Estimating the Carbon Footprint of 

Imagine Land 2040

1. Introduction

The film Imagine Land 2040 represents a paradigm shift in storytelling, utilizing artificial intelligence to produce visually stunning narratives. As AI-driven production becomes more prevalent, assessing its environmental impact is critical to aligning technological innovation with sustainability goals. While AI democratizes creativity, it requires intensive computational resources, leading to a significant carbon footprint.

This report estimates the carbon emissions associated with the production of Imagine Land 2040, focusing on:

1. Video Generation using Runway Gen-3.

2. Image Generation using Midjourney.

The analysis incorporates multiple scenarios for GPU usage, energy efficiency, and grid carbon intensity. Additionally, a sensitivity analysis highlights how variations in assumptions affect results, and uncertainties are explicitly addressed. Furthermore, a comparative analysis will be provided to understand how AI production contrasts with traditional animation and video production. These findings provide a foundation for sustainable practices in AI-driven filmmaking.

2. Scope of Analysis

2.1 Production Elements

1. Video Generation: Approximately 32,750 seconds of video were created using Runway Gen-3.

2. Image Generation: Around 45,000 images were generated on Midjourney.

2.2 Assumptions

1. GPU Power Draw: 400 W (representing an NVIDIA A100 or RTX 3090).

2. PUE (Power Usage Effectiveness): 1.2 (a highly efficient modern data center).

3. Grid Carbon Intensity:

   • Low (0.15 kg CO₂e/kWh): Renewable-heavy grids.

   • Medium (0.40 kg CO₂e/kWh): Mixed grids.

   • High (0.60 kg CO₂e/kWh): Fossil-heavy grids.

2.3 Limitations and Constraints

• Estimation of GPU Usage: The exact GPU-seconds per second of video or per image are not disclosed by Runway or Midjourney. Calculations rely on assumptions grounded in typical AI workloads.

• Regional Variability: Grid carbon intensity and data center efficiency vary geographically, introducing variability into emission estimates.

3. Carbon Footprint Calculations

3.1 Video Generation with Runway Gen-3

ScenarioGPU-sec/secondGPU-hourskWh (direct)kWh (PUE 1.2)Carbon Intensitykg CO₂eLow1090.9736.3943.670.156.550.4017.470.6026.20Mid20181.9472.7887.340.1513.100.4034.940.6052.40High30273.61109.44131.330.1519.700.4052.530.6078.80

3.2 Image Generation with Midjourney

ScenarioGPU-sec/imageGPU-hourskWh (direct)kWh (PUE 1.2)Carbon Intensitykg CO₂eLow112.505.006.000.150.900.402.400.603.60Mid562.5025.0030.000.154.500.4012.000.6018.00High10125.0050.0060.000.159.000.4024.000.6036.00

3.3 Combined Carbon Footprint

ScenarioVideo (kg CO₂e)Images (kg CO₂e)Total (kg CO₂e)Low6.550.907.4517.472.4019.8726.203.6029.80Mid13.104.5017.6034.9412.0046.9452.4018.0070.40High19.709.0028.7052.5324.0076.5378.8036.00114.80

4. Sensitivity Analysis

4.1 Impact of Doubling GPU Usage

• Doubling GPU usage (e.g., increasing GPU-seconds per output to 20 for images and 60 for video) nearly doubles the carbon footprint:

  • Example: Mid Video Scenario at Medium CI (0.40 kg CO₂e):

    • From 34.94 kg CO₂e → 69.88 kg CO₂e.

4.2 Impact of Increasing PUE

• Increasing the PUE to 1.4 (less efficient cooling) raises energy consumption by 16.7%:

  • Example: High Video Scenario at High CI (0.60 kg CO₂e):

    • From 78.80 kg CO₂e → 91.99 kg CO₂e.

5. Uncertainties and Limitations

5.1 GPU Usage

• The actual GPU-seconds per unit of output depend on specific implementation details, which remain proprietary for both Runway Gen-3 and Midjourney.

5.2 Data Center Operations

• The PUE of 1.2 is an optimistic estimate based on modern facilities. Older or smaller data centers may have PUE values exceeding 1.5.

5.3 Regional Carbon Intensity

• Carbon intensity values vary significantly by region and time. Without information on the data centers’ locations, the analysis relies on generalized ranges.

6. Carbon Footprint Range

The estimated carbon footprint for the production of Imagine Land 2040 is:

• Low Scenario: 7.45 kg CO₂e

• High Scenario: 114.80 kg CO₂e

This range reflects variability in GPU usage, energy efficiency, and grid carbon intensity.

7. Comparative Analysis: AI-Driven Filmmaking vs. Traditional and Animated Production

7.1 Carbon Footprint of Traditional Productions

Statistics on Emissions

Traditional film and television productions generate substantial carbon emissions due to their reliance on physical resources, transportation, and energy-intensive activities. Key statistics include:

• Average Film Production: Emits 3,370 metric tons (MT) of CO₂, equivalent to driving over 7 million miles in a car.

• Large Productions:

  • Major films like Oppenheimer or Barbie can emit up to 3,370 MT CO₂, comparable to powering 656 homes for a year or equivalent to 7 million miles driven.

  • The Screen New Deal report highlights that the average big-budget film generates enough CO₂ emissions that it would take 3,709 acres of forest an entire year to absorb.

• Case Study: The Day After Tomorrow (2004) was the first film to calculate and offset its carbon emissions, totaling 10,000 MT CO₂ during production.

• Medium Productions: Average carbon footprint is 769 MT CO₂, with 47% of emissions stemming from fuel consumption.

• Small Productions: Smaller films emit approximately 391 MT CO₂ per production.

• Television Series:

  • 1-hour scripted dramas emit 77 MT CO₂ per episode.

  • Half-hour single-camera shows emit 26 MT CO₂ per episode.

  • Unscripted series emit 13 MT CO₂ per episode.

Sources of Emissions

A study by the Sustainable Production Alliance of 161 feature films and 266 television series revealed that emissions primarily stem from:

• Fuel Consumption (48%):

  • Includes generators to power sets and equipment, particularly during remote or on-location shoots.

• Air Travel (24%):

  • Transporting cast, crew, and equipment over long distances.

• Utilities (22%):

  • Energy usage on set, including lighting and HVAC systems.

• Housing (6%):

  • Accommodations for cast and crew during production.

7.2 Animated Production

General Emissions

Animation productions, while eliminating many of the physical emissions associated with traditional filmmaking, still have substantial carbon footprints due to the computational energy required for rendering and production processes. 

Key statistics include:

• Average Emissions: According to BAFTA’s February 2016 report, the average animation production emits 5.5 metric tons (MT) of CO₂ per hour.

• Feature-Length Films: A typical 90-minute animated film produces approximately 8.25 MT CO₂.

  • It can be roughly estimated that the waste heat obtained from making a 90-minute animated film could provide enough water for 20,000 homes.

Rendering Emissions

Rendering is one of the most energy-intensive aspects of animation production, especially for high-resolution 3D animation.

Example Calculation

For a 90-minute animated film (130,000 frames), with each frame requiring 3 hours to render on a system consuming 270W:

1. Electricity Consumption:
   Total kWh=130,000 frames × 3 hours/frame × 0.27kW=100,000kWh

2. Carbon Emissions:
   Emissions (MT CO₂)=0.233kg CO₂/kWh × 100,000kWh=23.3MT CO₂

Case Study: Plankton Invasion (2011)

The production of Plankton Invasion highlights the potential emissions of large-scale 3D animation projects:

• Production Stats:

  • Total runtime: 546 minutes of 3D animation.

  • Total emissions: 32,500 MT CO₂.

  • Emissions per minute: 59.52 MT CO₂/min.

• Feature-Length Film Comparison:

  • Using the same emissions rate, a 90-minute animated film would generate: Emissions (MT CO₂)=90 minutes × 59.52MT CO₂/min=5,356.8MT CO₂

7.3 Conclusion

Comparison of Emissions

The carbon footprint of Imagine Land 2040, ranging from 7.45 to 114.80 kg CO₂e, is a fraction of the emissions associated with traditional or animated productions:

• Traditional Productions: Average feature films emit 3,370 metric tons (MT) CO₂, with large-scale productions exceeding 10,000 MT CO₂.

• Animated Productions: Rendering alone for a typical 90-minute animated film generates approximately 23.3 MT CO₂, while large-scale projects like Plankton Invasion emit over 5,000 MT CO₂.

Even under high computational assumptions, Imagine Land 2040 represents a remarkably energy-efficient production model, producing only 0.002%–0.05% of the emissions of these traditional methods.

A Case Study in Energy Efficiency

Imagine Land 2040 highlights the significant environmental advantages of AI-driven filmmaking, offering a low-carbon alternative to traditional production methods. This efficiency stems from:

1. Digital-Only Workflow: Eliminating on-location shoots, physical set construction, and large-scale logistics.

2. Efficient Resource Use: Streamlined GPU-focused energy consumption without material waste.

3. Scalable, Accessible Production: Enabling smaller teams to create high-quality content with minimal overhead.

However, it is important to emphasize that this report reflects a single case study. While the results demonstrate AI filmmaking’s potential for carbon efficiency, broader conclusions cannot be drawn without analyzing additional productions across varying scales, workflows, and technological conditions.

Context for Future Comparisons

Several factors must be considered when comparing Imagine Land 2040 to traditional and animated productions:

• Production Scale: Larger AI-driven projects or more computationally intensive outputs would increase emissions beyond those observed here.

• Energy Source: The carbon footprint depends heavily on the electricity grid powering data centers, with renewable-heavy grids drastically reducing emissions.

• Creative Constraints: While AI excels at certain types of storytelling, its current capabilities may not fully replicate the creative depth or fidelity of traditional or animated productions.

Final Summary

Imagine Land 2040 serves as a compelling example of how AI-driven filmmaking can achieve remarkable energy efficiency, producing emissions orders of magnitude lower than traditional or animated productions. While this case study offers no firm conclusions for the broader industry, it demonstrates the potential for AI to revolutionize filmmaking as a sustainable and accessible medium, balancing creative innovation with environmental responsibility.