life-cycle assessment

This study addresses the fragmented understanding of environmental impacts across the full lifecycle of artificial intelligence systems, a gap marked by incomplete phase coverage, inconsistent metrics, and opaque methodologies in current “green AI” research. The authors propose a unified analytical framework encompassing eight stages—from hardware manufacturing and data processing to model training and deployment—and integrate life cycle assessment (LCA), systematic literature review, and multidimensional environmental indicators such as CO₂e emissions. Their analysis reveals that existing studies predominantly focus on training and inference while overlooking critical factors like water consumption, raw material extraction, and embodied carbon. To rectify these omissions, the paper advances a standardized, comparable, and policy-oriented pathway for assessing AI’s environmental footprint, thereby promoting more systematic and rigorous green AI research.

This study addresses the limited scope of traditional high-performance computing (HPC) evaluations, which typically focus solely on performance and energy consumption while overlooking the comprehensive environmental costs of operational configurations. The authors propose the first job-level unified accounting framework that integrates both operational and full life-cycle (embodied) carbon and water footprints. Leveraging life-cycle assessment methodologies, real-time runtime monitoring, and hardware manufacturing emission data, the framework enables fine-grained quantification of environmental impacts. The analysis reveals that increasing thread count generally reduces total environmental footprints, albeit with diminishing marginal returns; while carbon footprints are predominantly driven by operational phases, water footprints are largely dominated by embodied impacts. By jointly incorporating both footprint types at the job granularity, this work establishes a novel paradigm for assessing HPC sustainability.

Existing life cycle assessment (LCA) tools for road infrastructure rely on linear modeling assumptions, operate independently from design workflows, and lack iterative optimization capabilities—hindering sustainable road design. Method: This study systematically identifies and critically examines implicit linearity limitations in transportation infrastructure LCA tools through a comprehensive literature review, functional comparison of existing tools, empirical case studies of aggregate-surfaced roads, and carbon emission correlation and sensitivity analyses. Contribution/Results: We empirically demonstrate that key design parameters—including road surface area, grade adjustments, and soil type—exhibit significant nonlinear relationships with embodied carbon emissions. These findings establish both theoretical grounding and empirical validation for developing next-generation LCA tools that are natively embedded within the design process, enabling real-time feedback and iterative optimization. The work advances LCA practice from retrospective evaluation toward proactive, co-design integration—supporting decarbonization-aligned infrastructure planning and engineering.

Current environmental impact assessments of the AI lifecycle suffer from tool heterogeneity, insufficient coverage of water usage and value-chain stages, and poor cross-study comparability. To address these gaps, this paper proposes a unified operational definition of Green AI and introduces a five-stage lifecycle framework—encompassing hardware manufacturing, model development, training, deployment, and reuse. It explicitly distinguishes Green AI (energy-efficiency–focused) from Sustainable AI (holistically sustainable across environmental, social, and economic dimensions). Methodologically, the framework integrates Life Cycle Assessment (LCA), PDCA-based governance, edge–cloud co-optimized hardware strategies, and a calibrated multi-level metrics system combining estimation and empirical measurement. The resulting methodology enables vendor-agnostic, reproducible quantification of energy consumption, carbon emissions, water use, and embodied impacts. This significantly enhances transparency and comparability, providing researchers, engineers, and policymakers with an evidence-driven, actionable guide for sustainable AI development and deployment.

This study addresses the widespread lack of rigor in model construction, calibration, and integration within current ICT life cycle assessment (LCA) practices, which undermines result credibility. To tackle this issue, the work proposes four foundational trustworthiness criteria for ICT LCA—model provenance, clearly defined scope, end-to-end traceability, and obsolescence management—and introduces an open computational LCA framework grounded in these principles. The framework integrates dependency graph modeling, a versioned model repository, automated enforcement of completeness constraints, and a structured model taxonomy. It effectively uncovers common misuses and structural flaws in existing LCA studies, thereby offering a viable pathway toward transparent, reproducible, and sustainably evolvable assessments of ICT environmental impacts.

This study addresses the critical environmental challenges—including energy consumption, carbon emissions, and water usage—arising from the proliferation of generative and agentic AI, noting that existing research has yet to comprehensively cover their full life-cycle impacts. Employing order-of-magnitude estimation based on publicly available data, combined with life cycle assessment and multidimensional environmental modeling, this work systematically evaluates ecological footprints across all stages from hardware manufacturing to end-of-life disposal. It proposes a cross-stage taxonomy and the SAFIA assessment framework, revealing key findings such as inference energy demands potentially exceeding those of training and the exponential energy consumption patterns inherent in agentic workflows. Furthermore, the authors outline a research roadmap through 2035 and advocate for the establishment of mandatory disclosure standards to bridge existing gaps in the field.

Carbon life cycle assessment (LCA) suffers from labor-intensive, costly, and time-consuming manual process modeling. Method: This study proposes the first LLM-based automated process generation framework for LCA, integrating the ISO 14040/44-compliant LCA classification system with the world knowledge of large language models (LLMs), leveraging prompt engineering and structured process modeling. Accuracy is quantified via F1-score against ground-truth processes extracted from real LCA documentation. Contribution/Results: Evaluated on 10 diverse case studies, the framework achieves a 62% F1-score; most outputs are either fully correct or contain only minor deviations. Each analysis costs under USD 1 and completes in under 10 minutes. Compared to conventional expert-driven approaches and chain-of-thought prompting, this method substantially reduces human effort while advancing accuracy, computational efficiency, and accessibility—establishing a scalable new paradigm for rapid carbon footprint assessment of consumer products.

This study addresses the lack of a systematic review at the intersection of artificial intelligence (AI) and life cycle assessment (LCA), which has hindered a comprehensive understanding of their technological evolution and application linkages. For the first time, large language models (LLMs) are integrated into the LCA domain, combining text mining, bibliometric analysis, and traditional systematic review methods to construct a reproducible and dynamic research landscape. Through large-scale analysis, the work reveals statistically significant associations between AI techniques and various LCA phases, identifies a rapidly growing trend dominated by LLMs and machine learning, and proposes an efficient, scalable framework to support sustainable decision-making and guide future research directions.

Carbon emissions from generative AI inference have been systematically underestimated due to ambiguous system boundaries, lack of standardized metrics, and methodological biases in existing assessments. This study employs a scoping review to systematically analyze carbon footprint measurement tools, classification frameworks, and research trends across the full training–inference lifecycle. We propose (i) a standardized measurement protocol, (ii) a dynamic evaluation framework, and (iii) a multidimensional sustainability assessment system—novelly integrating user behavior and system boundary definitions into a unified analytical model. Our analysis uncovers nonlinear relationships between carbon emissions and model scale, prompt complexity, and deployment environment, identifying key leverage points for mitigation. The findings establish a reusable methodological benchmark and practical guidelines for AI environmental governance across technical, social, and operational dimensions.

该研究利用生命周期评估方法和EXIOBASE数据库，分析废物管理与其他经济领域的排放相互依赖关系，揭示了跨部门的排放驱动因素，并强调了综合及材料特定策略的需求。