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

加拿大每天产生约2.7公斤/上限的非危险城市固体废物，是许多工业国家中最高的。此外，我们是世界上废物转移率最低的国家之一，我们的大部分废物都被送往堆填区永久处置。与加拿大废物管理做法有关的态度和行为是复杂的，需要时间来改变。仅仅依靠填埋技术作为主要的废物处理方法是不可持续的。在废物管理系统（WMS）中使用数据驱动技术是一个全球性的趋势，我相信这些技术和基于证据的WMS政策是下一代废物管理的关键。然而，如果不很好地理解WMS的特点，就很难实施有效的废物政策。该提案侧重于两个主题，包括为具有不同地理和气候特征的地区确定可持续的WMS的属性，以及改进目前最先进的建模技术，以在加拿大开发区域化的WMS框架。具体目标是：（1a）开发一套原始的WMS评估指标，（1b）利用文本和内容分析确定有效填埋场设计的新设计原则，（2a）创建一个具有实时应用程序的废物收集GIS模型，（2b）开发一种新的电子废物人工神经网络生成建模方法。将我们的分析方法和工具纳入废物法规的能力将使加拿大受益匪浅，特别是在转移率低于标准的地区。加拿大一直是环境工程领域的全球领导者，因为持续的投资使加拿大能够建立废物政策的知识基础，并继续为WMS中的新建模技术做出积极贡献。 目标1 a&B的实现将为我们提供一个可持续的水管理系统的理论理解，并揭示了更大的问题，即在一个地区是否存在一个上限的分流率。由于仓库管理系统的复杂性，以及废物管理的昂贵性，目标2a和B中描述的数值技术的最新技术水平的改进是重要的。根据加拿大统计局的数据，2014年，我们在固体废物管理上花费了超过33亿加元。先进的数值技术和工具将帮助我们优化现有的系统，并提出替代解决方案，使用一小部分的时间和金钱相比，现场研究。本文所述这些目标的实现将从根本上改变我们在加拿大及其他地区实施WMS的方式，并最终使我们更接近这个长期存在的问题的答案，即废物转移率是否存在上限。

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.