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 Spatio-Temporal Analysis of CO2 Emission Driving Force in Various Provinces in China Using the Extended STIRPAT-GWR Model

2021 ◽  
Author(s):  
Yulin Zhang
Keyword(s):  
Co2 Emissions ◽  
Co2 Emission ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Temporal Analysis ◽  
Energy Structure ◽  
High Effect ◽  
Northern Regions ◽  
Spatio Temporal ◽  
Gwr Model

To fill the shortcomings of traditional research that ignores the driver’s own spatial characteristics and provide a theoretical support to formulate suitable emission reduction policies in different regions across China. In this pursuit, based on the panel data of provincial CO2 emission in 2007, 2012, and 2017, the present study employed the extended environmental impact assessment model (STIRPAT-GWR model) to study the effect of population, energy intensity, energy structure, urbanization and industrial structure on the CO2 emissions in 29 provinces across China. The empirical results show that the effect of drivers on the CO2 emissions exhibited significant variations among the different provinces. The effect of population in the southwest region was significantly lower than that of the central and eastern regions. Provinces with stronger energy intensity effects were concentrated in the central and western regions. The effect of energy structure in the eastern and northern regions was relatively strong, and gradually weakened towards the southeast region. The areas with high urbanization effect were concentrated in the central and the eastern regions. Furthermore, significant changes were observed in the high-effect regions of the industrial structure in 2017. The high-effect area showed a migration from the northwest and northeast regions in 2007 and 2012, respectively, to the southwest and southeast regions in 2017. Urbanization showed the strongest effect on the CO2 emissions, followed by population and energy intensity, and the weakest effect was exhibited by the energy and industrial structure. Thus, the effects of population and energy structure showed a downward trend, in contrary to the effect of urbanization on the CO2 emissions in China.

scholarly journals Spatial Correlation, Influencing Factors and Environmental Supervision on Mechanism Construction of Atmospheric Pollution: An Empirical Study on SO2 Emissions in China

2019 ◽  
Vol 11 (6) ◽  
pp. 1742 ◽  
Author(s):  
Ruoyu Yang ◽  
Weidong Chen
Keyword(s):  
Air Pollution ◽  
Spatial Correlation ◽  
Influencing Factors ◽  
Atmospheric Pollution ◽  
Industrial Development ◽  
Population Pressure ◽  
Spatial Data Analysis ◽  
So2 Emissions ◽  
Political Movement ◽  
The Impact

In order to study the present situation regarding SO2 emissions in China, problems are identified and countermeasures and suggestions are put forward. This paper analyzes spatial correlation, influencing factors and regulatory tools of air pollution in 30 provinces on the Chinese mainland from 2006–2015. The results of exploratory spatial data analysis (ESDA) show that SO2 emissions have obvious positive spatial correlations, and atmospheric pollution in China shows obvious spatial overflow effects and spatial agglomeration characteristics. On this basis, the present study analyzes the impact of seven socioeconomical (SE) factors and seven policy tools on air pollution by constructing a STIRPAT model and a spatial econometric model. We found that population pressure, affluence, energy consumption (EC), industrial development level (ID), urbanization level (UL) and the degree of marketization can significantly promote the increase of SO2 emissions, but technology and governmental supervision of the environment have significant inhibitory effects. The reason why China’s air pollution is curbed at present is because the government has adopted a large number of powerful command-controlled supervision measures, to a large extent. Air pollution treatment is like a government-led “political movement”. The effect of the market is relatively weak and public force has not been effectively exerted. In the future, a comprehensive use of a variety of regulation tools is needed, as well as encouraging the public to participate, strengthening the supervision of third parties and building a diversified and all-encompassing supervision mechanism.

Research on the Regional Low-Carbon Industrial Development: A Case of Tianjin Binhai New Area

2011 ◽  
Vol 219-220 ◽  
pp. 250-253 ◽  
Author(s):  
Xiao Ying Cui ◽  
Hui Ming Li ◽  
Lei Wang
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Industrial Development ◽  
Carbon Emission ◽  
Large Scale ◽  
Industrial Structure ◽  
Industrial Sector ◽  
Energy Structure ◽  
Low Carbon ◽  
Tianjin Binhai New Area

Energy consumption induced by industry sector is the main source of carbon emission. So it is important to the policy making that research on the low-carbon industrial development, which is aiming to establish an industry system with low-carbon character. There are four restrictive factors on low-carbon industrial development in Tianjin Binhai New Area of China: the rather large scale of the secondary industry, the heavy industrial structure which strongly caused the increasing energy consumption, the high-carbon energy structure of industrial sector, and the lower industrial energy efficiency which has certain gap compared with other regions. Several countermeasures are proposed to reduce carbon emission induced by industrial sector, such as improving energy efficiency, optimizing energy structure, establishing multiple-access financing mechanism to encourage R&D on low-carbon technology, enhancing the development of low carbon industry, participating in the international cooperation actively, and making strategic plan of low-carbon industrial development.

scholarly journals Analysis of China’s Industrial Green Development Efficiency and Driving Factors: Research Based on MGWR

2021 ◽  
Vol 18 (8) ◽  
pp. 3960
Author(s):  
Ke Liu ◽  
Yurong Qiao ◽  
Qian Zhou
Keyword(s):  
Economic Development ◽  
Technological Innovation ◽  
Yangtze River ◽  
Industrial Development ◽  
Government Regulation ◽  
Industrial Structure ◽  
Driving Factors ◽  
The Yangtze River ◽  
Green Development ◽  
The Government

With increasingly severe constraints on resources and the environment, it is the mainstream trend of economic development to reduce industrial pollution emissions and promote green industrial development. In this paper, a super-efficiency slacks-based measure (SBM) model is adopted to measure the industrial green development efficiency (IGDE) of 289 cities in China from 2008 to 2018. Moreover, we analyze their spatiotemporal differentiation pattern. On this basis, the multiscale geographical weighted regression (MGWR) model is used to analyze the scale differences and spatial differences of the driving factors. The results show that the IGDE is still at a low level in China. From 2008 to 2018, the overall polarization of IGDE was relatively serious. The number of high- and low-efficiency cities increased, while that of medium-efficiency cities greatly decreased. Secondly, the IGDE presented an obvious spatial positive correlation. MGWR regression results show that the technological innovation, government regulation, and consumption level belonged to the global scale, and there was almost no spatial heterogeneity. Other driving factors were urbanization, industrial structure, economic development, and population density according to their spatial scale. Lastly, the influence of economic development and technological innovation had a certain circular structure in space; the influence of population size mainly occurred in the cities of the southeast coast and northeast provinces; the influence of urbanization was more obvious in the most northern provinces of the Yangtze River, while that of industrial structure was mainly concentrated in the most southern cities of the Yangtze River Economic Belt (YREB). Spatially, the influence of consumption was manifested as a distribution trend of decreasing from north to south, and the government regulation was manifested as increasing from west to east and then to northeast.

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.

Driving Factors of Carbon Emissions in China's Municipalities: A LMDI Approach

2021 ◽  
Author(s):  
Yuanxin Liu ◽  
Yajing Jiang ◽  
Hui Liu ◽  
Bo Li ◽  
Jia-hai Yuan
Keyword(s):  
Carbon Emissions ◽  
Emission Reduction ◽  
Industrial Structure ◽  
Driving Factors ◽  
Policy Implications ◽  
Growth Effect ◽  
Structure Effect ◽  
Energy Structure ◽  
Intensity Effect ◽  
Population Effect

Abstract China, as the world’s largest carbon dioxide emitter, is bound to assume the important responsibility of energy conservation and emission reduction. To this end, each city, led by representative municipalities directly under the Central Government, must enhance efforts in carbon emission reduction to jointly realize China’s low-carbon transition. Taking four representative municipalities, namely, Beijing, Tianjin, Shanghai, and Chongqing as the case cities, this paper establishes a decomposition analysis for the driving factors of carbon emissions by applying the LMDI method covering data from 2007 to 2017. Kaya identity is used to decompose the effects into eight driving factors: GDP effect, industrial structure effect, energy intensity effect, overall energy structure effect, population effect, urbanization effect, per capita energy consumption effect, urban and rural energy structure effect. The results show that at the municipality level, the driving factors that promote the growth of carbon emissions are the GDP growth effect and the population effect, with the former still being the most important factor in the municipalities with faster economic growth; and industrial structure effect is the most important factor that inhibits the growth of carbon emissions, followed by energy structure effect. The paper thereby puts forward policy implications for China's economic policies.

scholarly journals Exploring the Effects of Industrial Structure, Technology, and Energy Efficiency on China’s Carbon Intensity and Their Contributions to Carbon Intensity Target

2020 ◽  
Vol 12 (19) ◽  
pp. 8016
Author(s):  
Feng Wang ◽  
Min Wu ◽  
Jiachen Hong
Keyword(s):  
Energy Efficiency ◽  
Energy Saving ◽  
Historical Development ◽  
Industrial Development ◽  
Industrial Structure ◽  
Electricity Production ◽  
Mitigation Measures ◽  
Carbon Intensity ◽  
Intermediate Input ◽  
Residential Energy

To achieve the national carbon intensity (NCI) target, China should adopt effective mitigation measures. This paper aims to examine the effects of key mitigation measures on NCI. Using the input-output table in 2017, this paper establishes the elasticity model of NCI to investigate the effects of industrial development, intermediate input coefficients, energy efficiency, and residential energy saving on NCI, and further evaluates the contributions of key measures on achieving NCI target. The results are shown as follows. First, the development of seven sectors will promote the increase of NCI while that of 21 sectors will reduce NCI. Second, NCI will decrease significantly with the descending of intermediate input coefficients of sectors, especially electricity production and supply. Third, improving energy efficiency and residential energy saving degree could reduce NCI, but the latter has limited contribution. Fourth, the development of all sectors will reduce NCI by 10.11% in 2017–2022 if sectors could continue the historical development trends. Fifth, assuming that sectors with rising intermediate input coefficients would keep their coefficients unchanged in the predicting period and sectors with descending coefficients would continue the historical descending trend, the improvement of technology and management of all sectors will reduce NCI by 14.02% in 2017–2022.

The Scenario Analysis of Carbon Emissions Based on Improved IPAT Model in China

2012 ◽  
Vol 616-618 ◽  
pp. 1484-1489 ◽  
Author(s):  
Xu Shan ◽  
Hua Wang Shao
Keyword(s):  
Carbon Dioxide ◽  
Economic Development ◽  
Carbon Dioxide Emissions ◽  
Industrial Structure ◽  
Energy Structure ◽  
Low Carbon ◽  
Policy Scenario ◽  
Social And Economic Development ◽  
Energy Environment ◽  
Development Goals

The coordination development of economy-energy-environment was discussed with traditional environmental loads model, combined with "decoupling" theory. Considering the possibilities of social and economic development, this paper set out three scenarios, and analyzed quantitatively the indexes, which affected carbon dioxide emissions, including population, per capita GDP, industrial structure and energy structure. Based on this, it forecasted carbon dioxide emissions in China in future. By comparing the prediction results, it held that policy scenario was the more realistic scenario, what’s more it can achieve emission reduction targets with the premise of meeting the social and economic development goals. At last, it put forward suggestions to implement successfully policy scenario, from energy structure, industrial structure, low-carbon technology and so on.

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