玻利维亚液化石油气补贴改革、能源补偿与社会风险：基于机器学习智能体的微观模拟

中文摘要 AI 辅助

本研究采用机器学习智能体微观模拟框架，评估玻利维亚液化石油气（LPG）补贴改革的不同方案设计。研究整合家庭调查数据、支出数据、人口与健康信息，以及月度碳氢化合物生产与商业化序列，模拟多种改革情景下的财政节约、贫困影响、能源替代、行政成本及社会风险。模型对比了无补偿的补贴取消、固定能源转移、对脆弱LPG用户的全额补偿、基于凭证的补偿、母婴转移、清洁能源过渡套件及混合政策组合。机器学习模型用于学习家庭脆弱性、燃料使用模式、粮食不安全风险及行为倾向，这些因素被纳入月度智能体模拟。结果显示，无补偿地取消补贴虽带来最大财政节约，但会加剧贫困、极端贫困，并增加向固体燃料替代的压力；对Q1-Q2 LPG用户的全额货币补偿可大幅减少社会伤害，同时保留显著的财政节约，且在正常市场条件下因行政摩擦更低，优于同等的凭证设计；最具社会稳健性的设计是结合有针对性的货币能源补偿、母婴强化措施及为使用固体燃料的家庭提供清洁能源套件。研究结果支持逐步将普惠LPG补贴替换为有针对性、行政精简且符合行为规律的补偿机制。

英文摘要

This study evaluates alternative designs for reforming Bolivia's liquefied petroleum gas subsidy using a machine-learning agent-based microsimulation framework. The analysis harmonizes household survey data, expenditure data, demographic and health information, and monthly hydrocarbon production and commercialization series to simulate fiscal savings, poverty effects, energy substitution, administrative costs, and social risks under multiple reform scenarios. The model compares uncompensated subsidy removal, fixed energy transfers, full compensation for vulnerable LPG users, voucher-based compensation, maternal-child transfers, clean-energy transition kits, and hybrid policy packages. Machine-learning models are used to learn household vulnerability, fuel-use patterns, food insecurity risk, and behavioral propensities that feed into a monthly agent-based simulation. The results show that eliminating the subsidy without compensation generates the largest fiscal savings but increases poverty, extreme poverty, and pressure toward solid-fuel substitution. Full monetary compensation for Q1-Q2 LPG users substantially reduces social harm while preserving significant fiscal savings and dominates an equivalent voucher design under normal market conditions because of lower administrative friction. The most socially robust design combines targeted monetary energy compensation, maternal-child reinforcement, and clean-energy kits for households using solid fuels. The findings support a gradual replacement of the universal LPG subsidy with targeted, administratively lean, and behaviorally informed compensation mechanisms.

发表机构

• Universitat Politècnica de Catalunya – BarcelonaTech (UPC)（加泰罗尼亚理工大学）

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