Observer Research Foundation America, Washington, District of Columbia 20036, United States.
The Energy and Resources Institute (TERI), New Delhi 110003, India.
Environ Sci Technol. 2022 Jul 5;56(13):9237-9250. doi: 10.1021/acs.est.1c07500. Epub 2022 Jun 24.
Emission factors from Indian electricity remain poorly characterized, despite known spatial and temporal variability. Limited publicly available emissions and generation data at sufficient detail make it difficult to understand the consequences of emissions to climate change and air pollution, potentially missing cost-effective policy designs for the world's third largest power grid. We use reduced-form and full-form power dispatch models to quantify current (2017-2018) and future (2030-2031) marginal CO, SO, NO, and PM emission factors from Indian power generation. These marginal emissions represent emissions changes due to small changes in demand. For 2017-2018, spatial variability in marginal CO emission factors range 3 orders of magnitude across India's states. There is limited seasonal and intraday variability with coal generation likely to meet changes in demand more than half the time in more than half of the states. Assuming the Government of India approximate 2030 targets, the median marginal CO emission factor across states decreases by approximately a factor of 2, but emission factors still span 3 orders of magnitude across states. Under 2030-2031 assumptions there is greater seasonal and intraday variability by up to factors of two and four, respectively. Estimates provide emission factors to evaluate interventions such as electric vehicles, increased air conditioning, and energy efficiency.
尽管印度电力的排放因子具有明显的时空可变性，但仍缺乏特征描述。有限的公开可用排放和发电数据细节不足，使得人们难以了解排放对气候变化和空气污染的影响，从而可能错失了为世界第三大电网设计具有成本效益的政策的机会。我们使用简化形式和全形式的电力调度模型来量化当前（2017-2018 年）和未来（2030-2031 年）印度发电的边际 CO、SO、NO 和 PM 排放因子。这些边际排放代表了由于需求的微小变化而导致的排放变化。对于 2017-2018 年，印度各州的边际 CO 排放因子的空间变异性范围跨越 3 个数量级。季节性和日内变化有限，煤炭发电在一半以上的州中有一半以上的时间满足需求变化的可能性超过一半。假设印度政府在 2030 年左右的目标，各州的边际 CO 排放因子中位数下降约 2 倍，但排放因子仍跨越 3 个数量级。在 2030-2031 年的假设下，季节性和日内变化分别增加了 2 倍和 4 倍。这些估计值提供了排放因子，可用于评估干预措施，如电动汽车、增加空调和提高能源效率。
