LMDI Method Research Articles

Research on energy consumption of Beijing transportation industry based on LMDI method

Beijing, as the capital of China, has a rapid economic development and developed transportation industry. In recent years, the output value of transportation industry of Beijing has been growing rapidly. At the same time, it also accompanied by a large amount of energy consumption. With the energy and environmental problems increasingly prominent, it is particularly important to build a resource-saving and environment-friendly transportation industry. Based on this, the paper used LMDI method to analyse the factors affecting the energy consumption of Beijing transportation industry for the year 2005-2016. The results show that the energy structure, energy intensity and freight turnover of per GDP inhibit the energy consumption, and the freight turnover promote the energy consumption, in which, the freight turnover is the key factor to restrain energy consumption. Therefore, certain technical means should be taken out to reduce energy consumption and the pollution on environment, which has a certain practical significance in improving the environment of Beijing.

Decomposition of influencing factors and its spatial-temporal characteristics of vegetable production: A case study of China

China is the largest producer and consumer of vegetables, its vegetable industry is playing an important role in the domestic agricultural sector and global vegetable export market. It is important to promote the long-term sustainable development of Chinese vegetable industry for food security and quality of people’s lives. To find out the intrinsic way to promote the development of Chinese vegetable industry, this paper analyzed the influencing factors of Chinese vegetable production by utilizing the LMDI method and demonstrated the spatial-temporal characteristics of vegetable production through application of the ArcGIS spatial autocorrelation analysis method. The results showed that the influencing factors of vegetable production were the cultivated land area, multiple cropping index, vegetable planting proportion and vegetable yield per hectare in China. The major driving factor had changed from vegetable planting proportion to vegetable yield per hectare. The influencing degrees of factors on vegetable production are different in different regions, regionalization is therefore a major feature of Chinese vegetable production. The government should take production technology, regionalization-driven effect, and marketing integration into consideration to promote the development of Chinese vegetable industry.

Quantitative analysis of the impact factors of conventional energy carbon emissions in Kazakhstan based on LMDI decomposition and STIRPAT model

Quantitative analysis of the impact factors in energy-related CO2 emissions serves as an important guide for reducing carbon emissions and building an environmentally-friendly society. This paper aims to use LMDI method and a modified STIRPAT model to research the conventional energy-related CO2 emissions in Kazakhstan after the collapse of the Soviet Union. The results show that the trajectory of CO2 emissions displayed U-shaped curve from 1992 to 2013. Based on the extended Kaya identity and additive LMDI method, we decomposed total CO2 emissions into four influencing factors. Of those, the economic active effect is the most influential factor driving CO2 emissions, which produced 110.86 Mt CO2 emissions, with a contribution rate of 43.92%. The second driving factor is the population effect, which led to 11.87 Mt CO2 emissions with a contribution rate of 4.7%. On the contrary, the energy intensity effect is the most inhibiting factor, which caused–110.90 Mt CO2 emissions with a contribution rate of–43.94%, followed by the energy carbon structure effect resulting in–18.76 Mt CO2 emissions with a contribution rate of–7.43%. In order to provide an in-depth examination of the change response between energy-related CO2 emissions and each impact factor, we construct a modified STIRPAT model based on ridge regression estimation. The results indicate that for every 1% increase in population size, economic activity, energy intensity and energy carbon structure, there is a subsequent increase in CO2 emissions of 3.13%, 0.41%, 0.30% and 0.63%, respectively.

Comparative analysis on the socioeconomic drivers of industrial air-pollutant emissions between Japan and China: Insights for the further-abatement period based on the LMDI method

Abstract Japan has achieved the decoupling of economic growth from air-pollutant emissions since the rapid decrease in air-pollutant emissions in the 1970s and the further-abatement period. Air-pollutant emissions also decreased in China recently. We analyzed the factors driving the changes in air-pollutant emission, which enabled us to identify each factor's contribution and compare the further-abatement period in Japan with the primary reduction stage in China. This study performed the Logarithmic Mean Divisia Index analysis to decompose the industrial-emission changes (SO2 and NOx) in Japan and China into the socioeconomic factors that drive these changes. Results showed that changes in these factors, especially energy intensity and economic level, contributed differently to the emission reduction in Japan and China because the two countries differed in emission-reduction periods. The decline in emission coefficient, measured by emissions per unit of energy consumption, was the most important contributor to emission reduction in Japan. The other factors did not exert considerable influence. However, energy intensity decrease in China significantly contributed to emission reduction besides emission coefficient, and economic growth had a substantial negative impact on emission reduction. Differences also appeared in contributions from the industrial and economic structure between the two countries. Some factors may have reached their limitation after the rapid reduction period and thus contributed less significantly in the further-abatement period. A tendency toward a similar condition to Japan was also observed in some factorial contributions in China. From a sectoral perspective, emission change was distributed evenly among sectors in Japan than in China. Based on the comparison between the two countries and the “new normal” that China is experiencing, we provided insights for China for further abating industrial air-pollutant emissions in succeeding years.

Does forest matter regarding Chilean CO2 international abatement commitments? A multilevel decomposition approach

ABSTRACTThis paper assesses the role of the forestry sector in the CO2-eq emissions change in Chile from 1990 to 2013. Due to its relevance, this sector is considered as a proxy LULUCF activities. A second objective of this paper is to explore whether the forestry sector has always contributed to a decoupling process between CO2-eq emissions and economic growth. To address the first objective, the LMDI method has been used. For the second objective, the decoupling status between CO2-eq emissions comes from the Tapio index, which was used for the first step. For the second step, a second level of decomposition was carried out. Major findings from the LMDI and second-level decomposition analysis reveal that Chile's forestry sector clearly acted as a sink but failed to outweigh the role played by all other sectors taken into consideration. The results show that Chile has also failed to reach decoupling between economic growth and CO2-eq emissions. Efforts made in the forestry sector to reduce CO2 emissions were interesting but not strong enough to meet decoupling objectives. It is recommended that native forest management be reinforced and strengthened, mainly by reforestation with oak, raulí and coigüe varieties.

Analysis of the changes in the scale of natural gas subsidy in China and its decomposition factors

This paper applied the price-gap approach to estimate natural gas subsidy in China during 2007–2015 and employed the LMDI method to analyze the influencing factors of the changes in the subsidy scale. The results showed that the Chinese government raised the domestic gas price in contrast to the slump in global gas price in 2009, which accelerated the huge decrease in the natural gas subsidy scale. But the stagnation in price adjustment immediately led to a rebound in natural gas subsidy during 2010–2012. However, the level of natural gas subsidy has declined substantially in the industrial and commercial sectors since the natural gas pricing mechanism reform began in 2013. But the level of residential natural gas subsidy still remained high because the city gate pricing mechanism was not extended to this sector. The pricing mechanism was the most important decomposition factor of changes in the level of natural gas subsidy. The contribution rates of competitive gas price, pricing mechanism, consumption structure and natural gas consumption on decrease in natural gas subsidy scale were 11.08%, 101.21%, −3.15% and −9.14% respectively during 2013–2015. A reasonable and well-implemented pricing mechanism can prevent a rebound in natural gas subsidy. Therefore, the government should seize the current opportunity of sufficient natural gas supply and relatively low gas price to deepen and advance the natural gas pricing mechanism reform.

Investigating driving forces of aggregate carbon intensity of electricity generation in China

In addition to total CO2 emissions, an intensity indicator is also important to assess CO2 emissions from electricity generation, allowing the spatial differences to be analyzed, which is crucial for regionally heterogeneous countries like China. This study analyzed the driving forces of aggregate carbon intensity (ACI), a measure of CO2 emissions per unit of electricity generation, in China between 1995 and 2014, using the LMDI method incorporating geographical effects. Both the entire study period and individual 5-year periods were analyzed. The results show that the geographic distribution effect, utilization efficiency effect, and thermal power proportion effect were responsible for a decrease in ACI during this period, whereas the energy composition effect showed an inhibitory influence on reduction. The dominant factor was the utilization efficiency effect, and the thermal power proportion effect showed the largest growth. Geographic distribution is a non-negligible factor in the reduction of ACI, and the regional ACI can be reduced by redistributing electricity generation to areas with lower ACI through trans-regional policy guidance. For future policies on reduction of ACI from electricity generation, this study proposes focusing on the thermal power proportion and geographic distribution effects to the same extent as on the utilization efficiency effect.

Accounting carbon emission changes under regional industrial transfer in an urban agglomeration in China's Pearl River Delta

Industrial sectors account for a large part of urban carbon emissions. To address this, regional industrial transfer has recently been proposed in China, especially for areas consisting of urban agglomerations. In this manuscript, by applying LMDI method to the industrial transfer theory, driving forces of carbon emission changes from manufacturing industries were evaluated in the Pearl River Delta (PRD) of China. Firstly, the theories of industrial transfer were thoroughly analyzed to systematically study the pattern and trend. Then, a multi-region LMDI model was made to simulate the effect of the driving forces of regional transfer on carbon emissions, considering the production element flows of capital, labor, and technology under the background of industrial transfer through the cities. Finally, using the integrated model with spatial analysis, the temporal and spatial characteristics of carbon emission changes were quantified. The results revealed that the economic growth triggered the high speed of carbon increases, while industrial structure effect especially for labor-intensive industries and technology-intensive industries, help to prevent the carbon emissions from ever increasing. The developed cities effectively reduce high energy consuming industries, mainly the capital-intensive industries, by transferring to the developing places, causing the inequality distribution of carbon emissions of manufacturing industries in the PRD. The results may implied the adjustments of coordinated development to reduce the carbon disparities.

Technological progress and rebound effect in China's nonferrous metals industry: An empirical study

As one of China's mainstay and six major energy-intensive industries, the nonferrous metals industry faces the intense contradiction between economic growth and energy & environment constraints. Technological progress does not only realize the energy savings, but also causes rebound effect by promoting output growth. Although the rebound effect is an important topic, there is still very little empirical research that focuses on the nonferrous metals industry in China. Using the LMDI method and the total factor productivity model (to calculate parameters), we estimate the size of the rebound effect in China's nonferrous metals industry over the period 1985–2014. According to the results, the average rebound effect is approximately 83.02% with a downward trend, which indicates that most of the expected energy savings are mitigated. The rebounds with a strong fluctuation are comparatively discussed in various periods, suggesting that China cannot count only on technological progress to save energy and reduce emission. Furthermore, the government should apply economic instruments, such as energy pricing mechanism reform, resource tax, and carbon tax, as supplements to realize the targets of energy conservation and emission reduction. Additionally, optimizing sub-sector structure and promoting substitution also play a significant role in the mitigation of the rebound effect.

Analysis on the influencing factors of carbon emissions from energy consumption in China based on LMDI method

Based on the time series decomposition of the Log-Mean Divisia Index, this paper analyzes the driving factors of carbon emissions from energy consumption by introducing the indicators of energy trade in China during the period of 2000–2014. The carbon emissions are decomposed into carbon emission coefficient, population, economic output, energy intensity, energy trade, energy structure and industrial structure effect in the manuscript. The result indicates that economic activity has the largest positive effect on the variation of carbon emissions. The energy trade has a greatest opposite effect on carbon emission change. At the same time, China has achieved a considerable decrease in carbon emission mainly due to the improvement of energy intensity and the optimization of energy and industrial structure. However, the influences of those changes in energy intensity, energy and industrial structure are relatively small. In addition, through the analysis by using a suitable index of energy trade, it was found that improving the conditions of energy trade can effectively optimize the energy structure and reduce the carbon emission in China.

Comparative decoupling analysis of energy-related carbon emission from electric output of electricity sector in Shandong Province, China

As a major contributor to carbon dioxide emissions, the electric power sector has instigated significant changes in environmental issues. To show the effectiveness of the program, research on whether the changes of electricity production and CO2 emissions are out of sync are conducted by applying a decoupling elasticity analysis method. Then the decoupling index from the electricity analysis on the basis of the extended multilevel LMDI method are applied to study Shandong Province, covering the period from 1995 to 2012. Finally, a comparative decoupling stability analysis is applied. Our results indicate electricity output and coal consumption play significant roles in determining levels of CO2 emissions. Also, “relative decoupling” and “no decoupling” were the main states during the study period. We also found that the decoupling index performed better (in terms of stability) than did the electricity output elasticity of CO2 emission.

Research on Carbon Emission of Residents’ Consumption—Based on the City of Guangzhou

Carbon dioxide can cause greenhouse effects and that have a bad effect on the lives of residents. With the continuous improvement of economic development level, the consumption structure of residents is also changing, and then the carbon dioxide emission produced by residents is also changing. Therefore, this paper used LMDI decomposition method to calculate and analyze the indirect energy consumption of Guangzhou residents carbon emissions, which creatively used the LMDI method in the study of urban residents on the carbon emissions. At the same time, the research of this paper is helpful to strengthen the residents’ awareness of carbon emission reduction. This paper first explored the factors that affect the indirect energy carbon emissions of residents, then defined the research frame of residents’ indirect energy carbon emission, and therefore accounted for the indirect energy consumption of residents. According to the demographic characteristics of the population of Guangzhou, the emission from residents’ carbon consumption is further divided into urban consumer carbon emissions and rural consumer carbon emissions. The research results are as follows: The main factor that stimulates the carbon dioxide emission of Guangzhou’s residential indirect energy consumption is per capita income; the main factor that suppresses the emission is the intensity of energy consumption Population size, per capita income, and the resident consumption structure have a stimulating effect on the emissions of carbon dioxide from Guangzhou’s residential indirect energy consumption of urban and rural residents. The intensity of energy consumption and the emission intensity of carbon dioxide have a suppressing effect on the emissions of carbon dioxide from Guangzhou’s residential indirect energy consumption of urban and rural residents. Urban and rural population structure has a stimulating effect on the emissions of carbon dioxide from Guangzhou’s residential indirect energy consumption of urban residents, but has a suppressing effect on rural residents.

Decomposition and scenario analysis of CO2 emissions in China’s power industry: based on LMDI method

The power industry is a major contributor to CO2 emissions in China and thus plays a critical role in achieving the targets of CO2 emission reduction. This study analyzes the historical trajectory and feature of CO2 emissions in China’s power industry, explores the driving factors of CO2 emission change using LMDI method, and develops two emission reduction scenarios to evaluate the reduction potential of CO2 emissions. Results show the following: (1) China’s power industry has experienced a significant but unstable increase in CO2 emissions from 343.18 Mt in 1985 to 3447.57 Mt in 2013, a growth rate of 904.60%. (2) Industrial-scale effect plays a dominant role in promoting CO2 emission growth in China’s power industry, and the corresponding contribution degree reaches 111.73%. Energy intensity effect contributes most to the decrease in CO2 emissions, with a contribution degree of −16.82%. Capital productivity effect is another important factor leading to the increase in CO2 emissions. (3) The aggregate CO2 emission reduction in China’s power industry would reach 18,031.62 Mt in the ideal scenario and 15,466.03 Mt in the current policy scenario during 2014–2030. Finally, this study provides policy implications for energy-saving and CO2 emission reduction in China’s power industry.

The Influence Factors Decomposition of Grain Output Increase in China: 2003-2014

<p>As the world’s most populous country taking up 18.84% of the world’s total population, China’s grain supply has attracted high concerns in the world. This article takes the grain output increase in China from 2003 to 2014 as the research object, and wants to explore the influence factors of China’s grain growth and spatial effect analysis. It employs LMDI method to decompose China’s total grain output increase during 2003-2014. The increase of OFPPU paid maximum contribution to China’s total grain output increase among ACL, MCI and GPP. The conclusion of China’s grain output provincial spatial analysis is that the six provinces including Heilongjiang, Henan, Inner Mongolia, Jilin, Anhui and Shandong, contributed 62.2% share to China’s increase. The results of predicted OFPPU by Exponential Smoothing Method of Hoter-Winter No-Seasonal are that grain growth in China can meet requirements of grain security for China’s population growth and living improvement. The author wishes China can increase investment on land consolidation and renovate facilities for farmland and water conservancy projects.</p>

Urbanization and Its Effects on Industrial Pollutant Emissions: An Empirical Study of a Chinese Case with the Spatial Panel Model

Urbanization is considered a main indicator of regional economic development due to its positive effect on promoting industrial development; however, many regions, especially developing countries, have troubled in its negative effect—the aggravating environmental pollution. Many researchers have addressed that the rapid urbanization stimulated the expansion of the industrial production and increased the industrial pollutant emissions. However, this statement is exposed to a grave drawback in that urbanization not only expands industrial production but also improves labor productivity and changes industrial structure. To make up this drawback, we first decompose the influence of urbanization impacts on the industrial pollutant emissions into the scale effect, the intensive effect, and the structure effect by using the Kaya Identity and the LMDI Method; second, we perform an empirical study of the three effects by applying the spatial panel model on the basis of the data from 282 prefecture-level cities of China from 2003 to 2014. Our results indicate that (1) there are significant reverse U-shapes between China’s urbanization rate and the volume of industrial wastewater discharge, sulfur dioxide emissions and soot (dust) emissions; (2) the relationship between China’s urbanization and the industrial pollutant emissions depends on the scale effect, the intensive effect and the structure effect jointly. Specifically, the scale effect and the structure effect tend to aggravate the industrial wastewater discharge, the sulfur dioxide emissions and the soot (dust) emissions in China’s cities, while the intensive effect results in decreasing the three types of industrial pollutant emissions; (3) there are significant spatial autocorrelations of the industrial pollutant emissions among China’s cities, but the spatial spillover effect is non-existent or non-significant. We attempt to explain this contradiction due to the fact that the vast rural areas around China’s cities serve as sponge belts and absorb the spatial spillover of the industrial pollutant emissions from cities. According to the results, we argue the decomposition of the three effects is necessary and meaningful, it establishes a cornerstone in understanding the definite relationship between urbanization and industrial pollutant emissions, and effectively contributes to the relative policy making.

Study on decoupling analysis between energy consumption and economic growth in Liaoning Province

Since 1978, Liaoning province has experienced spectacular economic growth, which has led to more energy consumption. The purpose of this paper is to explore the decoupling status between energy consumption and economic growth in Liaoning Province. Firstly, the generalized LMDI method is used to explore the driving forces governing production energy consumption in Liaoning province. Then, the combination of Tapio decoupling indicator and generalized LMDI method is utilized to study what the decoupling status occurred in Liaoning Province and why the decoupling status appeared. During the study period, only four decoupling status occurred: expansive negative decoupling, expansive coupling, weak decoupling, and strong decoupling. The energy intensity decoupling effect played a positive role in the appearance of decoupling. However, the economic structure decoupling effect and investment decoupling effect played a negative role in the appearance of decoupling. Over the study period, the energy structure decoupling effect and labour decoupling effect played a relative small role in the appearance of decoupling.

Decomposing the influencing factors of energy consumption in Tunisian transportation sector using the LMDI method

Due to rapid economic development and accelerated urbanization, Tunisia's transport sector has experienced a dramatic growth that leads to excessive demand for fossil fuel energy. This study identifies the driving factors and measures their corresponding contributions in transportation energy consumption for the case of Tunisia by using the logarithmic-mean Divisia index method (LMDI) over the period 1985–2014. The transport related energy consumption is decomposed into energy intensity, transportation structure effect, transportation intensity effect, economic output, and population scale effects according to the driving mechanism. Results indicate that the overall effect of economic output, transportation intensity, population scale, and transportation structure on energy consumption is positive, whereas the overall effect of energy intensity is negative. It was shown that energy intensity played the dominant role in decreasing energy consumption during the study period. Improving the transport intensity exerts significant effect on saving energy. Our empirical findings provide scientific supports for the policy measures based on low greenhouse gas emissions integrated transport.

Analysis of energy-related CO2 emissions and driving factors in five major energy consumption sectors in China.

Continual growth of energy-related CO2 emissions in China has received great attention, both domestically and internationally. In this paper, we evaluated the CO2 emissions in five major energy consumption sectors which were evaluated from 1991 to 2012. In order to analyze the driving factors of CO2 emission change in different sectors, the Kaya identity was extended by adding several variables based on specific industrial characteristics and a decomposition analysis model was established according to the LMDI method. The results demonstrated that economic factor was the leading force explaining emission increase in each sector while energy intensity and sector contribution were major contributors to emission mitigation. Meanwhile, CO2 emission intensity had no significant influence on CO2 emission in the short term, and energy consumption structure had a small but growing negative impact on the increase of CO2 emissions. In addition, the future CO2 emissions of industry from 2013 to 2020 under three scenarios were estimated, and the reduction potential of CO2 emissions in industry are 335Mt in 2020 under lower-emission scenario while the CO2 emission difference between higher-emission scenario and lower-emission scenario is nearly 725Mt. This paper can offer complementary perspectives on determinants of energy-related CO2 emission change in different sectors and help to formulate mitigation strategies for CO2 emissions.

Decomposition analysis for assessing the progress in decoupling relationship between coal consumption and economic growth in China

This paper has four purposes. Firstly, the status of coal production and consumption is analyzed. Secondly, the temporal and spatial evolution of the coal consumption in China is studied. Furthermore, the LMDI method is used to explore the factors influencing coal consumption in China. Finally, a new decoupling index is developed based on the decomposition results, which is used to study the decoupling relationship between coal consumption and economic growth in China. This paper draws the following results: (1) Coal for transformation consumption gradually exceeded that for end-use over the study period. During 1991–2013, power generation was the biggest coal consumer among all transformation sectors. Among all end-use sectors, industry sector was the biggest coal consumer. (2) In 1991, Liaoning province was the biggest coal consumer. However, the biggest coal consumer was Shandong in 2013. The center of gravity for coal consumption was an overall movement towards the southwest. (3) The energy intensity effect played the dominant role in decreasing coal consumption over the study period. However, The economic activity effect and population effect maked the continual increase of coal consumption in China. (4) Only three decoupling statuses occurred over the study period: weak decoupling, expansive coupling, and expansive negative decoupling.

Decomposition of intensity of energy-related CO2 emission in Chinese provinces using the LMDI method

Uncovering the driving factors of CO2 emission intensity declining is important for China. This paper improves the logarithmic mean Divisia index technique, which includes energy density and energy consumption intensity, to explore the driving factors of carbon emission intensity (CI) in 29 Chinese provinces from 1995–2012. The main results are: (1) energy consumption intensity plays a more important role than carbon emission density (CD) for a rapid decrease in CI during the research period, so a much room is left for a significant CD reduction through carbon emission reduction technology, energy structural reduction, and energy consumption proportional reduction. (2) The decrease in energy consumption technology and energy structure in secondary industries contributes the most reduction in energy consumption intensity. (3)The energy consumption proportions of secondary and tertiary industries are the two most important drivers to decrease CD. (4) During the research period, the energy consumption proportions of secondary industries result in the most decrease in CD, whereas the energy consumption proportions of tertiary industries cause the most increase in CD.