What Really Matters for Green Calculations: A Practical Perspective

Rosemary J Thomas, PhD
Version 1
Published in
6 min readNov 29, 2023
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Since joining the UN Global Compact, Version 1 has been proud to be part of a global movement of sustainable companies. We are integrating a principles-based approach to sustainability into all our business operations and our AI Labs are no exception.

So how green is our AI? This is a question we are asking ourselves, together with many business leaders, researchers, practitioners, and policymakers are asking. AI models, algorithms and systems have increasingly become powerful and universal in various domains, such as generative AI, natural language processing, computer vision, speech technology, and more. However, these models also come with a significant environmental cost, as they need large datasets and computational resources to train and run.

For instance, to create GPT-3, a huge AI model with 175 billion parameters, 1,287 megawatt hours of electricity were used and 552 tons of carbon dioxide equivalent were emitted, which is the same as driving 123 petrol-powered cars for a year. Can you imagine how much would it be for the latest Claude model with 200k token limit? Therefore, it is important to consider the sustainable aspects of AI models, such as their energy efficiency, carbon emissions, and environmental impact. We aim to provide a brief overview of the current green calculations for AI models and their progress so far, which are essential for the long-term sustainability and development of AI and humanity.

Various tools and methods exist to help us calculate environmental impact. Green Software Foundation (GSF) is a non-profit organisation that strives to establish a reliable network of people, standards and best practices for building software and AI that are environmentally friendly. First, let us investigate some of the projects of measurements by GSF: Impact Framework, Software Carbon Intensity specification and Green Software Maturity Matrix.

The Impact Framework is a project by GSF that allows anyone running a model, application or process to estimate their energy and carbon impact. It stresses that measurement is not the end-result but should be able to guide where we should make advances and how quickly. By providing some basic configuration data in a simple manifest file, you can get energy and carbon usage estimates. This is in the alpha stage and a promising development towards transparent sustainability measurements with extreme flexibility for any organisation.

The GSF also developed the Software Carbon Intensity (SCI) specification to score any model, software or application, providing a common language to describe its carbon emissions behaviour. The SCI equation is simple and applicable to various scenarios. The equation is:

The equation SCI=RC quantifies the Sustainability of Carbon Intensity (SCI), a measure of carbon emissions for a given process. In this equation, R represents a core characteristic of the SCI, serving as a functional unit that converts the SCI from a total measure to an intensity measure. Meanwhile, C stands for the Carbon emissions per unit of R. Thus, this equation provides a more detailed view of carbon emissions, focusing on their intensity rather than their total amount.

A unique feature of the SCI is that it cannot be reduced by purchasing carbon offsets such as neutralisations, compensations, or renewable energy credits. This means that organisations can’t simply buy carbon credits to lower their SCI. They must make legitimate efforts to reduce their emissions. The SCI promotes the development of models that are more energy-efficient, hardware-efficient, or carbon-aware. This equation has been found to be effective in case studies and practical applications, demonstrating its utility in promoting sustainability.

GSF has also developed the Green Software Maturity Matrix, which is a self-assessment tool in the alpha stage designed to help organisations understand the extent to which they have applied green principles, patterns, and processes for building and operating their models. The tool addresses four key questions and attempts to respond to: ‘Where do I start?’, ‘Where am I now?’, ‘What am I heading towards?’, and ‘How do I know I am making progress?’. This seems to be a good foundation tool to start the sustainability conversation in your organisation.

Next, let’s investigate CodeCarbon, an open-source tool developed to track the carbon footprint of energy consumed during the execution of AI algorithms, models and systems. It is a light package that integrates smoothly into Python code. By recording power usage and estimating carbon emissions based on the energy grid’s carbon intensity, the tool provides developers with visibility into the sustainability of their code. It also includes a dashboard to weigh carbon emissions across geographies where cloud infrastructure is hosted. This is a step towards greater transparency and consciousness in the AI community, enabling users to measure and report the emissions generated by various AI algorithms, models and systems.

Finally, let’s investigate the two significant equations that are instrumental in understanding the energy consumption and carbon emissions associated with computational processes.

The first equation calculates the energy consumption in kilowatt-hours (kWh) for training models over a certain period. This is specifically used to calculate energy consumption during the training of models. However we could use this for inference too i.e. ‘Hours to Train’ can be replaced by ‘Hours of Execution’. The former equation is:

kWh = Hours to Train × Number of Processors × Average Power per Processor (in Watts) × PUE / 1000


Hours to Train is the time taken for the training process in hours.

Number of Processors is the total number of processors used.

Average Power per Processor is the average power consumption of each processor in watts.

PUE (Power Usage Effectiveness) is a measure of how efficiently a computer data centre uses energy; specifically, how much energy is used by the computing equipment.

The division by 1000 is to convert the power from watts to kilowatts, as 1 kilowatt is equal to 1000 watts.

Figure 1. Emissions from Prominent Language Models during Training

The second equation calculates the carbon dioxide equivalent emissions (tCO2e) resulting from energy consumption measured in kilowatt-hours (kWh).

The equation is:

tCO2e = kWh × kg CO2e per kWh / 1000


kWh is the energy consumption in kilowatt-hours.

kg CO2e per kWh is the carbon intensity factor, which represents the amount of CO2 equivalent emissions produced per kilowatt-hour of energy consumed.

The division by 1000 is to convert the emissions from kilograms to metric tonnes, as 1 metric tonne is equal to 1000 kilograms.

Both these equations provide a simplified model, and actual energy consumption and carbon emissions can be influenced by various other factors. Nonetheless, they offer a valuable starting point for understanding the environmental impact of computational processes.

There is an important factor to consider when investigating into these tools and equations is that organisations can decide what to include and exclude in the calculation depending on their sustainability objective or plans. However, when we need data that is publicly available in order to make these calculations. The data availability might be restricted to certain publicly available models for now. We have to wait and see how open businesses like Microsoft, AWS and others are likely to share this information. However, things might change when new regulations and laws come into place.

Let’s conclude with some strategies that can be exercised to create greener models. By implementing these strategies, we can make strides towards a more sustainable future with AI.

Develop Computationally Efficient Models: Strive to create models that require less computational power, thereby reducing energy consumption.

Reuse Existing Models: Whenever possible, use pre-existing models instead of training new ones from scratch. This approach can save both time and energy.

Optimise Model Parameters: Efficient use of parameters can lead to optimised models that perform well without excessive energy use.

Choose Energy-Efficient Hardware: Select cloud providers who utilise renewable energy sources. This choice can significantly reduce the carbon footprint of your AI operations.

Track and Report Carbon Footprints: Use AI to monitor and report variables that affect carbon footprints. This data can provide valuable insights for further optimisation.

About the author

Rosemary J Thomas, PhD, is a Senior Technical Researcher at the Version 1 AI Labs.