{"id":788783,"date":"2021-10-26T21:57:46","date_gmt":"2021-10-27T04:57:46","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&#038;p=788783"},"modified":"2023-01-24T12:05:44","modified_gmt":"2023-01-24T20:05:44","slug":"carbon-neutrality","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/carbon-neutrality\/","title":{"rendered":"Carbon Neutrality"},"content":{"rendered":"<section class=\"mb-3 moray-highlight\">\n\t<div class=\"card-img-overlay mx-lg-0\">\n\t\t<div class=\"card-background bg-gray-200 has-background- card-background--full-bleed\">\n\t\t\t\t\t<\/div>\n\t\t<!-- Foreground -->\n\t\t<div class=\"card-foreground d-flex mt-md-n5 my-lg-5 px-g px-lg-0\">\n\t\t\t<!-- Container -->\n\t\t\t<div class=\"container d-flex mt-md-n5 my-lg-5 align-self-center\">\n\t\t\t\t<!-- Card wrapper -->\n\t\t\t\t<div class=\"w-100 w-lg-col-5\">\n\t\t\t\t\t<!-- Card -->\n\t\t\t\t\t<div class=\"card material-md-card py-5 px-md-5\">\n\t\t\t\t\t\t<div class=\"card-body \">\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\n<h1 id=\"carbon-neutrality\">Carbon Neutrality<\/h1>\n\n\n\n<p><\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/section>\n\n\n\n\n\n<p>As Paris Agreement entered into force, countries take further steps to limit Greenhouse Gases emissions. China pledges to peak its CO<sub>2 <\/sub>emissions by 2030 and achieves net-zero no later than 2060, implying rapid and dramatic decarbonization actions across all sectors. Transportation accounts for about 25% of global carbon emissions and 10% of China\u2019s emission.<\/p>\n\n\n\n<p>Modeling scenarios of deep decarbonization in the transportation sector, as stipulated by large-scale EV adoption, requires a combined assessment of technical and behavioral factors. Here, we develop a machine-learning-based method (RL-GCAM) to model the optimal technical and behavioral pathways to directly achieve a specific share of EV in China. In contrast with traditional stylized perturbation scenarios, we automate and improve the parameterization of technical and behavioral factors within a well-established global integrated assessment model through a reinforcement learning framework.<\/p>\n\n\n","protected":false},"excerpt":{"rendered":"<p>As Paris Agreement entered into force, countries take further steps to limit Greenhouse Gases emissions. China pledges to peak its CO2 emissions by 2030 and achieves net-zero no later than 2060, implying rapid and dramatic decarbonization actions across all sectors. Transportation accounts for about 25% of global carbon emissions and 10% of China\u2019s emission.<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"research-area":[13556],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-788783","msr-project","type-msr-project","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"","related-publications":[],"related-downloads":[],"related-videos":[],"related-groups":[],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[{"type":"user_nicename","display_name":"Lin Huang","user_id":40456,"people_section":"Section name 0","alias":"hul"},{"type":"user_nicename","display_name":"Jia Zhang","user_id":41075,"people_section":"Section name 0","alias":"zhangjia"}],"msr_research_lab":[199560,851467],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/788783"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":2,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/788783\/revisions"}],"predecessor-version":[{"id":914394,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/788783\/revisions\/914394"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=788783"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=788783"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=788783"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=788783"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=788783"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}