Can AI save the planet? Applying machine learning in carbon credits
Can AI save the planet? The answer is yes, and we are utilizing it to improve the process of creating carbon credits
In a world that’s relentlessly racing towards a net-zero future, carbon credit has become the new gold rush. But there’s a roadblock — The “Measuring, Reporting and Verification” (MRV) of carbon credits is a complex, time-consuming, and costly process.
Then comes AI and Machine Learning, a promising duo that’s reshaping the carbon credit landscape and revolutionizing MRV.
🧐 What’s wrong with the current process
MRV is a standardized process to measure, certify and ensure the emissions reduced or avoided by a project are credible. But, it’s currently held back by laborious manual measurements and over assumption.
Traditionally, MRV for carbon credits relies heavily on technical expertise and years of experience. Not only does this approach require significant resources, it also often leads to inaccuracies and delays.
As the demand for carbon credits soars, it’s clear that the current MRV model is not built to scale with the speed and precision we need.
🌍 3 applications of machine learning for carbon credit MRV
This is where AI and Machine Learning kicks in, wielding the power to reimagine MRV for carbon credits. Here are the 3 potentials:
A. Enhancing Satellite Imagery 🛰
Utilizing AI technology, we can integrate Geo-Information System (GIS) via satellite imagery to efficiently monitor extensive areas in real-time. Coupled with Computer Vision with Deep Learning, these images can be better analyzed and interpreted to identify carbon stocks and sequestration activities, enabling a more precise calculation of carbon credits.
B. Recommending the most suitable methodology 💻
Carbon registries each use a unique way to calculate carbon stocks, with methods ranging from 25 to 75 pages long. This can be a headache for carbon project developers trying to choose the right one, especially for smaller or newer teams. They typically rely on experience and technical advice to make the choice.
But there’s a solution to this maze — machine learning. With its ability to recognize patterns in data, machine learning can match each project to the most suitable methods based on its type and location. So, it’s like a guide through the complex jungle of carbon calculation methods, making life easier for everyone in the field.
C. Improving Carbon Additionality Calculation 🌳
Machine learning is a tool that can pull and study data from many certified carbon projects. As it learns from this data, it improves, becoming more accurate and reliable. This power can be extended to develop predictive models for carbon credits. These models, trained on historical and real-time data, can better estimate the carbon stock baseline and expected additionality.
Such forecasting capability not only provides valuable insights for the project investors but also enhances the reliability and credibility of the project in the market. his ability to forecast not only gives useful information to project investors, but it also boosts the project’s trustworthiness and reputation in the market.
Conclusion
AI and Machine Learning are no longer the realm of fiction. They are practical, powerful, and transformative forces shaping the new era of carbon credit MRV.
By adopting these technologies, we can streamline MRV, improve the reliability of carbon credits, and ultimately expedite our journey towards a net-zero world. And the best part? It’s all based on real data, not just human guesswork. So, machine learning is like a smart assistant for project developers that keeps getting smarter!
At PLENO, we’re pioneering an AI and Machine Learning revolution in MRV for carbon projects. Whether you’re a project developer, auditor, or an expert in carbon-related fields, we invite you to join us.
Let’s collaborate to redefine the future of carbon credits together. Reach out now, and let’s make a difference!
