PLENO’s Reference Area Algorithm for Carbon Removal Forecasting 🌿

#bluecarbon #carbonremoval #biomass #machinelearning #ai

💡 Introduction

In our urgent battle against climate change, accurate data and efficient environmental assessment is crucial. PLENO stands at the forefront, introducing an innovative approach to assess and forecast estimated carbon removal from nature-based projects using a state of the art machine learning algorithm.

Our novel methodology focuses on selecting reference areas, enhancing the precision of emission estimates in nature based carbon projects, such as mangroves.

🔥 The Problem: A Need for Speed & Accuracy

We don’t have time. In 2023, as we witness the hottest year in modern history, the need to rapidly assess potential locations for nature-based carbon projects has never been more critical.

Traditional methods often falter, hampered by slow processes and reliance on secondary data that may be outdated or ill-suited for specific regions (Source: Randazzo et al., 2023). This leads to a situation where data integrity and reliability are constantly under question, especially for nature based carbon projects .

🚀 PLENO’s Solution: Reference Area Algorithm

PLENO is rewriting the narrative with a system that harnesses primary data from strategically chosen reference areas. Our machine learning algorithm seeks out similar ecosystems within an extended range from the project area.

For example, if it’s a mangrove ecosystem, we will automatically find other mangrove ecosystems in the close proximity, and analyzes the historical growth of environmental parameters such as tree height and biomass.

Here’s how we’re making a difference:

1. Accurate Data:

By leveraging primary data from historical reference areas, PLENO ensures the information is not only current but also highly relevant and specific to the area of interest. This method marks a significant improvement over current processes, eliminating the need for manually gathering secondary data, thereby enhancing the accuracy of biomass and emission forecasting.

2. Lightning Speed:

The days of manual work collecting secondary journal data are behind us. With PLENO, tasks that used to take weeks are now completed in minutes thanks to our machine learning pipeline. This cutting-edge approach streamlines the entire process.

3. Continuous improvement:

Our machine learning model improves with time. We continuously validate and refine our estimations by analyzing data from registered projects and scientific journals. Initially, minor differences in analyses are expected, but our results become increasingly accurate and plausible over time.

💨 Take the Leap with PLENO

Embrace this transformative era in carbon project assessment with PLENO. Our method promises and delivers efficiency and a level of precision previously unattainable in the field.

It doesn’t stop here! At PLENO, we are thinking big and acting fast. The application of our reference area algorithm is not just for carbon project assessment. In the future, it could be applied to assess biodiversity, ecosystem health, natural disaster risks, and much more.

🙌🏽 Try our solution now

See how PLENO can transform your carbon projects. Our 2nd software version will launch in January. Register for early access here or email Nura for details at nura@pleno.earth.