Envisioning surprises: How social sciences could help models represent ‘deep uncertainty’ in future energy and water demand
| Autor: | Dana Abi Ghanem, Josephine Mylan, Maria Sharmina, Saska Petrova, Alison Browne, Sarah Marie Hall, Ruth Wood |
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| Rok vydání: | 2019 |
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Sustainable Consumption Institute
Emerging technologies water sector 0211 other engineering and technologies Energy Engineering and Power Technology ResearchInstitutes_Networks_Beacons/sustainable_consumption_institute 02 engineering and technology Rebound effect (conservation) 010501 environmental sciences 01 natural sciences Regional planning Human geography Economics 021108 energy Social science uncertainty 0105 earth and related environmental sciences paradigm change Energy Renewable Energy Sustainability and the Environment decision-making ResearchInstitutes_Networks_Beacons/03/04 Demand forecasting demand forecasting Fuel Technology Nuclear Energy and Engineering Paradigm shift energy sector nexus Futures contract Nexus (standard) Social Sciences (miscellaneous) |
| Zdroj: | Sharmina, M, Abi Ghanem, D, Browne, A, Hall, S, Mylan, J, Petrova, S & Wood, F 2019, ' Envisioning surprises: How social sciences could help models represent ‘deep uncertainty’ in future energy and water demand ', Energy Research & Social Science, vol. 50, pp. 18-28 . https://doi.org/10.1016/j.erss.2018.11.008 |
| ISSN: | 2214-6296 |
| Popis: | Medium- and long-term planning, defined here as 10 years or longer, in the energy and water sectors is fraught with uncertainty, exacerbated by an accelerating ‘paradigm shift’. The new paradigm is characterised by a changing climate and rapid adoption of new technologies, accompanied by changes in end-use practices. Traditional methods (such as econometrics) do not incorporate these diverse and dynamic aspects and perform poorly when exploring long-term futures. This paper critiques existing methods and explores how interdisciplinary insights could provide methodological innovation for exploring future energy and water demand. The paper identifies four attributes that methods need to capture to reflect at least some of the uncertainty associated with the paradigm shift: stochastic events, the diversity of behaviour, policy interventions and the ‘co-evolution’ of the variables affecting demand. Machine-learning methods can account for some of the four identified attributes and can be further enhanced by insights from across the psychological and social sciences (human geography and sociology), incorporating rebound effect and the unevenness of demand, and acknowledging the emergent nature of demand. The findings have implications for urban and regional planning of infrastructure and contribute to current debates on nexus thinking for energy and water resource management. |
| Databáze: | OpenAIRE |
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