Master of Science
As food waste increases globally, many cities have implemented curbside collection of food waste (aka green bin programs) to divert food waste from landfills. However, not all municipalities in Ontario have green bin programs. A factor responsible for the adoption of green bin programs is the community support for the program. The study results are based on 407 completed surveys from randomly selected households in London, Ontario (a municipality without a green bin program) and Kitchener-Waterloo, Ontario (a municipality with a green bin program). Surveys were used to collect data to understand: i) the predictors of household green bin support and, ii) the difference in green bin support between both cities. Household food wasting and waste diversion variables were used to predict green bin support. As hypothesized, food wasting, and waste diversion variables were able to predict green bin support and Kitchener-Waterloo respondents were more supportive than those from London. Concern for environmental impact, convenience and norms favouring green bin use were the strongest predictors of green bin support in all three models (Kitchener-Waterloo, London and pooled sample). Composting, amount of food wasted, good provider identity, personal norms against food wasting, and food waste education were predictors in two models (London and pooled sample) while age was only a predictor one model (pooled sample). Municipalities looking to improve green bin support should consider educating their residents on food waste reduction and future research should investigate whether green bin support translates to green bin behaviour.
Summary for Lay Audience
Around the world, the amount of food wasted continues to increase creating several known economic, social and environmental issues. To combat some of the economic and particularly environmental issues (such as greenhouse gas emissions), cities around the world have implemented food waste diversion programs (commonly known as green bin programs). Green bin programs help divert food waste from landfill. However, in Ontario, Canada, not all municipalities have green bin programs – a reason for the inconsistency is linked with the support for the program. Support for diversion programs (e.g., blue bin) is a common theme observed in waste diversion literature. Therefore, this thesis attempts to understand green bin support in and between a city with a green bin and a city without a green bin. This study was conducted in two similar, mid-sized Ontario cities – London (without green bin) and Kitchener-Waterloo (with green bin). Surveys were sent to random households across both cities to understand: i) the predictors of green bin support and, ii) the difference in green bin support between both cities. The study used several variables (such as the amount of food people reported they wasted, food overprovisioning, having time and space to sort food waste and concern for environmental impact etc.) normally found in food waste and waste diversion studies to predict green bin support. Environmental concerns, convenience and norms favouring green bins were significant predictors of support in all models. Alongside these variables, age, food waste education and the amount of food wasted were also significant predictors of green bin support for both London and the pooled sample. Additionally, Kitchener-Waterloo residents were more supportive of a green bin program. The findings suggest that if municipalities are looking to improve green bin support, an important step to take is educating their residents on food waste reduction. Food waste education has a two-fold impact: 1) it makes people aware of their food wasting amount (and its consequences) so they can actively try to reduce it and, ii) it shows the (environmental) benefits of a green bin program thus improving the support. Future research should investigate whether green bin support translates to green bin behaviour.
Ladele, Oluwatomilola, "Understanding the Support for Municipal Green Bin Programs" (2020). Electronic Thesis and Dissertation Repository. 7341.
Available for download on Friday, January 01, 2021