Transform Carbon Data into Strategic Insights with Machine Learning
VisionCO2 is a web-based corporate carbon management platform that integrates mathematical emission modeling with AI-powered analytics. Designed in compliance with the GHG Protocol, our system enables organizations to collect, monitor, analyze, and report carbon emissions across Scope 1, Scope 2, and Scope 3 categories through a unified, transparent, and scalable digital infrastructure.
Design and Implementation of a Web-Based Corporate Carbon Management System with AI-Supported Analytics
This research project addresses the critical gap between traditional carbon accounting practices and the growing need for data-driven, transparent sustainability management. Developed at Altınbaş University, VisionCO2 represents a systematic application of modern web technologies and software engineering principles to create a sustainability-oriented information system.
The increasing complexity of corporate sustainability requirements has highlighted the need for digital platforms that can support carbon data management in an integrated, transparent, and scalable manner. Current corporate practices often rely on fragmented, manual, or spreadsheet-based approaches that lack integration, scalability, and traceability.
Türkiye signed the Paris Agreement on April 22, 2016, emphasizing its commitment as a developing country. This necessitates systems for monitoring, managing, and reporting corporate carbon footprints.
A comprehensive suite of features designed for end-to-end corporate carbon management, from data collection to AI-powered strategic recommendations.
Track and calculate emissions across all three scopes in accordance with internationally recognized GHG Protocol standards.
Move beyond static calculations to predictive analytics powered by AI and machine learning algorithms.
Centralized dashboards provide immediate overview of corporate carbon performance with rich visualizations.
An integrated conversational AI assistant that provides emission summaries, risk assessments, and sustainability guidance.
Structured data entry interfaces ensure GHG Protocol-compliant data collection with full traceability.
Generate comprehensive sustainability reports with visual representations and export capabilities.
Evaluate alternative decarbonization strategies through scenario modeling with cost-benefit analysis.
Manage and monitor sustainability projects with progress tracking, timeline management, and ROI calculation.
From raw activity data to actionable decarbonization strategies — powered by deterministic calculation and machine learning.
Structured input through GHG Protocol-compliant interfaces for activity-level data across all scopes.
Mathematical emission modeling using official emission factors from IPCC, EPA, DEFRA, and national authorities.
Clustering algorithms identify emission-intensive groups; regression models capture operational-emission relationships.
AI-supported scenario generation for conservative, aggressive, and optimal mitigation strategies with projected outcomes.
All calculations follow the GHG Protocol classification framework using emission factors from official governmental and academic institutions.
Direct Emissions
CO₂e (kg) = Fuel Consumption (liters) × Emission Factor (kg CO₂/liter)
Source: IPCC 2006, EPA 2024
CO₂ (t) = Natural Gas Consumption (m³) × 0.00202
Source: Turkish Ministry of Environment 2019
CO₂e (kg) = Distance Traveled (km) × Emission Factor (kg CO₂e/km)
Source: DEFRA 2025
Energy Indirect Emissions
CO₂e (t) = [Consumption (kWh) × EF] / 1000
Source: Turkish Ministry of Energy 2024
CO₂e (kg) = Cooling Energy (kWh) × EF (kg CO₂e/kWh)
Source: IPCC 2006
CO₂e = [(FC × d × 10⁻³) × NCV × 10⁻³] × EF × 10⁻³ × OF × GWP
Source: IPCC 2006, ITU 2022
Other Indirect Emissions
CO₂e (t) = Distance (km) × [CO₂ + CH₄×28 + N₂O×265] / 1000
Source: EPA 2025, IPCC 2014
CO₂e (t) = [Waste (kg) × EF (tCO₂e/short ton)] / 907.18
Source: EPA 2024
Spend-based, Material-based, Hybrid, or Recycling-adjusted methods
Source: GHG Protocol 2023
Evaluate alternative decarbonization strategies through AI-powered scenario modeling with cost-benefit projections.
Minimal operational changes with gradual transition and lower organizational disruption.
Substantial operational changes with rapid transition and significant emission reductions.
Balanced approach with phased implementation achieving significant but sustainable reduction.
The platform successfully functions as an integrated corporate carbon management information system — not just a standalone calculation or visualization tool.
Systematic collection, storage, and processing of emission data across Scope 1, 2, and 3 with persistent storage for longitudinal analysis.
Unified digital infrastructure improves accessibility, transparency, and consistency of carbon-related information across organizations.
Clear differentiation among conservative, aggressive, and optimal mitigation strategies with cost-benefit projections.
Clustering algorithms successfully identify emission-intensive activity groups; regression models capture operational-emission relationships.
AI-generated recommendations with estimated emission reduction potential, economic impact, and confidence scores.
| Feature | VisionCO2 | Spreadsheets | Generic ERP |
|---|---|---|---|
| Integration | Unified system | Fragmented files | Partial |
| Scalability | Automatic | Manual effort | Variable |
| Traceability | Full audit trail | None | Limited |
| GHG Protocol | Built-in compliance | Manual | Partial |
| AI Analytics | ML-powered insights | None | Basic |
| Scope Coverage | Scope 1, 2 & 3 | Varies | Limited |
Academic contributions advancing the fields of sustainability information systems, digital transformation, and web-based decision support.
Addresses corporate carbon management from a holistic information systems perspective with end-to-end sustainability support.
Systematic application of web technologies and software engineering to sustainability-oriented information systems.
Mitigates greenwashing through data-driven, model-based guidance for achieving carbon neutrality.
Aligned with SDG 13 (Climate Action), SDG 9 (Industry & Innovation), and SDG 12 (Responsible Production).
Focus: System architecture and implementation
Focus: AI integration and analytical capabilities
A multidisciplinary team from Altınbaş University and Istanbul University driving innovation in sustainability information systems.
Assistant Professor — Altınbaş University
Management Information Systems
Project coordination, research methodology, academic supervision
Undergraduate Student — Altınbaş University
Management Information Systems
System architecture, frontend and backend development
Undergraduate Student — Altınbaş University
Jewelry and Jewelry Design
UI/UX design and visualization components
Undergraduate Student — Altınbaş University
Management Information Systems
Frontend development and user interface design
Undergraduate Researcher — Istanbul University
Faculty of Economics — Business Administration
Machine learning architecture, predictive model design, and AI-driven analytics integration
Common questions about the VisionCO2 platform and research project.
Whether you're a researcher, organization, student, or policy maker — we welcome collaboration opportunities in sustainability information systems, AI-driven environmental analytics, and carbon management.