Overcoming bottlenecks in pre-issuance NbS project screening
Many investors and project developers struggle to move beyond early-stage NbS project screening without committing significant time, cost, and internal resources – often to assess only a small number of viable opportunities. Generic geospatial tools, manual workflows, and uncalibrated datasets slow the funnel and introduce risk and delays.
We’ve continued to iterate our NbS Project Screening tool to address these challenges directly. In this webinar, our carbon experts will walk through the most common pre-issuance screening bottlenecks and show how they can be solved in practice.
We’ll cover:
- The new IFM screening module
- Automatic ARR performance benchmarking
- Automatic growth curve calculation
- Screening complex areas of interest efficiently
- Viewing deforestation and vegetation context without specialist GIS tools
Date and time: January 27 @ 11am GMT / 6pm Jakarta
Speakers
Dr Alexis Moyer
Customer Solutions Lead
Dr. Alexis Moyer is a remote sensing scientist with expertise in combining satellite and field-based data to provide innovative solutions to complex environmental problems. She has a PhD in Environmental Sciences from the University of Edinburgh, where she used satellite data to explore the impact of climate change in the Arctic. As the Customer Solutions Lead at Space Intelligence, Alexis explores ways satellite data can be used to provide insight and support for nature-based solutions and nature-positive financial decision-making.
Dr Sophie Flack-Prain
Reporting and Insights Lead
Dr Sophie Flack-Prain is the reporting and insights lead at Space Intelligence. Her role focuses on harnessing high quality science and nature mapping data to support VCU verification and validation requirements, and integrity-driven due diligence assessments for NBS projects. With extensive experience harnessing remote sensing data and machine learning approaches, she has worked from data generation, with a detailed understanding of map creation, right through to data application and use in decision making.