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

在加拿大，有机垃圾的垃圾处理占全国甲烷排放量的24%。加拿大废物部门到2050年实现温室气体净零排放有赖于实施“废物资源化”办法。这一办法减少了传统的报废处理做法造成的温室气体排放。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市政当局采用“废物资源化”技术。然而，目前关于最佳做法选择的决策是有限的，原因是：1)现有技术缺乏全面的数据库；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各种生物和热化学技术性能的数据。数据将通过实验调查、全尺寸设施、过程建模和文献收集。第二阶段包括通过考虑温室气体排放的所有可能途径(例如，气体、液体和固体)，对每项工艺进行技术经济评估，以及每项技术的生命周期评估。第三阶段涉及开发包含关键绩效指标(KPI)、TEA和生命周期评价数据的决策工具，以构建不确定情况下的预测模型。金斯敦公用事业公司和金斯顿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完整的设施数据，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会引起政府政策制定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.