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

Bao, Chao; Liu, Ruowen

doi:10.3390/su11092494

Cited by 10 publications

(7 citation statements)

References 32 publications

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“…The existence of regional differences had already been shown in the analyzed literature [20,21,35], and, in general, the scholars showed that the standardized economic model is prone to accentuating territorial differences in terms of economic growth and employment, without the intervention of the public sector. For this reason, the EU, aware of this situation, has been developing and implementing mechanisms, generally of a budgetary nature-as is the case of the European Funds-to reduce these differences.…”

Section: Discussionmentioning

confidence: 92%

“…Analyzing the existing literature, it was detected that the authors frequently use the so-called shift-share analysis in this type of studies. Among the main contributions using this methodology are the studies of [20][21][22][23][24][25][26]. Additional studies were conducted specifically by implementing the shift-share analysis in the energy sector of Asiatic [20,22,27] and European countries [22,28].…”

Section: Regional Analysis Of the Impact Of Renewable Energiesmentioning

confidence: 99%

“…Then, the results revealed significant differences among the spatial decomposition related to efficiency changes when analyzing the Chinese provinces. The shift-share analysis was also applied by Bao and Liu [21], aiming to identify the driving factors for changing electricity consumption in Chinese regions, identifying differences across provinces related to the changes' components regarding electricity consumption.…”

Section: Regional Analysis Of the Impact Of Renewable Energiesmentioning

confidence: 99%

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Evaluation of the Effects on Regional Production and Employment in Spain of the Renewable Energy Plan 2011–2020

2021

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The Renewable Energy Plan for the period 2011–2020 established as a general goal to ensure that renewable sources represent at least 20% of final energy consumption in 2020, together with a minimum contribution of 10% from renewable energy sources in transportation for that year. Then, the goal of this research is to evaluate the effects of the regional production of clean energy, identifying the employment generated in the renewable sector. The adopted methodology was the shift-share analysis, frequently used by researchers to analyze territorial differences. Main results showed important differences, at regional level, in the production of this type of energy. Likewise, we used constant shift and constant share methodology to make a forecast on the evolution of the sector from the data of last published years. Pending the approval of the new Renewable Energy Plan for the period 2021–2030, the results obtained in this research allow the identification of the regions that showed a favorable evolution to the energy change and identifies the projects that generate employment and production in the sector.

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Section: Discussionmentioning

confidence: 92%

Section: Regional Analysis Of the Impact Of Renewable Energiesmentioning

confidence: 99%

Section: Regional Analysis Of the Impact Of Renewable Energiesmentioning

confidence: 99%

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Evaluation of the Effects on Regional Production and Employment in Spain of the Renewable Energy Plan 2011–2020

2021

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“…Therefore, using statistical data to express the spatiotemporal dynamics of EC in rural areas is still a big challenge. Nighttime light data have shown certain advantages in simulating urbanization [57,58], economy [59,60], population distribution [50,61,62], the effects of war [63,64], poverty distribution [65,66], PM2.5 pollution [52], electric consumption [67,68], and other aspects of human geography. Furthermore, the results of our study were in consistence with previous studies [24,47].…”

Section: A Nighttime Light Data Provide a New Perspective For Simulamentioning

confidence: 99%

Detecting Spatiotemporal Dynamic of Regional Electric Consumption Using NPP-VIIRS Nighttime Stable Light Data–A Case Study of Xi’an, China

Guo

Zhang

et al. 2020

IEEE Access

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The available statistics on electric consumption (EC) can hardly show the spatial heterogeneity within political boundaries, and the previous studies paid few attention on the relations between EC and urban planning. The present study developed models between nighttime light values and EC using nighttime light images (NLI) from the Suomi National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) and statistical EC data from local government of Xi'an. A geographic information system (GIS) was utilized to map the distribution of EC on a grid scale, and the spatiotemporal dynamic of EC was obtained. In addition, a slope model and the EC grading threshold were conducted to determine the urban functional districts (UFD). The results were shown as follows: (1) the relation between TNL and EC is significant positive, and the performance of linear regression model (R 2 =0.76，Root Mean Square Error (RMSE) =9.44×10 4 , Mean Absolute Error (MAE) =6.73×10 3 , Mean Percent Error (MPE) =-3.71%) is better than logarithmic model (R 2 =0.68, RMSE =1.09×10 5 , MAE =7.77×10 4 , MPE =-5.44%). (2) In Xi'an, the EC continuously increased during 2013-2017, and the change of EC demonstrated obvious features that increased slowly in the city center, and spread outward to the surrounding region especially to the south of the study area. (3) The UFD have been divided into five sections including tourist district, commercial district, industrial zone, residential community, and ecological zone, respectively. This study will thus provide a reference and scientific basis for urban planning and rational allocation of electric power.

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“…China's rapid economic development in the last 20 years has resulted in increased demand for electricity and ensuing shortages in electric power supply [7]. China's growing EPC has become an important factor to improving socio-economic development, as well as aggravating environmental degradation [8]. EPC is one of the basic indices for evaluating electric power use [9].…”

Section: Introductionmentioning

confidence: 99%

Research on the Spatio-Temporal Dynamic Evolution Characteristics and Influencing Factors of Electrical Power Consumption in Three Urban Agglomerations of Yangtze River Economic Belt, China Based on DMSP/OLS Night Light Data

et al. 2021

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In this paper, based on electrical power consumption (EPC) data extracted from DMSP/OLS night light data, we select three national-level urban agglomerations in China’s Yangtze River Economic Belt(YREB), includes Yangtze River Delta urban agglomerations(YRDUA), urban agglomeration in the middle reaches of the Yangtze River(UAMRYR), and Chengdu-Chongqing urban agglomeration(CCUA) as the research objects. In addition, the coefficient of variation (CV), kernel density analysis, cold hot spot analysis, trend analysis, standard deviation ellipse and Moran’s I Index were used to analyze the Spatio-temporal Dynamic Evolution Characteristics of EPC in the three urban agglomerations of the YREB. In addition, we also use geographically weighted regression (GWR) model and random forest algorithm to analyze the influencing factors of EPC in the three major urban agglomerations in YREB. The results of this study show that from 1992 to 2013, the CV of the EPC in the three urban agglomerations of YREB has been declining at the overall level. At the same time, the highest EPC value is in YRDUA, followed by UAMRYR and CCUA. In addition, with the increase of time, the high-value areas of EPC hot spots are basically distributed in YRDUA. The standard deviation ellipses of the EPC of the three urban agglomerations of YREB clearly show the characteristics of “east-west” spatial distribution. With the increase of time, the correlations and the agglomeration of the EPC in the three urban agglomerations of the YREB were both become more and more obvious. In terms of influencing factor analysis, by using GWR model, we found that the five influencing factors we selected basically have a positive impact on the EPC of the YREB. By using the Random forest algorithm, we found that the three main influencing factors of EPC in the three major urban agglomerations in the YREB are the proportion of secondary industry in GDP, Per capita disposable income of urban residents, and Urbanization rate.

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

Cited by 10 publications

References 32 publications

Evaluation of the Effects on Regional Production and Employment in Spain of the Renewable Energy Plan 2011–2020

Evaluation of the Effects on Regional Production and Employment in Spain of the Renewable Energy Plan 2011–2020

Detecting Spatiotemporal Dynamic of Regional Electric Consumption Using NPP-VIIRS Nighttime Stable Light Data–A Case Study of Xi’an, China

Research on the Spatio-Temporal Dynamic Evolution Characteristics and Influencing Factors of Electrical Power Consumption in Three Urban Agglomerations of Yangtze River Economic Belt, China Based on DMSP/OLS Night Light Data

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