A spatial shift-share decomposition of electricity consumption changes across Italian regions

Grossi, Luigi; Mussini, Mauro

doi:10.1016/j.enpol.2017.10.043

Cited by 21 publications

(16 citation statements)

References 17 publications

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“…It measures the effect of changes in the electricity intensities of various sectors during the study period. The equation is as follows [21,22]:…”

Section: Methodsmentioning

confidence: 99%

“…As every region may be affected by the positive or negative impact of neighboring regions [33], the traditional shift-share decomposition analysis (SSA) can be extended to a spatial shift-share decomposition model (SSS), which considers the electricity efficiency of neighboring regions [19,21,22]. The calculation procedures are as follows:…”

Section: Methodsmentioning

confidence: 99%

“…Letě i,r present the electricity intensity in sector i in neighboring regions. It is calculated by the following formula [21,22]:…”

Section: Methodsmentioning

confidence: 99%

“…Subsequently, Espa et al [21] suggested a spatial shift-share decomposition of regional business changes at the plant level in Italy. Grossi and Mussini [22] recently applied SSS to explore the changes in regional electricity consumption across Italy over the period 2000-2013. This approach may help us to understand the geography of electricity consumption change with respect to both the spatial and sectoral influence.…”

Section: Introductionmentioning

confidence: 99%

“…This approach may help us to understand the geography of electricity consumption change with respect to both the spatial and sectoral influence. It may reduce the risk of misleading results [14,22].…”

Section: Introductionmentioning

confidence: 99%

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Electricity Consumption Changes across China’s Provinces Using A Spatial Shift-Share Decomposition Model

Bao

Liu

2019

Sustainability

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China’s growing electricity consumption has become an important factor to improving socio-economic development, as well as aggravating environmental degradation. Based on the provincial level data in China for the entire period and every five years during 2000–2015, this paper used a spatial shift-share analysis (SSS) to detect the driving factors of electricity consumption changes in China, mainly focusing on the spatial spillover effects of electricity consumption which have been ignored by previous literature. Results show that economic growth and industry structure change have increased China’s electricity consumption by 8919 and 746 billion kWh, respectively, while the electricity efficiency improvement has reduced China’s electricity consumption by 5337 billion kWh for the entire period. Among the total decrease in China’s electricity consumption caused by electricity efficiency improvement, about 20% is caused by spatial spillover effects, which cannot be ignored. Moreover, there are great differences in electricity consumption changes’ components across China’s provinces. The results provide a quantitative and better understanding of the determinants of China’s electricity consumption changes, and practical implications for differentiated electricity consumption regulation policies and regional energy cooperation strategies for China, as well as for other similar countries.

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“…It measures the effect of changes in the electricity intensities of various sectors during the study period. The equation is as follows [21,22]:…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Letě i,r present the electricity intensity in sector i in neighboring regions. It is calculated by the following formula [21,22]:…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Electricity Consumption Changes across China’s Provinces Using A Spatial Shift-Share Decomposition Model

Bao

Liu

2019

Sustainability

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Food processing industry changes across China regions: The case of flour, rice, oil, and other cereal derivative food

Dou

Feng-ting

2022

Food Science & Nutrition

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Faced with the pressure of slowing industrial growth and industrial transformation requirements, it is crucial to analyze the changes and the corresponding driving factors of the food processing industry in China. An analysis using traditional and spatial shift‐share models was conducted to decompose the changes in the food processing industry in each region of China from 2009 to 2019 into five effects: national growth effect (NG), industrial mix effect (IM), competitive effect (CE), neighbor‐nation competitive effect (NNC), and region‐neighbor competitive effect (RNC). Among the five effects from 2009 to 2019, the NG contributed the most to the growth in most regions, indicating that the development of the food processing industry in China was greatly influenced by the industrial base and that China's food processing industry has entered a “growth bottleneck period.” During the period 2009–2014 to period 2014–2019, compared to the IM and CE, the influence of spatial spillover effects was stronger and significantly enhanced. Moreover, the IM, CE, NNC, and RNC in most southern regions were stronger than those in most northern regions. Therefore, China's food processing industry needs and is transforming into high‐quality development. It is necessary to innovate the mode of development of food processing industry and strengthen interregional exchanges and cooperation.

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A Reconnaissance Through the History of Shift-Share Analysis

Lahr¹,

Ferreira²

2020

Handbook of Regional Science

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A spatial shift-share decomposition of electricity consumption changes across Italian regions

Cited by 21 publications

References 17 publications

Electricity Consumption Changes across China’s Provinces Using A Spatial Shift-Share Decomposition Model

Electricity Consumption Changes across China’s Provinces Using A Spatial Shift-Share Decomposition Model

Food processing industry changes across China regions: The case of flour, rice, oil, and other cereal derivative food

A Reconnaissance Through the History of Shift-Share Analysis

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