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The hidden environmental impacts of digital products can be effectively addressed by systematically applying ecodesign principles to software and digital products. Significant improvements in data center energy consumption can often be achieved without restricting functions or technical features, but rather by focusing on critical details in key design parameters. Interdisciplinary collaborations, targeted analysis of these design parameters, and data-driven improvements serve as the foundation for creating smart and sustainable software solutions. At Helbling, such ecodesign principles are an integral part of project work across various industries, ensuring sustainable outcomes in the development of innovative products.
The Growing Energy Consumption of Digital Services
Accessing cloud storage anytime and anywhere, streaming media during commutes, sharing high-resolution photos, and using AI-powered chatbots instead of traditional search engines have all become integral to our life. However, these advanced services come with an environmental cost. The growing demand for digital services has led to a significant surge in electricity consumption for data centers
The graph below illustrates this trend, highlighting the estimated electricity consumption of data centers and their share of total electricity demand across key regions in 2022 as well as projections for 2026.
Understanding Ecodesign for Digital Products
When discussing ecodesign, physical products often come to mind: low-impact materials, minimum-waste generation, or sustainable production processes. However, the same principles can and should be applied to digital products, though the process is less tangible. The ecodesign of software solutions requires identifying the key drivers of environmental impact and addressing them systematically.
At Helbling, ecodesign is a core element in product development. By integrating ecodesign principles into various projects, Helbling ensures that energy efficiency, resource optimization, and sustainability are prioritized from the early stages of development. This approach has been successfully implemented in areas such as industrial automation, Medtech, and IoT solutions.
This structured approach follows three key steps: first, identifying the main drivers of environmental impact; second, linking environmental footprints with key design parameters; and third, interpreting the system to develop targeted improvements.
1. Identifying the Main Drivers of Environmental Impact
Digital products and their underlying systems are inherently complex. Therefore, interdisciplinary teams are the key to developing meaningful solutions. A comprehensive understanding of the system architecture is essential in order to identify key environmental impact drivers. This can be achieved using a screening life cycle assessment (screening LCA), which helps to pinpoint major sources of emissions and energy consumption.
For physical products, impacts are typically traced to specific components, and it is clearly visible which parts are most responsible for the environmental impact of a system. However, in digital systems, environmental impacts primarily stem from energy consumption at data centers, which is influenced by numerous design parameters.
The environmental impact of digital systems can be computed in regard to a specific functional unit. While in the case of streaming services this functional unit can be one gigabit of uploaded data (i.e. environmental impact per upload of one gigabit), in the case of other cloud services this unit could be one hour of computing time.
2. Linking Environmental Footprints to Key Design Parameters
Once the environmental impact of the functional unit is known, it needs to be allocated to digital system functions and features and ultimately linked to the key design parameters. The level of detail required in the modeling and analysis depends on the scope and decision-making power of stakeholders involved. Granularity can range from a broad overview to highly detailed system insights. Below are examples of relevant model inputs at different levels of granularity:
Low granularity
- Total energy consumption of data center per year
- Carbon intensity of the energy mix
- Embedded emissions of hardware
Medium granularity
- User level: Location, time of usage, service level agreements (SLA)
- Application/Hypervisor: Data redundancy settings
- Data center: Power usage effectiveness (PUE), hardware type, refrigerants, building infrastructure, water use, energy sources (time, location, power purchase agreements)
- Embedded emissions of hardware
High granularity
- Computation: Average/minimum power usage (watts), vCPU utilization, computation hours
- Storage: Redundancy factors, energy for storage operations
- Memory: RAM utilization, latency-related energy overheads
(Note: This list is non-exhaustive and depends on the system's complexity.)
3. System Interpretation and Improvements
Once the key parameters are determined, the analysis not only identifies the environmental impact of individual software services, such as single data uploads, but also uncovers opportunities for improvement. These insights enable the development of innovative strategies to reduce energy consumption and, consequently, the environmental footprint of digital products.
A few examples of possible measures related to key design parameters:
- Refining the data management strategy to minimize redundancy needs
- Integrating the demand-side management strategy, e.g., if possible, running larger computing jobs at a time of the day where renewable power generation is in excess
- Using lightweight containers instead of full virtual machines to reduce overheads
- Choosing architecture with ecodesign in mind
- Offloading some computations to edge devices closer to the end user
- Optimizing indexing, caching and redundancy strategies
- Optimizing code efficiency to reduce computation load
Applying such measures may bring advantages in terms of environmental impacts, but may also have a cost in terms of longer development time or reduced performance, for example. The role then of development teams committed to the ecodesign approach is to help software development to prioritize the most meaningful ecodesign measures, which are the ones with the most favorable environmental benefit-cost ratio.
Summary: Ecodesign paves the way for a sustainable digital future
Ecodesign principles, which are traditionally applied to physical products, can also significantly reduce the environmental impact of digital products like software and cloud services. This becomes more and more relevant as the growing demand for digital services, such as data storage and AI-powered tools, leads to increased electricity consumption, particularly in energy-intensive data centers. A structured ecodesign approach involves identifying key environmental impact drivers, analyzing relevant parameters (e.g., data transfer or computation time), and implementing targeted improvements. Techniques like screening LCAs and parameter-based analyses enable actionable insights. These help with optimizing energy efficiency, reducing environmental impact, and using more renewable energy. Helbling has successfully integrated these principles into its projects, achieving sustainable outcomes in various industries.
Authors: Lukas D’Olif, Jonathan Demierre, Adrian Roth
Main Image: AdobeStock
