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Greenhouse Gas Inventory Improvement Programme - 2  Energy, Waste, and Industrial Processes and Product Use · Department for Energy Security & Net Zero

Published 3 August 2026

Tender brief

DESNZ is seeking to procure a supplier(s) to deliver and lead the second UK Greenhouse Gas (GHG) Inventory Improvement Programme of research (IIP-2).

The research programme will involve an organisation with expert technical understanding of inventory issues as they relate to issues across the UK economy leading a consortium to develop methodologies, deliver sector-specific original research and engage relevant stakeholder, generating new or updated emission factors and activity data.

This will continue to ensure that the UK GHG inventory is in alignment with Intergovernmental Panel on Climate Change guidelines and reflects government decarbonisation efforts. This programme is being published as two procurements. This one is the  Energy, Waste, and Industrial Processes and Product Use (IPPU) emissions reporting sectors.

Refer to Page 55 of the ITT Document Both programmes will require a multidisciplinary teams with expertise spanning but not limited to climate science, mathematical emissions modelling, IPCC guidelines, UNFCCC accredited experts and international emissions reporting standards, specifically as applied to sectoral processes (excluding the agriculture sector but a knowledge of the agriculture sector is essential).

In addition, a dedicated programme management office will be essential to coordinate and oversee delivery. To ensure comprehensive and high-quality outcomes, the programme will employ a blend of qualitative, quantitative, modelling, and collaborative research approaches. These will include desk-based analysis, stakeholder engagement, laboratory testing, model and methodology development, fieldwork, and rigorous QA/QC procedures.

The intended outcome is a scientifically robust and transparently enhanced UK GHG inventory. The inventory will be more resilient to future changes and capable of quantifying emissions uncertainty in a reproducible and interpretable manner. It will also improve the representation of emissions from diffuse sources, using open and transparent models and methods that allow for the identification and correction of risks and weaknesses.

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