Related papers: Leveraging AI-derived Data for Carbon Accounting: …
This paper examines the integration of AI's carbon footprint into the risk management frameworks (RMFs) of the banking sector, emphasising its importance in aligning with sustainability goals and regulatory requirements. As AI becomes…
Climate change is one of the most pressing challenges of our time, requiring rapid action across society. As artificial intelligence tools (AI) are rapidly deployed, it is therefore crucial to understand how they will impact climate action.…
Provisioning dynamic machine learning (ML) inference as a service for artificial intelligence (AI) applications of edge devices faces many challenges, including the trade-off among accuracy loss, carbon emission, and unknown future costs.…
With the rapid rise of InsurTech, traditional insurance companies are increasingly exploring alternative data sources and advanced technologies to sustain their competitive edge. This paper provides both a conceptual overview and practical…
As multiple crises threaten the sustainability of our societies and pose at risk the planetary boundaries, complex challenges require timely, updated, and usable information. Natural-language processing (NLP) tools enhance and expand data…
The carbon footprint of algorithms must be measured and transparently reported so computer scientists can take an honest and active role in environmental sustainability. In this paper, we take analyses usually applied at the industrial…
In the evolving field of corporate sustainability, analyzing unstructured Environmental, Social, and Governance (ESG) reports is a complex challenge due to their varied formats and intricate content. This study introduces an innovative…
With the ever-growing adoption of AI-based systems, the carbon footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to hold themselves accountable for the carbon emissions of the AI models they…
This paper explores the environmental impact of the super-linear growth trends for AI from a holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize the carbon footprint of AI computing by examining the model…
The dual-carbon goals of China necessitate precise accounting of company carbon emissions, vital for green development across all industries. Not only the company itself but also financial investors require accurate and comprehensive…
Regulations govern many aspects of citizens' daily lives. Governments and businesses routinely automate these in the form of coded rules (e.g., to check a citizen's eligibility for specific benefits). However, the path to automation is long…
This paper introduces a comprehensive framework aimed at advancing research and policy development in the realm of decarbonization within electric power systems. The framework focuses on three key aspects: carbon accounting, carbon-aware…
Large language models (LLMs) like GPT-3 and BERT have revolutionized natural language processing (NLP), yet their environmental costs remain dangerously overlooked. This article critiques the sustainability of LLMs, quantifying their carbon…
Open-source AI is scaling rapidly, and model hubs now host millions of artifacts. Each foundation model can spawn large numbers of fine-tunes, adapters, quantizations, merges, and forks. We take the position that compute efficiency alone is…
Integration of AI into environmental regulation represents a significant advancement in data management. It offers promising results in both data protection plus algorithmic fairness. This research addresses the critical need for…
The volume of textual data available in aviation safety reports presents a challenge for timely and accurate analysis. This paper examines how Artificial Intelligence (AI) and, specifically, Natural Language Processing (NLP) can automate…
By integrating Artificial Intelligence (AI) with the Internet of Things (IoT), Artificial Intelligence of Things (AIoT) has revolutionized many fields. However, AIoT is facing the challenges of energy consumption and carbon emissions due to…
This paper presents a methodology for allocating energy consumption to multiple users of shared data center machines, infrastructure, and software. Google uses this methodology to provide carbon reporting data for enterprise customers of…
Climate Finance Bench introduces an open benchmark that targets question-answering over corporate climate disclosures using Large Language Models. We curate 33 recent sustainability reports in English drawn from companies across all 11 GICS…
This paper presents a solution to the challenge of mitigating carbon emissions from hosting large-scale machine learning (ML) inference services. ML inference is critical to modern technology products, but it is also a significant…