scholarly journals Uncovering Variations, Determinants, and Disparities of Multisector-Level Final Energy Use of Industries Across Cities

2019 ◽  
Vol 11 (6) ◽  
pp. 1806 ◽  
Author(s):  
Xianrui Liao ◽  
Wei Yang ◽  
Yichen Wang ◽  
Junnian Song
Keyword(s):  
Energy Use ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Industrial Output ◽  
Contribution Rate ◽  
Index Method ◽  
Intensity Effect ◽  
Output Effect ◽  
Final Energy ◽  
Two Stages

With continuous industrialization and urbanization, cities have become the dominator of energy consumption, to which industry is making leading contribution among all sectors. Given the insufficiency in comparative study on the drivers of energy use across cities at multisector level, this study selected seven representative cities in China to quantify and analyze the contributions of factors to changes in final energy use (FEU) in industrial aggregate and sectoral levels by using Logarithmic Mean Divisia Index method. Disparities in the drivers of industrial FEU across cities were explicitly revealed within two stages (2005–2010 and 2010–2015). Some key findings are presented as follows. Alongside the increase in industrial output of seven cities within two stages, the variation trends in industrial FEU are different. Industrial output effect (contribution rate 16.7% ~ 184.0%) and energy intensity effect (contribution rate −8.6% ~ −76.5%) contributed to the increase in aggregate FEU positively and negatively, respectively. Beijing had the largest contribution share of industrial structure effect (−24.4% and −12.8%), followed by Shenyang and Xi’an. Contributions of energy intensity effect and industrial output effect for Chemicals, Nonmetals, Metals, and Manufacture of equipment were much larger than those of other sectors. The results revealed that production technological innovations, phase-out of outdated capacities of energy intensive industries, and industrial restructuring are crucial for reduction in industrial FEU of cities. This study also provided reference to reasonable industrial layout among cities and exertion of technological advantages from a national perspective.

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.

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 Analysis of the Influencing Factors of Regional Carbon Emissions in the Chinese Transportation Industry

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1100 ◽  
Author(s):  
Changzheng Zhu ◽  
Meng Wang ◽  
Yarong Yang
Keyword(s):  
Size Effect ◽  
Carbon Emissions ◽  
Energy Intensity ◽  
Eastern Coast ◽  
Southern Coast ◽  
Transportation Industry ◽  
Fourth Stage ◽  
Intensity Effect ◽  
Output Effect ◽  
Economic Output

Global warming caused by excessive emissions of CO2 and other greenhouse gases is one of the greatest challenges for mankind in the 21st century. China is the world’s largest carbon emitter and its transportation industry is one of the fastest growing sectors for carbon emissions. However, China is a vast country with different levels of carbon emissions in the transportation industry. Therefore, it is helpful for the Chinese government to formulate a reasonable policy of regional carbon emissions control by studying the factors influencing the carbon emissions of the Chinese transportation industry at the regional level. Based on data from 1997 to 2017, this paper adopts the logarithmic mean divisia index (LMDI) decomposition method to analyze the influencing degree of several major factors on the carbon emissions of transportation industry in different regions, puts forward some suggestions according to local conditions, and provides references for the carbon reduction of Chinese transportation industry. The results show that (1) in 2017, the total carbon emissions of the Chinese transportation industry were 714.58 million tons, being 5.59 times of those in 1997, with an average annual growth rate of 9.89%. Among them, the carbon emissions on the Eastern Coast were rising linearly and higher than those in other regions. The carbon emissions in the Great Northwest were always lower than those in other regions, with only 38.75 million tons in 2017. (2) Economic output effect is the most important factor to promote the carbon emissions of transportation industry in various regions. Among them, the contribution values of economic output effect to carbon emissions on the Eastern Coast, the Southern Coast and the Great Northwest continued to rise, while the contribution values of economic output effect to carbon emissions in the other five regions decreased in the fourth stage. (3) The population size effect promoted the carbon emissions of the transportation industry in various regions, but the population size effect of the Northeast had a significant inhibitory influence on the carbon emissions in the fourth stage. (4) The regional energy intensity effect in most stages inhibited carbon emissions of the transportation industry. Among them, the energy intensity effects of the North Coast and the Southern Coast in the two stages had obvious inhibitory influences on carbon emissions of the transportation industry, but the contribution values of the energy intensity effect in the Great Northwest and the Northeast were positive in the fourth stage. (5) Except for the Great Southwest, the industry-scale effects of other regions had inhibited the carbon emissions of transportation industry in all regions. (6) The influences of the carbon emissions coefficient effect on carbon emissions in different regions were not significant and their inhibitory effects were relatively small.

scholarly journals Decoupling and decomposition analysis on the CO2 emissions of tourism industry: A case study of Hainan

2019 ◽  
Vol 118 ◽  
pp. 04042
Author(s):  
Yong Yang ◽  
Junsong Jia ◽  
Chunyan Liu ◽  
Duanqian Mao
Keyword(s):  
Energy Use ◽  
Industrial Structure ◽  
Decomposition Analysis ◽  
Tourism Industry ◽  
Primary Role ◽  
Intensity Effect ◽  
Use Efficiency ◽  
Countermeasures And Suggestions ◽  
Tourism Economy

Currently, little attention was paid to the tourism’s CO2 emission (CE) at province level. Thus, taking Hainan as a case, we computed this province’s CE, and analyzed the relationship between Hainan’s tourism economy and its CE, and the drivers of the CE. The results showed that Hainan’s tourism CE increased rapidly from 99.88×104 t in 2001 to 475.07×104 t in 2015. Particularly, Tourism transport always accounted for the largest proportion of tourism CE (more than 74%). Moreover, Hainan presented a holistic weak decoupling (0.68) during 2001-2015. But the decoupling rate was only 57.14%. Thus, Hainan still has much potential to improve the energy-use efficiency of tourism industry for accelerating the decoupling process. In addition, the effect of population was the dominant driver to promote Hainan’s tourism CE followed by expenditure size effect with the contribution rates of 132.52% and 11.78%, respectively. Whereas energy intensity effect played the most primary role in inhibiting CE followed by industrial structure effect, and their contribution rates were -38.65% and -5.58%, respectively. Last, based on these results above, some reasonable countermeasures and suggestions are proposed.

scholarly journals Analysis of Labor Embodied in China’s Inter-regional Trade in Value-Added: Re-Examination of Leontief’s Paradox from the Perspective of Energy Use

2021 ◽  
Author(s):  
Bingqian YAN ◽  
Zhenxia WANG
Keyword(s):  
Industrial Development ◽  
Energy Use ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Coastal Region ◽  
Value Added ◽  
Embodied Energy ◽  
Interregional Trade ◽  
Northwestern Region ◽  
Extensive Growth

Based on the multi-regional input-output framework, this paper analyzed the labor and energy transfer embodied in interregional trade in China. Meanwhile, through estimating the energy intensity per unit of labor embodied in final products in each region, this study examines whether the empirical results are consistent with the theoretical hypothesis and provides relevant explanations and industrial development suggestions. Results show that east coastal region and central region are the two main participants in interregional trade. As for the labor embodied in trade, east coastal region is the largest exporter of embodied labor, though it has the highest wage among eight regions; in contrast, north coastal and southwestern region, with relatively low wage, are the two largest importer of embodied labor. As for the energy embodied in trade, northwestern region is the largest exporter of embodied energy. Further analysis indicates that the energy intensity per unit of labor in region with relatively low GRP (such as northwestern region) is the highest, whereas those in Beijing-Tianjin Region and south coastal region (with relatively high GRP) are the lowest. By analyzing the Revealed Comparative Advantage in each region, the paper finds that the main reason for this inconsistency lies in the industrial structure in northwestern and north coastal region, which are highly dependent in primary industries. Improving the infrastructure and upgrading industrial structure are important steps for these regions to transform the extensive growth mode.

Research on Carbon Emissions Estimation and Decomposition of Industry in Hebei Province

2012 ◽  
Vol 518-523 ◽  
pp. 4941-4947
Author(s):  
Dong Heng Hao ◽  
Guo Zhu Li ◽  
Dian Ru Wang
Keyword(s):  
Carbon Emissions ◽  
Large Scale ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Hebei Province ◽  
Industrial Output ◽  
Factors Affecting ◽  
Industrial Enterprises ◽  
Industrial Energy ◽  
Major Factors

This paper estimated the carbon emissions of the large-scale industrial enterprises in Hebei Province, and analyzed their changing factors using the LMDI method. The results shows that major factors affecting the carbon emissions of industry in Hebei province are energy intensity, industrial structure and output changes . Seen from the absolute value , industrial output has the greatest impact on carbon emissions followed by energy intensity. Industrial structures has the least impact. The combined impact of industrial energy saving technological progress and industrial structure adjustment on carbon emissions is less than that of industrial output.

scholarly journals Sectoral Decomposition of CO2 Emissions in China and India for the Period 1980-2010

2013 ◽  
Author(s):  
Hasan Rüstemoğlu ◽  
Sevin Uğural
Keyword(s):  
Co2 Emissions ◽  
Scale Effect ◽  
Energy Use ◽  
Energy Intensity ◽  
Public Awareness ◽  
Carbon Intensity ◽  
Composition Effect ◽  
Intensity Effect ◽  
China And India ◽  
Laspeyres Index

Increasing amount of CO2 emissions and global warming is one of the hottest topics of world’s agenda. At the same time there exists a public awareness about this important chapter. A lot of researcher proved that in order to live in a sustainable world, necessary regulations should be done and CO2 emissions should be reduced immediately. For this study our aim is to decompose the CO2 emissions of the world’s new super powers, China and India, over 1980-2010. In order to see the accelerating and decelerating factors for CO2 emissions, the Refined Laspeyres Index used as a method. Consistent data gathered from the official web sites of the World Bank and United Nations. Five main sectors, agriculture, manufacturing, construction, transportation and other services are used. Four different impacts, scale effect, composition effect, energy intensity effect and carbon intensity effect have been discussed to see the increasing or decreasing factors of CO2 emissions. The results were interesting. The dominant impacts were the scale effect and energy intensity effect. The minor impacts were composition effect and carbon intensity effect. Fuel switching, efficient energy use and increasing usage of renewable resources are efficient tools to reduce the emissions.

scholarly journals Decomposing the Influencing Factors of Energy Intensity in the Passenger Transportation Sector in Indonesia

2020 ◽  
Vol 22 (3) ◽  
pp. 49-58
Author(s):  
Dhani Setyawan
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Energy Intensity ◽  
Structural Effect ◽  
Transport Sector ◽  
Passenger Transport ◽  
Intensity Effect ◽  
Passenger Transportation ◽  
Transportation Sector ◽  
Final Energy Consumption

Indonesia's transport sector has experienced rapid growth that has caused excessive fossil fuel energy consumption. Over 2000 to 2016 total final energy consumption in Indonesia’s transport sector has grown by 10% per annum so that transport now provides a large and rapidly growing component of total energy use. This study analyzes the specific characteristics of energy intensity in the transportation sector in Indonesia from 2000 to 2016 by employing a multiplicative Log Mean Divisia Index-II. The passenger transport sector in Indonesia, including the four modes of road, rail, water and air is examined in this study. Overall, the decline in energy intensity in passenger transport is attributed to the intensity effect. In passenger transport, the improvement of intensity effect was found to have significantly reduced the overall aggregate energy intensity, while the change in structural effect was found to have a relatively small reduction in the aggregate energy intensity.

Analysis of Energy Consumption in China and the Driving Factors Based on MRCI

2014 ◽  
Vol 521 ◽  
pp. 855-858 ◽  
Author(s):  
Hong Hai Sun ◽  
Yan Bin Sun ◽  
Yan Qiu Wang
Keyword(s):  
Energy Consumption ◽  
Energy Intensity ◽  
Structure Effect ◽  
Contribution Rate ◽  
Complete Structure ◽  
Output Analysis ◽  
Intensity Effect ◽  
Input Output Analysis ◽  
Analysis Theory ◽  
Consumption Saving

Using the complete structure of input output analysis theory and no residual decomposition method (MRCI) effect on the growth of energy consumption China and corresponding contribution rate of quantitative analysis of scale effect, the contribution rate of 100.99%, the structure of production effect, the contribution rate of 38.16%, per capita energy efficiency reached 9.05%, population scale contribution rate of 12.11%, the use of the structure effect reached 6.09%, distribution structure effect is up to 5.83%, pushing the energy consumption growth Chinese role; energy intensity effect contribution rate of-72.23% China, inhibit the growth of energy consumption, saving energy and reducing consumption of our country plays an important role.

scholarly journals Decomposing drivers of changes in productive and domestic water use based on the logarithmic mean Divisia index method: a regional comparison in Northern China

Water Policy ◽  
2021 ◽  
Author(s):  
Wenfei Lyu ◽  
Yuansheng Chen ◽  
Zhigang Yu ◽  
Weiwei Yao ◽  
Huaxian Liu
Keyword(s):  
Water Use ◽  
Industrial Structure ◽  
Scale Effects ◽  
Northern China ◽  
Logarithmic Mean ◽  
Domestic Water ◽  
Index Method ◽  
Divisia Index ◽  
Intensity Effect ◽  
Productive Water

Abstract It is crucial to consider regional heterogeneity while analyzing drivers of changes in sectoral water use for developing differentiated and effective demand-regulation strategies in China. By using the logarithmic mean Divisia index method, this study compares dynamic influences of intensity, structure and scale factors on changes in productive and domestic water use during 2003–2017 between Tianjin (a socio-economic developed region) and Hebei (less-developed). The results show that the scale effect stimulated the growth of productive water use in both regions, while structure and intensity effects restrained such growth. The three effects all stimulated the growth of domestic water use in most years in both regions. In both regions, the largest contributor to changes in productive and domestic water use was the scale and intensity effect, respectively. However, in the two regions, the synergies of three effects resulted in different change trends of productive water use, and cumulative contributions of sub-sectors to the intensity, structure and scale effects were not exactly the same. Tianjin and Hebei need to keep on adjusting industrial structure and lowering water-use intensity to control future growth of productive water use and take strict measures to tackle the increasing trend of domestic water use but should have different policy implementation focuses.

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