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 CO₂, SO₂, NO_X, and PM_2.5 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 CO₂ 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 CO₂ 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.

中文翻译：

---

印度发电边际排放因子的当前和未来估计

尽管已知的空间和时间可变性，印度电力的排放因子特征仍然很差。公开的排放和发电数据有限且足够详细，因此很难理解排放对气候变化和空气污染的影响，可能会错过世界第三大电网的具有成本效益的政策设计。我们使用简化形式和完整形式的电力调度模型来量化印度发电的当前（2017-2018 年）和未来（2030-2031 年）边际 CO ₂、SO ₂、NO _X和 PM _{2.5排放因子。}这些边际排放代表由于需求的微小变化而导致的排放变化。对于 2017-2018 年，边际 CO _{2的空间变异性}印度各州的排放因子范围为 3 个数量级。在一半以上的州，煤炭发电可能在超过一半的时间内满足需求变化，季节性和日内变化有限。假设印度政府接近 2030 年的目标，各州的边际 CO ₂排放因子中值下降约 2 倍，但各州的排放因子仍跨越 3 个数量级。在 2030-2031 年的假设下，季节性和日内变化分别高达 2 倍和 4 倍。估算提供排放因子来评估电动汽车、增加空调和能源效率等干预措施。