Figure source: IPCC AR5 WGIII. Available from: http://www.ipcc.ch/report/ar5/wg3/.
The above IPCC figure depicts possible future trends in greenhouse gas emissions. Underpinning this display is a set of well over a thousand scenarios. The range reflects different technological and economic trajectories, in addition to uncertainties. If we want to limit warming to no more than 2 ℃, we should look at the ‘RCP2.6’ path. How did the climate analysts produce this large collection of future outlooks? Not with crystal balls, but with an army of computer models. Or, somewhat more precisely, integrated assessment models. In a recent article, lead-authored by Stefan Pauliuk, we review these models from the perspective of our field, industrial ecology.
Integrated assessment models (IAMs) are widely used to explore strategies for halting climate change. The models mimic human decision and mechanisms in natural systems over long time-scales. They operate by selecting or substituting alternatives – this can be natural resource alternatives or technology alternatives – so that costs are minimized, while respecting constraints, such as limited emission budgets. Among numerous applications, IAMs have been used to explore which fossil fuel reserves need to remain unexploited in a 2 ℃ future, to study the potential role of natural gas as a “bridge fuel” during a shift to a low-carbon society, and to compare energy system transformations in 2 ℃ or 1.5 ℃ futures.
In industrial ecology, on the other hand, we study how energy and matter flow through society, how they are transformed or used in a network of industrial processes to satisfy human needs and desires, and how the natural environment is affected as a result. Key industrial ecology methods include material flow analysis (MFA), environmental input-output analysis (IOA) and environmental life cycle assessment (LCA). Some examples of applications of industrial ecology methods are: to analyse emissions associated with products and services consumed by households; to investigate resource use, emissions and wastes associated with developing and operating material stocks in the future; to study increased material efficiency (that is, to use less materials to deliver a given service) as a strategy to reduce emissions; and to analyse environmental impacts associated with future electricity supply.
The IAM and industrial ecology fields are both concerned with understanding industrial systems. Such systems help to create goods and services that humans utilize. They also create emissions and waste.
Looking into the future in order to evaluate possible strategies for sustainable development is, in a way, the heart of what IAMs do. Looking into the future is also at the core of what dynamic MFA is about, and is a growing trend in IOA and LCA. Furthermore, there is a shared ambition to address various types of environmental concerns, including greenhouse gas emissions, air pollution and water and land use.
Despite their common interests, each modelling approach has distinct differences. IAMs are strong at representing the dynamics that shape evolutions in human and natural systems. They are cost-led and parsimonious, weighing the costs of alternative means to an end in order to identify lowest-cost solutions. However, with few exceptions, IAMs lack explicit descriptions of physical linkages related to capital stocks and materials. This includes relationships between capital stocks and the materials you need to build the stocks, between the stocks/materials and emissions associated with producing them, and the factors that govern material cycles.
On the other hand, industrial ecology methods are less comprehensive in scope (and you could also say less integrated) than the IAMs. They focus their attention on specific types of linkages in industrial or ecological systems (as indicated in Figure 2 in our article), and often on specific products (LCA) or materials (MFA). Dynamic MFA is the only industrial ecology method that can generate scenarios itself; scenario-based LCAs and IOAs rely on exogenous scenarios as data inputs.
Potentials for interaction
Potential synergies between the two approaches exist. We believe that IAMs can generate more robust and credible emissions mitigation scenarios by adding industrial ecology linkages related to capital stocks and materials. We argue that, one the one hand, this can open the door for widening the set of potential mitigation solutions in the models, because material efficiency solutions – such as recycling, lifetime extensions, or using lightweight materials – can be added. On the other hand, it can introduce constrained availability of scrap for recycling as a new impediment to mitigation. In both cases where new solutions or obstacles come into play, model outcomes may become more realistic and useful for decision-making.
Industrial ecology can make use of IAM scenarios to improve its capability to analyse future change. One way to do this is to apply industrial ecology methods to analyse IAM scenarios; another way is to integrating IAM scenario data into industrial ecology core databases. IAMs provide insights into cost-minimizing strategies – a major concern of policy makers – something that industrial ecology does not offer frequently.
Environmental impact assessment methods developed for LCA have a potential to broaden the range of impact types considered in IAMs, while IAMs can be used to capture cross-sectoral interactions that matter for environmental impacts, such as between food and bioenergy production. Further discussions of potentials for interaction and improvement are available in our article.
In summary, IAM and industrial ecology share several important common interests, but employ entirely different approaches to achieve them. In our article, we call for more interaction between the integrated assessment and industrial ecology communities to the benefit of sustainability science as a whole.
Full article citation: Pauliuk, S., Arvesen, A., Stadler, K., Hertwich, E.G., 2017. Industrial ecology in integrated assessment models. Nature Climate Change 7, 13-20.
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