• Energy Research
• 2025-2025close
• Open Access close
• Closed Access close
• Embargo close
• 11. Sustainability close
• 12. Responsible consumption close
• 2. Zero hunger close

Creating and delivering products and services that promote sustainability is increasingly important in today’s economy. Novel services based on digital technologies and infrastructure can significantly contribute to sustainable development, as demonstrated by digitally enabled car-sharing services where increased asset utilization reduces production-related greenhouse gas emissions. However, there is still limited knowledge on how digital service innovation can purposefully be applied to promote sustainability. To address this gap, we conduct a systematic literature review and perform a qualitative inductive analysis of 50 articles on the impact of digital service innovation on social, environmental, and economic sustainability. We provide a comprehensive overview of real-world applications and identify five underlying mechanisms through which innovation with digital services can drive sustainable development. In doing so, we aim to pave the way to purposefully conceive, design, and implement digital services for sustainability.

Creating and delivering products and services that promote sustainability is increasingly important in today’s economy. Novel services based on digital technologies and infrastructure can significantly contribute to sustainable development, as demonstrated by digitally enabled car-sharing services where increased asset utilization reduces production-related greenhouse gas emissions. However, there is still limited knowledge on how digital service innovation can purposefully be applied to promote sustainability. To address this gap, we conduct a systematic literature review and perform a qualitative inductive analysis of 50 articles on the impact of digital service innovation on social, environmental, and economic sustainability. We provide a comprehensive overview of real-world applications and identify five underlying mechanisms through which innovation with digital services can drive sustainable development. In doing so, we aim to pave the way to purposefully conceive, design, and implement digital services for sustainability.

The adoption of electric vehicles (EVs), including electric taxis and buses, as a mode of transportation, is rapidly increasing in cities. In addition to providing economic and environmental benefits, these fleets can potentially participate in the energy arbitrage market by leveraging their mobile energy storage capabilities. This presents an opportunity for EV owners to contribute to a more sustainable and efficient energy system while also reducing their operational costs. The present study introduces deterministic and single-stage stochastic optimization frameworks that aim to maximize revenue by optimizing the charging, discharging, and travel of a fleet of electric vehicles in the context of uncertainty surrounding both spatial and temporal energy prices. The simulations are performed on a fleet of electric delivery trucks, which have to make deliveries to certain locations on specific dates. The findings indicate the promising potential of bidirectional electric vehicle charging as a mobile grid asset. However, it is important to note that significant revenue is only realized in scenarios where there is substantial variation in prices between different areas, and when these price variations can be accurately forecasted with a high level of confidence.

The adoption of electric vehicles (EVs), including electric taxis and buses, as a mode of transportation, is rapidly increasing in cities. In addition to providing economic and environmental benefits, these fleets can potentially participate in the energy arbitrage market by leveraging their mobile energy storage capabilities. This presents an opportunity for EV owners to contribute to a more sustainable and efficient energy system while also reducing their operational costs. The present study introduces deterministic and single-stage stochastic optimization frameworks that aim to maximize revenue by optimizing the charging, discharging, and travel of a fleet of electric vehicles in the context of uncertainty surrounding both spatial and temporal energy prices. The simulations are performed on a fleet of electric delivery trucks, which have to make deliveries to certain locations on specific dates. The findings indicate the promising potential of bidirectional electric vehicle charging as a mobile grid asset. However, it is important to note that significant revenue is only realized in scenarios where there is substantial variation in prices between different areas, and when these price variations can be accurately forecasted with a high level of confidence.

By providing various services, such as Demand Response (DR), buildings can play a crucial role in the energy market due to their significant energy consumption. However, effectively commissioning buildings for such desired functionalities requires significant expert knowledge and design effort, considering the variations in building dynamics and intended use. In this study, we introduce an adaptive data-driven prediction scheme based on Willems' Fundamental Lemma within the building control hierarchy. This scheme offers a versatile, flexible, and user-friendly interface for diverse prediction and control objectives. We provide an easy-to-use tuning process and an adaptive update pipeline for the scheme, both validated through extensive prediction tests. We evaluate the proposed scheme by coordinating a building and an energy storage system to provide Secondary Frequency Control (SFC) in a Swiss DR program. Specifically, we integrate the scheme into a three-layer hierarchical SFC control framework, and each layer of this hierarchy is designed to achieve distinct operational goals. Apart from its flexibility, our approach significantly improves cost efficiency, resulting in a 28.74% reduction in operating costs compared to a conventional control scheme, as demonstrated by a 52-day experiment in an actual building. Our findings emphasize the potential of the proposed scheme to reduce the commissioning costs of advanced building control strategies and to facilitate the adoption of new techniques in building control.

By providing various services, such as Demand Response (DR), buildings can play a crucial role in the energy market due to their significant energy consumption. However, effectively commissioning buildings for such desired functionalities requires significant expert knowledge and design effort, considering the variations in building dynamics and intended use. In this study, we introduce an adaptive data-driven prediction scheme based on Willems' Fundamental Lemma within the building control hierarchy. This scheme offers a versatile, flexible, and user-friendly interface for diverse prediction and control objectives. We provide an easy-to-use tuning process and an adaptive update pipeline for the scheme, both validated through extensive prediction tests. We evaluate the proposed scheme by coordinating a building and an energy storage system to provide Secondary Frequency Control (SFC) in a Swiss DR program. Specifically, we integrate the scheme into a three-layer hierarchical SFC control framework, and each layer of this hierarchy is designed to achieve distinct operational goals. Apart from its flexibility, our approach significantly improves cost efficiency, resulting in a 28.74% reduction in operating costs compared to a conventional control scheme, as demonstrated by a 52-day experiment in an actual building. Our findings emphasize the potential of the proposed scheme to reduce the commissioning costs of advanced building control strategies and to facilitate the adoption of new techniques in building control.

Predicted changes in rainfall intensity due to climate change are likely to influence key soil health parameters, especially structural attributes and crop growth. Variations in rainfall intensity will impact crop production negatively. It is therefore imperative to investigate the interaction between predicted increases in rainfall intensity and key soil health parameters, particularly in relation to soil structural attributes and plant growth. The objectives of this study were to determine the effects of rainfall intensity on soil crust formation and mode of seedling emergence in soils dominated by primary minerals. Soil samples were collected from the top 200 mm, air dried and then packed uniformly into plastic pots, which were perforated at the bottom. Three maize seeds of equal size were planted in a triangular pattern in each pot at a depth of 30 mm, after which the pots were pre-wetted by capillary. The samples were then subjected to simulated rainfall at 3 intensities, i.e., 30, 45 and 60 mm/h, for 5 min. Rainfall intensity significantly (P < 0.05) affected crust strength and mean emergence day (MED), but not emergence percentage (EMP) and shoot length (P > 0.05). The 60 mm/h rainfall intensity resulted in the highest crust strength and MED. The strength of crust for all three rainfall intensities was influenced by quartz content, soil organic matter, clay and hematite. Most seedlings emerged through cracks, which resulted in rainfall intensity having no significant effects on seedling EMP and shoot length. We concluded that any increase in rainfall intensity is likely to increase the severity of crusting in these soils. However, soils with extensive cracking are likely to have higher EMP and lower MED and more vigorous seedlings despite the strength of the crust. As a result, post-planting tillage methods that enhance crust cracking may be employed to enhance seedling emergence and growth in these soils.

Predicted changes in rainfall intensity due to climate change are likely to influence key soil health parameters, especially structural attributes and crop growth. Variations in rainfall intensity will impact crop production negatively. It is therefore imperative to investigate the interaction between predicted increases in rainfall intensity and key soil health parameters, particularly in relation to soil structural attributes and plant growth. The objectives of this study were to determine the effects of rainfall intensity on soil crust formation and mode of seedling emergence in soils dominated by primary minerals. Soil samples were collected from the top 200 mm, air dried and then packed uniformly into plastic pots, which were perforated at the bottom. Three maize seeds of equal size were planted in a triangular pattern in each pot at a depth of 30 mm, after which the pots were pre-wetted by capillary. The samples were then subjected to simulated rainfall at 3 intensities, i.e., 30, 45 and 60 mm/h, for 5 min. Rainfall intensity significantly (P < 0.05) affected crust strength and mean emergence day (MED), but not emergence percentage (EMP) and shoot length (P > 0.05). The 60 mm/h rainfall intensity resulted in the highest crust strength and MED. The strength of crust for all three rainfall intensities was influenced by quartz content, soil organic matter, clay and hematite. Most seedlings emerged through cracks, which resulted in rainfall intensity having no significant effects on seedling EMP and shoot length. We concluded that any increase in rainfall intensity is likely to increase the severity of crusting in these soils. However, soils with extensive cracking are likely to have higher EMP and lower MED and more vigorous seedlings despite the strength of the crust. As a result, post-planting tillage methods that enhance crust cracking may be employed to enhance seedling emergence and growth in these soils.