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

废物管理系统中的数据驱动技术和循证政策

• 批准号: RGPIN-2019-06154
• 负责人: Ng, KelvinTsunWai
• 金额: $ 1.89万
• 依托单位: University of Regina
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=681417
• 关键词: Data; driven; techniques; evidence; based

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.**

加拿大人每天产生约2.7公斤/帽的无害城市固体废物，是许多工业国家中最高的。此外，香港是世界上废物转移率最低的地区之一，把大部分废物运往堆填区作永久弃置。与加拿大废物管理做法相关的态度和行为是复杂的，需要时间来改变。仅依靠垃圾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种世界性的趋势，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，如果不能很好地了解水资源管理系统的特点，就很难实施有效的废物政策。*本建议侧重于两个主题，包括为地理和气候特征不同的区域确定可持续的水资源管理系统的属性，以及改进目前最先进的建模技术，以便在加拿大建立一个区域化的水资源管理系统框架。具体目标是：(1a)制定一套用于废物管理系统评估的原始指标，(1b)利用文本和内容分析确定有效垃圾填埋场设计的新设计原则，(2a)创建具有实时应用的废物收集地理信息系统模型，以及(2b)开发一种新的电子废物人工神经网络生成建模方法。能够将我们的分析方法和工具纳入废物法规，对加拿大将是一个巨大的好处，特别是在转移率低于平均水平的地区。加拿大传统上一直是环境工程领域的全球领先者，因为持续的投资使加拿大能够为废物政策建立知识库，并继续积极推动WMS中的新建模技术。*目标1a和b的实现将使我们从理论上理解可持续的水资源管理系统，并对一个更大的问题--一个区域是否存在分水率上限--提供新的认识。由于水资源管理系统的复杂性以及废物管理费用昂贵，改进目标2a和b中所述的数值技术的技术水平是重要的。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33亿美元。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，与实地研究相比，只需很少的时间和金钱。这里描述的这些目标的实现将从根本上改变我们在加拿大和其他地方实施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是否存在废物转移率上限。**