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Read to find out how AI is good for not just our comfort of life, but also helps in sustainable development efforts.

Words By Varnika Srivastava


Let us start at the basics. Artificial Intelligence (AI) is the capacity of any machine or computer to replicate human skills such as learning from examples and experience, identifying things, comprehending and responding to language, making decisions, and solving problems. The machine can therefore execute complicated functions such as driving a car by combining these diverse skills. AI has grown in popularity as a result of its effective implementation in previously unimaginable applications, such as setting up your schedule with Siri or Alexa.

AI may be used to help manage environmental effects and climate change in a variety of economic sectors and circumstances. AI-infused clean distributed energy networks, precision agriculture, sustainable supply chains, environmental monitoring and enforcement, and improved weather and catastrophe prediction and response are some examples of applications. According to the report, utilizing AI for environmental applications may contribute up to $5.2 trillion USD to the global economy in 2030, representing a 4.4 percent increase over current business practices. Simultaneously, the deployment of AI levers may cut global greenhouse gas (GHG) emissions by 4% in 2030, equivalent to 2.4 Gt CO2e – the total yearly emissions of Australia, Canada, and Japan in 2030. At the same time as productivity gains, AI has the potential to produce 38.2 million net new jobs throughout the global economy, resulting in more skilled positions. 

It doesn’t stop just there. Automated data collecting, decision-making, and corrective actions via robots are used in AI-augmented agriculture to allow early detection of crop illnesses and problems, give scheduled nourishment to animals, and optimize agricultural inputs and returns depending on supply and demand. This has the potential to improve the agriculture industry’s resource efficiency by reducing the use of water, fertilizers, and pesticides that harm key ecosystems, as well as increasing resistance to climate extremes. Moreover, a new area known as “Climate Informatics” is emerging, which employs artificial intelligence to alter weather forecasting and enhance our knowledge of climate change’s consequences.

This sector has historically required high-performance, energy-intensive computation, but deep-learning networks may make computers operate considerably quicker and incorporate more complexity from the ‘real-world’ system into calculations. In just over a decade, improvements in AI and computing power will enable household computers to have the same processing capacity as today’s supercomputers, decreasing research costs, increasing scientific output, and speeding up discoveries. AI methods may also be used to correct model biases, retrieve the most important data in order to minimize data deterioration, forecast severe occurrences, and simulate consequences.

Additionally, through the synchronization of emergency information capabilities, AI can analyze simulations and real-time data (including social media data) of meteorological events and catastrophes in an area to identify weaknesses and improve disaster preparation, give early warning, and prioritize response. Deep reinforcement learning, similar to how AI is now being used to find the greatest move in games like AlphaGo, might one day be integrated into catastrophe simulations to establish ideal response methods. 

All in all, by detecting energy emission reductions, CO2 removal, assisting in the development of greener transportation networks, monitoring deforestation, and forecasting extreme weather events, AI has the ability to speed global efforts to preserve the environment and save resources. With better grid systems that are more predictable and efficient, as well as the usage of renewable energy, the application of machine learning to optimize energy output and demand in real-time is becoming increasingly common. Smart sensors and meters may be installed in buildings to gather data and to monitor, analyze, and optimize energy consumption. Machine learning algorithms are already being utilized in smart transportation, such as Google Maps and Waze, to enhance navigation, boost safety, and give information on traffic flows and congestion (e.g. Nexar). 

Not to forget, AI can identify changes in land use, vegetation, forest cover, and the aftermath of natural disasters when coupled with satellite images. Invasive species may be monitored, recognized, and tracked using the technologies described above. Machine learning and computer vision are used to identify and track their presence, as well as to eliminate them.

Blue River Technology is utilizing artificial intelligence to identify the presence of invasive species and other changes in biodiversity. Anti-poaching squads have used predictive software to assist them design patrol routes. AI can also collect data from ocean places that are difficult or impossible to visit, allowing animals and ecosystems to be protected. It may also be used to track illegal fishing. Ocean conditions, such as pollution levels, temperature, and pH, may be monitored using AI-powered robots. 

Water scientists utilize AI to estimate water use in a specific geographic region and create weather forecasts in order to make educated policy decisions. Weather, soil and subsurface water conditions, and droughts may all be predicted using AI and satellite data. 

Lastly, Air purifiers with AI can collect real-time air quality and environmental data and adjust filtration efficiency accordingly. People living in metropolitan areas can receive alerts about pollution levels via AI-powered simulations. There are techniques that can rapidly and reliably detect pollution sources. Consequently, AI can assist reduce air pollution by using data from cars, radar sensors, and cameras.