In the face of increasing global consciousness of the threat posed by climate change, engineers are leading the charge in the development and crafting of solutions to achieve sustainability. Harnessing the power of artificial intelligence (AI) in the engineering sector as a tool for combating the climate emergency is certainly a present reality. Moreover, this trend is predicted to further grow the long term.
The COP26 UN Climate Change Conference held in Glasgow (31 Oct 2021 – 12 Nov 2021), served as a platform for an agreement to be reached on an intergovernmental roadmap for how AI might be used to address climate change. This was predicated on the many positive outlooks that AI can provide, one of which is its potential of reducing global greenhouse gas emissions by approximately 4 %. It has also been estimated that when used for environmental applications, AI can contribute about $5.2 trillion USD to the global economy by 2030.
What is Artificial Intelligence?
Artificial Intelligence (AI) is used as an umbrella term to refer to a set of algorithms and digital technologies that enable computers to learn, understand, reason, and act. There are many sub-fields and areas of programming such as machine learning, data analytics, deep learning, robotics, etc that are sometimes being used as words or phrases when referring to AI.
AI in the engineering sector
Machine learning as a subfield of AI was reported as being one of the most in-demand engineering skills in 2022. Meanwhile, big data analytics as a skillset required for engineering roles is being forecast to increase by 50 % each year. In the construction sector for instance, the market for AI is predicted to be worth approximately $2 billion by 2026.
Addressing the climate change question
Depending on the field of engineering, there are wide ranging possibilities and opportunities in the chain of processes (conception, design, manufacture, production, construction, maintenance, and decommissioning) for which AI can be leveraged or applied. Notwithstanding, ‘Big data’ is what forms the cornerstone for robust and successful AI implementation in almost all engineering processes. Big data is used to describe datasets that are too large, complex, and too complicated for traditional data processing applications. It is important to stress that the performance and success of AI-algorithms are only as good as the data that they feed on.
Nevertheless, virtually all engineers believe that the emission of greenhouse gases through energy sourcing, production, energy use and efficiency are some of the most critical elements of engineering processes for which AI has become imminently paramount. Quite a substantial leap has been made by relying on ‘big data’ in this regard.
AI has been adopted to forecast future energy supply and demand trends. By analysing historical data from various sources such as weather forecasts, electricity usage records, and energy prices, AI models can now predict near-term fluctuations in energy prices or spikes in power consumption. This has helped in the prediction of the time and duration of effectiveness of renewable energy sources for optimal production and usage.
AI-powered algorithms based on computer vision have aided the detection of anomalies and real-time monitoring of energy turbines to prevent unplanned downtimes due to failure. AI models have also enabled improved efficiency of solar photovoltaic (PV) panels by automatically adjusting their orientation and tilt for capturing maximum sunlight.
Challenges and ethical dilemmas
Although AI brings lots of opportunities, there are also important ethical trade-offs, and challenges. One of these problems is the development of large storage capacities for ‘big data’.
Also, if not understood and used responsibly, AI may carry some climate risk itself. For instance, the usage of some deep algorithms can generate unwanted emissions due to factors such as server location and the hardware used for training.
There are also concerns surrounding the possibility of AI replacing some jobs in the future. Fears of job loss in the engineering sector due to automation are understandable, but it appears there may not yet be sufficient data to back these claims on a grand scale. Hence, more research is needed on this front.
Final thoughts
As the engineering sector looks to the future, AI should be viewed as a digital trend that brings lots of opportunities, some of which are the benefits it can offer in achieving sustainability. Through responsible use of AI, engineers can continue to set the pace for other business sectors by ensuring that more positive outcomes are achieved. It is also important to ensure that both known or unforeseen negative effects on the environment do not disproportionately affect the most vulnerable and marginalized communities.
Dr Eyo Eyo is a lecturer and researcher in Geotechnical Engineering at the University of the West of England (UWE)
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