技术进步对中国电力行业环境效率提升影响的实证研究

doi:10.16381/j.cnki.issn1003-207x.2021.0883

Abstract

Abstract:

TSBM super efficiency model and ML model are adopted to measure the environmental efficiency and its growth rate of China's power industry in 30 provinces from 2009 to 2018 from a static and dynamic perspective， the static efficiency and dynamic efficiency are splitted， and the impact of technological progress on them is analyzed； Using Tobit regression model， the impact of technological progress and other factors on the environmental efficiency and growth rate of power industry in China and six power grid regions is tested. The results show that the annual average environmental efficiency of China's power industry during the study period is 0.769， which still has a large room for improvement； The improvement of pure technical efficiency in Central China Power Grid and Northeast China Power Grid has a low contribution to the improvement of environmental efficiency； The regional pure technical efficiency of China Southern Power Grid has significantly promoted the improvement of environmental efficiency； The environmental efficiency of China's power industry has increased at an average annual rate of 5.6%， of which the average annual growth rate of technological progress is 3.7%. Technological progress has made a great contribution to the improvement of environmental efficiency of China's power industry. Since the 12th Five Year Plan， the technological progress of China's power industry has accelerated； Technological progress is a key factor affecting the environmental efficiency and growth rate of power industry， which has a significant positive impact on it. It is suggested to continue to increase scientific and technological investment and strengthen scientific and technological innovation， so as to continue to promote the improvement of environmental efficiency of China's power industry； All regions should be proactive and take effective measures according to local conditions， accelerate the development of clean electric energy such as nuclear power and optimize the power supply structure.

Key words: power industry, environmental efficiency, SBM super efficiency model, ML model, Tobit regression model, technological progress

CLC Number:

F206

Jian-jun PAN,Guang-ming HOU,Ji-fa GU,Hua-feng ZHANG,Jun-peng WANG,Ze-wei CHEN. Empirical Study on the Impact of Technological Progress on the Environmental Efficiency of China's Power Industry: Based on SBM Super Efficiency-ML-Tobit[J]. Chinese Journal of Management Science, 2023, 31(12): 215-227.

Figures/Tables 18

省份	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	均值	排名
北京	1.03	1.031	1.035	1.041	1.042	1.055	1.118	1.171	1.223	1.278	1.102	1
江苏	1.043	1.035	1.046	1.076	1.031	1.031	1.032	1.057	1.041	1.029	1.042	2
浙江	1.065	1.08	1.077	1.076	1.003	1.002	0.838	0.858	1.001	1.012	1.009	3
湖北	1.002	1.006	1.004	1.004	1.006	1.007	1.036	1.011	1.005	1.002	1.000	4
青海	1.108	1.135	1.141	1.108	1.086	1.046	1.027	0.617	0.595	1.001	0.986	5
云南	0.721	0.723	1.003	1.002	1.01	1.059	1.094	1.083	1.092	1.040	0.983	6
四川	1.001	1.001	1.004	1.003	1.006	1.002	0.775	0.776	1.077	1.113	0.972	7
上海	1.002	0.916	0.902	1.002	0.925	0.935	0.677	1.002	1.002	1.067	0.971	8
福建	0.691	0.718	1.012	1.003	1.018	1.023	1.028	1.076	1.039	1.019	0.938	9
海南	0.714	0.756	0.88	0.829	0.938	1.007	0.773	1	0.936	0.818	0.865	10
宁夏	0.646	0.635	0.774	0.707	1.023	1.018	0.797	0.751	1.001	0.876	0.823	11
安徽	0.782	0.794	0.908	0.86	0.748	0.742	0.727	0.743	0.85	0.937	0.801	12
广东	0.794	0.764	0.803	0.759	0.798	0.824	0.787	0.758	0.784	0.809	0.796	13
天津	0.672	0.787	0.736	0.67	0.74	0.705	0.776	0.781	0.824	1.008	0.770	14
山西	0.755	0.758	0.743	0.713	0.796	0.753	0.696	0.672	0.669	0.769	0.732	15
内蒙古	0.771	0.692	0.755	0.737	0.672	0.684	0.702	0.557	0.616	1.002	0.731	16
山东	0.708	0.702	0.664	0.631	0.714	0.673	1.006	0.712	0.657	0.790	0.726	17
广西	0.664	0.661	0.693	0.667	0.738	0.748	0.728	0.696	0.717	0.716	0.690	18
贵州	0.741	0.652	0.634	0.693	0.604	0.656	0.707	0.628	0.633	0.681	0.663	19
河北	0.64	0.65	0.655	0.613	0.696	0.651	0.64	0.619	0.639	0.679	0.648	20
新疆	0.659	0.64	0.663	0.627	0.666	0.616	0.581	0.572	0.573	0.781	0.638	21
河南	0.548	0.53	0.556	0.539	0.621	0.653	0.736	0.7	0.719	0.661	0.626	22
陕西	0.602	0.557	0.566	0.558	0.612	0.618	0.623	0.633	0.634	0.709	0.621	23
辽宁	0.552	0.573	0.64	0.642	0.65	0.655	0.584	0.594	0.612	0.622	0.603	24
湖南	0.64	0.633	0.659	0.634	0.584	0.492	0.496	0.501	0.506	0.582	0.583	25
江西	0.578	0.575	0.62	0.608	0.629	0.613	0.528	0.529	0.565	0.643	0.580	26
重庆	0.484	0.512	0.559	0.571	0.588	0.57	0.602	0.638	0.636	0.647	0.576	27
甘肃	0.553	0.542	0.583	0.53	0.586	0.599	0.559	0.594	0.571	0.579	0.572	28
吉林	0.589	0.543	0.54	0.525	0.525	0.52	0.48	0.493	0.499	0.555	0.527	29
黑龙江	0.516	0.517	0.538	0.491	0.502	0.477	0.472	0.46	0.469	0.562	0.500	30

References 14

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指标类型	一级指标	二级指标	数据来源
投入指标	资本投入	装机容量	《中国电力年鉴》
	劳动力	电力行业从业人员数	《中国劳动统计年鉴》
	能源	能源耗量	《中国电力年鉴》
产出指标	期望产出指标	发电量	《中国电力年鉴》
	非期望产出指标	CO₂	《中国能源统计年鉴》
		SO₂	国家统计局官网、《中国能源统计年鉴》
		NO_X	《中国电力年鉴》

变量	变量符号	变量定义	数据来源
环境效率	EE	电力行业环境效率	本文计算
ML指数	ML	电力行业环境效率增长率	本文计算
技术水平	X₁	R&D投入费用占全国总的R&D投入费用的比重	中国科技统计年鉴
经济规模	X₂	地区GDP占全国GDP的比重	国家统计局官网
产业结构	X₃	第二产业增加值占GDP的比重	国家统计局官网
电源结构	X₄	非火力发电装机容量占全国非火力发电装机容量	中国电力年鉴

效率	区域	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	均值
环境效率	华北	0.761	0.786	0.767	0.734	0.798	0.767	0.847	0.791	0.802	0.905	0.796
	华东	0.968	0.944	0.952	0.969	0.953	0.960	0.861	0.921	0.982	1.013	0.952
	华中	0.672	0.678	0.752	0.736	0.748	0.737	0.689	0.711	0.731	0.775	0.723
	东北	0.593	0.570	0.584	0.560	0.594	0.591	0.576	0.571	0.580	0.716	0.593
	南方	0.746	0.723	0.836	0.824	0.794	0.830	0.801	0.802	0.825	0.813	0.799
	西北	0.699	0.701	0.754	0.726	0.793	0.773	0.728	0.633	0.687	0.789	0.728
纯技术效率	华北	0.788	0.838	0.827	0.817	0.863	0.796	1.086	1.278	0.847	0.896	0.904
	华东	0.978	0.973	0.961	0.975	0.965	0.969	0.978	0.972	1.000	1.030	0.980
	华中	0.694	0.741	0.768	0.752	0.760	0.747	0.712	0.721	0.738	0.789	0.742
	东北	0.613	0.589	0.591	0.574	0.613	0.605	0.596	0.580	0.592	0.696	0.605
	南方	1.675	1.373	1.361	1.282	1.205	1.218	1.151	1.215	1.225	1.235	1.294
	西北	0.821	0.781	0.851	0.827	0.880	0.889	0.981	0.813	0.818	0.898	0.856
规模效率	华北	0.965	0.945	0.940	0.909	0.933	0.967	0.858	0.805	0.951	1.009	0.928
	华东	0.990	0.971	0.991	0.993	0.988	0.990	0.883	0.948	0.983	0.984	0.972
	华中	0.969	0.932	0.979	0.980	0.984	0.987	0.966	0.985	0.990	0.979	0.975
	东北	0.970	0.970	0.990	0.977	0.972	0.978	0.967	0.985	0.981	0.982	0.977
	南方	0.775	0.781	0.818	0.807	0.834	0.850	0.837	0.813	0.834	0.808	0.816
	西北	0.880	0.936	0.914	0.916	0.930	0.920	0.845	0.841	0.886	0.909	0.898

省份	2009-2010	2010-2011	2011-2012	2012-2013	2013-2014	2014-2015	2015-2016	2016-2017	2017-2018	均值	排名
北京	1.004	1.021	1.023	1.005	1.061	1.367	1.387	1.536	2.092	1.277	1
四川	1.011	1.061	0.963	1.063	1.019	1.024	1.237	0.846	1.432	1.073	2
上海	1.002	1.013	0.994	1.014	0.982	0.971	0.996	1.438	0.954	1.040	3
云南	0.995	1.042	0.993	1.032	1.236	1.186	0.947	1.046	0.782	1.029	4
黑龙江	1.010	1.001	0.989	0.978	1.023	0.959	0.982	1.055	1.150	1.016	5
江西	1.018	1.016	1.000	1.014	1.000	1.015	0.965	1.027	1.056	1.012	6
新疆	0.960	0.989	1.008	1.036	1.032	1.011	1.005	1.018	1.044	1.011	7
陕西	1.002	1.020	1.004	0.999	0.992	0.957	0.996	1.033	1.079	1.009	8
山东	1.008	0.987	0.999	1.014	0.992	1.096	0.932	0.984	1.065	1.009	9
福建	0.993	0.995	1.014	0.984	1.005	1.000	1.010	1.002	1.072	1.008	10
湖南	0.995	1.000	0.997	1.010	0.989	0.936	1.010	1.022	1.115	1.008	11
天津	1.023	1.007	0.972	1.016	0.976	1.039	0.964	1.025	1.050	1.008	12
内蒙古	1.004	1.002	0.985	1.012	1.016	0.957	0.988	1.02	1.087	1.008	13
吉林	0.991	1.008	0.988	0.981	0.963	0.963	1.005	1.017	1.155	1.008	14
重庆	0.985	0.995	1.013	0.983	1.022	0.961	1.028	1.001	1.082	1.008	15
河北	1.009	1.001	0.987	1.008	0.986	0.98	0.995	1.045	1.055	1.007	16
辽宁	0.975	1.008	1.001	1.012	1.004	0.987	1.003	1.016	1.053	1.007	17
山西	1.001	0.988	0.994	1.029	0.992	0.979	1.002	1.003	1.068	1.006	18
浙江	1.017	0.991	0.999	1.010	1.004	1.128	0.901	0.975	1.029	1.006	19
宁夏	0.974	1.031	0.992	1.144	0.979	0.909	0.973	1.055	0.994	1.006	20
河南	0.990	1.015	0.989	0.998	0.98	0.956	0.975	1.027	1.118	1.005	21
甘肃	0.990	1.005	0.991	0.988	0.958	0.984	1.009	1.013	1.107	1.005	22
贵州	0.975	1.008	1.004	0.967	1.024	1.004	0.977	1.021	1.054	1.004	23
海南	0.993	1.020	0.996	1.009	1.035	0.923	1.053	1.000	1.000	1.003	24
广东	0.994	1.017	0.995	0.982	0.993	0.984	1.007	1.045	1.004	1.002	25
安徽	0.992	1.011	0.991	0.995	1.007	0.989	0.984	1.021	1.028	1.002	26
广西	0.983	1.003	0.990	0.987	1.013	1.007	0.93	1.035	1.063	1.001	27
江苏	0.964	1.051	1.149	0.815	0.998	1.005	1.117	0.934	0.947	0.998	28
湖北	1.074	0.985	0.995	0.727	0.996	0.974	1.003	1.022	1.003	0.975	29
青海	1.167	1.034	0.830	0.893	0.828	0.922	0.854	1.020	1.029	0.953	30

指数	2009-2010	2010-2011	2011-2012	2012-2013	2013-2014	2014-2015	2015-2016	2016-2017	2017-2018	均值
ML指数	1.038	1.016	1.03	1.008	1.057	1.171	1.084	1.094	1.004	1.056
技术效率变化指数	1.003	1.011	0.995	0.990	1.004	1.006	1.008	1.043	1.092	1.017
技术进步率变化指数	1.034	1.005	1.036	1.019	1.053	1.156	1.069	1.047	0.912	1.037

Empirical Study on the Impact of Technological Progress on the Environmental Efficiency of China's Power Industry: Based on SBM Super Efficiency-ML-Tobit

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		C	X₁	X₂	X₃	X₄
全国	系数	0.695895	4.297995	-2.331126	-0.218506	0.338330
全国	Z-Stat	12.01617^***	7.385046^***	-2.791346^***	-1.782989^*	7.617636^***
华北	系数	1.499266	-0.309786	0.463094	-1.442326	-0.651310
华北	Z-Stat	17.92297^***	-0.529457	0.629400	-9.278702^***	-4.777859^***
东北	系数	0.153727	-13.79289	21.37642	0.019479	0.340273
东北	Z-Stat	1.716542^*	-6.479973^***	7.199715^***	0.137301	2.154534^**
华东	系数	0.971721	0.898294	1.910288	-0.479271	0.272345
华东	Z-Stat	11.11114^***	0.966053	1.481866	-2.529472^**	2.714000^***
华中	系数	-0.224190	10.93346	-1.077101	0.938279	0.544412
华中	Z-Stat	-1.312276	3.395166^***	-0.435119	2.902993^***	5.998177^***
西北	系数	-0.463769	-14.33996	9.824286	2.281228	0.279674
西北	Z-Stat	-2.524140^**	-3.913038^***	2.473179^**	6.312325^***	3.455249^***
南方	系数	0.987181	1.889016	0.989052	-1.545564	0.714818
南方	Z-Stat	13.24386^***	1.918856^*	0.702892	-6.314325^***	7.179503^***

		C	X₁	X₂	X₃	X₄
全国	系数	1.215827	0.448681	-0.001595	-0.471864	0.158894
全国	Z-Stat	1629.285^***	3.327504^***	-0.999941	2.259236^**	-8.397651^***
华北	系数	2.644047	-6.767042	9.334972	-3.209369	-1.271124
华北	Z-Stat	10.60675^***	-3.730591^***	4.054885^***	-6.921195^***	-3.052293^**
东北	系数	1.021161	-1.275438	1.414037	-0.070850	0.118058
东北	Z-Stat	29.02753^***	-1.495380	1.207764	-1.341049	1.728208^*
华东	系数	1.343858	-0.136735	0.453931	-0.678599	0.015495
华东	Z-Stat	11.94881^***	-0.115696	0.278934	-2.802326^***	0.117955
华中	系数	0.783071	-14.16570	12.24204	-0.003676	0.508895
华中	Z-Stat	2.791012^***	-2.724602^***	3.034847^***	-0.006887	3.504241^***
西北	系数	2019.913	4.052454	1.082056	133.3721	-22.19214
西北	Z-Stat	611.2442^***	3.422390^***	0.445724	3.778897^***	-5.375517^***
南方	系数	0.854976	1.185210	-0.909081	-0.028032	0.431634
南方	Z-Stat	4.509708^***	0.479525	-0.252965	-0.046300	1.706462^*

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