Green HRM has shifted from compliance to a strategic enabler that supports seven distinct sustainable business models—yet its effectiveness depends on firm resources, sector and technological readiness. Firms that pair AI adoption with targeted GHRM (reskilling, governance and culture) are likeliest to capture larger productivity and sustainability gains, while uneven adoption risks distributional harms.

Main Finding

Green Human Resource Management (GHRM) has shifted from compliance-focused practices to a strategic enabler of sustainability-oriented business models. Across the literature (2014–2025), GHRM links to seven distinct sustainable business model pathways via mechanisms such as employee capabilities, organizational culture, green innovation, leadership, stakeholder alignment and technological capabilities. The effects are context-dependent — shaped by organizational resources, technological readiness and sectoral conditions.

Key Points

• Research growth: Bibliometric analysis of 237 Scopus-indexed studies (2014–2025) shows expanding interest and cluster emergence in GHRM research.
• Emerging thematic clusters: Green Culture, Green Innovation, Circular Economy, and Digital Transformation are dominant and growing themes.
• Mechanisms by which GHRM supports sustainability:
  • Developing employee capabilities and green skills
  • Shaping pro-environment organizational culture and behaviours
  • Stimulating green innovation through knowledge and incentives
  • Leadership and governance aligning HR practices with strategic sustainability goals
  • Aligning stakeholder expectations and external partnerships
  • Leveraging technological capabilities for implementation
• Seven Sustainable Business Model pathways identified from detailed SLR (24 high-quality studies):
• Technology-driven models
• Circular and regenerative models
• Capability-based models
• Governance and strategic-alignment models
• Behavioural and human-centric models
• Triple Bottom Line (social, environmental, financial) models
• Sector-specific models
• Heterogeneity: GHRM impacts vary by firm resources, tech readiness, and sector—no uniform effect across contexts.
• Theoretical integration: The study synthesizes diverse theories using the TCCM (theoretical, contextual, conceptual, methodological) framework to map intellectual progression and interrelationships.

Data & Methods

• Bibliometric analysis:
  • Dataset: 237 articles indexed in Scopus spanning 2014–2025.
  • Methods: bibliometric mapping to identify publication trends, influential authors/journals, co-citation and keyword co-occurrence clusters.
• Systematic literature review (SLR):
  • Subset: 24 studies selected for methodological quality and direct relevance.
  • Approach: qualitative synthesis to extract mechanisms, model typologies, and contextual moderators.
• Theoretical synthesis:
  • Framework: TCCM used to integrate theoretical propositions, contextual contingencies, conceptual links, and methodological gaps.
  • Outcome: taxonomy of seven sustainable business model pathways and mapped mechanisms linking GHRM to outcomes.

Implications for AI Economics

• AI as an accelerator of GHRM-driven sustainability:
  • AI-enabled HR tools (skill diagnostics, personalized training, recruitment algorithms) can scale capability-building and match labor to green roles faster, strengthening the capability-based and technology-driven pathways.
  • AI can optimize resource use, logistics, and design (supporting circular and regenerative models) but requires complementary human skills and governance.
• Complementarity and heterogeneity:
  • Economic returns to AI investments are conditional on complementary GHRM practices (reskilling, culture, leadership). Firms that pair AI adoption with GHRM are likely to capture larger productivity and sustainability gains.
  • Sectoral differences matter: capital- and data-intensive sectors may show stronger AI × GHRM complementarities than low-tech sectors.
• Labor market and distributional effects:
  • GHRM-focused reskilling mediated by AI could mitigate displacement risks, but economists should evaluate distributional outcomes (who gains skills and who is left behind).
  • AI-driven task substitution may change the demand for green skills; policies to support retraining are crucial.
• Measurement & empirical strategies for researchers:
  • Key variables to collect: firm-level AI adoption indicators, GHRM practice indices, green patenting/innovation, emissions/energy intensity, workforce skill composition, and sectoral characteristics.
  • Recommended methods: panel data with firm fixed effects, difference-in-differences exploiting exogenous AI-adoption shocks, instrumental variables for adoption, structural models capturing complementarities, and matched employer-employee datasets.
  • Need for causal evidence on AI × GHRM interactions and heterogenous treatment effects across firm types and sectors.
• Governance, transparency and regulation:
  • Algorithmic transparency, fairness in recruitment and monitoring, and worker privacy are important policy considerations; poor governance can undermine human-centric GHRM pathways and create negative externalities.
• Investment and policy design:
  • Public incentives that tie AI adoption funding to parallel investments in workforce retraining and sustainable HR practices can improve aggregate returns.
  • Sector-targeted policies (e.g., for manufacturing, energy, services) can recognize differing technological readiness and tailor support for GHRM-enabled transitions.
• Research agenda for AI economists:
  • Quantify productivity and environmental returns to integrated AI + GHRM strategies.
  • Study long-run equilibrium effects on wages, employment composition, and firm survival across sustainable business model types.
  • Model spillovers: how firm-level GHRM and AI adoption influence supply-chain emissions and regional labor markets.
  • Evaluate policy instruments (training subsidies, adoption grants, regulation) in randomized or quasi-experimental designs.

If you want, I can convert these implications into testable hypotheses or suggest datasets and empirical designs to study AI × GHRM complementarities empirically.