So how can sustainability be supported by technology? Well, we know that our reliance upon fossil fuels is untenable, so it’s vital that we find alternative fuel sources.
This will require research and development into energy production, encouraging us to move towards solar and wind alternatives. Whether it’s a wind turbine or reactor that uses solar energy, we need to develop reliable processes and infrastructure to make it easier for people to move away from non-renewable sources. According to research conducted by Centrica Business Solutions (CBS), over 81% of UK businesses that currently generate energy onsite plan to increase their self-generated production in the future and are looking towards solar and battery technology to enable this.
A recent study conducted by PwC UK, commissioned by Microsoft, reported that the use of artificial intelligence (AI) across a number of industries—including the agriculture, energy, transport and water sectors—could result in a 4% reduction in greenhouse gas emissions globally by 2030. Even data centre businesses, which offer anything from the basic provision of power, web hosting or other IT cloud services, are making changes to operate more efficiently and benefit both their customers and the environment. According to the Storage Networking Industry Association (SNIA), 5% of energy consumption globally is attributed to electronics and this is set to rise to 40% by 2030.
Another way that technology can contribute to sustainability initiatives in these data centres includes the cooling of machinery, such as servers, which is an energy-intensive process. Centres are developing water-cooling systems enabling machines to self-cool, reducing their reliance on energy and giving them the ability to optimise air flows.
The advancement of cloud technology also contributes to cost reduction for business operations, which in turn improves the environmental footprint of organisations. When setting up these cloud servers, companies are now looking towards hydro, solar and wind power available from low carbon supply sites or to have the option to work with energy suppliers that can trace their supply to renewable sources.
It’s not just machines that will help us reduce emissions, but also the way that this technology thinks. Take machine learning (ML) algorithms, which can calculate how technology can be used most efficiently. Factoring in weather patterns and historical data, this information can help in both the production and consumption of energy. This means that ML can predict the optimum times and methods that wind or sun power can be harnessed, for instance.
In addition to such algorithms, other predicative analytics can help ensure that all processes are running with utmost precision; this means that throughout manufacturing, businesses can monitor their energy consumption and water use. Faults, such as leaks, can be detected and fixed to minimise wastage during production.
While this is just a snapshot of what technological innovations are being developed to help companies shift towards more sustainable practices, it still provides a clear sense of how crucial technology is in the ongoing fight to reduce our impact on the environment.
Looking at the impact of these advancements on hiring, candidates are now looking at firms that have green energy offerings. Younger talent in particular think it’s important that businesses have strong corporate values. In addition to this, it ostensibly offers a safe bet for career longevity compared to companies are not diversifying into green energies. I’ve spoken with candidates that have named Orsted, the Danish power company, as an organisation that they would consider working for based on their green initiatives alone.
The aptitudes sought after in this space include: analysts, data scientists, and experience with artificial intelligence (AI)/ ML efforts in physical or agricultural trading. The ability to predict the supply and/or supply chain issues of physical commodities is better forecast by technical data scientists and AI/ML efforts, as environmental changes will have to rely on predictive, not descriptive, analytics.
by Stuart MacSweenview my profile