A panel empirical modeling on the driving factors of provincial electricity consumption in China

Yuan, Jiahai; Zhao, Defu

doi:10.1007/s11356-021-16261-8

Cited by 9 publications

(5 citation statements)

References 42 publications

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“…The estimates show that a one percentage point increase in urbanization rate leads to 0.37 percent increase in electricity consumption. While the magnitude of this effect may be lower compared to some findings in the literature which can reach up to 23 percent [ [ 7 , 8 ]], this result highlights the presence of inefficiencies and potential poor consumption habits in urban areas. The result suggests that urban areas may exhibit higher electricity consumption and potentially inefficient energy usage patterns compared to rural areas.…”

Section: Resultscontrasting

confidence: 58%

“…Pertaining to the consumers' socioeconomics conditions, factors such as income, education, household's size, and structure are mainly analysed. Most of the literature suggests a positive association between household income and electricity consumption, regardless of the type of household [ [ 7 , 8 , 11 , 17 , 37 ]]. However, Blasch et al [ 24 ] find that the effect of income on electricity consumption becomes insignificant after controlling for residential and household characteristics, confirming the findings of [ 38 ].…”

Section: Related Literaturementioning

confidence: 99%

“…As a result, the total residential electricity consumption has almost doubled, from 3562 TWh in 2000 to 6072 TWh in 2019. Second, urbanisation plays an import role in increasing electricity consumption [ [ 7 , 8 ]]. Existing literature shows that urban households consume more electricity than rural ones [ 9 ], and the urban-rural electricity consumption gap might be as high as 23 percent.…”

Section: Introductionmentioning

confidence: 99%

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Elicitation of electricity consumption habit formation among new subscribers

Moussa,

Kanga

2024

Heliyon

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Section: Resultscontrasting

confidence: 58%

Section: Related Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Elicitation of electricity consumption habit formation among new subscribers

Moussa,

Kanga

2024

Heliyon

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“…A variety of factors can influence the electricity consumption, such as population, economic development, urbanization, industrial structure, technological progress and foreign direct investment. Existing studies have conducted many tentative discussions on those factors (Lean and Smyth, 2014;Shahbaz et al, 2014;Acaravci et al, 2015;Lin et al, 2016;Wang et al, 2018;Ohlan, 2018;Khan et al, 2018;Yin et al, 2018;Liu et al, 2020;An et al, 2020;Ma, 2020;Yuan and Zhao, 2021), but most of them ignore the impact of financial factors (Mahalik et al 2017). In fact, finance is the core of modern economy, which has an important influence on the production and consumption of energy as the material basis in the process of social and economic development.…”

Section: Figure 1 Electricity Consumption In China During 1978-2019mentioning

confidence: 99%

Spatial spillover effects and action paths of electricity consumption driven by China’s financial development based on global co-integration

2022

Environ Sci Pollut Res

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Although studies on the influencing factors of electricity consumption are rich, the focus on the relationship between financial development and electricity consumption is scarce due to the characteristics of financial sector. In fact, the financial development cannot only increase electricity consumption, but also have the spatial spillover effects. Based on the global spatial modelling techniques, the long-term and short-term relationship between financial development and electricity consumption is examined, and the intermediary effect of financial development on electricity consumption through economic growth, urbanization and industrial structure optimization is also verified. Results show that there is a global co-integration relationship between financial development, economic growth, urbanization, industrial structure optimization and China's electricity consumption, rather than a local co-integration relationship. When the short-term change of electricity consumption deviates from the equilibrium state, the global error correction mechanism can promote the unbalanced system to return to equilibrium from time and spatial dimension. This study not only confirms the spatial spillover effects, but also heterogeneous influences of financial development on electricity consumption, which provides new evidence to make relevant policies.

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“…On the one hand, electricity demand would be afected by a large number of factors, including population, economic growth, and climate change [14,15]. Tese factors also have a large uncertainty and fuctuation, which adds the difculty in forecasting the future demand in electricity [16]. On the other hand, the time series of electricity demand itself displays less stability, which makes the prediction of electricity demand be full of challenges [17].…”

Section: Introductionmentioning

confidence: 99%

A Novel Robust Grey Model for Forecasting Chinese Electricity Demand

Yao

Jin

Wei

et al. 2022

Discrete Dynamics in Nature and Society

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The grey model, which is abbreviated as GM (1, 1), has been widely applied in the fields of decision and prediction, particularly in the prediction of time series with few observations, referred to as the poor information and small sample in the literature related to grey model. Previous studies focus on improving the accuracy of prediction but pay less attention to the robustness of the grey model to outliers, which often occur in practice due to an incorrect record by chance or an accidental failure in equipment. To fill that void, we develop a robust grey model, whose structural parameters are obtained from the least trim squares, to forecast Chinese electricity demand. Also, we use the last value in the first-order accumulative generating time series as the initial value, according to the new information priority criterion. We name the novel grey model, proposed in this paper, the novel robust grey model integrating the new information priority criterion, which could be abbreviated as NIPC-GM (1, 1). In addition, we introduce a novel approach, that is, the bootstrapping test, to investigate the robustness against outliers for the novel robust grey model and the classical grey model, respectively. Using the data on Chinese electricity demand from 2011 to 2021, we find that not only does the novel robust grey model integrating the new information priority criterion have a better robustness to outliers than the classical grey model, but it also has a higher accuracy of prediction than the classical grey model. Finally, we apply the novel robust grey model integrating the new information priority criterion to forecasting the future values in Chinese electricity demand during the period 2022 to 2025. We see that Chinese electricity demand would continue to rise in the next four years.

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A panel empirical modeling on the driving factors of provincial electricity consumption in China

Cited by 9 publications

References 42 publications

Elicitation of electricity consumption habit formation among new subscribers

Elicitation of electricity consumption habit formation among new subscribers

Spatial spillover effects and action paths of electricity consumption driven by China’s financial development based on global co-integration

A Novel Robust Grey Model for Forecasting Chinese Electricity Demand

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