ARTIFICIALINTELLIGENCEFOR
CLIMATE CHANGEMITIGATIONROADMAP
December 2023

Innovation for Cool Earth ForumICEF Roadmap 2023: AI for Climate Change Mitigation

AUTHORSDavid Sandalow
Chair, ICEF Innovation Roadmap Project
Columbia University

Colin McCormick
Georgetown University and Carbon Direct

Alp Kucukelbir
Columbia University and Fero Labs

Julio Friedmann
Carbon Direct

Trishna Nagrani
Climeworks

Zhiyuan Fan
Columbia University

Antoine Halff
Columbia University and Kayrros

Alexandre d'Aspremont
Centre National de la Recherche Scientifique and Kayrros

Ruben Glatt
Lawrence Livermore National Laboratory

Elena Méndez Leal
Columbia University

Kevin Karl
Columbia University

Alex Ruane
NASA Goddard Institute for Space Studies

Cite report as: David Sandalow, Colin McCormick, Alp Kucukelbir, Julio Friedmann, Trishna Nagrani,
Zhiyuan Fan, Antoine Halff, Alexandre d’Aspremont, Ruben Glatt, Elena Méndez Leal, Kevin Karl and Alex
Ruane, Artificial Intelligence for Climate Change Mitigation Roadmap (ICEF Innovation Roadmap Project,
December 2023). Available at https://www.icef.go.jp/roadmap/.

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PREFACE
Artificial intelligence (AI) is a hot topic. One business leader recently called it “the defining
technology of our time.” Another said “It is difficult to think of a major industry that AI will nottransform.”1
Meanwhile countries around the world are struggling to respond to the challenge of climate change.
Despite encouraging developments, including steep declines in the price of renewable power, global
emissions of greenhouse gases keep rising. Scientists warn that heat waves, floods, droughts andsevere storms will get far worse in the decades ahead unless we change course.
Can AI help cut emissions of greenhouse gases? This roadmap explores that question. Our goal is toprovide a useful resource for experts and non-experts alike. In Part I of the roadmap, we provide
brief introductions to both AI and climate change. In Part II, we explore six areas in which AI is
helping respond to climate change and could do much more. In Part III, we explore cross-cutting
barriers, risks and policies. We finish with findings and recommendations, including the following:
•

•

•
•
•

AI could make significant contributions to climate mitigation. Areas of especial opportunity
include greenhouse gas emissions monitoring, the power sector, manufacturing, materials
innovation, the food system and road transport.
Barriers to AI helping reduce greenhouse gas emissions include the lack of trained personnel
and access to high-quality data. All institutions with a role in climate change mitigation
should prioritize AI skills-development and explore ways to develop and share relevant data.
Risks related to AI – including bias, privacy and safety issues – require careful attention when
AI is used to reduce emissions.
Greenhouse gas emissions from computing infrastructure for AI are currently modest. The
amount of such emissions in the years ahead is highly uncertain.
All government agencies with responsibility for climate change should create an Artificial
Intelligence Office with responsibility for assessing opportunities, barriers and risks withrespect to AI in all aspects of the agency’s mission.

The relationship between AI and climate change is a big topic. Among the questions we do not
explore in this roadmap are (1) how AI could contribute to climate change adaptation (an importantarea for work and study) and (2) whether the broad societal forces that AI may unleash are more
likely to help or hinder the response to climate change (a difficult question in light of the many
uncertainties with respect to AI’s impacts in the years ahead). Instead, we aim to provide a resourcethat will make favorable outcomes more likely, pointing toward ways in which AI can contribute toclimate solutions.

1“15 Quotes,” ai.nl (January 3, 2022). https://www.ai.nl/artificial-intelligence/15-quotes-from-technology-

leaders-about-artificial-intelligence-ai/
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This roadmap builds on the body of literature produced annually in connection with the ICEF
conference. Previous roadmaps have addressed the following topics:••

Low-Carbon Ammonia (2022)
Blue Carbon (2022)

•
•
•
•

Carbon Mineralization (2021)
Biomass Carbon Removal and Storage (BiCRS) (2020)
Industrial Heat Decarbonization (2019)
Direct Air Capture (2018)•••

Carbon Dioxide Utilization (2017 and 2016)
Energy Storage (2017)
Zero Energy Buildings (2016)• Solar and Storage (2015)
This roadmap is a team effort. We are deeply grateful for the support provided by the ICEF
Secretariat, the ICEF Steering Committee (including in particular its chair, Nobuo Tanaka), the New
Energy and Industrial Technology Development Organization (NEDO), experts at the Institute ofEnergy Economics – Japan, and our design and copy edit team (including in particular Ms. Jeannette
Yusko, Dr. Kathryn Lindl, Dr. Melanie Mendez and Ms. Cat Lee).
The ICEF Innovation Roadmap Project aims to contribute to the global dialogue about solutions tothe challenge of climate change. We welcome your thoughts, reactions and suggestions.
David Sandalow
Chair, ICEF Innovation Roadmap Project
Inaugural Fellow, Center on Global Energy Policy,
Columbia University

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TABLE OF CONTENTSEXECUTIVE SUMMARY

1PART I – BACKGROUND
Chapter 1. INTRODUCTION TO ARTIFICIAL INTELLIGENCE

8

Alp Kucukelbir
A. What is AI?

8

B. How does AI work?

10

C. What is AI capable of?

11

D. What kind of data does AI need?

12

E. Why is AI developing so rapidly?

15

F. Further reading

16

Chapter 2. INTRODUCTION TO CLIMATE CHANGE

18David Sandalow, Trishna Nagrani and Julio Friedman
A. Climate change background18

B. Contributions of artificial intelligence to climate science20

C. Further reading

22

PART II – HIGH-POTENTIAL OPPORTUNITIES
Chapter 3. GREENHOUSE GAS EMISSIONS MONITORING

25

Antoine Halff, Colin McCormick and Alexandre d'Aspremont
A. Incumbent GHG emissions monitoring

25

B. AI-enabled GHG emissions monitoring

26(i) Methane emissions(ii) Carbon dioxide emissions(iii) Barriers(iv) Next Steps
C. Conclusion
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ICEF Roadmap 2023: AI for Climate Change Mitigation

Chapter 4. POWER SECTOR

37

Zhiyuan Fan and David Sandalow
A. Generation infrastructure

38

B. Transmission and distribution infrastructure40

C. End uses

41

D. Energy-storage infrastructure

42

E. Barriers and risks

43

F. Conclusion

44

Chapter 5. MANUFACTURING SECTOR

49

Alp Kucukelbir
A. How can AI help decarbonize manufacturing?

49

B. Common applications of AI in manufacturing

50(i) Decarbonizing the process of making things(ii) Decarbonizing supply chains and adopting dematerialization strategies(iii) Decarbonizing the impact of maintaining industrial equipment
C. Barriers and risks for AI in manufacturing

Chapter 6. MATERIALS INNOVATION

54

58

Colin McCormick
A. Materials innovation in climate technologies

59

B. Computational materials development

61

C. Applications of AI in materials discovery and design

62

D. Barriers

63

Chapter 7. FOOD SYSTEMS

67

Elena Méndez Leal, Kevin Karl and Alex Ruane
A. Food system greenhouse gas emissions

67

B. Using AI to mitigate food system emissions

68

C. Barriers and risks to AI applications in food systems

71

D. Key considerations in using AI to mitigate food-system emissions

72

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Chapter 8. ROAD TRANSPORT

75

Ruben Glatt and David Sandalow
A. Using AI to cut road transport emissions

76(i) Batteries(ii) Biofuels(iii) Intelligent transportation systems and smart infrastructure(iv) Modal shift
B. Barriers and Risks

82

PART III – CROSS-CUTTING TOPICS
Chapter 9. BARRIERS

87

Alp Kucukelbir and David Sandalow
A. Data87

B. People

88

C. Computing power88

D. Cost89

E. Institutions

89

Chapter 10. RISKS

91

David Sandalow, Julio Friedmann and Alp Kucukelbir
A. Bias91

B. Privacy

92

C. Security

93

D. Safety

94

E. Greenhouse gas emissions

95(i) Background(ii) Current GHG emissions from AI(iii) Future GHG emissions from AI(iv) Conclusion

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Chapter 11. POLICY

102

David Sandalow
A. Promoting use of AI for climate mitigation

103(i) Data(ii) People(iii) Computing power(iv) Cost(v) Institutions
B. Managing Risks

108(i) Bias(ii) Privacy(iii) Greenhouse gas emissions
Box: AI Policies in Brief

Chapter 12. FINDINGS AND RECOMMENDATIONS

111

118

A. Findings

118

B. Recommendations

120

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EXECUTIVE SUMMARY
Chapter 1 – INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Artificial intelligence (AI) is the science of making computers perform complex tasks typicallyassociated with human intelligence. Modern AI relies on machine learning (ML)—a type of softwarein which algorithms detect patterns from large datasets without being explicitly programmed. Thisdiffers from traditional software, which requires explicit programming of domain knowledge. AI
instead relies on implicit programming by using historical data and simulations to "train" models to
extract patterns.
Access to large, high-quality datasets is important for complex real-world applications of AI. These
data can come from various public and private sector organizations. Tabular, time series, geospatial
and text data are all commonly used in AI. Data must be properly measured, digitized and accessible
for AI applications.
The release of ChatGPT in November 2022 generated extraordinary public attention to AI. ChatGPT
quickly became the most rapidly adopted product in human history, with more than 100 million
users by January 2023. The website now receives roughly 1.8 billion visits per month. Large language
models (LLMs), such as ChatGPT, are one type of AI system.

Chapter 2 – INTRODUCTION TO CLIMATE CHANGE
Atmospheric concentrations of heat-trapping gases are now higher than at any time in humanhistory. This is changing the Earth’s climate. July 2023 was the hottest month ever recorded. The
nine warmest years ever recorded have been the last nine years. Severe storms, droughts, floods andwildfires—all made more likely by global warming—have caused extraordinary damage in recent
years. Sea-level rise threatens coastal cities around the world.
The Paris Agreement—adopted by over 190 nations in 2015—calls for holding the global averagetemperature increase to well below 2 °C (3.6 °F) above pre-industrial levels and pursuing efforts tolimit the increase to 1.5 °C (2.7 °F). The world is not on a path to achieve these goals. Policies
currently in place would result in a global average temperature increase of roughly 3 °C (5.4 °F) by
2100, and many of these policies are not being fully implemented.
AI is making important contributions to scientific understanding of climate change. AI is improving
climate-model performance, providing more advanced warning of extreme weather events andhelping attribute extreme weather events to the increase in heat-trapping gases in the atmosphere.
AI’s contributions to climate science will grow in the years ahead.

Chapter 3 – GREENHOUSE GAS EMISSIONS MONITORING
Good information on the sources of greenhouse gas (GHG) emissions is essential for responding toclimate change. AI is helping to significantly improve such information by analyzing vast amounts ofdata from earth-observation satellites, airplanes, drones, land-based monitors, the Internet of Things(IoT), social media and other technologies.
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AI has been particularly important for improving methane-emissions monitoring. AI helps to (1)process data from methane sensors at scale, (2) combine input from multiple satellites to overcome
each particular satellite’s limitations and (3) integrate satellite information with data generated byother types of sensors to build multi-scale monitoring and reporting systems.
AI is being used to better understand the sources of CO2 emissions as well. AI algorithms can betrained to survey the world’s vegetation at high spatial resolution and to precisely measure theamount of biomass carbon sequestered in forestry and other forms of vegetation, at scale and at anegligible cost.
Barriers to using AI for emissions monitoring include lack of AI literacy, conflicting data, sovereignty
concerns and uncertain financial models for providing AI-enabled GHG emissions data. Measures tohelp address these barriers include promoting AI literacy, establishing best practices andmechanisms to validate AI-enabled GHG emissions data, setting up one or more global “owners” of
AI-enabled GHG emissions data and elevating AI for climate in international dialogue andnegotiations.

Chapter 4 – POWER SECTOR
More than a third of global carbon dioxide (CO2) emissions come from the power sector. AI isbecoming an essential part of this sector due to the power grid’s complexity and rapid growth inpower sector data. AI can help with scenario development, short-term predictions based on timeseries data, optimization problems and systems integration.
AI has the potential to make decarbonization of the power sector cheaper, faster and smoother.
Opportunities abound in generation infrastructure, transmission and distribution networks, end-use
sectors and energy storage. Examples include:•••

determining the optimal size and location of solar- and wind-power projects;
predicting weather relevant to solar and wind generation;
improving fault detection, outage forecasting and stability assessments on distribution grids
and•facilitating deployment of demand response and vehicle-to-grid programs

Several barriers limit adoption of AI for decarbonizing the power sector. AI models and methods are
not yet sufficiently robust or well-developed for widespread deployment, standards for performance
evaluation are lacking, and knowledgeable workers are in short supply. Security risks must be studied
and properly addressed before deploying AI for most grid infrastructure.

Chapter 5 – MANUFACTURING SECTOR
The manufacturing sector accounts for roughly one-third of global GHG emissions. AI has significant
potential to help decarbonize manufacturing by optimizing existing industrial processes andoperations in cost-effective ways.
For example, AI can play an important role in steelmaking with electric arc furnaces—an important
decarbonization technology in which steel is made with recycled scrap metal instead of coal. AI can

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help address the variability in each batch of scrap metal, recommending optimal production settings
to adapt to the variability. Using AI tools, one Brazilian steel manufacturer achieved an 8% reduction
in alloy additive consumption using AI, cutting both costs and emissions.
More broadly, AI can help decarbonize manufacturing by enabling manufacturers to adapt to
production issues faster and better, avoid past mistakes by leveraging historical data, improve
production yields, promote recycling and circularity by adapting to variable recycled feedstocks,
minimize energy consumption, adopt alternative energy sources and optimize manufacturingschedules and supply chains to reduce logistical overhead.

Chapter 6 – MATERIALS INNOVATION
Advanced materials with special properties are essential for decarbonizing many parts of theeconomy. Products including catalysts, battery anodes, solar photovoltaics, wind turbine blades,
refrigerants, superconductors, carbon-capture sorbents and high-strength magnets depend on
advanced materials.
Historically, advanced materials have been discovered through accident or tedious, expensive trial
and error. Advances in computing power and material-science theory enabled a transition to a morecomputational basis for materials discovery several decades ago. However, the methods for
identifying potentially valuable novel materials through computation require large computing
resources and are still too slow to fully meet the needs of materials innovation for a decarbonizedeconomy.
Computational materials science has begun using AI methods, and they are already having animportant impact. In some cases, AI models can replace fully science-based computations, greatly
speeding up processing times. AI can also help interpret results of material-characterization
experiments, enabling rapid, high-throughput testing of advanced materials candidates. Naturallanguage AI can scour the vast materials-science technical literature, summarizing thousands ofpublished research articles to enable rapid, accurate literature reviews and surface harmonizedprocess steps for materials production. Most recently, generative AI methods have begun to suggest
entirely novel classes of advanced materials that had not previously been envisioned as relevant toemissions-reduction applications. While these advances are highly promising, much better
integration between materials science and AI research is still needed to realize the full potential forclimate mitigation.

Chapter 7 – FOOD SYSTEMS
Food systems—including food production, processing distribution, consumption and disposal—are
critical to health and livelihood worldwide. Food systems are responsible for more than 30% of global
GHG emissions. Climate change, in turn, has a significant impact on food systems.
AI has significant potential to help reduce GHG emissions in food systems, including by (1) integrating
data from multiple sources—such as soil sensors and satellites—to recommend fertilizer application
schedules that mitigate nitrous oxide emissions while maximizing crop yields; (2) anticipating futureneeds for precision fertilizer applications under a range of projected climate conditions; (3) analyzing

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data on biomass characteristics, growth rates and carbon-sequestration potential to optimize
feedstocks for biomass carbon removal and storage (BiCRS); (4) increasing renewable energy
generation by optimizing land use for multiple purposes; (5) forecasting pest and disease pressure;(6) developing alternative protein products, which have a much lower carbon footprint than animalsourced foods, and (7) reducing food loss and waste through intelligent harvest-timing to prevent
food spoilage.
AI provides a set of complementary tools for reducing food-system emissions, not a solution in itself.
Proper use of AI technologies should be grounded in scientific knowledge, physical constraints, welldefined public-policy objectives, ethical considerations and a nuanced understanding of the complex
operations of food-system stakeholders.

Chapter 8 – ROAD TRANSPORTRoad transport is a critical part of the global economy. Current modes of road transport rely heavily
on fossil fuels, producing roughly 18% of global energy-related CO2 emissions.
AI has significant potential to help reduce GHG emissions from road transport. AI can play animportant role in several important areas including (1) batteries, (2) sustainable biofuels, (3)intelligent transportation systems and (4) shifts toward modes of transportation that emit lesscarbon.
While the potential of AI in revolutionizing road transportation is immense, several barriers and risks
must be addressed. Barriers include a lack of data, the absence of uniform standards for data and ashortage of personnel with training in AI. Risks include bias, invasion of privacy and increases in GHG
emissions caused by deployment of autonomous vehicles, which are likely to increase total vehicle
miles traveled.

Chapter 9 – BARRIERSFive groups of barriers impede the use of AI for climate change mitigation: data, people,
computation, cost and institutions.
Data and people barriers are among the most significant. AI depends on available, accessible andstandardized data; such data are often lacking. The shortage of trained personnel can also be asignificant barrier. AI for climate mitigation requires skilled AI developers, collaboration between
those developers and experts in diverse fields (such as atmospheric chemistry, materials science,electrical engineering, finance and political science), and users who are broadly educated on thebasics of AI.
Other important barriers can include a lack of computing power to train, tune and run AI models; alack of financial resources; and a lack of leadership attention or clear AI policies within organizations.

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Chapter 10 – RISKS
Risks from using AI can include bias, invasion of privacy, security, safety and increased GHG
emissions.
Bias-related risks when using AI for climate mitigation include prioritizing certain groups due todifferences in data availability. Privacy-related risks include unauthorized data leaks to third parties,
personal identification and even surveillance. Security risks can arise when AI systems expand theattack surface for hackers beyond that found in conventional software programs. Safety risks can beserious when AI systems fail to work as intended or have unintended consequences. Security andsafety risks are especially acute if AI is used for real-time decision-making (such as in operating
factories or the electric grid).
GHG emissions from AI computing operations are currently modest—significantly less than 1% of the
global total. Better data collection and assessment methodologies are needed to provide a more
precise estimate with high confidence. The amount of future GHG emissions related to AI is highly
uncertain. In some scenarios, GHG emissions from AI decline in the years ahead. In other scenarios,
such emissions increase significantly.

Chapter 11 – POLICY
Government policies with respect to AI are evolving rapidly. Policymakers around the world areconsidering a range of topics with respect to AI, including security, bias, privacy, job displacement
and international competitiveness.
Very few policies that specifically address the use of AI for climate mitigation have been adopted todate. Those policies fall into two broad categories: (1) policies that promote the use of AI for climate
mitigation and (2) policies that manage risks related to the use of AI for climate mitigation.
Governments could promote the use of AI for climate mitigation by addressing barriers related todata, people, computing power, cost and institutions. Such policies could include funding collectionof climate-related data, encouraging or requiring standardization and harmonization of climaterelated data, launching AI skills-development programs, making computing infrastructure available
for projects that use AI for climate change mitigation, creating AI offices within government
ministries, and using international institutions—such as the Clean Energy Ministerial and World
Meteorological Organization—as platforms for international cooperation on using AI for climate
mitigation.
Governments could help manage risks related to the use of AI for climate mitigation, including bias,
privacy and increased emissions. Such policies could include standards requiring diverse andrepresentative data sets for AI models; standards with respect to transparency in the development ofAI models; legal frameworks that hold entities accountable for biased outcomes resulting from AI
applications; requiring that privacy considerations be expressly integrated in the design of AI models;establishing independent oversight boards responsible for monitoring privacy protections related to
AI and climate mitigation; investing in research and development on energy-efficient AI algorithms
and hardware; and promoting low-carbon data centers.

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Chapter 12 – FINDINGS AND RECOMMENDATIONSFINDINGS
1. AI is currently contributing to climate change mitigation in important ways.
2. AI has the potential to make significant additional contributions to climate change mitigationin the years ahead.
3. AI is not a panacea when it comes to climate change.
4. The lack of trained and experienced personnel is a critical barrier to using AI for climate
mitigation.
5. The lack of access to high-quality data is a critical barrier to using AI for climate mitigation.
6. Other barriers to using AI for climate mitigation include cost, lack of available computing
power and institutional issues.
7. Significant resources—by governments, corporations and other stakeholders—will be
required for AI to reach its potential in helping mitigate climate change.
8. General risks of using AI include bias, invasion of privacy and security issues. These risks also
exist when using AI for climate mitigation.
9. GHG emissions from computing infrastructure for AI are currently modest—significantly lessthan 1% of the global total.
10. The amount of future GHG emissions from AI computing infrastructure is highly uncertain.

RECOMMENDATIONS
1. AI tools should be integrated into many aspects of climate change mitigation.
2. AI skills-development and capacity-building should be a priority in all institutions with a rolein climate mitigation.
A. Educational institutions at all levels should offer courses relevant to AI.
B. Governments and foundations should launch AI-climate fellowship programs.
C. Government agencies with responsibility for climate issues should regularly review theirstaffs’ AI capabilities.
D. All organizations working on climate mitigation should require minimum AI literacy froma broad cross-section of employees.
3. Governments should assist in developing and sharing data for AI applications that mitigate
climate change.
A. Governments should systematically consider opportunities to generate and share datathat may be useful for climate mitigation.
B. Governments should establish policies to promote standardization and harmonization ofclimate and energy-transition data.
C. Governments should establish climate-data task forces composed of key stakeholders
and experts.

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4. Companies with datasets relevant to climate change mitigation should consider sharing
portions of those datasets publicly.
5. Governments should provide substantial funding for developing and applying AI tools forclimate mitigation.
A. Government funding for AI in the climate area should focus on emissions-reduction
potential, not just new AI methods.
B. Governments should help increase the availability of computing power for climatechange–related AI projects.
6. All government agencies with responsibility for climate change, including environment andenergy ministries, should create an Artificial Intelligence Office, with responsibility forassessing opportunities, barriers and risks with respect to AI in all aspects of the agency’s
mission.
7. Electric utilities should be incentivized to deploy AI, with regulated returns for investments in
AI and other tools.
8. Governments should launch international platforms to support cooperative work on AI forclimate change mitigation.
A. One or more member countries should launch a Clean Energy Ministerial initiative on AI
and climate mitigation.
B. The UN Framework Convention on Climate Change (UNFCCC), International Energy
Agency (IEA) and Food and Agriculture Organization (FAO), among other organizations,should build AI-for-climate issues centrally into their work programs.
C. One or more global organizations should be tasked with helping to reconcile any
conflicting AI-enabled data on GHG emissions.
9. Governments should work to minimize GHG emissions from AI’s computing infrastructure.
10. Avoiding unfair bias should be a core, high-priority principle guiding development of all AI
tools for climate change mitigation.
11. Governments should address privacy risks related to AI-climate programs with dataprotection regulations, cybersecurity standards, oversight boards and techniques that makepersonal data less identifiable.

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Chapter 1:

INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Alp Kucukelbir

Artificial intelligence (AI) is part of our everyday lives. Email providers use AI to filter spam.
Postal services apply AI to route hand-written envelopes. Technology companies leverage
AI to identify faces in photographs, while radiologists reach for AI to interpret medicalscans. Economists use AI to forecast elections, and digital retailers turn to AI to optimize
prices.1, 2
The release of ChatGPT in November 2022 generated extraordinary public attention to AI.
ChatGPT quickly became the most rapidly adopted product in human history, with morethan 100 million users by January 2023. The website now receives more than 1.5 billion
visits per month.3, 4 This increased attention has led to questions about how AI could help
address major global challenges, including climate change—the topic of this report.

A. What is AI?
AI is the science of making computers
perform complex tasks typicallyassociated with human intelligence.
Modern AI relies on a branch ofcomputer science called machinelearning (ML). ML refers to a set ofalgorithms that detects patterns fromlarge and sometimes messy data
without explicit programming (i.e.,
without a human-crafted descriptionof each pattern). This is a task often
associated with human learning—for Figure 1-1. A visualization of a deep neural network, a type of AI
model that powers popular AI systems such as ChatGPT.
example, learning to walk, speak or
identify objects.
How does AI differ from traditional computation? Consider a computer program that plays
chess. The traditional approach to building such an algorithm involves explicitly
programming the rules of chess, encoding basic principles of good game play, andspecifying a method to search over all possible moves to pick the best one. Even in a gameas seemingly simple as chess, this is an enormous task for a computer—the number ofchess positions is about the same as the number of atoms on Earth.5 (For the curious, thatis about 1,000,000,000,000,000,000,000,000,000,000, 000,000,000,000,000,000

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positions.) No existing computer, even the most powerful supercomputing clusters, canefficiently play chess this way.
Now consider an AI approach to playing chess. The core idea is to replace human input onwhat constitutes good strategy with a system that only uses the rules of the game to playagainst itself to find good strategies. Leveraging clever mathematics that significantly
reduce the need to search over all possible moves, an AI system can efficiently simulate
games against itself millions of times. This repeated simulation enables the AI system to“learn” the principles of good play, in a way that exceeds the ability of humanprogrammers to explicitly encode them in software. This approach to AI uses branches ofML known as deep neural networks (see Figure 1-1) and reinforcement learning, which are
ideally suited to problems where simulation plays a prominent role.6 Table 1 summarizes
the key difference between AI and traditional software.
Supervised and unsupervised ML are two other ways to build AI systems—both rely on
historical data to “learn” patterns.
••

Supervised learning requires historical data with labels or explicit targets. One
common example includes handwritten digit recognition—used by many postal
services around the world—which pairs many thousands of scanned pictures ofwritten digits with their corresponding number to “train” the AI system.
Unsupervised learning only requires historical data, without any corresponding labels.
The AI system is trained to search for patterns and associations hidden in the data
itself. This form of AI is commonly used in recommendation engines, which cansuggest movies you might like based on movies you have previously watched andhistorical patterns of the likes and dislikes of other people watching similar movies.

Requirements

Outputs

Traditional software

Artificial intelligence (AI)No historical data needed

Historical data or simulator

Explicit programming of domain
knowledge

Implicit programming of expectationsof patterns from data

No “training” needed (everything is
explicitly programmed)

Need to “train” the AI algorithm toextract patterns

Deterministic results

Statistical results: can sometimes
make mistakes

Can efficiently solve simpler problems

Can offer solutions to more complex
problems

Table 1: AI differs from traditional software in its requirements and its outputs.

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B. How does AI work?
With AI, there is no longer a need to explicitly program every detail of how to solve a
problem. Instead, we rely on data, a model and simulation.
Data. To replace explicit programming, supervised and unsupervised AI methods require
historical data—observations and measurements that pertain to the problem at hand. In
postal routing, these are images of handwritten letters and digits mapped to their correct
digital representations. In facial recognition, these are many photographs of the same
individual, labeled with their name. Access to high-quality data is essential for AI training.
More data directly improves the odds of finding useful patterns—up to a point, after whichmore data provide diminishing benefits. (In reinforcement learning, data sets are typicallysimulated.)
Model. AI methods require implicit programming of the types of patterns that lie hidden indata. This part of an AI program is called the “model”—a mathematical description ofpattern types expected in data. For example, if a sequence of chess moves appears
frequently in winning games, the model should pick this up as a successful strategy. If somepeople write the letter “t” with a straight line and others with a curve at the bottom, themodel should identify both as valid forms of a “t.” The scientific community has beensteadily developing increasingly sophisticated models over the past several decades.
Computation. Models by themselves are useless—they provide nonsense answers until
they are “trained” on data. Collectively, the various statistical approaches to achieving thisgoal and the hardware that enables such algorithms fall under the term “computation”—aset of mathematical methods for using a model to find and evaluate the quality of patterns(“training”), while simulating multiple scenarios. In chess, this involves making thousands ofclever hypothetical moves to evaluate a particular strategy. In postal routing, this involves
quantifying the uncertainty in differentiating a “3” from an “8” to recognize such digits
reliably. Computation integrates the ideathat AI programs do not contain
explicitly programmed rules; rather,computation is the mechanism by which
AI unravels and leverages implicit
patterns from data (Figure 1-2).
AI has been steadily improving since its
inception in the early days of computing.
A combination of better access to richdata sources, better models for complex
applications and better computing
technology (software and hardware) for
simulation has led to AI’s proliferation.

December 2023

Figure 1-2. AI systems work by using a model to identify patterns
in data. Models by themselves are not useful and must be“trained” on data through computation. Computation integrates
the idea that AI systems do not contain explicit information,rather computation is the mechanism by which AI unravels andleverages implicit patterns from data.

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C. What is AI capable of?
While chess contrasts AI to traditional software, it does not fully capture AI’s capabilities; achess program is effectively playing a game. To dive deeper into a practical discipline that is
evolving with AI,7 we turn to radiology—a branch of medicine in which specialist doctors
use medical imaging (data) to diagnose and treat diseases.
Radiologists are experts at pattern recognition. After years of training, these doctors spend
much of their time detecting anatomical and physiological deviations from blurry and noisy
medical scans—which are themselves proxies for tissue and biology, not the real thingitself. AI can provide an important boost to performing this task.
In cancer medicine, for instance, medical imaging data sets with expert-verified labeling of
the location and type of tumors are increasingly available. Armed with these data sets, AI
systems can be trained to detect the patterns in the medical images that expert humans
have labeled as a tumor. Once trained, these systems can be directed to examine new
medical images, searching for similar patterns in the data that would imply the existence ofa tumor.
Once a tumor has been identified, an AI system can begin to simulate various treatment
scenarios. How big would the tumor be after one session of radiotherapy? How about after
the second? What if the parameters of the radiotherapy are slightly different? Do we end
up with a better outcome? These are the types of questions radiologists can explore using
AI to assist them in designing a treatment plan, which they execute using tested traditional
software that operates medical equipment. The AI outputs a series of outcome
probabilities, which themselves recommend treatment actions.
AI technologies not only help radiologists in their practice but also help push the scientificboundaries of their field. AI is enabling radiologists to process and search for patterns
across huge databases, paving the way toward personalized treatments. This movement isso significant, it has its own name:radiomics.8
The rise of AI in radiology has neither
usurped traditional software nor
displaced its practitioners. But ithighlights a particular type of AI
success story. When AI is combinedwith traditional software and humandomain experts, the results arestronger than what AI can produce
alone. Keeping “humans in the loop”
Figure 1-3. Keeping "humans in the loop" is essential to using
is key to using AI to solve many realAI to solve real-world problems.
world problems (Figure 1-3).

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Box 1-1
LARGE LANGUAGE MODELS AND THE
FUTURE OF AI
Large language models (LLMs), such as ChatGPT, are one type of AI system. LLMs analyze vast
amounts of text data and can string together responses to queries by predicting the most likely
next word in a sentence. The user interface is similar to conversing with a human,
expanding the potential user base for such technology to anyone who can typea question into a computer.
The success of these systems has revived questions around the futurecapabilities of AI. ML and AI experts are divided on the transformational
potential of LLMs and the best balance between rapid innovation andcaution.

D. What kind of data does AI need?
Unlike traditional software, AI requires access to historical data. These data can come inmany different forms and be hosted by different types of entities. The availability andaccessibility of these data are both important considerations for their potential role in AI
systems.

(i) Data types
AI systems can work with many different data types.
•

Tabular data. Measurements that follow a generic row and column structure. Often
associated with spreadsheet applications, tabular data can represent multiple
measurements (rows) of a set of things (columns). Common across many
applications.

•Time-series data. Measurements that have a time ordering and can be plotted overtime. While small time-series data sets can also be considered tabular, they are often
stored in database software that can handle large volumes of data. Common in signal
processing (audio, remote sensing), finance and econometrics.
Geospatial and raster data. Measurements that have a spatial ordering and can often
be viewed as images. This kind of data no longer looks tabular; they are often stored
as files or in special databases. Common in satellite imaging and climate science.

•

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Network data. Measurements that come with a graph of nodes and edges. This kindof data is often stored in special graph databases. Common in power systems andsocial networks.
Text and sequential data. Measurements that comprise sequences of symbols, suchas words. This kind of data is typically stored as text files but can also be encoded indatabases. Common in language applications.

Box 1-2
HOW MUCH DATA IS NEEDED FOR AI?
The answer to this important question depends on the “resolution” of the problem AI is solving.
In chess, the number of moves in each game in a data set has no effect—the“resolution” of the task is at the game-level. The more games, the better.
In time-series tasks, if a common event is being studied, a few days of data
may be sufficient. But for rare events, years if not decades of historicalmeasurements will be needed. In general, data size is not a usefulmetric—the amount of data to drive successful AI applications canrange from megabytes9 to terabytes.10(ii) Data hosts and owners
Data that can be used for AI applications may be hosted by different organizations andentities. Public sector data hosts include government agencies, state-owned enterprises,
public universities, national research laboratories and multilateral institutions. Private
sector data hosts include for-profit companies, not-for-profit organizations (e.g., private
universities, think tanks, private research laboratories) and individuals. For both public
and private sector organizations, data can have varying degrees of availability andaccessibility.

(iii) Data availability
The term “data” loosely refers to some amount of measured information. But for AI
applications, the way in which data are measured and digitized matters (Figure 1-4).
•

Measured and well-digitized. Properly designed and deployed instrumentation will
provide high-quality data that can power AI applications. Such data typicallyexhibit a high degree of spatial and temporal resolution, covering relevant areas insufficient precision over an appropriate number of experiments and amount ofDecember 2023

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time. Examples include industrial production data, high-fidelity weather data andfine-resolution satellite data.
•

Measured but poorly digitized. Data where instrumentation is either insufficient or
improperly configured may not be able to drive successful AI applications. These
cases can occur in underfunded application areas (biodiversity studies), rapidlychanging application areas (agriculture) and broader geographies (weather data indeveloping nations). For example, digitizing the monthly total energy usage at thebuilding-level is not sufficient to drive AI-based individual household energy
optimization.

•

Measured but not digitized. Measurements that could support AI applications may
be measured but not digitized. Digital instruments without connectivity, analog
instrumentation and manual observations constitute much of this category.
Examples include digital thermometers without internet connectivity, analogpressure gauges and visual observations of the weather.

•Not measured. Facts and quantities that would be required to drive an AI
application may not be measured at all. In these situations, the ideal outcome is toleapfrog to measured and well-digitized data.

(iv) Data accessibility
Data that are measured and (ideally well) digitized may have varying levels ofaccessibility (Figure 1-4).
•

Open-source data. These are the most easily accessed data. Open-source data setsare often hosted on public websites or other public data services. While opensource data sets are widely accessible, they may be subject to licensing9
agreements that limit their use. Such data may also lack the specificity required in
AI applications, as they may have been anonymized to protect individual privacy ortrade secrets. Examples include government databases, academic datarepositories and data sets shared for data-science competitions.

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Figure 1-4. Data availability and accessibility are key aspects of enabling AI applications. The ideal zone for AI
development relies on accessible, measured and well digitized data.

•

•

•

Data at cost. These are data that are governed by some sort of usage agreement at a
cost dictated by their host. Such data are often high-quality and specific toapplication areas and may also be governed by additional licensing agreements.
Examples include imaging data sold by satellite-operating corporations, curated data
for self-driving vehicle development and transportation data from shipping
corporations.
Internal data. These data are kept by their hosts to be used internally. Such data are
typically proprietary, containing confidential or private information. Examples include
industrial production data, material-science research and development records, andGPS location data at the individual level.
Inaccessible data. These data are generated but not stored. Such data are often
temporarily created by computer programs and used in some way. Derived results
may be stored, but the raw data are frequently discarded. Examples include physicalsystem simulators and intermediate data used in the processing of other data.
Inaccessible data prevents AI development.

E. Why is AI developing so rapidly?
The speed and scale of recent AI development and deployment are remarkable.
Improvements in computational technology and exponential reductions in cost are fuelinglarger and more complex AI systems.10 The sharing of pre-trained models has also lowered
costs by enabling transfer learning instead of building AI systems from scratch. These
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decreasing costs are enabling more widespread use of advanced AI like large language
models for chatbots.

F. Further reading
There is a vast literature on AI, including many books and articles introducing computation,
ML and AI to non-experts. The following sources may be helpful:••••

New York City Mayor’s Office, AI Primer (2021)
Silver, N., The Signal and the Noise: Why So Many Predictions Fail—but Some Don't(Penguin Publishing Group, 2012)
Pearl, J., Mackenzie, D., The Book of Why: The New Science of Cause and Effect(Penguin Books Limited, 2018)
Christian, B., Griffiths, T., Algorithms to Live By: The Computer Science of Human
Decisions (Henry Holt and Company, 2016)

The following textbooks may be helpful to those seeking additional technical depth in AI
and ML:••••

Kevin P. Murphy, Probabilistic Machine Learning: An introduction (MIT Press, 2022)
Moritz Hardt, & Benjamin Recht, Patterns, predictions, and actions: Foundations ofmachine learning. (Princeton University Press 2022)
Russell, S., Russell, S., & Norvig, P., Artificial Intelligence: A Modern Approach.
(Pearson, 2020)
Sutton, R. S., Barto, A. G. Reinforcement Learning: An Introduction (MIT Press, 2018)

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Optical character recognition. Available from https://facts.usps.com/98-percent-of-handaddressed-letters/.

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Bibb Allen, et al., 2020 ACR Data Science Institute Artificial Intelligence Survey. Journal of theAmerican College of Radiology, 2021. 18(8): p. 1153-1159,
https://doi.org/10.1016/j.jacr.2021.04.002.

3.

Robert & Cai. ChatGPT Statistics 2023. 2023 Available fromhttps://www.tooltester.com/en/blog/chatgpt-statistics/.

4.

Rohit Shewale. 32 Detailed ChatGPT Statistics — Users, Revenue and Trends. 2023 Available
from https://www.demandsage.com/chatgpt-statistics/.

5.

Stefan Steinerberger, On the number of positions in chess without promotion. International
Journal of Game Theory, 2015. 44(3): p. 761-767, https://doi.org/10.1007/s00182-014-0453-7.

6.

David Silver, et al., A general reinforcement learning algorithm that masters chess, shogi, andGo through self-play. Science, 2018. 362(6419): p. 1140-1144,
https://doi.org/10.1126/science.aar6404.

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Garry Choy, et al., Current Applications and Future Impact of Machine Learning in Radiology.
Radiology, 2018. 288(2): p. 318-328, https://doi.org/10.1148/radiol.2018171820.

December 2023

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ICEF Roadmap 2023: AI for Climate Change Mitigation
8.

Kaustav Bera, et al., Predicting cancer outcomes with radiomics and artificial intelligence inradiology. Nature Reviews Clinical Oncology, 2022. 19(2): p. 132-146,
https://doi.org/10.1038/s41571-021-00560-7.

9.

Creative Commons. Available from https://creativecommons.org/.

10.

AI and compute. 2018 Available from https://openai.com/research/ai-and-compute.

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ICEF Roadmap 2023: AI for Climate Change Mitigation

CHAPTER 2:

INTRODUCTION TO CLIMATE CHANGE
David Sandalow, Trishna Nagrani and Julio Friedmann

A. Climate change background
Concentrations of heat-trapping gases in the atmosphere are now higher than at any time in humanhistory.1 This is changing the Earth’s climate.2 (See Figures 2-1 and 2-2.)
The Earth’s average global temperature has risen by at least 1.1 °C (1.9 °F) since 1880.3 (See Figure 23.) Based on global average temperatures:•••

July 6, 2023 was the hottest day ever recorded.4
July 2023 was the hottest month ever recorded.5
The nine hottest years on record are the past nine years.6

The principal heat-trapping gases
are carbon dioxide (CO2), methane(CH4), nitrous oxide (N2O) andfluorinated gases (such as HFCs andSF6). (These are commonly calledgreenhouse gases or GHGs. See
Figure 2) Carbon dioxide is
responsible for roughly 76% of thewarming impact of GHGs globally.
Methane is responsible for roughly
18%, nitrous oxide for 4% andfluorinated gasses for 2%.7
Human activities are the principal
cause of the buildup of GHGs in theatmosphere.1 Human activities are
responsible for more than 50 Gt
CO2e of emissions each year—anFigure 2-1. Greenhouse Effect.
increase of roughly 40% since1990.8 Those activities include
burning fossil fuels (coal, oil and gas), land use and land-use change, and lifestyles and patterns ofconsumption and production.1 Roughly 34% of global GHG emissions come from electricity and heatproduction, 24% from industry, 22% from agricultural, forestry and other land use, 15% fromtransport and 5% from buildings.7

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Figure 2-2. Carbon dioxide concentrations for past 800,000 years. Source: NASA, Global Climate Change, Vital Signsof the Planet, Carbon Dioxide, https://climate.nasa.gov/vital-signs/carbon-dioxide/

Impacts of a changing climate are being felt across the globe.
•

Storms, heat waves and droughts have increased in frequency and intensity in recent
decades.9, 10•

Warming air temperatures and droughts made more likely by climate change have directly
contributed to increased fire risk in many parts of the world. Changes in the climate over the
past 30 years are associated with a doubling of extreme fire weather conditions in California.11
Between June and August 2022, Pakistan experienced unprecedented floods, which affected
33 million individuals. Over 1,700 lives were lost and more than 2.2 million houses weredestroyed or damaged.12•

Billions of people face extraordinary risks unless the buildup of heat-trapping gases in theatmosphere slows and then reverses in the decades ahead.13 Those risks include even more severe
and frequent storms, floods, droughts and heat waves, as well as sea-level rise.14 One study foundroughly a dozen locations across the Mediterranean and Middle East temperatures are likely to reach
50 °C every year in the latter part of this century. Such temperatures were extremely rare or
impossible in these locations in the pre-industrial world.15
Climate change is expected to increase heat-related mortality rates and the incidence of lung andheart disease associated with poor air quality. Higher temperatures and more frequent flooding

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events caused by climate change
contribute to the spread of
infectious and vector-borne
communicable diseases such as
dengue, malaria, hantavirus andcholera.16
In 2015, more than 190 nations
adopted the Paris Agreement,
which calls for “holding theincrease in global averagetemperature to well below 2 °C
(3.6 °F) above pre-industrial
levels” and “pursuing efforts to
Figure 2-3. Global average temperatures 1880-2022. Source: NASA Global
limit the temperature increase to Climate Change, Vital Signs of the Planet, Carbon Dioxide,
1.5 °C (2.7 °F) above pre-industrial https://climate.nasa.gov/vital-signs/carbon-dioxide/
levels.”17 However, policies currently in place around the world would result in a global averagetemperature increase of 2.9 °C (5.2 °F) by 2100, and many policies to limit emissions are not being
fully implemented.18 The world is not on a path to meet globally-agreed climate change goals.

B. Contributions of artificial intelligence to climate science
Artificial intelligence (AI) is making important contributions to the scientific understanding of climatechange. While AI applications are still in relatively early stages of development, the progress to datesuggests real opportunity for better monitoring of anthropogenic climate impacts, better
understanding of how the Earth’s climate is likely to evolve and better predictions of climate impacts.

(i) Improving climate model performance
The best scientific understanding of climate dynamics and forecasts of climate impacts are based oncomputer simulations of complex climate models. To validate these simulations, their results arecompared across models (“model intercomparison”) and to historical actual weather data(“hindcasting”). AI can help improve this comparison process, identifying biases in specific models
and extracting the most useful physical results from increasingly massive amounts of climate model
output data.19
AI can also complement conventional physics-based climate modeling in hybrid approaches,
dramatically reducing the need for certain very intensive computations20 or improving the resolutionof model outputs.21 In some cases, AI can analyze the voluminous output of high-resolution climate
models and assess potential biases in their predictions. A Stanford study using AI to analyze maps oftemperature anomalies, for example, suggested that climate models underestimate the average rateof warming and that temperature increases are likely to exceed 1.5 °C by 2030–2035.22 Already, AI
has improved both the pre-processing23 and post-processing24 of climate models and numerical
weather prediction.

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A potential drawback of incorporating AI into climate simulations is less reproducibility (meaning that
calculations cannot necessarily be repeated and arrive at essentially identical results). The complexityand probabilistic nature of some AI and machine learning (ML) techniques make this morechallenging.25(ii) Improving the understanding of climate processes and feedbacks
The ability of AI to ingest and interpret immense volumes of climate and weather data has helped
illuminate natural processes and important hidden feedbacks within the climate system. For
example, one study identified the role of US Midwestern precipitation in modulating North Atlantic
salinity.26 Another AI-driven analysis of river floods illustrated that data-driven, empirical modelingusing AI could perform as well as science-based simulations in many situations.27 AI can also reduce
uncertainties in certain key climate drivers; for example, a recent study improved the understandingof the interactions between aerosols and clouds, which has long been a challenge for climate models
to accurately represent.28(iii) Providing more advanced warning for extreme weather
Already, AI is beginning to improve the weather forecasts associated with extreme events, providing
accurate, near-term advanced warning in critical contexts.29 This work has made major strides in thepast two years and could ultimately transform climate adaptation responses. Some of the most
crucial areas in which this AI-enabled “nowcasting” (within 6 hours) capability are being applied
include extreme precipitation30 and extreme wind speeds,31 with additional work on predicting
extreme heat over timescales of days to weeks.32(iv) Attributing extreme events to human influence
Climate attribution is a rapidly changing field, and understanding how climate change leads toextreme events is important for governments, companies and public stakeholders. AI has alreadyprovided insights into human attribution around specific phenomena and mechanisms. These include
river flooding in Europe,33 tropical cyclone intensity,34 growing period frost occurrence35 and many
more. New organizations and government programs like Europe’s XAIDA36 are dedicated to thisimportant task.

(v) Revealing additional climate drivers
The ability of AI to analyze visual and numerical data for patterns has greatly improved theunderstanding of certain man-made climate drivers. For example, AI-based analysis of satellite datafrom the National Aeronautics and Space Administration (NASA) revealed much higher ship-trackcloud formation than was previously known (10 times greater) and detected a long-term reduction
over 20 years due to sulfur reductions in maritime fuels.37 (See Chapter 3 for additional examples.)December 2023

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C. Further reading
There is a vast literature on climate change, including many books and articles introducing climatechange to non-experts. The following sources may be helpful:

Books• Gates, B., How to Avoid a Climate Disaster (Penguin Books Limited, 2021)• Figueres, C., Rivett-Carnac, T., The Future We Choose: Surviving the Climate Crisis (Alfred A.
Knopf, 2020)• Smil, V., How the World Really Works: The Science Behind How We Got Here and Where
We’re Going (Penguin Publishing Group, 2022)• Doerr, J., Panchadsaram, R., Speed and Scale: An Action Plan for Solving Our Climate Crisis
Now (Penguin Publishing Group, 2021)• Wallace-Wells, D., The Unhabitable Earth: Life After Warming (Crown, 2020)
Reports• International Panel on Climate Change (IPCC), Sixth Assessment Report (2023)• International Energy Agency (IEA), World Energy Outlook 2023 (2023)• World Meterological Organization (WMO), State of Climate Services Report (2023)• United Nations Environment Programme (UNEP), Emissions Gap Report 2022 (2022)

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Fangqun Yu, et al., Use of Machine Learning to Reduce Uncertainties in Particle Number Concentration andAerosol Indirect Radiative Forcing Predicted by Climate Models. Geophysical Research Letters, 2022.
49(16): p. e2022GL098551, https://doi.org/10.1029/2022GL098551.
Matthew Chantry, Zied Ben Bouallegue, Linus Magnusson, Michael Maier-Gerber, Jesper Dramsch. The
rise of machine learning in weather forecasting. European Centre for Medium-Range Weather Forecasts(ECMWF). 2023. Available from https://www.ecmwf.int/en/about/media-centre/science-blog/2023/risemachine-learning-weather-forecasting.
Yuchen Zhang, et al., Skilful nowcasting of extreme precipitation with NowcastNet. Nature, 2023.
619(7970): p. 526-532, https://doi.org/10.1038/s41586-023-06184-4.
Sandy Chkeir, et al., Nowcasting extreme rain and extreme wind speed with machine learning techniquesapplied to different input datasets. Atmospheric Research, 2023. 282: p. 106548,
https://doi.org/10.1016/j.atmosres.2022.106548.
Ignacio Lopez-Gomez, et al., Global Extreme Heat Forecasting Using Neural Weather Models. Artificial
Intelligence for the Earth Systems, 2023. 2(1): p. e220035, https://doi.org/10.1175/AIES-D-22-0035.1.
S. Jiang, E. Bevacqua, and J. Zscheischler, River flooding mechanisms and their changes in Europe revealed
by explainable machine learning. Hydrol. Earth Syst. Sci., 2022. 26(24): p. 6339-6359,
https://doi.org/10.5194/hess-26-6339-2022.
Davide Faranda, et al., Correcting biases in tropical cyclone intensities in low-resolution datasets using
dynamical systems metrics. Climate Dynamics, 2023, https://doi.org/10.1007/s00382-023-06794-8.
Carl J Talsma, et al., Frost prediction using machine learning and deep neural network models. Frontiers in
Artificial Intelligence, 2023. 5: p. 963781, https://doi.org/10.3389/frai.2022.963781.
XAIDA, eXtreme events: Artificial Intelligence for Detection and Attribution. Accessed October 2023.
Available from https://xaida.eu/.
Tianle Yuan, et al., Global reduction in ship-tracks from sulfur regulations for shipping fuel. Science
Advances, 2022. 8(29): p. eabn7988, https://doi.org/10.1126/sciadv.abn7988.

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ICEF Roadmap 2023: AI for Climate Change Mitigation

Chapter 3:

GREENHOUSE GAS EMISSIONS MONITORING
Antoine Halff, Colin McCormick and Alexandre d'Aspremont

Good information on the sources of greenhouse gas (GHG) emissions is essential for responding toclimate change. Accurate and timely data are needed to design mitigation strategies, prioritize abatement
opportunities and track the effectiveness of climate policies. Historically, however, data concerningsources of GHG emissions have often been partial and approximate, with significant time lags. In manycases, a lack of definitive information on GHG emissions has been an important hurdle to climate action.
Artificial intelligence (AI) is helping to address this challenge. AI tools can analyze vast amounts of datafrom Earth-observation satellites, airplanes, drones, land-based monitors, the Internet of Things (IoT),
social media and other technologies. This capability dramatically improves our ability to monitor GHG
emissions from specific sources accurately in near real-time. AI-enabled emissions monitoring has thepotential to accelerate climate mitigation in many areas.

A. Incumbent GHG emissions monitoring
Scientists began regularly measuring GHG concentrations in the atmosphere in the 1950s. These
measurements have shown a steady increase in GHG concentrations (see Figure 3-1) and have been

Figure 3-1. The Keeling Curve, showing measurements of CO2 concentrations at the Mauna Loa Observatory in Hawaii
since 1958, is named after the scientist Charles David Keeling who started the monitoring program.
Source: https://keelingcurve.ucsd.edu/

instrumental in raising awareness of the climate crisis. These data, from ground-mounted instruments
and Earth-observation satellites, such as NASA’s OCO series and JAXA’s GOSAT/IBUKI series (see Figure 3-

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ICEF Roadmap 2023: AI for Climate Change Mitigation2), are foundational for climate science. However, they provide very limited or no information on thesources, spatial distribution, timing and rates of GHG emissions.
To understand the sources of GHG emissions, the climate community primarily uses estimated emission
factors based on generic categories of equipment and processes. Unfortunately, these emission factors
often systematically underestimate real emissions.1-5 In addition, the use of emission factors creates no
incentive for improving operational performance. For example, a natural gas pipeline operator will beassigned the same level of emissions—based on pipeline length and diameter—whether or not its
operators engage in routine venting, flaring or other climate-adverse, high-emitting and avoidable
practices.
Different GHGs pose very different detection and measurement challenges.
••

CO2 emissions are mainly caused by fossil fuel combustion and deforestation. CO2 emissions fromfossil fuel combustion can be estimated with reasonable accuracy using fuel-consumption data,
while deforestation emissions can be estimated with a lower level of accuracy using land-usechange data.
CH4 emissions, in contrast, come from a range of sources (the energy sector, food system and waste
management) and are much less correlated with consumption. Energy-related methane emissions
are largely avoidable byproducts of fossil fuel production and transport, uncorrelated with
consumption rates and very unevenly distributed across fossil-fuel supply chains.

AI tools are critical for overcoming these challenges.

Figure 3-2. Japan’s GHG-observing satellite "IBUKI-2" (GOSAT-2).
Source: https://global.jaxa.jp/projects/sat/gosat2/

B. AI-enabled GHG emissions monitoring
The use of satellites, drones and ground sensors to measure GHG emissions at the source has
increased significantly in recent years. These instruments produce vast amounts of data. AI
technologies are essential to process and analyze these data. Progress on the “software” side—thecapacity to process and analyze raw satellite imagery and other data at scale in near-real time—is acritical enabler of advances on the “hardware” side.

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Further progress in AI, together with a new generation of satellites, will further improve methaneemissions monitoring and abatement. Progress in real-time carbon-emissions monitoring, as well as inmeasurement and monitoring of natural carbon sinks—such as vegetation—offers the same potential.

(i) Methane emissions
Methane has more than 80 times the warming power of CO2 in the first 20 years after release and isas big a source of near-term warming as CO2.6
AI has been particularly important in improving methane-emissions monitoring. AI helps to (1) processdata from methane sensors at scale, (2) combine input from multiple satellites to overcome eachparticular satellite’s limitations and (3) integrate satellite information with data generated by othertypes of sensors to build multi-scale monitoring and reporting systems.

a. Processing data at scale
AI algorithms that process large amounts of remote-sensing data related to methane have beendeveloped by scientists at leading research institutions, including the Netherlands Institute forSpace Research (SRON), the French Laboratory for Climate and Environmental Science (LSCE) andthe Wofsy group at Harvard University. These algorithms have been further developed andoperationalized by Kayrros, a French-based start-up company, which has enabled the automatic
detection and measurement of large methane emission events at scale on a global basis (Figure 33). (Two of the co-authors of this chapter are principals of Kayrros.) The International Methane
Emissions Observatory (IMEO), launched by the UN Environment Programme (UNEP) and theEuropean Union in 2021, has contracted with Kayrros, SRON and GHGSat (a Canadian company
that operates methane-tracking satellites) to collect information on super-emitters and work withthe responsible parties and their governments to reduce emissions.7
Advances in AI-enabled image processing capacity have considerably advanced the monitoring
power of satellites, which can now detect methane at the same spatial resolution as aerial surveys
at much lower cost and much higher temporal resolution.8 AI-enabled global methane monitoringhas shown that super-emitters are more ubiquitous than previously thought and that eliminating
most super-emitters from the oil and gas industry could be achieved at negligible cost.9-11
Eliminating energy-related super-emitters would help significantly reduce anthropogenic methane
emissions, which could cut the increase in global average temperatures by 0.3 °C by 2045 and by
0.5 °C by 2100.12, 13 This set of abatement measures—the fastest known way to reduce global
warming—is entirely dependent on the use of AI.

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ICEF Roadmap 2023: AI for Climate Change Mitigation

Figure 3-3. Methane super-emitters identified from satellite data processed with AI algorithms. Source: Kayrros.

The policy implications of these developments are already being felt. AI breakthroughs have played
an important role in advancing understanding of methane emissions and have enabled climate policy
initiatives, including the Global Methane Pledge, the EU Methane Strategy, the methane provisionsof the US Inflation Reduction Act, upcoming new methane regulations from the US Environmental
Protection Agency (EPA) and IMEO and its Methane Alert and Response System (MARS).

b. Combining data from multiple satellites
AI has been particularly valuable in enabling GHG monitoring based on data from satellites withdiverse capabilities, including satellites that were not initially intended for GHG monitoring.14 For
example, satellites such as Sentinel-5P and Sentinel-2 from the European Space Agency, PRISMA
from the Italian Space Agency, EnMAP from the German space agency DLR and Landsat 8 and 9 fromthe National Aeronautics and Space Administration (NASA) have different orbital timing (daysbetween revisiting a given location), spatial resolutions (the ground size of a single image pixel) andspectral (color) sensitivity. Input from these and other satellites, with distinct sets of benefits anddisadvantages, has been combined to build a more powerful and comprehensive monitoring andreporting system.15 This system makes it possible to confidently attribute methane emissions at thefacility level (versus basin-level), overcome terrain challenges (such as mountainous terrain in China’s
coal-producing Shanxi province) and even pinpoint the individual pieces of equipment responsible
for methane emissions (such as specific elevators or ventilation units in mines).
c. Integrating satellite data with other data
Some regulations call for very low detection thresholds at the asset level that cannot be detected via
satellite and can only be detected with local in situ sensors. However, the latter have inherently
limited range and are not configured to detect large intermittent events. AI can be used to integrate
data from various types of sensors to build comprehensive, multi-scale monitoring and reporting

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ICEF Roadmap 2023: AI for Climate Change Mitigation

systems. This capability is critical for establishing differentiated natural gas and liquified natural gas(LNG) markets based on lifecycle GHG emissions and for establishing carbon border adjustment
mechanisms.

(ii) Carbon dioxide emissions
AI is increasingly being used to better understand and quantify the sources of CO2 emissions. AI is
helping to build on existing datasets and dramatically improve the timeliness, granularity andaccessibility of CO2 information.
AI can analyze and integrate large quantities of data from highly diverse real-time or near-real-timedatasets from industry, power generation, ground transportation and other sectors. This approach has
been used to produce near-real-time trackers of CO2 emissions by sector, with continuous
improvements made to the underlying datasets and AI-based emissions analysis methods.16, 17
AI-enabled CO2 emissions data allow policymakers to assess the effects of emission-abatement
measures with timeliness and precision. For example, AI can model and monitor CO2 emissions fromurban environments with high spatial and temporal resolution, helping city managers and urban
planners assess the effects of abatement measures, sharpen their toolkit and respond to changing
circumstances in a timely manner.18-20
More use-cases for AI-enabled CO2 emissions data will undoubtedly emerge as AI algorithms continue
to improve, helped in part by new underlying data from Earth-observation satellites scheduled to belaunched soon.

a. Providing near real-time information on CO2 emissions and demand forcarbon credits
Climate Trace, Carbon Monitor and other organizations are using AI to more accurately monitor CO2
emissions. Their methods include:•••

combining computer vision with data from remote-sensing satellites, such as detecting water
vapor (a proxy for CO2 emissions) that is released from large natural-draft cooling towers at
power plants;21, 22
measuring the daily amount of vehicle traffic on roads over large regions and the GHG emissions
that these vehicles collectively produce;23 andimproving plume-inversion techniques to translate direct CO2 concentration measurements into
estimates of CO2 emissions rates at large power plants.24

Related work has used AI to create a much more accurate estimate of GHG emissions per nauticalmile from cargo ships and has combined this with satellite-relayed ship tracking data fromautomated identification system (AIS) transponders.25
Such transparency carries far-reaching consequences for carbon abatement. In particular, AIenabled measurements can support and improve cap-and trade systems, amplifying their impact byproviding carbon-market participants with up-to-date information on implied demand for carbon
credits.

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ICEF Roadmap 2023: AI for Climate Change Mitigation

AI-enabled measurements of carbon emissions can also be deployed across extended supply chainsto assess the lifecycle emissions of commodities and other products. This type of information may
be of critical importance for carbon border adjustment mechanisms. For example, AI-enabledmeasurements could be used to assess the amount of carbon (and methane) emissions embedded
in products including crude oil, gasoline, LNG, electric vehicles or wind turbines, by collecting
emissions associated with each link of their respective supply chains. This information could be usedby importing countries to assess the product’s GHG intensity and any associated GHG tariff.
Finally, AI tools can provide policymakers with a powerful resource to track the effect of emissions
regulations, identify and prioritize CO2 abatement opportunities, detect swings in CO2 emissions andcraft appropriate reaction measures in a timely manner. This is particularly the case for urban CO2emissions, which are estimated to account for up to 60% of total CO2 emissions and which can beanalyzed with AI technologies in great detail.26

b. Achieving near real-time transparency on negative CO2 emissions
In its Sixth Assessment Report, the Intergovernmental Panel on Climate Change (IPCC) highlights thecritical importance of vegetation to achieving our climate goals. Forestry and other forms ofvegetation constitute a vital carbon sink. Monitoring this carbon sink has been challenging withtraditional techniques, but AI algorithms can be trained to survey the world’s vegetation at highspatial resolution with radar and optical satellite imagery and can precisely measure the amount ofbiomass carbon sequestered in forestry and other forms of vegetation, at scale and at reasonablecost.
Traditional ways of monitoring forest projects involve sending teams of inspectors on the ground atlarge intervals of 5 to 10 years to inspect sample sections of the forests, measure the circumferenceof their tree trunks, and extrapolate from those measurements. Inspections are (1) too few and far
between to detect deforestation or degradation in time to take corrective measures, (2) do notaccount for carbon leakage (whereby deforestation is pushed from carbon-offset projects tosurrounding areas) and (3) do not provide data sufficient to assess the baselines used to set the
number of carbon credits issued (i.e., the assumed growth trajectory of the forest parcel in theabsence of a carbon offset project).
In contrast, AI can be used to process radar and optical satellite imagery to survey forestry and build
a strong monitoring, reporting and verification (MRV) architecture around carbon-offset projects. AI
technologies make it possible to not only monitor entire projects comprehensively at relatively highfrequency, cost-efficiently and non-intrusively, but also to detect carbon leakage virtually from theonset and to test the projects’ baselines by using archival imagery to observe underlying trends intheir respective areas over extended periods of time. This transparency has the potential to rebuild
confidence in carbon-offset projects, prevent and crack down on unsavory practices in NBS markets,
set strong safeguards around our shared forestry endowment and safely channel capital from North
to South.
Many start-up companies are currently engaged in AI-assisted biomass carbon monitoring,competing commercially in this emerging sector. As with the monitoring of positive carbon
emissions, there are several use cases for this application of AI technology. These include
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ICEF Roadmap 2023: AI for Climate Change Mitigation

strengthening forest protection through robust MRV of carbon offsetting projects, supporting carbon
markets with the provision of near real-time data on the supply of carbon credits, and facilitating theimplementation of anti-deforestation policies.
These AI-assisted technologies are a potential game changer for developing a robust and transparent
nature-based solutions (NBS) sector. NBS projects have been plagued by a lack of transparency thathas shielded dubious and sometimes fraudulent business practices, caused market inefficiencies andfailures, and severely undermined market confidence in NBS as a viable climate tool. 27, 28 AI
technologies can provide carbon traders with real-time information about the supply of carbon
offsets, supplementing implied demand data produced from the monitoring of carbon emissions.
Near-real-time transparency on carbon-credit supply and demand fundamentals can facilitate priceformation in carbon markets and help send the price signals needed to support investment in offset
projects.29(iii) Barriers
The use of AI to harness satellite imagery and other data
sources is one of the most promising developments for GHG
emissions abatement. However, there are important barriers.

a. Barrier: Lack of AI literacy
Lack of AI literacy limits the ability of data users to analyze
GHG data, integrate these data into their operations andgenerate customized products and applications based onthese data. Lack of AI literacy could also adversely affectpublic trust in GHG data and create a fertile ground for themisuse of data. To realize the full potential benefits of AI
for GHG-emissions monitoring, AI literacy must be broadly
improved, including in developing economies.
b. Barrier: Conflicting AI-enabled greenhouse gas (GHG) emissions data
With a proliferation of Earth-observation satellites being launched in the near future and new start-up
companies competing in the AI-for-climate space, different providers could potentially releaseconflicting measurements. This could undermine public confidence in the accuracy of AI-enabled GHG
emissions data.
This challenge could emerge with respect to methane emissions, in particular, in the years ahead. The
Environmental Defense Fund plans to launch MethaneSat in 2024; Carbon Mapper, a public-private
joint venture between the California Air Resources Board (CARB), NASA, Planet Labs and others is also
set to launch in 2024; GHGSat will expand its fleet; Absolut Sensing, a French private company, ispreparing to launch its own fleet of methane tracking nano-satellites; and many new start-up
companies are seeking to tap into the wealth of raw data from multiple satellite constellations togenerate actionable methane data. The proliferation of methane tracking sources could lead to real orapparent data inconsistencies, requiring integration and harmonization.

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ICEF Roadmap 2023: AI for Climate Change Mitigationc. Barrier: Sovereignty concerns
Sovereignty concerns may emerge as a significant impediment to the use of AI-enabled GHG emissions
data. Some countries may object to foreign monitoring and analysis of emissions within their territories.
AI-enabled analysis of GHG emissions data may come under assault or face a trust deficit if it isperceived as biased in favor of certain economic actors.
Independent verification of global GHG data and international consensus about the accuracy of AIenabled analyses will be required to fully realize the potential benefits of AI tools in GHG emission
monitoring.30

d. Barrier: Uncertain financial models
A tension exists in the current development of AI tools for GHG emissions data. The technologicalinnovations behind these new tools are developed by private-sector enterprises that must
generate revenue from the sale of data to recoup their investments and fund further research anddevelopment. However the data must be as widely-shared as possible and ideally made available tothe public in open access to facilitate global acceptance of their accuracy. Protecting theintellectual property in many AI-enabled data technologies is essential to the financial success ofthese private-sector enterprises and thus to innovation in AI data technologies but may limit public
acceptance of GHG emissions data.

(iv) Next steps
Several measures could help address the barriers described above and could promote the use of AI
tools for GHG emissions monitoring.

a. Promote AI literacy
AI literacy could be promoted in education worldwide, both by integrating AI into students’ broad
curriculum and by establishing AI as an independent, specialized field of study. In addition,
professional accreditation standards could include AI literacy metrics. For example, certified public
accountants and civil engineers could be required to demonstrate minimal AI proficiency and theability to use basic AI technologies for professional certification.
Furthermore, organizations in both the public and private sectors could enhance in-house AI
proficiency, whether by requiring minimum AI literacy standards from a broad cross-section of
employees or by building up dedicated AI-focused units and data-science centers within theirorganizations. Minimum AI literacy may be essential for these organizations to deploy AI-enabled
GHG emissions data and integrate those data into proprietary databases and operational systems.

b. Promote mechanisms to validate AI-enabled greenhouse gas (GHG) emissions data
Because AI-enabled data often detect and measure things that cannot be otherwise detected or
measured, proving their accuracy can be challenging. Governments could encourage publicresearch to develop ways of independently replicating and corroborating these data anddocumenting their accuracy based on well-calibrated ground-truth measurements. For example, ateam of scientists at Stanford University recently performed controlled blind releases of methane
in the Arizona desert to test the capacity of various companies to detect and accurately measure
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ICEF Roadmap 2023: AI for Climate Change Mitigationthese releases. Similar studies could be funded and their findings disseminated to build confidence
in AI-enabled data.

c. Establish best practices/regulations for AI-enabled data
To strengthen public trust in AI-enabled
GHG data and foster their use, firewalls
could be built between companies in thedata-generation business and thoseselling low-carbon certification products
based on these measurements. If thebusiness model of companies thatmeasure GHG emissions with theirproprietary AI technologies is based on
the sale of low-emission guarantees orother “certificates of origin” (e.g., certificates offering the “guaranty” that certain volumes of LNG or
petroleum products have been “responsibly” produced and are free of methane or other emissions)that creates an incentive for the data provider to boost sales by providing favorable data. Data
generation and certification could be decoupled, so that the sale of AI-enabled GHG measurements
is not conditioned on the favorability of the outcome to customers.
AI data providers could also be subject to some degree of regulatory oversight, similar to the way
energy Price Reporting Agencies methodologies were subjected to some form of regulatory guidancefrom the International Energy Agency (IEA), the Organization of the Petroleum Exporting Countries(OPEC) and the International Organization of Securities Commissions (IOSCO) under a G20 mandatein the wake of the 2011 London Interbank Offered Rate (LIBOR) scandal.31 For example, regulations
could seek to address and avoid any conflict of interest from AI data providers that would also
engage in the certification of so-called “responsibly produced liquefied natural gas,” requesting thatthe functions of emissions measurement and responsible gas certification be performed by separate
actors.

d. Set up one or several global “owners” of AI-enabled greenhouse gas (GHG)
emissions data
To manage the risk of conflicting AI-enabled data on GHG emissions, one or several global “owners”
of these data could be established and tasked with reconciling potentially contradictorymeasurements. These data owners may also be tasked with identifying and adopting best practices.
In the area of methane emissions measurement, the IMEO may fulfill this role. A challenge for the
IMEO and other such multilateral organizations is to engage with and ensure the participation andbuy-in of a broad diversity of energy consuming and producing countries and state and non-state
actors, notably to get buy-in from key stakeholders, including major oil and gas exporting countries
and large economies, such as China and India.

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ICEF Roadmap 2023: AI for Climate Change Mitigation

Existing organizations such as the World Meteorological Organization and Food and Agriculture
Organizations could handle CO2 and other GHG emissions datasets, as well. Alternatively, a single
new, centralized organization could be set-up to serve as a one-stop clearinghouse for all climate
data.
Finally, the data “owner” in charge of harmonizing and reconciling data may not necessarily
warehouse all types of GHG data but may choose instead to focus only on high-level data, while also
providing a form of certification for private providers of more granular GHG measures.

e. Elevate AI for climate in international dialogue and negotiations
A first step in managing some of the issues above may be to foster international cooperation on
addressing them. Climate policy hinges to a large extent on sound international policies with respect
to these AI issues. For example, it is important to acknowledge and address AI-for-climate issues insuch institutions as the UN Framework Convention on Climate Change (UNFCCC), the World Trade
Organization (WTO) and IEA.

C. Conclusion
AI is making great strides in providing near-real-timetransparency on GHG emissions by enabling data collection
on metrics that were previously unavailable (with CH4) andby enhancing the timeliness, coverage and accuracy of
existing datasets (with CO2 emissions). This information isprovided at scale, globally and cost-efficiently.
Rapid developments in the use of AI for GHG emissions
measurements are opening new opportunities for climate
action on many fronts and at many levels. AI-enabled GHG
emissions data provide fresh fodder for climate research andmay lead to new and more effective climate policies, such as
the methane rules currently under development in various
jurisdictions. AI-enabled emissions data are also providing key
metrics and key performance indicators (KPIs) to guideenvironmental, social and governance (ESG) practices in the financial sector. For example, banks can use
data on methane emissions from oil and gas companies as criteria for lending decisions or investors
could use them to monitor the footprint of their portfolio companies and help them reduce theiremissions. Industrial actors in carbon-intensive sectors and energy producers can use AI-enabled data tomonitor their own footprint and improve their operating practices. The fast-growing volume of AIenabled data on GHG emissions in open access also helps raise public awareness of climate change,
empowering civil society to hold public officials and private companies accountable for their climate
footprint.
Progress in AI-enabled GHG emissions data technologies is ongoing, enabled by new algorithms, cloudcomputing and the falling cost of data storage, growing computing capacity, and a proliferation of newsources of raw data, including satellite imaging, geolocation data, the IoT, etc. The growth of underlying

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ICEF Roadmap 2023: AI for Climate Change Mitigation

Earth-observation data is itself fueled by falling costs in satellite construction and launching. AI
technologies are also supported by the promotion of AI literacy in education and across all sectors ofsociety.
Because the GHG emissions data enabled by AI are often new, their use cases are still being establishedand are highly versatile. But the wealth and breadth of potential applications of these technologies are
already apparent, making AI-enabled GHG data services a critical “climate tech” sector that is vitally
important to the energy transition.

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Deep Learning and Multi-Source Heterogeneous Data Fusion Methods, in Proceedings of the 5th International
Conference on Information Technologies and Electrical Engineering. 2023, Association for Computing
Machinery: Changsha, China. p. 232–237, https://doi.org/10.1145/3582935.3582972.
Angel Hsu, et al., Predicting European cities’ climate mitigation performance using machine learning. Nature
Communications, 2022. 13(1): p. 7487, https://doi.org/10.1038/s41467-022-35108-5.
Chuanlong Zhou, et al. Building Integral Gridded Carbon Emission Disaggregating Model (BIGCarbonEDM):
Near real-time community-level CO2 emission evaluations for twelve cities in Egypt, South Africa, and Turkey.
2023. Copernicus Meetings, https://doi.org/10.5194/egusphere-egu23-4138.
Heather D Couture, et al. Towards tracking the emissions of every power plant on the planet. (2020). 3; pgs.
NeurIPS Workshop, https://www.climatechange.ai/papers/neurips2020/11.
Madison Hobbs, Ali Rouzbeh Kargar, Heather Couture, Jeremy Freeman, Isabella Söldner-Rembold, André
Ferreira, Jeyavinoth Jeyaratnam, Joseph O'Connor, Jordan Lewis, Hannes Koenig, Colin McCormick, Tiffany
Nakano, Charmaine Dalisay, Aaron Davitt, Lee Gans, Christy Lewis, Gabriela Volpato, Matthew Gray, Gavin
McCormick, Inferring carbon dioxide emissions from power plants using satellite imagery and machinelearning. 2023, https://2023.ieeeigarss.org/view_paper.php?PaperNum=5326.
Derek Rollend, Tomek Kott, Kevin Foster, Rohita Mocharla, Rai Muñoz, Neil Fendley, Chase Ashcraft, Marisa
Hughes, Transportation Sector: Road transportation (asset). 2022,
https://github.com/climatetracecoalition/methodologydocuments/blob/main/2022/Transportation/Transportation%20Sector%20Road%20transportation%20(asset)%20Methodology.pdf.
J. Dumont Le Brazidec, et al., Deep learning applied to CO2 power plant emissions quantification using
simulated satellite images. Geosci. Model Dev. Discuss., 2023. 2023: p. 1-30, https://doi.org/10.5194/gmd2023-142.
Brett Mayes, Mark Powell, Dan Knights, Max Schofield, Ted Mackereth, Transportation sector: Shipping.
2023, https://github.com/climatetracecoalition/methodologydocuments/blob/main/2023/Transportation/Transportation%20sector%20Domestic%20and%20International%20Shipping%20Emissions%20methodology.docx.pdf.
Da Huo, et al., Carbon Monitor Cities near-real-time daily estimates of CO2 emissions from 1500 cities
worldwide. Scientific Data, 2022. 9(1): p. 533, https://doi.org/10.1038/s41597-022-01657-z.
Monica Guy. COP26: Is corruption on the agenda? 2021 Available fromhttps://baselgovernance.org/blog/cop26-corruption-agenda.
Patrick Greenfield. Revealed: more than 90% of rainforest carbon offsets by biggest certifier are worthless,analysis shows. 2023 Available from https://www.theguardian.com/environment/2023/jan/18/revealedforest-carbon-offsets-biggest-provider-worthless-verra-aoe.
International Panel on Climate Change. AR6 Synthesis Report: Climate Change 2023. 2023 Available fromhttps://www.ipcc.ch/report/sixth-assessment-report-cycle/.
Kit Million Ross. China clamps down on information leaks in energy sector. 2023 Available fromhttps://www.power-technology.com/news/china-clamps-down-on-information-leaks-in-energy-sector/?cfview.
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Chapter 4:

POWER SECTOR
Zhiyuan Fan and David Sandalow

The power sector will play a central role in meeting economy-wide decarbonization targets. The scaleof the challenge is enormous.
•

In 2022, global emissions of carbon dioxide (CO2 ) from the power sector were 14.5 Gt—
roughly 40% of energy-related CO2 emissions globally.1•

Most strategies for deep decarbonization foresee growing reliance on the power sector in thedecades ahead. In the Net Zero by 2050 scenario prepared by the International Energy Agency(IEA), for example, the share of electricity in final energy use increases from 20% in 2020 to
50% in 2050.2 To achieve economy-wide net-zero emissions targets, the power sector mustgrow significantly as it decarbonizes.
In 2022, global investment in the power sector was about $1 trillion.3
In IEA’s Net Zero by 2050 scenario, annual power sector investment surges to roughly $3 trillion
by 2030 and stays at or near that level for decades.2••

Artificial intelligence (AI) provides an important set of tools to help decarbonize the power sector.
The growing capabilities of AI tools and increasing amounts of power sector data both play a role.

Figure 4-1. Data and AI applications for power grid infrastructure.5

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Machine learning (ML) tools use data from satellites to predict solar and wind power generation anddata from the “Internet of things” to forecast electricity demand.4 Indeed due to the profoundcomplexity of the power grid’s structure and operations, AI is becoming an essential component of
power-grid infrastructure. Power-sector management is one of the most important opportunities forusing AI to counter climate change.5, 6This chapter gives an overview of AI’s current and potential impact on four parts of the powersystem: (1) generation infrastructure (both utility-scale and distributed), (2) transmission anddistribution networks, (3) end-use sectors and (4) energy storage.
(This chapter mostly uses the term “AI” when referring to programs that perform tasks throughinference of patterns and learning from data. In technical literature, “ML” is more common.)A. Generation infrastructure
The planning, operation and maintenance of generation infrastructure are complex tasks, requiring
attention to a variety of factors including the size and expected lifespan of equipment, renewable
resource availability and transmission congestion. AI can play an important role in performing thesetasks.
AI can be especially valuable in siting large-scale renewable projects.
••

Determining the optimal size and location of solar projects requires complex calculations,which must consider weather patterns, grid constraints and other factors. Engel and Engel(2022) reviewed AI’s application for optimal sizing of solar plants.7 Ahmed et al. (2020)
summarized how both short-term (<1 day) and long-term (seasonal) solar-potential predictions
can help optimize site-specific solar plant planning.8
Similar complex planning challenges also exist for wind farm planning, which must consider
terrain morphology, wind speed and direction, turbine type and costs.9 Ashwin Renganathan etal. (2022) applied AI models to study economic performance of wind farm design and windfarm wake interactions for planning decisions.10

AI also has significant potential to assist with integrated electric-grid planning. This is an importantarea for research and innovation.

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Figure 4-2. AI for renewable predictions.11

After renewable generation capacity is installed, operational decisions can have significant impacts
on power output and costs. Predicting variable solar and wind power is one of the most well-studied
topics in the use of AI in the power sector (see Figure 4-2).11 For example:
•
•

•
•

AI can predict weather relevant to wind/solar generation, such as wind speed12 and solar
radiation.13
AI can integrate weather forecasts and power production forecasts. (These forecasts typicallyfocus on short-term predictions (<72 hours, mostly 24 hours) that rely on robust historical andreal-time data.)14
Other applications for maximizing renewable power generation using AI include reinforcement
learning control for wind turbines,15, 16 solar system operation4 and solar shading.17
AI can help accelerate deployment of non-conventional renewables such as wave energy18 andgeothermal energy.19 In geothermal energy, AI can help improve numerical reservoir modeling,
exploration, drilling and production.18

AI can also be applied for power generation infrastructure maintenance. Wind power facilities areoften located in tough environments and must endure high wind-speed, extreme temperatures
and/or low population density, making maintenance expensive (usually higher than procurement
costs).20 AI can be used for maintenance prediction and scheduling to minimize turbine failure andmalfunction, as well as for reducing maintenance frequency and costs.20, 21 Similarly, maintenanceoptimization using AI can be used for solar,22 nuclear,23 hydropower,24 and general power plants.25 AI
for utility-scale infrastructure maintenance applies data-driven predictions for failure, risk detection
and age/life predictions to minimize cost and production downtime.
AI can be especially helpful in operating rooftop solar PV. AI can predict rooftop solar installation
potential,26 generate forecasts27 and reduce customer acquisition costs.28 Overall, AI can help with
deployment of rooftop PV by predicting assets’ production and reducing uncertainty that may
damage the grid.

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Federated learning (FL)—a special type of AI—can be very useful in distributed infrastructure. FL is acollaboratively decentralized privacy-preserving learning method that allows training to be
performed on local devices.29 In FL, only training output (updated model parameters) is shared withthe global model while data are kept private within users’ local devices. FL can also perform tasks
such as rooftop solar generation prediction,30 which provides an alternative to AI models based on
centralized data and can perform various tasks in the “smart city” and “smart grid” context.31
In summary, AI can help improve power generation infrastructure, especially wind and solar, from allphases of the project lifetime: planning, operation and maintenance. Compared to operation andmaintenance, generation-infrastructure planning using AI is less well-studied and potentially a point
for innovation. Data support is critical for predictive approaches and proposed AI-based methods are
rarely deployed for commercial applications.

B. Transmission and distribution infrastructure
Transmission and distribution infrastructure is needed to bring electricity to end-users.
Interconnected lines form the transmission and distribution network is the backbone of the electricgrid. Planning and operating this infrastructure involves solving complicated nonlinear problems. AI
can improve optimization methods and provide completely new perspectives.
Transmission expansion planning (TEP) is a process that determines the optimal location and capacityof new transmission lines, as well as the best timing for new construction. TEP involves large-scale
combinatorial and nonconvex optimization problems in which finding a feasible solution can bedifficult.32 As a result, heuristic methods are sometimes adopted to search line-by-line to locate a“good solution.” Recent studies highlight the potential for AI to contribute to TEP:•

Borozan et al. (2023) integrated AI with well-established TEP decomposition methods toimprove computational efficiency while preserving solution quality.33

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ICEF Roadmap 2023: AI for Climate Change Mitigation•

Wang et al. (2021a and b) showed that AI can be used to solve multi-stage TEP based on thestatic model, which can be flexibly adjusted and incorporate uncertainties on wind power anddemand projections.34, 35•

Similarly for transmission networks, Fu et al. (2020) studied the stochastic optimal planning of
distribution networks using AI, considering both renewable power and demand variability.36

Although AI has been used to investigate TEP problems since at least 2002,37 its application fortransmission and distribution planning is not widely studied. This topic is an opportunity forinnovation.
In grid operation, AI can be used to improve both economics and resilience. The famous optimal
power flow (OPF) problem is a good example of AI’s application for grid operation. As Hasan et al.
(2020) mention in their review, the research community is still in search of reliable andcomputationally efficient OPF solution techniques 60 years after its initial formulation.38 AI canobtain a more cost-effective solution and reduce the computational burden.38 Xie et al. (2020)
summarize AI’s application in power system resilience, showing AI’s great potential in outage
forecasting, stability assessment, power system control and power restoration.39
AI can also help distribution network operations. Historically, the distribution grid is too complex andtangled to be mapped accurately, leading to difficulties with fault detection. Recent progress indigitalization has increased the observability and controllability of the distribution grid and enabled
AI to assist in fault detection.40 Studies have shown that AI methods outperform traditional methods
in fault detection accuracy but demand large amounts of data and significant computational
resources.40, 41
In conclusion, AI can be applied to transmission and distribution infrastructure to improveplanning/operational economics and resilience. As the optimization-based solution is largely limitedby computational load and expansion in problem size/complexity, AI is becoming increasingly
important in providing not just better but potentially more robust solutions.

C. End uses
Analyses of electric grid infrastructure sometimes treat end-use sectors such as buildings andindustrial facilities as out of scope. These sectors are often studied as separate research questions.
However, electricity-demand forecasting, demand side management and new end-use infrastructure(such as EV charging stations and hydrogen systems) are becoming increasingly important to theelectric grid. AI can play an important role in these areas.
Demand prediction and scenario projections using AI already exhibit great potential. AI can be used
to perform general demand prediction (time-series decomposition and prediction),42 national longterm demand scenario forecasting43 and demand predictions for specific sectors such as buildings44
and EV charging.45 These demand predictions can be used for system operation such as unit
commitment (short-term) and system planning (long-term).
Aside from passively predicting electricity demand, AI can also be used to actively manipulate
demand in limited scope. Known as demand response, this refers to programs in which certain users

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volunteer to limit electricity consumption for financial reward, providing the grid operator more
flexibility and minimizing the need for capacity charges, spinning reserves and ancillary service
storage purchases. Antonopoulos et al. (2020) reviewed AI approaches for demand response,
demonstrating that AI can capture human feedback, cluster users and tasks without prior knowledge,
and activate participants in demand response programs.46 Demand-side AI research requires
significant data support with high spatial-temporal resolution.
EV charging infrastructure offers
another great example of AI’svalue. In addition to predicting EV
charging demand (see above), AI
can help with EV charging
infrastructure planning. Deb(2021) reviewed ML approaches
to solving charging infrastructure
planning problems, highlightingthat AI can be used to optimize
charging station placement,
charger utilization (station size)and scheduling for minimum
pricing.47 Federated learning canperform tasks such as EV demand prediction48 and optimal EV charging station location.49
AI can also assist with integrating the electric grid and emerging low-carbon hydrogen networks.
Green hydrogen production will consume enormous power. Optimizing integration of the electric
grid with green hydrogen production can deliver significant savings.50 AI can help optimize greenhydrogen production by predicting renewable power potential,51 curtailed renewable energy52 andwater sustainability.53 AI can also help plan hydrogen refueling stations, optimizing station-based
production and storage.54 AI can be used to integrate renewable power with hydrogen-energy
storage to increase grid stability and lower peak loads.55
In summary, AI for demand prediction and demand-side response can assist with planning andoperating other grid infrastructure components. AI can also help plan new end-use infrastructure,such as EV charging stations and low-carbon hydrogen networks.

D. Energy-storage infrastructureWith more and more variable renewables in the power system, energy storage is becoming anessential part of grid infrastructure. Energy storage balances supply-demand temporal mismatch,serving as both generation and load. The scale of energy storage, although quickly expanding, is still
limited. AI can help plan for energy storage, schedule its operation and optimize its lifetime value.
AI has demonstrated ability to help predict and plan for the energy storage that will be required on a
power grid.56 Shams et al. (2021) used ML-based prediction of curtailed renewable power to planhydrogen and battery energy storage systems.57 Zhou et al. (2022) applied AI for energy storage

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scheduling.58 AI has also been used to help battery owners plan for maintenance, replacement andoptimal use of their energy storage assets.59, 60
EVs have significant potential as
distributed energy storage,
sometimes referred to as “vehicle
to grid (V2G)” or “vehicle toeverything (V2X)”.61 EV sales are
growing rapidly. Aggregatedvolumes of energy storage in EV
are very large in scale – manytimes greater than deployedamounts of stationary storage.
Most vehicles are parked most of
the time. However in order to use
EVs as grid assets, grid managers
must understand and pay carefulattention to drivers’ use of theirvehicles for mobility services, which will be a priority for most drivers in most situations. AI can beused for predicting user charging behaviors,62 helping solve vehicle routing optimization problems,63
and improving vehicle-to-grid performance.64, 65 AI can maximize the value of data collected fromvehicles, facilitating deployment of V2G technologies.

E. Barriers and risks
Several barriers limit the adoption of AI for decarbonization of the power sector.
First, AI models and methods are not yet sufficiently robust or well-developed for widespread
deployment. Notwithstanding some studies that date back to the early 2000s, most studies on this
topic are very recent and applications of AI for the grid are at an early stage of research andtechnology readiness level (TRL). Further, actual deployment timelines and impacts on commercial
systems are still highly uncertain. AI applications have greatly benefited from both the explosion of
power-grid system data and AI algorithm research. As the “digital infrastructure” of the grid becomes
more universal, AI is increasingly necessary to effectively use the growing amount of grid-related
data. However, more fundamental work on models and methods is needed for large-scale
deployment.
Second, a lack of general guidance and standards for performance evaluation is another barrier to AI
deployment. Nearly all types of AI methods (e.g., supervised, unsupervised and reinforcement
learning) explored in the literature can be applied to grid infrastructure. These methods have thepotential to deliver big improvements over non-AI methods. Quantifying the improvements in asystematic way requires guidance and standards.
Finally, the lack of a knowledgeable workforce is an important barrier. AI’s application in grid
infrastructure requires a workforce that is knowledgeable on both the electric grid and AI. This

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knowledge base is important for research and development (R&D), technology deployment andpolicy design.
As AI is deployed in the power sector, security risks will be an important topic. Grid security is aparamount concern for grid operators. In most literature on the use of AI in the power sector,
security risks receive scant attention. Important questions include (1) Is there a security risk fromusing AI in the proposed way?; (2) If not, how does one prove that?; and (3) If yes, how does one
minimize it? Whether AI will introduce security risks to grid infrastructure remains unclear, but risk
assessment and minimization strategies are essential. Security risks must be studied and properly
addressed before deploying AI for most grid infrastructure. (See Chapter 10.)

F. Conclusion
In summary, AI has significant potential to help decarbonize the power sector in several areas.
•

•

•

•

Short-term predictions based on time-series data. Predictions of electricity demand, solar
availability and wind speed are necessary for operating electric grids and power markets. Thesetypes of data follow certain physical laws and patterns of human behavior, but are intrinsicallystochastic. Prediction is possible but difficult with conventional non-AI algorithms. AI can detect
patterns in historical data that improve predictive abilities enormously.
Scenario development, such as for EV charging and renewable power deployment. These
scenarios are important to guide grid planning, especially in light of uncertainties related toclimate change impacts and the deployment of new technologies. If rich historical data are
available, AI tools can help significantly with these tasks.
Improving optimization, such as for planning problems. Many power grid optimization
problems involve work with large, nonlinear models. AI can speed computation, improvefeature extraction and help solve “optimization unsolvable” problems such as stochastic
planning. Data support for these model-based problems is generally less critical than in otherareas.
System integration, operation and optimization. The grid infrastructure is becoming more andmore inclusive and increasingly exposed to real-time uncertainties such as wind/solar
fluctuation. Taking a systematic view, instead of focusing on certain grid components, is morecritical than in the past. Furthermore, grid operation have objectives related to cost, reliability,
resilience, equity and greenhouse gas emissions. AI shows great promise in helping gridmanagers understand more complex and quickly evolving grid infrastructure.

AI has potential application in nearly all aspects of power-sector management, including planning,monitoring, maintenance and operations. AI is becoming an important tool to help decarbonize thepower sector, however AI tools for decarbonization are not yet widely deployed. Barriers must be
overcome and risks addressed before that happens.

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Resilience. 2020 IEEE Power & Energy Society General Meeting (Pesgm), 2020,
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P. Stefanidou-Voziki, et al., A review of fault location and classification methods in distribution grids.
Electric Power Systems Research, 2022. 209, https://doi.org/10.1016/j.epsr.2022.108031.
M. Shafiullah, K. A. AlShumayri, and M. S. Alam, Machine learning tools for active distribution grid fault
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R. Olu-Ajayi, et al., Building energy consumption prediction for residential buildings using deep learningand other machine learning techniques. Journal of Building Engineering, 2022. 45,
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Z. Y. Yi, et al., Electric vehicle charging demand forecasting using deep learning model. Journal ofIntelligent Transportation Systems, 2022. 26(6): p. 690-703,
https://doi.org/10.1080/15472450.2021.1966627.
I. Antonopoulos, et al., Artificial intelligence and machine learning approaches to energy demand-side
response: A systematic review. Renewable & Sustainable Energy Reviews, 2020. 130,https://doi.org/10.1016/j.rser.2020.109899.
S. Deb, Machine Learning for Solving Charging Infrastructure Planning Problems: A Comprehensive Review.
Energies, 2021. 14(23), https://doi.org/10.3390/en14237833.
Y. M. Saputra, et al., Energy Demand Prediction with Federated Learning for Electric Vehicle Networks.
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18(9): p. 6464-6473, https://doi.org/10.1109/Tii.2022.3148997.
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Mohammad H. Shams, et al., Machine learning-based utilization of renewable power curtailments under
uncertainty by planning of hydrogen systems and battery storages. Journal of Energy Storage, 2021. 41: p.
103010, https://doi.org/10.1016/j.est.2021.103010.
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Chapter 5:

MANUFACTURING SECTOR
Alp Kucukelbir

The manufacturing sector makes products on which the modern world depends. Billions of tons ofsteel and cement are used in buildings, bridges and roads each year. Chemicals, including ammonia,
provide fertilizers and other essential building blocks for modern society.1
At the same time, the manufacturing sector is responsible for roughly one third of global greenhousegas emissions. Steelmaking has the largest carbon footprint in the manufacturing sector, followed bycement-making and then chemicals. The remaining emissions come from aluminum, glass, paper,and other light manufacturing.2-5
Decarbonizing the manufacturing sector will be challenging. Many industrial processes require highand sustained heat, which fossil fuels are well-suited to delivering. Some industrial processes,
including cement-making, rely on chemical reactions that emit CO2. Many industrial products areglobally traded commodities, subject to significant loss of market share due to small increases in
production costs.6, 7
Artificial intelligence (AI) is showing promise in helping address the challenge of decarbonizing the
manufacturing sector. This chapter discusses that potential and explores opportunities for further
work.

A. How can AI help decarbonize manufacturing?
Consider the following example: AI can play a central role in reducing costs and emissions for electricarc furnaces (EAFs)—a key technology for decarbonizing steelmaking. EAFs melt scrap metal using
electricity instead of coal. Using recycled/circular feedstock, such as scrap, is a core idea thatpervades the effort to decarbonize all types of manufacturing. This idea introduces a novel challenge:
how to manage new sources of variability.
Virgin raw materials are stable.
Mining operators control theiroperations, packaging and shipping
raw ingredients that meet specificquality criteria. Steelmakers areaccustomed to this stability. But
every batch of scrap is different. One
batch of scrap may contain too muchof an alloy, another possibly too littleof it. Modern steelmakers can adjust
for this variation by enhancing the
Figure 5 – 1. Factories are increasingly digitalizating their operations.
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scrap with costly additives. The most common strategy is simple: plan for the “worst batch” scenario.
This strategy has led to a consistent, industry-wide overuse of additives. No matter what scrap metal
comes in, unnecessary amounts of additives are added. The extent of this practice is such that thebiggest portion of EAF steel’s carbon footprint is the upstream emissions from sourcing these
additives.8
AI offers a better approach to this challenge:
instead of over-designing for the “worst
batch,” AI can help steelmakers “adapt toeach batch” with a prediction that has
accuracy unavailable in existing softwaresystems (Figure 5-1). The idea is to use AI torecommend optimal production settings,
adapting to the variability in each batch.
Manufacturing remains a challenging
segment of the economy to decarbonize andwill require significant long-term hardware
research and investments. Many
governments are sponsoring capitalexpenditure-heavy projects to adopt recycled
feedstock, switch to greener sources of fuel,and make clever use of industrial heat.9, 10

Figure 5 – 2. AI enables manufacturers to adapt to recycled

feedstock. Factories typically address the increased variability ofrecycled feedstock by planning for the “worst case” scenario;
this leads to unnecessary waste and excess emissions. Instead,factories can use AI to optimize operations and produce equally
reliable products with net CO2e reductions.

AI provides a complementary benefit that is(1) available today and (2) can be applied to existing manufacturing infrastructure. In many cases, AI
can be applied today without any capital equipment change-out—it is ultimately just an operationalchange. As a result, AI can be orders of magnitude faster and cheaper to adopt than deeper
decarbonization pathways that require significant capital expenditures.

B. Common applications of AI in manufacturing(i) Decarbonizing the process of making things
The steelmaking example highlights one way AI can reduce a manufacturer’s emissions. There are many
more. Here are a few proven ways AI can help reduce emissions across many sectors:•

Adapt to volatility faster. Manufacturing plants are designed to minimize variation and consistently
produce high-quality goods. The idea of using data to control quality variation dates to Walter A.
Shewhart, who established the field of statistical process control at Bell Laboratories in the
1920s.11 AI extends the notion of statistical process control, enabling manufacturers to adapt toissues more quickly—any amount of time avoided making low-quality goods reduces scrap andminimizes a plant’s waste and energy usage.

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•

•

•

•

•

Adapt to volatility better. Without AI, reducing the time wasted making low-quality commodities
may be difficult because existing statistical methods may not be accurate enough to explain theroot cause of production issues. AI-based production can pinpoint the specific root cause of anissue in real-time during production. AI’s precision and ability to handle large numbers of potential
root-cause factors is what drives this capability.
Avoid past mistakes and enable expertise retention. Over three quarters of manufacturing firms
are concerned about their aging workforces.12 A primary component of their concern is losing theexpertise that their skilled workers have amassed at specific manufacturing sites (e.g., the exact
setting for a temperature for a particular product type). These sorts of insights are rarely recorded
in an accessible manner, but skilled engineers and operators leave their marks in historical
production data. Thus, while the experienced operator may know what to do in any scenario, anovice may leverage AI to sift through prior production runs and extract insights that resemble anissue at hand. AI can map challenges happening today to historical periods, filtering out
interventions that did not work and focusing on those that did. In this way, AI can help new talent
perform more efficiently, reducing waste and energy consumption during onboarding and beyond.
Improve yield. Production at scale is never 100% efficient: while 10 grams of ingredients may yield
10 grams of a final product in the laboratory, 10 tonnes of ingredients may yield only 9 tonnes offinal product at scale. Scaling production introduces inefficiencies caused by the challenge of
operating large-scale machinery and prioritizing production speed.13 AI can help minimize this yield
loss. By analyzing historical production data, AI can identify unexpected points in production wherecomplex operational changes may lead to improved yields. AI is uniquely suited to learning theidiosyncrasies of large-scale manufacturing facilities and can provide specific recommendations on
how to improve production yield for each site individually.
Enable recycling and circularity. Having traditionally relied on high-quality, low-variability raw
ingredients, many industrial sectors are embracing recycled feedstock to reduce their carbon
footprint, as well as increasing use of prior components and parts. Both could be considered
increased circularity, potentially helping with cost, as well as carbon intensity. However, recycled
and circular feedstocks typically exhibit low quality and certainly have high variability. This is theexample from the steelmaking case study, with direct parallels in the chemicals, aluminum, glass,and paper sectors, among others. Embracing recycled feedstock not only reduces emissions during
manufacturing, but also relieves demand on mining virgin ingredients in the first place. This aligns
with the materials-efficiency objective highlighted in the sixth assessment report14 of theIntergovernmental Panel on Climate Change (IPCC).
Minimize energy consumption. Manufacturing facilities are not designed to minimize energy
consumption; they are designed for safety. This means plants operate with conservative safetymargins factored into all parts of production. This presents an opportunity for energy
improvements while maintaining safety standards. This topic is a focus of the fifth assessment
report15 of the IPCC and serves as an optimization target for AI as well. Digital control systems
which automatically operate much of the machinery at modern manufacturing sites, can beorchestrated using AI to adapt to operating conditions to safely reduce energy consumption.
Reinforcement learning techniques can explore energy efficiencies in a gradual and safe way,
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safety margins that matter. Applications like these can provide net energy emissions reductions forplants with no hardware changes needed.
••

Adopt alternative energy sources. In some sectors, such as scrap-based steelmaking, production isshifting to using clean electricity, which provides a pathway to shifting towards green production.
In other cases, however, the switch may not be so simple. In direct steelmaking, manufacturers are
shifting towards hydrogen, biomass, and carbon capture. In cement, the use of alternative fuels at
the kiln is steadily increasing, including hydrogen and biomass, as well as carbon capture. Adopting
alternative energy sources, however, comes with its own new source of volatility. Alternative
cement fuels can negatively impact clinker quality, forcing cement mills to continue usinghydrocarbon-based fuels for stability.16 AI can help adapt to this new source of variability, enablingan increased, if not full conversion, to newer greener sources of fuels during production.
Adopt smaller and quicker batch manufacturing. Batch production, which encapsulates much of
the steel and chemicals sectors, embodies a tradeoff between size and speed. Larger batches offer
more opportunity to correct for mistakes and adapt to production issues, while smaller and quicker
batches use less energy and offer production flexibility. Reducing the 17cycle time—the amount oftime it takes to make a batch from start to finish—is a common challenge, compounded by theswitching between different product types between batches. AI can help analyze patterns in highdimensional historical production data and recommend operational set points as production shifts
quickly from batch to batch. Reducing cycle time comes with direct emissions reduction along withenergy minimization, and typically requires no hardware changes to the plant.

Box 5-1
CASE STUDY: ALLOY ADDITIVE REDUCTION
IN STEELMAKING
In 2022, a Brazilian steel manufacturer using AI achieved 8% reduction in alloy additive
consumption. This reduction came with a commensurate $3/metric ton cost savings and a 7.5%
reduction in CO2e/metric ton.17
This company achieved these results by
1. Acquiring recycled scrap metal for their production
2. Measuring the chemical composition of each batch of scrap
3. Leveraging AI recommendations during melting to add as little (if any)
additives as possible
4. Predicting the risk of producing each batch of steel, trading off
potential quality issues with emissions
5. Reducing the quality variation of their final product.
Adopting AI as part of a plant’s operating workflow, manufacturers canprogressively target high-opportunity use cases within their production.

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ICEF Roadmap 2023: AI for Climate Change Mitigation(ii) Decarbonizing supply chains and adopting dematerialization
strategies•••

Optimize manufacturing schedules. The production and storage of commodities are
driven by market demands. Factories optimize their production schedules to minimizeorder wait-time while reducing switching costs between product types or grades.
Inefficiencies in scheduling lead to superfluous production being stored on-site (leading
to unnecessary emissions associated with moving large volumes of material) andswitching costs (leading to unnecessary emissions due to keeping equipment runningwithout producing any goods). AI can help with this scheduling process by optimizing
complex production schedules to minimize such transitions and it can do so at greater
speeds and accuracy than manual approaches. AI can also help forecast market demands,
enabling manufacturers to prepare for anticipated market demand ahead of time.18
Minimize logistics overhead. Manufacturers and shipping companies collaborate todeliver billions of tonnes of material across the globe. Handling and routing such large
amounts of material with precision is a complex operational task. Shipments that are
kept in storage and/or unnecessarily shuffled around during this process lead to energy
waste. Poorly planned shipping routes can add to the indirect emissions that come withtransporting goods to their final destinations. AI can help with this process in two ways.
First, AI can optimize shipping operations, such as terminals and ports, to minimize
container movement while correctly loading and unloading shipments from one mode oftransport to another. Second, AI can help with forecasting both weather conditions andmarket demand, enabling logistics companies to plan and reduce operational
inefficiencies.19
Evaluate and adopt dematerialization strategies. The 6th IPCC Assessment Reporthighlights material efficiency as a key strategy in reducing the carbon footprint of
manufacturing. This strategy involves increasing circularity of materials used during
production, while consuming the smallest amount of new ingredients possible. It also
involves designing and manufacturing of stronger, lighter, and better materials to reduce
how much is needed for downstream applications. AI can assist with both objectives bytargeting production practices that reduce waste—increasing stability with recycled
feedstock—and precisely matching product specifications to production.20 AI can also be
used to design materials for easier disassembly and recycling. However, material
efficiency is not tracked the same way as energy efficiency, which poses a systematicchallenge in this endeavor.21

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ICEF Roadmap 2023: AI for Climate Change Mitigation(iii) Decarbonizing the impact of maintaining industrial equipment••

Monitor processes. Industrial
facilities are typically designed tooperate for long stretches of time,
ranging from chemical plants that
operate with one day of downtime
per week, to steel blast furnaces
that can operate continuously for
years at a time. Any unexpectedissues or downtime cause
unnecessary and often
preventable additional emissions.
Aluminum smelters can sometimes Figure 5 – 3. Factories comprise thousands of interconnected sensors.
unexpectedly fail in a way that releases perfluorocarbons—a potent greenhouse gas. AI
forecasting models can predict when this is about to happen, enabling operators an
opportunity to proactively avoid such scenarios.22 Similarly, silicon levels in tapped iron of blast
furnaces can indicate an unexpected cooling of the furnace—but only when it is too late to act.
AI can forecast silicon levels in a blast furnace, enabling operators to pre-emptively avoid any
furnace cooldowns that would cause avoidable emissions.23
Plan for maintenance. Scheduling maintenance for batch production is reasonably
straightforward since downtime between batches can be used to service equipment. However,continuous-process machinery requires regular maintenance that causes a reduction incapacity, if not direct downtime for the plant. Like cleaning a filter that clogs over time, thesemaintenance procedures are typically conducted on a regular basis—regardless of the state of
the equipment. However, as manufacturing plants adopt increasing variable feed- and fuelstock, continuous-process machinery can degrade at wildly differing rates. AI can be used toforecast the optimal time to service machinery, thus reducing downtime and the resulting
unnecessary emissions that come from winding a plant down and up again.24

C. Barriers and risks for AI in manufacturing
Several barriers prevent the widespread adoption of AI in the decarbonization of manufacturing.
They include the following:••

Lack of incentive to decarbonize. A threshold issue is the incentive of manufacturers todecarbonize, which can involve expense, market risk, adoption of unfamiliar technologies anddisruption of longstanding ways of doing business. Regulatory requirements or clear market
rewards are the two reasons why most factories and logistics companies pursue
decarbonization, but such requirements or rewards are often lacking. In the absence of
incentives to decarbonize, AI tools that could help with this process will rarely be considered or
adopted.
Lack of investment in digitalization. Manufacturing companies are often—culturally andoperationally—anchored to the pre-digital era of the industrial revolution. While large

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manufacturing companies are at various stages of embracing digitalization across theirproduction and supply chains, small- to medium-sized businesses may need to first invest indigitizing their operations. This process may involve installing sensors, connecting them todatabases, and maintaining an information technology foundation to support connecting allparts of the business.
•••

Low digital literacy. Digitalization requires manufacturers to develop, hire or outsource
personnel with expertise. Developing such talent in-house involves training internal domain
experts with data literacy, storage, and manipulation skills. Hiring for digital talent often
involves recruiting data scientists and data engineers to enhance existing staff in their work inthis field. Some manufacturers may prefer to outsource such activities to consulting groups andother companies that provide such services.
Need for coordination across large organizations. Adopting AI in day-to-day workflows requires
buy-in from many stakeholders. Manufacturing companies execute complex workflows that
can involve up to dozens of departments. Team members must be given sufficient resources
and time to build trust in AI-based strategies, which in turn should have clear deployment
ownership. Results should be quantified and shared among stakeholders to further incentivize
adoption.
Availability of recycled feedstock. Not every geography and economic market may have accessto the same levels or quality of recycled feedstock. Individual recycling is an important
challenge in recycling plastic products.25 Commercial recycling of commodities, such as steel, iswell established in the United States, Europe, and Japan; similar workflows and markets aredeveloping in South America, China, and India. Companies that lack consistent access torecycled feedstock may hesitate to adopt workflows, with or without AI, that rely on such
sources.

The adoption of AI in manufacturing also comes with a variety of risks.
•

Need for regular maintenance. Factories and logistics change over time. Any AI-based systemthat operates on real-time data must be carefully maintained. Deployed AI models should bechecked regularly and updated based on the requirements of their applications. This may be a
new workflow to which existing information technology groups may not be accustomed.
Regular workflows to assess and maintain software systems apply to AI-based applications.

•

Additional/new safety procedures. Manufacturing and logistics facilities are dangerous places
to work. Any change to a workflow must be accompanied with a careful investigation of any
source of risk to human safety and health. AI-based systems must be validated to perform as
expected under normal and abnormal operating conditions. Any personnel working with AI
systems may require additional training.

•

Application of AI to maintain or increase emissions. As a general-purpose technology, we haveno way of stopping bad manufacturing actors from applying AI toward use cases that either
maintain or increase their carbon footprint. Regulatory pressure and market dynamics, alongwith other incentivization, are ways to minimize this risk.

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CHAPTER 6:

MATERIALS INNOVATION
Colin McCormick

Materials innovation is important for decarbonization, andartificial intelligence (AI) can play a major role in accelerating it.
This chapter examines how improved materials can reduce
emissions and enable carbon management, as well as specificareas in which AI can help.
The search for novel materials with useful properties has been
central to technology innovation for centuries. Ancient Romans
developed novel concrete for bridges, aquifers and otherstructures, some of which have survived for millennia.1 In themodern era, Thomas Edison’s discovery of carbon filament forelectric light bulbs in 1879 enabled these bulbs to last for longenough to be practical, leading to a fundamental transformationof lighting technologies and the eventual phase-out of whale oiland kerosene lamps.3 Similarly, Charles Goodyear’s discovery of aprocess to vulcanize rubber in the 1830s helped overcome thelimitations of natural rubber, which melts in heat and cracks incold. Goodyear (among others) worked for years to address thischallenge, eventually discovering how to cross-link the longmolecules in natural rubber to create a much stronger and more
durable material.5

Roman concrete enabled extraordinary
construction projects, including the Pantheon,
the world’s oldest building still in active use.

These examples illustrate that most materials innovation
throughout history has relied on insight, experimentation and
The discovery of the vulcanization processserendipity. Edison’s search for an appropriate filament depended transformed natural rubber into a highly
durable, extremely useful material.
on general scientific insight and exhaustive material testing: hislaboratory tried thousands of carbonized plants before finallyidentifying one that worked well. Goodyear’s discovery of vulcanization was largely due to a stroke ofluck. Many other key materials—including carbon steel, ceramics, catalysts and polymers—have
followed similar paths. Without a systematic, quantitative framework for determining how amaterial’s properties depend on its chemical and structural nature, there is only one feasible
approach: innovators must laboriously find or synthesize many different materials, or many variationsof the same basic material with slight modifications, and exhaustively test them. This is costly andtime-consuming and creates a barrier to technological progress.

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A. Materials innovation in climate technologies
The performance of many clean-energy technologies islimited by the properties of key materials, including
photovoltaics (PVs), semiconductors, magnets, catalysts,
polymers, alloys and composites. Identifying new materials
with improved properties could enable these technologies
to achieve higher energy efficiency, lower costs, greater
performance, longer service lifetime, higher energy
densities and many other desirable characteristics. This inturn would allow these technologies to provide identical or
improved services with lower net greenhouse gas
emissions.

Materials innovation enabled the developmentof lithium-ion batteries for electric vehicles(EVs), long-duration grid storage and otherlow-carbon technologies.

Lithium-ion batteries are a good example of a technologythat was greatly improved through the discovery of novel
materials. Specifically, the cathode, anode and electrolytematerials in modern lithium-ion batteries are all the resultof extensive fundamental and applied research. This
includes the identification of lithium cobalt oxide (LiCoO2),
lithium iron phosphate (LiFePO4) and other cathodematerials beginning in the 1970s, as well as theidentification of graphite for anodes and a variety of liquidand solid materials for the electrolyte.6 Before thesematerials were identified and successfully integrated intofull systems the performance of batteries was much worse
Solar photovoltaic (PV) systems are thethan today (lower energy density and total capacity). The
product of years of materials innovation andcost of building battery-enabled technologies was
optimization.
correspondingly higher. Advances in these key materials
therefore enabled improved performance that brought batteries into new applications, such as
electric vehicles (EVs) and bulk storage of renewable electricity. Research into advanced battery
materials is still ongoing and may open a path to even higher-performing batteries, such as all-solidstate7 and sodium-ion technologies.8
Advanced materials also play important roles in carbon capture and management technologies.
Properties such as CO2 binding energy and kinetics, as well as long-term stability, determine theoverall performance of materials used as sorbents and solvents for carbon capture and direct air
capture (DAC) applications.9 Similar properties also determine the performance of catalyst materials
in applications such as electrocatalytic reduction of CO2.10 Even in the case of CO2 transport for
sequestration or utilization, material properties influence the durability and overall performance ofbulk transport systems. 11

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ICEF Roadmap 2023: AI for Climate Change MitigationBox 6-1
INNOVATION IN MATERIALS SYNTHESISIn some cases, the limitation to the performance of a technology could be overcome by a wellknown material that has superior properties, but a practical method to produce this material is notknown. One such case is the general illumination LED bulb, now in common use. Although LEDs
were originally invented in the 1960s, they were based on a material (gallium arsenide, GaAs) thatcan only emit red light. Researchers knew that gallium nitride (GaN) and zinc selenide (ZnSe) could
enable white LEDs that could be used for general applications like building and street lighting.
However, it was not until the development of the two-flow MOCVD (metal organic chemical vapor
deposition) reactor in the 1990s that GaN crystals could be reliably produced.2
This development led directly to commercial, white-colored LED lights withdramatically higher energy efficiency than incandescent and fluorescentbulbs, which are now gradually being replaced. (Notably, although LEDs
have reduced the energy intensity of lighting significantly, global CO2
emissions from lighting have not fallen because the demand for more
lighting has offset these efficiency gains.4)

There are many other use cases of advanced materials that are, or wouldbe, valuable in enabling technologies to reduce greenhouse gas emissions in energy, industrial,
transportation and other applications. These include solar PVs,12 wind turbine blades,13 hydrogenstorage,14 fuel-cell electrodes andelectrolytes,15 lightweight alloys andcomposites for vehicles,16 low-global
warming potential (GWP)
refrigerants,17 thermal-barrier
coatings,18 desiccants for advanced
HVAC,19 high-voltage direct-current(HVDC) power transmission,20 hightemperature superconductors, 21 andhigh-strength permanent magnets(used in everything from windturbines to fusion reactors). 22

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Innovative materials are important for enabling carbon capture and removal systems, such as this direct air capture(DAC) plant in Iceland (photo credit: Julio Friedmann).

B. Computational materials development
Key scientific advances in the 1960s changed the way materials
are designed and discovered. New computational methods
finally enabled researchers to go beyond simply relying on
intuition and incremental experiments; these methods allowed
them to directly calculate the properties of new materials justfrom their chemical makeup and structure (“ab initio”). For
example, following the discovery of the first high-temperature
superconductor (which was largely an Edisonian process guided
by intuition), other researchers quickly applied computationalmodeling to better understand the superconducting effect. Thisapproach led to the discovery of other, better high-temperature
superconductors.23, 24 Ab initio modeling also led to materials
discoveries for batteries, hydrogen storage, thermoelectrics,
nuclear fuels and semiconductors.25
Yttrium barium copper oxide (YBCO)
As a result, materials research has increasingly shifted tocomputation. Advances in computing power, algorithms anddata science have accelerated this trend. Governments havefunded broadly integrated materials science projects thatleverage information science tools to share advanced

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was one of the first high-temperature
superconductors to be discovered.
Image was created using publishedcrystallographic information and theCrystalmaker® program. Author: Gadolinist

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algorithms, provide compute resources, and disseminate the results of computations and experiments
in increasingly massive material property databases. Some examples include The Materials Project
coordinated by UC Berkeley,26 the NOMAD database hosted by Humboldt University of Berlin,27 andthe MateriApps project hosted by the University of Tokyo.28 These projects contain hundreds ofthousands to millions of entries on material properties and provide methods for users to run
numerical calculations of material properties on high-performance computers. The scale of materials
datasets is a consequence of the enormous number of stable materials that could theoretically exist
by the laws of chemistry and physics (estimated to be more than the number of atoms on Earth29)
even though only a tiny fraction have actually been synthesized.
Notably, modern computational materials science consumes enormous computing resources. In
recent years, roughly one-third of available supercomputing has been dedicated to these materialsrelated calculations.30

C. Applications of AI in materials discovery and design
The complex nature of materials
property predictions and the enormous
amount of available data have sparked
interest in using AI methods incomputational materials science forseveral years. One key area where AI
has been applied is directly predicting
properties of new materials without
performing full ab initio calculations.
This approach trains AI models on large
databases of previously computed
and/or tested materials to learn
quantitative relationships between
atomic structure and relevant
properties. This can save enormous compute time and cost. A recent application of this was the use ofgraph neural networks trained on data from the Materials Project to screen 31 million hypotheticallypossible crystal structures to identify roughly two thousand of them with promising properties forfurther investigation.31 This AI approach will probably remain less accurate than ab initio calculations
for a long time but can provide enormous benefits by down-selecting candidate materials for more
intensive, high-accuracy studies.
AI can also be used to accelerate experimental characterization of materials, leading to much more
efficient use of limited experimental resources. For example, x-ray diffraction (XRD) is a common
technique for examining the crystal structure of materials (such as changes in cathode phases during
battery charging) by measuring the pattern of diffraction of x-rays that hit a sample. AI models trainedon large experimental datasets of diffraction patterns and material crystal structures can directlyinterpret new XRD data in real time, dramatically speeding up experiments.32

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An enormous amount of prior materials research is available in scientific journal articles. Researchers
typically survey the scientific literature before approaching a new problem, but the large number of
relevant articles (often tens of thousands for a single material subtype) makes this process extremely
difficult and prone to error and bias. AI in the form of natural language processing (NLP) can be used
to extract information from these research articles and structure it systematically, known as“knowledge discovery.”33, 34 NLP models trained on non-technical language struggle to handle
scientific text, but materials-research-specific language models with better performance have begun
to emerge.35 With the broad introduction of large language models in 2022, progress in materials
science knowledge discovery has begun to accelerate dramatically. 36
The complexity of advanced materials means that the process used to synthesize (produce) themmust be tightly controlled. Small changes in process parameters can result in different, less usefulmaterials, so identifying and optimizing synthesis parameters is crucial. AI-based knowledge discovery
techniques have been successfully applied to published materials literature to identify precise
synthesis steps for key materials from thousands of research papers. For example, a neural-networkbased NLP method was used to search 22,000 journal articles and extract precise synthesis
parameters for optimized titania nanotubes.37
The use of generative AI is also growing rapidly within materials discovery and design. Generative AI
can propose new hypothetical materials that are not currently in any materials database and may bedramatically different from those that are. This is particularly powerful for the materials “inverse
design” problem, which starts with a desired property and uses an AI method to propose possible
material structures that may have it. As an example, a generative adversarial network (GAN) was used
to propose 23 entirely novel structures made from three atoms (magnesium, manganese and oxygen)that displayed excellent properties as photoanodes for water splitting.38

D. BarriersSome important progress has already been made in applying AI techniques to computational materials
discovery and design. More progress would be possible with expanded research budgets, including
additional funding for AI-specific applications in materials science.
While access to materials datasets and high-performance computing has been partly equalized across theglobe thanks to high-speed internet connections (with notable exceptions), the same is not true forphysical materials testing facilities. Real breakthroughs will ultimately depend on coupling AI-enabled
computational materials discovery with high-throughput synthesis and testing/characterization. To enable
this capability and broaden access to physical testing facilities, new automated and partly autonomous
materials testing laboratories are needed, which would allow remote operation for materials
characterization experiments. By combining machine learning (ML), AI, and robotics, these facilities could
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unlock broad global access to rapid iterations in materials design and testing, reducing the challenges ofparticipating in advanced materials development for researchers in resource-limited countries.39
The vast and growing network of materials databases also poses a challenge for progress. Better
integration of these datasets, including better harmonization of their metadata, is needed. This would
improve the ability of researchers to train models and query material properties across the full spectrum of
existing data, avoiding silos and misinterpretations due to conflicting definitions. Explicitly encouraging theinclusion of null results or failed experiments on materials—an uncommon step in most scientificresearch—could broaden the value of these datasets and provide more balanced training data for AI
models. Governments have difficulty acting on these issues unilaterally since the global materials science
community must align on data exchange and metadata protocols. However, international standards bodies
and scientific societies can lead the way through cooperative standards-setting efforts, potentially withgovernment funding for support.40
At a system level, the full life-cycle emissions implications of advanced materials are dependent on boththe key property of interest (e.g., PV efficiency, CO2 adsorption capacity, etc.) and the emissions caused bysynthesis (production) of the material. Unfortunately, relatively little attention has been paid to synthesis
emissions when discovering or optimizing novel materials, even though different synthesis pathways canhave significantly different emissions.41 More use of AI tools is needed in predicting greenhouse gas
emissions that would be caused by synthesizing novel materials, preferably in parallel with materials
discovery and design efforts. This application of AI would allow better understanding of the complete lifecycle emissions that would result from using a novel material in energy and related technologies.
Finally, advances in accelerating materials discovery and design with AI depend on improving the AI
knowledge and skills of the materials science workforce. Key issues in AI, such as understanding theapplicability of trained AI models to problems outside the domain of their training data and quantifying theuncertainty of model predictions, are challenging and likely unfamiliar to conventionally trained materials
scientists.40 AI tools should therefore be incorporated as a central part of materials science education, andtraining should also be offered to AI experts who are interested in applying their skills to novel materials
development. These education and training efforts could take place within traditional materials sciencecurricula or as part of external courses that can ensure the most recent models, numerical algorithms anddatasets are presented and continually updated.

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Chapter 7:

FOOD SYSTEMSElena Méndez Leal, Kevin Karl and Alex Ruane

Food systems—including foodproduction, distribution,
consumption, and waste disposal—
are critical to sustaining livelihoods
and delivering nutrition worldwide.
However, food systems contributesignificantly to the climate crisis,
accounting for over 30 percent of
human-caused global greenhouse gas
emissions1 (e.g., methane, carbon
dioxide, and nitrous oxide). Climatechange, in turn, has a significant andgrowing impact2 on food systems.
Climate change increases heat stressand can lead to deteriorating soil
health, for example, slowing
agricultural productivity and reducing
the nutritional content of crops andlivestock. The cascading impacts ofclimate extremes on agricultural
production are likely to destabilize
global food security, endangering the
livelihoods of billions of people andthreatening public health.

A. Food system greenhouse gas emissions
All parts of the food system – from land use change to waste disposal – result in greenhouse gas
emissions.
(i) Land-use change The conversion3 of natural ecosystems such as forests, marshes and grasslands
into land for agriculture . Eighty percent of deforestation and land degradation is due to land-use
change intended for agricultural production. This is a significant contributor4 to carbon dioxide andnitrous oxide emissions. Sustainable intensification can spare land from conversion and take land outof agricultural production. Well-designed land-use management strategies5—such as agroforestry,
cover cropping and reductions in tillage—can preserve soil health and enhance carbon sequestration
in working lands.
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ICEF Roadmap 2023: AI for Climate Change Mitigation(ii) Production The inputs and operations needed to produce food with agriculture. For example,
existing agricultural practices6 contribute to copious7 methane production (e.g., through flooded rice
fields and enteric fermentation in livestock) and nitrous oxide production (e.g., over-application ofsynthetic fertilizers). Improving livestock management, including rotational grazing and optimized
feed, and implementing precision agriculture techniques to reduce fertilizer and pesticide use canhelp lower total emissions.
(iii) Supply chains The processing, distribution, storage, trade, marketing and handling of food
following production and before consumption. Food-system supply chains have considerable
potential to employ more sustainable strategies, from increasing penetration of renewable energy
into food cold chains8 and decreasing energy expenditures across all steps of the food supply chain
via improved logistics and reductions in post-harvest food loss.
(iv) Consumption and Waste Processes related to consumer food preparation and to the disposal offood waste. Substantial carbon dioxide emissions occur9 in the production and combustion of
biomass and fossil fuels for cooking. The disposal of food waste in landfills emits significant amountsof methane10 globally. Food waste and consequent emissions also occur during food production,
storage, and transportation.
There is substantial opportunity to mitigate the climate impact of food systems while improving theresilience of food systems to climate shocks. Mitigating greenhouse gas emissions from food systems
will require, among other efforts, the adoption and improvement of sustainable practices6 in landuse, agriculture, supply chains and consumption processes. Integrating renewable energy sourcesand energy-efficient technologies will also be essential to reducing greenhouse gas emissions. All
these efforts could benefit from leveraging technological innovation to optimize the dual needs ofimproving food security and reducing food system greenhouse gas emissions. With sufficient,
relevant and accessible data, the emergence of artificial intelligence (AI) carries unique potential tocomplement existing strategies to improve food-system mitigation efforts.

B. Using AI to mitigate food system emissions
AI tools represent a sea change in terms of how data are identified, defined, interpreted andoptimized. By leveraging AI technologies, real-time and historical data can be efficiently harnessed tomodel and anticipate outcomes and drive significant improvements in each stage of the food system
in order to minimize environmental impacts.
AI can make food systems more efficient. For instance, AI can optimize
agricultural practices by integrating data from various sources like soil
sensors and satellite imagery to create precise nutrient and fertilizer
management plans. Machine learning (ML) simulations11 can further
analyze these data to recommend fertilizer application schedules thatminimize nitrous oxide emissions while maximizing crop yields. ML
simulations can also analyze optimal fertilizer applications across a range of
projected climate conditions. AI can aid in monitoring and predicting soil
health, moisture levels and disease risks, enabling proactive crop
management and minimizing the demand for agricultural inputs. Similarly,
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AI applications can explore optimal feedstock for Biomass with Carbon Removal and Storage(BiCRS)12 by analyzing data on biomass characteristics, growth rates and carbon sequestration
potential. AI can identify the most suitable biomass species or crop varieties for efficient carbon
removal and storage by considering factors such as regional climate. Such analyses can accelerate
the development climate-smart systems in ways that are both traditional and completely novel—for
example, AI models can test combinations of farm management, equipment, breeding, financing,and policy instruments that do not yet exist.
Another example of precision agriculture is CYCLESGYM, an innovative tool that employs
simulations13 (i.e., reinforcement learning) to improve crop management strategies. CYCLESGYM cancreate virtual farms simulating different crops, weather conditions and soil properties. Withadjustments to different agriculture management strategies, researchers and farmers can set custom
observational spaces and reward mechanisms that explore various crop growth and environmental
outcomes over short-term intervals that provide a balance between economic viability, greenhousegas emission mitigation and general environmental sustainability in food production. For example,
the CYCLESGYM environment can fine-tune the use of nitrogen-based fertilizers and water supply,
strategize how to minimize nitrogen loss and environmental impact, maintain crop yield objectives
and reduce crop wastage.
Other applications of AI in food systems include augmenting renewable energy generation by
optimizing land use for multiple purposes, forecasting pest and disease pressure for proactive
controls, identifying opportunities for multi-cropping or intercropping, improving logistics and energy
consumption during food transportation and storage and enhancing soil carbon sequestration
efforts. AI tools are also used to rapidly develop alternative protein products14 with a much lower
carbon footprint than most animal-sourced foods. AI is being used to scale food loss and waste
reduction, such as intelligent harvest timing to prevent food spoilage. As an overarching technology
for data identification and integration, AI can also help compile information15 on successful
greenhouse gas mitigation strategies in food systems to evaluate best practices across a variety ofcontexts (see Table 1 below).

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Title

Description

Land-use change Map renewable energy solutions and classify land. Through AI methods, such as deep learning, AI is
improving land classifications16, which can improve efforts to integrate biomass production (as a renewableenergy source) into agricultural landscapes. Other models using simulations can indicate17 land use as theclimate changes as well as analyze18 and optimize future land uses to assist in emission-reduction efforts
Balance solar resources for food and energy production. AI models can predict how to use land forboth sustainable food and energy production. When solar panels are located over crop fields, for example,
AI can predict optimal tilt and shading19 to maximize power production while protecting crop growth
Observe and predict deforestation events. Deforestation is a significant contributor to greenhouse gas
emissions. Monitoring and predicting deforestation and related events20 for agricultural expansion could
help minimize the carbon footprint of food production
Soil carbon sequestration to reduce emissions and mitigate climate change. Satellite data fed into an
image-based simulation model21 can accurately predict the storage potential and management of carbon
dioxide in soils and inform policy and agricultural practices around land-use change and regenerative
agriculture activities (e.g., agroforestry and tillage)
Food Production

Enhance productivity. Precision agriculture employing AI can increase efficiency and conservation gains
without sacrificing yield outcomes. Examples include watering and feed management, controlled crop
management22, 23, automation24, and advanced livestock and crop health monitoring, including identifying
field stresses and impacts from pests and pathogens25 through image processing and neural networks. In
addition, AI can help decrease food loss during harvest26 and maximize the potential of using bioenergy
and biomass27 for greener farming inputs, such as fertilizer
Derive new or enhanced models of farming systems28. This can include exploration of alternative
solutions, filling observational gaps to better calibrate existing models, creating low-cost model emulators toscale analyses, and develop data-driven hybrid models that enhance predictive power
Forecast crop yield and loss. With remote sensing and AI, farmers can better prepare for crop and yield
loss to weather29 events or other impacts of climate change (e.g., reducing on-farm and post-harvest loss).
Develop alternative proteins. AI methods can be leveraged to improve innovation30 around plant-basedand cultivated meat, which can lead to the consumption31 of food with smaller environmental footprints32.
Nonetheless, at scale, the production of alternative proteins also carries an emission risk and currently
depends on limited crops that could be at risk in future climate scenarios

Supply Chains

Optimize food supply chains. AI can increase the efficiency33 of food supply chains using current andhistorical data. Through simulations, AI can predict and reduce actual energy usage in procedures needed
for transportation34, such as in cold chains35. This application can include providing optimal routes while
weighing other relevant considerations, such as transportation resources and route maintenanceImprove food packaging. Through simulations and predictive analytics, AI can assist in improving thedesign, materials and energy usage for packaging36 food items

Consumption
and Waste

Forecast consumption patterns. AI simulations can help predict and emulate food demand, nutritional
needs, product shelf life and actual restocking37 requirements to limit food disposal38 and reduceoverallocation of products38. Accurately meeting real needs for consumption can improve unnecessary39
emission-contributing activities
Streamline disposal processes and sort waste. AI can assist in improving40 management of disposal
processes, including forecasting waste amounts, sorting recyclables and compostable waste, anddecreasing methane gas emissions41 from landfills

Table 1: Selected examples of current AI applications for food-system emission reduction.

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AI is best conceived as a set of complementary tools, rather than a solution in itself. To ensuresustainable and equitable outcomes, proper implementation of AI technologies should be groundedin scientific knowledge, physical constraints, well-defined public policy objectives, ethicalconsiderations and a nuanced understanding of the complex operations of food system actors andchanging baselines. AI models are susceptible to misuse and misinterpretation if not applied correctlyor if used in an incorrect context, leading to unsuccessful interventions or maladaptation. For
example, an AI model that seeks to maximize crop yields in the short-term with limited information
on sustainable agriculture practices, local knowledge, or climate and weather variations could overemphasize the promotion of monoculture crop production and harmful fertilizer use. As such, AI
models must be trained with diverse datasets, consider optimization across multiple objectives andimplement policy interventions that promote sustainable and responsible AI use in food systems.
Nonetheless, the possibilities for integration and application are limitless—by strategically harnessing
the potential of AI within a comprehensive framework, we can accelerate progress in mitigating
food-system emissions and foster a resilient and sustainable global food system.

C. Barriers and risks to AI applications in food systems
Using AI simulations and models
for food-system emission
mitigation is associated withbarriers and risks that require
careful attention. First, a lack of
capital may constrain widespread
adoption of AI technology since
the cost of AI tools (e.g., remotesensing monitors, computational
power), data availability,
maintenance needs and otheraccess limitations are likely to prevent resource-constrained farmers from employing them.
Moreover, concerns surrounding data privacy, security and market competitiveness may limit thewillingness of food-system actors across the supply chain to employ AI-driven solutions andparticipate in data-sharing.
Importantly, the efficacy of AI models heavily relies on the quality and availability of input and output
datasets. Insufficient or incomplete data on food-system processes can lead to inaccurate or
misleading forecasts, especially under novel climate scenarios, potentially undermining the efficacy
of emission-reduction strategies. Data access and intellectual property barriers from agribusinesses
can impede the use of AI in designing comprehensive and inclusive mitigation strategies. In somecases, just because AI models could be applied does not mean they should be applied, or that they
are an improvement on existing empirical models and analyses. Establishing proficient technicalexpertise and knowledge-sharing across food-system actors is important to ensure appropriate andeffective application of AI technologies.

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Finally, striking a balance between food-emission reduction objectives and other food-security
imperatives is both a key design constraint and a significant obstacle. Finding a way to limit our footprint
while increasing food security globally is not straightforward, especially with many regions relying onimports for general access and nutrition security.

D. Key considerations in using AI to mitigate food-system emissions(i) Develop protocols for stakeholder-driven AI applications and tools across food systems. AI solutions
should employ human-centered design principles focusing on the end user, such as farmers, factory
managers and retailers. In addition, applications should be developed with input from relevant experts,scientists, engineers and policymakers to increase opportunities for seamless and effective AI use in foodsystems.
(ii) Increase transparency, accountability and standardization in AI decision-making and data collection infood systems. Encouraging larger and more shareable data collection alongside standardized AI
approaches to food-system problems can increase understanding of factors influencing emissionreduction strategies and knowledge sharing around practical AI applications and methods. Such practices
lead to more informed and targeted interventions across the food supply chain.
(iii) Expand and scale existing technologies to effectively mitigate food-system emissions. Capitalizing on
existing AI technologies and initiatives that have demonstrated promise in curtailing emissions andimproving food-system processes is crucial. Scaling AI-driven interventions in areas such as precision
agriculture and energy-efficient supply chains can lead to more sustainable and efficient resource
management and global food provision.
(iv) Invest in AI research and innovation explicitly tailored to food systems. By dedicating resources todeveloping cutting-edge AI technologies and models for food production, distribution, consumption andwaste disposal networks, novel and more effective approaches for mitigating food-system emissions canbe identified and implemented.
(v) Promote collaboration and public-private partnerships to facilitate scalable emission-reduction
strategies across land use, production, distribution, consumption and waste. Collaboration between
government, researchers, private companies, farmers and other relevant stakeholders allows for moreseamless integration of AI technologies and practices across various stages of the food supply chain, suchas optimizing transportation logistics, accurately predicting demand and reducing food waste.
(vi) Ensure inclusivity and accessibility so relevant stakeholders within the food system have theopportunity to benefit from AI-driven strategies and contribute to a lower-emission future. Efforts
should consider improving access, knowledge sharing and technical assistance for small-scale farmers
and marginalized communities. Initiatives to reduce food-system emissions using AI technologies should
also align and consider other objectives of food security, health and safety.
By optimizing agriculture practices through AI, embracing data-driven decision making, investing in AI
research for food systems, fostering collaborative partnerships and prioritizing technology access for all
stakeholders, AI can play a transformative role in mitigating food-system emissions, enabling a moresustainable and resilient global food and climate future.

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Chapter 8:

ROAD TRANSPORT
Ruben Glatt and David Sandalow

Road transport is a critical partof the global economy. Current
modes of road transport rely
heavily on fossil fuels, producing
roughly 18% of global energyrelated carbon dioxide (CO2)emissions.1, 2 Strategies for
reducing CO2 emissions fromroad transport include deploying
electric vehicles (EVs),
sustainable biofuels andintelligent transportation
systems, as well as greater useof more efficient modes of transit.
Vehicle electrification is an especially important strategy. Life-cycle greenhouse gas emissions from
EVs are already significantly lower than those from comparable vehicles with internal combustion
engines. (Emissions benefits vary based on regional differences in energy generation. One recent
study found EV lifecycle emissions were lower by 66–69% in Europe, 60–68% in the United States,
37–45% in China and 19–34% in India.3) As electric grids decarbonize, EVs will contribute even more
to reducing emissions. Barriers to more rapid deployment of EVs include their up-front purchaseprice and driving range, both of which can be addressed with battery innovations.
Sustainable biofuels also have an important potential role in decarbonizing road transport. Theenergy density of biofuels make them an attractive option for heavy duty vehicles, including trucks
carrying large loads over long distances.
Intelligent transportation systems (ITSs) also have potential to reduce carbon emissions from roadtransport. ITSs integrate sensor and communication technologies with data processing, analyzing
vast amounts of real-time data to plan, monitor and control transit. Shifts from personal vehicles toshared vehicles or public transport are also important. Data-driven algorithms can be used topromote this shift by optimizing public transport to make sure riders have low wait times and can getto their destinations in a timely and cost-effective manner.

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A. Using artificial intelligence (AI) to cut road transport emissions
AI has significant potential to help
reduce greenhouse gas emissions
from road transport. Many solutions
are still in the research stage butshow great promise in experimental
settings or simulations. To realize
AI’s immense potential to reduceroad transport emissions, AI
solutions must be built into
commercial products, integrated
into public infrastructure anddeployed in a safe and responsible
manner.
The emerging capabilities of machine learning (ML) are opening up new opportunities to reduce
emissions throughout the road transport value chain.4 AI can play an important role in three coreareas: (1) batteries and biofuels, (2) intelligent transportation systems and smart infrastructure and(3) shifts towards modes of transportation that emit less carbon.
Realizing the full potential of AI in these areas will require more data and improved ML methods.
Together, they can provide a robust foundation for predictive analytics and decision-making in
scenario simulations. While algorithm development and improved computing hardware areimportant, near- to mid-term advances primarily depend on the availability of data. In combination,
new data sources and cutting-edge theoretical insights can maximize the impact of recent ML
advances.

(i) Batteries
AI has the potential to play a major role in reducing carbon emissions by improving battery design,
optimizing battery usage and promoting battery recycling.
For instance, AI has been shown to help improve battery design by speeding the process of material
discovery. Discovering new materials is a complex task comprising two core challenges.5 The firstchallenge involves determining the right chemical components that, in combination, exhibit certain
characteristics and develop desired properties. The second challenge involves finding a structure thatprovides a stable solution. The key to this process often lies in reducing the very large number ofpossible solutions to a small number that can be evaluated in real-world experiments in a more costand time-effective manner.6 ML can increase accuracy when predicting the properties of materials
and speed down-selection of possible solutions.7

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Major ML-driven breakthroughs
and innovations in battery
materials – including nickel
cathodes, silicon anodes and novel
electrolytes – are alreadyincreasing capacity and reducing
the cost of EV batteries.8 Moreprogress could be achieved
through ML investments that
support collaborations between
industry and academia based ondata, model and knowledge
sharing such as the U.S. Joint
Center for Energy Storage Research and the European Battery 2030+ Initiative.9
AI can significantly enhance battery use-phase processes. With data on energy prices, grid load,
driving patterns, battery health and other factors, AI methods can optimize charging schedules for
EVs with reinforcement learning.10 AI-assisted battery charging can cut electricity costs, prevent
overburdening of the power grid, prolong battery lifespans and increase vehicle availability,
particularly for EV fleet providers.11 AI tools can also optimize the charging process directly whileconsidering battery-aging effects and environmental conditions (such as temperature) to prevent
chemical aging. Examples include (1) replacing rule-based charging strategies with Bayesian
optimization combined with a linear-regression prediction model to define an extreme fast-charging
protocol that maximizes battery cycle-life and reduces the traditional experimental-based approachfrom 500 to 16 days6 and (2) adaptive multistage constant current/constant voltage charging
strategies based on a particle swarm optimization approach.12
Another way to decrease the carbon footprint of EV batteries is to improve recycling and reuse.13 AI
can improve processes based on pyrometallurgical, hydrometallurgical and biological recycling torecover precious raw materials, while supporting diagnostics to evaluate the fit and expectedcharacteristics for a second life. Examples of these applications are (1) useful-life forecasting,14 (2)ML-enhanced automated disassembly and quality control that integrates computer vision and timeseries prediction,15 (3) optimal parameter setting for bioleaching processes for material recovery
based on a random forest regression model,16 and (4) applications for battery life-cycle, waste
recycling and material recovery.17

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Box 8-1
RECOMMENDATIONS••••

Encourage collaboration between industry and academia to build better data sets andencourage joint model development and knowledge exchange for material research
Support development of large-scale non-linear surrogate models toimprove prediction of material properties
Integrate AI-driven management systems in the battery life-cycle toextend time of use and optimize raw material efficiency
Increase simulation capabilities to evaluate life-cycle andinfrastructure impact of innovative fuels(ii) Biofuels
Sustainable biofuels can help decarbonize road transport. The most promising applications are withheavy duty vehicles, such as trucks carrying large loads long distances. (Sustainable biofuels can also
help decarbonize air and marine travel.)
AI can play an important role in developing sustainable biofuels. Applications include image
segmentation for cell analysis in microalgae and modeling time series in the bioenergy conversion
process. For new biofuels, AI already plays an important role in predicting and optimizing highly
complex non-linear bioenergy systems. When it comes to producing biofuels from biomass, so far
most of the literature involving AI focuses on thermochemical processes,18 however biologicalprocesses offer a promising research direction.19 AI models can also help evaluate biofuelinfrastructure requirements and support policy making and long-term planning.20(iii) Intelligent transportation systems and smart infrastructure
ITSs integrate advanced information collection, data processing, communication and sensor
technologies into transportation networks. ITSs include technologies such as real-time traffic data
analysis, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, automatic
number plate recognition and advanced vehicle control systems.21
Smart infrastructure refers to physical infrastructure embedded with sensor technology, digitalconnectivity and data analytics. In the context of road transport, smart infrastructure includes
intelligent traffic signals that adjust to real-time traffic conditions, road sensors that detect andreport issues such as potholes or icy conditions, and digital signs that provide real-time information
to drivers.
ITSs and smart infrastructure have significant potential to help reduce carbon emissions in roadtransport. These technologies could be at the heart of a more sustainable and carbon-freetransportation system.
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AI plays a central role in maximizing thepotential of ITSs and smart infrastructure.
AI’s power to analyze extensive real-timedata from multiple sources could be agame-changer. Theoretical studies haveshown AI’s ability to optimize traffic flow,
decrease congestion, enable dynamic
traffic light sequencing, suggest smart
routes and model traffic predictively toforesee and alleviate congestion. While
real-world adoption and insights are still
scarce and not well-documented, somecities already have implemented pilot studies to investigate real-world implications. The city of
Phoenix in the United States saw a 40% decrease in vehicle delay time after implementing an AIdriven traffic management system. In Calabria, Italy, a pilot program reduced total travel time by up
to 55% through adaptive real-time control of traffic signals for connected vehicles (CV).22
In public transport, AI can be used to predict passenger loads and optimize schedules and routes,
enhancing service efficiency and user satisfaction.23 AI’s role in predictive maintenance can also help
foresee potential infrastructure issues in public transit, preventing failures or delays. Finally, byprocessing and analyzing ITS data, AI will be able to aid in informed policy decisions and strategicplanning, leading to greener, more efficient public transit systems. The infusion of AI into ITSs is
emerging as a cornerstone strategy in the shift toward lower emissions and heightened efficiency inpublic transit.
The data needed for successful AI applications can be provided by static or mobile sensors. Sensordriven infrastructure components—collecting and transmitting data—are essential.24 These include
traffic sensors at intersections or along roadways, smart traffic lights with sensors to monitor traffic
and pedestrian activity, road weather information systems that track atmospheric and pavement
conditions, and smart parking sensors that detect vehicle presence. Sensors on bridges, tunnels androads to facilitate predictive maintenance, as well as environmental sensors to monitor conditions
like air quality and emissions, are also important. In the realm of CVs, sensor-driven infrastructure
can dynamically integrate vehicle sensors—such as LiDAR, radar and cameras—in ITSs to perceive
the surrounding environment through edge analytics.25 By offering continuous, real-time data, asensor-driven infrastructure enables AI systems to significantly enhance the operational capabilities
of infrastructure, helping route emergency services, control traffic and respond to demand changes
in public transport. However, the massive amounts of data require smart integration with cloudbased storage and potentially large computing capabilities that may have a negative impact on net
emissions.26
AI-driven simulation has significant potential to reduce road transport emissions, delivering better
results than conventional algorithm-based simulations by capturing complex patterns andrelationships in transportation data.27 This can provide a wealth of insights, including in optimizing
infrastructure planning, forecasting energy demand and evaluating potential transportation system
policies.28 AI simulations can help identify where investments in charging stations and bicycle lanes
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can best reduce emissions, for example. Linking transportation and energy systems in AI-driven
simulations can significantly advance the evolution of ITSs, contributing to more sustainable andefficient transport networks. A 2021 Latvian study, for example, showed the potential of different
policy instruments to reduce CO2 emissions 30% by 2030, concluding that more research and atighter coupling between the transportation and energy sectors are needed to reach the ambitious
goals of the European Green Deal.29
AI can play an important role in bidirectional EV charging.30 With bidirectional charging capabilities,
EVs can deliver power to homes (V2H), businesses (V2B) or the electric vehicle grid (V2G). Together,
these applications are sometimes referred to as V2X or “vehicle-to-everything”. V2X technologies
provide homeowners and businesses with energy security and help grid managers overcome
shortages or deliver ancillary grid services. Reinforcement learning algorithms based on user
preference and price signals are a potent tool for guiding the charging and discharging in V2X
systems.31 ML can also be used in charge-management systems to guide EVs to charging stations to
reduce negative effects during peak charging times.32 The mobile storage can also be used to
improve energy performance of public buildings by using an ML-based V2G strategy to reduce thecarbon footprint of buildings supported by energy consumption and cost predictions.33
As simulations become more powerful, more data are needed and real-world experiences canprovide the best insights. Communities, utility providers, fleet operators and vehicle manufacturers
could initiate more pilot projects such as dynamic traffic light control systems, which leverage realtime data from GPS, traffic flow sensors, transportation network health and weather updates tooptimize the sequence and timing of traffic lights using ML methods. These pilot projects canenhance traffic flow, reduce congestion and curtail fuel consumption, however securing a large
enough number of participants will be key to gaining meaningful insights. Other initiatives could

Box 8-2
RECOMMENDATIONS
•
•
•

•

•

Increase investment in sensor-driven infrastructure components that can provide data for
ML models to support real-time decision making and planning efforts
Establish privacy regulations for data collection, storage and use in AI applications intransportation.
Support infrastructure research that extends simulation capabilities tobetter capture the interactions between transportation and energy
infrastructure
Establish large-scale pilot projects for intelligent transportation
systems in collaboration with utility provider, fleet operator andvehicle manufacturer
Establish standards for communication technology and protocols
for vehicle-infrastructure communication

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involve predictive maintenance of road infrastructure with sensors that monitor wear and tear,schedule preemptive maintenance and mitigate critical failures.34 Such collaborative, large-scale
projects not only improve transportation efficiency, safety and user experience, but also contributesignificantly to reducing carbon emissions.

(iv) Promoting modal shift
Modal shifts—moving from one type of transportation to another—can significantly reduce
emissions from road transport. Leading examples include shifts from private vehicles to public transit
and from solo driving to car sharing. Such modal shifts require behavior changes and often dependon transit systems that offer an array of mobility options.
AI can serve as a powerful tool in driving behavioral change that contributes to sustainable mobility.
AI-driven approaches encourage the use of public transportation in several ways:
•

•

•

•

First, by harnessing ML algorithms to analyze various data sources, AI-driven approaches canpredict public transit demand, allowing for optimal route planning and strategic relocation thatenhances the convenience of public transit.
Second, by underpinning integrated mobility platforms, which process real-time informationfrom multiple transport modes and propose optimal route options, AI platforms can nudge
users towards public or shared transport. In addition, AI-guided autonomous public transit
could extend the reach of public transport to regions where traditional services may not be
economically viable, thus decreasing reliance on private vehicles.
Third, by producing personalized recommendations and effective gamification techniques suchas reward systems, challenges or social competitions, AI-driven approaches can incentivize andengage commuters in choosing sustainable transportation options.
Finally, by predicting maintenance issues in public transport vehicles, AI-driven approaches canimprove the dependability of these services by minimizing downtime. Consequently, AI canmake public transportation more efficient, reliable and appealing, playing a crucial role incurtailing private vehicle usage and overall transport activity.

AI can also enable shared mobility solutions, which can significantly cut down on energy
consumption and greenhouse gas emissions.35•

AI can help manage shared vehicle fleets, ensuring that vehicles are distributed effectively
based on anticipated demand, reducing waiting times and making shared mobility more
effective and attractive.36•

AI can also personalize the shared mobility experience by understanding users, suggesting themost suitable shared options and facilitating dynamic pricing with prices based on supply anddemand to balance resource utilization and maintain service attractiveness.37
AI-driven predictive maintenance can keep shared vehicles in optimal condition, maintaining
energy efficiency, reducing downtime and enhancing the reliability of shared mobility
services.38•

Thus, through these measures, AI can make shared mobility a more appealing alternative to private
vehicle use, leading to a significant reduction in overall transport activity.
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Autonomous vehicles (AVs) and more specifically autonomous electric vehicles (AEVs) have thepotential to significantly shift transportation modalities, steering us away from a dependence on
conventional, individual-owned internal combustion engine vehicles and toward a new era of sharedelectric and autonomous transport. AI can be used to enhance accessibility and convenience, as
route optimization and vehicle distribution make AEVs that are integrated into shared mobility
platforms highly reliable and accessible. These features encourage a shift away from private vehicle
ownership. Furthermore, AEVs can potentially lower operating costs due to their electric drivetrains,
a benefit that AI can augment by optimizing energy usage. In terms of infrastructure use, AI enables
AEVs to operate more efficiently, through measures like platooning, smart parking management androute selection. This efficiency reduces congestion, energy use and urban space requirements.
Additionally, the environmental impact is minimized as AEVs produce no tailpipe emissions and AI
aids in optimizing energy usage. Lastly, AI can facilitate the integration of AEVs with public transit,
enhancing first-mile and last-mile connectivity, making public transit a more appealing choice andfurther driving the modal shift.

Box 8-3
RECOMMENDATIONS•••

Encourage technology adoption in public transport to increase
analytics capabilities
Develop regulations and incentives to facilitate the use of sharedmobility solutions
Support development and implementation of AEVs to
increase vehicle efficiency

B. Barriers and risks
While the potential of AI in revolutionizing road transportation is immense, several barriers must be
addressed to realize this potential.
A first barrier is lack of data. As noted above, data on a wide range of topics are required to deploy AI
in integrated road transportation systems. Sensors, smart infrastructure, CVs and other tools will beneeded to collect such data.
Second, uniform standards and protocols for sensor data collection and communication are
essential. In a modern grid, a vehicle can serve as a communication node and operate as a channel tointerconnect the electricity grid, traffic network and communication network.39 In this context,
developing common standards in V2V and V2I communication is important for promoting seamless
interoperability. A standardized communication framework enables vehicles to exchange information
effectively with their environment. This capability provides additional data that can inform local
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predictions and decision making, reducing emissions while increasing the efficiency and safety of thetransportation system.
A third barrier is a shortage of personnel with the needed training in and familiarity with AI. AI
experts and software developers are needed, but—at least as important—transport operators andregulators must be equipped with the necessary skills to consider and evaluate AI options.
The use of AI in road transport also creates risks that must be addressed.
First, privacy interests can be threatened by the extensive data collection needed for many
applications. Those data could potentially reveal a great deal about an individual’s habit and actions.
Societal norms are only beginning to be established with respect the collection, distribution and useof data in this area.
Second, the use of AI in road transport creates risks of bias. For example, training data sets may
sample more heavily from wealthy neighborhoods than poor ones. Inadvertent discrimination
against certain groups or areas is possible. Close attention is required to minimize the risk of
inadvertent bias emerging from use of AI.
A third and serious risk is higher emissions as a result of deploying AVs.40•

Cheap, convenient on-demand mobility may overshadow alternatives such as walking, cycling
and public transport. Drivers may be more prone to take longer trips when driving requires
little attention. The result could be more vehicle kilometers traveled and greater emissions.41•

In addition, rebound effects can occur when savings from efficiency improvements lead toincreased demand for a product, reducing or even negating the original savings.42
As AVs and smart infrastructure are algorithm-driven, malfunctions could result in significant
inefficiencies, unexpected behaviors or accidents that require corrective actions, potentiallyleading to additional carbon emissions.

•

Predicting the impact of AVs on road transport emissions is challenging due to several factors
including ongoing technological development, market evolution and regulatory actions. To address
potential negative impacts, a holistic and sustainable approach to integrating AI in thetransportation sector is crucial. Careful planning will be necessary to prevent unintended
consequences and manage potential increases in vehicle usage.
As a final consideration, the advent of foundation models, prompted by recent advances in largelanguage and vision models, has marked a significant shift in our approach to problem-solving. Thesemodels, with their capacity to handle multi-modal input and domain-specific expertise, have thepotential to revolutionize numerous fields. However, their applicability in the realm of road transport
is relatively uncharted. Potential applications for foundation models may include autonomous driving
and the control of intelligent transportation infrastructure, however their impact is not yet clear.43

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Chapter 9:

BARRIERS
Alp Kucukelbir and David Sandalow

Five groups of barriers impede the development and application of artificial intelligence (AI) to climatechange mitigation: data, people, computation, cost and institutions.

A. Data
AI requires significant amounts of data, from which it extracts patterns. Data-related barriers to the useof AI for climate change mitigation include the following:•

Data availability. This is a threshold issue for AI. Without available data, AI tools have limited
value. Data quality1 can be as important as data quantity, as more data is typically good but notalways better.2• Data accessibility. To be useful for AI, data must not only exist but also be accessible. Inaccessible
data inhibits application of AI. Both AI experts and domain experts will struggle to conceive of AI
applications without access to key data. Inaccessible data may further impede deployment of AI,
even if it has been developed using alternative data sources. For example, power-network
optimization software may need regular access to real-time data from a specific grid to make an
impact on that grid’s carbon emissions.
• Data standardization. Climate applications of AI, particularly those that require collaborative
efforts by stakeholders, can be severely impeded by lack of data standardization. Standardization
is especially important when data are gathered in a distributed fashion. The parties who gather
and store data can remove significant barriers to the use of their data by adhering to standardpractices in their individual efforts.
Data-related barriers can be overcome with a range of strategies. ClimateChangeAI has released a Data
Set Wishlist—“a list of datasets whose availability could accelerate progress or remove bottlenecks” inusing AI to fight climate change.3 Such guidance can help spur development and release of datasetswith high potential for impact. In addition, some businesses may find it helpful to release datasets while
protecting personally identifiable information. A leading example is Netflix, which released anonymized
user data and offered a prize for the development of the best recommender system using those data.
The “Netflix Prize” accelerated the development of machine learning algorithms important to thecompany’s business model and beneficial to the entire media sector.4

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B. People
The success of AI depends on the people who develop, use and evaluate it. Three primary groups ofpeople are involved:•••

AI developers. Developing AI requires a mixture of specialized skills: a strong fluency in statistics
and information theory, built upon a foundation of computer science and software engineering.
Many AI developers have graduate-level degrees in computer science, statistics, engineering andapplied sciences, applied mathematics, and operations research. Like any other highly technicalfield, the reliance on such specialist skill-sets poses a barrier to scaling the development anddeployment of AI, as the difficulty of the topic and the time required for its study produces a small
group of people every year with these credentials. Moreover, AI developers are under significant
market demand from non-climate-focused institutions, such as technology, finance and media
companies, leading to extraordinary competition to attract and retain such individuals.5
Climate application experts. AI developers cannot address climate change mitigation alone.
Expertise in many domains is needed. For AI to have an impact on climate change mitigation, AI
developers must work closely with experts in fields such as atmospheric chemistry, materials
science, electrical engineering, finance and political science.6 A lack of collaboration between AI
developers and these experts poses a barrier for successfully developing and deploying AI. Suchcollaborations are catalyzed when groups of skilled workers develop an elementary understandingof each others’ fields. These collaborations are stymied when these groups lack experience
working together or organizational structures that facilitate common efforts.
AI users. AI users can be anyone who makes decisions based on the output of AI systems. This can
include chemists exploring novel materials, economists evaluating policy impacts, pilots making
decision on flight paths and many others.7 The training needed to interact effectively with AI
systems will vary from application to application. In general, the more familiarity AI users havewith basic AI principles, the more likely they are to be able to use AI tools effectively for climatechange mitigation.

C. Computing power
The ability to simulate and run computations is a fundamental building block for AI. Obstacles tocomputation include the following:•

Computation hardware access. As the amount of data continues to grow across all application
areas, so does the demand on AI computation.8 This includes access to hardware, either physicallyor through an online computation platform. With access to computation hardware comes costs
associated with executing simulations; for physical hardware, this includes energy costs and theneed to maintain computational infrastructure. With online computational platforms, such as
cloud computing vendors, these costs are typically charged by the vendors based on the amountof time taken for computation and the quantity of data processed during that time.

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Infrastructure maintenance and upkeep. Institutions that decide to procure and operatecomputational infrastructure must also plan for its maintenance and upkeep. Computational
infrastructure comprises many electronic components, ranging from servers to networking andtemperature-control equipment. All components are subject to gradual degradation, which
requires redundancy in their design and a well-executed maintenance workflow. Upkeep ofindividual components may include using chips with lower energy requirements and increasing
the capabilities of computation infrastructure as demand for AI increases.

D. CostCost is a fundamental barrier that permeates and compounds the barriers discussed above:•

Financing data for AI. Insufficient financial resources can pose a significant obstacle to data
availability. Collecting, standardizing and digitizing the data that are needed to power a broad
range of AI applications for climate use-cases requires funds to buy and operate measurement
equipment; pay salaries of technicians and data analysts; and pay for data processing, transfer
and storage.

•

Financing people for AI. With AI-developer talent in strong demand, institutions seeking to draw
such skilled workers to climate applications require commensurate funding to compete in the
recruitment market. Climate-application experts are also in demand; lack of sufficient funding can
impede the ability to bring both sets of people together.

•

Financing computation for AI. Whether institutions choose to procure their own infrastructure oroutsource their computation to third-party vendors, cost is a primary obstacle to gaining access tocomputation.9 Without sufficient funding, lack of access to computation can bring AI development
and deployment to a standstill.

•

Financing access to AI. In many climate applications, end-users of AI will likely need to procure AI
systems and software from vendors. Institutions may decide to build these AI systems “in house,”financing the people and computation as above. Or they may choose to buy the AI systems from avendor, in which case lack of funding can prohibit adoption of AI into these climate applications.

E. Institutions
AI is poised to transform the work of many organizations that have a role in climate change mitigation,
including government agencies, private companies and non-governmental organizations (NGOs). A lackof leadership attention and thoughtful internal policies in these organizations could be a barrier torealizing AI’s potential to help reduce emissions. Potential barriers include the following:•••

Lack of institutional focus. To integrate AI into operations, many organizations will require seniormanagement commitment and an organizational structure that ensures attention to AI (such as
an AI department).
Internal policies around AI. Good data practices, adopting AI-based workflows and committing torequisite investments, both human and technical, are all essential.
Lack of standard-setting. Effective scaling of AI requires data standardization, but the institutions
with responsibility for setting these standards may be unclear.

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REFERENCES
1.

Andy McDonald. Data Quality Considerations for Machine Learning Models. 2022. Medium. Available fromhttps://towardsdatascience.com/data-quality-considerations-for-machine-learning-models-dcbe9cab34cb.

2.

Rhys Compton, et al., When More is Less: Incorporating Additional Datasets Can Hurt Performance By
Introducing Spurious Correlations. arXiv preprint arXiv:2308.04431, 2023,
https://doi.org/10.48550/arXiv.2308.04431.

3.

Climate Change AI. Climate Change AI Dataset Wishlist. Accessed November 2023. Available fromhttps://www.climatechange.ai/dataset-wishlist.pdf.

4.

Medium. Medium: Sign up to discover human stories that deepen your understanding of the world. Accessed
November 2023. Available from https://medium.com/codex/hybrid-recommender-system-netflix-prizedataset-e9f6b4a875aa.

5.

Chris Cutter. The $900,000 AI Job Is Here. 2023. The Wall Street Journal. Available fromhttps://www.wsj.com/articles/artificial-intelligence-jobs-pay-netflix-walmart-230fc3cb.

6.

Dong Kwan Kim. How cross-sector collaboration is driving the global climate agenda. 2020. World Economic
Forum. Available from https://www.weforum.org/agenda/2020/01/how-cross-sector-collaboration-isdriving-the-global-climate-agenda/.

7.

Carl Elkin and Dinesh Sanekommu. How AI is helping airlines mitigate the climate impact of contrails. 2023.
Google Blog. Available from https://blog.google/technology/ai/ai-airlines-contrails-climate-change/.

8.

Erin Griffith. The Desperate Hunt for the A.I. Boom’s Most Indispensable Prize. 2023. The New York Times.
Available from https://www.nytimes.com/2023/08/16/technology/ai-gpu-chips-shortage.html.

9.

Aaron Mok. AI is expensive. A search on Google's chatbot Bard costs the company 10 times more than aregular one, which could amount to several billion dollars. 2023. Business Insider. Available fromhttps://www.businessinsider.com/ai-expensive-google-chatbot-bard-may-cost-company-billions-dollars2023-2.

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Chapter 10:

RISKS
David Sandalow, Julio Friedmann and Alp Kucukelbir

Risks from using artificial intelligence (AI) can include bias, invasion of privacy, security, safety andincreased greenhouse gas emissions.

A. BiasBias in AI refers to discrimination that can arise when algorithms make decisions that perpetuate
prejudices, reflect stereotypes or amplify social inequalities. The causes of bias include skewed
training data and flawed algorithm design. Prominent examples include (1) an AI-enabled hiring toolthat favored male over female applicants because it was trained on historic data in which men werefavored in the hiring process and (2) a healthcare algorithm that prioritized healthier white patientsover sicker black patients because the algorithm used health care spending as a proxy for health careneeds.1, 2
When using AI in the context of climate mitigation, risks related to bias include:
Unrepresentative data: Data used to train AI models can be biased due to the very different levels ofdata availability in different countries. For example, data on vehicle traffic patterns in Organization
for Economic Cooperation and Development (OECD) countries is relatively rich because of the ability
to collect measurements from radio frequency identification (RFID), connected vehicles and in situ
traffic sensors. However, these data may be significantly different from measurements that would becollected in non-OECD countries if more sensor systems were available in those countries. As aresult, AI models for improving traffic flow trained on the most accessible data might be badly
adapted or entirely unusable in many countries.3, 4
Misaligned Objectives: AI models are designed to optimize specific objectives. If these objectives are
not carefully aligned with broad, inclusive climate goals, the models might inadvertently favor certain
groups or regions over others.
•

Undermining personal autonomy. AI may increasingly be used to provide climate mitigation“nudges,” subtly encouraging people to choose options resulting in lower emissions. An
example of this is Google Maps’ decision to bias route recommendations toward those withlower projected greenhouse gas emissions, even at the expense of slightly longer travel times.5
These biases in recommendations can be quite powerful at influencing behavior, especiallywhen informed by AI algorithms. However, there is no clear line between “minor” nudges andmore intrusive provision of information that excessively manipulates individual decisionmaking. Without clearer guidelines, AI-enabled “green nudges” may exceed reasonablesafeguards and undermine personal autonomy in decision-making.6•

Reinforcing environmental injustice in communities. AI will increasingly be used to determine
the optimal locations for siting green infrastructure, such as electric transmission lines and low-

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carbon fuel production facilities. However, the process of optimization includes many different
goals, including reducing overall costs and minimizing additional (marginal) environmental
impacts to air and water quality. Based on those criteria, an AI optimal-siting algorithm may
conclude that low-income communities with low land values that are already burdened byextensive air and water pollution are the “optimal” sites for further industrial development.
Without appropriate safeguards, this would further reinforce historical environmental
injustices in those communities. AI-enabled algorithms used to identify sites for beneficial
green infrastructure may suffer from similar issues. For example, when siting electric vehicle(EV) charging stations, an AI algorithm that prioritizes high rates of EV ownership in siting
decisions may neglect low-income communities, making EV ownership in those communities
more difficult.
Economic Bias: AI solutions could be developed with economic motivations that prioritize wealthiernations or communities. As a result, mitigation strategies might overlook developing countries, whichmight be more vulnerable to climate change but less represented in global data sets, even when
viable, cost-effective strategies are available.
Cultural Bias: Climate-related AI models might inadvertently prioritize or deprioritize certain
mitigation strategies based on the cultural biases of the researchers or developers. This could
overlook indigenous knowledge or local practices.
Feedback Loops: AI models that use real-time data to adapt can sometimes create feedback loops
that reinforce existing biases. For example, if renewable energy installations are primarily in
wealthier areas due to economic factors, an AI system optimizing for energy distribution might
continue to prioritize these areas, further widening the gap.

B. Privacy
Collection and analysis of vast amounts of data are essential for AI to contribute to climate change
mitigation. Some of these data may be sensitive, proprietary or personal, presenting privacy
concerns. Privacy risks related to using AI for climate mitigation include:
Surveillance: With the increasing use of sensors, drones and Internet of Things (IoT) devices tomonitor environmental change and human behaviors related to carbon emissions, some data could
be used for unauthorized surveillance of individuals or businesses.
Personal identification: When data from multiple sources are aggregated, individuals who werepreviously anonymous in isolated data sets could become identifiable. For example, combining
smart-meter data with property records or other public data sets can reveal detailed information
about an individual's habits.
Data Sharing: Data collected for climate-mitigation purposes, such as energy-consumption patterns,
might be shared with third parties like advertisers, insurers or utilities, potentially without the explicit
consent of the individuals involved. The data could be shared by the host of the data or as the resultof a data breach or cyberattack by a third party.

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These risks may have repercussions beyond the realm of commerce or privacy. For example,
criminals could use these data to gain access to personal information. AI-based systems that leverage
distributed data collection could be appropriated for political use. For example, AI models that
optimize crop growth in developing countries (increasing food production per unit of total
greenhouse gas emissions) may inadvertently provide previously unavailable network and datacollection infrastructure for government surveillance systems.7 While private developers may initiallybuild data-collection and analysis technology for purely climate mitigation systems, they may beunable to prevent a repurposing of these system.
Ultimately, any kind of data that are granular enough to contain information at the individual levelare of concern. Examples may include information about individuals (gathered through their usage ofa device connected to the internet), including personal and potentially private aspects, such as their
medical history, age or gender.
Not all data about people are of concern. Examples include data aggregated to the point whereindividuals cannot be identified or data about individuals where they cannot be identified, such as
satellite images of people in cars. Some operational and mathematical tools can fully anonymize
data, allowing for safe and insightful derivative data and results.
Practical applications are possible without overstepping important privacy bounds. Workers using
and developing AI tools can start with design around specific use cases (e.g., traffic or power use).
This can start first with awareness-building and operational guides, as well as training and education
regarding privacy concerns. In addition, straightforward technical approaches, such as federating
data, restricted access, differential privacy and encryption, can assist workers in both maintaining
privacy and delivering high-impact mitigation outcomes.

C. Security
All software programs are subject to attacks by hackers, creating security risks. AI systems expand
the “attack surface” for hackers beyond that found in conventional software programs, increasing
such risks. For example, AI systems ingest large amounts of data from potentially many different
sources. Attacking these data sources can steer an AI system’s behavior in unpredictable ways. The
consequences can be especially serious when AI is used in real-time workflows.
Using AI for planning/static use cases poses similar security risks to regular software infrastructure. AI
systems store and process data, thereby exposing such data to parties without authorized access,both within and outside of organizations. AI systems produce results that are themselves data
subject to similar risks. Both can be managed with data security protocols that are well established inorganizations that produce and operate with data.
Using AI in real-time/live workflows carries additional security risks beyond regular software systems.
AI systems that are integrated into real-time workflows may be subject to stringent reliability
requirements. Since AI systems are so complex, it becomes difficult to fully audit them for potential
issues when adopted into live workflows. Risks specific to AI include but are not limited to:

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•

•

•

Cases where data are accepted as an input during operations. For example, researchers haveidentified security risks in large language models (LLMs) by adding extra text in clever ways todrive AI systems to act unpredictably.8
Cases where the environment in which an AI system operates is modified. For example,
researchers were able to spoof street signs and confuse a self-driving AI autopilot system using
subtle stickers. By printing modified copies of signs or adding small black and white strips, they
could reliably cause a stop sign to be misread as a speed limit sign.9
Cases where systems must be regularly and carefully updated with new data to maintain theireffectiveness, giving attackers the opportunity to introduce data that may steer the AI systemastray.10

The rapid development of new AI technologies also means security risks may emerge faster thanorganizations can address them.11 Many of the AI applications in this report pertain to criticalinfrastructure sectors12 which are accompanied by additional security risk-mitigation requirements.
Overall, while promising significant climate mitigation opportunities, AI systems share similarities toregular software in how vulnerabilities can inadvertently be introduced if not properly managed froma security perspective.
The key is for both governments and organizations to take a proactive approach focused ontransparency, oversight, education and governance to manage the faster-moving security challenges
associated with adopting AI.

D. Safety
AI systems can create safety risks when they fail to operate as intended or have unintended
consequences. (This is different than the security risks discussed above, in which someone
intentionally uses an AI system to cause harm.)
The Cybersecurity and Infrastructure Security Agency of the United States maintains a list of sectors
considered “so vital […] that their incapacitation or destruction would have a debilitating effect on
security, national economic security, national public health or safety, or any combination thereof.”12More than half of these sectors, such as chemicals, manufacturing and energy, are covered in thisroadmap. Some of these sectors, such as manufacturing, have dangerous workplaces. Others, suchas food and agriculture, carry public health risks.
Almost every application of AI described in this roadmap could create safety risks. For example:••••

AI systems in real-time grid-optimization operations could lead to unsafe physical conditions
and/or other threats to human safety in the field.
Manufacturing facilities under AI-driven recommendations could lead to unstable operating
conditions and increase the risk of injury in physical facilities.
Agricultural AI applications with inadequate data or poorly developed models could lead toover-utilization of arable land and/or adverse public-health consequences.
Transportation systems optimized by AI could increase the risk of accidents by proposing
unsafe routes and traffic patterns.

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In each of these examples, the safety risk can be minimized with several strategies:•••

First, AI systems should be fully tested and validated in static (non-real-time) settings, where
the above-mentioned risks are mitigated because no live action is taken based on the AI
system’s outputs. This validation phase must be carefully conducted to properly represent“real-world” conditions of what the AI system would encounter once deployed live.
Second, AI systems in dangerous settings should be deployed under the same safeguards that
apply to regular software. The outputs of AI systems should pass through deterministic andvetted workflows that ensure these outputs cannot lead to dangerous physical conditions.
Finally, AI systems should be deployed in phased rollouts across large systems. This enablesenough resources to be dedicated to the required monitoring and validation of real-time AI
systems, before scaling across entire systems, such as large power grids. Existing guidelines for
adopting new workflows apply to AI systems and can help mitigate the risk to public health andsafety.

E. Greenhouse gas emissions
At present, greenhouse gas emissions from AI are modest—significantly less than 1% of the global
total. (See discussion below.) Better data collection and assessment methodologies are needed toprovide a more precise estimate with high confidence.
The amount of future greenhouse gas emissions related to AI is highly uncertain. In some scenarios,
greenhouse gas emissions from AI decline in the years ahead. In other scenarios, such emissions
increase significantly.
This section discusses current and future greenhouse gas emissions from AI, explaining theuncertainties.

(i) BackgroundAI systems require energy. Manufacturing integrated circuitry for AI systems requires energy formineral extraction, construction of complex machinery and sophisticated operations. Training andtuning of AI models requires energy for electricity. AI inference (in which users query models forresults) also requires energy for electricity. Infrastructure supporting these tasks, including data
storage, data transfer and cooling of hardware, adds to AI’s energy footprint.
This energy use does not necessarily result in significant greenhouse gas emissions. When theelectricity at a data center comes from solar, wind or nuclear power, for example, the direct
greenhouse gas emissions from data-center operations are modest. Current data centers are major
consumers of renewable power. Indeed in 2021, Amazon, Microsoft, Meta and Google—which
operate a significant percentage of the world’s data centers—led the world in renewable-powerpurchase agreements.13 However other parts of the AI value chain—including mineral extraction forequipment manufacturing—rely much less heavily on low-carbon power.
The term “emissions from AI” is potentially quite broad. The term could include emissions from:1) manufacturing hardware for AI;2) training and tuning of AI models;
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3) AI inference (i.e., use of AI models);4) applications of AI, some of which may increase emissions (e.g., those that make burning fossil
fuels cheaper or easier) and some of which may reduce emissions (e.g., the AI applications
discussed in this roadmap); and5) broad societal changes caused by AI, such as in labor markets or political dialogue.14
Estimating current and future greenhouse gas emissions from each of these processes is challenging
for several reasons:•••

Data collection and assessment methodologies are generally inadequate for providing precise
and confident estimates.14, 15
In modern cloud-based computing, computer hardware and its associated infrastructure(including HVAC) are frequently shared among many software programs. While some of thesesoftware programs use AI-based algorithms, many do not. As a result, it is difficult to correctly
allocate overall emissions from computing infrastructure to the subcategory of AI applications.
(In some cases, specialized computing chips are used for purely AI-based software, but the useof chips for both AI-based and non-AI-based software is more common.) This leads tosignificant uncertainty in emissions estimates, although the overall emissions from theinformation, communications and technology (ICT) sector serves as an upper bound on AI’semissions.
Estimating greenhouse gas emissions from items 4 and 5 above is especially challenging due tothe considerable unknowns with respect to how AI will be used in the years ahead.

In this section, we focus on items 1-3 above. Using the terminology of greenhouse gas accounting,these are Scope 1, Scope 2 and upstream Scope 3 emissions related to computing operations for AI.

(ii) Current greenhouse gas emissions from AI
There are no precise, widely accepted estimates of current lifecycle greenhouse gas emissions fromcomputing operations for AI. However, the existing literature suggests that such emissions are
significantly less than 1% of total global greenhouse gas emissions.
•

According to the International Energy Agency (IEA), in 2020, data centers and data transmission
networks were responsible for 0.6% of global greenhouse gas emissions (330 Mt CO2e). This
figure includes embodied emissions (i.e., emissions from the manufacturing of equipment usedin data centers and data transmission networks).13– No figure is available for the percentage of data-center and data-transmission-network
activity that relates to AI. However, that figure is less than 100% and likely much less than100%.
– On the other hand, AI activity has grown significantly since 2020—including most notably
with the development and public release of ChatGPT, which has roughly 100 million
active users. 16, 17

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In a Nature Climate Change paper published in 2022, Lynn Kaack et al. estimate that cloud andhyperscale data centers are responsible for 0.1–0.2% of global greenhouse gas emissions andthat roughly 25% of their workloads were related to machine learning (ML). 14–••

This estimate is based on data that are now several years old. AI activity has increased
significantly in the past several years.

In a 2022 study, Sasha Luccioni et al. found that greenhouse gas emissions from training
several current LLMs, including GPT-3 and BLOOM, ranged from roughly 30 to 550 tonnes
CO2e.18 In a 2021 paper, David Patterson et al. provided similar estimates (noting that the
average commercial plane emits roughly 180 tonnes CO2e flying from San Francisco to NewYork).19 550 tonnes CO2e is 0.000001% (1x10-8) of global greenhouse gas emissions. (Global
greenhouse gas emissions were 55 ± 5.2 GtCO2e in 2021.) 20
In 2021, Scope 1 and Scope 2 emissions from semiconductor manufacturing were roughly 76.5
Mt CO2e globally (0.15% of global emissions).21 The share of this manufacturing that related to
AI is unknown, but in light of the pervasive use of semiconductors in countless products, it islikely not significant.

The literature on greenhouse gas emissions from AI is growing.22-24 Unfortunately, no clear, uniform
standards exist for measuring greenhouse gas emissions from AI systems. Improved measurement
standardization and more research are needed to provide precise and confident estimates of current
emissions.

(iii) Future greenhouse gas emissions from AI
Future greenhouse gas emission from computing operations for AI depend on a number of factors,
including:1) the processes used to manufacture AI computing equipment,2) the energy efficiency of AI computing equipment,3) optimization techniques used to reduce the size of AI models and make training them more
efficient,4) the use of zero carbon electricity in AI operations, and
5) demand for AI applications.
Projecting most of these five variables over the medium- to long-term is very challenging. Each is
discussed below.
1) Manufacturing AI equipment. The demand for hardware related to AI is likely to growsignificantly in the years ahead. That could lead to an increase in greenhouse gas emissions
from manufacturing AI hardware, although energy efficiency improvements have thepotential to slow that increase and renewable-power deployment has the potential toreverse the increase. With sufficient deployment of low-carbon power, greenhouse gas
emissions from manufacturing AI hardware could fall even as demand grows.25
2) Energy efficiency of computation. Between 2015 and 2021, data center workload increased
by 260% while data center energy use increased by only 10%.26 If such improvements

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continue, they would help to significantly limit AI’s greenhouse gas footprint. Studies ofpower usage effectiveness (PUE) at data centers suggest significant continued energyefficiency improvements are possible.27 In 2020, average PUE (defined as the ratio of totalenergy use at a data center divided by the energy used by its computing equipment) acrossthe industry was 1.58, while newer data centers demonstrated PUEs of 1.1. More efficientand higher-performing computational equipment such as tensor processing units (TPUs)
offer the promise of continued energy-efficiency improvements as well.28-30 More radical
design concepts, such as analog-AI chips, may also result in major improvements in energy
efficiency.31
3) AI model improvement. Historically, advances in ML model architectures (such as sparsemodels versus dense models) have reduced computation needs by 5–10 times while
improving quality. Significant work is underway to further improve model architectures, suchas by reducing the size and improving efficiency with techniques such as pruning, low-rankfactorization, quantization and knowledge distillation.28, 32 Whether these improvements
outpace the growth in demand for AI is uncertain.
4) Use of low-carbon electricity. Electricity for data centers is the single largest source of energy
consumption related to AI. Many operators of large data centers are committed to the use of
low-carbon electricity and have committed to achieving net-zero emissions.33 Howeverachieving those commitments could be difficult due to a number of factors, including landuse constraints, inadequate transmission infrastructure, permitting delays and the cost ofenergy storage. The ability of data-center operators to use 24/7 low-carbon electricity in theyears ahead will vary from jurisdiction to jurisdiction and will have a significant impact ongreenhouse gas emissions from AI.
5) Demand for AI. Between 2017 and 2022, companies’ demand for AI applications more thandoubled.25 Total AI-related private investment grew 18 times from 2013 to 2021.34 Many
forecasters predict that AI will grow dramatically in the years ahead—at compound annual
growth rates in the range of 20–40% or more.35-37
Each of the factors above will have a material impact on greenhouse gas emissions fromcomputing operations for AI in the years ahead. The range of uncertainty with respect toeach of them is significant. If demand for AI increases at an exponential rate in the years
ahead but energy efficiency, model improvements and/or renewable power deployment
proceed slowly, greenhouse gas emissions could be quite large. If AI grows slowly and energy
efficiency, model improvements and/or renewable-power deployment proceed rapidly,
greenhouse gas emissions from computing operations from AI could be modest. Manyintermediate scenarios are possible as well.

(iv) Conclusion
Widening the aperture to include the greenhouse gas impacts of AI applications and broad societal
impacts of AI, the uncertainties become even greater.
There is significant potential for the overall greenhouse gas benefits of AI to exceed its costs. This
could happen, for example, if strategies for minimizing emissions from AI succeed and some of theemissions-reducing applications of AI discussed in this roadmap deliver significant results. However,December 2023

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the opposite is possible as well. AI could increase greenhouse gas emissions if strategies for reducing
emissions from AI fail and applications of AI that increase emissions overwhelm beneficial
applications, such as those discussed in this roadmap.
Rigorous analysis of the broad sweep of AI’s impact on greenhouse gas emissions will require further
research, as well as better data and assessment methodologies than exist today.15

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Chapter 11:

POLICYDavid Sandalow

Government policies with respect to
artificial intelligence (AI) are evolving
rapidly. Policymakers around the world areconsidering a range of topics with respect to
AI including security, bias, privacy, job
displacement and international
competitiveness.
AI policy began to receive considerable
attention during the latter part of the 2010s,
due in part to the growing capabilities of AI
models and emergence of applications suchas facial recognition and autonomousvehicles. Important developments included:•a White House report on Preparing for the Future of Artificial Intelligence (2016),1•••

China State Council’s Artificial Intelligence Development Plan (2017),2-4
the European Union’s General Data Protection Regulation (2018),5 andthe OECD AI Principles, issued by the Organization for Economic Cooperation andDevelopment (OECD) (2019).6

The release of ChatGPT in fall 2022 focused extraordinary public attention on AI, leading tounprecedented interest from policymakers. The text box at the end of this chapter summarizes
recent AI policy developments in key jurisdictions around the world.
This chapter examines policies that specifically address the use of AI for climate mitigation. Few
such policies have been adopted to date. Some general AI policies have implications for climatechange, and some climate change policies will be implemented using AI, but policies specificallydesigned to promote or manage the use of AI in climate mitigation are only beginning to emerge.
Against that mostly blank slate, this chapter explores two questions:
1. What policy tools could governments use to promote the use of AI for climate mitigation?
2. What policies could governments use to manage risks related to the use of AI for climate
mitigation?
These questions are discussed below.

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A. Promoting use of AI for climate mitigation
Barriers to the use of AI for climate mitigation include those related data, people, computing
power, cost and institutions. (See Chapter 9.) Government policies could help overcome barriers
in all these areas.

(i) Data
Data issues can be a significant barrier
to the use of AI for climate mitigation.
Such issues can include the absence of
data, the failure to digitize data, thelack of standardized or harmonized
data, and shortfalls in funding for datacollection and management.
Governments can play an importantrole in addressing these challenges.
Possible measures include:
•
•
•
•
•

collecting, curating and hostingclimate-related data;
funding the collection and hosting of climate-related data by others;
convening task forces or similar groups to encourage the collection, standardization andharmonization of climate-related data;
adopting regulations that encourage or require the collection, standardization andharmonization of climate-related data; andaddressing the global digital divide, which limits the creation of relevant data relevant tobillions of people worldwide.

Governments already collect and host significant amounts of data related to climate change. The
European Space Agency, US National Aeronautics and Space Administration (NASA), Japan
Meteorological Agency and China Meteorological Administration, for example, all collect and hostlarge amounts of historical and current weather data. Multilateral organizations, including theWorld Meteorological Organization, do the same. Several government programs, including theEuropean Space Agency’s Climate Change Initiative and NASA’s Climate Data Service focus
specifically on ways that weather data can contribute to understanding of climate change.
Government agencies collect other types of data related to climate mitigation as well. NASA
collects data on forest loss.7 The Japan Meteorological Agency collects data on sea-level rise.8
Hundreds of cities around the world collect traffic data.9 Most national governments—as well as
the World Bank, International Monetary Fund (IMF) and OECD—collect economic data.10
Governments often support the collection of climate-related data sets with grants, procurement
or other spending. Many universities, for example, rely on government grants for data collection
and analysis with respect to climate change. The US, EU, Japanese and Chinese governments,

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among others, provide extensive grant funding for climate change and clean-energy research,which often involves data collection.11-13
Governments can also play a central role in promoting standardization and harmonization of datafor climate mitigation.
First, governments can establish policies that emphasize the importance of standardization andharmonization of climate data. These policies could include:•

data management guidelines, such as the “FAIR Guiding Principles” (Findability, Accessibility,
Interoperability and Reusability) proposed by a diverse group of stakeholders in 2016;14•

data standardization and harmonization requirements in connection with governmentfunded research and development (R&D);••

measures to ensure transparency, including access to metadata and core data; andfunding for data standardization organizations and activities.

Examples of such data standardization policies today include:••

the German Standardization Roadmap, which establishes “data infrastructure and data
quality standards for the development and validation of AI systems,” specifically noting that“[data] standardization contributes to Germany's transformation into a climate-neutral
industrialized country”15; andthe European Space Agency’s Climate Change Initiative Data Standards, which set forth
requirements “to ensure consistency, harmonization and ease of use” of varied climate data
sets.16

Second, governments can participate in standardization bodies and initiatives focused on data forclimate mitigation. By joining international organizations—such as the International Standards
Organization—and supporting industry-specific groups, governments contribute to development
of data standards, protocols and best practices. Governments can also provide resources,
expertise and endorsements to encourage the adoption of these standards by industries andorganizations. One example is the European Telecommunications Standards Institute (ETSI), a
European standard-setting organization supported in part by the European Union that sets
Internet of Things (IoT) standards, including those for “achieving the green and digitaltransformation.”17
Third, governments can foster collaboration and knowledge-sharing among stakeholders, thereby
promoting standardization and harmonization of climate-related data. The UK Energy Data Task
Force is a good example. Established in 2019 as a collaboration between government, industry
and academia, the Task Force develops standards and best practices with respect to data quality,interoperability and data sharing protocols in the energy sector.18 Similarly, Global Open Data for
Agriculture and Nutrition (GODAN) is an international initiative that promotes the use of open
data in the agriculture and nutrition sectors. GODAN brings together governments, organizations
and individuals to advocate for data standardization, sharing and interoperability.19
Finally, governments can take steps to address the global digital divide. Today, more than 2.5
billion people globally are not connected to the Internet, and roughly half the world’s population
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lacks access to high-speed broadband.20 That significantly limits the creation of data on a range of
topics relevant to climate mitigation, including energy usage, travel patterns and more.
Connecting people to high-speed Internet has far-reaching benefits for economic development, ofcourse, and should be pursued for many reasons far more immediate than creating useful data for
AI-climate applications. Steps that governments can take include investing in broadband
infrastructure in remote and underserved areas; establishing public wifi hotspots in community
centers, libraries and schools; and launching digital literacy training programs to teach basic digitalskills.21(ii) People
One of the principal barriers to the use of AI for climate mitigation is a lack of trained personnel.
Not only are trained data scientists and engineers in short supply, but many professionals working
on climate issues lack basic familiarity with AI issues.
Governments could help overcome these barriers in several ways.
First, governments could launch skills-development programs for professionals working on AI andclimate issues. Some programs would target professionals with climate expertise, teaching them
about AI; other programs would target professionals with AI expertise, teaching them aboutclimate. The programs could be workshops, short lecture series or full courses. Government
agencies could run such programs or fund others to do so.
Second, governments could launch AI-climate fellowship programs, modeled after Marshall
Scholarships, Schwarzman Scholarships and similar programs. The programs would identify
promising university graduates (perhaps focusing on those from developing countries) and fundresidential fellowships to study topics related to AI and climate change. Governments could
explore partnerships with leading foundations for these programs.22-24
Third, governments could pay for the education of university students learning skills related to theuse of AI for climate mitigation. In some countries, paying the tuition and living expenses ofuniversity students developing such skills could help significantly to increase enrollment inrelevant courses.
Fourth, governments could integrate AI and climate change–related topics into educational
curricula at all levels. AI skills rest on a foundation of science, technology, engineering and math
(STEM) education, with a curriculum that includes quantitative reasoning, logic, computer
programming and related topics. Governments can commit to strengthening STEM education as aplatform for developing a new generation prepared for AI-specific education/training, withparticular applications related to climate change.

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Fifth, government agencies working on
climate mitigation could systematicallyreview the capabilities of their own
staff with respect to AI and launch
programs to ensure their staff remain
up-to-date with respect to AI
developments. This could be especiallybeneficial for grant managers, toensure government funds are
disbursed with an up-to-dateunderstanding of AI’s potential andattention to AI-related data
management practices.
Sixth, governments could commit extra funds to recruit and retain skilled AI professionals. AI
specialists often command high salaries in the private sector, making it challenging forgovernment agencies to hire them. Providing government human resources (HR) departmentswith the authority and resources to compete (at least partially) with the private sector for the best
AI professionals could deliver significant benefits.
Finally, as a core feature of education and training programs for AI and climate change,
governments could pay attention to the global digital divide. As noted, billions of people globallycurrently lack Internet connectivity. Education and training programs focusing on basic digitalskills in many regions will contribute enormously to a workforce able to fully utilize AI for climatechange mitigation over the long-term.

(iii) Computing PowerAI projects require computing power. The lack of adequate computing power can be a barrier
limiting the ability to pursue valuable AI projects related to climate change. Governments can takeseveral steps to address this challenge.
Governments could help increase the availability of computing power for climate change-related
AI projects by (1) investing in computing infrastructure and (2) making computing infrastructure
available for projects that use AI for climate change mitigation.
Governments already play an important role in this regard. Within the US Department of Energy(US DOE), for example, some of the world’s most powerful supercomputers support a global
network of partners as part of the Earth System Grid Federation (above). In connection with thisproject, Oak Ridge National Laboratory (ORNL), Lawrence Livermore National Laboratory (LLNL)
and other US DOE National Labs provide computational services for climate change—including
baselining, simulations and projections—in part using AI tools.25-27
As opportunities to use AI for climate mitigation grow in the years ahead, the role of governments
in helping provide computing power for such opportunities will be important. Many climatechange–related AI projects will have large potential public benefits but little, if any, near-termcommercial return. (Examples include projects that analyze satellite data to identify deforestation
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ICEF Roadmap 2023: AI for Climate Change Mitigationand traffic data to help minimize congestion.) Investing in such projects is a classic governmental
function.
Government investment could take several forms. Governments could invest in their own
computing infrastructure, provide grants for others to develop such infrastructure, and/or provide
tax incentives to encourage development. The approaches that work best will vary from country
to country.
One powerful tool could be to (i) solicit proposals for projects that use AI for climate change
mitigation and then (ii) make computing power available without cost for the proposals that offer
the greatest potential benefits. Microsoft AI for Earth and other private companies already dothis;28 governments could play an important role as demand for computing time increases in theyears ahead. Government high-performance computing (HPC) facilities could expand their review
process and reviewer pool to include more AI expertise and emphasize allocating HPC time for AIenabled research with direct impacts on climate mitigation.29(iv) Cost
Cost is a cross-cutting barrier, relevant to each of the three barriers discussed above (data, people
and computing power). Each of these three barriers could be mostly addressed, at least in themedium-term, with greater funding.
As noted above, many climate change–related AI projects will have little if any near-termcommercial return, making government funding essential. Many advances in the use of AI forclimate mitigation will depend on government funding in the years ahead.
A key question will be how such government funding for AI will be allocated. Some governments
may focus funding on new and innovative AI methods. Other governments may prioritize
greenhouse gas (GHG) reductions, which will often be achievable with existing AI methods. The
allocation of funding between these two types of projects—those investigating new AI methods
and targeting maximum emissions reduction—could have a significant impact.30
Governments also have an important role in making sure that electric utilities that use AI tools to
reduce emissions receive compensation for such projects. Electric utilities that are paid aregulated return based on their capital spending may lack the incentive to invest in AI tools that
reduce emissions and costs. Unlessregulators approve rules that provide
compensation for the value created by AI,
electric utilities may not pursue emissionsreducing projects such as those for demand
response or vehicle-grid integration.31(v) Institutions
A final barrier to the deployment of AI forclimate mitigation is institutional.
Some recent history provides usefulbackground. The modern computing era
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began in the 1960s, as mainframe computers became increasingly central to many business
functions. But the term “Chief Information Officer” wasn’t coined until 1981. Until the 1980s,
few large organizations had executives solely responsible for information and communications
technologies in their top leadership teams.32
In a similar manner, despite significant recent advances in AI, many institutions are only
beginning to incorporate AI into their organization and mission. A range of steps to do so are
possible. For governmental organizations with responsibility for climate change mitigation,
including environment and energy ministries, such steps could include:•

creating an Artificial Intelligence Office, with responsibility for assessing opportunities,
barriers and risks with respect to AI in all aspects of the ministry’s mission;33•

hiring a Special Advisor to the Minister or Cabinet Secretary, with responsibility foradvising the Minister on all matters related to AI;••

creating a unit to improve AI skills throughout the organization; and/or
launching a strategic planning process to consider ways that topics related to AI can best
be addressed within the ministry on an ongoing basis.

Governments can also create or help create public-private partnerships or other stakeholder
groups, bringing together diverse groups to discuss and implement opportunities for using AI
for climate mitigation. Governments could help fund such public-private partnerships and/or
provide the convening power to help assemble and sustain such groups.
Finally, international cooperation could also pay dividends. The Clean Energy Ministerial could
launch an initiative on the use of AI in promoting clean energy. The World MeteorologicalOrganization could launch a program to reconcile potentially contradictory GHG emissions datausing AI tools. (See Chapter 3.) Other international fora could launch work programs in thisarea as well.

B. Managing risks
Risks related to the use of AI for climate mitigation include those related to bias, privacy andincreased emissions. Government policies can play an important role in helping manage eachof these risks.

(i) Bias
Unrepresentative data, poorly-designed algorithms and other factors create risks of bias inmany AI applications. These biases can affect siting recommendations, suggesting (for example)
that new polluting infrastructure be located in low-income communities and new electricvehicle (EV) charging infrastructure be located in high-income communities, in part because
that’s where such infrastructure is found in existing data sets. AI modes can produce poor or
inaccurate results when developers fail to realize that data collected from one socioeconomic
group is not representative of patterns in another socioeconomic group.
Governments can address these risks with a range of tools.

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Data collection standards. Governments could set standards data collection for AI models,
highlighting the importance of diverse and representative data sets. These standards could
be binding or non-binding
Transparency. Similarly, governments could set standards with respect to transparency indeveloping AI models, giving all stakeholders a better opportunity to identify possible biases.
These standards could be binding or non-binding•

Third-party audits. Governments could recommend or require that AI developers retain
third party auditors to assess any bias in their products and establish accreditation
standards for organizations conducting such audits•

Legal accountability. Governments could establish legal frameworks that hold entities
accountable for biased or discriminatory outcomes resulting from AI applications
Convening. Governments could convene diverse stakeholders to evaluate AI products,
bringing people with a wide range of views together and making sure all are heard
Education and training. Governments could offer AI developers, data scientists and otherstakeholders training programs on the importance of bias recognition and mitigation•••

Research and development (R&D). Governments could allocate funding for research into
reducing bias in AI generally and for climate mitigation(ii) Privacy
As set forth in Chapter 10, privacy risks related to the use of AI for climate mitigation include
surveillance, personal identification and data sharing. First, the increasing use of sensors, drones
and IoT devices to monitor environmental change and human behaviors related to carbon
emissions creates a risk that some data could be used for unauthorized surveillance. Second,
when data from multiple sources are aggregated (such as smart meter data and property
records), individuals who were previously anonymous in isolated datasets could become
identifiable. Third, data on energy consumption patterns or other topics could be shared withthird parties, either by the host of that data or as the result of a cyberattack.
Governments can address these risks with policies including:•

Data protection regulations. Governments could enact laws requiring organizations toensure the privacy and protection of personal data; provide transparency on how data areprocessed; and give individual’s rights to access, correct and delete their data. The European
Union’s General Data Protection Regulation (GDPR) is the strongest such law passed globallyto date.

•Privacy by design for all AI models. Governments could require that privacy considerations
be expressly integrated in the design of AI models throughout development and during useof the models.

•

Cybersecurity standards. Governments could mandate cybersecurity measures fororganizations that collect, process or store climate-related data.
Anonymization. Governments could require use of techniques that render personal data lessidentifiable.

•

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Oversight and governance bodies. Governments could establish independent oversightboards or agencies responsible for monitoring and ensuring privacy protections related to AI
and climate mitigation.

(iii) Greenhouse gas (GHG) emissions
As set forth in Chapter 10, AI systems consume energy and, therefore, create a risk of increased
GHG emissions. A number of policies can help mitigate this risk and lower GHG emissions from AI
systems.
•

Research & development (R&D). Governments could invest in R&D on energy-efficient AI
algorithms and hardware. That could include research on methods that require less data orcomputational power for training AI models, such as few-shot learning or transfer learning.

•

Low-carbon data centers. Governments can promote data centers that emit little or no
carbon dioxide through a range of measures, including (1) tax incentives or subsidies fordata centers powered with zero-carbon electricity (renewables, nuclear or fossil generation
with carbon capture); (2) regulations requiring data centers to use a certain percentage ofzero-carbon power and (3) guidelines and incentives for energy-efficient data centers,
accelerating the adoption of energy-efficient cooling, energy-management systems andother technologies.
Energy consumption disclosures. Governments could require AI companies to disclose GHG
emissions associated with their operations on a full lifecycle basis.

•••

Government procurement. Governments can prioritize AI systems with low GHG emissions
when procuring AI solutions for their own use.
Carbon pricing. Governments can implement carbon taxes or cap-and-trade systems to
incentivize a wide range of companies, including AI and data center operators , to reduce
their GHG emissions.

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AI POLICIES IN BRIEF
as of November 2023

UNITED STATESThe Biden administration has devoted considerable attention to AI. Leading announcements
include:•the Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial
Intelligence, released in October 2023, which requires developers of large AI models to share
information about their products with the US government, streamlines visa processes for
noncitizens working on AI and directs federal agencies to issue AI guidelines, among dozens
of provisions;34•

the AI Risk Management Framework, released by the National Institute for Standards andTechnology (NIST) in January 2023 to help “manage risks to individuals, organizations, andsociety associated with artificial intelligence;”35 andthe Blueprint for an AI Bill of Rights, released by the White House in October 2022 to guidethe design and use of AI with five principles—safe and effective systems; algorithmic
discrimination protection; data privacy; notice and explanation; and human alternatives,
considerations and fallback.36•

In July 2023, President Biden met at the White House with the CEOs of leading AI companies,
who pledged “to develop and deploy advanced AI systems to help address society’s greatest
challenges,” including climate change.37 The National Artificial Intelligence Initiative Office, partof the White House Office of Science and Technology Policy, coordinates the federal
government’s National AI Initiative.38
Members of the US Congress have been paying attention as well, with high-profile hearings andbriefings, as well as bills introduced on a number of AI-related topics.39 Legislation to date has
focused on the use of AI within the federal government. This includes:••••

the Advancing American AI Act of 2022, which defines principles for government use of AI;
the AI Training for the Acquisition Workforce Act of 2022, which requires AI training forgovernment acquisition employees;
the National AI Initiative Act of 2020, which established the National Artificial Intelligence
Initiative Office and directed NIST to develop the AI Risk Management Framework; andthe AI in Government Act of 2020, which created the AI Center of Excellence within the
General Services Administration (GSA) and directed the Office of Management and Budget(OMB) to develop guidance on AI for federal agencies.40

EUROPEAN UNION
As of November 2023, the European Parliament, European Commission and Member State
governments were in negotiations concerning an Artificial Intelligence Act that would
comprehensively regulate AI in Europe. If enacted, the legislation would apply a risk-based
approach to AI, with high-risk AI systems subject to the most stringent controls. Drafts of the AI
Act include provisions prohibiting some activities considered to be especially risky, such as live
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facial recognition and scraping biometric data from social media platforms. Final action is
expected in late 2023 or early 2024.41, 42
In September 2022, the European Commission proposed the AI Liability Directive, which isintended to ensure that AI operators can be held liable for damages caused by AI systems. (In theabsence of such a directive, the lack of transparency and complexity of AI systems could makerecovery of damages difficult.) The European Parliament and Council of the European Union have
not yet acted on the European Commission’s proposal. If the AI Liability Directive is adopted, EU
Member States would then be required to incorporate its terms into national laws.43-47
Other important EU AI policies include (1) the Coordinated Plan on Artificial Intelligence, updated
in 2021, which aims to accelerate investments in AI technologies and align AI throughout theEuropean Union48 and (2) the General Data Protection Regulation (GDPR) of 2016. AI is not
explicitly mentioned in the GDPR, but many of its provisions—including those on purpose
limitation, data minimization, the special treatment of “sensitive data” and limitations on
automated decisions—are relevant to AI.49, 50
CHINA
In July 2023, the Cyberspace Administration of China (CAC) and other entities published theProvisional Regulations on Management of Generative Artificial Intelligence Services. The
Provisional Regulations require that any generative AI technologies used to provide services tothe public in the China “reflect socialist core values” and prohibit content that “may harm
national security and hurt the national image.”51
In June 2023, China’s State Council announced that it will submit a draft AI law to the Standing
Committee of the National People's Congress by the end of the year.52 This would be China’s firstnational AI legislation.
In the past several years, the Chinese government has released a number of binding policy
documents on AI. These include:•

Provisions on the Administration of Deep Synthesis Internet Information Services, released by
the CAC, the Ministry of Industry and Information Technology (MIIT) and the Ministry of
Public Security (MPS) in November 2022. This policy document requires the labeling ofsynthetically generated content and prohibits AI tools from generating “fake news
information.”53•

Provisions on the Management of Algorithmic Recommendations in Internet Information
Services, released by CAC, MIIT, MPS and the State Administration for Market Regulation in
December 2021. This document includes provisions for content control and worker
protection and created China’s “algorithm registry,” an online database. Developers are
required to submit information to the registry on the training and deployment of theiralgorithms.54, 55

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In their May 2023 meeting in Hiroshima, Japan, G7 heads of state agreed to launch an initiative
to strengthen collaboration on the governance of generative AI. The initiative will be known as
the “Hiroshima AI process.”56 Also in May 2023, the Japanese government held the first meetingof its Artificial Intelligence Strategy Council, attended by Prime Minister Fumio Kishida.57
In April 2023, Japan’s governing Liberal Democratic Party released an AI White Paper with morethan two dozen recommendations for promoting and managing the development of AI in Japan,
including:••••“Accelerate applied research and development by accumulating domestic knowledge on
foundation models”“Immediately initiate multiple pilot projects with visible results in a short period of time as
specific examples of utilizing AI for basic administrative services“Provide strong support for AI-based smart city initiatives by local governments”“Position the improvement of AI literacy in the public education curriculum in anticipation of
the AI native era, when active use of AI in daily socioeconomic activities will be the norm”58, 59

The AI White Paper builds on Japan’s AI Strategy 2022, released in April 2022 by the Secretariatof Science, Technology and Innovation Policy within the Cabinet office. The AI Strategy 2022 sets
forth five strategic objectives for AI development in Japan:
•
•
•
•
•

“A technological infrastructure that will enable Japan to protect its people in the face ofimminent crises such as pandemics and large-scale disasters”“Japan should become the world's most capable country in the AI era by developing humanresources”“Japan should become a top runner in the application of AI in real-world industries”“In Japan, a series of technology systems to realize a sustainable society with diversity isestablished and a mechanism to operate them is realized”“Japan should lead in building an international network in the AI field for research, educationand social infrastructure”60

INDIA
In April 2023, India’s Ministry of Electronics and Information Technology announced that the
Indian government “is not considering bringing a law or regulating the growth of artificial
intelligence in the country.” The ministry referred to AI as a “kinetic enabler of the digital
economy.”61, 62 In February 2023, the Indian government announced the establishment of three
new Centers of Excellence for Artificial Intelligence.63
In 2021, Nitii Ayog published a Responsible AI/AIforALL report, proposing seven “principles forthe responsible management of AI systems: 1. Safety and Reliability 2. Equality
3. Inclusivity and Non-discrimination 4. Privacy and Security 5. Transparency 6. Accountability 7.
Protection and Reinforcement of Positive Human Values.”64 In 2018, Nitii Ayog released an AI
Strategy calling for investment in education and training, privacy protections and use of AI acrossthe value chain.65 The Indian Government maintains an AI website at https://indiaai.gov.in/.66, 67

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Chapter 12:

FINDINGS AND RECOMMENDATIONS
A. Findings
1. Artificial intelligence is currently contributing to climate change
mitigation in important ways. AI tools play a central role inmonitoring methane emissions and deforestation; in integrating
solar and wind power into electric grids; in improving the energy
efficiency of industrial operations; in optimizing agricultural
practices to reduce emissions and increase yields; and muchmore.
2. Artificial intelligence has the potential to make significant
additional contributions to climate change mitigation in theyears ahead. Areas in which artificial intelligence could makesignificant contributions include greenhouse gas emissions
monitoring; decarbonization of the power sector; the discovery ofnovel materials; and reducing emissions from manufacturing, the food system
and road transport.
3. Artificial intelligence is not a panacea when it comes to climate change. Significant changes
beyond the scope of artificial intelligence will be required to achieve net zero emissions. AI does
not build infrastructure, move molecules or shift supply chains. While emissions reduction fromthe use of AI may be substantial, these savings alone cannot deliver deep decarbonization.
4. The lack of trained and experienced personnel is a critical barrier to the use of AI for climate
mitigation. More computer programmers and data engineers with the skills to create AI
applications for climate mitigation are needed. Equally important, policymakers, businessleaders, factory operators, climate professionals and many others need greater familiarity withthe potential for AI to contribute to their work and risks posed by AI.
5. The lack of access to high-quality data is a critical barrier to the use of AI for climate
mitigation. Successful AI applications are built on data that is available, accessible, andstandardized. Poorly measured and non-standardized data will limit the quality of AI
development, while private ownership and the cost of accessing data will limit the scale of AI
deployment. In addition, current limits of data gathering may introduce bias to AI analyses andconclusions. Strong policy, investment in education, and public sector action will be needed toovercome these barriers.

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6. Other barriers to the use of AI for climate mitigation include cost, lack of available computing
power and institutional issues. More resources are needed for training programs, RD&D andother purposes. Some promising ideas may falter from lack of access to the computing power
needed to fully develop them. Many organizations working on climate mitigation – including
government agencies, businesses and NGOs – are only beginning to incorporate AI into theiroperations and organizational structures.
7. Significant resources—by governments, corporations and other stakeholders—will be
required for AI to reach its potential in helping mitigate climate change.
Providing the human resources needed will require hiring and mission priority. Expansion ofboth funding and personnel are essential for delivering climate solutions at scale.
8. Risks of using AI include bias, invasions of privacy and security issues. These risks exist whenusing AI for climate mitigation. Bias in AI data and algorithms can lead to injustice, failure toachieve program objectives, unneeded expense and many other harmful outcomes. Failures tobuild in privacy safeguards can lead to unauthorized data sharing, personal identification andsurveillance. Negligence or misuse of AI technologies can lead to accidents and serious security
problems.
9. Greenhouse gas emissions from the computing infrastructure for AI are currently modest –significantly less than 1% of the global total. Better data collection and assessment
methodologies are needed to provide a more precise estimate with high confidence.
10. The amount of future greenhouse gas emissions from AI computing infrastructure is highly
uncertain. Such emissions could rise or fall in the years ahead. Future greenhouse gas emission
from computing operations for AI depend on a number of factors, including: (i) the processes
used to manufacture AI computing equipment, (ii) the energy efficiency of AI computing
equipment, (iii) optimization techniques used to reduce the size of AI models, (iv) the use ofzero carbon electricity in AI operations, and (v) demand for AI applications. Each of these ishighly uncertain.

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B. Recommendations
1. Artificial intelligence tools should be integrated into many
aspects of climate change mitigation. Government agencies
should use AI tools in policy making and funding decisions.
Businesses should use AI tools in sustainability programs. All
institutions with a role in climate mitigation should examine
opportunities for AI to support their mission and identify
priority areas in which AI could contribute.
2. AI skills-development and capacity-building should be apriority in all institutions with a role in climate mitigation.
A. Educational institutions at all levels should offer
courses relevant to AI. This should include basic skills
development in primary and secondary schools, more advanced
courses in universities and continuing education for professionals of all
kinds. Academic institutions should develop classes, internships, certification programs andexecutive training programs that provide familiarity with AI.
B. Governments and foundations should launch AI-climate fellowship programs. These
programs should identify promising students (from developing countries in particular) andfund fellowships in AI and climate-focused topics.
C. Government agencies with responsibility for climate issues should regularly review thecapabilities of their staffs with respect to AI. The goals should be to continually enhancethese capabilities and ensure that opportunities for AI to advance their mission arerecognized and accurately evaluated.
D. All organizations working on climate mitigation should require minimum AI literacy from a
broad cross-section of employees. Understanding of AI’s capabilities and experience
working with AI will contribute to employees’ impact and effectiveness in the years ahead.
Training and education are essential.
3. Governments should assist in the development and sharing of data for AI applications thatmitigate climate change.
A. Governments should systematically consider opportunities to generate and share datathat may be useful for climate mitigation. This should include data with respect to weather
forecasting, electricity generation and use, manufacturing, hydrocarbon production andconsumption, and transport.
B. Governments should establish policies to promote standardization and harmonization ofclimate and energy-transition data. These policies should include: (i) data management
guidelines such as the “FAIR Guiding Principles” (Findability, Accessibility, Interoperability
and Reusability), (ii) data standardization and harmonization requirements in connection
with government-funded RD&D, (iii) measures to ensure transparency, including access tometadata as well as core data, and (iv) funding for data standardization organizations andactivities.
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C. Governments should establish climate data task forces composed of key stakeholders andexperts. The UK’s Energy Data Task Force provides a good model. The climate data task
forces should start by identifying potential barriers to data access. They should plan ways tofederate, share and anonymize data for AI climate applications.
4. Companies with datasets relevant to climate change mitigation should consider sharing
portions of those datasets publicly. Public release of a company's datasets can provide direct
benefits to that company by encouraging development of algorithms helpful to the company,
attracting AI talent and facilitating integration with related datasets. Public release may provide
broader social benefits as well. In releasing datasets, companies must anonymize and strictly
protect personally identifiable information.
5. Governments should provide substantial funding for the development and application of AI
tools for climate mitigation.
A. Government funding for AI in the climate area should focus on emissions reduction
potential, not just new AI methods. Innovations in AI methodologies are important but may
not be required for high-impact climate mitigation programs. Some funding programs
should make emissions reduction potential using AI a priority selection criterion.
B. Governments should help increase the availability of computing power for climatechange-related AI projects. They should do so by (i) investing in computing infrastructure,(ii) soliciting proposals for projects that use AI for climate change mitigation, and (iii) making
computing power available without cost for the proposals that offer the greatest potential
benefits. This could include solicitations from the private sector in partnership withgovernments.
6. All government agencies with responsibility for climate change, including environment andenergy ministries, should create an Artificial Intelligence Office, with responsibility forassessing opportunities, barriers and risks with respect to AI in all aspects of the agency’s
mission. These agencies should also consider (i) hiring a Special Advisor to the head of theagency, with responsibility for advising the head on all matters related to AI, (ii) creating a unit
to improve AI skills throughout the organization; and (iii) launching a strategic planning process
to consider ways that topics related to AI can best be addressed within the ministry on an
ongoing basis.
7. Electric utilities should be incentivized to deploy artificial intelligence, with regulated returns
for investments in AI and other tools. Governments should provide utilities, generators andbalancing authorities with financial incentives and technical support to integrate AI into grid
systems. These programs should recognize the significant benefits AI tools can provide in gridplanning as well as risks related to the use of AI in grid operations.
8. Governments should launch international platforms to support cooperative work on AI forclimate change mitigation.
A. One or more member countries should launch a Clean Energy Ministerial initiative on AI
and climate mitigation.

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B. The UNFCCC, International Energy Agency and Food and Agriculture Organization, among
other organizations, should build AI-for-climate issues centrally into their work programs.
C. One or more global organizations should be tasked with helping to reconcile any
conflicting AI-enabled data on GHG emissions. The International Methane Emissions
Observatory (IMEO) could fulfill this role with respect to methane emissions. The World
Meteorological Organization (WMO) and Food and Agriculture Organizations (FAO) could
fulfill this role for CO2 and some other GHG emissions datasets. Alternatively, a single new,centralized organization could be set-up to serve as a one-stop clearinghouse for all AIenabled GHG emissions data.
9. Governments should work to minimize greenhouse gas emissions from AI’s computing
infrastructure. This should include (i) investing in RD&D on energy-efficient AI algorithms andhardware; (ii) requiring reporting on GHG emissions in all government-funded AI work; (iii)
prioritizing AI systems with low greenhouse gas emissions when procuring AI solutions; (iv)promoting data centers that emit little or no carbon dioxide through a range of measures
including regulations, guidelines and/or financial incentives, (v) establishing standardized
methods for measuring emissions from AI; (vi) requiring AI companies to disclose greenhousegas emissions associated with their operations, on a full lifecycle basis; and (vii) implementing
ambitious climate programs that incentivize all companies, including AI and data center
operators, to reduce their greenhouse gas emissions.
10. Avoiding unfair bias should be a core, high-priority principle guiding the development of all AI
tools for climate change mitigation. Businesses, governments, and researchers should
continually pay attention to the possibility of data and algorithmic bias in their work.
Governments should address the risk of bias with tools including (i) standards for the collectionof data for AI models, highlighting the importance of diverse and representative data sets; (ii)standards with respect to transparency in the development of AI models, giving all stakeholders
a better opportunity to identify possible biases; (iii) legal frameworks that hold entities
accountable for biased or discriminatory outcomes resulting from AI applications; (iv) regularreviews to consider the potential for bias in all AI programs related to climate change; and (v)training programs on the importance of bias recognition and mitigation.
11. Governments should address privacy risks related to AI-climate programs with dataprotection regulations, cybersecurity standards, oversight boards and techniques that makepersonal data less identifiable.

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This roadmap was prepared to facilitate dialogue at the Tenth Innovation for
Cool Earth Forum (October 2023), for final release at COP28 in December 2023.
We are deeply grateful to the Ministry of Economy,
Trade and Industry (METI) and New Energy and Industrial Technology
Development Organization (NEDO), Japan for launching and supporting the ICEF
Innovation Roadmap Project of which this is a part.