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<p>This study integrates Bayesian Structural Time Series modelling with Fermatean fuzzy programming to deliver accurate forecasting and robust decision-making for achieving the United Nations Sustainable Development Goals. The Bayesian Structural Time Series model, supported by a local linear trend and Markov Chain Monte Carlo estimation, effectively captures latent economic and environmental dynamics under uncertainty. Fermatean fuzzy programming further resolves vagueness in policy and resource allocation, ensuring optimal outcomes with minimal error. A comparative analysis with intuitionistic and Pythagorean fuzzy frameworks demonstrates that the Fermatean approach provides superior flexibility and precision in handling higher-order uncertainty. Together, these methods combine strong predictive performance with adaptable optimization, creating a unified pathway for economic growth, environmental sustainability, energy security, workforce development, and water management. The integrated framework offers a scientifically grounded foundation for setting feasible targets and formulating actionable policies for Vision 2030, supporting long-term environmental stability and economic resilience.</p>

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A Multi-Objective Fermatean Fuzzy Optimization Model Coupled with Bayesian Robust Trend Analysis for Sustainable Development

  • 期刊论文

  • 发表日期：

    2026年4月28日

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  • Nabil Ahmed Khan,
  • Ahmad Yusuf Adhami,
  • Mohammad Parvej

摘要

This study integrates Bayesian Structural Time Series modelling with Fermatean fuzzy programming to deliver accurate forecasting and robust decision-making for achieving the United Nations Sustainable Development Goals. The Bayesian Structural Time Series model, supported by a local linear trend and Markov Chain Monte Carlo estimation, effectively captures latent economic and environmental dynamics under uncertainty. Fermatean fuzzy programming further resolves vagueness in policy and resource allocation, ensuring optimal outcomes with minimal error. A comparative analysis with intuitionistic and Pythagorean fuzzy frameworks demonstrates that the Fermatean approach provides superior flexibility and precision in handling higher-order uncertainty. Together, these methods combine strong predictive performance with adaptable optimization, creating a unified pathway for economic growth, environmental sustainability, energy security, workforce development, and water management. The integrated framework offers a scientifically grounded foundation for setting feasible targets and formulating actionable policies for Vision 2030, supporting long-term environmental stability and economic resilience.