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A key factor to energy-efficiency of heating in buildings is the behavior of households, in particular how they ventilate rooms. Energy demand can be reduced by behavioral change; devices can support this by giving feedback to consumers on their behavior. One such feedback device, called the "CO2 meter", shows indoor air-quality in the colors of a traffic light to motivate so called "shock ventilation", which is energy-efficient ventilation behavior. The following effects of the "CO2 meter" are analyzed: (1) the effect of the device on ventilation behavior within households, (2) the diffusion of "CO2 meter" to other households, and (3) the diffusion of changed behavior to households that do not adopt a "CO2 meter". An agent-based model of these processes for the city of Bottrop (Germany) was developed using a variety of data sources. The model shows that the "CO2 meter" would increase adoption of energy-efficient ventilation by c. 12% and reduce heating demand by c. 1% within 15 years. Technology diffusion was found to explain at least c. 54% of the estimated energy savings; behavior diffusion explains up to 46%. These findings indicate that the "CO2 meter" is an interesting low-cost solution to increase the energy-efficiency in residential heating.
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.
Industrial demand response can play an important part in balancing the intermittent production from a growing share of renewable energies in electricity markets. This paper analyses the role of aggregators - intermediaries between participants and power markets - in facilitating industrial demand response. Based on the results from semi-structured interviews with German demand response aggregators, as well as a wider stakeholder online survey, we examine the role of aggregators in overcoming barriers to industrial demand response. We find that a central role for aggregators is to raise awareness for the potentials of demand response, as well as to support implementation by engaging key actors in industrial companies. Moreover, we develop a taxonomy that helps analyse how the different functional roles of aggregators create economic value. We find that there is considerable heterogeneity in the kind of services that aggregators offer, many of which do create significant economic value. However, some of the functional roles that aggregators currently fill may become obsolete once market barriers to demand response are reduced or knowledge on demand response becomes more diffused.
Participatory modeling - the involvement of stakeholders in the modeling process - can support various objectives, such as stimulating learning processes or promoting mutual understanding of stakeholders. Participatory modeling approaches could therefore be useful for the governance of transitions, but a systematic account of potential application areas of participatory modeling methods in transition governance is still lacking. This article addresses this gap by providing a review of participatory modeling methods and linking them to phases and objectives of transition governance. We reviewed participatory modeling studies in transition research and related fields of social-ecological modeling, integrated assessment and environmental management. We find that participatory modeling methods are mostly used for participatory visioning and goal setting as well as for interactive strategy development. The review shows the potential for extending the application of participatory modeling methods to additional phases of transition governance and for the exchange of experiences between research fields.
Integrated assessment models (IAMs) are commonly used by decision makers in order to derive climate policies. IAMs are currently based on climate-economics interactions, whereas the role of social system has been highlighted to be of prime importance on the implementation of climate policies. Beyond existing IAMs, we argue that it is therefore urgent to increase efforts in the integration of social processes within IAMs. For achieving such a challenge, we present some promising avenues of research based on the social branches of economics. We finally present the potential implications yielded by such social IAMs.
Was ist synthetisches Gas?
(2019)