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利用夜间灯光卫星图像预测新冠疫情对印度的经济影响。

Using satellite images of nighttime lights to predict the economic impact of COVID-19 in India.

作者信息

Dasgupta Nataraj

机构信息

Imperial College Business School, Imperial College London, London SW7 2BU, UK.

出版信息

Adv Space Res. 2022 Aug 15;70(4):863-879. doi: 10.1016/j.asr.2022.05.039. Epub 2022 May 24.

DOI:10.1016/j.asr.2022.05.039
PMID:35645448
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9128334/
Abstract

The outbreak of COVID-19 in early 2020 heralded a deep global recession not seen since the Second World War. With entire nations in lockdown, burgeoning economies of countries like India plunged into a downward spiral. The conventional instruments of estimating the short-term economic impact of a pandemic is limited, and as a result, it is challenging to implement timely monetary policies to mitigate the financial impact of such unforeseen events. This study investigates the promise of using nighttime images of lights on Earth, also known as nightlight (NTL), captured by the Visible Infrared Imaging Radiometer Suite (VIIRS) instrumentation onboard the Suomi National Polar-Orbiting Partnership (Suomi NPP) satellite mission to measure the economic cost of the pandemic in India. First, a novel data processing framework was developed for a recently released radiance dataset known as VNP46A1, part of NASA's Black Marble suite of NTL products. Second, the elasticity of nightlight to India's National Gross Domestic Product (GDP) was estimated using panel regression followed by machine learning to predict the Year-over-Year (YoY) change in GDP during Fiscal Year (FY) 2020Q1 (Apr-Jun, 2020). Electricity consumption, known to closely track economic output and precipitation were included as additional features to improve model performance. A strong relationship between both electricity usage and nightlight to GDP was observed. The model predicted a YoY contraction of 24% in FY2020Q1, almost identical to the official GDP decline of 23.9% later announced by the Indian Government. Based on the findings, the study concludes that nightlight along with electricity usage can be invaluable proxies for estimating the cost of short-term supply-demand shocks such as COVID-19, and should be explored further.

摘要

2020年初新冠疫情的爆发预示着自第二次世界大战以来最严重的全球经济衰退。随着各国进入封锁状态,印度等国家蓬勃发展的经济陷入了螺旋式下降。传统上用于估计大流行病短期经济影响的手段有限,因此,要实施及时的货币政策来减轻此类意外事件的金融影响具有挑战性。本研究探讨了利用苏米国家极地轨道伙伴关系(Suomi NPP)卫星任务搭载的可见红外成像辐射计套件(VIIRS)仪器拍摄的地球夜间灯光图像(也称为夜光(NTL))来衡量印度疫情经济成本的前景。首先,针对最近发布的辐射数据集VNP46A1开发了一种新颖的数据处理框架,该数据集是美国国家航空航天局(NASA)夜光产品黑大理石套件的一部分。其次,利用面板回归估计夜光与印度国内生产总值(GDP)的弹性,随后运用机器学习预测2020财年第一季度(2020年4月至6月)的GDP同比变化。将已知与经济产出密切相关的电力消耗和降水量作为额外特征纳入,以提高模型性能。研究发现电力使用和夜光与GDP之间都存在很强的关系。该模型预测2020财年第一季度GDP同比收缩24%,几乎与印度政府后来宣布的官方GDP下降23.9%相同。基于这些发现,该研究得出结论,夜光和电力使用可以作为估计新冠疫情等短期供需冲击成本的宝贵替代指标,应进一步探索。

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