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.

Revealing spatiotemporal heterogeneity of water conservation and its drivers: Enlightenment to water ecology protection and restoration.

Water conservation (WC) has emerged as one of the most vital services provided by basin ecosystems. Climate change, the conversion of farmland to forests, and the implementation of check dam projects significantly impact the WC function in the Malian River Basin (MRB) of the gully region, Loess Plateau. This study systematically and comprehensively reveals the variation rules of WC and the mechanisms of action of influence factors in the MRB and selects factors representing natural environmental changes and human activities, such as climate, geomorphology, vegetation, and soil, influencing the WC. The InVEST model and a modified formula were used to evaluate the WC and its spatial-temporal changes in the MRB. The response of influence factors to the WC was explored using a "geographical detection - spatial drive/inhibition - influence degree" framework. The results indicate that under the comprehensive influence of multiple factors, the spatial distribution of WC in the MRB remained relatively consistent over different periods, characterized by higher values in the southeast and lower in the northwest. The WC values in 1990, 2000, 2010, and 2020 were 2.57 × 104, 1.48 × 104, 2.19 × 104, and 1.93 × 104 m3, respectively. The interaction of two factors on WC had a more significant effect than single-factor interactions, particularly the interaction between Soil Saturated Water Conductivity (Ksat) and Annual Precipitation (Pre), Annual Evapotranspiration (AET), and Net Primary Productivity (NPP). Pre, Plant Available Water Content (PAWC), and Ksat are key positive drivers, while AET, Temperature (Temp), and Elevation (DEM) are crucial negative drivers. Climate factors had the largest explanatory power for the WC spatial pattern (34.03-36.54%), geomorphic factors had the least (16.60-17.50%), and vegetation factors more than soil factors. This study provides valuable insights for optimal water resource allocation and sustainable development of the gully region, Loess Plateau.

Analysis of a SARIMA-XGBoost model for hand, foot, and mouth disease in Xinjiang, China.

Hand, foot, and mouth disease (HFMD) is a common childhood infectious disease. The incidence of HFMD has a pronounced seasonal tendency and is closely related to meteorological factors such as temperature, rainfall, and wind speed. In this paper, we propose a combined SARIMA-XGBoost model to improve the prediction accuracy of HFMD in 15 regions of Xinjiang, China. The SARIMA model is used for seasonal trends, and the XGBoost algorithm is applied for the nonlinear effects of meteorological factors. The geographical and temporal weighted regression model is designed to analyze the influence of meteorological factors from temporal and spatial perspectives. The analysis results show that the HFMD exhibits seasonal characteristics, peaking from May to August each year, and the HFMD incidence has significant spatial heterogeneity. The meteorological factors affecting the spread of HFMD vary among regions. Temperature and daylight significantly impact the transmission of the disease in most areas. Based on the verification experiment of forecasting, the proposed SARIMA-XGBoost model is superior to other models in accuracy, especially in regions with a high incidence of HFMD.

Spatial and temporal evolution characteristics and factors of heat vulnerability in the Pearl River Delta urban agglomeration from 2001 to 2022.

To explore the spatiotemporal evolution characteristics of heat vulnerability in the Pearl River Delta urban agglomeration during heatwave disasters, this research employs the Entropy Weight Method (EWM) to calculate the heat vulnerability assessment results for nine cities in the region spanning from 2001 to 2022. Through the application of kernel density estimation, Moran's I, and the Geographically and Temporally Weighted Regression (GTWR) model, which is proven to be superior to traditional model such as OLS, this study analyzes the dynamic distribution patterns of heat vulnerability in the study area and dissect the trends of influencing factors. The results reveal that from 2001 to 2022, the overall heat vulnerability index in the study area demonstrates a fluctuating downward trend. Key contributors to heat vulnerability include high-frequency and long-duration heatwaves, population sensitivity, and changes in residents' consumption levels. Throughout this period of development, the disparity in heat vulnerability among cities has gradually widened, indicating an overall pattern of uneven development in the region. Future attention should be focused on formulating heat adaptation strategies in areas with high vulnerability to enhance the overall sustainability of the study area.

Investigating spatio-temporal variations and contributing factors of land use-related carbon emissions in the Beijing-Tianjin-Hebei Region, China.

The land use change is the primary factor in influencing the regional carbon emissions. Studying the effects of land use change on carbon emissions can provide supports for the development policies of carbon emission. Using land use and energy consumption data, this study measures carbon emissions from land use dynamics in the Beijing-Tianjin-Hebei region from 2000 to 2020. The standard deviation ellipse model is employed to investigate the distribution characteristics of the spatial patterns of carbon emissions, while the Geographically and Temporally Weighted Regression (GTWR) model is used to examine the contributing factors of carbon emissions and their spatial and temporal heterogeneity. Results indicate a consistently increasing trend in carbon emissions from land use in the Beijing-Tianjin-Hebei region from 2000 to 2020. Construction land is characterized with both the primary source and an increasing intensity of carbon emissions. Besides, the spatial distribution of carbon emissions from land use in the Beijing-Tianjin-Hebei region demonstrates an aggregation pattern from in the northeast-southwest direction towards the center, with a greater aggregation trend in the east-west direction compared to that in the south-north direction. During the study period, a positive correlation was documented between carbon emissions and factors including total population, economic development level, land use degree, and landscape patterns. This correlation showed a decreasing trend and reached a stable level at the end of the study period. Moreover, the analysis showed a negative correlation between industrial structure and carbon emissions, which showed an increasing trend and reached a relatively high level at the end of the study period.

An empirical analysis of the coupling and coordinated development of new urbanization and ecological welfare performance in China's Chengdu-Chongqing economic circle.

New urbanization (NU) and ecological welfare performance (EWP) play pivotal roles in achieving sustainable urban development, with both emphasizing social equity and environmental management. Exploring the coordinated relationship between EWP and NU is invaluable for understanding the symbiotic interplay between humans and nature. We constructed a framework to elucidate the coupling mechanism of EWP and NU from the perspective of systems theory. We quantified the levels of NU and EWP utilizing the entropy weighting method and the super-efficient SBM method, respectively. Furthermore, we assessed the degree of coupling coordination between the two using the coupling coordination degree model (CCDM). Spatial and temporal evolution analysis was conducted, and factors influencing the degree of coupling coordination between EWP and NU were explored through a spatial-temporal geographically-weighted regression model (GTWR). The results indicate: (1) During the study period, the average annual increase in EWP in the study area was 2.59%, with a narrowing relative gap between cities. Conversely, the average annual increase in the level of NU was 7.6%, with demographic and economic dimensions carrying the highest weights. (2) The type of coupling coordination between EWP and NU transitions from basic coordination to moderate coordination, with the development of EWP lagging behind that of NU. (3) City size demonstrates a positive yet diminishing trend on the coupling coordination level, with economic development exerting the greatest influence and exhibiting a "V" trend, while the impact of green technology innovation diminishes negatively. Additionally, regional disparities are significant, with city size exhibiting a negative impact in areas of high population density and low economic levels, and green technology innovation showing notable polarization characteristics in core cities. These findings serve as a foundation for fostering coordinated ecological development amid the rapid urbanization process of the Chengdu-Chongqing Economic Circle.

Remote sensing estimation of regional PM2.5 based on GTWR model -A case study of southwest China.

Air pollution in China has becoming increasingly serious in recent years with frequent incidents of smog. Parts of southwest China still experience high incidents of smog, with PM2.5 (particulate matter with diameter ≤2.5 µm) being the main contributor. Establishing the spatial distribution of PM2.5 in Southwest China is important for safeguarding regional human health, environmental quality, and economic development. This study used remote sensing (RS) and geographical information system (GIS) technologies and aerosol optical depth (AOD), digital elevation model (DEM), normalized difference vegetation index (NDVI), population density, and meteorological data from January to December 2018 for southwest China. PM2.5 concentrations were estimated using ordinary least squares regression (OLS), geographic weighted regression (GWR) and geographically and temporally weighted regression (GTWR). The results showed that: (1) Eight influencing factors showed different correlations to PM2.5 concentrations. However, the R2 values of the correlations all exceeded 0.3, indicating a moderate degree of correlation or more; (2) The correlation R2 values between the measured and remote sensed estimated PM2.5 data by OLS, GWR, and GTWR were 0.554, 0.713, and 0.801, respectively; (3) In general, the spatial distribution of PM2.5 in southwest of China decreases from the Northeast to Northwest, with moderate concentrations in the Southeast and Southwest; (4) The seasonal average PM2.5 concentration is high in winter, low in summer, and moderate in spring and autumn, whereas the monthly average shows a "V" -shaped oscillation change.

Spatial and temporal analysis of influential factors on motor vehicle carbon monoxide emissions in China considering emissions trading scheme.

The number of cars is increasing every year and the environmental aspects of transport are becoming a hot topic. The spatial and temporal patterns of motor vehicle carbon monoxide (CO) emissions are still unclear due to the unbalanced economic development and heterogeneous geographic conditions of China. With the objective of realizing a reduction in motor vehicle CO emissions, his study explores the transport carbon emission reduction pathways of China from motor vehicle CO emission. Firstly, the entropy method is adopted to comprehensively evaluate the CO emissions from motor vehicles in each province; secondly, the development of a Geographically and Temporally Weighted Regression (GTWR) model facilitates the examination of the spatiotemporal dynamics pertaining to the influencing factors of motor vehicle CO emissions within each province.; finally, the characteristics of motor vehicle CO emissions in ETS pilot areas and non-ETS pilot areas are compared. The results show that: (1) After the completion of the six ETS pilot areas in 2011, the CO emission from motor vehicles is reduced by 18% compared with 2010.(2)The entropy method shows that the largest CO emissions from motor vehicles are from Beijing, Shanghai, Guangdong and other provinces with high economic levels.(3) The results of the GTWR model show that the positive effects of economic level, population size, road mileage intensity and motor vehicle intensity on motor vehicle CO emissions are decreasing year by year. The negative effect of metro line intensity on CO emission decreases year by year. This study can help decision makers to identify the high emission areas, understand the influencing factors, and formulate emission reduction measures to achieve the purpose of carbon emission reduction in transport.

Exploring the impact of rural labor transfer on the production and ecological sustainability of crop planting structure in China.

With the improvement of industrialization, numerous rural laborers migrate to urban areas in search of off-farm jobs. Farmers change agricultural production decisions to adapt to the change of labor force, which will inevitably affect the crop planting structure. However, few studies have explored the sustainability of crop planting structure. Based on the calculation of the multiple cropping index (MCI), grain crops planting rate (GCR), economic crops planting rate (ECR), and ecological sustainability index (ESI) of crop planting structure, this study analyzes the impact of labor transfer rate (LTR) and labor cost (LC) on the sustainability of crop planting structure using a geographically and temporally weighted regression (GTWR) model. The results show that the scale of rural labor transfer and labor cost in China remains on the rise, but the growth rate has slowed down. The total carbon absorption of crops in China shows a U-shape trend, and the rice and maize have the largest carbon absorption. The impact of LTR on MCI is mainly positive, especially in the North China Plain in the early stage and some provinces in the Southwest China in the later stage. The impact of LTR on ECR and ESI is negative in most provinces. And the negative influence of LC on MCI is increasing, showing the spatial distribution characteristics of large influence in the southeast and small influence in the northwest. The impact of LC on ESI shows a negative effect in most provinces in the early stage, and the negative effect is more concentrated in some provinces in the southwest in the later stage.

Spatiotemporal Dynamic Evolution and Its Driving Mechanism of Carbon Emissions in Hunan Province in the Last 20 Years.

Global warming caused by carbon emissions is an environmental issue of great concern to all sectors. Dynamic monitoring of the spatiotemporal evolution of urban carbon emissions is an important link to achieve the regional "double carbon" goal. Using 14 cities (prefectures) in Hunan Province as an example, based on the data of carbon emissions generated by land use and human production and life, and on the basis of estimating the carbon emissions in Hunan Province from 2000 to 2020 using the carbon emission coefficient method, this paper uses the Exploratory Spatial-Temporal Data Analysis (ESTDA) framework to analyze the dynamic characteristics of the spatiotemporal pattern of carbon emissions in Hunan Province from 2000 to 2020 through the Local Indicators of Spatial Association (LISA) time path, spatiotemporal transition, and the standard deviation ellipse model. The driving mechanism and spatiotemporal heterogeneity of urban carbon emissions were studied by using the geographically and temporally weighted regression model (GTWR). The results showed that: (1) In the last 20 years, the urban carbon emissions of Hunan Province have had a significant positive spatial correlation, and the spatial convergence shows a trend of first increasing and then decreasing. Therefore, priority should be given to this relevance when formulating carbon emission reduction policies in the future. (2) The center of carbon emission has been distributed between 112°15'57″~112°25'43″ E and 27°43'13″~27°49'21″ N, and the center of gravity has shifted to the southwest. The spatial distribution has changed from the "northwest-southeast" pattern to the "north-south" pattern. Cities in western and southern Hunan are the key areas of carbon emission reduction in the future. (3) Based on LISA analysis results, urban carbon emissions of Hunan from 2000 to 2020 have a strong path dependence in spatial distribution, the local spatial structure has strong stability and integration, and the carbon emissions of each city are affected by the neighborhood space. It is necessary to give full play to the synergistic emission reduction effect among regions and avoid the closure of inter-city emission reduction policies. (4) Economic development level and ecological environment have negative impacts on carbon emissions, and the population, industrial structure, technological progress, per capita energy consumption, and land use have a positive impact on carbon emissions. The regression coefficients are heterogeneous in time and space. The actual situation of each region should be fully considered to formulate differentiated emission reduction policies. The research results can provide reference for the green and low-carbon sustainable development of Hunan Province and the formulation of differentiated emission reduction policies, and provide reference for other similar cities in central China.

Research on the coupling coordination and driving role of urbanization and ecological resilience in the middle and lower reaches of the Yangtze River.

Background: The growth of urbanization in the 20th and 21st centuries has resulted in unprecedented ecological security issues. The imbalance between urban development and internal ecological security is a growing concern. Methods: Based on the urban development process and the characteristics of ecosystem resilience, the corresponding urbanization evaluation system ("scale-structure-benefit") and ecosystem resilience assessment model ("resistance-adaptability-restoring") were constructed to explore the changes in each dimension as well as to analyze the spatial and temporal changes and driving effects of the coupled coordination level of urbanization and ecological resilience using the coupled coordination degree (CCD) model and geographically and temporally weighted regression (GTWR). Results: (1) From 2005 to 2020, urbanization levels increased (from 0.204 to 0.264, respectively), whereas the level of ecological resilience gradually decreased (from 0.435 to 0.421, respectively). The spatial distribution of urbanization is rather steady, with a "high-northeast low-southwest" pattern of regional distribution; however, the spatial distribution pattern of ecological resilience is essentially the reverse. (2) During the study period, there was an improvement in the level of coordination between urbanization and ecological resilience, with an increase from 0.524 to 0.540. However, the main coordination type remained the same, with over 46% being at the basic coordination stage. The relative development type was dominated by the lagging urbanization stage, with the lagging ecological resilience and synchronous development stages accounting for a smaller proportion, and the space was distributed in a block-like cluster. (3) The running results of the GTWR show that the core dimensions of the whole region are scale, benefit, and structure, and the impact of each dimension shows obvious spatial heterogeneity. Cities with different levels of relative development also have different central dimensions. This research will provide support for the coordination of urban development in areas where economic construction and ecological resilience are not coordinated, and will contribute to the sustainable development of urban areas.

Spatio-Temporal Variation and Its Driving Forces of Soil Organic Carbon along an Urban-Rural Gradient: A Case Study of Beijing.

Rapid urbanization has reshaped land cover and the ecological environment, potentially improving or deteriorating soil organic carbon (SOC). However, the response of SOC to urbanization has not yet been fully exploited. Herein, by using the land-use transfer matrix, the Sen & Mann-Kendall tests, the Hurst index, and a geographical and temporal weighted regression (GTWR) model, as well as an urban-rural gradient perspective, we assessed the dynamic response of SOC to Beijing's urbanization from 2001 to2015 and identified the main drivers. The results found that SOC stock decreased by 7651.50 t C during the study period. SOC density varied significantly along an urban-rural gradient, with high value areas mainly being located in remote mountainous rural areas and low value areas mainly being located in urban areas on the plains. There was an uneven variation in SOC density across the urban-rural gradient, with suburban areas (25-40 km away from urban cores) losing the most SOC density while urban areas and rural areas remained relatively unchanged. GTWR model revealed the spatio-temporal non-flat stability of various driving forces. Precipitation, the proportion of forest, the proportion of grassland, the population, distance to the urban center, the slope, and the silt content are the main factors related to SOC stock change. As a result, we suggest policy makers reconceptualize the uneven variation in the SOC between urban and rural areas, emphasize suburban areas as a target for controlling SOC loss, and take into consideration the spatial and temporal heterogeneity of the factors influencing SOC stock when evaluating policies.