scholarly journals Measurement of the Energy Intensity of Human Well-Being and Spatial Econometric Analysis of Its Influencing Factors

2020 ◽  
Vol 17 (1) ◽  
pp. 357
Author(s):  
Ruyin Long ◽  
Qin Zhang ◽  
Hong Chen ◽  
Meifen Wu ◽  
Qianwen Li
Keyword(s):  
Energy Consumption ◽  
Influencing Factors ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Northern China ◽  
Well Being ◽  
Spillover Effects ◽  
Local Area ◽  
Energy Structure ◽  
Positive Effects

Current energy efficiency indicators (such as energy intensity) do not properly reflect the inherent relationship between “energy-environment-health”. Therefore, this study introduces the indicator of energy intensity of human well-being (EIWB) to depict the efficiency problem between energy consumption and residents’ health. In this paper, panel data of 30 provinces in mainland China from 2005 to 2016 is used to calculate the EIWB of each province and analyze its spatial distribution. Moreover, the effect of influencing factors on EIWB is investigated by using the spatial Durbin model. The results show that: (1) The EIWB presents a spatial agglomeration. The provinces with high EIWB mostly cluster in the northern China. (2) Industrial structure and energy structure have positive effects on EIWB in local area through increasing energy consumption and damaging residents’ health. (3) The effect of urbanization and income on local EIWB is significantly positive because it will promote energy consumption. (4) Industrial structure, health expenditure, foreign direct investment and technological progress have spatial spillover effects due to its significant impact on residents’ health in neighboring areas. Based on conclusions, the corresponding policy recommendations are proposed.

Influencing Factors Decomposition of the Carbon Emission of Industrial Energy Consumption in Nanning Downtown Based on LMDI

2015 ◽  
Vol 737 ◽  
pp. 925-934 ◽  
Author(s):  
Jing Yang ◽  
Huan Mei Yao ◽  
Meng Lin Qin
Keyword(s):  
Energy Consumption ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Energy Structure ◽  
Driving Factor ◽  
Low Carbon ◽  
Industrial Carbon ◽  
Industrial Energy ◽  
Industrial Energy Consumption

According to IPCC carbon emission calculation instruction, the amount of industrial carbon emission of downtown of Nanning from 2003-2012 is evaluated. With LMDI element decomposition method, the carbon emission of industrial energy consumption in Nanning downtown is decomposed into effect of five aspects such as energy structure, energy intensity, industrial structure, economic scale and population size. It turns out that: the energy structure change can promote the increase of carbon emission. The energy consumption structure should be optimized and the proportion of high-carbon energy consumption should be reduced; The energy intensity is the leading driving factor of carbon emission. The energy efficiency should be further improved to control the increase of carbon emission to some degree; The industrial structure restrains the increase of carbon emission in a great degree. Industrial restructuring should be strengthened and low-carbon industry should be developed; The scale of economy is the main driving factor of the increase of carbon emission. The extensive way of economic growth which depends on the large input of production factors should be changed; The population has a promoting function the increase of carbon emission, while the driving effect is weak, and the growth rate of the population should be strictly controlled.

scholarly journals The Regional Inequity of CO2 Emissions per Capita in China

2017 ◽  
Vol 9 (7) ◽  
pp. 228 ◽  
Author(s):  
Ting Liu ◽  
Wenqing Pan
Keyword(s):  
Co2 Emissions ◽  
Emission Reduction ◽  
Co2 Emission ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Theil Index ◽  
Energy Structure ◽  
Co2 Emission Reduction ◽  
Index Method ◽  
Per Capita

This paper combines Theil index method with factor decomposition technique to analyze China eight regions’ inequality of CO2 emissions per capita, and discuss energy structure, energy intensity, industrial structure, and per capita output’s impacts on inequality. This research shows that: (1) The trend of China regional carbon inequality is in the opposite direction to the per capita CO2 emission level. Namely, as the per capita CO2 emission levels rise, regional carbon inequality decreases, and vice versa. (2) Per capita output factor reduces regional carbon inequality, whereas energy structure factor and energy intensity factor increase the inequality. (3) More developed areas can reduce the carbon inequality by improving the energy structure, whereas the divergence of energy intensity in less developed areas has increased to expand the carbon inequity. Thus, when designing CO2 emission reduction targets, policy makers should consider regional differences in economic development level and energy efficiency, and refer to the main influencing factors. At the same time, upgrading industrial structure and upgrading energy technologies should be combined to meet the targets of economic growth and CO2 emission reduction.

scholarly journals Probing the carbon emissions in 30 regions of China based on symbolic regression and Tapio decoupling

2021 ◽  
Author(s):  
Haiying Liu ◽  
zhiqun zhang
Keyword(s):  
Influencing Factors ◽  
Carbon Emissions ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Symbolic Regression ◽  
Car Ownership ◽  
Emissions Reduction ◽  
Policy Recommendations ◽  
The World ◽  
Urbanization Rate

Abstract Against the background of energy shortages and severe air pollution, countries around the world are aware of the importance of energy conservation and emissions reduction; China is actively achieving emissions reduction targets. In this study, we use a symbolic regression to classify China's regions according to the degree of influencing factors, and calculate and analyze the inherent decoupling relationship between carbon emissions and economic growth in each region. Based on our results, we divided the 30 regions of the country into six categories according to the main influencing factors: GDP (13 regions), energy intensity (EI; 7 regions), industrial structure (IS; 3 regions), urbanization rate (UR; 3 regions), car ownership (CO; 2 regions), and household consumption level (HCL; 2 regions). Then, according to the order of the average carbon emissions in each region from high to low, these regions were further categorized as type-EI, type-UR, type-GDP, type-IS, type-CO, or type-HCL regions. The decoupling index of each region showed a downward trend; EI and GDP regions were the most notable contributors to emissions, based on which we provide policy recommendations.

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.

Study on the industrial structure optimization under constraint of energy intensity

2020 ◽  
pp. 0958305X2092159
Author(s):  
Xiongfeng Pan ◽  
Mengna Li ◽  
Chenxi Pu ◽  
Haitao Xu
Keyword(s):  
Energy Consumption ◽  
Computable General Equilibrium ◽  
Technological Progress ◽  
Energy Intensity ◽  
Industrial Structure ◽  
High Energy ◽  
Industrial Sector ◽  
Added Value ◽  
Tertiary Industry ◽  
Intensity Control

This study establishes a multi-sector dynamic computable general equilibrium framework that integrates energy intensity module to explore the reverse feedback effect of energy intensity control on industry structure. The results indicate that (1) the tightening effect of energy intensity constrains on the Industrial sector is most significant, followed by the Tertiary Industry, with the least impact on Agriculture; (2) when there is no technological progress in the departments, the change of industrial structure is mainly reflected in the sharp decline in the proportion of Industry and the significant increase in the proportion of Tertiary Industry. When technological progress exists in high energy-consumption departments, the tightening effect of energy intensity constraints on the industrial sector will be reduced; when there is technological progress in all departments, the industrial structure will have a smaller change, and the technology progress can alleviate the tightening effect of the energy intensity target on various sectors; (3) under the constraint of energy intensity, the high energy-consuming industry shifts to the Equipment Manufacturing with low energy-consumption and high-added value. The increasing proportion of Tertiary Industry mainly comes from two industries including Wholesale, Retail, Hoteling and Catering, and Transportation, Storage, and Post.

scholarly journals The Determinants of Carbon Emissions in the Chinese Construction Industry: A Spatial Analysis

2020 ◽  
Vol 12 (4) ◽  
pp. 1428 ◽  
Author(s):  
Na Lu ◽  
Shuyi Feng ◽  
Ziming Liu ◽  
Weidong Wang ◽  
Hualiang Lu ◽  
...  
Keyword(s):  
Construction Industry ◽  
Carbon Emissions ◽  
Industrial Structure ◽  
Spillover Effects ◽  
Energy Structure ◽  
Chinese Government ◽  
Low Carbon ◽  
Industry Agglomeration ◽  
Technology Innovations ◽  
Growth Energy

As the largest carbon emitter in the world, China is confronted with great challenges of mitigating carbon emissions, especially from its construction industry. Yet, the understanding of carbon emissions in the construction industry remains limited. As one of the first few attempts, this paper contributes to the literature by identifying the determinants of carbon emissions in the Chinese construction industry from the perspective of spatial spillover effects. A panel dataset of 30 provinces or municipalities from 2005 to 2015 was used for the analysis. We found that there is a significant and positive spatial autocorrelation of carbon emissions. The local Moran’s I showed local agglomeration characteristics of H-H (high-high) and L-L (low-low). The indicators of population density, economic growth, energy structure, and industrial structure had either direct or indirect effects on carbon emissions. In particular, we found that low-carbon technology innovation significantly reduces carbon emissions, both in local and neighboring regions. We also found that the industry agglomeration significantly increases carbon emissions in the local regions. Our results imply that the Chinese government can reduce carbon emissions by encouraging low-carbon technology innovations. Meanwhile, our results also highlight the negative environmental impacts of the current policies to promote industry agglomeration.

scholarly journals Decomposition of China’s Carbon Emissions Intensity from 1995 to 2010: An Extended Kaya Identity

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Wei Li ◽  
Qing-Xiang Ou
Keyword(s):  
Carbon Emissions ◽  
Emission Reduction ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Energy Structure ◽  
Logarithmic Mean ◽  
Development Path ◽  
Optimal Outcomes ◽  
Emissions Intensity ◽  
Kaya Identity

This paper employs an extended Kaya identity as the scheme and utilizes the Logarithmic Mean Divisia Index (LMDI II) as the decomposition technique based on analyzing CO2emissions trends in China. Change in CO2emissions intensity is decomposed from 1995 to 2010 and includes measures of the effect of Industrial structure, energy intensity, energy structure, and carbon emission factors. Results illustrate that changes in energy intensity act to decrease carbon emissions intensity significantly and changes in industrial structure and energy structure do not act to reduce carbon emissions intensity effectively. Policy will need to significantly optimize energy structure and adjust industrial structure if China’s emission reduction targets in 2020 are to be reached. This requires a change in China’s economic development path and energy consumption path for optimal outcomes.

Research on Energy Efficiency and Environmental Pollution in China: a Simultaneous Equations Approach

2011 ◽  
Vol 361-363 ◽  
pp. 974-977 ◽  
Author(s):  
Ying Nan Dong ◽  
Yu Duo Lu ◽  
Jiao Jiao Yu
Keyword(s):  
Energy Efficiency ◽  
Sustainable Development ◽  
Energy Consumption ◽  
Environmental Pollution ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Economic Loss ◽  
Simultaneous Equations ◽  
Consumption Structure ◽  
The Relationship

This paper examined the relationship between the energy efficiency and the environmental pollution. By using the data of energy intensity and economic loss caused by environmental pollution (ELP) in China from 1989-2009, a simultaneous equations was developed. The result of two-stage OLS estimation suggested that the energy had exerted positive influences on the decreasing of the environmental pollutions. By enhancing the energy efficiency and adjusting the industrial structure and energy consumption structure, China is exploring a road for sustainable development in the energy conservation.

scholarly journals Identifying Driving Factors of Jiangsu’s Regional Sulfur Dioxide Emissions: A Generalized Divisia Index Method

2019 ◽  
Vol 16 (20) ◽  
pp. 4004
Author(s):  
Junliang Yang ◽  
Haiyan Shan
Keyword(s):  
Energy Consumption ◽  
Sulfur Dioxide ◽  
Emission Reduction ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Structure Effect ◽  
Index Method ◽  
Intensity Effect ◽  
Energy Mix ◽  
Sulfur Dioxide Emissions

The Chinese government has made some good achievements in reducing sulfur dioxide emissions through end-of-pipe treatment. However, in order to implement the stricter target of sulfur dioxide emission reduction during the 13th “Five-Year Plan” period, it is necessary to find a new solution as quickly as possible. Thus, it is of great practical significance to identify driving factors of regional sulfur dioxide emissions to formulate more reasonable emission reduction policies. In this paper, a distinctive decomposition approach, the generalized Divisia index method (GDIM), is employed to investigate the driving forces of regional industrial sulfur dioxide emissions in Jiangsu province and its three regions during 2004–2016. The contribution rates of each factor to emission changes are also assessed. The decomposition results demonstrate that: (i) the factors promoting the increase of industrial sulfur dioxide emissions are the economic scale effect, industrialization effect, and energy consumption effect, while technology effect, energy mix effect, sulfur efficiency effect, energy intensity effect, and industrial structure effect play a mitigating role in the emissions; (ii) energy consumption effect, energy mix effect, technology effect, sulfur efficiency effect, and industrial structure effect show special contributions in some cases; (iii) industrial structure effect and energy intensity effect need to be further optimized.

Exploration of the Development of Schemes to Achieve a District-Aggregate Energy-Intensity Goal - Taking the 12th Five-Year Period Energy Intensity Goal of Daxing District, Beijing as a Study Case

2014 ◽  
Vol 962-965 ◽  
pp. 1455-1460
Author(s):  
Xiang Qian Li ◽  
Li Juan Yang ◽  
Ling Ling Chen
Keyword(s):  
Energy Consumption ◽  
Gross Domestic Product ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Energy Losses ◽  
Residential Energy ◽  
Initial Value ◽  
Study Case ◽  
Per Capita

The paper explored how to develop schemes to achieve a district’s energy consumption per gross domestic product (ECPGDP) target. It first analysed the available measures regarding the reduction of ECPGDP. These measures include optimising the industrial structure, reducing the energy intensity of different industries, reducing the per capita residential energy consumption, and reducing the energy losses. Next, the procedure and methods of developing schemes to achieve the target ECPGDP were proposed. The procedure contains five steps: determine the target ECPGDP, predicting the initial value of the ECPGDP, analysing the availability of different measures of reducing the ECPGDP, forming the schemes of achieving the target, and summarising the proposed schemes. Finally, the paper considered the 12th Five-Year period ECPGDP target of Daxing District, Beijing as a study case. In the case study, four quantitative schemes to achieve the target ECPGDP were considered.

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