量化人工智能在能源转型中的影响：能源正义影响评估（EJIA）框架
Quantifying AI impact in energy transitions: The Energy Justice Impact Assessment (EJIA) framework
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中文总结 AI 辅助
针对AI在能源系统中影响难以量化的问题，提出EJIA框架，结合AI生命周期与能源正义三原则，生成多维可量化指标，按利益相关者群体测量并与反事实对比，以归因AI应用的实际影响。
中文摘要 AI 辅助
人工智能正越来越多地部署在能源系统中，以优化效率、平衡供需并整合可再生能源。这些应用改变了能源系统的运作方式，改变了利益与负担的分配方式、系统设计中代表谁的需求，以及谁能够影响决策，从而对能源正义产生影响，但这些影响很少被量化。本文通过系统回顾人工智能影响评估方法，并开发一个新框架来填补这一空白。我们采用PRISMA方法对26个经过同行评审的框架进行了回顾，发现大多数框架仅涉及单一影响维度，只有三个框架提供了可量化的指标，尽管有21个框架提及了正义影响，但只有两个框架通过可测量指标将正义相关构念操作化。现有框架中没有一个能将多维影响覆盖与按利益相关者群体分类的可量化指标相结合。因此，我们引入了能源正义影响评估（EJIA）框架。将人工智能生命周期的四个阶段——数据收集、模型开发、部署和持续适应——与能源正义的三大原则（分配正义、承认正义、程序正义）交叉，生成了一幅不公正可能产生的图谱，我们利用该图谱推导出一组环境、社会和经济结果指标。这些指标按受影响的利益相关者群体分别测量，并在定义的阶段内与反事实进行比较，从而能够将观察到的效应归因于人工智能应用。EJIA框架是描述性而非规范性的：它使差异影响可见且可比，而判断某种模式是否构成不公正仍是实践者根据具体情境做出的决定。将该框架应用于基于人工智能的社会住房需求响应系统的示例性案例，展示了该框架的用途。
英文摘要
Artificial intelligence is increasingly deployed across energy systems to optimize efficiency, balance supply and demand, and integrate renewable sources. These applications alter how energy systems function, changing how benefits and burdens are distributed, whose needs are represented in system design, and who can influence decision-making, with consequences for energy justice that are rarely quantified. This paper addresses that gap through a systematic review of AI impact assessment approaches, followed by the development of a new framework. Reviewing 26 peer-reviewed frameworks using PRISMA methodology, we find that most address a single impact dimension, only three provide quantifiable indicators, and although 21 reference justice implications, only two operationalize justice-relevant constructs through measurable indicators. No existing framework combines multidimensional impact coverage with quantifiable indicators disaggregated by stakeholder group. We therefore introduce the Energy Justice Impact Assessment (EJIA) framework. Crossing four AI lifecycle stages - data collection, model development, deployment, and continuous adaptation - with the three tenets of energy justice (distributional, recognition, procedural) produces a map of where injustice can arise, which we use to derive a set of environmental, social, and economic outcome indicators. These are measured separately per affected stakeholder group and assessed against a counterfactual across defined phases, enabling observed effects to be attributed to the AI application. The EJIA framework is descriptive rather than normative: it makes differential impacts visible and comparable, while judging whether a pattern constitutes injustice remains a context-dependent decision for practitioners. An exemplary application to an AI-based demand-side response system in social housing demonstrates the framework's use.
发表机构
- Seeburg Castle University（塞堡城堡大学）
- University of Konstanz（康斯坦茨大学）
机构由 AI 辅助整理，请以论文原文为准。
