A dive into AI’s vast potential in tackling climate change
Artificial Intelligence (AI) assists us in numerous unique ways in our daily lives. It is only logical, then, for scientists to look towards leveraging its immense computational power to address major challenges like climate change.
Terms like ‘green AI’, ‘sustainable AI’, and ‘eco-friendly AI’ all refer to the same concept, which involves creating and using AI technologies in ways that minimise environmental impact and promote long-term sustainability.
Energy Efficiency
AI improves energy efficiency in buildings and industries by predicting energy usage patterns and adjusting consumption accordingly. It identifies inefficiencies and provides suggestions for improvement by analysing extensive datasets on energy consumption. For example, data from sensors comparing indoor and outdoor temperatures with the energy supplied enables the AI system to adjust heating and ventilation based on weather forecasts, ensuring comfort for occupants.
Grid Management
Predictive analytics improve grid management, with AI transforming grid operations. AI runs complex calculations daily to forecast electricity demand and find cost-effective energy dispatch methods. Machine learning models perform these tasks much faster. Utility companies are also integrating AI into operations like inspecting and managing transmission lines and transformers, helping to detect potential disasters so that pre-emptive action is possible.
Reducing Carbon Footprint
AI can measure greenhouse gas emissions, identify improvement areas, and help companies reduce their carbon footprints by optimising inventory, delivery, and employee schedules. It ensures sufficient stock to meet customer demand while minimising the ecological impact of moving and storing goods. AI combines demand forecasting, last-mile delivery, and routing optimisation to address these issues. Scheduled optimisation aligns talent effectively, similar to inventory management.
Smart Waste Management
AI can revolutionise waste management, particularly through automating waste sorting. AI-powered robots use sensors, cameras, and machine learning to identify, sort, and separate waste more accurately and quickly than manual methods, improving recycling efficiency and reducing contamination.
Researchers are developing smart systems using hyperspectral cameras to better identify and characterise organic materials in non-recyclable waste. These cameras capture more spectral bands than digital cameras, revealing chemical characteristics invisible to the naked eye. They also scrutinise physical and chemical properties of waste to enhance sorting accuracy.
AI can further aid in disassembling complex electronics, examining waste generation patterns, optimising collection routes, and reducing the carbon footprint of waste collection vehicles. Predictive analytics help forecast waste trends, allowing for better resource planning.
Climate experts and scientists are becoming increasingly interested in the integration of AI into climate systems, intrigued by the vast potential that the technology holds. The numerous different ways in which AI can help combat the effects of climate change have already been explored, of which quite a few have to do with the energy sector.
Water Management
AI improves water management by monitoring and analysing water cycle data, including water quality and usage, and identifying potential issues in water supply infrastructure. It processes large data sets in real-time, allowing utilities to detect problems ahead of time. AI predicts water demand and adjusts supply, reducing waste and ensuring effective distribution. It also identifies leaks and pressure issues, and augments water flow, improving overall water supply efficiency.
Biodiversity Conservation
AI is a powerful tool in nature conservation, used for monitoring wildlife and collecting environmental DNA. AI, particularly computer vision, transforms data (pertaining to population sizes and species) into meaningful information, helpful in speeding up responses to threats like disease. Additionally, it forecasts the impacts of climate change and human activities on species and ecosystems, creating models of future biodiversity changes.
AI-powered technologies, like sensors, are also used for wildlife monitoring and anti-poaching. Additionally, AI optimises resource management in conservation, including deploying park rangers efficiently, predictive maintenance of equipment, and intelligent project monitoring.
Climate Models
Global climate models, which simulate the Earth’s future climate, rely on complex equations and interconnected system components like the atmosphere, ocean, and land surface. These models use a grid mesh for accuracy, with finer meshes being more precise but computationally expensive, requiring supercomputers.
Even then, some important processes remain unresolved. AI can enhance these models by making them faster and more efficient, reducing energy consumption.
AI helps climate scientists predict patterns and develop mitigation strategies by cracking data on greenhouse gas emissions and weather patterns, promoting coordinated environmental preservation efforts.
The Downsides of Climate AI
While it is true that the benefits associated with the powerhouse of a technology like AI far outweigh the drawbacks, there are still some areas of improvement that warrant attention and need addressing.
Take, for instance, the electronic waste generated during the creation of sustainable AI, impacting the environment. To reduce e-waste, AI hardware should be designed for durability, repairability, and recycling. AI systems can also perpetuate bias and discrimination if trained on biased data, leading to social and environmental inequalities.
Additionally, AI systems in data centres consume significant energy and water. Larger models, like those with deep learning algorithms, have substantial carbon footprints. For example, training a model with 110 million parameters emits 0.64 tonnes of CO2, while a 75 billion-parameter model emits 550 tonnes. Most emissions come from running these models on live data. Researchers are working on smaller, more energy-efficient models. Data centres, especially in hot regions, require extensive cooling, contributing to their carbon footprint, which is over 2% of global emissions and is projected to rise to 14% by 2040.
Embracing AI as a solution to climate change indicates a pivotal step towards a sustainable future. The transformative potential of AI, from enhancing energy efficiency to revolutionising waste and water management, is undeniable. Yet, this process must be coupled with mindful practices to mitigate its environmental and social impacts.
By ensuring sustainable development in AI’s lifecycle, we can harness its power responsibly, making sure that the technology not only addresses immediate climate challenges but also paves the way for long-term ecological balance and equity. Efforts towards this dual approach now will ensure that AI becomes a true ally in our fight against climate change in the future.
