Role of AI in World Climate Change
Alain Onesti, IT Executive in the HR Industry, Author of the Book, My New Colleague and Co-Author of the Journal Article (AI based – Green Banking Technologies and Bank Stability – Moderating role of Climate Change) describes that the Artificial intelligence and advanced analytics can be utilized to manage complex issues connected to reducing climate change and enhancing adaptation and resilience.
Global economic, social, and environmental systems will be significantly impacted by the changing climate. From longer droughts to more destructive storms, Onesti believes that we are already witnessing many of its environmental repercussions.
To achieve climate impact by 2050, as well as other mitigation measures, is crucial. But in order to reduce the damage, we must also put more effort into adapting and being resilient from quick rapid response to long-term strategy. Research, financing, and educational efforts, among others, will be needed to support these initiatives.
AI as a tool is ideally suited to assist in managing these intricate problems. It can be utilized to assist all stakeholders in adopting a more educated and data-driven strategy to reducing carbon emissions and establishing a greener society because of its capacity to collect, compile, and analyze big, complicated datasets on emissions, climate effect, and more. It can also be used to shift the focus of international climate efforts to the areas that are most at risk.
With the effective use of facts, learning algorithms, and sensor systems, Artificial Intelligence (AI) is a revolutionary paradigm that has a larger ability to analyze, anticipate, and mitigate the danger of climate change. It makes calculations, forecasts, and judgements to lessen the effects of climate change.
Onesti says that AI helps us better comprehend the effects of climate change across a variety of geographical places by creating efficient models for weather forecasting and environmental monitoring. It analyses climatic information and makes forecasts for severe weather, other socioeconomic effects of climate change, and precipitation. From a technical standpoint, AI improves climate forecasts, illustrates the effects of extreme weather, determines the real source of carbon emissions, and makes a number of other logical advances.
This helps the decision-makers to be aware of the changing climate, eroding natural habitats, hurricanes, rising sea levels, and extinction of species. However, the scientific community and professionals have already begun concentrating on climate analytics with AI paradigms. The predictive models diverge from long-term prediction, assessment, and mitigation and are better ideal for short-term forecasting models. More advanced research is required in this area to get the most out of AI for climate change mitigation.
Role of AI in World Climate Change
AI and machine learning are being used by scientists to fight climate change. Let’s look at some of the ways in which this technology can lower greenhouse gas emissions and aid nations in adapting to the effects of a warming climate.
Transportation
One of the most polluting businesses in the world is transportation, which includes cars, trucks, trains, and ships. But AI can also improve the sector’s cleanliness and safety. Vehicles with AI capabilities can reduce energy use by choosing the most energy-efficient routes.
Additionally, by transmitting risks between vehicles, such technology can lower the number of accidents. Currently, there are self-driving cars on the road, and completely autonomous vehicles that don’t need any human interaction at all might not be far behind.
Weather Forecasting
Meteorologists are never completely correct at predicting the weather. But using AI, scientists can reliably identify meteorological shifts, torrential downpours, and tropical cyclones. People can better anticipate for storms and reduce hazards with earlier and more accurate forecasts, whether by fleeing the area or erecting storm barriers. Additionally, such technology can more accurately predict wildfires, warmer temperatures, and other extreme weather occurrences.
Urban Planning
By utilizing enormous amounts of data, AI can improve the livability and efficiency of cities in the face of a changing climate. After extreme weather occurrences, AI can help with disaster response, for example, by processing information rapidly and allocating resources where they are most needed. By maximizing water and energy utilization, “smart cities” powered by AI can help cut down on resource waste. This technology is already being implemented in some of the world’s fastest-growing cities.
Mitigation
AI may be used to measure emissions at the macro and micro levels, cut emissions and the effects of greenhouse gases (GHGs), and remove already-emitted emissions from the atmosphere. According to the experience of BCG, for instance, AI can be used to reduce GHG emissions by 5% to 10% of an organization’s carbon footprint, or 2.6 to 5.3 giga tons of CO2e on a worldwide scale.
Distributed Energy Grids
Energy storage, efficiency, and load management can all be improved, along with the integration and dependability of renewable energy sources. AI can also enable pricing structure and trading, which can lead to market incentives.
Smart Food and Agricultural Systems
Automation of data collection, decision-making, and corrective actions through robotics are all part of AI-augmented agriculture. This allows for the early detection of crop diseases and problems, the timing of livestock nutrition, and general optimization of farm products and returns based on demand and supply. By reducing the use of water, fertilizers, and pesticides that harm significant ecosystems, this is expected to improve the agriculture sector’s resource efficiency and raise resilience to climate changes.
Conclusion
What is preventing organizations from using Artificial Intelligence (AI) more effectively when there are so many potent potential for it to make a difference in this conflict? While certain A.I. solutions are already well-established and prepared for widespread use, most of them are dispersed, occasionally difficult to reach, and lacking the resources to scale. 78% of poll participants claim that the challenges are brought on by a lack of AI knowledge, 77% mention availability issues, and 67% mention a lack of trust in data and analysis relating to artificial intelligence.
From the above article, Alain Onesti concludes that AI is not the only solution. It’s one of several tools we should employ to deal with this major problem. However, it can assist us in taking a faster, more data-driven, and more educated course, and we don’t have time to waste.
