The energy and the climate crisis reinforced the urgency of energy efficiency in residential buildings. Yet, we observe that the energy transition in residential buildings is greatly hampered as individual citizens and citizen collectives bear high information, coordination and transaction costs. At the same time, policymakers are expected to orchestrate a complex process, in which a myriad of critical investments determine the overall energy system efficiency and decarbonisation costs.
The 4-year project ALIGN4Energy, funded by the Netherlands Science Foundation (NWO), aims to contribute to deploying clean energy technologies in residential homes rapidly and at scale. The ALIGN4energy consortium provides a total of 11 PhD positions aimed to simultaneously address the citizens, policy and business perspectives of the residential energy transition by: 1) mapping citizens' preferences and investment barriers, 2) modelling the impact of diverse investments on overall energy system efficiency, 3) developing an adaptive decision-support system for citizens and policymakers supported by artificial intelligence that simultaneously optimises investments at the individual and collective level, and by 4) co-creating and field-testing behavioural interventions to stimulate investments in energy efficiency in combination with participation mechanisms. The Department of Environmental Economics of the Institute for Environmental Studies (IVM) at Vrije Universiteit Amsterdam will coordinate ALIGN4Energy and will host two of the 11 PhD positions with a strong focus on the behavioural and quantitative economic sciences. Both PhDs hosted at IVM will strongly collaborate with researchers from other Dutch universities and research institutes and will form part of a larger PhD network.
For PhD 1 you will co-design tailored decision-support for investments in residential energy efficiency, including behavioural interventions such as nudges. Based on the insights on the different types of decision-makers, you will co-design personalised behavioural interventions in a collaborative way with our societal partners (e.g. municipalities, ministries, digital platform developers, utilities, banks). These personalised behavioural interventions will be tested in selected test beds in several Dutch municipalities through one of the energy transition platforms we have access to (i.e. Habitata, BuurtWarmteWijzer). In this step, we will compare the outcomes of the interventions to a control group without personalised interventions. The objective is to design and test interventions which are the most effective in stimulating energy efficiency, and thereby, make an important contribution to the sustainable energy transition.
For PhD 2 you will test the digital tailored individual and collective decision-making support on a large scale through one of the energy transition platforms we have access to (i.e. habitata, BuurtWarmteWijzer). Amongst others, you will compare whether such a mechanism helps to effectively coordinate the investment decisions within citizen collectives (such as a VVE or a group of tenants), comparing the treated group to a comparable group without participation mechanism. Moreover, you will implement the personalised interventions at a large scale on the habitata platform and apply the learning algorithms developed to further refine the prediction model. The objective is to design and test decision-making mechanisms to stimulate energy efficiency of citizens and citizen collectives at scale, and thereby, to make an important contribution to the sustainable energy transition.Your duties
- analysis of survey data and field experimental data
- co-design of tailored decision-support & behavioural interventions
- testing of tailored decision-support & behavioural interventions (incl. nudges) in online and offline test beds
- testing of collective decision-making mechanism in selected online and offline test beds
- large-scale field-testing of interventions based on learning algorithm