Evidence (6869 claims)
Adoption
8570 claims
Productivity
7631 claims
Governance
6869 claims
Human-AI Collaboration
6491 claims
Org Design
4175 claims
Innovation
4114 claims
Labor Markets
3566 claims
Skills & Training
2966 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 758 | 199 | 100 | 900 | 2007 |
| Governance & Regulation | 826 | 400 | 191 | 122 | 1563 |
| Organizational Efficiency | 777 | 193 | 124 | 84 | 1189 |
| Technology Adoption Rate | 635 | 233 | 124 | 97 | 1098 |
| Research Productivity | 422 | 128 | 57 | 336 | 954 |
| Output Quality | 476 | 179 | 59 | 47 | 761 |
| Decision Quality | 328 | 177 | 81 | 47 | 640 |
| Firm Productivity | 435 | 57 | 88 | 20 | 606 |
| AI Safety & Ethics | 218 | 277 | 65 | 33 | 599 |
| Market Structure | 180 | 170 | 123 | 24 | 502 |
| Task Allocation | 213 | 64 | 72 | 33 | 387 |
| Skill Acquisition | 170 | 61 | 61 | 17 | 309 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 54 | 107 | 13 | 281 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 153 | 48 | 26 | 3 | 230 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 56 | 56 | 26 | 13 | 154 |
| Training Effectiveness | 94 | 21 | 13 | 19 | 149 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 31 | 18 | 8 | 3 | 61 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Governance
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DPPs generate high-quality, structured product and lifecycle data that are non-rivalrous and highly reusable, raising firm-level incentives to invest in AI models (forecasting, optimization, provenance verification) that exploit this data to capture value across production, secondary markets, and services.
Economic/technical implication drawn from the empirical characterization of DPP data and stakeholder interviews; this is an inferential claim linking DPP data properties to incentives for AI investment rather than a directly measured outcome in the surveys.
Practical DPP deployment must combine standards, governance, and user-centric design to unlock circular-economy benefits.
Inference from empirical mapping of barriers/drivers (survey and qualitative stakeholder input) and multivariate analyses showing interplay of technical and organizational factors; sample sizes not reported.
DPPs should be seen as both technical data platforms and participatory tools that enable collaborative value creation and responsible consumption (thus supporting SDG 12: responsible consumption and production).
Conceptual interpretation synthesized from empirical findings (surveys + multivariate analyses) and theoretical framing in the paper; empirical grounding via stakeholder responses but largely a conceptual contribution.
Successful DPP adoption requires matching technical functionalities (data granularity, interoperability, user interfaces) with firm-level readiness and strategies to engage different consumer segments.
Logistic regression and PCA mapping relationships among DPP features, organizational practices and consumer profiles arising from the two online surveys and mixed-method analysis; sample sizes not reported.
DPPs facilitate knowledge sharing and open innovation across firms, embedding sustainability and knowledge management into operational practice.
Qualitative and survey responses from industry stakeholders in the two sectors; analyses reported include mapping of cross‑firm knowledge exchange and organizational practices (methods: mixed methods, logistic regression/PCA); sample sizes not reported.
DPPs enhance transparency and traceability across complex supply chains, enabling material circularity and more resilient sourcing decisions.
Survey-based evidence and multivariate analyses (PCA, logistic regression) from stakeholders in Italian fashion and cosmetics indicating perceived/observed links between DPP functionalities (data granularity, interoperability) and traceability/circularity outcomes; sample sizes not reported.
Digital Product Passports (DPPs) function as a socio-technical, cognitive infrastructure that, when DPP technical capabilities are aligned with organizational readiness and consumer engagement, materially support circularity (raw-material reuse), supply-chain resilience, and cross-firm knowledge exchange, thereby turning sustainability from a compliance burden into a source of innovation and value in fashion and cosmetics.
Mixed-methods empirical study in Italian fashion and cosmetics using two online surveys, PCA and logistic regression to map relationships among technical features, organizational practices and consumer profiles; sample sizes not reported in the summary.
Economists and AI practitioners will need capacity-building in Earth-system knowledge to ensure models capture cumulative and systemic environmental risks rather than only firm-level signals.
Recommendation based on gap analysis between current disciplinary skills and systemic-environmental modeling needs; no survey or training-efficacy data offered.
There is a need for standards for data provenance, auditability, and adversarial robustness to prevent greenwashing and model manipulation.
Policy recommendation grounded in conceptual risk analysis; no technical standard proposals or threat-model evaluations provided.
Open environmental disclosure data supports reproducible empirical research in AI economics (causal inference, counterfactuals, macro-financial modeling) on effects of regulation and capital flows on environmental outcomes.
Logical argument about data availability enabling reproducible research; no empirical examples or reproducibility metrics provided.
More reliable environmental disclosures enable algorithmic investors and market models to price externalities more accurately and to implement sustainability-aligned strategies at scale.
Conceptual argument about improved information enabling market mechanisms; no empirical market-impact study included.
Open data facilitates automated, lower-cost reporting tools (NLP extraction, sensor/IoT integration, ETL pipelines) that reduce administrative burden and increase reporting frequency and timeliness.
Conceptual claim linking open standardized data to automation potential; no implemented cases or cost estimates provided.
Improved, standardized environmental disclosures improve training data quality for predictive models, reducing measurement error and bias.
Theoretical claim about data quality effects on model performance; no empirical evaluation provided.
Better, standardized, open environmental data unlocks AI/ML opportunities, enabling scalable models for firm- and system-level environmental risk assessment, scenario analysis, stress testing, and portfolio optimization.
Conceptual implications and use-case enumeration; no empirical model-building or benchmarking presented.
Applying assurance standards and regulatory oversight analogous to financial reporting will improve environmental data quality.
Normative recommendation; argument by analogy to financial assurance practices; no empirical assessment included.
Standardization (common taxonomies, units, definitions) and machine-readability are necessary to ensure comparability of environmental disclosures.
Methodological recommendation based on conceptual analysis of data interoperability issues; no empirical demonstration provided.
Treating environmental data with the same rigor as financial data (governance, standardization, auditing) would markedly improve investor, regulator, and public agency ability to assess environmental pressures, hold firms accountable, and align capital with sustainability objectives.
Conceptual causal claim argued from analogy to financial reporting; no empirical testing provided in the paper.
Corporate sustainability reporting is a powerful lever for changing corporate behavior; improving it can influence investment flows and corporate practice.
Normative/conceptual claim supported by literature synthesis and policy reasoning rather than new empirical testing.
The 2018 Supply Chain Innovation and Application Pilot Program can be used as a quasi‑natural experiment (treatment) to identify causal effects of SCD on firm outcomes.
Difference-in-differences design comparing treated (pilot-designated) versus control firms pre/post-2018; treatment defined by designation as pilot enterprise under the 2018 program.
The SCD → green innovation effects are larger for large firms (by firm size).
Heterogeneity analysis splitting sample by firm size (large vs small) with results indicating stronger SCD effects on green innovation for larger firms.
The SCD → green innovation effects are larger for state‑owned enterprises (SOEs).
Heterogeneity analysis by ownership type (SOE vs non‑SOE) showing larger and more significant coefficients for SOEs in the SCD effect on green innovation.
Carbon information disclosure (CID) is a key mediating channel: SCD increases the likelihood and quality of CID, which in turn promotes substantive green innovation.
Mediation analysis using observed CID indicators (likelihood/quality of carbon disclosure) in a causal pathway framework; SCD raised CID metrics in first-stage regressions and CID was positively associated with subsequent substantive green innovation in mediation tests.
Practical recommendation: incorporate uncertainty quantification (e.g., confidence intervals, Bayesian approaches) for ESG features in economic and ML models to reflect disclosure unreliability.
Applied recommendation in the implications section based on observed noise and manipulation risk in ESG data; not empirically tested in this review.
Market design and regulation should standardize ESG reporting and require audit/assurance, and AI can be used to monitor compliance at scale and target audits.
Policy recommendation synthesized from literature citing heterogeneity in ESG reporting and benefits of standardization; the paper presents this as an implication rather than reporting new empirical evidence.
Regulatory intervention and standardized ESG reporting/assurance are urgently required to mitigate misuse and information asymmetries.
Normative conclusion drawn from synthesis of empirical findings on disclosure heterogeneity, manipulation risk, and stakeholder harms; supported by cited calls in the literature but not empirically tested in this paper.
Strong ESG practices can reduce a firm's cost of capital (for equity and/or debt).
Synthesis of previous empirical studies linking higher ESG scores/disclosure to lower perceived risk and lower cost of equity/bond yields; literature review (secondary analysis), sample sizes and methods vary across cited studies.
ESG information can enhance long‑term firm value.
Qualitative synthesis of peer‑reviewed empirical studies in the literature review that report positive associations between stronger ESG practices and measures of firm valuation (e.g., Tobin's Q, market value). Evidence drawn from multiple prior studies with varied samples and methodologies; no new primary data in this paper.
Effective teams tend to evolve from ad-hoc interpretive methods toward systematic evaluation by (a) formalizing prompts/tests, (b) instrumenting outputs, (c) mapping failure modes to remediation paths, and (d) creating organizational decision rules.
Pattern observed in the qualitative coding of interviews where participants described trajectories or steps their teams took to formalize evaluation.
Successful teams close the results-actionability gap by systematizing interpretive practices and creating clearer pathways from evaluation signals to product changes.
Interview accounts and cross-case analysis showing some teams adopting formalization steps (e.g., standardized prompts/tests, instrumentation, remediation mappings) that participants described as enabling action.
Policy responses (active labor-market interventions, reskilling, lifelong learning, social insurance, redistribution) are needed to manage transitional inequality caused by AI-driven structural shifts in labor demand.
Policy implication drawn from reviewed empirical and theoretical literature on labor-market transitions and distributional impacts; presented as a recommendation without new empirical evaluation in this paper.
Economists should refine methods to measure AI adoption and incorporate AI-driven productivity gains into growth accounting while accounting for measurement challenges (quality change, task reallocation).
Methodological recommendation based on the review's identification of measurement difficulties in the existing empirical literature; the paper itself provides conceptual guidance rather than new measurement results.
AI has materially increased operational efficiency and productivity in industry, changing production processes and firm organization.
Qualitative integration of prior empirical studies and firm-level case studies cited in the literature review (industry analyses, adoption case examples); the paper itself does not provide new quantitative estimates or causal identification.
There is demand and market potential for usable, solutions-oriented AI-driven decision tools and risk-data products that support municipal and national MHEWS and resilience planning.
Stakeholder engagement and needs assessments reported in the project's synthesis indicating practitioner demand and potential market opportunities.
Progress was made on the six-point research agenda proposed in 2022; results and remaining gaps were evaluated across MYRIAD-EU activities.
Comparative synthesis of MYRIAD-EU activities and outputs (2021–2025) mapping achievements against the six-point agenda and documenting gaps.
Quantum diffusion will amplify demand for high-skilled workers (quantum engineers, hybrid systems integrators), requiring upskilling and causing sectoral labor reallocation and potential wage pressures in specialized talent markets.
Labor reallocation outputs from macro models with sectoral shocks; historical analogs for labor demand shifts after new compute technologies; qualitative workforce analysis.
Quantum algorithms that accelerate subroutines used in machine learning (sampling, optimization, simulation) would raise returns to AI investments and could speed model development or reduce training costs in specialized domains.
Conceptual analysis of quantum-classical complementarities, scenario modeling of cross-technology effects on investment returns; suggested need for empirical estimation.
Quantum computing could alter the landscape of available compute for AI workloads, potentially reducing or redirecting compute constraints for specific algorithmic tasks (e.g., optimization subroutines, certain quantum-native ML models).
Theoretical mapping of quantum algorithmic advantages to AI subroutines, scenario analysis of compute supply complements/substitutes; limited empirical grounding from specialized use-cases.
Realizing macro gains requires complementary investments in classical compute, data infrastructure, workforce training, and hybrid classical–quantum integration tools.
Model sensitivity analyses showing that augmenting quantum adoption parameters without sufficient complementary inputs yields smaller macro impacts; calibration to historical complements for enabling technologies.
Quantum offers sectoral advantages (optimization, materials discovery, cryptography-safe transitions, drug discovery, finance, logistics) that could raise productivity in targeted industries rather than producing uniform economy-wide shocks.
Productivity mapping that converts sectoral adoption into Hicks-neutral TFP shocks based on micro evidence and case studies (materials discovery, optimization deployments); diffusion models parameterized with sectoral heterogeneity.
Quantum computing has the potential to generate substantial long-run productivity gains across multiple sectors.
Scenario-based macroeconomic modeling that translates sectoral quantum adoption into TFP shocks and simulates outcomes in multi-sector CGE/growth models; parameters calibrated with micro evidence of quantum advantages and historical analogs (cloud, GPUs, AI toolchains); Monte Carlo / scenario ensembles.
The pilot policy is associated with increases in firm-level ESG scores and green-investment flows (direct effects of policy on the mediators).
Reduced-form DID estimates using ESG scores and green-investment flows as dependent variables show positive, statistically significant treatment effects.
When executives have both high green cognition and high digital cognition, the two cognitions reinforce each other, producing a significantly positive enabling effect on the policy's impact (facilitating integrated green+digital innovation and reducing adjustment frictions).
Triple-interaction or subgroup analysis combining high-green and high-digital executive cognition indicators within the DID framework, showing a significant positive effect larger than either cognition alone.
High executive green cognition strengthens the marginal positive effect of the green data center pilot policy on firms' energy utilization efficiency.
Moderation analysis interacting the policy treatment with an executive-level green-cognition measure in DID regressions; positive and significant interaction coefficients reported.
The policy effect on energy utilization efficiency is more pronounced for mature-stage firms than for early-stage firms.
Subsample analysis by firm life-cycle stage (firm-level lifecycle classification) showing statistically larger policy effects for mature firms in the DID estimates.
Firms operating in more competitive industries experience larger energy-efficiency gains from the green data center pilot policy.
Heterogeneity tests by industry competition (industry-level competition measure) within the DID framework, showing larger policy coefficients for firms in high-competition industries.
The policy's positive impact on energy utilization efficiency is stronger in resource-based cities than in non-resource-based cities.
Heterogeneity analysis splitting the sample by city type (resource-based indicator) and estimating DID effects separately; larger and statistically stronger coefficients reported for resource-based city subsample.
Policy-induced increases in firms' green investment constitute another primary channel through which the pilot policy improves energy utilization efficiency.
Mediation/channel analysis using firm green-investment flow measures in DID regressions; policy assignment is associated with increases in green investment and these increases account for part of the policy's effect on energy efficiency.
Improved firm ESG performance mediates part of the positive effect of the green data center pilot policy on corporate energy utilization efficiency.
Regression-based mediation tests within the DID framework using firm-level ESG scores as the mediator; inclusion of ESG reduces the estimated policy coefficient and mediator effects are reported as significant.
Immediate research priorities for AI economists include: field experiments testing NLP‑driven acquisition/personalization (measuring CAC, LTV, retention, consumer welfare); structural/empirical models of adoption that include data access costs and complementarities; and analyses of privacy regulation impacts on external text data availability and value.
Authors' set of recommended research directions derived from identified gaps in the systematic review and implications for AI economics.
Policy priorities to improve China's digital services exports include: strengthening participation in global rule‑making, building internationally competitive platforms and cloud infrastructure, expanding targeted support for firms (especially SMEs) to internationalize, and refining data governance to balance security/privacy with cross‑border interoperability.
Derived recommendations from the integrative literature and policy review and comparative diagnosis (interpretive, not empirically validated within the paper).