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Sensitivity Analysis of Net Zero Pathways for UK Industry


Citation Gailani, A., Cooper, S., Allen, S., Taylor, P. and Simon, R. Sensitivity Analysis of Net Zero Pathways for UK Industry. UKERC. 2021.
Author(s) Gailani, A., Cooper, S., Allen, S., Taylor, P. and Simon, R.
Publisher UKERC
Download UKERC_WP_Sensitivity-Analysis-of-Net-Zero-Pathways-for-UK-Industry.pdf document type
Abstract The deep decarbonisation of industry is vital if the UK is to reach net zero emissions by 2050. In this paper, we use the Net-Zero Industry Pathways (N-ZIP) model, developed by Element Energy to inform the work of the Climate Change Committee (CCC) and the Department of Business, Energy and Industrial Strategy (BEIS), to explore the impact of changing key input parameters on the industrial decarbonisation pathways to 2050. This is used to analyse the robustness of the model results that are being used to inform policy decisions. The Net-Zero Industrial Pathways (N-ZIP) model has been used to inform the sixth carbon budget advice from the CCC and the Governments Industrial Decarbonisation Strategy. N-ZIP is a spatially disaggregated, bottom-up model that contains a detailed description of the industrial activities that drive demand for energy and cause greenhouse gas (GHG) emissions.It also includes information on carbon prices, alternative fuel and technology options, and the projected future deployment of hydrogen and carbon capture and storage (CCS) infrastructure. The model then combines this information to produce a decarbonisation pathway for industry to 2050, with outputs including the level of emissions in each year and the technologies and fuels being used. The characteristics of the resulting pathways are dependent on these assumptions. Element Energy, the CCC and BEIS have undertaken some sensitivity analysis to explore how changes in key assumptions affect the results. However, given the importance of the model in informing policy on industrial decarbonisation, in this working paper we undertake further analysis to explore how the model results are affected by changing a wider range of inputs than previously studied. We find that the model resultsare generally robust to relatively wide variations in the input assumptions that we have explored. Key assumptions that significantly affect the results include the level of resource efficiency and energy efficiency (REEE), the costs of carbon capture and storage infrastructure and the level of carbon taxes. Assumptions about supply chain constraints also impact the profile of emissions reductions to 2050. A number of areas are also highlighted for further analysis. More work is needed to understand the trade-off between REEE and the technology and fuel options in the model and to explore potential alternatives to CCS for decarbonising sites that are not part of large industrial clusters and where the cost of CO2 transport is likely to be very high.