补贴政策对中国光伏装机市场的影响——基于面板数据回归的实证分析

doi:10.18402/resci.2021.06.01

摘要/Abstract

摘要：

准确评估补贴对于新兴产业发展的影响是科学制定政府补贴政策的重要依据。一方面,补贴过高会带来财政压力和产能过剩;另一方面,补贴过低会造成产业发展动力不足。中国光伏产业的高速增长伴随着亟待解决的“弃光限电”和财政补贴缺口问题,其背后是光伏补贴政策科学评估的缺乏。为此,本文以中国光伏产业为例,运用面板数据回归和模拟仿真方法,严谨评估中央补贴对光伏产业发展的影响大小。研究发现：①光伏补贴每提升0.1元/度电,全国光伏装机将增加540万~660万kW/年,大大超过现有文献估计。②反事实模拟表明,如果一开始完全取消光伏补贴,中国光伏装机市场规模将在原有基础上缩水85%左右。③以2019年弃光限电问题严重的9省为例,如果光伏补贴调整至不存在弃光的水平,这些省份的光伏补贴缺口每年可减少13亿元。本文研究结论可用于补贴政策调整优化、缓解弃光限电和补贴缺口等多个现实政策问题。

关键词: 光伏产业, 补贴政策, 政策评估, 弃光限电, 补贴缺口, 反事实模拟, 日照资源

Abstract:

Accurately estimating the impact of government subsidy on the development of strategic new industries is a key to evidence-based decision making of subsidy policies because, on the one hand, over-subsidization will bring about fiscal pressure and overcapacity to the society, and on the other hand, under-subsidization will slow down the development of these new industries. The rapid growth of China’s photovoltaic (PV) industry is accompanied by the problems of solar curtailment and fiscal pressure which need to be solved urgently. Behind this is the lack of scientific evaluation of PV subsidy policy. This research took China’s PV industry as an example, and used panel data regression and counterfactual analysis to rigorously estimate the impact of government subsidy on PV market development. Our findings have the following indications. (1) With all other factors controlled, an increase of 0.1 yuan/kWh in PV subsidy will bring about 5.4-6.6 GW/year of installed capacity to the Chinese PV market, which is much greater than that estimated in the literature. (2) From a different perspective, if China were not having any PV subsidy in the first place, the PV market size would likely shrink by 85% compared to the actual case. (3) Taking the nine provinces with serious PV curtailment in the first half year of 2019 as the case, if PV subsidies were adjusted to a level where there is no curtailment, the subsidy deficit will be reduced by about RMB 1.3 billion yuan per year for these provinces. The conclusions of this research can be applied to optimizing the subsidy policy design and mitigating policy problems including PV curtailment and subsidy deficit.

Key words: solar PV industry, subsidy policy, policy evaluation, PV curtailment, subsidy deficit, counterfactual simulation, solar resource

董长贵, 周润民, 李佳颖. 补贴政策对中国光伏装机市场的影响——基于面板数据回归的实证分析[J]. 资源科学, 2021, 43(6): 1065-1076.

DONG Changgui, ZHOU Runmin, LI Jiaying. The effect of feed-in tariff on China’s photovoltaic capacity development: An empirical analysis based on panel data regression[J]. Resources Science, 2021, 43(6): 1065-1076.

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资源区划分	资源区	2012.1— 2013.12	2014.1— 2016.6	2016.7— 2017.6	2017.7— 2018.6	2018.7—2019.6
I	宁夏,青海海西,甘肃嘉峪关、武威、张掖、酒泉、敦煌、金昌,新疆哈密、塔城、阿勒泰、克拉玛依,内蒙古除赤峰、通辽、兴安盟、呼伦贝尔以外地区	1.00	0.90	0.80	0.65	0.50
II	北京,天津,黑龙江,吉林,辽宁,四川,云南,内蒙古赤峰、通辽、兴安盟、呼伦贝尔,河北承德、张家口、唐山、秦皇岛,山西大同、朔州、忻州,陕西榆林、延安,青海、甘肃、新疆除I类外其他地区	1.00	0.95	0.88	0.75	0.60
III	除I类、II类资源区以外的其他地区（该政策未包含西藏、香港、澳门、台湾）	1.00	1.00	0.98	0.85	0.70

资源区	甘肃	新疆	青海	内蒙古	河北	山西	陕西
Ⅰ	0.639	0.195	0.459	0.605
Ⅱ	0.361	0.805	0.541	0.395	0.530	0.297	0.390
Ⅲ					0.470	0.703	0.610

变量	描述	数据来源	预期符号
Capacity	每个省每半年度的新增光伏装机/万kW	国家能源局	/
FIT	标杆上网电价/（元/kWh）	发改委	+
LCOE	平准化成本,根据2018年美元兑人民币汇率,将单位换算为元/kWh	IEA	-
CF	容量因子,用以衡量全年发电量的大小	文献[26]	/
Res	潜在日照资源/万kW	文献[26]	+
Coal_lag	滞后一期的人均煤炭消费量/（万t标准煤/万人）	国家统计局	+
Elec_lag	滞后一期的人均电力消费量/（亿kWh/万人）	国家统计局	+
GDP_lag	滞后一期的人均GDP/（万元/人）	国家统计局	-
Scale	年初和年中调整的光伏发展指标/万kW	国家能源局	-
Carbon	碳交易市场（试点所在省份为1,否则为0）	碳排放交易网http://tanpaifang.com/	+/-
FisHe	地方政府当年财政收入与财政支出之比	国家统计局	+/-

变量	观测值	均值	标准差	最小值	最大值
Capacity	420	31.060	46.219	0.000	353.000
FIT	420	0.901	0.130	0.500	1.000
LCOE	420	0.705	0.273	0.211	1.264
CF	420	0.189	0.022	0.151	0.260
LCOE_v2	420	0.705	0.284	0.151	1.557
Res	420	732.112	1766.914	0.850	7498.000
Coal_lag	420	3.786	3.351	0.236	16.772
Elec_lag	420	0.474	0.273	0.186	1.548
GDP_lag	420	2.624	1.235	0.801	7.183
Scale	420	410.690	474.114	1.000	999.000
Carbon	420	0.155	0.362	0.000	1.000
FisHe	420	0.507	0.186	0.156	0.931

	RE (1)	RE (2)	RE (3)
FIT	112.120***	148.756***
	(27.991)	(47.089)
LCOE	-70.336***
	(15.479)
LCOE_v2		-42.254
		(40.628)
FIT_v2			91.534***
			(30.082)
Res	0.004	0.006	0.005
	(0.004)	(0.005)	(0.005)
Coal_lag	0.720	0.659	0.886
	(1.958)	(2.133)	(2.070)
Elec_lag	33.075*	44.616*	34.005
	(19.713)	(24.052)	(22.045)
GDP_lag	-0.146	-10.727**	-11.136**
	(3.987)	(4.781)	(4.743)
Scale	-0.0002	-0.019**	-0.020**
	(0.006)	(0.009)	(0.009)
Carbon	-14.169**	-1.327	-1.818
	(6.386)	(6.704)	(6.712)
FisHe	-34.754	34.594	51.012*
	(26.254)	(29.576)	(28.921)
省级固定效应	是	是	是
时间固定效应	否	是	是
F统计量	68.132	158.057	159.457
样本量	420	420	420
R²	0.157	0.258	0.255
豪斯曼检验	13.445（不显著）	4.757（不显著）	5.896（不显著）