AI helps combat Climate Change

Anjali Sharma

GG News Bureau

UNITED NATIONS, 5th Nov. UN-led AI Advisory Body on Saturday said that the Artificial intelligence is making inroads worldwide in health, education and industry, but how can this cutting-edge technology help the world combat and mitigate the effects of climate change?

UN-led AI Advisory Body was launched and it advanced a growing global trend to harness machine learning to find solutions to common challenges.

According to AI, the data crunching game and a growing number of governments, businesses and civil society partners are working together to reap its many benefits.

That includes speeding up and scaling efforts to realize such global ambitions as the 2030 Agenda and its 17 SDGs which serve as the world’s blueprint to make the planet greener, cleaner and fairer.

UN Climate Change Conference (COP 28) begins at the end of November in Dubai, to discuss how AI helps the world, from communities to corporations to lawmakers, tackle climate change:

According to the UN’s World Meteorological Organization AI driven technologies offer unheard-of capabilities to process enormous volumes of data, extract insightful knowledge and improve predictive models.

That means improved modelling and predicting climate change patterns that can help communities and authorities to draft effective adaptation and mitigation strategies.

UN agencies support vulnerable communities in Burundi, Chad and Sudan through an AI-driven project to investigate past environmental change around displacement hotspots and deliver future projections to inform adaptation measures and anticipatory action for integration in humanitarian programming.

On the ground, enhanced data can be a game-changer.

MyAnga app helps Kenyan pastoralists brace for drought. With data from global meteorological stations and satellites sent to their mobile phones, herders can plan ahead, better manage their livestock and save hours of scouting for green pastures.

AI can help communities around the world to better brace for climate disasters As extreme weather events unfold with more frequency and intensity,.

AI-driven initiatives are targeting high-risk areas and feeding into local and national response plans. For areas susceptible to landslides, for example, mapping can help local authorities plan and implement sustainable development measures, reduce risks and ensure the safety of residents in vulnerable communities.

The developments in AI and robotics were among the tools identified in a recent project led by WMO, UNEP and International Telecommunication Union in enhancing accuracy in weather forecasts to reduce disaster risks.

WMO said that AI is helping in those efforts which operates a disaster risk reduction programme and multi-hazard early warning system that serves countries, communities and humanitarian agencies.

Secretary-General’s groundbreaking Early Warnings for All initiative benefits is also part of the UN.

It was launched this year, and its action plan aims to ensure everyone on Earth is protected from hazardous weather, water or climate events through early warning systems by the end of 2027.

Cities around the world track pollution to alert the public in cases of dangerous levels.

Using AI, susceptibility maps can support local governments in making decisions to improve public health and urban resilience.

AI can improve urban planning as well as traffic and waste management, making cities more sustainable and liveable.

AI can revolutionize the world’s approach to carbon neutrality and usher in an era of intelligent sustainability on a global scale at a time when the race is on to keep Earth from heating up to dangerous levels.

AI’s algorithms have a key role to play in minimizing environmental impact and maximizing efficiency as a critical catalyst in realizing global carbon neutrality goals.

In terms of realizing the global goal for affordable and clean energy for all by 2030 (SDG 7), AI can optimize grids and increase the efficiency of renewable sources. Predictive maintenance using AI can also reduce downtime in energy production. That can mean reducing the planet’s carbon footprint.

As an industry with a record of high emissions, fashion can benefit from AI-driven research and development to accelerate innovation.

The $2.4 trillion-dollar global industry that employs 300 million people across the value chain, many of whom are women, and the scale of the industry is only expected to grow over the coming years.

AI has a global reach, unsustainable practices within the fashion sector have important impacts on social and environmental development indicators, and without major changes to production processes and consumption patterns in fashion, the social and environmental costs of the sector will continue to mount, according to the UN Alliance for Sustainable Fashion.

AI can step in. Machine learning can optimize supply chains to reduce waste, monitor resource consumption and promote sustainable manufacturing processes.

AI can help to accelerate the energy transition by optimizing savings and improving efficiency across energy-intensive sectors.

It accounts for 22 per cent of global greenhouse gas emissions, according to a UN climate assessment report, but AI-driven efforts can change that.

From corporations to small-scale farmers facing extreme weather events, water scarcity and land degradation, AI can help optimize their practices, reduce waste and minimize the environmental impact of food production.

AI-driven smart grids can balance supply and demand, facilitating the integration of renewables into energy systems and reducing the reliance on fossil fuels.

This year’s Science and Innovation Forum  held in mid-October focused on climate action.

FAO hosted a week-long event showcasing technologies that aim to transform traditional practices into data-driven systems that protect people and the planet.

AI and digital tools are pivotal in building climate-resilient agrifood systems that are more efficient, sustainable and adaptable to climate change challenges, according to the agency.

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