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Improving Environment Data

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  2. Awarded decided in 43 days
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Description

The National Lottery Community Fund want to better understand and communicate the wide range of activities that community organisations undertake that benefit the environment. This project will establish a sound foundation from which we can generate quality data about the environment projects we fund. An external supplier (possibly a partnership of specialist expertise) is being procured to work with our Evidence and Impact Team and others in the Fund. It will combine two related (but distinct) workstreams into one project for efficiency and synergies: A. Environment Projects Typology - This will establish a common Proof of Concept (PoC) typology for the types of environmental projects that we fund. This work will: review, further develop, and test our typology(s) of environmental projects using machine learning (AI) that classifies the nature of the work we receive applications for and support, and proposes a sound typology for the future, including considerations on how we use and improve it in the short and medium terms. B. Improve Climate Action Fund (CAF) programme monitoring data - This will create a Monitoring Framework that works for the current CAF funded projects as well as future environmental funding in our UK Portfolio. This work will review current monitoring requirements and identify future requirements; assess options for approaches for data capture, analysis and reporting; refine and test existing survey tool; create implementation plan; and complete report on what is known about CAF to date.

Official source

Source: Contracts Finder (UK Cabinet Office)

Always verify details on the official notice.

Last verified against Contracts Finder (UK Cabinet Office) 1 month ago

Documents on Contracts Finder (UK Cabinet Office) (English)

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Avg. open tender · Research & development
€2,979,104
532 open across Europe right now 97% below sector average
Buyer track record on Aperlena
10 notices tracked · 9 awarded
€437,513 in awarded contracts

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