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Given the intricate spatial dependencies and dynamic trends among diverse road segments, the prediction of spatio-temporal traffic flow data presents a formidable challenge. To address this challenge within the complexity of urban multi-mode transportation systems, this paper introduces an innovative solution. Anchored by the TSHDNet framework, the proposed methodology presents a novel spatio-temporal heterogeneous decoupling network that adeptly captures the inherent relationships between traffic patterns and temporal-spatial fluctuations. By seamlessly integrating temporal and nodal embeddings, dynamic graph learning, and multi-scale representation modules, TSHDNet demonstrates remarkable efficacy in unraveling the subtle dynamics of traffic flow. Empirical evaluations and ablation experiments conducted on four real-world datasets affirm the framework’s capability and the effectiveness of the decoupling approach.The source codes are available at: [ABSTRACT FROM AUTHOR]

The spatiotemporal nonstationarity of shared bicycle usage, a sustainable and eco-friendly mode of transportation, is believed to be influenced by the built environment. However, the specific spatial and temporal impacts of built environment factors on shared bicycle trips are not yet fully understood. This study investigates the relationship between the built environment and shared bicycle ridership in Shenzhen, a city where the distribution of shared bicycles is relatively dense, by utilizing multisource urban big data. Key independent variables were selected based on the "5Ds" dimensions of the built environment, and the performance of two models—Geographically Weighted Regression (GWR) and Geographically and Temporally Weighted Regression (GTWR)—were compared. The analysis evaluates the impact of the built environment on the density of shared bicycle ridership, incorporating both spatial and temporal dimensions. The results of the study found that the GTWR model used in this paper can effectively explain the spatio-temporal heterogeneity of built environment-related variables on shared bicycle trips with high goodness of fit. And the regression fit coefficients of the model show that the effects of different built environment indicators on the density of shared bicycle ridership are significantly different in both time and space. Among them, road network density, catering POI density, traffic POI density and POI diversity have a facilitating effect on shared bicycle travels, particularly during peak hours on weekdays and in central urban areas. Shopping POI density shows different effects on shared bike use in different times and spaces. While the distance from the city center and the nearest distance to the bus station have a suppressive effect on shared bicycle use, they show opposite degrees of influence in the spatial distribution. The results can provide more precise guidance for future rational transportation strategies or sustainable urban planning. [ABSTRACT FROM AUTHOR]

Abstract The spatiotemporal nonstationarity of shared bicycle usage, a sustainable and eco-friendly mode of transportation, is believed to be influenced by the built environment. However, the specific spatial and temporal impacts of built environment factors on shared bicycle trips are not yet fully understood. This study investigates the relationship between the built environment and shared bicycle ridership in Shenzhen, a city where the distribution of shared bicycles is relatively dense, by utilizing multisource urban big data. Key independent variables were selected based on the “5Ds” dimensions of the built environment, and the performance of two models—Geographically Weighted Regression (GWR) and Geographically and Temporally Weighted Regression (GTWR)—were compared. The analysis evaluates the impact of the built environment on the density of shared bicycle ridership, incorporating both spatial and temporal dimensions. The results of the study found that the GTWR model used in this paper can effectively explain the spatio-temporal heterogeneity of built environment-related variables on shared bicycle trips with high goodness of fit. And the regression fit coefficients of the model show that the effects of different built environment indicators on the density of shared bicycle ridership are significantly different in both time and space. Among them, road network density, catering POI density, traffic POI density and POI diversity have a facilitating effect on shared bicycle travels, particularly during peak hours on weekdays and in central urban areas. Shopping POI density shows different effects on shared bike use in different times and spaces. While the distance from the city center and the nearest distance to the bus station have a suppressive effect on shared bicycle use, they show opposite degrees of influence in the spatial distribution. The results can provide more precise guidance for future rational transportation strategies or sustainable urban planning.

The significant increase in building energy consumption poses a major challenge to environmental sustainability. In this process, urban morphology plays a pivotal role in shaping building energy consumption. However, its impact may exhibit latent heterogeneity due to differences in temporal resolution and spatial scales. For urban energy planning and energy consumption modeling, it is crucial to pinpoint when and where urban morphology parameters matter, an overlooked aspect in prior research. This study quantitatively explores this heterogeneity, utilizing a detailed dataset from old residential buildings within a university campus. Spatial lag models were employed for cross-modeling across various temporal and spatial dimensions. The results show that annual and seasonal spatial regression models perform best within a 150 m buffer zone. However, not all significant indicators fall within this range, suggesting that blindly applying the same range to all indicators may lead to inaccurate conclusions. Moreover, significant urban morphology indicators vary in quantity, category, and directionality. The green space ratio exhibits correlations with energy consumption in annual, summer, and winter periods within buffer zones of 150 m, 50~100 m, and 100 m, respectively. It notably displays a negative correlation with annual energy consumption but a positive correlation with winter energy consumption. To address this heterogeneity, this study proposes a three-tiered framework—macro-level project decomposition, establishing a key indicator library, and energy consumption comparisons, facilitating more targeted urban energy model and energy management decisions. [ABSTRACT FROM AUTHOR]

The prediction of land subsidence is of significant value for the early warning and prevention of geological disasters. Although numerous land subsidence prediction methods are currently available, two obstacles still exist: (i) spatio-temporal heterogeneity of land subsidence is not well considered, and (ii) the prediction performance of individual models is unsatisfactory when the data do not meet their assumptions. To address these issues, we developed a spatio-temporal heterogeneous ensemble learning method for predicting land subsidence. Firstly, a two-stage hybrid spatio-temporal clustering method was proposed to divide the dataset into internally homogeneous spatio-temporal clusters. Secondly, within each spatio-temporal cluster, an ensemble learning strategy was employed to combine one time series prediction model and three spatio-temporal prediction models to reduce the prediction uncertainty of an individual model. Experiments on a land subsidence dataset from Cangzhou, China, show that the prediction accuracy of the proposed method is significantly higher than that of four individual prediction models. [ABSTRACT FROM AUTHOR]

Abstract 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.

Rural areas provide ecosystem services (ESs) to urban metropolitan regions. These services are threatened by the constant pressure of urbanisation and new interest in rural development. This has heightened the conflict between environmental concerns and developmental needs, thereby presenting significant land management and rural planning challenges. Employing a quantitative measurement and optimisation framework, we investigate six representative ES variables to assess planning strategies that can address this contradiction. We used a suburban rural area around Nanjing, China, as our study area. We collected spatial data from 2005 to 2020 at two scales (village level and 500 m grid) to map ESs, quantify interactions (trade-offs and synergies among ES bundles), and identify the social, ecological, and landscape drivers of rural change. Based on this, rural planning strategies for optimising ESs at different scales have been proposed. Our findings include (1) spatial heterogeneity in the distribution of ESs, (2) the identification of seven synergistic and eight trade-off pairs among ESs, (3) a spatial scale effect in suburban rural areas, and (4) the spatial trade-offs/synergies of ESs exhibiting a 'Matthew effect'. The identification of key trade-offs and synergistic ES pairs and the categorisation of ES bundles form the basis for a multi-scale hierarchical management approach for ESs in the region. By examining the commonalities and variations in drivers across diverse scales, we established connections and focal points for spatial planning. We use these findings to propose spatial planning and landscape policy recommendations for rural suburban areas on multiple scales. This study aims to provide a comprehensive and detailed spatial optimisation strategy for rural areas that can help contribute to their revitalisation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The rise of ride-hailing services presents innovative solutions for curbing urban carbon emissions, yet poses challenges such as fostering fair competition and integrating with public transit. Analyzing the factors influencing ride-hailing emissions is crucial for understanding their relationship with other travel modes and devising policies aimed at steering individuals towards more environmentally sustainable travel options. Therefore, this study delves into factors impacting ride-hailing emissions, including travel demand, land use, demographics, and transportation networks. It highlights the interplay among urban structure, multi-modal travel, and emissions, focusing on network features such as betweenness centrality and accessibility. Employing the COPERT (Computer Programme to Calculate Emissions from Road Transport) model, ride-hailing emissions are calculated from vehicle trajectory data. To mitigate statistical errors from multicollinearity, variable selection involves tests and correlation analysis. Geographically and temporally weighted regression (GTWR) with an adaptive kernel function is designed to understand key influencing mechanisms, overcoming traditional GTWR limitations. It can dynamically adjust bandwidth based on the spatio-temporal distribution of data points. Experiments in Shenzhen validate this approach, showing a 9.8% and 10.8% increase in explanatory power for weekday and weekend emissions, respectively, compared to conventional GTWR. The discussion of findings provides insights for urban planning and low-carbon transport strategies. [ABSTRACT FROM AUTHOR]

Abstract The watershed system has a complex game relationship between the benign operation and coordinated development of various elements of flood-sediment transportation, eco-environment, and socio-economy (FES). With the increasing breadth, depth, and intensity of human activities in watersheds, it is urgent to coordinate the FES. The relationship of water–sediment in the Yellow River Basin (YRB) is complex, with a prominent contradiction in water supply and a fragile ecosystem. This research tries to build a comprehensive evaluation model for FES and explore the complex interaction between FES in the YRB from 2000 to 2020. The results demonstrated that (1) the comprehensive flood-sediment transportation index (CFTI) and comprehensive eco-environment index (CEI) presented fluctuating growth. In contrast, the comprehensive socio-economy index (CSI) revealed a linear growth trend. The CFTI of Sanmenxia, CEI of Toudaokuan, and CSI of Ningxia had the highest growth rates, with 36.03%, 6.48%, and 107.5%, respectively. (2) FES's positive and negative effects were alternating, with heterogeneity in both time and space. (3) The coupling coordination degree (CCD) in the YRB indicated an increasing trend, ranging from 0.53 to 0.87, from reluctantly coordinated development to good coordinated development. The lagging subsystem was CFTI (2000–2001 and 2008–2020) and CSI (2002–2007), and the CEI was not lagging. (4) Exploratory Spatial Data Analysis (ESDA) demonstrated significant differences in the CCD of the YRB, and areas with similar CCD within the basin tend to be centrally distributed in space. At the same time, there was negative spatial autocorrelation in coordination. The results provide a scientific theoretical and methodological framework for strategic research on the YRB system's governance, protection, and management.

Invasive species can alter the structure and functioning of the invaded ecosystem, but predictions of the impact of invasive species on ecosystem functioning are weak. Invasion is determined by the interplay of invasive species traits, the recipient community, and the environmental context. However, efficient approaches to assess the spatial dimension of functional changes in heterogeneous environments and altered plant-plant interactions are lacking. Based on recent technological progress, we posit a way forward to i) quantify the fine-scale heterogeneity of the environmental context, ii) map the structure and function of the invaded system, iii) trace changes induced by the invader with functional tracers, and iv) integrate the different spatio-temporal information from different scales using (artificial intelligence-based) modelling approaches to better predict invasion impacts. An animated 3-D model visualisation demonstrates how maps of functional tracers reveal spatio-temporal dynamics of invader impacts. Merging fine- to coarse-scale spatially explicit information of functional changes with remotely sensed metrics will open new avenues for detecting invader impacts on ecosystem functioning. [ABSTRACT FROM AUTHOR]

Groundwater level (GWL) is a significant indicator for quantifying groundwater availability. Currently, hydrologists worldwide are actively engaged in modelling and predicting GWL. In karst regions, GWL exhibit varying responses to rainfall events across different locations and the impact of rainfall events on GWL within the same location also varies. Despite incorporating rainfall as an input variable, most existing data‐driven GWL prediction models inadequately account for the spatio‐temporal heterogeneity of karst water areas. Therefore, this study proposes a new analysis method to investigate the response patterns of GWL to rainfall events in karst regions with typical spatio‐temporal variations, known as the sensitivity analysis of rainfall‐GWL response. The method introduces the rainfall response coefficient to describe the response characteristics of GWL to rainfall. Through the rainfall response coefficient, the rainfall response variable (RR) is calculated and incorporates it as an input in the RR‐long short‐term memory (LSTM) GWL prediction model. The effectiveness of proposed method was validated by GWL prediction in karst aquifers located in Jinan City, China, renowned for its spatial–temporal heterogeneity in karst development. Through the analysis and validation conducted by integrating geographical multi‐feature, the study revealed a significant improvement in the accuracy of the RR‐LSTM model after integrating RR as a variable, particularly during significant rainfall events. These findings affirm that the method proposed in this study is highly effective in karst regions characterized by anisotropic karst features. [ABSTRACT FROM AUTHOR]

Copyright of Geography & Geographic Information Science is the property of Geography & Geo-Information Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The watershed system has a complex game relationship between the benign operation and coordinated development of various elements of flood-sediment transportation, eco-environment, and socio-economy (FES). With the increasing breadth, depth, and intensity of human activities in watersheds, it is urgent to coordinate the FES. The relationship of water–sediment in the Yellow River Basin (YRB) is complex, with a prominent contradiction in water supply and a fragile ecosystem. This research tries to build a comprehensive evaluation model for FES and explore the complex interaction between FES in the YRB from 2000 to 2020. The results demonstrated that (1) the comprehensive flood-sediment transportation index (CFTI) and comprehensive eco-environment index (CEI) presented fluctuating growth. In contrast, the comprehensive socio-economy index (CSI) revealed a linear growth trend. The CFTI of Sanmenxia, CEI of Toudaokuan, and CSI of Ningxia had the highest growth rates, with 36.03%, 6.48%, and 107.5%, respectively. (2) FES's positive and negative effects were alternating, with heterogeneity in both time and space. (3) The coupling coordination degree (CCD) in the YRB indicated an increasing trend, ranging from 0.53 to 0.87, from reluctantly coordinated development to good coordinated development. The lagging subsystem was CFTI (2000–2001 and 2008–2020) and CSI (2002–2007), and the CEI was not lagging. (4) Exploratory Spatial Data Analysis (ESDA) demonstrated significant differences in the CCD of the YRB, and areas with similar CCD within the basin tend to be centrally distributed in space. At the same time, there was negative spatial autocorrelation in coordination. The results provide a scientific theoretical and methodological framework for strategic research on the YRB system's governance, protection, and management. [ABSTRACT FROM AUTHOR]

With the active promotion of the "carbon peaking and carbon neutrality" goals, science and technology finance (STF) is the important driving force of low-carbon development, and financial networks facilitate the aggregation and transformation of resources in space, so it is of great theoretical and practical significance to investigate the impact of science and technology finance networks (STFN) on carbon emissions (CE). Based on the 30 provinces of China from 2011 to 2019, this article used the STF development level in each province as the main indicator to construct the STFN. The complex network analysis and econometric models are combined, with the weighted degree values and betweenness centrality selected as typical network structure indicators incorporating into the econometric model to explore their impact on CE. Then, the Geographically and Temporally Weighted Regression (GTWR) model is applied to analyse the spatio-temporal heterogeneity of influencing factors. The results show the following: (1) From 2011 to 2019, the spatial structure of China's STFN has changed significantly, and the status of the triangle structure consisting of Beijing–Tianjin–Hebei (BTH)–Yangtze River Delta (YRD)–Pearl River Delta (PRD) is gradually consolidated in the overall network, and the network structure tends to be stable. (2) The results of the benchmark regression show that the weighted degree value of the STFN has a significant inhibitory effect on CE, while betweenness centrality shows a certain positive effect on CE. (3) The weighted degree value has a more significant effect on CE reduction in the eastern region, while the betweenness centrality has a more significant effect on CE reduction in the central and western regions, but shows a significant promotion effect in the eastern region. (4) There is spatio-temporal heterogeneity in the effects of residents' affluence, energy consumption, industrial structure, and environmental pollution on CE. [ABSTRACT FROM AUTHOR]

With the rapid development, shared bicycles have gradually become an important part of slow urban traffic in China and have played an important role in satisfying the travel needs and facilitating the transfer of residents. Exploring the spatial and temporal characteristics of the impact of the built environment on shared bike travel is of practical importance to reshape the construction of low-carbon transportation and an urban-friendly cycling environment dominated by slow traffic and public transportation. We analyzed the spatio-temporal characteristics of shared bicycle travel through multi-source big data including Shenzhen's shared bicycle OD data, OSM road network data, Baidu Street View, and POIs and used a multi-scale geographical weighted regression model (MGWR) based on the "5D" index of the built environment to analyze the spatial difference characteristics of the impact of different built environment on shared bicycle flow. The findings of the research indicate that: (1) In terms of time, the shared bicycle flow in the morning and evening peaks of both working and rest days is more significant than that of other periods, and the peak period of the remaining days lags behind that of the working days. (2) In terms of space, the spatial distribution characteristics of the traffic flow of shared bicycles during each peak period exhibit a spatial pattern of "multiple aggregation cores and several extended belts." (3) Significant differences were observed in the impact of various built environmental factors on the flow of shared bicycle travel, among which, employment facility density, enclosure degree and population density had a positive effect in each period; their influences were globally significant; and the remaining factors demonstrated varied characteristics in each period. (4) Factors with significant influence showed different spatial scales in different periods. The spatial changes of employment facility density and enclosure in each period were generally flat; the spatial changes of proximity, density of shopping facilities, and the nearest distance to subway stations in some periods were generally flat; the spatial changes of building continuity and relative walking width were obvious in some periods. Moreover, population density and green vision rate had different spatial characteristics in different periods. This study restores the travel track of shared bicycles, analyzes the spatiotemporal characteristics of shared bicycle travel in multiple periods of working days and rest days and long-term series, and increases micro-built environment factors of subjective perception of people and experience dimension based on existing objective material space environment variables, to explore the spatiotemporal differences of the impact of different built environments on the travel flow of shared bicycles which compensate for the existing shared-bike travel time and space characteristics, build a shortage of environmental impact research, and provide references for the construction of an urban-friendly cycling environment and the creation of a slow walking space.

The urban mobility structure is a summary of individual movement patterns and the interaction between persons and the urban environment, which is extremely important for urban management and public transportation route planning. The majority of current research on urban mobility structure discovery utilizes the urban environment as a static network to detect the relationship between people groups and urban areas, ignoring the vital problem of how individuals affect urban mobility structure dynamically. In this paper, we propose a spatio-temporal representational learning method based on reinforcement learning for discovering urban mobility structures, in which the model can effectively consider the interaction knowledge graph of individuals with stations while accounting for the spatio-temporal heterogeneity of individual travel. The experimental results demonstrate the advantages of individual travel-based urban mobility structure discovery research in describing the interaction between individuals and urban areas, which can account for the intrinsic influence more thoroughly.

The significant increase in building energy consumption poses a major challenge to environmental sustainability. In this process, urban morphology plays a pivotal role in shaping building energy consumption. However, its impact may exhibit latent heterogeneity due to differences in temporal resolution and spatial scales. For urban energy planning and energy consumption modeling, it is crucial to pinpoint when and where urban morphology parameters matter, an overlooked aspect in prior research. This study quantitatively explores this heterogeneity, utilizing a detailed dataset from old residential buildings within a university campus. Spatial lag models were employed for cross-modeling across various temporal and spatial dimensions. The results show that annual and seasonal spatial regression models perform best within a 150 m buffer zone. However, not all significant indicators fall within this range, suggesting that blindly applying the same range to all indicators may lead to inaccurate conclusions. Moreover, significant urban morphology indicators vary in quantity, category, and directionality. The green space ratio exhibits correlations with energy consumption in annual, summer, and winter periods within buffer zones of 150 m, 50~100 m, and 100 m, respectively. It notably displays a negative correlation with annual energy consumption but a positive correlation with winter energy consumption. To address this heterogeneity, this study proposes a three-tiered framework—macro-level project decomposition, establishing a key indicator library, and energy consumption comparisons, facilitating more targeted urban energy model and energy management decisions.

The prediction of land subsidence is of significant value for the early warning and prevention of geological disasters. Although numerous land subsidence prediction methods are currently available, two obstacles still exist: (i) spatio-temporal heterogeneity of land subsidence is not well considered, and (ii) the prediction performance of individual models is unsatisfactory when the data do not meet their assumptions. To address these issues, we developed a spatio-temporal heterogeneous ensemble learning method for predicting land subsidence. Firstly, a two-stage hybrid spatio-temporal clustering method was proposed to divide the dataset into internally homogeneous spatio-temporal clusters. Secondly, within each spatio-temporal cluster, an ensemble learning strategy was employed to combine one time series prediction model and three spatio-temporal prediction models to reduce the prediction uncertainty of an individual model. Experiments on a land subsidence dataset from Cangzhou, China, show that the prediction accuracy of the proposed method is significantly higher than that of four individual prediction models.

Conducting ecological monitoring assessments and revealing the effects of driving factors are crucial for enhancing ecological safety and promoting sustainable development. Taking the Chushandian Reservoir basin as the research object, this paper employed the Remote Sensing Ecological Index (RSEI), constructed based on remote sensing data, to monitor and assess the ecological environment of the study area from 1990 to 2021, and predicted its future development trend through the Hurst index. On this basis, we integrated land use data to elucidate the response of the ecological environment to human activities. The results show that: (1) The mutation test indicates that selecting 1990, 2004, 2008, 2013, and 2021 as the study time nodes can comprehensively reflect the spatio-temporal information regarding changes in ecological quality in the study area. Specifically, both 1990 and 2021 exhibit higher ecological quality ratings, while 2008 has the lowest ecological quality rating. The spatial distribution of ecological quality is strongly clustered, with high–high clustering and low–low clustering dominating. (2) The overall trend of ecological quality in the study area appears in a pattern of initial decline followed by subsequent improvement. From 1990 to 2004, the degraded area constituted the largest proportion, accounting for 87.82%. After 2008, the quality of the ecological environment began to rebound. Between 2008 and 2013, the proportion of regions with improved ecological conditions was 57.91%, and from 2013 to 2021, 46.74% of the regions showed improvement. (3) In the research area, 36.70% of the regions exhibit a trend of sustainable stability into the future, representing the highest proportion. Approximately 34.3% of the areas demonstrate a trend of sustainable improvement, while the regions exhibiting sustainable degradation account for only 5.72%. While the ecological environment is demonstrating a positive overall developmental trend, it is crucial to stay vigilant regarding areas of ongoing degradation and implement appropriate protective measures. (4) Land use change significantly impacts the ecological environment, with the expansion of land for urban build up causing some ecological deterioration, while the later expansion of forest improves ecological quality. The results provide theoretical approaches and a foundation for decision-making in the ecological management of the Chushandian Reservoir basin. [ABSTRACT FROM AUTHOR]

Based on the panel data of China's 284 prefecture-level cities from 2006 to 2020, this study employs spatial econometric and geographically weighted regression models to systematically analyze the influencing factors and their spatial–temporal heterogeneity of urban transport carbon emissions. The findings reveal the following: (1) GDP per capita, population, urban road area, and private car per capita are important factors causing the increase in urban transport carbon emissions, while the improvement of urban density, public transportation effectiveness, and government environmental protection can mitigate emissions and promote low-carbon development in urban transportation. (2) The worsening impact of GDP per capita on urban transport carbon emissions shows a decreasing trend over time, forming a spatial gradient pattern of gradually increasing from southwest to northeast. However, a similar effect of population increase during the research period, which currently displays an increasing spatial differentiation from north to south in sequence. (3) As another key deteriorating urban transport carbon emission, the influencing degree of private car per capita has gradually decreased from 2006 to 2020 and represented certain spatial gradient patterns. (4) Although the urban road area is favorable to urban transport carbon reduction in the early stage, it gradually begins to change in an unfavorable direction. The urban density is the contrary, i.e., the increase in that begins to play a positive role in promoting the development of low-carbon transportation among more cities. In addition, the influence coefficient of the former also presents an increasing distribution characteristic from south to north. (5) The reduction effect of public transportation effectiveness and government environmental protection on transport carbon emissions are both gradually prominent, where the former also shows space inertia of "increasing gradient from north to south and from north to northeast". [ABSTRACT FROM AUTHOR]

Copyright of Tropical Geography is the property of Tropical Geography Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

AbstractSpatio-temporally correlated data appear in many environmental studies, and consequently, there is an increasing demand for estimation methods that take account of spatio-temporal (ST) correlation and thereby improve the accuracy of estimation. In this paper, we propose an estimation procedure that improves efficiency, which is based upon a nonparametric pre-whitening transformation of the dependent variable that must be estimated from the data. The asymptotic normality of the proposed estimators is established under mild conditions. We demonstrate, using both simulation and case studies, that the proposed estimators are more efficient than the traditional locally linear methods which fail to account for ST correlation. [ABSTRACT FROM AUTHOR]

Rural areas provide ecosystem services (ESs) to urban metropolitan regions. These services are threatened by the constant pressure of urbanisation and new interest in rural development. This has heightened the conflict between environmental concerns and developmental needs, thereby presenting significant land management and rural planning challenges. Employing a quantitative measurement and optimisation framework, we investigate six representative ES variables to assess planning strategies that can address this contradiction. We used a suburban rural area around Nanjing, China, as our study area. We collected spatial data from 2005 to 2020 at two scales (village level and 500 m grid) to map ESs, quantify interactions (trade-offs and synergies among ES bundles), and identify the social, ecological, and landscape drivers of rural change. Based on this, rural planning strategies for optimising ESs at different scales have been proposed. Our findings include (1) spatial heterogeneity in the distribution of ESs, (2) the identification of seven synergistic and eight trade-off pairs among ESs, (3) a spatial scale effect in suburban rural areas, and (4) the spatial trade-offs/synergies of ESs exhibiting a ‘Matthew effect’. The identification of key trade-offs and synergistic ES pairs and the categorisation of ES bundles form the basis for a multi-scale hierarchical management approach for ESs in the region. By examining the commonalities and variations in drivers across diverse scales, we established connections and focal points for spatial planning. We use these findings to propose spatial planning and landscape policy recommendations for rural suburban areas on multiple scales. This study aims to provide a comprehensive and detailed spatial optimisation strategy for rural areas that can help contribute to their revitalisation.

Research on digital economy (DE) and carbon environment governance (CEG) holds paramount importance in attaining sustainable development objectives. By focusing on 30 provinces within China as specific instances, this research constructed a comprehensive evaluation framework to assess the overall progress of DE and CEG from 2011 to 2020. Moreover, it employed a coupling coordination model alongside spatio-temporal evolution patterns to examine the state of coupling coordination development (CCD) and the temporal and spatial features within DE-CEG. First, the overall levels of China's DE and CEG were both showing an upward trend, with the average level of CCD improving year by year. The spatio-temporal arrangement exhibited a distinctive “bell-shaped” pattern, characterized by narrow at both ends, wide in the middle. The average level of CCD in three major regions was east, central and west followed a descending order, underscoring notable spatial and temporal disparities within the DE-CEG. Second, kernel density estimation and exploratory spatial data analysis substantiated the spatio-temporal differentiation for the CCD within DE-CEG. Notably, these analyses unveiled prevailing spatial patterns of “high-high” and “low-low” agglomeration, spatial agglomeration effect became significant over time. Finally, among the absolute differences, the central region exhibited the least disparity, while the western region displayed the greatest variation, with absolute differences undergoing a fluctuating trajectory. Among the relative differences, the most pronounced contrast was observed between the eastern and western regions, whereas the disparities between the central-east and central-west regions were comparable. Remarkably, relative differences accounted for over 40% of the total variation, emerging as the predominant contributor to the overall divergence in CCD within China's DE-CEG framework. Consequently, it is imperative for future developmental efforts to focus on diminishing the relative disparities among regions. In light of the research findings, a set of strategies and recommendations had been formulated to propel the CCD of China's DE and CEG.

Tourism sustainability is a significant approach to forming a synergistic model of industry and ecology in ecologically vulnerable areas. Scientifically detecting the effect mechanism of tourism development on eco-environment resilience is important in achieving regional social-ecological system sustainability. In this work, empirical exploration is conducted on the tourism development index (TDI) and eco-environment resilience index (ERI) in the Yangtze River Economic Belt (YREB) to study the spatio-temporal heterogeneity of TDI's effect on the ERI. The results indicate significant growth in the TDI in the YREB, with the formation of tourist clusters around Shanghai and Chongqing as the core. Although the ERI typically exhibits a declining trend, the rate of decline has notably slowed, forming a "high at the sides and low in the middle" spatial pattern. The TDI and ERI are spatially dependent in the YREB, with predominantly high-high (HH) and low-high (LH) clusters in Shanghai, Zhejiang, and Jiangsu. Conversely, upstream regions with strong eco-environmental foundations exhibit low-low (LL) and high-low (HL) clusters. In general, the TDI promotes the ERI, but there is significant spatio-temporal heterogeneity in the YREB. Positive impact regions are expanding, while negative impact regions are shrinking. These results could provide scientific evidence for differentiated classification and control policies in the YREB. [ABSTRACT FROM AUTHOR]

To better fit the actual data, this paper will consider both spatio-temporal correlation and heterogeneity to build the model. In order to overcome the "curse of dimensionality" problem in the nonparametric method, we improve the estimation method of the single-index model and combine it with the correlation and heterogeneity of the spatio-temporal model to obtain a good estimation method. In this paper, assuming that the spatio-temporal process obeys the α mixing condition, a nonparametric procedure is developed for estimating the variance function based on a fully nonparametric function or dimensional reduction structure, and the resulting estimator is consistent. Then, a reweighting estimation of the parametric component can be obtained via taking the estimated variance function into account. The rate of convergence and the asymptotic normality of the new estimators are established under mild conditions. Simulation studies are conducted to evaluate the efficacy of the proposed methodologies, and a case study about the estimation of the air quality evaluation index in Nanjing is provided for illustration. [ABSTRACT FROM AUTHOR]

Urban vitality is an important reflection of a city's development potential and urban quality. This study used exploratory spatio-temporal big data such as social media check-ins to portray the spatio-temporal evolution of urban vitality at the subdistrict scale in Changsha, a city in central China, from 2013 to 2021, finding that urban vitality in Changsha exhibited central agglomeration and outward circling expansion over time, and then we used Geodetector and spatial regression analyses to explain the interactive effects and spatio-temporal heterogeneity of the spatial elements of subdistrict form, subdistrict function, and subdistrict economy on urban vitality. The results show the following: (1) The subdistrict form and subdistrict function dimensions had a significant effect on urban vitality, and the effect of the economic dimension of the subdistrict was not significant. (2) The interaction effect of the density of entertainment and leisure facilities and the density of business office facilities in subdistrict function was the dominant factor in the change of urban vitality. (3) Under the spatio-temporal effect, land use diversity and park facility density had the strongest positive effect on urban vitality; road density and shopping facility density had the weakest effect. The study aimed to provide a reference for the optimization and allocation of spatial elements of subdistricts in sustainable urban development and urban renewal, in order to achieve the purpose of urban vitality creation and enhancement. [ABSTRACT FROM AUTHOR]

Vegetation cover is a crucial component of regional ecological environments that plays a vital role in maintaining ecosystem balance. This investigation utilized Google Earth Engine and MODIS NDVI products to examine the spatiotemporal heterogeneity of regional vegetation coverage based on the multi-year average NDVI in China. Using the multi-year average NDVI, multi-year change trend slope, coefficient of variation, and Hurst exponent, the spatial and temporal heterogeneity of provincial and prefectural administrative regions were quantified. The results indicated an upward trend in vegetation coverage from 2000 to 2021 at both provincial and prefectural levels, with growth rates of 0.032/10a and 0.03/10a, respectively. Moreover, the multi-year average NDVI significantly correlated with regional precipitation. Notably, vegetation growth was fastest in the Loess Plateau, while degradation was observed in southern Jiangsu and northern Zhejiang. Additionally, the degree of vegetation cover change in Ningxia and Macau was particularly prominent. These findings support the effectiveness of the Loess Plateau greening project and highlight the potential cost of economic and population growth on the ecosystem in eastern and southeastern coastal areas, where local vegetation degradation occurs. This study can serve as a valuable reference for ecosystem restoration and developmental planning at the administrative regional level, with the goal of enhancing vegetation management and conservation efforts in China. [ABSTRACT FROM AUTHOR]

Equilibrium phase transitions are influenced by fluctuations and often discussed within the framework of the Gibbs free energy, wherein the exchange of energy between system and thermal bath is stationary and all regions of the sample exhibit the same phase. Presence of spatial heterogeneity in the magnetic structures such as pinning centers, domain walls, topological defects, etc. may cause temporal heterogeneity that modifies the nature of the magnetic phase transition. This study reports that interplay of nanoscale thermodynamics with spatio‐temporal heterogeneity gives rise to complex phase transition pathways in amorphous FexGe1‐x thin films with temperature and Fe‐concentration (x). Coherent resonant soft X‐ray scattering experiments that have simultaneous spatial, temporal, and spectral sensitivity show that the origin of helical to paramagnetic phase transition in amorphous Fe‐Ge thin films lies in the appearance of enhanced‐fluctuation spots deep inside the ordered state. The fluctuations are heterogeneous, starting over a small fraction of the domains that increases and becomes isotropic over the entire film as the temperature increases or the Fe‐concentration decreases. The fluctuating‐fraction, when normalized to magnetization for different Fe‐concentrations, follows a single power law behavior, suggesting that the nature of the transition can be described in terms of the underlying spatio‐temporal fluctuations. [ABSTRACT FROM AUTHOR]

Ecosystem services are the beneficial goods and services that ecosystems provide to humans. Urbanization is an important feature of human social development. While promoting economic and social development, it also brings about land degradation, resource depletion, environmental pollution and other problems, intensifying the transformation of natural ecosystems into semi-natural and artificial ecosystems, ultimately leading to the loss of ecosystem service functions and declining value. The study of the impact of urbanization on the value of ecosystem services is of critical importance for the conservation of ecosystems and sustainable development. This study examined the spatio-temporal patterns of urbanization's impacts on ecosystem service value in the Yellow River Basin from the perspective of spatio-temporal heterogeneity. Findings: (1) Both the ecosystem service value (ESV) and urbanization level (UL) in the Yellow River Basin were on the rise on the whole, but they were significantly spatially negatively correlated and mainly characterized by the high–low spatial clustering of "low ESV–high UL" and "high ESV–low UL". This negative correlation was gradually weakened with the transformation of the urbanization development mode and ecological restoration projects in the Yellow River Basin. (2) The impacts of the five urbanization subsystems on the value of ecosystem services were diverse. Landscape urbanization had a negative impact on the value of ecosystem services in all regions; economic urbanization and innovation urbanization changed from having a negative to a positive impact; and demographic urbanization and social urbanization had both a positive and a negative impact. (3) To promote the coordinated development of ecological environmental protection and urbanization in the YRB, this paper proposes to change the urbanization development model, implement ecological restoration by zoning, and formulate classified development plans. This study compensates for the shortcomings of current studies that ignore the different impacts of urbanization subsystems on ecosystem service value and lack sufficient consideration of the spatio-temporal heterogeneity characteristics of urbanization and ESVs, enriches the theoretical understanding of the interrelationships between natural and human systems in basin areas, and provides a scientific basis for the rational formulation of urban planning and ecological protection policies in the region, which is of great theoretical and practical significance. [ABSTRACT FROM AUTHOR]

With the active promotion of the “carbon peaking and carbon neutrality” goals, science and technology finance (STF) is the important driving force of low-carbon development, and financial networks facilitate the aggregation and transformation of resources in space, so it is of great theoretical and practical significance to investigate the impact of science and technology finance networks (STFN) on carbon emissions (CE). Based on the 30 provinces of China from 2011 to 2019, this article used the STF development level in each province as the main indicator to construct the STFN. The complex network analysis and econometric models are combined, with the weighted degree values and betweenness centrality selected as typical network structure indicators incorporating into the econometric model to explore their impact on CE. Then, the Geographically and Temporally Weighted Regression (GTWR) model is applied to analyse the spatio-temporal heterogeneity of influencing factors. The results show the following: (1) From 2011 to 2019, the spatial structure of China’s STFN has changed significantly, and the status of the triangle structure consisting of Beijing–Tianjin–Hebei (BTH)–Yangtze River Delta (YRD)–Pearl River Delta (PRD) is gradually consolidated in the overall network, and the network structure tends to be stable. (2) The results of the benchmark regression show that the weighted degree value of the STFN has a significant inhibitory effect on CE, while betweenness centrality shows a certain positive effect on CE. (3) The weighted degree value has a more significant effect on CE reduction in the eastern region, while the betweenness centrality has a more significant effect on CE reduction in the central and western regions, but shows a significant promotion effect in the eastern region. (4) There is spatio-temporal heterogeneity in the effects of residents’ affluence, energy consumption, industrial structure, and environmental pollution on CE.

In-depth exploration of the coupled and coordinated relationship among regional digital economy (DE), technological innovation (TI) and ecological environment (EE) is a vivid embodiment of implementing sustainable development. In order to show the spatiotemporal features of the coupling coordination degree (CCD) from 2011 to 2020, this study builds an evaluation index system from three target levels, namely, the digital economy DE, science and technology innovation TE, and ecological environment EE. Based on this, global and local spatial econometric models, namely the global Moran's I index and the spatio-temporal geographically weighted regression (GTWR) model, are used to identify the spatio-temporal heterogeneity features of each explanatory variable on the CCD. The study results include: (1) The comprehensive evaluation index shows a rising trend, but the development is uneven among systems. (2) The CCD continues to rise steadily, and the regional disparity is widening; the transformation from the near-disorder level to the primary coordination level is realized over the research period. Spatially, the coupling coordination is higher in the eastern and southern regions, while the western and northern regions are relatively low. (3) The GTWR model demonstrates that human capital, urbanization rate, and openness to the outside world promote the CCD. In contrast, the social unemployment rate inhibits CCD, among which human capital is the main force behind coupled and coordinated development.

Influenced by the topography, the spatial variation of soil thickness on karst slopes is very large, and accordingly the spatial variation of soil moisture is also large. Therefore, analyzing the spatial heterogeneity of soil moisture on hillslopes is important for maintaining ecosystem stability. Combining geostatistical methods and GIS technology, the spatial variability and distribution pattern of soil moisture and the influencing factors of spatial variation and surface soil moisture (0–7 cm) on a typical karst shrub–grass hillslope were analyzed. The results showed that the mean soil moisture and coefficient of variation (CV) ranged between 25.7–42.6% and 10.3–20.9%, respectively, showing a moderate variation. The soil moisture presented a moderate or strong spatial autocorrelation in the sampling scale. The occurrence of rainfall events can exert a great influence on reducing the spatial heterogeneity of soil moisture. The spatial distribution pattern of soil moisture showed roughly plaque or stripe distribution. When soil moisture was much lower, the patch space fragmentation of soil moisture was higher. The soil moisture was higher in the low and middle parts of the plot. We can conclude that factors such as topography, vegetation, and weather conditions will exert a significant effect on soil moisture spatial variability. Areas with lower slope and higher vegetation coverage were more conducive to the retention of soil moisture. [ABSTRACT FROM AUTHOR]

With the emergence and development of sharing economy, Airbnb has established a global impact. Relatively low price is a major factor of its popularity, but the special pricing mode leads to risk of irrational decisions. Understanding the mechanism is of great practical significance, while existing research is limited in investigation into multidimensional built environment features and heterogeneity analysis from both perspectives of space and time. Based on Hedonic Price Method, combining Ordinary Least Squares and Geographically Weighted Regression, this research explores the influence of built environment features on Airbnb listing price and the spatio-temporal heterogeneity using datasets from December 2022 (low tourist season) and June 2023 (high tourist season) in Copenhagen. It is found that six indicators in three dimensions present statistically significant influence at the local scale, which exhibits great spatial dependence as well as certain temporal non-stationarity. The framework proposed provides methodological reference for subsequent research, and the findings will facilitate more targeted regulation of the pricing of Airbnb by authorities, promote the grasp of the attributes of listings for smarter pricing by hosts, support more reasonable choices by consumers, and contribute to more refined built environment practices by urban designers. [ABSTRACT FROM AUTHOR]

The coastal reclamation, as one of the most extreme transformations of the ocean space by humans, still lacks scientific quantitative evaluating methods to a large extent, compared with the evolution of land use patterns. A cumulative ecological impacts of reclamation (RCEI) was established in our study based on ecological influence characteristics of different reclamation types, and the attenuation effect of reclamation on adjacent areas. It was characterized by spatio-temporal features in decades. Here, we estimated that the cumulative reclamation area in the Bohai Sea from 1985 to 2018 was 5839.5 km2. Under the influence of human activity, proportions of the industrial and urban boundary, marine construction boundaries (e.g., ports, wharves, and bridges), and protective dams were increased significantly, which led to a sharp increase of the RCEI. In addition, spatio-temporal changes of reclamation were affected by the combination of population growth, economic development, urbanization, industrialization, and marine industry development in coastal cities. These results provided an important historical reference for tracking future development of the Bohai Sea by humans and provided basic data support for the development and protection of the ocean. [ABSTRACT FROM AUTHOR]

The ecological changes have attracted extensive international attention at Jiangsu Yancheng coastal wetland on the west coast of the Pacific Ocean in recent decades. Tidal creek is an important channel for material exchange between sea and land and plays an important role in the connectivity of water in coastal wetlands. The tidal flats from Sheyang Port to Liangduo Estuary in Jiangsu Province were selected and divided into five sub-study areas using each port as a split point. Based on the remote sensing image data from 1987 to 2020, this study used seven parameters (grade, number, length, density, curvature, bifurcation ratio, and drainage efficiency) to analyze the spatio-temporal divergence patterns and hydrological connectivity of tidal creek network. The results showed that (1) the area of tidal flat was reduced from 1024.87 to 352.05 km2, the total length of the tidal creek was directly reduced from 1061.27 to 640.74 km, the average density increased from 1.00 to 1.82 km/km2, and the total number increased by 33% in 1987–2020, indicating the tidal creeks tended to be "short, fragmented, and parallelized." (2) The development degree of tidal creek networks showed a trend of gradually increasing from north to south, in which the γ index in areas II and V were closer to 1/3 of tree shape after 2002, indicating that the development of tidal creeks in these two areas was better. (3) The spatial heterogeneity of hydrological connectivity levels of tidal creeks in the study area was obvious, which gradually increased from north to south. In 2020, Area I was the lowest, and ICmin was 0.14, and Area V was the highest, and ICmax was 0.90. (4) Reclamation was the main factor leading to the shrinkage and degradation of tidal creeks, but it also increased tidal creek density and hydrological connectivity per unit area; the expansion of Spartina alterniflora had a certain influence on the development of tidal creeks. The results of this study are expected to provide data support for understanding and predicting the evolution of the morphological characteristics of tidal creeks under the influence of human and natural activities and provide scientific reference for the protection and restoration of hydrological connectivity in coastal wetlands. [ABSTRACT FROM AUTHOR]

COVID-19 has led to the interruption of personnel flow, and the tourism industry has become one of the most seriously affected industries. With the gradual improvement of the domestic epidemic situation, the tourism industry has recovered in various provinces and regions, but that recovery shows the characteristics of temporal and spatial heterogeneity. From the perspective of "resilience", this study characterizes the resistance and recovery of the tourism industry in the face of the epidemic impact, analyzes the trends of change, spatial pattern and phased characteristics of tourism resilience, and explores the factors influencing the differences in tourism resilience. The results indicate that China's tourism industry shows obvious resilience characteristics, and the trend of tourism resilience in most provinces and regions fluctuates and rises. For example, Gansu, Hainan, Guizhou, Hebei and Shandong have a high level of comprehensive toughness, while Tibet, Ningxia, Shanxi and Beijing have a very low level of comprehensive toughness, and most other provinces and regions show the characteristic pattern of "weak in the north and strong in the south". This study shows that China's tourism resilience has experienced three stages: hard resistance, accelerated recovery and increasing with fluctuation. The resistance of the tourism industry to the impact of the epidemic is generally weak, and the ability to recover is significantly variable. The severity of the epidemic, the strictness of prevention and control policies, the joint influences of tourist source-destination, tourism foundation, geographical location and other factors will have a certain impact on tourism resilience. [ABSTRACT FROM AUTHOR]

Rural vulnerability is used to understand the potential multi-hazard threats and describe the fragile state of rural areas, which engage decision-makers in developing policies and strategies to reduce vulnerability. Existing studies about rural vulnerability focused on the single exogenous disturbance but paid inadequate attention to the multi-disturbances of rural coupled human-environment system. In addition, current studies mainly analyzed the spatial differentiation of vulnerability degree, ignoring the temporal evolution and the internal elements' relationships of rural systems. In this study, we structured the cognition of rural vulnerability with a framework for understanding coupled human-environment system, evaluated rural vulnerability with the dimensions of exposure, sensitivity and adaptability, and analyzed the driving mechanism based on spatial-temporal heterogeneity. Taking 117 county units in Heilongjiang Province as study cases, we found that (1) rural vulnerability was indeed significant, as the area of county units with extreme or high vulnerability levels accounts for 50.4% of the total area, indicating a trend of high vulnerability in the counties on the north and south sides and low vulnerability in the center. (2) The spatial-temporal heterogeneity of rural vulnerability presented a clustering trend, shifting from a relatively balanced spatial distribution from 2010 to 2013 to a state of vulnerability aggregation at all levels from 2016 to 2019. (3) Rural vulnerability was mainly affected by changes in the principal factors of sensitivity and adaptability, and driving sources mainly generated by human activities, which was largely derived from rural construction activities and government policy guidance on rural regulation. Based on the results, we classified county units into different rural vulnerability types, put forward a rural resilience planning mode of "General + Special" with planning strategies for each type, which can be used as a reference for rural planning positioning of county and township level land spatial planning in the national territory spatial planning. [Display omitted] • Rural vulnerability is affected by multi-disturbances from coupled human-environment system. • The evolution of rural vulnerability is mainly determined by land use, population and human construction. • The spatial-temporal heterogeneity of rural vulnerability presents a clustering trend. • The rural vulnerability of resource-based county units has increased significantly. • Policy has a significant guiding effect on reducing rural vulnerability. [ABSTRACT FROM AUTHOR]

Taking 736 counties in the Yellow River Basin of China as the research area, the comprehensive urbanization development level and ecosystem service capacity from 2000 to 2020 were measured. Combined with spatial autocorrelation, the spatial pattern evolution characteristics of the two systems in the Yellow River Basin were revealed. The spatio–temporal geographically weighted regression (GTWR) model was used to analyze the spatio–temporal heterogeneity of the impact of various elements of the system on urbanization and ecosystem service capacity. The results showed that (1) the urbanization level and ecosystem service capacity of the Yellow River Basin were on the rise but the urbanization level and ecosystem service capacity were low, while the spatial and temporal heterogeneity was significant. (2) The two systems are positively correlated in space, and the agglomeration characteristics are significant. The evolution trend of urbanization from an L–L agglomeration area to an H–H agglomeration area is occurring gradually. The spatial change in the ecosystem service agglomeration area is small, and the stability is strong. (3) The impact of ecosystem services on comprehensive urbanization is enhanced by time, and the spatial 'center–periphery' diffusion characteristics are significant. (4) The influence of urbanization on the comprehensive ecosystem service capacity is enhanced and shows the law of east–west differentiation in space. There are obvious transition zones in the spatial heterogeneity interval of the interaction between the two systems. [ABSTRACT FROM AUTHOR]

This paper considers GTFP of energy consumption and environmental pollution from a sustainable perspective as a measure of the evolutionary efficiency of manufacturing industries. It uses the super-efficiency SBM model to calculate the GTFP efficiency values of manufacturing industries in 30 Chinese provinces from 2011 to 2019. Moran's index and the GTWR model were used to study the spatial correlation and impact factors of GTFP. The results found that the following. (1) The overall level of GTFP in China's manufacturing industry from 2011 to 2019 rose progressively, and the level of GTFP between different regions gradually reduced. (2) The spatial correlation of GTFP in China's manufacturing industry is significantly positive, with a positive spatial spillover effect. (3) The level of manufacturing GTFP is affected by economic development, investment, and other factors. (4) There is spatiotemporal heterogeneity in the impact factors of manufacturing GTFP. According to empirical research focusing on the goal of sustainable development, it is proposed to increase the use of clean energy and reduce environmental pollution. To carry out green development according to local conditions, the eastern region will strengthen the development of new energy manufacturing and continue to increase investment in innovation, and the central and western regions will strengthen environmental supervision, accelerate industrial transformation, and attract more foreign investment. [ABSTRACT FROM AUTHOR]

Carbon control has become a key strategy in the high-quality development (HQD) phase of emerging countries, but the spillover effects of implementing carbon control instruments on HQD remain to be verified. In order to explore the realistic level of HQD in China and the mechanism of how carbon controls impact on it, this paper analyzes the regional differences and spatio-temporal dynamics of HQD in China by using Chinese provincial panel data from 2006 to 2019. This study evaluated the implications of a regionally implemented carbon emissions trading scheme (ETS) on HQD by using the difference-in-differences (DID) model. The results show that the overall level of HQD in China's provinces continues to rise. The level of HQD in most provinces showed a transition from low to high and then stabilization. Over the sample period, the national average HQD index increased from 18.95 to 29.96, a growth rate of 58.1%. There was significant regional heterogeneity in HQD. The highest HQD indices in the eastern, central and western regions were 35.67, 27.52 and 24.78, respectively. The level of HQD in the eastern regions was much higher than in the central and western areas. Further analysis revealed that ETS was able to significantly increase the overall level of HQD. Having found that there is regional heterogeneity in HQD, this research examines the specific effects of ETS on HQD and discovers that ETS supports regional HQD in the eastern and central areas, but has no significant influence on HQD in the western region. The robustness of the results was confirmed by the use of parallel trend tests, lagged effects, the removal of environmental disturbances and the replacement of evaluation variables or models. The above findings can be used as a reference for formulating low-carbon policy and promoting HQD levels. [ABSTRACT FROM AUTHOR]