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The carbon emissions of modern information and communication technologies (ICT) present a significant environmental challenge, accounting for approximately 4% of global greenhouse gases, and are on par with the aviation industry. Modern internet services levy high carbon emissions due to the significant infrastructure resources required to operate them, owing to strict service requirements expected by users. One opportunity to reduce emissions is relaxing strict service requirements by leveraging eco-feedback. In this study, we explore the effect of the carbon reduction impact of allowing longer internet service response time based on user preferences and feedback. Across four services (i.e., Amazon, Google, ChatGPT, Social Media) our study reveals opportunities to relax latency requirements of services based on user feedback; this feedback is application-specific, with ChatGPT having the most favorable eco-feedback tradeoff. Further system studies suggest leveraging the reduced latency can bring down the carbon footprint of an average service request by 93.1%. 

Exploring the shared, intersectional problem space of empowered users making carbon-aware decisions that guide computer systems operation. 

As edge devices see increasing adoption across a wide range of applications, understanding their environmental impact has become increasingly urgent. Unlike cloud systems, edge deployments consist of tightly integrated microcontrollers, sensors, and energy sources that collectively shape their carbon footprint. In this paper, we present a carbon-aware design framework tailored to embedded edge systems. We analyze the embodied emissions of several off-the-shelf microcontroller boards and peripheral components and examine how deployment context—such as workload type, power source, and usage duration—alters the carbon-optimal configuration. Through empirical case studies comparing battery- and solar-powered scenarios, we find that the lowest-emission choice is often workload- and context-specific, challenging assumptions that energy-efficient or renewable powered systems are always the most sustainable. Our results highlight the need for fine-grained, system-level reasoning when designing for sustainability at the edge and provide actionable insights for researchers and practitioners seeking to reduce the carbon cost of future deployments.