Artificial Intelligence - Mitigating the Effects of Climate Change

Published on 22 Sep, 2021

The adverse effects of climate change are evident across the world. The spike in their occurrence has mobilized governments and organizations to act decisively and take definitive steps to control the damage. Countries are now understanding the benefits of using cutting-edge technologies such as artificial intelligence and machine learning to accelerate the net-zero emission goals put forth by International Climate Change bodies.

With increase in erratic weather patterns and climatic conditions worldwide, scientists and institutions across the globe have expressed deep concerns over the adverse impact of climate change. In addition to the environmental initiatives being taken to mitigate these effects, several technology firms and startups have discovered how to use new-age technologies such as artificial intelligence (AI) and machine learning (ML) to contribute to this cause and combat climate change.

These companies are making an impact and setting the foundation for future entrepreneurs to further explore the capabilities of AI.

Problem Statement
It is now evident that adverse weather conditions can potentially wreak havoc on core operations and affect business continuity. There have been supply chain disruptions and mass evacuations due to wildfires, flash flood, and other calamities, which have made several regions inhabitable. The traditional approach of depending on historical data and weather patterns is no longer a reliable means to prepare a contingency plan. Thus, companies have decided to incorporate innovative technologies to bolster efforts toward a sustainable future.

Observed Solution
With the recent developments in the fields of AI and ML, certain promising applications have been developed. They can intelligently interpret the vast amounts of data generated across various industrial and commercial sectors and help monitor earth’s climatic conditions.

There are several ways in which AI can potentially mitigate the negative impact of climate change. For instance, in the field of environmental and climate intelligence, AI has helped researchers achieve 89% to 99% accuracy in identifying and predicting cyclones and other erratic climatic activities, according to the Columbia University Climate Institute. AI, combined with deep learning algorithms, aids in rapidly analyzing dynamic systems and simulating them to produce accurate models of weather prediction systems and augment decision-making for scientists.

Firms and Startups Actively Involved in this Domain

  • Google uses ML to analyze precipitation in high resolution, which helps in instantaneous predictions of weather patterns. They achieve this by making predictions about simple radar data as a proxy for rainfall rather than modeling complex weather systems.
  • An initiative known as the "Ocean Data Alliance" implements AI and has collaborated with a global network of cities to develop comparative smart ocean city action plans that utilize open ocean data to address the climate crisis. Their objective is to launch smart ocean cities that can enhance waste-water treatment to prevent algae blooms and restore oxygen to coastal dead zones.
  • Several startups have also associated themselves in the collective effort to combat climate change. One Concern is developing a "digital twin" of the world’s natural and built environments to dynamically and hyper-locally model the effects of climate change, offering what it terms "Resilience-as-a-Service."
  • London-based Cervest's approach is to make its climate intelligence platform available to all with a freemium business model, a strategy it expects will lead to network effects.

Team Aranca's Suggestions
As next steps, government institutes, private enterprises, and public sector companies must be educated on and made aware of how AI and ML technologies can potentially contribute toward curbing the adverse effects of climate change. The focus should be on significantly transforming data-driven solutions that can be promoted at scale. With gradual increase in advancements and further developments in the field of AI, we can expect heterogenous solutions being implemented in the future.