In recent years, there has been an increased emphasis on reducing the carbon emissions from electricity consumption. Many organizations have set ambitious targets to reduce the carbon footprint of their operations as a part of their sustainability goals. The carbon footprint of any consumer of electricity is computed as the product of the total energy consumption and the carbon intensity of electricity. Third-party carbon information services provide information on carbon intensity across regions that consumers can leverage to modulate their energy consumption patterns to reduce their overall carbon footprint. In addition, to accelerate their decarbonization process, large electricity consumers increasingly acquire power purchase agreements (PPAs) from renewable power plants to obtain renewable energy credits that offset their "brown" energy consumption. There are primarily two methods for attributing carbon-free energy, or renewable energy credits, to electricity consumers: location-based and market-based. These two methods yield significantly different carbon intensity values for various consumers. As there is a lack of consensus which method to use for carbon-free attribution, a concurrent application of both approaches is observed in practice. In this paper, we show that such concurrent applications can cause discrepancies in the carbon savings reported by carbon optimization techniques. Our analysis across three state-of-the-art carbon optimization techniques shows possible overestimation of up to 55.1% in the carbon reductions reported by the consumers and even increased emissions for consumers in some cases. We also find that carbon optimization techniques make different decisions under the market-based method and location-based method, and the market-based method can yield up to 28.2% less carbon savings than those claimed by the location-based method for consumers without PPAs.

翻译：近年来，减少电力消耗产生的碳排放日益受到重视。许多组织已将降低运营碳足迹作为可持续发展目标之一，并设定了雄心勃勃的减排指标。电力消费者的碳足迹计算方式为总能源消耗量乘以电力碳排放强度。第三方碳信息服务提供各区域碳排放强度数据，消费者可据此调整用电模式以减少总体碳足迹。此外，为加速脱碳进程，大型电力消费者越来越多地通过可再生能源发电厂签订购电协议，获取可再生能源信用额度以抵消其"棕色"电力消费。将无碳能源（即可再生能源信用额度）分配给电力消费者主要有两种方法：基于位置的方法和基于市场的方法。这两种方法对不同消费者产生的碳排放强度值存在显著差异。由于缺乏对无碳能源分配方法的统一共识，实践中两种方法常被同时使用。本文证明，这种并行应用可能导致碳优化技术报告的碳减排量出现偏差。通过分析三种最先进的碳优化技术，我们发现消费者报告的碳减排量最高可能被高估55.1%，某些情况下甚至会导致消费者实际碳排放增加。研究还表明，碳优化技术在使用市场法与传统位置法时会做出不同决策，对于未签订购电协议的消费者，市场法计算的碳减排量比位置法最多低28.2%。