The Empirical Research of the Causality Relationship Between CO2 Emissions Intensity, Energy Consumption Structure, Energy Intensity and Industrial Structure in China

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.

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Analysis of Energy Consumption Influence Factors in Japan Based on LMDI Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.71-78.2416 ◽

2011 ◽

Vol 71-78 ◽

pp. 2416-2419

Author(s):

Hong Qin Liu ◽

Xu Yan ◽

Hai Yan Duan ◽

Xian En Wang

Keyword(s):

Economic Development ◽

Energy Consumption ◽

Energy Intensity ◽

Industrial Structure ◽

Japanese Economy ◽

Consumption Structure ◽

Structure Adjustment ◽

Industrial Structure Adjustment ◽

Rapid Industrialization ◽

Positive Effect

After World War Ⅱ Japanese economy has undergone three periods: the rapid industrialization period, the industrial structure adjustment period and the economy depression period. Affected by the speed of economic development, industrial structure and other factors, Japanese energy consumption has shown different features during specific period of time. This article use the LMDI model, analyze the effect of different factors on Japanese energy consumption which include economic development, energy intensity, energy consumption structure and population size, research on the weight of specific factors during each developing period. The results show that all the factors show positive effect in the rapid industrialization period; in the industrial structure adjustment period, economic development factor shows positive effect while energy consumed factor shows negative; and in the depression period, the trend of all the factors contribution rate are slowly, economic development and energy consumption structure also show negative effect besides the energy intensity factor.

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Prediction of China’s Energy Consumption Based on Robust Principal Component Analysis and PSO-LSSVM Optimized by the Tabu Search Algorithm

Energies ◽

10.3390/en12010196 ◽

2019 ◽

Vol 12 (1) ◽

pp. 196 ◽

Cited By ~ 3

Author(s):

Lihui Zhang ◽

Riletu Ge ◽

Jianxue Chai

Keyword(s):

Principal Component Analysis ◽

Energy Consumption ◽

Tabu Search ◽

Industrial Structure ◽

Principal Component ◽

Component Analysis ◽

Support Vector ◽

Forecasting Model ◽

Robust Principal Component Analysis ◽

Consumption Structure

China’s energy consumption issues are closely associated with global climate issues, and the scale of energy consumption, peak energy consumption, and consumption investment are all the focus of national attention. In order to forecast the amount of energy consumption of China accurately, this article selected GDP, population, industrial structure and energy consumption structure, energy intensity, total imports and exports, fixed asset investment, energy efficiency, urbanization, the level of consumption, and fixed investment in the energy industry as a preliminary set of factors; Secondly, we corrected the traditional principal component analysis (PCA) algorithm from the perspective of eliminating “bad points” and then judged a “bad spot” sample based on signal reconstruction ideas. Based on the above content, we put forward a robust principal component analysis (RPCA) algorithm and chose the first five principal components as main factors affecting energy consumption, including: GDP, population, industrial structure and energy consumption structure, urbanization; Then, we applied the Tabu search (TS) algorithm to the least square to support vector machine (LSSVM) optimized by the particle swarm optimization (PSO) algorithm to forecast China’s energy consumption. We collected data from 1996 to 2010 as a training set and from 2010 to 2016 as the test set. For easy comparison, the sample data was input into the LSSVM algorithm and the PSO-LSSVM algorithm at the same time. We used statistical indicators including goodness of fit determination coefficient (R2), the root means square error (RMSE), and the mean radial error (MRE) to compare the training results of the three forecasting models, which demonstrated that the proposed TS-PSO-LSSVM forecasting model had higher prediction accuracy, generalization ability, and higher training speed. Finally, the TS-PSO-LSSVM forecasting model was applied to forecast the energy consumption of China from 2017 to 2030. According to predictions, we found that China shows a gradual increase in energy consumption trends from 2017 to 2030 and will breakthrough 6000 million tons in 2030. However, the growth rate is gradually tightening and China’s energy consumption economy will transfer to a state of diminishing returns around 2026, which guides China to put more emphasis on the field of energy investment.

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The Regional Inequity of CO2 Emissions per Capita in China

International Journal of Economics and Finance ◽

10.5539/ijef.v9n7p228 ◽

2017 ◽

Vol 9 (7) ◽

pp. 228 ◽

Cited By ~ 2

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.

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

10.3233/faia210304 ◽

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.

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Study on the industrial structure optimization under constraint of energy intensity

Energy & Environment ◽

10.1177/0958305x20921592 ◽

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.

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

Mathematical Problems in Engineering ◽

10.1155/2013/973074 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 5

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.

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

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16204004 ◽

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.

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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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.962-965.1455 ◽

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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Effects of Energy Prices on Energy Efficiency in China

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.962-965.1767 ◽

2014 ◽

Vol 962-965 ◽

pp. 1767-1772

Author(s):

Zun Ming Ren

Keyword(s):

Energy Efficiency ◽

Energy Consumption ◽

Industrial Structure ◽

Time Series Data ◽

Series Data ◽

Energy Prices ◽

Electricity Prices ◽

Consumption Structure ◽

Integration Test

The paper utilized the co-integration test, error correction model and Granger causality test, and other methods to verify the influence of the coal, oil and electricity prices, industrial and energy consumption structures on China's energy efficiency based on time-series data from 1979 to 2010. Test results show that: there is long-term equilibrium relationship of the energy prices, industrial structure, energy consumption structure and energy efficiency; coal prices, industrial structure and energy consumption structure are the Granger reasons of energy efficiency both in the short and long run; while the oil and electricity prices only constitute the long-term Granger reasons of energy efficiency. Finally, it analyzed the implications of policies of the empirical results and provided some constructive suggestions.

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