Factors Affecting Wind Power Efficiency: Evidence from Provincial-Level Data in China

As a significant energy consumer, China is under tremendous pressure from the international community to address climate change issues by reducing carbon emissions; thus, the use of clean energy is imperative. Wind power is an essential source of renewable energy, and improving the efficiency of win...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Xiaoyan Sun, Wenwei Lian, Hongmei Duan, Anjian Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	wind power efficiency DEA spatial econometric model China Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/a0035f8790a147d5804254d6048b4335
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a0035f8790a147d5804254d6048b4335
record_format	dspace
spelling	oai:doaj.org-article:a0035f8790a147d5804254d6048b43352021-11-25T19:04:08ZFactors Affecting Wind Power Efficiency: Evidence from Provincial-Level Data in China10.3390/su1322127592071-1050https://doaj.org/article/a0035f8790a147d5804254d6048b43352021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12759https://doaj.org/toc/2071-1050As a significant energy consumer, China is under tremendous pressure from the international community to address climate change issues by reducing carbon emissions; thus, the use of clean energy is imperative. Wind power is an essential source of renewable energy, and improving the efficiency of wind power generation will contribute substantially to China’s ability to achieve its energy-saving and emission reduction goals. This paper measured the wind power efficiency of 30 provinces in China from 2012 to 2017 using the data envelopment analysis (DEA) method. Moran’s I index and the spatial Durbin model were applied to analyse the spatial distribution of the wind power efficiency and the spatial effects of influencing factors. The results show obvious differences in the spatial distribution of wind power efficiency in China; specifically, the wind power efficiency in the eastern and western regions is higher than that in the central areas. Moreover, wind power efficiency has a significant positive spatial correlation between regions: the eastern and western regions show certain high-high clustering characteristics, and the central area shows certain low-low clustering characteristics. Among the influencing factors, the fixed asset investment and carbon emission intensity of the wind power property have a negative impact on the efficiency of regional wind power production, while the urbanization process and carbon emission intensity have significant spatial spillover effects. The optimization of the economic structure, technological innovation and the construction of energy infrastructure are expected to improve the regional wind power efficiency. The results present a new approach for accurately identifying the spatial characteristics of wind power efficiency and the spatial effects of the influencing factors, thus providing a reference for policymakers.Xiaoyan SunWenwei LianHongmei DuanAnjian WangMDPI AGarticlewind power efficiencyDEAspatial econometric modelChinaEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12759, p 12759 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	wind power efficiency DEA spatial econometric model China Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	wind power efficiency DEA spatial econometric model China Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Xiaoyan Sun Wenwei Lian Hongmei Duan Anjian Wang Factors Affecting Wind Power Efficiency: Evidence from Provincial-Level Data in China
description	As a significant energy consumer, China is under tremendous pressure from the international community to address climate change issues by reducing carbon emissions; thus, the use of clean energy is imperative. Wind power is an essential source of renewable energy, and improving the efficiency of wind power generation will contribute substantially to China’s ability to achieve its energy-saving and emission reduction goals. This paper measured the wind power efficiency of 30 provinces in China from 2012 to 2017 using the data envelopment analysis (DEA) method. Moran’s I index and the spatial Durbin model were applied to analyse the spatial distribution of the wind power efficiency and the spatial effects of influencing factors. The results show obvious differences in the spatial distribution of wind power efficiency in China; specifically, the wind power efficiency in the eastern and western regions is higher than that in the central areas. Moreover, wind power efficiency has a significant positive spatial correlation between regions: the eastern and western regions show certain high-high clustering characteristics, and the central area shows certain low-low clustering characteristics. Among the influencing factors, the fixed asset investment and carbon emission intensity of the wind power property have a negative impact on the efficiency of regional wind power production, while the urbanization process and carbon emission intensity have significant spatial spillover effects. The optimization of the economic structure, technological innovation and the construction of energy infrastructure are expected to improve the regional wind power efficiency. The results present a new approach for accurately identifying the spatial characteristics of wind power efficiency and the spatial effects of the influencing factors, thus providing a reference for policymakers.
format	article
author	Xiaoyan Sun Wenwei Lian Hongmei Duan Anjian Wang
author_facet	Xiaoyan Sun Wenwei Lian Hongmei Duan Anjian Wang
author_sort	Xiaoyan Sun
title	Factors Affecting Wind Power Efficiency: Evidence from Provincial-Level Data in China
title_short	Factors Affecting Wind Power Efficiency: Evidence from Provincial-Level Data in China
title_full	Factors Affecting Wind Power Efficiency: Evidence from Provincial-Level Data in China
title_fullStr	Factors Affecting Wind Power Efficiency: Evidence from Provincial-Level Data in China
title_full_unstemmed	Factors Affecting Wind Power Efficiency: Evidence from Provincial-Level Data in China
title_sort	factors affecting wind power efficiency: evidence from provincial-level data in china
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a0035f8790a147d5804254d6048b4335
work_keys_str_mv	AT xiaoyansun factorsaffectingwindpowerefficiencyevidencefromprovincialleveldatainchina AT wenweilian factorsaffectingwindpowerefficiencyevidencefromprovincialleveldatainchina AT hongmeiduan factorsaffectingwindpowerefficiencyevidencefromprovincialleveldatainchina AT anjianwang factorsaffectingwindpowerefficiencyevidencefromprovincialleveldatainchina
_version_	1718410354184159232

Factors Affecting Wind Power Efficiency: Evidence from Provincial-Level Data in China

Ejemplares similares