The survey protocol

Research Plan

The cities we chose for the study are similar in their importance in their respective countries and are all “environmentally conscious”. However, there are interesting differences when it comes to climate and economical characteristics and CO2 reduction goals. Thus, we will survey households in a Mediterranean area opposite to polar climate, households exposed to natural hazards, households from multicultural sites and households living in a rather industrial area compared to a touristic location.

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The consortium combines a large experience in household survey research, including quali-tative methods. The simulation tool was developed by Ghislain Dubois in a previous pilot project and will be adapted to fit the research questions and contexts in the four partici-pating countries. The sample of households will be chosen in order to also allow a country-by-country comparison of mid-size urban populations. The sample size was calculated on the basis of the following assumptions: What is, on average over the four participating countries (the 4 towns specifically), the item Xk (k=1, …, K) that contributes most to the CO2 reduction according to its relative importance given by the households, multiplied by the reported reduction percentage. Aiming for the length of the 95% confidence interval of this estimate to be +/-1%, we calculated the overall sample size was calculated to comprise n= 600 households (n=150 per country) .
The household sample will be drawn based on administrative population registers in each town. Since we offer a benefit to household, i.e. a “free” expert in-house assessment of its GHG footprint, we assume that non-participation rates will be lower than in comparable household surveys. Nevertheless we will use standard procedures to attain the required sample size and to evaluate the potential bias this might introduce.

1. At the first visit to the household (average time 2 hours according to pilot by Dubois) we will (i) apply a questionnaire on climate relevant views and behavior and (ii) establish a computer-assisted assessment of the household’s current GHG footprint.

2. At the second visit, the adapted simulation tool will be applied running households step by step through their own preferences from a list of 80 choices, allowing them to review and change their choice when given the prompt information in three areas (i) the amount of GHG this choice saves, (ii) the financial savings or expenses the choice generates. This allows to feedback to households the information on “eco-efficiency, i.e. (€/t of CO2 avoided), (iii) Households will be informed on the potential health benefits that they would gain, based on their respective choices. The spreadsheet on potential health co-benefits of individual behavior change was developed by Paul Wilkinson (HD team) in another study (see HD team). It will be adapted and integrated in the simulation tool. The households are asked to propose GHG reduction options in the rank order of their preferences until they have reached the imposed target.

3. The sample for the quantitative study will be used as a sampling frame for the in-depth qualitative study after the results for the questionnaire survey, the carbon footprint as-sessment and the simulation results will be available. Based on this information, we pro-pose to select 20 households in each town for subsequent in depth interview. The selection of the households will be based on a set of scientifically and policy relevant criteria, to be identified by the SAB. The qualitative study component will explore household views on potential barriers and motivation for reducing their carbon footprint. A flexible thematic interview guide will be used in the in-depth interviews. When developing the interview guide information attained from the first stage of the household survey will be used. In the interviews examples of areas covered are understanding knowledge, attitudes and practice about climate change and civil society mitigation, households‘ perceptions of the drivers behind their own GHG emission profile and their own responsibility to reduce GHG emissions, understanding if households are fully rational in their decision-making or only partially, their own priorities and the rationale they put behind their choices, in particular to discriminate financial and non-financial values and the motivation behind such choices and issues regarding behavioral change. The issue of values, quality of life and the role of health as a motivation for behavioral change are particularly important. A descriptive con-tent analysis applied by Graneheim & Lundmann (2004) will be employed with the use of text analysis software.