Intent-Driven Internet Sustainability: Modeling Environmental Costs and Energy-Aware Execution of Autonomous Digital Tasks

The development of generative artificial intelligence (GenAI), autonomous agents, cloud computing, search engines, and interconnected digital services is creating new possibilities for autonomous Internet task execution. Rather than manually navigating individual applications and services, users increasingly express intended outcomes that can be interpreted and executed through the orchestration of heterogeneous computational capabilities. However, as autonomous Internet systems become capable of invoking multiple AI models, external APIs, search services, cloud resources, and computational tools, the environmental costs associated with task execution may become increasingly important. A system that successfully completes a task does not necessarily accomplish it in an energy-efficient or environmentally sustainable manner. Unnecessary model invocations, excessive computational complexity, redundant service calls, inefficient resource allocation, and carbon-intensive execution environments may introduce environmental costs without providing proportional improvements in task outcomes. This paper proposes Intent-Driven Internet Sustainability (IDIS) as a conceptual research direction for integrating environmental sustainability into intent-driven Internet architectures. The central principle is that autonomous digital tasks should be executed not only according to their intended outcomes, but also according to their computational requirements, energy consumption, environmental impact, and acceptable quality constraints. To operationalize this perspective, this paper introduces the Intent-Driven Internet Sustainability Framework (IDISF), a conceptual architecture comprising eight interconnected functional components: intent-based sustainability requirement extraction, computational footprint estimation, capability-level environmental assessment, energy-aware capability selection, carbon-aware execution scheduling, sustainable multi-service orchestration, execution footprint monitoring, and sustainability-aware outcome evaluation. The framework further introduces an environmental cost-aware capability selection model that considers energy consumption, carbon intensity, computational resource requirements, execution latency, monetary cost, and task quality when determining an appropriate execution strategy. A distinguishing feature of the proposed approach is its integration of environmental considerations into the decision-making process of autonomous Internet orchestration. Rather than treating sustainability as an independent infrastructure optimization problem or a post-execution reporting activity, IDIS investigates how environmental information can influence which capabilities are selected, how tasks are decomposed, where and when computation is performed, and whether additional computational operations are justified. It also examines the potential for comparing local execution, cloud execution, lightweight models, high-capacity models, and multi-model collaboration according to their environmental costs and expected task outcomes. The paper further develops an energy–quality trade-off model, an intent-driven computational footprint representation, sustainability-aware scheduling strategies, research propositions, and an evaluation methodology. Potential advantages concerning reduced unnecessary computation, environmentally informed service selection, carbon-aware scheduling, resource efficiency, and sustainable autonomous Internet infrastructure are discussed. Importantly, these advantages are presented as theoretically motivated possibilities rather than experimentally established outcomes. The proposed framework is a conceptual research proposal that requires further formalization, implementation, and empirical evaluation.