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Data envelopment analysis cross-like efficiency model for non-homogeneous decision-making units: The case of United States companies’ low-carbon investment to attain corporate sustainability

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  • Zhu, Weiwei
  • Yu, Yu
  • Sun, Panpan

Abstract

Traditional data envelopment analysis (DEA) models assume directly comparability among decision-making units (DMUs). However, this assumption is not necessarily applied in practice; DMUs do not always utilize similar inputs to produce similar outputs. Sometimes, a DMU may not choose to produce a certain output (e.g., a steel plant may not produce tool steels) or cannot produce the certain output for some certain reasons. Meanwhile, DMUs may choose different resources to achieve the general productions or services (e.g., papermaker mills consume different raw materials to produce paper). How to compare a DMU to other units objectively becomes an issue when evaluating efficiency in the absence of homogeneity. In this study, a cross-like efficiency model for the DEA of non-homogeneous DMUs is established to handle the aforementioned issue. The proposed method can even assign unique rankings to DMUs with missing outputs or inputs. Furthermore, prior information on the appropriate bound for the share of resources is not needed. The proposed model is applied to an existing data set used in previous studies. 39 companies on S&P 500 corporations in 2013 are also studied with this model. It is confirmed that investors focus on the company's green thoughts and long-term sustainability by the empirical investigation. Furthermore, this study shows that the Information Technology sector has the highest low-carbon investment performance among the nine sectors investigated. The measurement of low-carbon technology investment can be an available benchmark for a specific industry to attain corporate sustainability.

Suggested Citation

  • Zhu, Weiwei & Yu, Yu & Sun, Panpan, 2018. "Data envelopment analysis cross-like efficiency model for non-homogeneous decision-making units: The case of United States companies’ low-carbon investment to attain corporate sustainability," European Journal of Operational Research, Elsevier, vol. 269(1), pages 99-110.
    • Handle: RePEc:eee:ejores:v:269:y:2018:i:1:p:99-110
    DOI: 10.1016/j.ejor.2017.08.007
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    References listed on IDEAS

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    1. Sueyoshi, Toshiyuki & Wang, Derek, 2014. "Radial and non-radial approaches for environmental assessment by Data Envelopment Analysis: Corporate sustainability and effective investment for technology innovation," Energy Economics, Elsevier, vol. 45(C), pages 537-551.
    2. Yu, Yu & Wang, Derek D. & Li, Shanling & Shi, Qinfen, 2016. "Assessment of U.S. firm-level climate change performance and strategy," Energy Policy, Elsevier, vol. 92(C), pages 432-443.
    3. Wu, Jie & Chu, Junfei & Sun, Jiasen & Zhu, Qingyuan, 2016. "DEA cross-efficiency evaluation based on Pareto improvement," European Journal of Operational Research, Elsevier, vol. 248(2), pages 571-579.
    4. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    5. Imanirad, Raha & Cook, Wade D. & Aviles-Sacoto, Sonia Valeria & Zhu, Joe, 2015. "Partial input to output impacts in DEA: The case of DMU-specific impacts," European Journal of Operational Research, Elsevier, vol. 244(3), pages 837-844.
    6. Wang Hong Li & Liang Liang & Sonia Valeria Avilés-Sacoto & Raha Imanirad & Wade D. Cook & Joe Zhu, 2017. "Modeling efficiency in the presence of multiple partial input to output processes," Annals of Operations Research, Springer, vol. 250(1), pages 235-248, March.
    7. Wang, Derek & Li, Shanling & Sueyoshi, Toshiyuki, 2014. "DEA environmental assessment on U.S. Industrial sectors: Investment for improvement in operational and environmental performance to attain corporate sustainability," Energy Economics, Elsevier, vol. 45(C), pages 254-267.
    8. Liang, Liang & Cook, Wade D. & Zhu, Joe, 2016. "DEA models for non-homogeneous DMUs with different input configurationsAuthor-Name: Li, WangHong," European Journal of Operational Research, Elsevier, vol. 254(3), pages 946-956.
    9. T Kuosmanen, 2009. "Data envelopment analysis with missing data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1767-1774, December.
    10. Haas, David A. & Murphy, Frederic H., 2003. "Compensating for non-homogeneity in decision-making units in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 144(3), pages 530-544, February.
    11. Wade D. Cook & Julie Harrison & Raha Imanirad & Paul Rouse & Joe Zhu, 2013. "Data Envelopment Analysis with Nonhomogeneous DMUs," Operations Research, INFORMS, vol. 61(3), pages 666-676, June.
    12. Tobias S. Schmidt, 2014. "Low-carbon investment risks and de-risking," Nature Climate Change, Nature, vol. 4(4), pages 237-239, April.
    13. Cook, Wade D. & Harrison, Julie & Rouse, Paul & Zhu, Joe, 2012. "Relative efficiency measurement: The problem of a missing output in a subset of decision making units," European Journal of Operational Research, Elsevier, vol. 220(1), pages 79-84.
    14. Sueyoshi, Toshiyuki & Wang, Derek, 2014. "Sustainability development for supply chain management in U.S. petroleum industry by DEA environmental assessment," Energy Economics, Elsevier, vol. 46(C), pages 360-374.
    15. Ya Chen & Yongjun Li & Huaqing Wu & Liang Liang, 2014. "Data Envelopment Analysis With Missing Data: A Multiple Linear Regression Analysis Approach," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 13(01), pages 137-153.
    16. C Kao & S-Tai Liu, 2000. "Data envelopment analysis with missing data: an application to University libraries in Taiwan," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(8), pages 897-905, August.
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    Cited by:

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    5. Cao, Ting & Cook, Wade D. & Kristal, M. Murat, 2022. "Has the technological investment been worth it? Assessing the aggregate efficiency of non-homogeneous bank holding companies in the digital age," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    6. Zeng, Ximei & Zhou, Zhongbao & Gong, Yeming & Liu, Wenbin, 2022. "A data envelopment analysis model integrated with portfolio theory for energy mix adjustment: Evidence in the power industry," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    7. Zhaojun Yang & Xiaoting Guo & Jun Sun & Yali Zhang, 2021. "Contextual and organizational factors in sustainable supply chain decision making: grey relational analysis and interpretative structural modeling," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(8), pages 12056-12076, August.
    8. Josef Jablonský, 2019. "Data Envelopment Analysis Models in Non-Homogeneous Environment," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 67(6), pages 1535-1540.
    9. Xiong, Xi & Yang, Guo-liang & Guan, Zhong-cheng, 2020. "A parallel DEA-based method for evaluating parallel independent subunits with heterogeneous outputs," Journal of Informetrics, Elsevier, vol. 14(3).

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