Abstract
Energy scenarios currently in use for policy advice are based on a number of simplifying assumptions. This includes, in particular, the linear extrapolation of trends. However, this approach ignores the fact that central variables were highly dynamic in the past. For an assessment of energy futures and the specification of measures, novel approaches are necessary which can implement non-linear trends. In this paper, we show how cross-impact balance (CIB) analysis can be applied to map dynamic trends. Using a small CIB model, we highlight the need for novel approaches in the creation and evaluation of energy futures and the possible contribution of CIB analysis.
Highlights
Socio-economic systems, like the energy system, are evolutionary systems
Path dependencies and persistence resulting for instance from long lifetimes of technological infrastructures (i. e., power plants) and incumbent energy companies with low interest in radical innovations stabilize the dynamics of the systems (Patel and Pavitt 1997; Safarzynska and van den Bergh 2010)
As long as the dynamics of the system do not change, possible futures of the corresponding system can be assessed more or less. The dynamics of these systems do depend on technical innovations (Grubler and Wilson 2013) and on changes in institutions, socio-economic structures, and policies on local, national, and global level, within and outside the system (Nelson and Winter 2002; Nelson and Winter 1982; Fagerberg 2003; Metcalfe 1994; Witt 2008). Examples for such occurrences are the liberalization of energy markets, or long-run changes in the attitudes of the government and the public towards nuclear power plants in Germany, Sweden, Switzerland and Belgium, which were caused in particular by changes in the composition of the government (World Nuclear Association 2019)
Summary
Socio-economic systems, like the energy system, are evolutionary systems. Path dependencies and persistence resulting for instance from long lifetimes of technological infrastructures (i. e., power plants) and incumbent energy companies with low interest in radical innovations stabilize the dynamics of the systems (Patel and Pavitt 1997; Safarzynska and van den Bergh 2010). S ource: Authors’ own compilation ing sub-periods and crucial descriptors (see Vögele et al 2018a for more information on advantages and disadvantages of the Advanced energy modelling – different approaches) Those descriptors can either trigger a change in interdependencies within the CIM as soon as a cerconsidering non-linearities tain threshold is reached, or they exhibit cyclic behaviour Starting with the first sub-period a CIM ing point, relevant variables or parameters are extrapolated into is constructed and several scenarios are identified, each describthe future This exercise is generally done, without changing the ing a possible future. The Cross-Impact-Balance (CIB) approach allows for speci- subsequent periods following future C This exercise is repeated fying consistent socio-economic storylines
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