Data Data-Driven Sustainability in Plastics Manufacturing: A Thematic Review of AI Applications and Sustainability

Selma Ayşegül Güneş; Hatice Camgöz Akdağ

doi:10.1007/978-3-032-22784-3_65

Data Data-Driven Sustainability in Plastics Manufacturing: A Thematic Review of AI Applications and Sustainability

Selma Ayşegül Güneş
, Hatice Camgöz Akdağ^*

^*Corresponding author for this work

Department of Management Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This systematic review examines the role of artificial intelligence (AI) in advancing sustainability in the plastics manufacturing sector. Following PRISMA 2020 guidelines, literature from Scopus (2016–2025) was screened, yielding 75 peer-reviewed studies after exclusions. Thematic synthesis revealed that AI is applied to optimize waste sorting, improve material recovery, enhance manufacturing efficiency, and support decision-making. Core sustainability themes include plastic recycling, waste management, and circular economy strategies, with frequent alignment to SDG 12 (Responsible Consumption and Production) and related goals. While AI demonstrates substantial technical potential, its industrial-scale deployment remains limited, and integration with life cycle assessment or economic feasibility analysis is rare. The review emphasizes the need for real-world validations, techno-economic evaluations, and policy alignment to translate laboratory successes into scalable industrial solutions, positioning AI as a key enabler for achieving circularity and environmental targets in the plastics sector.

Original language	English
Title of host publication	AI-Driven Production with Green Sustainability - Selected Papers from ISPR2025
Editors	Numan M. Durakbasa, Hatice Camgöz Akdag, Kemal Güven Gülen
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	767-777
Number of pages	11
ISBN (Print)	9783032227836
DOIs	https://doi.org/10.1007/978-3-032-22784-3_65
Publication status	Published - 2026
Event	International Symposium for Production Research, ISPR 2025 - Istanbul, Turkey Duration: 9 Oct 2025 → 11 Oct 2025

Publication series

Name	Lecture Notes in Mechanical Engineering
ISSN (Print)	2195-4356
ISSN (Electronic)	2195-4364

Conference

Conference	International Symposium for Production Research, ISPR 2025
Country/Territory	Turkey
City	Istanbul
Period	9/10/25 → 11/10/25

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Keywords

Artificial intelligence
Carbon Footprint
Circular economy
Digital Twin
Machine learning
Optimization
Plastic recycling
Plastics industry
Plastics Recycling
Recyclability
Sustainable manufacturing

Access to Document

10.1007/978-3-032-22784-3_65

Cite this

Güneş, S. A., & Camgöz Akdağ, H. (2026). Data Data-Driven Sustainability in Plastics Manufacturing: A Thematic Review of AI Applications and Sustainability. In N. M. Durakbasa, H. C. Akdag, & K. G. Gülen (Eds.), AI-Driven Production with Green Sustainability - Selected Papers from ISPR2025 (pp. 767-777). (Lecture Notes in Mechanical Engineering). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-032-22784-3_65

Güneş, Selma Ayşegül ; Camgöz Akdağ, Hatice. / Data Data-Driven Sustainability in Plastics Manufacturing : A Thematic Review of AI Applications and Sustainability. AI-Driven Production with Green Sustainability - Selected Papers from ISPR2025. editor / Numan M. Durakbasa ; Hatice Camgöz Akdag ; Kemal Güven Gülen. Springer Science and Business Media Deutschland GmbH, 2026. pp. 767-777 (Lecture Notes in Mechanical Engineering).

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title = "Data Data-Driven Sustainability in Plastics Manufacturing: A Thematic Review of AI Applications and Sustainability",

abstract = "This systematic review examines the role of artificial intelligence (AI) in advancing sustainability in the plastics manufacturing sector. Following PRISMA 2020 guidelines, literature from Scopus (2016{\textendash}2025) was screened, yielding 75 peer-reviewed studies after exclusions. Thematic synthesis revealed that AI is applied to optimize waste sorting, improve material recovery, enhance manufacturing efficiency, and support decision-making. Core sustainability themes include plastic recycling, waste management, and circular economy strategies, with frequent alignment to SDG 12 (Responsible Consumption and Production) and related goals. While AI demonstrates substantial technical potential, its industrial-scale deployment remains limited, and integration with life cycle assessment or economic feasibility analysis is rare. The review emphasizes the need for real-world validations, techno-economic evaluations, and policy alignment to translate laboratory successes into scalable industrial solutions, positioning AI as a key enabler for achieving circularity and environmental targets in the plastics sector.",

keywords = "Artificial intelligence, Carbon Footprint, Circular economy, Digital Twin, Machine learning, Optimization, Plastic recycling, Plastics industry, Plastics Recycling, Recyclability, Sustainable manufacturing",

author = "G{\"u}ne{\c s}, \{Selma Ay{\c s}eg{\"u}l\} and \{Camg{\"o}z Akdağ\}, Hatice",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; International Symposium for Production Research, ISPR 2025 ; Conference date: 09-10-2025 Through 11-10-2025",

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doi = "10.1007/978-3-032-22784-3\_65",

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Güneş, SA & Camgöz Akdağ, H 2026, Data Data-Driven Sustainability in Plastics Manufacturing: A Thematic Review of AI Applications and Sustainability. in NM Durakbasa, HC Akdag & KG Gülen (eds), AI-Driven Production with Green Sustainability - Selected Papers from ISPR2025. Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH, pp. 767-777, International Symposium for Production Research, ISPR 2025, Istanbul, Turkey, 9/10/25. https://doi.org/10.1007/978-3-032-22784-3_65

Data Data-Driven Sustainability in Plastics Manufacturing: A Thematic Review of AI Applications and Sustainability. / Güneş, Selma Ayşegül; Camgöz Akdağ, Hatice.
AI-Driven Production with Green Sustainability - Selected Papers from ISPR2025. ed. / Numan M. Durakbasa; Hatice Camgöz Akdag; Kemal Güven Gülen. Springer Science and Business Media Deutschland GmbH, 2026. p. 767-777 (Lecture Notes in Mechanical Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Data Data-Driven Sustainability in Plastics Manufacturing

T2 - International Symposium for Production Research, ISPR 2025

AU - Güneş, Selma Ayşegül

AU - Camgöz Akdağ, Hatice

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

PY - 2026

Y1 - 2026

N2 - This systematic review examines the role of artificial intelligence (AI) in advancing sustainability in the plastics manufacturing sector. Following PRISMA 2020 guidelines, literature from Scopus (2016–2025) was screened, yielding 75 peer-reviewed studies after exclusions. Thematic synthesis revealed that AI is applied to optimize waste sorting, improve material recovery, enhance manufacturing efficiency, and support decision-making. Core sustainability themes include plastic recycling, waste management, and circular economy strategies, with frequent alignment to SDG 12 (Responsible Consumption and Production) and related goals. While AI demonstrates substantial technical potential, its industrial-scale deployment remains limited, and integration with life cycle assessment or economic feasibility analysis is rare. The review emphasizes the need for real-world validations, techno-economic evaluations, and policy alignment to translate laboratory successes into scalable industrial solutions, positioning AI as a key enabler for achieving circularity and environmental targets in the plastics sector.

AB - This systematic review examines the role of artificial intelligence (AI) in advancing sustainability in the plastics manufacturing sector. Following PRISMA 2020 guidelines, literature from Scopus (2016–2025) was screened, yielding 75 peer-reviewed studies after exclusions. Thematic synthesis revealed that AI is applied to optimize waste sorting, improve material recovery, enhance manufacturing efficiency, and support decision-making. Core sustainability themes include plastic recycling, waste management, and circular economy strategies, with frequent alignment to SDG 12 (Responsible Consumption and Production) and related goals. While AI demonstrates substantial technical potential, its industrial-scale deployment remains limited, and integration with life cycle assessment or economic feasibility analysis is rare. The review emphasizes the need for real-world validations, techno-economic evaluations, and policy alignment to translate laboratory successes into scalable industrial solutions, positioning AI as a key enabler for achieving circularity and environmental targets in the plastics sector.

KW - Artificial intelligence

KW - Carbon Footprint

KW - Circular economy

KW - Digital Twin

KW - Machine learning

KW - Optimization

KW - Plastic recycling

KW - Plastics industry

KW - Plastics Recycling

KW - Recyclability

KW - Sustainable manufacturing

UR - https://www.scopus.com/pages/publications/105046820671

U2 - 10.1007/978-3-032-22784-3_65

DO - 10.1007/978-3-032-22784-3_65

M3 - Conference contribution

AN - SCOPUS:105046820671

SN - 9783032227836

T3 - Lecture Notes in Mechanical Engineering

SP - 767

EP - 777

BT - AI-Driven Production with Green Sustainability - Selected Papers from ISPR2025

A2 - Durakbasa, Numan M.

A2 - Akdag, Hatice Camgöz

A2 - Gülen, Kemal Güven

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 9 October 2025 through 11 October 2025

ER -

Güneş SA, Camgöz Akdağ H. Data Data-Driven Sustainability in Plastics Manufacturing: A Thematic Review of AI Applications and Sustainability. In Durakbasa NM, Akdag HC, Gülen KG, editors, AI-Driven Production with Green Sustainability - Selected Papers from ISPR2025. Springer Science and Business Media Deutschland GmbH. 2026. p. 767-777. (Lecture Notes in Mechanical Engineering). doi: 10.1007/978-3-032-22784-3_65

Data Data-Driven Sustainability in Plastics Manufacturing: A Thematic Review of AI Applications and Sustainability

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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