journal article·2018-09-13Open access

Potential Benefit of Carbon Benchmarking: Analysis of Semiconductor Industry in Taiwan

Je-Liang Liou, Pei-Ing Wu, Ching-Ren Chiu

Applied Sciences· Volume 8· Issue 9

Abstract

The main purpose of this study is to explore the carbon reduction environmental benefits that can be achieved if carbon benchmarking is applied when semiconductor manufacturers in Taiwan work to improve the technical efficiency of their carbon reduction efforts. The evaluation method used is as follows. First, a technical efficiency measurement method that is capable of considering both desirable outputs and undesirable outputs is used to measure the technical efficiency of the carbon reduction efforts and identify the benchmark firms with the best technical efficiency. Next, an attempt is made to estimate the greenhouse gas reduction that is realized by the sample if their carbon reduction efforts are accompanied by the implementation of a benchmarking system. Finally, the monetary value of the greenhouse gas reduction is estimated so as to develop a better understanding of the carbon reduction benefits for the adoption of the process outlined above. The empirical results show that using carbon benchmarking to raise the technical efficiency of carbon reduction management would, on average, boost the annual sales revenue of each decision-making unit by about US$486.6 million, while also bringing up the average annual CO2 reduction benefits per decision-making unit by US$11.11 million per year.
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Research context

OpenAlex topics: Environmental Impact and Sustainability · Energy, Environment, Economic Growth · Climate Change Policy and Economics

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

The Center for Green Economy, Chung-Hua Institution for Economic Research, No. 75 Chang-Hsing Street, Taipei 10672, Taiwan

Department of Agricultural Economics, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan

Department of Recreation and Sport Management, University of Taipei, No.101, Section 2, Zhongcheng Road, Shilin District, Taipei 11153, Taiwan

Funding

Ministry of Science and Technology, Taiwan

References (36)

Show source-provided references
1. United Nations (2018, July 15). Guidance to Assist Developing Country Parties to Assess the Impact of the Implementation of Response Measures, Including Guidance on Modelling Tools. Available online: https://unfccc.int/sites/default/files/resource/docs/2016/tp/04.pdf.
2. Productivity and undesirable outputs: A directional distance function approach10.1006/jema.1997.0146 ↗
3. Accounting for air pollution emissions in measures of state manufacturing productivity growth10.1111/0022-4146.00223 ↗
4. Measuring ecological efficiency with data envelopment analysis (DEA)10.1016/S0377-2217(00)00154-5 ↗
5. Modeling undesirable factors in efficiency evaluation10.1016/S0377-2217(01)00293-4 ↗
6. Characteristics of a polluting technology theory and practice10.1016/j.jeconom.2004.05.010 ↗
7. Yang, H., and Pollitt, M. (2007). Distinguishing Weak and Strong Disposability among Undesirable Outputs in DEA: The Example of the Environmental Efficiency of Chinese Coal-Fired Power Plants, Electricity Policy Research Group, University of Cambridge. Cambridge Working Papers in Economics No. 0741.
8. A survey of data envelopment analysis in energy and environmental studies10.1016/j.ejor.2007.04.042 ↗
9. Directional distance functions revisited: Selective overview and update
10. Optimal directions for directional distance functions: An exploration of potential reductions of greenhouse gases10.1093/ajae/aau035 ↗
11. On modeling pollution-generating technologies10.1016/j.jeem.2012.02.005 ↗
12. Bayesian measurement of productivity and efficiency in the presence of undesirable outputs: Crediting electric utilities for reducing air pollution10.1016/j.jeconom.2004.05.009 ↗
13. Plant level productivity, efficiency, and environmental performance of the container glass industry10.1023/A:1020236517937 ↗
14. Estimation of the shadow prices of pollutants with production-environment inefficiency taken into account a nonparametric directional distance function approach10.1006/jema.2001.0480 ↗
15. Productivity differences across OECD countries in the presence of environmental constraints10.1057/palgrave.jors.2601942 ↗
16. Directional distance functions and environmental regulation10.1016/j.reseneeco.2004.07.001 ↗
17. Shadow prices and pollution costs in U.S. agriculture10.1016/j.ecolecon.2004.12.022 ↗
18. Environmental sensitive productivity growth: A global analysis using Malmquist-Luenberger index10.1016/j.ecolecon.2005.02.004 ↗
19. Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach10.1016/j.ejor.2012.04.022 ↗
20. Measuring the environmental efficiency of countries: A directional distance function metafrontier approach10.1016/j.jenvman.2013.01.015 ↗
21. Measuring total-factor CO2 emission performance and technology gaps using a non-radial directional distance function: A modified approach10.1016/j.eneco.2016.04.005 ↗
22. Wang, K., Xian, Y., Lee, C.-Y., Wei, Y.-M., and Huang, Z. (2017). On selecting directions for directional distance functions in a non-parametric framework: A review. Ann. Oper. Res., 1–34.10.1007/s10479-017-2423-5 ↗
23. Xing, Z., Wang, J., and Zhang, J. (2017). CO2 Emission performance, mitigation potential, and marginal abatement cost of industries covered in China’s nationwide emission trading scheme: A meta-frontier analysis. Sustainability, 9.10.3390/su9060932 ↗
24. Statistical inference for DEA estimators of directional distances10.1016/j.ejor.2012.02.030 ↗
25. Judicial productivity, delay and efficiency: A directional distance function (DDF) approach10.1016/j.ejor.2014.07.014 ↗
26. Is there a difference between the energy and CO2 emission performance for China’s thermal power industry? A bootstrapped directional distance function approach10.1016/j.apenergy.2015.02.066 ↗
27. Profit efficiency of banks in Colombia with undesirable output: A directional distance function approach10.5018/economics-ejournal.ja.2018-30 ↗
28. Multilateral productivity comparisons when some outputs are undesirable: A nonparametric approach10.2307/1928055 ↗
29. Inference by the m out of n bootstrap in nonparametric frontier models10.1007/s11123-010-0200-4 ↗
30. Interagency Working Group on Social Cost of Carbon, United States Government (2018, July 15). Technical Support Document: Social Cost of Carbon for Regulatory Impact Analysis—Under Executive Order 12866, Available online: https://www.epa.gov/sites/production/files/2016-12/documents/scc_tsd_2010.pdf.
31. United States Environmental Protection Agency (2018, July 15). EPA Fact Sheet: Social Cost of Carbon, Available online: https://www.arb.ca.gov/regact/2016/capandtrade16/attach11.pdf.
32. Critical issues for the calculation of the social cost of CO2: Why the estimates from PAGE09 are higher than those from PAGE200210.1007/s10584-012-0633-z ↗
33. Nordhaus, W., and Sztorc, P. (2018, July 15). DICE 2013R: Introduction and User’s Manual. Available online: http://www.econ.yale.edu/~nordhaus/homepage/homepage/documents/DICE_Manual_100413r1.pdf.
34. Anthoff, D., and Tol, D.S.J. (2018, July 15). FUND—Climate Framework for Uncertainty, Negotiation and Distribution. Available online: http://www.fund-model.org/publications.
35. Paul, W. (2018, July 15). The Social Cost of Carbon. Available online: http://www.oecd.org/env/cc/37321411.pdf.
36. (2018, July 15). Market Observation Post System (MOPS). Available online: http://emops.twse.com.tw/server-java/t58query.