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UID:682e78ab74a11
DTSTART;TZID=America/Toronto:20250422T091500
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URL:/future-cities-institute/events/simulation-based-me
 thods-using-urban-climate-data-inform
SUMMARY:Simulation-based methods using urban climate data to inform policy
CLASS:PUBLIC
DESCRIPTION:Summary \n\nDAWN PARKER & RODRIGO COSTA\nThis presentation focu
 ses on methods to harness city climate data to\ndevelop policy solutions.\
 nWhile emerging AI methods are exciting\, often for policy analysis\, we\n
 need to design scenarios\nthat ask how specific policy levers might impact
  social\, economic\, and\nclimate metrics in our\ncities. To conduct such 
 analysis\, we need models of how shifts in\npolicy levers change the\ndeci
 sions of key actors\, and how these decisions interact to drive\noutcomes 
 of interest. From a\nscience viewpoint\, these are often referred to as 
 “process-based\nmodels.” Such models can\ncomplement\, and build on\, 
 pattern-based AI and other statistical\nmodels. In this presentation\, we\
 nwill offer a brief introduction to several process-based simulation\nmode
 ls that can be applied to\nclimate challenges in cities: systems dynamics\
 , cellular automaton\,\nand agent-based models.\n 
DTSTAMP:20250522T010651Z
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BEGIN:VEVENT
UID:682e78ab79830
DTSTART;TZID=America/Toronto:20250421T150000
SEQUENCE:0
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DTEND;TZID=America/Toronto:20250421T160000
URL:/future-cities-institute/events/ai-powered-solution
 s-bridging-urban-climate-policy-data-gaps
SUMMARY:AI-Powered Solutions for Bridging Urban Climate Policy Data Gaps
CLASS:PUBLIC
DESCRIPTION:Summary \n\nANGEL HSU\nMachine learning\, generative AI\, inclu
 ding Large Language Models\n(LLMs) hold immensepotential in addressing cri
 tical data and\ninformation gaps within city climate policy. Thesetechnolo
 gies enable\ncities to estimate greenhouse gas emission sources\, identify
 \nenvironmentalhotspots\, evaluate policy performance trends\, and\ncompre
 hend the diverse impacts of climatechange. This presentation\nwill showcas
 e several case studies that illustrate how\nAI-drivenapproaches can innova
 te data collection\, analysis\, and policy\nformulation in the context of 
 urbanclimate management\, including\nevaluating net-zero climate policy an
 d strategy with LLMs andtopic\nmodeling\, distributional climate and envir
 onmental impacts within\nurban areas\, theintegration of satellite remote 
 sensing for\nparticipatory heat-stress mapping\, among otherexamples. It w
 ill also\ndiscuss some of the challenges and pitfalls of applying AI to ur
 ban\nclimatepolicy and management\, such as the \"black box\" AI problem a
 nd\nbiases of underlying trainingdata.\n 
DTSTAMP:20250522T010651Z
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BEGIN:VEVENT
UID:682e78ab7a153
DTSTART;TZID=America/Toronto:20250421T093000
SEQUENCE:0
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DTEND;TZID=America/Toronto:20250421T103000
URL:/future-cities-institute/events/challenges-related-
 city-climate-data-governance
SUMMARY:The challenges related to city climate data governance
CLASS:PUBLIC
DESCRIPTION:Summary \n\nCities and the ecosystem of actors that support the
 m face a plethora\nof challenges related to climate data and action. Meanw
 hile\, recent\nadvancements in geospatial technology and AIpresent both ne
 w\nchallenges and opportunities. The diverse data challenges and variety\n
 ofecosystem actors that seek to support city climate action present an\nov
 erarching “orchestrationchallenge”\; connecting diverse\nperspectives 
 and challenges with on-the-ground climate actionrequires\na community of p
 ractice and collaborative approach. This session will\nintroduce thechalle
 nges of city climate data governance\, as well as\nemerging challenges and
  opportunitiesrelated to geospatial tools and\nAI. Next\, several practiti
 oners will present about\non-the-groundchallenges they face related to cit
 y climate data. The\nsession will conclude with an invitation fordiscussio
 n of the diverse\nperspectives on these challenges and opportunities and a
  call\nforcollaborative solutions.\n 
DTSTAMP:20250522T010651Z
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