Bosch Center for Artificial Intelligence , Pittsburgh , Pennsylvania 15222 , United States.
Department of Energy Resources Engineering, School of Earth, Energy and the Environment , Stanford University , Stanford , California 94305 , United States.
Environ Sci Technol. 2019 Aug 20;53(16):9905-9914. doi: 10.1021/acs.est.8b06586. Epub 2019 Aug 5.
In recent years, several methods have emerged to estimate the emissions and health, environmental, and climate change damages avoided by interventions such as energy efficiency, demand response, and the integration of renewables. However, differing assumptions employed in these analyses could yield contradicting recommendations regarding intervention implementation. We test the magnitude of the effect of using different key assumptions-average vs marginal emissions, year of calculation, temporal and regional scope, and inclusion of nonemitting generation-to estimate Mid-Atlantic region power pool (PJM) emissions and damage factors. We further highlight the importance of factor selection by evaluating three illustrative 2017 power system examples in PJM. We find that for a simple building lighting intervention, using average emissions factors incorporating nonemitting generation avoided damages by 45% compared to marginal factors. For PJM demand response, outdated marginal emissions factors from 2016 avoided damages by 25% compared to 2017 factors. Our assessment of PJM summer load further suggests that fossil-only average emissions factors damages by 63% compared to average factors incorporating nonemitting generation. We recommend that energy modelers carefully select appropriate emissions metrics when performing their analyses. Furthermore, since the U.S. electric grid is rapidly changing, we urge decision-makers to frequently update (and consider forecasting) grid emissions factors.
近年来，出现了几种方法来估算能源效率、需求响应和可再生能源整合等干预措施避免的排放和健康、环境及气候变化损害。然而，这些分析中使用的不同假设可能会对干预措施的实施产生相互矛盾的建议。我们检验了使用不同关键假设（平均排放与边际排放、计算年份、时间和区域范围以及非排放发电的纳入）来估算中美大西洋地区电力池（PJM）排放和损害因素的影响程度。我们通过评估 PJM 中三个有代表性的 2017 年电力系统示例，进一步强调了因素选择的重要性。我们发现，对于简单的建筑物照明干预措施，使用包含非排放发电的平均排放因子可将损害减少 45%，而使用边际排放因子则可将损害减少 45%。对于 PJM 的需求响应，2016 年的过时边际排放因子可比 2017 年的因子多避免 25%的损害。我们对 PJM 夏季负荷的评估进一步表明，与包含非排放发电的平均排放因子相比，仅化石燃料的平均排放因子会使损害增加 63%。我们建议能源建模者在进行分析时仔细选择适当的排放指标。此外，由于美国电网正在迅速变化，我们敦促决策者经常更新（并考虑预测）电网排放因子。
