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Abstract
This paper presents a novel distributionally robust risk-aware approach that aims to tackle the increasingly complex sustainable development model by taking into account job creation and economic growth. To alleviate the inherent conservatism in existing robust sustainable development model, we develop a distributionally robust chance constrained sustainable development model. This innovative approach, incorporating adjustable value-at-risk measures, offers a formal theoretical guarantee for sustainable economic development. However, its practical implementation presents considerable difficulty due to the non-convex nature of the optimization problem associated with the sustainable development model. Moreover, acquiring accurate probability distribution functions for uncertainties in the sustainable development model poses a challenge due to the limited availability of historical data. To address these challenges, we propose a distributionally robust risk-aware reformulated method that approximates the chance constrained sustainable development model as a semidefinite programming problem, taking into account the mean and variance of uncertainties. This operational strategy provides decision-makers with a robust market-optimal solution. A comparative analysis reveals that our proposed method outperforms alternative approaches by achieving a 5% higher GDP within the sustainable development model, underscoring its strong potential as a reliable and secure decision-making tool for the next generation of sustainable development.