Innovation and environmental performance: An empirical study of 31 cities in China

Document Type : Article


1 School of Business, Sichuan University, Wangjiang Road No. 29, Chengdu, 610064, China, P.R.

2 Department of Economics, Soochow University, 56, Kueiyang St., Sec. 1, Taipei 100, Taiwan, R.O.C.


After its rapid economic growth, China is facing a very serious problem of atmospheric pollution with major long-term atmospheric problems appearing in large cities. Air pollution not only affects people’s normal lives, but also has a greater negative impact on their bodies, causing diseases, impacting productivity, and influencing people’s creativity. Due to past articles, the discussion on the efficiency of innovation and research has not been considered the impact of environmental variables. This study combines energy consumption, economics, environmental variables and innovative research and development capabilities to analyze and explore the relationship between consumption, environment, economy, and innovative R&D capabilities, this is the feature of this article.
This study employ the Dynamic Data Envelopment Analysis (DEA) model to calculate energy consumption efficiency, R&D input efficiency, innovation patent output efficiency, carbon dioxide emission efficiency, and AQI efficiency of each city and further compare each city to find their space for improvement.
The results of the study show that 10 cities have a total efficiency score of 1, implying the improvement space is already 0, whereas the total efficiency scores of the other 21 cities mean there is still much room for improvement, and there are big differences among the cities.


Main Subjects

1. Hu, J.L. andWang, S.C. "Total factor energy efficiency of regions in China", Energy Policy, 34(17), pp. 3206- 3217 (2006).
2. Kumar, S. "Environmentally sensitive productivity growth: A global analysis using Malmquist-Luenberger index", Ecological Economics, 56, pp. 280-293 (2006).
3. Gomes, E.G. and Lins, M.P.E. "Modeling undesirable outputs with zero sum gains data envelopment analysis models", Journal of the Operational Research Society, 59, pp. 616-623 (2007).
4. Zhou, P., Ang, B.W., and Poh, K.L. "Measuring environmental performance under different environmental DEA technologies", Energy Economics, 30(1), pp. 1- 14 (2008).
5. Yeh, T.-l., Chen, T.-y., and Lai, P.-y. "A comparative study of energy utilization efficiency between Taiwan and China", Energy Policy, 38, pp. 2386-2394 (2010).
6. Zhang, X.P., Cheng, X.M., Yuan, J.H., and Gao, X.J. "Total-factor energy efficiency in developing countries", Energy Policy, 39, pp. 644-650 (2011).
7. Choi, Y., Zhang, N., and Zhou, P. "Efficiency and abatement costs of energy-related CO2 emissions in China: A slacks-based efficiency measure", Applied Energy, 98, pp. 198-208 (2012).
8. Wang, K., Wei, Y.-M., and Zhang, X. "Energy and emissions efficiency patterns of Chinese regions: A multi-directional efficiency analysis", Applied Energy, 104, pp. 105-116 (2013).
9. Lin, B. and Yang, L. "Efficiency effect of changing investment structure on China's power industry", Renewable and Sustainable Energy Reviews, 39, pp. 403- 411 (2014).
10. Zhou, C., Chung, W., and Zhang, Y. "Measuring energy efficiency performance of China's transport sector: A data envelopment analysis approach", Expert System with Application, 41(2), pp. 709-722 (2014).
11. Pan, X., Liu. Q., and Peng, X. "Spatial club convergence of regional energy efficiency in China", Ecological Indicators, 51, pp. 25-30 (2015).
12. Wang, Z. and Feng, C. "A performance evaluation of the energy, environmental, and economic efficiency and productivity in China: an application of global data envelopment analysis", Applied. Energy, 147, pp. 617- 626 (2015).
13. Wu, J., Lin, L., Sun, J., and Ji, X. " A comprehensive analysis of China's regional energy saving and emission reduction efficiency : from production and treatment perspectives", Energy Policy, 84, pp. 166-178 (2015).
14. Liou, J.-L., Chiu, C.-R., Huang, F.-M., and Liu, W.-Y. "Analyzing the relationship between CO2 emission and economic efficiency by a relaxed two-stage DEA model", Aerosol and Air Quality Research, 15, pp. 694-701 (2015).
15. Meng, F., Su, B., Thomson, E., Zhou, D., and Zhou, P. "Measuring China's regional energy and carbon emission efficiency with DEA models: A survey", Applied Energy, 183, pp. 1-21 (2016).
16. Wang, Q., Chiu, Y.H., and Chiu, C.R. "Non-radial metafrontier approach to identify Carbon Emission Performance and intensity", Renewable and Sustainable Energy Reviews, 69, pp. 664-672 (2017).
17. Du, K., Xie, Ch., and Ouyang, X. "A comparison of carbon dioxide (CO2) emission trends among provinces in China", Renewable and Sustainable Energy Reviews, 73, pp. 19-35 (2017).
18. Feng, Ch., Zhang, H., and Huang, J.-B. "The approach to realizing the potential of emissions reduction in China: An implication from data envelopment analysis", Renewable and Sustainable Energy Reviews, 71, pp. 859-872 (2017).
19. Vardanyan, M. and Noh, D. "Approximating pollution abatement costs via alternative specifications of a multi-output production technology: a case of the US electric utility industry", Journal of Environmental Management, 80, pp. 177-190 (2006).
20. Rao, X., Wu, J., and Zhang, Z. "Energy efficiency and energy saving potential in China: an analysis based on slacks-based measure model", Computers and Industrial Engineering, 63(3), pp. 578-584 (2012).
21. Long, X., Oh, K., and Cheng, G. "Are stronger environmental regulations effective in practice? The case of China's accession to the WTO", Journal of Cleaner Production, 39, pp. 161-167 (2013).
22. Bi, G.B., Song, W., and Zhou, P. "Does environmental regulation affect energy efficiency in China's thermal power generation? Empirical evidence from a slacksbased DEA model", Energy Policy, 66, pp. 537-546 (2014).
23. Bian, Y., Liang, N., and Xu, H. "Efficiency evaluation of Chinese regional industrial systems with undesirable factors using a two-stage slacks-based measure approach", Journal of Cleaner Production, 87, pp. 348-356 (2015).
24. Wang, Q., Hang, Y., Sun, L., and Zhao, Z. "Twostage innovation efficiency of new energy enterprises in China: A non-radial DEA approach", Technological Forecasting and Social Change, 112, pp. 254-261 (2016).
25. Li, H., You, Sh., Zhang, H., Zhang, W., Zheng, X., Jia, J., Ye, T., and Zou, L. "Modelling of AQI related to building space heating energy demand based on big data analytics", Applied Energy, 203, pp. 57-71 (2017).
26. Li, H., You, Sh., Zhang, H., Zhang, W., and Zou, L. "Investigating the environmental quality deterioration and human health hazard caused by heating emissions", Science of the Total Environment, 629, pp. 1209-1222 (2018).
27. Xu, X., Gonzalez, J.E., Shen, Sh., Miao, Sh., and Dou, J. "Impacts of urbanization and air pollution on building energy demands - Beijing case study", Applied Energy, 225, pp. 98-105 (2018).
28. Tong, Zh., Chen, Y., Malkawi, A., Liu, Zh., and Freeman, R.B. "Energy saving potential of natural ventilation in China: The impact of ambient air pollution", Applied Energy, 179, pp. 660-668 (2016).
29. Bidokhti, A.A., Shariepourb , Z., and Sehatkashanic, S. "Some resilient aspects of urban areas to air pollution and climate change, case study: Tehran, Iran", Scientia Iranica, Transactions A, Civil Engineering, 23(5), 1994-2004 (2016).
30. Ahmadzadehtalatapeh, M. "Solar assisted desiccant evaporative cooling system for office buildings in Iran: An annual simulation model", Scientia Iranica, Transactions B, Mechanical Engineering, 25(1), pp. 280-298 (2018).
31. Liao, Zh., Gao, M., Sun, J., and Fan S, "The impact of synoptic circulation on air quality and pollutionrelated human health in the Yangtze River Delta region", Science of the Total Environment, 608, pp. 838-846 (2017).
32. Li, H., You, Sh., Zhang, H., Zhang, W., Lee, W.-L., Ye, T., and Zou, L. "Analyzing the impact of heating emissions on air quality index based on principal component regression", Journal of Cleaner Production, 171(10), pp. 1577-1592 (2018).
33. Khreis, H., Hooghf,, and Nieuwenhuijsen, M.J. "Full-chain health impact assessment of traffic-related air pollution and childhood asthma", Environment International, 114, pp. 365-375 (2018).
34. Maji, K., Dikshit, A.K., Arora, M., and Deshpande, A. "Estimating premature mortality attributable to PM2.5 exposure and benefit of air pollution control policies in China for 2020", Science of the Total Environment, 612, pp. 683-693 (2018).
35. Laitinen, E.K. "A dynamic performance measurement system: evidence from small Finnish technology companies", Scandinavian Journal of Management, 18, pp. 65-99 (2002).
36. Urpelainen, J. "Export orientation and domestic electricity generation: Effects on energy efficiency innovation in select sectors", Energy Policy, 39(9), pp. 5638- 5646 (2011).
37. Bai, J. and Li, J. "Regional innovation efficiency in China: The role of local government", Innovation: Management, Policy & Practice, 13(2), pp. 142-153 (2011).
38. Chen, K. and Guan, J. "Two-stage innovation efficiency of new energy enterprises in China: A nonradial DEA approach", Regional Studies, 46, pp. 341- 355 (2012).
39. Bai, J. "On regional innovation efficiency: Evidence from panel data of China's different provinces", Cambridge, 47(5), pp. 773-776 (2013).
40. Moon, H.S. "The relative efficiency analysis of innovation activities with uncertainty: The case of Korean electronic equipment industry", Innovation: Management, Policy & Practice, 15(3), pp. 305-314 (2013).
41. Chen, J. and Meng, L. "Research on technological innovation efficiency of China's high-tech industry based on network SBM model and DEA window analysis", Proceedings of 2013 4th International Asia Conference on Industrial Engineering and Management Innovation (IEMI2013), Springer Berlin Heidelberg (2014).
42. Chen, K. and Kou, M. "State of an innovation system: theoretical and empirical advance towards an innovation efficiency index", The Annals of Regional Science, 52(2), pp. 627-657 (2014).
43. Suh, Y. and Kim, M.S. "State of an innovation system: Theoretical and empirical advance towards an innovation efficiency index", International Journal of Services Technology and Management, 20(4-6), pp. 267-289 (2015).
44. Wan, L., Luo, B., Li, T., Wang, Sh., and Liang, L. "Effects of technological innovation on eco-efficiency of industrial enterprises in China", Nankai Business Review International, 6(3), pp. 226-239 (2015).
45. Kou, M., Chen, K., and Wang, S. "Measuring efficiencies of multi-period and multi-division systems associated with DEA: An application to OECD countries' national innovation systems", Expert Systems with Applications, 46, pp. 494-510 (2016).
46. Huang, D., Lu, D., and Luo, J.H. "Corporate innovation and innovation efficiency: does religion matter?", Nankai Business Review International, 7(2), pp. 150- 191 (2016).
47. Greco, M., Grimaldi, M., and Cricelli, L. "Hitting the nail on the head: Exploring the relationship between public subsidies and open innovation efficiency", Technological Forecasting and Social Change, 118, pp. 213- 225 (2017).
48. Chen, W. and Lei, Y. "The impacts of renewable energy and technological innovation on environmentenergy- growth nexus: New evidence from a panel quantile regression", Renewable Energy, 123, pp. 1- 14 (2018).
49. Tone, K. "Resampling in DEA", GRIPS Discussion Paper, National Graduate Institute for Policy Studies, pp. 13-23 (2013).
50. Tone, K. and Tsutsui, M. "Dynamic DEA: A slacksbased measure approach", Omega, 38, pp. 145-156 (2010).
51. Simar, L. and Wilson, P.W. "Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models", Management Science, 44(1), pp. 49- 61 (1998).
52. Simar, L. and Wilson, P.W. "A General methodology for bootstrapping in non-parametric frontier models", Journal of Applied Statistics, 27(6), pp. 779-802 (2000).
53. Tziogkidis, P. "Bootstrap DEA and hypothesis testing", Cardiff Economics Working Papers, No. E2012/18 (2012).
54. Charnes, A., Cooper, W.W., and Rhodes, E. "Measuring the efficiency of decision making units", European Journal of Operational Research, 2(6), pp. 429-444 (1978).
55. Banker, R.D., Charnes, A., and Cooper, W.W. "Some models for estimating technical and scale inefficiencies in data envelopment analysis", Management Science, 30(9), pp. 1078-1092 (1984).
56. Tone, K. "A slacks-based measure of efficiency in data envelopment analysis", European Journal of Operational Research, 130, pp. 498-509 (2001).