Carbon-Efficient Transportation via Spatially Explicit Modelling of Large-Scale Bioenergy with Carbon Capture and Storage Supply Chains

Student thesis: Phd


This research focuses on the impact of a series of scenarios on the carbon performances of large-scale agricultural residue and industrial waste derived Bioenergy with Carbon Capture and Storage supply chains (BECCS) transportation emissions at a high spatial resolution in the UK. This analysis combines three novel research disciplines, high spatial resolution biomass mapping, transportation digital twin modelling and macro-energy system analysis, to simulate the carbon-optimal transportation aspects of BECCS supply chains at high spatial resolution in the UK. The three supply chains modelled in the analysis are a Municipal-Solid-Waste (MSW) derived BECCS-waste-to-energy supply chain, a Wheat Straw derived BECCS-Power supply chain and a Sawmill Residue derived BECCS-Hydrogen supply chain. The three supply chains were applied through a novel digital twin model called the Carbon Navigation System (CNS) created during the PhD, which can simulate a BECCS supply chain anywhere in the UK to determine the optimal siting locations for the facilities. The model can also carbon-efficiently switch between HGVs, rail, shipping and pipeline transportation to minimise produced emissions. The routings calculated by the CNS model also provide improved ground-truthed transportation assumptions for BECCS Life Cycle Assessments (LCAs), as the current assumptions are drastically underestimating the emissions associated with BECCS resource transportation. The three BECCS supply chains were applied through a range of scenarios to determine the impact on the carbon performance of the supply chains by changing parameters within the CNS methodology. This analysis found that the optimal siting locations for the MSW and Sawmill Residue supply chains are in Connah's Quay, while the optimal siting location for the Wheat Straw supply chain is in Barrow-Upon-Humber when capturing 1 MtCO2/yr, although the optimal siting location does change depending on how much CO2 is captured. Shifting the siting location for the supply chains away from the optimal location will increase the supply chain transportation emissions between 8.9 to 12.6% per 10km, and the improper siting may dampen the carbon balance of the project as, in the worst-case scenario, the improper siting of a project may increase supply chain transportation emissions by 1327.0%. On average for the UK, the optimal facility scale for the MSW supply chain is 0.59 MtCO2/yr, 0.88 MtCO2/yr for the Wheat Straw supply chain and 0.46 MtCO2/yr for the Sawmill Residue supply chain. The carbon performances of the three supply chains are marginally impacted by increases in biomass yield and biomass availability, with a 3 to 5% decrease in supply chain transportation emissions when biomass yield is increased by 50%, and biomass availability is increased to 100%. The carbon performances of the supply chains were only impacted when biomass yields and availabilities were extremely low, with supply chain transportation emissions increasing by 5 to 10% for a 50% decrease in biomass yield and 8 to 23% when biomass availability is reduced to their knock-out values. The decarbonisation of HGVs was the most impactful on the carbon performances of the supply chains, with the transition to high degree decarbonised fuels resulting in a 73-74% decrease in supply chain transportation emissions. The analysis was designed to help decision-making for policy-makers and industry to aid the deployment of BECCS across the UK to meet Net-Zero, and this analysis offers heuristics to aid their deployment to ensure a sustainable deployment of the technology.
Date of Award1 Aug 2023
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorClair Gough (Supervisor), Andrew Welfle (Supervisor) & Amanda Lea-Langton (Supervisor)


  • Bioenergy
  • Transportation Modelling
  • Net-Zero
  • Bioenergy with Carbon Capture and Storage
  • Rail
  • Shipping
  • GIS
  • UK
  • Climate Change
  • HGV
  • Carbon-Efficient
  • Hydrogen
  • Infrastructure
  • Digital Twin
  • Supply Chains
  • Spatially Explicit
  • Macro-Energy System Analysis
  • Power
  • Waste to Energy
  • Carbon Capture and Storage
  • High Spatial Resolution

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