Context: Recently, Union government in the recent budget described AI as sunrise technology while there is an allure to national dreams of economic prosperity and global competitiveness, underwritten by AI, there is an environmental cost.

• In this article we talks about the interconnection between the development of AI Technology and Climate Change.

About Artificial Intelligence (AI)

• AI describes the action of machines accomplishing tasks that have historically required human intelligence. 
• It is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. 

Some examples of Artificial Intelligence are:-

• Siri, Alexa and other smart assistants; Self-driving cars; Robo-advisors; Conversational bots; Email spam filters and Netflix’s recommendations.

How AI can help in tackling Climate Change?

Collection of Data: The great strength of AI lies in its ability to learn by experience, collecting massive amounts of data from its environment, intuiting connections that humans fail to notice, and recommending appropriate actions on the basis of its conclusions. 

• Entities looking to reduce their carbon footprint should turn the AI spotlight on all three components of the effort:

Monitoring Emissions: Entities can use AI-powered data engineering to automatically track emissions throughout their carbon footprint. 

• They can arrange to collect data from operations and from every part of the value chain, including materials and components suppliers, transporters, and even downstream users of their products. 
• By layering intelligence onto the data, AI can generate approximations of missing data and estimate the level of certainty of the results.

Predicting Emissions: Predictive AI can forecast future emissions across a the entities carbon footprint, in relation to current reduction efforts, new carbon reduction methodologies, and future demand. As a result, they can set, adjust, and achieve reduction targets more accurately.

• Reducing Emissions: By providing detailed insight into every aspect of the value chain, prescriptive AI and optimization can improve efficiency in production, transportation, and elsewhere, thereby reducing carbon emissions and cutting costs.

Issues with AI
Unfair race for dominance in AI:  A few developed economies possess certain material advantages right from the start, they also set the rules.

• They have an advantage in research and development, and possess a skilled workforce as well as wealth to invest in AI.

Inequality in terms of governance: We can also look at the state of inequity in AI in terms of governance: How “tech fluent” are policymakers in developing and underdeveloped countries?

• What barriers do they face in crafting regulations and industrial policy?
• At the same time, there is an emerging challenge at the nexus of AI and climate change that could deepen this inequity.

Climate impact of AI

• The climate impact of AI comes in a few forms: The energy use of training and operating large AI models is one.
• In 2020, digital technologies accounted for between 1.8 per cent and 6.3 per cent of global emissions.
• In November 2021, UNESCO adopted the  In November 2021, UNESCO adopted the Recommendation on the Ethics of Artificial Intelligence, calling on actors to “reduce the environmental impact of AI systems, including but not limited to its carbon footprint.” , calling on actors to “reduce the environmental impact of AI systems, including but not limited to its carbon footprint.”

Inequitable access to resources

• Both global AI governance and climate change policy (historically) are contentious, being rooted in inequitable access to resources.

Developing and underdeveloped countries face a challenge on two fronts

• AI’s social and economic benefits are accruing to a few countries.
• Most of the current efforts and narratives on the relationship between AI and climate impact are being driven by the developed West.

Road Ahead

• Governments of developing countries, India included, should also assess their technology-led growth priorities in the context of AI’s climate costs. It may be worth thinking through what “solutions” would truly work for the unique social and economic contexts of the communities in our global village.