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Optimizing Efficiency of Energy-Saving Service Industry Based on SE-SBM Model

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Proceedings of the 27th International Symposium on Advancement of Construction Management and Real Estate (CRIOCM 2022)

Part of the book series: Lecture Notes in Operations Research ((LNOR))

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Abstract

China actively responds to climate change and promotes green and low-carbon economic development. The energy-saving service companies (ESCOs) are key forces in energy-saving emission reduction, which provides services such as energy-consumption diagnosis, design, financing, transformation, and operation management of energy-saving projects, especially construction projects. This study adopts a data envelopment analysis (DEA) model to calculate the optimal scale of the number of employees and investment of ESCOs that effectively influences the energy-saving efficiency of energy-saving service industry. The study found that the optimal scale of employees and investment of ESCOs under the average production level is 140 people and $3.6 million by comparing the super-efficiency value of the top three years (i.e., the years 2006, 2020, and 2007). Some policy suggestions are proposed to develop the energy-saving service industry.

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6. References

  1. Zheng, S., He, C., Hsu, S.C., Sarkis, J., Chen, J.H.: Corporate environmental performance prediction in China: an empirical study of energy service companies. J. Clean. Prod. 266, 121395 (2020). https://doi.org/10.1016/j.jclepro.2020.121395

    Article  Google Scholar 

  2. Y. Li, S. Lan, M. Ryberg, J. Pérez-Ramírez, X. Wang, A quantitative roadmap for China towards carbon neutrality in 2060 using methanol and ammonia as energy carriers. IScience 24 (2021). https://doi.org/10.1016/j.isci.2021.102513

  3. Shi, X., et al.: Air quality benefits of achieving carbon neutrality in China. Sci. Total Environ. 795, 148784 (2021). https://doi.org/10.1016/j.scitotenv.2021.148784

    Article  Google Scholar 

  4. Zheng, S., Wang, R., Mak, T.M.W., Hsu, S.C., Tsang, D.C.W.: How energy service companies moderate the impact of industrialization and urbanization on carbon emissions in China? Sci. Total Environ. 751, 141610 (2021). https://doi.org/10.1016/j.scitotenv.2020.141610

    Article  Google Scholar 

  5. Hannon, M.J., Foxon, T.J., Gale, W.F.: The co-evolutionary relationship between energy service companies and the UK energy system: Implications for a low-carbon transition. Energy Policy 61, 1031–1045 (2013). https://doi.org/10.1016/j.enpol.2013.06.009

    Article  Google Scholar 

  6. Li, J., Colombier, M.: Managing carbon emissions in China through building energy efficiency. J. Environ. Manage. 90, 2436–2447 (2009). https://doi.org/10.1016/j.jenvman.2008.12.015

    Article  Google Scholar 

  7. Limaye, D.R., Limaye, E.S.: Scaling up energy efficiency: the case for a Super ESCO. Energ. Effi. 4, 133–144 (2011). https://doi.org/10.1007/s12053-011-9119-5

    Article  Google Scholar 

  8. Serrasqueiro, Z.S., MaçÃs Nunes, P.: Performance and size: Empirical evidence from Portuguese SMEs. Small Bus. Econ. 31 (2008) 195–217. https://doi.org/10.1007/s11187-007-9092-8

  9. Izadikhah, M., Farzipoor Saen, R.: A new data envelopment analysis method for ranking decision making units: an application in industrial parks. Expert Syst. 32, 596–608 (2015). https://doi.org/10.1111/exsy.12112

  10. Vine, E.: An international survey of the energy service company ESCO industry. Energy Policy 33, 691–704 (2005). https://doi.org/10.1016/j.enpol.2003.09.014

    Article  Google Scholar 

  11. Larsen, P.H., Goldman, C.A., Satchwell, A.: Evolution of the U.S. energy service company industry: market size and project performance from 1990–2008. Energy Policy 50, 802–820 (2012). https://doi.org/10.1016/j.enpol.2012.08.035

  12. Stuart, E., Larsen, P.H., Goldman, C.A., Gilligan, D.: A method to estimate the size and remaining market potential of the U.S. ESCO (energy service company) industry. Energy 77, 362–371 (2014). https://doi.org/10.1016/j.energy.2014.09.003

  13. Marino, A., Bertoldi, P., Rezessy, S., Boza-Kiss, B.: A snapshot of the European energy service market in 2010 and policy recommendations to foster a further market development. Energy Policy 39, 6190–6198 (2011). https://doi.org/10.1016/j.enpol.2011.07.019

    Article  Google Scholar 

  14. Bertoldi, P., Boza-Kiss, B.: Analysis of barriers and drivers for the development of the ESCO markets in Europe. Energy Policy 107, 345–355 (2017). https://doi.org/10.1016/j.enpol.2017.04.023

    Article  Google Scholar 

  15. Zheng, S., Alvarado, V., Xu, P., Leu, S.Y., Hsu, S.C.: Exploring spatial patterns of carbon dioxide emission abatement via energy service companies in China. Resour. Conserv. Recycl. 137, 145–155 (2018). https://doi.org/10.1016/j.resconrec.2018.06.004

    Article  Google Scholar 

  16. Kostka, G., Shin, K.: Energy conservation through energy service companies: empirical analysis from China. Energy Policy 52, 748–759 (2013). https://doi.org/10.1016/j.enpol.2012.10.034

    Article  Google Scholar 

  17. Liu, G., Zheng, S., Xu, P., Zhuang, T.: An ANP-SWOT approach for ESCOs industry strategies in Chinese building sectors. Renew. Sustain. Energy Rev. 93, 90–99 (2018). https://doi.org/10.1016/j.rser.2018.03.090

    Article  Google Scholar 

  18. Yang, G., Wenbin, L.: Analysis of returns to scale of the biological institutes based on DEA method. Sci. Res. Manage. 36, 105–111 (2015). https://doi.org/10.19571/j.cnki.1000-2995.2015.01.013

  19. Madlener, R., Antunes, C.H., Dias, L.C.: Assessing the performance of biogas plants with multi-criteria and data envelopment analysis. Eur. J. Oper. Res. 197, 1084–1094 (2009). https://doi.org/10.1016/j.ejor.2007.12.051

    Article  Google Scholar 

  20. Carvalho, P., Marques, R.C.: Computing economies of vertical integration, economies of scope and economies of scale using partial frontier nonparametric methods. Eur. J. Oper. Res. 234, 292–307 (2014). https://doi.org/10.1016/j.ejor.2013.09.022

    Article  Google Scholar 

  21. Banker, R.D., Charnes, A., Cooper, W.W.: Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage. Sci. 30, 1078–1092 (1984). https://doi.org/10.1287/mnsc.30.9.1078

    Article  Google Scholar 

  22. Lee, G., Yu, M.M., Wang, L.C.: DEA-based integrated relationship of returns to scale - an application to road maintenance in Taiwan. J. Civ. Eng. Manag. 18, 709–723 (2012). https://doi.org/10.3846/13923730.2012.723394

    Article  Google Scholar 

  23. Hung, S.W., Lu, W.M., Wang, T.P.: Benchmarking the operating efficiency of Asia container ports. Eur. J. Oper. Res. 203, 706–713 (2010). https://doi.org/10.1016/j.ejor.2009.09.005

    Article  Google Scholar 

  24. Lozano, S., Gutiérrez, E.: Efficiency analysis and target setting of Spanish airports, networks and spatial. Economics 11, 139–157 (2011). https://doi.org/10.1007/s11067-008-9096-1

    Article  Google Scholar 

  25. Sueyoshi, T., Goto, M.: Measurement of returns to scale and damages to scale for DEA-based operational and environmental assessment: How to manage desirable (good) and undesirable (bad) outputs? Eur. J. Oper. Res. 211, 76–89 (2011). https://doi.org/10.1016/j.ejor.2010.11.013

    Article  Google Scholar 

  26. Chan, X., Yiyuan, M., **aobin, H., Renqiao, X.: A study on operational efficiency of hi-tech startups in China based on DEA methods. J. Manage. Sci. 27, 26–37 (2014)

    Google Scholar 

  27. Shabanpour, H., Yousefi, S., Saen, R.F.: Forecasting efficiency of green suppliers by dynamic data envelopment analysis and artificial neural networks. J. Clean. Prod. 142, 1098–1107 (2017). https://doi.org/10.1016/j.jclepro.2016.08.147

    Article  Google Scholar 

  28. Lo, S.F., Lu, W.M.: An integrated performance evaluation of financial holding companies in Taiwan. Eur. J. Oper. Res. 198, 341–350 (2009). https://doi.org/10.1016/j.ejor.2008.09.006

    Article  Google Scholar 

  29. Athanassopoulos, A.D., Curram, S.P.: A comparison of data envelopment analysis and artificial neural networks as tools for assessing the efficiency of decision making units. J. Oper. Res. Soc. 47, 1000–1016 (1996). https://www.jstor

  30. Kwon, H.B.: Exploring the predictive potential of artificial neural networks in conjunction with DEA in railroad performance modeling. Int. J. Prod. Econ. 183, 159–170 (2017). https://doi.org/10.1016/j.ijpe.2016.10.022

    Article  Google Scholar 

  31. Emrouznejad, A., Shale, E.: A combined neural network and DEA for measuring efficiency of large scale datasets. Comput. Ind. Eng. 56, 249–254 (2009). https://doi.org/10.1016/j.cie.2008.05.012

    Article  Google Scholar 

  32. Wu, D.: Supplier selection: a hybrid model using DEA, decision tree and neural network. Expert Syst. Appl. 36, 9105–9112 (2009). https://doi.org/10.1016/j.eswa.2008.12.039

    Article  Google Scholar 

  33. Aldamak, A., Zolfaghari, S.: Review of efficiency ranking methods in data envelopment analysis. Meas.: J. Int. Meas. Confed. 106, 161–172 (2017). https://doi.org/10.1016/j.measurement.2017.04.028

  34. Huang, Y., Liu, S.: Efficiency evaluation of a sustainable hydrogen production scheme based on super efficiency SBM model. J. Clean. Prod. 256 (2020). https://doi.org/10.1016/j.jclepro.2020.120447

  35. Cooper, W.W., Seiford, L.M., Tone, K.: Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software: Second edition, 2007. https://doi.org/10.1007/978-0-387-45283-8

  36. Tone, K.: Slacks-Based measure of efficiency. Eur. J. Oper. Res. 130, 498–509 (2001). https://doi.org/10.1007/978-1-4419-6151-8_8

    Article  Google Scholar 

  37. Du, J., Liang, L., Zhu, J.: A slacks-based measure of super-efficiency in data envelopment analysis: a comment. Eur. J. Oper. Res. 204, 694–697 (2010). https://doi.org/10.1016/j.ejor.2009.12.007

    Article  Google Scholar 

  38. Guancen, W.U., Hao, Y., Dan, C.: Efficiency and comparative analysis of compact development in cities with undesirable output, China soft. Science 2, 79–89 (2021)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Building and Real Estate, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China

    Hui He & Albert P. C. Chan

  2. School of Economics and Management, Tongji University, 1500 Si** Road, Shanghai, 200092, China

    Hui He & Qinghua He

Authors
  1. Hui He
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  2. Albert P. C. Chan
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  3. Qinghua He
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Corresponding author

Correspondence to Hui He .

Editor information

Editors and Affiliations

  1. Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China

    **g Li

  2. Department of Real Estate and Construction, University of Hong Kong, Hong Kong, Hong Kong

    Weisheng Lu

  3. Department of Emergency Management, Nan**g University, Nan**g, China

    Yi Peng

  4. School of Management, Guangzhou University, Guangzhou, Guangdong, China

    Hong** Yuan

  5. Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China

    Daikun Wang

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He, H., Chan, A.P.C., He, Q. (2023). Optimizing Efficiency of Energy-Saving Service Industry Based on SE-SBM Model. In: Li, J., Lu, W., Peng, Y., Yuan, H., Wang, D. (eds) Proceedings of the 27th International Symposium on Advancement of Construction Management and Real Estate. CRIOCM 2022. Lecture Notes in Operations Research. Springer, Singapore. https://doi.org/10.1007/978-981-99-3626-7_28

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