Scenario modeling tools, like the global integrated assessment framework maintained at IIASA, are widely used to evaluate the costs, potentials, and consequences of different energy, climate, and human development futures over the medium-to-long term. Representation of the global energy-economy within such models is inevitably—and often intentionally—stylized, simplified, and selective. Their purpose is to derive robust insights on the systemic consequences of socioeconomic development and technology and policy choices (Krey, 2014). In particular, these models typically represent consumer behavior and the end use of energy as a simple, perfectly rational choice between available alternatives, for the most part depicting social actors as ‘representative agents’ who describe aggregate behavior at the mean (Conlisk, 1996, Laitner et al., 2000).
The Eco-Spill project will lead to the development of two Spatial-Behavioral Models of Energy Technology Diffusion, with the ultimate aim of linking them to the Model for Energy Supply Strategy Alternatives and their General Environmental Impact (MESSAGE).
Recent work at IIASA has incorporated a mechanism for representing spatial energy technology diffusion characteristics into a simplified integrated assessment model (Leibowicz et al., 2016). In addition, an agent-based model has been developed to study the importance of networks in driving energy technology choices (Grubler et al., 2016). Given the recognized importance of behavior for energy technology adoption, a fitting extension to each of these work-streams is the role of cultural spillovers and social interactions across countries. Previous research related to Hofstede’s cultural dimensions suggests similar purchase patterns evolve within culturally similar countries (de Mooij and Hofstede, 2011). No research, as yet, has explored such dynamics in the energy space, and no models have attempted to capture them.
This project (a partnership between the IIASA Energy and Transitions to New Technologies Programs, as well as external collaborators) aims to develop and test (through sensitivity analyses) alternative methods for the endogenous updating of consumer preferences over time and space. First, we will derive reduced-form relationships based on empirical findings for describing technology adoption propensity for different consumer types in the context of the broader adoption environment. Next, the empirical relationships will inform the two different modeling tools to be developed in this project. The spatial technology diffusion model described above will be extended into the socio-behavioral domain; forming a hybrid optimization model/discrete-choice model. In parallel, an agent-based model will be extended to study the importance of social interactions among diverse social networks. We focus our efforts on the transportation sector, where behavioral barriers to adoption of alternative-fuel vehicles have been shown to play an important role in the transformation toward a low-carbon energy system (Lin and Greene, 2011, McCollum et al., 2016). Future work could extend the research into other energy domains. In short, the models will permit (i) an assessment of how influential transport technology outcomes and travel/lifestyle choices in one part of the world may at be driving the speed of adoption (or non-adoption, i.e. aversion) and behavioral change in another, and (ii) the identification of behavioral tipping points, whether aiding or hindering the transition to a more sustainable future.
Hazel Pettifor, University of East Anglia, UK
Zhenhong Lin, Oak Ridge National Laboratory, USA
CONLISK J (1996) Why bounded rationality? Journal of Economic Literature, 34, 669-700.
DE MOOIJ M & HOFSTEDE G (2011) Cross-cultural consumer behavior: A review of research findings. Journal of International Consumer Marketing, 23, 181-192.
GRUBLER A, KREY V, TIEJU MA & MCCOLLUM D (2016) New Approaches for Modeling Energy End-use Technology Adoption and Behavior in IAM Networks (RITE-IIASA Collaborative Study). Laxenburg, Austria: IIASA.
KREY V (2014) Global energy-climate scenarios and models: a review. Wiley Interdisciplinary Reviews: Energy and Environment, 3, 363-383.
LAITNER JAS, DECANIO SJ & PETERS I (2000) Incorporating Behavioural, Social, and Organizational Phenomena in the Assessment of Climate Change Mitigation Options. In: JOCHEM, E., SATHAYE, J. & BOUILLE, D. (eds.) Society, Behaviour, and Climate Change Mitigation. Dordrecht, The Netherlands: Kluwer Academic Publishers.
LEIBOWICZ BD, KREY V & GRUBLER A (2016) Representing spatial technology diffusion in an energy system optimization model. Technological Forecasting and Social Change, 103, 350-363.
LIN Z & GREENE DL (2011) Promoting the Market for Plug-In Hybrid and Battery Electric Vehicles. Transportation Research Record: Journal of the Transportation Research Board, 2252, 49-56.
MCCOLLUM DL, WILSON C, PETTIFOR H, RAMEA K, KREY V, RIAHI K, LIN CB, EDELENBOSCH OY & FUJISAW S (2016). Improving the behavioral realism of global integrated assessment models: an application to consumers’ vehicle choices.
Last edited: 09 November 2016
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