CarbonCredible

An AI-driven solution to screen for feasible carbon projects

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Overview

You will find our pitch deck, pitch video, as well as our proposed UI design below. We have appended a one-page proposal as well (the fourth link).

Inspiration

In 2024, global carbon emissions reached a record high, and the credibility of carbon offset projects was increasingly questioned. We were inspired to tackle a growing problem: despite companies investing in carbon credits to offset emissions, many of these projects are ineffective or misleading, often contributing to greenwashing rather than real climate impact. We asked ourselves: how can companies, NGOs, and governments be confident in the quality of carbon offset projects they support?

The idea for CarbonCredible was born from this question — a platform that uses AI to assess the feasibility and credibility of carbon projects by combining document analysis, satellite imagery, and local community feedback. Our goal was to restore trust and transparency in the carbon offset market by designing a system that prioritizes data, explainability, and real impact.

What it does

CarbonCredible is a conceptual AI-powered web platform designed to screen carbon offset projects for feasibility, credibility, and impact. While our current submission focuses on the UI/UX, the proposed functionality includes:

  • Uploading and analyzing carbon project documents

  • Evaluating satellite imagery to assess reforestation, land use change, and biomass

  • Extracting and flagging issues in documentation using NLP

  • Understanding social and environmental impacts through local reports and community insights

  • Providing an overall feasibility score with visual indicators and improvement suggestions

The platform targets stakeholders including corporations, project developers, investors, NGOs, and governments looking to validate the quality of carbon credits before investing or certifying.

How we built it

We focused on designing an intuitive and data-rich user experience for the CarbonCredible platform. Using Vercel’s V0.dev platform, we prototyped key pages including:

  • Project Submission Page

  • Project Dashboard

  • Feasibility Score Breakdown

  • Document Analysis Page

  • Impact Assessment Page

The design process was grounded in real-world user needs — ensuring that both technical and non-technical stakeholders could understand and act upon the results. Each page includes space for visual analytics, document summaries, AI-generated insights, and risk flags. Although we did not implement the AI models themselves, we researched and outlined a modular architecture involving NLP, computer vision, and local context analysis for future development.

Challenges we ran into

Scope vs. feasibility: One major challenge was balancing the ambition of the platform with our current technical capacity. While the AI models are theoretically feasible, implementing them was beyond our current scope, so we focused on prototyping the interface and defining the AI architecture.

Designing for multiple user types: The platform serves many stakeholders — from technical teams to ESG officers to small NGOs — so we had to design UI elements that are clear, informative, and accessible to all.

Data visualization: Representing abstract concepts like "project feasibility" and breaking it down into interpretable scores and visuals was complex. We iterated multiple times to create a clean, structured interface.

Accomplishments that we're proud of

Developed a full UI/UX prototype that clearly communicates the functionality of a complex AI-driven evaluation tool

Performed user testing with sustainability professionals for our UI UX and received positive feedback, and incorporated areas for improvement

Created a detailed system architecture that can guide the development of a fully functional platform

Defined a novel value proposition: combining satellite imagery, AI-driven text analysis, and community feedback for carbon project screening

Proposed a scalable SaaS and PaaS business model aligned with real-world market needs and gaps

What we learned

We gained a deeper understanding of the carbon offset ecosystem — how carbon credits are created, verified, and often manipulated.

We explored the technical possibilities and limitations of using AI in sustainability contexts, especially in combining structured and unstructured data.

We learned how to design a user experience that balances technical depth with accessibility and visual clarity.

We understood the importance of trust, explainability, and transparency when designing sustainability tools.

What's next for CarbonCredible

We plan to:

  • Collaborate with AI/ML developers to implement the backend models proposed — including NLP for document analysis and computer vision for satellite imagery.

  • Develop a working MVP that can process basic project submissions and return feasibility assessments using public datasets and open-source models.

  • Explore partnerships with verification bodies, registries, and local organizations to improve data quality and credibility.

  • Expand into regions like Southeast Asia, where transparency in carbon offsetting is urgently needed, before scaling globally.

Ultimately, our vision is to make carbon offset accountability as automated, data-driven, and trustworthy as possible — so that every carbon credit purchased actually represents real climate impact.

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