基于DEA-Tobit模型的中国县域污水处理服务减排效率测评

doi:10.18402/resci.2017.03.07

摘要/Abstract

摘要：

县域污水处理服务快速增长,已成为重要的减排力量,但减排效率差异很大。为了提高县域污水处理服务的减排效率,本文应用数据包络分析法,以年处理能力、年运营费用、年耗电量、雇员人数为投入变量,以污水实际处理量、COD、BOD₅、氨氮消减量为产出变量,构建县域污水处理服务减排效率评价模型,以2014 年中国413个县域污水处理设施进行实证分析,并应用Tobit模型分析找出关键影响因素。结果表明：①全国县域污水处理服务减排综合效率平均值为0.637,具有较大的效率提升空间;技术效率为0.756,要素配比急需改善;规模效率为0.835,且73.85%的县域处于规模报酬递增阶段,有必要扩大处理规模;②污水处理服务减排效率的区域差异明显,特征各异;③处理规模、水量负荷率、运营年限、人口密度、经济发展水平对减排效率产生正向影响;④生物膜类工艺减排效率相对较高。因此,根据区域特征分类指导,加强县域污水处理服务运营管理交流,适当扩大处理规模,因地制宜选择污水处理工艺,加强配套管网建设,促进雨污分流,有利于提高污水处理服务的减排效率。

关键词: 县域, 污水处理服务, 减排效率, DEA-Tobit两步法, 影响因素

Abstract:

County sewage treatment services have become an important emissions reduction tool;there is however,a wide disparity in emissions reduction efficiency across county areas. In an effort to examine emissions reduction efficiency across county areas,we used data envelopment analysis to calculate efficiency levels in 413 counties using data from the year 2014. Capital,operating costs,labor and energy consumption were chosen as input indices. The actual treatment of sewage,ammonia,COD and BOD₅ reduction were utilized as output indices. An evaluation model of the efficiency of county-level sewage treatment services was established to analyze sewage treatment services and the Tobit model was used to identify key influencing factors. The results show that the combined efficiency of all county areas sewage treatment service is 0.637. The technical efficiency is 0.756 and the scale efficiency is 0.835. We found that 73.85% of counties are increasing their returns to scale. Emission reduction efficiency of sewage treatment service varies among regions. Scale,water load rate,location,population density and economic development levels all have a positive impact on emissions reduction efficiency;biological membrane technology is relatively superior. Many factors are useful in order for some of these sewage treatment facilities to improve their efficiency. Giving guidance according to regional characteristics,facilitating a culture of communication among sewage facilities in county areas,expanding or constructing sewage treatment facilities to appropriate size,utilizing local conditions to choose sewage treatment processes,strengthening the supporting pipe network construction and promoting rain and sewage diversion are all necessary to improve the efficiency of sewage treatment services in China.

Key words: county areas, sewage treatment service, emission reduction efficiency, DEA-Tobit two-step method, influencing factors

李鑫, 孙小霞, 苏时鹏. 基于DEA-Tobit模型的中国县域污水处理服务减排效率测评[J]. 资源科学, 2017, 39(3): 451-460.

Xin LI, Xiaoxia SUN, Shipeng SU. Emissions reduction efficiency of sewage treatment services in county areas in China based on DEA-Tobit Modeling[J]. Resources Science, 2017, 39(3): 451-460.

图/表 7

图1

表1

县域污水处理投入产出要素及可能影响因素说明"

变量		变量解释及单位	最大值	最小值	均值	标准差	预测方向
内部因素	主体处理工艺	活性污泥类;SBR类;氧化沟类等	-	-	-	-	-
	设计处理能力	日处理能力/（万t/d）	14.00	0.25	2.22	1.62	正向
	出水标准	1=二级;2=一级B;3=一级A	3.00	1.00	2.38	0.55	正向/负向
	负荷率	实际污水处理量/设计处理量	2.00	0.30	0.76	0.24	正向
	运营年限	污水处理设施投运年限/a	16.00	1.00	5.69	2.15	正向
外部环境	地理区位	1-5：排污系数由高至低	5.00	1.00	2.69	0.99	负向
	人口密度*	县域内人口密度/（人/km²）	7.26	1.13	5.43	0.99	正向
	人均GDP*	人均国民生产总值/元	12.04	8.53	10.15	0.51	正向
	第二产业	第二产业占GDP总比重/%	81.46	6.28	46.10	13.93	负向
	第三产业	第三产业占GDP总比重/%	64.01	10.87	33.58	8.67	负向
投入项	资本投入	年处理能力/万t	5 110.00	91.25	810.57	591.13	-
	资本投入	年运营费用/万元	11 402.00	50.00	618.65	891.74	-
	劳动投入	雇员数/人	150.00	8.00	33.31	20.32	-
	能源投入	年耗电量/（万kW $?$ h）	3 443.34	1.17	179.19	277.75	-
产出项	污染物削减量	污水处理量/万t	5 518.80	1.31	622.06	520.65	-
		COD削减量/t	22 042.09	2.27	1 416.38	1 832.16	-
		BOD₅削减量/t	10 179.43	1.15	619.57	803.55	-
		氨氮削减量/t	1 041.79	0.17	139.86	132.22	-

表1

表2

各地区DEA评价结果及各投入要素情况"

	区域	Ⅰ区	Ⅱ区	Ⅲ区	Ⅳ区	Ⅴ区	全国
效率单元	决策单元/个	61	86	197	55	14	413
	占比/%	14.76	20.82	47.69	13.31	3.39	100
综合效率	平均值	0.60	0.58	0.69	0.54	0.76	0.64
	有效单元/个	5	5	21	2	4	37
	有效单元占比/%	8.20	5.81	10.66	3.64	28.57	8.96
技术效率	平均值	0.76	0.69	0.76	0.81	0.85	0.76
	有效单元/个	11	9	30	12	9	71
	有效单元占比/%	18.03	10.47	15.23	21.82	64.29	17.19
规模效率	平均值	0.78	0.84	0.89	0.66	0.89	0.83
	有效单元/个	6	5	25	2	4	42
	有效单元占比/%	9.84	5.81	12.69	3.64	28.57	10.17
规模报酬	报酬递增	49	70	125	53	8	305
	占比/%	80.33	81.40	63.45	96.36	57.14	73.85
	报酬递减	6	10	46	0	2	64
	占比/%	9.84	11.63	23.35	-	14.29	15.50
	报酬不变	6	6	26	2	4	44
	占比/%	9.84	6.98	13.20	3.64	28.57	10.65
投入产出及	处理能力/（万t/a）	648.02	694.14	977.99	513.32	1 045.98	810.57
运营情况	实际处理量/（万t/a）	549.96	581.86	744.00	329.81	615.49	622.06
	年耗电量/（万 kW $?$ h）	105.85	123.29	255.03	76.53	178.32	179.19
	年运营费用/万元	454.60	554.84	749.44	437.92	594.99	618.65
	雇员数/人	24.82	29.09	41.31	19.82	36.57	33.31
	BOD₅进水浓度/（mg/l）	59.20	65.88	129.03	94.21	209.55	103.66
	COD进水浓度/（mg/l）	152.78	151.43	291.41	221.42	429.00	237.13
	氨氮进水浓度/（mg/l）	18.74	17.85	29.68	26.85	59.45	26.23
	运营年限/a	5.46	5.76	6.06	4.47	6.14	5.70
	负荷率	0.87	0.85	0.74	0.64	0.61	0.76

表2

图2

图3

图4

表3

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	变量	系数	标准差	T检验值	P值	预测方向	验证结果
内部因素	SBR类	-0.036*	0.022	-1.70	0.089	-	-
	氧化沟类	-0.037*	0.020	-1.84	0.067	-	-
	生物膜法类	0.115***	0.031	3.75	0	-	正向
	其他类	0.035	0.042	0.86	0.391	-	-
	设计处理能力	0.029***	0.006	4.91	0	正向	正向
	出水标准	0.007	0.016	0.45	0.653	正向/负向	无显著影响
	负荷率	0.646***	0.034	18.85	0	正向	正向
	运营年限	0.011***	0.004	2.63	0.009	正向	正向
外部环境	地理区位	0.071***	0.009	8.03	0	负向	正向
	人口密度	0.026**	0.010	2.71	0.007	正向	正向
	人均GDP	0.055***	0.019	2.92	0.004	正向	正向
	第二产业占比	-0.003***	0.001	-3.01	0.003	负向	负向
	第三产业占比	-0.002	0.001	-1.29	0.198	负向	无显著影响
	cons	-0.706***	0.201 372	-3.51	0.001	-	-
	Log likelihood	143.457
	LR chi2	363.69