Funding body: School of Civil Engineering, Northeast Forestry University, China
Principal Investigator: Helen Bao
Co-Investigator: Dr. Jie Liu, School of Civil Engineering, Northeast Forestry University, China,
Budget: RMB 340,000 (Approximately £42,000)
Abstract:
People’s residential location decisions have significant impacts on their choices of transportation modes and travel behaviours. A few recent studies found that if movers are nudged at the right time, they may move to places with better public transportation facilities and increase the share of environmentally friendly travel habits, such as more active travelling and less automobile usage (Bhattacharyya, Jin, Le Floch, Chatman, & Walker, 2019; Guo & Peeta, 2020). This research aims to extend existing studies by investigating the effects of four behavioural interventions on renters’ consideration of transportation in their residential location choices. The findings could shed light on whether and how behavioural interventions can encourage movers to choose properties that could reduce carbon footprint and travel time, and consequently improve the wellbeing of both the individual and the society in the long run.
In recent years behavioural interventions – interventions that are neither monetarily incentivizing nor legally/regulatorily coercive – have been extensively applied in environment and urban studies. In two recent literature review papers, Khanna et al. (2021) and Buckley (2020) contrasted the effect of both behavioural interventions and monetary incentives in reducing energy consumption and CO2 emissions in residential buildings. They not only confirmed the positive effect of both monetary and non-monetary interventions on reducing the energy consumption of households, but also highlighted the potential benefits of deploying the right combinations of behavioural interventions.
To better understand and choose among the wide range of behavioural interventions, it is helpful to classify these tools into two broad categories: nudges and boosts. Nudges (Thaler & Sunstein, 2008) leverage behavioural heuristics in the design of choice architecture to induce desirable actions for both the individual and the society, such as using green electricity defaults to increase the uptake of renewable energy (Kaiser, Bernauer, Sunstein, & Reisch, 2020). Boosts (Grune-Yanoff & Hertwig, 2016), on the other hand, focus on changing existing behavioural heuristics or establishing new ones to support environmentally friendly actions, such as providing home energy report with personalised energy use feedback and energy conservation information to encourage energy savings (Allcott & Rogers, 2014). In other words, nudges are manipulating tools, while boosts empower people. Generally speaking, nudges are easier and quick to implement, but the effects tend to be short-lived; boosts require more time and resources to affect behaviours, but tend to remain effective for a longer term because ‘they have become routinised and have instilled a lasting competence in the user” (Lorenz-Spreen, Lewandowsky, Sunstein, & Hertwig, 2020, page 1106).
Existing evidence indicates that both nudges and boosts are effective in encouraging positive actions in environmental conservation and sustainable urban development. Yet the effects vary significantly among studies. For example, boosts are effective only when combined with nudges in energy saving experiments in Monaco (Lazaric & Toumi, 2022), while video information boosts outperform nudges in increasing acceptance of recycled water in the US (Tanner & Feltz, 2022). Therefore, the effectiveness of behavioural interventions is context specific, and subject to the influence of many possible moderators and mediators such as environmental consciousness (see, for example, Lazaric & Toumi, 2022). The proposed research aims to provide an analytical framework and empirical evidence to help policymakers choose among these behavioural interventions.
Taking stock of existing literature, this proposed research will test several nudges and boosts interventions via online panel data (OPD) platform Credamo. This is one of the largest OPD service providers in China. We choose this platform because it is the only one that allows researchers to conduct multiple rounds of experiments with the same respondent anonymously, which is essential for the research project to obtain ethical approval. The platform also has one of the best designs to support the use of graphics in experiments, which is required in one of the behavioural intervention designs (i.e., visualisation).
The research will be carried out in three stages. In the first stage, a total of 1,500 participant will be recruited online. They should be renters who intend to move in the next one to three months. These respondents will be randomly allocated to the four treatment groups and one control group. In the treatment groups, respondents will be exposed to one of the four behavioural interventions, and their residential location choices will be recorded in the form of stated preferences (i.e., intention). Travel costs and time will be calculated and compared with existing ones, and the effect of behavioural interventions on environmentally friendly residential location choice is estimated accordingly.
In the second stage, all respondents will be invited back for a short survey to record any properties they have viewed before the survey and whether they have moved. The OPD allows anonymous correspondence with respondents from previous stage of the experiment at a cost. Information collected from this stage will be used to estimate the short-term effect of behavioural interventions (e.g., whether treatment groups viewed properties with more environmentally friendly transportation choices) and identify respondents for the next stage (i.e. movers).
In the final stage, movers will be contacted within the first month and again by the end of the third month of the move. Online interviews will be used to collect detailed and reliable information about current and previous residence attributes and travel behaviours. The information will also be compared with the self-reported information about previous residence and travel behaviours in the first stage of the experiment for data validation purposes. Information collected from this stage will be used to estimate long-term effect of behavioural interventions. It is estimated that about 20% of the participants from the first stage will actually move during the second stage, and also willing to participate in the final stage of the experiment.