emissions accounting

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 work addresses the challenge of balancing emission constraints, operational cost, and service quality in dynamic power grids where carbon intensity varies over time. Traditional fixed emission rate strategies prove inadequate under such conditions. To overcome this limitation, the authors propose a time-window-based emission budgeting mechanism that replaces static rates, enabling applications to accrue emission allowances during low-carbon periods and flexibly consume them during high-carbon intervals. Integrated within a MAPE-K adaptive control architecture, the approach leverages real-time monitoring of grid carbon intensity and system power consumption to dynamically schedule resources while adhering to long-term emission caps. Simulations using six weeks of real-world data from Germany, France, and Poland demonstrate that the method improves task completion rates by up to 36% in volatile grids while matching the performance of existing approaches in stable grids, achieving effective co-optimization of emissions, cost, and performance.

This study addresses the challenge of assessing the environmental impact of datacenter task and resource management policies. We propose a simulation-based methodology for quantifying carbon footprint, extending the Batsim simulator to enable fine-grained, dynamic CO₂ emission estimation within the SimGrid framework. Our approach integrates real-time platform power consumption models with time-series regional grid carbon intensity data, computing emissions per task and per node during scheduling simulations. The method is seamlessly embedded into existing simulation workflows, enabling reproducible and scalable evaluation of the carbon efficiency of distributed application scheduling strategies. Our primary contribution is the development of the first open-source, high-fidelity, carbon-aware simulation plugin tightly coupled with scheduling logic—providing a standardized, extensible assessment tool for green computing research.

This paper examines the adverse climate and environmental impacts of AI development, particularly focusing on data center energy consumption and indirect carbon emissions from AI applications. Methodologically, it employs policy modeling, regulatory interpretation, and multi-level energy efficiency analysis (server- and facility-level), complemented by cross-domain regulatory mapping between the EU AI Act and EU data center legislation. The study makes three novel contributions: (1) it proposes, for the first time, the inclusion of AI inference energy consumption within the EU AI Act’s mandatory reporting obligations; (2) it develops an actionable “Sustainability Impact Assessment” (SIA) framework; and (3) it advocates extending the Act’s scope to encompass full lifecycle environmental impacts and indirect emissions. Based on these findings, the paper formulates twelve concrete policy recommendations—covering energy disclosure requirements, regulatory clarification, transparency mechanisms, and anticipatory governance—to advance the EU’s AI regulatory regime toward climate resilience.

Operational deployment of greenhouse gas (GHG) plume detection systems remains hindered by challenges in fully automated, routine implementation. Method: We propose the first multi-task joint learning framework for remote sensing imagery—integrating instance detection and pixel-level segmentation—to jointly address three critical bottlenecks: data quality degradation, spatiotemporal misalignment, and objective-function mismatch. Our approach fuses multi-source airborne and spaceborne hyperspectral data within a unified CNN-based pipeline encompassing quality control, bias correction, and target alignment. We further introduce a deployability threshold assessment framework and standardized validation protocol. Contribution/Results: Experiments demonstrate operational-grade detection performance across heterogeneous platforms and geographic regions. We publicly release an analysis-ready dataset, trained models, and source code, and explicitly quantify deployability thresholds for diverse emission sources and geographical contexts—bridging the gap from algorithmic validation to engineering-scale GHG monitoring.

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.

This study addresses the structural tensions between regulatory transparency and data sovereignty in the global semiconductor value chain, intensified by the EU’s Carbon Border Adjustment Mechanism (CBAM). Building on the International Data Spaces Association (IDSA) framework, it proposes a RegTech reference architecture integrating Digital Product Passports (DPP), Agentic AI for autonomous compliance, and green fintech to enable trusted sharing of environmental telemetry data across semiconductor–petrochemical supply chains. The architecture differentiates CBAM’s mandatory obligations from Science-Based Targets initiative (SBTi) voluntary commitments and incorporates the complexities of Safe and Sustainable by Design (SSbD) principles. By transforming upstream physical vulnerabilities into closed-loop negative feedback mechanisms, the approach delivers a scalable blueprint for the Taipei–Penang technology corridor, advancing sovereign-controlled, transparent, and sustainable governance of global value chains.

This study addresses the absence of systematic methods for estimating carbon emissions from large-scale AI model training without requiring replication. Leveraging open-source models from Hugging Face, the work proposes the first scalable carbon footprint estimation framework centered on FLOPs, integrating a tiered metadata processing strategy and statistical regression models. It further introduces the novel metric “AI Training Carbon Intensity” (ATCI) to quantify training energy efficiency. Empirical analysis reveals that popular open-source models with over 5,000 downloads have collectively generated approximately 58,000 metric tons of CO₂ emissions, demonstrating the framework’s practicality, scalability, and potential to inform industry-wide sustainability standards.

Existing entity-level carbon emission forecasting lacks a unified, open, and multitask benchmark, with data highly fragmented in accessibility, granularity, and evaluation protocols. This work proposes GHGbench, the first multitask greenhouse gas emission prediction benchmark spanning both corporate and building levels. It integrates heterogeneous multisource data, establishes standardized data splits, and defines core tasks including cross-regional transfer and temporal forecasting. The study systematically evaluates diverse baselines—gradient boosting trees, MLPs, FT-Transformers, tabular foundation models, and multimodal remote sensing embeddings—and reveals that building-level emission prediction is substantially more challenging than at the corporate level. Performance gaps under distribution shift far exceed those attributable to model choice, while multimodal remote sensing embeddings notably mitigate representation generalization failure. Furthermore, tabular foundation models outperform tuned tree-based methods across multiple cities in building-level tasks.