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Special Issue "Scenarios and Indicators for Sustainable Development–Towards A Critical Assessment of Achievements and Challenges"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Social Ecology and Sustainability".

Deadline for manuscript submissions: 20 August 2018

Special Issue Editor

Guest Editor
Dr. Joachim H. Spangenberg

Helmholtz Centre for Environment Research UFZ, 04318 Leipzig, Germany; Sustainable Europe Research Institute SERI Germany, 51103 Cologne, Germany
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Special Issue Information

Dear Colleagues,

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
Guest Editor

Manuscript Submission Information

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Keywords

  • Sustainable Development
  • Scenarios
  • Models
  • Indicators
  • Uncertainties
  • Deficits

Published Papers (3 papers)

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Research

Open AccessArticle Sustainability Indicators Past and Present: What Next?
Sustainability 2018, 10(5), 1688; https://doi.org/10.3390/su10051688
Received: 27 March 2018 / Revised: 11 April 2018 / Accepted: 16 April 2018 / Published: 22 May 2018
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Abstract
This paper discusses the current state of thought amongst the Sustainability Indicator (SI) community, what has been achieved and where we are succeeding and failing. Recent years have witnessed the rise of “alternative facts” and “fake news” and this paper discusses how SIs
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This paper discusses the current state of thought amongst the Sustainability Indicator (SI) community, what has been achieved and where we are succeeding and failing. Recent years have witnessed the rise of “alternative facts” and “fake news” and this paper discusses how SIs fit into this maelstrom, especially as they are themselves designed to encapsulate complexity into condensed signals and it has long been known that SIs can be selectively used to support polarized sides of a debate. This paper draws from chapters in a new edited volume, the “Routledge Handbook of Sustainability Indicators and Indices”, edited by the authors. The book has 34 chapters written by a total of 59 SI experts from a wide range of backgrounds, and attempts to provide a picture of the past and present, strengths and weaknesses of SI development today. This paper is an “analysis of those analyses”—a mindful reflection on reflection, and an assessment of the malign and benign forces at work in 2018 within the SI arena. Finally, we seek to identify where SIs may be going over the coming, unpredictable years. Full article
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Open AccessArticle Global SDGs Assessments: Helping or Confusing Indicators?
Sustainability 2018, 10(5), 1540; https://doi.org/10.3390/su10051540
Received: 31 March 2018 / Revised: 2 May 2018 / Accepted: 9 May 2018 / Published: 12 May 2018
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Abstract
On 1 January 2016, the 17 Sustainable Development Goals (SDGs) of the 2030 Agenda for Sustainable Development—adopted by world leaders in 2015—came into force. They build on the Millennium Development Goals (MDGs) and call for action by all countries to promote prosperity while
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On 1 January 2016, the 17 Sustainable Development Goals (SDGs) of the 2030 Agenda for Sustainable Development—adopted by world leaders in 2015—came into force. They build on the Millennium Development Goals (MDGs) and call for action by all countries to promote prosperity while protecting the planet. Since the SDGs are not legally binding, governments are expected to take ownership and establish national frameworks for the achievement of the 17 Goals. Countries thus have the primary responsibility for follow-up and review of the progress made in implementing the Goals, which will require quality, accessible and timely data collection. This will be instrumental for both regional and global follow-up analyses and assessments—several such major global assessments have already appeared. It might be supposed that the SDGs framework, including indicators, is conceptually and methodologically well-designed and tested in order to function reliably and provide guidance for such assessments. However, while it seems that the current structure of the SDGs has provided a firm policy framework, the Goals and targets have been mostly operationalized by indicators. We demonstrate and argue that without a procedurally well-designed, conceptual indicator framework for selecting and/or designing indicators, the results of SDGs assessments may be ambiguous and confusing. Full article
Open AccessArticle Can ISO-Defined Urban Sustainability Indicators Be Derived from Remote Sensing: An Expert Weighting Approach
Sustainability 2018, 10(4), 1268; https://doi.org/10.3390/su10041268
Received: 8 March 2018 / Revised: 6 April 2018 / Accepted: 17 April 2018 / Published: 20 April 2018
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Abstract
In the context of the United Nations’ “Agenda 2030 for Sustainable Development” and the presented Sustainable Development Goals (SDGs), the process of developing and agreeing on indicators to monitor the SDGs implementation becomes fundamental. In this paper, we identify indicators for the sustainable
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In the context of the United Nations’ “Agenda 2030 for Sustainable Development” and the presented Sustainable Development Goals (SDGs), the process of developing and agreeing on indicators to monitor the SDGs implementation becomes fundamental. In this paper, we identify indicators for the sustainable development of cities that have the greatest potential for their underlying data to be measured by means of remote sensing. We first identified existing indicators, which are derived from the International Standard ISO 37120, “Indicators for city services and quality of life”, as being partly or fully measured by the use of remote sensing, and then presented these indicators to remote sensing experts in an assessment procedure. We then investigated Multi-Criteria Decision-Making (MCDM) weighting methods to identify the most relevant quality of life indicators that can be captured by means of remote sensing techniques. We assess the remote sensing experts’ knowledge in the context of Decision Support Systems (DSS), and by means of both a questionnaire-based approach and a pairwise comparison approach. The approaches are compared with each other regarding their complexity, their potentials and limitations, and the respectively identified remote sensing based indicators. We identified three indicators related to surface characteristics as having the highest remote sensing potential. When contrasted to the results of the pairwise comparison, the questionnaire-based approach revealed high usability and confirmability. In the end, this approach enables cities’ administrations to decide which indicators they want to cover by means of remote sensing, depending on the capacities of their departments. Full article
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