AI-driven E-Liability Knowledge Graphs: A Comprehensive Framework for Supply Chain Carbon Accounting and Emissions Liability Management

• URL: http://arxiv.org/abs/2312.00045v1
• Date: Sun, 26 Nov 2023 23:09:36 GMT
• Title: AI-driven E-Liability Knowledge Graphs: A Comprehensive Framework for Supply Chain Carbon Accounting and Emissions Liability Management
• Authors: Olamide Oladeji, Seyed Shahabeddin Mousavi, Marc Roston
• Abstract summary: We introduce the E-liability carbon accounting methodology and Emissions Liability Management (ELM) originally proposed by Kaplan and Ramanna. We introduce a novel data-driven integrative framework that leverages AI and computation - the E-Liability Knowledge Graph framework.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While carbon accounting plays a fundamental role in our fight against climate change, it is not without its challenges. We begin the paper with a critique of the conventional carbon accounting practices, after which we proceed to introduce the E-liability carbon accounting methodology and Emissions Liability Management (ELM) originally proposed by Kaplan and Ramanna, highlighting their strengths. Recognizing the immense value of this novel approach for real-world carbon accounting improvement, we introduce a novel data-driven integrative framework that leverages AI and computation - the E-Liability Knowledge Graph framework - to achieve real-world implementation of the E-liability carbon accounting methodology. In addition to providing a path-to-implementation, our proposed framework brings clarity to the complex environmental interactions within supply chains, thus enabling better informed and more responsible decision-making. We analyze the implementation aspects of this framework and conclude with a discourse on the role of this AI-aided knowledge graph in ensuring the transparency and decarbonization of global supply chains.

Related papers

• Integrating AI's Carbon Footprint into Risk Management Frameworks: Strategies and Tools for Sustainable Compliance in Banking Sector [0.0]
  This paper examines the integration of AI's carbon footprint into the risk management frameworks (RMFs) of the banking sector. Recent advancements in AI research, like the Open Mixture-of-Experts (OLMoE) framework, offer more efficient and dynamic AI models. Using these technological examples, the paper outlines a structured approach for banks to identify, assess, and mitigate AI's carbon footprint.
  arXiv  Detail & Related papers  (2024-09-15T23:09:27Z)
• Carbon Market Simulation with Adaptive Mechanism Design [55.25103894620696]
  A carbon market is a market-based tool that incentivizes economic agents to align individual profits with the global utility. We propose an adaptive mechanism design framework, simulating the market using hierarchical, model-free multi-agent reinforcement learning (MARL) Numerical results show MARL enables government agents to balance productivity, equality, and carbon emissions.
  arXiv  Detail & Related papers  (2024-06-12T05:08:51Z)
• Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Models [67.0243099823109]
  Generative AI (GAI) holds immense potential to reduce carbon emissions of Artificial Intelligence of Things (AIoT) In this article, we explore the potential of GAI for carbon emissions reduction and propose a novel GAI-enabled solution for low-carbon AIoT. We propose a Large Language Model (LLM)-enabled carbon emission optimization framework, in which we design pluggable LLM and Retrieval Augmented Generation (RAG) modules.
  arXiv  Detail & Related papers  (2024-04-28T05:46:28Z)
• Efficient Strategies on Supply Chain Network Optimization for Industrial Carbon Emission Reduction [0.0]
  This study investigates the efficient strategies for supply chain network optimization, specifically aimed at reducing industrial carbon emissions. We introduce Adaptive Carbon Emissions Indexing (ACEI), utilizing real-time carbon emissions data to drive instantaneous adjustments in supply chain operations.
  arXiv  Detail & Related papers  (2024-04-17T14:53:55Z)
• Towards Sustainable SecureML: Quantifying Carbon Footprint of Adversarial Machine Learning [0.0]
  We pioneer the first investigation into adversarial ML's carbon footprint. We introduce the Robustness Carbon Trade-off Index (RCTI) This novel metric, inspired by economic elasticity principles, captures the sensitivity of carbon emissions to changes in adversarial robustness.
  arXiv  Detail & Related papers  (2024-03-27T21:02:15Z)
• Leveraging AI-derived Data for Carbon Accounting: Information Extraction from Alternative Sources [0.0]
  We discuss the need for alternative, more diverse data sources that can play a significant role on our path to trusted carbon accounting procedures. We present a case study of the recent developments on real-world data via an NLP-powered analysis using OpenAI's GPT API on financial and shipping data.
  arXiv  Detail & Related papers  (2023-11-26T22:49:41Z)
• CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers [18.54380015603228]
  Training large-scale artificial intelligence (AI) models demands significant computational power and energy, leading to increased carbon footprint with potential environmental repercussions. This paper delves into the challenges of training AI models across geographically distributed (geo-distributed) data centers, emphasizing the balance between learning performance and carbon footprint. We propose a new framework called CAFE (short for Carbon-Aware Federated Learning) to optimize training within a fixed carbon footprint budget.
  arXiv  Detail & Related papers  (2023-11-06T23:59:22Z)
• Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples [70.84093873437425]
  This paper introduces the Accountable Offline Controller (AOC) that employs the offline dataset as the Decision Corpus. AOC operates effectively in low-data scenarios, can be extended to the strictly offline imitation setting, and displays qualities of both conservation and adaptability. We assess AOC's performance in both simulated and real-world healthcare scenarios, emphasizing its capability to manage offline control tasks with high levels of performance while maintaining accountability.
  arXiv  Detail & Related papers  (2023-10-11T17:20:32Z)
• Measuring the Carbon Intensity of AI in Cloud Instances [91.28501520271972]
  We provide a framework for measuring software carbon intensity, and propose to measure operational carbon emissions. We evaluate a suite of approaches for reducing emissions on the Microsoft Azure cloud compute platform.
  arXiv  Detail & Related papers  (2022-06-10T17:04:04Z)
• (Private)-Retroactive Carbon Pricing [(P)ReCaP]: A Market-based Approach for Climate Finance and Risk Assessment [64.83786252406105]
  Retrospective Social Cost of Carbon Updating (ReSCCU) is a novel mechanism that corrects for limitations as empirically measured evidence is collected. To implement ReSCCU in the context of carbon taxation, we propose Retroactive Carbon Pricing (ReCaP) To alleviate systematic risks and minimize government involvement, we introduce the Private ReCaP (PReCaP) prediction market.
  arXiv  Detail & Related papers  (2022-05-02T06:02:13Z)
• Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning [68.37641996188133]
  We introduce a framework for tracking realtime energy consumption and carbon emissions. We create a leaderboard for energy efficient reinforcement learning algorithms. We propose strategies for mitigation of carbon emissions and reduction of energy consumption.
  arXiv  Detail & Related papers  (2020-01-31T05:12:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.