Immersive technologies are rapidly reshaping our communication methods. Immersive experiences within virtual environments can elicit profound emotional responses and capture the viewer’s attention, enabling the more effective transmission of messages and ideas. VR technologies have been suggested as potentially effective tools to enhance awareness about environmental problems (Thoma et al., 2023), for example, by helping individuals visualize and comprehend future climate scenarios, otherwise perceived as distant and abstract. However, it is unclear whether VR is more effective in sustaining behavior change (Horen et al., 2024; Ferreira & Banerjee, 2024).

This project aims to assess whether VR technology can positively change multiple human behaviours in an environmental context. One way to study this is by evaluating spillover effects of behavioral interventions (Galizzi & Whitmarsh, 2019). Spillover effects assess whether individuals who have adopted more sustainable behaviour in one domain (eating less meat) will also become more susceptible to changing their behaviour in another domain (reduced energy consumption).

We propose developing an environment where participants interact with various scenarios to increase awareness and support behavior change regarding the environment through digital intervention. This will be tested using a randomized control trial with four conditions: (1) a 2D environment on a computer monitor, (2) a 3D environment in virtual reality (VR), (3) a placebo scenario unrelated to the environment in VR, and (4) a control condition where participants read scenario text. Participants in each condition will make two environmental choices, allowing us to determine the direct impact of VR versus 2D technology and any indirect spillover effects.

The study consists of multiple methods: we administer a digital intervention experiment embedded in a longitudinal study to measure real behaviors over time, using surveys to measure participants’ baseline characteristics and pre- and post-experimental attitudes. We will use state-of-the-art econometric methods to provide causal estimates of our treatments.

Researchers: