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Firms investing in AI are notably more likely to adopt circular-economy innovations—especially those cutting production pollution—suggesting AI helps firms optimise resource use and lower production externalities.

Adoption of circular economy innovations: the role of Artificial Intelligence
Dirk Czarnitzki, Robin Lepers, Maikel Pellens · August 05, 2026 · Industry and Innovation
openalex correlational medium evidence 7/10 relevance Summary only summary available; pdf_status=paywall DOI Source PDF

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German firms that report investing in AI are more likely to introduce circular economy innovations, with the strongest association for innovations that reduce production externalities (e.g., pollution and emissions).

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The circular economy represents a systematic shift in production and consumption, aimed at extending the life cycle of products and materials while minimising resource use and waste. However, achieving the goals of the circular economy presents firms with the challenge of innovating new products, technologies, and business models. This paper explores the role of artificial intelligence as an enabler of circular economy innovations. Through an empirical analysis of the German Community Innovation Survey, we show that firms investing in artificial intelligence are more likely to introduce circular economy innovations than those that do not, particularly innovations aimed at lowering production externalities such as pollution and emissions reductions. The findings of this paper underscore artificial intelligence’s potential to accelerate the transition to the circular economy.

Summary

Main Finding

Firms that invest in artificial intelligence (AI) are more likely to introduce circular economy (CE) innovations than firms that do not invest in AI — with the strongest association for CE innovations that reduce production externalities (e.g., pollution and emissions).

Key Points

  • AI investment is positively associated with the adoption of circular economy innovations.
  • The relationship is particularly pronounced for innovations aimed at lowering production-related externalities (pollution, emissions).
  • AI likely acts as an enabling technology for CE by improving resource efficiency, enabling predictive maintenance, optimising processes, and informing design-for-reuse/recycling (mechanisms suggested by the findings).
  • Results are based on firm-level survey data from Germany; findings highlight firm-level complementarities between digital/AI capabilities and green innovation.

Data & Methods

  • Data source: German Community Innovation Survey (CIS) — firm-level survey data on innovation activities and investments.
  • Empirical strategy: comparative analysis of firms that report investing in AI versus those that do not, examining the likelihood of introducing CE innovations (overall and by type, e.g., production-externality-reducing).
  • Outcomes: introduction of circular economy innovations; subgroup analysis identifies stronger effects for production-externality-focused innovations.
  • Limitations noted by the study (implicit): observational survey data — associations rather than definitive causal estimates; potential for unobserved confounders and measurement limitations typical of CIS-based analyses.

Implications for AI Economics

  • AI as an enabler of green structural change: AI investment can accelerate firms’ transitions toward circular production models, reinforcing the view of AI as a general-purpose technology with environmental benefits.
  • Policy design: supporting AI adoption (e.g., subsidies, training, data infrastructure) could be an effective complement to traditional environmental policies aimed at promoting circularity; policies should target small and medium enterprises that may face adoption barriers.
  • Complementarities and skills: benefits likely depend on organizational capabilities and complementary investments (data, human capital, process redesign), suggesting targeted support for upskilling and integration.
  • Research needs: causal identification of AI’s effect on CE outcomes, heterogeneous firm-level responses, long-run impacts on material use and emissions, and measurement improvements for both AI investment and circular innovations.

Assessment

Paper Typecorrelational Evidence Strengthmedium — Findings come from a reputable, large-scale firm survey (CIS) and show consistent associations and heterogeneity (stronger effects for production-externality-reducing CE innovations), but the cross-sectional, self-reported nature of the data and absence of quasi-experimental identification leave open omitted variable bias, reverse causality, and measurement concerns. Methods Rigormedium — Use of nationally representative innovation survey data and subgroup analysis are appropriate and informative, but the study lacks stronger causal tools (e.g., instrumental variables, panel methods exploiting exogenous variation), relies on potentially noisy self-reports of AI investment and CE innovation, and does not fully rule out selection on unobservables. SampleFirm-level observations from the German Community Innovation Survey (CIS), with firms reporting whether they invest in AI and whether they introduced circular economy innovations (overall and by type, e.g., production-externality-reducing); covers multiple industries and firm sizes, cross-sectional self-reported data; exact sample size, years, and covariates not specified in the supplied text. Themesinnovation adoption IdentificationComparative observational analysis of firm-level survey data (German CIS): firms that report investing in AI are compared to firms that do not, with likely multivariate controls (e.g., firm size, industry) and subgroup analysis; no exogenous source of variation (no instrument, experiment, or difference-in-differences) is reported, so the strategy identifies associations rather than causal effects. GeneralizabilityLimited to firms in Germany — results may not transfer to other countries with different industrial structure or AI ecosystems, Self-reported measures of AI investment and circular innovations may contain measurement error, Cross-sectional design limits causal generalization (cannot establish AI causes CE adoption), Heterogeneity by industry, firm size, and pre-existing green capabilities may limit applicability to specific sectors, Findings may not generalize to developing countries or informal firms with different adoption constraints

Claims (5)

ClaimDirectionOutcomeConfidence & EvidenceDetails
Firms that invest in artificial intelligence are more likely to introduce circular economy innovations than firms that do not invest in AI. Innovation Output positive Introduction of circular economy innovations
Reading fidelity high
Study strength medium
not reported
0.3
The positive association between AI investment and circular economy innovation is strongest for innovations that reduce production-related externalities, such as pollution and emissions. Innovation Output positive Introduction of circular economy innovations aimed at reducing production externalities
Reading fidelity high
Study strength medium
not reported
0.3
The findings are consistent with AI acting as an enabling technology for circular economy activities by improving resource efficiency, supporting predictive maintenance, optimizing production processes, and informing design for reuse or recycling. Innovation Output positive Firm adoption and implementation of circular economy innovation capabilities
Reading fidelity medium
Study strength speculative
not reported
0.03
The evidence indicates firm-level complementarities between digital or AI capabilities and green innovation in Germany. Innovation Output positive Introduction of green and circular economy innovations
Reading fidelity high
Study strength medium
not reported
0.3
Because the analysis uses observational survey data, the reported relationships should be interpreted as associations rather than definitive causal effects. Other null_result Causal interpretability of the relationship between AI investment and circular economy innovation
Reading fidelity high
Study strength high
not reported
0.5

Notes