用多目标优化平衡AI发展中的环境成本与经济韧性，提升可持续创业能力。
A Multi-Objective Optimization Approach for Sustainable AI-Driven Entrepreneurship in Resilient Economies
- 构建多目标优化框架，统筹可持续性、经济韧性与环保成本。
- 模型预测准确率超0.99，显著优于传统方法，识别出最优部署策略。
- 适合政策制定者与可持续科技创业者参考，尤其关注能源与投资效率。
人工智能的快速发展为可持续经济发展带来机遇与挑战。本文提出EcoAI-Resilience框架，通过数学优化同时实现三大目标：最大化可持续影响、增强经济韧性、最小化环境成本。该方法整合2015–2024年间53个国家14个行业的能源消耗、可持续性指标、经济绩效与创业成果数据。实验验证显示，各组件预测精度均超过0.99，显著优于线性回归（R=0.943）、随机森林（R=0.957）和梯度提升（R=0.989）。框架识别出最优策略：100%可再生能源接入、80%能效提升目标及人均202.48美元投资水平。结果显示，经济复杂度与韧性相关性达r=0.82，可再生能源与可持续性相关性r=0.71，全球AI准备度年均提升1.12点，可再生能源采纳率年均提升0.67点。
原文摘要 · Abstract (English)
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.
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