2001-2012年全球23国新能源发电效率测算与驱动因素分析

doi:10.18402/resci.2016.02.14

摘要/Abstract

摘要：

新能源替代化石能源发电,已成为“雾霾治理”的重要手段,并在世界各国节能减排政策制定上达成广泛共识。本文将新能源发电的投入细分为太阳能装机容量、风能装机容量、地热能装机容量和生物燃料产量等四种形式,以新能源发电量作为新能源发电的产出指标,基于2001-2012年全球23个国家的平衡面板数据,对新能源发电的综合效率进行了基于Bootstrap-DEA方法的纠偏测算,并运用面板Tobit模型实证研究了新能源发电综合效率的驱动因素。研究发现：全球新能源发电的综合效率均呈现逐年上升态势;各国碳排放量并未构成新能源发电效率提升的倒逼机制;自然资源租金比重的提高促进了新能源发电效率的提升;城镇化率提升阻碍了新能源发电效率提升;人均收入水平较高的国家新能源发电效率普遍较高。本文提出应当提高新能源装机容量扩张政策与发电量提升政策的协同性,着力提升新能源发电效率,增加储能技术的研发投入,充分发挥地区自然资源禀赋优势,以破解“弃风限电”、“弃光限电”等制约新能源发展的瓶颈。

关键词: 新能源, 发电效率, 节能减排, 驱动因素, Bootstrap-DEA模型, Tobit模型, 全球

Abstract:

The fossil energy substituted by new energy has become an important means of ‘fog and haze governance’, reaching broad consensus on energy saving and emission reduction policies around the world. Here we defined new energy generation inputs as four forms (installed capacity of solar energy, wind power, geothermal energy and biofuels production) and defined electricity from new energy as an output indicator. Based on balanced panel data from 2001 to 2012 for 23 countries we used the Bootstrap-DEA method to correctly calculate the comprehensive efficiency of new energy’s power generation, and used the Tobit Model to empirically analyze driving factors of efficiency. We found that the comprehensive efficiency of global new energy power generation is rising; carbon emissions do not constitute a forced mechanism of the efficiency of new energy power generation; the proportion of natural resource rent has positive effects on the efficiency of new energy power generation; the urbanization rate has hindered the efficiency of new energy power generation; and the higher the per capita income of a nation, the higher the efficiency of new energy power generation. Based on these findings we propose that more attention should be given to the coordination of policies between the expansion of installed capacity and acceleration of generating capacity in order to improve the power generation of installed capacity. Research and development should be increased for energy storage technology, giving full play to the advantage of regional natural resource endowments, in order to crack the bottlenecks of ‘abandoned wind power’ and ‘abandoned light power’.

Key words: New energy, generation efficiency, energy saving and emission reduction, driving factors, Bootstrap-DEA Model, Tobit Model, Global

李少林. 2001-2012年全球23国新能源发电效率测算与驱动因素分析[J]. 资源科学, 2016, 38(2): 321-332.

LI Shaolin. Research on calculation of new energy’s power generation efficiency and analysis on its driving factors[J]. Resources Science, 2016, 38(2): 321-332.

图/表 4

表1

表2

2012年23国新能源发电原始效率值与Bootstrap-DEA纠偏效率估计值比较"

国家	综合效率		偏误	置信区间
国家	$e m$		$e^m$ （纠偏）	下界（2.5%）	上界（97.5%）
加拿大	1.000	0.996	0.004	0.990	0.999
墨西哥	0.997	0.993	0.004	0.987	0.997
美国	0.994	0.990	0.004	0.984	0.993
奥地利	0.991	0.987	0.003	0.981	0.990
比利时	0.988	0.984	0.003	0.978	0.987
丹麦	0.985	0.981	0.003	0.975	0.984
芬兰	0.981	0.978	0.003	0.972	0.981
法国	0.978	0.975	0.003	0.969	0.978
德国	0.975	0.972	0.003	0.966	0.975
希腊	0.972	0.969	0.003	0.963	0.972
意大利	0.969	0.966	0.003	0.960	0.969
荷兰	0.966	0.963	0.003	0.957	0.966
挪威	0.963	0.960	0.003	0.954	0.962
葡萄牙	0.959	0.957	0.003	0.951	0.959
西班牙	0.956	0.953	0.003	0.948	0.956
瑞典	0.953	0.950	0.003	0.944	0.953
土耳其	0.950	0.947	0.003	0.941	0.950
英国	0.947	0.944	0.003	0.938	0.947
中国	0.943	0.941	0.003	0.935	0.943
日本	0.940	0.937	0.003	0.932	0.940
印度	0.937	0.934	0.003	0.929	0.937
韩国	0.934	0.931	0.003	0.926	0.934
澳大利亚	0.931	0.928	0.003	0.922	0.930
平均值	0.966	0.963	0.003	0.957	0.965

表2

表3

2001-2011年23个国家新能源发电Bootstrap-DEA纠偏效率估计值"

年份	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011
加拿大	0.608	-0.035	0.676	0.928	0.975	0.920	0.914	0.939	0.983	0.996	0.996
墨西哥	0.736	0.029	0.675	0.790	0.979	0.896	0.891	0.930	0.979	0.994	0.993
美国	0.874	0.097	0.679	0.857	0.976	0.871	0.868	0.918	0.974	0.993	0.990
奥地利	0.879	0.172	0.085	0.920	0.978	0.846	0.846	0.904	0.970	0.991	0.987
比利时	0.483	0.260	0.132	0.775	0.936	0.819	0.824	0.890	0.964	0.989	0.984
丹麦	0.578	0.367	0.182	0.846	0.940	0.794	0.802	0.874	0.959	0.988	0.981
芬兰	0.695	0.506	0.235	0.916	0.937	0.768	0.780	0.858	0.953	0.986	0.978
法国	0.833	0.682	0.290	0.766	0.938	0.743	0.759	0.840	0.947	0.984	0.975
德国	0.833	0.654	0.355	0.839	0.898	0.718	0.737	0.822	0.941	0.982	0.972
希腊	0.531	0.645	0.430	0.913	0.902	0.693	0.716	0.942	0.935	0.981	0.969
意大利	0.646	0.676	0.516	0.817	0.897	0.668	0.695	0.930	0.929	0.979	0.966
荷兰	0.757	-0.110	0.609	0.699	0.899	0.928	0.674	0.917	0.984	0.977	0.963
挪威	0.761	-0.046	0.510	0.757	0.859	0.898	0.654	0.901	0.979	0.975	0.960
葡萄牙	0.412	0.021	0.507	0.804	0.863	0.870	0.634	0.885	0.975	0.973	0.957
西班牙	0.496	0.093	0.510	0.680	0.858	0.840	0.614	0.868	0.970	0.971	0.954
瑞典	0.596	0.175	0.014	0.742	0.859	0.811	0.595	0.850	0.964	0.969	0.951
土耳其	0.703	0.270	0.059	0.797	0.820	0.782	0.576	0.833	0.959	0.967	0.948
英国	0.702	0.386	0.106	0.669	0.824	0.754	0.557	0.815	0.953	0.965	0.945
中国	0.453	0.511	0.155	0.731	0.818	0.727	0.539	0.797	0.947	0.963	0.941
日本	0.554	0.477	0.203	0.789	0.819	0.700	0.521	0.779	0.941	0.961	0.938
印度	0.636	0.464	0.257	0.705	0.781	0.674	0.502	0.761	0.935	0.958	0.935
韩国	0.639	0.497	0.315	0.607	0.785	0.648	0.843	0.744	0.929	0.956	0.932
澳大利亚	0.338	-0.197	0.372	0.654	0.779	0.849	0.781	0.726	0.923	0.945	0.929
均值	0.641	0.287	0.342	0.783	0.883	0.792	0.710	0.858	0.956	0.976	0.963

表3

表4

参考文献 37

[1]	贾科华. 2014年全球新能源发电量同比增19%[N]. 中国能源报, 2015-4-20(3).
	[JIA Kehua. Global new energy power generation increased by 19% in2014[N]. China Energy News, 2015-4-20(3)]
[2]	马晓舫. 德国可再生能源发电比例再破记录[EB/OL]. (2015-08-05) [2015-09-20]. .
	[Ma X H. Ratio of Renewable Energy Power Generation in German beating the Record[EB/OL]. (2015-08-05) [2015-09-20]. .
[3]	吴新竹. 可再生能源领域面临不堪重负的市场[EB/OL]. (2015-08-03)[2015-09-20]. .
	[Wu X Z. Renewable Energy Field Facing Overwhelmed Market[EB/OL]. (2015-08-03) [2015-09-20]. .]
[4]	连振祥, 张玉洁. 国家能源局: "两个200亿"成为中国可再生能源发展的最大问题[EB/OL]. (2015-08-08) [2015-09-20]. .
	[Lian Z X, Zhang Y J. National Energy Administration: "Two of 20 Billion" Becoming the Largest problem of Renewable Energy Development in China[EB/OL]. (2015-08-08) [2015-09-20]. .]
[5]	Fare R, Yoon B J.Returns to scale in U.S. surface mining of coal[J]. Resources & Energy, 1985, 7(4): 341-352.
[6]	Coelli T J.Total Factor Productivity Growth in Australian Coal-fire Electricity Generation: A Malmquist Index Approach[R]. Sydney: Paper Presented at the International Conference on Public Sector Efficiency, 1997.
[7]	Knittel C R.Alternative regulatory methods and firm efficiency: Stochastic frontier evidence from the U.S. electricity industry[J]. Review of Economics & Statistics, 2002, 84(3): 530-540.
[8]	刘新梅, 刘博. 发电企业基于AHP的DEA效率研究[J]. 生产力研究, 2006, (3): 244-245.
	[Liu X M, Liu B.Research on the Data Envelopment Analysis efficiency of power generation enterprises based on the Analytic Hierarchy Process[J]. Productivity Research, 2006, (3): 244-245.]
[9]	白雪洁, 宋莹. 中国各省火电行业的技术效率及其提升方向-基于三阶段DEA模型的分析[J]. 财经研究, 2008, 34(10): 15-25.
	[Bai X J, Song Y.Technical efficiency and its upgrade orientation of thermal power industry in provinces of China-based on the analysis of three-stage DEA model[J]. Journal of Finance and Economics, 2008, 34(10): 15-25.]
[10]	陶锋, 郭建万, 杨舜贤. 电力体制转型期发电行业的技术效率及其影响因素[J]. 中国工业经济, 2008, 22(1): 68-76.
	[Tao F, Guo J W, Yang S X.Technical efficiency of Chinese power generation and its determinants in the period of electric-system transition[J]. China Industrial Economics, 2008, 22(1): 68-76.]
[11]	张各兴, 夏大慰. 所有权结构、环境规制与中国发电行业的效率[J]. 中国工业经济, 2011, (6): 130-140.
	[Zhang G X, Xia D W.Ownership structure, environment regulation and efficiency of Chinese power generation industry[J]. China Industrial Economics, 2011, (6): 130-140.]
[12]	李宏舟, 邹涛. 我国电力行业发电技术效率及影响因素: 2000-2009年[J]. 改革, 2012, (10): 44-50.
	[Li H Z, Zou T.The efficiency and influencing factors of power generation industry in China during 2000-2009[J]. Reform, 2012, (10): 44-50.]
[13]	许晖, 尹忠东. 光伏发电的效率计算[J]. 科技视界, 2014, (9): 262-262.
	[Xu H, Yin Z D.The efficiency of photovoltaic power generation[J]. Science Technology Vision, 2014, (9): 262-262.]
[14]	尚华, 王惠荣. 太阳能光伏发电效率的影响因素[J].宁夏电力, 2010, (5): 48-50.
	[Shang H, Wang H R.Analysis on the factors affecting the efficiency of solar energy photovoltaic power generation[J]. Ningxia Electric Power, 2010, (5): 48-50.]
[15]	李思琢, 陈红兵. 自然冷却下太阳能PV板发电效率的影响研究[J]. 山西建筑, 2014, 40(14): 138-139.
	[Li S Z, Chen H B.Research on the influence of solar PV board power generation efficiency under natural cooling[J]. Shanxi Architecture, 2014, 40(14): 138-139.]
[16]	史君海, 孙丽兵, 张丽莹. 提高并网光伏发电效率分析与建议[J].电力与能源, 2013, 34(4): 402-404.
	[Shi J H, Sun L B, Zhang L Y.Analysis and suggestion on improving efficiency of photovoltaic power generation[J]. Power Energy, 2013, 34(4): 402-404.]
[17]	刘吉臻, 孟洪民, 胡阳. 采用梯度估计的风力发电系统最优转矩最大功率点追踪效率优化[J]. 中国电机工程学报, 2015, 35(10): 2367-2374.
	[Liu J Z, Meng H M, Hu Y.Efficiency optimization of optimum torque maximum power point tracking based on gradient approximation for wind turbine generator system[J]. Proceedings of the CSEE, 2015, 35(10): 2367-2374.]
[18]	卢志勇, 朱家玲, 张伟, 等. Kalina地热发电热力循环效率影响因素分析[J].太阳能学报, 2014, (2): 326-331.
	[Lu Z Y, Zhu J L, Zhang W, et al.Analysis of influencing factors on Kalina cycle geothermal power plant[J]. Acta Energiae Solaris Sinica, 2014, (2): 326-331.]
[19]	闫庆友, 陶杰. 中国生物质发电产业效率评价[J]. 运筹与管理, 2015, (1): 173-178.
	[Yan Q Y, Tao J.Evaluation of China biomass power generation industry efficiency[J]. Operations Research and Management Science, 2015, (1): 173-178.]
[20]	黄少鹏. 影响秸秆发电产业发展的制约因素分析-基于五河凯迪生物质能发电厂调研[J].再生资源与循环经济, 2014, 7(8): 17-19.
	[Huang S P.Analysis on the restricting factors for future development of straw power industry[J]. Recyclable Resources and Circular Economy, 2014, 7(8): 17-19.]
[21]	李虹, 董亮, 段红霞. 中国可再生能源发展综合评价与结构优化研究[J].资源科学, 2011, 33(3): 431-440.
	[Li H, Dong L, Duan H X.On comprehensive evaluation and optimization of renewable energy development in China[J]. Resources Science, 2011, 33(3): 431-440.]
[22]	陈艳, 朱雅丽. 新能源产业发展路径: 基于制度变迁的视角[J].资源科学, 2012, 34(1): 50-57.
	[Chen Y, Zhu Y L.Industry development path of renewable energy from perspectives of institutional transition[J]. Resources Science, 2012, 34(1): 50-57.]
[23]	肖兴志, 李少林. 光伏发电产业的激励方式、他国观照与机制重构[J]. 改革, 2014, (7): 75-86.
	[Xiao X Z, Li S L.Incentives of photovoltaic industry, other country’s experience and mechanism reconstruction[J]. Reform, 2014, (7): 75-86.]
[24]	俞萍萍. 国际碳贸易价格波动对可再生能源投资的影响机制-基于实物期权理论的分析[J]. 国际贸易问题, 2012, (5): 94-104.
	[Yu P P.Evaluating renewable energy investment options with uncertainty under volatile international carbon prices: A real options approach[J]. Journal of International Trade, 2012, (5): 94-104.]
[25]	苏竣, 眭纪刚, 张汉威, 等. 中国政府资助的可再生能源技术创新[J]. 中国软科学, 2008, (11): 34-44.
	[Su J, Sui J G, Zhang H W, et al.Government funded renewable energy innovation in China[J]. China Soft Science, 2008, (11): 34-44.]
[26]	孙鹏, 聂普焱. 动态视角下可再生对不新能源的替代[J]. 系统工程, 2014, (3): 92-98.
	[Sun P, Nie P.The substitution of renewable energy for depletable energy under dynamic perspective[J]. Systems Engineering, 2014, (3): 92-98.]
[27]	宋辉, 魏晓平. 我国可再生能源替代的动力学模型构建及分析[J]. 数学的实践与认识, 2013, 43(10): 58-70.
	[Song H, Wei X P.Research on renewable energy substitute model based on system dynamics in China[J]. Mathematics in Practice and Theory, 2013, 43(10): 58-70.]
[28]	赵新刚, 刘平阔, 刘璐, 等. 中国可再生能源发电对火力发电技术替代的实证研究-基于LVC模型[J]. 技术经济, 2011, 30(10): 40-44.
	[Zhao X G, Liu P K, Liu L, et al.Empirical analysis on technological substitution of electric power generation of renewable energy for thermal power generation in China: Based on Lotka-Volterra competition model[J]. Technology Economics, 2011, 30(10): 40-44.]
[29]	Masini A, Menichetti E.The impact of behavioural factors in the renewable energy investment decision making process: Conceptual framework and empirical findings[J]. Energy Policy, 2012, 40(1): 28-38.
[30]	Simar L, Wilson P.Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models[J]. Management Science, 1998, 44(1): 49-61.
[31]	方创琳, 关兴良. 中国城市群投入产出效率的综合测度与空间分异[J]. 地理学报, 2011, 66(8): 1011-1022.
	[Fang C L, Guan X L.Comprehensive measurement and spatial distinction of input-output efficiency of urban agglomerations in China[J]. Acta Geographica Sinica, 2011, 66(8): 1011-1022.]
[32]	周江, 李成东, 张鋆. 基于Bootstrap-DEA方法的我国区际能源生产效率分析[J]. 财经科学, 2014, (5): 124-130.
	[Zhou J, Li C D, Zhang J.China’s inter-regional energy production efficiency: Based on Bootstrap-DEA method[J]. Finance＆Economics, 2014, (5): 124-130.]
[33]	王亚华, 吴凡, 王争. 交通行业生产率变动的Bootstrap-Malmquist指数分析(1980-2005)[J].经济学(季刊), 2008, 7(3): 891-912.
	[Wang Y H, Wu F, Wang Z.Productivity in China’s transportation sector: A Malmquist Index and Bootstrap Approach(1980-2005)[J]. China Economic Quarterly, 2008, 7(3): 891-912.]
[34]	蔡立亚, 郭剑锋, 姬强. 基于G8与BRIC的可再生能源及新能源发电绩效动态评价[J]. 资源科学, 2013, 35(2): 250-260.
	[Cai L Y, Guo J F, Ji Q.Evaluation of new and renewable energy power generation performance in G8 and BRIC Nations[J]. Resources Science, 2013, 35(2): 250-260.]
[35]	刘婕, 魏玮. 城镇化率、要素禀赋对全要素碳减排效率的影响[J]. 中国人口·资源与环境, 2014, 24(8): 42-48.
	[Liu J, Wei W.Impact of urbanization level and endowment disparity on carbon reduction efficiency[J]. China Population, Resources and Environment, 2014, 24(8): 42-48.]
[36]	肖兴志, 姜莱. 战略性新兴产业发展对中国能源效率的影响[J]. 经济与管理研究, 2014, (6): 83-92.
	[Xiao X Z, Jiang L.The effect of the development of strategic emerging industries on China’s energy efficiency[J]. Research on Economics and Management, 2014, (6): 83-92.]
[37]	李伟. 城镇化率每增加1%, 需6000万吨标煤的能源消耗[EB/OL]. (2014-02-12)[2015-09-20]. .
	[Li W. 1% of Increase in Urbanization Rate needs 60 Million Tons of Standard Coal[EB/OL]. (2014-02-12) [2015-09-20]. .]

变量	变量涵义	样本量	均值	标准误	最小值	最大值
solar	太阳能容量/MW	276	920.799	3 168.056	0.000	32 643.000
wind	风能装机容量/MW	276	4 779.489	9 855.664	3.000	75 372.000
geothermal	地热能装机容量/MW	276	221.529	599.277	0.000	3386.000
biofuel	生物燃料投入/万toe	276	90.879	3 482.530	0.000	28 250.840
electricity	新能源发电量/亿kWh	276	190.216	314.618	1.070	2387.430
carbon	二氧化碳排放量（万t,取自然对数）	276	578.400	139.000	372.300	912.800
proportion	自然资源租金总额占GDP的比重/%	276	2.560	3.922	0.021	21.907
city	城镇化率/%	276	74.495	14.788	27.981	97.515
pgdp	人均国内生产总值（不变价美元,取自然对数）	276	10.050	1.047	6.156	11.505

发电效率	系数	标准误差	Z值	双尾检验概率	95%置信区间
发电效率	系数	标准误差	Z值	双尾检验概率	下限	上限
碳排放	0.065	0.041	1.590	0.112	-0.015	0.145
租金比重	1.588	0.902	1.760	0.078	-0.180	3.356
城镇化率	-1.548	0.496	-3.120	0.002	-2.519	-0.576
人均收入	0.469	0.063	7.400	0.000	0.345	0.594
常数项	-3.218	0.572	-5.620	0.000	-4.339	-2.096
个体效应标准差	0.265	0.065	4.100	0.000	0.139	0.392
随机干扰项标准差	0.206	0.010	20.910	0.000	0.187	0.226
RHO	0.624	0.122			0.376	0.828