Abstract
This presentation showcases a study conducted by Hatch Ltd. in collaboration with the International Council on Mining and Metals (ICMM)’s Cleaner Safer Vehicles (ICSV) programme to identify implementation pathways to achieve zero GHG emissions in surface haulage trucks. Mobile mining equipment can account for up to 30% of on-site emissions at a mine (or up to 80% if the mine does not have any smelting or refinery facilities). The ICSV programme aims to tackle this main source of Scope 1 emissions (i.e. direct emissions from owned or controlled sources) from the mining industry and is a collaborative initiative at the operational level, with the bold ambition of achieving greenhouse gas-free surface mining vehicles by 2040.
As different mining companies with different types of surface operations continue to strengthen their commitments to climate action, the requirement for a multi-disciplinary and cross-sectoral approach was paramount to providing guidance on decarbonization pathways, combining engineering and mining knowledge with technology identification and evaluation, investment and business planning opportunities, sustainable financing, advisory, environmental and social impact best practices. To this end, Hatch put together a diverse team of experts to support the ICSV programme in identifying zero emission implementation pathways by: establishing data collection templates to be completed by ICMM members, constructing simulation models, conducting equipment value propositions, analyzing technology landscapes, and developing GHG abatement strategies for surface haulage trucks.
For the moment, Hatch’s current results and approach towards delineating GHG abatement solution pathways and technologies provides only conceptual-level results and directional insight as to which surface truck decarbonization options are most attractive in a given context, as the work being conducted by Hatch and the ICSV programme is still at an early stage of development and was based on static views of underlying, collated assumptions and inputs.