Research on the spatiotemporal evolution and non-stationarity effect of urban carbon balance: Evidence from representative cities in China.

Accelerating the attainment of carbon balance in Chinese cities has become pivotal in addressing global climate change and promoting green, low-carbon development. This study, encompassing 277 prefecture-level and above cities from 2007 to 2020, reveals a positive overall trend in China's urban carbon balance index. The evolution unfolds in two stages, demonstrating a distinct "tiered development" pattern across the eastern, central and western regions. Moreover, significant spatial agglomeration characteristics characterize China's carbon balance hot and cold spots throughout the study period, with their spatial agglomeration degree remaining stable. The standard deviation ellipse analysis confirms these hot and cold spots' alignment with China's economic development level and population distribution. The GTWR test results highlight the pronounced non-stationary characteristics of different driving factors in space and time, exhibiting variations in strength and direction among regions. Consequently, enhancing China's urban carbon balance requires tailored measures based on different areas' unique conditions and development characteristics, emphasizing a hierarchical and classified approach to leverage distinct driving factors and foster a green development system in China.

Phylogenomic and population genomic analyses reveal the spatial-temporal dynamics of diversification of the Nigella arvensis complex (Ranunculaceae) in the Aegean archipelago.

The continental-shelf islands of the Aegean Sea provide an ideal geographical setting for evolutionary-biogeographical studies but disentangling the relationships between palaeogeographical history and the times, orders of modes of taxon divergence is not straightforward. Here, we used phylogenomic and population genomic approaches, based on orthologous gene sequences and transcriptome-derived SNP data, to reconstruct the spatial-temporal evolution of the Aegean Nigella arvensis complex (Ranunculaceae; 11 out of 12 taxa). The group's early diversification in the Early/Mid-Pliocene (c. 3.77 Mya) resulted in three main lineages (Greek mainland vs. central Aegean + Turkish mainland/eastern Aegean islands), while all extant taxa are of Late Plio-/Early Pleistocene origin (c. 3.30-1.59 Mya). Demographic modelling of the outcrossing taxa uncovered disparate modes of (sub)speciation, including divergence with gene flow on the Greek mainland, para- or peripatric diversification across eastern Aegean islands, and a 'mixing-isolation-mixing (MIM)' mode of subspeciation in the Cyclades. The two selfing species (N. stricta, N. doerfleri) evolved independently from the outcrossers. Present-day island configurations are clearly insufficient to explain the spatial-temporal history of lineage diversification and modes of (sub)speciation in Aegean Nigella. Moreover, our identification of positively selected genes in almost all taxa calls into question that this plant group represents a case of 'non-adaptive' radiation. Our study revealed an episodic diversification history of the N. arvensis complex, giving new insight into the modes and drivers of island speciation and adaption across multiple spatiotemporal scales.

Analysis of the spatial-temporal evolution of Green and low carbon utilization efficiency of agricultural land in China and its influencing factors under the goal of carbon neutralization.

Agricultural land is the most basic input factor for agricultural production and an essential component of terrestrial ecosystems, which plays a vital role in achieving carbon neutrality. Giving full play to the carbon-neutral contribution of agricultural land is a crucial part of China's economic transformation and green development. It incorporates carbon and pollution emissions from agricultural land use into the unexpected outputs of the Green and Low-carbon Utilization Efficiency of Agricultural Land (GLUEAL) evaluation system. The study utilized several advanced analytical tools, including the super-efficient Slacks-Based Measure (SBM) model, Exploratory Spatial-Temporal Data Analysis (ESTDA) method, Geodetector, and Geographically and Temporally Weighted Regression (GTWR) model. The objective was to examine the spatial-temporal evolution of GLUEAL and identify the factors that influenced it in all 31 provinces of China from 2005 to 2020. The results show that: (1) The overall spatial-temporal evolution of GLUEAL showed an increasing trend, but the disparity between provinces and regions became wider. (2) Most provinces have not yet made significant spatial and temporal jumps. They have high spatial cohesion with specific "path-dependent" characteristics. (3) The Geodetector results reveal that the Number of Rural Labor Force with Higher Education (NRLFHE) and Technology Support for Agriculture (TSA) have insufficient explanatory power on average for GLUEAL. Agricultural Economic Development Level (AEDL), Urbanization Level (UL), Multiple Crop Index (MCI), Planting Structure (PS), Degree of Crop Damage (DCD), Financial support for agriculture (FSA), and Agricultural mechanization level (AML) had stronger explanatory power on average for GLUEAL and were important factors influencing GLUEAL levels. (4) The average influence of AEDL, UL, FSA, and AML on GLUEAL changed from negative to positive. The average influence of MCI and DCD on GLUEAL was negative, and the average influence of PS on GLUEAL changed from positive to negative. This study provides a comprehensive description of the spatial and temporal evolution of GLUEAL in China. It reveals the key factors influencing GLUEAL and analyzes their spatial variations and impact patterns. These findings offer robust evidence for government policymakers to formulate policy measures for sustainable agricultural development and optimized resource allocation, promoting the transformation of agricultural land towards green and low-carbon practices and advancing the achievement of sustainable development goals.

The spatial-temporal evolution mechanism of PM2.5 concentration based on China's climate zoning.

Increasing attention has been given to the impact of PM2.5 concentration on human health. Exploring the influential factors of PM2.5 is conducive to improving air quality. Most existing studies explore the factors that influence the PM2.5 concentration from the perspective of cities or urban agglomerations, while few studies are conducted from the perspective of climate zones. We used the standard deviation ellipse and spatial autocorrelation analysis to explore the spatial-temporal evolution of the PM2.5 concentration in different climate zones in China during 2000-2018. We used differentiated EKC to construct panel regression models to explore the differences in the influential factors of the PM2.5 concentration in three climate zones. The number of cities with PM2.5 concentration less than 35 µg/m3 increased in the different climate zones. The center of gravity of the PM2.5 concentration has remained at the junction of the temperate and subtropical monsoon climate zones. The PM2.5 concentration had a high positive spatial autocorrelation in the different climate zones. The high-high clustering areas were located in the south of the temperate monsoon climate zone and the north of the subtropical monsoon climate zone. There was an inverted "U-shaped" curve between the PM2.5 concentration and economic development in China that varied in different climate zones. Identifying the differences in the influential factors of PM2.5 concentration in different climate zones will help to accelerate the implementation of the EKC inflection point.

Exploring the convergence patterns of PM2.5 in Chinese cities.

Economic development and ongoing urbanization are usually accompanied by severe haze pollution. Revealing the spatial and temporal evolution of haze pollution can provide a powerful tool for formulating sustainable development policies. Previous studies mostly discuss the differences in the level of PM2.5 among regions, but have paid little attention to the change rules of such differences and their clustering patterns over long periods. Therefore, from the perspective of club convergence, this study employs the log t regression test and club clustering algorithm proposed by Phillips and Sul (Econometrica 75(6):1771-1855, 2007. 10.1111/j.1468-0262.2007.00811.x) to empirically examine the convergence characteristics of PM2.5 concentrations in Chinese cities from 1998 to 2016. This study found that there was no evidence of full panel convergence, but supported one divergent group and eleven convergence clubs with large differences in mean PM2.5 concentrations and growth rates. The geographical distribution of these clubs showed significant spatial dependence. In addition, certain meteorological and socio-economic factors predominantly determined the convergence club for each city.

Spatial-temporal evolution of coupling relationship between land development intensity and resources environment carrying capacity in China.

Land development poses challenges to the sustainable use of resources and environmental health in regions. This study explores the coupling relationship and its spatial-temporal evolution trend between land development intensity and resources environment carrying capacity of 31 provinces in China from 2005 to 2017. The information entropy method, coupling degree model, and coupling coordination degree model are used to calculate the index weight, coupling degree, and coupling coordination degree. The results show that: (1) Three change types of resources environment carrying capacity are presented with land development intensity increasing: first decrease and then increase; first increase and then decrease; and alternating fluctuations. (2) The proportion of construction land, GDP per land, and population density are dominate determinants of land development intensity, while the water resources per capita, energy consumption per unit of GDP, and per capita cultivated land area are that of resources environment carrying capacity. (3) From the perspective of temporal evolution, both coupling and coordination relationship were found to have continuously strengthened. (4) In terms of spatial evolution, the coupling level presented a constantly narrowing inter-regional gap, and the coordination level has changed from initial two-level differentiation to final regional gap narrowing. These findings can provide evidence in support for integrating land development with resources environmental protection to promote regional coordinated development.

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.

[Spatiotemporal Evolution and Influencing Factors of Ecosystem Service Value in the Yellow River Basin].

The external spatiotemporal evolution and intrinsic impact mechanisms of ecosystem service value are of great significance for understanding regional ecosystem issues and enhancing human ecological well-being. Based on grid data, this study used the equivalent factor method and NDVI to measure the ecosystem service value of the Yellow River Basin, analyzed the spatial-temporal evolution of urban ecosystem service value along the basin, and established a GWR model to explore the spatial heterogeneity of each influencing factor on the basis of determining the main influencing factors via geographic detector. The results showed that:① The ecosystem service value of the Yellow River Basin increased first, then decreased, and finally increased from 2000 to 2020, showing a spatial distribution pattern of "the south was higher than the north;" "the lower reaches were lower, and the upper and middle reaches were higher;" and the regulation service contributed the most to the ecosystem service value of the basin. ② The results of geographical exploration showed that the degree of influence of various factors was different. Social factors played the strongest role in explaining the ecosystem service value of the Yellow River Basin, followed by economic factors, and natural factors played the weakest role. The high value areas in the upper reaches were mainly related to rivers and lakes, and the high value areas in the middle reaches were mainly related to mountains. ③ The results of the GWR model showed that population density and land reclamation rate were negatively correlated with ecosystem service value, whereas average annual precipitation was positively correlated, and the effects increased from east to west. The GDP per unit area was negatively correlated with the overall ecosystem service value but positively correlated in the upstream region.

[Spatial-temporal Evolution of Ecosystem Health and Its Influencing Factors in Beijing-Tianjin-Hebei Region].

Exploring ecosystem health and its influencing factors is of great significance for promoting regional sustainable development. An ecosystem health assessment model was constructed, and the spatial-temporal evolution characteristics of ecosystem health in the Beijing-Tianjin-Hebei Region in 2000, 2010, and 2020 were analyzed. The geographical detector and GWR were used to identify the dominant factors affecting ecosystem health. The main conclusions were as follows:during the study period, the index of ecosystem natural health in the Beijing-Tianjin-Hebei Region was generally better in the north and west than that in the southeast, and it showed an overall upward trend. The index of ecosystem services in the Beijing-Tianjin-Hebei Region presented as a spatial differentiation pattern of high in the north and low in the south, and it showed a downward trend. The ecosystem health level in the Beijing-Tianjin-Hebei Region showed a trend of rising first and then declining, showing significant heterogeneity in spatial distribution. The ecological health level in the central urban area of large cities was mostly poor, and the ecosystem health level in the Yanshan and Taihang Mountains and Bohai Rim was better. During the study period, the spatial pattern of ecosystem health in the Beijing-Tianjin-Hebei Region remained relatively stable. The hot spots and sub-hot spots were mainly distributed in the northern mountainous areas of Hebei Province and the Taihang Mountains, and the cold spots and sub-cold spots were mainly distributed in the southeast plain and the surrounding areas of some big cities. Population density, annual average temperature, per capita cultivated land area, and urbanization level were the dominant factors of ecosystem health in the Beijing-Tianjin-Hebei Region, and they were all negatively correlated with ecosystem health.

Spatial-temporal evolution and driving factors of rural resilience in the urban agglomerations in the middle reaches of the Yangtze River, China.

How to maintain the balanced stability and resilient development of rural systems is an important issue that needs urgent attention in the field of sustainable rural development at present. In this paper, the entropy method, spatial autocorrelation model, and Geodetector were used to explore the rural resilience level, spatial distribution characteristics, and driving factors of 31 cities in the urban agglomerations in the middle reaches of the Yangtze River (UAMRYR), and to put forward corresponding policy suggestions. The results are as follows: (1) From 2005 to 2020, rural resilience in the UAMRYR showed an upward trend with an average annual growth rate of 8.26%. The ranking of the three major urban agglomerations is Changsha-Zhuzhou-Xiangtan urban agglomerations > Wuhan urban agglomerations > Poyang Lake urban agglomerations. (2) During the study period, the spatial distribution of rural resilience gradually developed from a negative correlation to a strong positive correlation and generally showed the characteristics of high in the west and low in the east. (3) The urbanization rate, the size and structure of the economy, the difference in consumption and income between urban and rural areas, the local fiscal revenue, and the number of village committees are the key factors affecting the level of rural resilience. On this basis, we proposed policy recommendations to improve the economic, social, and ecological resilience of rural areas in the UAMRYR. The findings of this paper are expected to provide insights into the policy formulation of China's rural revitalization strategy.

Spatial-temporal evolution of pumped hydro energy storage potential on the Qinghai-Tibet Plateau and its future trend under global warming.

Global warming has brought extensive and far-reaching impacts on human life and production. A pumped hydro energy storage contributes to the large-scale development of renewable energy and serves as an important measure to mitigate climate change. Despite considerable efforts in estimating the potential of the pumped hydro energy storage, research gaps in response to global warming remain. In this regard, this study conducts a novel assessment of the pumped hydro energy storage's potential from a dynamic perspective, taking the Qinghai-Tibet Plateau as the study area. The spatiotemporal evolution of the pumped hydro energy storage's potential over the past few decades (the 1970s-2017) is analyzed, and its response to precipitation is identified innovatively. On this basis, the trend in the future period is further predicted for the first time, which is divided into near, short, medium, and long terms. Results show that the pumped hydro energy storage potential has a generally upward but not monotonic trend, decreasing from the 1970s to 1995 and then rising more dramatically, with slopes of 5548.5 ± 69.2 GWhyr-1 and -238.1 ± 90.4 GWhyr-1. In the majority (68.6 %) of lake basins (68.6 %), changes in precipitation positively contribute to the pumped hydro energy storage potential, resulting in a noticeable growth in the future. Under the representative concentration pathway of 8.5, the mean potential density is projected to rise by 23.4 %, 25.2 %, 28.3 %, and 30.6 % in the near, short, medium, and long terms, respectively. This result indicates that high-intensity greenhouse gas emissions under this scenario will lead to a greater potential for the pumped hydro energy storage in the future.

Characteristics and Driving Mechanism of Water Resources Trend Change in Hanjiang River Basin.

Studying the historical and future trends of water resources in a basin and explaining the causes of water resource changes is very important, which is key to the management of water resources in a basin. The Hanjiang River Basin is an important water supply source for southwestern Fujian and eastern Guangdong, but it has an uneven spatial and temporal distribution of water resources and an outstanding conflict between supply and demand. In this study, the SWAT model was used to simulate the conditions of the Hanjiang River Basin in the last 50 years, using long time series climate data to study the characteristics and driving mechanism of water resources trend change. The results show that the water resources in the basin have not increased significantly in the last 50 years, but evapotranspiration has increased significantly. The forecast results for water resources in the future are reduced. The water resource changes in the basin have been unevenly distributed in the last 50 years. Climate change has been the main factor in total water resource change in the basin, while the difference in water resource change trends within the basin is caused by land use. The key reason for the decrease in water resources in the Hanjiang River Basin is the significant increase in evapotranspiration due to the significant increase in temperature. If this situation continues, the available water resources in the basin will continue to decline. In fact, many basins around the world are currently likely to have such problems, such as the 2022 summer drought in the Danube River Basin in Europe and the Yangtze River Basin in China, so this article is informative and representative of future water resources management in the basin.

Spatio-temporal Analysis of Demand for Elderly Care Services and Its Key Influencing Factors in Chinese Cities: A Web Search Engine Approach.

The demand for elderly care services (DECS) in Chinese Cities is one of the most concerned issues. The aim of this study was to understand the spatial and temporal evolution and external factors of DECS in Chinese cities and support the formulation of elderly care policies. We collected Baidu Index data for 287 prefecture-level and above cities and 31 provinces in China from January 1, 2012 to December 31, 2020. The Thiel Index was employed to describe the differences of DECS at different regional levels, and multiple linear regression was used to explore the external factors affecting DECS by calculating the variance inflation factor (VIF) to identify multicollinearity. The DECS of Chinese cities increased from 0.48 million in 2012 to 0.96 million in 2020, and the Thiel Index decreased from 0.5237 in 2012 to 0.2211 in 2020. Per capital GDP, number of primary beds, proportion of population aged 65 and over, number of primary care visits, and proportion of illiterate population over the age of 15 have significant influences on DECS (P < .05). DECS was on the rise in Chinese cities, with significant regional differences. At the provincial level, regional differences were influenced by level of economic development, primary care provision, aging population, educational attainment, and health status. It is suggested to pay more attention to DECS in small and medium-sized cities or regions, to strengthen primary care, and to improve the health literacy and health status of the elderly population.

[Spatial-temporal Evolution and Heterogeneity of Carbon Emissions at County-level in China].

Counties are the key spatial units in achieving the reduction and control of carbon emissions. It is of great significance to study and reveal the spatial-temporal evolution characteristics and influencing mechanism of carbon emissions for realizing the "carbon peak and carbon neutral" goal. In this study, the spatial-temporal evolution and heterogeneity of carbon emissions at the county level in China from 2000 to 2017 were analyzed by using mathematical statistics and panel data regression modeling, and the influencing mechanism was explored. The results showed that: ① from 2000 to 2017, the annual growth rate of carbon emissions was 7.12%, which experienced the three stages of "sharp rise, slow rise, and high fluctuation" and finally stabilized at approximately 90×108 t. At the county scale, there was a significant positive spatial autocorrelation. ② The general panel regression model showed that GDP, construction land area, population, per capita GDP, and per capita deposit balance of financial institutions were significantly correlated with carbon emissions, and the former three had the strongest promoting effect on carbon emissions. ③ The goodness of fit of the geographically and temporally weighted regression model was high, and the direction and intensity of the other impact factors changed greatly in spatial-temporal characteristics, except that GDP showed a stable promoting effect globally. The results showed that carbon emission levels and main influencing factors varied among counties in China. This study revealed the heterogeneity of carbon emissions at the county level, which is helpful to optimize the spatial-temporal implementation path of the "dual carbon" target.

What is affecting the improvement of green innovation efficiency in the old industrial base: evidence from Northeast China.

Green innovation is an important driving force for high-quality development and is vital for reinvigorating the old industrial bases in Northeast China. As such, this study investigates the spatial-temporal evolution characteristics and factors influencing green innovation efficiency (GIE) in Northeast China from 2005 to 2020, using the super-efficient EBM-Malmquist model, kernel density estimation, and random forest model. The results show the following. (1) The "growth effect" of technological change is the main force driving GIE improvement; the "horizontal effect" of pure technical efficiency change has started to play an important role; and the club convergence characteristics of GIE in Northeast China have started to be optimized. (2) GIE in Northeast China shows significant spatial differentiation. The urban agglomeration of Mid-southern Liaoning and Harbin-Changchun has had high values for GIE, indicating that green innovation has a cyclic cumulative effect and the spatial pattern of green innovation needs to be further optimized. (3) The random forest model is more accurate and provides more trustworthy results compared with the traditional multiple linear regression model. The results of random forest model measurement illustrate that the number of digital economy enterprises, public finance expenditure, GDP per capita, and vegetation coverage play a positive role in promoting GIE. The proportion of the non-farm population and industrial agglomeration plays a negative role in GIE. In the same period, the contribution of the number of digital economy enterprises≥0.41, public expenditure ≥0.47, GDP per capita≥0.39, and vegetation coverage≥0.36 to GIE reach maximum values and then remain unchanged.

Temporal-Spatial Evolution and Trend Prediction of the Supply Efficiency of Primary Medical Health Service-An Empirical Study Based on Central and Western Regions of China.

China has established a comprehensive primary medical health service system, but the development of primary medical health services in the central and western regions is still unbalanced and insufficient. Based on data from 2010 to 2019, this paper constructs a super efficiency Slack-Based Measure model to calculate the supply efficiency of primary medical health services in 20 provinces and cities in central and western China. Using Kernel density estimation and Markov chain analysis, this paper further analyzes the spatial-temporal evolution of the supply efficiency of primary medical health services in central and western China, and also predicts the future development distribution through the limiting distribution of Markov chain to provide a theoretical basis for promoting the sinking of high-quality medical resources to the primary level. The results show that firstly, during the observation period, the center of the Kernel density curve moves to the left, and the main peak value decreases continuously. The main diagonal elements of the traditional Markov transition probability matrix are 0.7872, 0.5172, 0.8353, and 0.7368 respectively, which are significantly larger than other elements. Secondly, when adjacent to low state and high state, it will develop into convergence distributions of 0.7251 and 0.8243. The supply efficiency of primary medical health services in central and western China has the characteristics of high (Ningxia) and low (Shaanxi) aggregation respectively, but the aggregation trend is weakened. Thirdly, the supply efficiency of health services has the stability of keeping its own state unchanged, but the transition of state can still occur. The long-term development of the current trend cannot break the distribution characteristics of the high and low clusters, the efficiency will show a downward trend in the next 10-20 years, and still the problem of uneven long-term development emerges.

Spatial-temporal and structural differences in the carbon footprints embedded in households food waste in urban and rural China.

Household food waste (HFW) accounted for about 66% of global food waste's total carbon footprints (CF). Based on China's macro-panel food consumption data, this paper measures the urban-rural and provincial differences in the HFW CF from scale, structure, and temporal-spatial evolution perspectives. The results indicate that HFW and CF continue to grow, and the total CF and per capita HFW in urban households are higher than in rural households. The structural differences between urban and rural HFW CF vary significantly over time and spaces, which reflected that rural households in the southeastern coastal areas have higher per capita HFW CF than urban in 2019. The research results help to clarify the distribution and evolution pattern of HFW CF in China and offer new ideas for the differentiated governance of CF reduction in the food system.

Dynamic evolution characteristics and driving factors of carbon emissions in prefecture-level cities in the Yellow River Basin of China.

This paper focuses on the spatiotemporal evolution characteristics, as well as the driving factors, of carbon emissions in the prefecture-level cities in the Yellow River Basin (YB). The paper's findings will aid in promoting ecological conservation and high-quality development in the region. The initiatives undertaken in the YB are a significant national strategy towards achieving carbon peaking and carbon neutrality. To fully investigate the spatiotemporal evolution process, as well as the typical characteristics of their carbon emissions, conventional, and spatial Markov transition probability matrices were developed utilizing YB's panel data for 55 prefecture-level cities from 2003 to 2019. The generalized Divisia index decomposition method (GDIM) cleverly uses this data to conduct a complete analysis of the dynamics and driving processes influencing the change in carbon emissions in these cities. However, the evolution of carbon emissions in prefecture-level cities has reached a point of stability that maintains the original state, making it challenging to make meaningful short-term progress. The data indicates that prefecture-level cities in the YB are emitting more carbon dioxide on average. Neighborhood types in these cities significantly influence the transformation of carbon emissions. Low-emission areas can encourage a reduction in carbon emissions, whereas high-emission areas can encourage an increase. The spatial organisation of carbon emissions exhibits a "high-high convergence, low-low convergence, high-pulling low, low-inhibiting high" club convergence phenomenon. Carbon emissions rise with per capita carbon emissions, energy consumed, technology, and output scale, whereas it falls with carbon technology intensity and output carbon intensity. Hence, instead of enhancing the role of increase-oriented variables, prefecture-level cities in the YB should actively engage these reduction-oriented forces. The YB's key pathways for lowering carbon emissions include boosting research and development, promoting and applying carbon emission reduction technologies, lowering output carbon intensity and energy intensity, and improving energy use effectiveness.

Study on the Spatial-Temporal Pattern and Driving Mechanism of Tourism Eco-Security in the Yellow River Basin.

Tourism eco-security evaluation is an effective tool for facilitating the coordinated and sustainable economic and environmental development of tourist destinations. Based on system theory, this study established a comprehensive evaluation index system for the DPSIR model, applying the entropy-TOPSIS method, spatial autocorrelation, spatial econometric model and geo-detector to investigate the spatial and temporal evolution and drivers of tourism eco-security of the Yellow River basin. The results showed that the tourism eco-security of the Yellow River basin steadily and significantly increased from 2003 to 2020, reaching a peak in 2019, while there was a low level of overall tourism eco-security and improvement possibility. The results show a spatial evolution pattern of expansion from provincial capital cities to nearby prefecture-level cities from the middle and lower reaches to the middle and upper reaches, with significant spatial clustering and spillover effects. Factors affecting the tourism eco-security of the Yellow River basin vary in and between regional basins. Because there are many influencing factors, the key factors were further identified by spatial effect decomposition. The results of this study have important theoretical and practical value in promoting the coordinated and sustainable development of the tourism economy and ecological environment in the Yellow River basin.

Spatial and temporal patterns of supply and demand risk for ecosystem services in the Weihe River Main Stream, NW China.

The rapid development of society and economy in the post-industrial era has exacerbated the spatial matching contradiction between the demand of humans and the supply of the natural environment, while ecosystem service (ES) as a bridge linking nature and society, identifying and assessing its supply-demand risk, was beneficial to ecosystem management and promoted regional high-quality development. Based on the data of multi-source remote sensing and statistics, the supply and demand levels of four ESs, which contain food supply, carbon storage, soil conservation, and water yield in the main stem of the Weihe River in 2000, 2010, and 2020, were quantitatively measured. The spatial and temporal analysis of the supply, demand, and supply-demand ratio of each service was carried out using spatial mapping. The spatio-temporal pattern of the supply-demand risk was recognized by the method of spatial overlay, which means overlaying the supply and demand for material quality, ratio, trend, and the degree of trade-off coordination together between each service. The results showed that (1) the demand for water yield decreased slightly while the demand for food and the supply of carbon storage remained stable. In addition, the supply and demand of other services showed an upward trend. (2) The spatial distribution of the supply-demand ratio of each service shows "high in the south and low in the north" and "high in the east and low in the west," among which the supply-demand ratio of carbon storage is decreasing. (3) The overall supply-demand risk of soil conservation in the study area is low with characteristics of small range and high degree, the risk distribution characteristics of the other services are high in the east and low in the west, and the risk is high in the city center and low around. Otherwise, the supply-demand risk of food supply showed a downward trend, the risk of carbon storage showed an upward trend, the risk of soil conservation remained stable, and the risk of water yield showed a significant downward trend. Based on static supply-demand risk identification, this study assesses supply-demand risk over two periods and analyzes the trend of supply-demand risk changes over time. It clarifies the extent and direction of supply-demand risk shifts, as well as provides improved theoretical support for ecosystem service management.