In my PhD project I connect people's expenditure patterns across products with international value chain data and the value chain respective energy footprints. This way we can trace how consumption shapes not only value chains but the energy requirements of a society. This is useful in order to identify which consumption induces excessive energy flows and which consumption only brings reasonable energy footprints with it. The analysis will comprise more than 80 countries, break consumption down into 41 to 87 product groups while incorporating information about people's income class. Loosely speaking, the project aims constructing a novel fine-grained "image" of the global energy metabolism.
Within the context of the LiLi (Living well within limits) project, frameworks have been developed that aim to pinpoint which human needs actually require energy supply and which ones not (for example in Brand-Correa, L. I. and J. K. Steinberger (2017)). There is also considerable work been conducted on which societal (energy-)systems satsify human needs efficiently(O’Neill, D. W., A. L. Fanning, W. F. Lamb and J. K. Steinberger (2018)). Together with these insights, my project will be capable of identifying consumption patterns that are unsustainable and inefficient on national and international scales. Policy makers will be able to draw conclusions on how to satisfy people's needs with different consumption patterns and more sustainable provisioning systems.
Another mid-term goal of mine is to trace what kind of consumption requires which kind of fuel. I want to trace which life-styles motivate the use of which energy fuels. For example, for satisfying mobility through cars and planes it is convenient to refine petrol to diesel or to jet-fuel. These fuels however are very emission intensive. Satisfying mobility through light-weight e-bikes and the corresponding energy manifestation, namely electricity, is surely less harming to environment and people. Drawing these links, and building particularly on the conceptual work of Elizabeth Shove, can be understood as analyzing how social practice and social structure determine the energy metabolism of a society and vice versa.
All kinds of quantitative sustainability science, data-driven modelling and scenarios. Currently I use Matlab, Excel and/or Netlogo. Learning Python as well.
Global Patterns in Energy and Material Use, Sustainability, Complexity, Emergence, Networks (for example Input-Output data as a network), Socio-economic data sets of all kinds, Maths, Sociophysics, AI.