A systematic decision support system to objectively evaluate retrospective energy efficiency modelling options
DOI:
https://doi.org/10.17159/2413-3051/2019/v30i2a5740Keywords:
Energy efficiency, tax incentives, baseline models, decision support methods, eAbstract
Tax incentives are one of the methods used by the South African government to incentivise energy efficiency. One of these incentives is Section 12L of the Income Tax Act (1962), which allows a significant tax deduction as a result of quantifiable energy efficiency (EE) savings. The associated EE savings are calculated by means of baseline models and must be in accordance with the national standard for measurement and verification, i.e. SANS 50010, which is based on international practice. The present study developed a methodology that assists EE projects with incentive applications to objectively evaluate potential modelling options and ultimately select a final model. This methodology is based on the weighted sum method. It is verified by applying it to three actual case studies and is further validated by comparing the results obtained from the case studies to independent results of formal and successful incentive applications. The methodology allows for a transparent selection of a modelling option that is compliant with the relevant tax incentive regulatory requirements and untainted by personal bias.
Downloads
References
Azar, F.S. 2000. Multiattribute Decision-Making: Use of Three Scoring Methods to Compare the Performance of Imaging Techniques for Breast Cancer Detection, Philadelphia: University of Pennsylvania.
Bergh, C. 2012. Energy Efficiency in the South African Crude Oil Refining Industry: Drivers, Barriers and Opportunities, University of Cape Town, South Africa: Masters Dissertation.
Booysen, W. 2014. Measurement and verification of industrial DSM projects, South Africa: PhD Thesis, North-West University.
Botes, L. 2017. Objective evaluation of industrial energy efficiency models for the RSA Section 12L tax incentive, South Africa: Masters Dissertation, North-West University.
Bruen, M. 2008. Systems Analysis - A new paradigm and decision support tools for the water framework directive. Hydrology and Earth System Sciences, 12(2008): 739-749. https://doi.org/10.5194/hess-12-739-2008
Campbell, K. 2016. Evaluating the feasibility of the 12L tax incentive for energy intensive industries, South Africa: Masters Dissertation, North-West University.
Campbell, K., Booysen, W. and Vosloo, J. 2017. Evaluating the feasibility of the 12L tax incentive for energy-intensive industries. South African Journal of Industrial Engineering, 28(3): 15-28. https://doi.org/10.7166/28-3-1836
Carrico, N.J.G, Goncalves, F.V., Covas, D.I.C., do Céu Almeida, M. and Alegre, H. 2014. Multi-criteria analysis for the selection of the best energy efficient option in urban water systems. Procedia Engineering, 7(2014): 292-301. https://doi.org/10.1016/j.proeng.2014.02.033
Chedid, R.B. and Ghajar, R.F., 2004. Assessment of energy efficiency options in the building sector of Lebanon. Energy Policy, 32(2004): 647-655. https://doi.org/10.1016/S0301-4215(02)00328-2
Department of Energy, 2010. Digest of South African Energy Statistics 2009, Pretoria: Energy Information Management, Process Design and Publications. Available online at http://www.energy.gov.za
Du Toit, E.F. 2011. Energy efficiency savings allowance in South Africa: An international comparison, South Africa: Masters Dissertation, University of Pretoria.
Eastman, J.R. 1999. Multi-criteria evaluation and GIS. In: Geographical information systems. New York: Wiley, 493-502.
Efficiency Valuation Organisation, 2012. International Performance Measurement & Verification Protocol: Concepts and options for determining energy and water savings, Oak Ridge: U.S. Department of Energy.
Gous, A.G.S., Booysen, W. and Hamer, W. 2016. Data quality evaluation for measurement and verification processes. Cape Town, IEEE.
GreenCape, 2015. 12L Income Tax Allowance on Energy Efficiency Savings, Cape Town: GreenCape. Available online at https://greencape.co.za
Grobler, L.J. 2010. International M&V benchmarks and Eskom practices, South Africa: North-West University.
Hamer, W. 2016. A practical approach to quantify RSA Section 12L EE tax incentives for large industry, South Africa: PhD Thesis, North-West University.
Hamer, W., Booysen, W. and Mathews, E. 2017. A practical approach to managing uncertainty in the measurement and verification of energy efficiency savings. South African Journal of Industrial Engineering, 28(3): 128-146. https://doi.org/10.7166/28-3-1850
Hancerliogullari, G., Hancerliogullari, K.O. and Koksalmis, E. 2017. The use of multi-criteria decision making models in evaluating anasthesia method options in circumcision surgery. BMC Medical Informatics and Decision Making, 17: 14. https://doi.org/10.1186/s12911-017-0409-5
Hasanbeigi, A., Morrow, W., Sathaye, J., Masanet, E. and Xu, T. 2012. Assessment of Energy Efficiency Improvement and CO2 Emission Reduction Potentials in the Iron and Steel Industry in China, Berkeley: Lawrence Berkeley National Laboratory.
Janse van Rensburg, H. M., 2015. Structuring mining data for RSA Section 12L EE tax incentives, South Africa: Masters Dissertation, North-West University.
Kolios, A., Mytilinou, V., Lozano-Minguez, E. and Salonitis, K. 2016. A Comparative Study of Multiple-Criteria Decision-Making Methods under Stochastic Inputs. Energies, 9(566): 21. https://doi.org/10.3390/en9070566
Kolios, A. and Read, G. 2013. A political, economic, social, technology, legal and environmental (PESTLE) Approach for risk identification of the tidal industry in the United Kingdom. Energies, 6(2013): 5023-5045. https://doi.org/10.3390/en6105023
Mateo, J.R.S.C. 2012. Multi Criteria Analysis in the Renewable Energy Industry. Berlin, Germany: Springer Science & Business Media.
Morrow, W.R., Hasanbeigi, A., Sathaye, J. and Xu, T. 2013. Assessment of Energy Efficiency Improvement and CO2 Emission Reduction Potentials in India's Iron and Steel Industry, Berkeley: Lawrence Berkeley National Laboratory.
Nagappan, N. and Ball, T. 2005. Use of relative code churn measures to predict system defect density. St. Louis, IEEE. https://doi.org/10.1109/ICSE.2005.1553571
National Treasury, 2013. Regulations in terms of section 12L of the income tax act, 1962, on the allowance for energy efficiency savings, Pretoria: Government Gazette, No. 37136, Government Printing Works. Available online at http://www.energy.gov.za.
Ogryczak, W. 2007. Multicriteria models for fair resource allocation. Control and Cybernetics, 36(2007): 303-32.
Republic of South Africa, 2013. Section 12L of the Income Tax Act (no. 58 of 1962) Deduction in respect of energy efficiency savings, Pretoria: Government Gazette, No. 37019, Government Printing Works. Available online at https://www.greengazette.co.za.
Republic of South Africa, 2015. Taxation Laws Amendment Bill No 29 of 2015, Cape Town: Creda Communications. Available online at http://www.treasury.gov.za.
Saaty, T.L. 2008. Decision making with the analytical hierarchy process. International Journal Services Science, 1(1): 83-98. https://doi.org/10.1504/IJSSCI.2008.017590
SABS, 2017. SANS 50010:2017 Measurement and Verification of energy savings, Pretoria: South African Bureau of Standards.
SANEDI, 2016. Section 12L of the Income Tax Act National Road Show. Midrand, SANEDI. Available online at https://www.sanedi.org.za/12L.html.
Schutte, A.J. 2013. An integrated energy-efficiency strategy for deep-mine ventilation and refrigeration, North-West University, South Africa: Masters Dissertation.
Talbot, D. and Boiral, O. 2013. Can we trust corporates GHG inventories? An investigation among Canada's large final emitters. Energy Policy, 63(2013): 1075-1085. https://doi.org/10.1016/j.enpol.2013.09.054
Triantaphyllou, E. 2013. Multi-criteria Decision Making Methods: A Comparative Study. Berlin, Germany: Springer Science & Business Media.
Trotta, G., 2018. Factors affecting energy-saving behaviours and energy efficiency investments in British households. Energy Policy, 114(2018): 529-539. https://doi.org/10.1016/j.enpol.2017.12.042
Tzeng, G.H., Lin, C.W. and Opricovic, S. 2005. Multi-criteria analysis of alternative-fuel buses for public transportation. Energy Policy, 33(2005): 1373-1383. https://doi.org/10.1016/j.enpol.2003.12.014
U.S. Department of Energy, 2008. M&V Guidelines: Measurement and verification for federal energy projects, Washington, DC: US DOE Federal Energy Management Program.
Volkart, K., Weidmann, N., Bauer, C. and Hirschberg, S. 2017. Multi-criteria decision analysis of energy system transformation pathways: A case study for Switzerland. Energy Policy, 106(2017): 155-168. https://doi.org/10.1016/j.enpol.2017.03.026
Worrel, E. and Galitsky, C. 2005. Energy Efficiency Improvement and Cost Saving Opportunities For Petroleum Refineries, Berkeley: Lawrence Berkeley National Laboratory.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2019 L.A Botes, W. Booysen, M.J. Mathews, M. Kleingeld
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
.png){kind=spacer}