Log Mean Divisia Index Method Research Articles

Driving force analysis of the agricultural water footprint in China based on the LMDI method.

China's water scarcity problems have become more severe because of the unprecedented economic development and population explosion. Considering agriculture's large share of water consumption, obtaining a clear understanding of Chinese agricultural consumptive water use plays a key role in addressing China's water resource stress and providing appropriate water mitigation policies. We account for the Chinese agricultural water footprint from 1990 to 2009 based on bottom up approach. Then, the underlying driving forces are decomposed into diet structure effect, efficiency effect, economic activity effect, and population effect, and analyzed by applying a log-mean Divisia index (LMDI) model. The results reveal that the Chinese agricultural water footprint has risen from the 94.1 Gm3 in 1990 to 141 Gm3 in 2009. The economic activity effect is the largest positive contributor to promoting the water footprint growth, followed by the population effect and diet structure effect. Although water efficiency improvement as a significant negative effect has reduced overall water footprint, the water footprint decline from water efficiency improvement cannot compensate for the huge increase from the three positive driving factors. The combination of water efficiency improvement and dietary structure adjustment is the most effective approach for controlling the Chinese agricultural water footprint's further growth.

Using LMDI method to analyze the influencing factors of carbon emissions in China’s petrochemical industries

China’s petrochemical industries are playing an important role in China’s economic development. However, the industries consume large amounts of energy and have become primary sources of carbon emission. In this paper, the change in carbon emissions from China’s petrochemical industries between 2000 and 2010 was quantitatively analyzed with the Log-Mean Divisia Index method, which was decomposed into economic output effect, industrial structural effect and technical effect. The results show that economic output effect is the most important factor driving carbon emission growth in China’s petrochemical industries; industrial structural effect has certain decrement effect on carbon emissions; adjustment of industrial structure by developing low-carbon emission industrial sectors may be a better choice for reducing carbon emissions; and the impact of technical effect varies considerably without showing any clear decrement effect trend over the period of year 2000–2010. The biggest challenge is how to make use of these factors to balance the relationship between economic development and carbon emissions. This study will promote a more comprehensive understanding of the inter-relationships of economic development, industrial structural shift, technical effect and carbon emissions in China’s petrochemical industries and is helpful for exploration of relevant strategies to reduce carbon emissions.

Mitigating greenhouse gas emissions from China's cities: Case study of Suzhou

Knowledge of the factors driving greenhouse gas (GHG) emissions from cities is crucial to mitigating China's anthropogenic emissions. In this paper, the main drivers increasing GHG emissions from the Chinese city of Suzhou between 2005 and 2010 were identified and quantitatively analyzed using the Kaya identity and the log-mean Divisia index method. We found that economy and population were the major drivers of GHG emissions in Suzhou, having contributed 162.20% and 109.04%, respectively, to the increase in emissions. A decline in carbon intensity, which was caused by the declining energy intensity and an adjustment to the mixture of power and industrial structures, was the major determinant and accounted for a reduction of 171.24% in GHG emissions. Slowing and maintaining healthy growth rates of economy and population could be the primary and most effective means if Suzhou tries to curb the total emissions over the short term. It may be more realistic for Suzhou to control emissions by optimizing the economic structure for low-carbon industrial development because of the city's relative high energy requirements and low potential to mitigate GHGs by adjusting the energy mixture.

Study on temporal and spatial evolution of China’s oil supply and consumption

To begin with, energy flow chart is used to analyze the status of China’s oil supply and consumption. Moreover, the temporal and spatial evolution of the oil production and oil products consumption in China is studied based on the gravity model. Finally, the decoupling index combined with the log mean Divisia index method is used to explore the contribution of the factors which influence terminal oil product consumption in China over the period 1991–2010. This paper draws the following results: (1) China’s net oil import dependency soared from 7.5 % in 1993 to 58.63 % in 2010. (2) The center of gravity for crude oil production and oil products consumption is an overall movement toward the southwest. (3) The economic activity effect is the critical factor in the growth of oil products consumption in China. However, industrial energy intensity effect plays the dominant role in decreasing oil products consumption. (4) The value of the decoupling index represented a re-coupling effect in 1996–1997 and 2003–2004. The other time interval showed weak decoupling effect.

Analysis of rural residential commercial energy consumption in China

The objective of this paper is to identify the factors that contribute to the change in rural residential commercial energy consumption in China by utilizing the LMDI (Log Mean Divisia Index) method. Then the decoupling index is developed based on those factors to evaluate the progress in decoupling energy consumption from per capita annual net income of rural households. The results show that: (1) Rural residential commercial energy consumption in China grew at a yearly rate of 2.15% over the period 1991–2010; however, the rural residential commercial energy consumption per capita increased from 75.96 in 1991 to 143.74 kgtce in 2010. (2) The income effect is the critical factor in the growth of rural residential energy consumption in China and the energy intensity effect plays the dominant role in decreasing rural energy consumption. (3) The period 1995–1996, 2001–2005 and 2007–2008 represents a re-coupling effect, the period 1992–1993, 1994–1995, 1996–1998 and 1999–2000 indicates strong decoupling effect, while the other time interval shows weak decoupling effect.

Decomposing the decoupling of energy-related CO2 emissions and economic growth in Jiangsu Province

Jiangsu Province has become one of the most developed regions in China. Economic growth in Jiangsu has occurred along with rising CO2 emission levels. A deeper understanding of how energy-related CO2 emissions have evolved in Jiangsu Province is very important in formulating future policies. Thus it is very necessary to investigate the driving forces governing CO2 emissions and their evolution. The decoupling index combined with the LMDI (Log Mean Divisia Index) method is used to analyze the contribution of the factors which influence energy-related CO2 emissions in Jiangsu Province over the period 1995–2009. The results show that economic activity is the critical factor in the growth of energy-related CO2 emissions in Jiangsu Province, and the energy intensity effect plays the dominant role in decreasing CO2 emissions. The period from 2003 to 2005 represents a re-coupling effect; the periods 1996–1997 and 2000–2001 indicate strong decoupling effect, while the remaining time intervals show weak decoupling effect.

Using LMDI method to analyze the change of industrial CO2 emission from energy use in Chongqing

Low-carbon economy is becoming a new approach to optimize economic development, ensuring energy security and coping with climate change. As one of the important emission sources of greenhouse gases (GHG), the industrial sector should be prioritized in the development of low-carbon economy. In this study, the carbon emission from industrial energy use of Chongqing is accounted. On basis of industrial carbon emission (ICE) accounting, main factors responsible for industrial CO2 emission are identified and quantitatively analyzed using the Log-Mean Divisia Index method. The factors influencing ICE include energy mix, energy intensity, industrial structure and industrial output. It is found that the industrial output is the main driving force of ICE. The energy structure performs as a negative factor in carbon emission growth. By means of decomposing the influencing factors, several policy proposals were suggested for policy makers to build a low carbon city.

Decomposing the influencing factors of industrial carbon emissions in Shanghai using the LMDI method

Abstract Knowledge of influencing factors of industrial carbon emissions (ICE) is crucial to the efforts of reducing anthropogenic greenhouse gas emissions. In this paper, main factors responsible for the ICE in Shanghai between 1996 and 2007 were identified and quantitatively analyzed using the Log-Mean Divisia Index method. It was found that the industrial output was the main driving force of ICE. The decline in energy intensity and the adjustment of energy and industrial structure are major determinants for reduction of ICE, with the former alone accounting for 90% of the reduction. To better investigate the relative contribution of different industrial sectors and their changes over time, we divided the study period into two equal time intervals and analyzed some high-carbon emission sectors. The results suggested that the intensity of energy use should be reduced further, for it was far higher than the world average. Adjustment of industrial structure by developing low-carbon emission industries is more crucial than energy mix.

Decomposition analysis and mitigation strategies of CO 2 emissions from energy consumption in South Korea

Energy-related CO 2 emissions in South Korea have increased substantially, outpacing those of Organisation for Economic Co-operation and Development (OECD) countries since 1990. To mitigate CO 2 emissions in South Korea, we need to understand the main contributing factors to rising CO 2 levels as part of the effort toward developing targeted policies. This paper aims to analyze the specific trends and influencing factors that have caused changes in emissions patterns in South Korea over a 15-year period. To this end, we employed the Log Mean Divisia index method with five energy consumption sectors and seven sub-sectors in terms of fuel mix (FM), energy intensity (EI), structural change (SC) and economic growth (EG). The results showed that EG was a dominant explanation for the increase in CO 2 emissions in all of the sectors. The results also demonstrated that FM causes CO 2 reduction across the array of sectors with the exception of the energy supply sector. CO 2 reduction as a function of SC was also observed in manufacturing, services and residential sectors. Furthermore, EI was an important driver of CO 2 reduction in most sectors except for several manufacturing sub-sectors. Based on these findings, it appears that South Korea should implement climate change policies that consider the specific influential factors associated with increasing CO 2 emissions in each sector.

Factors affecting CO 2 emission from the power sector of selected countries in Asia and the Pacific

This study analyzes the key factors behind the CO 2 emissions from the power sector in fifteen selected countries in Asia and the Pacific using the Log-Mean Divisia Index method of decomposition. The roles of changes in economic output, electricity intensity of the economy, fuel intensity of power generation and generation structure are examined in the evolution of CO 2 emission from the power sector of the selected countries during 1980–2004. The study shows that the economic growth was the dominant factor behind the increase in CO 2 emission in ten of the selected countries (i.e., Australia, China, India, Japan, Malaysia, Pakistan, South Korea, Singapore, Thailand and Vietnam, while the increasing electricity intensity of the economy was the main factor in three countries (Bangladesh, Indonesia and Philippines). Structural changes in power generation were found to be the main contributor to changes in the CO 2 emission in the case of Sri Lanka and New Zealand.

Using LMDI method to analyze the change of China's industrial CO 2 emissions from final fuel use: An empirical analysis

Based on time series decomposition of the Log-Mean Divisia Index (LMDI), this paper analyzes the change of industrial carbon emissions from 36 industrial sectors in China over the period 1998–2005. The changes of industrial CO 2 emission are decomposed into carbon emissions coefficients of heat and electricity, energy intensity, industrial structural shift, industrial activity and final fuel shift. Our results clearly show that raw chemical materials and chemical products, nonmetal mineral products and smelting and pressing of ferrous metals account for 59.31% of total increased industrial CO 2 emissions. The overwhelming contributors to the change of China's industrial sectors’ carbon emissions in the period 1998–2005 were the industrial activity and energy intensity; the impact of emission coefficients of heat and electricity, fuel shift and structural shift was relatively small. Over the year 1998–2002, the energy intensity change in some energy-intensive sectors decreased industrial emissions, but increased emissions over the period 2002–2005. The impact of structural shift on emissions have varied considerably over the years without showing any clear trend, and the final fuel shift increased industrial emissions because of the increase of electricity share and higher emissions coefficient. Therefore, raw chemical materials and chemical products, nonmetal mineral products and smelting and pressing of ferrous metals should be among the top priorities for enhancing energy efficiency and driving their energy intensity close to the international advanced level. To some degree, we should reduce the products waste of these sectors, mitigate the growth of demand for their products through avoiding the excessive investment highly related to these sectors, increasing imports or decreasing the export in order to avoid expanding their share in total industrial value added. However, all these should integrate economic growth to harmonize industrial development and CO 2 emission reduction.

A new energy decomposition method: perfect in decomposition and consistent in aggregation

A new energy decomposition method, called the Log-Mean Divisia Index Method I (LMDI I), is presented. It has the desirable properties of perfect decomposition and consistency in aggregation. Perfect decomposition ensures that the decomposition results obtained do not contain a residual term. Consistency in aggregation allows estimates for sub-groups to be aggregated in a consistent manner. The formulation of the new method and the usefulness of aggregation consistency in energy decomposition studies are described. Two case studies on energy-related CO 2 emissions are presented.