Development of a data-driven Llfe-cycle greenhouse gas emissions decision tool for municipal organic waste management

有机废物的填埋处理占加拿大全国甲烷排放量的24%。到2050年实现加拿大废物部门温室气体净零排放依赖于实施“废物到资源”方法。这种方法减少了传统报废处理方法产生的温室气体排放。它还通过替代化石能源和资源减少温室气体，促进向低碳经济过渡。省级气候变化行动计划鼓励各城市采用“资源转换”技术。然而，目前关于选择最佳做法的决策是有限的，原因是：1）缺乏现有技术的综合数据库，2）在比较不同情景时忽视了温室气体的生命周期评估，3）几乎所有拟议的决策/优化模型都没有不确定性影响。本研究旨在开发一种基于数据驱动的生命周期评估（LCA）工具，支持基于证据的决策过程，以选择最佳的城市有机废物管理方案。研究的第一阶段涉及收集关于生物和热化学技术的各种配置的性能的数据。将通过实验研究、全尺寸设施、工艺建模和文献收集数据。第二阶段包括对每种工艺进行技术经济评估（TEA）沿着通过考虑温室气体排放的所有潜在途径（例如，气体、液体和固体）。第三阶段涉及开发一个决策工具，将关键绩效指标（KPI），TEA和LCA数据结合起来，以建立不确定性下的预测模型。公用事业金斯顿和金斯顿市是该项目的伙伴组织。他们的贡献包括但不限于提供全面的设施数据，提供有机废物样本，提供报告或信息，促进与其他潜在合作伙伴的联系，并在整个研究过程中提供反馈。这项研究的结果预计将引起政府决策者和私营部门的兴趣。

Landfill disposal of organic waste contributes to 24% of national methane emissions in Canada. Achieving net-zero greenhouse gas (GHG) emissions from the Canadian waste sector by 2050 rely on implementing the "Waste-To-Resource" approach. This approach mitigates GHG emissions from traditional end-of-life disposal practices. It also reduces GHGs by substituting fossil-based energy and resources, facilitating the transition to a low-carbon economy. Provincial Climate Change Action Plans encourage municipalities to incorporate "Waste-To-Resource" technologies. However, current decision-making on the selection of the best practices is limited due to 1) the lack of a comprehensive data repository for available technologies, 2) neglection of life cycle assessment (LCA) of GHGs when comparing different scenarios, and 3) the absence of uncertainty impact in almost all proposed decision/optimization models. This research aims to develop a data-driven life-cycle assessment (LCA)-based tool that supports the evidence-based decision-making process for selecting the best municipal organic waste management scenarios. The first phase of research involves data gathering on the performance of various configurations of bio and thermochemical technologies. The data will be collected through experimental investigations, full-scale facilities, process modeling, and the literature. The second phase includes a techno-economic assessment (TEA) of each process along with the LCA of each technology by considering all potential routes of GHG emissions (e.g., gas, liquid, and solids). The third phase involves developing a decision tool incorporating key performance indicators (KPIs), TEA, and LCA data to build predictive models under uncertainty. Utilities Kingston and The City of Kingston are partner organizations in this project. Their contribution involves but is not limited to providing full-scale facilities data, offering organic waste samples, giving access to reports or information, facilitating connections with other potential partners, and providing feedback throughout the research. The outcome of this research is expected to be of interest to both government policymakers and private sector.