- Project Overview
- The Problem We Address
- Our Solution: Key Features
- Unique Value Proposition
- Ethical Considerations
- Technology Stack
- Getting Started
- Project Structure
- Contributing
- License
- Contact
The AI-Powered Microclimate Carbon Sequestration Planner is an innovative web and mobile application designed to empower local communities, urban planners, and individual property owners to actively participate in climate action. Leveraging advanced Artificial Intelligence, the platform analyzes hyper-local microclimate data and soil conditions to recommend optimal locations and suitable plant species for effective carbon sequestration projects, such as urban forests and community gardens.
This project directly contributes to SDG 13: Climate Action, by providing actionable insights and tools that enable data-driven decisions for greening initiatives, fostering healthier ecosystems, and enhancing community resilience against climate change impacts.
Local communities, particularly in urban and peri-urban areas, struggle to effectively implement small-scale carbon sequestration projects due to a lack of precise, localized data and scientific expertise regarding optimal planting locations, suitable plant species, and their long-term carbon capture potential under specific microclimate and soil conditions. This challenge is compounded by limited access to user-friendly tools and actionable guidance, leading to inefficient resource allocation, sub-optimal environmental outcomes, and reduced community participation in climate action.
Our planner provides a suite of features tailored to guide users through the process of effective carbon sequestration:
- User Authentication & Profiles: Secure login and personalized dashboards for users (powered by Clerk).
- Intuitive Project Creation: Easily define new carbon sequestration projects by specifying location and area.
- Interactive Map Analysis: Visualize microclimate data (temperature, humidity, rainfall) and soil quality directly on an interactive map for precise site assessment.
- AI-Driven Plant Recommendations: Receive tailored suggestions for plant species (trees, shrubs, groundcovers) optimized for local microclimate, soil type, and desired carbon sequestration/biodiversity goals.
- Carbon Sequestration Projections: See estimated long-term carbon uptake projections for recommended planting schemes, aiding in impact assessment and fundraising.
- Comprehensive Implementation Guides: Access step-by-step resources covering planting techniques, maintenance, and funding opportunities for greening projects.
- Project Monitoring (Future): Tools to track the progress and estimated impact of implemented projects.
- Community Collaboration (Future): Features to share projects and collaborate with other community members.
- Microclimate Focus: Recommendations are tailored to hyper-local environmental conditions, ensuring higher success rates and maximized carbon capture.
- Community-Centric Empowerment: The platform is designed to be accessible and user-friendly, empowering individuals and communities to lead climate action initiatives directly.
- Biodiversity Integration: Plant suggestions prioritize not only carbon sequestration but also local ecological health and biodiversity enhancement.
- Scalable & Adaptable: The underlying AI model and platform architecture are designed to be adaptable for diverse urban, rural, and coastal environments globally.
We are committed to developing this project responsibly and ethically. Our approach includes:
- Data Privacy: Ensuring all user and community data is handled with the utmost care, with a focus on anonymization and aggregation where appropriate.
- Bias Mitigation: Actively working to detect and mitigate potential biases in AI recommendations to ensure equitable distribution of greening opportunities.
- Transparency & Explainability: Providing clear insights into how recommendations are generated and acknowledging AI model limitations.
- Accessibility: Designing the user interface to be accessible to diverse user groups, including those with varying levels of digital literacy or physical abilities.
- Community Engagement: Prioritizing stakeholder consultation and incorporating feedback from local communities throughout the development lifecycle to ensure the solution genuinely meets their needs.
- Avoiding Green Gentrification: Consciously considering the social impacts of greening projects to ensure they benefit all community members without displacement.
Our project leverages a modern and robust set of AI-powered and cloud-native tools:
- Frontend:
- React / Next.js: For building a dynamic and responsive user interface.
- V0.dev: Utilized for rapid UI prototyping and generating foundational React components.
- Clerk: For comprehensive and secure user authentication and management.
- Backend & Database:
- Supabase: Provides a powerful Postgres database, instant APIs, and authentication (integrated with Clerk) for robust backend services.
- LangChain: For orchestrating complex AI workflows, integrating diverse data sources (microclimate, soil, plant data) for our AI model, and potentially enabling natural language interactions.
- Custom AI/ML Models: (Developed in Python) for microclimate analysis, plant suitability, and carbon sequestration projections.
- Deployment & Infrastructure:
- Vercel: For seamless and automatic deployment of the frontend application (React/Next.js), with features like automatic deploys and previews.
- Railway: For deploying custom backend AI services, workers, or other infrastructure components that require more dedicated resources or specific environments.
- Documentation:
- Readme.so: Used to generate and maintain comprehensive project documentation.
- Feedback & Iteration:
- Lovable: The development mindset is guided by the principles of building a "lovable" product, focusing on continuous user feedback and iterative improvement.
This section will guide you through setting up and running the project locally for development.
Before you begin, ensure you have the following installed:
- Node.js (LTS version recommended)
- npm or yarn
- Git
- Python 3.x
- Docker (Optional, for specific backend services if containerized)
- Clone the repository:
git clone https://github.com/your-username/carbon-sequestration-planner.git cd carbon-sequestration-planner - Frontend Setup:
cd frontend # or whatever your frontend directory is named npm install # or yarn install
- Backend Setup:
cd backend # or whatever your backend directory is named pip install -r requirements.txt
- Supabase Configuration:
- Create a new project on Supabase.
- Set up your database schema (tables for users, projects, microclimate data, etc.).
- Retrieve your Supabase URL and
anonkey from your project settings. - Configure Row Level Security (RLS) as needed.
- Clerk Configuration:
- Create a new application on Clerk.
- Obtain your Clerk Public API Key and Secret Key.
- Configure webhooks for Supabase integration if necessary.
- Environment Variables:
- Create
.envfiles in yourfrontendandbackenddirectories based on provided.env.examplefiles. - Populate them with your Clerk, Supabase, and any other API keys/configurations.
- Create
- Start Supabase (if self-hosting or using local setup):
- Follow Supabase documentation for local development setup if not using their cloud service.
- Start the Backend Server:
cd backend python app.py # or whatever command starts your backend API
- Start the Frontend Development Server:
cd frontend npm run dev # or yarn dev
- Open your browser and navigate to
http://localhost:3000(or whatever port your frontend runs on).
We welcome contributions to the AI-Powered Microclimate Carbon Sequestration Planner! Please see our CONTRIBUTING.md (to be created) for guidelines on how to contribute, report bugs, and suggest features.
This project is licensed under the MIT License - see the LICENSE file for details.
For questions, feedback, or collaborations, please reach out to:
- [Members of Enigma]
- [[email protected]]