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A blended graph-MCMC framework for carbon emission reduction in oil & gas supply chain

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Sustainable Development Goals

  • SDG 13 - Climate Action
    SDG 13 Climate Action

Abstract

Amidst growing global concerns about climate change and heightened environmental awareness, this scholarly paper introduces an innovative approach to addressing the imperative of carbon emissions reduction in the Oil & Gas sector. Leveraging the analytical power of Monte Carlo Markov Models (MCMMs), this study responds to the pressing need for emission mitigation strategies within an industry that significantly contributes to global carbon emissions. Recent empirical data underscores the urgency of this endeavour. In 2021, the Oil & Gas industry accounted for a substantial 45% of global energy-related emissions, emitting approximately 34 billion metric tons of CO2-equivalent. Projections paint a dire picture, indicating a potential 50% increase in emissions by 2050 without substantial intervention. To tackle this challenge, our research introduces a robust framework for modelling, simulating, and optimizing supply chain operations in the Oil & Gas sector. This framework encompasses dynamic variables encompassing exploration, extraction, refining, transportation, and distribution. Monte Carlo simulations yield probabilistic forecasts of carbon emissions, empowering decision-makers with critical information to make informed choices within the supply chain. A comprehensive case study demonstrates substantial reductions in emissions while preserving operational efficiency, highlighting the practical significance of emission reduction strategies in the Oil & Gas industry. This research underscores the urgent necessity of mitigating emissions within the sector, given its significant contribution to global carbon emissions, while also offering a promising path towards sustainability.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 554-561 (8 pages)

Publication milestones

  • Published - 25/02/2024

Publication status

Published - 25/02/2024

Publisher

Springer, Japan, India, Australia, Germany, United States, United Arab Emirates, Austria, Switzerland, Italy, China, United Kingdom, Netherlands, Brazil, France, Singapore

Publication series

  • Publication series name: Lecture Notes in Electrical Engineering
    ISSN (Print): 1876-1100
    ISSN (Electronic): 1876-1119
    Volume: 1154 LNEE

ISBN (Print)

9789819706648

External Publication IDs

  • handle.net: 10547/626842
  • Scopus: 85187802822

Host publication title

Advanced Manufacturing and Automation XIII

Host publication editors

  • Yi Wang
  • Tao Yu
  • Kesheng Wang

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