Incentivizing Forest Preservation using AI
Client
Tech Company
Tech Company
Sector
Technology, Media & Telecom
Technology, Media & Telecom
Type of service
AI exploration
AI exploration
Artificial intelligenceDataSustainabilityEnvironment
The Challenge
Hatch Studio recently supported an incubator from a leading tech company in an ambitious initiative to transform carbon credit data collection. To discover how AI could enhance and facilitate this task, our client first needed to understand the dynamics behind forest measurement.
Our collaboration involved a deep dive into existing processes in the Brazilian biomes to uncover barriers and needs, meticulously mapping the journey of carbon stock data collection.
The team tackled the unique challenges posed by different forest types and leveraged cutting-edge AI technology to develop innovative, simplified solutions. Through this collaboration, Hatch Studio played a crucial role in making a significant impact on global sustainability, showcasing the power of technology and innovation.
Our Approach
1
Carbon stock data collection
We conducted a full immersion in the Brazilian rain forest and shadowed an organization dedicated to carbon stock data collection in the country.
We had meetings with the different areas in the organization, did an inventory of their tools and processes and also went to a plot of land to understand the measurement techniques.
2
AI Tool MVP
Finally, we showed the team in the organization an MVP of a potential AI tool to measure carbon stock data and got relevant feedback for iterating it.
We conducted an analysis session on the gathered information with the client team and did a draft mapping of the carbon stock data collection process.
3
Visual report
In the end, we delivered a full visual report containing findings and insights from all research activities, a full mapping of the process, as well as recommendations and potential next steps.
Outcomes
The most important aspects that came from the research were related to the lack of players in the sector and of innovation in the processes and tools employed, as well as contextual barriers coming from governmental limitations:
Context: The journey of a carbon stock project in Brazil faces challenges that are contextual, related to the biome but also to bureaucratic realities.
Stakeholders: There’s very few Carbon developers and their investment is large before they see any revenue from it. Auditors also have very high expectations before approving a project.
Data collection: In order for a project to be successful, carbon developers need to meet the carbon credit standards from auditors and therefore are concerned about carbon stock data transparency, because it defines earnings. The data points they need are many and created by different people. The projects become very lengthy and prone to errors
Lack of innovation: Field measurement is a manual and time consuming effort, with no technological advancements in the last years. There is a clear need to update techniques and make measurements more efficient. AI tools are welcomed but connectivity in the forest is an issue and can’t be reliant on the internet once there. Therefore, the MVP shown had to be iterated to fit the topography of the forest and the connectivity realities.
Context: The journey of a carbon stock project in Brazil faces challenges that are contextual, related to the biome but also to bureaucratic realities.
Stakeholders: There’s very few Carbon developers and their investment is large before they see any revenue from it. Auditors also have very high expectations before approving a project.
Data collection: In order for a project to be successful, carbon developers need to meet the carbon credit standards from auditors and therefore are concerned about carbon stock data transparency, because it defines earnings. The data points they need are many and created by different people. The projects become very lengthy and prone to errors
Lack of innovation: Field measurement is a manual and time consuming effort, with no technological advancements in the last years. There is a clear need to update techniques and make measurements more efficient. AI tools are welcomed but connectivity in the forest is an issue and can’t be reliant on the internet once there. Therefore, the MVP shown had to be iterated to fit the topography of the forest and the connectivity realities.
