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

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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Decomposition Analysis of CO₂ Emissions of the Electricity Generation Sector in Korea using a Logarithmic Mean Divisia Index Method

Journal of Climate Change Research ◽

10.15531/ksccr.2017.8.4.357 ◽

2017 ◽

Vol 8 (4) ◽

pp. 357-367

Author(s):

Yongsung Cho

Keyword(s):

Electricity Generation ◽

Decomposition Analysis ◽

Logarithmic Mean ◽

Index Method ◽

Divisia Index

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Coupling the driving forces of urban CO2 emission in Shanghai with logarithmic mean Divisia index method and Granger causality inference

Journal of Cleaner Production ◽

10.1016/j.jclepro.2021.126843 ◽

2021 ◽

Vol 298 ◽

pp. 126843

Author(s):

Yulong Luo ◽

Weiliang Zeng ◽

Xianbiao Hu ◽

Hong Yang ◽

Lin Shao

Keyword(s):

Granger Causality ◽

Co2 Emission ◽

Driving Forces ◽

Logarithmic Mean ◽

Index Method ◽

Divisia Index ◽

Causality Inference

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Is Urban Economic Output Decoupling from Water Use in Developing Countries?—Empirical Analysis of Beijing and Shanghai, China

Water ◽

10.3390/w11071335 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1335 ◽

Cited By ~ 3

Author(s):

Xiaowei Wang ◽

Rongrong Li

Keyword(s):

Economic Development ◽

Water Use ◽

Industrial Structure ◽

Urban Water ◽

Industrial Water ◽

Primary Industry ◽

Intensity Effect ◽

Development Level ◽

Urban Water Use ◽

Secondary Industry

Water issue is one of the challenges of urban sustainability in developing countries. To address the conflict between urban water use and economic development, it is required to better understand the decoupling states between them and the driving forces behind these decoupling states. The transformed Tapio decoupling model is applied in this paper to study the decoupling relationship between urban industrial water consumption and economic growth in Beijing and Shanghai, two megacities in China, in 2003–2016. The factors driving decoupling are divided into industrial structure effect, industrial water utilization intensity effect, economic development level effect, and population size effect through Logarithmic Mean Divisia Index (LMDI) method. The results show that: (1) the decoupling states of total water consumption and economic growth in Beijing and Shanghai are mainly strong decoupling and weak decoupling. In comparison, Shanghai’s decoupling effect is better than Beijing; (2) regarding decoupling elasticity, Beijing is higher than that of Shanghai in tertiary industry and lower in primary industry and secondary industry. As a result, Beijing’s decoupling level is worse than Shanghai in tertiary industry, while better in primary industry and secondary industry; (3) The common factors that drive the two megacities’ decoupling are industrial structure effect and industrial water utilization intensity effect. The effects of economic development level and population size mainly present weak decoupling in two megacities, but the decoupling state is optimized year by year. Finally, based on the results, some suggestions for achieving the sustainable development of urban water use are proposed.

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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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Analysis on the Influence Factors of CO2 Emission in France - Based on LMDI Approach

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.616-618.1537 ◽

2012 ◽

Vol 616-618 ◽

pp. 1537-1540 ◽

Cited By ~ 1

Author(s):

Shuo Wang

Keyword(s):

Energy Use ◽

Influence Factors ◽

Energy Structure ◽

Carbon Density ◽

Logarithmic Mean ◽

Index Method ◽

Divisia Index ◽

Structure Problem

This article uses Logarithmic Mean Divisia Index Method (LMDI) to analyze influence factors of emission in France during last 50 years, including energy use, GDP, carbon density, energy structure and population. Energy structure problem is proposed at the end of the article.

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Decomposing the decoupling of water consumption and economic growth in Jiangxi, China

Journal of Water Reuse and Desalination ◽

10.2166/wrd.2018.003 ◽

2018 ◽

Vol 9 (1) ◽

pp. 94-104 ◽

Cited By ~ 5

Author(s):

Mianhao Hu ◽

Yunlin Hu ◽

Juhong Yuan ◽

Fucai Lu

Keyword(s):

Economic Growth ◽

Water Consumption ◽

Industrial Structure ◽

Jiangxi Province ◽

Key Factors ◽

Logarithmic Mean ◽

Primary Industry ◽

Index Method ◽

Industrial Growth ◽

Factors Affecting

Abstract Current population growth coupled with industrial growth has caused water supply to be outstripped by human demand. Understanding water consumption (WC) decoupling patterns and the factors affecting the decoupling status are essential for balancing economic growth and WC. This study determines the decoupling relationship between WC and economic growth in Jiangxi Province, China, and the driving factors were determined by the Tapio decoupling model and the logarithmic mean Divisia index method. Results showed that changes in the industrial structure in Jiangxi Province resulted in corresponding changes in WC structure. Analysis of the decoupling relationship showed that the decoupling state between WC and economic growth for primary industry was very unstable and largely volatile from 1999 to 2015, but showed a good decoupling status for secondary and tertiary industries. The largest cumulative effects on WC were economic development and technology, which were positive and negative drivers of WC changes, contributing 1,406.14% and −902.96% to the total effect of WC, respectively. The findings can help Jiangxi government identify the key factors influencing the decoupling effect, and formulate effective policies to reduce WC, which will benefit the harmonious development of economy, society and water resources in Jiangxi Province.

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Decomposing Influencing Factors of Carbon Emissions in Liaoning Province for 1999–2009 with Logarithmic Mean Divisia Index Method

Advanced Science Letters ◽

10.1166/asl.2013.4429 ◽

2013 ◽

Vol 19 (4) ◽

pp. 1213-1217 ◽

Cited By ~ 2

Author(s):

Qianbin Di ◽

Jialu Wu ◽

Jie Zhang

Keyword(s):

Influencing Factors ◽

Carbon Emissions ◽

Liaoning Province ◽

Logarithmic Mean ◽

Index Method ◽

Divisia Index

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

Sustainability ◽

10.3390/su11061806 ◽

2019 ◽

Vol 11 (6) ◽

pp. 1806 ◽

Cited By ~ 1

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.

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Divergent Leading Factors in Energy-Related CO2 Emissions Change Among Subregions of the Beijing–Tianjin–Hebei Area from 2006 to 2016: An Extended LMDI Analysis

Sustainability ◽

10.3390/su11184929 ◽

2019 ◽

Vol 11 (18) ◽

pp. 4929 ◽

Cited By ~ 2

Author(s):

Zou ◽

Tang ◽

Keyword(s):

Co2 Emissions ◽

Industrial Structure ◽

Driving Forces ◽

Low Carbon ◽

Logarithmic Mean ◽

Index Method ◽

Positive Effects ◽

Energy Demands ◽

Rapid Economic Growth ◽

Bth Region

In recent decades, the Beijing–Tianjin–Hebei (BTH) region has experienced rapid economic growth accompanied by increasing energy demands and CO2 emissions. Understanding the driving forces of CO2 emissions is necessary to develop effective policies for low-carbon economic development. However, because of differences in the socioeconomic systems within the BTH region, it is important to investigate the differences in the driving factors of CO2 emissions between Beijing, Tianjin, and Hebei. In this paper, we calculated the energy-related industrial CO2 emissions (EICE) in Beijing, Tianjin, and Hebei from 2006 to 2016. We then applied an extended LMDI (logarithmic mean Divisia index) method to determine the driving forces of EICE during different time periods and in different subregions within the BTH region. The results show that EICE increased and then decreased from 2006 to 2016 in the BTH region. In all subregions, energy intensity, industrial structure, and research and development (R&D) efficiency effect negatively affected EICE, whereas gross domestic product per capita effect and population had positive effects on EICE. However, R&D intensity and investment intensity had opposite effects in some parts of the BTH region; the effect of R&D intensity on EICE was positive in Beijing and Tianjin but negative in Hebei, while the effect of investment intensity was negative in Beijing but positive in Tianjin and Hebei. The findings of this study can contribute to the development of policies to reduce EICE in the BTH region.

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