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

Olamide Oladeji, Seyed Shahabeddin Mousavi, Marc Roston
11/26/2023
cs.CYcs.AIcs.SI

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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.

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Cite this paper

@article{oladeji2023aidriven,
  title  = {AI-driven E-Liability Knowledge Graphs: A Comprehensive Framework for Supply Chain Carbon Accounting and Emissions Liability Management},
  author = {Olamide Oladeji and Seyed Shahabeddin Mousavi and Marc Roston},
  year   = {2023},
  eprint = {2312.00045},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2312.00045v1}
}

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