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Sustainability policy in the early 2000s is based on and therefore influenced by scientific literature on "transition". The importance of this link has inspired the authors to explore the structure of cooperating authors and citation networks in the field. In order to understand "transition" literature, we compare it with an alternative term for change, "transformation", which is also used in the context of socio-technical shifts towards sustainability. We expose the different structures of these fields with an overview of keywords, key references, key authors, and the coherence between references and authors. By analysing co-author and citation networks, we find large differences in these groups of documents. The transition literature is characterised by a large network of directly and indirectly cooperating authors with clear clusters; transformation literature contains smaller author networks. Key transition authors are predominantly Dutch. They repeatedly write together and cite each other's work. The transition literature is tightly knit with high degrees of internal references and a clearly distinguishable core. Transformation literature has fewer connections between authors and articles. The connecting articles, each with many global citations, form its basis. This analysis can be used as a step to continue the debate on the role of transition and transformation literature in sustainability and renewable energy policy. The transformation literature teaches us that older streams of thought are still relevant and may be used as "glue" for linking change with respect to sustainable energy to wider developments. Rediscovering existing literature in new combinations may lead to promising new views on sustainable energy.
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.
Societal transitions involve multiple actors, changes in institutions, values and technologies, and interactions across multiple sectors and scales. Given this complexity, this paper takes on the view that the societal transitions research field would benefit from the further maturation and broader uptake of modelling approaches. This paper shows how modelling can enhance the understanding of and support stakeholders to steer societal transitions. It discusses the benefits modelling provides for studying large societal systems and elaborates on different ways models can be used for transitions studies. Two model applications are presented in some detail to illustrate the benefits. Then, limitations of modelling societal transitions are discussed, which leads to an agenda for future activities: (1) better cooperation in the development of dynamic models, (2) stronger interaction with other transition scholars and stakeholders, and (3) use of additional modelling approaches that we think are relevant to and largely unexplored in transitions studies.
Heating behavior of households is key for reducing domestic energy demand and mitigating climate change. Recently, various technical devices have been developed, providing households with feedback on their heating behavior and supporting energy conservation behavior.
The impact of such devices on overall energy consumption depends on (1) the impact of a device within a household, (2) the diffusion of devices to other households and the number of adopters, and (3) the diffusion of the induced behavioral change beyond these households. While the first two processes are currently established in assessments of sustainable household devices, we suggest that adding behavior diffusion is essential when assessing devices that explicitly target behavioral change. We therefore propose an assessment framework that includes all three processes. We implement this framework in an agent-based model by combining two existing simulation models to explore the effect of adding behavior diffusion. In three simulation experiments, we identify two mechanisms by which behavior diffusion (1) spreads the effect of such devices from adopters to non-adopters and (2) increases the average speed of behavioral change of households. From these results we conclude that behavior diffusion should be included in assessments of behavior-changing feedback devices.
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.
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.
We present an approach to simulate climate and energy policy for the EU, using a flexible and modular agent-based modelling approach and a toolbox, called the Energy Modelling Laboratory (EMLab). The paper shortly reviews core challenges and approaches for modelling climate and energy policy in light of the energy transition. Afterwards, we present an agent-based model of investment in power generation that has addressed a variety of European energy policy questions. We describe the development of a flexible model core as well as modules on carbon and renewables policies, capacity mechanisms, investment behaviour and representation of intermittent renewables. We present an overview of modelling results, ongoing projects, a case study on current reforms of the EU ETS, and we show their relevance in the EU context.
Simulation modeling is useful to understand the mechanisms of the diffusion of innovations, which can be used for forecasting the future of innovations. This study aims to make the identification of such mechanisms less costly in time and labor. We present an approach that automates the generation of diffusion models by: (1) preprocessing of empirical data on the diffusion of a specific innovation, taken out by the user; (2) testing variations of agent-based models for their capability of explaining the data; (3) assessing interventions for their potential to influence the spreading of the innovation. We present a working software implementation of this procedure and apply it to the diffusion of water-saving showerheads. The presented procedure successfully generated simulation models that explained diffusion data. This progresses agent-based modeling methodologically by enabling detailed modeling at relative simplicity for users. This widens the circle of persons that can use simulation to shape innovation.
Feedback devices can be used to inform households about their energy-consumption behavior. This may persuade them to practice energy conservation. The use of feedback devices can also - via word of mouth - spread among households and thereby support the spread of the incentivized behavior, e.g. energy-efficient heating behavior. This study investigates how to manage the impact of these environmental innovations via marketing. Marketing activities can support the diffusion of devices. This study aims to identify the most effective strategies of marketing feedback devices. We did this by adapting an agent-based model to simulate the roll-out of a novel feedback technology and heating behavior within households in a virtual city. The most promising marketing strategies were simulated and their impacts were analyzed. We found it particularly effective to lend out feedback devices to consumers, followed by leveraging the social influence of well-connected individuals, and giving away the first few feedback devices for free. Making households aware of the possibility of purchasing feedback devices was found to be least effective. However, making households aware proved to be most cost-efficient. This study shows that actively managing the roll-out of feedback devices can increase their impacts on energy-conservation both effectively and cost-efficiently.
For many years, carbon capture and storage (CCS) has been discussed as a technology that may make a significant contribution to achieving major reductions in greenhouse gas emissions. At present, however, only two large-scale power plants capture a total of 2.4 Mt CO2/a. Several reasons are identified for this mismatch between expectations and realised deployment. Applying bibliographic coupling, the research front of CCS, understood to be published peer-reviewed papers, is explored to scrutinise whether the current research is sufficient to meet these problems. The analysis reveals that research is dominated by technical research (69%). Only 31% of papers address non-technical issues, particularly exploring public perception, policy, and regulation, providing a broader view on CCS implementation on the regional or national level, or using assessment frameworks. This shows that the research is advancing and attempting to meet the outlined problems, which are mainly non-technology related. In addition to strengthening this research, the proportion of papers that adopt a holistic approach may be increased in a bid to meet the challenges involved in transforming a complex energy system. It may also be useful to include a broad variety of stakeholders in research so as to provide a more resilient development of CCS deployment strategies.
Transition modelling is an emerging but growing niche within the broader field of sustainability transitions research. The objective of this paper is to explore the characteristics of this niche in relation to a range of existing modelling approaches and literatures with which it shares commonalities or from which it could draw. We distil a number of key aspects we think a transitions model should be able to address, from a broadly acknowledged, empirical list of transition characteristics. We review some of the main strands in modelling of socio-technological change with regards to their ability to address these characteristics. These are: Eco-innovation literatures (energy-economy models and Integrated Assessment Models), evolutionary economics, complex systems models, computational social science simulations using agent based models, system dynamics models and socio-ecological systems models. The modelling approaches reviewed can address many of the features that differentiate sustainability transitions from other socio-economic dynamics or innovations. The most problematic features are the representation of qualitatively different system states and of the normative aspects of change. The comparison provides transition researchers with a starting point for their choice of a modelling approach, whose characteristics should correspond to the characteristics of the research question they face. A promising line of research is to develop innovative models of co-evolution of behaviours and technologies towards sustainability, involving change in the structure of the societal and technical systems.
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.