[论文解读] A Multi-Objective Optimization Approach for Sustainable AI-Driven Entrepreneurship in Resilient Economies

Anas ALsobeh, Raneem Alkurdi|arXiv (Cornell University)|Mar 9, 2026

Economic and Technological Innovation被引用 0

一句话总结

论文提出 EcoAI-Resilience 框架，一种多目标优化方法，在53个国家和14个行业中最大化可持续性与经济韧性，同时降低 AI 部署的环境成本；通过 ML 模型和 SLSQP 优化进行验证。

ABSTRACT

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.

研究动机与目标

• Develop a mathematical multi-objective optimization model that integrates sustainability impact, economic resilience, and environmental cost functions
• Create and validate ML models for predicting sustainability and resilience outcomes in AI deployment contexts
• Identify optimal AI deployment strategies across different sectors and geographic regions

提出的方法

• Formulate a weighted-sum multi-objective optimization: max F(x)=α·S(x)+β·R(x)−γ·E(x) with α+β+γ=1
• Define sustainability S(x) with log-scaled renewable energy and quadratic efficiency terms
• Define economic resilience R(x) using innovation, market stability, and AI investment components
• Define environmental cost E(x) with energy, carbon, and water metrics and normalization
• Impose physical, economic, and environmental constraints on adoption, energy, investment, and emissions
• Integrate four datasets (LLM energy, sustainability metrics, renewable energy market, entrepreneurship) and perform ML validation (Random Forest, Gradient Boosting) to predict components

实验结果

研究问题

• RQ1 How can AI deployment strategies be optimized to balance sustainability, economic resilience, and environmental costs?
• RQ2 What are the optimal AI adoption, renewable energy, efficiency, and investment configurations across sectors and regions?
• RQ3 How do different weighting schemes affect the identified optimal deployment strategies and their robustness?
• RQ4 What are the key drivers of sustainability, resilience, and environmental costs in AI deployment according to the framework?

主要发现

• ML components achieve high predictive accuracy (S: R²≈0.997, resilience: R²≈0.999, environmental: R²=1.000) on sustainability metrics data
• Optimal deployment strategy features 100% renewable energy, 80% efficiency gain, AI adoption at max (10.0), innovation index 100, market stability 10, AI investment $202.48 per capita
• Baseline optimization yields composite objective value 2.05 with energy 798.9 MWh, CO2 297.8 tons, water 1499.8 L
• EcoAI-Resilience outperforms Linear Regression (R² 0.943), Random Forest (R² 0.957), Gradient Boosting (R² 0.989) with R² 0.996
• Sensitivity analyses show robustness to weight configurations; AI adoption and renewable energy percentage are the most impactful parameters
• Temporal trends indicate global improvements: sustainability +0.89 points/year, AI readiness +1.12 points/year, renewable energy +0.67%/year