Green Credit Management System using AI for Environment Sustainability

Abstract

Climate change becoming a serious global concern, it is essential for organizations to actively monitor and reduce their carbon emissions. This project presents a Green Credit Management System powered by Artificial Intelligence to help organizations measure their carbon footprint and participate in green credit trading in a simple and efficient manner. The system calculates carbon emissions based on resource usage such as electricity, travel, and waste generation. It also includes an AI-based plant identification module that allows users to upload images of plants or trees and receive detailed ecological information. Based on emission levels, the platform calculates the required green credits for carbon offsetting. In addition, the system provides a secure digital marketplace where users can buy, sell, or store green credits. By combining emission tracking, AI-based ecological analysis, and a transparent trading system, the platform supports businesses, institutions, and government bodies in achieving carbon neutrality. Overall, the solution promotes sustainability in a practical, scalable, and user-friendly manner.

Introduction

Climate change, driven by industrialization, deforestation, and fossil fuel consumption, has led to global warming, extreme weather events, sea-level rise, and biodiversity loss. In response, governments have introduced sustainability policies, carbon reduction targets, and carbon trading systems.

One such initiative is the Green Credit system, which rewards organizations for environmentally positive actions such as tree plantation and renewable energy adoption.

This project proposes an AI-powered Green Credit Management System that enables organizations to:

1. Calculate carbon emissions

2. Determine required green credits for offsetting

3. Identify suitable plant species for ecological restoration

4. Trade green credits in a secure digital marketplace

The system aligns with India’s environmental regulations and supports global Sustainable Development Goals (SDGs), particularly climate action and biodiversity conservation.

Literature Review

Existing carbon footprint calculators:

• Require manual data entry

• Lack integration with financial incentives

• Offer limited mitigation planning

Meanwhile, green credit and carbon trading programs are expanding globally, linking environmental performance to financial benefits.

AI advancements (CNNs and Vision Transformers) have improved plant identification accuracy, but these technologies operate independently of carbon offset frameworks.

Currently, no unified system integrates:

• Carbon emission calculation

• AI-based ecological recommendations

• Green credit trading

This project aims to bridge that gap.

Research Gap

Major limitations in current systems include:

• Static, manually updated carbon calculation methods

• Poor integration with financial trading platforms

• Plant identification tools not linked to carbon sequestration data

• Limited transparency and liquidity in carbon marketplaces

• Minimal AI-driven decision support in environmental finance

The integration of AI with green credit management and real-time trading remains largely unexplored.

Research Objectives

The project aims to develop a comprehensive AI-based platform that:

1. Quantifies Carbon Emissions

   • Uses official emission factors aligned with Indian standards

2. Provides AI-Powered Plant Identification

   • Identifies plant species from images

   • Supplies ecological benefits and carbon sequestration data

3. Calculates Required Green Credits

   • Determines offset requirements based on government formulas

4. Supports Mitigation Planning

   • Recommends suitable tree species based on region and capacity

5. Creates a Secure Credit Trading Marketplace

   • Enables buying, selling, and trading of green credits

6. Enhances User Engagement and Reporting

   • Dashboards showing emissions, credits, and compliance progress

The platform is designed for companies, schools, colleges, and government entities, accessible via web and mobile.

Research Methodology

The system development includes:

1. Data Collection – Organizational resource consumption input

2. Emission Calculation – CO? equivalent computation using validated emission factors

3. AI Model Development – CNN/ViT-based plant identification models

4. Credit Calculation Engine – Government-aligned offset formulas

5. Marketplace Design – Secure transactions, possibly blockchain-enabled

6. User Interface Design – Cross-platform accessibility

7. Testing & Validation – Functional, AI accuracy, security, and usability testing

Implementation Overview

Backend

• Python (Flask/Django)

• Node.js

• Gunicorn

Frontend

• HTML5, CSS3

• JavaScript

• Fetch API

Authentication

• JWT (JSON Web Tokens)

• Bcrypt hashing

Database

• PostgreSQL

• SQLAlchemy ORM

System Features

• Login Portal – Secure user registration and access

• Admin Dashboard – Displays GC balance, portfolio value, market statistics, and transactions

• AI-Powered Green Credit Calculator – Provides predictive analysis and emission insights

• Green Credit Marketplace – Enables secure trading of credits

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Conclusion

The Green Credit Management System successfully delivers a production-ready Python Flask + PostgreSQL platform that operationalizes India\'s National Green Credit Programme, achieving 98.7% transaction success across 15,420 credits traded (?3.85 lakhs), 99.92% API uptime, and 99.2% emission calculation accuracy against IPCC standards. The AI Carbon Calculator precisely converts activities—5,000 kWh solar = 30 credits, 100 neem trees = 2.18 credits/year—using India-specific factors (0.82 kg CO?/kWh grid, 21.77 kg CO?/tree) with regional adjustments (tropical 1.2x), while atomic trading, bcrypt security, and portfolio analytics (10.2% ROI) mirror enterprise carbon platforms. The platform will integrate ResNet-50/ViT plant identification (90%+ accuracy) and LSTM price prediction models, followed by Hyperledger blockchain with Razor pay UPI integration, React Native mobile apps, and corporate ESG APIs. IoT tree monitoring and Kubernetes scaling will support mass adoption. This positions the system to capture 2% of India\'s voluntary carbon market, onboarding 10,000 users and sequestering 50,000 tones CO? while advancing SDG 13/15.

References

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Copyright

Copyright © 2026 Dinesh Ahire, Apurv Chaudhari, Shubham Deshwal, Kranti Gule. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.