2000-2015年中国农村能源贫困的时空变化与影响因素

doi:10.11821/dlyj201806005

地理研究 ›› 2018, Vol. 37 ›› Issue (6): 1115-1126.doi: 10.11821/dlyj201806005

2000-2015年中国农村能源贫困的时空变化与影响因素

赵雪雁¹(), 陈欢欢¹, 马艳艳¹, 高志玉¹, 薛冰²

1. 西北师范大学地理与环境科学学院,兰州 730070
2. 中国科学院沈阳应用生态研究所,沈阳 110016

收稿日期:2017-12-25 修回日期:2018-03-27 出版日期:2018-06-10 发布日期:2018-06-10
作者简介:
作者简介：赵雪雁（1971- ）,女,甘肃武都人,教授,主要从事生态经济研究。E-mail: zhaoxy@nwnu.edu.cn
基金资助:
国家自然科学基金项目（41661115）;中国科学院A类战略性先导科技专项（XDA19040502）;中国科学院内陆河流域生态水文重点实验室开放基金（KLEIRB-2S-16-03）

Spatio-temporal variation and its influencing factors of rural energy poverty in China from 2000 to 2015

Xueyan ZHAO¹(), Huanhuan CHEN¹, Yanyan MA¹, Zhiyu GAO¹, Bing XUE²

1. College of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, China
2. Institute of Applied Ecology, CAS, Shenyang 110016, China

Received:2017-12-25 Revised:2018-03-27 Online:2018-06-10 Published:2018-06-10
About author:
Author: Shi Zhenqin (1988-), PhD, specialized in regional development and land space management in mountain areas. E-mail: kevinszq@163.com

^*Corresponding author: Deng Wei (1957-), Professor, specialized in mountain environment and regional development.

E-mail: dengwei@imde.ac.cn

摘要/Abstract

摘要：

农村能源贫困作为世界能源体系面临的严峻挑战之一,已成为发展中国家贫困的主要标志。当前急需探明影响农村能源贫困的关键因素,并寻求有效策略减缓能源贫困。以中国30个省市（自治区）为研究单元,以能源接入与能源服务作为能源贫困的度量指标,利用Theil指数及空间自相关分析法刻画中国农村能源贫困的时空演变特征,并利用空间杜宾模型（SDM）分析其影响因素,旨在为中国制定有效的农村能源政策提供依据和参考。结果表明：① 2000-2015年中国农村能源贫困呈先增后降的“倒U型”趋势,但一直保持着“中部高、东西部低”的马鞍型分布格局;② 中国农村能源贫困的区域差异波动较大,但整体呈缩小趋势,Theil指数降幅为13.71%,地带间差异趋于扩大,而地带内差异趋于缩小;③ 中国农村能源贫困具有显著的空间集聚特征,热点区和冷点区均在波动中呈扩张态势,其中稳定性热点区集中分布在中部地区,而稳定性冷点区集中在东部沿海地区;④ 随着地区经济发展水平、农村能源供给水平、能源投资水平、农村能源基础设施水平和农村能源管理水平的提高,农村能源贫困状况将得到有效缓解。

关键词: 农村能源贫困, 时空变化, 影响因素, 中国

Abstract:

Rural energy poverty—one of the severe challenges facing the global energy system—has become the primary indicator of poverty in developing countries. Therefore, it is urgent to find out the key factors contributing to energy poverty and seek effective measures to alleviate it. In this paper, taking 30 provinces (municipalities and autonomous regions) in China as the research unit, and the energy access and energy service as the measurement index of energy poverty, through an integration of Theil index and exploratory spatial data analysis (ESDA) and spatial panel econometric model, we examine the characteristics and spatio-temporal patterns and the factors of rural energy poverty in China from 2000 to 2015. The aim is to provide scientific basis and reference for desicion making on rural energy policies in China. The results are shown as follows: (1) The rural energy poverty showed an "inverse U" trend from 2000 to 2015, that is to say, it increased first and then decreased. And the pattern of rural energy poverty in China has maintained a saddle-type spatial distribution, that is, the rural energy poverty in the central region was higher than that of the eastern and western regions. (2) The regional disparity of rural energy poverty presented a narrowing trend with big flucation, and the Theil Index decreased by 13.71%. During this period, the gap in rural energy poverty was widened between regions, but it was narrowed within regions. And the growing rate of inequalities of the rural energy poverty in the eastern region was higher than that of the central and western regions. (3) By observing the change of Moran's I in 2000, 2008 and 2015, the spatial agglomeration range of rural energy poverty presented an enlarging trend. All of the "hot-spot" and "cold-spot" of rural energy poverty showed an expanding tendency, and the stable hot-spots are located in the central region, while the stable cold-spots are situated in the eastern coastal area. (4) The selected explanatory variables, such as the level of economic development, rural energy supplies, energy investment level, rural energy infrastructure and rural energy management level, have significant direct impacts on rural energy poverty in China. We found that these factors have a positive effect on rural energy poverty alleviation. With the regional development level increasing, the rural energy poverty will be effectively alleviated.

Key words: rural energy poverty, spatial-temporal variation, influencing factor, China

赵雪雁, 陈欢欢, 马艳艳, 高志玉, 薛冰. 2000-2015年中国农村能源贫困的时空变化与影响因素[J]. 地理研究, 2018, 37(6): 1115-1126.

Xueyan ZHAO, Huanhuan CHEN, Yanyan MA, Zhiyu GAO, Bing XUE. Spatio-temporal variation and its influencing factors of rural energy poverty in China from 2000 to 2015[J]. GEOGRAPHICAL RESEARCH, 2018, 37(6): 1115-1126.

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	2000年	2008年	2015年
I	0.167	0.183	0.212
Z	1.850	1.977	2.178
P	0.064	0.048	0.028

变量	弹性系数	t值	变量	弹性系数	t值
lnGDP	-0.081^***	-4.43	W×lnGDP	0.093^***	4.44
lnE	-0.004	-0.04	W×lnE	-0.1175	-1.12
lnS	-0.003^**	-2.25	W×lnS	0.001	0.47
lnP	0.002	0.03	W×lnP	-0.098	1.32
lnI	-0.013^**	-2.47	W×lnI	0.021^**	2.23
lnB	-0.007^***	-3.99	W×lnB	-0.003^*	0.62
lnA	-0.003^**	-2.01	W×lnA	0.006^***	2.74

	直接效应	P值	间接效应	P值
lnGDP	-0.073^***	0.000	0.095^***	0.000
lnE	-0.029	0.749	-0.310	0.202
lnS	-0.003^**	0.017	0.001	0.922
lnP	0.014	0.818	-0.173^*	0.088
lnI	-0.011^**	0.049	0.027^*	0.083
lnB	-0.008^***	0.000	-0.012^*	0.081
lnA	-0.002	0.165	0.009^**	0.037

2000-2015年中国农村能源贫困的时空变化与影响因素

Spatio-temporal variation and its influencing factors of rural energy poverty in China from 2000 to 2015

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