{"id":2694,"date":"2022-10-28T13:06:25","date_gmt":"2022-10-28T00:06:25","guid":{"rendered":"https:\/\/blogs.otago.ac.nz\/cefg\/?p=2694"},"modified":"2022-10-28T13:07:21","modified_gmt":"2022-10-28T00:07:21","slug":"2694-2","status":"publish","type":"post","link":"https:\/\/blogs.otago.ac.nz\/cefg\/2694-2\/","title":{"rendered":"CEFGroup’s Presentation to Stanford SFI Seminar"},"content":{"rendered":"
\n

CEFGroup’s Professor Ivan Diaz-Rainey<\/strong><\/a>\u00a0and\u00a0Dr Quyen Nguyen<\/strong><\/a>\u00a0presented to\u00a0Stanford University<\/a>‘s Sustainable Finance and Investment Seminar today. They introduced how transition risk is measured and highlighted associated challenges with particular focus on corporate carbon footprints,<\/p>\n

Diaz-Rainey and Nguyen showed that the application of machine learning algorithms to fill the carbon data gaps lead to improved prediction accuracy especially in Scope 1 and Scope 2 emissions. For Scope 3 emissions, firms\u2019 voluntary disclosures have measurement divergence and incomplete composition problems, and prediction accuracy is primarily limited by low observations in particular categories.<\/p>\n

Finally, they used actual and predicted corporate carbon footprint data to examine US banks\u2019 exposure to climate transition risk using a bottom-up, loan-level methodology incorporating climate stress test based on the Merton probability of default model and transition pathways from the Intergovernmental Panel on Climate Change.<\/p>\n

The presentation drew from several research projects of Diaz-Rainey and Nguyen, including their work with EMMI<\/a><\/strong>.<\/p>\n

For more information, visit HERE<\/strong><\/a>.<\/p>\n

For Stanford Precourt Institute for Energy’s upcoming events, visit HERE<\/strong><\/a>.<\/p>\n

\"\"<\/a> \"\"<\/a><\/p>\n

\n","protected":false},"excerpt":{"rendered":"

CEFGroup’s Professor Ivan Diaz-Rainey\u00a0and\u00a0Dr Quyen Nguyen\u00a0presented to\u00a0Stanford University‘s Sustainable Finance and Investment Seminar today. They introduced how transition risk is measured and highlighted associated challenges with particular focus on corporate carbon footprints, Diaz-Rainey and Nguyen showed that the application of machine learning algorithms to fill the carbon data gaps lead to improved prediction accuracy especially […]<\/p>\n","protected":false},"author":41694,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-2694","post","type-post","status-publish","format-standard","hentry"],"post_mailing_queue_ids":[],"_links":{"self":[{"href":"https:\/\/blogs.otago.ac.nz\/cefg\/wp-json\/wp\/v2\/posts\/2694","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.otago.ac.nz\/cefg\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.otago.ac.nz\/cefg\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.otago.ac.nz\/cefg\/wp-json\/wp\/v2\/users\/41694"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.otago.ac.nz\/cefg\/wp-json\/wp\/v2\/comments?post=2694"}],"version-history":[{"count":0,"href":"https:\/\/blogs.otago.ac.nz\/cefg\/wp-json\/wp\/v2\/posts\/2694\/revisions"}],"wp:attachment":[{"href":"https:\/\/blogs.otago.ac.nz\/cefg\/wp-json\/wp\/v2\/media?parent=2694"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.otago.ac.nz\/cefg\/wp-json\/wp\/v2\/categories?post=2694"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.otago.ac.nz\/cefg\/wp-json\/wp\/v2\/tags?post=2694"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}