Memory lane

I was recently asked to tell about the experiences we have at 2.-0 LCA consultants with consequential LCA. That question sent me on a long trip down memory lane…..

You will therefore find the format of this blog-post a bit unusual, with a ‘fat’ bibliography and a focus on our contributions to the field. I hope you will anyway find it worthwhile to read.

All practical applications of LCA are ultimately concerned with potential improvements of the analysed systems. Therefore, LCA is designed to model the physical consequences of a change, tracing the physical and economic causalities that result from a decision. This type of modelling is is often referred to as consequential LCA, as opposed to attributional modelling that a trace specific aspect of a value chain or supply chain back to its contributing unit processes, and that cannot say anything about the consequences of changing the analysed system.

The idea of LCA as a model of changes was initially suggested by Heintz & Baisnée (1992) and Weidema (1993), pointing out that to determine what processes to include in a product system, it is necessary to use information on how markets react to changes in demand and supply. The consequential modelling principles were later built into the ISO standards on LCA, published in 1998 (ISO 14040, 14044 and 14049; see Consequential-LCA 2015) and supported by a number of scientific publications, notably Weidema et al. (1999) and Weidema (2001a), and summarized in Weidema (2003a).

Due to the lack of flexible and geographically differentiated background databases, the initial application of the consequential modelling principles was limited to specific parts of the foreground systems. Examples of early applications can be found for metals (ISII 1997, Weidema 1999a, Weidema & Norris 2004), renewable materials (Weidema 1999b), electricity and nitrogen fertiliser (Weidema 2001b), and fish (Thrane 2004).

That a consistent consequential model could be implemented in a background database based on the introduction of flexible market activity datasets was put forward in Weidema (2003b) but not implemented in practice until ten years later, in the ecoinvent database (Weidema et al. 2013).

That marginal modelling is also applicable in IO-LCA (LCA using input-output data from the national accounts) was already pointed out in Nielsen & Weidema (2001) and consequential procedures to handle co-production have already long been in use in IO modelling (Stone 1961; see also the discussion of the parallel but isolated developments of IO and LCA modelling in Suh et al. 2010). This parallel model structure allows current consequential LCA practice to combine the advantages of both process-based data (high degree of detail) and IO-data (economy-wide completeness).

The increasing global trade and the corresponding availability of geographically differentiated LCA data have increased the relevance of identifying the geographical location of marginal suppliers to the global markets; see examples for aluminium (Schmidt & Thrane 2009), pulp wood (Reinhard et al. 2010), biomass production capacity (Schmidt et al. 2015).

For changes that liberate or bind scarce resources, a consistent analysis requires inclusion of the marginal rebound effects of this change in resource availability (Weidema 2008). The most well-known rebound effect is the effect of price differences that change the availability of money for alternative consumption (see Thiesen et al. 2008 for an example of how to estimate this), but also other rebound effects can be of importance; see Weidema et al. (2008) for an example of systematic inclusion of rebound effects.

Because market reactions to changes in demand and supply can lead to both increases and decreases in environmental impacts, results of consequential studies may often be unexpected and counterintuitive compared to a more static analysis that ignore such market reactions. Examples of such initially counterintuitive and possibly even controversial findings are that:

  • Field application and emissions of manure are not related to crop production but related to (caused by) the animal husbandry, and the marginal crop production therefore needs to cover its full fertilisation need by artificial fertilisers exclusively (Dalgaard et al. 2014);
  • Bio-based and biodegradable products often have more environmental impact than the fossil-based products they are intended to substitute (Schmidt & Brandão 2013);
  • Intensive agriculture and plantation forestry often have less environmental impact than less efficient practices that initially appear more benign (Weidema 2013). An exception is dairy farming where further intensification is harmful due to the reduced displacement of stand-alone high-impact meat production (Weidema et al. 2008).

When specific data used in a consequential model are counterintuitive, controversial, or particularly important for the outcome of the analysis, the requirements to documentation of the data acquisition increases, and additional efforts and techniques may be required for data acquisition, for example the use of equilibrium models to identify the specific farm types that provide the marginal supply of different agricultural products (Jensen & Andersen 2003), and exhaustive uncertainty assessment (Weidema 2011).

Examples of companies that put particular emphasis on open and transparent reporting of the assumptions and data used are Novozymes (see Weidema & Wesnæs 2005, Wesnæs & Weidema 2006) and Arla Foods (Dalgaard et al. 2016). Examples of very structured and well-documented applications of the consequential procedures are Schmidt (2015) for the identification of the determining output of five oil crops and Schmidt et al. (2011) for country-specific consequential electricity mixes. Sharing well-documented consequential data has become easier with the recent availability of the community website


Consequential-LCA (2015). The ISO 14040 standards for consequential LCA.

Dalgaard R, Schmidt J H, Flysjö A (2014). Generic model for calculating carbon footprint of milk using four different LCA modelling approaches. Journal of Cleaner Production 73:146‑153

Dalgaard R, Schmidt J H, Cenian K (2016). Life cycle assessment of milk National baselines for Germany, Denmark, Sweden and United Kingdom 1990 and 2012. Arla Foods, Aarhus, Denmark

Heintz B, Baisnée P-F. (1992). System boundaries. Pp 35-52 in SETAC-Europe: Life-cycle assessment. Brussels: SETAC. (Report from a workshop in Leiden, 1991.12.02-03).

IISI (1997). Methodology report [of the IISI LCI study]. Brussels: International Iron and Steel Institute.

Jensen JD and Andersen M (2003). Marginale producenter af udvalgte landbrugsprodukter. FØI Working paper no. 08/2003 (in Danish).

Nielsen A M, Weidema B P (2001). Input/Output-analysis – Shortcut to life cycle data? Proceedings of a workshop held in Copenhagen on the 29th of September 2000. Copenhagen: Danish Environmental Protection Agency. (Environmental Project 581)

Reinhard J, Weidema B P, Schmidt J H. (2010). Identifying the marginal supply of pulp wood. Aalborg: 2.-0 LCA consultants.

Schmidt J H. (2015). Life cycle assessment of five vegetable oils. Journal of Cleaner Production 87:130‑138.

Schmidt J H, Brandão M (2013). LCA screening of biofuels – iLUC, biomass manipulation and soil carbon. This report is an appendix to a report published by the Danish green think tank CONCITO on the climate effects from biofuels: Klimapåvirkningen fra biomasse og andre energikilder, Hovedrapport (in Danish only). CONCITO, Copenhagen.

Schmidt J, Thrane M. (2009). Life cycle assessment of aluminium production in new Alcoa smelter in Greenland. Grønlands Hjemmestyre.

Schmidt J H, Merciai S, Thrane M, Dalgaard R (2011). Inventory of country specific electricity in LCA – Consequential and attributional scenarios. Methodology report v2. 2.‑0 LCA consultants, Aalborg, Denmark.

Schmidt J H, Weidema B P, Brandão M (2015). A framework for modelling indirect land use changes in life cycle assessment. Journal of Cleaner Production 99:230‑238

Stone, R. 1961. Input-output and national accounts. Paris: Organization for European Economic Cooperation.

Suh S, Weidema B P, Schmidt J H, Heijungs R. (2010). Generalized Make and Use Framework for Allocation in Life Cycle Assessment. Journal of Industrial Ecology 14(2):335-353.

Thiesen J, Christensen T S, Kristensen T G, Andersen R D, Brunoe B, Gregersen T K, Thrane M, Weidema B P. (2008). Rebound Effects of Price Differences. International Journal of Life Cycle Assessment 13(2):104-114.

Thrane M (2004b): Energy consumption in the Danish fishery – Identification of key factors. Journal of Industrial Ecology 8, 223–239.

Weidema B P. (1993). Market aspects in product life cycle inventory methodology. Journal of Cleaner Production 1(3-4):161-166.

Weidema B P (1999a). A reply to the aluminium industry: Each market has its own marginal. Letter to the Editor responding to previously published article on Marginal production technologies for LCI’s. International Journal of Life Cycle Assessment 4(6):309‑310

Weidema B P. (1999b). System expansions to handle co-products of renewable materials. Pp. 45-48 in Presentation Summaries of the 7th LCA Case Studies Symposium. Brussels: SETAC-Europe.

Weidema B P. (2001a). Avoiding co-product allocation in life-cycle assessment. Journal of Industrial Ecology 4(3):11-33.

Weidema B P. (2001b). Two cases of misleading environmental declarations due to system boundary choices. Presentation for the 9th SETAC Europe LCA Case Studies Symposium, Noordwijkerhout, 2001.11.14-15.

Weidema B P. (2003a). Market information in life cycle assessment. Copenhagen: Danish Environmental Protection Agency. (Environmental Project no. 863).

Weidema B P. (2003b). Flexibility for application. Market modelling in LCI databases. Presentation for International Workshop on LCI-Quality, Karlsruhe, 2003.10.20-21.

Weidema B P. (2008). Rebound effects of sustainable production. Presentation to the “Sustainable Consumption and Production” session of the conference “Bridging the Gap; Responding to Environmental Change – From Words to Deeds”, Portorož, Slovenia, 2008.05.14-16.

Weidema B P (2011). Uncertainty reduction in consequential LCA models. Presentation for the Life Cycle Assessment XI (LCA XI) Conference, Chicago, 2011.10.4‑6.

Weidema B P (2013). Reducing impacts of forestry – the fallacy of low-intensity management. Presentation for 6th International Conference on Life Cycle Management, Gothenburg 2013.08.25‑28.

Weidema B P, Norris G A. (2004). Avoiding co-product allocation in the metals sector. Pp. 81-87 in A Dubreuil: “Life Cycle Assessment and Metals: Issues and research directions.” Pensacola: SETAC. (Proceedings of the International Workshop on Life Cycle Assessment and Metals, Montreal, Canada, 2002.04.15-17).

Weidema B P, Wesnæs M (2005). Marginal production routes and co-product allocation for alcoholetoxylate from palm oil and palm kernel oil (zip-file). Study for Novozymes. Copenhagen: 2.‑0 LCA consultants.

Wesnæs M, Weidema B P (2006). Long-term market reactions to changes in demand for NaOH. Study for Novozymes. Copenhagen: 2.‑0 LCA consultants.

Weidema B P, Frees N, Nielsen A-M. (1999). Marginal Production Technologies for Life Cycle Inventories. The International Journal of Life Cycle Assessment 4(1):48-56.

Weidema B P, Wesnæs M, Hermansen J, Kristensen T, Halberg N (2008). Environmental improvement potentials of meat and dairy products. Eder P & Delgado L (eds.) Sevilla: Institute for Prospective Technological Studies. (EUR 23491 EN).

Weidema B P, Bauer C, Hischier R, Mutel C, Nemecek T, Reinhard J, Vadenbo C O, Wernet G (2013). Overview and methodology. Data quality guideline for the ecoinvent database version 3. Ecoinvent Report 1(v3). St. Gallen: The ecoinvent Centre.