New Energy Development and Pollution Emissions in China
Abstract
:1. Introduction
2. Literature Review
3. Research Method
3.1. Non-Oriented EBM: Simultaneous Assessment of Inefficiency from Both Input and Output Perspectives
3.2. Empirical Model in This Study: A Modified Undesirable EBM DEA Model
3.3. New Energy, Energy Consumption, and CO2, SO2, and NO2 Efficiency Indices
4. Empirical Analyses
4.1. Data Sources and Description
4.1.1. Input Variables
4.1.2. Output Variable
4.1.3. Undesirable Output
4.2. Statistical Analysis
4.3. Empirical Analysis of the Modified Undesirable EBM DEA
4.3.1. Epsilon Score Analysis
4.3.2. Annual Efficiency
4.3.3. Comparison of Radial and Non-Radial Inefficiency Analysis for the Input and Output Indicators
4.3.4. Efficiency of the Input and Output Indicators: Fixed Assets, Employees, GDP, Energy, New Energy, and CO2, SO2, and NO2
5. Conclusions and Policy Implications
- The comparison of the input and output indicator radial DEA and non-radial DEA inefficiency scores found that most input indicator inefficiencies were due to the radial DEA, with only a few municipalities/provinces having inefficiencies resulting from the non-radial DEA.
- The annual efficiency was 1 in Beijing, Inner Mongolia, Shanghai, and Tianjin for all four years from 2013–2016. The other 26 municipalities/provinces had large differences and required significant improvements. The annual total efficiency score changes in most municipalities/provinces had variable trends.
- The various input and output indicator efficiencies for employment, GDP, and fixed assets were generally higher. However, the traditional energy efficiency scores and new energy efficiency scores were generally low, with the new energy efficiency scores being lower than the traditional energy efficiency scores.
- The CO2, SO2, and NO2 efficiency scores varied widely, with the NO2 efficiencies being slightly better than the CO2 and SO2 efficiencies. However, the efficiency scores for these three undesirable outputs varied considerably across the municipalities/provinces.
Policy Implications
- Except for the municipalities/provinces that had efficiency scores of 1, only two or three provinces had overall upward efficiency trends; however, the overall annual efficiency in most other provinces declined, indicating that more effective measures are needed to improve the efficiency of new and traditional energy sources.
- Industrial restructuring needs to be accelerated and medium- and long-term development plans and energy plans need to be developed. In combination with the development and utilization of new technologies for traditional energy, we should actively promote the adjustment of energy structure and industrial structure. Traditional energy consumption plays and will continue to play an important role in urban development and economic growth for decades in the coming future. However, traditional energy consumption also brings problems such as increased CO2 emissions and air pollution, all of which affect sustainable development. Since China joined the Paris Climate Change Agreement on 3 September 2016, the Chinese government adopted a series of measures for domestic greenhouse gas emission reductions. The “13th Five-Year Plan” carbon intensity reduction target aims at controlling both total energy consumption and total energy intensity, strengthening low-carbon city pilot demonstrations, promoting the development of a national carbon trading market, and planning and implementing supporting policies and measures. At the same time, China is seeking to optimize its energy structure, with the proportion of coal being used for power generation dropping from 72% in 2005 to 64% in 2015, with a further drop to 60% expected by 2020. The empirical results suggested that increased CO2 emission reduction efforts are needed in Anhui, Jiangsu, Shandong, Shanxi, and Shaanxi, and further improvements are needed in Chongqing, Fujian, Gansu, Guangdong, Guangxi, Henan, Hubei, Hunan, Jiangxi, Qinghai, Sichuan, Xinjiang, Yunnan, and Zhejiang. NO2 emission reductions are needed in Guizhou and Liaoning, and all undesirable pollutant output indicators need to be improved in Jilin, Hebei, and Ningxia.
- Most regions need to actively strengthen the source control of air pollutant emissions. They need to actively develop and adopt new technologies and clean energy technologies to control the air pollutants of high-polluting manufacturing enterprises at the source and discharge process. The current main governance measure is end-of-pipe governance, whereby once mandatory end-of-pipe governance is not strictly enforced, as emissions of air pollutants from companies that need to recover from economic development still exist. Therefore, effective measures should be to encourage enterprises to adopt new technologies and clean energy use technologies to establish green ecological enterprise production through the production process of enterprises, and fundamentally reduce air pollutant emissions in the long run.
- Actively promoting the research, development, and utilization of clean renewable energy, and actively promoting the use of new energy in production are positive and effective measures to improve environmental efficiency. China’s renewable energy installed equipment capacity currently accounts for 16% to 20% of global capacity. Compared to traditional energy and other indicators, the new energy efficiencies in most municipalities/provinces were very low, except for Beijing, Inner Mongolia, Shanghai, and Tianjin, all of which had efficiencies of 1. Therefore, all municipalities/provinces need to put greater focus on new energy development and improving traditional energy efficiencies.
- Comprehensive governance plans and measures need to be developed to jointly manage carbon dioxide emissions and air pollutant emissions. In most regions, carbon dioxide emissions in recent years not only have room for improvement, but efficiency scores also showed a downward trend. Emissions and inefficiencies in air pollutants exacerbate the pressure on environmental protection efforts. It is necessary to explore and actively promote measures and policies to jointly manage carbon dioxide emissions and air pollutant emissions.
Author Contributions
Funding
Conflicts of Interest
References
- Hu, J.L.; Wang, S.C. Total-factor energy efficiency of regions in China. Energy Policy 2006, 34, 3206–3217. [Google Scholar] [CrossRef]
- Yeh, T.-L.; Chen, T.-Y.; Lai, P.-Y. A comparative study of energy utilization efficiency between Taiwan and China. Energy Policy 2010, 38, 2386–2394. [Google Scholar] [CrossRef]
- Shi, G.-M.; Bi, J.; Wang, J.-N. Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs. Energy Policy 2010, 38, 6172–6179. [Google Scholar] [CrossRef]
- Choi, Y.; Zhang, N.; Zhou, P. Efficiency and abatement costs of energy-related CO2 emissions in China: A slacks-based efficiency measure. Appl. Energy 2012, 98, 198–208. [Google Scholar] [CrossRef]
- Wu, A.-H.; Cao, Y.-Y.; Liu, B. Energy efficiency evaluation for regions in China: An application of DEA and Malmquist indices. Energy Effic. 2014, 7, 429–439. [Google Scholar] [CrossRef]
- Chang, M.-C. Energy intensity, target level of energy intensity, and room for improvement in energy intensity: An application to the study of regions in the EU. Energy Policy 2014, 67, 648–655. [Google Scholar] [CrossRef]
- Wang, K.; Wei, Y.-M. China’s regional industrial energy efficiency and carbon emissions abatement costs. Appl. Energy 2014, 130, 617–631. [Google Scholar] [CrossRef]
- Cui, Q.; Kuang, H.-B.; Wu, C.-Y.; Li, Y. The changing trend and influencing factors of energy efficiency: The case of nine countries. Energy 2014, 64, 1026–1034. [Google Scholar] [CrossRef]
- Wu, J.; Lv, L.; Sun, J. A comprehensive analysis of China’s regional energy saving and emission reduction efficiency: From production and treatment perspectives. Energy Policy 2015, 84, 166–176. [Google Scholar] [CrossRef]
- Pang, R.-Z.; Deng, Z.-Q.; Hu, J.-L. Clean energy use and total-factor efficiencies: An international comparison. Renew. Sustain. Energy Rev. 2015, 52, 1158–1171. [Google Scholar] [CrossRef]
- Guo, X.; Lu, C.-C.; Lee, J.-H.; Chiu, Y.-H. Applying the dynamic DEA model to evaluate the energy efficiency of OECD countries and China Energy. Energy 2017, 134, 392–399. [Google Scholar] [CrossRef]
- Feng, C.; Zhang, H.; Huang, J.-B. The Approach to realizing the potential of emissions reduction in China: An implication from data envelopment analysis. Renew. Sustain. Energy Rev. 2017, 71, 859–872. [Google Scholar] [CrossRef]
- Chien, T.; Hu, J.-L. Renewable energy and macroeconomic efficiency of OECD and non-OECD economies. Energy Policy 2007, 35, 3606–3615. [Google Scholar] [CrossRef]
- Honma, S.; Hu, J.-L. Total-factor energy efficiency of regions in Japan. Energy Policy 2008, 36, 821–833. [Google Scholar] [CrossRef]
- Hoang, V.-N.; Rao, D.S.P. Measuring and decomposing sustainable efficiency in agricultural production: A cumulative exergy balance approach. Ecol. Econ. 2010, 69, 1765–1776. [Google Scholar] [CrossRef]
- Shiau, T.-A.; Jhang, J.-S. An integration model of DEA and RST for measuring transport sustainability. Int. J. Sustain. Dev. World Econ. 2010, 17, 76–83. [Google Scholar] [CrossRef]
- Blokhuis, E.; Advokaat, B.; Schaefer, W. Assessing the performance of Dutch local energy companies. Energy Policy 2012, 45, 680–690. [Google Scholar] [CrossRef]
- Boubaker, K. A review on renewable energy conceptual perspectives in North Africa using a polynomial optimization scheme. Renew. Sustain. Energy Rev. 2012, 16, 4298–4302. [Google Scholar] [CrossRef]
- Menegaki, A.N.; Gurluk, S. Greece and Turkey: Assessment and Comparison of Their Renewable Energy Performance. Int. J. Energy Econ. Policy 2013, 3, 367–383. [Google Scholar]
- Fagiani, R.; Barquin, J.; Hakvoort, R. Risk-Based Assessment of the Cost-Efficiency and the Effectivity of Renewable Energy Support Schemes: Certificate Markets versus Feed-In Tariffs. Energy Policy 2013, 55, 648–661. [Google Scholar] [CrossRef]
- Sueyoshi, T.; Goto, M.; Sugiyama, M. DEA window analysis for environmental assessment in a dynamic time shift: Performance assessment of U.S. coal-fired power plants. Energy Econ. 2013, 40, 845–857. [Google Scholar] [CrossRef]
- Sueyoshi, T.; Goto, M. Environmental assessment for corporate sustainability by resource utilization and technology innovation: DEA radial measurement on Japanese industrial sectors. Energy Econ. 2014, 46, 295–307. [Google Scholar] [CrossRef]
- Azlina, A.A.; Law, S.H.; Mustapha, N.H.N. Dynamic linkages among transport energy consumption, income and CO2 emission in Malaysia. Energy Policy 2014, 73, 598–606. [Google Scholar] [CrossRef]
- de Castro Camioto, F.; Mariano, E.B.; do Nascimento Rebelatto, D.A. Efficiency in Brazil’s industrial sectors in terms of energy and sustainable development. Environ. Sci. Policy 2014, 37, 50–60. [Google Scholar] [CrossRef]
- Wang, H. A generalized MCDA–DEA (multi-criterion decision analysis–data envelopment analysis) approach to construct slacks-based composite indicator. Energy 2015, 80, 114–122. [Google Scholar] [CrossRef]
- Kim, K.-T.; Lee, D.J.; Park, S.-J.; Zhang, Y.; Sultanov, A. Measuring the efficiency of the investment for renewable energy in Korea using data envelopment analysis. Renew. Sustain. Energy Rev 2015, 47, 694–702. [Google Scholar] [CrossRef]
- Zhang, N.; Xie, H. Toward green IT: Modeling sustainable production characteristics for Chinese electronic information industry, 1980–2012. Technol. Forecast. Soc. Chang. 2015, 96, 62–70. [Google Scholar] [CrossRef]
- Guo, X.; Zhu, Q.; Lv, L.; Chu, J.; Wu, J. Efficiency evaluation of regional energy saving and emission reduction in China: A modified slacks-based measure approach. J. Clean. Prod. 2017, 140, 1313–1321. [Google Scholar] [CrossRef]
- Tone, K.; Tsutsui, M. Dynamic DEA: A Slacks-based Measure Approach. Omega 2010, 38, 145–156. [Google Scholar] [CrossRef]
- Zhu, J. Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets; Springer: Berlin/Heidelberg, Germany, 2014. [Google Scholar]
- Inman, O.L.; Anderson, T.R.; Harmon, R.R. Predicting US jet fighter aircraft introductions from 1944 to 1982: a dogfight between regression and TFDEA. Technol. Forecast. Soc. Chang. 2006, 73, 1178–1187. [Google Scholar] [CrossRef]
- Mardani, A.; Zavadskas, E.K.; Streimikiene, D.; Jusoh, A.; Khoshnoudi, M. A Comprehensive review of data envelopment analysis (DEA) approach in energy efficiency. Renew. Sustain. Energy Rev. 2017, 70, 1298–1322. [Google Scholar] [CrossRef]
- Cook, D.; Zhu, J. Modeling Performance Measurement Applications and Implementation Issues in DEA; Springer: New York, NY, USA, 2005. [Google Scholar]
- Martínez-Molina, A.; Tort-Ausina, I.; Cho, S.; Vivancos, J.-L. Energy efficiency and thermal comfort in historic buildings: A review. Renew. Sustain. Energy Rev. 2016, 61, 70–85. [Google Scholar] [CrossRef]
- Moya, D.; Torres, R.; Stegen, S. Analysis of the Ecuadorian energy audit practices: A review of energy efficiency promotion. Renew. Sustain. Energy Rev. 2016, 62, 289–296. [Google Scholar] [CrossRef]
- Bian, Y.; Hu, M.; Wang, Y.; Xu, H. Energy efficiency analysis of the economic system in China during 1986–2012: A parallel slacks-based measure approach. Renew. Sustain. Energy Rev. 2016, 55, 990–998. [Google Scholar] [CrossRef]
- Balitskiy, S.; Bilan, Y.; Strielkowski, W.; Štreimikienė, D. Energy efficiency and natural gas consumption in the context of economic development in the European Union. Renew. Sustain. Energy Rev. 2016, 55, 156–168. [Google Scholar] [CrossRef]
- Chandel, S.S.; Sharma, A.; Marwaha, B.M. Review of energy efficiency initiatives and regulations for residential buildings in India. Renew. Sustain. Energy Rev. 2016, 54, 1443–1458. [Google Scholar] [CrossRef]
- Apergis, N.; Payne, J.E. Renewable energy consumption and economic growth: Evidence from a panel of OECD countries. Energy Policy 2010, 38, 656–660. [Google Scholar] [CrossRef]
- Menegaki, A.N. Growth and renewable energy in Europe: A random effect model with evidence for neutrality hypothesis. Energy Econ. 2011, 33, 257–263. [Google Scholar] [CrossRef]
- Bildirici, M. The relationship between economic growth and energy consumption. Renew. Sustain. Energy Rev. 2012, 4, 31–35. [Google Scholar] [CrossRef]
- Apergis, N.; Payne, J.E. Renewable energy, Output, CO2 emission and fossil fuel prices in Central America: Evidence from a non-linear Panel Smooth transition vector error correction model. Energy Econ. 2014, 42, 226–232. [Google Scholar] [CrossRef]
- Solarin, S.A.; Ozturk, I. On the causal dynamics between hydroelectricity consumption and economic growth in Latin America countries. Renew. Sustain. Energy Rev. 2015, 52, 1857–1868. [Google Scholar] [CrossRef]
- Chang, T.; Gupta, R.; Inglesi-Lotz, R.; Simo-Kengne, B.; Smithers, D.; Trembling, A. Renewable energy and growth: Evidence from heterogeneous panel of G7countries using Granger causality. Renew. Sustain. Energy Rev. 2015, 52, 1405–1412. [Google Scholar] [CrossRef]
- Ozbugday, F.C.; Erbas, B.C. How effective are energy efficiency and renewable energy in curbing CO2 emissions in the long run? A heterogeneous panel data analysis. Energy 2015, 82, 734–745. [Google Scholar] [CrossRef]
- Jaforullah, M.; King, A. Does the use of renewable energy sources mitigate CO2 emissions? A reassessment of the US evidence. Energy Econ. 2015, 49, 711–717. [Google Scholar] [CrossRef]
- Bilgili, F.; Koçak, E.; Bulut, U. The dynamic impact of renewable energy consumption on CO2 emissions: A revisited environmental Kuznets curve approach. Renew. Sustain. Energy Rev. 2016, 54, 838–845. [Google Scholar] [CrossRef]
- Farrell, M.J. The Measurement of Productive Efficiency. J. R. Stat. Soc. 1957, 120, 253–281. [Google Scholar] [CrossRef]
- Charnes, A.; Cooper, W.W.; Rhodes, E. Measuring the Efficiency of Decision Making Units. Eur. J. Oper. Res. 1978, 2, 429–444. [Google Scholar] [CrossRef]
- Banker, R.D.; Charnes, A.; Cooper, W.W. Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Manag. Sci. 1984, 30, 1078–1092. [Google Scholar] [CrossRef]
- Tone, K. A Slacks-based Measure of Efficiency in Data Envelopment Analysis. Eur. J. Oper. Res. 2001, 130, 498–509. [Google Scholar] [CrossRef]
- National Bureau of Statistics of China. China Statistical Yearbook. 2017. Available online: http://www.stats.gov.cn/ (accessed on 8 April 2018).
- China Statistical Yearbooks Database. Demographics and the Employment Statistical Yearbook of China, and the Statistical Yearbooks of All Cities; China Academic Journals Electronic Publishing House, 2017. Available online: http://www.stats.gov.cn/ (accessed on 8 April 2018).
- China’s Environmental and Protection Bureau Reports; Ministry of Ecology and Environment of the People’s Republic of China, 2017. Available online: http://www.mep.gov.cn/ (accessed on 26 March 2018).
- China’s Energy Policy 2012; The State Council: Beijing, China, October 2012. Available online: http://www.china.org.cn (accessed on 24 October 2012).
Input Variables | Output Variables | Undesirable Output |
---|---|---|
Labor (lab) | GDP | CO2 |
Fixed assets (asset) | SO2 | |
Energy consumption (com) | NO2 | |
New energy |
Epsilon Score | 2013 | 2014 | 2015 | 2016 |
---|---|---|---|---|
Epsilon for EBM X | 0.2427 | 0.3584 | 0.2698 | 0.2771 |
Epsilon for EBM Y | 0.093 | 0.1450 | 0.1105 | 0.1264 |
No. | DMU | 2013 | 2014 | 2015 | 2016 |
---|---|---|---|---|---|
1 | Anhui | 0.6980 | 0.6680 | 0.6644 | 0.6454 |
2 | Beijing (m) | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
3 | Chongqing (m) | 0.6835 | 0.6542 | 0.6598 | 0.6493 |
4 | Fujian | 0.8130 | 0.7918 | 0.7760 | 0.7518 |
5 | Gansu | 0.4946 | 0.4673 | 0.4371 | 0.4147 |
6 | Guangdong | 0.8528 | 0.8411 | 0.8361 | 0.8264 |
7 | Guangxi | 0.7044 | 0.6921 | 0.7027 | 0.7070 |
8 | Guizhou | 0.5354 | 0.5366 | 0.5480 | 0.5478 |
9 | Hainan | 0.8228 | 0.8065 | 0.7693 | 0.7388 |
10 | Hebei | 0.7858 | 0.7261 | 0.6992 | 0.6577 |
11 | Heilongjiang | 0.6258 | 0.6593 | 0.6398 | 0.6553 |
12 | Henan | 0.6165 | 0.5955 | 0.5758 | 0.5567 |
13 | Hubei | 0.7500 | 0.7311 | 0.7272 | 0.7076 |
14 | Hunan | 0.8177 | 0.8008 | 0.8039 | 0.7977 |
15 | Jiangsu | 0.8475 | 0.8092 | 0.8259 | 0.8043 |
16 | Jiangxi | 0.6562 | 0.6158 | 0.5926 | 0.5666 |
17 | Jilin | 0.7298 | 0.7040 | 0.6829 | 0.6606 |
18 | Liaoning | 0.7399 | 0.7233 | 0.7983 | 1.0000 |
19 | Inner Mongolia | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
20 | Ningxia | 0.6280 | 0.5889 | 0.5818 | 0.5559 |
21 | Qinghai | 0.6083 | 0.5990 | 0.5960 | 0.5918 |
22 | Shandong | 0.8248 | 0.8070 | 0.7988 | 0.7808 |
23 | Shanghai (m) | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
24 | Shanxi | 0.5380 | 0.5061 | 0.4759 | 0.4503 |
25 | Shaanxi | 0.6070 | 0.5875 | 0.5686 | 0.5513 |
26 | Sichuan | 0.7174 | 0.7130 | 0.7099 | 0.7201 |
27 | Tianjin (m) | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
28 | Xinjiang | 0.5344 | 0.5124 | 0.4755 | 0.4546 |
29 | Yunnan | 0.5725 | 0.5743 | 0.5674 | 0.5677 |
30 | Zhejiang | 0.8457 | 0.8047 | 0.7992 | 0.7706 |
No. | DMU | Score | Input Inefficiency | Input Radial Inefficiency | Input Non-Radial Inefficiency | Output Inefficiency | Output Radial Inefficiency | Output Non-Radial Inefficiency |
---|---|---|---|---|---|---|---|---|
1 | Anhui | 0.6980 | 0.1981 | 0.1268 | 0.0712 | 0.1489 | 0.1268 | 0.0221 |
2 | Beijing | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
3 | Chongqing | 0.6835 | 0.2046 | 0.1516 | 0.0530 | 0.1637 | 0.1516 | 0.0121 |
4 | Fujian | 0.8130 | 0.1294 | 0.0641 | 0.0653 | 0.0708 | 0.0641 | 0.0067 |
5 | Gansu | 0.4946 | 0.3484 | 0.2912 | 0.0572 | 0.3175 | 0.2912 | 0.0263 |
6 | Guangdong | 0.8528 | 0.1069 | 0.0430 | 0.0639 | 0.0473 | 0.0430 | 0.0043 |
7 | Guangxi | 0.7044 | 0.1889 | 0.1384 | 0.0506 | 0.1515 | 0.1384 | 0.0132 |
8 | Guizhou | 0.5354 | 0.3162 | 0.2412 | 0.0750 | 0.2772 | 0.2412 | 0.0360 |
9 | Hainan | 0.8228 | 0.1374 | 0.0348 | 0.1026 | 0.0484 | 0.0348 | 0.0136 |
10 | Hebei | 0.7858 | 0.1445 | 0.0462 | 0.0983 | 0.0888 | 0.0462 | 0.0426 |
11 | Heilongjiang | 0.6258 | 0.2462 | 0.1789 | 0.0673 | 0.2045 | 0.1789 | 0.0255 |
12 | Henan | 0.6165 | 0.2503 | 0.1930 | 0.0573 | 0.2161 | 0.1930 | 0.0231 |
13 | Hubei | 0.7500 | 0.1652 | 0.1033 | 0.0619 | 0.1130 | 0.1033 | 0.0097 |
14 | Hunan | 0.8177 | 0.1227 | 0.0621 | 0.0606 | 0.0728 | 0.0621 | 0.0107 |
15 | Jiangsu | 0.8475 | 0.1075 | 0.0474 | 0.0601 | 0.0531 | 0.0474 | 0.0057 |
16 | Jiangxi | 0.6563 | 0.2204 | 0.1739 | 0.0466 | 0.1879 | 0.1739 | 0.0140 |
17 | Jilin | 0.7298 | 0.1767 | 0.1023 | 0.0744 | 0.1281 | 0.1023 | 0.0258 |
18 | Liaoning | 0.7399 | 0.1723 | 0.0947 | 0.0777 | 0.1187 | 0.0947 | 0.0240 |
19 | Inner Mongolia | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
20 | Ningxia | 0.6280 | 0.2486 | 0.1443 | 0.1044 | 0.1964 | 0.1443 | 0.0521 |
21 | Qinghai | 0.6083 | 0.2613 | 0.1818 | 0.0795 | 0.2144 | 0.1818 | 0.0326 |
22 | Shandong | 0.8248 | 0.1180 | 0.0442 | 0.0738 | 0.0694 | 0.0442 | 0.0252 |
23 | Shanghai | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
24 | Shanxi | 0.5380 | 0.3143 | 0.2338 | 0.0806 | 0.2745 | 0.2338 | 0.0407 |
25 | Shaanxi | 0.6070 | 0.2603 | 0.1921 | 0.0682 | 0.2186 | 0.1921 | 0.0266 |
26 | Sichuan | 0.7174 | 0.1839 | 0.1250 | 0.0589 | 0.1376 | 0.1250 | 0.0126 |
27 | Tianjin | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
28 | Xinjiang | 0.5344 | 0.3161 | 0.2416 | 0.0745 | 0.2798 | 0.2416 | 0.0382 |
29 | Yunnan | 0.5725 | 0.2858 | 0.2247 | 0.0611 | 0.2476 | 0.2247 | 0.0229 |
30 | Zhejiang | 0.8457 | 0.1157 | 0.0365 | 0.0793 | 0.0457 | 0.0365 | 0.0092 |
No. | DMU | Score | Input Inefficiency | Input Radial Inefficiency | Input Non-Radial Inefficiency | Output Inefficiency | Output Radial Inefficiency | Output Non-Radial Inefficiency |
---|---|---|---|---|---|---|---|---|
1 | Anhui | 0.6680 | 0.2206 | 0.1423 | 0.0783 | 0.1667 | 0.1423 | 0.0244 |
2 | Beijing | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
3 | Chongqing | 0.6542 | 0.2228 | 0.1765 | 0.0463 | 0.1881 | 0.1765 | 0.0117 |
4 | Fujian | 0.7918 | 0.1435 | 0.0737 | 0.0698 | 0.0817 | 0.0737 | 0.0080 |
5 | Gansu | 0.4673 | 0.3724 | 0.3142 | 0.0582 | 0.3431 | 0.3142 | 0.0289 |
6 | Guangdong | 0.8411 | 0.1119 | 0.0534 | 0.0585 | 0.0559 | 0.0534 | 0.0024 |
7 | Guangxi | 0.6921 | 0.1970 | 0.1471 | 0.0499 | 0.1601 | 0.1471 | 0.0130 |
8 | Guizhou | 0.5366 | 0.3149 | 0.2378 | 0.0771 | 0.2766 | 0.2378 | 0.0388 |
9 | Hainan | 0.8065 | 0.1467 | 0.0428 | 0.1039 | 0.0580 | 0.0428 | 0.0152 |
10 | Hebei | 0.7261 | 0.1798 | 0.0855 | 0.0943 | 0.1296 | 0.0855 | 0.0441 |
11 | Heilongjiang | 0.6593 | 0.2220 | 0.1416 | 0.0804 | 0.1801 | 0.1416 | 0.0384 |
12 | Henan | 0.5955 | 0.2652 | 0.2093 | 0.0559 | 0.2338 | 0.2093 | 0.0245 |
13 | Hubei | 0.7311 | 0.1775 | 0.1154 | 0.0621 | 0.1251 | 0.1154 | 0.0097 |
14 | Hunan | 0.8008 | 0.1330 | 0.0718 | 0.0611 | 0.0827 | 0.0718 | 0.0109 |
15 | Jiangsu | 0.8092 | 0.1312 | 0.0674 | 0.0637 | 0.0737 | 0.0674 | 0.0063 |
16 | Jiangxi | 0.6158 | 0.2494 | 0.2048 | 0.0446 | 0.2189 | 0.2048 | 0.0140 |
17 | Jilin | 0.7040 | 0.1928 | 0.1163 | 0.0765 | 0.1467 | 0.1163 | 0.0304 |
18 | Liaoning | 0.7233 | 0.1797 | 0.1058 | 0.0739 | 0.1341 | 0.1058 | 0.0283 |
19 | Inner Mongolia | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
20 | Ningxia | 0.5889 | 0.2767 | 0.1724 | 0.1043 | 0.2283 | 0.1724 | 0.0560 |
21 | Qinghai | 0.5990 | 0.2674 | 0.1870 | 0.0804 | 0.2230 | 0.1870 | 0.0360 |
22 | Shandong | 0.8070 | 0.1278 | 0.0504 | 0.0774 | 0.0808 | 0.0504 | 0.0304 |
23 | Shanghai | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
24 | Shanxi | 0.5061 | 0.3404 | 0.2585 | 0.0819 | 0.3032 | 0.2585 | 0.0446 |
25 | Shaanxi | 0.5875 | 0.2753 | 0.2034 | 0.0719 | 0.2336 | 0.2034 | 0.0302 |
26 | Sichuan | 0.7130 | 0.1863 | 0.1284 | 0.0578 | 0.1412 | 0.1284 | 0.0128 |
27 | Tianjin | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
28 | Xinjiang | 0.5124 | 0.3341 | 0.2568 | 0.0773 | 0.2994 | 0.2568 | 0.0426 |
29 | Yunnan | 0.5743 | 0.2834 | 0.2219 | 0.0615 | 0.2477 | 0.2219 | 0.0258 |
30 | Zhejiang | 0.8047 | 0.1317 | 0.0707 | 0.0611 | 0.0791 | 0.0707 | 0.0084 |
No. | DMU | Score | Input Inefficiency | Input Radial Inefficiency | Input Non-Radial Inefficiency | Output Inefficiency | Output Radial Inefficiency | Output Non-Radial Inefficiency |
---|---|---|---|---|---|---|---|---|
1 | Anhui | 0.6644 | 0.2266 | 0.1342 | 0.0924 | 0.1642 | 0.1342 | 0.0300 |
2 | Beijing | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
3 | Chongqing | 0.6598 | 0.2209 | 0.1672 | 0.0537 | 0.1808 | 0.1672 | 0.0135 |
4 | Fujian | 0.7760 | 0.1559 | 0.0787 | 0.0772 | 0.0877 | 0.0787 | 0.0090 |
5 | Gansu | 0.4371 | 0.4014 | 0.3378 | 0.0636 | 0.3696 | 0.3378 | 0.0318 |
6 | Guangdong | 0.8361 | 0.1196 | 0.0493 | 0.0703 | 0.0531 | 0.0493 | 0.0038 |
7 | Guangxi | 0.7027 | 0.1936 | 0.1334 | 0.0602 | 0.1476 | 0.1334 | 0.0142 |
8 | Guizhou | 0.5480 | 0.3079 | 0.2215 | 0.0865 | 0.2629 | 0.2215 | 0.0414 |
9 | Hainan | 0.7693 | 0.1694 | 0.0635 | 0.1059 | 0.0797 | 0.0635 | 0.0162 |
10 | Hebei | 0.6992 | 0.1999 | 0.0956 | 0.1043 | 0.1442 | 0.0956 | 0.0485 |
11 | Heilongjiang | 0.6398 | 0.2374 | 0.1482 | 0.0893 | 0.1919 | 0.1482 | 0.0437 |
12 | Henan | 0.5758 | 0.2821 | 0.2202 | 0.0620 | 0.2467 | 0.2202 | 0.0265 |
13 | Hubei | 0.7272 | 0.1837 | 0.1094 | 0.0743 | 0.1225 | 0.1094 | 0.0131 |
14 | Hunan | 0.8039 | 0.1361 | 0.0585 | 0.0776 | 0.0747 | 0.0585 | 0.0162 |
15 | Jiangsu | 0.8259 | 0.1261 | 0.0476 | 0.0785 | 0.0580 | 0.0476 | 0.0104 |
16 | Jiangxi | 0.5926 | 0.2692 | 0.2154 | 0.0537 | 0.2333 | 0.2154 | 0.0179 |
17 | Jilin | 0.6829 | 0.2084 | 0.1241 | 0.0843 | 0.1592 | 0.1241 | 0.0351 |
18 | Liaoning | 0.7983 | 0.1347 | 0.0371 | 0.0976 | 0.0839 | 0.0371 | 0.0468 |
19 | Inner Mongolia | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
20 | Ningxia | 0.5818 | 0.2841 | 0.1697 | 0.1143 | 0.2305 | 0.1697 | 0.0607 |
21 | Qinghai | 0.5960 | 0.2707 | 0.1855 | 0.0851 | 0.2238 | 0.1855 | 0.0382 |
22 | Shandong | 0.7988 | 0.1356 | 0.0429 | 0.0927 | 0.0822 | 0.0429 | 0.0393 |
23 | Shanghai | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
24 | Shanxi | 0.4759 | 0.3685 | 0.2803 | 0.0882 | 0.3269 | 0.2803 | 0.0466 |
25 | Shaanxi | 0.5686 | 0.2920 | 0.2105 | 0.0815 | 0.2451 | 0.2105 | 0.0346 |
26 | Sichuan | 0.7099 | 0.1880 | 0.1325 | 0.0555 | 0.1439 | 0.1325 | 0.0114 |
27 | Tianjin | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
28 | Xinjiang | 0.4755 | 0.3681 | 0.2844 | 0.0837 | 0.3289 | 0.2844 | 0.0445 |
29 | Yunnan | 0.5674 | 0.2899 | 0.2250 | 0.0649 | 0.2516 | 0.2250 | 0.0266 |
30 | Zhejiang | 0.7992 | 0.1392 | 0.0651 | 0.0741 | 0.0772 | 0.0651 | 0.0121 |
No. | DMU | Score | Input Inefficiency | Input Radial Inefficiency | Input Non-Radial Inefficiency | Output Inefficiency | Output Radial Inefficiency | Output Non-Radial Inefficiency |
---|---|---|---|---|---|---|---|---|
1 | Anhui | 0.6454 | 0.2397 | 0.1418 | 0.0979 | 0.1779 | 0.1418 | 0.0361 |
2 | Beijing | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
3 | Chongqing | 0.6493 | 0.2289 | 0.1682 | 0.0607 | 0.1876 | 0.1682 | 0.0194 |
4 | Fujian | 0.7518 | 0.1709 | 0.0925 | 0.0784 | 0.1027 | 0.0925 | 0.0102 |
5 | Gansu | 0.4147 | 0.4221 | 0.3566 | 0.0656 | 0.3935 | 0.3566 | 0.0370 |
6 | Guangdong | 0.8264 | 0.1224 | 0.0575 | 0.0649 | 0.0620 | 0.0575 | 0.0044 |
7 | Guangxi | 0.7070 | 0.1924 | 0.1252 | 0.0672 | 0.1423 | 0.1252 | 0.0171 |
8 | Guizhou | 0.5478 | 0.3075 | 0.2178 | 0.0897 | 0.2641 | 0.2178 | 0.0463 |
9 | Hainan | 0.7388 | 0.1876 | 0.0805 | 0.1071 | 0.0997 | 0.0805 | 0.0192 |
10 | Hebei | 0.6577 | 0.2266 | 0.1219 | 0.1047 | 0.1758 | 0.1219 | 0.0539 |
11 | Heilongjiang | 0.6553 | 0.2254 | 0.1241 | 0.1013 | 0.1821 | 0.1241 | 0.0580 |
12 | Henan | 0.5567 | 0.2970 | 0.2325 | 0.0646 | 0.2628 | 0.2325 | 0.0303 |
13 | Hubei | 0.7076 | 0.1962 | 0.1212 | 0.0750 | 0.1359 | 0.1212 | 0.0147 |
14 | Hunan | 0.7977 | 0.1405 | 0.0554 | 0.0852 | 0.0774 | 0.0554 | 0.0220 |
15 | Jiangsu | 0.8043 | 0.1399 | 0.0559 | 0.0840 | 0.0694 | 0.0559 | 0.0135 |
16 | Jiangxi | 0.5666 | 0.2899 | 0.2306 | 0.0593 | 0.2533 | 0.2306 | 0.0228 |
17 | Jilin | 0.6606 | 0.2226 | 0.1341 | 0.0885 | 0.1768 | 0.1341 | 0.0427 |
18 | Liaoning | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
19 | Inner Mongolia | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
20 | Ningxia | 0.5559 | 0.3022 | 0.1867 | 0.1155 | 0.2552 | 0.1867 | 0.0685 |
21 | Qinghai | 0.5918 | 0.2714 | 0.1883 | 0.0832 | 0.2311 | 0.1883 | 0.0428 |
22 | Shandong | 0.7808 | 0.1468 | 0.0441 | 0.1026 | 0.0928 | 0.0441 | 0.0487 |
23 | Shanghai | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
24 | Shanxi | 0.4503 | 0.3906 | 0.3006 | 0.0900 | 0.3533 | 0.3006 | 0.0527 |
25 | Shaanxi | 0.5513 | 0.3051 | 0.2193 | 0.0858 | 0.2603 | 0.2193 | 0.0410 |
26 | Sichuan | 0.7201 | 0.1812 | 0.1212 | 0.0600 | 0.1370 | 0.1212 | 0.0158 |
27 | Tianjin | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
28 | Xinjiang | 0.4546 | 0.3859 | 0.2996 | 0.0863 | 0.3507 | 0.2996 | 0.0511 |
29 | Yunnan | 0.5677 | 0.2880 | 0.2238 | 0.0642 | 0.2541 | 0.2238 | 0.0304 |
30 | Zhejiang | 0.7706 | 0.1574 | 0.0779 | 0.0795 | 0.0935 | 0.0779 | 0.0156 |
DMU | 2013 Assets | 2014 Assets | 2015 Assets | 2016 Assets | 2013 em | 2014 em | 2015 em | 2016 em |
---|---|---|---|---|---|---|---|---|
Anhui | 0.7356 | 0.7281 | 0.7322 | 0.7308 | 0.8732 | 0.8577 | 0.8660 | 0.7356 |
Beijing | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
Chongqing | 0.7337 | 0.8235 | 0.8328 | 0.8318 | 0.8484 | 0.8235 | 0.8330 | 0.7337 |
Fujian | 0.8294 | 0.8055 | 0.7898 | 0.7946 | 0.9359 | 0.9263 | 0.9210 | 0.8294 |
Gansu | 0.7088 | 0.6858 | 0.6622 | 0.6434 | 0.7088 | 0.6858 | 0.6620 | 0.7088 |
Guangdong | 0.7707 | 0.8714 | 0.8100 | 0.9425 | 0.9570 | 0.9466 | 0.9510 | 0.7707 |
Guangxi | 0.8616 | 0.8529 | 0.8184 | 0.7839 | 0.8617 | 0.8529 | 0.8670 | 0.8616 |
Guizhou | 0.7588 | 0.7622 | 0.7785 | 0.7822 | 0.7588 | 0.7622 | 0.7790 | 0.7588 |
Hainan | 0.3228 | 0.3501 | 0.3933 | 0.4648 | 0.9652 | 0.9572 | 0.9370 | 0.3228 |
Hebei | 0.8111 | 0.8008 | 0.7934 | 0.7840 | 0.9538 | 0.9145 | 0.9040 | 0.8111 |
Heilongjiang | 0.8211 | 0.8584 | 0.8518 | 0.8759 | 0.8211 | 0.8584 | 0.8520 | 0.8211 |
Henan | 0.8070 | 0.7907 | 0.7798 | 0.7675 | 0.8070 | 0.7907 | 0.7800 | 0.8070 |
Hubei | 0.8492 | 0.8426 | 0.8283 | 0.8373 | 0.8967 | 0.8846 | 0.8910 | 0.8492 |
Hunan | 0.9111 | 0.9017 | 0.8739 | 0.8588 | 0.9379 | 0.9282 | 0.9420 | 0.9111 |
Jiangsu | 0.9526 | 0.9326 | 0.9524 | 0.9441 | 0.9526 | 0.9326 | 0.9520 | 0.9526 |
Jiangxi | 0.7798 | 0.7952 | 0.7846 | 0.7694 | 0.8261 | 0.7952 | 0.7850 | 0.7798 |
Jilin | 0.8977 | 0.8837 | 0.8759 | 0.8659 | 0.8977 | 0.8837 | 0.8760 | 0.8977 |
Liaoning | 0.7501 | 0.8561 | 0.9629 | 1.0000 | 0.9053 | 0.8942 | 0.9630 | 0.7501 |
Inner Mongolia | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
Ningxia | 0.7033 | 0.6799 | 0.6951 | 0.6890 | 0.8557 | 0.8276 | 0.8300 | 0.7033 |
Qinghai | 0.6715 | 0.6391 | 0.6385 | 0.6246 | 0.8182 | 0.8130 | 0.8140 | 0.6715 |
Shandong | 0.9558 | 0.9496 | 0.9571 | 0.9559 | 0.9558 | 0.9496 | 0.9570 | 0.9558 |
Shanghai | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
Shanxi | 0.7662 | 0.7415 | 0.7197 | 0.6994 | 0.7663 | 0.7415 | 0.7200 | 0.7662 |
Shaanxi | 0.8079 | 0.7966 | 0.7895 | 0.7807 | 0.8079 | 0.7966 | 0.7900 | 0.8079 |
Sichuan | 0.8224 | 0.8306 | 0.8593 | 0.8462 | 0.8750 | 0.8716 | 0.8670 | 0.8224 |
Tianjin | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
Xinjiang | 0.7584 | 0.7432 | 0.7156 | 0.7004 | 0.7584 | 0.7432 | 0.7160 | 0.7584 |
Yunnan | 0.7753 | 0.7781 | 0.7750 | 0.7762 | 0.7753 | 0.7781 | 0.7750 | 0.7753 |
Zhejiang | 0.6749 | 0.9293 | 0.9349 | 0.9221 | 0.9635 | 0.9293 | 0.9350 | 0.6749 |
No. | DMU | 2013 Con | 2014 Con | 2015 Con | 2016 Con | 2013 New | 2014 New | 2015 New | 2016 New | 2013 GDP | 2014 GDP | 2015 GDP | 2016 GDP |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Anhui | 0.5056 | 0.4822 | 0.4371 | 0.4063 | 0.1842 | 0.0826 | 0.0488 | 0.0213 | 0.8988 | 0.8754 | 0.8942 | 0.8895 |
2 | Beijing | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
3 | Chongqing | 0.8484 | 0.8235 | 0.7965 | 0.7282 | 0.0288 | 0.0247 | 0.0298 | 0.0289 | 0.8837 | 0.8500 | 0.8747 | 0.8741 |
4 | Fujian | 0.8252 | 0.7872 | 0.7786 | 0.7541 | 0.0167 | 0.0166 | 0.0135 | 0.0135 | 0.9432 | 0.9314 | 0.9320 | 0.9219 |
5 | Gansu | 0.4335 | 0.4034 | 0.3603 | 0.3267 | 0.0064 | 0.0063 | 0.0066 | 0.0069 | 0.8160 | 0.7609 | 0.7984 | 0.7919 |
6 | Guangdong | 0.9570 | 0.9466 | 0.9259 | 0.8891 | 0.0240 | 0.0218 | 0.0271 | 0.0190 | 0.9604 | 0.9493 | 0.9552 | 0.9484 |
7 | Guangxi | 0.8133 | 0.8369 | 0.8339 | 0.8257 | 0.0120 | 0.0102 | 0.0093 | 0.0100 | 0.8916 | 0.8718 | 0.8947 | 0.8999 |
8 | Guizhou | 0.2533 | 0.2632 | 0.2648 | 0.2644 | 0.0072 | 0.0058 | 0.0059 | 0.0060 | 0.8373 | 0.8079 | 0.8465 | 0.8483 |
9 | Hainan | 0.8132 | 0.7866 | 0.7543 | 0.7164 | 0.0358 | 0.0380 | 0.0731 | 0.0167 | 0.9675 | 0.9590 | 0.9437 | 0.9307 |
10 | Hebei | 0.3435 | 0.3445 | 0.3097 | 0.2988 | 0.0705 | 0.0696 | 0.0649 | 0.0459 | 0.9577 | 0.9212 | 0.9197 | 0.9020 |
11 | Heilongjiang | 0.4404 | 0.3430 | 0.3023 | 0.2441 | 0.0614 | 0.0644 | 0.0762 | 0.0536 | 0.8682 | 0.8759 | 0.8857 | 0.9006 |
12 | Henan | 0.5239 | 0.5132 | 0.4871 | 0.4652 | 0.1117 | 0.1451 | 0.1398 | 0.1298 | 0.8608 | 0.8269 | 0.8471 | 0.8413 |
13 | Hubei | 0.7540 | 0.7568 | 0.6987 | 0.6825 | 0.0082 | 0.0079 | 0.0089 | 0.0081 | 0.9144 | 0.8965 | 0.9102 | 0.9024 |
14 | Hunan | 0.8288 | 0.8356 | 0.7535 | 0.7112 | 0.0093 | 0.0096 | 0.0097 | 0.0099 | 0.9448 | 0.9330 | 0.9476 | 0.9502 |
15 | Jiangsu | 0.7549 | 0.7306 | 0.6613 | 0.6191 | 0.1075 | 0.0654 | 0.0502 | 0.0376 | 0.9567 | 0.9368 | 0.9565 | 0.9497 |
16 | Jiangxi | 0.8261 | 0.7851 | 0.7029 | 0.6347 | 0.0441 | 0.0382 | 0.0329 | 0.0224 | 0.8710 | 0.8300 | 0.8494 | 0.8422 |
17 | Jilin | 0.5012 | 0.4671 | 0.4390 | 0.4087 | 0.0296 | 0.0430 | 0.0495 | 0.0401 | 0.9151 | 0.8958 | 0.9006 | 0.8943 |
18 | Liaoning | 0.6005 | 0.5621 | 0.4275 | 1.0000 | 0.0477 | 0.0439 | 0.0394 | 1.0000 | 0.9204 | 0.9043 | 0.9655 | 1.0000 |
19 | Inner Mongolia | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
20 | Ningxia | 0.1263 | 0.1164 | 0.1049 | 0.0953 | 0.0138 | 0.0106 | 0.0088 | 0.0066 | 0.8880 | 0.8530 | 0.8733 | 0.8641 |
21 | Qinghai | 0.4422 | 0.4811 | 0.5215 | 0.5908 | 0.0021 | 0.0022 | 0.0025 | 0.0027 | 0.8667 | 0.8425 | 0.8647 | 0.8632 |
22 | Shandong | 0.5497 | 0.4948 | 0.4371 | 0.3886 | 0.1094 | 0.1379 | 0.0937 | 0.0398 | 0.9594 | 0.9520 | 0.9605 | 0.9594 |
23 | Shanghai | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
24 | Shanxi | 0.1449 | 0.1252 | 0.1130 | 0.0986 | 0.0505 | 0.0372 | 0.0257 | 0.0122 | 0.8407 | 0.7946 | 0.8204 | 0.8123 |
25 | Shaanxi | 0.4063 | 0.3623 | 0.3164 | 0.2786 | 0.0565 | 0.0558 | 0.0482 | 0.0458 | 0.8612 | 0.8310 | 0.8519 | 0.8476 |
26 | Sichuan | 0.7674 | 0.7975 | 0.8663 | 0.8788 | 0.0054 | 0.0049 | 0.0048 | 0.0049 | 0.9000 | 0.8862 | 0.8953 | 0.9024 |
27 | Tianjin | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
28 | Xinjiang | 0.2546 | 0.2186 | 0.1804 | 0.1529 | 0.0130 | 0.0123 | 0.0109 | 0.0094 | 0.8371 | 0.7957 | 0.8187 | 0.8127 |
29 | Yunnan | 0.5002 | 0.5294 | 0.5580 | 0.5992 | 0.0030 | 0.0025 | 0.0027 | 0.0023 | 0.8450 | 0.8184 | 0.8448 | 0.8454 |
30 | Zhejiang | 0.7970 | 0.7703 | 0.6963 | 0.6405 | 0.0671 | 0.0590 | 0.0423 | 0.0353 | 0.9660 | 0.9340 | 0.9424 | 0.9326 |
No. | DMU | 2013 CO2 | 2014 CO2 | 2015 CO2 | 2016 CO2 | 2013 SO2 | 2014 SO2 | 2015 SO2 | 2016 SO2 | 2013 NO2 | 2014 NO2 | 2015 NO2 | 2016 NO2 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Anhui | 0.5056 | 0.4822 | 0.4371 | 0.4063 | 0.6489 | 0.6426 | 0.5844 | 0.5584 | 0.5415 | 0.5295 | 0.5166 | 0.4991 |
2 | Beijing | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
3 | Chongqing | 0.8484 | 0.8235 | 0.7965 | 0.7282 | 0.3622 | 0.3936 | 0.3932 | 0.3901 | 0.8270 | 0.8129 | 0.8303 | 0.7789 |
4 | Fujian | 0.8252 | 0.7872 | 0.7786 | 0.7541 | 0.7625 | 0.7611 | 0.7462 | 0.7434 | 0.9359 | 0.9263 | 0.9213 | 0.9075 |
5 | Gansu | 0.4335 | 0.4034 | 0.3603 | 0.3267 | 0.2052 | 0.1942 | 0.1671 | 0.1514 | 0.3848 | 0.3721 | 0.3385 | 0.3147 |
6 | Guangdong | 0.9570 | 0.9466 | 0.9259 | 0.8891 | 0.8190 | 0.8634 | 0.8403 | 0.8581 | 0.9151 | 0.9375 | 0.9507 | 0.9425 |
7 | Guangxi | 0.8133 | 0.8369 | 0.8666 | 0.8748 | 0.5192 | 0.5066 | 0.4923 | 0.4633 | 0.7010 | 0.7249 | 0.7413 | 0.7602 |
8 | Guizhou | 0.2533 | 0.2632 | 0.2648 | 0.2644 | 0.1423 | 0.1538 | 0.1617 | 0.1691 | 0.3738 | 0.4043 | 0.4467 | 0.4843 |
9 | Hainan | 0.8132 | 0.7866 | 0.7543 | 0.7164 | 0.9652 | 0.9572 | 0.9365 | 0.9195 | 0.5514 | 0.5580 | 0.5522 | 0.5359 |
10 | Hebei | 0.3435 | 0.3445 | 0.3097 | 0.2988 | 0.3478 | 0.3570 | 0.3312 | 0.3302 | 0.3887 | 0.3790 | 0.3608 | 0.3476 |
11 | Heilongjiang | 0.4404 | 0.3430 | 0.3023 | 0.2441 | 0.4847 | 0.4007 | 0.3484 | 0.2901 | 0.4691 | 0.3832 | 0.3640 | 0.3102 |
12 | Henan | 0.5239 | 0.5132 | 0.4871 | 0.4652 | 0.4245 | 0.4270 | 0.4058 | 0.3952 | 0.5065 | 0.5078 | 0.5110 | 0.5085 |
13 | Hubei | 0.7540 | 0.7568 | 0.6987 | 0.6825 | 0.6298 | 0.6431 | 0.6171 | 0.6186 | 0.8967 | 0.8846 | 0.8906 | 0.8788 |
14 | Hunan | 0.8288 | 0.8356 | 0.7535 | 0.7112 | 0.5961 | 0.6080 | 0.5775 | 0.5604 | 0.9379 | 0.9282 | 0.9187 | 0.8809 |
15 | Jiangsu | 0.7549 | 0.7306 | 0.6613 | 0.6191 | 0.9109 | 0.8950 | 0.8713 | 0.8493 | 0.9526 | 0.9326 | 0.9524 | 0.9441 |
16 | Jiangxi | 0.8261 | 0.7851 | 0.7029 | 0.6347 | 0.4388 | 0.4551 | 0.4163 | 0.4001 | 0.6270 | 0.6177 | 0.6040 | 0.5735 |
17 | Jilin | 0.5012 | 0.4671 | 0.4390 | 0.4087 | 0.5612 | 0.5408 | 0.4848 | 0.4546 | 0.5511 | 0.4988 | 0.4679 | 0.4211 |
18 | Liaoning | 0.6005 | 0.5621 | 0.4275 | 1.0000 | 0.4355 | 0.4234 | 0.3052 | 1.0000 | 0.6730 | 0.6300 | 0.5048 | 1.0000 |
19 | Inner Mongolia | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
20 | Ningxia | 0.1263 | 0.1164 | 0.1049 | 0.0953 | 0.1136 | 0.1138 | 0.1071 | 0.1045 | 0.1455 | 0.1433 | 0.1383 | 0.1338 |
21 | Qinghai | 0.4422 | 0.4811 | 0.5215 | 0.5908 | 0.2403 | 0.2357 | 0.2136 | 0.2026 | 0.4093 | 0.3648 | 0.3628 | 0.3279 |
22 | Shandong | 0.5497 | 0.4948 | 0.3177 | 0.2624 | 0.5156 | 0.5109 | 0.4793 | 0.4634 | 0.7451 | 0.6951 | 0.6852 | 0.6395 |
23 | Shanghai | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
24 | Shanxi | 0.1449 | 0.1252 | 0.1130 | 0.0986 | 0.1713 | 0.1600 | 0.1505 | 0.1410 | 0.2776 | 0.2558 | 0.2513 | 0.2364 |
25 | Shaanxi | 0.4063 | 0.3623 | 0.3164 | 0.2786 | 0.3566 | 0.3537 | 0.3214 | 0.3083 | 0.8877 | 0.5343 | 0.5094 | 0.4901 |
26 | Sichuan | 0.7674 | 0.7975 | 0.8663 | 0.8788 | 0.4526 | 0.4627 | 0.4675 | 0.4529 | 0.8750 | 0.8716 | 0.8675 | 0.8187 |
27 | Tianjin | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
28 | Xinjiang | 0.2546 | 0.2186 | 0.1804 | 0.1529 | 0.1771 | 0.1701 | 0.1627 | 0.1534 | 0.2456 | 0.2342 | 0.2355 | 0.2242 |
29 | Yunnan | 0.5002 | 0.5294 | 0.5580 | 0.5992 | 0.2985 | 0.2963 | 0.2970 | 0.2940 | 0.5669 | 0.5351 | 0.5331 | 0.5025 |
30 | Zhejiang | 0.7970 | 0.7703 | 0.6962 | 0.6405 | 0.7425 | 0.7696 | 0.7441 | 0.7193 | 0.9635 | 0.9293 | 0.9349 | 0.9221 |
DMU | CO2 | SO2 | NO2 | |
---|---|---|---|---|
Anhui | All three undesirable outputs had low efficiency scores. The efficiency of CO2 was relatively low and showed a downward trend and should be treated first. | ▲ | ||
Chongqing | SO2 had the lowest efficiency; NO2 had the best efficiency; therefore, more effective measures are needed to reduce SO2 emissions. | ▲ | ||
Fujian | NO2 had the best efficiency; the SO2 efficiency was lower than the others; therefore, more effective measures are needed to reduce SO2 emissions. | ▲ | ||
Gansu | All three emission indicators had poor efficiency scores, but SO2 had the lowest efficiency, and the CO2 efficiency was better; therefore, more effective measures are needed to reduce SO2 emissions. | ▲ | ||
Guangdong | SO2 efficiency was lower than the others; therefore, more effective measures are needed to reduce SO2 emissions. | ▲ | ||
Guangxi | SO2 efficiency was lower than the others; therefore, more effective measures are needed to reduce SO2 emissions. | ▲ | ||
Guizhou | All three undesirable outputs had low efficiencies below 0.4, with the SO2 efficiency being the lowest at below 0.2. Comprehensive management should be strengthened, after strengthening the governance of SO2 emissions. | ▲ | ||
Hainan | NO2 had the worst emission efficiency, The SO2 efficiency was high, but declined; thus, more effective measures are needed to reduce NO2 emissions. | ▲ | ||
Hebei | All three undesirable indicator efficiencies were lower than 0.4, with the NO2 efficiency being slightly higher than CO2 and SO2. Comprehensive management should be strengthened, after strengthening the governance of CO2 emissions. | ▲ | ▲ | ▲ |
Heilongjiang | All three undesirable indicator efficiencies were lower than 0.4, with the NO2 efficiency being slightly higher than CO2 and SO2. Comprehensive management should be strengthened, after strengthening the governance of CO2 emissions. | ▲ | ▲ | |
Henan | The SO2 efficiency was the worst at only 0.4, and the CO2 efficiency score was between 0.4 and 0.6 but declined. The NO2 efficiency was similar to SO2, but with less fluctuation; Overall, more effective measures are needed to reduce SO2, CO2, and NO2 emissions, but the governance of SO2 emissions should be strengthened first. | ▲ | ||
Hubei | The NO2 efficiency was the highest at over 0.8, the CO2 efficiency was slightly lower, and the SO2 efficiency score was much lower; therefore, more effective measures are needed to reduce SO2 emissions. | ▲ | ||
Hunan | The SO2 efficiency score was much lower at 0.6; therefore, more effective measures are needed to reduce SO2 emissions. | ▲ | ||
Jiangsu | The CO2 efficiency was much lower than the other indicators at between 0.6 and 0.75; the reduction of CO2 emissions should become the focus of governance in Jiangsu. | ▲ | ||
Jiangxi | SO2 had the lowest efficiency at between 0.4 and 0.5 and declined; therefore, more effective measures are needed to reduce SO2 emissions. NO2 also declined at 0.6, with the CO2 efficiency being slightly better, declining to between 0.6 and 0.8; therefore, there is room for improvement. | ▲ | ||
Jilin | The NO2, SO2, and CO2 efficiencies were all between 0.6 and 0.8; therefore, the room for improvement was similar and these emissions should be treated equally. CO2 efficiency was slightly lower than others, and can be prioritized. | ▲ | ▲ | ▲ |
Liaoning | The NO2, SO2, and CO2 efficiencies were similar, maintaining a continuous upward trend and reaching 1 in 2016. The state of input and output should be maintained in 2016, and attention should be paid to and sufficient measures should be taken to maintain the existing input and output status. | ▲ | ▲ | ▲ |
Ningxia | All NO2, SO2, and CO2 efficiencies were very low at between 0.1 and 0.2, and declined; therefore, all three indicators need to improve. CO2 and SO2 emission efficiency were lower and can be prioritized. | ▲ | ▲ | ▲ |
Qinghai | The SO2 efficiency was below 0.2 and had a downward trend. The CO2 efficiency was slightly higher at between 0.4 and 0.6, and the NO2 efficiency was between 0.2 and 0.4; therefore, all three indicators need to improve. The governance of SO2 emissions should be strengthened first. | ▲ | ||
Shandong | The CO2 efficiency score was between 0.2 and 0.8 and declined. The SO2 efficiency also declined; however, the minimum value of 0.4 was slightly better than the CO2 minimum. The efficiency of CO2 dropped sharply and should be treated first. | ▲ | ||
Shanxi | All NO2, SO2, and CO2 efficiencies were very low, with the CO2 efficiency being the lowest at 0.2 and continuing to decline. The SO2 efficiency was slightly better at around 0.2, but also decreased, and the NO2 efficiency was slightly better, but was only 0.3; therefore, all three indicators need to improve. | ▲ | ▲ | |
Shaanxi | The CO2 efficiency was between 0.2 and 0.4, and declined. The SO2 efficiency also declined, with the minimum value being between 0.2 and 0.4. The NO2 efficiency was between 0.4 and 0.6, but also showed a downward trend; therefore, all three indicators need to improve. | ▲ | ▲ | |
Sichuan | The SO2 efficiency was 0.5, and the CO2 efficiency was 0.8 and rising. The NO2 had a higher rising efficiency score between 0.8 and 0.9; therefore, more effective measures are needed to reduce SO2 emissions. | ▲ | ||
Xinjiang | All NO2, SO2, and CO2 efficiencies were very low. The SO2 efficiency score of 0.2 was the lowest and declined. The SO2 efficiency score was the worst overall and should be prioritized. However, the emission efficiency of the other two indicators was not high, and comprehensive management is also needed. | ▲ | ||
Yunnan | SO2 had the lowest efficiency at around 0.3 and declined, while the CO2 efficiency was better between 0.5 and 0.8 and rising. The NO2 efficiency at 0.5 to 0.6 was falling; therefore, more effective measures are needed to reduce SO2 emissions. | ▲ | ||
Zhejiang | The CO2 and SO2 efficiencies were between 0.6 and 0.8 and falling; therefore, more effective measures are needed to reduce CO2 and SO2 emissions. | ▲ | ▲ |
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Li, Y.; Chiu, Y.-h.; Lu, L.C. New Energy Development and Pollution Emissions in China. Int. J. Environ. Res. Public Health 2019, 16, 1764. https://doi.org/10.3390/ijerph16101764
Li Y, Chiu Y-h, Lu LC. New Energy Development and Pollution Emissions in China. International Journal of Environmental Research and Public Health. 2019; 16(10):1764. https://doi.org/10.3390/ijerph16101764
Chicago/Turabian StyleLi, Ying, Yung-ho Chiu, and Liang Chun Lu. 2019. "New Energy Development and Pollution Emissions in China" International Journal of Environmental Research and Public Health 16, no. 10: 1764. https://doi.org/10.3390/ijerph16101764