Achieving Net Zero with Artificial Intelligence
Climate change is one of the most pressing challenges of our time, and it requires immediate action to reduce greenhouse gas emissions and transition to a low-carbon economy. The UK has set ambitious goals to achieve net-zero emissions by 2050. In 2022, The UK Government launched a new programme to support the use of artificial intelligence (AI), titled ‘AI for Decarbonisation Programme’.
The programme supports the development of AI technology in the quest towards achieving net zero. ‘The programme will promote coordination and collaboration between AI and carbon-emitting sectors in the UK in order to maximise the economic and carbon benefits of AI solutions in solving our most critical decarbonisation challenges.’ Projects are encouraged to include uses of AI which could enable a faster transition to renewable energy, assist in the decarbonisation of the transport industry, enhance energy storage and management and reduce emissions from heavy industries like agriculture and manufacturing.
The focus of the programme is on the delivery of real-world solutions to enable progress towards net zero. AI won’t solve the complexity around climate change without the human in the loop and the subsequent ease of access to data in an open and secure way. These technologies will only succeed if they are developed and deployed in partnership with stakeholders, working closely with end users, industry experts, scientists and policymakers to ensure that the systems are fit for purpose.
According to the Boston Consulting Group, AI technologies could reduce global emissions by up to 10 per cent by 2030 – a significant chunk of the 2050 net zero goal. At the rate at which AI is advancing, we expect the balance of its contribution to the net zero goal to increase year on year. In a strange paradox, the advances in AI technology that we as humans have made, offer us part of the way out of the mess we’ve created. Experts must also consider the negative implications of computing and data storage on the environment and find ways to mitigate this so that we’re not expending more energy on AI solutions than the energy we’re trying to save.
Optimising Energy Consumption
Data solutions and AI technologies can optimise energy consumption in buildings and transportation systems by analysing large amounts of data to identify energy waste and opportunities for optimisation. For example, a building’s energy management system can be integrated with smart sensors and AI algorithms to analyse occupancy patterns, weather data, and energy usage to optimise heating and cooling systems and reduce energy waste. According to a report by Carbon Trust, optimising energy consumption in commercial buildings can reduce energy consumption by up to 20%.
Similarly, AI-based transportation systems can optimise routes and schedules to reduce energy consumption and improve efficiency. For example, London’s public transportation system uses an AI-based system called the Bus Priority System to optimise bus routes and schedules to reduce congestion and improve efficiency. This system has reduced bus travel times by up to 10%, resulting in a reduction of 22,000 tonnes of CO2 emissions per year.
Transitioning to Renewable Energy
Renewable energy sources such as wind and solar power are critical for reducing greenhouse gas emissions and achieving net-zero targets. However, integrating these sources into the grid requires advanced data solutions and AI technologies to manage the fluctuating supply and demand of renewable energy. AI algorithms can predict energy generation and demand to balance the grid accordingly, ensuring that renewable energy sources are integrated efficiently and reliably.
One example of AI-based renewable energy integration is the Virtual Power Plant (VPP) in Australia. The VPP uses AI algorithms to manage the energy output of distributed solar panels and batteries, ensuring that energy is supplied to the grid when demand is high and stored when demand is low. The VPP has reduced the need for fossil fuel-based energy sources and has reduced electricity costs for consumers.
Enhancing Energy Storage and Management:
Energy storage systems such as batteries and pumped hydro storage are critical for integrating renewable energy sources into the grid and ensuring a reliable and stable energy supply. Data solutions and AI technologies can enhance energy storage and management by predicting energy demand and usage patterns to optimise the performance of storage systems. AI algorithms can predict the lifespan of batteries and adjust their charging and discharging cycles to optimise their performance.
One example of AI-based energy storage is Tesla’s Powerpack system. The Powerpack uses AI algorithms to optimise the performance of batteries and integrate renewable energy sources into the grid. The system can store excess energy generated by solar panels and wind turbines, ensuring a stable and reliable energy supply even when renewable energy sources are not generating power.
Improving Transportation Efficiency:
Transportation is a major source of greenhouse gas emissions, and reducing emissions from transportation requires data solutions and AI technologies to optimise routes, schedules, and vehicle performance. AI-based transportation systems can predict traffic patterns and identify the most efficient routes for vehicles, reducing travel times and energy consumption.
One example of AI-based transportation efficiency is the City of Helsinki’s Transport Agency. The agency uses an AI-based system called the Journey Planner to optimise routes and schedules for buses and trains, reducing travel times and improving efficiency. The system has reduced emissions from public transportation by up to 15%.
Reducing Emissions from Industries:
Industries such as Agriculture and Manufacturing are a major source of greenhouse gas emissions, and reducing emissions from these industries requires a collaborative approach across departments and supply chains. ESG frameworks have become a collective driving force for managing, tracking and improving environmental credentials alongside social and organisational governance.
AI technologies are being developed to optimise production processes and reduce waste in heavy industries such as Agriculture, Engineering and Manufacturing. AI algorithms can analyse data from sensors and other sources to identify opportunities for process optimisation and energy/waste reduction.
One example of AI-based industrial emissions reduction is the steel manufacturer SSAB in Sweden. SSAB uses an AI-based system called the SmartSteel system to optimise the production process and reduce waste. The system analyses data from sensors and other sources to identify opportunities for process optimisation and waste reduction, resulting in a reduction of 5% in CO2 emissions.
Prof. Andrea Renda, Head of Global Governance and Digital Economy expert at the Centre for European Policy Studies in Brussels, Belgium, and also a member of the expert group advising the European Commission says ‘it’s time to merge the two big debates of today. One is on digital technology and the other one is on sustainable development, and in particular the environment. If we use the former to save the latter, I think we will have made the best possible use of the resources that we have,’ he said. ‘Otherwise we’re just wasting time.’
It’s clear that the development of AI based solutions is critical for achieving the UK’s net-zero emissions target by 2050 and by association be a key enabler in tackling global climate change. By integrating innovative AI technologies into the energy and transportation systems as well as industry, the UK can reduce greenhouse gas emissions, save costs, and move toward a sustainable and low-carbon future – a net zero future, for all.
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