June 1, 2020 by admin
Our company, 2.-0 LCA consultants, celebrated its 20 years birthday on May 1st 2020 by issuing a quiz, where the below text had blanks instead of years, and the task was to fill in the correct years.
Out of the 12 blanks, the year that turned out to be most difficult to get correct was 2014, the year our sponsorship for BONSAI begun. In fact, only one person got that right, namely our winner.
The winner, with the largest number of correct answers, 10 out of 12, is Guillermo García-García, University of Sheffield, who has chosen as his prize a EUR 2400 voucher towards the course fee for his next course at the International Life Cycle Academy.
A prize was also drawn by lot among all who participated. The lucky winner of this prize is Daina Romeo, EMPA, who has chosen as her prize a free membership of the 2.-0 SDG club.
Thank you to all that quizzed with us.
The Quiz (with correct answers)
Our work with IO- and hybrid-LCA databases began in year 2000 with an International workshop in Copenhagen financed by the Danish EPA. Already then, the idea of a global multi-regional hybrid LCA database was aired. The work resulted in a Danish LCA database and later several EU projects, notably FORWAST, CREEA and DESIRE, culminating in the latest hybrid version of Exiobase 3. The work is currently continued in our Exiobase Update club.
Our work with Social LCA began in year 2002 with a presentation in conjunction with the ISO TC207 meeting in Johannesburg. The presentation concluded that “The principles of life cycle impact assessment are also relevant for social impact assessment”. From 2004, we contributed with a vice-chair to the work of the UNEP/SETAC Life Cycle Initiative cross-cutting “Task Force on Social Aspects in LCA”, which resulted in the 2009 Guidelines for Social Life Cycle Assessment of Products. The work is currently continued in our Social LCA club.
Since 2004 we have offered privileged access to a range of working documents and tools to members of our Executive club.
Our work on monetary valuation as part of Life Cycle Impact Assessment began with a project for the EU JRC in Ispra. Based on the year where we finished this project, the resulting monetary valuation method was baptised Stepwise2006. Later, we participated in drafting the ISO 14008 on monetary valuation, published in 2019, and now we continue the improvement of the scientific quality of the method in our Monetarisation club.
Indirect land use change has been on our agenda since 2007, and we continue to develop the quality of the data and models through our iLUC club.
Since 2011 we have sponsored the International Life Cycle Academy, to ensure the continued provision of high-quality training opportunities in quantitative sustainability assessment.
Since 2014 we have sponsored BONSAI, with the aim that all data, software and algorithms to produce “product footprints” are maintained as open source
Consequential modelling has always been indispensable for our work with LCA. During the years, we have given a lot of advice to developers of standards and guidelines, and in 2015 we collected much of this as a free web resource: consequential-lca.org.
In 2017, we launched the SDG club, a crowd-funded project to place each of the indicators for 169 targets of the 17 UN Sustainable Development Goals into a comprehensive, quantified and operational impact pathway framework. Since 2018 this project runs under the auspices of the UN Life Cycle Initiative.
February 18, 2020 by admin
A government hearing on biomass in the Danish Parliament has being going around social media lately, not so much because of the content, but because American star-journalist Michael Grunwald tweeted about the sober way the politicians behaved during the hearing: “I couldn’t tell which pols were in which party or what biases any of them had about the topic being discussed. It really seemed like they were there to learn. And by the end it was clear they had.”
We are proud to say that our CEO, Jannick Scmidt was an invited expert witness at the hearing, alongside foreign expert witnesses such as Searchinger. In his allotted 10 minutes, Jannick managed to clarify the intricacies of direct and indirect land use consequences as well as the overall climate consequences of burning biomass for energy. The take-home-message to the politicians were: If you ask if biomass is climate neutral, then a resounding ‘no’ is the only possible answer.
Then, Jannick explained why: When you burn forest biomass you release CO2. The re-growth of the forest takes time, so the uptake of CO2 from the air happens over a long time compared to the instantaneous release when burning. This difference in timing of the release and uptake of CO2 is important because less CO2-emissions are needed now, while a reduction in CO2 has less importance if it happens later. Secondly, if the biomass is grown in biomass plantations, then the land cannot be used for food production, and this will in the end lead to expansion into nature as well as increased fertilizer use on other land. This mechanism is called indirect land use change (iLUC). Globally, according to IPCC, CO2 from deforestation contributes with around 11% of the global greenhouse gas emissions. Finally, even if biomass is harvested without affecting the cultivated land, for example, when tree tops, smaller branches, and forest debris are removed as part of a forestry operation and used as fuel, this leads to CO2-emissions now, instead of the slower decomposition on the forest floor with a more gradual CO2 release. So when the politicians decide to allow burning of forest residues, this leads to CO2 emissions and subsequent environmental impact right now, on their watch.
We can only hope that Michael Grunwald is right that the politicians listened. At least the message is clear.
The meeting (in Danish) is recorded and can be found via this link, as can the Danish abstract
Michael Grunwalds tweet about the Danish hearing on Threadreader
October 22, 2019 by David Font Vivanco
Resource efficiency has traditionally been a key pillar of energy and broader environmental policy. This is why more efficient cars, lighting, and irrigation systems, to name a few, have been widely endorsed by both private and public management. Yet this pervasive efficiency narrative is now being challenged by the so-called rebound effect. So what is this rebound effect and how does it affect everyday choices?
Let’s assume a case where Mike and Penny want to replace their old car with a new one that is 10% more fuel efficient, believing they will save the environment a 10% energy use.
But what happens if the couple actually change their driving behavioural due to the perceived efficiency change? For example, they might now spend the resulting economic savings from reduced fuel use on other products, including additional driving. Also, their driving behaviour may change in light of their perceived ‘good deed’. Indeed, they may feel morally licensed to buy a bigger car, turn the AC more regularly, or even buy a second car (Santarius and Soland 2018). All of these indirect consequences constitute the so-called rebound effect. When a given resource efficiency measure leads to overall increased resource use, we speak of a backfire effect or the Jevon’s Paradox in relation to the seminal work by William Stanley Jevons (1865).
Estimating the rebound effect entails isolating the effect that any perceived efficiency plays on resource use. In other words, Penny and Mike would have needed to first estimate their current energy use, and then estimate which share would be attributable not just to driving the car, but to the change in efficiency of the new car with respect to the old one. Such a daunting task has led to multiple approaches and ultimately a polarised debate between those who argue that rebound effects are modest in size, easily addressed, and generally overplayed (Gillingham et al. 2013) and those who argue that the Jevon’s Paradox takes place in multiple contexts and a better understanding of rebound effects is needed to guide environmental policy worldwide. Some of us from the latter group have edited a Research Topic in Frontiers in Energy Research and Frontiers in Sociology entitled “The Rebound Effect and the Jevons’ Paradox: Beyond the Conventional Wisdom” (editorial).
This special issue focuses on unconventional approaches to study rebound effects. The Research Topic includes seven theoretical works and case studies that shed new insights into the study of rebound effects: from the theories of complex adaptive systems and moral licensing to applications of system dynamics and industrial ecology models.
Dr. Tamar Makov from Yale University and I contributed a paper on rebound effects from smartphone reuse going beyond the traditional focus on energy efficiency to show how rebound effects may apply to circular economy strategies (Makov and Font Vivanco 2018). We show how imperfect substitution between recycled and new products, together with re-spending of the cost savings, could erode around one third—and potentially all—of the emission savings from smartphone reuse. Could this also apply to Penny and Mike’s car?
The special issue demonstrates the limitations of the current framing of rebound effects and show how this phenomenon has deeper roots in system behaviour, human psychology, and social organisation. The articles reinforce the argument that rebound effects are larger than many have assumed, and therefore present a critical challenge for environmental sustainability.
The critical challenge is to reconcile the economic growth with sustainability ambitions and bring the rebound effect issue into the policy arena (Font Vivanco et al. 2016a). For genuine sustainability, a good understanding of rebound effects is needed to avoid unintended consequences. I believe the life cycle-based approaches combined with tools that capture complex human and broader systemic behaviour, such as econometric (Font Vivanco et al. 2016b), quasi-experimental (Makov and Font Vivanco 2018), and macro-economic (Font Vivanco et al. 2019) tools offer untapped potential for business and governmental organisations to mitigate rebound effects and achieve their sustainability targets.
Font Vivanco, D., Kemp, R., and van der Voet, E. (2016a). How to deal with the rebound effect? A policy-oriented approach. Energy Policy, Elsevier, 94, 114–125.
Font Vivanco, D., Nechifor, V., Freire-González, J., and Calzadilla, A. (2019). Economy-wide rebound makes UK’s electric car subsidy fall short of expectations. Renewable & Sustainable Energy Reviews, (accepted).
Font Vivanco, D., Tukker, A., and Kemp, R. (2016b). Do Methodological Choices in Environmental Modeling Bias Rebound Effects? A Case Study on Electric Cars. Environmental Science and Technology, 50(20).
Gillingham, K., Kotchen, M. J., Rapson, D. S., and Wagner, G. (2013). Energy policy: The rebound effect is overplayed. Nature, Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved., 493(7433), 475–476.
Jevons, W. S. (1865). The Coal Question. An inquiry concerning the progress of the nation and the probable exhaustion of our coal-mines. Macmillan and co., Cambridge, UK.
Makov, T., and Font Vivanco, D. (2018). Does the Circular Economy Grow the Pie? The Case of Rebound Effects From Smartphone Reuse. Frontiers in Energy Research, Frontiers, 6, 39. https://www.frontiersin.org/articles/10.3389/fenrg.2018.00039/full
Santarius, T., and Soland, M. (2018). How Technological Efficiency Improvements Change Consumer Preferences: Towards a Psychological Theory of Rebound Effects. Ecological Economics, Elsevier, 146, 414–424.
September 9, 2019 by Bo Weidema
I recently responded to a questionnaire from Copper8 on “social valuation” and thought that some of the answers might be of general interest, so I share them here:
1. What is your definition of social valuation?
We normally speak of social assessment, where assessment is the full study including human activities, their interaction, the externalities and their cause-effect chains (as in ‘Life cycle sustainability assessment’), while valuation is only the last step in the impact assessment where the relative severity of different impacts are valued by individuals in order to make comparisons and trade-offs.
We normally speak of social in the sense of ‘aggregated at the level of society’, which is the common usage in economics since Marx (1885), but acknowledge that it can also be used in the more narrow sociological sense of ‘pertaining to the interactions between people’; see also reply to question 4. Maybe it would be best to talk only of social in the societal sense and call the rest inter-personal?
2. What is the scope of social valuation in current methodologies?
The scope for our assessments is the full product life cycle, i.e. the system of interlinked activities that change as a consequence of producing and consuming a product. We may additionally assess the impacts of the supply chain (the system of interlinked activities that contribute one or more specified and intrinsically linked physical properties to a product), or the value chain (the system of interlinked activities that contribute added value to a product). The three types of systems are described in our article at http://lca-net.com/p/2919.
3. What is the status of quantifying social impacts currently? We want to measure social impact on a product or material level, how would you do this? What is the best way to link social impact to (components of) products or materials?
First, social impacts are quantified per product at the organizational level and these are then aggregated over the analysed product system, using the standard life cycle assessment methodology.
4. Is there an overlap between economic, environmental and social valuation and how do you deal with that?
The social assessment (=Life Cycle Sustainability Assessment) includes the internalised costs and benefits by the use of Life Cycle Costing, as well as the social (inter-personal) externalities, the economic externalities, and the biophysical externalities by the use of Life Cycle Assessment. At the level of pressure indicators, it is possible to distinguish between the social (inter-personal), economic and biophysical pressures (see http://lca-net.com/p/3289), but at the level of midpoint impacts it only possible to distinguish socio-economic and biophysical indicators, and in the end all impacts contribute to a single indicator of social (sustainable) wellbeing.
The same human activity or the same pressure indicator (such as toxic substance emissions) or the same midpoint impact indicator (such as unemployment) can contribute to more than one impact pathway, where some impact pathways may be classified as economic, some as social (inter-personal) and some as biophysical. However, this classification is irrelevant for the actual assessment, which will proceed in the same way, disregarding the classification. Each impact is assessed individually and then aggregated at the societal level, taking into account synergies and dysergies as well as the (dis-)utility to the affected population.
5. Could you give an example of social value that you have measured?
We assess indicators quantitatively at different levels of detail. The most generic level (for simple screenings) is what we call the ‘Social footprint’, which is composed of three elements: the income redistribution impact, the productivity impact of missing governance, and the potential credits for positive action. This can then be broken down into quantified contributions from more specific impacts, such as the impacts from insufficient education, insufficient health care, insufficient clean water, or undernutrition, as described in http://lca-net.com/p/2858.
We have two crowdfunded projects that contribute to further detailing and improving these impact pathways, their indicators and their characterisation factors; one specifically addressing social (inter-personal) impacts (https://lca-net.com/clubs/social-lca/) and one addressing each of the SDG indicators (https://lca-net.com/clubs/sdg/). The latter includes all impacts that contribute to sustainable development. Both crowdfunded projects are based on the same top-down approach to valuation.
6. How do you assess the value of ambiguous social impacts, e.g. child labour can normatively be seen as a negative social impact, but it also adds to income and employment.
An activity (or a pressure such as child labour) can have several impacts. When one is detrimental and the other is beneficial, they may counteract each other. In practice, each impact is modelled separately and from that the net result can be calculated.
7. How to deal with feedback loops?
Generally, we deal with loops as a standard part of the models. Our models are system of linear equations, and when solving these, either by iteration or matrix inversion, the effects of loops are implicitly a part of the solution.
8. We want to make choices based on the right metrics, not only the measurable ones. How can we make this happen? How do you deal with this?
Our approach is top-down, starting from an assessment of the current annual level of impacts on human wellbeing (people), species loss (planet), and the productivity gap (prosperity). The causes of these impacts are then traced backwards to human activities. To avoid that something is left out due to missing knowledge or missing metrics, we always identify one impact pathway as ‘default’ to take care of the residual impact that is not explained by any of the other impact pathways.
9. What social impact data should/could be collected?
The analysed system will normally be divided in a foreground system for which activity-specific data are collected, and a background system where data come from average statistics for an industry and/or a geographical area. The minimum data that are required per activity or industry are work hours and value added.
10. How do you collect social impact data?
Companies usually supply data for the foreground systems, sometimes these may be verified by an external accountant. Background data are collected from statistical data sources. In the best case, data are available from several independent sources, allowing for triangulation.
11. What is the reliability of social impact data?
Reliability of social impact data is very variable. We use uncertainty estimates that include an assessment of reliability.
12. How to deal with a lack of benchmark data? And what data collection questions are relevant without benchmark data?
The global averages can always be used as benchmark. National and industry averages are also often available. For specific companies, the data for the previous years can be used as benchmark, giving a time series of impact, e.g. per unit of revenue. Using competitor’s performance is more difficult, since these data will seldom be publicly available. An option is when companies cooperate in industry associations.
13. For environmental impact absolute quantitative thresholds have been defined, i.e. the planetary boundaries. Can product social impact be linked to absolute thresholds and would that be meaningful? And can these thresholds be expressed in a monetary value?
In general, the socially relevant threshold is where marginal abatement costs are equal to marginal damage costs. Both these metrics are expressed in monetary units. In our top-down approach, monetary valuation is applied to the endpoints (human wellbeing measure in quality-adjusted person-life-years, species loss measured in numbers, and the productivity gap measured in productivity-adjusted person-life-years). All other contributing indicators – and therefore also thresholds – can then implicitly be expressed in monetary values.
14. Would it be possible to attribute organisational social impact to separate products?
15. Can organizational social impact indicators be translated in product social impact data?
Organisational impacts are attributed to individual products by the standard allocation procedures (for combined production by modelling the changes when producing more of one product and no more of the others; for joint production by modelling how the dependent co-products affect the markets they supply).
Final question: What is the best way to quantify qualitative indicators? E.g. could we use 12 yes/no indicators, of which 8 are scored with a “yes” which accounts to 8/12 = 66.7%?
By nature, qualitative indicators are used when possibility for quantification is insufficient. Quantifying these indicators therefore would be violating the nature and purpose of these data. The purpose of qualitative data is to identify missing perspectives and indicators, and to provide richer local context and a deeper understanding of the motivations, culture, values, power relations and change potentials of the affected societal groups.