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The benefits of certified palm oil – measured with LCA?

November 7, 2016 by Jannick Schmidt

This week, I am at the Round Table on Sustainable Palm Oil (RSPO) Annual Meeting. Once again you might say. I have been fortunate enough to also attending the Annual Meeting in Medan in 2013. 2.-0 LCA consultants have a long history of providing data and methodology to enable a more sustainable production of palm oil from 2004 where I started my Ph.D. study on LCA of palm oil and rapeseed oil. You can see my speech at the meeting in Medan here: https://www.youtube.com/watch?v=cGlHzailfG4

Palm oil is used in a multitude of products and palm oil is the oil that is affected when there are changes in the demand for unspecified vegetable oil (Schmidt and Weidema 2008). Therefore, it is important to address the potential environmental impacts that the palm oil production might have in an informed and facts based way – using life cycle thinking.

Fortunately, consumers are increasingly demanding products containing palm oil produced without harm to the environment. The industry has responded to this demand by creating the Roundtable on Sustainable Palm Oil (RSPO). Furthermore, a certification system has been developed to ensure sustainable palm oil production.

But how much better is the environmental profile of RSPO certified palm oil actually when compared to non-certified palm oil in the market? And what does the certification mean from a life cycle perspective? These answers we do not yet have.

Therefore we have initiated a crowd-funded initiative: Certified Palm oil Club

The initiative aims to provide a complete cradle-to-gate LCA study, including oil palm cultivation, palm oil mill and refinery, as well as other relevant upstream processes. We will cover a wide set of environmental impact categories, including GHG emissions and biodiversity impacts and offsetting hereof from nature conservation. Furthermore, the initiative will address both direct and indirect land use changes, which are also important in relation to a sustainable palm oil production.

With this project, we both provide stakeholders in the palm oil value chain with highly valuable information, and we demonstrate what LCA should be used for – i.e. fostering improvements instead of just document the current status.

You can read more about the initiative on our project page.

References:

Schmidt (2013). Video of presentation in Medan 2013 https://www.youtube.com/watch?v=cGlHzailfG4

Schmidt J H, Weidema B P (2008). Shift in the marginal supply of vegetable oil. International Journal of Life Cycle Assessment 13(3):235‑239. https://lca-net.com/p/995

LCA for decision support

October 21, 2016 by Bo Weidema

Today, I give a keynote presentation to the “LCA Food 2016” conference in Dublin, on the topic of “Potentials and limitations of LCA for decision support”. The below figure is taken from one of my slides.

The three circles in the figure show our current knowledge, and the smaller circles within each illustrate how much of this knowledge is typically used by current LCA practice.

A wealth of knowledge is available for and from Life Cycle Assessment (LCA) as it combines three areas of knowledge:

• Knowledge on how supply and demand for products change the flows of products between economic activities (blue). Completeness is ensured by the use of IO-databases, based on data from national statistical agencies, and rebound effects are included based on marginal consumer behaviour. But many practitioners still do not use IO-databases in the background, resulting in up to 50% incompleteness in typical LCAs. Also it is still not common LCA practice to include the rebound effects, which can really change a result from beneficial to not or vice versa.
• Knowledge on the impacts from these activities on our natural, social and economic environment (green). Interdisciplinary research tells us what is the global annual loss of habitats, the global burden of diseases and social impacts, and the causal pathways from the human activities. Yet, most current LCAs are still limited to a few selected impacts on nature and human health, that account for maybe only 10% of the global impacts on the natural, social and economic environment.
• Knowledge on the values that humans place on and obtain from the existence and services of the environment and the economic activities (red). Welfare economics provides values (costs and benefits) expressed in comparable units of utility, including science-based equity-weighting and discounting. These values are based on market prices and survey data of representative selections of the relevant population. But most LCAs today are either presented in incomparable units or aggregated on the basis of the values of a small number of experts, mainly from westernized economies.

The above considerations can be extended to cover additional aspects of data and model quality, such as the models used for linking data into product systems, the spatial detail of data, the age of the data used, the transparency of the data, the data quality indicators used, and the review procedures applied. For all of these aspects, current LCA practice leaves much to be desired.

The main question for my keynote presentation is therefore: Why is most of current LCA practice so limited?

I have three suggestions for an answer to this question:

• Value of Information Analyses are generally not performed to identify the cost of additional information versus the costs of false negative or positive outcomes.
• Even when these costs are known, we still face the challenge of willful ignorance – the tendency that humans have to ignore information that does not fit with their prior worldview or interests. Willful ignorance is often caused by what you could call market failures, namely that:
• The ones who work with LCA – whether practitioners or decision-makers – often do not carry the (full) costs of making a wrong choice or of delaying a precautionary choice.

My conclusion is that for our knowledge to be used in practice, we need to make these costs matter to LCA practitioners and decision-makers, which means that we need to become involved in the power game around decision-making.

In this power game, we must not only provide knowledge but also empowerment of those stakeholders that have the winning (more environmentally friendly) solutions but currently have too low power to have them implemented. One powerful tool in this game is to call for due diligence by the more powerful players that have the losing (less environmentally friendly) solutions. Because these players are powerful, it may be necessary to find ways to temporarily compensate their losses, to ensure that the best possible compromises can be implemented. To maintain our scientific integrity, we need to lose our political virginity.

Memory lane

September 7, 2016 by Bo Weidema

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.org.

References

Consequential-LCA (2015). The ISO 14040 standards for consequential LCA. http://consequential-lca.org/clca/why-and-when/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 https://lca-net.com/p/580

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 https://lca-net.com/p/2324

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). http://curis.ku.dk/ws/files/135447941/8.pdf.pdf

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) https://lca-net.com/p/1125

Reinhard J, Weidema B P, Schmidt J H. (2010). Identifying the marginal supply of pulp wood. Aalborg: 2.-0 LCA consultants. https://lca-net.com/p/198

Schmidt J H. (2015). Life cycle assessment of five vegetable oils. Journal of Cleaner Production 87:130‑138. https://lca-net.com/p/1719

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. https://lca-net.com/p/227

Schmidt J, Thrane M. (2009). Life cycle assessment of aluminium production in new Alcoa smelter in Greenland. Grønlands Hjemmestyre. https://lca-net.com/p/183

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. https://lca-net.com/p/212

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 https://lca-net.com/p/1863

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. https://lca-net.com/p/200

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. https://lca-net.com/p/169

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 https://lca-net.com/p/1184

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. https://lca-net.com/p/1186

Weidema B P. (2001a). Avoiding co-product allocation in life-cycle assessment. Journal of Industrial Ecology 4(3):11-33. https://lca-net.com/p/1142

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. https://lca-net.com/p/1131

Weidema B P. (2003a). Market information in life cycle assessment. Copenhagen: Danish Environmental Protection Agency. (Environmental Project no. 863). https://lca-net.com/p/1078

Weidema B P. (2003b). Flexibility for application. Market modelling in LCI databases. Presentation for International Workshop on LCI-Quality, Karlsruhe, 2003.10.20-21. https://lca-net.com/p/1082

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. https://lca-net.com/p/175

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. https://lca-net.com/p/208

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. https://lca-net.com/p/229

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). https://lca-net.com/p/1042

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. https://lca-net.com/p/1028

Wesnæs M, Weidema B P (2006). Long-term market reactions to changes in demand for NaOH. Study for Novozymes. Copenhagen: 2.‑0 LCA consultants. https://lca-net.com/p/1014

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. https://lca-net.com/p/1188

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). https://lca-net.com/p/171

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. https://lca-net.com/p/234