Open Data Analytics for Decarbonization Strategies

该项目将制定一套开放数据分析原则，用于分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，使用可视化的基于网络的节能决策支持工具，支持基于循证气候解决方案的集体决策。我们的平台将支持现有气候评估计算模型与各种数据来源的更广泛集成，这些数据来源涉及现有战略的有效性，以履行联合国将全球变暖控制在工业化前温度低于2摄氏度的承诺。 我们工作中的核心思想是使用开放的可视化建模形式化，基于数据流编程范例，称为基于树的参数图(TBAG)。它们提供了一种结构，用于捕获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。我们之前的工作探索了使用TBAGs进行能源效率和碳核算方面的决策，我们开发了一个基于Web的原型工具，通过创建、共享和改进链接的论点图来支持大规模集体决策。 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工具的演示。我们还预计，随着这些工具允许更广泛地参与可持续发展数据的收集和管理，普通公众中将有更广泛的(但分散的)用户社区。从长远来看，我们设想该平台将有一个基于网络的前端，支持数据和建模的众包精选，作为一种定量分析的维基百科。 我们的长期目标是创建一个框架，通过这个框架，研究人员、政策制定者和非政府组织可以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。其目标是通过可操作的科学镜头支持各种建模方法(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整合现有不同的建模和数据分析方法，以更好地支持多模型决策。

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.