Using the industrial ecology toolbox to support the Sustainable Development Goals

The Sustainable Development Goals (SDGs) that launched on January 1st, 2016 include 17 goals, 169 targets and 303 indicators, which will help frame the agendas and policies of the United Nations’ member states through 2030.... [ view full abstract ]

The Sustainable Development Goals (SDGs) that launched on January 1^st, 2016 include 17 goals, 169 targets and 303 indicators, which will help frame the agendas and policies of the United Nations’ member states through 2030. SDGs set a new direction for development covering the environmental, economic and social pillars of sustainability. 

SDGs are obviously highly interconnected, making it essential to understand synergies, trade-offs and conflicts between them in order to support decisions. Without such understanding, a policy to improve on one goal could conflict with another goal. For example, policies targeting at improving energy provision could conflict with another goal on climate-change mitigation, or those aiming at the protection of marine ecosystems could clash with the provision of sustainable food for all. Incidentally, quantitative sustainability assessments (QSAs) from the industrial ecology community, such as life cycle sustainability assessment, the various footprinting approaches, environmental input-output models, material flow analyses, ecosystem evaluation approaches, and other QSAs have largely been excluded from the set of indicators and tools that made the final list proposed by the United Nations.

In this contribution, we try to understand how QSAs could support decision-making on SDGs. We reflect on whether trade-offs among SDGs can be understood through the lenses of QSAs, and whether the tools that we commonly use in the industrial ecology community allow, or not, understanding cause-effect mechanisms between policy actions and progress on SDGs, thus guiding effective decision-making for sustainability. We also address another important aspect of effective decision-making for sustainability, namely the necessity of using aggregation theory to condense the multiple aspects of sustainability into one or multiple composite indicators that can be compared, monitored, and communicated. Aggregation and composite indicators have been proliferating in the field of sustainability, and examples include the Human Development Index, the Ocean Health Index, the Living Planet Index, among many others. Recent approaches have proposed aggregation strategies over the list of SDGs that use normalization and weighting to obtain a composite sustainability index. We further address aggregation in the context of QSAs, using the case of life cycle sustainability assessment as an example. We show how the lack of aggregation (and normalization and weighting) in this context influences the ability of life cycle sustainability assessment to effectively guide policy- and decision-making. 

Our reflections on cause-effect mechanisms and aggregation are intended to stimulate collaborations across disciplines in our community and beyond, and are a conditio sine qua non for QSAs to be relevant for policy-making on SDGs, and for the success of any policy intervention aimed at jointlyaddressing the environmental, social and economic aspects of sustainability.