Ethical explainable artificial intelligence for green innovation: a systematic review and meta-analysis of transparency, fairness, and accountability in sustainable technologies

Artificial Intelligence (AI) is increasingly deployed across sustainability-oriented domains such as smart energy systems, precision agriculture, water management, and sustainable manufacturing to support the United Nations Sustainable Development Goals (SDGs). However, the growing use of complex and opaque models has raised concerns regarding transparency, accountability, fairness, and ethical governance in high-impact sociotechnical decision-making contexts. This study presents a PRISMA-guided systematic literature review and meta-analysis of 140 peer-reviewed empirical studies (2020–2025) that integrate Explainable Artificial Intelligence (XAI) within sustainability-focused AI applications. Following the structured identification, screening, and eligibility assessment of 550 records retrieved from Scopus, Web of Science, IEEE Xplore, ScienceDirect, and SpringerLink, quantitative synthesis was conducted using random-effects models to explicitly accommodate cross-study heterogeneity. The meta-analysis indicates that XAI-integrated AI systems reported consistently high descriptive performance levels within the analysed literature (pooled classification accuracy ≈ 95.84%, aggregated regression fit R2 ≈ 0.964). These pooled estimates do not represent transferable or generalisable performance benchmarks, as substantial heterogeneity exists across studies in terms of algorithmic paradigms, dataset characteristics, feature-engineering strategies, and experimental protocols (I2 ≈ 60–70%). Rather, the aggregated values summarise literature-level reporting tendencies under diverse experimental conditions. Importantly, the correlation analysis revealed no systematic evidence of performance degradation associated with the incorporation of explainability mechanisms (Spearman’s ρ = − 0.08, p > 0.05). This finding should be interpreted as a context-dependent tendency observed within the reviewed sustainability literature, rather than as definitive proof that no interpretability–performance trade-off exists under all modelling conditions. Across domains, post hoc, model-agnostic explainability techniques—particularly SHAP and LIME—dominate current practice, whereas model-specific approaches such as Grad-CAM are primarily applied in image- and sensor-based contexts. Despite technical advances, the explicit operationalisation of fairness, accountability, and governance mechanisms remains limited and uneven. The findings suggest that explainability functions as a performance-preserving design tendency in the existing literature, supporting transparency, epistemic justification, and calibrated stakeholder trust without systematically compromising predictive performance.