Co‑Adaptive Eco‑Nudging: A Privacy‑Preserving Contextual Bandit with User‑Taught Preferences in Everyday Browsing

Digital eco‑nudges are widely deployed, yet their long‑term efficacy, ethical acceptability, and net environmental impact remain unclear. We report two field studies targeting routine online behaviors under strict parity of message content and delivery budgets. Study 1 shows that minimal, factual tailoring improves compliance over generic prompts when opportunities are defined independently of delivery. Study 2 introduces a privacy‑preserving, on‑device contextual bandit that learns when to act and when to DoNothing, achieving higher compliance at comparable prompt intensity while maintaining autonomy. We operationalize an Ethical–Efficacy Frontier (EEF) to visualize autonomy–effectiveness trade‑offs, and compute an energy Return on Investment (ROI) that nets behavior‑driven savings against measured system overhead. Energy savings are estimated using literature‑calibrated proxies with sensitivity bands, and we discuss the energy trade‑offs of on‑device learning relative to a stylized cloud alternative. We probe short‑term persistence via withdrawal and a brief follow‑up; long‑term habit formation and rebound remain out of scope. We contribute design and reporting practices—ablation parity, opportunity denominators, EEF, and net‑impact accounting—that make digital sustainability interventions more rigorous, transparent, and respectful, advancing sustainable HCI beyond “small changes.”