Governance of sustainability and policymaking in the anthropocene
Governance of sustainability happens in the interaction of state, private and civil society organizations. I investigate the nuts and bolts of it.
I have always been fascinated with the environment and our role as humans in it. Or put differently, how we can create a safe and just space for humanity (and non-human animals) in the anthropocene. This has led me down an interdisciplinary path, combining political, network, and sustainability science.
In my research on governance, I am especially interested in understanding governance as a network of diverse actors. These actors from all societal sectors exchange, collaborate, and sometimes are in conflict with regard to specific challenges of the anthropocene. Governance manifests in the interactions and actions within these networks and I find it fascinating to both understand what shapes networks structures, what types of networks structures are associated with successful outcomes and how we might construct interventions to boost the problem-solving capacity of networks based on this evidence.
Starting summer 2021 I will have the privilege to put all of this into practice in a postdoc project I will lead at the UZH (University of Zurich) Digital Society Initiative (DSI). At the DSI, I (and a very small team) investigate how we can leverage digitalization to support actor networks in Zurich for urban sustainability. At the core of this is a persuasion that sustainability in the anthropocene is still “Handarbeit”, as we call it in German. Sustainability is material work carried out on the ground through many small actions. I want a role for digitalization in supporting, not disrupting this work. In this vein, we are going to explore digital tools and data sources to map actor networks; gather actor knowledge through gamified participatory modeling; and use the resulting data to bring the right actors together in urban sustainability transformations.
In my previous work on governance, predominantly during my PhD, I especially focused on water governance networks. I am to this day amazed by all the issues that arise in water governance, ranging from biodiversity to water supply management, flood protection, recreation or even the fate of beavers undermining roads close to watercourses. I believe that this should be a cause for humility on part of the governance researcher. Actors are already navigating incredibly complex systems with regard to governance and we can safely assume that our abstract understandings of governance problems as researchers are fundamentally different from their intuitive understanding of their day-to-day business.
Studying water governance led me down a widening path when I started studying broader, but still water-related systems during my first PostDoc. I studied success factors in wetlands governance in Switzerland, a project I am still associated with as an external partner. Studying wetlands governance from a broad perspective made it necessary to involve not directly water-related issues such as regional development or spatial planning.
My second PostDoc position at WSL continued along this line in tackling an ever more diverse set of sustainability governance challenges. I briefly worked on conceptual foundations of a project studying how rural municipalities in Switzerland can re-orient municipal decision-making and policy in order to facilitate resource-saving, sustainable lifestyles, which was valuable experience.
Researching governance as the networks within which decision-making, implementation or interpretation happens is one side of the coin. The other side relates to specific policies as inputs, throughputs or outputs of this governance process. I am interested in this part of the puzzle as well. I have for example been involved in research on policy preferences of actors in renewable energy policy.
I am passionate about thorough and reproducible ways to link data analysis to questions about governance, policy and sustainability. I take an unreasonable amount of joy in finding innovative or innovatively simple ways to measure complex concepts, such as gaps in governance systems, how governance is structured or what shapes how actors interact in governance.
My main methodological asset is a good knowledge about the tools, possibilities, and limits of network approaches to data analysis. I believe in the integration of relational aspects of data structures and the merits of studying networked phenomena in their own right, but I also think that treating network analysis as its own field in applied research misses out on a lot of advanced knowledge about data analysis from other established data analysis procedures (eg. clustering).
I am fascinated by representing and analyzing complex social-environmental settings as multi-level networks. I have spent quite some time working with Exponential Random Graph Models and developed a kind of love-hate relationship with them over time. Recently, this relationship has improved a bit with new developments in Bayesian ERGMs. Beyond this, I often end up combining generalized linear regression models with causal graph approaches to causality.
I have become ever more Bayesian, for the very simple reason that Bayesian Data Analysis fits the way I have always thought about how we gain knowledge in research. But it took me some time to realize that there was a whole area of statistics in line with it. I would also say that this way of thinking is also not particularly different from how most researchers I know treat the interpretation their models.
I do not care much about simplistic rejections of straw-man null hypotheses based on arbitrary threshold values nor the cult of statistical significance. I also do not care much about the fetish for super-large sample sizes - evidence is evidence and if you have less data points, you can still learn something, but you will probably just be more uncertain. With the caveat that this creates a responsibility to quantify that uncertainty. And I hate hiding behind reflexive statements of “correlation is not causation”" statements, even though we can often offer the people to which our research might matter more than that.
I am obviously influenced by a host of researchers and statisticians who have thought much more deeply than me about these things. This especially because as an applied researcher, I am dependent on these people to think things through properly and make them accessible. I would unequivocally recommend anyone to dive into Richard McElreath and his brillant “Statistical Rethinking”(v2) as well as Judea Pearl’s work most accessible in the “Book of Why”.
However, I feel that there is also a responsibility for an applied researcher to not only apply but also try to understand approaches they use, which has always been very important to me. This is not an easy line to walk obviously, but an important one to be aware of.