scholarly journals The Spatiotemporal Dynamics and Socioeconomic Factors of SO2 Emissions in China: A Dynamic Spatial Econometric Design

Atmosphere ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 534 ◽  
Author(s):  
Zhimin Zhou
Keyword(s):  
Spatial Data ◽  
Industrial Structure ◽  
Spatial Data Analysis ◽  
Spatial Effects ◽  
So2 Emissions ◽  
Socioeconomic Determinants ◽  
Long Run ◽  
Short Run ◽  
Spatial Panel Data ◽  
Adverse Influence

With the great strides of China’s economic development, air pollution has become the norm that is a cause of broad adverse influence in society. The spatiotemporal patterns of sulfur dioxide (SO2) emissions are a prerequisite and an inherent characteristic for SO2 emissions to peak in China. By exploratory spatial data analysis (ESDA) and econometric approaches, this study explores the spatiotemporal characteristics of SO2 emissions and reveals how the socioeconomic determinants influence the emissions in China’s 30 provinces from 1995 to 2015. The study first identifies the overall space- and time-trend of regional SO2 emissions and then visualizes the spatiotemporal nexus between SO2 emissions and socioeconomic determinants through the ESDA method. The determinants’ impacts on the space–time variation of emissions are also confirmed and quantified through the dynamic spatial panel data model that controls for both spatial and temporal dependence, thus enabling the analysis to distinguish between the determinants’ long- and short-term spatial effects and leading to richer and novel empirical findings. The study emphasizes close spatiotemporal relationships between SO2 emissions and the socioeconomic determinants. China’s SO2 emissions variation is the multifaceted result of urbanization, foreign direct investment, industrial structure change, technological progress, and population in the short run, and it is highlighted that, in the long run, the emissions are profoundly affected by industrial structure and technology.

scholarly journals Spatial-Temporal Characteristics in Grain Production and Its Influencing Factors in the Huang-Huai-Hai Plain from 1995 to 2018

2020 ◽  
Vol 17 (24) ◽  
pp. 9193
Author(s):  
Chunshan Zhou ◽  
Rongrong Zhang ◽  
Xiaoju Ning ◽  
Zhicheng Zheng
Keyword(s):  
Influencing Factors ◽  
Spatial Data ◽  
Spatial Data Analysis ◽  
Grain Production ◽  
Temporal Characteristics ◽  
Spatial Panel ◽  
Geographically Weighted Regression Model ◽  
Northern Areas ◽  
Spatial Panel Data ◽  
The Impact

The Huang-Huai-Hai Plain is the major crop-producing region in China. Based on the climate and socio-economic data from 1995 to 2018, we analyzed the spatial–temporal characteristics in grain production and its influencing factors by using exploratory spatial data analysis, a gravity center model, a spatial panel data model, and a geographically weighted regression model. The results indicated the following: (1) The grain production of eastern and southern areas was higher, while that of western and northern areas was lower; (2) The grain production center in the Huang-Huai-Hai Plain shifted from the southeast to northwest in Tai’an, and was distributed stably at the border between Jining and Tai’an; (3) The global spatial autocorrelation experienced a changing process of “decline–growth–decline”, and the area of hot and cold spots was gradually reduced and stabilized, which indicated that the polarization of grain production in local areas gradually weakened and the spatial difference gradually decreased in the Huang-Huai-Hai Plain; (4) The impact of socio-economic factors has been continuously enhanced while the role of climate factors in grain production has been gradually weakened. The ratio of the effective irrigated area, the amount of fertilizer applied per unit sown area, and the average per capita annual income of rural residents were conducive to the increase in grain production in the Huang-Huai-Hai Plain; however, the effect of the annual precipitation on grain production has become weaker. More importantly, the association between the three factors and grain production was found to be spatially heterogeneous at the local geographic level.

scholarly journals Characteristics of the Spatio-Temporal Trends and Driving Factors of Industrial Development and Industrial SO2 Emissions Based on Niche Theory: Taking Henan Province as an Example

2020 ◽  
Vol 12 (4) ◽  
pp. 1389 ◽  
Author(s):  
Pengyan Zhang ◽  
Yu Zhang ◽  
Jay Lee ◽  
Yanyan Li ◽  
Jiaxin Yang ◽  
...  
Keyword(s):  
Industrial Development ◽  
Industrial Structure ◽  
Temporal Trends ◽  
Driving Factors ◽  
Spatial Data Analysis ◽  
Energy Structure ◽  
Niche Theory ◽  
Industrial Output ◽  
So2 Emissions ◽  
Spatio Temporal

Industrial development is critical in improving a nation’s economy and in how it consumes energy resources. However, such development often causes environmental problems. Among others, the haze caused by industrial SO2 emissions is particularly prominent. Based on Niche theory and combined with Exploratory Spatial Data Analysis (ESDA, a decoupling index model, and a Logarithmic Mean Divisia Index (LMDI) factor decomposition model, this paper reports a study on the spatio-temporal distribution and the driving factors of industrial development and industrial SO2 emissions of cities in Henan, China between 2005 and 2014. The results showed that over the studied period in Henan: (1) SO2 emissions reduced by 4.56 × 105 tons and showed a slowly decreasing trend, which gradually transitioned to a “green health” industrial structure in Henan cities; (2) studied cities with high and low industrial niche values (0.038–0.139) showed an absolute decoupling relationship between industrial development and industrial SO2 emissions; (3) except for Zhengzhou city and Hebi city, other studied cities showed a trend of gradually increasing industrial output; (4) along with increases in the values of industrial output, studied cities showed increased levels of SO2 emissions but with energy intensity and energy structure showing a fluctuating trend of increases and decreases in their contributions to SO2 emissions; and (5) the energy consumption intensity and environmental technology were critical factors that were conducive to industrial SO2 emissions and the evolving industrial structure. These findings are important for the control of industrial SO2 emissions, though the levels of their influences are different in different cities. The scale of industrial production and the composition of energy structure in a region could lead to further deterioration of industrial SO2 emissions in the future.

scholarly journals The Effects of Foreign Direct Investment, Economic Growth, Industrial Structure, Renewable and Nuclear Energy, and Urbanization on Korean Greenhouse Gas Emissions

2020 ◽  
Vol 12 (4) ◽  
pp. 1625 ◽  
Author(s):  
Suyi Kim
Keyword(s):  
Economic Growth ◽  
Foreign Direct Investment ◽  
Greenhouse Gas ◽  
Direct Investment ◽  
Nuclear Energy ◽  
Manufacturing Industry ◽  
Industrial Structure ◽  
Ghg Emissions ◽  
Long Run ◽  
Short Run

This study analyzes the effects of foreign direct investment (FDI), economic growth, industrial structure, renewable and nuclear energy, and urbanization on Korean greenhouse gas (GHG) emissions from 1981 to 2014. The cointegration relationship of the variables is examined using autoregressive distributed lag (ARDL) bounds test. The test confirmed the long-run equilibrium among the variables. After that, the short-run and long-run coefficients are estimated by an ARDL error-correction model. The result shows that in the long run, economic growth and urbanization are the main contributors to the increase of GHG emissions, while manufacturing industry share, renewable energy and nuclear energy contributed to the reduction of GHG emissions. The inflow of FDI has led to the increase of greenhouse gases, but the coefficients is negligible. In the short run, economic growth has caused an increase in GHG emissions, while renewable and nuclear energy have contributed to the reduction in GHG emissions. FDI and urbanization did not play a role in increasing of GHG emissions in the short term.

scholarly journals Identifying the Driving Factors of Food Nitrogen Footprint in China, 2000–2018: Econometric Analysis of Provincial Spatial Panel Data by the STIRPAT Model

2021 ◽  
Vol 13 (11) ◽  
pp. 6147
Author(s):  
Chun Liu ◽  
Gui-Hua Nie
Keyword(s):  
Panel Data ◽  
Spatial Data ◽  
Driving Forces ◽  
Early Stage ◽  
Spillover Effect ◽  
Spatial Data Analysis ◽  
Ekc Hypothesis ◽  
Stirpat Model ◽  
Spatial Panel Data ◽  
Per Capita

This paper studies the EKC hypothesis and STIRPAT model. Based on the panel data of carbon emission intensity and other influencing factors of 30 provinces in China from 2000 to 2018, the spatial effect of per capita food nitrogen footprint (FNF) and the effect of different socio-economic factors in China were studied by using exploratory spatial data analysis and fixed effect spatial Durbin model for the first time. The results show that: (1) there is a spatial agglomeration effect and a positive spatial dependence relationship in China’s provincial per capita FNF (FNFP), which verifies that the relationship between China’s FNF and economy is in the early stage of EKC hypothesis curve. (2) The driving forces of China’s FNF were explored, including Engel’s coefficient of urban households (ECU), population density (PDEN), urbanization, nitrogen use efficiency (NUE) and technology. (3) The results show that there is a significant spatial spillover effect of FNFP. The ECU and NUE can reduce the regional FNFP, and can slow down the FNFP of surrounding provinces. (4) Policy makers need to formulate food nitrogen emission reduction policies from the food demand side, food consumption side and regional level.

scholarly journals An expanded knowledge production function

2022 ◽  
Vol 20 ◽  
pp. e021018
Author(s):  
Pedro Henrique Batista de Barros ◽  
Adirson Maciel de Freitas Júnior
Keyword(s):  
Production Function ◽  
Knowledge Production ◽  
Path Dependence ◽  
Knowledge Spillovers ◽  
Knowledge Production Function ◽  
Spatial Spillovers ◽  
Long Run ◽  
Short Run ◽  
Spatial Panel Data ◽  
Economies Of Agglomeration

This paper uses a theoretical motivation for an Expanded Knowledge Production Function(EKPF) that encompasses both path dependence and spatial spillovers to search for evidences inBrazil using a Dynamic Spatial Panel Data approach. The purpose is to identify the determinantsof knowledge production in the 2005-2015 period as well as its temporal evolution, usinginnovation patents as proxies. Regarding its spatial distribution, we identified a North-Southdisparity for the knowledge production in Brazil, with Southeast and South producing alarge part of the country’s patents. Based on the EKPF, we confirmed the importance ofpath dependence and knowledge spillovers to explain the Brazilian innovation. In addition,population density, which generates Jacobian externalities and economies of agglomeration, isan important structural feature in the short run while the number of researchers in universitiesand an increased economic scale are essential to knowledge production in the long run.

scholarly journals Spatial Analysis of Urban Water Use Management in the Northern Border Region of Mexico

2020 ◽  
Vol 15 (11) ◽  
pp. 126
Author(s):  
Gregorio Castro-Rosales ◽  
Ramiro Esqueda-Walle
Keyword(s):  
Spatial Econometrics ◽  
Spatial Data ◽  
Ordinary Least Squares ◽  
Border Region ◽  
Spatial Data Analysis ◽  
Water Price ◽  
Urban Water ◽  
Spatial Effects ◽  
Water Index ◽  
Northern Border

This paper estimates water price elasticity and examines spatial patterns of urban water management variables in 70 localities of more than 2 500 inhabitants of the six northern border Mexican states. By using ordinary least squares, spatial econometrics, Lagrange Multipliers, and exploratory spatial data analysis techniques, four variables were analyzed: water price (P), a Non-revenue water index (NRWI), total urban water connections, and water billed volume (BV). In accordance with the literature, we found that water demand is price sensitive but inelastic. Then price as an instrument for controlling water consumption does not offer an efficient alternative for reducing it, as water price increases would have to rise very high to reflect changes in consumption habits. Instead, it could be just a revenue-raising tool. Our findings also confirm a significant spatial autocorrelation in P and the NRWI. More interestingly, we found robust spatial effects on BV. This result implies that the performance of urban water utilities is determined by its counterparts' performance in the region. Given the results and characteristics of water resources in the region, we argue that management policies must consider a regional approach to be effective.

scholarly journals Environmental Protection, Industrial Structure and Urbanization: Spatiotemporal Evidence from Beijing–Tianjin–Hebei, China

2022 ◽  
Vol 14 (2) ◽  
pp. 795
Author(s):  
Shaojun Ma ◽  
Lei Li ◽  
Huimin Ke ◽  
Yilin Zheng
Keyword(s):  
Environmental Protection ◽  
Spatial Data ◽  
Industrial Structure ◽  
Process Development ◽  
Transformation Process ◽  
Urban Agglomeration ◽  
Evaluation Index System ◽  
Spatial Data Analysis ◽  
Development Level ◽  
Low Efficiency

The Beijing–Tianjin–Hebei urban agglomeration (BTH) is striving to realize the transformation process from a low-efficiency to a high-quality development mode; however, it still has problems regarding reducing energy consumption and ecological environment pressure. Based on panel data from 2013 to 2017, this paper proposes an evaluation index system based on BTH’s “environmental protection–industrial structure–urbanization” system. In the course of applying the coupling degree model (CDM) and the coupling coordination degree model (CCDM) with exploratory spatial data analysis (ESDA) methods, this paper discusses the spatiotemporal process, development level, and spatial agglomeration characteristics of the environmental protection–industrial structure–urbanization system in each city of the BTH area. The findings reveal that the coupling degree of the BTH system is gradually increasing, and that the development level of the BTH subsystem is unbalanced: the coupling coordination level of BTH shows a positive evolution process; however, it is in a stage of low-level collaborative development, and there are obvious differences in the level of BTH coupling coordination in space, revealing the convergence of low–high and high–low types. This paper concludes by putting forward the strategy of optimizing the regional spatial pattern of urban agglomeration and implementing integrated development in order to achieve the desired coupling and coordination effects.

Cluster identification

2019 ◽  
Vol 29 (4) ◽  
pp. 401-415
Author(s):  
Reinaldo Belickas Manzini ◽  
Di Serio Carlos Luiz
Keyword(s):  
Spatial Data ◽  
Regional Analysis ◽  
Spatial Association ◽  
Spatial Dimension ◽  
Spatial Data Analysis ◽  
Industry Concentration ◽  
Spatial Effects ◽  
Temporal Dimension ◽  
Content Type ◽  
Concentration Statistics

Purpose This paper aims to contribute to the approaches based on traditional industry concentration statistics for identifying clusters by complementing them with the techniques of exploratory spatial data analysis (ESDA). Design/methodology/approach Using a sample with 34,500 observations retrieved from the social information annual report released by Brazil Ministry of Labor and Employment, the methodology was designed to make a comparison between the application of industry concentration statistics and ESDA statistics. Findings As the results show, the geographic distribution measures proved to be fundamental for longitudinal studies on regional dynamics and industrial agglomerations, and the local indicator of spatial association statistic tends to overcome the limitation of the industry concentration approach. Research limitations/implications In the period considered, due to economic, structural and circumstantial questions, activities linked to the transformation industry have been losing ground in the value creation process in Brazil. In this sense, the study of other industries may generate other types of insights that should be considered in the process of regional development. Originality/value This paper offers a critical analysis of empirical approaches and methodological advances with an emphasis on the treatment of special effects: spatial dependence, spatial heterogeneity and spatial scale. However, the regional dynamic presents a temporal dimension and a spatial dimension. The role of space has increasingly attracted attention in the analysis of economic changes. This work has identified opportunities for incorporating spatial effects in regional analysis over time.

Spatial Distribution of Employment in Hermosillo, 1999–2004

Urban Studies ◽  
2012 ◽  
Vol 49 (16) ◽  
pp. 3663-3678 ◽  
Author(s):  
Liz Rodríguez-Gámez ◽  
Sandy Dallerba
Keyword(s):  
Spatial Data ◽  
Central Business District ◽  
Developed Countries ◽  
Spatial Data Analysis ◽  
Spatial Effects ◽  
North West ◽  
Large Cities ◽  
The North ◽  
Historical District ◽  
Business District

While the suburbanisation process has been well documented in some large cities of several developed countries, much less attention has been devoted to the case of small and middle-sized cities in developing countries. This article focuses on an exploratory spatial data analysis to investigate the location of the central business district (CBD) and other employment centres in Hermosillo, Mexico. The results reveal the significant presence of spatial dependence and spatial heterogeneity, although their extent varies with the sector under study. These spatial effects take the form of a persistent cluster of high values of employment around the historical district of the city shaping a huge CBD, although a sub-centre of high values emerges to the south and to the north-west of the CBD in 2004. Overall, Hermosillo is still characterised by a traditional monocentric model, but the role of its CBD has changed.

scholarly journals Influencing Factors of New Energy Development in China: Based on ARDL Cointegration and Granger Causality Analysis

2021 ◽  
Vol 9 ◽  
Author(s):  
Fuliang Xue ◽  
Xiaotong Feng ◽  
Jing Liu
Keyword(s):  
Economic Growth ◽  
Power Generation ◽  
Energy Consumption ◽  
Carbon Emissions ◽  
Industrial Structure ◽  
Energy Development ◽  
Energy Prices ◽  
Long Run ◽  
New Energy ◽  
Short Run

The development and competition of the new energy industry will become an important battlefield of a new round of technological and industrial competition. This study use the annual data from 1990 to 2019 to understand the factors affecting the development of new energy development in China by examining the long-run and causal relationship among the proportion of new energy consumption, energy prices, carbon emissions, industrial structure, economic growth, and new energy power generation in a multivariate model for China. The findings indicate that in the long run, new energy generation is positively linked with new energy consumption, whereas energy prices and carbon emissions have a negative and significant impact on new energy consumption. In the short run, economic growth can promote the growth of new energy consumption. However, this positive effect is gradually formed and is unlikely to happen soon. However, whether the impact of industrial structure optimization on new energy consumption is a long- or short-run estimate is not significant. Causality results suggest that a one-way Granger causality exists between each factor and new energy consumption in different lag orders, except for industrial structure. Re-examining the energy price mechanism and carbon emission mechanism policy, maintaining stable GDP growth, increasing the installed capacity of new energy power generation, and improving power generation conversion efficiency are vital for ensuring new energy development.

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