Industrial structure, energy-saving regulations and energy intensity: Evidence from Chinese cities

2017 ◽  
Vol 141 ◽  
pp. 1539-1547 ◽  
Author(s):  
Ben Ma ◽  
Yihua Yu
Keyword(s):  
Energy Saving ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Chinese Cities

Energy Saving and Emission Reduction Potential Analysis Based on Industrial Structure and Scientific Technology Progress in China

2013 ◽  
Vol 827 ◽  
pp. 417-421
Author(s):  
Le Ya Wu ◽  
Wei Hua Zeng ◽  
Hao Wu
Keyword(s):  
Energy Saving ◽  
Emission Intensity ◽  
Emission Reduction ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Carbon Dioxide Emission ◽  
Structure Evolution ◽  
Structure Adjustment ◽  
Industrial Structure Adjustment ◽  
Scientific Technology

Energy saving and emission reduction is a necessary way for China to develop low carbon economy. Based on the knowledge of industrial structure adjustment and scientific technology progress, a potential analysis is addressed through four general models, namely modified structure evolution-energy intensity interconnection model, modified structure evolution-carbon dioxide emission intensity interconnection model, scientific technology progress-energy intensity interconnection model and scientific technology progress-carbon dioxide emission intensity interconnection model. It shows that both industrial structure adjustment and scientific technology have a great significance on energy saving and carbon emission reduction in China during the period from 1995 to 2010, and industrial structure adjustment shows more energy saving and emission reduction potential than scientific technology progress. With the development of industrial structure and scientific technology, during the 12th five-year plan period, the energy intensity saving potential of them are 0.5166 and 1.0526tce/10,000RMB respectively; and the carbon emission intensity potential of them are 1.2883 and 2.9536t/10,000RMB respectively. So it is very important to make full use of scientific technology progress potential to service for energy saving and emission reduction in the future.

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.

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 Analysis of the electric energy consumption components for mine train traction

2019 ◽  
Vol 294 ◽  
pp. 01001 ◽  
Author(s):  
Serhii Arpul ◽  
Viktor Artemchuk ◽  
Mykola Babyak ◽  
Viacheslav Vasilyev ◽  
Hennadii Hetman ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Energy Intensity ◽  
Electric Energy ◽  
Rolling Stock ◽  
List Type ◽  
Opencast Mining ◽  
Study Results ◽  
Transportation Process ◽  
The Cost

The paper considers the issues of reducing the energy intensity of transportation at opencast mining enterprises, the relevance of which has now increased due to the rise in the cost of fuel and energy resources. It presents the study results concerning the cost structure of the electricity consumed by electric mine transport, which form the basis for the development of technical and operational measures to reduce the energy intensity of the transportation process. It is shown that the work to reduce the electricity consumption for mined rock transportation should be aimed at: Reduction of losses in the power circuits of the traction rolling stock due to the use of more advanced electric rolling stock and regulation of the degree of utilization of the installed traction power; Introduction of new contact materials for electrical circuits with the lowest possible resistivity, including for current collector plates; Introduction of measures to reduce energy consumption for power supply of auxiliary circuits; Development and implementation of rational train control techniques. The introduction of energy-saving measures should include the development and application of effective methods for calculating individual norms of energy consumption and incentives for energy saving of the employees involved in the organization of the transportation process.

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.

Sign in / Sign up

Export Citation Format

Share Document