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美国住宅建筑中天然气使用的驱动因素。

Drivers of natural gas use in U.S. residential buildings.

作者信息

Mittakola Rohith Teja, Ciais Philippe, Schubert Jochen E, Makowski David, Zhou Chuanlong, Bazzi Hassan, Sun Taochun, Liu Zhu, Davis Steven J

机构信息

Laboratoire des Sciences du Climat et de l'Environnement, IPSL CEA CNRS UVSQ, Gif-sur-Yvette, France.

Atos France, Technical Services, 80 Quai Voltaire, 95870 Bezons, France.

出版信息

Sci Adv. 2024 Apr 5;10(14):eadh5543. doi: 10.1126/sciadv.adh5543. Epub 2024 Apr 3.

DOI:10.1126/sciadv.adh5543
PMID:38569031
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10990266/
Abstract

Natural gas is the primary fuel used in U.S. residences, yet little is known about its consumption patterns and drivers. We use daily county-level gas consumption data to assess the spatial patterns of the relationships and the sensitivities of gas consumption to outdoor air temperature across U.S. households. We fitted linear-plus-plateau functions to daily gas consumption data in 1000 counties, and derived two key coefficients: the heating temperature threshold () and the gas consumption rate change per 1°C temperature drop (Slope). We identified the main predictors of and Slope (like income, employment rate, and building type) using interpretable machine learning models built on census data. Finally, we estimated a potential 2.47 million MtCO annual emission reduction in U.S. residences by gas savings due to household insulation improvements and hypothetical behavioral change toward reduced consumption by adopting a 1°C lower than the current value.

摘要

天然气是美国居民使用的主要燃料，但人们对其消费模式和驱动因素知之甚少。我们使用县级每日天然气消费数据来评估美国家庭天然气消费关系的空间模式以及天然气消费对室外气温的敏感度。我们对1000个县的每日天然气消费数据拟合了线性加平台函数，并得出两个关键系数：供暖温度阈值（）和每1°C温度下降时的天然气消费率变化（斜率）。我们使用基于人口普查数据构建的可解释机器学习模型确定了和斜率的主要预测因素（如收入、就业率和建筑类型）。最后，我们估计，通过改善家庭隔热措施以及假设采取比当前值低1°C的温度从而减少消费的行为变化，美国居民每年可通过节省天然气减排约247万吨二氧化碳。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b03/10990266/fa0f6bbf00cf/sciadv.adh5543-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b03/10990266/f173b95abc08/sciadv.adh5543-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b03/10990266/b70c51042d3b/sciadv.adh5543-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b03/10990266/5ca37db14f90/sciadv.adh5543-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b03/10990266/fa8d5c905707/sciadv.adh5543-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b03/10990266/fa0f6bbf00cf/sciadv.adh5543-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b03/10990266/f173b95abc08/sciadv.adh5543-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b03/10990266/b70c51042d3b/sciadv.adh5543-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b03/10990266/5ca37db14f90/sciadv.adh5543-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b03/10990266/fa8d5c905707/sciadv.adh5543-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b03/10990266/fa0f6bbf00cf/sciadv.adh5543-f5.jpg

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本文引用的文献

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Nat Commun. 2023 Oct 18;14(1):6434. doi: 10.1038/s41467-023-41458-5.
2
Unveiling hidden energy poverty using the energy equity gap.揭示能源权益差距中的隐性能源贫困
Nat Commun. 2022 May 4;13(1):2456. doi: 10.1038/s41467-022-30146-5.
3
The carbon footprint of household energy use in the United States.美国家庭能源使用的碳足迹。
Proc Natl Acad Sci U S A. 2020 Aug 11;117(32):19122-19130. doi: 10.1073/pnas.1922205117. Epub 2020 Jul 20.