A comparison of brain retraction mechanisms using finite element analysis and the effects of regionally heterogeneous material properties

Finite element (FE) simulations of the brain undergoing neurosurgical procedures present us with the great opportunity to better investigate, understand, and optimize surgical techniques and equipment. FE models provide access to data such as the stress levels within the brain that would otherwise be inaccessible with the current medical technology. Brain retraction is often a dangerous but necessary part of neurosurgery, and current research focuses on minimizing trauma during the procedure. In this work, we present a simulation-based comparison of different types of retraction mechanisms. We focus on traditional spatulas and tubular retractors. Our results show that tubular retractors result in lower average predicted stresses, especially in the subcortical structures and corpus callosum. Additionally, we show that changing the location of retraction can greatly affect the predicted stress results. As the model predictions highly depend on the material model and parameters used for simulations, we also investigate the importance of using region-specific hyperelastic and viscoelastic material parameters when modelling a three-dimensional human brain during retraction. Our investigations demonstrate how FE simulations in neurosurgical techniques can provide insight to surgeons and medical device manufacturers. They emphasize how further work into this direction could greatly improve the management and prevention of injury during surgery. Additionally, we show the importance of modelling the human brain with region-dependent parameters in order to provide useful predictions for neurosurgical procedures.

Construction of China's food security evaluation index system and spatiotemporal evolution

Food security is a vital material foundation for a nation's development and has been a topic of significant concern on the international stage in recent years. With a population exceeding 1.4 billion, China is not only a major producer but also a substantial consumer of food. Ensuring food security in China is not only a top priority for its socio-economic development but also a driving force in maintaining the stability of the global food supply chain and reducing the number of hungry people worldwide. However, a lack of comprehensive research into the Chinese food security system remains. This study addresses this gap by constructing a comprehensive evaluation framework encompassing four dimensions: food supply, accessibility, production stability, and sustainability. Utilizing the Moran's Index and generating LISA (Local Indicators of Spatial Association) maps, we analyze the spatial correlations of food security. The Dagum Gini coefficient and kernel density estimation are applied to assess heterogeneity and spatial disparities. Furthermore, this research employs the Exponential Smoothing (ETS) model to forecast food security trends. The findings reveal that the overall composite food security score exhibited fluctuations, initially increasing and reaching its peak of 0.407 in 2003, followed by a subsequent sharp decline after 2019. Spatially, food security exhibits correlations, with the Huang-Huai-Hai Plain and Northeast regions consistently showing high-high clustering. In contrast, the Western and Southern regions exhibit low-low clustering at specific periods. The Dagum Gini coefficient indicates that overall food security disparities are relatively small. However, these disparities have gradually expanded in recent years, with inter-group differences becoming predominant after 2005. As indicated by the kernel density estimation, the dynamic distribution of food security initially widens and then narrows, suggesting a shift from dispersed to concentrated data distribution. This phenomenon is accompanied by polarization and convergence trends, particularly evident after 2015. According to the ETS model, the study forecasts a substantial risk of declining food security in China over the next decade, largely influenced by the ongoing pandemic. In conclusion, this research provides a comprehensive assessment of the changing status of food security in China. It offers early warnings through predictive analysis, addressing the existing research gaps in the field of food security.

Enhancing environmental sustainability in transferred farmlands through rural e-commerce: insights from China

The issue of farmers neglecting environmental concerns in transferred farmlands poses a serious challenge, contradicting the long-term ecological goals of establishing resource-efficient and environment-friendly agriculture. Amidst the pivotal trend of moderately scaled agricultural operations, rural e-commerce holds promise as a catalyst and driving force for enhancing long-term environmental governance of transferred lands. The effectiveness and mechanisms of this potential, however, remain to be empirically examined. This study gathers panel data on environmental positive and negative externalities from six provinces in China, spanning the period from 2013 to 2022, encompassing 6372 farmers. A quasi-natural experiment of farmers' e-commerce participation is designed using difference-in-differences methodology (DID), propensity score matching (PSM), and moderating models. The primary findings are as follows: E-commerce participation increases farmers' positive environmental inputs on transferred lands, such as water-saving irrigation, adoption of social services, and preservation of traditional varieties. Simultaneously, it decreases negative environmental inputs, such as the consumption of fertilizers, pesticides, and agricultural films. The environmental sustainability effects of e-commerce vary across the eastern, central, and western regions of China. E-commerce has a more pronounced impact on agricultural social services and chemical pollutants in the eastern and central regions, while its influence is more significant on water-saving irrigation and variety preservation in the western region. Land transfer forms and supply order contracts do not directly promote farmers' environmentally friendly cultivation practices. Instead, they catalyze the environmental effects of e-commerce through a significant positive interaction term. These conclusions hold after matching for e-commerce participation propensity, while passing sensitivity tests, parallel trend tests, and placebo tests. Consequently, rural e-commerce, without compromising farmers' income, enhances the proactiveness of farmers in environmental conservation, transforms agricultural management practices, and effectively reduces rural non-point source pollution. Policy recommendations include reducing institutional barriers to rural e-commerce participation at the national level, encouraging the establishment of region-specific agricultural environmental sustainability goals, and leveraging the rural e-commerce industry chain to establish a nationwide environmental credit database and incentive mechanism.

Household size and transport carbon emissions in China: Direct, heterogeneity and mediating effects

Shrinking household sizes presents a significant sustainable challenge by reducing the sharing of means of transportation and increasing individual resource consumption and carbon emissions. Research from the historical literature reveals that larger households generally exhibit lower per capita energy consumption and carbon emissions. However, it remains uncertain how widely these trends extend and their implications for carbon emissions within the expanding transportation industry. This paper employs inter-provincial data from China spanning 2003-2021 to investigate the effects, regional heterogeneity, and mechanisms by which household size influences carbon emissions from the transport sector. The findings show that the expansion of household size in China significantly reduces carbon emissions from transport by 0.2805 %. Households with 2 to 4 members are more effective in achieving transport carbon emission reductions, with an average reduction level of 0.1853 %. Moreover, in terms of geographic factors, reducing transport carbon emissions is more effective in low-density areas than in high-density areas. At the income and carbon emissions level, household size significantly reduces transport carbon emissions in high-income and low-emission regions, and to a lesser extent in low-income and high-emission regions. Additionally, the study revealed that transport consumption expenditure and energy consumption indirectly strengthen the effect of household size on reducing transport carbon emissions. Future sustainable development strategies should focus on regulating household size and promoting moderate household size to decrease personal resource consumption and transportation carbon emissions, and to achieve the objective of sustainable development.

Using POI and multisource satellite datasets for mainland China's population spatialization and spatiotemporal changes based on regional heterogeneity

Geospatial big data and remote sensing data are widely used in population spatialization studies. However, the relationship between them and population distribution has regional heterogeneity in different geographic contexts. It is necessary to improve data processing methods and spatialization models in areas with large geographical differences. We used land cover data to extract human activity, nighttime light and point-of-interest (POI) data to represent human activity intensity, and considered differences in geographical context to divide mainland China into northern, southern and western regions. We constructed random forest models to generate gridded population distribution datasets with a resolution of 500 m, and quantitatively evaluated the importance of auxiliary data in different geographical contexts. The street-level accuracy assessment showed that our population dataset is more accurate than WorldPop, with a higher R2 and smaller deviation. The improved datasets provided broad potential for exploring the spatial-temporal changes in grid-level population distribution in China from 2010 to 2020. The results indicated that the population density and settlement area have increased, and the overall pattern of population distribution has remained highly stable, but there are significant differences in population change patterns among cities with different urbanization processes. The importance of the ancillary data to the models varied significantly, with POI contributing the most to the southern region and the least to the western region. Moreover, POI had relatively less influence on model improvement in undeveloped areas. Our study could provide a reference for predicting social and economic spatialized data in different geographical context areas using POI and multisource satellite data.

Research on the impact and mechanism of digital capabilities and digital finance on household wealth in the context of aging

China has entered a period of synchronous development between digitalization and aging. Based on the data from the China Household Finance Survey (CHFS), the partial least squares structural equation model (PLS-SEM) and multi-group analysis were used to analyze the impact mechanism of digital capabilities and digital finance on the wealth of elderly households. The results indicate that digital capabilities and digital finance can improve the wealth level of households headed by the elderly through direct and indirect paths. The indirect effects of digital capabilities and digital finance on elderly household wealth are all exerted through the node of business and property income, and entrepreneurship/investment are mediating variables. Moreover, digital capabilities have a greater impact on the wealth of elderly households in the central and western China regions, while digital finance has a greater impact in the eastern China regions. In addition, there is no significant difference in the effect of digital capabilities on business and property income across regions, while digital finance has a larger effect in the eastern region. The above conclusions can provide theoretical and practical support for realizing active aging and common prosperity in different countries and regions.

Testing for stationary of housing prices in China: An examination using efficient unit root tests

Changes in housing prices affect all aspects of production and life, and have always been a hot spot of social concern. This paper uses the sequence panel selection method (SPSM) to study the time series properties of housing prices in 100 cities in China from June 2010 to December 2022. It is found that there are large differences in the stationary of housing prices in first/second/third-tier cities. Using the SPSM test method, it is found that housing prices in first-tier cities are all non-stationary series, the samples of second- and third-tier cities can be significantly divided into stable housing prices and non-stable housing prices. After further using the Fourier function to approximate the structural mutation of the data, more second-tier cities show stable housing prices, while less third-tier cities show stable housing prices. These findings provide an important decision-making basis for the government to implement regulatory policies according to local conditions based on the differential characteristics of changes in housing prices.

Influence mechanism of green finance on regional emission reduction

The implementation of green finance is crucial in achieving a reduction in regional emissions. As such, understanding how green finance affects regional emission reduction is essential. Using provincial panel data from 2008 to 2019, we employed the fixed effects model to examine the impact of green finance on regional emission reduction. The empirical results reveal the following: (1) Green finance has a negative effect on sulfur dioxide intensity, and the development of green finance can significantly reduce the emission of regional pollutants. (2) Among the different instruments of green finance, green credit and green investment exhibit more substantial emission reduction effects than green securities and green insurance. (3) The mechanism by which green finance affects regional emission reduction is mainly through the advanced industrial structure and green technology innovation. (4) The development of green finance shows geographical discrepancies: The eastern region of China is more effective in reducing emissions than the central and western regions. To fully maximize the role of green finance in emission reduction, this paper offers pertinent suggestions for strengthening the green financial system, improving the advanced industrial process, increasing investment in green energy technology, and formulating specific development tactics that consider the prominent characteristics of distinct regions.

Industrial agglomeration, FDI, and carbon emissions: new evidence from China's service industry

In the context of economic servitization and low carbonization, the problem of carbon emissions in the service industry is worthy of attention. An essential channel for restraining carbon emissions from the service industry is industrial agglomeration. Based on provincial panel data from 2004 to 2021 in China, this study empirically analyzes the influence of the service industry's agglomeration on its CO2 emissions. The findings indicate that agglomeration significantly reduces the industry's carbon emissions. Next, producer services agglomeration has a significant carbon-reduction effect, whereas non-producer services agglomeration does not. Moreover, service industry agglomeration helps to restrain carbon emissions from the service industry in East China. However, it does not significantly affect carbon emissions in Central or West China. Regarding the moderating effect, foreign direct investment can enhance service industry agglomeration's carbon-reduction effect. Based on the results, relevant policy implications are provided.

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.

Environmental decentralization and carbon emissions: evidence from China

A robust environmental management system holds great significance for the Chinese government in effectively managing the country's carbon emissions. This study delves into the spatial impact of environmental decentralization on the carbon emissions of 30 Chinese provinces spanning from 2000 to 2019. First, we found that the increase in environmental decentralization promotes carbon emissions, and this effect is primarily driven by the delegation of environmental monitoring authority and environmental supervision authority. Second, we analyzed the heterogeneity of the carbon emission effects of environmental decentralization across different regions and observed that the impact of environmental decentralization is more significant in the western region compared to the central and eastern regions. Furthermore, this study investigates how the industrial structure, government competition, and environmental regulation exert an influence on the carbon emission effects of environmental decentralization. This article presents empirical evidence from the perspective of environmental management systems that underscores the rapid escalation of carbon emissions. Additionally, it contributes to an enhanced comprehension of the economic ramifications linked to the process of environmental decentralization. At the same time, the conclusions of this article have significant practical implications for the rational design of levels of environmental decentralization, thereby accelerating the achievement of carbon neutrality.

Water-soluble organic aerosols over South Asia - Seasonal changes and source characteristics

Water-soluble organic carbon (WSOC) has been identified as a key component in atmospheric aerosols due to its ability to act as cloud condensation nuclei (CCN) owing to their highly hygroscopic nature. This paper discusses about the spatio-temporal variability in WSOC mass concentration, sources (primary and secondary contributions), the role of long-range air-mass transport in modulating their abundance, at distinct sectors over South Asia. We found from our observations that, photochemical ageing of primary organic aerosols that are derived from biomass emissions, significantly contribute to the total WSOC budget over South Asia. The wide range of water-soluble compounds released by biomass burning can contribute directly to the WSOC fraction or undergo further atmospheric processing, such as oxidation or ageing, leading to the formation of additional WSOC. WSOC/OC (organic carbon) ratio and the correlation between the WSOC and secondary organic carbon (SOC) are used for assessing the contribution from secondary sources. The three different ratios are used to delineate different source processes; OC/EC (elemental carbon) for source identification, WSOC/OC for long-range atmospheric transport (ageing) and WSOC/SOC to understand the primary and secondary contribution of WSOC. The present investigation revealed that, the primary OC that have undergone significant chemical processing as a result of long-range transport have a substantial influence on WSOC formation over South Asia, especially in Indo Gangetic Plain outflow regions such as southern peninsular and adjacent marine regions. Overall, oxidation and ageing of primary organic aerosols emitted from biomass burning was found to serve as an important source of WSOC over South Asia.

Impact of aging-related consumption trend on carbon emission efficiency in China: mediation effect model based on industrial structure adjustment

With the deepening of China's aging process, changes in the age structure of the population affect the industrial structure and consumption structure in different ways and have a knock-on effect on the whole economic system. Therefore, aging is one of the objective factors affecting future carbon emissions in China. This study analyzes the impact mechanism of aging-related consumption trend on carbon emission efficiency (CEE) based on panel data of 30 Chinese provinces from 2000 to 2019. The results show that the aging-related consumption trend is conducive to the improvement of regional CEE, and the mediation transmission mechanism of industrial structure adjustment is obvious, with a coefficient of 0.1496. The core industry closely linked to the demand for aging-related consumption is consumer services. The promotion of the consumption demand of the aging in the eastern region on the CEE and the transmission stimulation of the industrial structure adjustment are the most obvious. The mediation effect in the central and western regions is relatively weak, and the aging-related consumption demand has not formed a positive interaction with the aging industry. Therefore, improving the market construction of products and services for the aging is beneficial to achieve a virtuous cycle of aging-related consumption upgrading and carbon emission efficiency. This research can provide insights for China to promote industrial structure transformation within the aging trend and also help China meet its carbon neutrality target on schedule.

Health impact assessment for air pollution in the presence of regional variation in effect sizes: The implications of using different meta-analytic approaches

The estimated health effects of air pollution vary between studies, and this variation is caused by factors associated with the study location, hereafter termed regional heterogeneity. This heterogeneity raises a methodological question as to which studies should be used to estimate risks in a specific region in a health impact assessment. Should one use all studies across the world, or only those in the region of interest? The current study provides novel insight into this question in two ways. Firstly, it presents an up-to-date analysis examining the magnitude of continent-level regional heterogeneity in the short-term health effects of air pollution, using a database of studies collected by Orellano et al. (2020). Secondly, it provides in-depth simulation analyses examining whether existing meta-analyses are likely to be underpowered to identify statistically significant regional heterogeneity, as well as evaluating which meta-analytic technique is best for estimating region-specific estimates. The techniques considered include global and continent-specific (sub-group) random effects meta-analysis and meta-regression, with omnibus statistical tests used to quantify regional heterogeneity. We find statistically significant regional heterogeneity for 4 of the 8 pollutant-outcome pairs considered, comprising NO2, O3 and PM2.5 with all-cause mortality, and PM2.5 with cardiovascular mortality. From the simulation analysis statistically significant regional heterogeneity is more likely to be identified as the number of studies increases (between 3 and 30 in each region were considered), between region heterogeneity increases and within region heterogeneity decreases. Finally, while a sub-group analysis using Cochran's Q test has a higher median power (0.71) than a test based on the moderators' coefficients from meta-regression (0.59) to identify regional heterogeneity, it also has an inflated type-1 error leading to more false positives (median errors of 0.15 compared to 0.09).

Can urban land market reform mitigate industrial emissions? Environmental evidence from 257 prefecture-level cities in China

In the period of planned economy, China used low-cost land investment to drive economic development for a long time, which brought problems such as low efficient use of natural resources and environmental pollution. Under the dual pressure of economic development and environmental protection, Chinese government has begun to implement the urban land market reform, hoping to gradually change the extensive economic development model with high input and low output through the market mechanism. In order to investigate whether the urban land market reform can reduce industrial emissions and promote the transformation of socioeconomic, this paper constructs industrial land transactions and socioeconomic panel data of 257 prefecture-level cities in China from 2007 to 2020. The research results show that (1) The urban land market reform has reduced industrial emissions through changing the behavior of local governments in fiscal decentralization and the behavior of industrial enterprises. (2) The regional competition and cooperation brought by urban land market reform have regional heterogeneity in the emission reduction effect of industrial emissions. (3) The emission reduction effect of urban land market reform has a spatial spillover effect, which not only affects the local area, but also affects neighboring cities. We suggest that Chinese government needs to continue to carry out ULMR and increase scientific investment and technological innovation, promote the transformation of the industrial economy, and promote healthy competition among local governments. The findings not only provide valuable insights for the future reform of China's market economy, but also provide practical reference for social changes for other countries in the transition period of the world.

Spatial evolution and decomposition of energy-related CO2 emissions in China's mining industry: from the perspective of regional heterogeneity

To ensure China's energy security, the mining industry faces increasing emissions reduction and energy conservation pressures. This study combined index and production-theoretical decomposition analyses to decompose the energy-related CO2 emissions in mining industry (ERCEMI) influencing factors into seven major effects and adopted a gravity model to dynamically visualize the transfer path and gravity distribution from 2000 to 2015. As investment effects were introduced into the decomposition analysis, the results fully considered the regional heterogeneity and spatiotemporal dynamics. The main findings were as follows: (i) a typical heavy emissions trend along the Heihe-Tengchong line, with a concentration of large ERCEMI values; (ii) the gravity center of ERCEMI had shifted to the southwest, and the migration trends were divided into three stages; (iii) the ERCEMI had strong regional heterogeneity, with a diffusion trend from north to south and shrinking from east to west; (iv) the potential energy intensity and investment efficiency effects had significantly inhibited the ERCEMI, while the investment scale had boosted it. Implications for regional layouts, energy intensity reductions, and investment optimization are discussed. This research provides a comprehensive regional analysis for ERCEMI reductions and the sustainable development of the mining industry and provides a reference for local industrial development planning.

The impact of artificial intelligence on pollution emission intensity-evidence from China

Artificial intelligence (AI) is a crucial component of sustainable economic development and an indicator of the next wave of technological progress. This study examines the effects and mechanisms of AI on the intensity of pollution emissions, using China as an example. Theoretical analysis demonstrates that the scale expansion effect and the technological innovation effect of AI can reduce the intensity of pollution emissions. In the meantime, AI can have a positive structural influence on reducing the intensity of pollution emissions through the upgrading of industrial structures. Therefore, we use panel data for 30 Chinese provinces from 2006 to 2019 to test the effect of AI on pollution emission intensity using a fixed effects model, employ explanatory variable substitution, endogenous analysis, regression after tailing, and eliminate related policy interference for robustness analysis. The results indicate that AI can significantly decrease the intensity of pollution emissions, with a 6.63% reduction for every 10% increase in AI utilization. We use the mediating effect model to conclude that AI can reduce the intensity of pollution emissions via the rationalization of industrial structure and advanced industrial structure, with the rationalization of industrial structure being the main mechanism. The examination of heterogeneity revealed that the implementation of AI in technology-intensive industries is an effective method for reducing the intensity of pollution emissions and that the positive impact of AI on the intensity of pollution emissions is more pronounced in the western region.

Labor market distortion and air pollution: An empirical analysis based on spatial effect modeling

In China, along with the rapid development of economy, air pollution has become a hot issue of public concern, particularly in many cities. The distortion in the labor factor market can cause air pollution, but the underlying mechanism is not yet clear. To investigate this question, this article examines the effect of labor market distortion on air pollution focusing on SO₂ emissions based on data of China's 283 cities during 2003-2015. The main objectives are to examine the direct and spillover effects of labor market distortion on air pollution using panel fixed-effects models, including the spatial Durbin model and the mediated-effects model. Results show that labor market distortion directly aggravates air pollution in cities. Mechanism analysis suggests that labor market distortion incurs air pollution through mechanisms of suppressing technological progress, hindering the upgrading of industrial structure, and reducing the efficiency of energy use. Divided the cities by their locations into those in eastern, central, and western regions, we find that such unfavorable effects are more prominent in eastern and western regions of the country. These findings highlight the impetus of mitigating the distorted labor market to ameliorate air quality and promote sustainable development.

Effects of climate change and grazing intensity on grassland productivity-A case study of Inner Mongolia, China

In the last 30 years, grassland productivity has declined seriously due to climate variations and unreasonable human activities. Therefore, to analyze the impact of different factors on grassland productivity, we selected three grassland stations of the Typical Steppe from west to east and collected 38 years of data. The Pearson Correlation and Fixed Effect Model were used to analyze the impact of precipitation, temperature, and grazing intensity on grassland productivity. The empirical results show that precipitation positively and significantly affected grassland productivity. The effects of climate change are more significant than human activities, but the impact of temperature is greater than precipitation. The synergy between precipitation and temperature was greater than between precipitation and temperature separately. In addition, the effects of climate change and human activities on grassland productivity have evident regional heterogeneity. The variation trend gradually increases from west to east in factors that affect grassland productivity. Therefore, we suggest some implications for grassland risk management, such as utilizing some financial products for climate risk and focusing on the synergy index to design financial products, such as design weather derivatives. Lastly, we should strengthen the research on the relationship between climate change and grassland productivity to provide a scientific basis for revealing the intrinsic relationship between climate, human activities, and grassland productivity.

The impact of regional heterogeneity in whole-brain dynamics in the presence of oscillations

Large variability exists across brain regions in health and disease, considering their cellular and molecular composition, connectivity, and function. Large-scale whole-brain models comprising coupled brain regions provide insights into the underlying dynamics that shape complex patterns of spontaneous brain activity. In particular, biophysically grounded mean-field whole-brain models in the asynchronous regime were used to demonstrate the dynamical consequences of including regional variability. Nevertheless, the role of heterogeneities when brain dynamics are supported by synchronous oscillating state, which is a ubiquitous phenomenon in brain, remains poorly understood. Here, we implemented two models capable of presenting oscillatory behavior with different levels of abstraction: a phenomenological Stuart-Landau model and an exact mean-field model. The fit of these models informed by structural- to functional-weighted MRI signal (T1w/T2w) allowed us to explore the implication of the inclusion of heterogeneities for modeling resting-state fMRI recordings from healthy participants. We found that disease-specific regional functional heterogeneity imposed dynamical consequences within the oscillatory regime in fMRI recordings from neurodegeneration with specific impacts on brain atrophy/structure (Alzheimer's patients). Overall, we found that models with oscillations perform better when structural and functional regional heterogeneities are considered, showing that phenomenological and biophysical models behave similarly at the brink of the Hopf bifurcation.

Regional heterogeneity and driving factors of road runoff pollution from urban areas in China

Due to the multiple influences of natural and anthropogenic factors, stormwater runoff from urban roads generally presents heterogeneous pollution among cities. The identification of regional heterogeneity and related driving factors of road runoff pollution is of significance for the optimal management of road runoff pollution according to the local circumstances. In this study, the regional heterogeneity of urban road runoff pollution from fourteen representative cities in China is analyzed for four typical pollutants including total suspended solids (TSS), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP). The results show wide variations in TSS, COD, TN and TP pollution among cities, with the average event mean concentrations ranging from 77.0 to 1347.9, 31.4 to 488.1, 0.81 to 8.46, 0.139 to 1.930 mg/L, respectively. One-way ANOVA analyses demonstrate significant differences in road runoff pollution among cities. The TSS pollution is significantly heavier for northern and northwestern inland cities than that for eastern and southern cities. Pearson correlation analysis and Stepwise linear regression analysis are performed to identify and rank the influence of climate, population, economy, industry structure, traffic and environmental quality. Direct relationships of road runoff pollution are detected with PM_2.5, PM₁₀, secondary industry, tertiary industry, annual rainfall, and urban green coverage, among which PM₁₀ and urban green coverage are the most important and common factors exerting positive and negative influences on road runoff pollution, respectively. Based on the findings of this work, improvement of atmospheric particulate pollution and increase in urban greenness are recommended measures to manage the road runoff pollution. Furthermore, the traffic-related emissions accompanying the upgrading of industry structure should be effectively controlled to attenuate the TSS and COD pollution in road runoff.

Analysis of technological innovation on provincial green development levels of logistics industry in China

The transition from traditional logistics to green and low-carbon logistics is crucial and inevitable due to the pressure of climate change and sustainable development in China. Meanwhile, technological innovation is perceived as an important factor affecting the development of the logistics industry. To explore the impacts of technological innovation and other factors and to propose proper policies based on the results, this study utilizes a generalized estimating equations (GEE) regression model to analyze panel data of 30 provinces during 2001-2019. Firstly, the entropy weight method is applied to calculate the green logistics development level based on an index system considering green factors. Secondly, a GEE model which considers the correlation among different observations is used to investigate the impacts of crucial factors on the green logistics development level. Moreover, regional heterogeneity is also analyzed in this paper by comparing the regression results of the Eastern region, Central region, and Western region. Based on the above analysis, several conclusions are drawn: (1) In terms of the average green logistics development levels, the Eastern region ranks 1st, the Central region ranks 2nd, and the Western region ranks 3rd. (2) GEE regression model is proved effective in our sample. (3) For the full sample, technological innovation, trade openness, and logistics infrastructure positively affect the green logistics development level; while, government regulation and energy intensity negatively influence the green logistics development level. (4) Regional heterogeneity is confirmed in our sample. Related policy recommendations are proposed based on our regional regression results. Take the Eastern region as an example, the local governments in the Eastern region should upgrade the manufacturing industry, reduce government financial investment in the transportation sector, and enhance environmental control expenditure in the transportation sector.

How does mandatory energy conversion affect sustainable development: Perspectives of regional heterogeneity and efficiency decomposition

Clean energy conversion is a core approach and development trend to tackle climate change, while the severe drawbacks such as supply deficiency and cost increase restrict regional sustainable development. This paper employs a natural experiment of coal-to-gas conversion of the Chinese government to study the effect of such policy on regional sustainable development, as well as the underlying mechanism. Based on a city-level dataset from 2006 to 2019, this paper measure green total factor productivity (GTFP) using data envelopment analysis (DEA) combined with the Malmquist‒Luenberger productivity index. Then, this paper evaluates the impact of the CTG policy in pilot cities using the Difference-in-Difference (DID) with Propensity Score Matching (PSM) approach. This paper finds that the CTG policy increased the GTFP of the pilot cities by 2.25% (0.0229/1.02). A series of robustness tests confirmed the findings. Subsequent mechanism analysis shows that the CTG policy increases the GTFP of pilot cities mainly by increasing technical efficiency. In addition, the mechanism of the CTG policy's impact differs between central and noncentral cities. In particular, the CTG policy increases the technological innovation indicator (TC) of provincial capital cities by 2.35% while it increases the technical efficiency indicator (EC) of other cities by 1.89%, which proves the Porter effect in provincial capital cities. Finally, several implications are provided for policymakers to promote other types of renewable energy.

Industrial digitization and synergy between pollution and carbon emissions control: new empirical evidence from China

Industrial digital transformation is a key engine to help developing countries reduce pollution and carbon emissions. We used the composite system synergy model (CSSM) and modified entropy weight method to measure the degree of synergy between pollution and carbon emissions control (SPCEC) and the level of industrial digitization in each province and city based on the Chinese inter-provincial panel data from 2011 to 2020. We then used the two-way fixed effects and panel quantile regression models to test the heterogeneous influence of industrial digitization on the SPCEC. We found that: (1) industrial digitization had a positive contribution to the SPCEC. (2) Digitization of industry contributes more to the SPCEC level than the digitization of agriculture and services. (3) The promotion of SPCEC by industrial digitization is significant in the western region, but not in the eastern, central and northeastern regions. (4) In provinces and municipalities with lower level of SPCEC, the contribution of industrial digitization to the SPCEC is higher. This paper reveals the impact of industrial digitization on the SPCEC and can provide a policy reference for the realization of the SPCEC from the perspective of the integration of industry and digitization.

What are the determinants of wastewater discharge reduction in China? Decomposition analysis by LMDI

Wastewater discharge reduction (WDR) is a key breakthrough point for China's environmental protection. Based on China's 30 provincial data from 2011 to 2017, this paper applied the logarithmic mean Divisia index (LMDI) method to clarify the determinants of WDR at national, regional, and provincial levels. Except for wastewater discharge factor, economic development, and total population, four innovative factors, total water application intensity, water environment cost, water treatment industry development level, and drainage infrastructure investment scale were first proposed in this study. The results indicated that from 2011 to 2017, at the national level, total water application intensity and water treatment industry development level were dominant contributors to WDR, while other factors all inhibited WDR. At the regional level, the results of wastewater discharge factor, economic development, and water environment cost were similar to the national level. The drainage infrastructure investment scale had a positive effect on WDR in Northeast and South China while having a negative effect on other regions. And except for Northeast China, the water treatment industry development level promoted WRD, while the total population inhibited WDR. Finally, the determinants of WDR at the provincial level were investigated. On this basis, targeted corresponding policies were provided in this paper.

Does distorted allocation of capital factors inhibit green technology innovation in Chinese cities? An empirical analysis based on spatial effect

Distorted allocation of capital factors will lead to the loss of capital market-based soil as the background support for green technology innovation, which will not be able to climb up the value chain and eventually become an economic "colony." This study empirically investigates the relationship between distorted capital factor allocation and green technology innovation using data from 2005 to 2018 for prefecture-level cities in China. The empirical results show that the distortion of capital factor allocation not only has a significant inhibiting effect on green technology innovation in the city, but also hinders the development of green technology innovation in neighboring cities. Mechanism test analysis suggests that there is negative impact via generating mismatch, crowding out, and rent-seeking effects. Further research shows that the effect of distorted capital factor allocation on urban green technology innovation is more influential in the eastern and western regions. The conclusions of this study have important practical significance for optimizing the rational allocation of factor resources, promoting green technology innovation, and achieving high-quality economic growth.

A dynamic view of environmental regulation influence mechanism on manufacturing agglomeration-a case study of the Yangtze River Delta city cluster

Rapid development and agglomeration of manufacturing industries in China inevitably lead to serious environmental pollution problems. The government restrains development of manufacturing industries through environmental regulation policies, but this may affect manufacturing industries agglomeration. Therefore, studying the impact of environmental regulation (ER) on manufacturing agglomeration (MA) is a critical prerequisite for the sustainability of China's manufacturing industry. Based on the panel data of 41 cities in Yangtze River Delta (YRD) region of China from 2003 to 2017, this paper explores the threshold effect of ER on MA using a panel threshold model and analyzes the heterogeneity impact of ER on MA in different regions and analyzes the dynamic mechanism and contribution of each influencing factor on MA using a panel vector autoregressive model. The results show that (1) there is a double threshold effect of ER on MA, showing an inverted "U"-shaped effect of rising and then falling; there are threshold effects of ER on MA based on foreign direct investment (FDI) and economic development level (EDL). The effect of ER on MA has the threshold effect under different FDI and EDL level, in which the effect based on FDI shows an inverted "U" shape relationship of rising and then falling; the effect based on EDL shows a growing trend. (2) There are 29 cities in YRD region in the "weak regulation" or "strong regulation" stage, both of which are not conducive to MA. (3) ER has a facilitating effect on MA, but with the extension of forecast period, this facilitating effect turns into an inhibiting effect. MA is mainly influenced by itself and transportation conditions (TC). Our study can provide some insights for improving the ecological environment and promoting the development of manufacturing clusters in YRD region.

How does new-type urbanization affect air pollution? Empirical evidence based on spatial spillover effect and spatial Durbin model

To alleviate the ecological and environmental problems caused by rapid urban development, China has formulated and implemented the new-type urbanization strategy. However, there is insufficient empirical research on the specific relationship between new-type urbanization and air pollution. Therefore, based on the panel data of 30 provinces in China from 2005 to 2018, this paper constructs a comprehensive evaluation index system of new-type urbanization using the five dimensions of population, economy, space, society, and green. The spatial Durbin model and the spatial mediating model are used to discuss the spatial effect, transmission mechanism, and regional heterogeneity of new-type urbanization on air pollution. The results show that China's air pollution mainly presents a spatial pattern of high-high agglomeration and low-low agglomeration, and there are spatial fluctuations. The construction of new-type urbanization significantly reduces local air pollution, and the industrial structure optimization, technological innovation, and energy structure adjustment are considered as important transmission mechanisms. However, under the fiscal decentralization and political tournament system in China, the policy implementation deviation may weaken the emission reduction effect of new-type urbanization, which is not conducive to regional environmental governance. From the sub-regional level, the impact of new-type urbanization on air pollution has regional heterogeneity. A robustness test confirms the reliability of our research conclusions. This study also proposes some policy suggestions that the government can utilize in grasping the policy focus of new-type urbanization construction to discover effective ways of controlling air pollution.

Regional heterogeneity of malaria prevalence and associated risk factors among children under five in Togo: evidence from a national malaria indicators survey

BACKGROUND

Malaria remains a major cause of morbidity and death among children less than 5 years of age. In Togo, despite intensification of malaria control interventions, malaria remained highly prevalent, with significant heterogeneity from one region to another. The aim of this study is to explore further such regional differences in malaria prevalence and to determine associated risk factors.

METHODS

Data from a 2017 cross-sectional nationally representative malaria indicator survey was used. Children aged 6-59 months in selected households were tested for malaria using a rapid diagnostic test (RDT), confirmed by microscopy. Univariate and multivariate logistic regression analysis were performed using Generalized Linear Models.

RESULTS

A total of 2131 children aged 6-59 months (1983 in rural areas, 989 in urban areas) were enrolled. Overall 28% of children tested positive for malaria, ranging from 7.0% in the Lomé Commune region to 4% 7.1 in the Plateaux region. In multivariate analysis, statistically significant differences between regions persisted. Independent risk factors identified were higher children aged (aOR = 1.46, 95% CI [1.13-1.88]) for those above 24 months compared to those below; households wealth quintile (aOR = 0.22, 95% CI [0.11-0.41]) for those richest compared to those poorest quintiles; residence in rural areas (aOR = 2.02, 95% CI [1.32-3.13]).

CONCLUSION

Interventions that target use of combined prevention measures should prioritise on older children living in poorest households in rural areas, particularly in the regions of high malaria prevalence.

Haze management: is urban public transportation priority effective?

Public transportation is often considered as a green travel mode to alleviate the negative externalities such as traffic congestion and haze pollution generated from transport. However, is prioritizing urban public transportation actually conducive to haze emission reduction? In this study, considering special emphasis on the cumulative effect of haze, a dynamic panel model is constructed to analyze and quantify the impact of public transportation on haze pollution by using the data of 284 cities in China, and the heterogeneity of the impact in cities with different pollution levels is examined. Several interesting findings are derived from the empirical results. First, the development of urban public transportation can significantly alleviate urban haze pollution. Second, the haze reduction effect of public transportation in cities with different pollution levels is non-universal. Comparatively speaking, the haze reduction effect of public transportation in lightly polluted cities is more evident than that in heavily polluted cities. Therefore, in order to reduce haze pollution in a more effective manner, China should continue to promote urban public transportation priority strategy. Moreover, the government should also formulate differentiated traffic development strategies to effectively alleviate the urban traffic burdens.

Measurement of green total factor productivity on Chinese pig breeding: from the perspective of regional differences

China has a vast territory and abundant resources, and there are significant differences in the development of pig breeding in different regions. Chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) produced in the process of pig breeding will affect China's environmental quality. In view of this, based on the Minimum Distance to Weak efficient frontier model, this paper constructs Metafrontier-Malmquist-Luenberger (MML) index considering negative output under the common frontier to comprehensively evaluate the green total factor productivity of Chinese pig breeding (GTCP). This has guiding significance for improving China's pork production and reducing pollution emissions. The results manifest that (1) no matter under the common frontier or the group frontier, GTCP presents large temporal and spatial differentiation characteristics. Compared with the central region and the western region, the eastern region has obvious advantages in GTCP. (2) GTCP has shown an upward trend as a whole, which is mainly due to the technical progress. (3) Compared with small-scale and medium-sized GTCP, large-scale GTCP has apparent superiorities. Based on the above outcomes, this paper finally raises policy recommendations for improving GTCP: (1) give full play to the advantages of pig breeding in different regions, (2) increase the research and introduction of pig breeding clean technology and improve the application efficiency, and (3) give full play to the scale effect and vigorously develop large-scale pig breeding.

Information entropy and elasticity analysis of the land use structure change influencing eco-environmental quality in Qinghai-Tibet Plateau from 1990 to 2015

Regional land use change affects eco-environmental quality by altering ecosystem structure and function. The primitive ecosystem and environment of the Qinghai-Tibet Plateau (QTP) occupies a special position in the world, but it is very fragile. Although land use activities on the plateau have increased gradually in past decades, its effects on eco-environmental quality and the underlying mechanisms of regional heterogeneity remain unclear. In this study, an eco-environmental quality assessment index system was established to characterize the QTP, and the information entropy and elasticity methods were introduced to quantify the impact of land use dynamic trajectory on the eco-environmental quality. It provides a statistical measurement of system structure and more information than the traditional methods to reveal the land use change. The area change in land use on QTP was small from 1990 to 2015. The unused land and forest decreased, but those of grassland, water body, built-up land, and cultivated land increased. The overall eco-environmental quality on the QTP was low, and increased at a rate of 9.39% over the past 25 years, presenting a distribution of decreasing from southeast to northwest. The improvement in eco-environmental quality attributed to land use change was mainly due to the conversion of unused land into grassland, and ecological conservation projects also improved the local ecological environment. Conversely, the expansion of built-up land and land degradation contributed to decline in local eco-environmental quality in the Hengduan Mountains, northeastern plateau, and Qaidam Basin. The results indicated that under the influence of climate change, the changes in land use and eco-environmental quality were inconsistent in part regions, mainly including the central and southern Tibet and the border zone. Regions in which eco-environmental quality has been degraded by unreasonable land use are urgent to optimize land use management.

Analysis of the mechanism of the impact of internet development on green economic growth: evidence from 269 prefecture cities in China

As the digital economy develops rapidly and the network information technology advances, new development models represented by the network economy have emerged, which have a crucial impact on green economic growth. However, the relevant previous studies lacked the role of analyzing the direct and indirect effects of internet development on green economic growth at the prefecture-level city level. For this purpose, this paper aims to examine the intrinsic mechanism of the impact of internet development on green economic growth and provide empirical support for cities and regions in China to increase internet construction. Furthermore, the mixed model (EBM), which includes both radial and non-radial distance functions, is applied to calculate the green economic growth index. Fixed effect model and mediation effect model are also employed to test influence mechanisms of the internet development on green economic growth using panel data of 269 prefecture-level cities in China from 2004 to 2019. The statistical results reveal that internet development has contributed significantly to green economic growth. When the internet development level increases by 1 unit, the green economic growth level increases by an average of 5.0372 units. However, regional heterogeneity is evident between internet development and green economic growth, that is, the promoting effect of internet development on green economic growth is gradually enhanced from the eastern region to the western region. We also find that internet development guides industrial structure upgrading improves environmental quality and accelerates enterprise innovation, which indirectly contributes to green economic growth. And internet development mainly achieves green economic growth through enterprise innovation. Based on the above findings, we concluded that policymakers should not only strengthen the guiding role of social actors to promote the stable development of the internet industry, but also foster the construction of the three models of "internet+industry integration," "internet+environmental governance," and "internet+enterprise innovation" to promote green economic growth.

Does energy efficiency make sense in China? Based on the perspective of economic growth quality

Whether energy efficiency can significantly improve economic growth quality is an important perspective to understand the relationship between energy and economic growth. Based on the provincial data during 2000-2017 in China, this study uses the Shepherd energy distance function and entropy weight method to calculate the energy efficiency and economic growth quality of each province, and investigates the impact of energy efficiency on economic growth quality. The results indicate that energy efficiency does not significantly improve economic growth quality, but there is an obvious U-shaped relationship between energy efficiency and economic growth quality. The influence of energy efficiency has significant regional heterogeneity. Energy efficiency significantly increases economic growth quality in the eastern regions but significantly reduces economic growth quality in the central and western regions. Meanwhile, energy efficiency has a positive U-shaped relationship with economic growth quality in the eastern and central regions. Moreover, the upgrading of industrial structure plays an important role in the process of energy efficiency affecting economic growth quality. In response to the above conclusions, this paper puts forward the targeted policy implications to improve China's energy efficiency and promote high-quality economic development.

Impact of regional heterogeneity on the severity of COVID-19

The main objective of the study is to assess the impact of regional heterogeneity on the severity of COVID-19 in Japan. We included 27,865 cases registered between January 2020 and February 2021 in the COVID-19 Registry of Japan, to examine the relationship between the National Early Warning Score (NEWS) of COVID-19 patients on the day of admission and the prefecture where the patients live. A hierarchical Bayesian model was used to examine the random effect of each prefecture in addition to the patients' backgrounds. Additionally, we compared the results of two models; one model included the number of beds secured for COVID-19 patients in each prefecture as one of the fixed effects, and the other model did not. The results indicated that the prefecture had a substantial impact on the severity of COVID-19 on admission, even when considering the effect of the number of beds separately. Our analysis revealed a possible association between regional heterogeneity and increased/decreased risk of severe COVID-19 infection on admission. This heterogeneity was derived not only from the number of beds secured in each prefecture but also from other factors.

The effect of daily mean temperature on hand, foot and mouth disease and the source of regional heterogeneity in Chongqing, China, 2010-2019

BACKGROUND

Hand, foot and mouth disease (HFMD) is a serious infectious disease which has become a public health problem. A multi-regional study was conducted in this study to explore the relationship between temperature and HFMD in different regions and the source of heterogeneity, and further detect the effect modifiers such as socio-economic factors, medical and health factors and meteorological factors.

METHODS

The data on daily reported HFMD cases and meteorological data from 2010 to 2019 in Chongqing were collected. Thirty-eight districts and counties of Chongqing were divided into 6 regions. The distributed lag nonlinear model (DLNM) was applied to assess the effect of daily mean temperature on HFMD at region level with the pooled effect estimates from multivariate meta-regression model analysis. Stratified analyses by gender, age and children's type were also conducted. Potential modifiers were considered in meta regression to explore the source of heterogeneity.

RESULTS

There were nonlinear relationships with an inverted V-shape between temperature and HFMD. A maximum cumulative relative risk (CRR) of 1.22 (95% confidence interval (CI): 1.12-1.34) peaked at 23.8 °C, and the risk appeared immediately and lasted for the whole 14 days. Compared with other groups, warm temperature had a stronger effect on children aged 0-1 and scattered children, while cold temperature had a stronger effect on female, children aged 3-6 and childcare children with an M-shape. We found that socio-economic factors, medical health factors and meteorological factors were significantly associated with heterogeneity. Density of medical technical personnel, urbanization rate and density of health care institutions were the main modifiers for explaining heterogeneity of 26.10%, 24.90% and 24.86% respectively which were revealed by meta-analysis.

CONCLUSIONS

There was a significant nonlinear correlation between temperature and HFMD. Compared with other groups, children aged 0-1 and scattered children were more susceptible to warm temperature, while female, children aged 3-6 and childcare children were more susceptible to cold temperature. Socio-economic factors, medical health factors and meteorological factors may be the source of the heterogeneity. Therefore, local governments should consider different temperature-HFMD relationships between different regions and populations when formulating appropriate preventive measures.

Study on the impact of environmental pollution on farmland abandonment

Farmland abandonment, as a manifestation of the low efficiency of the rural economy, has a profound impact on the process of agricultural and rural modernization. This study uses the 2016 China Labor Force Dynamic Survey data based on 8116 samples from 104 cities, constructs the Tobit model and IV Tobit model to analyze the land abandonment quantitatively from the perspective of environmental pollution. The results show that (1) environmental pollution can significantly increase the probability and area of land abandonment, (2) there is significant regional heterogeneity in the impact of environmental pollution on land abandonment, (3) the impact of environmental pollution on land abandonment varies significantly with different family sizes and land management scales, but the land management scale is relatively more sensitive. This study provides a deeper understanding of the relationship between environmental pollution and land abandonment in China and provides a basis for formulating relevant policies to strengthen the treatment of environmental pollution to solve the dilemma of land abandonment, which is of great practical significance to sustainable development of rural economy and the guarantee of food security in China.

The impact of green credit policy on energy efficient utilization in China

Green credit policy is an innovative measure in the financial industry and can help enterprises reduce energy consumption, reverse the situation of environmental pollution and resource waste, and promote sustainable development of the economy and society based on guiding fund flows into the green environmental protection industry. This research thus uses panel data of 30 provinces and cities in China from 2000 to 2017 to examine the impact of a green credit policy on the level of energy efficient utilization. We establish the EBM (epsilon-based measure) super-efficiency model to measure the level of energy efficient utilization in China, apply the regression discontinuity design (RDD) model to empirically study the net effect of the green credit policy on the level of energy efficient utilization, and assess the policy's regional heterogeneity. Finds present that the level of energy efficient utilization in China exhibits a fluctuating upward trend, and there are certain spatial heterogeneities across its regions. The overall level of the eastern region's energy efficient utilization is the best, followed by the central and western regions in that order. The results of RDD based on the national perspective indicate that the green credit policy has a significantly positive effect on the level of energy efficient utilization, while subregional regression results reveal that improvement caused by implementation of the green credit policy varies across regions in China. The performance of improving energy efficiency in the eastern region is excellent, but the improvement effect in the central and western regions is so far not desirable.

Does renewable energy consumption contribute to the development of low-carbon economy? Evidence from China

Stimulating renewable energy consumption has become a major strategic choice for China to both fulfill the international commitment to reduce carbon emissions and realize the high-quality growth of the domestic economy. On account of the provincial data during the period of 2000 to 2017, we creatively incorporate the ecological footprint into the measurement of low-carbon economy development level through super-efficient SBM model, so as to infer the coordinated development level of 3E system more precisely. Based on the factor substitution effect, energy path dependence effect, and scale effect, the complex nonlinear relationship between the two core research objects is further probed by constructing the threshold regressive model. On the foundation of theoretical research, the consumption of renewable energy, the intensity of energy use, and the level of regional economic development are respectively selected as the moderating variables of the model. Further, we divide different intervals of threshold values to distinguish the differences in the effects caused by regional heterogeneity. The following conclusions are drawn ultimately: There is an apparent threshold effect between renewable consumption and the advancement of the low-carbon economy. Only when renewable itself reaches a higher level of consumption can it show a significant advantage in green economic development. In addition, to make full use of renewable resources to boost the low-carbon and green economy, it is necessary to reduce the economy's dependence on energy, that is, to decrease the intensity of energy use while maintaining the process of improving coordination of regional economy.

The role of agricultural green production technologies in improving low-carbon efficiency in China: Necessary but not effective

The effects of agricultural green production technologies (AGPTs) on agricultural productivity and the environment have received increasing attention. With the panel data of agricultural production of mainland China from 2000 to 2017, this study investigates the role of AGPTs adoption rates in improving low-carbon efficiency by adopting a random-effects panel Tobit model. Results indicate that average adoption rates of AGPTs are less than 20% and unbalanced adoptions vary between the main and non-main grain-producing areas, as well as the northern and southern main grain-producing areas. Furthermore, AGPTs adoption reduces low-carbon efficiency at nationwide and main grain-producing areas. In the northern main grain-producing areas, water-saving irrigation and no-tillage seeding reduce low-carbon efficiency, while mechanized returning straw crushing promotes it. In the southern main grain-producing areas, deep tillage with fertilizer application and no-tillage seeding decrease low-carbon efficiency, while mechanized deep ploughing and scarification increases it. We also find that AGPTs can promote low-carbon efficiency through comprehensive mechanization level and mechanical input density. To improve low-carbon efficiency, we suggest that the improvement and diffusion of applicable AGPTs should be adapted to the local production conditions, and the agricultural machinery service, research and development system should be improved as well.

Impact of eco-city construction on firm innovation in the case of China

More and more scholars have paid attention to the importance of coordinated development between ecological environmental protection and economy. Eco-city construction has been implemented in many countries in recent years; however, quantitative research on its economic impact has only started. This study establishes a theoretical model of the impact of eco-city construction on a firm's research and development (R&D) investment. The numerical simulation results show that eco-city construction promotes a firm's R&D investment and long-term earnings from two aspects: (1) macro policies increase a firm's exogenous uncertainty and (2) ecological capital enhances a firm's business conditions. The empirical study matches the microscopic data of 115 cities and 2612 listed firms in China from 2008 to 2017, and results show that eco-city construction has a significant positive impact on firm innovation input and output. Further research shows that this positive impact mainly comes from ecological environment and ecological economy, and there is regional heterogeneity. For the first time, this study affirms the positive role of eco-city construction from the perspective of firm innovation activities at the micro level. It provides strong evidence for the government to realise the sustainable development of firms by accelerating eco-city construction.

Gauging the impacts of urbanization on CO2 emissions from the construction industry: Evidence from China

The construction industry has aided rapid urbanization in China, significantly contributing to CO₂ emissions. However, few studies have investigated the impacts of urbanization on CO₂ emissions from the construction industry and the regional heterogeneity or considered the construction-related factors for urban construction scale to represent urbanization. To compensate for these limitations, this study aimed to explore the impacts of urbanization on CO₂ emissions from the construction industry. Herein, the urban construction scale was used to represent urbanization, along with population size, economic growth, and technology level. An augmented Stochastic Impacts by Regression on Population, Affluence, and Technology model was used to estimate the cross-province panel data from three regions in China during 2008-2017. The heterogeneity due to regional differences in urbanization levels was addressed by classifying China into three regions- urbanized, urbanizing, and under-urbanized. The findings suggest that population size, economic growth, construction of residential buildings, and technology level were the primary factors impacting CO₂ emissions, and the impact presented a declining trend from the urbanized to the urbanizing and under-urbanized regions. Specifically, an inverted U-shaped relationship existed between CO₂ emissions and urban economic growth, and the urbanized region indicated a higher inflection point than other regions. The urbanization ratio was negatively correlated with CO₂ emissions, while the energy intensity, per capita floor space of urban residential buildings, and per capita length of drainpipes were positively correlated with the CO₂ emissions in all three regions. Further, the technology level was conducive to CO₂ emissions reduction, however, it requires further improvement. The per capita area of paved roads exerted significantly negative effects in the urbanized region and insignificant in the urbanizing and under-urbanized regions. Overall, these results can help formulate policies to mitigate the construction industry's carbon emissions.

The dynamic relationship between regional corruption and carbon emissions in China

ABSTRACT

Does regional corruption exacerbate regional carbon emissions? To answer this, based on the spatial Durbin model, this study empirically examines the impact of regional corruption on carbon emission, using panel data from 30 provinces in China during the period 2002-2017. The results show that: (1) there is an indistinctive N-shaped relationship between regional corruption and carbon emissions at the national level. Regional corruption tends to initially aggravate carbon emissions, then contributes to emission reduction, and then finally boosts carbon emissions. However, this effect is not statistically significant. The results suggest that the role of regional corruption on carbon emissions is twofold. Corruption can exacerbate and can also inhibit regional carbon emissions. (2) Pronounced regional heterogeneity exists with regard to the influence of corruption on carbon emissions. Regional corruption and carbon emissions show a significant N-shaped dynamic relationship in China's central region, while the relationship is not significant in the eastern and western regions. (3) The impact of regional corruption on carbon emissions varies with time. For 2002-2009, regional corruption did not have a significant effect on carbon emissions. For 2010-2017, the direct effect became significant, and an apparent N-shaped relationship formed between regional corruption and carbon emissions. Based on the empirical results, this paper proposes several policy recommendations regarding corruption and carbon governance.

Can urban sprawl be the cause of environmental deterioration? Based on the provincial panel data in China

Based on the provincial panel data in China for 2001-2017, this article explores the impact of incoordination between land and population urbanization on environmental quality from the perspective of urban sprawl. It can be found that: (1) Most provinces are suffering from the urban sprawl, land finance has made urban sprawl more serious. (2) The results of SDM show that the impact of urban sprawl on the CO₂ emissions can be regarded as a N-shaped curve. Both excessively rapid expansion of urban space and excessively rapid growth of urban population can intensify the CO₂ emissions. (3) The tests of regional heterogeneity show that in underdeveloped provinces, the effect of urban sprawl on CO₂ emissions could also be a N-shaped curve, but it is an inversed U-shaped for the developed provinces. Thus, it can diminish the emissions of CO₂ in developed provinces, through strengthening environmental regulations and restraining the excessively rapid growth of the urban population. However, the underdeveloped provinces should actively promote economic development and create more jobs to avoid the loss of labors. These conclusions are also applicable to the tests of regional heterogeneity based on total factor productivity.

The impact of regional astrocyte interferon-γ signaling during chronic autoimmunity: a novel role for the immunoproteasome

Background

In early autoimmune neuroinflammation, interferon (IFN)γ and its upregulation of the immunoproteasome (iP) is pathologic. However, during chronic multiple sclerosis (MS), IFNγ has protective properties. Although dysregulation of the iP has been implicated in neurodegeneration, its function remains to be fully elucidated. Here, we demonstrate that IFNγ signaling in regional astrocytes induces the iP and promotes protection of the CNS during chronic autoimmunity.

Methods

In a multiple sclerosis (MS) brain, we evaluated mRNA expression and labeled postmortem MS brainstem and spinal cord for iP subunits and indicators of oxidative stress. Primary regional human astrocytes were analyzed for iP regulation and function by quantitative reverse transcription-polymerase chain reaction (qRT-PCR), Western blot, OxyBlot, and reactive oxygen species and caspase activity detection assays. Following immunization with myelin oligodendrocyte glycoprotein (MOG)_35-55, the role of IFNγ signaling and the iP during chronic experimental autoimmune encephalomyelitis (EAE) were assessed using pharmacologic inhibition of the iP and genetic interruption of IFNγ signaling specifically in astrocytes. Central nervous system (CNS) tissues were analyzed by immunohistochemistry (IHC) and immunofluorescence, and cell-specific colocalization was quantified.

Results

In MS tissue, iP expression was enhanced in the spinal cord compared to brainstem lesions, which correlated with a decrease in oxidative stress. In vitro, IFNγ stimulation enhanced iP expression, reduced reactive oxygen species burden, and decreased oxidatively damaged and poly-ubiquitinated protein accumulation preferentially in human spinal cord astrocytes, which was abrogated with the use of the iP inhibitor, ONX 0914. During the chronic phase of an MS animal model, EAE, ONX 0914 treatment exacerbated the disease and led to increased oxidative stress and poly-ubiquitinated protein buildup. Finally, mice with astrocyte-specific loss of the IFNγ receptor exhibited worsened chronic EAE associated with reduced iP expression, enhanced lesion size and oxidative stress, and poly-ubiquitinated protein accumulation in astrocytes.

Conclusions

Taken together, our data reveal a protective role for IFNγ in chronic neuroinflammation and identify a novel function of the iP in astrocytes during CNS autoimmunity.

Bisphosphonate treatment changes regional distribution of trabecular microstructure in human lumbar vertebrae

BACKGROUND

In osteoporosis patients, antiresorptive treatments such as alendronate reduce the resorption of trabecular bone and thus minimize vertebral fracture risk. However, fracture risk reduction efficacy of antiresorptive drugs varies between skeletal sites and is highest for vertebral bone. In human vertebrae, cancellous bone is distributed heterogeneously between regions. This microstructural heterogeneity is changing with patient age and is likely to play a major role in vertebral failure mechanisms and fracture susceptibility. Whether antiresorptive treatment affects the heterogeneity of vertebral microstructure in osteoporosis has not been unraveled.

METHODS

Our aim was to assess whether antiresorptive treatment would have a region-dependent influence on vertebral trabecular bone. Therefore, we used high-resolution peripheral quantitative computed tomography (HR-pQCT), microcomputed tomography (microCT) and uniaxial compression testing to determine the structure and mechanical properties of trabecular bone cores from anterior and posterior regions of 22 lumbar vertebrae from elderly osteoporotic women. We analyzed age-matched ex vivo bone samples from bisphosphonate-treated female osteoporosis patients (age: 82 ± 7y, bisphosphonate treatment period: 4 ± 2 years) along treatment-naïve female controls (82 ± 7y).

RESULTS

MicroCT analysis showed a significantly lower bone volume fraction (p = 0.006) and lower trabecular number (p = 0.003) for the anterior bone cores compared to posterior bone cores in the treatment-naïve group. The bisphosphonate-treated group had a more homogeneous bone volume distribution and did not show significant regional differences in bone volume, it however also displayed significantly different trabecular numbers (p = 0.016). In bone cores of the bisphosphonate-treated group, trabeculae were thicker in comparison to treatment-naïve controls (p = 0.011). Differences in bone volume further resulted in different maximum forces during compression testing between the samples. In addition, the percental difference between BV/TV_μCT in anterior and posterior bone cores was lower in bisphosphonate-treated vertebrae when vertebrae with directly adjacent fractures (n = 3) were excluded.

CONCLUSION

In conclusion, regional trabecular bone microstructure in lumbar vertebrae of bisphosphonate-treated women was more homogeneous compared to treatment-naïve controls. Bisphosphonate treatment, which specifically targets resorption surfaces common in anterior vertebral bone, might have resulted in a region-specific preservation of vertebral microstructure and loading capacity. This could have positive implications for the reduction of wedge fracture risk and add to the explanation of the higher efficacy of fracture risk reduction in vertebrae in comparison to other fracture regions.

Agricultural susceptibility to monsoon variability: A district level analysis of Maharashtra, India

Indian Summer Monsoon (ISM) variability has always been a feature affecting Indian agriculture. However, nature of this variability is varying in the backdrop of climate change, and as a consequence, agriculture is getting significantly affected and subsequently threatening food security. To address the climate variability, most of the studies conducted so far have focused on a coarse scale such as Central India or Western Ghats. However, finer scale analysis for identification and quantification of the link between agriculture vulnerability and climate variables has not yet been attempted in a comprehensive manner. The latter is essential, as considering state and national domain as a single entity for regional level policy formulations have led to under-representation of grievances and issue related to agriculture and farmers in the past. In order to address these issues, focus of the current study is on finer scale analysis of districts of Maharashtra state in Western India. Inter and intra-regional spatio-temporal heterogeneity in monsoon variability parameters was found across districts of Maharashtra. Here, we ranked the districts of Maharashtra using monsoon variability index which includes six different monsoon variability parameters (Wet/Dry Spells, frequency/intensity of extreme rainfall events, deviation from the long-period average and daily-scale variability) by using factor analysis. Monsoon variability index indicated that districts under Vidarbha and Marathwada regions are at highest risk and need immediate attention from decision-makers and scientists. This index was further linked to average yield and cropped area using Structural equation modeling that will help to determine the ideal cropping pattern for the most vulnerable districts. An empirical model of monsoon variability is also proposed at district level for the state of Maharashtra that can contribute to the currently operating 'State Action Plan on Climate Change (SAPCC)' or can be used to formulate a new action plan at district level i.e. 'District Action Plan on Climate Change (DAPCC)'. The current study differs from other studies in terms of its application, levels of spatial aggregation and areas of coverage. The findings can be utilized by farmers and policy makers while formulating agricultural policies, risk reduction measures, and adaptation mechanisms to address the adverse impacts of climate change.

Correlation of Wall Microstructure and Heterogeneous Distributions of Strain in Evolving Murine Abdominal Aortic Aneurysms

A primary deficiency in predicting the progression and rupture-risk of abdominal aortic aneurysms (AAAs) is an inability to assign patient-specific, heterogeneous biomechanical properties to the remodelling aortic wall. Toward this end, we investigated possible correlations between three quantities having the potential for non-invasive measurement (diameter, wall thickness, and strain) and local wall microstructure within evolving experimental AAAs. AAAs were initiated in male C57BL/6J mice via in situ adventitial application of elastase and allowed to progress for 1-4 weeks. Regional in vitro Green strain was assessed using custom panoramic digital image correlation and compared to local geometry and histology. Diameter correlated mildly with elastin grade and collagen, when considering all circumferential locations and remodeling times. Normalized wall thickness correlated strongly with normalized collagen area fraction, though with outliers in highly cellular regions. Circumferential Green strain correlated strongly with elastin grade when measured over the range of 20-140 mmHg, though the correlation weakened across a physiologic range of 80-120 mmHg. Axial strain correlated strongly between in vitro and physiologic ranges of pressures. Circumferential heterogeneities render diameter a poor predictor of underlying regional microstructure. Thickness may indicate collagen content, though corrections are needed in regions of increased cellularity. In vitro circumferential strain predicts local functional elastin over large ranges of pressure, but there is a need to extend this correlation to clinically relevant pressures. Axial strain in the aneurysmal shoulder region may reflect the elastic integrity within the apical region of the lesion and should be explored as an indicator of disease severity.