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In Germany, doubling today's insulation rate of about 1% is an important element for reaching the government's target of reducing the demand for energy in the housing sector by 80% by 2050. A survey among 275 private homeowners was conducted to better understand their insulation activity. The results were incorporated into an agent-based model, which was applied to evaluate new policy options. The results of the survey show that policies should focus on homeowners' wall insulation activity. Homeowners' decision-making processes regarding insulation are largely unaffected by their financial resources, which raises the question of the usefulness of financial incentives. In contrast, non-economic factors were found to have a statistically significant influence: in the year following a house ownership change, a comparatively large number of insulation projects are carried out. The probability of insulating walls can be predicted from knowing the homeowner's age, attitude towards insulation, and the structural condition of the walls. The simulations indicate that information instruments lead to a comparatively small increase in the wall insulation rate, while obligating new homeowners to insulate the walls within the first year after moving in has the potential to increase the total insulation rate by up to 40%.
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.
Für die Umsetzung der Energiewende und speziell den Ausbau erneuerbarer Energien sind nicht nur energiewirtschaftliche oder Klimaschutz-Kriterien maßgeblich. Zu einer umfassenden Nachhaltigkeitsbewertung gehört unter anderem auch die Ressourcenbewertung. Hier ist unstrittig, dass die Gesamt-Ressourceninanspruchnahme eines Energiesystems generell erheblich niedriger ist, wenn dieses nicht auf fossilen, sondern auf erneuerbaren Energien basiert (und dabei nicht hauptsächlich auf Biomasse ausgerichtet ist). Bisher wurde jedoch insbesondere der Verbrauch und die langfristige Verfügbarkeit der mineralischen Rohstoffe, die in der Regel zur Herstellung von Energiewandlern und Infrastruktur benötigt werden, wenig untersucht.
Im Rahmen des Projekts KRESSE wurde daher erstmals analysiert, welche "kritischen" mineralischen Rohstoffe für die Herstellung von Technologien, die Strom, Wärme und Kraftstoffe aus erneuerbaren Energien erzeugen, bei einer zeitlichen Perspektive bis zum Jahr 2050 in Deutschland relevant sind. Die Einschätzung als "kritisch" umfasst dabei die langfristige Verfügbarkeit der identifizierten Rohstoffe, die Versorgungssituation, die Recyclingfähigkeit und die Umweltbedingungen der Förderung. Die Studie macht deutlich, dass die geologische Verfügbarkeit mineralischer Rohstoffe für den geplanten Ausbau der erneuerbaren Energien in Deutschland grundsätzlich keine limitierende Größe darstellt. Dabei kann jedoch möglicherweise nicht jede Technologievariante unbeschränkt zum Einsatz kommen.
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.