Special Issue "Scenarios and Indicators for Sustainable Development–Towards A Critical Assessment of Achievements and Challenges"
Deadline for manuscript submissions: closed (15 October 2018) | Viewed by 30228
A printed edition of this Special Issue is available here.
The global ecosphere is a complex, evolving system, and the anthroposphere another, more rapidly evolving one. Globalization and telecoupling are enhancing their complexity, and even more that of coupled socio-ecological systems. Sustainable development as a global normative development concept, as defined by Agenda 2030 and the SDGs, adds another level of complexity. As a result, the demand for tools to assess future risks and support precautionary decision making for sustainability is growing by the day in business and politics.Scenarios are means for simplification, reducing the real-world complexity to a potentially high but limited number of factors, analyzing their interaction, and supporting policy formulation. Indicators help monitoring selected trends recognized as decisive, and support communication and decision making. However, political or management demands can emerge rather spontaneously, while scenario development takes time—the demand for climate scenarios with a maximum 1.5 °C global warming took the IPCC by surprise. How can the scientific community prepare itself, and make sure that decision makers' demands can be met in due time, while the window of opportunity for decisions is still open?Integrated models have been a great success story and are used all over the world for sustainable development assessment and strategy development. Still they are criticized (and in particular the economic computable global equilibrium models most of them incorporate) for a lack of transparency, covered implicit assumptions, impossibility to capture stark and structural changes of effect driving mechanisms, technical insufficiencies and political bias. For instance, the model-based IPCC scenarios' warnings are ever more severe with every new report—is that only due to newly found facts, or can one of the reasons be the implicit habit of scientists of avoiding type 2 errors (claiming a relationship when it does not exist) at the expense of making type 1 errors (not finding a relationship when it exists)? Which role do other habits and routines, and the world views of scholars, play in the assumptions made and the interpretations given? The assumptions made regarding the effects on social, economic or environmental policies on society, its structures and processes are heavily influenced by the choice of the social theory applied, and there are many to choose from. For instance, there is no scenario analyzing in any detail how a no-growth, steady state or even degrowth economy would work out on social structures, economic prospects and community flourishing. Even the impacts this has on the environment, nature's contributions to people and human well-being are rather speculative so far. Scenarios tend to be path-dependent themselves: Some more assumptions, some more details are added, but the basics remain unchanged (like the current IPPC scenarios, derived from the SRES scenarios published by IPCC et al. in the year 2000).In particular, how does “the social” as one of the core dimensions of sustainable development enter scenarios and models? It includes the effects and dynamics of public orientations including values and preferences, decision making mechanisms including equity, gender issues, power statures and democracy, and implementing organizations, their role and functions—all factors which lend themselves often better to qualitative description (at best ordinal scale measurement as used by IPCC and IPBES) rather than to quantification. They can be rather well accommodated in scenario narratives, but hardly so in quantitative models: shouldn't scenarios be considered to consist of narratives with certain elements illustrated by occasional modelling? This would imply that that 'hard' figures are soft facts which have to be interpreted and modified in the narrative context to be hardened.However, in that case uncertainties would become less quantifiable—and which decision maker understands the scientific concept of uncertainties? How can it be explained to the users? Anyway, how reliable are uncertainty figures? A decade ago, assuming a collapse of the gulf stream was considered a futile assumption; these days its details are being modelled. The economic shock following a pandemic when hundreds of millions of people desert their working place for retreats in the country side assuming to be to be safer there goes beyond what models can model, but is in line with what the WHO warns about. So how do we deal with shocks, defined as single events changing the prevailing development trajectory? What do we consider plausible assumptions to be used in scenario development, and why?In a nutshell: Which are the most urgent tasks in improving the existing scenarios and their presentation (making assumptions and caveats more explicit), and complementing them with new ones filling the existing gaps? How should scientists communicate the uncertainties and deficits without reducing the policy impact of their work (which was, for instance, one of the core foundations for the Paris Climate Accord)?Similar questions apply to indicators, a main tool to communicate scenario results. Which systems are chosen, how does the choice reflect the world view of its designers? Are the relevant, newly emerging trends the issue of reporting, or only those where good time series of data exist (which implies that the problem has been recognized as such at least a quarter century ago)? Such questions have been, implicitly and explicitly, a matter of dispute in the process of developing and agreeing on indicators to monitor the SDG implementation, and a critical examination of reporting so far could probably identify significant room for improvement.Indicators simplify even more than models, but they make communication easier: the question then is, how to strike a balance? How to avoid that the information lost by indicator design makes the result easy to communicate, but potentially misguiding? What are the limits of aggregation (again, communication gets easier, but information gets lost, and negative trends in one aspect can be camouflaged by positive ones in another)? Finally, if implicit assumptions and methodology choices influence the results, how can it be avoided that influential groups produce their models and scenarios, seemingly scientific but interest-driven, and produce fake facts to undermine scientifically informed decision making (not that farfetched an assumption as we all know)?For all past successes, scenario development, model building and deriving indicators deserve and require a permanent critical assessment, and in particular a critical self-reflection of scholars if they are to maintain and enhance their usefulness in supporting better decision in an increasingly complex world.
Dr. Joachim H. Spangenberg
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- Sustainable Development