The impact of transportation accessibility on industrial investment in the urban agglomeration around Poyang Lake in China-based on the perspective of ecological security constraints

Based on the perspective of ecological security constraints, this research takes panel data of 42 counties (cities) in the urban agglomeration around Poyang Lake in China from 2000 to 2020 and uses a spatial econometric model to investigate the impact of transportation accessibility on industrial investment. The findings herein present an obvious spatial relationship between industrial investment among cities under ecological security constraints and reveal how transportation accessibility has a significant spatial effect on industrial investment in this area. Transportation accessibility has promoted industrial investment in the local region but restrained industrial investment in the surrounding areas. A series of endogenous and robustness tests strengthen this conclusion. Lastly, the effect of transportation accessibility on industrial investment in the UAAPYL is influenced by the lake's circle structure and shows obvious heterogeneity.

Spatial correlation and influencing factors of industrial agglomeration and pollution discharges: a case study of 284 cities in China

Exploring the spatial correlation characteristics and influencing factors of industrial agglomeration and pollution discharge, which is of great significance to reduce industrial pollution discharge and promote China's construction of an ecological civilization. Taking 284 prefecture-level cities in China in 2017 as the research object, this study used spatial autocorrelation analysis method to explore the spatial agglomeration characteristics and spatial correlation of industrial agglomeration and industrial pollution discharge, and spatial econometric analysis method was used to explore the main factors affecting industrial pollution discharge. The research results showed that the level of industrial agglomeration in China exhibited a spatial distribution characteristic of "high in the east and low in the west". The total discharge and discharge intensity of industrial pollutants showed a spatial pattern of "high in the north and low in the south" in general, and industrial agglomeration, total discharge, and discharge intensity of industrial pollution showed significant spatial autocorrelation. Moreover, industrial agglomeration had a strong local spatial correlation with the total and intensity of industrial wastewater, industrial SO₂, and industrial smoke and dust, and the main agglomeration types were high agglomeration-low pollution, low agglomeration-high pollution, and low agglomeration-low pollution. In addition, industrial agglomeration had a positive impact on the total industrial wastewater discharge, and had a negative impact on the total industrial smoke and dust discharge, industrial wastewater discharge intensity, industrial SO₂ discharge intensity, and industrial smoke and dust discharge intensity.

The influence of FDI on GHG emissions in BRI countries using spatial econometric analysis strategy: the significance of biomass energy consumption

Indeed, the Belt and Road Initiative (BRI) plays an increasingly important role in global economic and climate change mitigation. However, scientists have insufficient attention to the issues related to the elements that contribute to justifying these impacts and bolstering its response in BRI nations. Accordingly, the existent study executed an in-depth examination of the spatial direct and spillover effects of foreign direct investment inflows (FDI) and biomass energy consumption (BEC) on greenhouse gas emissions (GHG) for 57 BRI countries (1992-2012). We applied the spatial lag model (SLM), the spatial error model (SEM), and the spatial Durbin model (SDM) with five different weights matrices to verify the existence of the pollution haven hypothesis (PHH), the pollution halo hypothesis (P-HH), and the N-shaped environmental Kuznets curve (EKC). We linked the study results with the implementation level of the sustainable Development Goals (SDGs). The findings of local Moran's I (LMI) and Lagrange Multiplier (LM) tests confirm the existence of spatial autocorrelation (SAR). The empirical results revealed that FDI has a positive direct and spillover influence on GHG emissions, which supports the presence of PHH. Also, the nexus between economic growth and GHG emission is an N-shaped curve. The results revered that BEC has a negative sign for direct and spillover effects. In contrast to BEC, Fossil Fuel Energy Consumption (FFEC) and population positively sign for direct and indirect impact. Some policy proposals and future research directions are discussed for BRI countries.

Gambling Outlets Location in Urban Areas: A Case Study of Madrid

Cities are certainly a key factor in the location of gambling facilities. This paper aims to map the location of gambling outlets in urban areas and to examine potential links between neighborhoods socioeconomic and demographic characteristics and gambling supply, taking into account spatial dependencies of neighboring areas. This correlation is of interest because neighborhood characteristics may attract sellers, and because the presence of gambling sellers may cause changes in neighborhood demographics. Using detailed official data from the city of Madrid for the year 2017, three spatial econometric approaches are considered: spatial autoregressive (SAR) model, spatial error model (SEM) and spatial lag of X (explicative variables) model (SLX). Empirical analysis finds a strong correlation between neighborhoods characteristics and co-location of gambling outlets, highlighting a specific geographic patterning of distribution within more disadvantaged urban areas. This may have interesting implications for gambling stakeholders and for local governments when it comes to the introduction and/or increase of gambling availability.

Spatiotemporal variations and determinants of water pollutant discharge in the Yangtze River Economic Belt, China: A spatial econometric analysis

Water pollution is an urgent problem that needs to be controlled via green transformation and the development of the Yangtze River Economic Belt (YREB). Based on the water pollutant discharge and socio-economic database of prefecture-level cities in the YREB from 2011 to 2015, this study explores the spatiotemporal variations in water pollutant discharge in the YREB via two main indicators: chemical oxygen demand (COD) and ammonia nitrogen (NH₃-N). Further, the spatial effects and determinants of water pollutant discharge are quantitatively estimated. The results show that (1) the water pollutant discharge in the YREB has decreased significantly, with the COD and NH₃-N discharge reduced by 10.46% and 10.79%, respectively, and the discharge reduction in the lower reaches was the most prominent; (2) the spatial pattern of water pollutant discharge in the YREB was generally stable and partially improved, and cities with a high rate of water pollutant reduction in the YREB were distributed in the main stream region of the Yangtze River and the intersection of the main stream and tributaries; (3) spatial effects had a significant impact on water pollutant discharge in the YREB, with regional cooperation and economic radiation through environmental management and control initially showing a combined reduction trend in regional water pollutants; and (4) determinants of population size and agricultural economic share declined to varying degrees at the end of the study period, although the urbanization level continued to increase, indicating that urbanization in the YREB occurred too quickly and that water pollutant discharge reduction was limited. However, economic development leading to the deterioration of the water environment was alleviated. In addition, foreign direct investment (FDI) inflows and rapid industrialization processes must be monitored to increase the reduction in characteristic water pollutants.

Effects of socioeconomic and natural factors on air pollution in China: A spatial panel data analysis

China's energy use has increased significantly in recent years with the country's rapid economic growth and large-scale urbanization. Therefore, air pollution has become a major issue. In this study, we conducted spatial autocorrelation and spatial panel regression analyses of sulfur dioxide (SO₂) and nitrogen oxide (NO_X) emissions using the panel data of 31 provincial-level administrative units in China during the period 2011-2017 to comprehensively understand the factors affecting air pollutant emissions. This study contributes to the literature by considering comprehensive factors and spatial effects in the panel-data econometric framework of the whole country of China. The analysis of spatial characteristics shows that during the study period, pollutant emissions in China declined, although emissions in northern regions were still relatively high. Furthermore, SO₂ and NO_X emissions showed significant positive spatial autocorrelations. The results of a fixed-effect spatial lag model showed that both socioeconomic and natural factors were statistically significant for air pollutant emissions, although the degree differed by the type of pollutant. The population, the urbanization rate, the share of added value of secondary industry, and heating and cooling degree days positively affected emissions, while population density, per-capita gross regional product, precipitation, and relative humidity negatively affected emissions. Based on these results, we have put forward suggestions to address the issue of air pollution and achieve environmental sustainability, such as the promotion of regional cooperation and a transition of the economic structure.

Influence of FDI on environmental pollution in selected Arab countries: a spatial econometric analysis perspective

This study investigates the spatial influence and spillover effects of foreign direct investment (FDI) on environmental pollution (EP) by using panel spatial data in 1970-2016 for 12 selected Arab countries. It employs the STochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. The spatial econometric approach is applied to examine the validity of the pollution haven hypothesis (PHH) and the pollution halo hypothesis (P-HH) (from now on, we will use the acronyms PHH and P-HH to denote the pollution haven hypothesis and pollution halo hypothesis, respectively). The Sustainable Development Goals (SDGs) are linked to the study results with a focus on cleaner production practices. The global Moran's I, local Moran's I, and Lagrange multiplier (LM) tests are used to ascertain the existence of spatial autocorrelation (SAR) and determine its trend. We also apply the spatial lag model (SLM), the spatial error model (SEM), and the spatial Durbin model (SDM) to achieve the study objectives. Data are analyzed by using the SDM on the basis of the results of the Wald and likelihood ratio tests. The results of the LM and global and local Moran's I tests confirm the existence of SAR. The SDM results reveal that a slight increase in CO₂ is an influence of the FDI on EP. Findings support the existence of PHH in the Arab countries. The direct effect of the FDI is increased CO₂ and environmental degradation, and the spatial spillover effects are statistically insignificant. This study suggests a set of policies for managing and directing FDI toward clean technology-based industries and reduced CO₂ emissions. Such policies may contribute to the achievement of some SDGs and balancing economic development and environmental sustainability according to the cleaner production practice perspective in the Arab countries and other states with similar conditions.

Spatial econometric analysis of environmental total factor productivity of ranimal husbandry and its influencing factors in China during 2001-2017

Pollution discharge from animal husbandry deteriorates the environment and is of global concern. In this study, data envelopment analysis (DEA) was used to indirectly estimate the pollution discharge of livestock and poultry breeding in terms of sustainable development in China. Land, biogas, and grassland were included as input indexes, and the inverted values of indexes of organic matter, nitrogen, and phosphorus produced by pigs, dairy cattle, beef cattle, poultry, and sheep were included as output indexes. Based on the DEA model with variable returns to scale, the "pollution intensity index of livestock and poultry breeding" was estimated using the principle of output maximization. This study focused on livestock and poultry farming pollution emissions, and the slack-based measure directional distance and metafrontier efficiency functions were used to measure the environmental total factor productivity (ETFP) of animal husbandry in each province and six major animal husbandry production regions of China during 2001-2017. Additionally, a spatial econometric model was employed to analyze the factors affecting animal husbandry ETFP. The results show that the mean value of animal husbandry ETFP was higher than that of conventional total factor productivity. The driver of increased animal husbandry ETFP was technological progress. Overall, China's animal husbandry was developing sustainably, and there was little scope for group technology to catch up. According to an empirical analysis of influencing factors, farmers' improved per capita income level and environmental governance helped to increase animal husbandry ETFP. Furthermore, various measures to improve animal husbandry ETFP in China according to local conditions are needed. Finally, animal husbandry should continue to develop sustainably, using environmental regulations that continuously exert the "Porter Effect."

Financial development and carbon emissions in China since the recent world financial crisis: Evidence from a spatial-temporal analysis and a spatial Durbin model

China's financial development boomed after the recent world financial crisis in 2007. Financial development may affect an economy and hence carbon emissions significantly. No research has examined their relationship since this crisis up to now. Against this background, the effect of financial development on carbon intensity in China from 2007 to 2016 is investigated. The spatial and temporal patterns, and the dynamic evolution of both China's provincial carbon emissions and financial development were firstly investigated. Then a Spatial Durbin Model was employed to explore the effects of financial development, since this crisis, on carbon intensity, controlling related variables. The results show that financial development will significantly increase the local province's emissions but significantly decrease adjacent areas' emissions to a larger extent, so that the overall effect is that financial development will curb carbon emissions. The paper concludes that carbon emission reductions in China since 2007 may not be caused by the financial resources being channeled into the research and development of emission-reduction technologies or into high value-added firms. Hence, it is suggested that measures be taken to channel financial resources into the right projects and firms.

The assessment of forest ecological security and its determining indicators: A case study of the Yangtze River Economic Belt in China

This study put forward an evaluation index system for measuring forest ecological security index (FESI). Taking the 1086 counties located in the Yangtze River Economic Belt as a case study, we investigated the change and its spatial pattern of FESI, as well as the determining indicators (both natural and socio-economic), with the support of Arcmap and GeoDA software. The average FESI value of the study counties in 2010 and 2015 was found to be 0.4226 and 0.4990, increased by 18.08%. Spatially, an evident spatial gradient change was identified, with FESI values in the upstream areas of the Yangtze River being higher than those in midstream areas, and the values of midstream areas in turn being higher than those in downstream areas. The eight tributary basins within the economic belt witnessed significantly different FESI values. Based on the results of this evaluation of FESI and its sub-evaluation indexes, we identified 46.04% of the total counties as constituting "problem areas". These problem areas were mainly concentrated in Shanghai, Jiangsu and Anhui provinces, followed by counties around Dongting Lake, Poyang Lake and in Sichuan province. A regression analysis was conducted in order to identify the determining indicators behind forest ecological security, with results indicating that the ratio of secondary industry, the urbanization rate, the per capita financial institution loan balance, accumulated temperature and wind speed all negatively impacted on FESI values, while population structure, soil organic matter and rainfall were revealed to play a positive role; all of these indicators were highly significant. Given these findings, we also set out a series of policy measures intended to promote the sustainable forest development of the study region. These include the vigorous development of tertiary industry and moves to reduce the proportion of the secondary industry in the national economy, the development of a circular economy, slowing the pace of urbanization, and continued increases in forestry investment in central cities - particularly in problem areas.