Uncovering the drivers of CO2 emissions in the United States: The hidden spillover effects.

This study explores the applicability of the Environmental Kuznets Curve (EKC) hypothesis in the United States (US) from 2006 to 2020, employing the Spatial Durbin Model (SDM) to analyze the cross-border effects of pollution among states. The results indicate that although economic growth initially decreases environmental degradation, it subsequently contributes to more significant environmental degradation, challenging the EKC hypothesis's validity at the US state level. Factors such as higher energy prices and reliance on fossil fuels are also identified as significant drivers of environmental deterioration, with varying impacts observed across states. Conversely, adopting renewable energy sources is crucial in mitigating pollution levels. The study underscores the importance of coordinated state-level efforts to harmonize economic growth with sustainable environmental practices. It highlights the complexities of policymaking in balancing economic development with environmental conservation and emphasizes the need for targeted interventions to address environmental challenges effectively. This research enhances our understanding of sustainable development pathways amidst diverse regional dynamics within the US by providing empirical evidence and policy insights.

Detrended seasonal relationships and impact of climatic factors combined with spatiotemporal effect on the prevalence of human brucellosis.

Human brucellosis (HB) is a seasonal and climate-affected infectious disease that is posing an increasing threat to public health and economy. However, most of the research on the seasonal relationships and impact of climatic factors on HB did not consider the secular trend and spatiotemporal effect related to the disease. We herein utilized long-term surveillance data on HB from 2008 to 2020 using sinusoidal models to explore detrended relationships between climatic factors and HB. In addition, we assessed the impact of such climatic factors on HB using a spatial panel data model combined with the spatiotemporal effect. HB peaked around mid-May. HB was significantly correlated with climatic factors with 1-5-month lag when the respective correlations reached the maximum across the different lag periods. Each 0.1 °C increase in temperature led to 0.5% decrease in the 5-month lag incidence of HB. We also observed a positive spatiotemporal effect on the disease. Our study provides a detailed and in-depth overview of seasonal relationships and impact of climatic factors on HB. In addition, it proposes a novel approach for exploring the seasonal relationships and quantifying the impacts of climatic factors on various infectious diseases.

Green infrastructure for coastal flood protection: The longitudinal impacts of green infrastructure patterns on flood damage.

The importance of implementing green infrastructure (GI) for flood protection is supported by multiple substantial cross-sectional analyses. Yet, limited longitudinal research has been conducted which addresses how to maintain and improve the configuration of GI in order to minimize the cost of losses resulting from flooding. Structural damage from devastating storm events has repeatedly imposed substantial financial burdens on local governments in coastal regions. This study longitudinally examines the impacts of changes in GI patterns on flood damage cost in coastal Texas areas. Major flood events in the 36 Texan coastal watershed counties along the Gulf of Mexico were monitored from 2000 to 2017. Along with non-spatially weighted panel data models, we developed an advanced statistical model controlling for spatially correlated errors in flood loss and predicting flood loss with a set of time-series socioeconomic and environmental control variables. The results of the spatial panel data model reveal that long-term maintenance of larger, more irregular, more dispersed, less fragmented, and less connected patterns of GI will help to reduce county-level flood damage costs per capita over time. Most importantly, protecting larger patches within a closer proximity was found to be of the utmost importance for retaining the flood regulation services provided by GI. These findings suggest that planners and natural resource managers should enhance supportive land use policies to preserve existing GI and strategically locate new implementations in order to achieve long-term flood protection.

Migration and Democratization in Brazil: The Case of Electoral Participation and Competition.

Deepening democratization in Brazil has coincided with sustained flows of domestic migration, which raises an important question of whether migration deepens or depresses democratic development in migrant-sending regions. Whereas earlier perspectives have viewed migration as a political "brain drain," we contend that out-migration can generate resources that promote democratic processes back home. We investigate the role of migration in two aspects of democratization: electoral participation and competition. The analyses are based on spatial panel data models of mayoral election results across all municipalities between 1996 and 2012. The results show that migration increases electoral participation and competition in migrant-sending localities in Brazil. This study also identifies the sociopolitical context that conditions the impact of migration: the effect is most often present in the context of rural-urban migration and is more pronounced in sending localities with less democratic political structures. Moreover, using spatial network models, we find evidence for the transmission of political remittances from migration destination municipalities to origin municipalities. The present study extends the research on the migration-development nexus to the political arena, thus demonstrating the value of integrating demographic processes into explanations of political change.

Convergence analysis of regional marginal abatement cost of carbon dioxide in China based on spatial panel data models.

China has announced to launch a national emission trading system (ETS). The heterogeneity of marginal abatement cost (MAC) is prerequisite for trading, and the knowledge about the evolutionary characteristics of MAC is particularly necessary. However, the ß convergence theory has been proved to be suitable yet rarely applied to the study of MAC of CO2. To fill this gap, this paper connects them creatively, and the convergence of MAC during 2001-2015 and the influential factors are explored by spatial panel data models. Results show that China's MAC converges during the study period whether the spatial effect is considered or not. When evaluating the convergence of MAC, the spatial effect should not be ignored, because it will improve the explanatory power of models and promote the convergence. The size of labor force, emission level, coal consumption, foreign direct investment, and industrial structure significantly affect the growth rate of MAC. Low-carbon policies could be formulated fully considering the factors and their spillover effects. Those findings are certainly significant in imposing carbon reduction targets and adopting policy instruments. In addition, a national ETS is more applicable to China's reality at this stage and suggested to introduce carbon tax in due course in the future.

Spatiotemporal clustering and meteorological factors affected scarlet fever incidence in mainland China from 2004 to 2017.

OBJECTIVE: To analyze the spatiotemporal dynamic distribution and detect the related meteorological factors of scarlet fever from an ecological perspective, which could provide scientific information for effective prevention and control of this disease. METHODS: The data on scarlet fever cases in mainland China were downloaded from the Data Center of the China Public Health Science, while monthly meteorological data were extracted from the official website of the National Bureau of Statistics. Global Moran's I, local Getis-Ord Gi⁎ hotspot statistics, and Kulldorff's retrospective space-time scan statistical analysis were used to detect the spatial and spatiotemporal clusters of scarlet fever across all settings. A spatial panel data model was conducted to estimate the impact of meteorological factors on scarlet fever incidence. RESULTS: Scarlet fever in China had obvious spatial, temporal, and spatiotemporal clustering, high-incidence spatial clusters were located mainly in the north and northeast of China. Nine spatiotemporal clusters were identified. A spatial lag fixed effects panel data model was the best fit for regression analysis. After adjusting for spatial individual effects and spatial autocorrelation (ρ = 0.5623), scarlet fever incidence was positively associated with a one-month lag of average temperature, precipitation, and total sunshine hours (all P-values < 0.05). Each 10 °C, 2 cm, and 10 h increase in temperature, precipitation, and sunshine hours, respectively, was associated with a 6.41% increment and 1.04% and 1.41% decrement in scarlet fever incidence, respectively. CONCLUSION: The incidence of scarlet fever in China showed an upward trend in recent years. It had obvious spatiotemporal clustering, with the high-risk areas mainly concentrated in the north and northeast of China. Areas with high temperature and with low precipitation and sunshine hours tended to have a higher scarlet fever incidence, and we should pay more attention to prevention and control in these places.

Identifying the driving factors of NO2 pollution of One Belt One Road countries: satellite observation technique and dynamic spatial panel data analysis.

To recover the global economy, China in 2013 called for a new global strategy, namely, "One Belt and One Road Initiative" (BRI), which aims at reinforcing regional economic cooperation, enhancing regional collaboration of economic policy, and realizing the goal of rapid economic development of member countries. Accelerating industrialization not only has been recognized as an effective way to stimulate economic development, but also lead to the serious issue of environmental pollution, which challenges the environmental sustainability. In this study, we focus on the industrializing region as a study area to investigate the driving factors of environmental pollution. Technically, we utilized satellite observation technique to obtain NO2 columns data to denote environmental pollution and then applied dynamic spatial panel data models to evaluate what affects NO2 pollution levels. The findings are the following. (1) NO2 pollution exhibits significant and positive spatial autocorrelation, indicating spatial spillovers of NO2 pollution. (2) Lebanon, Bangladesh, Kyrgyzstan, and India experienced the largest increase of NO2 pollution while NO2 pollution in Singapore, Hungary, Greece, and Ukraine was substantially reduced. (3) The results of the dynamic spatial panel data models show that both the time dynamics effects and the spatial spillover effects are found to be significant and positive. In other words, both effects should be considered. Population is the foremost contributor to increase NO2 pollution while urbanization is an effective way to reduce pollution. An EKC relationship between NO2 pollution and per capita income was verified. Besides, industrialization, foreign direct investment, and trade openness have positive impacts on NO2 pollution.

Does Environmental Innovation Improve Environmental Productivity?-An Empirical Study Based on the Spatial Panel Data Model of Chinese Urban Agglomerations.

Environmental productivity comprehensively measures economic growth and environmental quality. Environmental innovation is considered to be the key to solving economic and environmental problems. Therefore, discussing the impact of environmental innovation on environmental productivity will reveal its economic and environmental effects. This paper measures environmental productivity by value added per unit of pollution emissions (four types of emissions are used) using panel data of 10 Chinese urban agglomerations from 2003 to 2016 to analyze the spatial correlation of environmental productivity, and constructs a spatial panel data model to empirically test the impact of environmental innovation on environmental productivity. It was found that environmental productivity measured by value added per unit of carbon dioxide emissions (gross domestic product (GDP)/CO2) had a significant positive spatial spillover effect, and measured by value added per unit of sulfur dioxide emissions (GDP/SO2), smoke (dust) emissions (GDP/SDE), and industrial sewage emissions (GDP/IS) had a significant negative spatial spillover effect. Environmental innovation has a significant negative inhibitory effect on environmental productivity measured by GDP/SDE and GDP/IS, but no obvious effect measured by GDP/CO2 and GDP/SO2. Control variables such as economic development level, industrial agglomeration, foreign direct investment, and endowment structure factor also show significant differences in environmental productivity measured by value added per unit of pollution emissions. In addition, there are significant differences in direct effects of explanatory variables on environmental productivity of local regions and indirect effects on neighboring regions. These differences are also related to the types of pollution emissions. Therefore, policymakers should set different policies for different types of pollution and encourage different types of environmental innovation, so as to achieve reduced pollution emissions and improved environmental productivity.

West Africa's CO2 emissions: investigating the economic indicators, forecasting, and proposing pathways to reduce carbon emission levels.

This paper investigates the nexus between carbon emissions (CO2) and economic growth in West Africa based on the Environment Kuznets Curve (EKC) hypothesis by utilizing spatial panel data technique to check the possible effect of spatial dependence among countries in West Africa. Our empirical findings suggest the presence of spatial dependence of carbon emissions distribution in West Africa. By examining the existence of EKC embedded within the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) approach, we conclude an inverse N-trajectory of the relationship between carbon emissions and economic growth. Furthermore, to mitigate global carbon emissions, we utilize a recurrent neural network (RNN) bidirectional long short-term memory (BiLSTM) algorithm devoid of exogenous variables and assumptions to forecast carbon emissions from the year 2015 to the year 2030 based on the predictive accuracy of our formulated algorithm. Due to the upward trends in future emission levels, we propose emissions mitigation pathways for countries in West Africa to still hold carbon emissions-related global warming well below 1.5 and 2 °C. Such mitigation pathways proposed could help implement strategic policies to minimize carbon emissions to a considerable level. As a policy implication, drafting strict environmental regulations and utilizing renewable energy technologies will help mitigate carbon emissions for all West African countries.

Income inequality and CO2 emissions in belt and road initiative countries: the role of democracy.

This paper investigates the interaction effects of income inequality and democracy on CO2 emissions. The spatial panel model, which accounts for the spatial spillover effects across countries, is used. Using the panel data covering 41 Belt and Road initiative countries, the results indicate significant positive spatial spillovers effect to country-level CO2 emission activity. The Kuznets Curve hypothesis, which assumes that reverse U relation presents between income and CO2 emissions, is identified. Empirical results provide evidence that democracy levels promote the nonlinear nexus between income inequality and CO2 emissions. High levels of inequality, ceteris paribus, in conjunction with poor democratic institutions are likely to result in higher pollution. The findings are robust to various robustness tests.

Skew-normal Bayesian spatial heterogeneity panel data models.

This paper proposes a new regression model for the analysis of spatial panel data in the case of spatial heterogeneity and non-normality. In empirical economic research, the normality of error components is a routine assumption for the models with continuous responses. However, such an assumption may not be appropriate in many applications. This work relaxes the normality assumption by using a multivariate skew-normal distribution, which includes the normal distribution as a special case. The methodology is illustrated through a simulation study and application to insurance and gasoline demand data sets. In these analyses, a simple Bayesian framework that implements a Markov chain Monte Carlo algorithm is derived for parameter estimation and inference.

Spatiotemporal Dynamics of Ecological Total-Factor Energy Efficiency and Their Drivers in China at the Prefecture Level.

Improvement of ecological total-factor energy efficiency (ETFEE) is crucial for transformation of China's economic growth pattern, energy conservation and emissions abatement. Here we combined the epsilon-based measure (EBM) and the Global Malmquist-Luenberger (GML) productivity index to evaluate ETFEE and ecological total-factor energy productivity (ETFEP) and its decompositions for 283 prefecture-level cities in China between 2003 and 2013. A spatial econometric model is used to investigate factors influencing ETFEE and ETFEP. Results indicated that ETFEE, ETFEP and corresponding trends differ significantly depending on whether environmental constraints are considered. No convergence trend was found in ETFEE between prefecture-level cities. Technical progress plays the largest role in increasing ETFEP growth. Pure efficiency change and scale efficiency change, however, are the main hindering factors. Boosting cumulative technological progress, cumulative scale efficiency growth rate and cumulative pure efficiency growth rate are important means of increasing ETFEP. I also found that areas with high levels of economic development do not completely overlap with areas of high ETFEE. Surprisingly, the fiscal expenditure on scientific undertakings and technological spillover effects from foreign direct investment (FDI) have not substantially increased ETFEE. Whereas increased industrialization hinders the improvement of ETFEE. Furthermore, reducing per capita energy consumption help boost ETFEE. In addition, endowment advantages of factors of production have a positive overall effect on improving ETFEE. Lastly, important policy implications are inferred.

The influencing factors and spatial spillover effects of CO2 emissions from transportation in China.

CO2 emissions from transportation (TC) are one of the main causes of global climate change. China faces particularly severe pressures and challenges in transportation carbon reduction. Based on the panel data of 30 provinces in China from 2000 to 2015, this study explored the influencing factors and spatial spillover effects of TC by estimating spatial panel data models. It found that China's TC will continue to increase in the future, because the increase in per capita gross domestic product (GDP) is the primary driving force to accelerate the growth of TC, but an increasing proportion of tertiary industry (PTI) in the national economy will help reduce the growth in emissions. Moreover, urban road density (URD) and per capita highway mileage (PHM) are the other two major factors promoting the growth of TC. In contrast, urban population density (UPD) has a negative direct impact on per capita CO2 emissions from transportation (PTC) but also has a larger positive spatial spillover effect, which indicates that these three factors should be properly planned and controlled. Meanwhile, we should actively advocate the development of urban public transport because it plays an important role on reducing TC. The conclusions provide important inspiration and a scientific basis for formulating policies to effectively curb the growth of CO2 emissions in China.

Spatial transmission and meteorological determinants of tuberculosis incidence in Qinghai Province, China: a spatial clustering panel analysis.

BACKGROUND: Tuberculosis (TB) is the notifiable infectious disease with the second highest incidence in the Qinghai province, a province with poor primary health care infrastructure. Understanding the spatial distribution of TB and related environmental factors is necessary for developing effective strategies to control and further eliminate TB. METHODS: Our TB incidence data and meteorological data were extracted from the China Information System of Disease Control and Prevention and statistical yearbooks, respectively. We calculated the global and local Moran's I by using spatial autocorrelation analysis to detect the spatial clustering of TB incidence each year. A spatial panel data model was applied to examine the associations of meteorological factors with TB incidence after adjustment of spatial individual effects and spatial autocorrelation. RESULTS: The Local Moran's I method detected 11 counties with a significantly high-high spatial clustering (average annual incidence: 294/100 000) and 17 counties with a significantly low-low spatial clustering (average annual incidence: 68/100 000) of TB annual incidence within the examined five-year period; the global Moran's I values ranged from 0.40 to 0.58 (all P-values < 0.05). The TB incidence was positively associated with the temperature, precipitation, and wind speed (all P-values < 0.05), which were confirmed by the spatial panel data model. Each 10 °C, 2 cm, and 1 m/s increase in temperature, precipitation, and wind speed associated with 9 % and 3 % decrements and a 7 % increment in the TB incidence, respectively. CONCLUSIONS: High TB incidence areas were mainly concentrated in south-western Qinghai, while low TB incidence areas clustered in eastern and north-western Qinghai. Areas with low temperature and precipitation and with strong wind speeds tended to have higher TB incidences.

Different effects of meteorological factors on hand, foot and mouth disease in various climates: a spatial panel data model analysis.

BACKGROUND: Major outbreaks of hand, foot and mouth disease (HFMD) have been reported in China since 2008, posing a great threat to the health of children. Although many studies have examined the effect of meteorological variables on the incidence of HFMD, the results have been inconsistent. This study aimed to quantify the relationship between meteorological factors and HFMD occurrence in different climates of mainland China using spatial panel data models. METHODS: All statistical analyses were carried out according to different climate types. We firstly conducted a descriptive analysis to summarize the epidemic characteristics of HFMD from May 2008 to November 2012 and then detected the spatial autocorrelation of HFMD using a global autocorrelation statistic (Moran's I) in each month. Finally, the association between HFMD incidence and meteorological factors was explored by spatial panel data models. RESULTS: The 353 regions were divided into 4 groups according to climate (G1: subtropical monsoon climate; G2: temperate monsoon climate; G3: temperate continental climate; G4: plateau mountain climate). The Moran's I values were significant with high correlations in most months of group G1 and G2 and some months of group G3 and G4. This suggested the existence of a high spatial autocorrelation with HFMD. Spatial panel data models were more appropriate to describe the data than fixed effect models. The results showed that HFMD incidences were significantly associated with average atmospheric pressure (AAP), average temperature (AT), average vapor pressure (AVP), average relative humidity (ARH), monthly precipitation (MP), average wind speed (AWS), monthly total sunshine hours (MSH), mean temperature difference (MTD), rain day (RD) and average temperature distance (ATD), but the effect of meteorological factors might differ in various climate types. CONCLUSIONS: Spatial panel data models are useful and effective when longitudinal data are available and spatial autocorrelation exists. Our findings showed that meteorological factors were related to the occurrence of HFMD, which were also affected by climate type.

Detecting the association between meteorological factors and hand, foot, and mouth disease using spatial panel data models.

OBJECTIVES: The aim of this study was to quantify the relationship between meteorological factors and the occurrence of hand, foot, and mouth disease (HFMD) among children in Shandong Province, China, at a county level, using spatial panel data models. METHODS: Descriptive analysis was applied to describe the epidemic characteristics of HFMD from January 2008 to December 2012, and then a global autocorrelation statistic (Moran's I) was used to detect the spatial autocorrelation of HFMD in each year. Finally, spatial panel data models were performed to explore the association between the incidence of HFMD and meteorological factors. RESULTS: Moran's I at the county level were high, from 0.30 to 0.45 (p < 0.001), indicating the existence of a high spatial autocorrelation on HFMD. Spatial panel data models are more appropriate to describe the data. Results showed that the incidences of HFMD in Shandong Province, China were significantly associated with average temperature, relative humidity, vapor pressure, and wind speed. CONCLUSIONS: Spatial panel data models are useful when longitudinal data with multiple units are available and spatial autocorrelation exists. The association found between HFMD and meteorological factors makes a contribution towards advancing knowledge with respect to the causality of HFMD and has policy implications for HFMD prevention and control.