{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/156002"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/156002","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Data Roadmap for Last Mile Sustainability","abstract":"The final leg of e-commerce deliveries, often referred to as the \"last mile,\" carries a significant environmental impact. While carbon data analysis tools, such as, carbon emission forecasting tools offer meaningful insights into understanding and mitigating this impact, their effectiveness hinges on the quality, availability, and granularity of data. This research project proposes data recommendations for last-mile sustainability, acknowledging the nuances inherent in such initiatives. By integrating transportation-related metrics and operational data from delivery facilities, the project seeks to enhance the accuracy and availability of last mile carbon emission forecasts. The research consists of three primary components: data source analysis, development of a carbon emission forecasting tool, and drafting last mile sustainability data recommendations. We developed tools for carbon data analysis to assess the impact of last mile activity variables and predict carbon emission using both process and business-level data. Through this approach, we aim to provide actionable insights to support sustainability efforts within the last mile delivery sector.","abstract_html":"The final leg of e-commerce deliveries, often referred to as the "last mile," carries a significant environmental impact. While carbon data analysis tools, such as, carbon emission forecasting tools offer meaningful insights into understanding and mitigating this impact, their effectiveness hinges on the quality, availability, and granularity of data. This research project proposes data recommendations for last-mile sustainability, acknowledging the nuances inherent in such initiatives. By integrating transportation-related metrics and operational data from delivery facilities, the project seeks to enhance the accuracy and availability of last mile carbon emission forecasts. The research consists of three primary components: data source analysis, development of a carbon emission forecasting tool, and drafting last mile sustainability data recommendations. We developed tools for carbon data analysis to assess the impact of last mile activity variables and predict carbon emission using both process and business-level data. Through this approach, we aim to provide actionable insights to support sustainability efforts within the last mile delivery sector.","abstract_has_math":false,"creators":["Vaidya, Sajiree Vivek"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Sloan School of Management","school":null,"contributors":[],"advisors":["Simchi-Levi, David","Graves, Stephen"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:20:47Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"rights_urls":["https://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/156002","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Simchi-Levi, David","Graves, Stephen"]},{"key":"dc:contributor.department","label":"Department","values":["Sloan School of Management","Massachusetts Institute of Technology. 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While carbon data analysis tools, such as, carbon emission forecasting tools offer meaningful insights into understanding and mitigating this impact, their effectiveness hinges on the quality, availability, and granularity of data. This research project proposes data recommendations for last-mile sustainability, acknowledging the nuances inherent in such initiatives. By integrating transportation-related metrics and operational data from delivery facilities, the project seeks to enhance the accuracy and availability of last mile carbon emission forecasts. The research consists of three primary components: data source analysis, development of a carbon emission forecasting tool, and drafting last mile sustainability data recommendations. We developed tools for carbon data analysis to assess the impact of last mile activity variables and predict carbon emission using both process and business-level data. 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