SPECIAL ISSUE PAPER
Study of haze emission efficiency based on new co-opetition data envelopment analysis
Xianhua Wu,
Yufeng Chen,
Peng Zhao,
Ji Guo,
Zhanxin Ma,
Corresponding Author
Xianhua Wu
School of Economics and Management, Shanghai Maritime University, Shanghai, China
Collaborative Innovation Center on Climate and Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
Correspondence
Xianhua Wu, School of Economics and Management, Shanghai Maritime University, China.
Email: [email protected]; [email protected]
Search for more papers by this authorYufeng Chen
Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, China
Search for more papers by this authorPeng Zhao
School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China
Search for more papers by this authorJi Guo
School of Economics and Management, Shanghai Maritime University, Shanghai, China
Collaborative Innovation Center on Climate and Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
Search for more papers by this authorZhanxin Ma
School of Economics and Management, Inner Mongolia University, Hohhot, China
Search for more papers by this authorXianhua Wu,
Yufeng Chen,
Peng Zhao,
Ji Guo,
Zhanxin Ma,
Corresponding Author
Xianhua Wu
School of Economics and Management, Shanghai Maritime University, Shanghai, China
Collaborative Innovation Center on Climate and Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
Correspondence
Xianhua Wu, School of Economics and Management, Shanghai Maritime University, China.
Email: [email protected]; [email protected]
Search for more papers by this authorYufeng Chen
Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, China
Search for more papers by this authorPeng Zhao
School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China
Search for more papers by this authorJi Guo
School of Economics and Management, Shanghai Maritime University, Shanghai, China
Collaborative Innovation Center on Climate and Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
Search for more papers by this authorZhanxin Ma
School of Economics and Management, Inner Mongolia University, Hohhot, China
Search for more papers by this authorAbstract
As haze intensifies in China, controlling haze emission has become the country's top priority for environmental protection. Because haze moves across different regions, it is necessary to develop a data envelopment analysis (DEA) model underpinned by both competition and cooperation to evaluate the haze emission efficiency in different provinces. This study innovatively adopts the spatial econometrics to construct the co-opetition matrices of Chinese provinces, then builds the co-opetition DEA model to evaluate the haze emission efficiency of them, and finally uses the haze data of 2015 as an example to assess the applicability of the model. The results of the study include the following: First, compared with the traditional CCR (A. Charnes & W. W. Cooper & E. Rhodes) model, this study constructs the co-opetition DEA cross-efficiency model that integrates haze's feature of cross-border moving; thus, it is more in line with the reality of haze emission and movement. Second, compared with the efficiency value gained from the CCR model, the haze emission efficiency values for Tianjin and Guangdong, two decision-making units, register greater variance when using the DEA model. The reason might lie in that they have a different spatial transportation relationship with their surrounding provinces. Third, the haze emission efficiency of provinces, according to the evaluation based on the co-opetition DEA method, varies greatly: Those with high efficiency are mostly inland provinces with slow economic growth and adverse climatic conditions, whereas many of the provinces with low efficiency are located in the relatively prosperous East China. The specific co-opetition DEA model constructed in this study enriches the research on the DEA model, which can be applied to the emission efficiency evaluation of similar pollutants around the world and can contribute empirical support to the haze reducing efforts of the government with its empirical results.
REFERENCES
- An, Q., Wen, Y., & Ding, T. (2018). Resource sharing and payoff allocation in a three-stage system: Integrating network DEA with the Shapley value method. Omega, 85, 16–25. https://doi.org/10.1016/j.omega.2018.05.008
- Anselin, L. (2001). Spatial effects in econometric practice in environmental and resource economics. American Journal of Agricultural Economics, 83(3), 705–710. https://doi.org/10.1111/0002-9092.00194
- Borge, R., Lumbreras, J., & Vardoulakis, S. (2007). Analysis of long-range transport influences on urban PM10 using two-stage atmospheric trajectory clusters. Atmospheric Environment, 41(21), 4434–4450. https://doi.org/10.1016/j.atmosenv.2007.01.053
- Camarero, M., & Castillo (2013). Eco-efficiency and convergence in OECD countries. Environmental and Resource Economics, 55(1), 87–106. https://doi.org/10.1007/s10640-012-9616-9
- Cao, Y. (2016). Regional technical innovation efficiency evaluation and promotion strategies based on cross-efficiency model. Shenyang University of Technology.
- Chen, X., Shao, S., & Tian, Z. (2017). Impacts of air pollution and its spatial spillover effect on public health based on China's big data sample. Journal of Cleaner Production, 142, 915–925. https://doi.org/10.1016/j.jclepro.2016.02.119
- Cheng, Z., Li, L., & Liu, J. (2017). Identifying the spatial effects and driving factors of urban PM2.5 pollution in China. Ecological Indicators, 82, 61–75. https://doi.org/10.1016/j.ecolind.2017.06.043
- Choi, Y., Zhang, N., & Zhou, P. (2012). Efficiency and abatement costs of energy-related CO2 emissions in China: A slacks-based efficiency measure. Applied Energy, 98, 198–208.
- Coury, C., & Dillner, A. M. (2007). Trends and sources of particulate matter in the superstition wilderness using air trajectory and aerosol cluster analysis. Atmospheric Environment, 41(40), 9309–9323. https://doi.org/10.1016/j.atmosenv.2007.09.011
- Doyle, J., & Green, R. (1994). Efficiency and cross efficiency in DEA: Derivations, meanings and the uses. Journal of the Operational Research Society, 45, 567–578. https://doi.org/10.1057/jors.1994.84
- Fleishman, R., Alexander, R., Bretschneider, S., & Popp, D. (2009). Does regulation stimulate productivity? The effect of air quality policies on the efficiency of US power plants. Energy Policy, 37(11), 4574–4582. https://doi.org/10.1016/j.enpol.2009.06.012
- Gieré, R., Blackford, M., & Smith, K. (2006). TEM study of PM2.5 emitted from coal and tire combustion in a thermal power station. Environmental Science & Technology, 40(20), 6235–6240. https://doi.org/10.1021/es060423m
- Green, R. H., Doyle, J. R., & Cook, W. D. (1996). Preference voting and project ranking using DEA and cross-evaluation. European Journal of Operational Research, 90(3), 461–472. https://doi.org/10.1016/0377-2217(95)00039-9
- Guo, J., Zhu, D. D., Wu, X. H., & Yan, Y. Z. (2017). Study on environment performance evaluation and regional differences of strictly-environmental-monitored cities in China. Sustainability, 9(12), 2094. https://doi.org/10.3390/su9122094
- Guo, X., Lu, C. C., & Lee, J. H. (2017). Applying the dynamic DEA model to evaluate the energy efficiency of OECD countries and China. Energy, 134, 392–399.
- He, F., Ma, D. D., & Zhu, L. Y. (2016). Measurement and factors of environmental technology efficiency in China under the constraint of haze—Provincial panel data based on the SBM-undesirable model. R&D Management, 28(5), 34–43.
- Ho, K. F., Ho, S. S. H., & Huang, R. J. (2016). Chemical composition and bioreactivity of PM2.5, during 2013 haze events in China. Atmospheric Environment, 126, 162–170. https://doi.org/10.1016/j.atmosenv.2015.11.055
- Hosseini, H. M., & Rahbar, F. (2011). Spatial environmental Kuznets curve for Asian countries: Study of CO2 and PM10. Journal of Environmental Studies, 37(58), 1–14.
- Huang, H. J., Liu, H. N., & Jiang, W. M. (2006). Physical and chemical characteristics and source apportionment of PM2.5 in Nanjing. Climatic and Environmental Research, 11(6), 713–722.
- Li, F., Zhu, Q., & Liang, L. (2018). Allocating a fixed cost based on a DEA-game cross efficiency approach. Expert Systems with Applications, 96, 196–207. https://doi.org/10.1016/j.eswa.2017.12.002
- Liang, L., Wu, J., & Cook, W. D. (2008a). The DEA game cross-efficiency model and its Nash equilibrium. Operations Research, 56(5), 1278–1288. https://doi.org/10.1287/opre.1070.0487
- Liang, L., Wu, J., & Cook, W. D. (2008b). Alternative secondary goals in DEA cross-efficiency evaluation. International Journal of Production Economics, 113(2), 1025–1030. https://doi.org/10.1016/j.ijpe.2007.12.006
- Lin, R. (2019). Cross-efficiency evaluation capable of dealing with negative data: A directional distance function based approach. Journal of the Operational Research Society, 1–12. https://doi.org/10.1080/01605682.2019.1567652
- Liu, B., Song, N., & Dai, Q. (2016). Chemical composition and source apportionment of ambient PM2.5, during the non-heating period in Taian, China. Atmospheric Research, 170, 23–33. https://doi.org/10.1016/j.atmosres.2015.11.002
- Liu, H., Song, Y., & Yang, G. (2019). Cross-efficiency evaluation in data envelopment analysis based on prospect theory. European Journal of Operational Research, 273(1), 364–375. https://doi.org/10.1016/j.ejor.2018.07.046
- Liu, J., Wu, D., & Fan, S. (2017). A one-year, on-line, multi-site observational study on water-soluble inorganic ions in PM2.5 over the Pearl River Delta region, China. Science of the Total Environment, 601, 1720–1732.
- Liu, W., Wang, Y. M., & Lv, S. (2017). An aggressive game cross-efficiency evaluation in data envelopment analysis. Ann. Oper. Res., 259(1-2), 241–258. https://doi.org/10.1007/s10479-017-2524-1
- Liu, W. B., Meng, W., Li, X. X., & Zhang, D. Q. (2010). Dea models with undesirable inputs and outputs. Annals of Operations Research, 173(1), 177–194. https://doi.org/10.1007/s10479-009-0587-3
- Luo, N. S., & Li, J. M. (2018). Do industrial agglomeration and traffic links aggravate the spatial spillover effects of haze? —Analysis from the perspective of spatial distribution of industries. Industrial Economics Research, 4, 5.
- Ma, L. M., & Zhang, X. (2014). Spatial effects of regional air pollution and the impact of industrial structure. China Population, Resources and Environment, 24(7), 157–164.
- Maddison, D. (2006). Environmental Kuznets curves: A spatial econometric approach. Journal of Environmental Economics and Management, 51(2), 218–230. https://doi.org/10.1016/j.jeem.2005.07.002
- Miao, Z., Zhou, P., Wang, Y., & Sun, Z. R. (2013). Energy Saving, emission reduction and air pollutants emissions rights allocation research. China Industrial Economy, 6, 31–43.
- Pan, B. F., Wang, W., & Li, L. (2013). Analysis of the reason of formation and the characteristic of pollution about fog or haze at key cities in autumn and winter in China. Environment and Sustainable Development, 38(1), 33–36.
- Pan, H. F., Wang, X., & Zhang, S. Y. (2015). Duration and spatial spillover effects of haze pollution—Evidence from Beijing-Tianjin-Hebei region. China Soft Science, 12, 134–143.
- Picazo-Tadeo, A. J., Castillo-Giménez, J., & Beltrán-Esteve, M. (2014). An intertemporal approach to measuring environmental performance with directional distance functions: Greenhouse gas emissions in the European Union. Ecological Economics, 100(1), 173–182. https://doi.org/10.1016/j.ecolecon.2014.02.004
- Poon, P. H., Casaa, I., & He, C. (2006). The impact of energy, transport and trade on air pollution in China. Eurasian Geography and Economics, 47(5), 568–584. https://doi.org/10.2747/1538-7216.47.5.568
- Qiao, T., Xiu, G., & Zheng, Y. (2015). Preliminary investigation of PM1, PM2.5, PM10, and its metal elemental composition in tunnels at a subway station in Shanghai, China. Transportation Research Part D Transport & Environment, 41, 136–146. https://doi.org/10.1016/j.trd.2015.09.013
- Rashidi, K., Shabani, A., & Saen, R. F. (2015). Using data envelopment analysis for estimating energy saving and undesirable output abatement: a case study in the Organization for Economic Co-Operation and Development (OECD) countries. Journal of Cleaner Production, 105, 241–252. https://doi.org/10.1016/j.jclepro.2014.07.083
- Ren, Z. H., Wan, B. T., & Su, F. Q. (2014). Several characteristics of atmospheric environmental quality in China at Present. Research of Environmental Sciences, 17(1), 1–6.
- Saen, R. F. (2010). Developing a new data envelopment analysis methodology for supplier selection in the presence of both undesirable outputs and imprecise data. International Journal of Advanced Manufacturing Technology, 51(9-12), 1243–1250. https://doi.org/10.1007/s00170-010-2694-3
- Seiford, L. M., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 141(2), 16–20.
- Shao, S., Li, X., & Cao, J. H. (2016). China's economic policy choices for governing smog pollution based on spatial spillover effects. Economic Research Journal, 9, 73–88.
- Shi, D., Wu, L. X., Fu, X. X., & Wu, B. (2008). Study on regional differences and causes of energy efficiency in China—Based on variance decomposition of stochastic frontier production function. Management World, 2, 35–43.
- Shi, K., Liu, C. Q., & Wu, S. H. (2014). Self-organized evolution of trans-boundary PM10 pollution in Zhoushan City. Acta Scientiae Circumstantiae, 34(5), 1125–1132.
- Song, M., Zhu, Q., Peng, J., & Santibanez Gonzalez, E. D. R. (2017). Improving the evaluation of cross efficiencies: A method based on Shannon entropy weight. Computers & Industrial Engineering, 112, 99–106. https://doi.org/10.1016/j.cie.2017.07.023
- Song, M. L., Zhang, G. J., & Fang, K. N. (2016). Regional operational and environmental performance evaluation in China: Non-radial DEA methodology under natural and managerial disposability. Natural Hazards, 84(1), 243–265.
- Song, M. L., Zhang, J., & Wang, S. H. (2015). Review of the network environmental efficiencies of listed petroleum enterprises in China. Renewable & Sustainable Energy Reviews, 43, 65–71.
- Song, M. L., Zhang, L. L., Liu, W., & Fisher, R. (2013). Bootstrap-DEA analysis of BRICS' energy efficiency based on small sample data. Applied Energy, 112(C), 1049–1055. https://doi.org/10.1016/j.apenergy.2013.02.064
- Song, M. L., Zhou, Y. X., Zhang, R. R., & Fisher, R. (2017). Environmental efficiency evaluation with left–right fuzzy numbers. Operational Research, 17(3), 697–714. https://doi.org/10.1007/s12351-015-0202-0
- Su, W., Liu, Y., & Wang, S. (2018). Regional inequality, spatial spillover effects, and the factors influencing city-level energy-related carbon emissions in China. Journal of Geographical Sciences, 28(4), 495–513. https://doi.org/10.1007/s11442-018-1486-9
- Sueyoshi, T., & Yuan, Y. (2015). China's regional sustainability and diversified resource allocation: DEA environmental assessment on economic development and air pollution. Energy Economics, 49(8), 239–256. https://doi.org/10.1016/j.eneco.2015.01.024
- Sun, W. (2013). Research on modeling and similarity of qualitative direction relations based on the direction relation matrix. Jilin University.
- Tai, A. P. K., Mickley, L. J., & Jacob, D. J. (2010). Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States: Implications for the sensitivity of PM2.5 to climate change. Atmospheric Environment, 44(32), 3976–3984. https://doi.org/10.1016/j.atmosenv.2010.06.060
- Tang, D. L., Li, L., & Hong, X. F. (2017). The spatial spillover effect of energy consumption on haze pollution in China——An empirical research based on the static and dynamic spatial panel data model. Systems Engineering - Theory & Practice, 37(7), 1697–1708.
- Tolis, E. I., Saraga, D. E., & Lytra, M. K. (2015). Concentration and chemical composition of PM2.5 for a one-year period at Thessaloniki, Greece: A comparison between city and port area. Atmospheric Environment, 113, 197–207. https://doi.org/10.1016/j.atmosenv.2015.05.014
- Tsai, H., Wu, J., & Sun, J. (2013). Cross-efficiency evaluation of Taiwan's international tourist hotels under competitive and cooperative relationships. Journal of China Tourism Research, 9(4), 413–428. https://doi.org/10.1080/19388160.2013.841500
10.1080/19388160.2013.841500 Google Scholar
- Wang, F., Chen, D. S., & Cheng, S. Y. (2010). Identification of regional atmospheric PM10 transport pathways using HYSPLIT, MM5-CMAQ and synoptic pressure pattern analysis. Environmental Modelling & Software, 25(8), 927–934. https://doi.org/10.1016/j.envsoft.2010.02.004
- Wang, Y. M., & Chin, K. S. (2011). The use of OWA operator weights for SPI cross-efficiency aggregation. Omega, 39(5), 493–503. https://doi.org/10.1016/j.omega.2010.10.007
- Wang, H. L., Qiao, L. P., & Lou, S. R. (2016). Chemical composition of PM2.5, and meteorological impact among three years in urban Shanghai, China. Journal of Cleaner Production, 112, 1302–1311. https://doi.org/10.1016/j.jclepro.2015.04.099
- Wang, K. L., Meng, X. R., & Yang, B. C. (2017). Regional differences and influencing factors of China's air pollution emission efficiency considering technological heterogeneity. China Population, Resources and Environment, 27(1), 101–110.
- Wang, Q., Zhang, H., & Zhang, W. (2013). A Malmquist CO2, emission performance index based on a metafrontier approach. Mathematical and Computer Modelling, 58(5-6), 1068–1073. https://doi.org/10.1016/j.mcm.2012.05.003
10.1016/j.mcm.2012.05.003 Google Scholar
- Wang, S., Xiu, T. Y., Sun, Y., Meng, X. R., & Xu, J. C. (2014). The changes of mist and haze days and meteorological element during 1960-2012 in Xi'an. Acta Scientiae Circumstantiae, 34(1), 19–26.
- Wang, X. Q., Ju, X., & Feng, B. (2016). Analysis of performance variances of haze main precursor's emissions among China's provinces. Journal of Arid Land Resources and Environment, 30(4), 190–196.
- Wang, Y. M., Chin, K. S., & Jiang, P. (2011). Weight determination in the cross-efficiency evaluation. Computers & Industrial Engineering, 61(3), 497–502. https://doi.org/10.1016/j.cie.2011.04.004
- Wang, Y. M., & Wang, S. (2013). Approaches to determining the relative importance weights for cross-efficiency aggregation in data envelopment analysis. Journal of the Operational Research Society, 64(1), 60–69. https://doi.org/10.1057/jors.2012.43
- Wu, J., Liang, L., & Zha, Y. C. (2008). Determination of the weights of ultimate cross efficiency based on the solution of nucleolus in cooperative game. Systems Engineering - Theory & Practice, 28(5), 92–97. https://doi.org/10.1016/S1874-8651(09)60023-5
10.1016/S1874-8651(09)60023-5 Google Scholar
- Wu, J., Sun, J., & Liang, L. (2011). Determination of weights for ultimate cross efficiency using Shannon entropy. Expert Systems with Applications, 38(5), 5162–5165. https://doi.org/10.1016/j.eswa.2010.10.046
- Wu, J., Yu, Y., Zhu, Q., An, Q. X., & Liang, L. (2018). Closest target for the orientation-free context-dependent DEA under variable returns to scale. Journal of the Operational Research Society, 69(11), 1819–1833. https://doi.org/10.1080/01605682.2017.1409865
- Wu, X. H., Chen, S. S., & Guo, J. (2018). Effect of air pollution on the stock yield of heavy pollution enterprises in China's key control cities. Journal of Cleaner Production, 170(1), 399–406. https://doi.org/10.1016/j.jclepro.2017.09.154
- Wu, X. H., Chen, Y. F., & Guo, J. (2016). Spatial concentration, impact factors and prevention-control measures of PM2.5 pollution in China. Natural Hazards, 86, 1–18.
- Wu, X. H., Tan, L., Guo, J., & Zhu, W. W. (2016). A study of allocative efficiency of air pollutant emission rights based on a zero sum gains data envelopment model: Taking PM2.5 as an example. Journal of Cleaner Production, 113, 1024–1031. https://doi.org/10.1016/j.jclepro.2015.11.025
- Xu, J., Chang, L., & Qu, Y. (2016). The meteorological modulation on PM2.5 interannual oscillation during 2013 to 2015 in Shanghai, China. Science of the Total Environment, 572, 1138–1149. https://doi.org/10.1016/j.scitotenv.2016.08.024
- Xue, W. B., Fei, F. U., Wang, J. N., Ke-Bi, H. E., Yu, L., & Yang, J. T. (2014). Modeling study on atmospheric environmental capacity of major pollutants constrained by PM2.5 compliance of Chinese cities. China Environmental Science, 34(10), 2490–2496.
- Yang, F., Xia, Q., & Liang, L. (2011). DEA cross efficiency evaluation method for competitive and cooperative decision making units. Systems Engineering - Theory & Practice, 31(1), 92–98.
- Yang, H., Chen, J., & Wen, J. (2016). Composition and sources of PM2.5, around the heating periods of 2013 and 2014 in Beijing: Implications for efficient mitigation measures. Atmospheric Environment, 124, 378–386. https://doi.org/10.1016/j.atmosenv.2015.05.015
- Yang, S., Ma, Y. L., & Duan, F. K. (2017). Characteristics and formation of typical winter haze in Handan, one of the most polluted cities in China. Science of the Total Environment, 613, 1367–1375.
- You, W., & Lv, Z. (2018). Spillover effects of economic globalization on CO2 emissions: A spatial panel approach. Energy Economics, 73, 248–257. https://doi.org/10.1016/j.eneco.2018.05.016
- Zhang, R., Jing, J., & Tao, J. (2013). Chemical characterization and source apportionment of PM2.5 in Beijing: Seasonal perspective. Atmospheric Chemistry and Physics, 13(14), 7053–7074. https://doi.org/10.5194/acp-13-7053-2013
- Zhang, Y., Huang, W., & Cai, T. (2016). Concentrations and chemical compositions of fine particles (PM2.5) during haze and non-haze days in Beijing. Atmospheric Research, 174-175, 62–69. https://doi.org/10.1016/j.atmosres.2016.02.003
- Zhang, Y., Huang, W., Cai, T., Fang, D. Q., Wang, Y. Q., Song, J., … Zhang, Y. X. (2016). Concentrations and chemical compositions of fine particles (PM2.5) during haze and non-haze days in Beijing. Atmospheric Research, 174, 62–69.
- Zheng, C. D., & Liu, S. (2011). Empirical research of carbon emission and economic growth in China based on the spatial econometric analysis. China Population, Resources and Environment, 21(5), 80–86.
- Zheng, P. N., Chen, H. B., Chen, X. G., Zhou, D. B., & Zhang, B. Y. (2007). Study on distribution of reduction targets based on DEA model. Chinese Journal of Environmental Engineering, 11(1), 133–139.
- Zhou, P., Ang, B. W., & Han, J. Y. (2010). Total-factor carbon emission performance: A Malmquist index analysis. Energy Economics, 32(1), 194–201. https://doi.org/10.1016/j.eneco.2009.10.003
