AI for a sustainable environment-resilient future
USING AI to better manage the environment could reduce greenhouse gas (GHG) emissions while, at the same time, boosting global GDP by up to $5 trillion and creating up to 38 million jobs by 2030.
The PwC UK research study commissioned by Microsoft examines the potential opportunities of AI for economic growth and emissions reduction potential in the next seven years.
Titled “How AI can enable a sustainable future,” the study estimates that harnessing AI in four vital economic sectors in support of environmental management could yield productivity benefits, a higher GDP, reduced carbon emissions and up to 38 million jobs globally.
Benefits, risks
The research models scenarios for AI’s use across four sectors: agriculture, transport, energy and water. Some of the examples in the study are AI-infused clean distributed energy grids, precision agriculture, sustainable supply chains, environmental monitoring and enforcement, and enhanced weather and disaster prediction and response.
The study estimates that using environmental applications of AI in these four key sectors could contribute up to $5.2 trillion to the global economy in 2030, a 4.4 percent increase relative to business as usual. In parallel, the application of AI levers could reduce worldwide GHG emissions by 4 percent in 2030, an amount equal to the 2030 annual emissions of Australia, Canada and Japan combined.
The report also finds encouraging signs for AI’s potential to improve health. More accurate and localized early warning systems for air pollution, for example, could save an estimated $2.4 billion globally in reduced health care costs and health impacts.
Additional environmental benefits can be achieved in water quality, air pollution, deforestation, land degradation, and biodiversity, through greater data, insights and early warning systems that could save 32 million hectares of forest by 2030.
The report warns, however, that for all the potential that AI for environmental systems have, its application and uses could also exacerbate existing threats or create new risks. Broader AI risks linked to bias, security and control are all potential risks to the environment. In addition, there are substantial and wide-reaching barriers relating to these sectors that need to be overcome.
All hands on deck
The study recommends the following “key enablers” that will unlock the potential of AI for the environment:
– facilitate awareness, value alignment, collaboration and multidisciplinary partnerships, including technologists, industry, scientists, civil society and governments.
– Start with “Responsible AI” and extend this principled approach to include consideration of societal and environmental impact to ensure that sustainability principles are embedded alongside wider considerations of AI safety, ethics, values and governance.
– Address digital infrastructure, data and technology access, and wider complementary technologies through building appropriate cloudproviding infrastructure, including satellites and data infrastructure, facilitating fit-for-purpose data access and annotation.
– Provide opportunities and training for upskilling and reskilling to adapt to sectoral transformations — not only to unlock new innovations and scale applications, but to manage and govern AI-based systems to best serve people and the planet for markets, and the workforce of the future.
– Encourage R&D from research to scalable commercial deployment, with a focus on connecting stakeholders (industrial, academic and government research agencies) and encouraging interdisciplinary research and development that leads to scalable commercial deployment.
All stakeholders across the public, private and third sectors must be involved in unlocking AI to tackle environmental challenges to its fullest potential. Each has a role to play in creating this “enabling environment” to accelerate economic and environmental progress.
– Governments must take an agile approach to targeted regulation and policy support on data access, R&D, and digital infrastructure and skills investment.
– Tech developers should create, provide and improve data assets and provide access to AI tools, data and wider complementary technologies.
– Companies are urged to embed environmental impact considerations into AI strategies and deployment, identify disruption and transformation needs, and embrace upskilling and reskilling of workforces.
– Academia should encourage multi-disciplinary focus, combining AI and domain-relevant education and research, and industry partnerships, including facilitating multidisciplinary research and learning across technical and domain expertise and encouraging industry partnerships for skills training, and data and knowledge sharing.
– Nongovernmental organizations need to develop partnerships with technologists, invest in digital upskilling, and explore where AI and wider tech innovations can create benefits.
The researchers hope their work is a first step in a larger conversation to inject attention and investment into a tech-first approach to the most pressing environmental challenges.