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

填埋处理的有机废物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“废物转化为资源”的方法。这种方法减少了传统的报废处理做法产生的温室气体排放。它还通过替代化石能源和资源来减少温室气体排放，促进向低碳经济的过渡。省级气候变化行动计划鼓励各市采用“废物资源化”技术。然而，目前关于最佳实践选择的决策受到以下因素的限制：1)缺乏可用技术的综合数据存储库；2)在比较不同情景时忽略了温室气体的生命周期评估（LCA）； 3)几乎所有提出的决策/优化模型都缺乏不确定性影响。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。研究的第一阶段包括收集各种生物和热化学技术配置的性能数据。数据将通过实验调查、全尺寸设备、过程建模和文献收集。第二阶段包括通过考虑所有潜在的温室气体排放途径（如气体、液体和固体），对每个过程进行技术经济评估（TEA）以及每种技术的LCA。第三阶段涉及开发一个决策工具，该工具包含关键绩效指标（kpi）、TEA和LCA数据，以构建不确定性下的预测模型。金斯敦公用事业公司和金斯敦市是该项目的合作伙伴组织。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。预计这项研究的结果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.