Deep Learning-Driven Dynamic Network DEA for Cross-Industry ESG Resilience: Heterogeneous Threshold Identification and Carbon Policy Simulation
Year: 2026
Type: article
Abstract: Balancing production resilience with environmental, social and governance (ESG) performance is difficult when disruptions, policy constraints and stakeholder expectations interact over time. This paper proposes a six-node dynamic network data envelopment analysis architecture whose admissible weight intervals are adjusted by an LSTM learner using IoT-derived shock states. The application covers 96 aggregate monthly periods from 2018 to 2025 across three countries and four technology-based manufacturing groups. Figure-grounded diagnostics indicate mean resilience scores of 0.7016 for the basic IoT-DEA benchmark and 0.7134 for the complete model (paired difference = 0.0118; 12-month moving-block bootstrap 95% CI = 0.0015–0.0245; p = 0.001), while mean uncertainty falls from 0.0536 to 0.0205, a 61.8% reduction. Threshold sensitivity shows that governance-delay and skill boundaries vary by industry and carbon-constraint severity rather than constituting universal standards. In the calibrated policy simulation, the combined carbon-tax and green-subsidy path reaches approximately 0.90 by month 96, compared with 0.79 under no policy; this contrast is interpreted as a scenario result, not a firm-level causal treatment effect. NSGA-III and SHAP then connect the measured constraints to Pareto-efficient portfolios and sector-specific managerial priorities. The framework’s main supported contribution is more stable, temporally explicit ESG-resilience diagnosis with transparent limits on causal and cross-sectional inference.
Source: Technologies
Authors: Guiheng Zou, Kok Beng Gan
Institution: National University of Malaysia
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W7212169273