Delhi/Mumbai, April 13: The COVID-19 pandemic has forced governments around the world to impose tough restrictions on daily life to prevent the spread of the virus. With these restrictions, roads and airports are nearly empty, shops and restaurants are closed, and industrial activities are largely at a halt. In this environment, real-time information about economic activity is at a premium, but often hard to acquire. Yet, historically, electricity consumption has proven to be a reliable, early indicator of broader economic trends. This data tracker shows the impact of COVID-19 on electricity consumption and particulate pollution in countries and sub-regions throughout the world.
EPIC research shows fall in particulate emissions as well; new portal makes information available
In India, data suggests, that power consumption has fallen by 18.72 percent as of 4th April, 2020 compared to December 2019. The number of COVID 19 cases and deaths due to the spread have seen a rise in this time period and dip in consumption has fallen along with the rise. The interactive graph available at https://epic.uchicago.edu/area-of-focus/covid-19/ lets the user get a sense of how both these curves have progressed, side by side. The website also provides a country wise analysis of how India fairs in these aspects compared to US, China and a few European nations.
On the other hand, there is a difference of -6.8% in particulate emissions across India which also have fallen study along with the rise in COVID 19 cases.
As the world confronts COVID-19, researchers at EPIC have launched a portal that has a dynamic tracker showing the impact of the Coronavirus on electricity consumption and particulate pollution for countries like India, China, and the United States among others.
Daily Indian electricity data from 4/1/2013 to 4/6/2020 come from India’s Power System Operation Corporation Limited (POSOCO)’s daily reports, available here. We use energy met (in MU, or GWh), as our measure of Indian electricity consumption. To produce the data visualization, we regress energy met on a linear time trend, day-of-week fixed effects, and month-of-year fixed effects. We also control for quarter-of-year fixed effects interacted with cooling and heating degree days. We fit the model on data prior to January 2020, and use the fitted model to generate prediction errors for the January 2020-April 2020 period. Finally, we generate percent changes relative to December 2019 by subtracting the mean residual from December 2019 from our prediction errors, and dividing by observed electricity use. To construct the cooling and heating degree days (with a cutoff of 65F / 18.3C), we compile weather station information from NOAA’s Global Historical Climatology Network, available here. We construct CDD and HDD at the weather station level, and then average to the state level. We then weight these observations by state-level population to construct national averages.
India Particulate Matter Emissions
We assembled daily PM 2.5 data from Open AQ, a website which aggregates particulate matter readings from monitoring stations around the world, from March 5, 2016 to April 1, 2020. We create a grid of 0.5 by 0.5 degrees, and match pollution monitor readings with weather station readings from NOAA’s Global Historical Climatology Network within the same grid cell. We regress PM on temperature and week-of-year fixed effects for each grid cell individually from the beginning of the sample through December 31, 2019. We then generate prediction errors for 2020. We generate percent changes relative to December 2019 by subtracting the mean residual from December 2019 from our prediction errors, and dividing by observed electricity use. We generate national averages by weighting using gridded population data from the 2015 Land Scan Global Population Database.