Carbonada

Our project helps people learn about their carbon footprint with a fun game powered by AI. We use LLMs to estimate the carbon impact of different things and create an engaging way to understand and care about the environment.

Motivation

We are addressing the urgent need to increase awareness about carbon footprints. Many people do not understand the impact of their daily choices on emissions. For example, an apple generates 35g of CO2, while a portion of beef produces 7,700g.¹ Calculating and visualizing the environmental impact is complex, and current tools are tedious and unattractive. We aim to create a fun, accessible educational experience to promote carbon literacy.

Our project uses AI in two main components:

• Carbon Footprint Estimation Model: It estimates the carbon footprint of any concept entered by the user using GPT-4, function calling and semantic search.
• Educational Game: Players place cards on a "carbon line" based on their estimated carbon footprint. An AI agent supervises the game, ensuring fairness and evaluating the originality of each move.

Our innovative approach combines advanced AI with game mechanics to create an engaging educational experience about carbon footprints.

What is a carbon footprint?

Mike Bernes-Lee ² defines the term "carbon footprint" as follows:

I’m using “footprint” as a metaphor for the total impact that something has. And I’m using the word “carbon” as shorthand for all the different global warming greenhouse gases. So, I’m using the term “carbon footprint” to mean the best estimate we can get of the full climate change impact of something. That something could be anything—an activity, an item, a lifestyle, a company, a country, or even the whole world.

Exploration

Carbon Footprint Estimation Model

To estimate the carbon footprint of a product or service, we utilize two main tables:

1. Product Carbon Footprint Table:

   • Contains specific data on the carbon footprint of various products from multiple sources.

   product	carbon footprint [kg CO2e]	source
   Apple	0.3	Source A
   Banana	0.2	Source B
   Orange	0.25	Source C

2. Industry Carbon Footprint Table:

   • Provides information on the carbon footprint associated with different industries based on economic value in dollars and the country.

   industry	carbon footprint per USD [kg CO2e/USD]	country	source
   Agriculture	0.5	USA	Source D
   Manufacturing	0.8	Germany	Source E
   Transport	1.0	China	Source F

Estimation Process

1. Semantic Search in Product Table:

   • When a user inputs, for example, "an apple," the model performs a semantic search in the product column.
   • It returns the row with the smallest semantic distance.
   • Confidence Levels:
     • High Confidence: distance < u1.
     • Medium Confidence: u1 <= distance < u2.

2. Search in Industry Table:

   • If the distance is greater than u2, the second table is used.
   • An LLM determines the relevant industry and estimates its value in USD.
   • The closest row in the Industry Carbon Footprint Table is returned.
   • The result is considered to have low confidence.

Estimation Function

The function takes the user's input and returns:

• The estimated carbon footprint.
• The confidence level of the estimation (high, medium, low).
• The source used for the estimation.

Example

Input: "an apple"

Output:

{
  "estimated_carbon_footprint": "0.3",
  "carbon_footprint_unit": "kg CO2e",
  "confidence_level": "high",
  "source": "Source A"
}

Handling Complex Queries

For more complex queries, such as "an apple and a glass of water," we use function calling to decompose the query into individual calls to our estimation function. The final result is the aggregated response.

Example

Input: "an apple and a glass of water"

Process:

1. Decompose the query into "an apple" and "a glass of water".
2. Estimate the carbon footprint for each item.
3. Aggregate the results.

Output:

{
  "items": [
    {
      "item": "apple",
      "estimated_carbon_footprint": "0.3",
      "carbon_footprint_unit": "kg CO2e",
      "confidence_level": "high",
      "source": "Source A"
    },
    {
      "item": "glass of water",
      "estimated_carbon_footprint": "0.05",
      "carbon_footprint_unit": "kg CO2e",
      "confidence_level": "medium",
      "source": "Source B"
    }
  ],
  "total_estimated_carbon_footprint": "0.35",
  "carbon_footprint_unit": "kg CO2e",
}

Evaluation

We tested our custom GPT-4 models, labeled as carbonada, against standard GPT-4 models. The results show that our carbonada models are more accurate, with lower Mean Absolute Error (MAE). While these models take a bit more time to run, the difference in speed isn’t a big issue for our game, and we can work on making them faster later.

In regression tasks, relying solely on MAE can be misleading, especially for more challenging problems. To address this, we also evaluated our models based on how often they provided the most accurate predictions. The chart shows the average rank of each model, where a lower rank indicates better performance. Our custom carbonada models consistently outperformed the baseline models, demonstrating that they are more accurate overall, even if MAE alone doesn't fully capture this improvement.

Game Design

We have created an educational game inspired by the concept of a carbonada, a dish where players add ingredients—anything they can think of. The objective is to stay within a predetermined carbon footprint limit for the dish. The frontend code is available here.

Gameplay

• Each game sets a carbon footprint limit for the carbonada dish.
• Players take turns adding ingredients to the dish. These ingredients can be anything the player can imagine and write down.
• The system calculates the carbon footprint of each ingredient based on our carbon footprint estimation model.
• The goal is to avoid exceeding the carbon footprint limit. The player who adds an ingredient that causes the total carbon footprint to surpass the limit loses the game.

LLM as a Game Judge

• We implemented an AI agent to supervise the game in real-time.
• This agent detects strategies to "cheat" the system, such as multiplying concepts (e.g., "two apples").
• The agent evaluates the validity and originality of each move, blocking unfair or repetitive ones.
• In case of a block, the system asks the player to propose a new concept, keeping the game challenging and educational.

This way, we created a dynamic, fair, and educational game experience that promotes learning about the carbon footprint in an entertaining manner.

Datasets

Name	Author	Link
Carbon Catalogue	Christoph J Meinrenken et al	Springer
Idemat Database	SSIM, Delft University of Technology	Eco Cost Value
MRIO	Small World Consulting	SWC
RIVM	Dutch National Institute for Public Health	RIVM

More details about the datasets used in this project can be found here.

Team

Alonso Astroza, Víctor Navarro, Luis Miranda, Claudia Navarro and Bastián Araya.

Acknowledgment

We would like to take this opportunity to thank our mentors for their invaluable guidance and support: Alonso Silva, Eduardo Graells-Garrido, Sergio Concha, Rodrigo Zigante and Renzo Lüttges.

Installation

To get started with running the code in this repository, please refer to our Installation Guide.

References

Footnotes

1. Clever Carbon ↩

2. Berners-Lee, M. (2022). The carbon footprint of everything (2nd ed.). Greystone Books. ↩