A key challenge for conservation and decarbonization today is to obtain verifiable information in real time and verifiable of the environmental impact from the first mile. This is the point of origin of products such as agriculture, wood and minerals – and often where the most important environmental and ecological risks exist, but it has been traditionally the most opaque.
The AI transforms this by creating massive data sets – satellite imaging, IoT sensors and environmental risk models – achievable in almost real time. With the ability to analyze everything, from changes in land use to methane emissions, AI allows companies and regulators to detect deforestation, illegal activities and the risks of sustainability with unprecedented precision.
Beyond surveillance, AI plays a role in risk forecasting and compliance. It can model climatic risks – such as drought, forest fires or extreme weather conditions – and help organizations proactively adapting their supply and supply strategies. While regulations such as EU deforestation regulations (EUDR) become stricter, companies will rely more and more on an analysis powered by AI to guarantee compliance and mitigate the vulnerabilities of the supply chain.
CEO and co-founder of Treefera.
How does AI technology improve the accuracy and reliability of carbon shift measurements compared to traditional methods?
Historically, the carbon markets have relied on manual verification and estimates based on projections rather than a real impact. This has aroused concerns about credibility and market integrity.
AI and remote sensing technologies revolutionize this process by allowing close measurement, report and verification in real time (MRV). For example, AI can detect deforestation and land use changes, ensuring that forest conservation credits are truly additional and permanent. Advanced models can quantify carbon monitoring – such as methane emissions and discounts – in particular in agriculture and discharge projects. The analysis fueled by AI of soil carbon sequestration guarantees that the credits for carbon cultivation and regenerative agriculture are measurable and defendable.
This approach based on milestones, where credits are issued according to the progress verified rather than speculative complaints, helps to move the market to greater transparency and confidence.
What are the biggest challenges in the application of AI and automatic learning for forest preservation and carbon credit verification, and how can they be treated?
One of the biggest challenges is data integrity. AI models are as good as the data on which they are formed and for environmental applications, gaps in the data from the first mile have historically led to ineffectures and unverifiable complaints.
To remedy this, the emphasis is placed on the combination of several data sources – satellite imaging, Lidar analyzes, the observations of the soil key and automatic learning models – to ensure that carbon sequestration, deforestation and impacts of biodiversity are precisely measured.
Another challenge is the time and cost of verification of the project. Traditional methods can take years for a carbon credit project to be audited and approved. Automation fueled by AI now reduces the recording times of projects for several years to a few weeks, considerably accelerating climate action.
The regulations also catch up. Emerging policies are increasingly requiring verifiable environmental data and high resolution to ensure that the credits issued on the market represent real and additional carbon reductions.
How will the AI shape the fight against climate change in the next 5 to 10 years?
AI tools already prove its value in climate risk management and the reduction of emissions, and during the next decade, its impact will only develop.
For example, AI will improve carbon monitoring, in particular for emissions from scope 3, which remain the most difficult to quantify and manage. It will also optimize nature -based solutions, such as regenerative agriculture and reforestation projects, ensuring that they offer measurable carbon advantages. AI can also improve climate risk forecasts, help companies and governments anticipate disturbances and adapt before crises.
We also note an increasing intersection of AI and blockchain in sustainability. By integrating the measure and monitoring powered by AI with immutable blockchain recordings, companies can create verifiable and infiltrated sustainability complaints – essential for regulatory compliance and investor confidence.
How did technology directly contribute to more effective decarbonization efforts or improved sustainability practices?
Transparency has long been a challenge in sustainability efforts. Although satellites and AI can offer visibility on environmental impact, the real problem is verification and responsibility.
Historically, the recording and verification of a carbon project – whether reforestation or an initiative to reduce methane – was an expensive and slow process. But with the AI and the registers supported in blockchain, the project validation deadlines have been reduced from several years to a few months.
This acceleration is essential because it increases the speed at which capital can take place in high impact climate projects. Whether it is to reduce land use emissions, improve soil carbon storage or move to regenerative supply chains, technology makes it possible to measure, check and evolve these efforts faster than ever.
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