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The current and future use of artificial intelligence for systematic evidence synthesis in environmental management

Edited by:

Biljana Macura, PhD, Stockholm Environment Institute, Sweden
Shinichi Nakagawa, PhD, Evolution & Ecology Research Centre, The University of New South Wales, Australia
Samantha Cheng, PhD, World Wildlife Fund, United States of America

Submission Status: Open   |   Last submission date: 28 February 2025 

Environmental Evidence is calling for submissions to our Collection on the current and future use of artificial intelligence for
systematic evidence synthesis in environmental management. Submissions are currently open with last submission date 28 February 2025. 

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New Content ItemThis Collection supports and amplifies research related to SDG 3: Good Health & Well-Being, SDG 6: Clean Water and Sanitation, SDG 7: Affordable and Clean Energy, SDG 12: Responsible Consumption & Production, SDG 13: Climate Action, SDG 14: Life Below Water, SDG 15: Life on Land

Meet the Guest Editors

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Biljana Macura, PhD, Stockholm Environment Institute, Sweden

Biljana Macura is a Senior Research Fellow at Stockholm Environment Institute (HQ) in Stockholm. She is an environmental social scientist. Biljana produces rigorous syntheses of scientific evidence, including systematic reviews and maps, to inform decision-making in environment and development. Biljana is particularly interested in evidence-informed decision-making, knowledge co-production and stakeholder engagement, policy needs assessment and use of different forms of evidence to investigate complex environmental and development problems. She also works on the improvement and development of methodology for evidence synthesis and provides training in this field. Biljana is the Editor-in-Chief of Environmental Evidence journal.

Shinichi Nakagawa, PhD, Evolution & Ecology Research Centre, The University of New South Wales, Australia

Shinichi Nakagawa is a professor of evolutionary biology and synthesis and a newly appointed Canada Research Excellence Chair for Open Science and Synthesis in Ecology and Evolution at the University of Alberta. His work revolves around understanding variation using research synthesis methods, and the development of new methodologies, especially for meta-analysis.

Samantha Cheng, PhD, World Wildlife Fund, United States of America

Dr. Sam Cheng is WWF's Director for Conservation Evidence and works across the organization and with external organizations to advance evidence-informed practice in conservation programs. She has extensive experience partnering with government agencies, conservation non-profits, multilateral institutions, and foundations to systematically evaluate the impact of conservation on ecological and social outcomes and identify key insights to guide policy, practice, and research. Her work also focuses on the process of utilizing evidence to inform decisions in conservation and development by examining where, when, and how to provide timely and responsive evidence for different policy and program scenarios. She works to develop and improve open-source methods and tools for identifying, synthesizing, and delivering evidence for pressing policy questions for conservation and development.

About the Collection

The field of evidence synthesis plays a pivotal role in informing environmental decision-making processes by providing robust and comprehensive assessments of available evidence. With the rapidly increasing volume and complexity of scientific literature, traditional evidence synthesis methods may face challenges in terms of efficiency, accuracy, and scalability. The rapid pace of policy development also necessitates timely and updated syntheses of research. 

To address these limitations, the rise of artificial intelligence (AI) and related new technologies presents an opportunity to enhance and streamline various stages of the evidence synthesis process.

This collection focuses on applications of AI and related technologies in systematic evidence synthesis. Specifically, the collection is open fora wide range of topics -addressing both the existing applications of AI (with a special focus on large language models) in evidence synthesis and the potential future directions in this field. The collection aims to examine AI applications across various stages of the review process, offering a comprehensive analysis of both the benefits and limitations of AI in evidence synthesis. Through critical evaluations and addressing potential criticisms, the submissions to this collection should contribute to the development of responsible and ethical AI practices in the field.

We welcome submissions of commentaries, original research papers, or reviews covering a wide range of topics related to current and future use of AI for systematic evidence synthesis in environmental management. These may include, but are not limited to, the following:

- Innovative AI applications within the systematic evidence synthesis process.

- Rigorous examination and validation of AI-powered tools, methods, or approaches employed for evidence synthesis.

- Critical analysis of ethical considerations, biases, evolving relationships among stakeholders in the evidence ecosystem as a result of AI use in evidence synthesis

- Exploration of attitudes of evidence users towards AI use in systematic evidence synthesis.

There are currently no articles in this collection.

Submission Guidelines

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This Collection welcomes submission of commentaries, original research papers or reviews of the literature. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. 

Articles for this Collection should be submitted via our submission system, Editorial Manager. Please select the appropriate Collection title “The current and future use of artificial intelligence for systematic evidence synthesis in environmental management" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer-review process. The peer-review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.