风电上网电价政策地区差异及其产业效应

doi:10.18402/resci.2021.01.02

摘要/Abstract

摘要：

风力发电是发展新能源产业的重要任务之一,而国家对风力发电进行补贴的上网电价政策是促进风电产业发展的重要举措。分析上网电价政策的实施情况及其产业效应,对于有效落实政策、促进风电产业高质量发展具有重要意义。本文引入政策文本分析方法,从政策实施时间、政策数量、政策相似度多个角度量化评价上网电价政策实施的地区差异。然后,从资源区和省份两个层面,建立多水平模型分析上网电价政策的产业效应。结果表明：①不同资源区和省份实施风电上网电价政策的情况不同,多数属于国家发改委划分的第Ⅰ、Ⅱ、Ⅲ类资源区的省份,风电上网电价政策实施情况较好,政策发布较早、数量较多,而且政策具有针对性。②风电上网电价政策产生了显著的产业效应,而且政策实施时间越早、数量越多、政策相似度越低,越有利于风电产业发展。但是,这些政策变量对不同地区的风电产业发展的影响程度不同。政策实施时间、政策数量对风能资源丰富区的风电产业发展影响较大,政策相似度对风电发展模式不完全成熟的地区的风电产业发展影响较大。因此,各个省份应该尽早实施上网电价政策,并结合地区差异针对性地多发布政策,从而利用政府政策有序推进风电产业发展。

关键词: 风电, 上网电价政策, 地区差异, 产业效应, 文本相似度, 多水平模型

Abstract:

Wind power generation is one of the important tasks for the development of new energy industry, and feed-in-tariff policy is an important measure to promote the development of the wind power industry. It is of great significance to analyze the implementation of the policy and its industrial effect for effectively implementing the policy and promoting the high quality development of the wind power industry. This study first introduced the method of policy similarity in policy text analysis, and quantitatively evaluated the regional difference in the implementation of feed-in-tariff policy from the perspectives of policy implementation time, number of policies, and policy similarity. Then, a multilevel model was established to analyze the industrial effects of the feed-in-tariff policy at the resource area and provincial levels. The results show that: First, different resource areas and provinces have different implementation situation of feed-in-tariff policy. Most of the provinces that belong to the first, second, and third categories of resource areas classified by the National Development and Reform Commission have good implementation of the feed-in-tariff policy, and the policy was issued earlier, the number is large, and the policy is targeted. Second, the policy of wind power feed-in-tariff has significant industrial effects. The earlier the policy was implemented, the larger the number of policies, and the lower the policy similarity, the more favorable the development of the wind power industry. However, these policy variables have different impacts on the development of wind power industry in different regions. Implementation time and the number of policies have a great influence on the development of wind power industry in areas with abundant wind energy resources, while policy similarity has a great influence on the development of wind power industry in areas with immature wind power development model. Therefore, the provinces should implement the feed-in-tariff policy as soon as possible and issue more relevant policies in accordance with regional characteristics in order to promote the development of wind power industry.

Key words: wind power, feed-in-tariff policy, regional difference, industrial effects, text similarity, multilevel model

赵彦云, 李倩. 风电上网电价政策地区差异及其产业效应[J]. 资源科学, 2021, 43(1): 12-22.

ZHAO Yanyun, LI Qian. Regional difference and industrial effects of wind power feed-in-tariff policy[J]. Resources Science, 2021, 43(1): 12-22.

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参考文献 27

[1]	赵媛, 郝丽莎. 世界新能源政策框架及形成机制[J]. 资源科学, 2005,27(5):62-69.
	[ Zhao Y, Hao L S. New energy policy framework of the world and its forming mechanism[J]. Resources Science, 2005,27(5):62-69.]
[2]	彭月兰, 任晋晋. 促进我国风电发展的财税政策研究[J]. 经济问题, 2018, (8):28-31.
	[ Peng Y L, Ren J J. A study on fiscal and tax policies for the development of wind power in China[J]. On Economic Problems, 2018, (8):28-31.]
[3]	林伯强, 李江龙. 基于随机动态递归的中国可再生能源政策量化评价[J]. 经济研究, 2014,49(4):89-103.
	[ Lin B Q, Li J L. Quantitative evaluation of Chinese renewable energy policy based on recursive stochastic dynamic programme[J]. Economic Research Journal, 2014,49(4):89-103.]
[4]	何凌云, 张丽虹, 钟章奇, 等. 环境不确定性、外部融资与可再生能源投资: 兼论政策有效性[J]. 资源科学, 2018,40(4):748-758. doi: 10.18402/resci.2018.04.09
	[ He L Y, Zhang L H, Zhong Z Q, et al. The effect of environmental uncertainty, external financing and policy on renewable energy investment[J]. Resources Science, 2018,40(4):748-758.] doi: 10.18402/resci.2018.04.09
[5]	Tazi N, Bouzidi Y. Evolution of Wind Energy Pricing Policies in France: Opportunities and New Challenges[C]. Portugal: 6th International Conference on Energy and Environment Research, 2019.
[6]	Gnatowska R, Moryń-Kucharczyk E. Current status of wind energy policy in Poland[J]. Renewable Energy, 2019,135:232-237.
[7]	Saidur R, Islam M R, Rahim N A, et al. A review on global wind energy policy[J]. Renewable and Sustainable Energy Reviews, 2010,14(7):1744-1762.
[8]	侯建朝, 谭忠富, 谢品杰, 等. 世界风能资源开发现状和政策分析及对我国的启示[J]. 中国电力, 2008,41(9):65-68.
	[ Hou J C, Tan Z F, Xie P J, et al. Inspiration by analyzing situation and policy of overseas wind energy development[J]. Electric Power, 2008,41(9):65-68.]
[9]	张庆阳, 郭家康. 世界风能强国发展风电的经验与对策[J]. 中外能源, 2015,20(6):25-34.
	[ Zhang Q Y, Guo J K. Major wind power using countries’ experience and countermeasures in developing wind energy[J]. Sino-Global Energy, 2015,20(6):25-34.]
[10]	Yuan L Y, Xi J C. Review on China’s wind power policy (1986-2017)[J]. Environmental Science and Pollution Research, 2019,26:25387-25398. pmid: 31278643
[11]	Sahu B K. Wind energy developments and policies in China: A short review[J]. Renewable and Sustainable Energy Reviews, 2018,81:1393-1405. doi: 10.1016/j.rser.2017.05.183
[12]	李庆, 陈敏. 中国风电固定上网电价政策的实物期权理论与实证分析[J]. 中国管理科学, 2016,24(5):65-73. doi: 10.16381/j.cnki.issn1003-207x.2016.05.008
	[ Li Q, Chen M. Real option theoretical and empirical analysis on feed-in-tariff (FIT) policy of wind electricity in China[J]. Chinese Journal of Management Science, 2016,24(5):65-73.] doi: 10.16381/j.cnki.issn1003-207x.2016.05.008
[13]	郭晓丹, 闫静静, 毕鲁光. 中国可再生能源政策的区域解构、有效性与改进[J]. 经济社会体制比较, 2014, (6):176-187.
	[ Guo X D, Yan J J, Bi L G. Regional deconstruction, effectiveness and improvement of the renewable energy policy in China[J]. Comparative Economic & Social Systems, 2014, (6):176-187.]
[14]	Zhang R X X, Ni M, Shen G Q P, et al. An analysis on the effectiveness and determinants of the wind power Feed-in-Tariff policy at China’s national-level and regional-grid-level[J]. Sustainable Energy Technologies and Assessments, 2019,34:87-96.
[15]	裴雷, 孙建军, 周兆韬. 政策文本计算: 一种新的政策文本解读方式[J]. 图书与情报, 2016, (6):47-55.
	[ Pei L, Sun J J, Zhou Z T. Policy text computing: A new methodology of policy interpretation[J]. Library and Information, 2016, (6):47-55.]
[16]	邬龙, 王晓蓉, 迟远英. 我国大气污染治理政策主题变迁量化分析及预测[J]. 北京工业大学学报(社会科学版), 2019,19(6):80-89.
	[ Wu L, Wang X R, Chi Y Y. Quantitative analysis and prediction of policy topic changes of air pollution control policies in China[J]. Journal of Beijing University of Technology (Social Sciences Edition), 2019,19(6):80-89.]
[17]	Liao Z J. The evolution of wind energy policies in China (1995-2014): An analysis based on policy instruments[J]. Renewable and Sustainable Energy Reviews, 2016,56:464-472.
[18]	黄萃, 苏竣, 施丽萍, 等. 政策工具视角的中国风能政策文本量化研究[J]. 科学学研究, 2011,29(6):876-882.
	[ Huang C, Su J, Shi L P, et al. Textual and quantitative research on Chinese wind energy policy system from the perspective of policy tools[J]. Studies in Science of Science, 2011,29(6):876-882.]
[19]	王晓珍, 蒋子浩, 郑颖. 风电产业创新政策有效性研究[J]. 科学学研究, 2019,37(7):1249-1257.
	[ Wang X Z, Jiang Z H, Zheng Y. Research on the effectiveness of wind power industrial innovation policy[J]. Studies in Science of Science, 2019,37(7):1249-1257.]
[20]	王春柳, 杨永辉, 邓霏, 等. 文本相似度计算方法研究综述[J]. 情报科学, 2019,37(3):158-168.
	[ Wang C L, Yang Y H, Deng F, et al. A review of text similarity approaches[J]. Information Science, 2019,37(3):158-168.]
[21]	Wu H C, Luk R W P, Wong K F, et al. Interpreting TF-IDF term weights as making relevance decisions[J]. ACM Transactions on Information Systems, 2008,26(3):1-37.
[22]	谷重阳, 徐浩煜, 周晗, 等. 基于词汇语义信息的文本相似度计算[J]. 计算机应用研究, 2018,35(2):391-395.
	[ Gu C Y, Xu H Y, Zhou H, et al. Text similarity computing based on lexical semantic information[J]. Application Research of Computers, 2018,35(2):391-395.]
[23]	王尚坤, 石磊. 多水平面板数据模型的估计理论及模拟研究[J]. 统计与信息论坛, 2011,26(9):16-22.
	[ Wang S K, Shi L. Estimation theory and simulation studies on multilevel panel data model[J]. Statistics & Information Forum, 2011,26(9):16-22.]
[24]	洪福星, 邱皓政. 多层次模式方法论: 阶层线性模式的关键问题与试解[M]. 北京: 经济管理出版社, 2015.
	[ Hong F X, Qiu H Z. Multilevel Model Methodology: Key Problems and Trial Solutions of Hierarchical Linear Model[M]. Beijing: Economic Management Press, 2015.]
[25]	张文珺, 喻炜. 中国风电建设的区域分布及其对风力发电水平的影响[J]. 经济问题探索, 2014, (1):77-84.
	[ Zhang W J, Yu W. Regional distribution of wind power construction in China and its impact on wind power level[J]. Inquiry into Economic Issues, 2014, (1):77-84.]
[26]	周迪, 周丰年, 王雪芹. 低碳试点政策对城市碳排放绩效的影响评估及机制分析[J]. 资源科学, 2019,41(3):546-556.
	[ Zhou D, Zhou F N, Wang X Q. Impact of low-carbon pilot on the performance of urban carbon emission and its mechanism[J]. Resources Science, 2019,41(3):546-556.]
[27]	张敏, 鲁筠, 石磊. 基于高层次结构数据的多水平发展模型设计及应用[J]. 数量经济技术经济研究, 2017,34(6):134-147.
	[ Zhang M, Lu J, Shi L. Design and application based on the high-level structure data of multilevel development model[J]. The Journal of Quantitative & Technical Economics, 2017,34(6):134-147.]

实施时间	实施省份及价格（元/kWh）
2009年	辽宁（0.61）、黑龙江（0.58或0.61）、河北（0.54或0.61）、甘肃（0.54或0.58）、新疆（0.51或0.58）、北京（0.61）、天津（0.61）
2010年	吉林（0.58或0.61）、内蒙古（0.51或0.54）、山东（0.61）、宁夏（0.58）、江苏（0.61）、浙江（0.61）、海南（0.61）、云南（0.61）
2011年	广东（0.61）、陕西（0.61）、福建（0.61）、青海（0.61）
2012年	安徽（0.61）、湖北（0.61）、广西（0.61）

地区	政策相似度
地区	2009年	2014年	2016年	平均值
北京	0.75	0.80	0.82	0.79
天津	0.77	0.73	0.78	0.76
河北	0.85	0.83	0.72	0.80
山西	0.90	1.00	0.89	0.93
内蒙古	0.81	0.76	0.78	0.78
辽宁	1.00	0.95	0.98	0.98
吉林	0.95	0.97	0.93	0.95
黑龙江	0.80	0.85	0.83	0.83
上海	1.00	1.00	1.00	1.00
江苏	1.00	0.95	0.98	0.98
浙江	1.00	1.00	1.00	1.00
安徽	0.95	1.00	1.00	0.98
福建	0.98	0.92	0.94	0.95
江西	0.90	1.00	0.95	0.95
山东	0.80	0.72	0.67	0.73
河南	0.95	1.00	0.92	0.96
湖北	0.95	0.93	0.93	0.94
湖南	1.00	1.00	0.98	0.99
广东	0.90	0.88	0.88	0.89
广西	0.98	0.92	0.90	0.93
海南	1.00	1.00	1.00	1.00
重庆	0.90	0.90	0.88	0.89
四川	0.70	0.76	0.68	0.71
贵州	1.00	1.00	1.00	1.00
云南	1.00	1.00	1.00	1.00
陕西	0.95	0.87	0.90	0.91
甘肃	0.72	0.92	0.90	0.85
青海	0.90	0.86	1.00	0.92
宁夏	0.83	0.82	0.87	0.84
新疆	0.88	0.86	0.85	0.86

固定效应及显著性检验				随机效应及显著性检验
参数	回归系数	t	P	参数	标准差	方差	P（>Chisq）
γ₀₀	0.255	2.635	0.000	u_0j	1.244	1.547	0.000
				e_ij	1.541	2.376

固定效应	回归系数	t值	P值
intercept	0.336	0.972	0.435
time	0.349	4.233	0.000
number	0.065	2.518	0.012
similarity	-1.860	-12.584	0.000
emission	0.148	3.599	0.000
consumption	-0.042	-1.813	0.072
time×second	-0.527	-0.764	0.526
time×third	-0.230	-2.298	0.022
time×fourth	-0.289	-3.465	0.001
number×second	-0.179	-0.731	0.466
number×third	-0.006	-0.027	0.978
number×fourth	-0.235	-2.344	0.020
similarity×second	2.071	12.314	0.000
similarity×third	1.855	10.955	0.000
similarity×fourth	1.857	12.483	0.000
随机效应	方差	标准差	P值
Intercept	0.352	0.593	0.000
Residual	0.006	0.077	0.000

变量	回归系数	t值	P值
intercept	0.354	10.540	0.000
time	0.068	4.097	0.000
number	-0.151	-2.765	0.006
similarity	-0.018	-0.747	0.456
emission	0.254	4.981	0.000
consumption	-0.026	-0.456	0.650
second	-0.216	-6.528	0.000
third	-0.281	-0.907	0.000
fourth	-0.402	-15.254	0.000
F	65.042
调整R²	0.656