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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=753121
• 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公斤的无害城市固体废物，在许多工业国家中是最高的。此外，香港是世界上废物转用率最低的国家之一，大部分废物都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世界性的趋势，我认为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，如果不能很好地理解WMS的特征，则很难实施有效的废物政策。本提案侧重于两个主题，包括确定具有不同地理和气候特征的地区可持续WMS的属性，以及改进目前最先进的建模技术，以促进加拿大区域化WMS框架的发展。具体目标是：（1a）开发一套用于WMS评估的原始度量标准，（1b）使用文本和内容分析确定有效填埋场设计的新设计原则，（2a）创建具有实时应用的废物收集GIS模型，以及（2b）开发一种新的人工神经网络生成电子废物建模方法。将我们的分析方法和工具纳入废物法规的能力将对加拿大产生巨大的好处，特别是在转移率低于标准的地区。加拿大传统上一直是环境工程领域强大的全球领导者，因为持续的投资使加拿大能够建立废物政策知识库，并始终是WMS新建模技术的积极贡献者。实现目标a和b将使我们对可持续水资源管理有一个理论上的认识，并对一个地区是否存在调水量上限这一更大的问题有了新的认识。由于WMS的复杂性和废物管理的昂贵性，改进目标2a和b中所述的数值技术的最新水平是很重要的。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系统，并提出替代解决方案，与现场研究相比，只需花费一小部分时间和金钱。本文所描述的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于回答这个长期存在的问题，即是否存在废物转移率上限。