Structuring complexity for tailoring research contributions to sustainable development: a framework

Research aiming at generating effective contributions to sustainable development faces particular complexity related challenges. This article proposes an analytical framework disentangling and structuring complexity issues with which research for sustainable development is confronted. Based on theoretical conceptions from fields like policy sciences and transdisciplinary research as well as on an in-depth analysis of the concept of sustainable development, three meta-perspectives on research for sustainable development are introduced and elaborated. The first perspective focuses on notions of sustainable development, sorting out the problem of unclear or ambiguous interpretations of the general sustainability objectives in specific contexts. The second perspective introduces a broad conception of the policy process representing the way societal change towards sustainable development is brought about. It supports identifying those academic and non-academic actors and stakeholders that are relevant for coming up with effective knowledge contributions. The third perspective identifies different forms of knowledge that are needed to tackle sustainability problems as well as the significance of their mutual interrelations. How the framework perspectives support reflecting on the fundamental complexity issues research for sustainable development is confronted with is illustrated using a case example from natural scientific research in the field of land use. We argue that meeting the complexity inherent in the concept of sustainable development requires joint learning in policy processes, working out shared visions being in line with the core objectives of sustainable development and generating knowledge about empirical, normative and pragmatic aspect

On which common ground to build? Transferable knowledge across cases in transdisciplinary sustainability research

To support societal problem solving, transdisciplinary research (TDR) uses knowledge co-production focusing on relevance and validity in a studied case and its particular social–ecological context. In the first instance, the resulting situated knowledge seems to be restricted to these single cases. However, if some of the knowledge generated in TDR could be used in other research projects, this would imply that there is a body of knowledge representing this special type of research. This study used a qualitative approach based on the methodology of grounded theory to empirically examine what knowledge is considered transferable to other cases, if any. 30 leaders of 12 Swiss-based TDR projects in the field of sustainable development were interviewed, representing both academia and practice. The transferable knowledge we found consists of the following: (1) Transdisciplinary principles, (2) transdisciplinary approaches, (3) systematic procedures, (4) product formats, (5) experiential know-how, (6) framings and (7) insights, data and information. The discussion of TDR has predominantly been focusing on transdisciplinary principles and approaches. In order to take knowledge co-production in TDR beyond an unmanageable field of case studies, more efforts in developing and critically discussing transferable knowledge of the other classes are needed, foremost systematic procedures, product formats and framings

Researchers' roles in knowledge co-production: experience from sustainability research in Kenya, Switzerland, Bolivia and Nepal

Co-production of knowledge between academic and non-academic communities is a prerequisite for research aiming at more sustainable development paths. Sustainability researchers face three challenges in such co-production: (a) addressing power relations; (b) interrelating different perspectives on the issues at stake; and (c) promoting a previously negotiated orientation towards sustainable development. A systematic comparison of four sustainability research projects in Kenya (vulnerability to drought), Switzerland (soil protection), Bolivia and Nepal (conservation vs. development) shows how the researchers intuitively adopted three different roles to face these challenges: the roles of reflective scientist, intermediary, and facilitator of a joint learning process. From this systematized and iterative self-reflection on the roles that a researcher can assume in the indeterminate social space where knowledge is co-produced, we draw conclusions regarding trainin

Lessons for Science from the "Year without a Summer" of 1816. What does it take for science to respond to climate change?

Science is responding in manifold ways to current climate change. What are the perquisites for response, and how can we structure the response? By studying the historical climatic event “Year without a Summer” of 1816 and by relating to Fleck’s theory of genesis and development of a scientific fact, we posit that responding refers to making interlinkages between different notions of climatic change

Which Methods Are Useful to Justify Public Policies? An Analysis of Cost–Benefit Analysis, Multi-Criteria Decision Analysis, and Non-Aggregate Indicator Systems

Science-based methods for assessing the practical rationality of a proposed public policy typically represent assumed future outcomes of policies and values attributed to these outcomes in an idealized, that is, intentionally distorted way and abstracted from aspects that are deemed irrelevant. Different types of methods do so in different ways. As a consequence, they instantiate the properties that result from abstraction and idealization such as conceptual simplicity versus complexity, or comprehensiveness versus selectivity of the values under consideration to different degrees. I hold that none of these methods is best in general. Instead, I opt for the valuation method that is useful for the policy issue in question both in terms of its relevance and in terms of its practicability. Relevance requires that the method can represent and account for what is at stake in the policy issue. Practicability refers to aspects such as easy versus difficult handling of the method. To argue for the claim, I evaluate three types of valuation methods: (1) cost–benefit analysis that rests on unidimensional measurement and ranking, (2) multi-criteria decision analysis that applies multi-dimensional measurement but unidimensional ranking, and (3) non-aggregate indicator systems that operate with multi-dimensional measurement and sometimes also multi-dimensional ranking. Second-order justification indicating whether and how the valuation method chosen is capable of accounting for the substantive value considerations that constitute the real-world policy issue in question renders the conditions on which the results of a proposed policy evaluation rest transparent.ISSN:0925-4560ISSN:1572-858

On Rationales for Cognitive Values in the Assessment of Scientific Representations

Cognitive values like simplicity, broad scope, and easy handling are properties of a scientific representation that result from the idealization which is involved in the construction of a representation. These properties may facilitate the application of epistemic values to credibility assessments, which provides a rationale for assigning an auxiliary function to cognitive values. In this paper, I defend a further rationale for cognitive values which consists in the assessment of the usefulness of a representation. Usefulness includes the relevance of a representation regarding the investigation of a given problem and its practicability for the users. This rationale builds on the claim that any evaluation of scientific representations should pursue two aims: providing information about their credibility and providing information about their usefulness. Cognitive values relating to the usefulness of a representation and epistemic values relating to its credibility both perform a first-order function. Cognitive values are abstract, and several values with first-order functions may conflict in their application. Thus, in order for cognitive values to account for the sort of problem that is to be investigated by means of a representation, they need to be appropriately specified and weighed. Comprehensiveness, complexity, high resolution, and easy handling, for instance, may be required in a first-order function for model-based prediction of regional climate impacts but not for explaining how the global climate system works. Specifying and weighing cognitive and epistemic values relative to a given problem is a legitimate second-order function of social values.ISSN:0925-4560ISSN:1572-858