Spatial impact of the digital economy on low-carbon logistics efficiency in RCEP countries.

A thorough study of the spatial impact of the digital economy on low-carbon logistics efficiency would be greatly significant for Regional Comprehensive Economic Partnership (RCEP) countries to improve low-carbon logistics efficiency and achieve sustainable cooperation. This study constructs a theoretical framework from the perspective of spatial effects on the impact of the digital economy on low-carbon logistics efficiency in RCEP countries. The entropy method was used to measure the level of digital economic development. The super-efficiency SBM model was used to measure low-carbon logistics efficiency. Spatial feature analysis was conducted using kernel density estimation and Moran's index, followed by empirical analysis using spatial econometric models to examine the spatial impact of the digital economy on low-carbon logistics efficiency in RCEP countries. The results indicate that in RCEP countries, both low-carbon logistics efficiency and the level of digital economic development exhibit significant spatial positive correlation. Furthermore, the digital economy can promote low-carbon logistics efficiency in economically neighboring countries through spatial spillover effects. The improvement of domestic low-carbon logistics efficiency can also promote low-carbon logistics efficiency in neighboring countries. This conclusion was supported by endogeneity tests and a convergence analysis. Additionally, the mechanism analysis revealed that improving the level of green energy can enhance the spatial spillover effects of the digital economy and promote low-carbon logistics efficiency. Finally, countermeasures and suggestions was proposed to improve the low-carbon logistics efficiency of RCEP countries through the digital economy.

Understanding the spread of infectious diseases in edge areas of hotspots: dengue epidemics in tropical metropolitan regions.

Identifying clusters or hotspots from disease maps is critical in research and practice. Hotspots have been shown to have a higher potential for transmission risk and may be the source of infections, making them a priority for controlling epidemics. However, the role of edge areas of hotspots in disease transmission remains unclear. This study aims to investigate the role of edge areas in disease transmission by examining whether disease incidence rate growth is higher in the edges of disease hotspots during outbreaks. Our data is based on the three most severe dengue epidemic years in Kaohsiung city, Taiwan, from 1998 to 2020. We employed conditional autoregressive (CAR) models and Bayesian areal Wombling methods to identify significant edge areas of hotspots based on the extent of risk difference between adjacent areas. The difference-in-difference (DID) estimator in spatial panel models measures the growth rate of risk by comparing the incidence rate between two groups (hotspots and edge areas) over two time periods. Our results show that in years characterized by exceptionally large-scale outbreaks, the edge areas of hotspots have a more significant increase in disease risk than hotspots, leading to a higher risk of disease transmission and potential disease foci. This finding explains the geographic diffusion mechanism of epidemics, a pattern mixed with expansion and relocation, indicating that the edge areas play an essential role. The study highlights the importance of considering edge areas of hotspots in disease transmission. Furthermore, it provides valuable insights for policymakers and health authorities in designing effective interventions to control large-scale disease outbreaks.

The effect of environmental regulation competition on haze pollution: evidence from China's province-level data.

Haze pollution has drawn lots of public concern due to its potential damages to human health. Strategic interaction of environmental regulation among local governments may lead to a race to the bottom and hinder air quality improvement. Still, current empirical evidence is scarce, especially from developing countries. Based on province-level panel data from 2004 to 2015, the paper employs a dynamic fixed effect spatial Durbin model to identify interactive patterns of environmental regulation and then investigate its environmental impact. Empirical results indicate that regional differences are observed in environmental regulation and haze pollution, and high-high and low-low clusters dominate the spatial pattern. Interactive patterns of economically similar provinces are dominated by strategic substitution, whereas provinces sharing common borders or belonging to the same region are dominated by strategic complementation. Further, both race to the bottom and race to the top effect are discovered in the asymmetric test. The reaction coefficient values are much more extensive when competitors implement laxer policies, indicating a more significant racing trend to the bottom. Overall, after controlling for the spillover effect and hysteresis effect of haze pollution, the strategic interaction of environmental regulation among provinces is not conducive to improve air quality. The consequence might be correlated with low environmental standards, weak regulation enforcement, and the "free-ride" motive in China. These findings will be of great significance for optimizing local government behavior and improving air quality.

Spatiotemporal distribution and determinants of gonorrhea infections in mainland China: a panel data analysis.

OBJECTIVES: Gonorrhea remains a major public health concern worldwide. This study aims to explore the spatiotemporal distribution and sociodemographic determinants of gonorrhea rates during 2004-2014 in mainland China. STUDY DESIGN: Space-time scan statistics and spatial panel regression model. METHODS: The gonorrhea infection data and sociodemographic data during 2004-2014 at the provincial level in mainland China were extracted from the China Public Health Science Data Center and China Statistical Yearbooks, respectively. The space-time scan statistics were used to identify the high-risk clusters of gonorrhea, and the spatial panel regression model was adopted to examine the sociodemographic determinants. RESULTS: One most likely and five secondary high-risk clusters of gonorrhea rates were identified, which were mainly located in southern and eastern China. The regions with higher GDP per capita, larger floating population, less access to healthcare, higher male-female ratio, and higher divorce rate were more likely to become high-risk areas of gonorrhea. CONCLUSIONS: Gonorrhea rates were distributed unevenly through space and time and affected by various sociodemographic variables. The space-time scan statistics and spatial panel regression are viable tools for identifying clusters and examining determinants of gonorrhea rates. The findings provide valuable implications for developing targeted prevention and control programs in public health practice.

The physical, economic and policy drivers of land conversion to forestry in Ireland.

Land use change is fundamentally a product of the interaction of physical land characteristics, economic considerations and agricultural and environmental policies. Researchers are increasingly combining physical and socio-economic spatial data to investigate the drivers of land-use change in relation to policy and economic developments. Focusing on Ireland, this study develops a panel data set of annual afforestation over 2811 small-area boundaries between 1993 and 2007 from vector and raster data sources. Soil type and other physical characteristics are combined with the net returns of converting agricultural land to forestry, based on the micro-simulation of individual farm incomes, to investigate land conversion. A spatial econometric approach is adopted to model the data and a range of physical, economic and policy factors are identified as having a significant effect on afforestation rates. In addition to the financial returns, the availability and quality of land and the implementation of environmental protection policies are identified as important factors in land conversion. The implications of these factors for the goal of forest expansion are discussed in relation to conflicting current and future land use policies.

Epidemiological characteristics of tuberculosis incidence and its macro-influence factors in Chinese mainland during 2014-2021.

BACKGROUND: Tuberculosis (TB) remains a pressing public health issue, posing a significant threat to individuals' well-being and lives. This study delves into the TB incidence in Chinese mainland during 2014-2021, aiming to gain deeper insights into their epidemiological characteristics and explore macro-level factors to enhance control and prevention. METHODS: TB incidence data in Chinese mainland from 2014 to 2021 were sourced from the National Notifiable Disease Reporting System (NNDRS). A two-stage distributed lag nonlinear model (DLNM) was constructed to evaluate the lag and non-linearity of daily average temperature (℃, Atemp), average relative humidity (%, ARH), average wind speed (m/s, AWS), sunshine duration (h, SD) and precipitation (mm, PRE) on the TB incidence. A spatial panel data model was used to assess the impact of demographic, medical and health resource, and economic factors on TB incidence. RESULTS: A total of 6,587,439 TB cases were reported in Chinese mainland during 2014-2021, with an average annual incidence rate of 59.17/100,000. The TB incidence decreased from 67.05/100,000 in 2014 to 46.40/100,000 in 2021, notably declining from 2018 to 2021 (APC = -8.87%, 95% CI: -11.97, -6.85%). TB incidence rates were higher among males, farmers, and individuals aged 65 years and older. Spatiotemporal analysis revealed a significant cluster in Xinjiang, Qinghai, and Xizang from March 2017 to June 2019 (RR = 3.94, P < 0.001). From 2014 to 2021, the proportion of etiologically confirmed cases increased from 31.31% to 56.98%, and the time interval from TB onset to diagnosis shortened from 26 days (IQR: 10-56 days) to 19 days (IQR: 7-44 days). Specific meteorological conditions, including low temperature (< 16.69℃), high relative humidity (> 71.73%), low sunshine duration (< 6.18 h) increased the risk of TB incidence, while extreme low wind speed (< 2.79 m/s) decreased the risk. The spatial Durbin model showed positive associations between TB incidence rates and sex ratio (ß = 1.98), number of beds in medical and health institutions per 10,000 population (ß = 0.90), and total health expenses (ß = 0.55). There were negative associations between TB incidence rates and population (ß = -1.14), population density (ß = -0.19), urbanization rate (ß = -0.62), number of medical and health institutions (ß = -0.23), and number of health technicians per 10,000 population (ß = -0.70). CONCLUSIONS: Significant progress has been made in TB control and prevention in China, but challenges persist among some populations and areas. Varied relationships were observed between TB incidence and factors from meteorological, demographic, medical and health resource, and economic aspects. These findings underscore the importance of ongoing efforts to strengthen TB control and implement digital/intelligent surveillance for early risk detection and comprehensive interventions.

Live and let live: understanding the temporal drivers and spillovers of life expectancy in Europe for public planning.

The European continent has one of the longest life expectancies in the world, but still faces a significant challenge to meet the health targets set by the Sustainable Development Goals of the United Nations for 2030. To improve the understanding of the rationale that guides health outcomes in Europe, this study assesses the direction and magnitude effects of the drivers that contribute to explain life expectancy at birth across 30 European countries for the period 2008-2018 at macro-level. For this purpose, an aggregated health production function is used allowing for spatial effects. The results indicate that an increase in the income level, health expenditure, trade openness, education attainment, or urbanisation might lead to an increase in life expectancy at birth, whereas calories intake or quantity of air pollutants have a negative impact on this health indicator. This implies that health policies should look beyond economic factors and focus also on social and environmental drivers. The results also indicate the existence of significant spillover effects, highlighting the need for coordinated European policies that account for the synergies between countries. Finally, a foresight analysis is conducted to obtain projections for 2030 under different socioeconomic pathways. Results reveal significant differences on longevity projections depending on the adoption, or not, of a more sustainable model of human development and provides valuable insight on the need for anticipatory planning measures to make longer life-spans compatible with the maintenance of the welfare state.

Can the Content of Social Networks Explain Epidemic Outbreaks?

People share and seek information online that reflects a variety of social phenomena, including concerns about health conditions. We analyze how the contents of social networks provide real-time information to monitor and anticipate policies aimed at controlling or mitigating public health outbreaks. In November 2020, we collected tweets on the COVID-19 pandemic with content ranging from safety measures, vaccination, health, to politics. We then tested different specifications of spatial econometrics models to relate the frequency of selected keywords with administrative data on COVID-19 cases and deaths. Our results highlight how mentions of selected keywords can significantly explain future COVID-19 cases and deaths in one locality. We discuss two main mechanisms potentially explaining the links we find between Twitter contents and COVID-19 diffusion: risk perception and health behavior.

The impact of digital finance on pollutants emission: evidence from chinese cities.

This paper investigates whether emerging digital finance can reduce environmental pollution in China based on data from 273 of China's prefecture-level cities spanning the period from 2010 to 2017. The dynamic spatial econometric models (DSDM) find a significant negative association between digital finance and pollutants emissions, and the impacts vary among regions and urban development stages. The impact mechanism test proves that digital finance reduces pollutants emissions through technological innovation, structural adjustment, and capital allocation effects. In addition, we explore the different dimensions of digital finance and find that the depth of use has a more practical effect on reducing emissions. Further analyses based on the threshold model show an inverted N-shaped nexus between digital finance and emissions. The threshold effect also exists in terms of the traditional financial level. Our study proves that emerging digital finance crucially affects its potential benefits to environment and provides an empirical basis for policy-makers to accelerate the digitalization of financial markets, particularly paying attention to its emission-reduction effects.

Uncovering the spatio-temporal impact of the COVID-19 pandemic on shared e-scooter usage: A spatial panel model.

This study examines the spatio-temporal effects of the COVID-19 pandemic on shared e-scooter usage by leveraging two years (2019 and 2020) of daily shared micromobility data from Austin, Texas. We employed a series of random effects spatial-autoregressive model with a spatially autocorrelated error (SAC) to examine the differences and similarities in determinants of e-scooter usage during regular and pandemic periods and to identify factors contributing to the changes in e-scooter use during the Pandemic. Model results provided strong evidence of spatial autocorrelation in the e-scooter trip data and found a spatial negative spillover effect in the 2020 model. The key findings are: i) while the daily e-scooter trips reduced, the average trip distance and the average trip duration increased during the Pandemic; ii) the central part of Austin city experienced a major decrease in e-scooter usage during the Pandemic compared to other parts of Austin; iii) areas with low median income and higher number of available e-scooter devices experienced a smaller decrease in daily total e-scooter trips, trip distance, and trip duration during the Pandemic while the opposite result was found in areas with higher public transportation services. The results of this study provide policymakers with a timely understanding of the changes in shared e-scooter usage during the Pandemic, which can help redesign and revive the shared micromobility market in the post-pandemic era.

Temporal and spatial evolution and influencing factors of urban ecological total factor productivity in the Yellow River basin under strong sustainable development.

A growing consensus worldwide has indicated the need to protect the ecological environment and achieve sustainable development. Ensuring ecological protection and high-quality development of the Yellow River basin have become China's major national strategy. We reviewed extant literature, summarised government reports and guidance documents on the Yellow River basin, and proposed introducing a strong sustainable development theory into the study of total factor productivity (TFP). The spatial-temporal evolution and influencing factors of urban ecological TFP in the Yellow River basin are of great practical significance. We proposed a new ecological TFP indicator: the modified input-oriented Luenberger productivity indicator (MIL). Using panel data from 78 cities in the Yellow River basin during 2003-2019, we measured the urban ecological TFP. We adopted the geographic information system tool and kernel density estimation to analyse the temporal and spatial evolution of the indicator, as well as its spatial effects and influencing factors, using the global Moran's I index and dynamic spatial Durbin model (SDM). Our results show that, during the sample period, our indicator increased in cities in the region with an average annual growth rate of 0.627%, driven by technological progress. The average annual growth rate in urban areas showed a decreasing distribution of 'downstream-midstream-upstream'. Fiscal decentralisation (FD), industrial structure (IND), financial development (FIN), urbanisation level (URB) and research and development (RD) investment improved growth rates in this and the adjacent regions through direct and indirect effects. However, environmental regulation (ER), opening level (OPEN) of cities and population density (POP) were obstacles to TFP growth.

Estimating the Distribution and Convergence of County-level Healthcare Resources Allocative Efficiency in China Based on DEA and Spatial Panel Model.

Although China's 2009 New Healthcare Reform aimed to correct the imbalance in the spatial allocation of healthcare resources with a focus on the county level, its impact on county-level allocative efficiency evolution and convergence remains unclear. This paper for the first time performs a spatial analysis to explore the distribution, evolution, and convergence of the allocative efficiency of healthcare resources with county-level data. This paper uses the sample data of 158 countries in Henan Province, China, to evaluate the evolution and convergence of the allocative efficiency of healthcare resources. Based on the estimated Data Envelopment Analysis (DEA) allocative efficiency, analysis of variance (ANOVA), and spatial descriptive analysis, we explore the county heterogeneity and efficiency evolution; a spatial panel model is then utilized to test the county-level convergence of the allocative efficiency of healthcare resources. Although the number of efficient counties has not increased, the number of inefficient individuals keeps decreasing, and the allocative efficiency of municipal districts is lower than that of nonmunicipal counties. There exists a positive spatial correlation of allocative efficiency in Henan Province, and significant and robust convergence results can be found at the county level after China's 2009 reform. This study reveals a diversified picture of China's county-level spatial evolution of allocative efficiency in healthcare resources, showing a more balanced spatial distribution of allocative efficiency since the triggering of China's 2009 reform. However, long-term investment incentives and a targeted allocation of healthcare resources are still needed to promote further efficiency convergence and increase the number of counties with efficiency.

Market Segmentation and Green Development Performance: Evidence from Chinese Cities.

This study is based on 2006-2019 panel data from 282 Chinese cities. Market segmentation and green development performance are empirically investigated to examine their non-linear relationship using static panel, dynamic panel, and dynamic spatial panel models. The results reveal the following: (1) Green development performance is found to have a high degree of temporal and spatial path dependence, exhibiting spatial linkage between cities. (2) Market segmentation stemming from local government protection has a clear inverted U-shaped structure in relationship with the green development performance. (3) Our analysis suggests that the upgrading of industrial structures significantly enhances green development, while factor price distortion inhibits it. The relationship between market segmentation and industrial structure upgrading is also an inverted U-shape. (4) The analysis further reveals that market segmentation has an inverted U-shaped correlation with the green development performance in western, central, and eastern cities. However, the different rates of development of industrial structures within the three regions result in varying degrees of market segmentation according to inflection point values. Moreover, aligned with the theoretical hypothesis of "resource curse," in resource-based cities (exclusively), market segmentation still affects the green development performance with a significant inverted U-shaped structure.

Convergence study of water pollution emission intensity in China: evidence from spatial effects.

One of the challenges that China currently faces is how to reduce the emissions of water pollution. However, the study of water pollution convergence has a certain policy significance for controlling the emissions of water pollution. This article firstly uses chemical oxygen demand (COD) and ammonia nitrogen (NH3-N) as indicators of water pollution. Due to the obvious spillover effect of water in space, this article adds a spatial effect to the convergence model. Based on panel data of 30 provinces and cities from 2006 to 2017, this article uses a dynamic spatial Dubin model to analyze the convergence of water pollution emission intensity to address the endogenous problem in the model. The empirical results of this paper show that there is absolute ß-convergence and conditional ß-convergence in the intensity of water pollution emissions. The spatial autocorrelation test shows that there is a positive spatial autocorrelation of water pollution emissions, which means that the pollution emissions in neighboring areas will affect the emissions in the local area. The industrial structure has a certain promoting effect on the emission of water pollution, which means that adjusting the industrial structure and alleviating the structure of the secondary industry is the trend of future development. Economic growth can curb the emissions of water pollution. The influences of urbanization and foreign investment on the emissions of the two pollutants are inconsistent, and policies can be formulated according to local conditions in the future.

Spatial Spillover Effects of Resource Misallocation on the Green Total Factor Productivity in Chinese Agriculture.

Continuous resource misallocation not only results in total factor productivity loss but also leads to ecological degradation. Therefore, in the process of changing from extensive growth to intensive growth, Chinese agriculture should pay attention to the problem of resource misallocation. There is currently a lack of relevant research, especially concerning the spatial spillover effects of resource misallocation at the city level. To fill this gap, we employ a spatial panel model for empirical testing on the basis of measuring agricultural green total factor productivity (GTFP) in 306 cities in China from 1996-2017. We found that there is positive spatial autocorrelation in Chinese agricultural GTFP, but it decreases year by year. Misallocation in land, labor, machinery and fertilizer all directly hinder the local GTFP. The eastern is mainly negatively affected by neighbor resource misallocation, while the central and western are mainly negatively affected by local resource misallocation. Finally, the indirect effect of neighbor resource misallocation on GTFP gradually shifts from inhibiting effect to a facilitating effect with increasing spatial distance. These findings have clear policy implications: Chinese government should strengthen agricultural green technology innovation and diffusion, strengthen environmental regulation and promote the free movement of labor between regions and sectors.

Agricultural chemicals and sustainable development: the agricultural environment Kuznets curve based on spatial panel model.

Excessive delivery of agricultural chemicals seriously threatens the ecology and environment of agricultural areas and restricts the sustainable development of agriculture. The analysis of agrochemical Environmental Kuznets Curve (EKC) adopting spatial econometric tools is limited. Therefore, this study adopted the spatial panel regression approach to analyze the agricultural chemicals EKC Three Gorges Reservoir Region (TGRR). The results show that (1) both EKC curves of chemical fertilizer and pesticide of the TGRR are inverted U-shaped, and there are 53.8% and 42.3% of the counties/districts did not meet the inflection point of the EKC as regards to chemical fertilizer and pesticide. (2) The EKC of agricultural chemicals of the TGRR are stable, and the variables such as cultivated area and the urban-rural income disparity have impact on the occurrence of the inflection point of EKC. (3) There is the spatial "imitation and convergence" of agricultural chemicals among the counties in the TGRR. The findings indicate that the ecological and environmental situations of agriculture in the TGRR need urgent attention. Countermeasures aiming to alleviate the contradiction between ecological and economic development should be put forward.

Ecological Livability Assessment of Urban Agglomerations in Guangdong-Hong Kong-Macao Greater Bay Area.

This paper proposes the "citizen-ecology-city" evaluation framework for urban ecological livability theoretically and studies the ecological livability of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) empirically. In addition, we analyze the factors of urban ecological livability in a spatial dynamic panel model. The results are as follows. (1) Ecological livability levels of Macao and Hong Kong are significantly higher than the nine cities in the PRD; (2) Shenzhen and Guangzhou lead the nine cities in the PRD, while Jiangmen and Zhaoqing perform poorly; (3) GBA cities can be divided into three categories: Macao, Hong Kong, Shenzhen, and Guangzhou in the first tier; Zhuhai, Foshan, and Dongguan in the second tier; Huizhou, Zhongshan, Jiangmen, and Zhaoqing in the third tier; and (4) The ecological livability of the GBA cities has a characteristic of spatial correlation. In terms of the international value, the three-dimensional evaluation framework can apply to other bay areas in the world.

Do double-edged swords cut both ways? Housing inequality and haze pollution in Chinese cities.

Housing inequality, where the wealthy hold much more housing than those 5 less so, is common worldwide, but how it correlates with haze pollution and hence air pollution has not been studied to date. Due to the market-oriented and finance-driven evolution of its housing system over the last two decades, housing inequality is particularly prominent in post-reform urban China while air pollution has become an increasingly serious problem too. This study explores the relationship between housing inequality and air pollution using 2002-2009 spatial panel data of 65 Chinese cities, to find that housing inequality contributes to exacerbating air pollution. It is also shown that an excessive demand for real estate development due to the uneven distribution of the housing stock is an important mechanism underlying the correlation between housing inequality and air pollution. In addition, such factors as economic development, city size, urbanization level, industrial structure, and capital stock all have different degrees of impact on the correlation between housing inequality and air pollution. The paper concludes by discussing the policy implications of this research and offering some policy recommendations.

Does environment-biased technological progress reduce CO2 emissions in APEC economies? Evidence from fossil and clean energy consumption.

Environment-biased technological progress plays a critical role in carbon reduction, while the association among environment-biased technological progress, energy consumption, and carbon emissions has not been paid enough attention. Working with a unique spatial panel dataset of APEC economies spanning the 2000-2017 period, we employed the nonspatial panel model and the spatial panel model to investigate the role of fossil energy (FE) and clean energy (CE) consumption in carbon dioxide (CO2) abatement through environment-biased technological progress (EBTP). We decomposed EBTP into both emission-reducing biased technological progress (ErBTP) and energy-saving biased technological progress (EsBTP). The results show that the direct effect of EBTP on CO2 emissions was significantly negative and that the direct effect of ErBTP was significantly larger than that of EsBTP. EBTP reduced CO2 emissions through CE consumption, whereas it increased CO2 emissions through FE consumption, that is, EBTP had a "backfire effect" on FE consumption. More into detail, ErBTP had a larger effect on CO2 emissions in developing economies, while EsBTP played a more important role in developed economies. Furthermore, the results of the robustness test were consistent with our findings. Finally, several policy options were suggested to reduce CO2 emissions in APEC economies.

Industrial structure, technological innovation, and total-factor energy efficiency in China.

The promotion of industrial restructuring and technological innovation is the most important and realistic way of improving energy efficiency. This thesis uses the modified Super-SBM method to measure China's total-factor energy efficiency and then uses the dynamic spatial panel model (DSPM) to verify the effect of industrial structure and technological innovation on total-factor energy efficiency. The study found that from 2003 to 2016, China's total-factor energy efficiency showed a fluctuating trend of "falling first and then rising." The inflection point appeared in 2012; total-factor energy efficiency in the Eastern region was significantly higher than the national average, while in the Central and Western regions, it was significantly lower. The results of the analysis show that both the service adjustment of the inter-industry structure and the productivity growth of the intra-industry structure significantly promote improvements in total-factor energy efficiency. However, due to the low conversion rate of scientific and technological achievements in China, the impact of technological innovation input on total-factor energy efficiency is not significant. This is in contradistinction to technological innovation output which does significantly improve total-factor energy efficiency. The above research conclusion is still robust and reliable after changing the measurement method and spatial weight matrix.