Open Data Analytics for Decarbonization Strategies

脱碳策略的开放数据分析

• 批准号: RGPIN-2019-07042
• 负责人: Easterbrook, Steve
• 金额: $ 2.04万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716218
• 关键词: Open; Data; Analytics; Decarbonization; Strategies

This project will develop a set of principles for open data analytics applied to sharing and integrating models and analyses of global decarbonization goals to address climate change. We will build on our existing work with visual web-based decision support tools for energy conservation, to support collective decision-making based on evidence-based climate solutions. Our platform will support the broader integration of existing computational models for climate assessment with a variety of data sources on the effectiveness of available strategies to meet the UN's commitment to keep global warming below 2C over pre-industrial temperatures. The core idea in our work is the use of an open visual modeling formalism, based on the dataflow programming paradigm, known as Tree-Based Argument Graphs (TBAGs). These provide a structure for capturing quantitative and qualitative evidence in support of specific decisions, and linking it to source data. Our previous work has explored the use of TBAGs for decision-making around energy efficiency and carbon accounting, and we have developed a prototype web-based tool to support large-scale collective decisions, by creating, sharing, and improving linked argument graphs. During this research program, we will work with three primary user groups: Policy analysts at various levels of government; NGOs concerned with climate and energy; and academic researchers in focussed on decarbonization strategies and sustainability science. A secondary use of our approach is for education settings, where we expect to develop demonstrations of how the tools can be used in a classroom setting. We also anticipate a broader (but diffuse) community of users among the general public, as the tools allow a broader participation in the collection and curation of sustainability data. In the longer term, we envisage a web-based front-end to the platform that supports crowd-sourced curation of data and modeling, as a kind of Wikipedia for quantitative analysis. Our long-term goal is to create a framework through which researchers, policymakers, and NGOs can compare and integrate their analyses of decarbonization strategies, while facilitating greater openness and data sharing. The goal is to support integration of a wide variety of modeling approaches (agent-based models, physics-based models, equilibrium models, statistical models, and machine learning) and data sources, through an actionable science lens. The resulting software platform will support the integration of existing disparate approaches to modeling and data analytics, to better support multi-model decision-making.

该项目将制定一套开放数据分析原则，用于共享和整合全球脱碳目标的模型和分析，以应对气候变化。我们将在现有工作的基础上，利用基于网络的可视化节能决策支持工具，支持基于循证气候解决方案的集体决策。我们的平台将支持将现有的气候评估计算模型与各种数据源进行更广泛的整合，以评估现有战略的有效性，以实现联合国将全球变暖保持在工业化前温度2摄氏度以下的承诺。 我们工作的核心思想是使用基于数据流编程范式的开放视觉建模形式主义，称为基于树的参数图（TBAG）。这些数据库提供了一个结构，用于获取支持具体决策的定量和定性证据，并将其与源数据联系起来。我们以前的工作已经探索了使用TBAGs进行能源效率和碳核算的决策，我们已经开发了一个基于Web的原型工具，通过创建，共享和改进链接的参数图来支持大规模的集体决策。 在这项研究计划中，我们将与三个主要用户群体合作：各级政府的政策分析师;关注气候和能源的非政府组织;以及专注于脱碳战略和可持续发展科学的学术研究人员。我们的方法的第二个用途是用于教育环境，我们希望在那里开发如何在课堂环境中使用这些工具的演示。我们还预计，由于这些工具允许更广泛地参与可持续性数据的收集和管理，因此公众中的用户群体将更广泛（但分散）。从长远来看，我们设想一个基于网络的平台前端，支持众包的数据和建模管理，就像量化分析的维基百科一样。 我们的长期目标是创建一个框架，使研究人员、政策制定者和非政府组织能够比较和整合他们对脱碳战略的分析，同时促进更大的开放性和数据共享。目标是通过可操作的科学透镜支持各种建模方法（基于代理的模型、基于物理的模型、平衡模型、统计模型和机器学习）和数据源的集成。由此产生的软件平台将支持现有不同建模和数据分析方法的集成，以更好地支持多模型决策。