Spatial-temporal Evolution Characteristics Research Articles

The spatial-temporal evolution characteristics and influencing factors of the coupling between the industrial chain and innovation chain in China

Exploring the theoretical logic, development characteristics and improvement paths of the deep coupling of innovation chains and industrial chains (two-chain) is of great importance for building a modern industrial system and developing productive forces. However, empirical evidence on the spatial-temporal characteristics and influencing factors of two-chain coupling is still lacking. In this study, the degree of coupling coordination model is applied to assess the degree of two-chain coupling in China using provincial panel data from 2013 to 2022. In addition, spatial analysis is applied to investigate the spatial-temporal evolution characteristics of the variables in question. On this basis, the fuzzy set qualitative comparative analysis (fsQCA) method is applied to identify influential factors. The results indicate that: (1) Temporally, the coupling level of the two-chain shows a consistent upward trend and gradually develops a distribution pattern characterized by high values in the east and weak values in the west. (2) Spatially, there is a significant positive spatial correlation between the two-chain coupling. (3) In terms of drivers, the increase in two-chain coupling is attributed to multiple factors. Three models can be identified: human-driven, capital-driven and information-driven, which are influenced by the cooperation between government and market. The novelty of this article lies in its exploration of the theoretical logic and developmental characteristics of two-chain coupling, while empirically demonstrating its driving factors. This study provides a new perspective for governments and relevant departments to improve two-chain linkages and promote economic development.

Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm

China’s total carbon emissions account for one-third of the world’s total. How to reach the peak of carbon emissions by 2030 and achieve carbon neutrality by 2060 is an important policy orientation at present. Therefore, it is of great significance to analyze the characteristics and driving factors of temporal and spatial evolution on the basis of effective calculation and prediction of carbon emissions in various provinces for promoting high-quality economic development and realizing carbon emission reduction. Based on the energy consumption data of 30 provinces in China from 2000 to 2021, this paper calculates and predicts the total carbon emissions of 30 provinces in China from 2000 to 2035 based on ARIMA model and BP neural network model, and uses ArcGIS and standard elliptic difference to visually analyze the spatial and temporal evolution characteristics, and further uses LMDI model to decompose the driving factors affecting carbon emissions. The results show that: (1) From 2000 to 2035, China’s total carbon emissions increased year by year, but the growth rate of carbon emissions gradually decreased; The carbon emission structure is “secondary industry > residents’ life > tertiary industry > primary industry”, and the growth rate of carbon in secondary industry and residents’ life is faster, while the change trend of primary industry and tertiary industry is smaller; (2) The spatial distribution of carbon emissions in China’s provinces presents a typical pattern of “eastern > central > western” and “northern > southern”, and the carbon emission centers tend to move to the northwest; (3) The regions with high level of digital economy, advanced industrial structure and new quality productivity have relatively less carbon emissions, which has significant group difference effect; (4) The intensity effect of energy consumption is the main factor driving the continuous growth of carbon emissions, while the per capita GDP and the structure effect of energy consumption are the main factors restraining carbon emissions, while the effects of industrial structure and population size are relatively small. Based on the research conclusion, this paper puts forward some policy suggestions from energy structure, industrial structure, new quality productivity and digital economy.

Spatial–Temporal Evolution and Driving Force Analysis of Blue–Green Space in the Chengdu–Chongqing Economic Circle, China

In the rapid process of urbanization, revealing the patterns and driving forces behind the evolution of blue–green spaces holds significant value for optimizing urban blue–green environments. This study systematically investigates the spatial–temporal evolution characteristics and driving forces of blue–green space in the Chengdu–Chongqing Economic Circle from 1990 to 2020, utilizing GIS technology, landscape pattern analysis, and geographic detectors. The research findings indicate the following: (1) The area of blue–green space in the study area exhibits a general trend of initial growth followed by decline, with significant changes occurring between 2010 and 2020. (2) The fragmentation degree of blue–green space is gradually increasing, while connectivity among landscapes is decreasing; however, there has been an increase in landscape distribution uniformity. More than 90% of blue–green spaces expanded mainly through adjacency patterns. (3) In examining driving forces, it was found that temperature, topographic relief, elevation, population density, and construction intensity are the primary driving factors. Notably, the influence of natural factors has diminished over time while human social factors have significantly intensified. This study offers solutions for optimizing the configuration of blue–green spaces within the Chengdu–Chongqing Economic Circle. It also serves as a reference case for promoting high-quality urbanization in developing countries undergoing rapid urbanization.

Spatial–Temporal Variation and the Influencing Factors of NO2 Column Concentration in the Plateau Mountains of Southwest China

Given the complex terrain and economic development status of Guizhou Province, research on tropospheric NO2 column concentration using satellite remote sensing is still insufficient. Observing the spatial–temporal evolution characteristics of tropospheric NO2 column concentration can ensure the stable development of air quality. Based on the Google Earth Engine (GEE) platform, NO2 column concentration data retrieved from Sentinel-5P TROPOMI were analyzed using spatial autocorrelation, hotspot analysis, and geographic detector methods (Geodetector). The results show that NO2 column concentration in Guizhou Province exhibits seasonal variation, characterized by higher levels in winter and lower levels in summer, with transitional values in spring and autumn. The annual average concentration was highest in 2021 at 3.47 × 10−5 mol/m2 and lowest in 2022 at 2.85 × 10−5 mol/m2. Spatially, NO2 column concentration displays a distribution pattern of “high in the west, low in the east; high in the north, low in the south”, with significant spatial clustering. The distribution of cold and hot spots aligns with areas of high and low values. NO2 column concentration is primarily influenced by socio-economic factors, with the interaction between any two factors enhancing the explanatory power of individual factors on NO2 column concentration.

Regional Coexistence in the Digital Era: Spatial–Temporal Evolution and Sustainable Strategies of the Coupled System in the Yangtze River Basin, China

Against the backdrop of globalization and ecological civilization, this study aims to analyze the patterns of system coupling coordination development in the Yangtze River Basin under the interacting influences of population growth, ecological conservation, energy utilization, and digital economic development. Using a multisource model, this paper explores the state of coordinated development, spatial–temporal evolution characteristics, and influencing factors in the Yangtze River Basin from 2011 to 2020. The results indicate the following: (1) The overall degree of coupling coordination in the Yangtze River Basin shows better performances in the eastern coastal areas compared to the central and western regions. Over time, the spatial autocorrelation of coupling and coordination increases, exhibiting a significant spatial clustering trend. (2) The Moran’s I index increased from 0.327 to 0.370, with high–high clusters primarily distributed in economically developed coastal provinces, while low–low clusters were observed in remote provinces in the central and western regions, revealing regional development imbalance issues. (3) The driving force analysis shows that green coverage and GDP are the core factors influencing the spatial differentiation of coupling coordinated development. Factors such as the urbanization rate, nighttime light index, and energy consumption had significant impacts in certain years but are generally considered minor factors. The results of this study not only contribute to understanding the dynamic mechanisms of regional coupling and development but also provide a scientific basis for formulating regional coordinated development policies, promoting the achievement of win–win goals of economic growth and ecological civilization in the Yangtze River Basin and similar regions.

Exploring the complex relationship between industrial upgrading and energy eco-efficiency in river basin cities: A case study of the Yellow River Basin in China

The synergistic advancement of industrial upgrading and energy eco-efficiency is crucial for the attainment of the Sustainable Development Goals. This study explored the coupling coordination relationship and its spatial-temporal evolution characteristics between industrial structure advancement and energy eco-efficiency, industrial structure rationalization and energy eco-efficiency in Yellow River Basin cities from 2010 to 2020. The super-SBM model and coupling coordination degree model were used to calculate the energy eco-efficiency and coupling coordination degree. Moran's I and Tobit model were used to analyze the spatial autocorrelation and the factors of coupling coordination degree. The coupling coordination degree of industrial structure advancement and energy eco-efficiency decreased from 0.535 to 0.489, with a growth rate of −8.60 %, showing a fluctuating downward trend. The coupling coordination degree of industrial structure rationalization and energy eco-efficiency decreased from 0.296 to 0.246, with a growth rate of −16.88 %, showing an "M" -shaped downward trend. The coupling coordination degree had a significant spatial correlation, and the global Moran's I value was stable around 0.2. Additionally, public transport and degree of opening-up can significantly promote the coordination relationship between industrial upgrading and energy eco-efficiency. These consequences can supply evidence for industrial and energy structure adjustment in river basin cities.

Spatial-temporal evolution characteristics and driving factors analysis of regional energy supply and demand in China

Under the requirements of the Sustainable Development Goals, the study of China's energy development is an important reference for other countries to plan their energy policies. China is facing the problem of slow energy transformation processes and a continuously widening gap between supply and demand. Using panel data for 2012–2021, this study analyzes the characteristics of energy in China's 30 provinces, and clarifies the problems faced by each province. GM (1,1) model is employed to forecast the level of energy development from 2023 to 2030. The LMDI (Logarithmic mean Divisia index) model is utilized to analyze factors impacting energy demand in each province, according to which the design of energy development direction in each region is completed. The study finds that: (1) Overall, energy supply and demand in China are marked by “high in the west, low in the east” and “low in the west, high in the east,” respectively. The energy structure of each region is continuously optimized, but coal still has a high share. (2) The pace of clean energy development is satisfactory. However, natural gas's increase in share is relatively slight on both supply and demand sides. The substitution effect is not in line with the expectations of the transition policy. (3) Economic and investment effect are major contributing factors to increase energy demand, while the technical effect plays a significant part in reducing energy demand. A few insights from this study are provided for the formulation of transition policies in China.

Research on the relationship of coupling coordination between digitalization and green development

The coupling coordination of digitalization and green development has become an inevitable requirement for building a new development pattern and achieving high-quality development of China’s economy. Based on the panel data of 284 Chinese cities from 2011 to 2021, this study uses the coupling coordination degree model, Dagum Gini coefficient, spatial convergence, Markov transfer probability matrix, and panel Tobit model to quantitatively analyze the spatial–temporal evolution characteristics and influencing factors of the coupling coordination degree of digitization and green development. The study results show that the coupling coordination degree (CCD) of digitalization and green development has an overall increasing trend during the study period, and the eastern region is higher than other regions which showing spatial non-equilibrium characteristics. The spatial difference of the CCD continues to downward, and inter-regional differences are the main source of the CCD. In the long run, there is a “catch-up effect” between cities with low CCD and those with high CCD, and the CCD of digitalization and green development will tend to a steady state. The transfer type of the CCD has the characteristics of “club convergence”, the probability of maintaining the original state is high, and the overall CCD shows a good development trend in the future. Factors such as environmental regulation, green innovation, industrial structure upgrading and financial efficiency can significantly contribute to the CCD of digitalization and green development. The above findings provide empirical evidence of the CCD of digitalization and green development to achieve high-quality economic development.

Spatial–temporal evolution characteristics of cultivated land structure transition: a case study of Hubei Province, Central China

Spatial–temporal evolution characteristics of cultivated land structure transition: a case study of Hubei Province, Central China

Exploring the spatial-temporal evolution characteristics of extreme heat events and driving factors in seven geographic regions of China (1980–2021)

Extreme heat events in China are occurring more frequently due to global warming. However, there is a lack of comprehensive studies analyzing the movement direction and factor interaction of extreme heat events in different regions of China. Based on daily temperature data from 2252 meteorological stations in China from 1980 to 2021, three extreme heat indicators were calculated: Hot days (SU35), Tropical nights (TR20), and Extreme maximum temperature (TXx). The study updated and compared the characteristics of spatiotemporal evolution and driving factors of extreme heat events in China and its seven geographical regions.The results show that: (1) SU35 and TR20 increase faster in summer, while TXx increases faster in winter. Nighttime high-temperature events have increased at a faster rate. (2) Extreme heat events in North, Northeast, and Northwest China have gradually moved southward and eastward, while in Central, East, and South China, such events have moved northward. (3) In northeast and northwest China, the Atlantic Multi-decadal Oscillation (AMO) is a strong driving force for extreme heat events, while urbanization is the most potent driving force in the remaining regions. The interaction between urbanization and atmospheric circulation, including AMO, Pacific Decadal Oscillation (PDO), and East Atlantic/West Russia (EA/WR), is non-linear or double factor enhancement and exhibits significant spatial heterogeneity. These results will be significant for urban adaptation to climate change and sustainable development.

Spatial-Temporal Evolution Characteristics Analysis of Color Steel Buildings in Lanzhou City

With the continuous acceleration of China’s urbanization process, color steel plate, as a new type of building material, has been widely used in all kinds of temporary buildings and has become the spatial carrier of the specific development stage of urbanization. This study focuses on Lanzhou City as a case study to deeply analyze the spatiotemporal distribution and evolution of color steel plate buildings. Utilizing data extracted from Google imagery and GF-2 satellite images of the built-up areas in Lanzhou, spatial statistical and analytical methods such as centroid analysis, compactness index, and patch density are applied. Systematic analysis is conducted across different time periods and spatial scales to examine the evolution of indicators, including quantity, centroid distribution, spatial clustering, and distribution direction. The results show that from 2013 to 2021, the prevalence of color steel buildings in Lanzhou city initially increased and then decreased, and the number peaked in 2017, but there is a significant difference between distinct areas in the urban area. By quantitatively analyzing the spatial and temporal evolution characteristics of color steel plate buildings, this study reveals the important role it plays in promoting the urbanization process and provides a scientific basis for relevant planning decisions.

Regional Differences and Spatial-Temporal Evolution Characteristics of Digital Economy Development in the Yangtze River Economic Belt

Digital economy has emerged as one of the primary driving forces for economic globalization. However, assessing digital economy development in a robust and scientific manner remains a great challenge. This paper proposes an evaluation system with measurement errors correction to accurately research the regional differences in and the spatial-temporal evolution characteristics of digital economy development in the Yangtze River Economic Belt (YEB), combining the entropy method, the Dagum–Gini coefficient, an σ convergence model and grey correlation analysis. The results present that the digital economy development index in the YEB rose from 2012 to 2021, with the greatest weight being social livelihood benefits. Meanwhile, there were noticeable regional differences in digital economy development in the YEB; in particular, the middle reaches showed obvious convergence. The grey correlation degree between the influence factors and the digital economy development ranged from 0.5286 to 0.9144, demonstrating a robust positive correlation. The theoretical framework of this paper integrates economic development models with advanced statistical analysis techniques, providing a robust analytical perspective for examining the complexities of digital economy evolution. The insights offer a blueprint for policymakers seeking to foster a robust and equitable digital economy, underscoring the potential of data-driven policy formulations in navigating the intricate landscape of economic globalization.

Synergy level of urban resilience and urban land use efficiency in the Yellow River Basin: Spatial-temporal evolution characteristics and driving factors

The complex global context, including globalization, rapid urbanization, and climate change, poses significant challenges to urban stability and development. Balancing urban land use efficiency and resilience is crucial for sustainable progress. Focusing on the vulnerable Yellow River Basin (YRB), this study examines the interplay between urban resilience and land use efficiency. Panel data from 2013 to 2020 for 54 cities in the YRB were used, it quantifies the Coupling Coordination Degree of Urban Resilience and Urban Land Use Efficiency (CCDUU), explores its spatiotemporal evolution and influencing factors. Key findings include: The CCDUU exhibits a sustained and discernible growth trend. Notably, CCDUU is higher in downstream areas in comparison to the middle reaches, reaching its lowest point in the upstream areas; however, the increase in CCDUU in the upstream areas surpasses that observed in other regions. Concurrently, regional disparities in CCDUU are diminishing. Despite the presence of a notable positive spatial correlation in CCDUU within the YRB, the strength of this spatial association is not sufficiently robust. Of paramount importance factor is the role of regional innovation, which significantly influences the enhancement of CCDUU. Following closely is the degree of openness, whereas the positive effects of government support and population density are concentrated predominantly in the upper YRB region. In contrast, urban-rural disparity exerts an adverse impact on CCDUU in most regions. Policy recommendations for enhancing CCDUU in YRB cities include strengthening government support and planning control, particularly in upstream regions, to achieve efficient resource utilization and environmental protection. Implementing population density management policies, encouraging rational movement, and promoting population migration to upstream areas can alleviate pressure in downstream cities. Enhancing openness, attracting foreign investment, and promoting innovation and industrial upgrading will drive economic structural upgrades and improve CCDUU.

Spatial-Temporal Evolution Characteristics and Driving Force Analysis of NDVI in Hubei Province, China, from 2000 to 2022

Exploring the characteristics of vegetation dynamics and quantitatively analyzing the potential drivers and the strength of their interactions are of great significance to regional ecological environmental protection and sustainable development. Therefore, based on the 2000–2022 MODIS NDVI dataset, supplemented by climatic, topographic, surface cover, and anthropogenic data for the same period, the Sen+Mann–Kendall trend analysis, coefficient of variation, and Hurst exponent were employed to examine the spatial and temporal characteristics and trends of NDVI in Hubei Province, and a partial correlation analysis and geographical detector were used to explore the strength of the influence of driving factors on the spatial differentiation of NDVI in vegetation and the underlying mechanisms of interaction. The results showed that (1) the mean NDVI value of vegetation in Hubei Province was 0.762 over 23 years, with an overall increasing trend and fluctuating upward at a rate of 0.01/10a (p < 0.005); geospatially, there is a pattern of “low east and high west”; the spatial change in NDVI shows a trend of “large-scale improvement in the surrounding hills and mountains and small-scale degradation in the middle plains”; it also presents the spatial fluctuation characteristics of “uniform distribution in general, an obvious difference between urban and rural areas, and a high fluctuation of rivers and reservoirs”, (2) the future trend of NDVI in 70.76% of the region in Hubei Province is likely to maintain the same trend as that of the 2000–2022 period, with 70.78% of the future development being benign and dominated by sustained improvement, and (3) a combination of partial correlation analysis and geographical detector analysis of the drivers of vegetation NDVI change shows that land cover type and soil type are the main drivers; the interactions affecting the distribution and change characteristics of NDVI vegetation all showed two-factor enhancement or nonlinear enhancement relationships. This study contributes to a better understanding of the change mechanisms in vegetation NDVI in Hubei Province, providing support for differentiated ecological protection and project implementation.

Spatial–temporal evolution and peer effects of urban green development efficiency in China

In the process of global urban development, there are urgent ecological security and environmental pollution problems, green development is the fundamental way for urban sustainable development, economic transformation and mitigation of ecological and environmental problems. Based on the panel data of 283 cities at prefecture level and above in China from 2003 to 2017, this paper analyzes spatial–temporal evolution characteristics of urban green development efficiency (UGDE) and the peer effects of UGDE between cities of different grades. It is found that during the study period, in terms of temporal evolution, the average UGDE in China increased from 0.47 in 2003 to 0.61 in 2017, with a cumulative growth rate of 29.79%, showing a rising trend in general. In terms of spatial evolution, the number of low-efficiency cities and medium-efficiency cities continued to decrease. The eastern region has always been the main distribution area of higher-efficiency cities and high-efficiency cities; in the central region, UGDE in most cities improved significantly; in the western region, UGDE has always lagged behind that in the eastern and central regions. In addition, the center of gravity of UGDE presented a trend of northwest migration in general, with a total displacement of 100.07 km, and UGDE showed a spatial dispersion trend. The empirical results indicate that the improvement of UGDE in large cities has a driving effect on that in neighboring medium cities and small cities through the positive peer effect, and the growth of UGDE in medium cities has a promoting effect on that in neighboring small cities through the positive peer effect; the increase of UGDE in medium cities has a positive peer effect on that in neighboring large cities, and the growth of UGDE in small cities has a positive peer effect on that in neighboring medium cities; UGDE promotes each other between large cities through the positive peer effect.

Measurement, Spatial-Temporal Evolution, and Optimization Path of the Level of Coordinated Development of Ecological Civilisation: The Case of China

Based on the energy–economy–environment–society (3E1S) system theory, this paper constructed a comprehensive evaluation system for the coordinated development of China’s ecological civilisation construction using panel data from 31 provinces and cities from 2005 to 2020, which is specifically divided into four sub-systems, namely, energy, economy, environment, and society, and covers 10 ordinal covariates and 40 ordinal variable indicators. In terms of measurement and evaluation methods, this paper adopted the composite system synergy model to measure the level of coordinated development of ecological civilisation construction of the country as a whole and 31 provinces and cities and adopted Arcgis maps, standard deviation ellipses, kernel density curves, and Theil index methods to explore the spatial-temporal evolution characteristics and regional differences in the level of coordinated development of ecological civilisation construction. It was found that the coordinated development level of China’s ecological civilisation construction increased year by year from 2005 to 2020, with the most obvious upward trend from 2011 to 2015, with an average increase of 26.5%. Secondly, the spatial distribution pattern of the coordinated development level of China’s ecological civilisation construction was relatively stable, basically showing a dominant southwest–northeast direction, but the spatial centre of gravity shows a trajectory shifting towards the southwest. Finally, there were large regional differences in the level of coordinated development of China’s ecological civilisation, with an overall decreasing regional pattern of “Central-Northeast-East-West”. However, the overall differences among the four regions showed a decreasing trend. The overall differences among the four regions were mainly intra-regional, with the western region having the greatest intra-regional differences and contributing to the overall differences. Although this paper takes China as a case study, its research methodology and conclusions can provide references for the construction of ecological civilisation and green sustainable development in other countries.

Deep fault sliding rates for Ka-Ping block of Xinjiang based on repeating earthquakes

Abstract The seismically active Ka-Ping Block within Xinjiang, China, represents a zone of potential earthquake hazards, and existing surface measurements cannot fully reflect the area’s sub-surface slip rates. To determine these slip rates and characterize the level of hazard that the study area may face in future years, using a cross-correlation of waveforms, we identified 432 repeat sequences along large faults (F1–F5) in the Ka-Ping Block between September 2009 and April 2022 and computed the annual slip rate for every sequence with the empirical relationship of the moment and the seismic magnitude. Spatial distribution images and temporal evolution characteristics of deep deformation in fault zones were constructed. We obtained five asperities in the Ka-Ping Block, the western portion of the PiQiang fault (F4) had a larger yearly slide rate than its eastern portion, and the southwest and eastern areas of the KEP station show an elevated risk of seismic activity within the next two years.

Comprehensive evaluation and spatial-temporal evolution characteristics of urban resilience in Chengdu-Chongqing Economic Circle

To clarify the connotations and extensions of urban resilience, this study focuses on the Chengdu-Chongqing Economic Circle with 16 cities as research subjects. A comprehensive evaluation index system was constructed to measure the resilience of each city from 2003 to 2020. The spatial-temporal evolution characteristics were analyzed using Kernel density estimation, standard deviation ellipse, and spatial Markov chain analysis, and the spatial Tobit model was introduced to discover the influencing factors. The results indicate the following: ① Urban resilience in the Chengdu-Chongqing Economic Circle displays an upward trend, with the center of gravity moving to the southwest, and the polarization phenomenon intensifying. ② The urban resilience level in a region has certain spatial and geographical dependence, while the probability of urban resilience transfer differs in adjacent cities with different resilience levels. ③ Urban centrality, economic scale, openness level, and financial development promote urban resilience, whereas government scale significantly inhibits it. Finally, this paper proposes countermeasures and suggestions to improve the urban resilience of the Chengdu-Chongqing Economic Circle.

Spatial-temporal evolution characteristics and critical factors identification of urban resilience under public health emergencies

Public health emergencies have a massive impact on the safety of people's lives and property and the sustainable development of cities. Much scholarly attention has focused on the index system, measurement method, and operation mechanism of urban resilience. But those debates typically overlook the particularity of public health emergencies and the new features of urban data and dynamic intelligence. Building on this literature, this study takes 26 cities in the Yangtze River Delta Urban Agglomerations from 2000 to 2022 as research samples to scientifically propose a six-dimensional evaluation index system (economic, social, ecological, infrastructure, organizational, and institutional) by meta-analysis. This study also discusses the spatial-temporal static (dynamic) evolution law in depth based on the comprehensive weight of the CRITIC method and entropy method, and spatial Markov chain. Finally, with the help of combining the interpretative structural model and the complex network, critical factors are quantitatively identified from two aspects of node local and network global. This study proposes a novel approach to understanding urban resilience in public health emergencies. Furthermore, the evaluation index system, spatial-temporal evolution characteristics, and critical factor identification provide theoretical references and policy recommendations in developing safe development strategies for high-quality, safe, and resilient cities.

Spatial–temporal evolution characteristics and influence factors of extreme precipitation indices based on bias-corrected and gauge-measured precipitation data in the main river basins of China, 1980–2020

Spatial–temporal evolution characteristics and influence factors of extreme precipitation indices based on bias-corrected and gauge-measured precipitation data in the main river basins of China, 1980–2020