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Review of 269 studies finds human-AI teamwork boosts green supply chains

Study title: Meta analytic study of human AI co decision systems and workforce intelligence in enhancing sustainability performance in circular supply chains

Evidence type
Systematic review or meta-analysis
Sample
n = 269
Sources
1 sourcePeer-reviewed
Published
2 min read
Truvace record
TRV-2026-2739cite or check it

Study title: Meta analytic study of human AI co decision systems and workforce intelligence in enhancing sustainability performance in circular supply chains

A meta-analysis of 269 Scopus-indexed empirical studies published from 2018 to 2025 examined how human-AI co-decision systems affect circular supply chains. It found a statistically significant positive association between these systems and circular behaviour, which in turn improved environmental performance.

Read the study abstract

Abstract The shift to circular supply chains (CSC) in the context of Industry 4.0 has further driven the adoption of artificial intelligence (AI) for production planning, logistics and sustainability considerations. But an increasing body of evidence suggests that the environmental implications are less a matter of AI adoption and more about how effectively AI complements human judgment and human skills. The paper contributes by introducing a meta-analysis on the function of human–AI co-decision systems and labo…

60Gain side

How the two sides scoreClose call

56Problem side

The gain

A meta-analysis of 269 empirical studies (2018 to 2025) found human-AI co-decision systems were significantly and positively linked to circular behaviour and better environmental performance in supply chains.

The problem

The same 269-study meta-analysis found economic gains from human-AI co-decision systems in circular supply chains were rather modest, well below the social sustainability gains, pointing to real trade-offs.

A fixed formula sets both scores from the evidence and its sources. Reader votes don’t move them.

What the scores mean

G is the gain side, out of 100. It goes up when the gain is specific, when it measurably helped people and when the sources behind it are strong.

P is the problem side, out of 100. It goes up when the problem is specific, when it measurably hurt people and when the sources behind it are strong.

In brief

Workforce intelligence, meaning digital skills, collaborative capability and organisational learning readiness, strengthened these links. Models built on close human-AI collaboration outperformed both AI-dominant models and heavily assistive systems.

Social sustainability outcomes improved the most, while economic gains were rather modest, which the authors describe as real-world trade-offs. They argue the results point to human-centred AI design and workforce readiness as drivers of resilient supply chains.

Main points

  1. The review screened and meta-analysed 269 Scopus-indexed empirical studies published between 2018 and 2025.
  2. It followed the PRISMA-2020 checklist and used a random-effects meta-analytic method.
  3. Human-AI co-decision systems were significantly and positively associated with circular behaviour and environmental performance.
  4. Digital skills, collaborative capability and organisational learning readiness strengthened the relationship.
  5. Closely collaborative human-AI models outperformed AI-dominant and highly assistive systems.
  6. Social sustainability outcomes improved most, while economic gains were modest.

The full story

The study responds to the spread of AI in production planning, logistics and sustainability work within circular supply chains under Industry 4.0. It argues environmental effects depend less on whether firms adopt AI and more on how well AI complements human judgment and skills.

Using a systematic PRISMA-2020 screening of 269 studies and a random-effects meta-analysis, the authors tested whether co-decision systems and workforce intelligence support circular economy practice in production systems.

The results favour designs where humans and AI work closely together over systems dominated by AI or limited to assisting. The gains were largest for social sustainability, with only modest economic returns.

What this doesn’t fix

The authors note that economic gains were modest and that implementation involves real-world trade-offs, so benefits were uneven across sustainability dimensions.

Sources

1 on file
  1. Discover Sustainabilitydoi:10.1007/s43621-026-04157-x
    Peer-reviewed2026-08-06

Talk it over

Four questions to start onThis record found a gain and a problem in the same place. Which matters more to you, and who might answer differently?

Start with a question

Review of 269 studies finds human-AI teamwork boosts green supply chainshttps://truvace.com/item/meta-analytic-study-of-human-ai-co-decision-systems-and-workforce-intelligence-i

  1. This record found a gain and a problem in the same place. Which matters more to you, and who might answer differently?

    The gain A meta-analysis of 269 empirical studies (2018 to 2025) found human-AI co-decision systems were significantly and positively linked to circular behaviour and better environmental performance in supply chains.

    The problem The same 269-study meta-analysis found economic gains from human-AI co-decision systems in circular supply chains were rather modest, well below the social sustainability gains, pointing to real trade-offs.

    Answer in the thread

  2. How do we know? What can this kind of evidence show, and what can’t it?

    The evidence This rests on a systematic review or meta-analysis with a sample of 269, from Discover Sustainability.

    Answer in the thread

  3. What does it leave open? What else would you want to know before acting on it?

    The record’s own caveat The authors note that economic gains were modest and that implementation involves real-world trade-offs, so benefits were uneven across sustainability dimensions.

    Answer in the thread

  4. Have you seen AI used this way where you study, work or live? Did it go the same way?

    Answer in the thread

The questions are the same on every record. The details under them are taken from this record as written.

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