Towards zero-emission urban mobility: Leveraging AI and LCA for targeted interventions
Urban mobility is a critical contributor to greenhouse gas emissions, accounting for over 30% of urban carbon emissions in the United States in 2021. Addressing this challenge requires a comprehensive and data-driven approach to transform transportation systems into sustainable networks. This paper...
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| Veröffentlicht in: | Building simulation 2024, Vol.17 (10), p.1653-1657 |
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| creator | Wang, Qi R. |
| description | Urban mobility is a critical contributor to greenhouse gas emissions, accounting for over 30% of urban carbon emissions in the United States in 2021. Addressing this challenge requires a comprehensive and data-driven approach to transform transportation systems into sustainable networks. This paper presents an integrated framework that leverages artificial intelligence (AI), machine learning (ML), and life cycle assessment (LCA) to analyze, model, and optimize urban mobility. The framework consists of four key components: AI-powered analysis and models, synthetic urban mobility data generation, LCA for environmental footprint analysis, and data-driven policy interventions. By combining these elements, the framework not only deciphers complex mobility patterns but also quantifies their environmental impacts, providing actionable insights for policy decisions aimed at reducing carbon emissions and promoting sustainable urban transportation. The implications of this approach extend beyond individual cities, offering a blueprint for global sustainable urban mobility. |
| doi_str_mv | 10.1007/s12273-024-1193-7 |
| format | Article |
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The implications of this approach extend beyond individual cities, offering a blueprint for global sustainable urban mobility.</description><subject>Artificial intelligence</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Building Construction and Design</subject><subject>Carbon</subject><subject>Data analysis</subject><subject>Emissions</subject><subject>Engineering</subject><subject>Engineering Thermodynamics</subject><subject>Environmental impact</subject><subject>Footprint analysis</subject><subject>Greenhouse gases</subject><subject>Heat and Mass Transfer</subject><subject>Life cycle assessment</subject><subject>Machine learning</subject><subject>Monitoring/Environmental Analysis</subject><subject>Perspective</subject><subject>Transportation systems</subject><subject>Urban transportation</subject><issn>1996-3599</issn><issn>1996-8744</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><recordid>eNp1kM9LwzAUx4MoOOb-AG8Bz9GXpG0Sb2P4Y1DwMr2GtElLx5bMpJvMv96WCp58l_cOn-_3wQehWwr3FEA8JMqY4ARYRihVnIgLNKNKFUSKLLv8vXmu1DVapLSFcQTkGZ-hj034MtEm_O1iIG7fpdQFj4-xMh7vQ9Xtuv78iEt3ctG0nW_xco2Nt7hcLXETIu5NbF3vLO587-LJ-X7Ipxt01ZhdcovfPUfvz0-b1Ssp317Wq2VJapbRnggJtsqka2ghwcmiUpbWylqoQRaysApo7ayoGHAuB5DVoBh1OVWVgYLlfI7upt5DDJ9Hl3q9Dcfoh5eaUyaAC1GMFJ2oOoaUomv0IXZ7E8-agh4N6smgHgzq0aAWQ4ZNmTSwvnXxr_n_0A-IYnJu</recordid><startdate>2024</startdate><enddate>2024</enddate><creator>Wang, Qi R.</creator><general>Tsinghua University Press</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>2024</creationdate><title>Towards zero-emission urban mobility: Leveraging AI and LCA for targeted interventions</title><author>Wang, Qi R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c241t-780db48ef1680e86b9d1c9dd0c08686d901ced7b20338b482c0921e519ba06253</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Artificial intelligence</topic><topic>Atmospheric Protection/Air Quality Control/Air Pollution</topic><topic>Building Construction and Design</topic><topic>Carbon</topic><topic>Data analysis</topic><topic>Emissions</topic><topic>Engineering</topic><topic>Engineering Thermodynamics</topic><topic>Environmental impact</topic><topic>Footprint analysis</topic><topic>Greenhouse gases</topic><topic>Heat and Mass Transfer</topic><topic>Life cycle assessment</topic><topic>Machine learning</topic><topic>Monitoring/Environmental Analysis</topic><topic>Perspective</topic><topic>Transportation systems</topic><topic>Urban transportation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Qi R.</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>CrossRef</collection><jtitle>Building simulation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Qi R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Towards zero-emission urban mobility: Leveraging AI and LCA for targeted interventions</atitle><jtitle>Building simulation</jtitle><stitle>Build. 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| ispartof | Building simulation, 2024, Vol.17 (10), p.1653-1657 |
| issn | 1996-3599 1996-8744 |
| language | eng |
| recordid | cdi_proquest_journals_3127037765 |
| source | SpringerLink Journals - AutoHoldings |
| subjects | Artificial intelligence Atmospheric Protection/Air Quality Control/Air Pollution Building Construction and Design Carbon Data analysis Emissions Engineering Engineering Thermodynamics Environmental impact Footprint analysis Greenhouse gases Heat and Mass Transfer Life cycle assessment Machine learning Monitoring/Environmental Analysis Perspective Transportation systems Urban transportation |
| title | Towards zero-emission urban mobility: Leveraging AI and LCA for targeted interventions |
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