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

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.