Decomposition analysis of China's electricity intensity with LMDI method

He, Yongxiu; Tao, Weijun; Zhang, Songlei; Yang, Wei; Li, Furong

doi:10.1504/ijgei.2009.027972

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…To further investigate the drivers of electricity intensity in Madagascar, we employ the LMDI decomposition technique. The LMDI method is widely used in the energy economics literature to identify the contributing factors to changes in energy consumption or intensity (Ang, 2004(Ang, , 2005He et al, 2009;Inglesi-Lotz and Blignaut, 2011;Praene et al, 2021). Applying the decomposition approach to n sectors, the electricity intensity can be presented as follows:…”

Section: Decomposition Methodologymentioning

confidence: 99%

“…Shifting the focus to China, the study by He et al (2009) provides a detailed empirical analysis of the drivers of changes in China's Aggregate Electricity Intensity (CAEI) from 1995 to 2007 using the LMDI decomposition method. Decomposing CAEI at a granular 29-sub-sector level, the authors find that intensity effects played a major role in the decline of CAEI during 1995-1999, accounting for nearly half the observed decrease.…”

Section: Index Decomposition Of Energy Demandmentioning

confidence: 99%

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LMDI decomposition and macroeconomic drivers of electricity intensity in Madagascar

Ramaharo

2024

Preprint

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This study employs the Logarithmic Mean Divisia Index (LMDI) decomposition method to analyze the drivers of overall electricity intensity in Madagascar over the period 2007-2022. The decomposition results reveal that the residential sector is the primary driver of overall electricity intensity, followed by the service and industry sectors. Notably, the industry sector has contributed to a decrease in intensity. Furthermore, the results indicate that the intensity effect has had a significant impact on increasing overall electricity intensity, whereas the structure effect has played a role in reducing total intensity. Building on these findings, we investigate the impact of key macroeconomic factors on the observed electricity intensity trend using the Auto Regressive Distributed Lag (ARDL) bounds testing approach with quarterly data over the same study period. The ARDL cointegrating approach estimation results reveal a long-run association between financial development, foreign direct investment, industry development, consumer energy inflation, and trade openness. Moreover, the long-run coefficients, which are further supported by the Dynamic Ordinary Least Squares (DOLS) estimates, indicate that financial development and trade openness have a positive and significant impact on electricity intensity in Madagascar, while foreign direct investment, industry development, and consumer energy inflation have a negative and significant impact, suggesting that these factors contribute to electricity efficiency. The Toda-Yamamoto non-causality approach uncovers unidirectional causal relationships, specifically from financial development, industrial development, and consumer energy inflation to electricity intensity, as well as from electricity intensity to foreign direct investment. These causal directions are further corroborated by the forecast-error variance decomposition, providing strong evidence for these relationships. These findings provide valuable insights for policymakers and stakeholders in Madagascar's energy sector, highlighting the importance of promoting energy efficiency initiatives.

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Section: Decomposition Methodologymentioning

confidence: 99%

Section: Index Decomposition Of Energy Demandmentioning

confidence: 99%

LMDI decomposition and macroeconomic drivers of electricity intensity in Madagascar

Ramaharo

2024

Preprint

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“…Although different types of electricity generation are present in China, some papers did not consider separate sources of energy and treated the electricity generation sector as a whole when analyzing the driving forces of CO 2 emissions in this sector [39][40][41][42][43]. This can be partly explained by the fact that the structure of the Chinese electricity generation sector had been rather simple prior to the expansion of the renewables.…”

Section: Literature Review On China's Thermal Electricity Generationmentioning

confidence: 99%

Energy-Related CO2 Emission in China’s Provincial Thermal Electricity Generation: Driving Factors and Possibilities for Abatement

et al. 2018

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China's electricity sector mainly relies on coal-fired thermal generation, thus resulting that nearly 50% of China's total CO 2 emissions coming from the electricity sector. This study focuses on the provincial CO 2 emissions from China's thermal electricity generation. Methodologically, Index Decomposition Analysis (IDA), facilitated by the Shapley Index, is applied to discover the driving factors behind CO 2 emission changes at the provincial level. In addition, the Slack-based Model (SBM) is used to identify which provincial power grids should be allocated with a higher (lower) CO 2 reduction burden. The IDA results indicate that economic activity pushed the CO 2 emissions up in all 30 provincial power grids, excluding Beijing and Shanghai; the carbon factor contributed to a decrease in the CO 2 emissions in all 30 provincial power grids, with the exception of Jilin, Guangdong, and Ningxia; though the effect of energy intensity varied across the 30 provinces, it played a significant role in the mitigation of CO 2 emissions in Beijing, Heilongjiang, Liaoning, Jilin, Shanghai, Anhui, and Sichuan. According to the SBM results, the lowest carbon shadow prices are observed in Yunnan, Shanghai, Inner Mongolia, Jilin, Qinghai, Guizhou, Anhui, and Ningxia. These provincial power grids, thus, should face higher mitigation targets for CO 2 emissions from thermal electricity generation.

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Driving factors of consumption-based PM2.5 emissions in China: an application of the generalized Divisia index

Sun

Huang

2021

Environ Dev Sustain

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Decomposition analysis of China's electricity intensity with LMDI method

Cited by 7 publications

References 16 publications

LMDI decomposition and macroeconomic drivers of electricity intensity in Madagascar

LMDI decomposition and macroeconomic drivers of electricity intensity in Madagascar

Energy-Related CO2 Emission in China’s Provincial Thermal Electricity Generation: Driving Factors and Possibilities for Abatement

Driving factors of consumption-based PM2.5 emissions in China: an application of the generalized Divisia index

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