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Insulating existing buildings offers great potential for reducing greenhouse gas emissions and meeting Germany's climate protection targets. Previous research suggests that, since homeowners' decision-making processes are inadequately understood as yet, today's incentives aiming at increasing insulation activity lead to unsatisfactory results. We developed an agent-based model to foster the understanding of homeowners' decision-making processes regarding insulation and to explore how situational factors, such as the structural condition of houses and social interaction, influence their insulation activity. Simulation experiments allow us furthermore to study the influence of socio-spatial structures such as residential segregation and population density on the diffusion of renovation behavior among homeowners. Based on the insights gained, we derive recommendations for designing innovative policy instruments. We conclude that the success of particular policy instruments aiming at increasing homeowners' insulation activity in a specific region depends on the socio-spatial structure at hand, and that reducing financial constraints only has a relatively low potential for increasing Germany's insulation rate. Policy instruments should also target the fact that specific renovation occasions are used to undertake additional insulation activities, e.g. by incentivizing lenders and craftsmen to advise homeowners to have insulation installed.
The buildings sector accounts for more than 30% of global greenhouse gas emissions. Despite the well-known economic viability of many energy-efficient renovation measures which offer great potential for reducing greenhouse gas emissions and meeting climate protection targets, there is a relatively low level of implementation. We performed a citation network analysis in order to identify papers at the research front and intellectual base on energy-efficient renovation in four areas: technical options, understanding decisions, incentive instruments, and models and simulation. The literature was reviewed in order to understand what is needed to sufficiently increase the number of domestic energy-efficient renovations and to identify potential research gaps. Our findings show that the literature on energy-efficient renovation gained considerable momentum in the last decade, but lacks a deep understanding of the uncertainties surrounding economic aspects and non-economic factors driving renovation decisions of homeowners. The analysis indicates that the (socio-economic) energy saving potential and profitability of energy-efficient renovation measures is lower than generally expected. It is suggested that this can be accounted for by the failure to understand and consider the underpinning influences of energy-consuming behaviour in calculations. Homeowners׳ decisions to renovate are shaped by an alliance of economic and non-economic goals. Therefore, existing incentives, typically targeting the economic viability of measures, have brought little success. A deeper understanding of the decisions of homeowners is needed and we suggest that a simulation model which maps the decision-making processes of homeowners may result in refining existing instruments or developing new innovative mechanisms to tackle the situation.
The German Energiewende is a deliberate transformation of an established industrial economy towards a nearly CO2-free energy system accompanied by a phase out of nuclear energy. Its governance requires knowledge on how to steer the transition from the existing status quo to the target situation (transformation knowledge). The energy system is, however, a complex socio-technical system whose dynamics are influenced by behavioural and institutional aspects, which are badly represented by the dominant techno-economic scenario studies. In this paper, we therefore investigate and identify characteristics of model studies that make agent-based modelling supportive for the generation of transformation knowledge for the Energiewende. This is done by reflecting on the experiences gained from four different applications of agent-based models. In particular, we analyse whether the studies have improved our understanding of policies' impacts on the energy system, whether the knowledge derived is useful for practitioners, how valid understanding derived by the studies is, and whether the insights can be used beyond the initial case-studies. We conclude that agent-based modelling has a high potential to generate transformation knowledge, but that the design of projects in which the models are developed and used is of major importance to reap this potential. Well-informed and goal-oriented stakeholder involvement and a strong collaboration between data collection and model development are crucial.