A new study released by the Bengaluru-based think tank Center for Study of Science, Technology, and Policy (CSTEP) lays the groundwork for incorporating artificial intelligence (AI) into India’s climate change mitigation and adaptation initiatives.
The study looked at AI’s potential to boost climate action in India, with a focus on transportation and water management. According to the study, the use of AI in the transportation sector has the potential to significantly reduce GHG (greenhouse gas) emissions and, as a result, can be useful in mitigating the effects of climate change.
“One such high-impact application is in planning the electric vehicle (EV) charging infrastructure — a crucial factor for large-scale EV uptake and another is in flood prediction and warning, which can enable efficiently, targeted emergency responses. The study acknowledges AI as a powerful tool to boost and speed up our response to climate change. Recognizing that how AI is deployed soon will determine India’s progress towards achieving its climate action goals, the report furnishes a comprehensive roadmap to guide the way forward,” the CSTEP said in its report.
It identified the use of water, including drinking, agriculture, and industrial, and applications of AI in water management and conservation.
“Diminishing glaciers pose a long-time critical risk to water availability and security in India. AI’s can be deployed for the frequent and more expansive monitoring of glacial retreat and advance, better simulations/estimates of impacts of glacial changes in river water systems,” the report said.
“In the Indian context, some glacier mapping datasets (like those on Hindu Kush Himalayas) are readily available. It is crucial to deploy appropriate AI, ML (machine learning) or DL (deep learning) technologies to monitor or map the effects of climate change on the glacial structure and assess the impact,” the report added.
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Explaining the use of AI and ML in the transport sector the report further said, “India can also explore the possibility of using AI tools or writing suitable AI/ML algorithms for energy optimization. EV penetration in India, especially of four-wheelers, is not very impressive. The penetration of heavyweight EV vehicles (like trucks, buses, etc.) is almost non-existent. Though the rate of penetration seems to be increasing in the case of two and three-wheelers, there is a long way to go. The nature of datasets, i.e. the demand, charging pattern, load on the grid, etc. can also be obtained only when there is a substantial number of EVs plying on the Indian roads.”
“Thus, this AI use case has the potential to generate a positive end-user impact and mitigate climate change issues over the mid to long term. One of the factors for the low demand for EVs can also be the high purchase price of an EV for the consumer. Evaluating their costs versus overall returns in the Indian context will be useful in enabling informed decision-making by the end user. In India, traffic data (such as the number of road users and pedestrians, traffic density), information on intersections, and types of vehicles at an intersection are not readily available. AI can be deployed to gather these big and raw datasets from traffic video feed that are accessible. The use case will result in high positive returns in terms of reducing the overall traffic activity, and hence traffic congestion on roads. Moreover, it would certainly lead to efficient fuel consumption by vehicles and a reduction in overall emissions. With the availability of datasets, intelligent traffic signal systems can prove to be a solution to traffic congestion in the short term, with immediate and direct impact on the end users,” the report added.
