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Addressing climate change is an urgent challenge, bringing together experts from various fields to implement solutions to mitigate the worst of the impact.
The rapid adoption of artificial intelligence (AI) will potentially contribute to greenhouse gas emissions owing to the electricity supply required to power massive data center capacity. But AI is also making possible solutions that may previously have been theoretical.
A survey by Boston Consulting Group (BCG) found that 87% of global public- and private-sector climate and AI leaders view advanced analytics and AI as helpful tools in the fight against climate change. Additionally, 67% of those in the private sector stated that governments should do more to support the use of AI in fighting climate change.
What are some ways AI algorithms and models can help to reduce carbon emissions?
Monitoring and Predicting Weather Trends
AI models trained on weather data, such as satellite data imagery, climate models, and historical weather patterns, can predict extreme conditions and the long-term impact of changing climate conditions on different regions with increasing accuracy. They can then provide decision-making tools to identify effective preparation and response. These models can also help identify risk factors and develop ways to mitigate them.
Accurate predictions can help provide early warnings of hurricanes, droughts, or floods, enabling governments and communities to reduce their impact proactively.
For instance, the FloodNet project, a collaboration between the City of New York, the New York University (NYU) Tandon School of Engineering, the City University of New York (CUNY), and community partners, has deployed open-source sensors in flood-prone areas across New York City. The project uses deep learning algorithms to analyze sensor data and satellite images of rivers to predict flooding in real-time.
In sub-Saharan Africa, where climate change is expected to amplify drought and other extreme conditions, UN agencies are collaborating on the AI-driven IKI project centered in Burundi, Chad, and Sudan, which is making projections based on past environmental change around displacement hotspots to help communities adapt to climate change and prepare humanitarian aid.
In Kenya, the MyAnga mobile phone app helps Kenyan herders manage their livestock and prepare for drought based on data from global meteorological stations and satellites.
Policymakers can use AI models to identify and implement the most effective strategies for reducing emissions, enhancing the resilience of local systems, and helping citizens to adapt to changing conditions. They can also use AI to track how effective those policies are in implementation.
Smart Grid Management
Cloud-connected, AI-powered systems can enhance smart grids to manage electricity demand and the generation required to meet it, reducing excess emissions.
Machine learning, data analytics, and the Internet of Things (IoT) devices enable smart grids to manage complex power generation, transmission, and distribution.
AI modeling can help smart grids manage intermittent and variable renewable energy generation using sensors, software, and communication networks to predict power loads. It can also optimize the use of battery storage systems to store and release excess energy as needed. Increasing the share of wind, solar, and hydropower in the grid reduces the demand for fossil fuel generation to reduce carbon emissions.
Advanced metering infrastructure, voltage and frequency regulation, fault detection, and isolation combined with AI data analytics can increase energy efficiency by reducing energy losses during transmission and the need for polluting peaking plants operating during high-demand periods.
Using AI and smart meters in homes and offices can improve demand-side management by monitoring power consumption and analyzing large volumes of data from Heating, Ventilation, and Air Conditioning (HVAC) systems to assist with planning and scheduling. AI algorithms can optimize energy use and supply across buildings, increasing energy efficiency. Architectural modeling can also help design more energy-efficient building structures, layouts, and materials.
The U.S. Federal Energy Regulatory Commission (FERC) found that demand response can have a significant impact on consumption (PDF), with peak load reduced by up to 150 gigawatts (GW). The Electric Power Research Institute (EPRI) has estimated these smart grids could reduce summer peaks in energy demand by 175GW by 2030.
For instance, Equans, which provides companies with technical and energy services, uses predictive AI modeling to identify energy inefficiencies and emissions sources in complex datasets to optimize industrial processes and energy performance.
Predictive Maintenance
Predictive maintenance uses real-time data to identify the best time to carry out equipment maintenance and warn of failures before they occur to prevent unexpected downtime.
AI uses machine learning algorithms to analyze historical sensor data and build modeling to recognize normal operations, and then identify abnormal conditions from real-time data to send alerts of impending risks or failures.
By identifying potential problems with equipment before they occur, organizations can reduce downtime, optimize their maintenance schedules, and improve their energy efficiency.
In manufacturing, this can minimize equipment breakdowns, preventing the release of pollutants, while in the oil and gas industry, predicting failure rates to carry out maintenance when needed rather than replacing assets at fixed intervals can reduce excessive flaring.
Flaring, which intentionally burns natural gas to prevent damaging infrastructure during maintenance and outages, is estimated to contribute 5-10% of annual global greenhouse gas emissions. AI can also detect methane leaks in natural gas infrastructure so that they can be patched to limit how much is released into the atmosphere.
For instance, US-based AI solutions provider SparkCognition’s SparkPredict software aggregates historical data and creates models that learn over time as they process more data. A significant oil and gas producer deployed SparkPredict onshore at its remote control center to provide alerting, 10-minute diagnostics, and increased operational visibility.
One particular alert identified a temperature sensor providing faulty values, which could be addressed quickly to prevent maintenance that would have taken up to two days.
In the renewable energy sector, AI-powered predictive maintenance can limit equipment failures and optimize power production, reducing the need for fossil fuel generation to meet demand.
Waste and Recycling Management
AI-powered systems can optimize waste management and recycling by analyzing historical and real-time data on waste production, collection, and disposal.
Modeling can predict traffic conditions and the level to which bins are filled. This can help companies and municipalities optimize their waste management systems to reduce waste, increase recycling rates, save on costs, and reduce carbon emissions from collection vehicles.
Machine learning algorithms can analyze data to identify opportunities for recycling and composting, reducing landfill waste by improving the separation of recyclable materials. At recycling facilities, advanced robotics and AI vision systems can sort materials more effectively than manual labor, increasing the volume and quality of recyclable materials. This reduces emissions from managing landfill waste while also increasing recycling capacity to limit the need for new carbon-intensive production.
For instance, Canada-based Waste Robotics integrates computer vision, deep learning algorithms, and robotic technologies into waste handling processes. Its hyperspectral camera technology enables detailed material analysis and identification for precise sorting and separation. At the same time, its multi-sensor model can accurately recognize the composition and characteristics of materials to maximize resource recovery.
Increasing Transportation Efficiency
AI systems can optimize traffic management and public transportation routes to increase fuel efficiency and reduce carbon emissions.
AI-powered traffic management systems can analyze real-time data from sensors, GPS devices, cameras, and other sources to reduce congestion and fuel consumption from idled vehicles. AI models can analyze passenger demand to optimize bus, train, and tram routes, schedules, and capacity allocation, increasing fuel efficiency.
AI can also optimize route planning for supply chain and logistics operations, resulting in more efficient resource allocation and faster deliveries.
Google’s Project Green Light aims to help improve transportation by allowing city traffic engineers to use AI to help optimize traffic lights at intersections by making cost-effective infrastructure updates to reduce the number of stops cars make at red lights. Early data from the project indicates the potential to reduce stopping by up to 30% and cut emissions at intersections by up to 10%.
Tracking Air Quality
Air pollution in cities around the world contributes to severe health conditions, including cardiorespiratory diseases, prenatal complications, and cancers, increasing hospitalization rates.
AI-assisted air quality monitoring can enhance smart city management with predictive systems that can help authorities anticipate increases in air pollution and make decisions to reduce carbon emissions and improve public health.
Interpreting and projecting air pollution requires complex numerical models that simulate weather and chemistry. AI systems can easily handle this and analyze data generated by sensors and measurements from monitoring stations to provide detailed information.
The Global Environment Monitoring System for Air (GEMS/Air), co-founded by the UN Environment Program (UNEP) and IQAir, is the world’s largest global air quality information network. IQAir aggregates data from more than 25,000 monitoring stations in more than 140 countries and uses AI to provide insights on the impact of real-time air quality to inform health protection measures.
AI systems can automate the analysis of satellite images of power plants to generate regular updates on carbon emissions. They can also measure local infrastructure and electricity use to measure the impact of a power plant — including gas-fired plants, which have less visible emissions than coal-fired plants. For instance, the Carbon Tracker non-profit think tank uses satellite imagery, sensors, and AI technology to monitor emissions from coal and gas plants, track where air pollution originates, and make this information available to the public.
The Bottom Line
There are many ways AI can help mitigate the worst effects of climate change, from projecting trends and providing warning systems to optimizing energy generation, transportation systems, and waste management to reduce carbon emissions.
The rapid adoption of generative AI models will help increase the deployment of these AI-powered systems by companies and governments worldwide. This will provide more data analytics and insights than traditional data systems, making AI a valuable tool in meeting emissions reduction targets.
