Unraveling birth weight determinants: Integrating machine learning, spatial analysis, and district-level mapping.

Despite a decrease in the prevalence of low birth weight (LBW) over time, its ongoing significance as a public health concern in Bangladesh remains evident. Low birth weight is believed to be a contributing factor to infant mortality, prolonged health complications, and vulnerability to non-communicable diseases. This study utilizes nationally representative data from the Multiple Indicator Cluster Surveys (MICS) conducted in 2012-2013 and 2019 to explore factors associated with birth weight. Modeling birth weight data considers interactions among factors, clustering in data, and spatial correlation. District-level maps are generated to identify high-risk areas for LBW. The average birth weight has shown a modest increase, rising from 2.93 kg in 2012-2013 to 2.96 kg in 2019. The study employs a regression tree, a popular machine learning algorithm, to discern essential interactions among potential determinants of birth weight. Findings from various models, including fixed effect, mixed effect, and spatial dependence models, highlight the significance of factors such as maternal age, household head's education, antenatal care, and few data-driven interactions influencing birth weight. District-specific maps reveal lower average birth weights in the southwestern region and selected northern districts, persisting across the two survey periods. Accounting for hierarchical structure and spatial autocorrelation improves model performance, particularly when fitting the most recent round of survey data. The study aims to inform policy formulation and targeted interventions at the district level by utilizing a machine learning technique and regression models to identify vulnerable groups of children requiring heightened attention.

Innovating from the ground up: the impact of key technological advancements on collaborative carbon and haze governance.

Strengthening the synergistic management of carbon and haze is an important means to realize China's "carbon peaking and carbon neutrality goals" and green development. In this paper, the entropy method is used to measure the key core technology innovation level of 30 provinces in China from 2011 to 2021, and the fixed-effect model is used to empirically test the impact of key core technology innovation on carbon haze synergistic governance and the internal mechanism. The study found that (1) key core technological innovation helps to promote carbon haze synergistic governance. (2) The mechanism test shows that key core technology innovation promotes the synergistic management of carbon haze by improving the clean energy structure. (3) The moderating effect shows that both market incentives and government environmental regulations will strengthen the positive relationship between key core technology innovation and carbon haze synergistic governance. The main contribution of this paper is to reveal the influence mechanism of key core technology innovation on carbon haze synergistic governance, and also to provide theoretical basis for the mechanism and law of carbon haze synergistic governance.

Outcome expectations and working alliance may be more important for patients from rural areas during the transition to college life: An exploratory within-patient analysis.

OBJECTIVE: Research has given limited attention to the distinction between patients from rural and urban areas, especially concerning the frequent overlap between rural living and low socio-economic status (SES). To shed more light on this, we explored the differential treatment processes between patients from rural and urban areas. METHOD: Seven hundred and fourteen patients recruited from a university counseling center in China filled out the questionnaires for Outcome Expectation (OE), Session Alliance Inventory (SAI) and Clinical Outcomes in Routine Evaluation-Outcome Measure (CORE-OM) each session. Data was analyzed using the disaggregated cross-lagged panel model and the asymmetric fixed-effect model. RESULTS: The findings indicated a reciprocal within-patient relation between OE and SAI for the whole sample. SAI mediated the effect of OE on next-session CORE-OM for patients from rural areas, with a significantly greater indirect effect than for patients from the urban areas. Asymmetric effects were found for OE among patients from urban areas, for whom drops in OE predicted worse next-session CORE-OM more strongly than improvements in OE predicted improved CORE-OM. CONCLUSION: This study provided preliminary evidence for differential OE-alliance-outcome predictions between patients with different SES and affirmed a reciprocal OE-alliance relation in a Chinese sample during the transition period of college.

Moving to an Activity-Friendly Community Can Increase Physical Activity.

BACKGROUND: Creating activity-friendly communities (AFCs) is an important strategy to increase physical activity (PA). While cross-sectional links between community environments and PA are well documented, their causal relationships remain insufficiently explored. METHODS: Using the accelerometer and survey data collected from adults who moved to an AFC (cases) and similar non-AFC-residing adults who did not move (comparisons), this pre-post, case-comparison study examines if moving to an AFC increases PA. Data came from 115 participants (cases = 37, comparisons = 78) from Austin, Texas, who completed 2 waves of 1-weeklong data collection. Difference-in-difference analyses and fixed-effect models were used to test the significance of the pre-post differences in moderate-to-vigorous PA (MVPA) between cases and comparisons, for the full sample and the subsample of 37 pairs matched in key covariates using the Propensity Score Matching method. RESULTS: Average treatment effect generated based on Propensity Score Matching and difference-in-difference showed that moving to this AFC led to an average of 10.88 additional minutes of daily MVPA (76.16 weekly minutes, P = .015). Fixed-effect models echoed the result with an increase of 10.39 minutes of daily MVPA after moving to the AFC. We also found that case participants who were less active at baseline and had higher income increased their MVPA more than their counterparts. CONCLUSIONS: This study showed that, among our study sample, moving to an AFC increased residents' PA significantly when compared to their premove level and the comparison group. This causal evidence suggests the potential of AFCs as sustainable interventions for PA promotion.

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.

Effect of air pollution on the prevalence of breast and cervical cancer in China: a panel data regression analysis.

The association between the prevalence of breast and cervical cancer in Chinese women and air pollution is obscure. The study aims to analyze the correlation between air pollution and the prevalence of breast and cervical cancer, and whether the gross domestic product (GDP) has a modifying effect on the impact of air pollution on the prevalence of breast and cervical cancer. Extracting panel data from 31 provinces and cities between 2006 and 2020, we evaluated the association between breast and cervical cancer prevalence and pollutant emissions from 2006 to 2015 with two-way fixed-effect models. We also analyzed the interaction between GDP and pollutant emissions and further check the robustness of the moderating effect results using group regression from 2016 to 2020. Cluster robust standard errors were used to correct for the heteroskedasticity and autocorrelation. The coefficients of models show that the coefficients of logarithmic soot and dust emissions are estimated to be significantly positive, and the coefficients of their square terms are significantly negative. The robust results suggest that the relationship between soot and dust emissions and breast or cervical cancer prevalence is non-linear, from 2006 to 2015. In the analysis of particulate matter (PM) data in 2016-2020, the PM-GDP interaction term was also significantly negative, indicating that GDP growth weakened the effect of PM on the prevalence of breast cancer and cervical cancer. In provinces with higher GDP, the indirect effect of PM emissions concerning breast cancer is -0.396 while in provinces with lower GDP, it is about -0.215. The corresponding coefficient concerning cervical cancer is about -0.209 in provinces with higher GDP but not significant in provinces with lower GDP. Our results suggest that there is an inverted U-shaped relationship between the prevalence of breast cancer and cervical cancer and air pollutants from 2006 to 2015. GDP growth has a significant negative moderating effect on the impact of air pollutants on the prevalence of breast cancer and cervical cancer. PM emissions have a higher effect on the prevalence of breast and cervical cancer in provinces with higher GDP and a lower impact in provinces with lower GDP.

The Impact of Education and Culture on Poverty Reduction: Evidence from Panel Data of European Countries.

The 2030 Agenda has among its key objectives the poverty eradication through increasing the level of education. A good level of education and investment in culture of a country is in fact necessary to guarantee a sustainable economy, in which coexists satisfactory levels of quality of life and an equitable distribution of income. There is a lack of studies in particular on the relations between some significant dimensions, such as education, culture and poverty, considering time lags for the measurement of impacts. Therefore, this study aims to fill this gap by focusing on the relationship between education, culture and poverty based on a panel of data from 34 European countries, over a 5-year period, 2015-2019. For this purpose, after applying principal component analysis to avoid multicollinearity problems, the authors applied three different approaches: pooled-ordinary least squares model, fixed effect model and random effect model. Fixed-effects estimator was selected as the optimal and most appropriate model. The results highlight that increasing education and culture levels in these countries reduce poverty. This opens space to new research paths and policy strategies that can start from this connection to implement concrete actions aimed at widening and improving educational and cultural offer.

Research on the Heterogeneity Threshold Effect of Foreign Direct Investment and Corporate Social Responsibility on Haze Pollution.

Carrying out environmental protection and governance in the process of using foreign capital to develop the economy is a realistic problem that China needs to solve urgently. In order to reduce environmental pollution, all enterprises are called upon by the local government to fulfil CSR and improve the quality of FDI use. However, previous studies have rarely explored the threshold effect of FDI and CSR on haze pollution. This paper employs the threshold effect model to explore the above problem based on panel data of 30 provinces in China from 2009 to 2018. The empirical study found the following: (1) FDI has a significantly positive double-threshold effect on haze pollution. Meanwhile, the promotion effect of FDI on haze pollution is the strongest in the two threshold ranges. (2) CSR has a significantly negative single-threshold effect on haze pollution; that is, the increase in CSR intensity inhibits haze pollution. Such a negative effect shows the characteristics of increasing marginal efficiency. (3) In addition, the provinces in different thresholds display obvious geographical distribution characteristics. Through the above analysis, it can be observed that FDI and CSR have distinct impacts on haze pollution. Thus, the country and the government can reduce haze pollution by improving the investment structure, using environmentally friendly technology, guiding enterprises to abide by business ethics and promoting social responsibilities fulfilment.

The driving factors of corporate carbon emissions: an application of the LASSO model with survey data.

Corporate carbon performance is a key driver of achieving corporate sustainability. The identification of factors that influence corporate carbon emissions is fundamental to promoting carbon performance. Based on the carbon disclosure project (CDP) database, we integrate the least absolute shrinkage and selection operator (LASSO) regression model and the fixed effects model to identify the determinants of carbon emissions. Furthermore, we rank determining factors according to their importance. We find that Capx enters the models under all carbon contexts. For Scope 1 and Scope 2, financial-level factors play a greater role. For Scope 3, corporate internal incentive policies and emission reduction behaviors are important. Different from absolute carbon emissions, for relative carbon emissions, the financial-level factors' debt-paying ability is a vital reference indicator for the impact of corporate carbon emissions.

Does the Digital Economy Promote the Reduction of Urban Carbon Emission Intensity?

The impact of the digital economy is increasing, and its environmental effect has attracted more and more attention. The digital economy promotes the improvement of production efficiency and the government's environmental governance capacity, and contributes to the reduction of urban carbon emission intensity. In order to study the impact of digital economy development on urban carbon emission intensity, this paper analyzes the theoretical basis of the digital economy on the reduction of carbon emission intensity, and then, based on the panel data of cities from 2011 to 2019, uses the two-way fixed effect model for empirical testing. The regression results show that the development of the digital economy has promoted the reduction of carbon emission intensity of cities, promoted the green transformation and upgrading of cities, and lays a foundation for China to achieve carbon peaking and carbon neutralization through the improvement of human capital investment and green innovation level. The basic conclusion is robust by changing core explanatory variables, changing samples, replacing regression methods, and shrinking and truncating tests. The impact of the digital economy on urban carbon emission intensity varies with the location, grade and size of the city. Specifically, the development of the digital economy in cities in the eastern and central regions, cities at or above the sub provincial level, large cities and non-resource-based cities has promoted the reduction of urban carbon emission intensity. In terms of resource-based cities, the development of the digital economy in renewable resource-based cities and resource-based cities dominated by iron ore and oil mining has promoted the decline in urban carbon emission reduction intensity.

Does green finance improve the industrial eco-efficiency in China?

Improving industrial eco-efficiency is of great significance for building a beautiful China and achieving its carbon peak and neutrality targets. Based on the panel data of 30 provinces in China from 2007 to 2018, this paper uses the super-efficiency SBM model to measure industrial eco-efficiency and empirically tests the influence of green finance on Chinese industrial eco-efficiency from the national and regional levels. The results show that the average level of industrial eco-efficiency in China is relatively stable during the study period with a large space for advancement. Second, there is spatial heterogeneity in Chinese industrial eco-efficiency, showing a gradually decreasing "southeast-northwest" ladder-like distribution. Third, the national-level regression results show that there is a significant "U-shaped" relationship between green financing and industrial eco-efficiency. In addition, the regression results at the regional level indicate that there is regional heterogeneity in the impact of green finance on industrial eco-efficiency. Finally, based on the research conclusions, specific suggestions on how green finance can improve industrial eco-efficiency in China are put forward, including vigorously developing green finance at the macro and micro levels, and exerting the positive effects of green finance in improving industrial eco-efficiency according to the area and the development level of green finance.

Collectivist culture, environmental regulation and pollution emissions: evidence from China.

Collectivist culture serves as a significant cultural foundation in China. It could, to some extent, shape public attitudes toward the environment and thus influence the implementation of related policies. To examine this hypothesis, this study constructs the collectivist culture intensity index for 25 Chinese provinces spanning from 2010 to 2020. Through a fixed-effect model, we explore how the collectivist culture intensity affects pollution emissions in China. The empirical results indicate the significance of collectivism in enhancing emission reduction through environmental regulations. This conclusion remains robust even when excluding the impact of endogeneity concerns by adopting the instrumental variable approach. Heterogeneity analysis shows that collectivism is more effective in enhancing market-based environmental regulations rather than those driven by policies. Further mechanism analysis confirms that green innovation is a crucial pathway through which collectivism influences pollution emissions. These findings here will offer guidance to policymakers when formulating environmental policies for contexts with different regional cultures.

Influencing factors of the thermal environment of urban green space.

Several heat records have been broken in recent years and decades. Extreme high temperature not only damages human health, but also increases the risk of wildfires. As a common urban infrastructure, urban green space has been proved to have a cooling effect. In this study, the physical indicators and temperature data of 36 green spaces in Xi'an were collected, and the influence of different physical indicators of green spaces on their thermal environment was explored through correlation analysis. The results suggest that the area of green space should be between 0.6-0.7 square kilometers or the perimeter should range from 4000 to 4500 m in order to obtain the lowest internal temperature. When the area of water body in the green space is between 0.3-0.4 square kilometers or the perimeter is about 5000 m, its internal temperature is the lowest. Indicators of green space in the conclusion can be directly understood and referred by urban planners and policy makers. Results of this study thus have implications for improving urban thermal comfort by controlling the physical indicators of green space.

Assessing the Impact of Air Pollution on Inbound Tourism along the Yangtze River across Space and Time.

The prevalent air pollution along the Yangtze River Economic Belt (YREB) possesses a significant threat to the natural environment, which further affects nearby tourism destination development. The paper seeks to assess the impact of air pollution on tourism in this region through a 2002-2012 panel data of 31 prefecture-level cities, along with geographic information system (GIS) and cluster analyses. The results reveal that air pollution is negatively associated with the number of inbound tourists along the YREB. In general, when air pollution intensifies by 1%, the number of inbound tourists decreases by 1.171%. This impact is more evident when air pollution is more severe, in the long term, and in areas that are larger, more central, and with more tourism resources. The paper contributes to the literature by addressing common limitations in previous studies and providing a more comprehensive evaluation of air pollution's impact on inbound tourism in the YREB. Practical implications regarding public policies and development directions based on air pollution periods, regions, and tourism resource allocations are provided.

Meteorological Factors Affecting Infectious Diarrhea in Different Climate Zones of China.

Meteorological factors and the increase in extreme weather events are closely related to the incidence rate of infectious diarrhea. However, few studies have explored whether the impact of the same meteorological factors on the incidence rate of infectious diarrhea in different climate regions has changed and quantified these changes. In this study, the time series fixed-effect Poisson regression model guided by climate was used to quantify the relationships between the incidence rate of various types of infectious diarrhea and meteorological factors in different climate regions of China from 2004 to 2018, with a lag of 0-2 months. In addition, six social factors, including per capita Gross Domestic Product (GDP), population density, number of doctors per 1000 people, proportion of urbanized population, proportion of children aged 0-14 years old, and proportion of elderly over 65 years old, were included in the model for confounding control. Additionally, the intercept of each province in each model was analyzed by a meta-analysis. Four climate regions were considered in this study: tropical monsoon areas, subtropical monsoon areas, temperate areas and alpine plateau areas. The results indicate that the influence of meteorological factors and extreme weather in different climate regions on diverse infectious diarrhea types is distinct. In general, temperature was positively correlated with all infectious diarrhea cases (0.2 ≤ r ≤ 0.6, p < 0.05). After extreme rainfall, the incidence rate of dysentery in alpine plateau area in one month would be reduced by 18.7% (95% confidence interval (CI): -27.8--9.6%). Two months after the period of extreme sunshine duration happened, the incidence of dysentery in the alpine plateau area would increase by 21.9% (95% CI: 15.4-28.4%) in that month, and the incidence rate of typhoid and paratyphoid in the temperate region would increase by 17.2% (95% CI: 15.5-18.9%) in that month. The meta-analysis showed that there is no consistency between different provinces in the same climate region. Our study indicated that meteorological factors and extreme weather in different climate areas had different effects on various types of infectious diarrhea, particularly extreme rainfall and extreme sunshine duration, which will help the government develop disease-specific and location-specific interventions, especially after the occurrence of extreme weather.

The Effect of Governance on Industrial Wastewater Pollution in China.

Water pollution not only aggravates the deterioration of the ecological environment and endanger human health, but also has a significantly negative impact on economic growth and social development. It is crucial to investigate the relationship between industrial wastewater governance and industrial wastewater pollution on the path to reduce water pollution. In this paper, we studied whether industrial wastewater governance affected industrial wastewater pollution using the panel fixed effect model and system generalized moment estimation model (SYS-GMM) with the panel data of 30 provinces from 2005 to 2020 in China. This is the only empirical analysis of the relationship between industrial wastewater governance and industrial wastewater pollution. We proxied industrial wastewater pollution by organic pollutants and inorganic pollutants and measured the per capita investment in industrial wastewater governance. The results shed light on the positive correlation between the per capita investment in industrial wastewater governance and industrial wastewater pollution. The increase in per capita investment in industrial wastewater governance promoted the increase of pollutant emissions from industrial wastewater. The estimation also indicated that there was an inverted U-shaped relationship between per capita GDP and inorganic /organic pollutants in industrial wastewater. Our empirical research shows that it is necessary to increase investment in industrial wastewater treatment and optimize the investment structure of environmental treatment, so as to pave the way for the comprehensive utilization of a variety of environmental treatment solutions.

Research on the interactive response relationship between thermal power carbon emission and industrial structure in Western China.

The thermal power industry takes the important social responsibility of energy conservation and environmental protection. The carbon emissions made by the thermal power industry are affected by the industrial structure. In this paper, the carbon emission of thermal power generation is divided into three links: energy combustion, desulfurization process, and power transportation. The total carbon emission of thermal power in 11 provinces in western China from 2000 to 2017 is calculated. Combined with industrial reform, this paper constructs a panel data fixed effect model to systematically analyze the interactive response relationship between thermal power carbon emission and industrial structure in the western region. The research shows that due to the continuous expansion of hydropower, wind power, and other new energy power generation scale and the improvement of energy efficiency in the western region, the growth trend of thermal power carbon emission has slowed down since 2010. The industrial development pattern is the main driving force of regional economic development, and the secondary industry in the western region is the main driving factor of thermal power carbon emission. High quality economic development in the western region can be promoted through technological upgrading, new energy development, and industrial multi-mode operation.

Healthcare Utilization Under a Comprehensive Public Welfare Program: Evidence From Japan.

The public assistance system in Japan provides detailed and comprehensive livelihood support for low-income families with various needs. As one example, and the beneficiaries of the public welfare program in Japan can receive the same medical treatments as those insured of the universal public health insurance without any financial burdens. This system has greatly contributed to maintaining and improving the health of public assistance beneficiaries but may cause excessive healthcare utilization: moral hazard. This study uses a large sample taken from two nationally representative claim data for public assistance and public health insurance patients to estimate the magnitude of moral hazard effect in basic outpatient utilization. The results of the fixed-effect regression analysis utilizing the concept of pseudo panel data analysis and those of propensity score matching show that the average treatment effect of public assistance assignment on healthcare utilization is significantly positive. Specifically, public assistance assignment increases monthly healthcare expenditure by 17.5 to 22.9 percent and the monthly number of doctor visits by 23.1 to 27.8 percent, respectively. In addition, the average treatment effects on the treated are also significantly positive, suggesting that monthly healthcare expenditure significantly decreases by 22.7 to 25.0 percent and the number of visits by 27.6 to 29.7 percent, respectively, when imposing a copayment on public assistance beneficiaries. However, the estimated price elasticity based on these results is very small, approximately -0.02, indicating that the level of copayment rate has little effect on the intensive margin of outpatient healthcare utilization.

Has China's New Round of Collective Forest Reforms Reduced Forest Fragmentation? A Case Study of the Beijing-Tianjin-Hebei Region.

The new wave of reform of collective forestland tenure (NRCFT) in China is considered an important policy for achieving sustainable management of forest resources. The purpose of this study is to investigate the influence of NRCFT on forest fragmentation in the Beijing-Tianjin-Hebei region of China based on a fixed-effects model. The forest fragmentation was analyzed based on the remote sensing images of Landsat and landscape pattern indices in the Beijing-Tianjin-Hebei region from 2000 to 2018. The results showed that (1) The NRCFT has significantly contributed to reducing forest fragmentation. (2) The effect of economic growth on forest fragmentation showed an inverted U-shape. (3) The implementation of the Grain for Green Program (GGP) and the transformation of rural energy consumption significantly reduce the degree of forest fragmentation. This study has crucial implications for formulating policies, achieving good forest governance, and reducing forest fragmentation.

Testing environmental Kuznets curve hypothesis: considering the impact of Sino-US trade on three main industries in China.

To achieve carbon neutrality and carbon reduction goals, China needs to consider industrial structure and trade. This study aims to test the validity of environmental Kuznets curve (EKC) hypothesis at the industry level in China and study the different impact of Sino-US trade in intermediate goods and trade in final goods on China's environment. To do so, we used the annual data of China's 25 sectors in 1990-2015 and classified 25 sectors into three main industries. Based on the Stochastic Impacts by Regression on Population, Affluence and Technology framework, we investigated the validity of EKC hypothesis and the driving factors of greenhouse gas (GHG) emissions. The results show that (1) EKC hypothesis is verified for the country and the tertiary industry. (2) Compared with the primary industry and the secondary industry, the economic growth of the tertiary industry brings less GHG emissions. (3) Intermediate goods exported to all sectors in the USA will increase GHG emissions in the country and the three main industries, but final goods exported to consumers in the USA will reduce GHG emissions except the tertiary industry. From our results, the EKC hypothesis is a suitable model for environmental policy in tertiary industry, but it does not apply to the primary industry and the secondary industry. Environmental policy in the primary industry and the secondary industry needs to focus on reducing energy intensity. In the case of exports to the USA, intermediate goods pollute the environment, while final improve the environment. Thus, China needs to increase controls on exports of intermediate goods with low added value and high pollution and gradually shift to the production of environmentally friendly final goods.