ENVIRONMENT/ SCIENCE & TECH
- GS-3: Awareness in the fields of IT
- GS-3: Environment Conservation
- GS-2: Government policies and interventions for development in various sectors and issues arising out of their design and implementation.
AI technologies and Climate
Context: Union government in the recent budget described AI as sunrise technology.
What is Artificial Intelligence?
Artificial intelligence (AI) 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
- Conversational bots
- Email spam filters
- Netflix’s recommendations
How AI can help in tackling Climate Change?
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.
Challenges with AI
- Carbon Emissions:
- On the one hand, it can help reduce the effects of the climate crisis, such as in smart grid design, developing low-emission infrastructure, and modelling climate change predictions. On the other hand, AI is itself a significant emitter of carbon.
- The carbon footprint of training a single big language model is equal to around 300,000 kg of carbon dioxide emissions. This is of the order of 125 round-trip flights between New York and Beijing.
- In 2020, digital technologies accounted for between 1.8 per cent and 6.3 per cent of global emissions.
- 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:
- First, AI’s social and economic benefits are accruing to a few countries,
- Second, most of the current efforts and narratives on the relationship between AI and climate impact are being driven by the developed West.
- Developing countries are not sufficiently represented and empowered at the international bodies that set rules and standards on AI.
- Transparency and Accountability
- The largest companies working in AI space are neither transparent nor meaningfully committed to studying to substantively limit the climate impact of their operations.
- Also, Policy makers are not “the fluent” in developing and underdeveloped countries that may create barriers in crafting regulations and industrial policy.
- 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.
Connecting the dots: