{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T06:12:36Z","timestamp":1773900756194,"version":"3.50.1"},"reference-count":78,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T00:00:00Z","timestamp":1745280000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["WEVJ"],"abstract":"<jats:p>The rapid growth of e-commerce has intensified the need for efficient and sustainable last-mile delivery solutions in urban environments. This paper explores the integration of electric vehicles (EVs) and artificial intelligence (AI) into a combined framework to enhance the environmental, operational, and economic performance of urban logistics. Through a comprehensive literature review, we examine current trends, technological developments, and implementation challenges at the intersection of smart mobility, green logistics, and digital transformation. We propose an operational framework that leverages AI for route optimization, fleet coordination, and energy management in EV-based delivery networks. This framework is validated through a real-world case study conducted in Lisbon, Portugal, where a logistics provider implemented a city consolidation center model supported by AI-driven optimization tools. Using key performance indicators\u2014including delivery time, energy consumption, fleet utilization, customer satisfaction, and CO\u2082 emissions\u2014we measure the pre- and post-AI deployment impacts. The results demonstrate significant improvements across all metrics, including a 15\u201320% reduction in delivery time, a 10\u201325% gain in energy efficiency, and up to a 40% decrease in emissions. The findings confirm that the synergy between EVs and AI provides a robust and scalable model for achieving sustainable last-mile logistics, supporting broader urban mobility and climate objectives.<\/jats:p>","DOI":"10.3390\/wevj16050242","type":"journal-article","created":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T20:11:48Z","timestamp":1745352708000},"page":"242","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Enhancing Sustainable Last-Mile Delivery: The Impact of Electric Vehicles and AI Optimization on Urban Logistics"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6662-0806","authenticated-orcid":false,"given":"Joao C.","family":"Ferreira","sequence":"first","affiliation":[{"name":"Faculty of Logistics, Molde University College, NO-6410 Molde, Norway"},{"name":"INESC INOV, 1000-029 Lisbon, Portugal"},{"name":"ISTAR, Instituto Universit\u00e1rio de Lisboa (ISCTE-IUL), 1649-026 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0064-2424","authenticated-orcid":false,"given":"Marco","family":"Esperan\u00e7a","sequence":"additional","affiliation":[{"name":"ISTAR, Instituto Universit\u00e1rio de Lisboa (ISCTE-IUL), 1649-026 Lisbon, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shuaibu, A.S., Mahmoud, A.S., and Sheltami, T.R. 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