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<p>Human well-being as a systemic nexus is ultimately regarded as the primary objective of sustainable development. Excavating methodologies aimed at reconciling the interplay among multiple human well-being objectives are demonstrated as a critical bottleneck. This study formulates a well-being optimization model that accounts for complex resource interdependencies among sectors, improves NSGA-III and MOEA/D for industrial restructuring strategies and validates it through a Beijing case demonstration, revealing synergies and trade-offs among human well-being objectives through Spearman correlation coefficient and elasticity coefficient. The main findings are as follows: (1) The improved MOEA/D-CCLP algorithm demonstrates superior performance compared to traditional NSGA-III, MOEA/D and improved NSGA-III-CCLP algorithm. (2) Income and education exhibit a synergistic relationship, yet both objectives demonstrate trade-offs with other objectives. Human health, ecosystem quality, resource scarcity and waste treatment cost display mutual synergies. Compared with other objectives, ecosystem quality exhibits greater sensitivity to treatment cost. (3) The tertiary sector should be prioritized as the focal point of future structural adjustments and should strive to develop capital-intensive industries into technology-intensive and knowledge-intensive industries. This research establishes the framework for resolving conflicting sustainability objectives and providing actionable policy blueprints for megacities balancing development and livability.</p>

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Quantifying the synergy and trade-offs among human well-being objectives: a perspective of industrial restructuring

  • 期刊论文

  • 发表日期：

    2026年6月24日

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  • Xin Ning,
  • Yu Qiu,
  • Wenjuan Wang,
  • Dan Lv

摘要

Human well-being as a systemic nexus is ultimately regarded as the primary objective of sustainable development. Excavating methodologies aimed at reconciling the interplay among multiple human well-being objectives are demonstrated as a critical bottleneck. This study formulates a well-being optimization model that accounts for complex resource interdependencies among sectors, improves NSGA-III and MOEA/D for industrial restructuring strategies and validates it through a Beijing case demonstration, revealing synergies and trade-offs among human well-being objectives through Spearman correlation coefficient and elasticity coefficient. The main findings are as follows: (1) The improved MOEA/D-CCLP algorithm demonstrates superior performance compared to traditional NSGA-III, MOEA/D and improved NSGA-III-CCLP algorithm. (2) Income and education exhibit a synergistic relationship, yet both objectives demonstrate trade-offs with other objectives. Human health, ecosystem quality, resource scarcity and waste treatment cost display mutual synergies. Compared with other objectives, ecosystem quality exhibits greater sensitivity to treatment cost. (3) The tertiary sector should be prioritized as the focal point of future structural adjustments and should strive to develop capital-intensive industries into technology-intensive and knowledge-intensive industries. This research establishes the framework for resolving conflicting sustainability objectives and providing actionable policy blueprints for megacities balancing development and livability.