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1. Sustainable Recovery from Shocks: Policies and Partnerships for Fresh Produce Rescue and Environmental Impact Reduction

   https://doi.org/10.3390/foods15030582  

   Koutsopoulos, Mariana T; Luna-Reyes, Luis F; Bozlak, Christine T; Neff, Roni; Mu, Tianhong; Romeiko, Xiaobo Xue; Guo, Zhijian; Hosler, Akiko S; Pettigrew, Stacy M; Pernicka, Natasha; et al (February 2026, Foods)

   Food policies that respond to shocks and support nutritious diets for vulnerable populations can enhance resilience, support social equity, and reduce environmental damage. Using a simulation model, we evaluated the effectiveness of two food redistribution policies—Nourish New York, a program providing funds to food rescue organizations to purchase food directly from farmers, and the Food Donation and Food Scraps Recycling Law (an organics “waste ban”)—in response to a shock such as the COVID-19 pandemic. We assessed policy based on recovered food and life cycle carbon and water footprints over 10 years. Both policies improved produce donations during post-shock. The waste ban increased waste at feeding organizations; diverting unavoidable food waste to composting and anaerobic digestion mitigated its carbon footprint. Enhanced coordination and partnerships within the food redistribution network were crucial for ensuring that produce reached those in need, ultimately reducing long-term environmental impacts. Implementing multiple strategies that enhance recovery from farms and retail, while strengthening the organizational capacity of the food redistribution network, can simultaneously advance food security and environmental goals. 

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2. Balancing Bits and Drops: Stress-Adjusted Water Management for Data Centers

   https://doi.org/10.1145/3744255.3811726  

   Talukder, Zahidur; Rahim, Imtiaz Bin; Gupta, Pranjol Sen; Ren, Shaolei; Islam, Mohammad A (June 2026, ACM)

   Data centers are critical to today’s digital economy, but are also among the largest industrial consumers of freshwater. Beyond the sheer volume of water use, the environmental impact of data center water consumption varies significantly across locations and seasons, depending on local and regional water stress. However, prior research has largely focused on reducing total water use, overlooking that the same unit of water can have drastically different environmental consequences depending on when and where it is consumed. In this paper, we introduce a stress-adjusted water framework that quantifies the true sustainability impact of data center water consumption by incorporating both spatial and temporal water stress. Using the AWARE-US model, we capture county-level monthly variations in water availability and extend this framework to account for the off-site water footprint of electricity generation. Based on this stress-aware accounting, we analyze stress-adjusted water-computing strategies spanning both the software and infrastructure layers. Specifically, we study workload scheduling policies that jointly optimize water and carbon efficiency, evaluate the potential of rainwater harvesting as a supplemental water source, and investigate the feasibility of dry cooling as a water-free alternative to evaporative cooling. Our evaluation across major U.S. data center markets shows that stress-aware workload scheduling can reduce stress-adjusted water consumption by up to 25% while preserving performance and balancing carbon emissions. We further show that rainwater harvesting can offset up to 100% of on-site cooling water in regions with sufficient precipitation, while offering diminishing returns in arid locations. Finally, our analysis of dry cooling reveals that its effectiveness depends critically on energy efficiency and off-site water stress, highlighting important trade-offs between water savings, energy use, and carbon emissions. Together, these results demonstrate that incorporating water stress as a first-class sustainability metric enables more informed and effective data center design and operation 

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3. LLMCARBON: MODELING THE END-TO-END CARBON FOOTPRINT OF LARGE LANGUAGE MODELS

   Faiz, Ahmad; Kaneda, Sotaro; Wang, Ruhan; Osi, Rita; Sharma, Prateek; Chen, Fan; Jiang, Lei (April 2024, The Twelfth International Conference on Learning Representations)

   The carbon footprint associated with large language models (LLMs) is a significant concern, encompassing emissions from their training, inference, experimentation, and storage processes, including operational and embodied carbon emissions. An essential aspect is accurately estimating the carbon impact of emerging LLMs even before their training, which heavily relies on GPU usage. Existing studies have reported the carbon footprint of LLM training, but only one tool, mlco2, can predict the carbon footprint of new neural networks prior to physical training. However, mlco2 has several serious limitations. It cannot extend its estimation to dense or mixture-of-experts (MoE) LLMs, disregards critical architectural parameters, focuses solely on GPUs, and cannot model embodied carbon footprints. Addressing these gaps, we introduce \textit{\carb}, an end-to-end carbon footprint projection model designed for both dense and MoE LLMs. Compared to mlco2, \carb~significantly enhances the accuracy of carbon footprint estimations for various LLMs. The source code is released at \url{https://github.com/SotaroKaneda/MLCarbon}. 

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4. Reducing Smart Phone Environmental Footprints with In-Memory Processing

   Yang, Zhuoping; Zhang, Wei; Ji, Shixin; Zhou, Peipei; Jones, Alex (October 2024, IEEE)

   Smart phones have revolutionized the availability of computing to the consumer. Recently, smart phones have been aggressively integrating artificial intelligence (AI) capabilities into their devices. The custom designed processors for the latest phones integrate incredibly capable and energy efficient graphics processors (GPUs) and tensor processors (TPUs) to accommodate this emerging AI workload and on-device inference. Unfor- tunately, smart phones are far from sustainable and have a substantial carbon footprint that continues to be dominated by environmental impacts from their manufacture and far less so by the energy required to power their operation. In this paper we explore the possibility of reversing the trend to increase the dedicated silicon dedicated to emerging application workloads in the phone. Instead we consider how in-memory processing using the DRAM already present in the phone could be used in place of dedicated GPU/TPU devices for AI inference. We explore the potential savings in embodied carbon that could be possible with this tradeoff and provide some analysis of the potential of in- memory computing to compete with these accelerators. While it may not be possible to achieve the same throughput, we suggest that the responsiveness to the user may be sufficient using in- memory computing, while both the embodied and operational carbon footprints could be improved. Our approach can save circa 10–15kgCO2e. 

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5. Toward Carbon-Aware Data Transfers

   https://doi.org/10.1109/MIC.2025.3565187  

   Goldverg, Jacob; Jamil, Hasibul; Rodrigues, Elvis; Kosar, Tevfik (March 2025, IEEE Internet Computing)

   The growing adoption of cloud, edge, and distributed computing, as well as the rise in the use of AI/ML workloads, have created a significant need to measure, monitor, and reduce the carbon emissions associated with these resource-intensive tasks. One significant but often overlooked source of emissions is data transfers over wide-area networks (WANs), primarily due to the challenges in accurately measuring the carbon footprint of end-to-end network paths. We introduce a novel mechanism to measure network carbon footprints and propose strategies for optimizing the scheduling of network-intensive tasks. We show that users can achieve significant carbon savings by shifting data transfer tasks across time and geographic regions based on local carbon intensity. 

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