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National Emergency Briefing on climate & nature

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An unfiltered assessment of the latest threats to UK food supply, health and national security from eight leading experts to an invitation-only audience - also covering positive solutions. Westminster Central Hall, 27 November.

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Blank Participant Details and Evidence Form

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Blank Participant Details and Evidence Form

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What to do next...

1. Complete and save your Carbon Literacy Evidence Form.

2. Email your completed form to the Lighthouse Sustainability team at hello@lighthouse-sustainability.co.uk

3. We will review your pledge to ensure it meets the Carbon Literacy Standard.

4. If any amendments are needed, we'll contact you with feedback and suggestions.

5. Once approved, your pledge will be submitted for Carbon Literacy certification.

6. You will receive your certificate by email once certification has been confirmed.

Please note: Certification can take several weeks, depending on submission volumes and processing times.

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£50 credit when you switch to Octopus Energy (£100 for business accounts)

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£50 credit when you switch to Octopus Energy (£100 for business accounts)

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Octopus customers pay less: Cheaper than price-cap prices, rewards for using less when the grid is stressed, and smart tariff savings for your home, car or battery

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We are a diverse community of therapeutic practitioners, thinkers, researchers, artists and others. We believe that attending to the psychology and emotions of the climate and ecological crisis is at the heart of our work.

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We are a diverse community of therapeutic practitioners, thinkers, researchers, artists and others. We believe that attending to the psychology and emotions of the climate and ecological crisis is at the heart of our work.

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Manish Karkera & Guy Griffiths (Institute for Environmental Analytics)

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Visualising how the climate has changed for every country across the globe

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Straight Talking Sustainability with Emma

Sustainable(ish)

Outrage and Optimism

BBC World Service- The Climate Question

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Welcome to Your Carbon Literacy Journey!

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Welcome to Your Carbon Literacy Journey!

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This is your launchpad for your Carbon Literacy for Business course. Here you'll find everything you need to get the most from your training.

This Padlet is your hub for everything you need to:

Deepen your understanding of the climate crisis

Revisit the course content with access to the slides

Connect with us on social media

Share your feedback to help us improve

Benefit from special offers and explore related partners

If you have any questions, don’t hesitate to get in touch at hello@lighthouse-sustainability.co.uk.

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The SME Climate Hub is a non-profit global initiative that empowers small to medium sized companies to take climate action and build resilient businesses for the future.

The Hub is an initiative of the We Mean Business Coalition, the Exponential Roadmap Initiative and the United Nations Race to Zero campaign in collaboration with Normative and the Net Zero team at Oxford University.

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The SME Climate Hub is a non-profit global initiative that empowers small to medium sized companies to take climate action and build resilient businesses for the future. The Hub is an initiative of the We Mean Business Coalition, the Exponential Roadmap Initiative and the United Nations Race to Zero campaign in collaboration with Normative and the Net Zero team at Oxford University.

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Lighthouse is built on firm foundations. For 25 years, we've supported organisations of all sizes, from start-ups to household names, with practical sustainability advice and training.

Our founder, Emma, is passionate about helping people take meaningful climate action. Since 2021, she has led the Lighthouse team in delivering Carbon Literacy training to organisations across a wide range of sectors. You can read more about the difference our training is making on our Impact page.

For more information, please visit https://lighthouse-sustainability.co.uk/

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Lighthouse Sustainability | Support and experience for businesses facing sustainability challenges

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Lighthouse Sustainability | Carbon Training for Businesses

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Struggling to understand carbon, sustainability, and net zero in the corporate world? Discover our carbon training, and empower your employees to take action,

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Weather Versus Climate Change | Cosmos: A Spacetime Odyssey

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Free Small Business Carbon Footprint Calculator - Small99

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Answer a few simple, non-technical questions to get your Carbon footprint Assessment and provide instant, actionable steps on how to improve.

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Building upon thousands of hours of analysis by scientific experts from around the world, the Drawdown Explorer provides detailed information on the many technologies and practices proven or proposed to effectively reduce greenhouse warming pollution in the atmosphere.

The Drawdown Explorer analyzes solutions in four categories:

Highly Recommended: solutions that meet all of Project Drawdown's criteria for global climate solutions

Worthwhile: solutions that can still help mitigate climate change but do not meet the scale to be considered a major climate solution

Keep Watching: solutions that have potential but are not yet available in the real world - or the technology still lacks clear effectiveness, evidence, or a reasonable cost - and are not yet ready to be deployed

Not Recommended: solutions that, at the end of the day, we do not recommend as a climate solution, either because they are not scientifically plausible or they present a high level of risk.

Browse the full complement of Drawdown Explorer solutions below, or filter by criteria of your choice using dropdowns or the table format. Then click through to individual solutions to get a close-up view of current and potential impact - including, for recommended solutions, the optimal timing, cost, additional benefits, interactive maps showing where action can have the greatest impact, and specific advice for how you, whatever your role, can best put the solution to work.

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We know what we need to do: stop climate change as quickly as possible. Now, with the Drawdown Explorer, we know how to do it.

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Accessible Participant Details and Evidence Form

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Accessible Participant Details and Evidence Form

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Use this guide to support the writing of your pledge form.

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Session slides will appear after you have completed each day of training.

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Session slides will appear after you have completed each day of training.

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Tell Frank and the team that we sent you!

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Explore how to eat for the climate. Answer fun quizzes and unlock easy recipes to put knowledge into action.

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Why reducing our carbon emissions matters (a little story about climate change)

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This course has been accredited by The Carbon Literacy Project.

Find out more:
https://carbonliteracy.com/

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Navigating the 1.5 Degrees Breach
Image credit: Photo by Pixabay. In 2023, the world witnessed an onslaught of extreme weather events, ranging from severe droughts and scorching heatwaves to devastating wildfires...

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Relevant climate change learning for everyone

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Department for Energy Security and Net Zero

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Greenhouse gas reporting: conversion factors 2025

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These emission conversion factors are for use by UK and international organisations to report on 2025 greenhouse gas emissions.

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1. Watch Sir David Attenborough's documentary Climate Change - The Facts (1 hour)

If you are not able to access iPlayer or are short of time, there are shorter clips on YouTube.

2. Complete the personal Carbon Footprint Calculator (15 minutes).

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Calculate your environmental footprint and learn how you can reduce your impact with WWF's Footprint Calculator.

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Lighthouse Reading List

How Bad are Bananas?  - Mike Berners-Lee

Drawdown - The Most Comprehensive Plan Ever Proposed to Reverse Global Warming - Paul Hawkin

The Future We Choose - The Stubborn Optimist’s Guide to the Climate Crisis – Christiana Figueres / Tom Rivett-Carnac

Net Positive - Paul Polman

You Can't Make Money From a Dead Planet: The Sustainable Method for Driving Profit - Mark Shayler

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Ecosia - the search engine that plants trees

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Ecosia uses the ad revenue from your searches to plant trees where they are needed the most. By searching with Ecosia, you're not only reforesting our planet, but you're also empowering the communities around our planting projects to build a better future for themselves. Give it a try!

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Join the network of businesses declaring a climate and ecological emergency & taking purposeful action.

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business declares a climate & ecological emergency by

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Climate Change: Vital Signs of the Planet

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Carbon dioxide (CO ₂) is an important heat-trapping gas, also known as a greenhouse gas, that comes from the extraction and burning of fossil fuels (such as coal, oil, and natural gas), from wildfires, and natural processes like volcanic eruptions. The first graph shows atmospheric CO ₂ levels measured by NOAA at Mauna Loa Observatory, Hawaii, since 1958. The second graph shows CO ₂ levels during Earth's last three glacial cycles, as captured by air bubbles trapped in ice sheets and glaciers.

Since the onset of industrial times in the 18th century, human activities have raised atmospheric CO ₂ by 50% - meaning the amount of CO ₂ is now 150% of its value in 1750. This human-induced rise is greater than the natural increase observed at the end of the last ice age 20,000 years ago.

The animated map shows how the historical changes in global carbon dioxide over time. Note the colors change as the amount of CO ₂ rises from 365 parts per million (ppm) in 2002 to over 420 ppm currently. It's important to understand that "parts per million" refers to the number of carbon dioxide molecules per million molecules of dry air. These measurements are from the mid-troposphere, the layer of Earth's atmosphere that is 8 to 12 kilometers (about 5 to 7 miles) above the ground. This data provides insights into the significant rise in atmospheric CO2 concentrations, highlighting the impact of human activities on Earth's climate.

CO₂ Through the Seasons

A closer look at the carbon dioxide measurements at Mauna Loa shows a series of wiggles in the data. Although total CO ₂ is increasing each year, there is also a short-term cycle visible within the larger trend.

This annual rise and fall of CO ₂ levels is caused by seasonal cycles in photosynthesis on a massive scale. In Northern Hemisphere spring, plants come to life and draw in CO ₂ to fuel their growth. This begins the process of lowering the amount of CO ₂ in the atmosphere. In northern autumn, plant growth stops or slows down, and the whole process reverses itself. Much of the plant matter decomposes, releasing CO ₂ back to the atmosphere.

A similar but less intense pattern repeats in the Southern Hemisphere in opposite seasons. Spring growth starts in September and winter decomposition begins in March, so CO ₂ records in the Southern Hemisphere show the opposite pattern of that seen in Mauna Loa. However, because there is a lot more land and vegetation in the Northern Hemisphere than the southern, the global seasonal cycle more closely aligns with the northern pattern.

See the cycle in action in the visualization Watching Earth Breathe: The Seasonal Vegetation Cycle and Atmospheric Carbon Dioxide.

This boom-and-bust cycle of plant growth gives the graph of CO ₂ a sawtooth pattern of ups and downs from year to year. At a larger scale, the upward climb of the trend line over the decades is caused by CO ₂ emissions, primarily from burning fossil fuels. Thus, the data illustrate both natural factors and human additions of CO ₂.

Learn more:

NASA's Climate Kids: Why is Carbon Important?

Missions That Observe CO₂
Atmospheric Infrared Sounder (AIRS) Orbiting Carbon Observatory (OCO-2) Orbiting Carbon Observatory (OCO-3)

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Carbon Dioxide Concentration | NASA Global Climate Change

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Vital Signs of the Planet: Global Climate Change and Global Warming. Current news and data streams about global warming and climate change from NASA.

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Contact emma@lighthouse-sustainability.co.uk for a personal referral to Dan at My Mother Tree.

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70% of all carbon emissions come from 100 companies.

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The carbon capture question | FT Climate Capital

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The fight for a habitable planet is the fight for our lives. But while we look at ways to make our lives more sustainable, most of us are also funding environmental catastrophe.

During the 7 years following the Paris Agreement, the world's top 60 private-sector banks pumped $5.5 trillion into fossil fuels.

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Find Ethical & Sustainable Banks In Your Area - Bank.Green

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Bank.Green is sounding the alarm on the climate-destroying activities of banks while recommending sustainable alternatives and empowering consumer action.

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www.lighthouse-sustainability.co.uk

https://uk.linkedin.com/company/lighthouse-sustainability-ltd

https://uk.linkedin.com/in/emmaburlow

Instagram

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We would be extremely grateful if you could take a moment to leave us a review on Trustpilot.

Leave your review here.

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Lighthouse Sustainability Ltd is rated "Excellent" with 4.6 / 5 on Trustpilot

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Do you agree with Lighthouse Sustainability Ltd's 4-star rating? Check out what 17 people have written so far, and share your own experience.

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We share the latest news and recommendations on sustainability, Carbon Literacy, circular economy, and biodiversity in business in our newsletter.

Sign up here to stay updated.

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It is time to upgrade our economic system.

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We don't just work with young people. Join our mission by levelling up your organisation.

From tackling eco-anxiety and burnout to developing a "Resilience Rucksack", our informative and impactful workshops help you to develop your organisational resilience.

Delivered by experienced young changemakers with heart, passion and insight.

Join the ranks of LEGO, Mind, Friends of the Earth Europe, Leeds Art University and many more by enquiring about a workshop now.

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Empowering Youth Climate Leaders for a Sustainable Future | The Resilience Project

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Empowering youth climate leaders. Combat climate anxiety. Join us for a sustainable future

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I was hesitant to attend initially, given that I don't run my own sustainable business, but I've been pleasantly surprised by how PPP has been such a positive platform for networking, connection, and a chance to mingle with many amazing like-minded people at various stages of their careers, who are all passionate about creating change in the sustainability space. Conversations alone won't necessarily change the world but they can be the impetus for ideas, collaborations, projects, networks, and paradigm shifts ... and so I'd like to share my gratitude for all the hard work that goes into such a vast schedule of regular events that are now happening globally. It's a very inspiring and worthwhile effort, and one I feel lucky to be able to continue to partake in People Planet Pint.

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People, Planet, Pint: Sustainability Meetup - Small99

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Pop down to your local People Planet Pint to find more about what's going on locally with sustainability and how you can get involved.

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Join a group near you | Friends of the Earth

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Friends of the Earth England, Wales and Northern Ireland has more than 200 local action groups working to protect people and planet. Find out if there’s a group near you.

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Transition Network encourage communities to come together and rebuild our world. Step up and address the big challenges we face by starting local.

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About us - Centre for Sustainable Energy

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CSE supports people and organisations across the UK to tackle the climate emergency and end the suffering caused by cold homes

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Sea level rise and coastal flood risk maps -- a global screening tool by Climate Central

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Interactive global map showing areas threatened by sea level rise and coastal flooding.

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Nigel Topping: UN Climate Action Champion for the UK.

TED Talk 4.00-6.15mins

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Miao Wang, Alok Sharma and Nigel Topping: The race to net-zero emissions by 2050 is on. Can we count you in?

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In this rousing call to action, three participants of the Race To Zero -- a global campaign for cities, businesses, investors and individuals to achieve net-zero carbon emissions by 2050 -- discuss how we can move forward with speed and urgency to create a more fair, healthy and sustainable world.

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Assaad Razzouk - Energy/resources articles

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Assaad Razzouk - Energy/resources articles

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Department for Energy Security and Net Zero

# data[].attributes.attachment_link.title
2025 UK greenhouse gas emissions: provisional figures - statistical release

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This publication provides provisional estimates of 2025 UK territorial greenhouse gas emissions, meaning emissions that occur within UK borders. Emissions are presented in carbon dioxide equivalent units (CO2e) throughout this statistical release. These estimates give an early indication of emissions in 2025 and are subject to revision when final estimates are published in February 2027. They also include estimates of quarterly emissions, impacts of external temperature changes, and UK-based international aviation and shipping emissions.

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Cecilia Keating, Tom Prater, Kerry Cleaver

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AMOC: Is global warming tipping key Atlantic ocean currents towards ‘collapse’?

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The AMOC is under threat from human-caused climate change, as warming seas, melting ice and increased rainfall upset the temperature and salt balance of the North Atlantic.

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You can download our complete Our World in Data CO ₂ and Greenhouse Gas Emissions database.

CO₂ and Greenhouse Gas Emissions Country Profiles

How are emissions changing in each country? Is your country making progress on reducing emissions? We built 207 country profiles which allow you to explore the statistics for every country in the world.

Each profile includes interactive visualizations, explanations of the presented metrics, and the details on the sources of the data.

See emissions in your country and how it compares to others:

Our 12 most visited country profiles

Every profile includes five sections

Why do greenhouse gas emissions matter?

Global average temperatures have increased by more than 1℃ since pre-industrial times

Human emissions of carbon dioxide and other greenhouse gases - are a primary driver of climate change - and present one of the world's most pressing challenges.¹ This link between global temperatures and greenhouse gas concentrations - especially CO ₂ - has been true throughout Earth's history.²

To set the scene, let's look at how the planet has warmed. In the chart, we see the global average temperature relative to the average of the period between 1961 and 1990.

The red line represents the average annual temperature trend through time, with upper and lower confidence intervals shown in light grey.

We see that over the last few decades, global temperatures have risen sharply - to approximately 0.7℃ higher than our 1961-1990 baseline. When extended back to 1850, we see that temperatures then were a further 0.4℃ colder than they were in our baseline. Overall, this would amount to an average temperature rise of 1.1℃.

Because there are small year-to-year fluctuations in temperature, the specific temperature increase depends on what year we assume to be 'pre-industrial' and the end year we're measuring from. But overall, this temperature rise is in the range of 1 to 1.2℃.³

Greenhouse gas emissions from human activities are the main driver of this warming

How much of the warming since 1850 can be attributed to human emissions? Almost all of it. The Intergovernmental Panel on Climate Change (IPCC) states clearly in its AR5 assessment report⁴:

"Anthropogenic greenhouse gas emissions have increased since the pre-industrial era, driven largely by economic and population growth, and are now higher than ever. This has led to atmospheric concentrations of carbon dioxide, methane and nitrous oxide that are unprecedented in at least the last 800,000 years. Their effects, together with those of other anthropogenic drivers, have been detected throughout the climate system and are extremely likely to have been the dominant cause of the observed warming since the mid-20th century. "

Aerosols have played a slight cooling role in global climate, and natural variability has played a very minor role. This article from the Carbon Brief, with interactive graphics showing the relative contributions of different forcings on the climate, explains this very well.

A changing climate has a range of potential ecological, physical, and health impacts, including extreme weather events (such as floods, droughts, storms, and heatwaves); sea-level rise; altered crop growth; and disrupted water systems. The most extensive source of analysis on the potential impacts of climatic change can be found in the 5th Intergovernmental Panel on Climate Change (IPCC) report.⁵

In some regions, warming has - and will continue to be - much greater than the global average

Local temperatures in 2019 relative to the average temperature in 1951-1980.⁶

When we think about the problem of global warming, a temperature rise of 1℃ can seem small and insignificant.

Not only is it true that 1℃ of rapid warming itself can have significant impacts on climate and natural systems, but also that this 1℃ figure masks the large variations in warming across the world.

In the map shown - taken from the Berkeley Earth global temperature report - we see the global distribution of temperature changes in 2019 relative to the period 1951 - 1980.⁷ This period from 1951 to 1980 is similar to the period global average time series shown in the section above.

There are a couple of key points that stand out.

Firstly, the global average temperature rise is usually given as the combined temperature change across both land and the sea surface. But it's important to note that land areas change temperature, both warming and cooling much more than oceanic areas.⁸ Overall, global average temperatures over land have increased around twice as much as the ocean. Compared to the 1951 - 1980 average, temperatures over land increased by 1.32 ± 0.04 °C. Whereas, the ocean surface temperature (excluding areas of sea ice) increased by only 0.59 ± 0.06 °C.

Since the Northern Hemisphere has more land mass, this also means that the change in average temperature north of the equator has been higher than in the south.

Secondly, from the map shown, we see that in some regions the temperature change has been much more extreme. At very high latitudes - especially near the Poles - warming has been upwards of 3°C, and in some cases exceeding 5°C. These are, unfortunately, often the regions that could experience the largest impacts such as sea ice, permafrost, and glacial melt.

Monitoring the average global temperature change is important, but we should also be aware of how differently this warming is distributed across the world. In some regions, warming is much more extreme.

How are greenhouse gas emissions and concentrations changing?

Atmospheric concentrations of CO₂ continue to rise

To slow down - with the eventual aim of halting - rising global temperatures, we need to stabilize concentrations of CO ₂ and other greenhouse gases in Earth's atmosphere.⁹ This link between global temperatures and greenhouse gas concentrations - especially CO ₂ - has been true throughout Earth's history.¹⁰ It's important to note that there is a 'lag' between atmospheric concentrations and final temperature rise - this means that when we do finally manage to stabilize atmospheric concentrations, temperatures will continue to slowly rise for years to decades.¹¹,¹²

But, far from stabilizing concentrations, greenhouse gases continue to accumulate.

In the chart here we see global average concentrations of CO ₂ in the atmosphere over the past 800,000 years. Over this period we see consistent fluctuations in CO ₂ concentrations; these periods of rising and falling CO ₂ coincide with the onset of ice ages (low CO ₂) and interglacials (high CO ₂).¹³ These periodic fluctuations are caused by changes in the Earth's orbit around the sun - called Milankovitch cycles.

Over this long period, atmospheric concentrations of CO ₂ did not exceed 300 parts per million (ppm). This changed with the Industrial Revolution and the rise of human emissions of CO ₂ from burning fossil fuels. We see a rapid rise in global CO ₂ concentrations over the past few centuries, and in recent decades in particular. For the first time in over 800,000 years, concentrations did not only rise above 300ppm but are now well over 400ppm.

It's not only the level of change CO ₂ in the atmosphere that matters, but also the rate that this has changed. Historical changes in CO2 concentrations tended to occur over centuries or even thousands of years. It took us a matter of decades to achieve even larger changes. This gives species, planetary systems, and ecosystems much less time to adapt.

Global emissions have not yet peaked

To stabilize (or even reduce) concentrations of CO ₂ in the atmosphere, the world needs to reach net-zero emissions. This requires large and fast reductions in emissions.

Are we making progress towards this? How far are we from this target?

At a time when global emissions need to be falling, they are in fact still rising, as the chart here shows. The world has not yet peaked.

Current climate policies will reduce emissions, but not quickly enough to reach international targets

Related charts

Current policies to reduce, or at least slow down growth, in CO ₂ and other greenhouse gas emissions will have some impact on reducing future warming.

As we see in the chart shown here, current implemented climate and energy policies would reduce warming relative to a world with no climate policies in place. This chart maps out future greenhouse gas emissions scenarios under a range of assumptions: if no climate policies were implemented; if current policies continued; if all countries achieved their current future pledges for emissions reductions; and necessary pathways which are compatible with limiting warming to 1.5 °C or 2 °C of warming this century.¹⁴

If countries achieved their current 'Pledges' (also shown on the chart), this would be an even further improvement. In this regard, the world is making some progress.

But if our aim is to limit warming to "well below 2°C" - as is laid out in the Paris Agreement - we are clearly far off track.

Robbie Andrew, a senior researcher at the Center for International Climate Research (CICERO), mapped out the global emissions reduction scenarios necessary to limit global average warming to 1.5°C and 2°C. Based on the IPCC's Special Report on 1.5°C and Michael Raupach's work, published in Nature Climate Change, these mitigation curves show that urgent and rapid reductions in emissions would be needed to achieve either target.¹⁵,¹⁶,¹⁷ And the longer we delay a peak in emissions, the more drastic these reductions would need to be.

We may be making slow progress relative to a world without any climate policies, but we are still far from the rates of progress we'd need to achieve international targets.

Which countries have set net-zero emissions targets?

Whilst current climate policies fall well short of what's needed to keep temperatures below 1.5 °C or 2 °C, countries have set more ambitious targets to reach net-zero emissions.

These interactive maps show the status of net-zero emissions targets across the world. This is based on the latest data from the Energy and Climate Intelligence Unit's Net Zero Scorecard.¹⁸

The target year to reach net zero varies by country - you can see the target year for each country by hovering over it on the map.

Note that the inclusion criteria may vary from country to country. For example, some countries may include international aviation and shipping in their net-zero commitment, while others do not. Or, some may allow for carbon offsets while others will not accept them. You can dig deeper into the specifics of each country's criteria here.

Can we make progress in reducing emissions?

Some countries reduced emissions whilst increasing GDP - even when we take into account outsourced production

There is a strong link between CO ₂ emissions, prosperity, and standards of living - we look at this in much more detail, with the data, on our page on Emissions Drivers.

Therefore, if we're to ask the question: "Have any countries demonstrated that we can make progress in reducing emissions?", they would have to achieve both:

1. High standards of living;

2. Low levels of emissions, or at least large reductions in emissions to maintain that standard of living.

There are many countries that meet one criterion: rich countries that have high standards of living, but also high levels of emissions; and poor countries that have low levels of emissions but poor standards of living.

But, some countries have shown signs of progress. A number of countries have shown in recent years that it is possible to increase GDP whilst also reducing emissions. We see this in the chart which shows the change in GDP and annual CO ₂ emissions. Both production- and consumption-based CO ₂ emissions are shown - consumption-based emissions are corrected for traded goods and services, so we can see whether emissions reductions were only achieved by "offshoring" production to other countries.

A number of countries - such as the USA, UK, France, Spain, Italy, and many others - have managed to reduce emissions (even when we correct for trade) whilst increasing GDP.

The more important question is "Can we make progress fast enough?"

So we can see numerous examples of countries, with high standards of living, which have been successful in reducing emissions. This is a clear signal that it is possible to make progress.

But the key question here is probably less: "can we make progress?", but rather "can we make progress fast enough?".

As we explored earlier in this article, the world is currently far off-track from our 2 ° C target. If this is our definition of "fast enough" then we have little historical evidence to suggest that most, or even some, countries can reduce emissions (whilst maintaining high living standards) at the speed needed to achieve this.

We can make progress, but it's currently too slow. We need a large-scale acceleration of these efforts across the world.

How do we make progress in reducing emissions?

To make progress in reducing greenhouse gas emissions, there are two fundamental areas we need to focus on: energy (this encapsulates electricity, heat, transport, and industrial activities) and food and agriculture (which includes agriculture and land use change, since agriculture dominates global land use).

Below we've listed some of the key actions we need to make progress in each area. At a very basic level they can be summarised by two core concepts: improving efficiency (using less energy to produce a given output; and using less land, fertilizer, and other inputs for food production, and reducing food waste); and transitioning to low-carbon alternatives (in energy, this means shifting to renewables and nuclear; for food, this means substituting carbon-intensive products for those with a lower carbon footprint).

How can we decarbonize our energy systems?

1. Shift towards low-carbon electricity (reduce carbon intensity - carbon per unit energy)
- Renewables
- Nuclear energy
- Shift from coal to gas (which emits less CO₂ per unit of energy) as an interim step
→ Explore our work on energy.
→ Explore our data on CO2 emissions by fuel type.

2. Shift sectors such as transport, towards electricity. Some energy sectors are harder to decarbonize - for example, transport. We, therefore, need to shift these forms towards electricity where we have viable low-carbon technologies.
→ Explore our data on greenhouse gas emissions by sector.

3. Develop low-cost low-carbon energy and battery technologies. To do this quickly, and allow lower-income countries to avoid high-carbon development pathways, low-carbon energy needs to be cost-effective and the default choice.

4. Improve energy efficiency - energy per unit GDP.
→ Explore our data on energy intensity.

How can we reduce emissions from food production and agriculture?

1. Reduce meat and dairy consumption, especially in higher-income countries. Shift dietary patterns towards lower-carbon food products. This includes eating less meat and dairy generally but also substituting high-impact meats (e.g. beef and lamb) for chicken, fish, or eggs. Innovation in meat substitutes could also play a large role here.
→ Read our article on the carbon footprint of meat and dairy versus alternative foods.
→ Explore our work on meat and dairy production.

2. Promote lower-carbon meat and dairy production. We are not going to cut out meat and dairy products completely any time soon (and doing so is unnecessary - large reductions would be sufficient). This makes the promotion of lower-carbon production methods essential.
→ Read our article on the large differences in carbon footprint for specific meat and dairy products.

3. Improve crop yields. Sustainable intensification of agriculture allows us to grow more food on less land. This could help to prevent deforestation from agricultural expansion, and frees up land for replanting, or giving back to natural ecosystems.
→ Explore our work on crop yields.

4. Reduce food waste. Around one-third of food emissions come from food that is lost in supply chains or wasted by consumers. Improving harvesting techniques, refrigeration, transport, and packaging in supply chains; and reducing consumer waste can reduce emissions significantly.
→ Read our article on GHG emissions from food waste.

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Who is emitting greenhouse gases? Which countries and which sectors? And what needs to happen to reduce emissions?

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These days, the buzz is all about artificial intelligence (AI), computer systems that can sense their environment, think, learn, and act in response to their programmed objectives. Because of AI's capabilities, there are many ways it can help combat climate change, but will its potential to aid decarbonization and adaptation outweigh the enormous amounts of energy it consumes? Or will AI's growing carbon footprint put our climate goals out of reach?

The artificial intelligence revolution

Earlier this year, several large language models-revolutionary types of AI trained on huge amounts of text that can generate human sounding text-were launched. The first large language model appeared in the 1950s, but today's models are vastly more sophisticated. The most popular new models are Microsoft's AI-powered Bing search engine, Google's Bard, and OpenAI's GPT-4.

When I asked Bard why large language models are revolutionary, it answered that it is "because they can perform a wide range of tasks that were previously thought to be impossible for computers. They can generate creative text, translate languages, and answer questions in an informative way." Large language models are a type of generative AI, because the models can generate new combinations of text based on the examples and information they have previously seen.

How do large language models work?

The goal of a large language model is to guess what comes next in a body of text. To achieve this, it first must be trained. Training involves exposing the model to huge amounts of data (possibly hundreds of billions of words) which can come from the internet, books, articles, social media, and specialized datasets. The training process can take weeks or months. Over time, the model figures out how to how to weigh different features of the data to accomplish the task it is given. At first a model's guesses are random, but as training progresses, the model identifies more and more patterns and relationships in the data. The internal settings that it learns from the data are called parameters; they represent the relationships between different words and are used to make predictions. The model's performance is refined through tuning, adjusting the values for the parameters to find out which ones result in the most accurate and relevant outcomes.

Each generation of large language models has many more parameters than the previous one; the more parameters, the more accurate and flexible they can be. In 2018, a large language model had 100 million parameters. GPT-2, launched in 2019, had 1.5 billion parameters; GPT-3 at 100 times larger, had 175 billion parameters; no one knows how large GPT-4 is. Google's PaLM large language model, which is much more powerful than Bard, had 540 billion parameters.

To process and analyze the vast amounts of data, large language models need tens of thousands of advanced high-performance chips for training and, once trained, for making predictions about new data and responding to queries.

Graphics processing units (GPUs), specialized electronic circuits, are typically used because they can execute many calculations or processes simultaneously; they also consume more power than many other kinds of chips.

AI mostly takes place in the cloud-servers, databases, and software that are accessible over the internet via remote data centers. The cloud can store the vast amounts of data AI needs for trainings and provide a platform to deploy the trained AI models.

How AI can help combat climate change

Because of its ability to analyze enormous amounts of data, artificial intelligence can help mitigate climate change and enable societies to adapt to its challenges.

AI can be used to analyze the many complex and evolving variables of the climate system to improve climate models, narrow the uncertainties that still exist, and make better predictions. This will help businesses and communities anticipate where disruptions due to climate change might occur and better prepare for or adapt to them. Columbia University's new center, Learning the Earth with Artificial Intelligence and Physics (LEAP) will develop next-generation AI-based climate models, and train students in the field.

AI can help develop materials that are lighter and stronger, making wind turbines or aircraft lighter, which means they consume less energy. It can design new materials that use less resources, enhance battery storage, or improve carbon capture. AI can manage electricity from a variety of renewable energy sources, monitor energy consumption, and identify opportunities for increased efficiency in smart grids, power plants, supply chains, and manufacturing.

AI systems can detect and predict methane leaks from pipelines. They can monitor floods, deforestation, and illegal fishing in almost real time. They can make agriculture more sustainable by analyzing images of crops to determine where there might be nutrition, pest, or disease problems. AI robots have been used to collect data in the Arctic when it is too cold for humans or conduct research in the oceans. AI systems can even green themselves by finding ways to make data centers more energy efficient. Google uses artificial intelligence to predict how different combinations of actions affect energy consumption in its data centers and then implements the ones that best reduce energy consumption while maintaining safety.

These are just a few examples of what AI can do to help address climate change.

How much energy does AI consume?

Today data centers run 24/7 and most derive their energy from fossil fuels, although there are increasing efforts to use renewable energy resources. Because of the energy the world's data centers consume, they account for 2.5 to 3.7 percent of global greenhouse gas emissions, exceeding even those of the aviation industry.

Most of a data center's energy is used to operate processors and chips. Like other computer systems, AI systems process information using zeros and ones. Every time a bit-the smallest amount of data computers can process-changes its state between one and zero, it consumes a small amount of electricity and generates heat. Because servers must be kept cool to function, around 40 percent of the electricity data centers use goes towards massive air conditioners. Without them, servers would overheat and fail.

In 2021, global data center electricity use was about 0.9 to 1.3 percent of global electricity demand. One study estimated it could increase to 1.86 percent by 2030. As the capabilities and complexity of AI models rapidly increase over the next few years, their processing and energy consumption needs will too. One research company predicted that by 2028, there will be a four-fold improvement in computing performance, and a 50-fold increase in processing workloads due to increased use, more demanding queries, and more sophisticated models with many more parameters. It's estimated that the energy consumption of data centers on the European continent will grow 28 percent by 2030.

With AI already being integrated into search engines like Bing and Bard, more computing power is needed to train and run models. Experts say this could increase the computing power needed-as well as the energy used-by up to five times per search. Moreover, AI models need to be continually retrained to keep up to date with current information.

Training

In 2019, University of Massachusetts Amherst researchers trained several large language models and found that training a single AI model can emit over 626,000 pounds of CO2, equivalent to the emissions of five cars over their lifetimes.

A more recent study reported that training GPT-3 with 175 billion parameters consumed 1287 MWh of electricity, and resulted in carbon emissions of 502 metric tons of carbon, equivalent to driving 112 gasoline powered cars for a year.

Inference

Once models are deployed, inference-the mode where the AI makes predictions about new data and responds to queries-may consume even more energy than training. Google estimated that of the energy used in AI for training and inference, 60 percent goes towards inference, and 40 percent for training. GPT-3's daily carbon footprint was been estimated to be equivalent to 50 pounds of CO2or 8.4 tons of CO2 in a year.

Inference energy consumption is high because while training is usually done multiple times to keep models current and optimized, inference is used many many times to serve millions of users. Two months after its launch, ChatGPT had 100 million active users. Instead of employing existing web searches that rely on smaller AI models, many people are eager to use AI for everything, but a single request in ChatGPT can consume 100 times more energy than one Google search, according to one tech expert.

Northeastern University and MIT researchers estimated that inference consumes more energy than training, but there is still debate over which mode is the greater energy consumer. What is certain, though, is that as OpenAI, Google, Microsoft, and the Chinese search company Baidu compete to create larger, more sophisticated models, and as more people use them, their carbon footprints will grow. This could potentially make decarbonizing our societies much more difficult.

"If you look at the history of computational advances, I think we're in the 'amazed by what we can do, this is great, let's do it phase,'" said Clifford Stein, interim director of Columbia University's Data Science Institute, and professor of computer science at the Fu Foundation School of Engineering and Applied Science. "But we should be coming to a phase where we're aware of the energy usage and taking that into our calculations of whether we should or shouldn't be doing it, or how big the model should be. We should be developing the tools to think about if it's worth using these large language models given how much energy they're consuming, and at least be aware of their energy and environmental costs."

How can AI be made greener?

Many experts and researchers are thinking about the energy and environmental costs of artificial intelligence and trying to make it greener. Here are just some of the ways AI can be made more sustainable.

Transparency

You can't solve a problem if you can't measure it, so the first step towards making AI greener is to enable developers and companies to know how much electricity their computers are using and how that translates into carbon emissions. The measurements of AI carbon footprints also need to be standardized so that developers can compare the impacts of different systems and solutions. A group of researchers from Stanford, Facebook, and McGill University have developed a tracker to measure energy use and carbon emissions from training AI models. And Microsoft's Emissions Impact Dashboard for Azure enables users to calculate their cloud's carbon footprint.

Renewable energy use

According to Microsoft, all the major cloud providers have plans to run their cloud data centers on 100 percent carbon-free energy by 2030, and some already do. Microsoft is committed to running on 100 percent renewable energy by 2025, and has long-term contracts for green energy for many of its data centers, buildings, and campuses. Google's data centers already get 100 percent of their energy from renewable sources.

Moving large jobs to data centers where the energy can be sourced from a clean energy grid also makes a big difference. For example, the training of AI startup Hugging Face's large language model BLOOM with 176 billion parameters consumed 433 MWh of electricity, resulting in 25 metric tons of CO2 equivalent. It was trained on a French supercomputer run mainly on nuclear energy. Compare this to the training of GPT-3 with 175 billion parameters, which consumed 1287 MWh of electricity, and resulted in carbon emissions of 502 metric tons of carbon dioxide equivalent.

Better management of computers

Data centers can have thousands or hundreds of thousands of computers, but there are ways to make them more energy efficient. "Packing the work onto the computers in a more efficient manner will save electricity," said Stein. "You may not need as many computers, and you can turn some off."

He is also currently researching the implications of running computers at lower speeds, which is more energy efficient. In any data center, there are jobs that require an immediate response and those that don't. For example, training takes a long time but usually doesn't have a deadline. Computers could be run more slowly overnight, and it wouldn't make a difference. For inference that's done in real time, however, computers need to run quickly.

Another area of Stein's research is the study of how accurate a solution needs to be when computing. "Sometimes we get solutions that are more accurate than the input data justifies," he said. "By realizing that you only need to really be computing things approximately, you can often compute them much faster, and therefore in a more energy efficient manner." For example, with some optimization problems, you are gradually moving towards some optimal solution. "Often if you look at how optimization happens, you get 99 percent of the way there pretty quickly, and that last one percent is what actually what takes half the time, or sometimes even 90 percent of the time" he said. The challenge is knowing how close you are to the solution so that you can stop earlier.

More efficient hardware

Chips that are designed especially for training large language models, such as tensor processing units developed by Google, are faster and more energy efficient than some GPUS.

Google claims its data centers have cut their energy use significantly by using hardware that emits less heat and therefore needs less energy for cooling. Many other companies and researchers are also trying to develop more efficient hardware specifically for AI.

The right algorithms

Different algorithms have strengths and weaknesses, so finding the most efficient one depends on the task at hand, the amount and type of data used, and the computational resources available. "You have to look at the underlying problem [you're trying to solve]," said Stein. "There are often many different ways you can compute it, and some are faster than others."

People don't always implement the most efficient algorithm. "Either because they don't know them, or because it's more work for the programmer, or they already have one implemented from five years ago," he said. "So by implementing algorithms more efficiently, we could save electricity." Some algorithms have also learned from experience to be more efficient.

The appropriate model

Large language models are not needed for every kind of task. Choosing to use a smaller AI model for simpler jobs is a way to save energy-more focused models instead of models that can do everything are more efficient. For instance, using large models might be worth the electricity they consume to try to find new antibiotics but not to write limericks.

Some researchers are trying to create language models using data sets that are 1/10,000 of the size in the large language models. Called the BabyLM Challenge, the idea is to get a language model to learn the nuances of language from scratch the way a human does, based on a dataset of the words children are exposed to. Each year, young children encounter between 2,000 to 7,000 words; for the BabyLM Challenge, the maximum number of words in the dataset is 100,000 words, which amounts to what a 13-year-old will have been exposed to. A smaller model takes less time and resources to train and thus consumes less energy.

Modifying the models

Fine tuning existing models instead of trying to develop even bigger new models would make AI more efficient and save energy.

Some AI models are "overparameterized." Pruning the network to remove redundant parameters that do not affect a model's performance could reduce computational costs and storage needed. The goal for AI developers is to find ways to reduce the number of parameters without sacrificing accuracy.

Knowledge distillation, transferring knowledge the model has learned from a massive network to a more compact one, is another way to reduce AI model size.

Virginia Tech and Amazon are studying federated learning, which brings the model training to the data instead of bringing data to a central server. In this system, parts of the model are trained on data stored on multiple devices in a variety of locations instead of in a centralized or cloud server. Federated learning reduces the amount of time training takes, and the amount of data that must be transferred and stored, all of which saves energy. Moreover, the data on the individual devices stays where it is, ensuring data security. After they are trained on the local devices, the updated models are sent back to a central server and aggregated into a new more comprehensive model.

New cooling methods

Because traditional cooling methods, such as air conditioning, cannot always keep data centers cool enough, Microsoft researchers are using a special fluid engineered to boil 90 degrees lower than water's boiling point to cool computer processors. Servers are submerged into the fluid, which does not harm electronic equipment; the liquid removes heat from the hot chips and enables the servers to keep operating. Liquid immersion cooling is more energy efficient than air conditioners, reducing a server's power consumption by 5 to 15 percent.

Microsoft is also experimenting with the use of underwater data centers that rely on the natural cooling of the ocean, and ocean currents and nearby wind turbines to generate renewable energy. Computers are placed in a cylindrical container and submerged underwater. On land, computer performance can be hampered by oxygen, moisture in the air, and temperature fluctuations. The underwater cylinder provides a stable environment without oxygen. Researchers say that underwater computers have one-eighth the failure rate as those on land.

Eventually, data centers may move from the cloud into space. A startup called Lonestar has raised five million dollars to build small data centers on the moon by the end of 2023. Lunar data centers could take advantage of abundant solar energy and would be less susceptible to natural disasters and sabotage.

Thales Alenia Space is leading a study on the feasibility of building data centers in space which would run on solar energy. The study is attempting to determine if the launch and production of space data centers would result in fewer carbon emissions than those on land.

Government support for sustainable AI

To speed the development of more sustainable AI, governments need to establish regulations for the transparency of its carbon emissions and sustainability. Tax incentives are also needed to encourage cloud providers to build data centers where renewable energy is available, and to incentivize the expansion of clean energy grids.

"If we're smart about AI, it should be beneficial [to the planet] in the long run," said Stein. "We certainly have the ability to be smart about it, to use it to get all kinds of energy savings. I think AI will be good for the environment, but to achieve that requires us to be thoughtful and have good leadership."

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Artificial intelligence has the potential to develop game-changing ways to fight climate change - but only if we can find ways to cut its carbon emissions.

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1. Explore Carbon Map (5 mins)

2. Watch How can business survive climate change? | The Economist (20mins) and COP26 Mia Mottley PM of Barbados and/or Vinisha Umashankar – Earthshot winner (both 5-6mins)

3. Read Climate change: IPCC report is 'code red for humanity' - BBC News and 10 Big Findings from the 2023 IPCC Report on Climate Change (WRI)

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Free introductory course

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In this 20-minute, interactive e-learning course delivered by the UN Global Compact Academy and the Science Based Targets initiative, you will learn about the Net-Zero Standard and receive guidance on how to set science-based net-zero targets consistent with limiting global temperature rise to 1.5C.

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Setting Science-Based Targets to Achieve Net-Zero

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This Academy e-learning course will guide you in the process of setting a science-based target in support of a net-zero future. It will also offer an opportunity to hear from other companies about why they think setting science-based targets makes good business sense.

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Subscribe to GHG Protocol's email list HERE ,Access information about the corporate suite of standards update process HERE

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Through the 2015 Paris Agreement, world governments committed to curbing global temperature rise to well-below 2°C above pre-industrial levels and pursuing efforts to limit warming to 1.5°C. In 2018, the Intergovernmental Panel on Climate Change warned that global warming must not exceed 1.5°C to avoid the catastrophic impacts of climate change.

To achieve this, GHG emissions must halve by 2030 - and drop to net-zero by 2050. As the major source of GHG emissions, the private sector must take action now.. Businesses with science-based targets are already cutting emissions at scale; all businesses must now join them.

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Lead the way to a zero-carbon economy, boost innovation and drive sustainable growth by setting ambitious, science-based emissions reduction targets

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Nouveau jalon dans la transparence environnementale de l’IA générative | Carbone 4

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Analyse du Cycle de Vie d’un LLM de Mistral AI

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Munich Center for Digital Sciences and AI (MUC.DAI), HM Hochschule München University of Applied Sciences, Munich, Germany

This study presents a comprehensive evaluation of the environmental cost of large language models (LLMs) by analyzing their performance, token usage, and CO ₂ equivalent emissions across 14 LLMs ranging from 7 to 72 billion parameters. Each LLM was tasked with answering 500 multiple-choice and 500 free-response questions from the MMLU benchmark, covering five diverse subjects. Emissions were measured using the Perun framework on an NVIDIA A100 GPU and converted through an emission factor of 480 gCO ₂/kWh. Our results reveal strong correlations between LLM size, reasoning behavior, token generation, and emissions. While larger and reasoning-enabled models achieve higher accuracy, up to 84.9%, they also incur substantially higher emissions, driven largely by increased token output. Subject-level analysis further shows that symbolic and abstract domains such as Abstract Algebra consistently demand more computation and yield lower accuracy. These findings highlight the trade-offs between accuracy and sustainability, emphasizing the need for more efficient reasoning strategies in future LLM developments.

1 Introduction

Artificial intelligence (AI) is transforming communication at all levels, from one-on-one interactions to organizational and societal exchanges, by enhancing speed, creativity, and personalization, while also raising challenges related to bias, privacy, and governance ( Polak and Anshari, 2024; Sonni, 2025). As AI technologies permeate the communication domain, it becomes essential to quantify their environmental costs. Natural Language Processing (NLP) is a subfield of artificial intelligence focused on enabling computers to understand, generate, and interpret human language. In particular, the rapid development and widespread adoption of large language models (LLMs) have profoundly impacted NLP, communication research, and adjacent fields. LLMs are deep neural networks trained on large corpora of text data to learn statistical patterns of language, enabling them to generate and interpret human-like text. With new architectures and benchmarks emerging on the scale of weeks or months, LLM capabilities and applications are expanding at an unprecedented pace ( Minaee et al., 2024; Movva et al., 2024). These applications include multilingual machine translation ( Zhu et al., 2024), text summarization ( Liu et al., 2024), question answering ( Arefeen et al., 2024), and code generation ( Jiang et al., 2024). Following the release of ChatGPT, the average daily rate of arXiv preprints mentioning "large language model" in their title or abstract rose from 0.40 to 8.58, highlighting the surge of interest in this domain ( Zhao et al., 2024).

The growing adoption of LLMs has opened new research opportunities while amplifying concerns about data security, cultural bias, and societal impact. Recent studies reveal systemic issues of linguistic and cultural dominance, mainly driven by English-language training corpora, which threaten the inclusion of AI systems ( Wang et al., 2024; LI et al., 2024). In response, researchers have proposed bias mitigation frameworks and culturally adaptive training methodologies to promote more equitable, globally representative LLMs ( Wang et al., 2024).

These ethical and societal dimensions are increasingly reflected in publication trends. Between 2018-2022 and 2023, the Computers and Society subcategory on arXiv grew 20× faster in its share of articles related to LLMs than other subfields. Submissions on Applications of ChatGPT and Societal implications increased eight times and four times, respectively ( Movva et al., 2024). Almost half (49.5%) of first authors in 2023 LLM papers and 38.6% of corresponding authors had no previous NLP publications, indicating a growing interdisciplinary research community exploring LLM applications in human-computer interaction, security, software engineering, and beyond ( Movva et al., 2024).

Despite extensive attention to social and security issues, sustainability remains critically underexplored. Generative AI models, including LLMs, are estimated to consume ~29.3 TWh annually, comparable to Ireland's total energy consumption ( de Vries, 2023), yet only a small fraction of LLM research addresses the carbon footprint or environmental impact. As model sizes grow, reaching hundreds of billions of parameters ( Zhao et al., 2024), their sustainability implications intensify. A Scopus analysis shows that LLM-related publications increased by 82.7% from 2020 to January 2025, but only 1.82% and 0.64% of those papers explicitly address "carbon emissions" or "CO ₂" impacts, respectively ( Elsevier, 2025). Moreover, existing work often relies on theoretical estimates rather than empirical measurements of energy consumption during training and inference.

2 Sustainability calculation for large language models

Accurate quantification of the environmental impact of a product requires a comprehensive Life Cycle Assessment (LCA), in which all environmental burdens are evaluated, from the extraction of raw materials to the end of life disposal ( Klöpffer, 1997). For LLMs, this assessment encompasses both the manufacture of computing infrastructure (including raw material acquisition, processing, and fabrication of server components such as GPUs) and all subsequent lifecycle stages: dataset generation, data preprocessing, iterative experimentation, model training, deployment, and eventual retirement ( Luccioni et al., 2023). Due to limited transparency across these phases, existing studies often rely on estimates of material and manufacturing impacts ( Khowaja et al., 2024; de Vries, 2023) or focus on directly measurable quantities, notably energy consumption during training and inference ( Luccioni et al., 2023; Liu and Yin, 2024; Faiz et al., 2024).

Standard analyses of LLM greenhouse gas (GHG) emissions typically focus on computing related impacts during deployment. However, a full LCA requires converting all GHGs, carbon dioxide (CO ₂), methane (CH ₄), and nitrous oxide (N ₂O), into carbon dioxide equivalents (CO ₂eq) by applying the global warming potential (GWP) of each gas relative to CO ₂ ( Luccioni et al., 2023; Faiz et al., 2024; Strubell et al., 2020; Liu and Yin, 2024).

Although CO ₂ eq is the de facto standard for reporting LLM sustainability, meaningful comparison across studies is impeded by methodological heterogeneity. Variations in system boundaries, estimation methods, functional units, model parameterizations, and architectural differences complicate direct benchmarking and the transferability of conclusions.

3 Method

In this study, we investigate the relationship between performance and carbon dioxide equivalent emissions (CO ₂eq) for various LLM families and evaluate multiple models with different numbers of parameters. The examined LLMs include Meta's Llama3.1 models ( Grattafiori et al., 2024) with 8 billion and 70 billion parameters, as well as the Llama3.3 model ( Grattafiori et al., 2024) with 70 billion parameters. Additionally, Alibaba's Qwen models ( Bai et al., 2023) and the Qwen2.5 models ( Qwen et al., 2025), each with 7 billion and 72 billion parameters, are used for comparison. Furthermore, two reasoning models developed by Deep Cogito, with 8 billion and 70 billion parameters, are included. These models operate in both standard text generation mode and reasoning mode. Also, Deepseek R1 models ( DeepSeek-AI et al., 2025), specifically designed for reasoning, are included, featuring variants with 7 billion, 8 billion, and 70 billion parameters.

All models were tasked with answering the same 500 questions drawn from different subject areas. The questions and their correct answers were extracted from the Massive Multitask Language Understanding (MMLU) dataset ( Hendrycks et al., 2021). The MMLU dataset evaluates multitask accuracy in diverse knowledge domains, comprising 15,908 multiple choice questions from 57 subjects including engineering, mathematics, humanities, and social sciences. The questions are sourced from publicly available practice exams and academic materials that span various educational levels and each question provides multiple choice answers, with correct responses derived from the original educational content created by experts ( Hendrycks et al., 2021). For the purposes of our study, the five subjects Philosophy, High School World History, International Law, Abstract Algebra, and High School Mathematics were selected to ensure a comprehensive range of questions from general historical and legal knowledge to mathematical logic across educational levels. Each model answered 100 questions per subject and underwent two distinct testing phases.

In the first phase, the models received the question accompanied by four multiple choice options and were required to select the correct answer. For standard text-generation models such as Qwen, Llama, and Cogito (in their default text-generation mode), the output was limited to a single word, indicating the index of the chosen answer. This allowed for straightforward and objective comparison between the model's chosen answer and the correct answer from the MMLU dataset. However, for reasoning-based models, output word limits were not imposed, as these models require generating additional text to complete their reasoning processes.

In the second testing scenario, models received only the question as a prompt, without restrictions on output length. To assess the correctness of the responses, we used the fast, cost-effective OpenAI o4-mini reasoning model as an evaluator. This evaluator was provided with the question, the multiple choice options, the correct answer, and the model-generated answer as input, tasked with judging the correctness of the answer based on the specific subject and the known correct answer from the MMLU dataset.

All experiments were carried out on a local Nvidia A100 GPU with 80 GB of memory, allowing accurate measurement of energy consumption, memory usage, and response time during model evaluation. Measurements were performed using the Perun framework, designed for energy benchmarking of high-performance computing applications ( Gutiérrez Hermosillo Muriedas et al., 2023). To calculate associated CO ₂eq emissions, an emission factor of 480 gCO ₂/kWh was applied. This emission factor was selected as it represents the latest global average. This value represents the global average emission factor, reflecting recent trends toward increased adoption of renewable energy, providing a realistic baseline to evaluate environmental impacts ( Wiatros-Motyka et al., 2024).

4 Results

Before relating model behavior to energy consumption and the resulting CO ₂eq emissions, we first present the raw task performance that underpins all subsequent analyzes. Specifically, we quantify the precision of each model in the 500 MMLU questions in two scenarios: a constrained multiple-choice phase and a free-response phase, whose outputs are adjudicated by the OpenAI o4-mini evaluator. Figure 1 shows the number of correct responses each model achieves across the five MMLU subjects in both phases. By comparing success rates across knowledge domains and parameter scales from 7 billion to 72 billion parameters, we find that the largest models consistently lead. In the multiple-choice phase, the reasoning-enhanced Cogito 70B model tops the field with 91.0% correct answers, followed by the Deepseek R1 70B reasoning model at 85.0% and the Qwen 2.5 72B model at 80.2%. In the free-response phase, the same Cogito variant again ranks first with 78.8%, narrowly ahead of Cogito 70B in standard text mode (76.4%) and Qwen 2.5 72B (75.0%). Among the compact 7-8 billion-parameter models, Deepseek R1 8B attains 67.4% correct answers in the multiple-choice phase and 49.8% in the free-response phase. In contrast, the weakest compact model, Qwen 7B, answers only 41.4% in the multiple-choice phase and 24.4% in the free-response phase. Subject-wise, multiple-choice performance peaks on High School World History with an average of 76.3% correct answers per model and bottoms out on Abstract Algebra at 51.4% correct answers. In free-response mode, models score highest on High School Mathematics with 69.4 correct answers and lowest on Philosophy with 52.1% correct answers.

Figure 1. Comparison of the performance of tested LLMs on multiple-choice and free-response tasks, with individual subject performance displayed.

In addition to the accuracy of the different models, we also analyze the token footprints incurred by each model when answering the 500 MMLU questions. A token is a unit of text, such as a word, part of a word, or an individual character, that is converted into a numerical representation, so that an LLM can process it ( Gastaldi et al., 2025). We distinguish between response tokens (the tokens comprising the model's final answer) and thinking tokens (the additional tokens generated by reasoning-enabled models before producing their answer). For each LLM, separate markers denote the average number of response tokens and, for reasoning-enabled variants, the average number of thinking tokens. In the multiple-choice setting the models generated 37.7 response tokens per question on average, while reasoning-enabled variants required an additional 543.5 thinking tokens. Subject-wise, High School Mathematics incurred the longest answers (83.3 tokens on average), while Abstract Algebra demanded the highest thinking overhead (865.5 tokens on average). In free-response mode, average response lengths increase to 435.2 tokens. The shortest free-text answers (two tokens) occur in the Qwen 2.5 72B model on Philosophy, while the longest single output (37.575 tokens) was generated by the Cogito 8B reasoning model on Abstract Algebra. Thinking tokens average 859.2 per reasoning run. Zero-token reasoning traces appear when no intermediate text is needed (e.g. Cogito 70B reasoning on certain History items), whereas the maximum reasoning burden (6.716 tokens) is observed for the Deepseek R1 7B model on an Abstract Algebra prompt. Figure 2 presents the distribution of token counts for every model in all five subjects in the free-response phase.

Figure 2. Average number of tokens required by each LLM to answer questions across different subjects. For reasoning models, the number of reasoning tokens generated prior to providing the final answer is indicated in a darker color.

The total CO ₂eq emissions, expressed in grams of CO ₂ equivalent, required to process the complete set of 500 MMLU questions in both the constrained multiple-choice and free-response phases are illustrated in Figure 3. In the multiple-choice phase, emissions spanned from just 1.25 g CO ₂eq for the default Cogito 8B model to 717.31 g CO ₂eq for the Deepseek-R1 70B model. Reasoning-enabled variants generated substantially more emissions than their standard counterparts (e.g. Cogito 70B reasoning: 411.72g vs. Cogito 70B default: 8.20g), and larger models (70-72B) uniformly consumed on the order of 100-700 g, whereas compact 7-8B systems emitted below 180g. In the free-response phase, the range expanded further, from a low of 26.28 g CO ₂eq for Qwen 7B to a high of 1,325.12 g CO ₂eq for Deepseek-R1 70B. Again, reasoning modes incurred a 4 × -6 × increase in emissions compared to text-only modes (e.g. Cogito 8B reasoning: 371.87g vs. Cogito 8B default: 56.30g), and high-parameter models (70-72B) emitted several hundred grams more CO ₂ eq than their 7-8B counterparts (e.g. Qwen2.5 72B: 418.12g vs. Qwen2.5 7B: 60.63g). These trends underscore the environmental trade-off of scale and reasoning depth in large language models.

Figure 3. Total CO ₂ emissions (measured in grams) produced by each LLM when answering all 500 questions, reported separately for the multiple-choice and free-response scenarios.

When examining the combined CO ₂eq emissions and overall accuracy across all 1,000 questions ( Figure 4), clear trade-offs emerge between model scale, reasoning depth, and environmental cost. The smallest model, Qwen 7B, emits only 27.7 g CO ₂eq, by far the lowest footprint, but achieves just 32.9% accuracy. Conversely, the largest reasoning model, Deepseek-R1 70B, incurs 2,042.4 g CO ₂eq and reaches 78.9% accuracy. Notably, 12 of the 14 evaluated systems require less than 500 g CO ₂eq, yet none of these exceeds 80% accuracy. In contrast, the reasoning-enabled Cogito 70B emits 1,341.1 g CO ₂ eq, 34.3% less than Deepseek-R1 70B, while delivering 84.9% correct answers, representing a 7.6% improvement over its non-reasoning counterpart.

Figure 4. Combined CO ₂ eq emissions (measured in grams) and overall accuracy of each LLM across 1,000 questions. The results illustrate trade-offs between model size, reasoning depth, and environmental impact.

5 Discussion

The analysis of combined CO ₂eq emissions, accuracy, and token generation across all 1,000 questions reveals clear trends and trade-offs between model scale, reasoning complexity, and environmental impact. As model size increases, accuracy tends to improve. However, this gain is also linked to substantial growth in both CO ₂eq emissions and the number of generated tokens. The largest reasoning model, Deepseek-R1 70B, achieved 78.9% accuracy but emitted 2,042.4 g CO ₂eq, significantly surpassing smaller counterparts like Qwen 7B, which only consumed 27.7 g CO ₂ eq but provided just 32.9% accuracy.

Notably, the reasoning-enabled Cogito 70B model demonstrates a superior performance-efficiency balance, achieving the highest accuracy of 84.9%, a relative improvement of 7.6 percentage points over the Deepseek-R1 70B, while emitting 34.3% less CO ₂ eq (1,341.1 g). This suggests that adding a reasoning component to large models can substantially improve accuracy without proportionally escalating environmental impact.

Subject-wise analysis highlights significant variability in performance across different domains. Multiple-choice accuracy was consistently highest in High School World History, averaging 76.3% correct responses per model, likely due to the factual nature of the questions enabling easier recall or recognition. In contrast, Abstract Algebra posed the greatest challenge, averaging only 51.4% correct answers, reflecting its higher complexity and abstract conceptual demands. In the free-response phase, High School Mathematics was answered most successfully, averaging 69.4% correct responses per model, likely benefiting from explicit numerical computations. Conversely, Philosophy questions, requiring nuanced and subjective reasoning, presented substantial challenges with an average of only 52.1% correct responses.

The token generation analysis further underscores the computational cost of reasoning. On average, reasoning-enabled models required significantly more tokens in both testing phases. Particularly in the multiple-choice phase, reasoning models frequently struggled to produce concise answers, despite explicit prompts instructing them to only return the choice index. For instance, Deepseek-R1 7B generated up to 14,187 tokens on a single mathematical question, while standard models consistently produced minimal single-token responses. This trend persisted in the free-response phase, with the Cogito 8B reasoning model generating extremely verbose answers, such as a maximum of 37,575 tokens in Abstract Algebra, indicating an inherent challenge in controlling response verbosity when using reasoning prompts.

From an environmental perspective, reasoning models consistently exhibited higher emissions, driven primarily by their elevated token production. For instance, the Qwen 2.5 model with 72 billion parameters achieved strong performance with 77.6% accuracy while emitting only 426.8 g CO ₂ eq, less than one-third of the emissions of Cogito 70B with reasoning. This efficiency is partly due to its response generation remaining concise. While Qwen 2.5 72B produced on average 1.0 word per answer for the multiple-choice test phase, compared to the Cogito 70B reasoning model, which required an average of 145.1 response tokens and 450.9 thinking tokens per question in mathematics alone. Such findings highlight how reasoning capability, while beneficial for accuracy, significantly increases emissions through longer outputs. The environmental footprint of the Cogito 70B reasoning model was substantially larger compared to non-reasoning models of similar scale, yet it maintained a favorable efficiency balance due to significantly higher accuracy.

In conclusion, while larger and reasoning-enhanced models significantly outperform smaller counterparts in terms of accuracy, this improvement comes with steep increases in emissions and computational demand. Optimizing reasoning efficiency and response brevity, particularly for challenging subjects like Abstract Algebra, is crucial for advancing more sustainable and environmentally conscious artificial intelligence technologies.

6 Limitations

Although this study compares a diverse range of language models with varying architectures, training datasets, parameter counts, and reasoning routines, the findings are not easily transferable to other model families. Due to these structural and architectural differences, generalizing the results to models with significantly different designs is limited. Moreover, the current findings do not allow for reliable conclusions about the behavior of much larger LLMs (several hundred billion parameters). To draw robust conclusions about the relationship between CO ₂ eq emissions and accuracy at this scale, these models would need to be included in future analyses.

It should also be noted that all emissions were measured under a specific hardware and energy profile, namely, using an NVIDIA A100 80GB GPU and an emission factor of 480 gCO ₂/kWh. These values depend heavily on the chosen infrastructure and local energy grid, and results may vary significantly with different hardware setups or emission baselines. Therefore, the CO ₂ eq results presented here cannot be directly extrapolated to other systems.

Future work could extend this investigation by including a broader range of models, including those fine-tuned for specific tasks across diverse domains. For instance, it would be valuable to analyze whether models specialized in code generation perform better on programming tasks, how many parameters are required to achieve high accuracy, and whether such specialization leads to lower CO ₂ eq emissions compared to general-purpose LLMs.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

MD: Writing - original draft, Writing - review & editing. GS: Writing - original draft, Writing - review & editing.

Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that Gen AI was used in the creation of this manuscript. Generative AI was used to check for typos and wrong spelling.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Qwen, Yang, A., Yang, B., Zhang, B., Hui, B., Zheng, B., et al. (2025). Qwen2.5 technical report. arXiv [Preprint]. arXiv:2412.15115. doi: 10.48550/arXiv.2412.15115

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