’s impact in the region. This study is based on land cover remote sensing data and GPP imagery data to monitor the long-term evolution trends of LU and GPP in MRYRU from the perspectives of townships and grids. It further investigates the spatial correlation and clustering characteristics between the two using bivariate spatial autocorrelation method. Additionally, a Geographically and Temporally Weighted Regression (GTWR) model was employed to analyze the spatial effects of LU on GPP. The results indicate that the GPP of MRYRU grows to 1572.88gCm-2a-1 with an average annual growth rate of 0.848% from 2000 to 2020. High-value areas are distributed in the central and eastern mountainous of the urban agglomeration, while low-value areas gradually formed an “n”-shaped clustered distribution pattern. The negative spatial autocorrelation between LU and GPP shows a general upward trend, and Low-High is the dominant agglomeration type, concentrated in Hengyang and some mountainous cities in Jiangxi Province. The overall negative impact of land urbanization on GPP in MRYRU is maintained in the spatially nonstationary pattern of driving coefficients, with negative impact areas mostly concentrated in the city’s main city zone and positive impact areas concentrated in the urban agglomeration’s mountainous townships. This study provides support for MRYRU to adopt more resilient land optimization management strategies and vegetation ecological restoration plans.

Global or Local Spatial Spillovers? Industrial Diversity and Economic Resilience in the Middle Reaches of the Yangtze River Urban Agglomeration, China

A growing body of literature has studied the empirical relationship between industrial diversity and economic resilience since the 2008 Great Recession. However, many existing studies are based on a nonspatial perspective, and little is known about the local or global spatial spillover effect of industrial diversity on economic resilience. This paper employs Bayesian spatial econometric methods to investigate the roles of related variety and unrelated variety on economic resilience in the middle reaches of the Yangtze River urban agglomeration, China and explores the possible local or global spatial spillover effect in the diversity–resilience relationship. The empirical results from the spatial Durbin error model estimation show that: (1) regions with high levels of related variety are economically resilient to the external shock in the postcrisis era, whereas unrelated variety has no significant direct effect on recovery resilience; (2) both related and unrelated variety have local spatial spillovers with respect to the one-year resilience of 2008–2009, but these spillovers are negligible in longer study periods. These results confirm the role of industrial relatedness and immediate neighbors in promoting regions’ short-run capabilities of recovery from external economic shocks.

Investigating the mechanism of urbanization on the net primary productivity of vegetation in the Yangtze River Economic Belt: a comprehensive analysis from global and local effects

The stressful effects of urbanization on vegetation net primary productivity (NPP) and vegetation–carbon cycle functions within the Yangtze River Economic Belt (YEB) under the sustainable development goals (SDG) and the concept of coordinated regional development, have garnered growing attention. Existing studies have been insufficient in comprehensively examining both the global effects and local variations resulting from urbanization in the region. Additionally, insufficient attention has been given to the heterogeneity of the ecological negative effects of urbanization in the three major urban agglomerations within the YEB. Based on multivariate remote sensing image and socioeconomic statistics data, this study integrates population, economic and land dimensions to construct comprehensive urbanization indexes and quantify the spatio-temporal evolution patterns of NPP and urbanization in the YEB. The focus is on detecting the global response of NPP to urbanization using the Spatial Durbin model and discussing the local heterogeneity of the effect in the Yangtze River Delta urban agglomeration (YRD), the middle reaches of Yangtze River urban agglomeration (MRYRU), and the Chengdu-Chongqing urban agglomeration (CCU) based on a geographically weighted regression model. The results show that the average NPP of the YEB increased from 592g*c/m2 to 670g*c/m2 at a rate of 0.621% from 2000 to 2020, with the most significant growth in the CCU, and the overall pattern of change is “increased in the north and decreased in the south”. The negative spatial autocorrelation between urbanization and NPP is becoming increasingly significant, and the negatively correlated clusters is the dominant type of local autocorrelation, among which the number of “High-Low” type cities is the largest and growing, mainly located in the northern regions of Anhui and Jiangsu. The significant negative effect of urbanization on the productive capacity of vegetation cover systems was verified, and a negative spillover effect that far exceeded the local negative effect was also confirmed. The effect of urbanization on NPP has significant local variability and gradually shifts to the effect pattern of positive effect in the east and negative effect in the west, while the pattern of urbanization-driven effects in the three urban agglomerations also continues to change. This study increases the concern about the negative ecological effects of urbanization, and more importantly, provides a basis for the joint action of ecological restoration and management in the Yangtze River Economic Belt and the implementation of differentiated governance policies around urban agglomerations.

Study on Spatial and Temporal Characteristics and Influencing Factors of Carbon Emissions in the Urban Agglomeration of the Middle Reaches of the Yangtze River

The industrial transfer of heavy industries such as non-metallic mineral manufacturing, metal smelting and manufacturing from the eastern coast of China to the central region is beneficial to the economic development of the central region on the one hand, but increases carbon emissions in the central region on the other hand. In February 2022, the National Development and Reform Commission approved the “14th Five-Year Plan for the Development of the Urban Agglomeration in the Middle Reaches of the Yangtze River”. This indicates that the urban agglomeration of the middle reaches of the Yangtze River is an important region for implementing green development in the central area. The spatial and temporal evolution of carbon emissions and influencing factors in this region are the foundation for achieving carbon peaking and the carbon neutrality goal. This paper calculates the total carbon emissions of the cities in the urban agglomeration of the middle reaches of the Yangtze River and uses models such as spatial autocorrelation, geographically weighted regression, and Geodetector to explore the spatial–temporal pattern of carbon emissions. The results show the following: (1) The total carbon emissions of the middle reaches of the Yangtze River urban agglomeration showed fluctuations during 2010–2020, and the carbon emission reduction effect is unstable. Additionally, the carbon emissions of the middle reaches of the Yangtze River city cluster show obvious spatial variability, but the high carbon emission area is always concentrated in Wuhan, and this remains unchanged. (2) In 2010, 2014 and 2017, population size was the most important factor affecting carbon emission divergence, and in terms of interaction, the interaction between energy intensity and GDP and urbanization is the reason for the increasing carbon emissions. (3) The influence of population size on carbon emissions decreases from north to south, the influence of energy intensity on carbon emissions shows a spread from the most influential region in the northwest to the centre and then to the northeast, and the GDP per capita has little influence on the difference of carbon emissions spatial distribution.

Collaborative innovation evolution of the logistics and manufacturing industry in China.

The logistics industry and the manufacturing industry are mutually productive factors and service objects, which determines that the two must develop cooperatively. In the increasingly fierce market competition, open collaborative innovation is more conducive to improving the level of linkage between the logistics industry and the manufacturing industry, and promoting industrial development. Based on the patent data of 284 prefecture-level cities in China from 2006 to 2020, this paper uses Gis spatial analysis, spatial Dubin model and other methods to analyze the collaborative innovation between the logistics industry and manufacturing industry. The results lead to several conclusions. (i) The overall collaborative innovation level is not high, and in terms of the evolution cycle, collaborative innovation has experienced three stages: embryo, rapid development and stable development. (ii) The spatial agglomeration characteristics of the collaborative innovation between the two industries are more and more obvious, and the Yangtze River Delta urban agglomeration and the middle reaches of the Yangtze River urban agglomeration play an important role in the collaborative innovation between the two industries. (iii) The hot spots of collaborative innovation between the two industries are concentrated in the eastern and northern coastal areas, while the cold spots are concentrated in the south of the northwest and southwest areas in the late stage of the study. (iiii) The positive influencing factors of local collaborative innovation between the two industries include economic development, scientific and technological level, government behavior, and employment, while the negative influencing factors include information technology level, and logistics infrastructure. Economic development has a negative spatial spillover effect on surrounding areas, while the spatial spillover effect of scientific and technological level is significantly positive. This article aims to explore the current situation and influencing factors of collaborative innovation between the two industries, with a view to proposing countermeasures and suggestions for improving the level of collaborative innovation between the two industries, and also providing new ideas for research on cross industry collaborative innovation.

Research on the dynamic evolution and influence factors of industrial energy efficiency in China Yangtze River Economic Belt

Improving the Yangtze River Economic Belt's industrial energy efficiency is not only an important measure to alleviate China's energy shortages but also a drive to promote green economic development. The Super-EBM model, Malmquist productivity index, exploratory spatial data analysis, and Spatial Dubin model are used in this article to investigate the spatial-temporal dynamic development characteristics and influencing factors of industrial energy efficiency in 108 cities of the Yangtze River Economic Belt from 2011 to 2020. The findings demonstrate that the industrial energy efficiency of the Yangtze River Economic Belt and its three urban agglomerations went through three stages, including the “oscillation period,” “stability period,” and “enhancement period,” and decreases from east to west in the spatial dimension. The Yangtze River Delta urban agglomeration has the highest industrial energy efficiency, followed by the Middle-reach Yangtze River urban agglomeration, and the Chengdu-Chongqing urban agglomeration is the lowest. Further, this article identifies seven influencing factors including government intervention, industrial structure, degree of openness, R&D investment, urbanization, economic development, and environmental regulation. This article provides suggestions for industrial energy efficiency improvement.

Incorporating ecological connectivity into ecological functional zoning: A case study in the middle reaches of Yangtze River urban agglomeration

Ecological functional zoning is critical for multiobjective sustainable management of social-ecological systems. However, the framework of ecological functional zoning needs to be further improved. In terms of the index system, the existing “element-structure–function” index system mainly considers the structural characteristics that have a negative impact on ecological processes, e.g., disturbances, while ignoring positive impacts, e.g., connectivity, which may weaken the role of key nodes in the ecosystem and is not conducive to regional ecological protection. In terms of research methodology, the widely used self-organizing feature mapping neural network (SOFM) produces a number of clusters that easily exceed expectations, thus increasing management difficulties. Therefore, there is an urgent need to explore new combining methods. Here, an improved index system combining ecological connectivity and ecological functions and a traditional index system including only ecological functions were constructed, and a zoning method combining a self-organizing feature mapping neural network and fuzzy mean clustering (SOFM-FCM) was used for functional zoning of the middle reaches of the Yangtze River urban agglomeration. The advantages of functional zoning incorporating ecological connectivity were verified by comparing the results of both zoning procedures before and after improvement. The results indicate that the study area could be divided into ecological conservation areas, biodiversity conservation areas, urban development areas, and grain production areas. The zoning schemes before and after the improved framework were reliable compared with the national planning scheme, but there was 20.6% inconsistency in the results after the improved framework. This was mainly manifested by the shift from biodiversity conservation areas to ecological conservation areas and from urban development areas to food production areas. This shift was more conducive to improving spatial continuity and landscape multifunctionality, thus maximizing ecological conservation, pushing back smart urban growth, and achieving food security. This study expands the research perspective concerned with ecological functional zoning, and these results can provide an important reference for regional ecological protection and related research.

Patterns of Typical Chinese Urban Agglomerations Based on Complex Spatial Network Analysis

The two prerequisites for monitoring SDG11.A “support positive economic, social and environmental links between urban, peri-urban and rural areas by strengthening national and regional development planning” are the classification of the urban–rural continuum and the extraction of spatial links. However, the complexity and diversity of urban patch distribution make it difficult to achieve a global rapid assessment. Based on the self-developed high-resolution global impervious surface area 2021 (Hi-GISA 2021) product, this study combined the complex network with remote sensing technology to propose a new method to delineate and evaluate the pattern and inner spatial links of the urban–rural continuum for five typical urban agglomerations in China, including the Beijing–Tianjin–Hebei urban agglomeration (BTHUA), the Yangtze River Delta urban agglomeration (YRDUA), the Greater Bay Area (GBAUA), the Chengdu–Chongqing urban agglomeration (CYUA), and the Middle Reaches of Yangtze River urban agglomeration (MRYRUA). The research results are in good agreement with Chinese government documents. First, the five urban agglomerations are all small-world networks with a low degree of overall polycentricity, and the urbanization degrees of GBAUA and YRDUA are higher than BTHUA, CYUA, and MRYRUA. Second, the imbalanced development of YRDUA is higher than the other regions, and the siphon effects of BTHUA and MRYRUA are more significant than YRDUA, CYUA, and GBAUA. Third, some multi-centers show significant siphon effects. The urbanization degree is highly correlated with the urbanization potential but not positively correlated with the degree of balanced development. The results can provide data, methods, and technical support for monitoring and evaluating SDG11.A.

长江中游城市群碳效率时空演化特征——基于三阶段SBM-DEA模型

PDF HTML阅读 XML下载 导出引用 引用提醒 长江中游城市群碳效率时空演化特征——基于三阶段SBM-DEA模型 DOI: 10.5846/stxb202211163319 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金面上项目(42071161); 湖南省自然科学基金面上项目(2021JJ30282); 湖南省教育厅科学研究项目(22A0666); 湖南省研究生科研创新重点项目(CX20221046)资助 Spatio-temporal evolutionary characteristics of carbon efficiency in the middle reaches of the Yangtze River urban agglomeration based on a three-stage SBM-DEA model Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:长江中游城市群作为我国区域发展格局的中坚力量,其碳效率的提升对助力我国实现"双碳"目标意义重大。采用三阶段SBM-DEA模型测度2006-2019年长江中游城市群28市的碳效率并进行环境变量影响分析,以探究长江中游城市群碳效率的影响路径;进而运用核密度估计、中心-标准差椭圆等方法分析碳效率的时空演化特征,以探明各区域碳效率差异并给出相应提升策略。研究结果表明:①长江中游城市群整体碳效率水平不高,但呈现出逐年波动上升的趋势;②长江中游城市群碳效率呈现出"武汉城市圈>环长株潭城市群>环鄱阳湖城市群"的空间分异格局,效率中心整体向东北方向移动,标准差椭圆长轴标准差整体缩小,短轴标准差相对稳定;③长江中游城市群碳效率存在正向空间溢出效应,城镇化进程、产业结构和科技支撑强度是影响碳效率的重要因素。 Abstract:As the backbone of China's regional development pattern, increasing carbon efficiency in the middle reaches of the Yangtze River urban agglomeration is of great significance to help China achieve the "double carbon" goal. In order to explore the carbon efficiency differences and influence paths of each region in the middle reaches of the Yangtze River urban agglomeration and propose corresponding improvement strategies, we applied a three-stage SBM-DEA model to measure the carbon efficiency of 28 cities in the middle reaches of the Yangtze River urban agglomeration from 2006 to 2019 and analyze the impact of environmental variables, which excluded the influence of external environment and random factors. Then, we also used kernel density estimation and center-standard deviation ellipses to analyze the spatial and temporal evolution of carbon efficiency, which aimed to find a site-specific approach to make targeted suggestions for carbon efficiency improvement. The results show that: ① the overall carbon efficiency of the middle reaches of the Yangtze River urban agglomeration is not high but shows a trend of the fluctuating upward year by year. There is no GDP output redundancy among cities, and the main reasons affecting carbon efficiency in the middle reaches of the Yangtze River urban agglomeration are focused on capital, labor, energy inputs, and CO2 emissions. ② The carbon efficiency of the middle reaches of the Yangtze River urban agglomeration shows a spatial divergence pattern of Wuhan city circle > Changzhutan urban agglomeration > Poyang Lake urban agglomeration, while the center of efficiency moves to the northeast. The standard deviation of the long axis of the standard deviation ellipse decreases as a whole, and the standard deviation of the short axis is relatively stable. The overall area of the standard deviation ellipse has decreased, which means that the difference in carbon efficiency between regions is reducing year by year. ③ There is a positive spatial spillover effect of carbon efficiency in the middle reaches of the Yangtze River urban agglomeration, and the urbanization process, industrial structure, and technology support intensity are crucial factors affecting carbon efficiency. Within the middle reaches of the Yangtze River urban agglomeration, Wuhan city circle, Changzhutan urban agglomeration, and Poyang Lake urban agglomeration have some competitive relationship in capital, labor, and energy input. Therefore, the better-developed areas siphon resources to the surrounding areas, which formed the negative spatial spillover effect within the middle reaches of the Yangtze River urban agglomeration. 参考文献 相似文献 引证文献

The driving effect of energy efficiency change: From the perspective of collaborative innovation development pattern

Energy efficiency enhancement and carbon reduction are essential means by which to promote carbon neutralization. However, is the constant pursuit of improving energy efficiency always conducive to urban development? The collaborative innovation development pattern has become the focus of regional competition. Thus, this paper establishes a new paradigm for analyzing the driving effect of energy efficiency change from the perspective of collaborative innovation development pattern. Using panel data from cities in the middle reaches of the Yangtze River urban agglomeration in China from 2009 to 2019, the DEA-Malmquist and projection pursuit models calculate the energy efficiency change and collaborative innovation city construction level. Then, we use the Tobit model to analyze the reverse forcing effect of energy efficiency change on collaborative innovation city construction level from the dual perspectives of energy efficiency changes at different levels and geographical locations. The results show that the driving effect of energy efficiency change is U-shaped. In addition, there are some city differences, and the U-shaped relationship only exists in high-efficiency cities, the Wuhan Metropolitan Area and the Poyang Urban Agglomeration. This paper also finds that industrial structure, government interaction, development level, and population density impact collaborative innovation development pattern. Finally, these findings provide policy-makers with new ideas to promote sustainable urban development.