emissions accounting

Widely deployed AI-assisted carbon footprint calculation systems lack standardized, credible evaluation criteria; existing guidelines are outdated, benchmark datasets are scarce, and uncertainty analysis remains infeasible at scale. Method: We propose the first comprehensive credibility verification framework specifically designed for AI-assisted carbon accounting systems. Departing from conventional itemized auditing, our system-level approach integrates three core metric categories: benchmark testing, data quality indicators, and uncertainty characterization—tailored to use cases such as corporate GHG accounting and product-level hot-spot identification. The framework was developed via iterative demand analysis, standards drafting, and empirical piloting, incorporating life cycle assessment modeling, AI-to-domain mapping techniques, and statistical uncertainty quantification. Contribution/Results: It enables automated, high-fidelity credibility assessment with scalability, reproducibility, and verifiability—serving practitioners, third-party auditors, and standardization bodies.

Existing global greenhouse gas (GHG) emission datasets suffer from low accuracy, incomplete coverage, coarse spatiotemporal resolution, and significant reporting lags—impeding effective climate monitoring and evidence-based policymaking. To address these limitations, this study develops Climate TRACE, an open-access platform that delivers facility-level, monthly, near-real-time estimates of anthropogenic emissions across all major sectors—including power generation, industry, and transportation. Leveraging multi-source data—including satellite remote sensing, ground-based measurements, and legacy inventories—the platform employs sector-specific, AI-driven models to perform dynamic gap-filling and synergistic inverse modeling. Emission estimates are published monthly starting from January 2021, with a latency of only two months, and cover subnational administrative units worldwide. The platform is designed for accessibility by non-technical users. This work substantially enhances transparency, traceability, and responsiveness in emissions monitoring, establishing a new paradigm for data-driven climate governance.

This study addresses the challenges of identifying software-related carbon emissions and clarifying responsibility boundaries in the ICT sector. We propose the first carbon accountability mapping framework spanning the entire software lifecycle. Leveraging systematic mapping, ICT energy consumption modeling, and multi-stakeholder governance analysis, we trace energy flows and emission sources across hardware-software interactions, and explicitly define climate accountability for developers, operators, and cloud service providers across design, deployment, and operation phases. Our key innovation lies in operationalizing abstract environmental responsibility into a traceable, allocatable role-based model, and developing a standardized Software Carbon Accountability Atlas tool. The framework supports compliance with EU regulatory frameworks—including the Corporate Sustainability Reporting Directive (CSRD) and Corporate Sustainability Due Diligence Directive (CSDD)—and significantly enhances transparency in ICT firms’ carbon disclosures and cross-organizational emission reduction coordination.

This study addresses the lack of standardized methodologies for Scope 3 Category 1 carbon accounting in enterprise AI inference services, where current practices often overestimate emissions by 10–40× due to reliance on coarse-grained industry-average emission factors. The authors propose the first four-tier hierarchical accounting framework aligned with CSRD compliance requirements, which degrades systematically—from a token-level physical energy consumption model to expenditure-driven environmentally extended input-output (EEIO) analysis—based on data availability. Integrating GPU energy benchmarks (ML.ENERGY v3), regional grid carbon intensities (EPA eGRID, Ember), and water efficiency metrics, the framework reveals a previously undocumented carbon-water trade-off: low-carbon regions may entail high water consumption, influencing data center siting decisions. An empirical assessment of a 200-person European firm shows annual AI inference emissions under 1 ton CO₂e, indicating that regulatory challenges stem from methodological gaps rather than emission magnitude, while also uncovering water-carbon synergies overlooked by mainstream ESG tools.

Scope 3 greenhouse gas emissions are challenging to analyze accurately due to sparse disclosure, heterogeneous formats, and a lack of traceable evidence. This work proposes the first dual-granularity framework for extracting Scope 3 emissions data at both organizational and building levels, featuring full traceability of supporting evidence. By integrating optical character recognition (OCR), large language models (LLMs), rule-based engines, and table reconstruction techniques, the framework enables end-to-end extraction of high-precision, interpretable Scope 1–3 emissions data from real-world ESG reports. Concurrently, the study introduces the first multimodal, evidence-annotated dataset designed to support reliable integration and transparent provenance tracking of heterogeneous emissions disclosures.

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。

This study addresses the disconnect between bottom-up energy consumption metrics and top-down reporting in cloud carbon accounting, which impedes unified accountability. To bridge this gap, we propose the Reconciled Software Carbon Intensity (rSCI) framework. By designing residual decomposition and allocation algorithms grounded in physical drivers and integrating cloud provider audit data, rSCI re-anchors bottom-level energy estimates to top-level emissions reports. The research exposes limitations in existing accounting methodologies and establishes standardized procedures for rSCI implementation. This approach effectively reconciles dual-source signals while preserving optimization incentives, enabling precise attribution of idle capacity and embodied carbon emissions. Ultimately, it offers a new paradigm for carbon accountability in cloud computing.

This study addresses the surging carbon emissions driven by the expansion of agent AI-powered data centers. Leveraging authoritative scenarios and life cycle assessment models, this work rigorously quantifies the dynamic emission trajectories of both operational and embodied carbon in AI data center operations. The findings confirm that industry expansion precipitates a dramatic increase in total emissions, with embodied carbon constituting a significantly rising proportion. By filling the critical gap in full-life-cycle carbon footprint quantification for the agent AI era, this research unveils previously overlooked embodied carbon risks. Consequently, it provides essential scientific evidence to inform precise low-carbon transition strategies for AI infrastructure development.

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.

为解决工业能源数据采集问题，提出STREAM框架，通过目标驱动和不确定性评估方法确保数据满足能源性能评估需求。