A Moonshot for Sustainability Assessment.

U.S. President John F. Kennedy, Rice University, 1962. Early in April this year, the United Nations deliberated on how to implement the 17 Sustainable Development Goals (SDGs) and their related 169 targets that will be launched in this September. The International Council for Science recognized that SDGs offer major improvements on the Millennium Development Goals that they substituted. The implementation list well covers the social, economic and environmental components of sustainability. Clearly the SGDs and their implementation processes pose an unprecedented challenge to policy makers; so do they to sustainability scientists, including those who are engaged in quantitative sustainability assessment (QSA). Science-based QSAs are the key to monitor the progress of and support the decisions on the 17 SDGs. However, as they currently stand, they are far from ready to undertake the challenge. First, QSAs need to account for potential conflicts, synergies and trade-offs between SDGs. SDG number 7, for example, reads “Ensure access to af fordable, reliable, sustainable and modern energy for all.” The achievement of this goal would likely require expanding the energy supply infrastructure especially in the developing and least developed world. However, this policy decision could clash with SDG number 13 that reads “Take urgent action to combat climate change and its impact”, since building a new infrastructure contributes to the emission of greenhouse gases (GHGs). Therefore, QSAs would need to take into account multiple criteria and their synergies and tradeoffs. Such criteria should include not only a multitude of environmental impacts but also social and economic aspects. Recent advances in life cycle assessment (LCA) to embrace socio-economic impacts are encouraging. LCA allows quantifying the life cycle impacts of a product system using a wide range of indicators (e.g., depletion of natural resources, contribution to climate change). However, LCA has yet to further evolve into a tool for a comprehensive sustainability assessment. Second, QSAs need to embrace causal relationships in their analysis. The implementation of the SDGs will create a complex network of policy actions and their outcomes, overlaid by ongoing changes in the society and the environment. Understanding the actions and their consequences under this condition is not easy. SDG number 14, for example, states “Conserve and sustainably use the oceans, seas and marine resources for sustainable development”, which includes, for example, the specific target of “reducing the loss of marine species” (i.e., target 14.2 in the SDGs). A statistically sound causal analysis can help determine the strength of the causal link between an action (e.g., reducing and enforcing fishing quota) and its outcome (e.g., increasing diversity of marine species). In the field of economics and ecology, innovative approaches have been developed to analyze causal relationships in complex systems. For the irreversible, large-scale changes that SDGs and corresponding policy actions may induce, however, existing approaches focusing on historical data exhibit significant limitations. Third, a policy-maker faces the problem of decision-making under uncertainty. Decisions are seldom supported by perfect information, and no QSA provides uncertainty-free results. A rigorous uncertainty analysis with the output of QSA provides policy-makers the opportunity to evaluate the value of the information that QSAs generate. Although the application of techniques of uncertainty analysis has yet to become standard practice in the sustainability sciences, recent advances allow identifying the key drivers of uncertainty and to conduct an uncertainty analysis for models of great complexity at a limited computational cost. Further efforts are needed to support the development of protocols to adequately characterize uncertainty and to communicate it in the results of QSAs. We highlighted what we believe to be three fundamental needs for QSAs to monitor the progresses of SDGs and to support decision-makers. These are (1) the use of QSAs in a multicriteria setting, (2) the embracing of causal relationships in the assessment, and (3) the adequate characterization of uncertainty of QSAs’ results. The biggest challenge to QSA