对比不同经济类型下可再生能源分配策略，发现短期优化更有效。
Comparing Socially-Equitable Renewable Energy Budget Allocation MDP Policies in Mature and Emerging Economies
- 将能源分配建模为马尔可夫决策过程，统一评估政策效果。
- 美国实现66%可再生能源渗透率，低收入群体供电缺口减少96%。
- 在印尼，追逐市场会恶化弱势地区供电，需谨慎设计激励机制。
公平的可再生能源规划是序列决策问题，但成熟经济体与新兴经济体的公共规划者可用决策变量差异显著：前者主要由政府建设发电设施，后者则通过激励和配额引导私营投资。本文将社会公平的可再生能源预算分配建模为马尔可夫决策过程（MDP），并使用同一无问题依赖的求解接口，在八个美国城市（成熟经济体）与印度尼西亚西爪哇省（新兴经济体）进行政策比较。结果表明，无论在何种经济背景下，滚动时域价值迭代策略均占优。在美国，该策略实现66%可再生能源渗透率，同时使低收入人群供电缺口减少96%（相比随机基准）。在西爪哇，该策略有效缩小低接入差距，并最大程度吸引私人资本投入。更令人关注的是，一种简单的市场追逐启发式在美国仅略逊于最优解，但在印尼却导致所有低接入区域被严重忽视——因为当规划者通过私营开发商行动时，追逐高回报市场与服务弱势群体的目标出现根本冲突。
原文摘要 · Abstract (English)
Equitable renewable-energy planning is a sequential decision problem, but the decision variables available to a public planner differ sharply between mature and emerging economies. In the former the government largely builds generation, while in the latter it steers private investment through incentives and quotas. We formulate socially-equitable renewable-energy budget allocation as a Markov Decision Process (MDP) and, using a single problem-agnostic solver interface, compare the same policies across the two settings: eight U.S. cities (a mature economy) and West Java, Indonesia (an emerging economy). The results show that across both settings, a receding-horizon value-iteration policy dominates. In the U.S., it reaches 66% renewable penetration while cutting the underserved low-income population by 96% versus a random baseline. In West Java it closes the low-access gap while crowding in the most private capital. More interestingly, a naive market-chasing heuristic, which is mildly sub-optimal in the U.S., could yield catastrophic outcomes in Indonesia, by underserving every low-access region, because chasing attractive markets and serving the underserved goals diverge once the planner acts through private developers.
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