AI-Guided Diversification of Green Technology Supply Chains

Erokhin, D. ORCID: https://orcid.org/0000-0002-5191-0579 (2026). AI-Guided Diversification of Green Technology Supply Chains. Sustainability 18 (19) e9696. 10.3390/su18199696.

[thumbnail of sustainability-18-09696.pdf]
Preview
Text
sustainability-18-09696.pdf

Download (12MB) | Preview

Abstract

Green technology supply chains combine rapid demand growth with concentrated production, uneven environmental performance, and exposure to trade disruption. This article links trade network analysis, next-year bilateral trade link prediction, multi-objective portfolio optimization, and disruption stress testing across 14 importing markets and six technology and material groups from 2015 to 2024. The cleaned sample contains 15,186 supplier-year observations and USD 696.1 billion in foreign supplier trade. Concentration rose most clearly in photovoltaic equipment, lithium-ion batteries, and wind equipment. In the chronological 2022–2023 test, gradient boosting achieved ROC AUC 0.911 and a Brier score of 0.119. Logistic regression produced slightly higher AUC, while persistence retained the strongest F1. Event-specific tests show that exits and reactivations are more predictable than rare first observed entries. A historical decision comparison finds only a small and statistically uncertain advantage of calibrated gradient boosting over calibrated logistic regression, while predictive continuity information improves allocation performance relative to a specification without the probability term. Latest-year portfolios remain highly concentrated, with median HHI of 0.632 and a median top-three supplier share of 95.5%. Under balanced priorities, median HHI falls by 53.7%, and sourcing exposure to supplier-country CO2 intensity of GDP falls by 32.3%. These changes are accompanied by a 5.2% median unit value increase and a 4.8 percentage point decline in predicted next-year link activity. Joint supplier budget scenarios show that individually feasible national diversification plans can compete for the same alternatives. The framework provides a transparent basis for screening sourcing options, identifying cases that require supplier-level verification, and assessing where coordinated investment may be needed.

Item Type: Article
Uncontrolled Keywords: artificial intelligence; sustainable supply chain; green technology; critical minerals; supplier diversification; trade networks; portfolio optimization; resilience; business transformation
Research Programs: Advancing Systems Analysis (ASA)
Advancing Systems Analysis (ASA) > Cooperation and Transformative Governance (CAT)
Depositing User: Michaela Rossini
Date Deposited: 23 Sep 2026 16:11
Last Modified: 23 Sep 2026 16:11
URI: https://pure.iiasa.ac.at/21894

Actions (login required)

View Item View Item