A Multi-Objective Optimization Approach for Sustainable AI-Driven Entrepreneurship in Resilient Economies

TL;DR

This paper presents EcoAI-Resilience, a multi-objective optimization framework that balances AI sustainability, environmental costs, and economic resilience, validated across diverse global data with high accuracy and strategic insights.

Contribution

The study introduces a novel multi-objective optimization framework for sustainable AI deployment, integrating diverse data sources and demonstrating superior performance over baseline models.

Findings

High model accuracy with R > 0.99 across components

Strong correlation between economic complexity and resilience (r=0.82)

Significant global improvements in AI readiness and renewable energy adoption

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.