Geographically And Temporally Weighted Regression Research Articles

Multiple driving factors and hierarchical management of PM2.5: Evidence from Chinese central urban agglomerations using machine learning model and GTWR

In the fast-developing urban agglomerations (UAs), it is of importance to make accurate judgments concerning the multiple driving factors, and establish hierarchical joint management policy. The impact of weather conditions on daily PM2.5 concentrations in the Chinese central UAs was studied using machine learning algorithm, and the analyzed results were integrated into “the proportion of day numbers with negative weather conditions (PDNW)”. Geographically and temporally weighted regression (GTWR) was used to analyze the driving factors of PM2.5 pollution. Results showed that PM2.5 pollution in central China decreased from north to south, and spatial gathering was becoming increasingly prominent. The PM2.5 predicted values decreased smoothly, with barometric pressure and humidity exerting a large effect, and wind speed and direction having a complex effect. Meteorological conditions had a small effect on the annual scale, but the timing of the effect varied in each city. The distribution of PDNW ranged from 23.3% to 55.6%. The proportion of the tertiary industry's GDP (mean − 0.191), education expenditure (mean − 0.057), and the greening rate of urban built-up areas (mean − 0.295) were found to be negatively correlated with PM2.5 pollution. Transportation, urban greening, innovation, and entrepreneurship were driving factors with obvious spatial differences.

Spatio-temporal heterogeneity of logistics CO2 emissions and their influencing factors in China: An analysis based on spatial error model and geographically and temporally weighted regression model

As one of the ultra-large carbon emitters in China, the logistics industry plays an increasingly essential role in mitigating carbon emissions. Previous studies on logistics CO 2 emissions (LCEs) seldom considered the issue of carbon leakage from electricity and the spatio-temporal nonstationarity of factors influencing LCEs. Therefore, using regional grid carbon emission factors, this study calculated the LCEs of 30 Chinese provinces from 2005 to 2019 and, thereafter, employed global and local spatial econometric models – Spatial Error Model (SEM) and Geographically and Temporally Weighted Regression (GTWR) model, respectively – to uncover the spatio-temporal heterogeneous impact of influencing factors on LCEs. The results suggest that: (1) The overall LCEs continue to rise, presenting significant spatial difference with an increasing gradient pattern of the “Western-Central-Eastern.” (2) The LCEs show a spatial agglomeration, with high–high (H–H) and low–low (L–L) clusters as the main types. (3) Economic development level, energy intensity, and electricity consumption significantly promote the LCEs, and the intensity of the promotion presenting a downward, steady, and upward trend, respectively. (4) Population urbanization inhibits LCEs in north China, but boosts them in south China; the inhibition effect of industrial structure on LCEs, which is mainly pronounced in the provinces along the Yangtze River Economic Belt, tends to transform into a promoting effect. • China’s logistics CO 2 emissions considering carbon leakage continue to rise. • Urbanization has both positive and negative impact on logistics CO 2 emissions. • The aggravating effect of economy on logistics CO 2 emissions has been alleviated. • The promotion of electricity on logistics CO 2 emissions is intensifying. • Industry restrains logistics CO 2 emissions in most areas along the Yangtze River.

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.

Geographical and temporal weighted regression model and its application in epidemiology: a review

Geographical and temporal weighted regression (GTWR) model is a local regression linear model, which indirectly reflects the spatio-temporal non-stationary characteristics of the study data by calculating the trends for changes in parameters with space and time. Recently, GTWR model has become one of the hot topics in the study on spatio-temporal heterogeneity of diseases. This review summarizes the basic principles and study methods of the GTWR model, and describes the applications of this model in epidemiology.

Analysis of water quality indexes and their relationships with vegetation using self-organizing map and geographically and temporally weighted regression

Natural vegetation has been proved to promote water purification in previous studies, while the relevant laws has not been excavated systematically. This research explored the relationships between vegetation cover and water quality indexes in Liaohe River Basin in China combined with self-organizing map (SOM) and geographically and temporally weighted regression (GTWR) innovatively and systematically based on the distributing heterogeneity of water quality conditions. Results showed that the central and northeast regions of the study area had serious organic and nutrient pollution, which needed targeted treatment. And SOM verified that high vegetation coverage with retention potential of organic and inorganic pollutants as well as nutrients improved water quality to some degree, while the excessive discharges of pollutants still had serious threats to nearby water environment despite the purification function of vegetation. GTWR indicated that the waterside vegetation was beneficial for dissolved oxygen increasing and contributed to the decreasing of organic pollutants and inorganic pollutants with reducibility. Natural vegetation also obsorbed nutrients like TN and TP to some degree. However, the retential potential of nitrogen and organic pollutants became not obvious when there were heavy pollution, which demonstrated that pollution sources should be controlled despite the purification function of vegetation. This study implied that natural vegetation purified water quality to some degree, while this function could not be revealed when there was too heavy pollution. These findings underscore that the pollutant discharge should be controlled though the natural vegetation in ecosystem promoted the purification of water bodies.

Government drivers of breast cancer prevention: A spatiotemporal analysis based on the association between breast cancer and macro factors.

BackgroundCurrently, breast cancer (BC) is ranked among the top malignant tumors in the world, and has attracted widespread attention. Compared with the traditional analysis on biological determinants of BC, this study focused on macro factors, including light at night (LAN), PM2.5, per capita consumption expenditure, economic density, population density, and number of medical beds, to provide targets for the government to implement BC interventions.MethodsA total of 182 prefecture-level cities in China from 2013 to 2016 were selected as the sample of the study. The geographically and temporally weighted regression (GTWR) model was adopted to describe the spatiotemporal correlation between the scale of BC and macro factors.ResultsThe results showed that the GTWR model can better reveal the spatiotemporal variation. In the temporal dimension, the fluctuations of the regression coefficients of each variable were significant. In the spatial dimension, the positive impacts of LAN, per capita consumption expenditure, population density and number of medical beds gradually increased from west to east, and the positive coefficient of PM2.5 gradually increased from north to south. The negative impact of economic density gradually increased from west to east.ConclusionThe fact that the degree of effect of each variable fluctuates over time reminds the government to pay continuous attention to BC prevention. The spatial heterogeneity features also urge the government to focus on different macro indicators in eastern and western China or southern and northern China. In other words, our research helps drive the government to center on key regions and take targeted measures to curb the rapid growth of BC.

Exploring the spatial and temporal driving mechanisms of landscape patterns on habitat quality in a city undergoing rapid urbanization based on GTWR and MGWR: The case of Nanjing, China

Landscape pattern significantly impacts habitat quality, especially in cities undergoing rapid urbanization, where landscape patterns are changing dramatically. However, the spatial and temporal driving mechanisms of landscape pattern on habitat quality are still unclear, and the proposed methods of Geographically and Temporally Weighted Regression (GTWR) and Multiscale Geographic Weighted Regression (MGWR) provide possibilities for the exploration of these mechanisms. This study was conducted in Nanjing from 2001 to 2020. Landscape pattern indices indicating aggregation, connectivity, diversity and compactness were calculated using Fragstats from 2001 to 2020. The habitat quality was computed using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. By combining two new spatial measurement models, GTWR and MGWR, the spatial and temporal driving mechanisms of landscape patterns on habitat quality were explored. The results show that (1) as Nanjing’s land under construction has expanded, habitat quality has decreased significantly, and the overall landscape pattern has fluctuated drastically. (2) GTWR and MGWR are well-suited to such analysis and provide important insights. (3) Overall, aggregation and compactness were negatively associated with habitat quality in areas of low-quality habitat. Increased connectivity on low habitat substrates had a positive effect on habitat. The increase of diversity in proximity had a positive effect on habitat, while the opposite was true in high habitat zones. (4) As the urbanization level increases, the negative effects of aggregation expand, as do the positive effects of connectivity and diversity. (5) The extent of influence of landscape pattern effects are ranked from largest to smallest: compactness, diversity, connectivity, and aggregation, while the intensity of effects is reversed. Based on these findings, a reference point for urban planners is provided to plan urban landscape patterns in a sustainable and rational manner. It also provides a new means of integrating GTWR and MGWR into the study of landscape ecology.

Europe-wide air pollution modeling from 2000 to 2019 using geographically weighted regression.

Previous European land-use regression (LUR) models assumed fixed linear relationships between air pollution concentrations and predictors such as traffic and land use. We evaluated whether including spatially-varying relationships could improve European LUR models by using geographically weighted regression (GWR) and random forest (RF). We built separate LUR models for each year from 2000 to 2019 for NO2, O3, PM2.5 and PM10 using annual average monitoring observations across Europe. Potential predictors included satellite retrievals, chemical transport model estimates and land-use variables. Supervised linear regression (SLR) was used to select predictors, and then GWR estimated the potentially spatially-varying coefficients. We developed multi-year models using geographically and temporally weighted regression (GTWR). Five-fold cross-validation per year showed that GWR and GTWR explained similar spatial variations in annual average concentrations (average R2=NO2: 0.66; O3: 0.58; PM10: 0.62; PM2.5: 0.77), which are better than SLR (average R2=NO2: 0.61; O3: 0.46; PM10: 0.51; PM2.5: 0.75) and RF (average R2=NO2: 0.64; O3: 0.53; PM10: 0.56; PM2.5: 0.67). The GTWR predictions and a previously-used method of back-extrapolating 2010 model predictions using CTM were overall highly correlated (R2>0.8) for all pollutants. Including spatially-varying relationships using GWR modestly improved European air pollution annual LUR models, allowing time-varying exposure-health risk models.

Investigating multi-way impacts of transportation on human footprint: Evidence from China

Transportation can indirectly impact the environment through affecting various human activities. Previous literature has not adequately discussed in what direction and to what extent transportation impacts human activities and in turn the environment in a large geographical context. This paper aims to investigate the multi-way impacts of transportation on human activities and their spatial-temporal variations. We adopt transportation dominance as a proxy for transportation, which is an integration of density, proximity, and location advantage. We employ human footprint as a proxy for human activities, which is compiled on built environments, population density, electric infrastructure, croplands, and pasture lands. Ordinary least squares (OLS) and geographically and temporally weighted regression (GTWR) models are used to estimate transportation impacts. The results show that multi-way impacts of transportation on human footprint are related to a host of variables and exhibit spatial-temporal heterogeneity, which can be positive, negative, and non-significant. Multi-way impacts are mainly manifested in spatial variation rather than temporal variation, with macroscopic east-west variation, mesoscopic core-periphery variation, and microscopic urban-rural variation. Our findings also suggest that human footprint in rural areas and central provinces is more susceptible to improved transportation conditions. Roadless areas should be protected from transportation-induced human activities and disturbance to promote sustainable transportation development.

TOD and vibrancy: The spatio-temporal impacts of the built environment on vibrancy

Urban vibrancy is described by the activities of residents and their spatio-temporal dynamics. The metro station area (MSA) is one of the densest and most populous areas of the city. Thus, creating a vibrant and diverse urban environment becomes an important goal of transit-oriented development (TOD). Existing studies indicate that the built environment decisively determines MSA-level urban vibrancy. Meanwhile, the spatio-temporal heterogeneity of such effects requires thoroughly exploration and justification. In this study, we first apply mobile signaling data to quantify and decipher the spatio-temporal distribution characteristics of the MSA-level urban vibrancy in Chengdu, China. Then, we measure the built environment of the MSA by using multi-source big data. Finally, we employ geographically and temporally weighted regression (GTWR) models to examine the spatio-temporal non-stationarity of the impact of the MSA-level built environment on urban vibrancy. The results show that: 1) The high-vibrant MSAs concentrate in the commercial center and the employment center. 2) Indicators such as residential density, overpasses, road density, road network integration index, enterprise density, and restaurant density are significantly and positively associated with urban vibrancy, while indicators such as housing price and bus stop density are negatively associated with urban vibrancy. 3) The GTWR model better fits the data than the stepwise regression model. The impact of the MSA-level built environment on urban vibrancy shows a strong non-stationarity in both spatial and temporal dimensions, which matches with the spatio-temporal dynamic patterns of the residents’ daily work, leisure, and consumption activities. The findings can provide references for planners and city managers on how to frame vibrant TOD communities.

Factors that dynamically affect provincial incidences of catastrophic health expenditure among middle-aged and elderly Chinese population-transition of disease financial risk protection from global to local

BackgroundThe high incidence of catastrophic health expenditure (ICHE) among middle-aged and elderly population is a major deterrent for reducing the financial risk of disease. Current research is predominantly based on the assumption of spatial homogeneity of nationwide population characteristics, ignoring the differences in regional characteristics. Thus, our study aimed to explore the impact of various influencing factors on the ICHE from a spatiotemporal perspective.MethodsWe used data from the China Health and Retirement Longitudinal Study (waves 1 to 4), to conduct a retrospective cohort study across 28 provinces, from 2011 to 2018. We measured regional incidences of catastrophic health expenditure using methods recommended by the World Health Organization. Ordinary least squares (OLS) and geographical and temporal weighted regression (GTWR) were used as the global and local estimation models, respectively. The Fortheringham method was used to test the spatiotemporal non-stationarity.ResultsNational ICHE showed a gradual increase from 2011 to 2015, but suddenly decreased from 2015 to 2018, also showing the spatial heterogeneity. And the southwest area and Hebei showed persistently high ICHE (Qinghai even reached the highest value of 27.5% in 2015). Out-of-pocket payment, gross domestic product, PM2.5, ageing, incidence of non-communicable diseases and disabilities, number of nurses, and health insurance coverage in the global estimation passed the significance test, and the GTWR model showed a better model fit (0.769) than the OLS model (0.388). Furthermore, except for health insurance coverage, all seven variables had spatiotemporal non-stationarity among their impacts on ICHE.ConclusionIn this longitudinal study, we found spatiotemporal non-stationarity among the variable relationships, supporting regional governments’ adoption of regional-target policies. First, after the completion of universal health insurance coverage, the spatiotemporal non-stationarity of the prevalence of non-communicable diseases and disability and ageing should be the focus of the next phase of health insurance design, where improvements to compensation coverage and benefit packages are possible policy instruments. Second, the governance and causes of catastrophic health expenditure need to be laid out from a macro perspective rather than only from the individual/household perspective, especially for the potential impact of economic development, air pollution and nursing resources.

The reduce of energy consumption intensity: Does the development of science and technology finance matter? Evidence from China

The paper aims to study the impact of the development of science and technology finance (STF) on energy consumption intensity (ECI) in 30 Chinese provinces, based on Chinese provincial panel data from 2012 to 2019. The ECI and the level of STF development index are calculated using the entropy weight method. Then, the interrelationship between them is studied using the coupling coordination degree (CCD) model. Finally, a variety of spatial econometric models as well as geographically and temporally weighted regression (GTWR) models are constructed for empirical testing by combining five indicators, including industrial structure. The results show that the development level of STF in China is characterized by a high level in the eastern and a low level in the central and western, while the distribution of ECI is basically reversed. The development of STF is to a certain extent conducive to reducing ECI, and there is spatial heterogeneity in the relationship between them. Factors such as industrial structure, opening up level, and urbanization level also have a significant impact on ECI. Therefore, this paper proposes that China should continuously improve the policies related to the development of STF, encourage science and technology innovation, and guide the industrial structure to rationalization and supererogation, to reduce the ECI.

Spatiotemporal Heterogeneity and Driving Factors of Water Resource and Environment Carrying Capacity under High-Quality Economic Development in China.

Rapid economic growth and social development in China have led to serious water pollution problems and water resource shortages, limiting the sustainable development that could support both the socio-economy and water resources carrying capacity (WRECC). However, the spatial heterogeneity and evolutionary characteristics of the coordination between the WRECC and economic development have not been adequately explored in China. In this study, we developed the support and pressure indicators of China’s 30 provinces and then analyzed the spatiotemporal distribution and evolution characteristics of their WRECC by using the geographically and temporally weighted regression (GTWR) model. The main findings are shown in the following: (i) From a temporal perspective, there has been an overall upward trend in the WRECC to support human activities; however, the WRECC level is not high. Approximately 63.7% of provinces remain in an overloaded state, indicating that the support indicator of most provinces is smaller than the pressure indicator imposed by human social activities. (ii) There are significant spatial differences in the WRECC indicators across provinces. Provinces with low-level WRECCs are concentrated in central China but decrease significantly from the country’s borders to its center. Eastern regions have a medium-level of WRECC with the greatest degree of regional difference, while western regions have a high-level of WRECC with the smallest degree of regional difference. The variation of WRECC is attributed to within-group differences in the three geographical regions in China. (iii) The factors that significantly impact the WRECC include population density, gross domestic product (GDP), temperature, urbanization, the added value of tertiary industry within the GDP, and R&D expenditures. GDP and R&D expenditures positively impact the WRECC, while the other four factors have different influences on the WRECC. (iv) The spatial distributions of driving factors show significant aggregation characteristics, with decreasing trends from the eastern to western regions and from the southern to northern regions. These findings present a comprehensive understanding of the current WRECC in China’s provinces which can be used as a reference for realizing environmentally sustainable water development strategies under high-quality economic development.

Spatiotemporal Correlation Analysis of Hydraulic Fracturing and Stroke in the United States.

Hydraulic fracturing or fracking has led to a rapid growth of oil and gas production in the United States, but the impact of fracking on public health is an important but underresearched topic. We designed a methodology to study spatiotemporal correlations between the risk of fracking and stroke mortality. An annualized loss expectancy (ALE) model is applied to quantify the risk of fracking. The geographically and temporally weighted regression (GTWR) model is used to analyze spatiotemporal correlations of stroke mortality, fracking ALE, and nine other socioeconomic- and health-related factors. The analysis shows that fracking ALE is moderately correlated with stroke mortality at ages over 65 in most states of fracking, in addition to cardiovascular disease and drug overdose being positively correlated with stroke mortality. Furthermore, the correlations between fracking ALE and stroke mortality in men appear to be higher than in women near the Marcellus Shale, including Ohio, Pennsylvania, West Virginia, and Virginia, while stroke mortality among women is concentrated in the Great Plains, including Montana, Wyoming, New Mexico, and Oklahoma. Lastly, within two kilometers of the fracking mining activity, the level of benzene in the air was found to be significantly correlated with the fracking activity in Colorado.

Identifying the Determinants of Distribution of Oncomelania hupensis Based on Geographically and Temporally Weighted Regression Model along the Yangtze River in China.

Background: As the unique intermediate host of Schistosoma japonicum, the geographical distribution of Oncomelania hupensis (O. hupensis) is an important index in the schistosomiasis surveillance system. This study comprehensively analyzed the pattern of snail distribution along the Yangtze River in Jiangsu Province and identified the dynamic determinants of the distribution of O. hupensis. Methods: Snail data from 2017 to 2021 in three cities (Nanjing, Zhenjiang, and Yangzhou) along the Yangtze River were obtained from the annual cross-sectional survey produced by the Jiangsu Institute of Parasitic Diseases. Spatial autocorrelation and hot-spot analysis were implemented to detect the spatio–temporal dynamics of O. hupensis distribution. Furthermore, 12 factors were used as independent variables to construct an ordinary least squares (OLS) model, a geographically weighted regression (GWR) model, and a geographically and temporally weighted regression (GTWR) model to identify the determinants of the distribution of O. hupensis. The adjusted coefficients of determination (adjusted R2, AICc, RSS) were used to evaluate the performance of the models. Results: In general, the distribution of O. hupensis had significant spatial aggregation in the past five years, and the density of O. hupensis increased eastwards in the Jiangsu section of the lower reaches of the Yangtze River. Relatively speaking, the distribution of O. hupensis wase spatially clustered from 2017 to 2021, that is, it was found that the border between Yangzhou and Zhenjiang was the high density agglomeration area of O. hupensis snails. According to the GTWR model, the density of O. hupensis was related to the normalized difference vegetation index, wetness, dryness, land surface temperature, elevation, slope, and distance to nearest river, which had a good explanatory power for the snail data in Yangzhou City (adjusted R2 = 0.7039, AICc = 29.10, RSS = 6.81). Conclusions: The distribution of O. hupensis and the environmental factors in the Jiangsu section of the lower reaches of the Yangtze River had significant spatial aggregation. In different areas, the determinants affecting the distribution of O. hupensis were different, which could provide a scientific basis for precise prevention and control of O. hupensis. A GTWR model was prepared and used to identify the dynamic determinants for the distribution of O. hupensis and contribute to the national programs of control of schistosomiasis and other snail-borne diseases.

Spatio-temporal disparity of water use efficiency and its influencing factors in energy production in China

Water resources are considered as the biggest obstacle to regional high-quality development in China. Increasing water use efficiency (WUE) is an effective way to alleviate the growing water scarcity. The study concentrates on the energy production in China, uses the input-output analysis (IOA), data envelopment analysis (DEA), and geographically and temporally weighted regression (GTWR), to determine the total water use, to measure WUE, and to identify the main influencing factors. The results indicated that water consumption exhibit an overall increasing trend, decreasing from the southeastern regions to the southwestern, the northeastern, and the northwestern regions, with an increasing proportion of intermediate input. Although WUE improves significantly from 2002 to 2015, there is still a large space for improvement. Globally, WUE is highly clustered in most regions, and the degree of clustering shows a continuously increasing trend. Locally, the coastal regions in north of the Yangtze River have higher efficiency, while regions in the northwestern and southwestern have lower efficiency. The positive influences of population size, economic level, and industrial structure are significant, and the negative influence of resource endowment is also significant. The influences of resource endowment, population size, economic level, and industrial structure are significantly non-stationary in both space and time, and the former is stronger than the latter. Overall, economic level and population size are the predominant factors, and their quantitative structures are extremely stable.

Uncovering the Structural Effect Mechanisms of Natural and Social Factors on Land Subsidence: A Case Study in Beijing

Understanding the effect mechanisms of various factors on land subsidence may help in the development of scientific measures to control land subsidence. Previous studies mainly focused on exploring local effect mechanisms, such as extracting hotspots and analyzing their spatiotemporal distribution characteristics and identifying the interaction mechanisms of the associated factors. However, the scarcely discussed structural effect mechanisms on a small scale suggests a need to further explore the effects on land subsidence. Therefore, in this paper, an analytical framework was proposed to elaborate the structural effect mechanisms of influencing factors on land subsidence. First, the local effect mechanisms were identified using the geographically and temporally weighted regression (GTWR) model, followed by a spatial clustering analysis and the detection of their aggregation pattern using the spatially constrained multivariate clustering (SCMC) model to show the structural mechanisms. Study datasets included the monitoring results of land subsidence during 2003–2010 and the related socioeconomic factors by using synthetic aperture radar (SAR) images from Beijing. Factors such as population, annual average rainfall, underground water, and static load were identified to measure the changes in land subsidence, and all of these had both negative and positive impacts. Among these, the annual average rainfall had the largest coefficient variation range. These four geographically associated factors revealed various spatiotemporal effects on land subsidence in Beijing, showing land subsidence changes resulting from the urbanization process of Beijing during that period.

Regional differences and driving factors analysis of carbon emissions from power sector in China

Carbon emissions from the power sector (CEPS11CEPS: Carbon emissions from the power sector.) contribute more than 40% of carbon emissions to both the world and China, which is the key link of carbon emission reduction. In the context of China’s “carbon peaking and carbon neutrality” goals, clarifying the regional differences and driving factors of CEPS is helpful to formulate carbon emission reduction policies. Based on the CEPS data of 30 provinces in China from 2004 to 2019, this study uses data statistics and spatial autocorrelation analysis to test the temporal and spatial differences of CEPS among provinces. Ten indicators are selected to construct a geographically and temporally weighted regression (GTWR) model for the regression analysis of driving factors of CEPS in the provinces. During 2004–2019, the CEPS in China’s 30 provinces show obvious differences. The CEPS in Shandong, Inner Mongolia and Shanxi maintain a high rise, while that in Sichuan and Yunnan shows a downward trend. The global Moran’s I of provincial CEPS in China shows significant positive spatial autocorrelation, high-high clustering area is concentrated in North and East China, and low-low clustering area is distributed in Southwest China. The effects of various driving factors on provincial CEPS show obvious temporal and spatial heterogeneity. The power structure (PS), the thermal power energy consumption rate (TE) and the per capita electricity consumption (PC) have positive effects on CEPS in most provinces, while the contribution of the power industry (IC) has a negative effect. The effects of other indicators on different provinces fluctuate in different periods. In order to realize the low-carbon sustainable development of power sector, relevant measures such as improving the efficiency of thermal power production, optimizing power structure by region, and support the major power producing provinces are proposed.

Spatiotemporal Patterns and Influencing Factors of Industrial Ecological Efficiency in Northeast China

Scientific measurement of regional industrial ecological efficiency and discussion of the development and changes of its spatiotemporal pattern are of great significance to accelerate the high-quality development of regional economy and coordinate the development of industrial economy and ecological environment. Taking the old industrial bases in Northeast China as the research case and 2004–2019 as the research period, a super-slack-based model was used to measure the industrial ecological efficiency of 34 prefecture-level cities in the region. Meanwhile, the spatial autocorrelation model and the geographically and temporally weighted regression (GTWR) model were used to analyze the spatiotemporal pattern characteristics and the spatiotemporal heterogeneity of influencing factors. The results showed that: (1) From a time change perspective, the overall industrial ecological efficiency of Northeast China declined, the mean of the 34 cities decreased from 0.675 to 0.612, the number of cities with a high level of industrial ecological efficiency decreased significantly, the number of cities with a low level of industrial ecological efficiency increased significantly, and the development gap between cities within the region widened. (2) In terms of spatial pattern, the difference in the spatial pattern in the east–west direction decreased, and the spatial pattern in the south–north direction was enhanced. The industrial ecological efficiency of the central part of Northeast China gradually became the highest in the whole region. (3) From 2017, the industrial ecological efficiency had stable spatial autocorrelation characteristics. The local spatial autocorrelation was dominated by L-H-type cluster areas in the mountainous regions and L-L-type cluster areas in central and southern Liaoning province. H-H and H-L types had small numbers. In addition, the trend of H-H cities transforming into H-L cities was obvious, and the high level of urban space spillover effect showed good development. (4) The science and technology input, industrial agglomeration intensity, and environmental regulation of the government generally had a promoting effect on the improvement in industrial ecological efficiency, while the economic extroverted degree had a negative impact. The high-value area of science and technology investment and industrial agglomeration intensity concentrated significantly in the central part. The government focused on ecological protection areas and ecologically sensitive areas, and the economic extroverted degree had a significant positive impact on the two major urban agglomerations in central Northeast China. Therefore, differentiating measures should be taken according to the actual situation of each city to improve the industrial ecological efficiency level in Northeast China.

Effects of Climate Change on Corn Yields: Spatiotemporal Evidence from Geographically and Temporally Weighted Regression Model

Food security has been one of the greatest global concerns facing the current complicated situation. Among these, the impact of climate change on agricultural production is dynamic over time and space, making it a major challenge to food security. Taking the U.S. Corn Belt as an example, we introduce a geographically and temporally weighted regression (GTWR) model that can handle both temporal and spatial non-stationarity in the relationship between corn yield and meteorological variables. With a high fitting performance (adjusted R2 at 0.79), the GTWR model generates spatiotemporally varying coefficients to effectively capture the spatiotemporal heterogeneity without requiring completion of the unbalanced data. This model makes it possible to retain original data to the maximum possible extent and to estimate the results more reliably and realistically. Our regression results showed that climate change had a positive effect on corn yield over the past 40 years, from 1981 to 2020, with temperature having a stronger effect than precipitation. Furthermore, a fuzzy c-means algorithm was used to cluster regions based on spatiotemporally changing trends. We found that the production potential of regions at high latitudes was higher than that of regions at low latitudes, suggesting that the center of productive regions may migrate northward in the future.