Regional socioeconomic characteristics and density of general practitioners in Germany: A nationwide cross-sectional and longitudinal spatial analysis.

OBJECTIVES: Limited healthcare availability impacts population health. Regional disparities in GP density across Germany raise questions about their association with regional socioeconomic characteristics. STUDY DESIGN: This longitudinal nationwide ecological German study used regional data at the county level (n = 401) from 2015 to 2019 provided by the Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR). The outcome was general practitioners (GPs) density, defined as the number of GPs per 10,000 inhabitants. METHODS: Univariate Moran's I, cluster analysis (LISA), and spatial lag of X (SLX) models were employed to analyse the spatial distribution of GP density and its correlation with various regional socioeconomic characteristics from a cross-sectional and longitudinal perspective. RESULTS: In contrast to the univariate analysis, rural counties showed the highest GP density the multivariate model. Several counties were identified as embedded in low- or high-GP-density clusters. In 2015 and 2019, larger household size (2015: std. ß = -2.31, p = 0.021; 2019: std. ß = -4.14, p < 0.001) and higher unemployment rate (2015: std. ß = -2.84, p = 0.005; 2019: std. ß = -5.47, p < 0.001) were associated with lower GP density. In the longitudinal model, a greater increase in the unemployment rate was related to a greater decrease in GP density (std. ß = -2.17, p = 0.030). CONCLUSION: A higher regional unemployment rate is linked to lower GP availability in Germany, and a greater increase in the unemployment rate was related to a greater decrease in GP availability over time. This necessitates policy intervention to avoid socioeconomic disparities in GP care.

Prevalence and Spatial Distribution Characteristics of Female Stress Urinary Incontinence in Mainland China.

Background and objective: Stress urinary incontinence (SUI) in women is a common condition that affects middle-aged and elderly women. Currently, there are still many limitations in the epidemiological research on SUI. This study aims to address the gap in the prevalence of female SUI in mainland China and provide theoretical support for the prevention and treatment of SUI. Methods: A comprehensive literature search was conducted on the prevalence of female SUI in mainland China, systematically searching Chinese and English databases including PubMed, Web of Science, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang Database, and Weipu Database as of April 1, 2024. Detailed criteria for screening and exclusion were established. The prevalence of SUI in the selected studies was synthesized using Stata MP (version 15) software, and a multisubgroup analysis, a sensitivity analysis, and publication bias detection of the prevalence of SUI were also performed using the software. Additionally, ArcGIS software (version 10.8) and Geoda software (version 1.2) were utilized to explore the geographical distribution characteristics of the prevalence of female SUI in mainland China. Key findings and limitations: A total of 688 articles were screened, and finally 85 articles were included. The overall rate of female SUI in mainland China was 24.5% (95% confidence interval: 22.5-26.5%). The heterogeneity of the study is statistically significant (I2 = 99.0%, p < 0.001). Based on significant heterogeneity, a multisubgroup analysis was conducted. The results showed that the prevalence of SUI varies among different publication years, literature quality scores, investigators, study settings, sampling methods, provinces, regions, coastal or inland areas, and rural or urban areas. A spatial econometric analysis indicated that the incidence of SUI in the east-west distribution showed a downward trend, while in the north-south distribution, the incidence rate of SUI showed a trend of first increasing and then decreasing. Additionally, a spatial metrology analysis showed similar trends in the distribution of SUI incidence. Conclusions and clinical implications: The high incidence rate of female SUI in mainland China and the regional differences observed indicate the need for further rigorous epidemiological investigation in the future. Patient summary: Stress urinary incontinence (SUI) is common among middle-aged and elderly women. The high prevalence of SUI in mainland China and the differences across regions emphasize the need for conducting more robust epidemiological studies in the future.

Determinants of spatiotemporal changes of land use carbon emissions for counties in Shaanxi Province, China.

In China, urban sprawl and developed land expansion challenge the country's "carbon peak" and "carbon neutrality" goals. Counties as the basic governance units are crucial for effective carbon reduction policies. This study examines land use carbon emissions (LUCE) in Shaanxi Province at the county level, essential for China's low-carbon strategy. Analyzing data from 107 counties between 2000 and 2020, we found that developed land, though increasing, is the primary carbon source with a slowing growth rate. The Conversion of Cropland to Forests and Grasslands national policy mitigated the impact on carbon absorption. Carbon emissions displayed positive autocorrelation and spatial heterogeneity, varying across the region. Using the Spatial Durbin Error Model, we linked county-level emissions to GDP per capita, population, urbanization rate, and research and development expenditure for direct and indirect influence. These factors correlate with fossil fuel use and high-quality industrial development. Promoting public transits and reducing private car use are vital for achieving local and regional low-carbon goals.

The past and future dynamics of ecological resilience and its spatial response analysis to natural and anthropogenic factors in Southwest China with typical Karst.

With the global land use/land cover (LULC) and climate change, the ecological resilience (ER) in typical Karst areas has become the focus of attention. Its future development trend and its spatial response to natural and anthropogenic factors are crucial for understanding the changes of ecologically fragile areas to human behavior. However, there is still a lack of relevant quantitative research. The study systematically analyzed the characteristics of LULC changes in Southwest China with typical Karst over the past 20 years. Drawing on the landscape ecology research paradigm, a potential-elasticity-stability ER assessment model was constructed. Revealing the characteristics and heterogeneity of the spatial distribution, annual evolution, and development trend of ER in the past and under different scenarios of shared socioeconomic pathways and representative concentration pathways (SSP-RCP) in the future. In addition, the spatial econometric model was utilized to reveal the spatial effect response mechanism of ER, and adaptive development strategies were proposed to promote the sustainable development of Southwest China. The study found that : (1) In the past 20 years, the LULC in Southwest China showed an accelerated change trend, the ER decreased declined in general, and there was significant spatial heterogeneity, showing the spatial distribution pattern of "west is larger than east, south is larger than north, and reduction in the west was slower than that in the east." (2) Under the same SSP scenario, with the increase of RCP emission concentration, the area of the lowest-resilience increased significantly, and the area of the highest-resilience decreased. (3) The woodland was the largest contributor to ER per unit area in the Southwest China, and grassland was the main LULC type, which had a prominent impact on the ER of the study area. (4) The average precipitation and the normalized difference vegetation index (NDVI) were significant natural drivers of ER in the study area, and the economic growth, innovation, and optimization of industrial structure contributed to the ER of Southwest China. Overall, the integration of quantitative assessment and multi-scenario-based modeling not only provides new perspectives for understanding the pattern of change and response mechanisms, but also provides valuable references for other typical Karst regions around the world to achieve sustainable development.

Spatial-structural analysis of macroeconomic factors' impact on carbon emissions in East Africa: a spatial econometric panel study.

Despite the abundance of research on reducing carbon emissions, there is a significant gap in understanding the influence of macroeconomic factors on carbon dioxide (CO2) emissions from a spatial-structural perspective. This study aims to contribute to the literature by investigating the impact of macroeconomic factors on carbon dioxide emissions in six East African countries between 1989 and 2020. Using spatial econometric panel models, the study analyzed spatial dependence among the variables. The empirical findings indicate that gross domestic product (GDP) per capita and electricity consumption have positive direct and indirect effects on carbon emissions, while fuel prices and exports have negative direct effects, but positive spillover effects on neighboring countries. Imports have a positive impact on local economies, but negative spillover effects. Additionally, the urban population has no significant impact on the environment. These findings provide important policy implications for optimizing spatial growth patterns and achieving a low-carbon economy in East African countries.

Study on the impact of air pollution on residents' health expenditures in Guangdong, Hong Kong, and Macao Greater Bay Area.

The Guangdong, Hong Kong, and Macao Greater Bay Area (GHMA) has experienced economic development and rapid growth of transportation infrastructure in recent years. However, the economic advancement is also accompanied by serious atmospheric pollution, which threatens the health of the residents, thus, it is of great significance to explore the impact of atmospheric pollution on the health expenditures of residents in the GHMA. The article establishes a spatial econometric model to study the impact of atmospheric pollution on residents' health expenditure in the GHMA based on panel data from 2014 to 2021, using nine prefectures in the GHMA as research objects. The results show that: (1) Atmospheric pollution in the GHMA has an obvious spatial agglomeration phenomenon and spatial spillover effect, and the impact of atmospheric pollution on the health of the residents is still very serious; (2) PM2.5 emissions are positively and significantly related to the actual health care cost per person, and the rise in air pollution is the main reason for the rise in public health spending; (3) Other factors also have different impacts on residents' health expenditures. Based on the above research, the article puts forward corresponding policy recommendations.

Evaluation of Food Security in North China Based on Food Production Level.

This paper focuses on county-level grain production and food security in North China; selects 17 indicators from both climatic conditions and human activities; applies yield fluctuation coefficients, spatial econometric modelling, the random forest method, and factor analysis to study the characteristics of grain production in North China and the influencing factors; and evaluates the situation of food security in North China based on grain production capacity. The following results were obtained: (1) The spatial and temporal changes in grain production located in North China from 2000 to 2020 are obvious. The grain output in North China from 2000 to 2020 maintains fluctuating growth at a rate of 0.38 × 1011 kg/10a. The east and south are the key areas for grain production in North China. Grain output was relatively stable except for 2003. with the cold spots of grain production being mainly in the northwestern area and the hot spots in the central and southern areas. (2) The changes in grain production in North China from 2000 to 2020 were less affected by climate and mainly influenced by human activity indicators. (3) As time progresses, the area of food shortage zones decreases in size, becoming evenly distributed and dispersed from the initial concentration in northern Hebei and most of Shanxi; the change in the supply-demand equilibrium zones is not obvious; and the area of surplus grain zones increases markedly in size, with a tendency to expand from the south and centre of the study area to the west and north. The grain production capacity of counties in the northwest and north is generally low, and even counties located in surplus grain areas have potential food security risks. However, in the east and south, due to their high grain production capacity, the per capita grain supply situation may be alleviated even in counties located in grain shortage areas. This study can deepen the understanding of the characteristics of food production in North China and enrich the research on food security. Analyses of factors influencing food production will improve a deeper understanding of food security. Food security evaluation based on food production capacity will contribute to a more precise and comprehensive understanding of the food security pattern in North China.

The impact of digital inclusive finance on the collaborative reduction of pollutant and carbon emissions: spatial spillover and mechanism analysis.

In light of the escalating global climate risks threatening human survival, there is a global consensus on the necessity for collaborative reduction of pollutant and carbon emissions (CRPC). Within this context, digital inclusive finance (DIF) is recognized for its unique inclusiveness and digital characteristics as a critical factor in promoting environmentally friendly and sustainable development. DIF provides advantageous channels for environmental governance, thereby making the achievement of CRPC objectives feasible. However, the impact of DIF on CRPC has not been fully explored. This study employs a spatial econometric model to investigate the impact of DIF on CRPC in 278 prefecture-level cities in China from 2011 to 2020. The findings indicate that DIF has a positive impact on CRPC, with significant spatial spillover effects. The analysis highlights the pivotal mediating roles played by technology effect and electrified effect of the energy mix, while environmental regulation effect plays a moderating role. Notably, disparities in the impact of DIF on CRPC are evident, particularly in non-resource-based cities, cities with low carbon intensity, and eastern regions where spatial spillover effects are more pronounced. These experiences enrich the relevant thesis in terms of DIF on CRPC, providing a theoretical basis for formulating CRPC schemes.

[Impacts of Urban Form on Carbon Emissions Under the Goal of Carbon Emission Peak and Carbon Neutrality: A Case Study of the Yangtze River Economic Belt].

Clarifying the mechanism of influence of urban form on carbon emissions is an important prerequisite for achieving urban carbon emission reduction. Taking the Yangtze River Economic Belt as an example, this study elaborated on the general mechanism of urban form on carbon emissions, used multi-source data to quantitatively evaluate the urban form, and explored the impacts of urban form indicators on carbon emissions from 2005 to 2020 at global and sub-regional scales with the help of spatial econometric models and geodetector, respectively. The results showed that:① The carbon emissions of the Yangtze River Economic Belt increased from 2 365.31 Mt to 4 230.67 Mt, but the growth rate gradually decreased. Its spatial distribution pattern was bipolar, with high-value areas mainly distributed in core cities such as Shanghai and Chongqing and low-value areas concentrated in the western regions of Sichuan and Yunnan. ② The area of construction land in the study area expanded over the past 15 years, but the population density of construction land had been decreasing. The degree of urban fragmentation was decreasing, and the difference between cities was also progressively narrowing. The average regularity of urban shape improved, and the compactness increased significantly. ③ All indicators of urban scale had significant positive effects on carbon emissions at the global scale, urban fragmentation had a significant negative effect in 2005, and the effective mesh size (MESH) indicator of urban compactness showed a significant negative correlation with carbon emissions in the study period. ④ Total class area, patch density, and effective mesh size had the most significant impacts on carbon emissions in upstream cities. Effective mesh size, mean perimeter-area ratio, and total class area had higher influences in midstream cities. Effective mesh size, percentage of like adjacencies, and largest patch index were the key factors to promote carbon reduction in downstream cities. Cities in different regions should comprehensively consider the impacts of various urban form indicators on carbon emissions and then optimize their urban form to promote sustainable development.

Identifying determinants of spatial agglomeration of healthcare resources by using spatial econometric methods: a longitudinal study in China.

Background: Healthcare resources are necessary for individuals to maintain their health. The Chinese government has implemented policies to optimize the allocation of healthcare resources and achieve the goal of equality in healthcare for the Chinese people since the implementation of the new medical reform in 2009. Given that no study has investigated regional differences from the perspective of healthcare resource agglomeration, this study aimed to investigate China's healthcare agglomeration from 2009 to 2017 in China and identify its determinants to provide theoretical evidence for the government to develop and implement scientific and rational healthcare policies. Methods: The study was conducted using 2009-2017 data to analyze health-resource agglomeration on institutions, beds, and workforce in China. An agglomeration index was applied to evaluate the degree of regional differences in healthcare resource allocation, and spatial econometric models were constructed to identify determinants of the spatial agglomeration of healthcare resources. Results: From 2009 to 2017, all the agglomeration indexes of healthcare exhibited a downward trend except for the number of institutions in China. Population density (PD), government health expenditures (GHE), urban resident's disposable income (URDI), geographical location (GL), and urbanization level (UL) all had positive significant effects on the agglomeration of beds, whereas both per capita health expenditures (PCHE), number of college students (NCS), and maternal mortality rate (MMR) had significant negative effects on the agglomeration of institutions, beds, and the workforce. In addition, population density (PD) and per capita gross domestic product (PCGDP) in one province had negative spatial spillover effects on the agglomeration of beds and the workforce in neighboring provinces. However, MMR had a positive spatial spillover effect on the agglomeration of beds and the workforce in those regions. Conclusion: The agglomeration of healthcare resources was observed to remain at an ideal level in China from 2009 to 2017. According to the significant determinants, some corresponding targeted measures for the Chinese government and other developing countries should be fully developed to balance regional disparities in the agglomeration of healthcare resources across administrative regions.

Decoding the impact of fiscal decentralization on urban pollution intensity in China: A spatial econometric analysis.

Utilizing city-level data from China, the paper employs a spatial econometric analysis to investigate the impact of fiscal decentralization on urban pollution. Empirical evidence indicates: (1) In the context of the emphasis of ecological civilization construction in China, an increase of fiscal autonomy for local governments is conducive to mitigating urban pollution intensity. Specifically, fiscal decentralization in one city not only promotes a reduction in local pollution intensity but alleviates environmental pollution problems in adjacent cities through spatial spillover effects. (2) Industrial structure upgrading and green technology progress become crucial measures for local governments to realize pollution reduction targets through fiscal expenditure. (3) Heterogeneity analysis reveals that the positive significance of decentralization is most prominent in the eastern China, while local governments with fiscal autonomy in central region tend to transfer pollution to neighboring cities. (4) There is a threshold characteristic for fiscal decentralization to promote a reduction in urban pollution intensity, and its marginal effect becomes more significant accompanied by continuous introduction of sophisticated foreign direct investment. Finally, the paper summarizes the potential significance of fiscal decentralization among Chinese local governments against the background of "Chinese-style decentralization" and proposes corresponding policy recommendations.

Spatial distribution and influencing factors of CDC health resources in China: a study based on panel data from 2016-2021.

Background: Measuring the development of Chinese centers for disease control and prevention only by analyzing human resources for health seems incomplete. Moreover, previous studies have focused more on the quantitative changes in healthcare resources and ignored its determinants. Therefore, this study aimed to analyze the allocation of healthcare resources in Chinese centers for disease control and prevention from the perspective of population and spatial distribution, and to further explore the characteristics and influencing factors of the spatial distribution of healthcare resources. Methods: Disease control personnel density, disease control and prevention centers density, and health expenditures density were used to represent human, physical, and financial resources for health, respectively. First, health resources were analyzed descriptively. Then, spatial autocorrelation was used to analyze the spatial distribution characteristics of healthcare resources. Finally, we used spatial econometric modeling to explore the influencing factors of healthcare resources. Results: The global Moran index for disease control and prevention centers density decreased from 1.3164 to 0.2662 (p < 0.01), while the global Moran index for disease control personnel density increased from 0.4782 to 0.5067 (p < 0.01), while the global Moran index for health expenditures density was statistically significant only in 2016 (p < 0.1). All three types of healthcare resources showed spatial aggregation. Population density and urbanization have a negative impact on the disease control and prevention centers density. There are direct and indirect effects of disease control personnel density and health expenditures density. Population density and urbanization had significant negative effects on local disease control personnel density. Urbanization has an indirect effect on health expenditures density. Conclusion: There were obvious differences in the spatial distribution of healthcare resources in Chinese centers for disease control and prevention. Social, economic and policy factors can affect healthcare resources. The government should consider the rational allocation of healthcare resources at the macro level.

The impact of urbanization and economic growth on carbon dioxide emission in sub-Saharan African countries: a perspective from the spatial-temporal approach.

Sub-Saharan Africa (SSA) is seeing exceptional urbanization and economic expansion rates. Therefore, the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) parameters and the spatial econometric framework are used in this work to examine the influence of economic growth and urbanization on SSA's CO2 emissions. Likewise, to determine the spatial effect and understand how factors influence the spatial dependence of carbon emissions, the study builds a spatial Durbin model (SDM). In line with the findings, the spatial correlation test revealed the spatial correlations across various countries. This indicates that the changes in sub-Saharan African country's CO2 emissions impacted nearby countries and the countries themselves. Additionally, the findings reveal that, in the SSA's countries, urbanization, economic growth, industrial structure, trade, and population, excluding energy intensity, which failed the significant test, all positively influence CO2 outflows, in line with the spatial econometric model's findings. Thus, energy intensity shares an adverse impact on carbon emissions. As an outcome, energy intensity reduces carbon dioxide emissions in nearby nations and the entire region. Thus, the study recommends that policymakers account for the effects of spatial spillover when establishing low-carbon policies, encouraging a low-carbon lifestyle, promoting environmentally friendly technologies, and improving regional collaboration.

Mechanism research on digital inclusive finance promoting high-quality economic development: Evidence from China.

This article aims to precisely evaluate the catalytic impact of digital inclusive finance on economic growth, enhance the implementation of policies pertaining to digital inclusive finance, and foster high-quality economic development. Based on China's provincial panel data and the digital inclusive finance index from 2011 to 2021, this research investigates the influence of digital inclusive finance on high-quality economic development and the associated underlying mechanisms. The findings suggest that digital inclusive finance exerts a notable spatial impact on high-quality economic development. Moreover, there is heterogeneity in the spatial effects between different dimensions of digital inclusive finance and high-quality economic development. Through the threshold model and intermediary effect model, it is found that the Internet penetration rate has a dual-threshold effect on the impact of digital inclusive finance on promoting high-quality economic development. Specifically, digital inclusive finance contributes to elevating the level of high-quality economic development through its role in promoting the transformation of consumption structure. The findings of this study offer valuable insights for countries aiming to attain high-quality economic development through the enhancement of digital inclusive finance.

Analysis of carbon emission equity degrees based on regional heterogeneity in China.

Carbon emission reduction is an environmental and development issue that needs to consider various factors, such as the economy and people's livelihood. Supporting the achievement of emission reduction targets has become an important planning goal for provincial governments; however, there are differences in provincial industrial structure and economic development, which cannot be ignored in goal setting. This study measures the equity degrees of carbon emissions based on economic output by using provincial panel data from 2000 to 2019 and evaluates the spatial distribution characteristics of the carbon emission inequity index (CII). Then, analysis of the influencing factors to CII is employed by spatial econometric methods. Furthermore, multi-index panel data factor analysis and cluster analysis are used to divide regions. The empirical results show that nearly half of the provinces have the problem of carbon emissions inequity with significant spatial correlation. For local development, economic growth and population expansion will significantly improve the equity degrees of carbon emissions. In contrast, the growth of urbanization level, the percentage of secondary industry, and increased energy intensity will significantly improve the equity degrees of carbon emissions in neighboring regions. Policymakers should consider the factors influencing CII and formulate emission reduction plans according to regional characteristics.

Distribution characteristics and influencing factors of household consumption carbon emissions in China from a spatial perspective.

Household consumption carbon emissions (HCCEs) have become the main growth point of China's carbon emissions in the future. It is important to investigate the factors affecting the demand-side carbon emissions in order to find the accurate entry point of emission reduction and achieve carbon peaking and carbon neutrality goals. Different from previous studies, this study analyzed the spatial and temporal evolution characteristics of provincial HCCEs in China from a spatial perspective by using the Theil index and spatial auto-correlation and explored the key influencing factors and spatial spillover effects of HCCEs in different regions by using an econometric model. The results of the study showed that: (1) Per capita HCCEs increased by 11.90% annually, and the eastern region > northeastern region > western region > central region. (2) There were regional differences in per capita HCCEs, but the decrease was significant at 40.32%. (3) The spatial agglomeration effect of per capita HCCEs was significant, and the hot spots were mainly concentrated in the eastern coastal areas. (4) From the national level, every 1% increase in residents' consumption power would increase HCCEs by 2.489%. Which was the main factor for the growth of HCCEs, while the increase in fixed asset investment would restrain HCCEs. At the regional level, the change in population size significantly increased the HCCEs in the eastern and central regions. While for the western region, a 1% increase in population would reduce the HCCEs by 0.542%. For the eastern and central regions, the degree of aging and the consumption structure of residents could suppress regional HCCEs. However, the consumption structure of residents drove the growth of HCCEs in the western region. For the Northeast region, residents' consumption capacity and cooling degree days were the main factors for the growth of residents' consumption, while fixed asset investment could inhibit the growth of HCCEs.

Provincial distribution differences and influencing factors of self-assessed health among elderly population in China / 环境与职业医学

Background Regional differences in economic development, natural environment, health care level, and social structure may lead to differences in the provincial distribution of the health status of the elderly population. Objective To explore the provincial distribution characteristics, regional differences, and influencing factors of the self-assessed health of the elderly population, with the aim of providing a policy basis for improving the health of the elderly population and promoting healthy aging according to local conditions. Methods Using 31 provinces (municipalities and autonomous regions) in China as the basicstudy unit and based on the method of Wagstaff, the self-rated health data of the elderly population (aged 60 years and above) in each province from the 2010 and 2020 national censuses and the 2015 1% National Population Sample Survey were converted into ill-health scores as a measure of self-assessed health, and higher scores represented worse health status perception. Global Moran's I was used to evaluate spatial autocorrelation, range [−1, 1], with a value of 1 as a perfect clustered pattern. Local Moran's I was used to evaluate the tendency of local autocorrelation, and high-high aggregation/low-low aggregation indicated that both target province and its neighboring provinces showed higher/lower ill-health scores. Spatial econometric models were selected by Lagrange multiplier test and Hausman test to explore influencing factors of the self-assessed health of the elderly population. Results In 2010, 2015, and 2020, the national ill-health scores of the elderly population were 1.831, 1.873, and 1.547, respectively, and the corresponding Global Moran's I statistics were 0.347, 0.482, and 0.511, respectively (P<0.01), indicating that the ill-health scores of the elderly population showed a significant spatial positive autocorrelation, and the degree of spatial aggregation was increasing gradually. From 2010 to 2020, the high-high aggregation of ill-health scores among the elderly population was concentrated in the inland northwest, while the low-low aggregation was concentrated in the southeast coast, gradually showing a "southeast-central-northwest" stepped incremental pattern of differentiation. The Lagrange multiplier test and Hausman test suggested that the fixed-effects spatial lagged model was a better choice, and the regression model showed a spatial autocorrelation in the ill-health scores of the elderly population, with an autocorrelation coefficient of 0.3969 (P<0.001); the ill-health scores of the elderly population were negatively correlated with the natural logarithms of gross regional product per capita, and the number of beds in health care facilities per 1000 population, with regression coefficients of −0.8297 and −0.0454 (P<0.05) respectively, and positively correlated with the annual average concentration of PM2.5, illiteracy rate, and the number of health technicians per 1000 population, with regression coefficients of 0.0033, 0.0297, and 0.0765 (P<0.05), respectively. Conclusion From 2010 to 2020, the overall self-assessed health level of China's elderly population showed an upward trend and a spatial positive autocorrelation, with better self-assessed health in the southeast coast and poorer ratings in the northwestern inland. Additionally, there was a gradual decline in self-assessed health of the elderly population from the southeast to the central regions and further to the northwest in terms of spatial distribution. Economic development level, environmental pollution, health resource allocation, and education level are important factors influencing the self-assessed health of the elderly population.

Importance of temporary and permanent snow for new second homes.

This study investigates empirically how natural snow depth and permanent snow affect the number of new second homes in Norway. One out of four Norwegian municipalities is partly covered by glaciers and permanent snow. In the winter seasons of 1983-2020, there is a decline in snow depth from 50 to 35 cm on average (based on 41 popular second-home areas in the mountains). Results of the fixed effects Poisson estimator with spatial elements show that there is a significant and positive relationship between natural snow depth in the municipality and the number of second homes started. There is also a significant and negative relationship between the number of new second homes in the municipality and a scarcity of snow in the surrounding municipalities. However, the magnitude of both effects is small. Estimates also show a strong positive relationship between the proportion of surface covered by permanent snow or glaciers in the municipality and new second homes. This implies that a decline in permanent snow and glaciers may make these areas less attractive for the location of second homes.

Spillover effects of urban form on urban land use efficiency: evidence from a comparison between the Yangtze and Yellow Rivers of China.

The contradiction between the basin's economic importance and its role as an ecological barrier impedes efficient urban land use. This study aims to propose an integrated approach to compare the urban land use of two representative basin areas of the Yangtze River Economic Belt and the Yellow River Basin and to investigate the impact of urban form on urban land use efficiency. Urban form was characterized by landscape indexes including Patch Density, Largest Patch Index, Edge Density, Patch Cohesion Index, and Agglomeration Index based on FRAGSTATS 4.0 software, and urban land use efficiency was measured by using Slack-Based Model-Undesirable, considering urban land becomes an emission source. Furthermore, spatial econometric models were adopted to explore direct effects and spatial spillover effects of urban form on urban land use efficiency. From 2000 to 2018, changes in urban form in both Yangtze River Economic Belt and Yellow River Basin showed increased fragmentation, enhanced heterogeneity, and more complex patch shapes. The high values of urban land use efficiency were concentrated in lower reaches of the Yangtze and Yellow Rivers. Spatial econometric models suggested that between different basins and various sized cities, the impact of urban form on urban land use efficiency had a spatial spillover effect and regional heterogeneity. Results indicated that input factors such as capital and labor should be more concentrated in metropolitan areas and urban agglomerations, thus promoting higher land use efficiency.

Carbon emission reduction effect of renewable energy technology innovation: a nonlinear investigation from China's city level.

There has always been controversy over how renewable energy technologies can play a role in reducing carbon emissions. Based on the energy patent data and the economic data of 244 prefecture-level cities from 2007 to 2017 in China, we explore the carbon reduction effect of renewable energy technology and its mechanism from the perspective of energy production, conservation, and management. The two-way fixed effect, instrumental variable, spatial Durbin, and mediation effect models are employed to explore empirical results. We found that (1) the impact of renewable energy technologies on carbon emissions is nonlinear, with an inverted U shape. However, this inverted U-shaped relationship only exists locally in cities and there are uncertainties in adjacent cities, which indicates that cross-regional cooperation in renewable energy technology needs to be improved. (2) The mechanism analysis shows that industrial agglomeration and energy consumption scale are the channels that renewable energy technologies affect carbon emissions. Thus, the implicit carbon emissions generated by industrial agglomeration and the failure to green upgrade energy consumption are the main reasons for the inverted U-shaped relationship. (3) The carbon reduction effect of renewable energy technologies of conservation type takes effect first, and renewable energy technologies of production type do not reduce carbon emissions in non-eastern cities, which means that non-eastern cities are likely to become pollution havens. This study provides evidence for renewable energy technologies to achieve efficient carbon emission reduction and cross-regional technical cooperation.