GreenZ: A Sustainable UX Framework for Complex Digital Systems

TL;DR

GreenZ is a comprehensive Sustainable UX Framework designed to reduce digital waste and AI overuse in complex systems through principles, operational models, and practical tools.

Contribution

It introduces a novel three-layer framework, including a waste taxonomy and AI sufficiency model, to promote sustainable digital system design.

Findings

Framework architecture and conceptual foundations are presented.

Core contributions include a digital waste taxonomy and AI sufficiency decision model.

Empirical validation is ongoing, with expert review planned.

Abstract

Digital systems have become simultaneously more powerful and more wasteful. Features accumulate that nobody uses. Data is collected that nobody analyzes. AI is deployed at significant energy and water costs for gains that a simpler approach could have achieved. And through all of it, the people who depend on these systems quietly absorb the consequences in cognitive load, lost time, and eroded trust. This paper introduces GreenZ, a three-layer Sustainable UX Framework for complex digital systems. Its three layers are a Philosophy Layer built around ten published principles, an Operational Frameworks Layer comprising five applied systems, and a Tools and Canvases Layer of practical audit instruments and decision models. Two contributions sit at the framework's core: a Digital Waste Taxonomy classifying eight distinct waste types, and an AI Sufficiency Decision Model that asks whether AI should exist in a given flow before any question of how to implement it. GreenZ v1 is theoretically grounded but empirically unvalidated. A practitioner expert review study is underway at the time of submission. The paper presents the framework's architecture, its conceptual foundations, its position relative to existing literature, and an honest account of what remains to be established.