AI to tackle climate change

How Can Artificial Intelligence Tackle Climate Change?

Muskan
Muskan

“Between 2030 and 2050, climate change is expected to cause approximately 250,000 additional deaths per year from malnutrition, malaria, diarrhea, and heat stress alone. The direct damage costs to health are estimated to be between USD 2-4 billion per year by 2030.” – World Health Organization.

Climate change is perhaps one of the most pressing threats that humanity has ever faced. The perils of climate change are undermining years of progress in global health by threatening air pollution, safe water supplies, and nutritious food supplies. However, despite the fact that some still believe that climate change is a farce, a majority of people are dedicated to doing everything in their power to slow down or solve the issue. 

There has been enormous technical progress in our fight against climate change. In recent times, promising applications of Artificial Intelligence and data science are helping us understand the large volumes of data generated across different sectors, which in turn is paving the way to better monitor the natural resources of the planet. In fact, many tech companies and investors are finding interest in machine learning solutions that glean patterns from data and use these patterns to predict, recommend, and make decisions in real or virtual settings. Here are some of the ways in which AI is helping humans tackle climate change and its impacts. 

Harnessing Swaths of Data and Deriving Actionable Insights

We can leverage AI to analyze the flood of data that is generated on a day-to-day basis that can help identify patterns and derive actionable insights. It is essential to have an accurate picture of how the world is changing due to climate change in order to devise solutions to tackle it. Assessing the present state of our climate can help us improve our solutions by identifying vulnerabilities and weaknesses. This information can be shared with policy-makers in order to deal with the ill effects of climate change effectively. 

Extreme Weather Predictions

One of the most important applications of AI is predictive analytics in order to help humans better prepare for extreme weather conditions. This can drastically reduce the damage caused to human lives and property as governments and citizens can take appropriate measures with early warnings. 

In the past few years, there has been significant progress in the use of machine-learning algorithms to identify tropical cyclones and other calamities based on data from extreme weather events. Furthermore, machines are being used to analyze the strengths and weaknesses of climate change models by reviewing the ones which are currently in use and extracting information from them on a regular basis. 

Researchers are actively studying climate informatics with AI paradigms. Research on climate informatics involves leveraging state-of-the-art techniques to understand the climate better and address climate change.

Renewable Energy

Grid operators, developers, and consumers are embracing Artificial intelligence as we move into the Fourth Industrial Revolution, clearing the way for a seamless transition to higher usage of renewables. With AI’s enhanced prediction capabilities, businesses can better forecast demand and manage assets, while automating processes can promote consistent operation excellence, thereby resulting in competitive advantage and cost savings for all. Artificial Intelligence is also contributing to the boost of renewables through improved material sciences. For instance, using machine learning, researchers were able to discover new compounds better able to harness solar power than photovoltaics. AI, coupled with other innovative technologies such as IoT, big data, and blockchain, can unlock the vast potential of renewable energy.

Optimizing Energy Generation and Consumption

The power grid is perhaps one of the most complex innovations of humankind. Grids must continuously balance supply and demand in real-time because electricity cannot be efficiently stored at scale. The use of machine learning can automate and optimize this complex system, allowing grid operators to forecast electricity flows better and eliminate inefficiencies that increase carbon emissions. 

Using AI, decentralized energy sources can send surplus electricity to the grid in order to direct that power to where it is actually needed. In a similar manner, energy storage facilities within industries, offices, homes, and cars can be used to store any excess energy, and AI can be used to deploy this energy whenever it is required. Changing from an infrastructure-driven system to one that is AI-based enables the grid to be more flexible and resilient to unforeseen events, creating a system that forecasts and reacts rapidly instead of taking days to do so.

The policy-makers of different countries should consider public financing of renewable energy projects and incentives to boost distributed energy generation, both around the home and in the private sector. Furthermore, we need global standards for AI software governance to ensure interoperability, transparency, and equal access across the energy sector. In the future, AI and software will be the key to a more sustainable world, allowing for a faster, more reliable grid.

Managing and Protecting the Planet’s Natural Resources

Emerging technologies have the potential to re-think how humans relate to the natural world, providing the tools for monitoring and managing natural resources in a more efficient way to meet business and society’s needs. Machine learning and artificial intelligence can help us make our planet more sustainable by enabling innovative products and services for better natural resource management. For instance, smart thermostats can save customers between 10-12% on heating and 15% on cooling costs; irrigation systems that can save households around 8,800 gallons of water per year are some of the consumer-facing AI devices that can help conserve natural resources.

Tracking Carbon Emissions

In order to cut down carbon emissions, an organization must first assess its carbon footprint. This can be pretty challenging as the company has to factor in emissions from its own operations, emissions due to the electricity consumed during the operations, and finally, the emissions that go into the production and consumption of the company’s products from suppliers to customers. Many startups are working on providing companies with a comprehensive view of their carbon footprint. However, it is not just limited to that. These startups also use AI to offer data-driven plans to reduce emissions. For instance, these startups advise companies on how they can switch to renewables, buy carbon offsets, push their suppliers to transition to low-carbon practices, etc. 

Some Startups Leveraging AI to Tackle Climate Change

Blue Sky Analytics

Cofounded by Abhilasha Purwar and Kshitij Purwar, Blue Sky Analytics is a Gurugram-based geospatial data intelligence startup that leverages satellite data, AI, and IoT to offer high frequency and near real-time data. The startup aims to fight pollution and provide its users with contextualized information on air quality, real-time air quality data, three-day forecasts, comparison between air quality of multiple stations, and gamification for user engagement, among others. 

One Concern

One Concern is one of the oldest startups fighting against the ill effects of climate change as its mission statement notes, “We’re working to make disasters less disastrous.” One Concern has created what it calls “a digital twin” of the world’s natural and built environments in order to model climate change in a dynamic and hyperlocal way. The company leverages AI/ML for a better understanding of the vast amounts of unstructured data so that humans can comprehend it and take appropriate actions to minimize disaster risks. 

Cervest

London-based startup Cervest combines machine learning, climate science, and scalable computing to process and analyze large volumes of data in order to offer insights on millions of assets and help in making decisions by quantifying climate risks. The platform provides a unified methodology for evaluating asset-level climate risk at a global level for enterprises, governments, insurance firms, and financial markets. 

Potential Pitfalls

Although AI has a massive potential to reduce global carbon emissions, data centers are contributing to the accelerating international chain of logistics, exploitation of resources, and fossil fuel emissions in ways we do not understand yet. Essentially, as we progress, we need to make sure that the benefits of AI will far outweigh its drawbacks. In order to shape a positive scenario for our future, we need collective action on multiple fronts: ensuring appropriate regulations that align with the interest of the planet, developing new standards to reduce environmental impacts, and formulating guidelines for the development of green AI initiatives. 

As our knowledge and technologies advance to the planetary scale, it is revealing new feedback loops and interdependencies between the natural environment and our engineered systems. Environmental impacts and the responsibility to care for our planet should be considered in tech infrastructures, working methods, and policies for fair, accountable, transparent, and ethical AI systems. The potential applications of Artificial Intelligence are endless. However, there is no application that matters more at this point than tackling climate change, and it’s ill-effects. 

Many entrepreneurs and researchers are devoting themselves to this cause. After all, the economic and societal value provided by working on an issue such as this opens up limitless opportunities. Therefore, the wiser we can become with AI and machine learning, the higher are our chances of reducing the damage caused by climate change.

Muskan
Technology

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