• Energy Research
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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 analysis of global catastrophic events often occurs in isolation, simplifying their study. In reality, risks cascade and interact. Therefore, it is essential to consider the interconnected nature of global risks. This investigation explores the interplay between nuclear winter and planetary boundaries. It may seem reasonable to assume that respecting planetary boundaries, which define a safe operating space for the planet, is preferable before a nuclear war. However, that does not always seem to be the case. For instance, increased nitrogen emissions today could serve as a nutrient buffer during nuclear winter. Contrastingly, mitigating climate change, means an even larger temperature drop in nuclear winter in comparison with pre-industrial times. This exploratory study also highlights planetary boundaries that could enhance human survival if we adhere to their limits, both presently and after a nuclear war. The best example being biosphere integrity, as conserving it has no direct downsides and would make the Earth system more resilient to resist the shock of a nuclear winter.

The climate crisis is the biggest threat to the health, development, and wellbeing of the current and future generations. While there is extensive evidence on the direct impacts of climate change on human livelihood, there is little evidence on how children and young people are affected, and even less discussion and evidence on how the climate crisis could affect violence against children.In this commentary, we review selected research to assess the links between the climate crisis and violence against children.We employ a social-ecological perspective as an overarching framework to organize findings from the literature and call attention to increased violence against children as a specific, yet under-examined, direct and indirect consequence of the climate crisis.Using such a perspective, we examine how the climate crisis exacerbates the risk of violence against children at the continually intersecting and interacting levels of society, community, family, and the individual levels. We propose increased risk of armed conflict, forced displacement, poverty, income inequality, disruptions in critical health and social services, and mental health problems as key mechanisms linking the climate crisis and heightened risk of violence against children. Furthermore, we posit that the climate crisis serves as a threat multiplier, compounding existing vulnerabilities and inequities within populations and having harsher consequences in settings, communities, households, and for children already experiencing adversities.We conclude with a call for urgent efforts from researchers, practitioners, and policymakers to further investigate the specific empirical links between the climate crisis and violence against children and to design, test, implement, fund, and scale evidence-based, rights-based, and child friendly prevention, support, and response strategies to address violence against children.

With the advent of employing bio-fuels along with the diesel in compression ignition engines the study of performance and emission characteristics have occupied the prominence, owing to diversified multi responses. As the limited information is available about the application of Taguchi based GTMA process to maximize the overall performance and emission characteristics of diesel engine, in the present work the investigation was carried out to maximize the overall utility by employing the Taguchi based GTMA process. By following the user preference rating, weights for the response characteristics namely brake thermal efficiency, brake specific fuel consumption, carbon monoxide and oxides of nitrogen were calculated using graph theory and matrix approach (GTMA). The parameter hydrogen induction played a major role to an extent of 78.62% while Injection opening pressure playing a minor role with a contribution of 7.06%. The optimal parameters condition was at mid-level of the governing parameters namely IOP, CR and volume of hydrogen inducted. The predicted results were within 95% of confidence interval of the optimal values. Therefore, the hydrogen inductance into the cylinder not only improving the performance but also minimizing the emission characteristics.

Batteries can effectively improve the security of energy systems and mitigate climate change by facilitating wind and solar power. The installed capacity of battery energy storage system (BESS), mainly the lithium ion batteries are increasing significantly in recent years. However, the battery degradation cannot be accurately quantified and integrated into energy management system with existing heuristic battery degradation models. This paper proposed a hierarchical deep learning based battery degradation quantification (HDL-BDQ) model to quantify the battery degradation given scheduled BESS daily operations. Particularly, two sequential and cohesive deep neural networks are proposed to accurately estimate the degree of degradation using inputs of battery operational profiles and it can significantly outperform existing fixed or linear rate based degradation models as well as single-stage deep neural models. Training results show the high accuracy of the proposed system. Moreover, a learning and optimization decoupled algorithm is implemented to strategically take advantage of the proposed HDL-BDQ model in optimization-based look-ahead scheduling (LAS) problems. Case studies demonstrate the effectiveness of the proposed HDL-BDQ model in LAS of a microgrid testbed. 12 pages

One year of continuous monitoring for Indoor Environmental Quality (IEQ) and energy use on a high school campus

We compare stochastic programming and robust optimization decision models for informing the deployment of ad hoc flood mitigation measures to protect electrical substations prior to an imminent and uncertain hurricane. In our models, the first stage captures the deployment of a fixed quantity of flood mitigation resources, and the second stage captures the operation of a potentially degraded power grid with the primary goal of minimizing load shed. To model grid operation, we introduce adaptations of the direct current (DC) and linear programming alternating current (LPAC) power flow approximation models that feature relatively complete recourse by way of an indicator variable. We apply our models to a pair of geographically realistic flooding case studies, one based on Hurricane Harvey and the other on Tropical Storm Imelda. We investigate the effect of the mitigation budget, the choice of power flow model, and the uncertainty perspective on the optimal mitigation strategy. Our results indicate the mitigation budget and uncertainty perspective are impactful, whereas choosing between the DC and LPAC power flow models is of little to no consequence. To validate our models, we assess the performance of the mitigation solutions they prescribe in an alternating current (AC) power flow model. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: This work was supported by the Energy Institute, The University of Texas at Austin. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0125 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0125 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .