International Workshop: Building global capacity for city climate data governance (CityClimateData)
Dates: April 21-22, 2025
Location: Balsillie School of International Affairs,67 Erb Street West, ݮƵ, Ontario, N2L 6C2
Data is crucial to enable city climate action. Effective reporting, collection, management and distribution of data can help cities, city networks, international organizations, and other actors to document and monitor progress, understand and improve policies and practices, and increase climate action. Leading city climate networks have developed data management frameworks and identified best practices. Yet, despite these preliminary efforts, the city climate data community continues to struggle with capacity deficits even while the world of city climate data is complex and rapidly evolving. Developments in geospatial data and artificial intelligence (AI), coupled with the pace and scale of climate change impacts mean that these actors are struggling to keep up. To keep pace with these changes and effectively manage city climate data, researchers and practitioners will need to develop and upgrade their skills and abilities.
The University of ݮƵ, in partnership with the Balsillie School of International Affairs, the ݮƵ Institute for Complexity and Innovation, and Future Cities Institute will host an international workshop to convene and build the capacities of the global climate city data governance community. This two-day in-person workshop will be held on April 21-22, 2025 at the Balsillie School of International Affairs in ݮƵ, Canada.
This workshop and associated project activities will bring together Canadian and international academics and practitioners to build global capacity for city climate data governance. Through presentations and interactive sessions, participants will learn about, reflect on, improve, and apply innovative approaches to manage city climate data. The workshop will focus on three themes:
- the capacity of the global city climate data community;
- advances in geospatial tools and technology; and
- new frontiers related to big data and AI.
Academics and practitioners involved in city climate data governance in Canada and internationally who will be involved in this workshop include organizations that collect and manage city climate data, city climate networks, city representatives, specialists with expertise in geospatial analysis and AI, and academics. The outreach and knowledge mobilization activities will also involve broader actors in the global ecosystem of networks supporting city climate action.
Participants will learn about the challenges and potential solutions related to city climate data management; access tools and resources to improve city climate action; gain skills related to data governance, geospatial tools, and AI; and develop relationships. Through this process, the community of practice for city climate data governance will identify concrete actions to improve city climate data governance.
The CityClimateData project is led by Dr. Christopher Orr, Adjunct Assistant Professor in the Department of Geography and Environmental Management at the University of ݮƵ. If you are interested in learning more about the project and workshop, please contact Dr. Orr directly. (christopher.orr1@uwaterloo.ca).
Open sessions at the CityClimateData Workshop
Participation in the following sessions is open either in person at the Balsillie School of International Affairs (67 Erb St. W., ݮƵ) or online via Zoom. Use the links below each session description to register via Eventbrite.ca for each session you are interested in.
Monday, April 21 from 9:30 - 10:30 a.m.
Cities and the ecosystem of actors that support them face a plethora of challenges related to climate data and action. Meanwhile, recent advancements in geospatial technology and AI present both new challenges and opportunities. The diverse data challenges and variety of ecosystem actors that seek to support city climate action present an overarching “orchestration challenge”; connecting diverse perspectives and challenges with on-the-ground climate action requires a community of practice and collaborative approach. This session will introduce the challenges of city climate data governance, as well as emerging challenges and opportunities related to geospatial tools and AI. Next, several practitioners will present about on-the-ground challenges they face related to city climate data. The session will conclude with an invitation for discussion of the diverse perspectives on these challenges and opportunities and a call for collaborative solutions.
with Angel Hsu
Monday, April 21 from 3-4 p.m.
Machine learning, generative AI, including Large Language Models (LLMs) hold immense potential in addressing critical data and information gaps within city climate policy. These technologies enable cities to estimate greenhouse gas emission sources, identify environmental hotspots, evaluate policy performance trends, and comprehend the diverse impacts of climate change. This presentation will showcase several case studies that illustrate how AI-driven approaches can innovate data collection, analysis, and policy formulation in the context of urban climate management, including evaluating net-zero climate policy and strategy with LLMs and topic modeling, distributional climate and environmental impacts within urban areas, the integration of satellite remote sensing for participatory heat-stress mapping, among other examples. It will also discuss some of the challenges and pitfalls of applying AI to urban climate policy and management, such as the "black box" AI problem and biases of underlying training data
with Dawn Parker & Rodrigo Costa
Tuesday, April 22nd from 9:15 - 10:15 a.m.
This presentation focuses on methods to harness city climate data to develop policy solutions. While emerging AI methods are exciting, often for policy analysis, we need to design scenarios that ask how specific policy levers might impact social, economic, and climate metrics in our cities. To conduct such analysis, we need models of how shifts in policy levers change the decisions of key actors, and how these decisions interact to drive outcomes of interest. From a science viewpoint, these are often referred to as “process-based models.” Such models can complement, and build on, pattern-based AI and other statistical models. In this presentation, we will offer a brief introduction to several process-based simulation models that can be applied to climate challenges in cities: systems dynamics, cellular automaton, and agent-based models.