Urban energy environment efficiency in China: Based on dynamic meta-frontier slack-based measures

Figures & data

Figure 1. Structural diagram for the DN-DEA.

Table 1. Statistical data analysis for the upper-middle and high-income cities from 2013 to 2016.

	2013				2014				2015				2016
	(I)em	(I)asset	(I)com	(O)GDP	(I)em	(I)asset	(I)com	(O)GDP	(I)em	(I)asset	(I)com	(O)GDP	(I)em	(I)asset	(I)com	(O)GDP
Total	8,787,700	4011.377	3715.788	6382.116	8,858,286	4480.663	3758.6	6949.584	8,951,853	4891.517	3735.649	7476.272	9,073,384	5155.027	3579.714	8104.097
Upper-middle	7,354,412	3252.997	2841.514	3804.406	7,376,063	3616.498	2900.507	4165.066	7,443,015	4003.958	2927.640	4494.396	7,560,900	4406.751	2819.574	4913.925
High	10,528,121	4932.265	4777.407	9512.193	10,658,128	5530.005	4800.570	10,330.783	10,784,014	5969.266	4716.802	11,097.121	10,909,971	6063.648	4502.742	11,977.8757
µm/hr, %	0.699	0.660	0.595	0.400	0.692	0.654	0.604	0.403	0.690	0.671	0.621	0.405	0.693	0.727	0.626	0.410

Notes. (I) = input index;(O) = output index. Units for index, em: 100 persons; asset: millions CNY; com: tonnes; GDP: millions CNY.

Table 2. Environmental efficiency scores from 2013 to 2016.

Number	DMU	Overall score	Rank	2013	2014	2015	2016
1	Beijing	1	1	1	1	1	1
2	Chengdu	0.4951	14	0.4664	0.4774	0.5273	0.5102
3	Changchun	0.4622	17	0.4479	0.4492	0.4734	0.4816
4	Changsha	0.5152	12	0.496	0.5203	0.5217	0.5232
5	Chongqing	0.5145	13	0.4809	0.5128	0.521	0.5527
6	Fuzhou	0.4758	15	0.5227	0.5204	0.4466	0.411
7	Guangzhou	1	1	1	1	1	1
8	Guiyang	0.2797	25	0.2479	0.2796	0.2975	0.2998
9	Harbin	0.4722	16	0.4517	0.4794	0.4847	0.4733
10	Haikou	0.3004	24	0.3131	0.3013	0.287	0.2997
11	Hangzhou	0.5847	8	0.5559	0.5859	0.5926	0.6054
12	Hefei	0.4234	18	0.4245	0.413	0.4206	0.4357
13	Huhehot	0.4133	20	0.3586	0.4512	0.4356	0.4076
14	Jinan	0.7732	5	0.6145	0.6908	0.8522	1
15	Kunming	0.3406	23	0.3188	0.3331	0.3531	0.3572
16	Lanzhou	0.2311	29	0.2375	0.2303	0.2256	0.2308
17	Lhasa	0.257	26	0.2705	0.2705	0.2351	0.2579
18	Nanchang	0.4209	19	0.4077	0.4307	0.4197	0.4259
19	Nanjing	0.5568	9	0.5028	0.5172	0.5954	0.6141
20	Nanning	1	1	1	1	1	1
21	Shanghai	1	1	1	1	1	1
22	Shenyang	0.715	6	0.5578	0.6076	0.7719	1
23	Shijiazhuang	0.232	27	0.2404	0.2313	0.2281	0.2287
24	Taiyuan	0.232	27	0.2345	0.2331	0.2251	0.2352
25	Tianjin	0.5503	10	0.5427	0.5487	0.5435	0.5661
26	Wuhan	0.5426	11	0.5089	0.5362	0.5263	0.6004
27	Urumqi	0.3455	22	0.3602	0.3477	0.3523	0.3259
28	Xian	0.3641	21	0.3462	0.3554	0.368	0.3868
29	Xining	0.1841	31	0.1781	0.1735	0.1706	0.2655
30	Yinchuan	0.2282	30	0.2442	0.234	0.2187	0.2159
31	Zhengzhou	0.6544	7	0.6489	0.6368	0.7076	0.626

Table 3. Wilcoxon signed rank test for average technical efficiency.

Years	High-income average	Upper-middle-income average	Wilcoxon test score
2013	0.6226	0.3684	0.0007**
2014	0.6461	0.3718	0.0005**
2015	0.6723	0.3757	0.0011**
2016	0.6985	0.3857	0.0011**

Notes. *For the two-tailed test, the confidence interval 0.05 was significant.

** For the two-tailed test, the confidence interval 0.01 was significant.

Table 4. Overall efficiency and regional efficiency differences.

			Total efficiency by year				Regional efficiency by year				Gaps between total and regional efficiency
	Total	Rank	2013	2014	2015	2016	2013	2014	2015	2016	2013gap	2014gap	2015gap	2016gap
Beijing	1	1	1	1	1	1	1	1	1	1	1	1	1	1
Chengdu	0.4951	14	0.4664	0.4774	0.5273	0.5102	1	1	1	1	0.4664	0.4774	0.5273	0.5102
Changchun	0.4622	17	0.4479	0.4492	0.4734	0.4816	1	1	1	1	0.4479	0.4492	0.4734	0.4816
Changsha	0.5152	12	0.496	0.5203	0.5217	0.5232	0.496	0.5203	0.5217	0.5232	1	1	1	1
Chongqing	0.5145	13	0.4809	0.5128	0.521	0.5527	1	1	1	1	0.4809	0.5128	0.5210	0.5527
Fuzhou	0.4758	15	0.5227	0.5204	0.4466	0.411	0.5727	0.5704	0.4666	0.4211	0.9127	0.9123	0.9571	0.9760
Guangzhou	1	1	1	1	1	1	1	1	1	1	1	1	1	1
Guiyang	0.2797	25	0.2479	0.2796	0.2975	0.2998	0.5187	0.5936	0.5573	0.5756	0.4779	0.4710	0.5338	0.5208
Harbin	0.4722	16	0.4517	0.4794	0.4847	0.4733	1	1	1	1	0.4517	0.4794	0.4847	0.4733
Haikou	0.3004	24	0.3131	0.3013	0.287	0.2997	0.6505	0.609	0.5156	0.5705	0.4813	0.4947	0.5566	0.5253
Hangzhou	0.5847	8	0.5559	0.5859	0.5926	0.6054	0.5559	0.5859	0.5926	0.6054	1	1	1	1
Hefei	0.4234	18	0.4245	0.413	0.4206	0.4357	1	1	1	1	0.4245	0.4130	0.4206	0.4357
Huhehot	0.4133	20	0.3586	0.4512	0.4356	0.4076	0.3586	0.4512	0.4356	0.4076	1	1	1	1
Jinan	0.7732	5	0.6145	0.6908	0.8522	1	1	1	1	1	0.6145	0.6908	0.8522	1
Kunming	0.3406	23	0.3188	0.3331	0.3531	0.3572	0.699	0.7573	0.715	0.7211	0.4561	0.4399	0.4938	0.4954
Lanzhou	0.2311	29	0.2375	0.2303	0.2256	0.2308	0.5036	0.4862	0.4358	0.4578	0.4716	0.4737	0.5177	0.5042
Lhasa	0.257	26	0.2705	0.2705	0.2351	0.2579	0.4887	0.4857	0.4108	0.4582	0.5535	0.5569	0.5723	0.5629
Nanchang	0.4209	19	0.4077	0.4307	0.4197	0.4259	0.4077	0.4307	0.4197	0.4259	1	1	1	1
Nanjing	0.5568	9	0.5028	0.5172	0.5954	0.6141	0.5128	0.5272	0.5994	0.6161	0.9805	0.9810	0.9933	0.9968
Nanning	1	1	1	1	1	1	1	1	1	1	1	1	1	1
Shanghai	1	1	1	1	1	1	1	1	1	1	1	1	1	1
Shenyang	0.715	6	0.5578	0.6076	0.7719	1	0.7149	1	1	1	0.7802	0.6076	0.7719	1
Shijiazhuang	0.232	27	0.2404	0.2313	0.2281	0.2287	0.5153	0.4936	0.4388	0.4516	0.4665	0.4686	0.5198	0.5064
Taiyuan	0.232	27	0.2345	0.2331	0.2251	0.2352	0.5309	0.5269	0.4609	0.5066	0.4417	0.4424	0.4884	0.4643
Tianjin	0.5503	10	0.5427	0.5487	0.5435	0.5661	0.5427	0.5487	0.5435	0.5661	1	1	1	1
Wuhan	0.5426	11	0.5089	0.5362	0.5263	0.6004	0.5189	0.5462	0.5353	0.6104	0.9807	0.9817	0.9832	0.9836
Urumqi	0.3455	22	0.3602	0.3477	0.3523	0.3259	1	1	1	1	0.3602	0.3477	0.3523	0.3259
Xian	0.3641	21	0.3462	0.3554	0.368	0.3868	0.7074	0.7344	0.6815	0.7452	0.4894	0.4839	0.5400	0.5191
Xining	0.1841	31	0.1781	0.1735	0.1706	0.2655	0.373	0.3571	0.3228	0.4067	0.4775	0.4859	0.5285	0.6528
Yinchuan	0.2282	30	0.2442	0.234	0.2187	0.2159	0.4918	0.4864	0.4144	0.4221	0.4965	0.4811	0.5278	0.5115
Zhengzhou	0.6544	7	0.6489	0.6368	0.7076	0.626	1	1	1	1	0.6489	0.6368	0.7076	0.6260

Table 5. Wilcoxon signed rank test for the technology gap.

Years	High income	Upper-middle income	Wilcoxon test score
2013	0.9226	0.4966	0.0001**
2014	0.915	0.4986	0.0001**
2015	0.9475	0.5328	0.0001**
2016	0.9701	0.5318	0.0001**

Notes. *For the two-tailed test, the confidence interval 0.05 was significant,

**For the two-tailed test, the confidence interval 0.01 was significant.

Table 6. PM_2.5 and SO₂ emissions from 2013 to 2016 (unit for PM_2.5 and SO₂ emission difference is %).

			2013		2014		2015		2016
DMU	Score	Rank	PM2.5	SO2	PM2.5	SO2	PM2.5	SO2	PM2.5	SO2
Beijing	1	1	0	0	0	0	0	0	0	0
Chengdu	0.4951	14	−36.7	−58.04	−66.57	−24.91	−49.18	−22.07	−52.05	−28.07
Changchun	0.4622	17	−75.83	−77.62	−76.17	−82.35	−76.08	−85.75	−71.62	−84.21
Changsha	0.5152	12	−71.24	−66.26	−68	−57.46	−69.9	−55.41	−67.28	−60.1
Chongqing	0.5145	13	−50.81	−37.49	−32.43	−36.58	−38.72	−27.26	−40.29	−17.37
Fuzhou	0.4758	15	−50.31	−30.47	−50.68	−32.84	−65.76	−58.92	−55.52	−92.42
Guangzhou	1	1	0	0	0	0	0	0	0	0
Guiyang	0.2797	25	−67.06	−76.27	−65.86	−76.31	−66.56	−74.42	−67.46	−69.13
Harbin	0.4722	16	−68.32	−75.99	−68.37	−86.69	−73.1	−84.79	−66.37	−82.32
Haikou	0.3004	24	−82.37	−72.83	−82.35	−75.5	−84.67	−81.27	−85.18	−82.71
Hangzhou	0.5847	8	−55.5	−65.56	−54.66	−54.38	−59.82	−52.82	−58.43	−54.62
Hefei	0.4234	18	−80.33	−64.81	−80.4	−76.35	−80.95	−74	−79.79	−74.4
Huhehot	0.4133	20	−80.57	−94.91	−75.5	−76.63	−80.05	−91.59	−80.69	−89.85
Jinan	0.7732	5	−21.41	−34.37	−3.28	−25.62	0	0	0	0
Kunming	0.3406	23	−50.06	−68.43	−43.74	−65.84	−45.2	−69.27	−43.42	−71.63
Lanzhou	0.2311	29	−79.47	−84.31	−77.9	−84.53	−78.91	−85.09	−80.69	−81.37
Lhasa	0.257	26	−95.65	−95.23	−95.75	−97.7	−96.16	−97.18	−97.07	−96.29
Nanchang	0.4209	19	−83.22	−91.12	−78.15	−84.9	−79.08	−88.26	−80.38	−82.23
Nanjing	0.5568	9	−63.59	−68.07	−64.34	−63.48	−63.25	−63.25	−58.79	−62.09
Nanning	1	1	0	0	0	0	0	0	0	0
Shanghai	1	1	0	0	0	0	0	0	0	0
Shenyang	0.715	6	−1.16	−54.08	−0.8	−43.01	0	0	0	0
Shijiazhuang	0.232	27	−73.01	−70.87	−69.31	−78.24	−65.65	−78.76	−70.5	−77.52
Taiyuan	0.232	27	−78.24	−88.27	−77.61	−90.26	−79.82	−94.19	−82.26	−94.29
Tianjin	0.5503	10	−39.52	−58.52	−36.47	−57.02	−40.02	−48.83	−43.85	−33.26
Wuhan	0.5426	11	−54.88	−48.89	−52.75	−37.94	−57.17	−44.63	−50.67	−16.37
Urumqi	0.3455	22	−85.17	−82.24	−81.14	−83.54	−85.89	−80.33	−88.58	−79.25
Xian	0.3641	21	−77.73	−78.57	−78.93	−76.2	−76.92	−75.73	−77.17	−71.7
Xining	0.1841	31	−87.24	−92.16	−86.73	−92.9	−89.25	−92.82	−87.9	−93.38
Yinchuan	0.2282	30	−91.49	−97.62	−90.67	−96.99	−93.16	−98.2	−93.91	−98.15
Zhengzhou	0.6544	7	−44.29	−57.71	−49.65	−64.43	−49.97	−49.18	−52.24	−55.8

Table 7. Air pollutant emissions changes from 2013 to 2016 and the governance focus.

DMU	PM2.5	SO2	Focus of governance
Chengdu	Up	Down	Priority treatment for PM2.5 followed by SO2.
Changchun	Down	Up	Priority treatment for SO2.5 followed by PM2.5.
Changsha	Up and down	Up and down	Priority treatment for PM2.5 followed by SO2.
Chongqing	Down	Down	Priority treatment for PM2.5 followed by SO2.
Fuzhou	Up	Quick up	Priority treatment for improvements for SO2 followed by PM2.5.
Guiyang	Down	Down	Same treatment for SO2 and PM2.5but additional treatment needed for SO2.
Harbin	Down	Up	Priority treatment for SO2.5 followed by PM2.5.
Haikou	Up	Up	Priority treatment for PM2.5 and concerted efforts also made for SO2 compared to other cities.
Hangzhou	Up	Down	Priority treatment for both PM2.5 and SO2, and a close watch on the PM2.5 changes.
Hefei	Down	Up	Priority treatment for PM2.5 followed by SO2 treatment.
Huhehot	Down and up	Down and up	Priority treatment for SO2 followed by PM2.5
Kunming	Down	Up	Priority treatment for SO2 treatment and more attention paid to industrial restructuring and resource-based development model strategies.
Lanzhou	Slightly up	Up and slightly down	Priority treatment for both PM2.5 and SO2, with a greater focus on SO2 than PM2.5. Increased attention on industrial restructuring is needed and PM2.5 increases monitored.
Lhasa	Slightly up	Up and slightly down	Priority treatment for both PM2.5 and SO2, further industrial restructuring required.
Nanchang	Slightly up	Down in high level	While both SO2 and PM2.5 need significant improvement, priority treatment should be given to SO2; however, comprehensive actions for both SO2 and PM2.5 treatments are needed. Focused attention needed on industrial restructuring and resource-based development model strategies.
Nanjing	Down	Down	Priority treatment for both PM2.5 and SO2.
Shijiazhuang	Down and slightly up	Up and slightly down	Priority treatment for SO2 with some attention paid to PM2.5. Focused attention needed on industrial restructuring and resource-based development model strategies.
Taiyuan	Up	Up in high level	Priority treatment for SO2 treatment. Focused attention needed on energy consumption, industrial restructuring, and resource-based development model strategies. Comprehensive actions needed for the treatment of both pollutants.
Wuhan	Down and up slightly	Down quickly	Priority treatment for PM2.5 followed by SO2, which needs greater attention than in other cities.
Urumqi	Up	Down	Priority treatment for PM2.5 followed by SO2, which needs greater attention than in other cities.
Xian	Slightly up and down	Down	Priority treatment for both PM2.5 and SO2, as both need significant improvement. Further industrial restructuring is also needed.
Xining	Up and down slightly	Up and down slightly	Priority treatment for both PM2.5 and SO2, as both need significant improvement. Further industrial restructuring is also needed to reduce both pollutants.
Yinchuan	Up	Up and down	Priority treatment for PM2.5 followed by SO2, as both pollutants require significant improvements. Further industrial restructuring is also needed to reduce both pollutants.
Zhengzhou	Up	Down and up	Priority treatment for PM2.5, with the SO2 treatment necessary being slightly higher than PM2.5. Further industrial restructuring is also needed to reduce PM2.5.