中国能源影子价格和能源环境效率省际差异

doi:10.18402/resci.2020.06.03

摘要/Abstract

摘要：

为响应政府推进能源定价机制改革,充分发挥市场配置资源的决定性作用。本文以能源影子价格为切入点,基于共同前沿下SBM-Undesirable模型,选取2000—2017年中国30个省份数据分别测算群组前沿和共同前沿下能源影子价格和能源环境效率,分析其地区差异性和演变趋势,然后利用面板数据模型对中国地区间能源影子价格影响因素进行回归分析。结果表明：①中国能源环境效率整体偏低,且地区间技术差异性显著,能源环境效率高、中、低3个群组之间技术落差比为1、0.571、0.614,表明高水平组拥有最优的生产技术;②群组前沿下中国能源影子价格呈现上升趋势,表明中国能源要素的经济价值逐年提升,而共同前沿下中国省际能源影子价格潜在提升空间呈现差异性,云南可提升空间最大,北京可提升空间最低;③各影响因素对国家和各地区影响程度和作用方向不同,市场化程度对高水平组省份有显著正向影响,政府干预对中水平组省份有显著负向影响,产业结构对低水平组省份有显著负向影响。据此提出：各地在推进能源市场化改革时,不应简单”一刀切“,应考虑群组技术异质性,制定差别化的能源定价机制。

关键词: 能源影子价格, SBM-Undesirable, 共同前沿, 全要素能源环境效率, 中国

Abstract:

In response to the government’s reform of the energy pricing mechanism, the market should play a fundamental role in allocating resources. Based on the meta-frontier and slacks-based measure (SBM)-undesirable model, provincial data from 2000 to 2017 in 30 selected provinces of China were employed to analyze energy shadow price and energy environment efficiency of group frontier and meta-frontier in this study. Then, a provincial panel data model was used to analyze the influencing factors of shadow energy prices in China. The results indicate that Chinese energy environment efficiency is low, and regional energy environment efficiencies differ. The technology gap ratio of A: B: C group are 1: 0.571: 0.614, which shows clear technological heterogeneity. Second under the group frontier, China’s energy shadow price is rising, indicating that the economic value of China’s energy factors has increased. The potential room for energy shadow price increase in various provinces is different under the meta-frontier,Yunnan has the greatest potential and Beijing has the lowest potential. Third, government intervention, industrial structure, marketization reform, energy endowment, and energy consumption structure all have remarkable impacts on energy shadow price in a country or region, with different influencing mechanisms. Regional marketization reform has significant positive impact on the energy shadow price in A group. Regional government intervention inhibits the promotion of the energy shadow price in B group. Regional industrial structure inhibits the promotion of the energy shadow price in C group. We conclude that a one size fits all approach is inappropriate when promoting energy market reform and that people should consider the heterogeneity of technology when developing different energy pricing mechanism.

Key words: energy shadow price, SBM-Undesirable, meta-frontier, total factor energy environment efficiency, China

闫庆友, 桂增侃, 张文华, 陈立忠. 中国能源影子价格和能源环境效率省际差异[J]. 资源科学, 2020, 42(6): 1040-1051.

YAN Qingyou, GUI Zengkan, ZHANG Wenhua, CHEN Lizhong. The heterogeneity of regional energy shadow price and energy environment efficiency in China[J]. Resources Science, 2020, 42(6): 1040-1051.

图/表 9

表1

图1

图2

表2

图3

Table 3

Energy shadow price under group-frontier and meta-frontier of selected provinces in China, 2000-2017"

	地区	群组前沿				共同前沿
	地区	2000—2005	2006—2011	2012—2017	2000—2017	2000—2005	2006—2011	2012—2017	2000—2017
A组	北京	0.623	0.755	1.296	0.891	0.625	0.758	1.302	0.895
	天津	0.476	0.562	0.649	0.562	0.478	0.574	0.657	0.570
	辽宁	0.376	0.455	0.675	0.502	0.382	0.458	0.678	0.506
	上海	0.561	0.574	0.653	0.596	0.563	0.576	0.658	0.599
	江苏	0.630	0.628	0.729	0.662	0.636	0.630	0.729	0.665
	浙江	0.594	0.696	0.773	0.688	0.600	0.702	0.773	0.692
	福建	0.655	0.694	0.939	0.763	0.669	0.699	0.942	0.770
	广东	0.736	0.825	0.947	0.836	0.740	0.830	0.949	0.840
	海南	0.740	0.806	0.952	0.833	0.744	0.821	0.958	0.841
	平均	0.599	0.666	0.846	0.704	0.604	0.672	0.850	0.709
B组	吉林	0.319	0.583	0.882	0.595	0.347	0.603	0.893	0.614
	黑龙江	0.361	0.424	0.632	0.472	0.442	0.471	0.708	0.540
	安徽	0.382	0.570	0.918	0.623	0.483	0.625	0.959	0.689
	江西	0.372	0.530	0.842	0.582	0.472	0.697	0.975	0.715
	河南	0.258	0.545	0.661	0.488	0.322	0.586	0.733	0.547
	湖北	0.480	0.531	0.812	0.608	0.527	0.550	0.844	0.640
	湖南	0.215	0.476	0.800	0.497	0.289	0.503	0.845	0.546
	广西	0.272	0.462	0.723	0.486	0.315	0.659	0.894	0.623
	重庆	0.503	0.556	0.796	0.618	0.539	0.577	0.860	0.658
	四川	0.414	0.457	0.710	0.527	0.448	0.515	0.796	0.586
	陕西	0.451	0.581	0.748	0.593	0.501	0.603	0.824	0.643
	平均	0.366	0.520	0.775	0.554	0.426	0.581	0.848	0.619
C组	河北	0.261	0.329	0.446	0.345	0.370	0.425	0.674	0.490
	山西	0.174	0.249	0.352	0.258	0.207	0.312	0.508	0.343
	内蒙古	0.170	0.256	0.313	0.246	0.218	0.368	0.554	0.380
	山东	0.275	0.330	0.465	0.357	0.497	0.556	0.750	0.601
	贵州	0.196	0.232	0.377	0.268	0.309	0.401	0.650	0.453
	云南	0.261	0.241	0.441	0.314	0.457	0.526	0.820	0.601
	甘肃	0.206	0.218	0.368	0.264	0.345	0.430	0.655	0.477
	青海	0.277	0.368	0.415	0.353	0.351	0.385	0.488	0.408
	宁夏	0.265	0.283	0.319	0.289	0.265	0.341	0.444	0.350
	新疆	0.219	0.233	0.279	0.243	0.398	0.424	0.461	0.427
	平均	0.230	0.274	0.377	0.294	0.342	0.417	0.600	0.453
全国平均		0.391	0.482	0.664	0.512	0.451	0.553	0.766	0.590

Table 3

图4

图5

表4

参考文献 30

[1]	Ju K Y, Su B, Zhou D Q, et al. Does energy-price regulation benefit China’s economy and environment? Evidence from energy-price distortions[J]. Energy Policy, 2017,105:108-119. doi: 10.1016/j.enpol.2017.02.031
[2]	冷艳丽, 杜思正. 能源价格扭曲与雾霾污染: 中国的经验证据[J]. 产业经济研究, 2016,80(1):71-79.
	[ Leng Y L, Du S Z. Energy price distortion and haze pollution: The evidence from China[J]. Industrial Economics Research, 2016,80(1):71-79.]
[3]	Wang X L, Bai M Q, Xie C P. Investigating CO₂ mitigation potentials and the impact of oil price distortion in China’s transport sector[J]. Energy Policy, 2019,130:320-327. doi: 10.1016/j.enpol.2019.04.003
[4]	Lai P H, Du M Z, Wang B, et al. Assessment and decomposition of total factor energy efficiency: An evidence based on energy shadow price in China[J]. Sustainability, 2016, DOI: 10.3390/su8050408. doi: 10.3390/su8101040 pmid: 28008371
[5]	Zhang X P, Xu Q N, Zhang F, et al. Exploring shadow prices of carbon emissions at provincial levels in China[J]. Ecological Indicators, 2014,46:407-414. doi: 10.1016/j.ecolind.2014.07.007
[6]	Tamaki T, Shin K J, Nakamura H, et al. Shadow prices and production inefficiency of mineral resources[J]. Economic Analysis and Policy, 2018,57:111-121. doi: 10.1016/j.eap.2017.03.005
[7]	陈欣, 刘延. 中国二氧化碳影子价格估算及与交易价格差异分析: 基于二次型方向性距离产出函数[J]. 生态经济, 2018,34(6):14-20.
	[ Chen X, Liu Y. Estimation of China’s carbon dioxide shadow price and analysis of the difference in transaction price: Output function based on quadratic directional distance[J]. Ecological Economy, 2018,34(6):14-20.]
[8]	Zhao Q Z, Yan Q Y, Zhang L J, et al. Dynamic and spatial character analysis of regional marginal abatement costs of CO₂ emissions from energy consumption: A provincial aspect[J]. Polish Journal of Environmental Studies, 2019,28(4):2501-2511. doi: 10.15244/pjoes/90479
[9]	Wang K, Che L N, Ma C B, et al. The shadow price of CO₂ emissions in China’s iron and steel industry[J]. Science of the Total Environment, 2017,598:272-281. doi: 10.1016/j.scitotenv.2017.04.089 pmid: 28445824
[10]	Singbo A G, Lansink A O, Emvalomatis G. Estimating shadow prices and efficiency analysis of productive inputs and pesticide use of vegetable production[J]. European Journal of Operational Research, 2015,245(1):265-272. doi: 10.1016/j.ejor.2015.02.042
[11]	Choi Y, Zhang N, Zhou P. Efficiency and abatement costs of energy-related CO₂ emissions in China: A slacks-based efficiency measure[J]. Applied Energy, 2012,98(5):198-208. doi: 10.1016/j.apenergy.2012.03.024
[12]	Yan Q Y, Wang Y X, Tomas B, et al. Energy-related CO₂ emission in China’s provincial thermal electricity generation: Driving factors and possibilities for abatement[J]. Energy, 2018,11(5):1096-1118.
[13]	Sheng P F, Yang J, Shackman J D. Energy’s shadow price and energy efficiency in China: A non-parametric input distance function analysis[J]. Energies, 2015,8(3):1975-1989. doi: 10.3390/en8031975
[14]	吴凡, 刘雪娇, 谢文秀. 要素配置优化与中国区域能源影子价格[J]. 现代财经(天津财经大学学报), 2017,37(3):93-101.
	[ Wu F, Liu X J, Xie W X. Factor configuration optimization and regional energy shadow price in China[J]. Modern Finance and Economics-Journal of Tianjin University of Finance and Economics, 2017,37(3):93-101.]
[15]	Fukuyama H, Weber W L. A directional slacks: Based measure of technical inefficiency[J]. Socio-Economic Planning Sciences, 2009,43(4):274-287. doi: 10.1016/j.seps.2008.12.001
[16]	Zhang N, Kong F B, Yu Y N. Measuring ecological total-factor energy efficiency incorporating regional heterogeneities in China[J]. Ecological Indicators, 2015,51:165-172. doi: 10.1016/j.ecolind.2014.07.041
[17]	Yu Y T, Huang J H, Zhang N. Modeling the eco-efficiency of Chinese prefecture-level cities with regional heterogeneities: A comparative perspective[J]. Ecological Modelling, 2019,402:1-17. doi: 10.1016/j.ecolmodel.2019.03.012
[18]	Battese G E, Rao D S P, O’Donnell C J. A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies[J]. Journal of Productivity Analysis, 2004,21:91-103. doi: 10.1023/B:PROD.0000012454.06094.29
[19]	方琳, 吴凤平, 王新华, 等. 基于共同前沿SBM模型的农业用水效率测度及改善潜力[J]. 长江流域资源与环境, 2018,27(10):2293-2304.
	[ Fang L, Wu F P, Wang X H, et al. Analysis of agricultural water efficiency measurement and improvement potential based on meta frontier SBM model[J]. Resources and Environment in the Yangtze Basin, 2018,27(10):2293-2304.]
[20]	Long X L, Luo Y S, Sun H P, et al. Fertilizer using intensity and environmental efficiency for China’s agriculture sector from 1997 to 2014[J]. Natural Hazards, 2018,92:1573-1591. doi: 10.1007/s11069-018-3265-4
[21]	国涓, 刘丰, 王维国. 中国区域环境绩效动态差异及影响因素: 考虑可变规模报酬和技术异质性的研究[J]. 资源科学, 2013,35(12):2444-2456.
	[ Guo J, Liu F, Wang W G. Dynamic differences and factors affecting regional environmental performance in China: Variable return to scale and technology heterogeneity[J]. Resources Science, 2013,35(12):2444-2456.]
[22]	Lin B Q, Tan R P. Ecological total-factor energy efficiency of China’s energy intensive industries[J]. Ecological Indicators, 2016,70:480-497. doi: 10.1016/j.ecolind.2016.06.026
[23]	Li J L, Lin B Q. Ecological total-factor energy efficiency of China’s heavy and light industries: Which performs better[J]. Renewable & Sustainable Energy Reviews, 2017,72:83-94.
[24]	聂雷, 郭忠兴, 彭冲. 基于SBM-Undesirable和Meta-frontier模型的城市建设用地利用效率研究[J]. 资源科学, 2017,39(5):836-845. doi: 10.18402/resci.2017.05.04
	[ Nie L, Guo Z X, Peng C. Construction land utilization efficiency based on SBM-Undesirable and Meta-frontier model[J]. Resources Science, 2017,39(5):836-845.]
[25]	陈诗一. 工业二氧化碳的影子价格: 参数化和非参数化方法[J]. 世界经济, 2010,33(8):93-111.
	[ Chen S Y. Shadow price of industrial carbon dioxide: Parametric and nonparametric approach[J]. The Journal of World Economy, 2010,33(8):93-111.]
[26]	Chiu C R, Liou J L, Wu P I, et al. Decomposition of the environmental inefficiency of the meta-frontier with undesirable output[J]. Energy Economics, 2012,34(5):1392-1399. doi: 10.1016/j.eneco.2012.06.003
[27]	张军, 吴桂英, 张吉鹏. 中国省际物质资本存量估算: 1952-2000[J]. 经济研究, 2004, (10):35-44.
	[ Zhang J, Wu G Y, Zhang J P. The estimation of China’s provincial capital stock: 1952-2000[J]. Economic Research Journal, 2004, (10):35-44.]
[28]	中华人民共和国国家统计局. 中国统计年鉴[M]. 北京: 中国统计出版社, 2001- 2018.
	[National Bureau of Statistics of the People’s Republic of China. China Statistical Yearbook[M]. Beijing: China Statistics Press, 2001- 2018.]
[29]	国家统计局能源统计司, 国家能源局综合司. 中国能源统计年鉴[M]. 北京: 中国统计出版社, 2001- 2018.
	[Department of Energy Statistics, National Bureau of Statistics of the People’s Republic of China, Department of General Affairs National Energy Administration of the People’s Republic of China. China Energy Statistical Yearbook[M]. Beijing: China Statistics Press, 2001- 2018.]
[30]	Yao X, Zhou H C, Zhang A Z, et al. Regional energy efficiency, carbon emission performance and technology gaps in China: A meta-frontier non-radial directional distance function analysis[J]. Energy Policy, 2015,84:142-154. doi: 10.1016/j.enpol.2015.05.001

	变量	单位	平均值	最大值	最小值	标准差
A组	资本存量	亿元	33712.932	177723.015	1157.632	33000.163
	劳动力	万人	2283.834	6310.014	330.974	1705.803
	能源消费量	万t标准煤	11880.717	32342.004	479.955	8389.987
	地区生产总值	亿元	14687.278	62401.513	526.829	12816.087
	环境污染指数		0.193	0.595	0.013	0.154
B组	资本存量	亿元	25605.809	141653.974	2375.282	24906.032
	劳动力	万人	3040.019	6746.432	1044.624	1415.241
	能源消费量	万t标准煤	9909.742	23647.109	2329.007	5124.148
	地区生产总值	亿元	8547.678	29348.583	1747.442	5666.173
	环境污染指数		0.216	0.509	0.091	0.092
C组	资本存量	亿元	20801.614	190365.189	710.265	29382.224
	劳动力	万人	1964.539	5960.014	238.578	1530.583
	能源消费量	万t标准煤	11319.868	38899.253	897.219	9259.562
	地区生产总值	亿元	6739.013	51736.438	294.534	8783.717
	环境污染指数		0.267	0.785	0.014	0.190

地区		E^Tol	E^TI	E^MI	改善策略		地区		E^Tol	E^TI	E^MI	改善策略
地区		E^Tol	E^TI	E^MI	技术	管理			E^Tol	E^TI	E^MI	技术	管理
A组	北京	0.251	0.000	0.251		√				广西	0.415	0.354	0.061	√
	天津	0.334	0.000	0.334		√				重庆	0.479	0.416	0.063	√
	辽宁	0.438	0.000	0.438		√				四川	0.591	0.449	0.141	√
	上海	0.293	0.000	0.293		√				陕西	0.564	0.419	0.146	√
	江苏	0.369	0.000	0.369		√				平均	0.493	0.380	0.113
	浙江	0.363	0.000	0.363		√			C组	河北	0.674	0.314	0.359	√	√
	福建	0.267	0.000	0.267		√	山西	0.796		0.176	0.620		√
	广东	0.007	0.000	0.007		√	内蒙古	0.619		0.235	0.384		√
	海南	0.037	0.000	0.037		√	山东	0.541		0.333	0.207		√
	平均	0.262	0.000	0.262			贵州	0.734		0.136	0.597	√
B组	吉林	0.475	0.259	0.216	√	√	云南	0.585		0.289	0.296	√	√
	黑龙江	0.503	0.345	0.158	√			甘肃	0.619	0.192	0.427		√
	安徽	0.485	0.423	0.062	√			青海	0.389	0.266	0.123	√
	江西	0.353	0.337	0.016	√			宁夏	0.522	0.332	0.190	√
	河南	0.550	0.420	0.130	√			新疆	0.702	0.134	0.568		√
	湖北	0.569	0.388	0.180	√			平均	0.618	0.241	0.377
	湖南	0.436	0.363	0.073	√			全国	0.465	0.219	0.246

变量	全国			A组			B组			C组
变量	系数	t值	P值	系数	t值	P值	系数	t值	P值	系数	t值	P值
GI	-0.268	-1.586	0.073	-0.507	-0.880	0.202	-0.388	-1.996	0.045	-0.295	-1.703	0.072
IS	-0.089	-1.551	0.079	-0.311	-1.045	0.181	-0.279	-2.071	0.040	-0.469	-5.063	0.000
MR	0.012	1.427	0.099	0.024	2.144	0.034	0.001	0.094	0.925	0.004	0.726	0.239
EE	-0.027	-5.042	0.000	-0.177	-1.246	0.116	-0.032	-2.448	0.015	-0.002	-2.181	0.037
EC	-0.536	-6.018	0.000	-1.184	-6.095	0.000	-0.521	-4.386	0.000	-0.191	-2.355	0.020