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

Abstract

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

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.

Figures/Tables 9

Table 1

Figure 1

Figure 2

Table 2

	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

Figure 4

Figure 5

Table 4

References 30

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	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

The heterogeneity of regional energy shadow price and energy environment efficiency in China

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