metadata: authors: 0: author: Shen,Chang
1: author: Alberini,Anna
2: author: Timilsina,Govinda R.
count: World
docna: 0: docna: The Impact of COVID-19 on Electricity
            Generation : An Empirical Investigation
docty: Policy Research Working Paper
abstracts: cdata!: During the 2020-2022 period, the COVID
            Pandemic affected, directly or indirectly, every economic
            sector globally. While the effects on some sectors, such as
            travel and tourism, were highly visible, those on other
            sectors, such as utility services, require investigation.
            Whether COVID-19 affected total electricity generation and
            the electricity supply mix is an empirical question, which
            this study attempts to answer using panel data of daily
            electricity generation between January 2018 and September
            2021 from 26 countries. The study finds that a 1-unit
            increase in the stringency measure of COVID-19 is linked
            with a 463 MWh decrease in the total daily electricity
            generation. The study does not find a significant change in
            the electricity generation mix due to COVID-19 responses,
            although there is some evidence of some substitution of wind
            with solar and coal with natural gas.
display_title: The Impact of COVID-19 on
            Electricity Generation : An Empirical Investigation
listing_relative_url: /research/2022/06/33858662/impact-covid-19-electricity-generation-empirical-investigation
topicv3: Energy Access m1325609 521,Electricity Access m1503452 3922,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2022-06-30T00:00:00Z
dois: 10.1596/1813-9450-10116
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 2077655
1: author_key: 670481
2: author_key: 940446
model_extractions: Policy Research Working Paper 10116 The Impact of COVID-19 on Electricity Generation An Empirical Investigation _Chang Shen Anna Alberini Govinda Timilsina_ Development Economics Development Research Group June 2022
Policy Research Working Paper 10116

# **Abstract**

During the 2020–2022 period, the COVID Pandemic affected, directly or indirectly, every economic sector globally. While the effects on some sectors, such as travel and tourism, were highly visible, those on other sectors, such as utility services, require investigation. Whether COVID19 affected total electricity generation and the electricity supply mix is an empirical question, which this study attempts to answer using panel data of daily electricity generation between January 2018 and September 2021 from 26 countries. The study finds that a 1-unit increase in the stringency measure of COVID-19 is linked with a 463 MWh decrease in the total daily electricity generation. The study does not find a significant change in the electricity generation mix due to COVID-19 responses, although there is some evidence of some substitution of wind with solar and coal with natural gas.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at cshen1@terpmail.umd.edu, aalberin@umd.edu, and gtimilsina@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **The Impact of COVID-19 on Electricity Generation: An Empirical Investigation**<sup>**1**</sup>

Chang Shen, Anna Alberini and Govinda Timilsina<sup>**2**</sup>

**Key Words:** COVID-19, Electricity, COVID-19 Impacts on electricity generation

## **JEL Classification:** Q43

1 The authors would like to thank Carolyn Fischer, Pravakar Sahoo and Jevgenijs Steinbuks for their valuable comments and suggestions. The views and interpretations are of authors and should not be attributed to the World Bank Group and the organizations they are affiliated with. We acknowledge World Bank’s Research Support Grant (RSB) for financial support.

2 Chang Shen (cshen1@terpmail.umd.edu) was a short-term consultant to World Bank for this study.; Anna Alberini (aalberin@umd.edu) is a professor in the Department of Agriculture and Resource Economics, University of Maryland, College Park. Govinda Timilsina (gtimilsina@worldbank.org) is a Senior Economist at the Development Research Group, World Bank, Washington, DC.
# **The Impact of COVID-19 on Electricity Generation: An Empirical Investigation**

# **1. Introduction**

Since early 2020, the COVID-19 pandemic has impacted human life in every corner of the world. During the first wave of COVID-19 (March–June 2020), the world went through unprecedented lockdowns that reduced the level of economic activity. However, the lockdowns also caused some positive environmental externalities, such as reduced local air pollution and greenhouse gas (GHG) emissions due to reduced energy consumption, particularly for transportation (Andreoni, 2021; Bauwens et al., 2020; Bao and Zhang, 2020; Liu et al., 2020; Le Quéré et al., 2020). The reductions in local air pollution and GHG were mainly seen in countries with the predominant use of fossil fuels for their energy supply.

Some studies (Sharma et al., 2020; Nakada and Urban, 2020; Menut et al., 2020; Chauhan and Singh, 2020) have sought to investigate the effect of COVID-19 and the accompanying public health measures on reducing local air pollutants in India, Brazil, China, Europe, and the United States. Most of these studies have reported a significant reduction in common pollutants due to the COVID-19 lockdowns. For example, using spaceborne NO2 column observations from January to April 2020, Bauwens et al. (2020) find that NO2 decreased by 40% in China and 20% - 38% in western Europe and the United States compared to the same period in 2019. In Hangzhou, a megacity in eastern China, aerosol particles data from January to March 2020 show that the concentrations of PM2.5-10, PM2.5, NOx, SO2, and CO fell by 58%, 47%, 83%, 11%, and 30%, respectively (Yuan et al., 2021). In contrast, Bekbulat et al. (2021) find that stay-at-home orders in the US did not significantly or permanently reduce pollution. Ozone, NO2, and CO returned to expected levels, and PM2.5 and PM10 levels were higher than expected after the lockdowns.

There are several questions regarding the impacts of COVID-19 on electricity demand and generation. For example, how much has the COVID reduced the electricity demand? Has electricity generation mix changed? Has COVID-19 increased the penetration of renewable energy sources (RES) in electricity generation, either through the change in electricity load profiles or through the partial use of COVID pandemic stimulus funds promoting renewables in various countries? IEA (2021) and McWilliams and Zachmann (2020) track electricity consumptions 2
during the COVID time and address some of these questions. The tracking, however, provide data on electricity demand and generation and offer some visual analysis, but no statistical analysis to establish the relationships between the public health measures to COVID-19 and electricity demand or the electricity generation mix.

During the early stages of the pandemic, governments instituted lockdowns, and many activities in the service sector (restaurants, recreation centers, etc.) closed down. Whether this affected electricity generation depends on the net change in electricity demand due to these activities. Electricity demand for transportation, service activities, public buildings, and laborintensive industries likely fell. On the other hand, electricity demand in private households might have increased due to more people working from home. The question is, what is the net effect on electricity demand? Sectoral shifts of electricity demand are expected to change the load profile. For example, peak load might shift from daytime to evening time if people prefer to work in the evening rather than during the day (Bauwens et al., 2020). Since nuclear, gas-combined cycle and coal-fired plants primarily provide baseload, whereas storage hydro, gas-turbine, and internal combustion (diesel) engines normally serve peak load, the change in daily load profile may impact the daily generation mix. Whether such changes in the generation mix were transitory or permanent is an empirical question, considering that COVID-19 lockdowns came and went and often did not last very long but were in some cases reintroduced during the subsequent waves of the pandemic.

Considering the change in load profiles due to COVID-19 and the typical generation profiles of renewables (solar, wind, and hydro<sup>3</sup> ), one wonders whether the change in electricity load profile impacted the contribution of renewables to the total electricity supply during the various phases of the pandemic. Since renewables—particularly solar, wind, and run-of-river hydro—are must-run facilities and their shares in most countries are small, the change in electricity demand and daily and seasonal load profiles would not be expected to change the total generation from renewables. We leave it to our empirical work to find out whether this is indeed the case: Lockdowns and other public health measures may have disrupted the scheduled and unscheduled maintenance of power plants, which in turn may have affected total generation or contributed to

> 3 Solar is available only during the day, with highest availability during the mid-day; wind availability depends on climate and weather conditions; the same is true for hydro, with greater availability during the rainy season.

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changing each fuel’s share of total generation. Lastly, the pandemic may have disrupted the logistics of resource extraction and fuel imports and exports, causing changes in prices large enough to stifle generation or encourage substitution from one type of generation to another.

This study investigates whether COVID-19 has affected total electricity generation, GHG emissions from the power sector, and electricity supply mix, particularly the penetration of solar and wind. Rather than focusing on a single system operator (Carvalho et al., 2021; Leach et al., 2020; Agdas and Barooah, 2020), this study uses daily electricity generation data from the United States, Brazil, India, and the majority of the European countries from January 2018 to September 2021. According to the latest data from the Emission Database for Global Atmospheric Research (EDGAR), these countries collectively account for 31% of global CO2 emissions in 2019.

To quantify the impact of the pandemic on electricity generation, we regress daily generation in each country on the stringency of the measures adopted to combat the pandemic, controlling for other factors thought to affect generation. We also regress the _share_ of a particular type of electricity (e.g., coal-fired or hydro) out of total generation on the same determinants and measures of the pandemic. Identification comes from the stringency of lockdowns and other public health measures that vary across countries, depending on how early the pandemic hit them and how proactive the governments were in implementing pandemic mitigation measures, as well as within a country over time.

The study finds that a 1-unit increase in the lockdown stringency measure is associated with a 0.473 GWh decrease in the total daily generation. The lockdown stringency is measured using the Oxford COVID-19 Government Response Tracker, which documents and compares worldwide government policy responses to the coronavirus using well-defined and consistent criteria across countries and over time. On average, COVID-19 response has decreased daily electricity generation by almost three percent, increased the share of solar electricity generation by 0.6% in generation mix, which is around 15% increase relative to its pre-COVID level. Regarding fossil fuels, coal-fired share decreased by 1.6%, largely substituted by natural gas. However, the impacts are significantly different across the countries selected in our study. These findings should be interpreted with caution, as changes in the shares may not only have been caused by the COVID19 pandemic – directly or indirectly via disruptions in the markets for fuel inputs to generation – but also by other causes, such as droughts or weak winds, which limit hydropower or wind generation.

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The remainder of this paper is organized as follows: Section 2 presents the data, while Section 3 describes the methodology employed for analysis. Results are interpreted and discussed in Section 4. Section 5 draws key conclusions and policy implications.

# **2. Data**

We retrieved necessary data for the United States, Brazil, India, the United Kingdom, 26 of the European Union (EU) member states,<sup>4</sup> and Norway and Switzerland<sup>5</sup> from January 2018 to September 2021 – a period that includes approximately two years before the beginning of the pandemic and 21 months within the pandemic. Data sources are summarized in Table 1.

# **Table 1: Summary of data sources**

|Category|Data|Source|
|---|---|---|
|Electricity generation|Daily electricity generation by energy type;<br>Monthly installed capacity by energy type|US: EIA<br>Brazil: ONS<br>India: Ministry of Power<br>Europe: ENTSO-E|
|COVID Severity|Daily new cases per million population;<br>Daily new death per million population;<br>Stringency index|CSSE at Johns Hopkins University<br>(JHU);<br>Oxford<br>COVID-19<br>Government<br>Response Tracker|
|Temperature|Daily population-weighted CDD;<br>Daily population-weighted HDD|NOAA<br>Global<br>Historical<br>Climatology<br>Network<br>Daily<br>(GHCN-D);<br>Eurostat<br>and<br>OECD<br>regional<br>database.|
|Fuel prices|Monthly fuel prices by major energy hub (Coal,<br>Australia; Coal, South Africa; LNG, Asia; Natural<br>Gas, EU; Natural Gas, US; Henry Hub Gas; Dubai<br>Crude; Brent Crude; WTI Crude)|<br> <br> <br>IMF Primary Commodity Prices|

As shown in Appendix Figure A. 1, fossil fuels account for around 61% and renewables (wind, solar, hydro, and others) for around 17% of electricity generation in the United States. As

> 4 All except for Malta due to data availability issues.

> 5 Norway and Switzerland are not part of the European Union but belong to the European Free Trade Association (along with Iceland and Liechtenstein). Iceland and Liechtenstein are not included in our sample due to lack of data.

5
to the European countries in our sample, fossil fuels account for 38% and renewables for 34%. Brazil and India are two countries that rely heavily on one energy type: hydroelectricity in Brazil (71% of electricity generation) and coal-fired generated electricity in India (73% of electricity generation). We gathered data about the price of oil, natural gas, and coal from the IMF Primary Commodity Prices. They are not available at a daily frequency, which prevents us from estimating a model of demand (by generators) and supply of the fuel inputs. Nevertheless, we can examine how these prices evolved during the pandemic using the monthly level fuel prices from major energy hubs.

# _2.1. Electricity generation and electricity mix_

As shown in the summary statistics in Table A. 1 and Table A. 2, the average daily generation in our sample was 953 GWh, with a max of 14,019 GWh. Capacity averaged 92 GW, with a max at 1,225 GW. Looking at the electricity generation by source, renewables saw a large increase after the pandemic started, led by solar and followed by hydropower and wind. Coal has seen the largest decrease in power generation.

Figure A. 2 presents daily generation in GWh. The figure shows that electricity generation is strongly seasonal. This is because the demand and generation profiles of renewables (hydro, solar and wind) are seasonal, depending on weather patterns and the more abundant availability of sunlight and wind at certain times of the year. Our econometric models (described below) must therefore control for month, temperature, and diffusion of air conditioning and various other types of heating/cooling equipment in the country.

Figure A. 2 also shows that all regions faced a significant drop in electricity generation during the first wave of COVID-19 – approximately starting in March 2020. All regions returned to their original level of electricity generation in the following months but recovered at different speeds. The United States recovered quickly, returning to its previous peak in July 2020. The European countries and Brazil recovered more slowly and reached their previous peak around March 2021. India was recovering slowly but plunged again in June 2021 due to the Delta variant of the novel coronavirus, reaching its previous peak around September 2021. On average, only the European countries continue to see reduced electricity generation in the post-pandemic world. The US, Brazil, and India experienced a slight increase in daily electricity generation (see Figure A. 3).

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The increase of RES in total generation could be caused by higher utilization of existing capacity as well as the addition of new capacity. Figure A. 5 suggests that installed capacity does not change much during our study period. Wind and solar capacity have slightly increased in all regions. Figure A. 4 displays histograms of the RES share of daily generation in the two-year periods before and since the beginning of the pandemic. All regions except for Brazil see a shift to the right in the distribution, implying that RES accounts for more electricity generation in the post-pandemic era. This effect could be due to COVID stimulus or recovery packages, pre-existing trends, or market forces. The decrease in Brazil’s RES share can be explained by the severe drought of 2021, when Brazil received the lowest rainfall in the last 91 years.

# _2.2 COVID severity_

We considered a total of four candidate indicators that capture the severity of the pandemic. The first is a simple time dummy denoting the pre-pandemic era and the pandemic era. This time dummy takes on a value of 0 before March 15, 2020, and 1 thereafter. Two additional indicators are new cases per million and new deaths per million, obtained from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). The average number of cases per million was 67, with a maximum of 3,197, while the average number of deaths per million was 1.2 in our sample. However, economic activity is more directly impacted by mitigation measures than by COVID-19 cases or mortality. Many countries adopted partial or total lockdowns, including travel bans, since the first wave of the pandemic. The time when these restrictions were subsequently lifted varies across the countries.

Our study captures these attributes through stringency indices developed by the Oxford COVID-19 Government Response Tracker, which documents and compares worldwide government policy responses to the coronavirus using well-defined and consistent criteria across countries and over time. The stringency index is a composite index that combines different indicators of a series of policy responses. The policy responses considered include school closings, workplace closings, suspension of public transport, restrictions on the size of gatherings, cancellation of public events, stay-at-home requirements, and restrictions on domestic and international travel. Each country is evaluated at a daily level and given a score from 0 to 100 for each indicator. These scores are then averaged to get a composite index for the country at each point in time, which gives a comprehensive picture of the stringency level of its lockdown policy 7
(Hale et al., 2020).<sup>6</sup> Figure A. 6 shows the changes in the stringency indices of major countries since the first wave of the pandemic. The stringency index averaged 25; at its highest, the index was 96. Figure A. 7 shows differences in maximum lockdown severity among selected countries.

Figure A. 8 plots three indicators of COVID-19 severity: new cases per million, new deaths per million, and the lockdown stringency index developed by Oxford University. The graphs on the left-hand side in each row display the new cases per million (scale shown on the left axis) and the new deaths per million (scale shown on the right axis). The scales of the two indicators are adjusted to allow the line graphs to have a similar height. The graphs on the right-hand side show the change in policy stringency throughout the study period. We define the start of the pandemic as March 15, 2020 (marked by the red vertical line).<sup>7</sup> As shown in the plots, the new cases per million population and the new deaths per million population mirror each other very well, with the death indicator lagging several days compared to the case indicator. The stringency index, however, does not necessarily follow either the cases or the deaths closely. For example, it rose promptly at the start of the pandemic when the new cases and deaths per million were relatively low compared with the rest of the post-pandemic era. The stringency of the policies then gradually decreases as economies reopen in the summer of 2020. With the arrival of colder weather (around October 2020), the caseload significantly increased. The stringency index rose slightly again while governments were re-imposing restrictions to mitigate the new COVID wave. The Delta variant was first detected in India in December 2020. It spread to other countries and generated another wave of cases in the summer and fall of 2021.

Different regions also show very different COVID-19 severity patterns. At the beginning of the pandemic, the European countries and the United States saw a relatively fast spread of COVID-19 in March 2020, whereas Brazil and India did not reach their first peak until July 2020. In the winter of 2020, the European countries were ravaged by the so-called “second wave,” whereas Brazil and India did not see a significant winter spike. Later, in May 2021, India’s case

> 6 For instance, Italy was the first European country to impose a full lockdown measure; we can see that the stringency index for Italy rose before that of other countries. Another good example is India: during the Delta surge, India’s stringency index rose again. Note that even within the same continent, European countries often had stringency profiles different from those of other countries. We exploit this variation as an identification strategy to examine the impact of COVID-19 on electricity generation.

> 7 The World Health Organization declared COVID a pandemic on March 11, 2020.

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numbers reached a peak due to the Delta variant, whereas other regions began to see Delta rise after June 2021.

# _2.3. Other Data_

**_CDD and HDD:_** Outside temperature is an important determinant of electricity demand, particularly in the building (residential and commercial) sector – with or without COVID (Jovanović et al., 2015; Alberini et al., 2019). Electricity generation fluctuates to balance the demand change induced by hourly, daily and seasonal temperatures. We capture the temperature using cooling degree days (CDD) and heating degree days (HDD).<sup>8</sup> For each country, data on daily temperature come from the weather stations in province capitals; we use the population of provinces as weights to calculate national-level CDDs and HDDs. The population data come from Eurostat and OECD regional databases, and the temperature data are from the NOAA Global Historical Climatology Network Daily (GHCN-D).

**_Fuel prices_** : Monthly fuel prices by major energy hubs (Coal, Australia; Coal, South Africa; LNG, Asia; Natural Gas, EU; Natural Gas, United States; Henry Hub Gas; Dubai Crude; Brent Crude; WTI Crude) are obtained from IMF Primary Commodity Prices. Figure A. 12 illustrates how closely the changes in fuel prices and the stringency index are linked. At the beginning of the pandemic, fuel prices fell quickly due to depressed economic activities directly and indirectly caused by the pandemic. Crude oil prices dropped by 75% from US$80/bbl. to US$20/bbl. As shown in  Figure A. 12, the first round of COVID deaths was at their peak when oil prices were at their bottom, most likely because of uncertainty about the virus and its impact on the economy. The price of oil later rebounded.

Visual inspection suggests that it is the stringency (lockdowns and other measures), rather than the number of cases, which drove the energy prices. Like crude oil, the price of coal dropped

> 8 A degree day compares the mean daily temperatures to a standard temperature of 65° Fahrenheit (18° Celsius). If the average temperature is below the standard temperature (cold weather), the temperature difference is the heating degree days (HDD), which indicates the magnitude of heating needs. On a hot day where the average temperature exceeds the standard temperature of 65 Fahrenheit (18 Celsius), we call the difference in temperature cooling degree days, which indicates the magnitude of cooling needs. The degree days measurement is designed to quantify the demand for energy needed for heating and cooling.

9
significantly – 50% from its peak (US$100/metric ton in 2018) – at the beginning of the pandemic. However, the price rebounded and continued to rise above its 2018 level, reaching US$160/metric ton. Coal prices were at their bottom when COVID cases, deaths, and stringency were at their highest in the first wave of COVID-19. The price then recovered and remained high during and even after the second wave of the pandemic. Again, visual inspection suggests that the price of coal responds to the stringency of public health measures. The story is somewhat different for the price of natural gas, whose path in the United States seems to have diverged from that in Europe and does not necessarily co-vary with COVID-19 cases or stringency.

# **3. Methods**

Our first goal is to establish whether COVID-19 severity affected total electricity generation and, if so, by how much. We take advantage of our panel dataset, which covers selected countries for two years before and almost two years into the pandemic, and fit the regression: i 1C C i 1 i 3 i 4<sup>C</sup> i i i i i i i (1) EE = ββ CC + ββ CC + ββ HHC<sup>+ ββCC+ ααHH+ δδ+ θθ</sup> + ηη + εε where _i_ denotes the country, _t_ the date, _COVID_ measures COVID severity, _C_ is generation capacity, _HDD_ and _CDD_ are the heating and cooling degree days, respectively, and _H_ is a holiday dummy.

Equation (1) contains a rich set of fixed effects (country-by-year, country-by-month, and country-by-day-of-the-week). It controls for installed capacity in an effort to capture the underlying generation patterns. We note, however, that daily data capacity has only very limited variation over time, and thus it tends to act almost like a country fixed effect. The interpretation of Equation (1) is straightforward: Essentially, we wish to see whether COVID-19 severity has any additional explanatory power once we control for the other determinants of generation.

One challenge is that both electricity generation and COVID-19 severity are seasonal. Electricity demand and supply are influenced by heating and cooling needs and activities, as well as weather, all of which are seasonal in nature. One common observation is that COVID-19 cases tend to rise during the fall and winter when school resumes and most activities are moved indoors. Different variations of the virus also tend to come in waves. We take advantage of the variation across countries in the timing of these waves and in the mitigation or prevention response by governments to identify the effect of COVID.

10
COVID-19 severity in Equation (1) is captured in two possible ways. In one specification, we simply enter a “pandemic dummy” (equal to one after March 15, 2020, in all countries). We use this specification – which is essentially a “before vs. after” design and as such cannot identify the effect of COVID per se – to get a sense of how the generation after the pandemic starts compares to the generation before the pandemic starts. In an alternate specification, COVID-19 severity in Equation (1) is captured by the stringency index (public health measures and mandates). Because the stringency index (i) is equal to zero in all countries before the pandemic, (ii) starts being different from zero only after March 15, 2020, (iii) is first different from zero on different dates in different countries, and (iv) has a different time profile in the different countries, depending on the number and types of measures adopted, it is effectively an “intensity” type of treatment in a natural experiment.

Countries that have not adopted any COVID-19 response measures yet, therefore, serve as controls for themselves at a later time and for other countries with measures already in place. The reasoning is reversed when restrictions are lifted. We interpret the coefficient on the stringency index as an average treatment effect of stringency on the treated. Note that Equation (1) assumes that the effect of COVID restrictions on generation is symmetric.

It is interesting to determine whether the pandemic affected total generation and how generation is distributed across the different types of fuels and renewables. We accomplish this goal by specifying variants of Equation (1), where the outcome variable is the share of daily generation from a particular energy source (hydro, solar, wind, coal, natural gas, fossil oil, nuclear). The regressors are the same as in Equation (1) – the only difference is that we exclude installed capacity because there is very little variation in the proportion of installed capacity for different energy sources during the study period:

# **4. Results**

# _4.1 Impact of the pandemic on total electricity generation_

Table 2 reports OLS estimates of Equation (1). The dependent variable is the daily generation in GWh. For good measure, we use heteroskedasticity-robust standard errors. In Column 1, the pandemic period is captured by a simple 0/1 dummy. The coefficient is -16.9 and 11
statistically significant at the 1% level, indicating that, on average, countries have seen a 16.9 GWh decrease in their daily electricity generation during the pandemic, after controlling for capacity and heating/cooling needs. Since the average daily electricity generation for countries in our sample during the study period is 952.6 GWh, the drop accounts for around 1.8% of total daily generation.

Column 2 in Table 2 uses the stringency index to measure the impact of the pandemic. The coefficient on the stringency index is -0.463. It is statistically significant at the 1% level, implying that a 1-unit increase in the stringency measure is associated with a 0.463 GWh decrease in the daily generation.

The stringency index was zero pre-pandemic and quickly rose to an average of 79.7 in April 2020, during the first wave of the illness. Its average value since the onset of the pandemic is 58.9. Our model predicts that going from zero to the average level of stringency (58.9) brings an average 27.8 GWh decrease in daily electricity generation (about 2.9%).

Of the other controls, installed capacity, heating degree days, and cooling degree days are all positively associated with total electricity generation. A 1 GW increase in installed capacity is associated with a 56 GWh increase in the total daily generation. However, this effect should be interpreted with caution since installed capacity does not vary much within one country during the study period (see Figure A.5), and this variable is likely to capture mostly between-country variation. This makes sense, as fossil fuel-fired and nuclear power plants usually take years to build, and in general, whether to add a new power plant is a long-term decision. Small solar and wind projects can be constructed in 3–6 months, which could explain a relatively larger increase in the proportion of these two types of energy among EU countries. Finally, a 1-unit increase in cooling degree days is associated with a 6.3 GWh increase in total daily generation, whereas a 1 unit increase in heating degree days is associated with a 7.8 GWh increase.

**Table 2: Impact of the pandemic on total electricity generation**

||(1)|(2)|
|---|---|---|
|Dep: Total daily generation|post|stringency_index|
|C19 Severity|-16.92***|-0.463***|
||(4.062)|(0.0582)|
|Capacity|56.51***|56.17***|
||(5.436)|(5.439)|
|CDD|6.191***|6.186***|

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||(0.398)|(0.398)|
|---|---|---|
|HDD|7.732***|7.879***|
||(0.334)|(0.334)|
|Observations|40,673|40,673|
|R-squared|0.994|0.994|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

# **_4.2_** _Impact of the pandemic on generation profile_

So far, we have found that the total daily generation falls as the stringency of COVID-19 mitigation measures increases. Is the effect the same or different across energy sources? How did renewables fare during the pandemic?

Tables A.3-A.9 in the Appendix report the estimation results from fitting Equation (1) to the share of each type of electricity. The same estimation results are summarized in Table 3 for the sake of clarity. Among renewable energy types, the share of solar has increased during the pandemic, while the share of wind has dropped. Columns 1 and 3 report the coefficients of the “post” dummy and stringency index effects on the share of each energy source. A coefficient of 0.01 on “post” should be interpreted as a 1 percentage point increase of that energy source’s share of the generation mix. For solar, the coefficient of the “post” variable is 0.006, which translates to an increase of 0.6 percentage points in solar share, suggesting that solar’ share has increased by 0.6 percentage point in the total daily generation compared with the pre-pandemic world after controlling for seasonality and fixed effects. The wind share has fallen by 2.3 percentage point during the pandemic. Among fossil fuel-fired electricity, both coal and natural gas have increased their share during the pandemic. Specifically, the coal share has increased, on average, by 0.3% percentage point, and natural gas has increased, on average, by 1% percentage point during the pandemic.

**Table 3: Summary of the pandemic’s impact on generation profile**

||(1)|(2)|
|---|---|---|
|Share of Energysource|Post|StringencyIndex|
|Hydropower|-0.00181|-8.14e-06|
|Solar|0.00573***|6.52e-05***|
|Wind|-0.0234***|-0.000159***|
|Coal|0.00328**|-0.000105***|
|Natural Gas|0.0107***|0.000100***|

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|Oil|0.00400***|-8.56e-05***|
|---|---|---|
|Nuclear|0.000822|8.25e-05***|

Note: Columns 1 and 2 report the coefficients of “post” dummy and stringency index on the share of generation profile by energy source. A coefficient of 0.01 on “post” should be interpreted as a 1 percentage point increase of that energy source’s share of the generation mix.

# **_4.3_** _Impact of the pandemic on generation profile, heterogeneous response_

From Table 3, the coefficient of pandemic dummy “post” ranges from +0.011 and -0.023. This means that compared with before the pandemic, the percentage points of different energy sources have changed between 1.1% to 2.3% of total daily generation. When looking at these changes by energy source, solar and wind appear to change the most in response to changes in the stringency of public health measures.

One concern with the main models and data is that the stringency index is based on an internal comparison within each country, which means that a change from zero to 50% in one country is not equivalent to a change from zero to 50% in another country. Moreover, countries have different generation profiles to start with, implying that a 10% change in the share of hydroelectricity in a country where generation is 30% hydro means something completely different in a country where hydro accounts for 8% of generation.

Table 4 reports results based on fitting Equation (1) separately to Brazil, the European Union plus UK, Norway and Switzerland, the US, and India. Country-by-time fixed effects are retained only for the Europe regression. Table 5 is based on fitting Equation (1) separately to a selection of European countries, namely the UK, Germany, France, Italy, Spain, Poland, Denmark, Norway, and Switzerland. We selected these countries based on their generation profile, the intensity of their pandemic experience, and the different public health approach to mitigating and preventing future waves of the pandemic.

**Table 4: Pandemic’s impact on generation profile (change in share of energy mix for each energy type, by continent)  -- Unit: Percentage Point**

|CTY|C19 Severity|Hydro|Solar|Wind|Coal|NG|Oil|Nuclea<br>r|
|---|---|---|---|---|---|---|---|---|
||Post|2.28%|-0.03%|**1.75%**|**-1.23%**|**-2.72%**|**-0.74%**|<br>**0.38%**|
|Brazil|Pre-post stringency|**4.68%**|**-0.07%**|**1.74%**|**-1.51%**|**-3.67%**|**-1.77%**|**0.31%**|
||max-min stringency|**5.77%**|**-0.08%**|**2.15%**|**-1.86%**|**-4.52%**|**-2.18%**|**0.38%**|
|Euroe|Post|-0.27%|**0.69%**|**-2.69%**|0.26%|**1.46%**|**0.51%**|0.05%|
|p|stringency_index|-0.25%|**0.45%**|**-1.09%**|**-0.71%**|**0.88%**|**-0.47%**|**0.53%**|

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||max-min stringency|**0.40%**|**3.32%**|**-10.31%**|1.01%|**4.01%**|**0.18%**|**0.71%**|
|---|---|---|---|---|---|---|---|---|
||Post|**0.62%**|**0.64%**|-1.28%|**0.35%**|0.06%|**0.03%**|-0.54%|
|France|stringency_index|**3.4%**|**20.2%**|**-22.8%**|**53.9%**|**-10.0%**|**-0.5%**|**1.1%**|
||max-min stringency|**4.8%**|**28.6%**|**-32.3%**|**76.4%**|**-14.2%**|**-0.7%**|**1.5%**|
||Post|0.55%|**0.85%**|-0.44%|**0.95%**|**-2.86%**|-0.01%||
|Italy|stringency_index|-0.19%|**1.02%**|-0.43%|**0.78%**|**-2.28%**|**-0.16%**||
||max-min stringency|-0.25%|**1.37%**|-0.58%|**1.05%**|**-3.07%**|**-0.22%**||
||Post|-0.80%|0.95%|2.84%|0.17%|-2.97%|0.35%|-0.69%|
|Spain|stringency_index|0.78%|-0.33%|1.15%|0.76%|-2.65%|0.30%|-0.22%|
||max-min stringency|1.05%|-0.45%|1.55%|1.03%|-3.57%|0.40%|-0.30%|
||Post|**0.26%**||**-4.04%**|1.63%|**1.63%**|**0.08%**||
|Poland|stringency_index|-0.07%|-0.43%|**-1.52%**|0.82%|**0.97%**|**0.09%**||
||max-min stringency|-0.11%|-0.65%|**-2.29%**|1.23%|**1.45%**|**0.13%**||
||Post||**2.79%**|**-14.0%**|**5.22%**|**3.11%**|**0.27%**||
|Denmark|stringency_index||**2.36%**|**-8.22%**|-0.38%|**3.19%**|**0.36%**||
||max-min stringency||**3.16%**|**-10.98%**|-0.50%|**4.26%**|**0.48%**||
||Post|**1.94%**||**-0.98%**||**-1.03%**|||
|Norway|stringency_index|**0.83%**||-0.33%||**-0.44%**|||
||max-min stringency|**1.23%**||-0.49%||**-0.66%**|||
||Post|**-4.30%**|**1.58%**|**-0.08%**||||**2.80%**|
|Switzerland|stringency_index|**-2.62%**|0.36%|-0.02%||||**2.28%**|
||max-min stringency|**-3.76%**|0.51%|-0.03%||||**3.28%**|

Note: The first row shows the effect using the pandemic dummy “post”. In the second row, we calculate how the stringency index translates to an overall effect during the pandemic. We multiply the change in the average stringency index before and during the pandemic by the coefficient on the stringency index for that country or continent. In the third row, we multiply the coefficient on the stringency index by the difference between the maximum and minimum stringency index values. Significant coefficients are shown in boldface.

To compare the coefficients of different COVID-19 severity measurements, we calculate how the stringency index translates to an overall effect during the pandemic for each country. First, we multiply the change in the average stringency index between pre-pandemic and pandemic levels for each country by the coefficient on that country’s stringency index. This tells us the average impact of lockdowns and other measures on the generation profile, and the result is presented in the second row. We are also interested in the effect on generation when the public health measures are at their most stringent. To see this, we multiply the coefficient on the stringency index by the difference between the maximum and minimum stringency index value, which is presented in the third row. Each column reports the result for an energy source. For each 16
country, the first row reports the coefficients of the “post” dummy, while the second and third rows report the result using the stringency index. Significant coefficients are shown in boldface.

# **5. Discussion**

We have documented the diverse effects of COVID-19 on electricity generation in different countries. We further illustrate our results by using our models to predict what a country’s generation would have been if it had had another country’s lockdown and public health measures. For simplicity, we limit these calculations to Sweden (a country with low-carbon electricity, as it relies on nuclear and hydropower, and high demand since electricity is used for space heating) – which implemented very limited COVID mitigation efforts – and the United States. We compute their electricity profiles assuming that Sweden had adopted France’s (stricter) mitigation measures and that the United States had adopted Italy’s (stricter) measures. In both cases, we rely on fitting Equation (1) separately to Sweden and the United States.

The results of this exercise are shown in Table 6. The average total daily generation of the fitted value using Sweden’s own stringency index is 398.8 GWh. It is 395.8 GWh if Sweden had had France’s lockdown policy – a 0.8% decrease.

We also simulate the generation profile using France’s lockdown policy. As shown in Figure 2, the fitted daily generation using Sweden’s stringency index is shown in the blue line, and the predicted daily generation using France’s stringency index is illustrated in the dashed red line, with the result summarized in Table 6 . If Sweden were to adopt a stricter policy similar to that of France, it would see a slight decrease in nuclear as well as wind generation profile (36% to 35.8% for nuclear, and 12.4% to 12.1% for wind) but an increase in hydro (from 46.7% to 47.2%).

**Table 6: Sweden’s energy mix with France’s lockdown**

|After pandemic hit|Predicted|Simulated|
|---|---|---|
|Total daily gen (GWh)|398.8|395.8|
|Hydro|46.7%|47.2%|
|Wind|12.4%|12.1%|
|Nuclear|36.0%|35.8%|

Note: The predicted column shows fitted value using Sweden’s own stringency index. The simulated column shows the simulation result by replacing Sweden’s stringency index with France’s stringency index.

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generation by 0.6 percentage point. Regarding fossil fuels, share of coal-fired electricity generation decreased by 1.6 percentage point, largely substituted by natural gas.

Further investigation shows that the share of nuclear power generation did not change much during the pandemic as it normally provides the baseload. Countries that relied heavily on natural gas saw a decrease in natural gas generation in the mix in response to the drop in total generation. The analysis also shows a widespread reduction in the wind energy mix across multiple European countries, likely due to the low wind speed in Europe during the study period. However, many countries expedited their solar development, and we see a drastic increase in solar generation during the pandemic to compensate for the wind loss as well as counterbalance the natural gas shortage.

Two simple simulation exercises further show renewables can well withstand the pandemic and accompanying lockdowns. If Sweden were to adopt a stricter lockdown policy that was similar to that of France, it would see a 0.8% decrease in the total daily generation since the pandemic hit, a slight decrease in nuclear and wind as a share of the energy mix, but an increase in hydro share. If the United States were to adopt a stricter lockdown policy that was similar to that of Italy, it would see a 0.4% decrease in the total daily generation since the pandemic hit, a decrease in natural gas as a share of the energy mix, and a slight increase in hydro and wind share.

Caution is, as always, needed when interpreting the results of this study. We have examined electricity _generation_ on a daily basis. But countries often meet demand through imports and/or exports of their excess generation. The US, for example, while a major energy exporter in 2020 (see <u>https://www.eia.gov/energyexplained/us-energy-facts/imports-and-exports.php), at times</u> imports electricity from its neighbors Canada and Mexico. Unfortunately, we do not have highfrequency electricity imports and exports data, nor is it possible for us to compute “net imports” as the difference between generation and final consumption, as we lack data about the latter.

Imports and exports of electricity are commonplace within EU countries, and both Norway and Switzerland export electricity to EU countries. Only a small share of the total electricity consumption is however imported into the EU from non-EU countries (e.g., Russia<sup>9</sup> ), suggesting that EU aggregate generation serves as a good approximation to final consumption of electricity in the EU.

> 9 See https://view.news.eu.nasdaq.com/view?id=b62aadb50d4d3bd34550a8d2b09b7f6df&lang=en

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Another caveat is that many countries passed stimulus packages to help the population survive the pandemic—both physically and financially. Several of these stimulus packages contained provisions supporting renewables.<sup>10</sup> We lack systematic, high-quality data about these stimulus packages, which presumably resulted in increased renewables capacity, and are thus unable to assess their effectiveness. In practice, we would expect a time lag between the fund allocations and project completion: The funds might be used now for expansion of renewables electricity projects, but it would take a few years to get these projects completed. We hope to be able to examine this issue in future research, relying on more precise data and a longer pandemic or post-pandemic sample period.

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