Black carbon (BC) is an important pollutant aerosol emitted by numerous human
activities, including gas flaring. Improper combustion in flaring activities
can release large amounts of BC, which is harmful to human health and has a
strong climate warming effect. To our knowledge, no study has ever directly
monitored BC emissions from satellite imagery. Previous works quantified BC
emissions indirectly, by applying emission coefficients to flaring volumes
estimated from satellite imagery. Here, we develop a deep learning framework
and apply it to Sentinel-2 imagery over North Africa during 2022 to detect and
quantify BC emissions from gas flaring. We find that BC emissions in this
region amount to about 1 million tCO$_{2,\mathrm{eq}}$, or 1 million passenger
cars, more than a quarter of which are due to 10 sites alone. This work
demonstrates the operational monitoring of BC emissions from flaring, a key
step in implementing effective mitigation policies to reduce the climate impact
of oil and gas operations.

通过卫星图像检测和量化燃气喷烧导致的黑碳排放量，结果显示该地区的黑碳排放量相当于 100 万吨 CO2 等效，大约占总排放量的四分之一，该研究为实施有效的减轻石油和天然气运营的气候影响的政策提供了关键性步骤。

利用深度学习从多光谱影像中识别的北非燃气燃烧引起的黑碳喷流

Black carbon plumes from gas flaring in North Africa identified from  multi-spectral imagery with deep learning

The climate impact of AI, and NLP research in particular, has become a
serious issue given the enormous amount of energy that is increasingly being
used for training and running computational models. Consequently, increasing
focus is placed on efficient NLP. However, this important initiative lacks
simple guidelines that would allow for systematic climate reporting of NLP
research. We argue that this deficiency is one of the reasons why very few
publications in NLP report key figures that would allow a more thorough
examination of environmental impact. As a remedy, we propose a climate
performance model card with the primary purpose of being practically usable
with only limited information about experiments and the underlying computer
hardware. We describe why this step is essential to increase awareness about
the environmental impact of NLP research and, thereby, paving the way for more
thorough discussions.

该论文讨论了 NLP 研究中使用计算模型所耗费的能量问题，提出了使用气候表现模型卡的可行性，以简化气候报告。