13 June 2022

Data promises to support climate action. Is it a double-edged sword?

by Kate Iida

Data sharing can support research into reducing carbon emissions, and open data on greenhouse gas emissions can help hold companies accountable to their sustainability pledges. But there are also concerns about the tech sector’s own carbon emissions.

‘Climate change’ by Quote Catalog. Photo from Flickr, CC-BY-2.0.

On September 9, 2020, I woke to an eerie, orange-tinted darkness. Flakes of ash blanketed the garden outside my northern California home. An hour away, residents saw the San Francisco skyline bathed in an apocalyptic orange glow, caused by the 500,000 acres of wildfire blazing across the state.

For days, my family and I had sheltered indoors, avoiding dangerous levels of smoke in the air. We were lucky not to receive evacuation orders, but we couldn’t help feeling that the dark sky was a sign of the impending climate crisis.

Experts say that keeping global warming below 1.5 degrees celsius, to avoid the worst effects of climate change, requires “rapid, far-reaching and unprecedented changes in all aspects of society.”

To do this, carbon emissions need to reach ‘net zero’ by 2050, the United Nations Intergovernmental Panel on Climate Change has said. Net zero essentially means “not adding to the greenhouse gases in the atmosphere.”

Towards this goal, in April the UK Government announced a new target of cutting carbon emissions by 78% by 2035 as compared to 1990 levels, keeping the country on track to meet net zero by 2050.

But how can countries like the UK meet their net zero goals?

According to a report by Arup and the Open Data Institute, open data, including information about carbon emissions and energy use, can help reduce emissions and avoid the worst-case climate scenario.

The term ‘open data’, according to The World Bank, means “data or content […] that is free to use, re-use or redistribute.” Open data must have minimal to no legal restrictions on its use, and be published in machine readable formats that anyone can access and use with free software.

Sharing data with researchers can help support investigations into policies that reduce emissions, and requiring companies to openly publish data on their greenhouse gas emissions can also help hold companies accountable to their sustainability pledges. But there are also concerns about the carbon emissions caused by big data and the tech sector itself.

Data is already being shared with researchers to help them explore public policies for reducing greenhouse gas emissions. One team in the Data for Climate Action competition, for example, used data provided by WAZE, EPA MOVES-Mexico, and Google Places’ Popular Times on congestion in Mexico City to estimate the carbon emissions caused by traffic jams in the capital.

The team then determined that if Mexico City implemented a policy to make transit buses run on electric power, this would reduce CO2 emissions by about a quarter. The competition demonstrated how sharing data can provide a new and powerful resource for innovative public policy research, and in this case leads to a strong argument for electric powered public transport.

Sustainability pledges

Open data in particular can have an important environmental benefit: it can help hold organisations accountable in keeping to their sustainability pledges.

On February 14th 2022, news broke that several large banks, including HSBC, Barclays and Deutsche Bank have invested billions of dollars in the expansion of oil and gas, despite promising to support the move to net zero as part of the Net Zero Banking Alliance.

This incident demonstrates a long running problem with private-sector net zero pledges: it’s hard to find out if companies actually follow their public commitments to reduce emissions, or to hold them accountable if they don’t. This is greenwashing – ‘the practice of businesses or investment funds making misleading or unsubstantiated claims about environmental performance’.

Requiring private companies to publicly share data about their carbon emissions and the environmental impact of the projects they’re funding will make it easier to track the sustainability changes made by the private sector. This, in turn, will help keep companies accountable in making progress towards their stated climate goals.

Costa Rica’s National System of Climate Change Metrics demonstrates the steps governments can take to make this happen. Their open data platform includes information on a wide range of drivers for climate change, risks and responses.

Gavin Starks, founder of the UK’s Icebreaker One initiative, has argued for automating the reporting of data on greenhouse gas emissions. This, he writes, could help countries ensure that they are on track to reach their net zero goals, ‘rather than waiting for the climate data to tell us that we’re not’.

Requiring private companies to publish data about their carbon emissions could also help to address one of the major concerns about using data and technology to support sustainability: the environmental impact of the tech sector itself.

Environmental costs of big data

Any solution to the climate crisis that looks to data and technology as an answer, however, must first address the tech sector’s environmental impacts.

In the report, Digital technology and the planet, The Royal Society recommends that the technology sector should be open about the impact of its activities on the climate. ‘The tech sector should lead by example and make data accessible’, The Royal Society proposes, ‘to allow the greater monitoring of its energy consumption and carbon emissions’.

Data centres – buildings which contain many powerful computers and the systems needed to keep them running so that large amounts of data can be processed – consume significant amounts of electricity.

The International Energy Agency says that data centres contribute 0.3% to global carbon dioxide emissions, and are responsible for 1% of the global demand for electricity.

Many data centres still run on fossil fuels. Atlas of AI author Kate Crawford writes that ‘China’s data centre industry draws 73 percent of its power from coal, emitting about 99 million tons of CO2 in 2018’.

And that doesn’t include other environmental impacts of technology, such as the pollution caused by mining for minerals like lithium, cobalt and the rare earth elements used in batteries, mobile phones and laptops.

Researchers are working to find ways to make data centres more sustainable, such as building them in cool climates or using water cooling systems that use less energy to keep the centres at appropriate temperatures. Others, such as the Condorcet data centre in Paris and and IBM data centre in Switzerland, use waste heat produced by the centres as a power source for nearby areas. Facebook, Google and Apple, furthermore, have all made commitments to use entirely renewable energy.

But, as The Royal Society expresses, to meet net zero ‘the tech sector will need to achieve actual zero emissions’. That’s still a long way off.

Yes, open data can help to support initiatives to reduce carbon emissions and hold companies accountable to their sustainability commitments. Investing in more data and data processing capabilities, however, needs to be measured against its environmental impacts.

Above all, researchers and organisations need to honestly evaluate ‘whether specific data and computing applications bring environmental or social benefits that outweigh their own emissions’.

If we’re to stop or reverse environmental damage like the California wildfires, companies around the world need to follow through on their sustainability commitments. More shared and open data arguably won’t solve the climate crisis on its own, but it can help the cause.


More insights

21 April 2017

Why Government is ready for AI

12 July 2017

Five levels of AI in public service

26 July 2017

Making it personal: civil service and morality

10 August 2017

AI: Is a robot assistant going to steal your job?

19 September 2017

AI and legitimacy: government in the age of the machine

06 October 2017

More Than The Trees Are Worth? Intangibles, Decision-Making, and the Meares Island Logging Conflict

16 October 2017

The UK Government’s AI review: what’s missing?

23 October 2017

Why unconference? #Reimagine2017

03 November 2017

AI: the ultimate intern

09 November 2017

Motherboard knows best?

23 November 2017

Beyond driverless cars: our take on the UK’s Autumn Budget 2017

05 December 2017

Why Black people don’t start businesses (and how more inclusive innovation could make a difference)

06 December 2017

“The things that make me interesting cannot be digitised”: leadership lessons from the Drucker Forum

23 January 2018

Want to get serious about artificial intelligence? You’ll need an AI strategy

15 February 2018

Economic disruption and runaway AI: what can governments do?

26 April 2018

Ranking governments on AI – it’s time to act

08 May 2018

AI in the UK: are we ‘ready, willing and able’?

24 May 2018

Mexico leads Latin America as one of the first ten countries in the world to launch an artificial intelligence strategy

05 July 2018

Beyond borders: talking at TEDxLondon

13 July 2018

Is the UK ready, willing and able for AI? The Government responds to the Lords’ report

17 July 2018

Suspending or shaping the AI policy frontier: has Germany become part of the AI strategy fallacy?

27 July 2018

From open data to artificial intelligence: the next frontier in anti-corruption

01 August 2018

Why every city needs to take action on AI

09 August 2018

When good intentions go bad: the role of technology in terrorist content online

26 September 2018

Actions speak louder than words: the role of technology in combating terrorist content online

08 February 2019

More than STEM: how teaching human specialties will help prepare kids for AI

02 May 2019

Should we be scared of artificial intelligence?

04 June 2019

Ethics and AI: a crash course

25 July 2019

Dear Boris

01 August 2019

AI: more than human?

06 August 2019

Towards Synthetic Reality: When DeepFakes meet AR/VR

19 September 2019

Predictive Analytics, Public Services and Poverty

10 January 2020

To tackle regional inequality, AI strategies need to go local

20 April 2020

Workshops in an age of COVID and lockdown

10 September 2020

Will automation accelerate what coronavirus started?

10 September 2020

Promoting gender equality and social inclusion through public procurement

21 September 2020

The Social Dilemma: A failed attempt to land a punch on Big Tech

20 October 2020

Data and Power: AI and Development in the Global South

23 December 2020

The ‘Creepiness Test’: When should we worry that AI is making decisions for us?

30 September 2022

Towards a human-centred vision for public services: Human-Centred Public Services Index

06 October 2022

Why You Should Know and Care About Algorithmic Transparency

26 October 2022

Harnessing data for the public good: What can governments do?

09 December 2022

Behind the scenes of the Government AI Readiness Index

06 February 2023

Reflections on the Intel® AI for Youth Program

01 May 2023

Canada’s AI Policy: Leading the way in ethics, innovation, and talent

15 May 2023

Day in the life series: Giulia, Consultant

15 May 2023

Day in the life series: Emma, Consultant

17 May 2023

Day in the life series: Kirsty, Head of Programmes

18 May 2023

Day in the life series: Sully, Partnerships Associate/Consultant

19 May 2023

LLMs in Government: Brainstorming Applications

23 May 2023

Bahrain: Becoming a regional R&D Hub

30 May 2023

Driving AI adoption in the public sector: Uruguay’s efforts on capacity-building, trust, and AI ethics

07 June 2023

Jordan’s AI policy journey: Bridging vision and implementation

12 June 2023

Response to the UK’s Global Summit on AI Safety

20 June 2023

 Unlocking the economic potential of AI: Tajikistan’s plans to become more AI-ready

11 July 2023

Government transparency and anti-corruption standards: Reflections from the EITI Global Conference in Dakar, Senegal

31 August 2023

What is quantum technology and why should policymakers care about it?

21 September 2023

Practical tools for designers in government looking to avoid ethical AI nightmares

23 October 2023

Collective Intelligence: exploring ‘wicked problems’ in National Security

23 October 2023

Exploring the concepts of digital twin, digital shadow, and digital model

30 October 2023

How to hire privileged white men

09 November 2023

Inclusive consensus building: Reflections from day 4 of AI Fringe

13 November 2023

AI for Climate Change: Can AI help us improve our home’s energy efficiency?

14 November 2023

Navigating the AI summit boom: Initial reflections

20 November 2023

AI for Climate Change: Improving home energy efficiency by retrofitting

24 November 2023

Will AI kill us all?

27 November 2023

AI for Climate Change: Preventing and predicting wildfires 

28 November 2023

Service Design in Government 2023: conference reflections

04 December 2023

AI for Climate Change: Using artificial and indigenous Intelligence to fight climate change

06 December 2023

Release: 2023 Government AI Readiness Index reveals which governments are most prepared to use AI

11 December 2023

AI for Climate Change: AI for flood adaptation plans and disaster relief

18 December 2023

AI for Climate Change: Managing floods using AI Early Warning Systems

28 May 2024

Strengthening AI Governance in Chile: GobLab UAI and the Importance of Collaboration

05 June 2024

Towards a Digital Society: Trinidad and Tobago’s AI Ambitions