Projecting carbon capture and electricity share in LNG lifecycle: GHG economics via regression and stochastic analysis

Two primary approaches (GHG reduction revenue and CO2 equivalent emission cost methods) have been established for emission reduction to motivate stakeholders to use more renewable sources and consume eco-friendlier fuels. Fuels must be clean not only during the combustion phase but also throughout their production lifecycle, which is the focus of the current study, addressing liquefied natural gas (LNG) and its comparison with diesel fuel. The production and transportation processes of the LNG lifecycle assessment (LCA) are modified in two ways. First, single-point parameter changes are implemented, and the emission factors are calculated. Second, a series of parameter modifications is performed using the Monte Carlo method, defining the distribution type within the permitted range. Subsequently, multi-regression analysis is conducted on the design variables of LNG production and emission economic parameters. The resulting linear equations can predict future scenarios where LNG properties, vessel operations, and renewable technology shares change. By single point analysis, the combinatory scenario (combustion and production phase) of LNG reduces the emission cost by 32.48 %. Through combining uncertainty simulation with probability functions and multilinear regression modeling, a linear fitting equation is extracted for emission cost based on electricity share and CO2 sequestration technology (R2 = 0.993).