Open Data Analytics for Decarbonization Strategies

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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.