Spatial-temporal Differentiation Research Articles

Rural Industrial Integration and New Urbanization in China: Coupling Coordination, Spatial–Temporal Differentiation, and Driving Factors

Understanding the coupling coordination status between rural industrial integration (RII) and new urbanization (NU) is critical for the Chinese government to optimize policies that promote the synergetic and sustainable development of RII and NU. Based on constructing evaluation index systems, this paper uses the entropy value method, coupling coordination degree model, exploratory spatial analysis method, gravity center model, and geographical detector model to reveal the characteristics of the spatial–temporal differentiation of the coupling coordination and its driving factors in China. The results show that: (1) The regional differences in the coupling coordination degree between RII and NU are obvious, and the coupling coordination degree of China’s three economic regions are all in an increasing trend, but the mean values in the central and western regions are always below the national average. (2) The coupling coordination degree has significant spatial agglomeration characteristics, but the regional differences are gradually decreasing. The whole country is still dominated by the low–low agglomeration, and the provinces with the high–high agglomeration are mainly located in the eastern region. (3) The gravity center of the coupling coordination degree has moved in both the east–west and north–south directions from 2011 to 2021, but the movement trend in the north–south direction is more obvious. (4) The economic development level, industrial structure, transportation conditions, government support capacity, financial support level, and agricultural mechanization level are important factors driving the spatial–temporal variation, and their interactions will enhance the differentiation. The results can provide a theoretical basis and decision-making reference for relevant government departments in China to promote the sustainable development of RII and NU.

Ecological risk assessment and spatial–temporal differentiation of soil and water resources in the Hefei metropolitan area

There are important ways to solve the ecological risk problems of regional water resources and soil resources, and to promote the benign development of soil and water resources, involving scientific evaluation of the ecological risk of soil and water resources in Hefei metropolitan area, clarifying the intrinsic evolution law of ecological risk and identifying the characteristics of spatial and temporal variations. Based on the conceptual model of “ST-QS-RR”, the evaluation indicator system is constructed, the CRITIC method is used to assign weights, and the TOPSIS method, kernel density method, markov chain and resistance model are used to measure and analyse the spatial and temporal characteristics of ecological risk of soil and water resources, and to explore the main factors that cause ecological risk of soil and water resources. The results of the study show that: (1) Hefei metropolitan area and its cities show a steady decline and the characteristics of “high in the north and low in the south, high in the west and low in the east”. (2) Most of the subsystems in the Hefei metropolitan area and the cities show a decreasing trend, with its resistance factors mainly concentrated in the QS system. (3) There is club convergence in Hefei metropolitan area. When the type of adjacent domain is higher, the change of risk type is more sensitive.

Spatial-temporal differentiation and influencing factors of carbon emission trajectory in Chinese cities - A case study of 247 prefecture-level cities

Cities, where human energy activities and greenhouse gas emissions are concentrated, contribute significantly to alleviating the impacts of global climate change. Utilizing the China Carbon Emissions Accounting Database (CEADs) to provide carbon dioxide emission inventories for urban areas in China at the prefecture level, this study closely examines the historical evolution trajectories of carbon emissions across 247 urban units from 2005 to 2019. The logarithmic cubic function model was employed to simulate these trajectories, evaluating urban emission peaks and classifying the different carbon emission trajectories. Further, the Geographical and Temporal Weighted Regression model was employed to explore spatiotemporal traits and essential variables that impact the variations in carbon emissions among four identified trajectory types. Our results showed that Chinese urban carbon emission trajectories can be classified into four categories: a) peaking emissions, b) fluctuating growth, c) continuous growth, and d) passive decline. Specifically, 43 cities, primarily in North China, proactively attained their emission peak post-2010, driven by the reduction in secondary industry and energy intensity. 90 cities, largely industrial hubs in the southeast coast and inland, reached an emission plateau around 2015, exhibiting fluctuating growth due to dependencies on secondary industries. 101 cities, predominantly located in western and central regions, demonstrated a clear upward trend in carbon emissions, propelled by rapid urbanization and heavy industry-oriented economic development. Lastly, 13 cities, typically in the northeastern and southwestern regions, experienced a passive decline in carbon emissions, attributable to resource depletion or economic downturns. It is evident that China's city-level carbon peaking has demonstrated some effectiveness, yet considerable progress is still required.

Spatial-temporal differentiation of urban eco-efficiency and its driving factors: A comparison of five major urban agglomerations in China.

This paper utilizes an improved undesirable output DEA model to measure the eco-efficiency of cities in five major urban agglomerations in China during 2006-2020. It employs the Theil Index and Geodetector to investigate the spatial-temporal distribution differentiation characteristics and driving factors of urban eco-efficiency. The main findings are as follows. Firstly, the eco-efficiency of all urban agglomerations showed a fluctuating upward trend, but the eco-efficiency performance of different urban agglomerations in China shows a stratification characteristic. Specifically, the Pearl River Delta urban agglomeration consistently ranks first in China, while the mean values of the Yangtze River Delta urban agglomeration, Beijing-Tianjin-Hebei urban agglomeration, and Chengdu-Chongqing urban agglomeration are lower than the national average. Secondly, the overall differences in the urban eco-efficiency of all sample cities show a consistently fluctuating downward trend. The factor that affects the level differences of eco-efficiency in different cities is the intra-regional differences. Last but not least, the top three factors affecting the spatial distribution difference of urban eco-efficiency in the whole sample are environmental pollution control investments, innovation level, and environmental infrastructure investments. In the end, this paper proposes that reducing the intra-regional differences is the primary task to achieve the coordinated improvement of urban eco-efficiency in urban agglomerations, and then puts forward some policy suggestions to improve eco-efficiency for the five major urban agglomerations.

Spatial–Temporal Differentiation and Trend Prediction of Coupling Coordination Degree of Port Environmental Efficiency and Urban Economy: A Case Study of the Yangtze River Delta

Green development is a primary path for ports and cities to achieve a low-carbon transition under the Sustainable Development Goals and a powerful driving force to elevate regional port–city relations to a high level of coordination. In this paper, twenty port cities in the Yangtze River Delta (YRD) were selected and port environmental efficiency (PEE) was calculated through the window SBM model, while the EW-TOPSIS model was used to evaluate high-quality urban economic development (HED). The coupling coordination degree (CCD) model, the kernel density model, GIS spatial analysis, and the grey prediction model were used to further explore the spatial–temporal dynamic evolution and prediction of the CCD between PEE and HED. The results suggested that: (1) PEE fluctuation in the YRD is increasing, with a trend of seaports achieving higher PEE than river ports; (2) HED in the YRD shows upward trends, and the polarization of individual cities is obvious; (3) Temporally, the CCD in the YRD has risen from 0.438 to 0.518. Shanghai consistently maintains intermediate coordination, and Jiangsu has experienced the most significant increase in CCD. Spatially, CCD is led by Lianyungang, Suzhou, Shanghai, and Ningbo-Zhoushan, displaying a decreasing distribution pattern from east to west. The projection for 2026 suggests that all port cities within the YRD will have transitioned to a phase of orderly development. To enhance the coordination level in the YRD, policymakers should consider the YRD as a whole to position the ports functionally and manage them hierarchically, utilize the ports to break down resource boundaries to promote the synergistic division of labor among cities, and then tilt the resources towards Anhui.

Spatial–Temporal Differentiation and Driving Factors of Cultivated Land Use Transition in Sino–Vietnamese Border Areas

Understanding the transformation of cultivated land use is crucial for advancing sustainable development goals related to food security. However, in mountainous regions, there is a lack of comprehensive studies that fully account for the diverse factors influencing cultivated land use transformation. This study aims to elucidate the temporal and spatial dynamics of cultivated land resource transformation in the mountainous Sino–Vietnam border area, uncover its underlying driving mechanisms, and offer insights for safeguarding cultivated land, promoting economic development, ensuring homeland security, enhancing ecological security, and bolstering border stability and prosperity. To investigate the cultivated land use transformation pattern in the Sino–Vietnam border area from 2000 to 2020, we employed kernel density estimation and geo-information spectra. Additionally, we developed a comprehensive driving force system tailored to the unique characteristics of cultivated land use in the border region. We applied a spatial econometric model to dissect the driving mechanisms governing cultivated land use transformation. Our findings revealed several key insights: (1) The density of cultivated land in the Sino–Vietnam border area exhibited an initial increase followed by a decrease. Notably, the transformation of cultivated land was most prominent in the eastern plains, intensifying over time. (2) The predominant type of transformation in the Sino–Vietnamese border area revolved around the mutual conversion of cultivated land and woodland, with the mutual conversion of cultivated land and grassland ranking second. (3) Against the backdrop of urban–rural integration, the transformation of cultivated land use at the border progressed from a phase of rapid decline to a phase of slower decline. (4) The transformation of cultivated land was influenced by a complex interplay of socio-economic factors, natural environmental conditions, policy management, and transportation infrastructure. The relative importance of these factors in driving cultivated land use transformation varied significantly across different time periods. In light of these findings, we recommend promoting agricultural modernization and industrialization in the Sino–Vietnamese border areas. It is essential to consider the region’s distinct cultivated land characteristics, implement tailored land policies, and develop diversified strategies for the utilization and management of cultivated land. Furthermore, harnessing land resources to stimulate economic development should be a focal point of future initiatives in the area.

Spatial-temporal differentiation and influencing factors of rural settlements in mountainous areas: an example of Liangshan Yi Autonomous Prefecture, Southwestern China

Spatial-temporal differentiation and influencing factors of rural settlements in mountainous areas: an example of Liangshan Yi Autonomous Prefecture, Southwestern China

Spatial-temporal differentiation pattern and influencing factors of land economic density at the township scale in Zhejiang Province.

Based on the land economic density of 892 town units, the spatial pattern of the land economic density in Zhejiang Province is analyzed using the coefficient of variation, spatial classification, and spatial correlation methods, and the influencing factors are analyzed using a spatial regression model. The results are as follows: (1) The coefficients of variation were 2.6 and 3.1 in 2014 and 2019, respectively, indicating that the degree of imbalance of the town's industrial economy at the county level increased. (2) The distribution of the high-level agglomeration areas was characterized by one core area and two sub-core areas. The main core area was located at the junction of Hangzhou City, Shaoxing City, and Jiaxing City, and the two sub-core areas were located in Yuyao City and the main urban area of Ningbo City. In addition, several small-scale agglomeration areas composed of medium and high-level units were distributed in Wenzhou City. (3) The high-value agglomeration and low-value agglomeration distribution in the spatial correlation patterns was identified using the spatial auto-correlation method. The hot spots and sub-hot spots were distributed in Northern Zhejiang, and the cold spots formed a large-scale agglomeration in Quzhou City, Lishui City, Taizhou City, and several other cities in Southern Zhejiang. (4) Compared with the county scale, the spatial scope of the high-level areas in Northern Zhejiang shrunk significantly at the township scale, and the high-level agglomeration areas along the southeast coast changed into a cluster of several townships. (5) According to the geographically weighted regression (GWR) model, the importance of influencing factors is as follows: population density > regional area > industrial output value per capita > total population > proportion of secondary and tertiary personnel > total employees.

Spatial-temporal differentiation characteristics and driving factors of China's energy eco-efficiency based on geographical detector model

As China is striving to build a modernization in which people and nature live in harmony, evaluating energy eco-efficiency (EEE) is an important part of achieving a balanced development of socio-economic construction and ecological civilization. From the perspective of the “energy-economy-ecology-society” system, this study included social welfare elements and PM2.5 into the EEE evaluation index, and the EEE of 30 provinces in China from 2000 to 2019 were examined more comprehensively. The distribution differences and sources of EEE were measured by Trend analysis and the Dagum Gini coefficient. A unique analytical method that reveals the drivers of spatial heterogeneity, the Geographical Detector model (Geodetector), was used to better account for the explanatory power of the drivers and their interactions. The results show that China's EEE has a weak “N"-shaped trend in time series, with significant spatial heterogeneity, and the contribution of this imbalance is ranked as inter-regional (39.83%) > hypervariable density (31.95%) > inter-regional (28.22%). The core factors that dominate the spatial-temporal differentiation in the East, Center and West are energy consumption structure (0.688), environmental regulation (0.414), and the level of urbanization (0.548), respectively. The factor interactions are all greater than the explanatory power of factor independent effects on EEE divergence, and the strongest interaction is between Environmental Regulation and Level of educational development in 2010(0.91). The interactions have regional characteristics, and targeted EEE enhancement paths should be selected for different regions.

Spatial–Temporal Coupling Analysis of Land Use Function and Urban–Rural Integration in Heilongjiang, China

Urban–rural integration relies on the rational flow of factors between urban–rural areas. Land represents a closely related factor between urban–rural areas, so the effective utilization of land resources can promote the flow of urban–rural factors. Therefore, there is a certain correlation between land use function and urban–rural integration. The purpose of this study is to explore the coupling and coordination relationship between the two systems and to find out the spatial–temporal differentiation characteristics in the process of land use function and urban–rural integration. The main conclusions are as follows: (1) The comprehensive level of land use function and urban–rural integration in Heilongjiang Province shows an overall upward trend, but there is a large differentiation on a municipal scale. (2) The coupling coordination degree of the two systems in Heilongjiang Province shows a spatial distribution pattern of “high in the north and low in the south, high in the middle and low in the east and west”. From 2013 to 2022, except for Harbin and Yichun, the overall trend in other regions is gradually upward. (3) The obstacle degree analysis of land use function and urban–rural integration in Heilongjiang Province shows that there is a close correlation of obstacle factors between the two systems.

Analysis on spatial-temporal variation characteristics of climate in Qinling-Huaihe demarcation zone since 1961

The emerging global environmental crisis, intensified by global warming, necessitates a deeper understanding of climate variations, especially in critical geographical divisions like the Qinling-Huaihe demarcation zone, which acts as a natural division between northern and southern mainland China. This study investigates the spatial–temporal differentiation of climatic elements within the Qinling-Huaihe demarcation zone from 1961 to 2018, employing data accumulated from 144 meteorological stations. Utilizing methodologies including the univariate linear regression, the Mann-Kendall test, and the Kriging interpolation, this investigation unveils the spatial–temporal variation characteristics of precipitation, relative humidity, and temperature. Temporally, the climatic alterations in the zone are predominantly marked by warming trends, significantly pronounced since the 1990s, with the elevation in minimum temperature being the most notable. Both precipitation and relative humidity exhibit a fluctuating ascending trend. Spatial analysis reveals a west-to-east increase in precipitation, while relative humidity presents lower values in the central region, escalating towards the eastern and western edges. The spatial distribution patterns for average, maximum, and minimum temperatures align closely, showcasing higher temperatures in the central region, extending to the Huaihe River area, with the Qinling Mountains exhibiting the lowest temperatures, particularly noticeable north of the Qinling Mountains.

Spatial–temporal differentiation and control strategies of nitrogen environmental loss in China's coastal regions based on flow analysis

Nitrogen pollution emissions from human production and living activities in coastal regions are important topics in the management of environmental pollution in coastal waters. However, to date, there has been relatively little research systematically assessing the environmental loss of nitrogen (NEL) from human activities that negatively affect marine ecosystems. This study categorised emission sources into five subsystems, namely livestock, farming, aquatic, industrial, and residential. Through flow analysis, the anthropogenic emissions of nitrogen in the gas, liquid, and solid phases from 11 coastal provinces in China in 2011, 2015, and 2020 were determined. A nitrogen cost index was constructed by combining the social indicators of each province. The effectiveness of nitrogen emission control since the land-sea coordination and the future challenges for the coastal region were discussed from various perspectives. The results of the study showed that the total NEL that poses a potential threat to marine ecosystems in coastal areas of China has decreased from 18.93 TgN to 14.66 TgN since the proposal of land-sea coordination, with livestock systems and aquatic systems emitting the most. The Bohai and Yellow Seas area were most threatened by nitrogen pollution. Among the three oceanic pathways, liquid-phase nitrogen discharge from each subsystem was effectively controlled, and the control of gas-phase nitrogen emissions is still the most numerous NEL state, although it has had a significant effect. The results of the correlation analysis suggest that NEL flow can characterize the regional management of nutrient-based organic pollutants. Past management tools and environmental investments in China have been more effective in controlling emissions from point and line sources involving artificial facilities, but less direct effect on mariculture. How to control surface source pollution from livestock and aquaculture will be an important challenge to reduce reactive nitrogen emissions in the future.

Study on Spatial-Temporal Differentiation of Water Resources Carrying Capacity Evaluation in Liaoning Province

Study on Spatial-Temporal Differentiation of Water Resources Carrying Capacity Evaluation in Liaoning Province

Spatial-Temporal Differentiation of Soil Biochemical Parameters and Their Relationship with Nitrogen Resources during the Vegetation Period of Selected Crops

Understanding the spatial–temporal variability of soil enzymatic activity and its relationship with nitrogen (N) resources in the soil and crop yield is crucial in rational management practices of mineral fertilization. The scarcity of comprehensive studies on geostatic analyses of agricultural soils and plant yields, which would take into account both temporal and spatial variability, was the reason for undertaking this research. The aim of this study was to determine the spatial and temporal variability of the activity of soil enzymes, such as acid (PAC) and alkaline (PAL) phosphatases, urease (URE) and protease (PROT), the content of N-NH4 (ammonium ions), N-NO3 (nitrate ions), phosphorus (P), pH, moisture, as well as crop yield on a conventionally managed farmland of 40 ha. During the two-year experiment, soil samples were collected from 37 measurement points. Wheat was the first tested crop, followed by oilseed rape. It was shown that all the tested soil parameters showed temporal and spatial variability, and a significant number of them were significantly higher in July. The creation of raster maps showing the distribution of the tested parameters allowed for the observation of the considerable activity of PAC, PAL, URE, and PROT, as well as a high application of N-NO3 in the southern part of the field during the growth of the plants. The statistical analysis revealed a negative interaction between the N-NH4 and N-NO3 and the urease in the soil under the cultivation of plants. The pH and the percentage of moisture in the soil also had higher values in the south of the field. This pointed to the existence of separate production zones in the south-central part of the field, characterized by a higher yield of wheat and rape. On the basis of the conducted research, it was unequivocally stated that the values of enzymatic and chemical parameters of the soil were reflected in the size of the yield obtained, which allows conclusions to be drawn with respect to the rational management of N in the production process, laying the foundations for precision agriculture.

Spatiotemporal Distribution and Driving Force Analysis of the Ecosystem Service Value: A Typical Case Study of the Coastal Zone, Eastern China

Identifying and assessing the drivers of change in ecosystem service value (ESV) is critical for integrated management and human well-being in coastal zone areas. This paper took a typical coastal zone in eastern China as the research object. Based on five periods of remote sensing monitoring data of land use status from 2000 to 2020, the ESV of Yancheng was estimated by adopting the equivalence factor method. Spatial statistical analysis and GeoDetector were applied to reveal the heterogeneous features of ESV and its driving mechanism. The results suggested that: (1) From 2000–2020, each land use type in the study area changed to different degrees, among which the most drastic change was in the construction land, which increased continuously by 962.69 km2, the cultivated land area decreased continuously by 784.1 km2, and the area of water body decreased by 163.34 km2. (2) ESV experienced a process of increasing and then decreasing, from 547.84 × 108 yuan to 570.86 × 108 yuan and then decreasing to 507.62 × 108 yuan, with farmland ecosystems having the largest ESV, accounting for more than 60%. Regulatory services were the core function of ecosystem services, accounting for more than 60%. (3) There was significant spatial-temporal differentiation in ESV, with extremely low ESV expanding in rapidly developing urbanized areas. The ESV distribution exhibited significant spatial autocorrelation and local spatial clustering, with the gravity center showing a general trend from north to southwest. (4) The ESV spatial and temporal evolutionary characteristics were the result of a multifactorial integration of land use, topography, socio-economics, and climate factors. The explanatory power of each factor in descending order was LDI > DEM > POP > GDP > RAI > TEM, and any two-factor interaction had higher explanatory power than the single factor.

Spatial-temporal differentiation and driving factors of ecological resilience in the Yellow River Basin, China

Ecological problems are considered to be the key constraint to high-quality development in the Yellow River Basin (YRB), so accurately grasping and understanding the spatial–temporal differentiation characteristics and driving factors of ecological resilience (ER) in the YRB is crucial for the sustainable development of the YRB. This study develops a three-dimensional ER evaluation index system comprising resistance (RE), adaptability (AD), and recovery (RC), measures the ER of 76 cities in the YRB using the entropy method, and investigates the spatial–temporal evolutionary characteristics and drivers of ER in the YRB using the Dagum Gini coefficient (DGC), Kernel density estimation (KDE) and geographic detector methods (GDM). The results show that the spatial differences in ER of the YRB showed a narrowing trend. The primary cause of the overall differences was the interregional differences. There is no significant polarization of ER in the basin as a whole, and the differentiation between the three regions is gradually weakening. Economic development (ED) is a key factor influencing the spatial differences in ER in the YRB and downstream regions. Average annual precipitation (AP) and adaptability (AD) are the most important drivers affecting ER in the upper and middle reaches, respectively. The combined impact of multiple factors on the spatial differences of ER in the YRB is significantly more pronounced than that of individual factors. This study provides empirical evidence and decision-making reference to comprehensively grasp the current ER development characteristics in the YRB, and to develop and implement differentiated regional ecological conservation strategies.

Investigating the Spatial-Temporal Variation of Pre-Trip Searching in an Urban Agglomeration

Search engines have been the primary tool for online information search before traveling. Timely detection and the control of peak tourist flows in scenic areas prevent safety hazards and the overconsumption of tourism resources due to excessive tourist clustering. This study focuses on the spatial-temporal interactions between the pre-trip stage and the after-arrival stage to investigate online information search behavior. Big data obtained from mobile roaming and search engines provide precise data on daytime and city scales, which enabled this paper to examine the relationship between daily tourist arrivals and their pre-trip searching from 40 cities within the Yangtze River Delta urban agglomeration. This study had several original results. First, tourists generally search for tourist information 2–8 days before arriving at destinations, while tourist volume and SVI from source cities show distance attenuation. Second, SVI is a precursor to changes in tourist volume. The precursory time rises with the increase of traffic time spatially. Third, we validated a VAR model and improved its accuracy by constructing it based on the spatial-temporal differentiation of search features. These findings would enhance the management and preservation of tourism resources and promote the sustainable development of tourism destinations.

THE SPATIAL-TEMPORAL DIFFERENTIATION IN AIR POLLUTION AND INDUSTRY STRUCTURE — EVIDENCE FROM THE METROPOLITAN AREA OF BEIJING–TIANJIN–HEBEI URBAN IN CHINA

In developing countries, environmental problems are often intertwined with industrial spatial distribution and migration. This paper is the first in this field to apply the standard deviational ellipse (SDE) method to the analysis of spatial agglomeration and spatiotemporal evolution of air pollution in 13 cities in the Beijing–Tianjin–Hebei region (BTH). According to the industrial classification, each industry could be further divided into five sub-industries: labor-intensive, capital-intensive, technology-intensive, pollution-intensive, and the mining industry. The discussion includes an exploration of the relationship between environmental quality and the spatial distribution of different sub-industries, along with their dynamic changes in migration. This paper also describes the innovative use of the geographically weighted regression (GWR) to study the impact of the whole industry and sub-industries on the environment. The main conclusions of this paper are as follows: (i) Both air pollution and the industry have obvious spatial agglomeration characteristics. (ii) The spatial difference coefficients between the secondary industry and environmental pollution and between various sub-industries and environmental pollution are small, and there is spatial consistency. (iii) Capital-intensive industries represent a primary source of air pollution. In addition to the slowdown of the tertiary industry, the contribution of various types of sub-industries to environmental pollution displays an increasing trend.

Spatial-temporal differentiation of coupling coordination degree for green finance and green innovation efficiency: a case study in China.

Continued investment in finance and innovation is beneficial to economic development, and the joining of green system can accelerate the process of economic recovery from environmental distress. To better enhance the relationship of green finance and green innovation, it is vital to demonstrate the synergy between the two thoroughly. Thirty provinces in China are selected to examine the coupling coordination relationship between the two, specifically testing the spatial aggregation and evolutionary differences in the coupling coordination by adopting the coupling coordination degree (CCD) model, spatial autocorrelation, and kernel density estimation. Conclusions of the paper show that green finance is calculated by the EW-TOPSIS method, and the overall score of provinces is low. Using super-SBM model to evaluate green innovation, the uneven distribution of efficiency is obvious, although it is gradually increasing. The CCD in most provinces is in low-level or basic coordination, with significant regional heterogeneity. The global Moran's index becomes gradually evident with time. The local Moran scatter diagram presents a downward trend from east to west, but with more L-L aggregation provinces emerging in 2020. The center of the national kernel density curve gradually shifts to the right, indicating that the national overall synergy level is improving. Deepening the understanding of the empirical results facilitates the formulation of reasonable policies that fit the four major regions.

Spatial–temporal differentiation and driving factors of water resources green efficiency in the Huaihe River Basin

Abstract Considering the regional economic and social characteristics and high-quality development goals, this paper improves the calculation method of green efficiency of water resources and adopts the SBM-DEA model to calculate the green efficiency of water resources in 35 cities of four provinces in the Huaihe River Basin. The results show that (1) the green efficiency of water resources in the Huaihe River Basin decreased first and then increased from 2011 to 2020. (2) In terms of spatial distribution, the provincial-level divisions of green efficiency of water resources in the Huaihe River Basin from high to low are Shandong, Henan, Jiangsu, and Anhui. The elliptical area of green efficiency of water resources keeps expanding, and the efficiency value radiates outward along ‘Yangzhou–Zhengzhou’. (3) At the level of total-factor productivity decomposition, TC (technological change) has a more noticeable impact on factors than EC (efficiency change). TC scale technology changes mainly come from the improvement of MATC (magnitude technical change). (4) Industrial output value has a positive driving effect on the green efficiency of water resources. Government participation, resource endowment, and economic growth rate reflect China's current incongruity between economic development and water resources green efficiency improvement.