{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T19:33:17Z","timestamp":1770751997137,"version":"3.50.0"},"reference-count":35,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T00:00:00Z","timestamp":1770595200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Portuguese Recovery and Resilience Plan","award":["C645112083-00000059"],"award-info":[{"award-number":["C645112083-00000059"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Ports are critical nodes in global supply chains and play a central role in sustainability transitions in trade and logistics. This study investigates how Artificial Intelligence (AI) contributes to sustainable innovation within port ecosystems, focusing on efficiency, transparency, resilience, and environmental performance. To address the research question\u2014how has AI supported sustainability in maritime ports?\u2014we conducted a systematic screening combined with bibliometric performance analysis and science mapping. A total of 80 peer-reviewed articles published between 2019 and 2025 (Scopus) were analysed. The results show a strong acceleration of publications in 2025, alongside a citation\u2013time lag for recent studies. The findings indicate three dominant application streams: (1) operational efficiency and optimisation (terminal operations, forecasting, routing, scheduling); (2) digital and smart-port enablement through IoT and data infrastructures; and (3) governance, risk, and compliance (e.g., Port State Control, inspection analytics, cyber-resilience). The mapping also evidences increasing convergence of AI with complementary technologies\u2014particularly IoT and, in a smaller but visible subset, blockchain\u2014to enhance trust, accountability, and interoperability. By synthesising the field\u2019s intellectual structure and thematic evolution, this study outlines research gaps and proposes future directions toward integrated frameworks for sustainable port ecosystems and Sustainable Commerce 4.0.<\/jats:p>","DOI":"10.3390\/systems14020187","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T09:41:02Z","timestamp":1770630062000},"page":"187","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Smart Ports to Sustainable Port Ecosystems: The Transformative Role of Artificial Intelligence"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6616-1881","authenticated-orcid":false,"given":"Marcela","family":"Castro","sequence":"first","affiliation":[{"name":"Instituto Polit\u00e9cnico de Set\u00fabal, Escola Superior de Ci\u00eancias Empresariais, 2910-765 Set\u00fabal, Portugal"},{"name":"NECE\u2014Research Centre for Business Sciences, Department of Management and Economics, Faculty of Human and Social Sciences, Universidade da Beira Interior, 6200-209 Covilh\u00e3, Portugal"},{"name":"CIEQV\u2014Life Quality Research Centre, Santar\u00e9m Polytechnic University, 2001-904 Santar\u00e9m, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8250-9748","authenticated-orcid":false,"given":"Maria Rosilene","family":"Sabino","sequence":"additional","affiliation":[{"name":"Instituto Polit\u00e9cnico de Set\u00fabal, Escola Superior de Ci\u00eancias Empresariais, 2910-765 Set\u00fabal, Portugal"},{"name":"UNIDEMI\u2014Research & Development Unit for Mechanical and Industrial Engineering, Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8394-2260","authenticated-orcid":false,"given":"Maria do Ros\u00e1rio","family":"Cabrita","sequence":"additional","affiliation":[{"name":"UNIDEMI\u2014Research & Development Unit for Mechanical and Industrial Engineering, Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal"},{"name":"LASI\u2014Intelligent Systems Associate Laboratory, 4800-058 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3229-4712","authenticated-orcid":false,"given":"Ana","family":"Mendes","sequence":"additional","affiliation":[{"name":"Instituto Polit\u00e9cnico de Set\u00fabal, Escola Superior de Ci\u00eancias Empresariais, 2910-765 Set\u00fabal, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2546-5392","authenticated-orcid":false,"given":"Tiago","family":"Pinho","sequence":"additional","affiliation":[{"name":"Instituto Polit\u00e9cnico de Set\u00fabal, Escola Superior de Ci\u00eancias Empresariais, 2910-765 Set\u00fabal, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1109\/TITS.2017.2789279","article-title":"Estimated Time of Arrival Using Historical Vessel Tracking Data","volume":"20","author":"Alessandrini","year":"2019","journal-title":"IEEE Trans. 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