河南省能源效率的空间格局与对策研究
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摘要
基于随机前沿生产函数模型,测算了2007—2015年河南省18个地级市的能源效率,并采用趋势面分析方法对河南省能源效率的空间结构特征进行了分析。研究结果表明:①河南省各地级市能源效率差异显著,郑州市、开封市、许昌市、漯河市和驻马店市为能源效率较高的区域,能源效率相对较低的区域主要位于河南省边缘地带;②趋势面分析结果显示,能源效率自东向西呈现先升后降的趋势,高点集中在中部,自北向南呈现先升后降的趋势,且北部地区高于南部地区;③中部地区作为能源效率的高点区域,周边地区与其差距越来越大。从优化能源结构、加强地区交流合作、产业结构调整和技术进步驱动等方面提出提高河南省城市能源效率的对策建议。
Based on panel data of 18 cities in Henan province from 2007 to 2015, the energy efficiency is estimated by means of SFA model, then analyzes the spatial pattern of energy efficiency by trend surface analysis. The results indicate that: 1) There are significant differences in energy efficiency in Henan province, among which cities like Zhengzhou, Kaifeng, Xuchang, Luohe and Zhumadian are the areas with high energy efficiency, whereas the low efficiency areas are mainly located along the edge of the province; 2) according to trend surface analysis, the energy efficiency trend rises and then falls like an inverted-U shape from east to west, with the peak at central Henan; a similar inverted-U shape can be seen from north to south, with a higher level of energy efficiency in the north; 3) there is an increasingly wide gap between central Henan and other areas. On this basis, some countermeasures to improve the energy efficiency are proposed, for example, optimizing the energy utilization structure, strengthening regional exchange and cooperation, adjusting industrial structure, making technology-driven innovation, and so on.
Based on panel data of 18 cities in Henan province from 2007 to 2015, the energy efficiency is estimated by means of SFA model, then analyzes the spatial pattern of energy efficiency by trend surface analysis. The results indicate that: 1) There are significant differences in energy efficiency in Henan province, among which cities like Zhengzhou, Kaifeng, Xuchang, Luohe and Zhumadian are the areas with high energy efficiency, whereas the low efficiency areas are mainly located along the edge of the province; 2) according to trend surface analysis, the energy efficiency trend rises and then falls like an inverted-U shape from east to west, with the peak at central Henan; a similar inverted-U shape can be seen from north to south, with a higher level of energy efficiency in the north; 3) there is an increasingly wide gap between central Henan and other areas. On this basis, some countermeasures to improve the energy efficiency are proposed, for example, optimizing the energy utilization structure, strengthening regional exchange and cooperation, adjusting industrial structure, making technology-driven innovation, and so on.
引文
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[18] 刘凤朝,杨玲,赵宸浩.辽宁省经济发展的空间结构分析:基于GIS平台的测度[J].大连理工大学学报(社会科学版),2011,32(3):1-5.
[2] 王家庭,赵亮.中国城市的资源集约效率及其影响因素研究[J].中国人口·资源与环境,2009,19(5):45-52.
[3] QI S R,XU L,COGGINS J S.Integrated environmental-economic accounting of GDP[R].Chicago:American Agricultural Economics Association,2001.
[4] 李双杰,李春琦.全要素能源效率测度方法的修正设计与应用[J].数量经济技术经济研究,2018,35(9):110-125.
[5] 孟庆春,黄伟东,戎晓霞.灰霾环境下能源效率测算与节能减排潜力分析:基于多非期望产出的NH-DEA模型[J].中国管理科学,2016,24(8):53-61.
[6] 张本照,刘淇,王海涛.中国交通运输业能源利用效率动态测算:基于产出指标改进DEA模型的实证分析[J].工业技术经济,2014,33(8):15-25.
[7] 王强,樊杰,伍世代.1990—2009年中国区域能源效率时空分异特征与成因[J].地理研究,2014,33(1):43-56.
[8] 许淑婷,王舒,赵湘宁.中国沿海地区能源效率与产业结构关系研究[J].辽宁师范大学学报(自然科学版),2018,41(3):403-412.
[9] 江静,马莹.中国服务业全要素能源效率:测度与影响因素[J].北京工商大学学报(社会科学版),2018,33(5):23-32.
[10] 周江,胡静锋,王波.中国能源产业效率测量及比较分析[J].经济问题,2018(8):60-65.
[11] 解文华,方虹,张军峰,等.基于SBM-DEA模型及Malmquist指数的中国与欧盟航空运输企业能源效率比较研究[J].数学的实践与认识,2017,47(17):194-201.
[12] 孙策,成金华,孙莉.长江中游地区能源效率评价研究:与京津冀、长三角和珠三角地区的比较[J].中国矿业,2017,26(8):69-74,111.
[13] 关伟,许淑婷.辽宁省能源效率与产业结构的空间特征及耦合关系[J].地理学报,2014,69(4):520-530.
[14] 武春友,岳良文,张米尔.基于MFA和DEA的煤炭资源效率测算方法的研究[J].中国人口·资源与环境,2012,22(12):135-142.
[15] 魏楚,沈满洪.能源效率及其影响因素:基于DEA 的实证分析[J].管理世界,2007(8):66-76.
[16] 揭懋汕,郭洁,陈罗烨,等.碳约束下中国县域尺度农业全要素生产率比较研究[J].地理研究,2016,35(5):898-908.
[17] 佟连军,宋亚楠,韩瑞玲,等.辽宁沿海经济带工业环境效率分析[J].地理科学,2012,32(3):294-300.
[18] 刘凤朝,杨玲,赵宸浩.辽宁省经济发展的空间结构分析:基于GIS平台的测度[J].大连理工大学学报(社会科学版),2011,32(3):1-5.