Determinants and their spatial heterogeneity of carbon emissions in resource-based cities, China

Global climate change associated with increased carbon emissions has become a global concern. Resource-based cities, by estimations, have emerged as major contributors to carbon emissions, accounting for approximately one-third of the national total. This underscores their pivotal role in the pursuit of carbon neutrality goals. Despite this, resource-based cities have long been neglected in current climate change mitigation policy discussions. Accordingly, using exploratory spatial data analysis and Geographical Weighted Regression method, this study investigates the determinants of carbon emissions and their spatial pattern in 113 resource-based cities in China. It can be concluded that: (1) The proportion of carbon emissions from resource-based cities in the national total has shown a marginal increase between 2003 and 2017, and the emissions from these cities have not yet reached their peak. (2) A relatively stable spatial pattern of "northeast high, southwest low" characterizes carbon emissions in resource-based cities, displaying significant spatial autocorrelation. (3) Population size, economic development level, carbon abatement technology, and the proportion of resource-based industries all contribute to the increase in carbon emissions in these cities, with carbon abatement technology playing a predominant role. (4) There is a spatial variation in the strength of the effects of the various influences.

Carbon sinks/sources' spatiotemporal evolution in China and its response to built-up land expansion

Terrestrial ecosystem carbon sink examination in China still faces great uncertainties. Determinant analysis has focused on climate change but ignored the influence of fast urban expansion. Using remote sensing images, climate variable data, and high-resolution land use data, this research improved net ecosystem productivity (NEP) simulation model based on a large number of field observations, and investigated spatial-temporal changes of NEP. This research calculated the NEP loss caused by built-up land expansion and used geographically weighted regression (GWR) model to explore the spatial heterogeneity of the relationship between NEP growth and built-up land expansion. The results showed that China contributed a carbon sink of 0.33 Pg C per year from 2000 to 2020. Southern China had a greater capacity to sequester carbon than northern China. The carbon sink capacity of most Chinese regions increased. Built-up land expansion caused 4.95 Tg C of carbon sink loss per year, which was mainly concentrated in eastern China. In GWR model, 50.8% of regions showed negative correlations between NEP growth and built-up land expansion. These two variables were mostly positively correlated in the northwest and negatively correlated in the southeast. Consequently, this study suggests that maintaining the capacity of carbon sinks in southern provinces is important for China to meet its carbon neutrality goal.

Identifying the spatial heterogeneity in the effects of the construction land scale on carbon emissions: Case study of the Yangtze River Economic Belt, China

Low-carbon emissions are a major research focus to solve the problem of global warming and an important area that China needs to focus on to achieve high-quality development. Construction land scale is a non-negligible factor affecting carbon emissions. However, carbon emission impacts of county-scale spatial heterogeneity in construction land scale are under addressed in contemporary research. To address this gap, this paper took 1042 counties in China's Yangtze River Economic Belt (YREB) and developed datasets of the influencing factors including the construction land scale, GDP, secondary industry output proportion in GDP, residential population, and fixed asset investment. After comparing the ordinary least squares and geographically weighted regression (GWR) models, we applied GWR for more in-depth analyses. The global regression model results showed that the effect of the scale of construction land on carbon emissions was exceedingly significant and that the directions of the impacts coincided with the predictions. Further, local regression model results showed that construction land scale had significant spatial heterogeneity in the impact on carbon emissions and most counties (69.58%) showed significant positive correlations. The counties with significant construction land scale impacts on carbon emissions were concentrated and contiguous in spatial distribution and spatially clustered areas varied, with the clearest impact in the downstream region. The findings help to further identify the spatial heterogeneity of construction land scale impacts on carbon emissions, which provides evidence-based and theoretical support for policymakers to develop spatially differentiated emission reduction measures.

A critical assessment of provincial-level variation in agricultural GHG emissions in China

China is a world leader on agriculture production; with only 8% of global cropland it feeds 20% of the world's population. However, the increasing production capacity comes with the cost of greenhouse gas (GHG) emissions. As a populous country with the highest GHG emissions in the world, determining how to achieve the dual goals of mitigating climate change and ensuring food security is of great significance for the agricultural sector. This requires assessing the spatial variation in agricultural greenhouse gases (GHGs) and their drivers. In this study, we conduct a spatial assessment of agricultural GHGs at the provincial level in China for the years 1997-2017, and then explore the effects of related factors on GHG emissions using a geographically weighted regression (GWR) model. The results suggest the following. 1) There have always been significant interprovincial variations, whether in the total amount, structure, intensity, or per capita level of agricultural GHG emissions. 2) The directions of the effects of selected factors on GHG intensity fall broadly into three categories: negative effects (urbanization, intensity of agricultural practices, and agricultural structure), positive effects (agricultural investment and cropland endowments), and mixed effects, with factors leading to reductions in some provinces and increases in others (economic level, frequency and intensity of disasters, and the level of mechanization). 3) The magnitude of the effects varies by factor and also by province. The results suggest synergetic province- or state-specific reduction policies in agricultural GHG for China, as well as for other developing and emerging economies.

Explicit Spatializing Heat-Exposure Risk and Local Associated Factors by coupling social media data and automatic meteorological station data

Extremely high temperatures, a major cause for weather-related public health issues, are projected to intensify and become more frequent. To mitigate the adverse effects, a low-cost and effective risk assessment method should be developed. Therefore, we applied automatic meteorological station data and population mobility data to develop a high spatiotemporal resolution temperature risk assessment method. The population mobility analysis results showed the working/residential complex pattern in Tianhe District, with hotspots of spatial clustering located in the north, southwest, and southeast of the study area. Taking the population mobility patterns into consideration, high-temperature risk assessment results with a resolution of 100 m were obtained. The total mortality cases in 2014 and 2015 were used to validate this result. The validation showed that the total mortality in the high-temperature risk areas accounted for over 36% of that in Tianhe District. Thus, the method introduced in this study is capable of reflecting weather-related risk. Furthermore, the high-temperature risk assessment results showed that most of the risky areas were located in the southwest of the study area. Two peak times of the risk areas were determined, being before dawn and in the evening. Compared with the risk areas during weekdays, those at weekends expanded. In addition, we used the geographically weighted regression model to investigate the potential influencing factors. Individual factor contributed more than 22.4% to the spatial distribution of heat exposure. Catering services, transportation services, and living services were higher than others, with mean R² values of 0.28, 0.23, and 0.25, respectively. More than 47.9% of spatial distribution of heat exposure was attributed to joint function of influencing factors, with global R² ranged from 0.23 to 0.34. Our research introduces a spatial-specific method to quantitatively assess high-temperature risk. Moreover, the mechanisms behind the spatial distribution of the high-temperature risk were discussed. The theoretical and management implications can help urban designers and energy governors to develop useful strategies to mitigate weather-related public health risks.

Examining the spatiotemporal variations and inequality of China's provincial CO2 emissions

Tremendous energy consumption appears as rapid economic development, leading to large amount of CO₂ emissions. Although plentiful studies have been made into the driving factors of CO₂ emissions, the existing literatures that take the spatial differences and temporal changes into consideration are few. Therefore, this study first analyzes the variations of total CO₂ emissions' spatial distribution from 2008 to 2017 and present the changes of driving factors, finding significant spatial autocorrelation in CO₂ emissions at the province level, and that urbanization rate, per capita GDP and per capita CO₂ emissions increased significantly, but energy consumption structure and trade openness decreased. We then compared the effects of different factors affecting CO₂ emissions, using classic linear regression model, panel data model, and the geographically weighted regression (GWR) model, and the three models roughly agree on the effects of factors. The GWR model considering spatial heterogeneity provides more detailed results. Population, urbanization rate, per capita carbon emissions, energy consumption structure, and trade openness all have positive effects, while per capita GDP has a two-way impact on CO₂ emissions. The influence of urbanization rate and energy consumption structure in the central and western regions increased even faster than in eastern regions, and the impacts of trade openness in lower and higher opening areas are more significant. The population and per capita CO₂ emission have declining influences, among which the influence of population in coastal areas declined more slowly, while the rate of decline of per capita CO₂ emission was positively correlated with the local total CO₂ emissions. The Lorenz curve and the Gini coefficient reveal the inequality distribution of CO₂ emissions in various regions, with the highest CO₂ emissions growth in the medium-economic-level areas, where the key area of carbon mitigation is. Finally, per capita GDP reveals that China as a whole has the trend of inverted N-shape Kuznets curve, and the underdeveloped regions are in the rising stage between the two inflection points, while developed regions are at the end of the rising stage and about to reach the second inflection point.

Association of Community Factors with Hospital-onset Clostridioides (Clostridium) difficile Infection: A Population Based U.S.-wide Study

BACKGROUND

Clostridioides (Clostridium) difficile ranks first among the pathogens of hospital-acquired infections with hospital-based preventive strategies being only partially successful in containing its spread.

METHODS

We performed a spatial statistical analysis to examine the association between population characteristics and parameters of community healthcare practice and delivery with hospital-onset Clostridioides (Clostridium) difficile infection (HO-CDI), using data from the Medicare Hospital Compare, Medicare Provider Utilization Part D, and other databases. Among the areas with the highest HO-CDI rates ("hot spots"), we conducted a geographically weighted regression (GWR) to quantify the effect of the decrease in the modifiable risk factors on the HO-CDI rate.

FINDINGS

Percentage of population > 85 years old, community claims of antimicrobial agents and acid suppressants, and density of hospitals and nursing homes within the hospital service areas (HSAs) had a statistically significant association with the HO-CDI incidence (p < 0.001). The model including the community claims of antimicrobial agents and number of hospital centers per HSA km² was associated with 10% (R² = 0.10, p < 0.001) of the observed variation in HO-CDI rate. The hot spots were organized into 5 Combined Statistical areas that crossed state borders. The association of the antimicrobial claims and HO-CDI rate was as high as 71% in the Boston-Worcester-Providence area (R² = 0.71, SD 0.19), with a 10% decrease in the rate of antimicrobial claims having the potential to lead to up to 23.1% decrease in the HO-CDI incidence in this area.

INTERPRETATION

These results outline the association of HO-CDI with community practice and characteristics of the healthcare delivery system and support the need to further study the effect of community and nursing home-based antimicrobial and acid suppressant stewardship programs in the rate of HO-CDI in geographic areas that may cross state lines.

Local spatial variations analysis of smear-positive tuberculosis in Xinjiang using Geographically Weighted Regression model

Background

The spatial interplay between socioeconomic factors and tuberculosis (TB) cases contributes to the understanding of regional tuberculosis burdens. Historically, local Poisson Geographically Weighted Regression (GWR) has allowed for the identification of the geographic disparities of TB cases and their relevant socioeconomic determinants, thereby forecasting local regression coefficients for the relations between the incidence of TB and its socioeconomic determinants. Therefore, the aims of this study were to: (1) identify the socioeconomic determinants of geographic disparities of smear positive TB in Xinjiang, China (2) confirm if the incidence of smear positive TB and its associated socioeconomic determinants demonstrate spatial variability (3) compare the performance of two main models: one is Ordinary Least Square Regression (OLS), and the other local GWR model.

Methods

Reported smear-positive TB cases in Xinjiang were extracted from the TB surveillance system database during 2004–2010. The average number of smear-positive TB cases notified in Xinjiang was collected from 98 districts/counties. The population density (POPden), proportion of minorities (PROmin), number of infectious disease network reporting agencies (NUMagen), proportion of agricultural population (PROagr), and per capita annual gross domestic product (per capita GDP) were gathered from the Xinjiang Statistical Yearbook covering a period from 2004 to 2010. The OLS model and GWR model were then utilized to investigate socioeconomic determinants of smear-positive TB cases. Geoda 1.6.7, and GWR 4.0 software were used for data analysis.

Results

Our findings indicate that the relations between the average number of smear-positive TB cases notified in Xinjiang and their socioeconomic determinants (POPden, PROmin, NUMagen, PROagr, and per capita GDP) were significantly spatially non-stationary. This means that in some areas more smear-positive TB cases could be related to higher socioeconomic determinant regression coefficients, but in some areas more smear-positive TB cases were found to do with lower socioeconomic determinant regression coefficients. We also found out that the GWR model could be better exploited to geographically differentiate the relationships between the average number of smear-positive TB cases and their socioeconomic determinants, which could interpret the dataset better (adjusted R² = 0.912, AICc = 1107.22) than the OLS model (adjusted R² = 0.768, AICc = 1196.74).

Conclusions

POPden, PROmin, NUMagen, PROagr, and per capita GDP are socioeconomic determinants of smear-positive TB cases. Comprehending the spatial heterogeneity of POPden, PROmin, NUMagen, PROagr, per capita GDP, and smear-positive TB cases could provide valuable information for TB precaution and control strategies.