Reducing CO₂ Emissions in Logistics with AI Tool
Leveraging AI & weather data to make sustainable transport decisions
PROJECT INTRODUCTION
PROBLEM STATEMENT
The logistics industry is under increasing pressure to reduce CO₂ emissions.
While weather conditions are often considered for optimizing delivery efficiency, their impact on emissions is rarely integrated into fleet management strategies.
This gap limits opportunities for sustainable decision-making and emission reduction.
PROJECT OBJECTIVE
Develop an AI-based tool that optimizes the use of transportation modes based on weather conditions, emissions, and operational costs, contributing to a more efficient and sustainable logistics system.
DATE
March 2025
TOOLS
Figma, Canva
ROLE
UX/UI Designer
RESEARCH & ANALISYS
Several leading logistics companies integrate Artificial Intelligence (AI) and real-time weather data to optimize fleet management and reduce emissions:
DHL – Uses AI to analyze weather data and mitigate shipment risks.
UPS – The ORION system incorporates real-time weather and traffic data to optimize delivery routes.
Tesla & Other Freight Companies – Utilize AI for predictive maintenance, minimizing downtime and emissions.
Amazon – Uses AI-based demand forecasting for efficient fleet management.
Note: While these companies leverage AI and weather data for operational efficiency, the proposed approach
—combining weather conditions, vehicle type, and environmental impact—
is a more targeted and innovative solution, particularly for a company with a diverse fleet.
IMPACT OF WEATHER CONDITIONS ON CO2 DISPERSION
Weather conditions significantly affect CO₂ dispersion:
Atmospheric Pressure: High pressure stabilizes air, trapping pollutants near the ground, while low pressure enhances vertical dispersion.
Humidity & Precipitation: High humidity can contribute to secondary particulate formation, while rain temporarily reduces CO₂ by washing pollutants from the air.
Wind: Facilitates horizontal dispersion, diluting local CO₂ concentrations and improving air quality.
Integrating these factors into fleet management can enhance operational efficiency and reduce environmental impact.
In the following three graphs , I illustrate how meteorological factors influence CO₂ dispersion.
Temperature vs. CO₂: Temperature inversions trap CO₂, preventing dispersion.
Wind Speed & Direction vs. CO₂: Higher wind speed disperses CO₂, lowering local concentration.
Atmospheric Stability vs. CO₂: Stable air traps CO₂ near the ground, increasing concentration.
WIREFRAMES
By entering the departure/ destination cities, and the preferred dates,
the system analyzes weather conditions along the route over the coming days, providing highly accurate forecasts based on real-time satellite data.
Additionally, the tool performs a scientific evaluation of CO₂ emissions, calculating the environmental impact of the selected vehicle.
This tool not only estimates CO₂ emissions
but also correlates them with specific weather conditions,
analyzing how they can be naturally dissipated in the atmosphere.
Based on this, it selects the
most efficient vehicle while also considering the best economic fit for the trip.
WIREFRAMES
In the Results screen, three interactive maps are displayed:
Weather Evolution – shows climate variations along the route over time.
CO₂ Emissions Overview – visualizes the amount of CO₂ released and how weather conditions impact its dispersion during the journey.
The Route
Additionally, the following key insights are provided:
CO₂ Dispersion Interval – the estimated amount of CO₂ (in tons) dispersed based on meteorological conditions.
Transport Cost with Selected Vehicle – the cost estimation for the optimal vehicle based on efficiency, CO₂ impact, and economic viability.
Alternative Transport Cost – the cost estimation for a slightly more polluting vehicle (next-best alternative), displaying the additional CO₂ emissions,and the potential cost savings for the company.
MAIN FLOW
MAIN FLOW
CONCLUSION & NEXT STEPS
By integrating satellite-based forecasts with cost-effective vehicle recommendations, the system provides actionable insights for logistics companies aiming to reduce their carbon footprint.
How an MVP Could Be Developed
To create a Minimum Viable Product (MVP), the following key steps should be taken:
Prototype Refinement – Improving UI/UX design based on initial feedback.
Data Integration – Connecting real-time weather APIs and emissions databases.
Algorithm Development – Enhancing the CO₂ dispersion model for more precise recommendations.
Testing & Validation – Collaborating with logistics companies to validate results and refine predictions.