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The role of AI in tackling climate change - A double-edged sword

Mon, 22/05/2023 - 11:58

As humanity grapples with the urgent threat of climate change, there is a growing reliance on finding new and innovative solutions to tackle this existential crisis. Among the many available tools at our disposal, perhaps none holds as much promise as artificial intelligence (AI). With its remarkable ability to analyze vast amounts of data, identify patterns, and make predictions, AI is quickly emerging as a vital tool in the fight against climate change. From predicting extreme weather events to optimizing renewable energy systems, AI is revolutionizing our approach to environmental sustainability. A study conducted by the accounting firm PricewaterhouseCoopers revealed that by 2030, AI could play a significant role in reducing global greenhouse gas emissions by up to 4%. This promising finding highlights the potential impact of technology in combating climate change. Furthermore, a 2022 BCG Climate AI Survey report finds that 87% of private and public sector chief executive officers (CEOs) with decision-making power in AI and climate believe AI is an essential tool in the fight against climate change.

However, the famous adage "with great power, comes great responsibility" holds true when it comes to AI, as its immense potential necessitates careful consideration of its usage. Therefore, it is essential to approach technological innovation with a critical eye, taking into account both the benefits and potential drawbacks. By evaluating the pros and cons of AI implementation, we can make informed decisions about its use and ensure that it is leveraged in a responsible and effective manner.

Promises of AI

Enhanced data analytics in improving climate modelling: One of the key benefits of AI in tackling climate change is its ability to process large amounts of data quickly and accurately. Predictive analytics using AI employs statistical algorithms and data to forecast future trends giving us more precise and timely insights. This can also help us better understand how climate change is affecting different regions and ecosystems, as well as identify areas where we need to focus our efforts. An example of AI being used to improve climate change prediction is the DeepCube project, which uses deep learning to analyze satellite data and make predictions about sea surface temperatures. This approach has been shown to outperform traditional climate models in terms of accuracy and efficiency. 

Optimisation of energy systems: AI is proving to be valuable in various aspects of energy transition, such as improving the accuracy of renewable energy forecasting, optimizing grid operations, coordinating distributed energy assets and demand-side management, and accelerating materials innovation and discovery to reduce greenhouse gas emissions. For example, researchers at Case Western Reserve University are using AI to analyze data from solar power plants and identify areas where improvements can be made to increase efficiency. Furthermore, AI can also be used to model wind turbine wake flows and optimize the layout of wind farms, making wind energy production more efficient and cost-effective

Precision agriculture: AI can support sustainable agriculture practices by monitoring various factors such as crop moisture, soil composition, and temperature, allowing farmers to optimise crop growth and yield while minimizing water and fertilizer usage. This technology can also be used to grow crops in urban areas, reducing the need for deforestation for food production.

It can also help protect the environment by locating and safeguarding carbon sinks, which absorb carbon dioxide from the atmosphere. Moreover, it can help farmers target weeds with the appropriate amount of herbicide, reducing the need for widespread chemical use and minimizing environmental pollution.

Decarbonising transportation: AI can enhance transportation usage estimates and model public transportation demand, leading to better infrastructure planning. AI can also optimize freight routing and scheduling, and promote the use of low-carbon transportation modes, such as trains. In terms of electric vehicles, AI can help optimize charging protocols and locations, as well as facilitate the development of new batteries and fuels. Additionally, AI plays a critical role in autonomous vehicle technology, although its impact on emissions is uncertain. While self-driving personal vehicles could potentially increase emissions, autonomous buses that integrate with public transportation and pool passengers have the potential to decrease emissions.

Climate change mitigation and adaptation: AI can help in climate risk and impact analytics by integrating environmental, climate, and weather data to analyse climate risk and precisely account for carbon emissions. It can further help researchers analyze satellite images to identify deforestation or land use changes that contribute to climate change. Additionally, AI can be used to create energy-efficient networks by using data analytics to optimize energy consumption and reduce carbon dioxide emissions across multiple layers. On a micro level, it has the potential to improve energy efficiency in cities by forecasting energy demand by incorporating data from smart meters and the Internet of Things(IOTs), All these will go a long way in reducing greenhouse gas emissions and help societies adapt to the effects of climate change by supporting more resilient infrastructure planning and natural resource management. 

Pitfalls of AI

Negative environmental impact: Training large algorithms, such as deep learning models, involves processing vast amounts of data through complex mathematical calculations. These calculations require high-performance computing resources, including powerful processors and specialized hardware like graphics processing units (GPUs). As a result, training these models can consume significant amounts of energy which can have a substantial impact on the environment. In fact, a study conducted by the Massachusetts Institute of Technology found that the energy consumption associated with training a large algorithm can result in the emission of as much as 284,000 kg of carbon dioxide. Furthermore, the study found that the energy consumption and carbon footprint of training algorithms are likely to increase as the demand for AI applications continues to grow.

Data privacy and security concerns: The use of AI requires the collection and processing of large amounts of data, which raises concerns about data privacy, security, and potential misuse. These concerns are valid as AI systems rely on data to learn and make decisions. The data used to train AI models can contain sensitive information, and if not handled properly, it can lead to data breaches and privacy violations. Additionally, AI systems can be vulnerable to cyber attacks, which can result in data theft or manipulation.

Exacerbate existing inequalities: Access to technology and digital resources is not equal worldwide, and this can create a scenario that is known as digital apartheid, which may leave certain communities behind in their efforts to mitigate climate change. The development and implementation of AI solutions can be costly, potentially creating disparities in access to AI-driven climate change mitigation technologies, further exacerbating existing inequalities between countries and regions which could give developed countries an unfair advantage over developing countries in addressing climate change. Over-reliance on AI also raises the risk of a lack of human oversight and accountability in decision-making processes. Therefore, it is crucial to strike a balance between utilizing AI as a tool for tackling climate change and ensuring it does not contribute to existing inequalities or undermine the human factor.

AI Automation and ethical concerns: There is a growing concern that AI could lead to significant job losses in the future as machines become increasingly capable of performing tasks that were previously done by humans. This would further compound the already pressing challenges of climate change, which has been shown to exacerbate existing social and economic inequalities. A study by the McKinsey Global Institute estimates that by 2030, up to 375 million workers (or roughly 14% of the global workforce) could be displaced by automation and AI. Additionally, AI-powered job automation is a pressing concern as the technology is adopted in industries like marketing, manufacturing, and healthcare, with 85 million jobs expected to be lost to automation between 2020 and 2025 alone. While AI is expected to create 97 million new jobs, many workers may not have the necessary skills for these technical roles, which could leave them behind if companies do not upskill their workforces.


In conclusion, while AI holds great promise in mitigating the devastating effects of climate change, we cannot overlook the potential risks associated with using AI to protect the environment. It is imperative that we take a proactive stance towards developing AI solutions that prioritize energy efficiency and limit carbon emissions, while also implementing measures to minimize the ecological footprint of AI development and deployment.

Moreover, AI is not a panacea for mitigating the effects of climate change. While it can certainly aid us in optimizing energy consumption and reducing emissions, we must not lose sight of the fact that addressing climate change requires a multi-pronged, systemic approach that necessitates collective action. By embracing a holistic outlook towards AI and sustainability, we can ensure that these cutting-edge tools continue to advance our collective progress while minimizing their ecological impact. In this era of the climate crisis, sustainability should be at the forefront of our minds in all spheres of society, and we should work in tandem to create a more environmentally conscious future.

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