Inputs zur gesellschaftlichen Entscheidungsfindung

Transitions from one socio-technical regime configuration to another entail long phases of institutional complexity, where two or more field logics co-exist in a sector and induce incompatibilities and frictions. This paper presents a dynamic phase model, which characterizes the types of institutional complexity that may build up and settle across various phases of a transition, illustrated with a case study from the diffusion of onsite water reuse in San Francisco. Results from semi-structured expert interviews and a focus group demonstrate that different forms of institutional complexity may follow each other in a transition trajectory and that formidable strategic agency is needed by the actors in a field in navigating prolonged phases of competing cultural demands. Gaining a more balanced perspective of both organizational and field-level reconfigurations may help better explain why transitions succeed in some places and fail in others.

Classic accounts of transitions research have predominantly built on reconstructions of historical transition processes and in-depth case studies to identify and conceptualize socio-technical change. While such approaches have substantively improved our understanding of transitions, they often suffer from methodological nationalism and a lack of generalizability beyond spatial and sectoral boundaries. To address this gap, we propose a novel methodology – socio-technical configuration analysis (STCA) – to map and measure socio-technical alignment processes across time and space. STCA provides a configurational and dynamic perspective on how social and technical elements get aligned into "configurations that work", allowing for the identification of differentiated transition trajectories at and across spatial and sectoral contexts. The methodology's value is illustrated with the empirical case of an ongoing shift from centralized to more modular infrastructure configurations in the global water sector. Building on this illustration, we outline potential contributions of STCA to configurational theorizing in transition studies, sketching the contours of what we believe could become a generative epistemological approach for this field.

Climate change threatens winter tourism in the Alps severely, and ski resorts are struggling to cope under uncertain climate change. We aim to identify under what conditions physical and economic tipping points for ski resorts may occur under changing climate in six Swiss ski resorts representing low, medium, and high elevation in the Alps. We use exploratory modeling (EMA) to assess climate change impacts on ski resorts under a range of futures adaptation options: (1) snowmaking and (2) diversifying the ski resorts' activities throughout the year. High-resolution climate projections (CH2018) were used to represent climate uncertainty. To improve the coverage of the uncertainty space and account for the climate models' intra-annual variability, we produced new climate realizations using resampling techniques. We demonstrate the importance of five factors, namely climate scenarios (RCPs), intra-annual climate variability, snow processes model, and two adaptation options, in ski resorts survival under a wide range of future scenarios. In six ski resorts, strong but highly variable decreases in the future number of days with good snow conditions for skiing (GSD) are projected. However, despite the different characteristics of the resorts, responses are similar and a shrunk of up to 31, 50, and 62 days in skiing season (Dec-April) is projected for the near-future (2020–2050), mid-future (2050–2080), and far-future (2070–2100), respectively. Similarly, in all cases, the number of days with good conditions for snowmaking (GDSM) will reduce up to 30, 50, and 74 days in the skiing season in the near-, mid-, and far-future horizons, respectively. We indicate that all ski resorts will face a reduction of up to 13%, 33%, and 51% of their reference period (1981–2010) revenue from winter skiing activities in the near-, mid-, and far-future horizons. Based on the outcomes of the EMA, we identify Dynamic Adaptive Policy Pathways (DAPP) and determine the adaptation options that ski resorts could implement to avoid tipping points in the future. We highlight the advantages of adaptive planning in a first of its kind application of DMDU techniques to winter tourism. We specify the possible adaptation options ranging from "low revenue diversification and moderate snowmaking" to "high revenue diversification and large snowmaking" and demonstrate when an adaptation action fails and a change to a new plan is needed. By the end of the century, we show that only ski resorts with ski lines above 1800–2000 m elevation will survive regardless of the climate scenarios. Our approach to decision-making is highly flexible and can easily be extended to other ski resorts and account for additional adaptation options.

In polycentric governance systems, decisions that influence a given policy issue are often made across a series of forums: venues where actors meet to resolve collective action problems. Here, we examine who does and does not participate in forums, and the factors driving that participation. We analyse forum participation patterns of 307 actors involved in Swiss water governance, who could participate in water governance forums. We find that the majority of actors do not participate in any forums. Results from a Bayesian multi-level logistic regression model show that especially those concerned with a broader range of policy issues and those that have more organizational resources at their disposal are more likely to participate. To a lesser extent, this also holds for organizations that represent policy beliefs consistent with median beliefs in the system. A belief that increased cross-sectoral coordination is needed to promote more effective governance does not have a discernible impact on participation. These results question the integrative characteristics often attributed to forums in polycentric governance more generally.

Digital technologies can be important to policy-makers and public servants, as these technologies can increase infrastructure performance and reduce environmental impacts. For example, utilizing data from sensors in sewer systems can improve their management, which in turn may result in better surface water quality. Whether such big data from sensors is utilized is, however, not only a technical issue, but also depends on different types of social and institutional conditions. Our article identifies individual, organizational, and institutional barriers at the level of sub-states that hinder the evaluation of data from sewer systems. We employ fuzzy-set Qualitative Comparative Analysis (fsQCA) to compare 23 Swiss sub-states and find that two barriers at different levels can each hinder data evaluation on their own. More specifically, either a lack of vision at the individual level or a lack of resources at the organizational level hinder the evaluation of data. Findings suggest that taking into account different levels is crucial for understanding digital transformation in public organizations.