The International Journal of Life Cycle Assessment

, Volume 19, Issue 12, pp 1974–1984 | Cite as

Consequential cradle-to-gate carbon footprint of water treatment chemicals using simple and complex marginal technologies for electricity supply

  • Juan Pablo Alvarez-GaitanEmail author
  • Michael D. Short
  • Gregory M. Peters
  • Iain MacGill
  • Stephen Moore



Chemicals produced via chlor-alkali electrolysis are widely used throughout the water industry worldwide, with treatment chemicals often the second largest source of environmental impacts from potable water production after electricity use. Population-driven increases in the future demand for potable water will require concomitant increases in the production of water treatment chemicals, with the associated environmental impacts of chemicals production primarily arising from the additional demand for electricity. Due to the dominance of electricity in the environmental performance of chlor-alkali chemicals, assessment of the future environmental impacts of potable water production is largely dependent on proper identification of the marginal source of electricity. In this paper, we present a consequential cradle-to-gate carbon footprint (cCF) for the most widely used chlor-alkali-produced disinfectant (sodium hypochlorite (13 % w/w)) and coagulant (ferric chloride (42 % w/w)) in Australia, with special emphasis placed upon the identification of future marginal electricity supply and the substitution of hydrogen gas and sodium hydroxide during production. While this analysis is presented in an Australian context, commonalities in potable water and chlor-alkali chemical production processes internationally give the findings a broader relevance.


Consequential models for sodium hypochlorite (13 % w/w) and ferric chloride (42 % w/w) production were developed, and the identification of the marginal source of electricity was modelled using a “simple marginal technology” approach via operationalisation of the Weidema framework and a “complex marginal technology” using a partial equilibrium model. For the simple marginal technology, the levelised cost of electricity was used to select the most competitive energy generation technologies and those most relevant for the Australian market. For the complex marginal technology, the energy sector model was used to simulate the most likely electricity supply mix. Details of the different paths taken in the substitution of hydrogen gas and sodium hydroxide are also presented. To allow for proper incorporation of uncertainties arising from these key factors in the cCF, several scenarios were developed covering fuel and carbon prices for identifying the marginal supply mix of electricity, as well as the likely production routes for sodium carbonate in the context of sodium hydroxide substitution.

Results and discussion

cCF results of sodium hypochlorite (13 % w/w) and ferric chloride (42 % w/w) are presented using simple and complex marginal technologies, and the implications of choosing one marginal technology over the other in the context of water treatment chemicals are presented. For the simple marginal technology approach, the global warming potential (GWP) per megagram of chemical varied from 68 to 429 kg CO2-eq for sodium hypochlorite (13 % w/w) and 59–1,020 kg CO2-eq for ferric chloride (42 % w/w). For the complex marginal technology approach, the GWP per megagram of chemical varied from 266 to 332 kg CO2-eq for sodium hypochlorite (13 % w/w) and 214–629 kg CO2-eq for ferric chloride (42 % w/w). Insights are given in relation to the impact of the price of fossil fuels, the carbon price, and the different substitution routes.


The use of a partial equilibrium model (PEM) has enabled a better understanding of the variability of the results in this study. For example, the use of PEM for the identification of the complex marginal source of electricity shows that, for the case of Australia, any benefit from a carbon price is lost with high prices of natural gas due to the incentive to use cheaper fuels such as black and brown coal. Likewise, the use of explorative scenarios was decisive to manage the inherent uncertainty of the parameters included in the model. In relation to substitution, the case of ferric chloride (42 % w/w) indicated that using only one substitution route was not enough to fully understand the potential continuum of cCF results. The simple marginal approach, where an exclusive marginal source of electricity or substitution route is considered, presents significant risks for the modelling accuracy of the cCF as shown here for sodium hypochlorite (13 % w/w) and ferric chloride (42 % w/w), therefore, it is not recommended.


Chlor-alkali chemicals Coagulation and disinfection Consequential carbon footprint Potable water treatment 



This research was undertaken as part of an Australian Research Council (ARC) Linkage project (LP0991017). The primary author was also supported by an ARC funded Australian Postgraduate Award Industry PhD scholarship. The authors would like to thank Thomas Brinsmead from the CSIRO Energy Technology Division for providing additional efuture data for the preparation of this manuscript.


  1. AEMO (2013) National Electricity Forecasting Report (NEFR). Aust Energy Mark Oper. Available at: Accessed September 25th 2013
  2. AER (2013) Generation capacity and output by fuel source. Aust Energy Regul. Available at: Accessed September 1st 2013
  3. Alvarez-Gaitan JP, Schulz M, Peters G (2011) Sustainability of water and wastewater treatment chemicals: development of Australian life cycle inventory data Proceedings of the 7th Australian life cycle assessment conference—revealing the secrets of a green market, 9th–10th March, MelbourneGoogle Scholar
  4. Alvarez-Gaitan JP, Peters GM, Rowley HV, Moore S, Short MD (2013) A hybrid life cycle assessment of water treatment chemicals: an Australian experience. Int J Life Cycle Assess 18:1291–1301CrossRefGoogle Scholar
  5. Alvarez-Gaitan JP, Peters GM, Short MD, Schulz M, Moore S (2014) Understanding the impacts of allocation approaches during process-based life cycle assessment of water treatment chemicals. Integr Environ Assess Manag 10(1):87–94CrossRefGoogle Scholar
  6. Australian Government (2011) Strong growth, low pollution. Modelling a carbon price. Available at: Accessed June 14th 2013
  7. BREE (2012) Australian energy technology assesment. Bur Resour Energy Econ. Canberra. Available at: Accessed March 13th 2013
  8. BREE (2013) The Australian energy assessment 2013 Model Update. Bur Resour Energy Econ. Canberra. Available at: Accessed May 31st 2014
  9. Crawford D, Jovanovic T, O’Connor M, Herr A, Raison J, Baynes T (2012) AEMO 100% renewable energy study: potential for electricity generation in Australia from biomass in 2010, 2030 and 2050. CSIRO energy transformed flagship, Newcastle, Australia Available at: Accessed July 14th 2013
  10. CSIRO (2012) eFuture Commonwealth scientific and industrial research organisation Available at: Accessed July 3rd 2013
  11. Dalgaard R, Schmidt J, Halberg N, Christensen P, Thrane M, Pengue WA (2008) LCA of soybean meal. Int J Life Cycle Assess 13:240–254CrossRefGoogle Scholar
  12. EIPPCB (2013) Best available techniques (BAT) reference document for the production of chlor-alkali. Eur Integr Pollut Prev Control Bur. Available at: Accessed July 12th 2013
  13. Elliston B, Diesendorf M, MacGill I (2012) Simulations of scenarios with 100 % renewable electricity in the Australian national electricity market. Energ Policy 45:606–613CrossRefGoogle Scholar
  14. Elliston B, MacGill I, Diesendorf M (2013) Least cost 100 % renewable electricity scenarios in the Australian national electricity market. Energ Policy 59:270–282CrossRefGoogle Scholar
  15. Franklin Associates (2011) Cradle-to-gate life cycle inventory of nine plastic resins and four polyurethane precursors available at: Accessed June 19th 2013
  16. Gaudreault C, Samson R, Stuart P (2010) Energy decision making in a pulp and paper mill: selection of LCA system boundary. Int J Life Cycle Assess 15:198–211CrossRefGoogle Scholar
  17. Geoscience Australia (2010) Australian energy resource assessment. Available at: Accessed March 14th 2013
  18. Graham PW, Brinsmead TS, Marendy P (2013) efuture sensitivity analysis 2013. Available at:;jsessionid=3523B91FCE0BB99EBA734ECED3B49A89. Accessed May 31st 2014 CSIRO
  19. Höjer M et al (2008) Scenarios in selected tools for environmental systems analysis. J Clean Prod 16:1958–1970CrossRefGoogle Scholar
  20. ILCD (2010) International reference life cycle data system. European Commission–Joint Research Centre–Institute for environment and sustainability. General guide for life cycle assessment—detailed guidance. Luxembourg publications, Office of the European UnionGoogle Scholar
  21. Jung J, von der Assen N, Bardow A (2013) Comparative LCA of multi-product processes with non-common products: a systematic approach applied to chlorine electrolysis technologies. Int J Life Cycle Assess 18:828–839CrossRefGoogle Scholar
  22. Lane JL, de Haas D, Lant PA (2012) Life cycle assessment perspectives on wastewater recycling, urban water security research alliance, technical report No 86. Available at: Accessed July 1st 2013
  23. Lund H, Mathiesen B, Christensen P, Schmidt J (2010) Energy system analysis of marginal electricity supply in consequential LCA. Int J Life Cycle Assess 15:260–271CrossRefGoogle Scholar
  24. Lundie S, Peters GM, Beavis PC (2004) Life cycle assessment for sustainable metropolitan water systems planning. Environ Sci Technol 38:3465–3473CrossRefGoogle Scholar
  25. Marvuglia A, Benetto E, Rege S, Jury C (2013) Modelling approaches for consequential life-cycle assessment (C-LCA) of bioenergy: critical review and proposed framework for biogas production. Renew Sust Energ Rev 25:768–781CrossRefGoogle Scholar
  26. Mathiesen BV, Münster M, Fruergaard T (2009) Uncertainties related to the identification of the marginal energy technology in consequential life cycle assessments. J Clean Prod 17:1331–1338CrossRefGoogle Scholar
  27. Muñoz E, Navia R (2011) Life cycle assessment of solid waste management strategies in a chlor-alkali production facility. Waste Manag Res 29:634–643CrossRefGoogle Scholar
  28. NHMRC (2011) Australian drinking water guidelines paper 6 National Water Quality Management Strategy National Health and Medical Research Council, National Resource Management Ministerial Council, Commonwealth of Australia, Canberra Available at: Accessed September 1st 2013
  29. Owen AD (2011) The economic viability of nuclear power in a fossil-fuel-rich country: Australia. Energ Policy 39:1305–1311CrossRefGoogle Scholar
  30. PE International (2013) GaBi 6 softwareGoogle Scholar
  31. Reinhard J, Zah R (2009) Global environmental consequences of increased biodiesel consumption in Switzerland: consequential life cycle assessment. J Clean Prod 17:S46–S56CrossRefGoogle Scholar
  32. Schmidt JH, Holm P, Merrild A, Christensen P (2007) Life cycle assessment of the waste hierarchy—a Danish case study on waste paper. Waste Manag 27:1519–1530CrossRefGoogle Scholar
  33. Skals P, Krabek A, Nielsen P, Wenzel H (2008) Environmental assessment of enzyme assisted processing in pulp and paper industry. Int J Life Cycle Assess 13:124–132CrossRefGoogle Scholar
  34. The Australian (2013) Jobs lost as Penrice shuts soda ash plant Available at: Accessed February 12th 2013
  35. Thomassen MA, Dalgaard R, Heijungs R, De Boer I (2008) Attributional and consequential LCA of milk production. Int J Life Cycle Assess 13:339–349CrossRefGoogle Scholar
  36. United Nations (2013) COMTRADE database. Available at: Accessed February 20th 2013
  37. USGS (2011) Historical statistics for mineral and material commodities in the United States. U. S Geol Surv. Available at: Accessed February 20th 2013
  38. Vázquez-Rowe I, Rege S, Marvuglia A, Thénie J, Haurie A, Benetto E (2013) Application of three independent consequential LCA approaches to the agricultural sector in Luxembourg. Int J Life Cycle Assess 18:1593–1604CrossRefGoogle Scholar
  39. Weidema B (2003) Market information in life cycle assessment: environmental project no 863, Danish Environmental Protection Agency, Danish Ministry of the EnvironmentGoogle Scholar
  40. Weidema BP, Ekvall T, Heijungs R (2009) Guidelines for application of deepened and broadened LCA. Deliverable D18 of work package 5 of the CALCAS project. Available at:, accessed June 14th 2013
  41. William EL, Luke JR, Liam DW, Colin FA, Anthony RS (2012) An economic evaluation of the potential for distributed energy in Australia. Energ Policy 51:277–289CrossRefGoogle Scholar
  42. WSAA (2010) Water Services Association of Australia. Report Card 2009–2010. Performance of the Australian urban water Industry and projection for the future http://www.clearwaterasnau/sites/clearwaterasnau/files/resources/ReportCard2010%20-%20FINALpdf. Accessed January 14th 2013
  43. Yellishetty M, Mudd GM, Ranjith PG, Tharumarajah A (2011) Environmental life-cycle comparisons of steel production and recycling: sustainability issues, problems and prospects. Environ Sci Pol 14:650–663CrossRefGoogle Scholar
  44. Zamagni A, Guinée J, Heijungs R, Masoni P, Raggi A (2012) Lights and shadows in consequential LCA. Int J Life Cycle Assess 17:904–918CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Juan Pablo Alvarez-Gaitan
    • 1
    Email author
  • Michael D. Short
    • 1
    • 2
  • Gregory M. Peters
    • 1
    • 3
  • Iain MacGill
    • 4
  • Stephen Moore
    • 5
  1. 1.UNSW Water Research Centre, School of Civil and Environmental EngineeringThe University of New South WalesSydneyAustralia
  2. 2.Centre for Water Management and Reuse, School of Natural and Built EnvironmentsUniversity of South AustraliaMawson LakesAustralia
  3. 3.Department of Chemical and Biochemical EngineeringChalmers University of TechnologyGothenburgSweden
  4. 4.School of Electrical Engineering and Telecommunications and the Centre for Energy and Environmental Markets (CEEM)The University of New South WalesSydneyAustralia
  5. 5.School of Civil and Environmental EngineeringThe University of New South WalesSydneyAustralia

Personalised recommendations