The rapid advancement of artificial intelligence (AI) technologies presents both unprecedented opportunities and significant challenges for sustainable economic development. While AI offers transformative potential for addressing environmental challenges and enhancing economic resilience, its deployment often involves substantial energy consumption and environmental costs. This research introduces the EcoAI-Resilience framework, a multi-objective optimization approach designed to maximize the sustainability benefits of AI deployment while minimizing environmental costs and enhancing economic resilience. The framework addresses three critical objectives through mathematical optimization: sustainability impact maximization, economic resilience enhancement, and environmental cost minimization. The methodology integrates diverse data sources, including energy consumption metrics, sustainability indicators, economic performance data, and entrepreneurship outcomes across 53 countries and 14 sectors from 2015-2024. Our experimental validation demonstrates exceptional performance with R scores exceeding 0.99 across all model components, significantly outperforming baseline methods, including Linear Regression (R = 0.943), Random Forest (R = 0.957), and Gradient Boosting (R = 0.989). The framework successfully identifies optimal AI deployment strategies featuring 100\% renewable energy integration, 80% efficiency improvement targets, and optimal investment levels of $202.48 per capita. Key findings reveal strong correlations between economic complexity and resilience (r = 0.82), renewable energy adoption and sustainability outcomes (r = 0.71), and demonstrate significant temporal improvements in AI readiness (+1.12 points/year) and renewable energy adoption (+0.67 year) globally.

翻译：人工智能（AI）技术的快速发展为可持续经济发展带来了前所未有的机遇，同时也带来了重大挑战。尽管AI在应对环境挑战和增强经济韧性方面展现出变革性潜力，但其部署往往伴随着巨大的能源消耗和环境成本。本研究提出了EcoAI-Resilience框架，这是一种多目标优化方法，旨在最大化AI部署的可持续性效益，同时最小化环境成本并增强经济韧性。该框架通过数学优化处理三个关键目标：可持续性影响最大化、经济韧性增强和环境成本最小化。该方法整合了多样化的数据源，包括能源消耗指标、可持续性指标、经济绩效数据以及2015年至2024年间53个国家和14个部门的创业成果。我们的实验验证表明，该框架在所有模型组件上均表现出卓越性能，R分数均超过0.99，显著优于基线方法，包括线性回归（R = 0.943）、随机森林（R = 0.957）和梯度提升（R = 0.989）。该框架成功识别了最优的AI部署策略，其特点包括100%可再生能源整合、80%的效率提升目标以及人均202.48美元的最优投资水平。关键发现揭示了经济复杂性与韧性之间存在强相关性（r = 0.82），可再生能源采用与可持续性成果之间也存在强相关性（r = 0.71），并展示了全球范围内AI准备度（+1.12分/年）和可再生能源采用（+0.67年）的显著时间性改善。