Tech heroes powering net-zero: AI & the race against climate change

Time remains the great, unstoppable force. It can be our ally and enabler – one of Tolstoy’s “most powerful warriors”. It unites us all in the race to net zero, a race in which we all have the same finish line, the same rules, and the same judge deciding if we have won. And within this race, industrial AI can help accelerate the pace of minimizing our environmental impacts. Here’s how.

Today, seismic changes are transforming the world we live in. Our ability to master these?‘megatrends’?– demographic change, urbanization, globalization, environmental change and resource efficiency, and digitalization – will shape our collective future.

The pressure of time weighs heavily on all of these. But this pressure becomes existential when it comes to the race to net-zero; net-zero in the broad sense encompassing all human-induced environmental impacts. Time is running out to achieve the goals of the Paris Agreement and, as highlighted in the Infrastructure Transition Monitor 2023,?fewer than 50% of organizations?currently expect to meet their decarbonization targets by 2030.

AI, however, holds promise to be a game-changer in our efforts here. One study published in Nature magazine, examining the?role of AI in achieving the Sustainable Development Goals, outlines the potential, alongside the considerations for realizing it. Other success factors are referenced in?research by PwC, which highlights that AI could help cut CO2 emissions by?0.9 to 2.4 gigatons by 2030, equal to the yearly emissions of Australia, Canada, and Japan.

When it comes to transforming the backbones of our economies, like manufacturing, infrastructure, and transportation, it is not about making one giant leap with AI. The strategic use of ‘Industrial AI’ (AI that is?industrial-grade?- reliable, secure, and trustworthy) can serve as an accelerator and enhancer. It can help us master more complex problems faster and at scale to increase efficiency and lower emissions. What does this progress look like? Here are five prime examples:

1. Digital Twins: Engineering a Net-Zero Future

At the heart of this AI-enhanced revolution are AI-powered digital twins, integral to the industrial metaverse. These sophisticated simulations of real-world assets enable 'time travel' for engineers, allowing them to analyze past data and forecast future scenarios. The environmental impact of products can thereby be simulated allowing engineers to make data-driven optimization decisions minimizing real-world footprints.

For example,?digital twin technology is helping ACC?significantly improve yield in electrode manufacturing. This not only speeds up their transition to net-zero factories but it also accelerates their ramp up of Gigafactories. As a result, it accelerates the shift towards electric mobility.

2. Smarter Infrastructure: Saving Water the Smart Way

Access to water is the most basic human need. Globally, however, the availability and management of water is a challenge – exacerbated by factors like climate change, rapid demographic shifts, and urbanization. We face a?1% increase?in water demand every year, against a reality where approximately?30%?of all produced water is getting lost worldwide.

Radically more efficient water management, therefore, is critical. And innovations such as those realized by the Swedish water company?VA SYD, which can effectively reduce water leakage, are essential to addressing this. Here, an?AI-based solution?detects leaks as small as 0.5 liters per second, delivering greater transparency and smarter asset management, to better sustain and transport the precious resource of water.

3. Generative Design: Accelerating Innovation

Generative design utilizes AI to explore a vast design space, generating numerous solutions for multicriteria requirements. This method not only offers a broad array of solutions but also efficiently narrows down the most effective ones, saving time and resources in testing. This leads to notable reductions in cost, environmental impact, and risk.

The AI-driven redesign of industrial robot grippers showcases this innovation. By mimicking natural designs through AI-powered topology optimization and harnessing the potential of additive manufacturing, this approach led to?an 82% reduction in CO2 emissions per robot.

4. Predictive Maintenance: A World with Higher Mobility Availability

Predictive maintenance, empowered by AI, is revolutionizing the way we manage and maintain complex systems. This approach involves using AI to analyze runtime-data and predict potential issues before they arise, effectively reducing downtime, enhancing efficiency and prolonging the lifetime of assets.

A standout example is the Rhein-Ruhr-Express project. Here, AI-driven analytics?based on?Railigent X?contributes to?over?99% service availability. This efficiency also allows?for?longer component lifetimes and reduced material use, directly leading to decreased resource and energy consumption, and lower emissions. Additionally, it significantly enhanced the reliability and appeal of the rail system for daily commuters.

5. Helping Balance AI’s Impact: Energy-Efficient Data Center Operations

While AI is a powerful tool in the fight against climate change, it's important to acknowledge its own energy-intensive nature. Innovation is underway to address the root causes of this, targeting LLM efficiency, how the infrastructure architecture is designed, the hardware the models run on, and more.

Alongside this, data centers, crucial for our digital activities, business operations, and the global economy, consume significant energy, particularly for cooling purposes. Yet, AI is also providing part of the solution here. A compelling example is the AI-powered cooling management at Greenergy Data Centers in Estonia. The innovative application of AI here has transformed the facility into one of the most sustainable data centers in the Baltics, enhancing efficiency by approximately 30% at?the mere push of a button.

Ultimately, our journey towards a more environmentally friendly future is marked by the synergetic convergence of AI with other technologies. Industrial AI, in particular, is transforming our ability to master complex multidimensional problems at unprecedented speed and scale, enhancing other technologies we have available and accelerating our learning and innovation capacities.

This is also a journey of collaboration, supported by digital business platforms like Siemens Xcelerator. No one can tackle our greatest challenges alone. Great minds, businesses, developers, and innovators must come together, across borders, and take advantage of an ecosystem-centric approach to innovation; to cocreate and leverage the synergies and interoperability of industrial AI to other technologies.

In embracing this, we have the opportunity to not just adapt or respond to environmental and resource challenges; we can, together, proactively drive and shape our future by combining the digital and real worlds. This will be a vital step towards a smarter, environmentally friendly world; with industrial AI one increasingly powerful tool helping accelerate our pace in the race to net-zero.

Want to know more about AI and sustainability? Check out:?

• “The power of AI for environmental stewardship and optimized industry”, produced with New Scientist
• Industrial AI and the sustainability revolution: podcast with Pina Schlombs , produced with New Scientist?
• “Solving the decarbonization equation” by Peter Koerte

Author: Pina Schlombs , Sustainability Lead, Siemens Digital Industries Software. This article was initially published on Siemens Insights