OSSOKINA

Energy transition in housing: economic behavior and customer segmentation


In the coming decades, 5 million Dutch dwellings need to be made energy-efficient through insulation, solar panels, new heating sources, etc. Environmental savings this energy transition will yield, largely depend on the behaviour of house owners and tenants. Will people timely invest in new technologies? Will they use the installed energy-efficient technologies in a sustainable way? How does this differ by population segment? My research group works on digital solutions and behavioural tools that can predict and affect clean energy choices of people. We use economic behavioural models, AI and big data.

See below for examples of the projects.

1. Does information encourage or discourage tenants to accept energy retrofitting of homes?

Finished, together with Stephan Kerperien and Theo Arentze. English working paper. Dutch article.

We ran a stated choice experiment in which 600 tenants of 4 public housing providers were repeatedly offered choices from different energy retrofitting packages. We find that between 70 and 80% of tenants are willing to agree with existing retrofitting packages, also when these include a rent increase. A communication strategy providing tenants with additional information on retrofitting can increase but also decrease this share with 3 to 5 percentage points. When comfort-related consequences of renovations are highlighted, people are more likely to choose for retrofitting. Information on financial consequences on the other hand makes people more critical and reduces the support for retrofitting. Support for retrofitting is further reduced when tenants have low trust in their housing association.

In collaboration with 4 public housing providers.

2. The effects of digital environments on people's renewable energy choices

PhD project (ongoing)

We evaluate the effectiveness of existing online platforms that help people choose the best energy investment for their home, and search for the best practices.

The project answers the following questions: How do artificial intelligence driven online platforms affect clean energy choices of house owners and neighbourhoods? How do population groups differ in the energy-efficient technologies they choose and in the way they respond to online campaigns?

The insights will be used to improve online platforms and develop tailor-made information and communication campaigns for energy transition in neighbourhoods.

In collaboration with Dutch municipalities, online platform providers and CBS statistics restricted access microdata.

3. Digital tools to model and affect clean energy choices of low-income people

PhD project (ongoing)

We evaluate the changes in energy consumption and living comfort of low-income families after energy retrofitting of their homes. Special focus is on those who have difficulty paying energy bills.

People change their behaviour after energy retrofitting of their homes, e.g. by showering longer or setting the temperature higher. This so-called rebound effect reduces energy- and environmental savings from retrofitting, but increases further the living comfort of residents. We evaluate the energy savings as well as increases in comfort after retrofitting, for low-income families and people facing energy poverty. We develop tools for public housing providers to choose the optimal retrofitting technology taking into account the behavioural responses of tenants.

In collaboration with 2 other universities, public housing providers and CBS statistics restricted access microdata.

See press-release.

4. Do people value the environmental footprint of their house? Choice of materials for insulation

Master thesis (ongoing)

Better insulation is the first and most widespread energy-saving technology people invest in when making their homes energy-efficient. Choosing the insulation material is however not as easy as it may look like. Materials differ in costs and energy saving capacity. But also in fire resistance, sound reduction and last, but not least, the CO2 emitted during their production.

At TU Eindhoven a tool is being designed to help collectives such as energy cooperatives or neighbourhoods figure out what insulation attributes their members value most, and thus support efficient and fast collective purchasing. We are now testing a beta-version and would like to invite members of energy cooperatives and other collectives to participate in this pilot, through an online experiment.

5. Energy retrofitting in public housing. Tools for participation and collective decision-making.

PDEng project and master thesis (ongoing)