A Four-Layer Hybrid Intelligence Framework for Resource-Efficient and Sustainable Data Asset Governance Prioritization
Year: 2026
Type: article
Abstract: Resource misallocation in data governance is an overlooked source of carbon emissions, since most prioritization methods disregard the environmental cost of computing and human effort. This study proposes a four-layer hybrid intelligence framework (FLHIF) that unifies cloud model uncertainty quantification, social network analysis of data lineage graphs, DeepSeek-V4-driven multi-agent semantic reasoning, and adaptive weight learning via proximal policy optimization (PPO). On a benchmark of 439 data asset instances, FLHIF achieves an NDCG@10 of 0.9500, outperforming all baselines. Beyond ranking accuracy, expert evaluation yields an interpretability score of 4.2/5.0 (ICC > 0.80), indicating that FLHIF’s recommendations are auditable as well as accurate, a requirement of growing importance for AI adoption in regulated governance settings. Holdout stability tests, sensitivity analysis, and zero-shot generalization across finance, healthcare, manufacturing, and government sectors further demonstrate the framework’s robustness across the tested benchmarks. Preliminary simulations indicate that FLHIF can reduce redundant governance operations by 15–22% in a typical enterprise setting, corresponding to an estimated annual reduction of 120–180 kg CO2-equivalent emissions per medium-sized data platform. FLHIF thus offers a systematic, resource-efficient, explainable, and adaptive approach to sustainable data governance, directly supporting SDG 9.1, SDG 11.3, and SDG 12.2/12.5.
Source: Sustainability
DOI: 10.3390/su18189277
Authors: Ming Li, Fang Bu, Yiwen Zhang, Fengyao Sun
Institution: Hohai University
Language: English
Topic: Green IT and Sustainability
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7212057838