Estimating the future prices and scale of engineered GGRs in Voluntary Carbon Markets

Description

This project will forecast the future prices and scale of engineered Greenhouse Gas Removals (GGRs) in Voluntary Carbon Markets (VCMs) to strengthen internal analysis in DESNZ and inform the design of the GGR Business Model, including how it can best complement integration of GGRs into the UK Emission Trading System (ETS). External VCM forecasts, which DESNZ currently relies on for internal analysis, have various limitations, including a lack of transparency in their assumptions and few attempts to isolate the price of engineered removals specifically, amongst others. We are interested in tenders that would deploy economic modelling to provide a probabilistic assessment of scenarios & assumptions that will affect the prices and volumes of engineered GGRs in VCM i.e. what will participants value most when purchasing a credit in the future and how will this impact volume sold/price? This modelling should also be preceded by literature reviews to highlight the existing landscape of engineered GGRs in VCMs. Modelling should consider both the demand and supply side of the market for engineered GGR credits to estimate the likely price and quantity of engineered GGR credits over time (from 2030 to 2050 in yearly intervals). Demand forecasts could estimate the Marginal Willingness to Pay curves of corporate buyers of engineered GGR credits. Supply forecasts could estimate Marginal Cost curves for different GGR technologies. DESNZ could use these improved forecasts to refine their internal analysis on exchequer funding costs. More accurate forecasts could also help inform decision-making on the GGR Business Model i.e. establishing optimal levels for any price floor and volume support. While research is unlikely to conclude in time for the next BM update, findings would help inform later updates, future iterations of the Business Model and strengthen our bargaining position in negotiations, hence the benefit of doing this research as early as possible. Outputs of