Data driven techniques and evidence-based policy in waste management system

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Canadians generate about 2.7kg/cap of non-hazardous municipal solid waste per day, the highest among many industrial nations. In addition, we have one of the lowest waste diversion rates in the world and send most of our waste to landfills for permanent disposal. Attitudes and behaviors related to Canadian waste management practices are complex and will take time to change. Reliance on landfill technology alone as the primary waste treatment method is not sustainable. There is a world-wide trend on the use of data-driven techniques in waste management system (WMS), and I believe these techniques and evidence-based policy to WMS is the key to the next generation of waste management. It is, however, difficult to implement effective waste policy if the WMS characteristics are not well understood. This proposal focuses on two themes, including identification of the attributes of a sustainable WMS for regions with diverse geographical and climatic features, and improvement of the current state of the art modelling techniques for the development of a regionalized WMS framework in Canada. The specific objectives are to: (1a) develop an original set of metrics for WMS evaluation, (1b) identify new design principles on effective landfill design using text and content analysis, (2a) create a waste collection GIS model with real-time applications, and (2b) develop a novel artificial neural network generation modelling approach for electronic waste. The ability to incorporate our analytical approaches and tools into the waste regulations will be a huge benefit to Canada, particularly in regions with subpar diversion rates. Canada has been traditionally a strong global leader in environmental engineering, as continued investment enables Canada to build a knowledge base for waste policy and to remain an active contributor of new modeling techniques in WMS. Fulfillment of objectives 1 a&b will provide us a theoretical understanding of a sustainable WMS and shed new light on the bigger question of whether an upper limit on diversion rate exist in a region. Improvements on the state of the art of numerical techniques described in objectives 2 a&b are important due to the complexity of the WMS, and that waste management is expensive. According to Statistics Canada, in 2014 we spent over $3.3 billion dollars on solid waste management. Advanced numerical techniques and tools will help us to optimize existing system and to propose alternative solution using a fraction of time and money compared to field study. The realizations of these objectives described herein will fundamentally change how we implement WMS in Canada and beyond, and will ultimately bring us closer to the answer of this long-standing question whether an upper limit on waste diversion rate exists.