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Metrics for green syntheses

  • Marco Eissen , Giacomo Trapasso ORCID logo , James Clark , Fabio Aricò ORCID logo EMAIL logo , John Andraos EMAIL logo und Pietro Tundo ORCID logo EMAIL logo
Veröffentlicht/Copyright: 15. September 2025
Pure and Applied Chemistry
Aus der Zeitschrift Pure and Applied Chemistry

Abstract

Despite being introduced approximately 30 years ago, green metrics are still not widely implemented in the practice of Green Chemistry. Nowadays, there is a general desire and fashion for Green Chemistry considering the modern global concerns of climate change and resource scarcity. However, the scientific literature reveals a confusing array of definitions and methodologies related to green metrics, particularly in both organic and inorganic chemistry. In this review we want to focus on organic synthesis, namely new reaction pathways that employ organic and inorganic catalysts, grounded in fundamental chemistry. The application of rigorous green metrics must go along with the experimental validation of synthetic procedures. This is essential to establish clear guidelines for defining truly green synthetic approaches, and to prevent misunderstandings or overreaching claims that are based on subjective rather than objective assessments. This work originated from an IUPAC project aimed at providing standardized guidance for the use of green metrics. Accordingly, we present a list of green metrics and related terminology currently employed to assess material usage, energy efficiency, and environmental impact in individual reactions and synthetic strategies.

Keywords: Algorithm database; green chemistry; green metrics; metric glossary
Corresponding authors: Fabio Aricò, Environmental Science, Informatics and Statistics, Cà Foscari University, Venice, Italy, e-mail: ; John Andraos, CareerChem, Toronto, ON, Canada, e-mail: ; and Pietro Tundo, University of Venice, Venice, Italy, e-mail:

Funding source: Ministero dell’Istruzione, dell’Universitàe della Ricerca

Award Identifier / Grant number: DoE 2023-2027 (MUR, AIS.DIP.ECCELLENZA2023_27.FF p

Acknowledgments

This work was supported by the DoE 2023–2027 (MUR,AIS.DIP.ECCELLENZA2023_27.FF project).

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: Conceptualization, writing and supervision: John Andraos, Pietro Tundo, Marco Eissen and Fabio Aricò; Data curation and Writing – original draft: Marco Eissen and Fabio Aricò; Revision and wring: Giacomo Trapasso, James Clark.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The author states no conflict of interest.

  6. Research funding: IUPAC Project No.: 2017-030-2-041.

  7. Data availability: Not applicable.

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Received: 2025-08-14
Accepted: 2025-09-08
Published Online: 2025-09-15

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