Imperial College London


Faculty of EngineeringDepartment of Chemical Engineering

Reader in Energy Systems



+44 (0)20 7594 9300a.hawkes




Ms Quasirat Hasnat +44 (0)20 7594 7250




C502Roderic Hill BuildingSouth Kensington Campus






BibTex format

author = {Guo, Y and Hawkes, A},
doi = {10.1016/j.enpol.2019.05.002},
journal = {Energy Policy},
pages = {132--155},
title = {Asset stranding in natural gas export facilities: An agent-based simulation},
url = {},
volume = {132},
year = {2019}

RIS format (EndNote, RefMan)

AB - © 2019 Elsevier Ltd This paper analyses the scale of asset stranding of global natural gas production and transmission infrastructure between 2015 and 2060 using Gas-GAME-Spot, an agent-based gas-sector model. It extends the existing modelling efforts by considering contract constraints in short-term gas sales and explicitly simulating trade in two types of spot markets. The study also contributes to the methodologies of stranded asset analysis by taking into account two aspects which are commonly overlooked: market fluctuation subject to the changing export capacities and the impacts of market signals on investor decision making. The results of the base scenario indicate that if gas demand follows current policy expectations, the scale of asset stranding is likely to be limited. This is attributed to supply capacity shortage of medium-sized exporters due to a slowing of their investment. Moreover, two alternative scenarios show that, when the markets face sudden reduction in demand, they leverage the flexibilities in their long-term contracts and opt more gas through spot trade. Though the issue of stranding is not significant globally in these scenarios, exporting regions with high short-run delivery costs, especially North America and Australia, have higher risks of asset stranding as they are arguably less competitive in spot sales.
AU - Guo,Y
AU - Hawkes,A
DO - 10.1016/j.enpol.2019.05.002
EP - 155
PY - 2019///
SN - 0301-4215
SP - 132
TI - Asset stranding in natural gas export facilities: An agent-based simulation
T2 - Energy Policy
UR -
VL - 132
ER -