In today's world, sustainability has become critical for business growth and success. It is an undoubted fact that the ongoing digitalization and networking of devices and systems are significantly impacting our energy consumption and the amount of greenhouse gas emissions that pollute our environment. The trend goes either way: Digitalization helps to reduce energy waste and CO₂ emissions in traditional sectors like logistics, manufacturing, energy production and distribution, agriculture, construction. Through the intelligent use of technology, companies can optimize and automate their processes to improve resource efficiency and waste reduction. On the other hand, digital technologies have immense and growing energy requirements themselves. The question arises regarding how we should deal with these energy-intensive technologies to minimize environmental damage by systematically measuring energy consumption of digital systems and innovate to reduce energy waste in developing and applying digital technologies.

The transportation sector is one of the foci of societal and environmental debate on energy consumption due to a broad range of central themes. The rise of global supply chains and an increased stakeholder consciousness has led to an increased interest in sustainability, influenced by a growing concern about climate change, its CO2 emissions, and the growing concern to become “lean and green.” AI is a crucial technology to de-carbonize transportation by providing data, gaining knowledge from data, planning, and automation. Moreover, AI is fundamental to shared mobility, AVs, EVs, and innovative public transit, which, with the right incentives, can enable significant reductions in GHG emissions.

Cement and steel production account for over 10% of all global GHG emissions. The cement industry alone emits more GHGs than every country except the USA and China. AI can help minimize these emissions by reducing the need for carbon-intensive materials, transforming industrial processes to run on low-carbon energy, and even redesigning the chemistry of structural materials. To reduce the use of cement and steel, researchers have combined ML with generative design to develop structural products that require less raw material, thus reducing the resulting GHG emissions.Cement and steel production account for over 10% of all global GHG emissions. The cement industry alone emits more GHGs than every country except the USA and China. AI can help minimize these emissions by reducing the need for carbon-intensive materials, transforming industrial processes to run on low-carbon energy, and even redesigning the chemistry of structural materials. To reduce the use of cement and steel, researchers have combined ML with generative design to develop structural products that require less raw material, thus reducing the resulting GHG emissions.

Electricity systems are responsible for about a quarter of annual human-caused GHG emissions. To reduce emissions from electricity systems, organizations must use advances in Artificial Intelligence (AI) to transform the energy sector, revolutionizing how energy is generated, distributed, and consumed. With AI, energy companies can optimize energy production, increase efficiency, and reduce costs while significantly reducing their environmental footprint. Implementing AI-powered intelligent grids and energy management systems paves the way for highly accurate forecasting and real-time energy consumption monitoring. Also, the importance of batteries in reducing greenhouse gas emissions cannot be overstated. Batteries allow us to store renewable energy sources like solar and wind, which can be used when demand is high and the sun isn't shining or the wind isn't blowing. AI can help to reduce the cost of developing and mass producing of highly efficient next generation batteries. Also, second-life batteries have a major role. They have been used in electric vehicles and are now being repurposed for stationary energy storage systems. This helps reduce the need for new batteries. Refurbishing used batteries is a complex problem, which can effectively supported by Machine Learning Algorithms. AI-driven energy systems can unlock the potential for widely adopting renewable energy sources.

The Internet of Things (IoT) is a game changer for manufacturers to reduce the carbon footprint and energy consumption of their plants while improving the efficiency and quality of their products. Machine learning (ML) is central to this scenario, as it is a powerful tool to address the complex problems of overproduction and overstocking. It enables predictive analysis of demand patterns and optimization of production processes, paving the way for a more sustainable manufacturing process. In addition, ML has the potential to address food waste by scrutinizing consumption patterns and supply chain processes while facilitating the development of environmentally friendly and efficient food production and distribution systems. Digitization and the Internet of Things (IoT) also profoundly impact 3D additive manufacturing. Integrating advanced materials and improved manufacturing processes significantly reduces waste, remarkably increases efficiency, and produces innovative and sustainable products.

Agriculture is fundamental for sustaining the livelihood of humans, animals, and the entire environmental ecosystem. At the same time, agricultural production is one of the most significant sources of pollution regarding GHG emissions, fertilizer use, and food supply chains. Digital technologies, specifically AI, can help reduce the pollutive use of fertilizers and excessive land and resource use and thereby help reshape ecosystems sustainably. By using technologies such as machine learning and data collection technologies, farmers can improve crop yields and reduce water consumption. Fertilizers and raw materials can be used precisely where and when needed. Digital systems can manage highly efficient farming warehouses and reduce extensive farmland.

The AI Service Center Berlin-Brandenburg has the task of simplifying the use of and access to artificial intelligence. After the launch on November 1, 2022 and a start-up phase in 2023, the second phase begins with the cleanIT-Conference: After building the team and procuring a powerful infrastructure, the KISZ-BB goes into regular operation. At this event, we will present the range of services offered by KISZ-BB and officially put the infrastructure into operation.

All data traffic requires energy. The annual internet traffic has risen exponentially during the last few years and continues to rise steeply. Already today, the total carbon emissions of digital technologies surpass those of global air traffic by a factor of two. In 2019 all air traffic combined accounted for about one billion tons of carbon emissions and 2 % of overall emissions. In the same year, digital technologies emitted about 2 billion tons, or about 4 %, of all human-induced carbon dioxide. Humanity is at the beginning of a massive acceleration of digitization on a global scale. These are only rough estimates. We still need to learn more about how much energy digital systems such as AI, Big Data Analytics, Blockchain, and others consume. There still needs to be a standardized way to truthfully measure energy demand due to the heterogeneous nature of hardware and software stacks for the countless applications. To effectively tackle the issue of energy consumption of digital systems, we must find a way to measure it correctly.

The rising carbon footprint of digitization is avoidable. Computer systems, which are based on the interrelationship of hardware and software, organized by algorithms, can be designed in many ways to produce the same outcome. Often unnecessarily complicated programming or computer systems design causes higher energy consumption when compared to more efficient algorithms. Innovative software architectures can achieve the same or slightly lower precision or data throughput while saving enormous energy. How algorithms and computer systems are designed matters because every algorithmic operation consumes energy. This can only be achieved if digital engineering follows the "Sustainability by Design paradigm." Therefore, we need increased public and engineering awareness of the digital carbon footprint, especially on wasteful algorithms' impact. Also, the trade-offs between precision/speed/data throughput and energy consumption must be brought to the forefront as a core principle of computer systems design to build digital energy-aware systems for the benefit of humanity.

Information about the panel will follow here shortly.