LMDI decomposition approach: A guide for implementation

Abstract:In 2013, China proposed the Bangladesh-China-India-Myanmar Economic Corridor initiatives to enhance the economic cooperation between Bangladesh, India, and Myanmar. To serve the priority setting of agricultural cooperation in the initiatives, an analysis is necessary to understand the food security situation and its key influencing factors in the three countries. For this purpose, this study applied a food security index (FSI) to measure the food security level at state scale and analyze its changes in the three countries during 1990-2013. Further, a factor decomposition method was used to quantify the contribution of major factors to the FSI changes. The results showed that the food demand was generally secured in Myanmar and India during 1990-2013 but in shortage by around 10% in most years before 2008 in Bangladesh. The annual FSI values showed a rather obvious periodic variation and a slightly increasing trend during the whole period. Although the grain production in the three countries increased significantly, the FSI was not accordingly improved, as a result of the simultaneous increase of grain consumption. In Bangladesh and India, the grain production increase was mainly driven by the improvement of crop yield, while the grain consumption increase was mainly caused by the population growth. In Myanmar, the production increase was largely caused by the expansion of cropping area, and the consumption growth was mainly due to the increased grain requirement per capita. To meet the increasing food demand in the future, it is essential to improve the irrigation and drainage systems and crop management to increase the land use efficiency and crop yield. In addition, the adaptation of appropriate policy for family planning could be important to slow down the quick population growth and thus the food demand.

Section: Quantifying Factor Contribution To Food Securitymentioning

confidence: 99%

“…For the given period between the beginning time 0 and the ending time t, ∆TGP and ∆TGC can be quantified by the following equations [36]:…”

Section: Quantifying Factor Contribution To Food Securitymentioning

confidence: 99%

A Comparative Analysis on Food Security in Bangladesh, India and Myanmar

Chen

2018

“…The result of the LMDI method does not produce residuals. Therefore, LMDI is a relatively superior decomposition method [72][73][74][75][76][77][78][79]. This paper uses the LMDI method to analyze the influencing factors of carbon emissions in the transportation sector of North China.…”

Section: The Logarithmic Mean Divisia Index (Lmdi) Modelmentioning

confidence: 99%

An Analysis of Decoupling and Influencing Factors of Carbon Emissions from the Transportation Sector in the Beijing-Tianjin-Hebei Area, China

Zhu

2017

Abstract:The transport sector is the major green-house gas emitter and most rapidly growing sector in terms of consuming energy in China. Understanding the driving forces behind carbon emission is a prerequisite for reducing carbon emissions and finding a balance between economic growth and carbon emissions. The purpose of this paper is to identify the impact of the factors which influence the level of carbon emissions from the transportation sector in the Beijing-Tianjin-Hebei (BTH) area, China, using decomposition model, combined with a decoupling elasticity index. The results of our study indicate that: (1) changes in the level of carbon emissions from the transportation sector are not always synchronized with changes in economic growth. (2) The decoupling state between the carbon emissions and economic growth of Tianjin and Beijing can be roughly divided into two phases. The first phase was during the 2005 to 2009 period, when the decoupling state was pessimistic. The second phase was from 2009 to 2013, when the decoupling state became better overall and was mainly dominated by weak decoupling. Conversely, the decoupling state of Hebei was mainly weak during this period. (3) Economic growth and population size play positive roles in increasing the levels of transportation-related carbon emissions in BTH. However, the energy structure is a negative force. The effect of energy intensity always plays a negative role in Tianjin and Hebei, but positive in Beijing. The industrial structure effect shows a fluctuating trend, but the cumulative effect value is negative, and negative interaction is prominent. Finally, this paper gives some suggestions on how to develop low-carbon transport in BTH area.

“…Ang et al [21][22][23][24][25] presented a review of the development and applications of IDA methods and recommended the logarithmic mean Divisia index I (LMDI) method because it is robust and convenient for many applications. Many studies have utilized the LMDI method to decompose the total CO 2 growth of various sectors and regions.…”

Section: Lmdi Decompositionmentioning

confidence: 99%

LMDI Decomposition of Energy-Related CO2 Emissions Based on Energy and CO2 Allocation Sankey Diagrams: The Method and an Application to China

Chong

Zhang

et al. 2018

This manuscript develops a logarithmic mean Divisia index I (LMDI) decomposition method based on energy and CO 2 allocation Sankey diagrams to analyze the contributions of various influencing factors to the growth of energy-related CO 2 emissions on a national level. Compared with previous methods, we can further consider the influences of energy supply efficiency. Two key parameters, the primary energy quantity converted factor (K PEQ ) and the primary carbon dioxide emission factor (K C ), were introduced to calculate the equilibrium data for the whole process of energy unitization and related CO 2 emissions. The data were used to map energy and CO 2 allocation Sankey diagrams. Based on these parameters, we built an LMDI method with a higher technical resolution and applied it to decompose the growth of energy-related CO 2 emissions in China from 2004 to 2014. The results indicate that GDP growth per capita is the main factor driving the growth of CO 2 emissions while the reduction of energy intensity, the improvement of energy supply efficiency, and the introduction of non-fossil fuels in heat and electricity generation slowed the growth of CO 2 emissions.