Energy Demand Changes Induced by Technological and Social Innovations

Levels and structure of energy and resource demands are increasingly recognized as a key critical determinant of feasibility, timing, and costs of climate mitigation actions and their SDG synergies and tradeoffs. The higher the demand, the earlier, the more stringent, and the more costly climate mitigation will have to be. Conversely, lower demands increase the temporal flexibility of climate mitigation and reduce the stringency and costs of mitigation actions, thus also reducing the risks of SDG tradeoffs. Energy and resource demands themselves are intermediary variables, and it is the services and amenities that the use of energy and other resources provides. The efficiency of resource use and the efficiency of alternative service provision models thus moves into center stage of climate mitigation from a demand, or end-use perspective. Because of the high heterogeneity of consumers and the multitude of demand types (food, shelter, mobility, communication, etc.) the theoretical understanding and modeling of “demand” (outside aggregated simplistic formulation) remains limited and fragmented, as are resulting capabilities to propose and to assess demand-side policy interventions from the twin angle of climate mitigation as well as of promoting the SDGs. Overall project objectives 1. to improve the state-of-art of demand modeling in environmental and climate policy analysis, via methods and model intercomparisons and assisting the transfer of conceptual and methodological improvements across disciplines, sectors, and environmental domains. 2. to better inform policy via structured model experiments and simulations that assess potential impacts, barriers, as well as synergies and tradeoffs to other SDG objectives of demand-side policy interventions, particularly in novel fields and service provision models such as digitalization, sharing economy, or the integration of SDG and climate objectives in synergistic policy designs. 3. EDITS focuses on both the human and the technical resources by launching an expert network and a demand-side model comparison exercise.