Abstract
Although the logarithmic mean Divisia index (LMDI) approach has been widely used in the field of energy and environmental research, it has a shortcoming. Since the LMDI approach only focuses on the base year and reporting year, in situations in which the research period is long, the annual changes during the research period may be difficult to capture. In particular, if there were huge fluctuations in the indicators (such as the energy consumption and carbon emissions) or their drivers during the middle of a research period, a substantial amount of information about the fluctuations will be ignored. Therefore, we propose four extended yearly LMDI approaches, including pure LMDI, weighted LMDI, comprehensive LMDI, and scenario LMDI approaches to better capture fluctuations and compensate for the original LMDI approach’s shortcomings. Additionally, we found that there are mathematical relationships among the four extended LMDI approaches. We further compare these four approaches’ advantages, disadvantages, and applicable situations and analyze a case study on China’s energy consumption based on the four proposed approaches.
Highlights
The logarithmic mean Divisia index (LMDI) approach has been widely used in the field of energy and environmental research, it has a shortcoming
Since the decomposition created by the original LMDI approach only relies on the base year and reporting year, substantial information during the middle of the research period will be missed if the research period is long
We use China’s energy consumption as a case study and first adopt the original LMDI approach to explore the effects of driving factors on China’s energy consumption from 1980 to 2016, thereby considering only the base and reporting years. en, we further analyze them based on the four extended LMDI approaches to analyze the yearly effects of the drivers and make comparisons between the original LMDI approach and the extended yearly LMDI approaches
Summary
The logarithmic mean Divisia index (LMDI) approach has been widely used in the field of energy and environmental research, it has a shortcoming. Many scholars have used the original LMDI approach to measure the impact of various incentives on energy consumption (or other indicators, such as carbon emissions) during consecutive two-year periods [15, 16], which may not ignore the yearly information, few studies further discuss and explore the applicable scenarios of this type of decomposition. This is one of the extended yearly LMDI methods we intend to introduce and called it as the weighted LMDI approach. We provide a case study of China’s energy consumption based on the four approaches
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