Determinants of fishing grounds footprint: Evidence from dynamic spatial Durbin model

Despite a growing literature on fishing grounds footprint, there is no study analyzing fishing footprint regarding spatial effects between neighboring countries. Thus, we explored whether the fishing grounds footprint of 156 countries is spatially correlated. For this purpose, we applied the dynamic spatial Durbin model to examine the direct and indirect effects of GDP per capita, biological capacity, trade openness, population, and urbanization on fishing grounds footprint in the short-term and the long-term during 2001-2021. The results revealed that: (1) there exists a positive and significant spatial dependence in fishing grounds footprint between countries; (2) inverted U-shaped environmental Kuznets curve hypothesis is valid in the short-term and the long-term; (3) fishing grounds footprint is negatively influenced by biocapacity and urbanization in neighboring countries, while population directly increases the fishing footprint. Finally, some suggestions were put forward to reduce fishing grounds footprint and to achieve a sustainable fisheries environment.

The spatial spillover effects of clean energy consumption and production on sustainable economic development in China

The massive consumption of fossil energy has resulted in high CO2 emissions, posing a formidable challenge to global sustainable economic development (SED). As countries endeavor to shift from fossil to clean energy sources to achieve SED, research on the impact of clean energy is scarce, and quantitative analysis is lacking. This study measured China's SED and used a spatial econometric model to examine the impact of clean energy consumption and production on SED across 30 provinces in China from 2008 to 2020. Results show that (1) China's SED exhibits significant positive spatial autocorrelation characteristics, forming a "point-to-area" development pattern. (2) Clean energy consumption, production, and consumption structure all contribute to the promotion of SED in the region and have positive spatial spillover effects. (3) A considerable regional disparity exists in the spatial impact of clean energy on SED. The eastern and central regions have significant positive spatial spillover effects, whereas the western region is opposite. Notably, the estimated coefficient of the spatial Durbin model is relatively small, reflecting China's ongoing transition to clean energy and its limited role in promoting economic sustainability. Joint efforts and differentiated policies are essential to develop clean energy and sustainable economic.

Advancing nuanced pollution control: Local improvements and spatial spillovers of policies on key enterprises

This paper examines the impact of air pollution control policies targeting key polluting enterprises, highlighting a strategic shift towards precision pollution control that concentrates on high-emission, high-risk businesses. The paper explores the efficacy of these policies and their potential spatial spillover effects, utilizing panel data from 259 Chinese cities from 2013 to 2021. Employing the difference-in-differences (DID) model and spatial Durbin model, the study analyzes both the direct local effects and the broader spatial consequences of these regulatory measures on air quality. The findings indicate a significant reduction in air pollutant concentrations in urban areas, attributing this improvement to factors such as industrial restructuring, increased investment in science and technology, and economic growth. Spatial econometric analysis further reveals a substantial positive correlation in air quality among Chinese cities. However, estimates of the spillover effect indicate that while such policies successfully reduce pollution locally, they could unintentionally degrade air quality in adjacent areas. The study highlights the need for nuanced policy strategies to mitigate unintended spatial spillovers and enhance overall effectiveness. It recommends tailored policies that integrate environmental and socioeconomic objectives, national and regional coordination for consistent enforcement, technology-driven compliance strategies, and incentives for sustainable enterprise practices.

The power of attention: Government climate-risk attention and agricultural-land carbon emissions

Climate change is a common challenge faced by all humanity. Promoting emission and carbon reduction in agricultural land is the most important priority for addressing climate change and realizing sustainable development. Based on data from 296 prefecture-level cities in China from 2011 to 2021, this study utilizes machine-learning and text-analysis methods to construct an indicator of government climate-risk attention (GCRA). It combines a two-way fixed-effects model to investigate how GCRA affects agricultural-land carbon emissions (ALCE) and carbon intensity (ALCI) and the mechanism of the impact. The results indicate that (1) GCRA substantially reduces ALCE and ALCI, and the conclusions are robust to a battery of tests. Furthermore, (2) mechanism analysis reveals that GCRA primarily uses three mechanisms-strengthening environmental regulation, promoting agricultural green-technology innovation, and upgrading agricultural-land mechanization-to reduce ALCE and lower ALCI. Additionally, (3) heterogeneity analysis suggests that the carbon-emission reduction effect of GCRA is more significant in the east, in arid and humid climate zones, and in non-grain-producing regions. Finally, (4) spatial-spillover effect analysis and quantile regression results demonstrate that GCRA also significantly inhibits carbon emissions and the carbon intensity of nearby agricultural land, with the inhibition effect becoming more pronounced at higher levels of government attention. This study's discoveries are helpful in promoting the emission reduction and carbon sequestration of agricultural land and provide references for developing countries to cope with climate change.

Towards inclusive green growth in China: Synergistic roles and mechanisms of new infrastructure construction

Inclusive green growth (IGG) has been widely discussed for its emphasis on coordinating economic growth quality, social equity, as well as environmentally sustainable development. New infrastructure, representing network and information infrastructure construction, has emerged as a pivotal national strategy to stimulate socioeconomic progress, and its impact on the inclusive green growth deserves careful exploration. Employing the staggered difference-in-difference (staggered DID) approach, this study investigates the influence of new infrastructure on IGG based on Chinese prefecture-level city data from 2011 to 2019, taking advantage of the "Broadband China" strategy (BCS) as a quasi-natural experiment. The results indicate a significant enhancement in IGG due to new infrastructure construction, which remains tenable after rigorous robustness assessments. Further testing with the spatial Durbin DID method reveals that BCS has a significant positive spillover impact on IGG in neighboring areas. For its underlying mechanisms, new infrastructure construction enhances IGG mainly by reinforcing industrial structure supererogation, improving the urban innovation level, and developing digital inclusive finance. There is also evidence that heterogeneity highlights the advancing effects of IGG in the central region, non-aging industrial base cities and non-resource-based cities. This research sheds new light on the understanding of the effect of new infrastructure on promoting IGG through both conceptual and empirical aspects and is conducive to future policymaking for developing countries.

Analysis of spatial spillover effects and influencing factors of transportation carbon emission efficiency from a provincial perspective in China

Globally, the transportation industry has become one of the leading sectors in carbon emission, and all countries are committed to environmental protection and energy conservation while experiencing rapid development. Under China's "dual-carbon" goal, the carbon emission problem hinders the construction of China's green transportation system and affects the high-quality development of transportation, so it is of great significance to study the spatial pattern of carbon emission efficiency in the transportation industry and the factors affecting it. Firstly, this paper measures the carbon emission value of transportation in 30 provinces in China from 2010 to 2020 based on the IPCC method and measures the carbon emission efficiency through the super-efficiency slack-based measurement model. Secondly, spatial autocorrelation analysis was conducted to determine the spatial clustering characteristics of the efficiency values. Finally, two spatial Durbin models are constructed to measure the spatial spillover effects and analyze the short-term immediate effects of each influencing factor on the static model and the long-term effects of the dynamic model considering the time lag of the transportation carbon emission efficiency. The results of the study show that (1) the average value of efficiency in the central and eastern regions is basically higher than 0.5; in the western and northeastern regions, it is basically lower than 0.3.The overall efficiency of carbon emission in the region shows a fluctuating upward trend but with increasing regional differences. (2) The number of regions with positive spatial correlation increased from 21 to 25 during the study period, and the degree of provincial transportation carbon emission efficiency agglomeration increased. (3) Although urbanization and energy intensity have a large detrimental influence on transportation carbon emission efficiency, environmental regulation has a major favorable effects on it both long and short term. Population scale, opening level, and urbanization all have significant spatial spillover effects. Accordingly, relevant policy recommendations are put forward to provide theoretical guidance for promoting the realization of low-carbon transportation.

The impacts of carbon emissions trading scheme on green finance: evidence from China

China enacted and implemented a carbon emissions trading pilot policy in 2011, and whether this carbon emissions trading scheme (ETS) can promote the development of green finance is crucial to realizing a win-win situation for both environmental and economic performance. Based on the panel data of 30 provinces in China from 2007 to 2019, this study constructs a multi-period double-difference model (DID) to explore the impact of carbon ETS on the development of green finance and uses the spatial Durbin model (SDM) to test whether there is a spatial spillover effect of the carbon ETS on the development of green finance. The results show that (a) the implementation of carbon ETS significantly promotes the development of green finance, and this conclusion still holds through a series of robustness tests; (b) the promotion effect of the carbon ETS on the development of green finance is more significant in eastern and western provinces, non-resource-based provinces, and provinces with a high level of openness to the outside world; (c) industrial structural upgrading and green innovation play pivotal roles in achieving the desired outcomes of carbon ETS; (d) carbon ETS have spatial spillover effects on the development of green finance, with the indirect effects being more significant than the direct effects. The findings of this study can serve as a valuable reference for expediting the establishment of a unified national carbon market and the development of a robust green financial system. This holds immense significance in effectively implementing the "dual-carbon" strategy.

The impact of digital economy on carbon total factor productivity: A spatial analysis of major urban agglomerations in China

Amid global climate imperatives and intensified economic competition, pivoting from China's conventional growth paradigms to innovative economic catalysts emerges as pivotal for its transformative agenda. Drawing on panel data from 141 principal urban conglomerates spanning 2011-2021, this investigation delves into the intricate nexus between the digital economy and carbon total factor productivity. Our empirical analysis unveils a U-shaped trajectory characterizing the digital economy - carbon total factor productivity interplay, accompanied by a congruent spatial spillover dynamic. While digital economy fortifies environmental governance mechanisms through amplified data and media channels, such regulatory frameworks, albeit efficacious in emission abatement, may inadvertently impede economic vitality, thus attenuating carbon total factor productivity. Progressing from digital economy's foundational phase to its comprehensive deployment, its reverberations on capital productivity manifest in a U-shaped curve, invigorating local carbon total factor productivity while potentially undermining adjacent regions. This digital economy - carbon total factor productivity interrelation is accentuated in advanced, non-resource-reliant metropolises with subdued innovation propensities. This discourse proffers nuanced policy implications for sculpting digital economy trajectories and bolstering carbon total factor productivity in a sustainable context.

Assessing the spatial spillover effects and influencing factors of carbon emission efficiency: a case of three provinces in the middle reaches of the Yangtze River, China

Studying urban carbon emission efficiency is vital for promoting city collaboration in combating climate change. Prior research relied on traditional econometric models, lacking spatial spillover effects understanding at the urban scale. To provide a more comprehensive and visually insightful representation of the evolving characteristics of carbon emission efficiency and its spatial clustering effects and to establish a comprehensive set of indicators to explore the spatial spillover pathways of urban carbon emission efficiency, we conducted an analysis focusing on 42 cities in the middle reaches of the Yangtze River. By employing the index decomposition method, the super-efficiency SBM model, spatial autocorrelation analysis, and the spatial Durbin model, the study calculates the urban carbon emission efficiency from 2011 to 2019 and analyzes the spatial spillover effects and influencing factors of urban carbon emission efficiency. The main conclusions are as follows: (1) Jiangxi Province displayed stable urban carbon emission efficiency evolution, while Hubei and Hunan showed significant internal disparities. (2) Positive spatial correlation exists in urban carbon emission efficiency, with an imbalanced distribution. (3) Various factors influence urban carbon emission efficiency. Technological innovation and economic development have positive direct and indirect impacts, whereas industrial structure, urbanization, population, and energy consumption have negative effects. Spatial spillover effects of vegetation coverage are insignificant. These methods and findings offer insights for future research and policy formulation to promote regional sustainable development and carbon emission reduction.

The carbon emission reduction effect of auditing outgoing officials' natural resource asset management--Evidence from China

Audit outgoing officials' natural resource asset management is an institutional innovation in the field of ecological civilization construction to promote the modernization of national governance system and governance capacity. Focusing on the carbon emission reduction effect of this policy, this paper takes the audit pilot as a quasi-natural experiment and constructs a difference-in-difference model and a spatial difference-in-difference model to explore the carbon emission reduction effect and spatial spillover effect of this policy. The results reveal that the audit pilot has a significant negative impact on carbon emission intensity. Additionally, the impacts are heterogeneous in the east, center, west, northeast, and on both sides of the "Heihe-Tengchong" Line. What's more, this policy influences the environmental performance of surrounding areas manifesting significant spatial spillover effects. Finally, based on the summary of findings, this study proposes a series of countermeasures and suggestions to optimize audit outgoing officials' natural resource asset management.

Study of spatial spillover effects and threshold characteristics of the influence of urbanization on grain green production efficiency in China under carbon constraints

Food security is a basic guarantee for maintaining social stability, and improving green and healthy grain production is the way to promote food security. China is currently experiencing rapid urbanization that has an impact on grain production and security, with reduced arable land, environmental pollution, insufficient agricultural population, and inadequate resource allocation. How to deal with the relationship between urbanization and grain production becomes the key to solve this problem. Therefore, this study constructs an evaluation system of grain green production efficiency (GGPE) with non-expected output containing carbon emissions, and uses the super-efficient SBM model to measure the level of GGPE, and constructs a spatial econometric model to examine the spatial correlation and spillover effect of urbanization on GGPE; then constructs a panel threshold model to analyze the nonlinear threshold characteristics between urbanization and GGPE. It was found that (1) from 2000 to 2019, China's GGPE showed an overall upward trend, and the performance of GGPE varied among different provinces. (2) According to the results of the spatial econometric model, China's GGPE shows obvious spatial characteristics, and the level of urbanization not only directly affects regional GGPE but also can indirectly affect neighboring regions' GGPE through spatial spillover effects, and the indirect effects among regions are larger than the direct effects. (3) Spatial threshold effect results, there is a significant non-linear threshold characteristic of the impact of urbanization rate on GGPE. In terms of the influence effects of other variables, the influence of each variable on GGPE is roughly in the same direction as the influence under the spatial spillover effect, but the degree of influence is slightly different.

How do intellectual property demonstration cities contribute to low-carbon development? Evidence from China

An intellectual property demonstration city (IPDC) not only promotes innovation, but also brings many unexpected gains, the most prominent of which is carbon reduction. Unfortunately, few scholars have included IPDC and carbon emissions in a unified research framework, ignoring the role of intellectual property protection in environmental governance. Therefore, this paper investigates the impact of IPDC on carbon emissions through a multi-period difference-in-difference (DID) model, a spatial DID model, and a mediating effect model with IPDC policy as a quasi-natural experiment. The research results are as follows: (1) IPDC policy has a significant inhibitory effect on carbon emissions. Compared to non-pilot cities, IPDC policy can reduce carbon emissions by about 20.6%. (2) There are temporal and regional heterogeneity of the IPDC policy on carbon emissions. More specifically, the carbon reduction effect of IPDC is more effective in large cities and cities with richer human capital, stricter environmental regulation, and higher financial development. Meanwhile, the policy effects in 2012 and 2015 are larger than those in 2018, while the policy effects in 2014, 2016, and 2019 are not significant. (3) IPDC policy reduces carbon emissions mainly by stimulating innovation and green innovation, and promoting R&D element agglomeration. (4) IPDC policy has obvious spatial spillover effects and leads to the surrounding cities becoming pollution havens. The above conclusions have implications for designing a better urbanization model to promote innovative development and reduce carbon emissions.

Environmental quality assessment and spatial spillover effects of three urban agglomerations in China: A Meta-EBM approach

The new development form of urban agglomeration has greatly promoted economic and social progress in recent years, but it is also facing severe environmental pollution problems. Understanding the status quo of environmental efficiency in urban agglomerations and its leading driving forces is an important prerequisite for formulating energy conservation and emission reduction policies. This research uses the Meta Epsilon Based Measure (Meta-EBM) model to measure the environmental emission efficiency of the Beijing-Tianjin-Hebei（BTH）, Yangtze River Delta (YRD) and Pearl River Delta (PRD) urban agglomerations in China from 2014 to 2018 so as to improve on the inability of traditional Data Envelopment Analysis (DEA) to combine linear and non-linear characteristics, and employs Moran's I index and spatial econometric methods to analyze their spatial dependence and main driving factors. The results demonstrate that the overall environmental efficiency of the three major urban agglomerations in the five years from 2014 to 2018 presents a wave-like development and then tends to be flat. The itemized efficiency of economic outputs has maintained a relatively high level with the environmental output index exhibiting the best efficiency for industrial wastewater, followed by industrial sulfur dioxide (SO2). The scores of the two indicators for inhalable fine particle emissions (PM2.5) and industrial smoke and dust in each urban agglomeration are not ideal, and there are obvious differences between regions. Among them, YRD and PRD are relatively inferior. From the perspective of spatial spillover effects, various indicators show diverse characteristics at different development stages of the regions. Population and Normalized Difference Vegetation Index (NDVI) have a positive effect on environmental efficiency, while both Gross Domestic Product (GDP) per capita and transportation tend to show greater negative effects on regional environmental optimization. This study proposes countermeasures as follows. Each urban agglomeration should set up measures suitable to local conditions and give full play to their location advantages. They can also use space radiation to promote sector economic development and optimize urban environmental benefits.

Impact of agricultural modernization on agricultural carbon emissions in China: a study based on the spatial spillover effect

Taking the data of 30 provinces (excluding Hong Kong, Macao, and Taiwan regions and Tibet) at the provincial level from 2010 to 2019 as the research object, this paper analyzes the current situation and characteristics of China's agricultural modernization and response to carbon emissions. Agricultural modernization is decomposed into production modernization, management modernization, and ecological modernization. This study uses the spatial Dobbin model to demonstrate the impact of agricultural modernization on carbon emissions and analyzes the impact of agricultural modernization on carbon emissions in the East. The direct effect and spatial spillover effect of the three western regions are to different degrees. The results show that agricultural carbon emissions are spatially dependent. The development of agricultural modernization and transportation of neighboring provinces and cities will have an impact on agricultural carbon emissions in this region. Therefore, under the background of rural revitalization and low-carbon agriculture, this paper further analyzes the impact of agricultural modernization on the spatial distribution of carbon emissions in the eastern, central, and western regions. Recommendations are proposed with a view to giving better play to the process of agricultural modernization.

Can heterogeneous environmental regulations improve industrial green total factor energy efficiency?

Whether heterogeneous environmental regulations in China can improve industrial green total factor energy efficiency (IGTFEE) is essential to sustainable industrial development nationwide. However, under China's fiscal decentralization system, the impact of heterogeneous environmental regulations on the IGTFEE and its underlying mechanism needs further exploration. This study incorporates capital misallocation and local government competition into the research framework and systematically investigates the mechanisms and effects of environmental regulations affecting the IGTFEE under China's fiscal decentralization system. Based on provincial panel data from 2007 to 2020, this study measured the IGTFEE using the Super-SBM model with undesirable outputs. Based on efficiency, this study uses a bidirectional fixed-effects model, an intermediary effect model, and a spatial Durbin model for empirical testing. The results show that the effect of command-and-control environmental regulation on the IGTFEE presents an inverted U shape, while the effect of market-incentive environmental regulation on the IGTFEE presents a U shape. Conversely, the effect of command-and-control environmental regulation on capital misallocation presents a U shape, while the effect of market-incentive environmental regulation on capital misallocation presents an inverted U shape. Capital misallocation is the mediating variable of heterogeneous environmental regulations affecting IGTFEE, but heterogeneous environmental regulations do not affect the IGTFEE through the same mechanisms. The spatial spillover effects of command-and-control and market-incentive environmental regulations on IGTFEE present a U shape. Local governments adopt a differentiation strategy for command-and-control environmental regulation and a simulation strategy for market-incentive environmental regulation. Environmental regulations have spillover effects on the IGTFEE under different competitive strategies, but only the imitation strategy, characterized by the race-to-the-top, can promote local and neighboring IGTFEE. Therefore, we propose the following recommendations: the central government should flexibly adjust the intensity of environmental regulations to maximize the capital allocation effect, set diversified performance assessment indicators to motivate local governments into the healthy competition and reform the modern fiscal system to correct distortions in the behavior of local governments.

Quantifying the spatial spillover effects of non-pharmaceutical interventions on pandemic risk

BACKGROUND

Non-pharmaceutical interventions (NPIs) implemented in one place can affect neighboring regions by influencing people's behavior. However, existing epidemic models for NPIs evaluation rarely consider such spatial spillover effects, which may lead to a biased assessment of policy effects.

METHODS

Using the US state-level mobility and policy data from January 6 to August 2, 2020, we develop a quantitative framework that includes both a panel spatial econometric model and an S-SEIR (Spillover-Susceptible-Exposed-Infected-Recovered) model to quantify the spatial spillover effects of NPIs on human mobility and COVID-19 transmission.

RESULTS

The spatial spillover effects of NPIs explain [Formula: see text] [[Formula: see text] credible interval: 52.8-[Formula: see text]] of national cumulative confirmed cases, suggesting that the presence of the spillover effect significantly enhances the NPI influence. Simulations based on the S-SEIR model further show that increasing interventions in only a few states with larger intrastate human mobility intensity significantly reduce the cases nationwide. These region-based interventions also can carry over to interstate lockdowns.

CONCLUSIONS

Our study provides a framework for evaluating and comparing the effectiveness of different intervention strategies conditional on NPI spillovers, and calls for collaboration from different regions.

Analysis of regional differences and spatial spillover effects of agricultural carbon emissions in China

In order to realize "double carbon" target in agriculture and high-quality development of the rural economy in China, it is crucial to study the regional differences and spatial spillover effects of agricultural carbon emissions (ACE). This paper measures ACE using panel data of 31 Chinese provinces from 2005 to 2020, examines the spatio-temporal evolution characteristics，the convergence of agricultural carbon emissions, compares and analyzes regional differences, and investigates the spatial correlation and spatial spillover effects. The study found that: (1) Total agricultural carbon emissions over the research period exhibit a rising and then reducing trend, the spatial distribution of total agricultural carbon emissions is described as high in east-central and low in west. The gap of agricultural carbon emissions is gradually declining in the east, and will eventually reach their respective steady-state levels in the west and northeast. (2) There is a strong spatial interprovincial link of ACE, which has a beneficial knock-on effect on the convergence of adjacent provinces. (3) Agricultural industrial structure, urbanization level, the size of the agricultural labor force, and the intensity of the agricultural machinery input all directly affect ACE in this province and indirectly affect ACE in adjacent provinces, with the exception of the negligible coefficient of economic development level on ACE. Hence, pertinent policy suggestions are put out to serve as a guide for reducing ACE.

Determinants of province-based health service utilization according to Andersen' s Behavioral Model: a population-based spatial panel modeling study

OBJECTIVE

The Andersen' s Behavioral Model was used to explore the impact of various factors on the utilization of health services. The purpose of this study is to establish a provincial-level proxy framework for the utilization of health services from a spatial perspective, based on the influencing factors of the Andersen' s Behavioral Model.

METHOD

Provincial-level health service utilization was estimated by the annual hospitalization rate of residents and the average number of outpatient visits per year from China Statistical Yearbook 2010-2021. Exploring the relevant influencing factors of health service utilization using the spatial panel Durbin model. Spatial spillover effects were introduced to interpret the direct and indirect effects influenced by the proxy framework for predisposing, enabling, and need factors on health services utilization.

RESULTS

From 2010 - 2020, the resident hospitalization rate increased from 6.39% ± 1.23% to 15.57% ± 2.61%, and the average number of outpatient visits per year increased from 1.53 ± 0.86 to 5.30 ± 1.54 in China. For different provinces, the utilization of health services is uneven. The results of the Durbin model show that locally influencing factors were statistically significantly related to an increase in the resident hospitalization rate, including the proportion of 65-year-olds, GDP per capita, percentage of medical insurance participants, and health resources index, while statistically related to the average number of outpatient visits per year, including the illiteracy rate and GDP per capita. Direct and indirect effects decomposition of resident hospitalization rate associated influencing factors demonstrated that proportion of 65-year-olds, GDP per capita, percentage of medical insurance participants, and health resources index not only affected local resident hospitalization rate but also exerted spatial spillover effects toward geographical neighbors. The illiteracy rate and GDP per capita have significant local and neighbor impacts on the average number of outpatient visits.

CONCLUSION

Health services utilization was a variable varied by region and should be considered in a geographic context with spatial attributes. From the spatial perspective, this study identified the local and neighbor impacts of predisposing factors, enabling factors, and need factors that contributed to disparities in local health services utilization.

Does agricultural factor misallocation hinder agricultural green production efficiency? Evidence from China

The sustainable development of agriculture is challenged by two major issues: increasing resource constraints and environmental pollution. Sustainable agricultural development is achievable by improving green total factor productivity from the perspective of resource allocation. To promote the green development of agriculture, this paper utilizes the SBM super-efficiency mode and thus calculates the agricultural resource misallocation index and agricultural green production efficiency index in China between 2001 and 2019. Furthermore, this paper discusses the temporal and spatial evolution characteristics of agricultural green production efficiency, using a fixed model and spatial econometric models to estimate the influence effect of agricultural resource misallocation on green production efficiency. Below are the results. First, China's agricultural green total factor productivity is growing at an impressive rate, with high efficiency in the northeast, northwest, and southeast coastal areas and low efficiency in the central and inland areas. Second, agricultural capital misallocation, labor misallocation, and land misallocation all negatively impact agricultural green production efficiency. Accordingly, the misallocation of agricultural factors will hamper the growth of agricultural green production efficiency in this region and also in the surrounding areas. Third, the indirect impact on the own agricultural green production efficiency exceeds its direct impact on neighboring regions' efficiency. Fourth, the mechanisms are the upgrading of agricultural industry structure and green technology innovation. According to the findings, reducing resource misallocation can substantially enhance agricultural green productivity, which is an imperative step in addressing agricultural green production. Hence, policies should be formulated that highlight the regional allocation of agricultural production factors and green production-oriented concept of agricultural production. Also, the government should promote the transformation and upgrading of the agricultural industrial structure, as well as the application of green agricultural technologies.

Spatial effects of air pollutants reduction on CO2 emissions

The sources of air pollutants and CO₂ are basically the same, hence the reduction of air pollutants will affect CO₂ emissions. Considering the regional integration of economic development as well as air pollution control, it is necessary to analyze the impact of air pollutants reduction in a region on CO₂ emissions in its surrounding regions. Furthermore, as different stages of air pollutants reduction have different effects on CO₂ emissions, it is also important to study the heterogeneity of this impact. In this article, we took China as the research case and built a spatial panel model based on the data of 240 cities above the prefecture level from 2005 to 2016 to study the impact of two different stages of air pollutants reduction-front reduction of air pollutants (FRAP) and end-of-pipe treatment of air pollutants (EPAP) on CO₂ emissions-and their spatial spillover effects. On this basis, we further modified traditional spatial weight matrix and constructed the matrices of cities in the same and different provinces to discuss the influence of provincial administrative boundaries on the spillover effect between cities. The results show that FRAP affects CO₂ emissions mainly through the local synergistic effect, and its spatial spillover effect is not significant. The local effect of EPAP on CO₂ emissions is antergic, and the spatial spillover effect is significant. The increase of a city's EPAP will increase the CO₂ emissions in surrounding regions. Besides, provincial boundaries weaken the spatial spillover effects of FRAP and EPAP on CO₂ emissions in prefecture-level cities. There is a significant spatial spillover effect between cities in the same province, but the spillover effect does not exist for cities in different provinces nearby.

The impact of carbon emissions trading policy on carbon emission of China's power industry: mechanism and spatial spillover effect

Carbon emission trading policy (CETP) is an important tool for energy savings and emission reduction. However, the effect of CETP on carbon emission reduction in power industry is still unknown. This paper uses the difference-in-differences (DID) model and the intermediary effect model to test the impact and mechanism of CETP on power industry carbon emissions. In addition, a spatial difference-in-differences (SDID) model is established to analyze the spatial spillover effect. The results show that CETP has a significant inhibitory effect on power industry carbon emissions and the results are still valid after endogenous and robust tests. The improvement of technology level and power conversion efficiency plays an intermediary role for CETP to reduce power industry carbon emissions. The optimization of power generation structure is likely to become another important way for CETP to play its role in the future. The spatial spillover effect test shows that CETP not only has a significant inhibitory effect on power industry carbon emissions in the pilot areas but also has a negative spatial spillover effect on power industry carbon emissions in the surrounding non-pilot areas. The heterogeneity tests show that CETP has the most significant reduction effect in the central region of China and the strongest spatial spillover inhibiting effect in the eastern region. The purpose of this study is to provide decision-making references for government to achieve China's dual-carbon goal.

Does digital finance promote the "quantity" and "quality" of green innovation? A dynamic spatial Durbin econometric analysis

Based on the panel data of 284 prefecture-level cities in China, this paper uses the dynamic spatial Durbin model to explore the impact of digital finance on green innovation from the dimensions of "quantity" and "quality." The results show that digital finance has a positive impact on both the quality and quantity of green innovation in local cities, but the development of digital finance in neighboring cities has a negative impact on the quantity and quality of green innovation in local cities, and the impact on the quality of green innovation is greater than that on the quantity of green innovation. And after a series of robustness tests, it was shown that the above conclusions are robust. In addition, digital finance can have a positive impact on green innovation mainly through industrial structure upgrading and informatization level. Heterogeneity analysis shows that the breadth of coverage and the degree of digitization are significantly related to green innovation, and digital finance has a more significant positive impact in eastern cities than in mid-western cities.

Spatial spillover effects of national-level eco-industrial park establishment on regional ecological efficiency: evidence from 271 cities in China

Using a panel dataset of 271 Chinese cities at the city level from 2004 to 2018, this study is the first to adopt a staggered spatial difference-in-differences (SDID) model to investigate the effect and mechanism of the national-level eco-industrial park (NEIP) policies on eco-efficiency. Moreover, this study deeply identifies the policy effect by using a spatial difference-in-difference-in-differences (SDDID) model, spatial attenuation boundary, and event study method. The results show that NEIP can significantly and consistently improve urban eco-efficiency. However, NEIP has a continuous negative effect on the eco-efficiency of the surrounding area. The siphoning effect of the NEIP on eco-efficiency is more pronounced in cities with more than one NEIP or in provincial capitals and municipalities with NEIPs. In addition, the spatial effect of the eco-efficiency of the NEIP can spread for approximately 100 km; i.e., there is a negative impact on the cities in the immediate vicinity of the NEIP (within 100 km). Moreover, the impact of the NEIP on urban and even regional eco-efficiency is mainly realized through the crowding out effect of heavily polluting enterprises and the technological innovation effect. Therefore, based on continuing to expand the NEIP pilot cities, the government should establish several regional eco-industrial city clusters centered on the pilot cities, adopt policies, and build the corresponding infrastructure. In addition, taking into account regional differences, the government should construct differentiated eco-industrial park goals and support policies to achieve regional economic development based on environmental protection.

Green location-oriented policies and carbon efficiency: a quasi-natural experiment from National Eco-industrial Demonstration Parks in China

This paper investigates how National Eco-industrial Demonstration Parks (NEDP) in China affects carbon emission efficiency. The difference-in-differences (DID) strategy is used for analysis. This paper finds that the construction of NEDP is conducive to the improvement of carbon emission efficiency, and the findings remain robust through placebo tests and propensity score matching. Heterogeneity analysis shows NEDP construction has greater utility on carbon efficiency in non-resource-based cities as well as in environmentally friendly cities. The mechanism analysis found that green technology innovation, industrial restructuring, and the relocation of industrial enterprises are effective ways to improve carbon efficiency in NEDP. Finally, this paper finds that the construction of NEDP has obvious spatial spillover effects on carbon efficiency, which can effectively heighten the carbon efficiency level of this locality and nearby areas.

Does the carbon emission rights trading pilot policy aggravate local government fiscal pressure? Evidence from China

The carbon emission rights trading pilot (CERTP) policy is an important measure to promote low-carbon economic development. This pilot policy also affects the entry and survival of enterprises and is thus related to local government fiscal pressure. The objective of this paper is to examine whether the CERTP policy increases local government fiscal pressure. Based on the quasi-natural experiment of China's CERTP policy, using a dataset from 314 prefecture-level cities in China over the period 2005 to 2019, this paper applies the staggered difference-in-differences (DID) model to examine the impact of the CERTP policy on local government fiscal pressure, and further tests the spatial spillover effects and potential mediation mechanisms of this pilot policy. The results indicate that the implementation of the CERTP policy significantly increases local government fiscal pressure, especially in the eastern regions and regions with low economic development levels, which provides further evidence of a causal relationship between the CERTP policy and fiscal pressure. The results of the spatial spillover effects confirm that the implementation of the CERTP policy in neighboring prefecture-level cities would increase local government fiscal pressure in the local region. The results of the mediation mechanism effect reveal that the CERTP policy aggravates local government fiscal pressure by inhibiting the progress of green technology by enterprises, hindering the emergence of new enterprises, and increasing the number of closures of high-carbon emissions enterprises. This paper recommends that when implementing the CERTP policy, it is necessary to weigh the overall impact of the policy, not just its effect on carbon emissions reduction. The fiscal sustainability of local governments cannot be ignored.

Spatial spillover effect of the synergistic development of inward and outward foreign direct investment on ecological well-being performance in China

Inward and outward foreign direct investment (FDI) can promote a country's economic growth, but the effect of inward and outward FDI on regional ecological well-being performance (EWP) is uncertain. Using the data of 30 Chinese provinces from 2004 to 2019, this study employs the spatial Dubin model to examine the spatial spillover effects of synergistic development of inward and outward FDI on regional ecological well-being performance and their mediating mechanisms. The result shows that the synergistic development of inward and outward FDI can significantly improve regional EWP and imply a positive spatial spillover effect. The dynamic effect analysis indicates that synergistic development of inward and outward FDI has a lag effect on the improvement of regional EWP. The mechanism test found that the synergistic development of inward and outward FDI can enhance the EWP of the region and the spatially related regions by promoting the rationalization, upgrading and technological innovation of the industrial structure. These findings have some insights into improving global ecological well-being in an open economy.

Quantifying the effect of administrative approval reforms on SO2 emissions: a quasi-experiment in Chinese cities

The effects of the Administrative Examination and Approval System Reform on economic growth and entry of businesses have drawn much attention. However, few scholars pay attention to the impacts of this policy on SO₂ emissions. Keeping in view the existing research gap, a spatial difference-in-difference (SDID) model is employed to assess the effects of the Administrative Examination and Approval System Reform on SO₂ emissions in 297 Chinese cities during the period 1995-2020 from the perspective of spatial spillover effects. The results show that the establishment of Administrative Examination and Approval Center (AEAC) has significantly positive effects on the local SO₂ emissions. The significant indirect (spatial spillover) effects are confirmed. That is, the establishment of AEAC of a given city has a significant positive impact on the SO₂ emissions of neighboring cities. The findings are confirmed by several robustness tests. Our study findings have significant implications for the cross-border coordination of environmental policies that aim to improve the quality of the environment across borders.

Influence mechanisms and spatial spillover effects of industrial agglomeration on carbon productivity in China's Yellow River Basin

The ecological protection and high-quality development of the Yellow River Basin have become major national strategies in China. Therefore, reducing carbon emissions in the Yellow River Basin through efficient industrial agglomeration is necessary for achieving the goals of carbon peak by 2030 and carbon neutrality by 2060. The Yellow River Basin is an important base for energy, chemicals, raw materials, and industry in China, making it important to study the effects of different industrial agglomeration types on carbon productivity from the perspective of agglomeration externalities. Therefore, taking 2009-2019 panel data for prefecture-level cities in the Yellow River Basin, this study uses a spatial Durbin model to investigate the spatial spillover effects of industrial agglomeration (i.e., specialized, diversified, and competitive agglomeration) on carbon productivity. Furthermore, the moderating effects of urbanization level and environmental regulation are analyzed. The results reveal, first, the existence of spatial correlation in carbon productivity across different cities in the Yellow River Basin. Second, diversified and competitive agglomeration significantly increase carbon productivity, although competitive agglomeration has beggar-thy-neighbor spillover effects. Meanwhile, the effect of specialized agglomeration is not significant. Third, the effects of different types of industrial agglomeration differ significantly between cities in different locations and with different resource endowments. Fourth, urbanization level and environmental regulation have different moderating effects in the relationship between different types of industrial agglomeration and carbon productivity. These findings provide evidence for further developing rational industrial agglomeration patterns to enhance carbon productivity in the Yellow River Basin.

Digital economy, spatial spillover and industrial green innovation efficiency: Empirical evidence from China

The digital economy is pushing more efficient and greener production and innovation processes, as well as quickening the mobility of production factors, which would have a critical impact on improving industrial green innovation efficiency. Based on the panel data of 30 Chinese provinces from 2005 to 2019, this study established a comprehensive index system to assess the level of provincial digital economy development, and adopted the SBM-DEA model including non-expected output to evaluate industrial green innovation efficiency, then adopted the Global Moran's I and Local Moran's I to test whether there is spatial autocorrelation, followed by the spatial Durbin model (SDM) and the mediating effect test model to investigate the direct impact, spatial spillover effect and indirect transmission mechanism of the digital economy on industrial green innovation efficiency. The results show that: both the development level of the digital economy and industrial green innovation efficiency show positive spatial autocorrelation; The digital economy not only has a significant direct role in promoting industrial green innovation efficiency but also has a spatial spillover effect; The digital economy can improve industrial green innovation efficiency by promoting manufacturing structure upgrading and stimulating enterprises' green technology innovation. The findings of this paper are helpful for policymakers to clarify the relationship between the digital economy and industrial green innovation efficiency and provide favorable policy directions for developing the digital economy to promote industrial green innovation efficiency.

Spatial effect analysis of heterogeneous green technology innovations on pollution emission reduction: evidence from China's power industry

Based on the provincial panel dataset of the power industry in China from 1997 to 2020, this study employed the dynamic spatial Durbin model (SDM) to investigate the spatial effects of heterogeneous green technology innovations (GTIs) of the power industry chain-clean energy GTIs (GTI1), fossil-fueled GTIs (GTI2), energy-saving GTIs (GTI3), and power transmission technology innovations (GTI4)-on three pollution emission reduction: SO_2, solid waste (SW), and waste water (WW). The empirical results revealed that three pollution emissions showed "path dependent" and "snowball effects." GTI1, GTI2, and GTI3 reduced local SO₂ and SW emissions, while GTI2 and GTI4 had no obvious reduction effects on WW emissions. Different GTIs had the same spatial "symbiotic effects" on SO₂ emission reduction in the short term, showing positive spatial spillover reduction effects. Finally, it is of great significance to make full use of the positive spatial spillover effects of GTIs to promote the regional collaborative linkage of pollutant governance in the power industry.

A comprehensive analysis of the relationships between the built environment and traffic safety in the Dutch urban areas

Built-environment factors potentially alleviate or aggravate traffic safety problems in urban areas. This paper aims to investigate the relationships of these factors with vehicle-bicycle and vehicle-vehicle property damage only (PDO) and killed and severe injury (KSI) crashes in urban areas. For this purpose, an area-level analysis using 100x100m² cells, along with a Spatial Hurdle Negative Binomial regression model were employed. The study area is composed of a selection of municipalities in the Netherlands-Randstad Area where major land-use developments have occurred since the 1970s. The study was conducted by developing a rich dataset composed of various national and local databases. The findings reveal that built-environment factors and land-use policies have substantial impacts on safety, which cannot be neglected. The factors explaining the land-use density and diversity in the area (e.g., urbanity and function mixing levels), as well as the land-use design characteristics (indicated by average age of the neighborhoods), traffic and road network characteristics, and proximity to different destinations influence the probability, frequency, and severity of crashes in urban areas. Furthermore, low socioeconomic levels are associated with a higher frequency of traffic crashes.

Spatial variations and social determinants of life expectancy in China, 2005-2020: A population-based spatial panel modelling study

BACKGROUND

Social determinants of health (SDOH) produce a broad range of life expectancy (LE) disparities. In China, limited literatures were found to report association between SDOH and LE at ecological level during a consecutive period of time from the spatial perspectives. This study aimed to determine the existence, quantify the magnitude, and interpret the association between SDOH and LE in China.

METHODS

Provincial-level LE were estimated from mortality records during 2005-2020 from National Mortality Surveillance System in China. A spatial panel Durbin model was used to investigate LE associated SDOH proxies. Spatial spillover effects were introduced to interpret direct and indirect effects caused by SDOH during long-term and short-term period on LE disparities.

FINDINGS

Nationwide, LE increased from 73.1 (95% confidence interval (CI): 71.3, 74.4) years to 77.7 (95%CI: 76.5, 78.7) years from 2005 to 2020. Unequally spatial distribution of LE with High-High clustering in coastal areas and Low-Low clustering in western regions were observed. Locally, it was estimated that SDOH proxies statistically significant related to an increase of LE, including GDP (coefficient: 0.02, 95%CI: 0.00, 0.03), Gini index (coefficient: 2.35, 95%CI: 1.82, 2.88), number of beds in health care institutions (coefficient: 0.02, 95%CI: 0.00, 0.05) and natural growth rate of resident population (coefficient: 0.02, 95%CI: 0.01, 0.02). Direct and indirect effects decomposition during long-term and short-term of LE associated SDOH proxies demonstrated that GDP, urbanization rate, unemployment rate, education attainment, Gini index, number of beds in health care institutions, sex ratio, gross dependence ratio and natural growth rate of resident population not only affected local LE, but also exerted spatial spillover effects towards geographical neighbors.

INTERPRETATION

Spatial variations of LE existed at provincial-level in China. SDOH regarding socioeconomic development and equity, healthcare resources, as well as population characteristics not only affected LE disparities at local scale but also among nearby provinces. Externalities of policy of those SDOH proxies should be took into consideration to promote health equity nationally. Comprehensive approaches on the basis of population strategy should be consolidated to optimize supportive socioeconomic environment and narrow the regional gap to reduce health disparities and increase LE.

FUNDING

National Key Research & Development Program of China (Grant No.2018YFC1315301); Ministry of Education of China Humanities and Social Science General Program (Grant No.18YJC790138).

Spatial and dynamic effects of air pollution on under-five children's lower respiratory infections: an evidence from China 2006 to 2017

Air pollution has been a deeply concerned issue posing an immediate and profound threat to human's lower respiratory health in China. The health of children under 5 years old, regarded as a key index of public health progress in a country, is closely related to the long-term human capital development. Hence, it is vital to investigate the potential association between air pollution and children's lower respiratory health outcomes and to explore related policy implications regarding the public health and the pollution regulation. As air pollutants diffuse across adjacent regions rather easily, considering the spatial spillover effect is meaningful in course of acquiring the aforementioned association. Based on the proposed province-level panel dataset of China during 2006-2017, this study constructs a dynamic spatial panel Durbin model to investigate the impact of air pollution on under-five children's lower respiratory infections. As a result, (1) both air pollution and children's respiratory health have obvious spatial spillover effects, and the latter has an outstanding characteristic of path dependence in time. (2) In the short term, air pollution presents significant negative impact on children's respiratory health, while in the long run, the impact decreases dramatically. (3) Regional comparison indicates that children in the western China are the most susceptible to air pollution followed by children in the central and eastern regions. (4) Other control variables have significant and varying impacts both in the short and long term. Particularly, this paper proves the existence of "siphon effect" in children healthcare system in China. From a broader and more comprehensive perspective, this study provides effective and constructive basis for policy making, in favor of improving children's health under air pollution and promoting sustainable development in China.

The health potential of neighborhoods: A population-wide study in the Netherlands

BACKGROUND

While differences in population health across neighborhoods with different socioeconomic characteristics are well documented, health disparities across neighborhoods with similar socioeconomic characteristics are less well understood. We aimed to estimate population health inequalities, both within and between neighborhoods with similar socioeconomic status, and assessed the association of neighborhood characteristics and socioeconomic spillover effects from adjacent neighborhoods.

METHODS

Based on Dutch whole-population data we determined the percentage of inhabitants with good or very good self-assessed health (SAH) and the percentage of inhabitants with at least one chronic disease (CD) in 11,504 neighborhoods. Neighborhoods were classified by quintiles of a composite neighborhoods socioeconomic status score (NSES). A set of spatial models was estimated accounting for spatial effects in the dependent, independent, and error components of the model.

RESULTS

Substantial population health disparities in SAH and CD both within and between neighborhoods NSES quintiles were observed, with the largest SAH variance in the lowest NSES group. Neighborhoods adjacent to higher SES neighborhoods showed a higher SAH and a lower prevalence of CD. Projected impacts from the spatial regressions indicate how modest changes in NSES among the lowest socioeconomic neighborhoods can contribute to population health in both low- and high-SES neighborhoods.

CONCLUSION

Population health differs substantially among neighborhoods with similar socioeconomic characteristics, which can partially be explained by a spatial socio-economic spillover effect.

Research on the influence of technological innovation on carbon productivity and countermeasures in China

Increasing carbon productivity is an important measure taken by China to deal with global climate change, and technological innovation is the fundamental way to promote industrial carbon productivity. To explore the low-carbon effects of technological innovation, based on the panel data of 30 provinces in China from 2009 to 2017, this paper established a spatial panel measurement model and a panel threshold regression model to explore the spatial spillover effects and threshold characteristics of technological innovation on industrial carbon productivity. The research shows the following: on the one hand, technological innovation and industrial carbon productivity each has obvious spatial correlation, and technological innovation has a significant spatial spillover effect on the improvement of industrial carbon productivity, and the indirect spillover between regions is greater than the direct spillover effect within the area. On the other hand, the impact of technological innovation on industrial carbon productivity has a double threshold effect. With the continuous improvement of technological innovation capabilities, the promotion of industrial carbon productivity has become increasingly more influential. Through the division of threshold values, the technological innovation capabilities of various regions in China are significantly heterogeneous, and the overall level is low. Although technological innovation capabilities have improved in recent years, there is still much room for improvement. Finally, this article puts forward relevant suggestions from the construction of regional technological innovation system, economical green circular development, and the establishment of a green technological innovation system.

Study on the spatial spillover effects of cement production on air pollution in China

Since 1985, China has become the largest cement producer and consumer in the world. The pollutants emitted from cement production and processing have aggravated China's pressure to conserve energy and reduce emissions. Considering the fact of cross regional transfer and capacity replacement of cement industry, this paper explores the influence of cement production on air pollution by using spatial econometric models. The results illustrate that the concentration of PM_2.5 is obviously spatially dependent and presents high-east and low-west agglomeration characteristic on a national scale. Moreover, the positive correlation between cement production and air pollution is quite obvious, the spatial spillover effects of cement production on air pollution increase progressively, and the indirect spillover effects are seven times greater than the direct spillover effects. The results also show that the phenomenon of cement industries obtaining benefits at the cost of hurting air quality in surrounding areas is the most severe in eastern China. Thus, rules should be based on local conditions when making policies in cement industries and the strong correlation between the pollution of adjacent areas should be fully considered.

How does China's air pollution influence its labor wage distortions? Theoretical and empirical analysis from the perspective of spatial spillover effects

OBJECTIVE

This study aims to analyze the impact of air pollution on Chinese labor wage distortions, especially the impact caused by neighboring air pollution, which is the spatial spillover effects of pollution.

METHODS

It first constructs a theoretical model to explain the effect of air pollution on wage distortions theoretically. Then, based on the measurement of wage distortions of 289 cities from 1998 to 2016, it further uses the Spatial Durbin Model to examine the influences and influencing mechanisms empirically.

RESULTS

The results show that labor wages are negatively distorted during the sample period, with the real wages being lower than the marginal product labor (MPL). Besides, local air pollution significantly exacerbates wage distortions through the production input effect that increases MPL, with a 1% increase in air pollution leading to a 0.0842% and 0.1038% increase in wage distortions and MPL, respectively. The spatial spillover effects suggest that air pollution from neighborhoods significantly reduces local wage distortions through the human capital effect that decreases MPL, and the elastic coefficients of neighboring air pollution on wage distortions and MPL are -0.6078 and - 0.8870, respectively. Besides, the heterogeneity test indicates the magnitude and the significance of the impacts vary in the eastern, central, and western regions.

CONCLUSION

Our analysis calls for collaborative governance in both air pollution and labor market liberalization and the simultaneous growth of real wages and labor productivity.

Spatial spillover effects of environmental regulations on air pollution: Evidence from urban agglomerations in China

Environmental regulations affects the environmental quality of not only local areas but also surrounding regions. It remains unknown whether the effect of environmental regulations on the surrounding regions is free riding or pollution shelter. Based on the data from 2006 to 2018, the spatial correlation of PM_2.5 in Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) urban agglomerations in China was examined in this study. In addition, the spatial spillover effects of environmental regulation on PM_2.5 concentrations were explored while the socio-economic driving factors of the heterogeneity of pollution spillover were identified via SDM based STIRPAT framework. Results showed that the characteristics of PM_2.5 concentrations spatial correlations varies from one urban agglomeration to another. This study revealed that the air pollution is affected by not only local environmental regulations, but also regulations implemented in surrounding cities. The PM_2.5 concentration of BTH, YRD and PRD increased by 0.76, 0.147 and 0.109 for each unit increase in environmental regulation of surrounding cities, respectively. In fact, cities with loose regulation become the pollution shelters. The spatial spillover effects offset the improvement effects of local environmental regulations on the air quality. Furthermore, the comparison amongst three urban agglomerations showed that the spatial spillover effects of PM_2.5 concentration in BTH and YRD are higher than that of PRD. This is attributed to differences in industrial structure, population density, economic development, FDI and geographical location. Therefore, the spatial spillover effects should be taken into consideration and joint regulation should be strengthened to address air pollution issues in urban aggregations.

Spatial patterns and social-economic influential factors of population aging: A global assessment from 1990 to 2010

The world's population is aging rapidly. In this paper, three population aging indicators were collected to represent the elderly population, the oldest-old population, and centenarians. The spatial patterns of three population aging indicators and the influencing social-economic factors and their spatial spillover effects in the world from 1990 to 2010 were investigated. The empirical strategy was based on application of spatial autocorrelation methods and spatial error modeling. The results revealed the significant positive spatial autocorrelation as well as the obvious spatial disparities and clusters of the aging indicators in the world. Furthermore, spatial spillover effects of population aging indicators were detected with positive influence of several social-economic factors (e.g., per capita GNI, urbanization rate, and life expectancy) not only of population aging in a country itself, but in its neighboring counties. In sum, these findings indicated that population aging are a spatio-temporal process, and the spatial spillover effects from neighbors also vary among these indicators, which should be considered into the differentiated policies in response to the challenge of an aging society.

Investigating the driving forces of China's carbon intensity based on a dynamic spatial model

In extant literature on China's carbon intensity, economic growth is considered an important determinant. However, the corresponding policy implications are slightly weak in subsequent practice because economic growth is an outcome of many economic activities, such as technological progress and capital stock accumulation. Furthermore, spatial spillover effects are ignored when using regional datasets. As a result, this study uses the dynamic spatial model to analyze the driving forces of China's provincial carbon intensity over the period 2000-2014. Results indicate that both technological progress and capital stock accumulation are important measures to carbon intensity reduction. China's current industrialization, urbanization, and special energy structure exert a negative effect on the decline in carbon intensity. In addition, China's provincial carbon intensity also exhibits considerable spatiotemporal distribution characteristics. As such, the corresponding policy measures are presented.

Neighbours matter: A nation-wide small-area assessment of environmental inequality in Germany

This study investigates the presence of environmental inequality in Germany and analyses its spatial pattern on a very fine grained level. Using the 2011 German census and pollution measures of the E-PRTR, the study relies on nearly 100,000 one squared km census cells over Germany. SLX and community-fixed SLX models incorporate spatial spillover-effects into the analysis to account for the spatial distribution of socio-demographic characteristics. Results reveal that the share of minorities within a census cell indeed positively correlates with the exposure to industrial pollution. Furthermore, spatial spillover effects are highly relevant: the characteristics of the neighbouring spatial units matter in predicting the amount of pollution. Especially within urban areas, clusters of high minority neighbourhoods are affected by high levels of environmental pollution. This highlights the importance of spatial clustering processes in environmental inequality research.