ISSN (Print) 0473-453X

Discussion Paper No. 1093

ISSN (Online) 2435-0982

ASSESSING
THE IMPACT OF COVID-19
ON GLOBAL FOSSIL FUEL CONSUMPTION
AND CO2 EMISSIONS
L. Vanessa Smith
Nori Tarui
Takashi Yamagatax

July 2020

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Assessing the impact of COVID-19
on global fossil fuel consumption and CO2 emissions∗
L. Vanessa Smith†
DERS, University of York, UK

Nori Tarui‡
Department of Economics, University of Hawaii

Takashi Yamagata§
DERS, University of York & ISER, Osaka University
25 June 2020

Abstract
The shock to the global economy from COVID-19 is predicted to be faster and more severe
than the 2008 global financial crisis and even the Great Depression. We assess its impact on
global fossil fuel consumption and CO2 emissions over a two-year horizon. For this purpose weemploy a global vector autoregressive (GVAR) model, which captures complex spatial-temporalinterdependencies across countries due to the spread of the virus and associated international
propagation of economic impact. The model makes use of a unique quarterly data set of coal,natural gas, and oil consumption, output and exchange rates, including global fossil fuel prices for
32 major CO2 emitting countries. We produce forecasts of coal, natural gas and oil consumption,
conditional on GDP growth scenarios based on alternative IMF World Economic Outlook forecaststhat were made before and after the outbreak. We also simulate the effect of a relative price change
in fossil fuels, due to carbon pricing in all countries in the sample, on consumption and output.
Our results show that fossil fuel consumption and CO2 emissions are expected to return to theirpre-crisis levels, and even exceed them, within the two-year horizon despite the large reductions in
the first quarter following the outbreak. Our forecasts anticipate more robust growth for emerging
than for advanced economies. Recovery to the pre-crisis levels is expected even if another wave of
pandemic occurs within a year. The results from our counterfactual carbon pricing scenario showthat an increase in coal prices is expected to have a smaller impact on GDP than on fossil fuelconsumption. Thus, the COVID-19 pandemic would not provide countries with a strong reason todelay climate change mitigation efforts.

JEL Classifications: C33,O50,P18,Q41,Q43,Q47
Keywords: COVID-19, CO2 emissions, fuel consumption, Global VAR (GVAR), conditional forecasts∗

We are grateful to Julia Touza-Montero, Joao Madeira and Ron Smith for helpful discussions. We thank Sara
Elsayed, Rui Lin and Ryo Mikami for excellent research assistance.
†vanessa.smith@york.ac.uk‡
Corresponding author. Address: 2424 Maile Way, 518 Saunders Hall, Honolulu, HI 96822 USA. E-mail:nori@hawaii.edu§
takashi.yamagata@york.ac.uk. This authour gratefully acknowledges support from JSPS KAKENHI (18K01545,
15H05728).

1

Introduction

After the first case of pneumonia of unknown cause in Wuhan, China, was reported at the end of
December 2019, the spread of novel coronavirus (COVID-19) has been causing a worldwide publichealth emergency. Social isolation and lockdown, which have been enforced to slow down the virus
spread, led to a virtual halt of major economic activities. The World Economic Outlook, published in
April 2020 by the International Monetary Fund (IMF, 2020b), predicts that the global economy will
shrink by 3 per cent in 2020. This forecast was further revised downwards to a 4.9 per cent shrink in
June 2020 (IMF, 2020c). This means that the shock to the global economy from COVID-19 could be
more severe than the 2008 global financial crisis and even the Great Depression.
According to latest research, the sharp drop in economic activity due to the enforced confinement
has dramatically reduced energy use, and hence carbon dioxide (CO2 ) emissions. Le Quere et al. (2020)
estimated daily changes in global CO2 emissions taking account of the levels of the confinement policy.
Based on the emissions data for six economic sectors across 69 countries, their results indicated a 17%
decline in daily global CO2 emissions by early April 2020 relative to the mean level in 2019. Liu et al.
(2020) report a decrease of 7.8% in global CO2 emissions due to fossil fuel use during the first quarterof 2020 relative to the first quarter of 2019.
Despite growing evidence of the instantaneous impact on energy consumption and CO2 emissions,longer-term effects have not been well understood. Studying such effects is important because related
evidence will provide policy makers with essential information to prepare post-COVID-19 economic
recovery packages. These packages should be designed to achieve strong economic growth as well as
the international emission targets agreed in COP21 in Paris.1 If significant CO2 emissions reduction
continues beyond a quarter or two, then one could further investigate which sectors of the economy
experience more sustained emissions reduction. In this case, sector-level investigations of relative declines in emissions and economic activities may provide useful insights for cost-effective CO2 emissions
in the future. If the emissions reduction over a year or two is limited, however, then continued efforts
for further emissions mitigation become more urgent. It is then of interest to investigate how carbon
pricing of global scale affects the countries’ economic performance and emissions.
In this work we assess the impact of the global economic shock from COVID-19 on global fossil
fuel consumption and CO2 emissions over a two-year horizon. For this purpose, we employ the global
vector autoregressive (GVAR) model of Pesaran et al. (2004), which is further developed in Dées etal. (2007). GVAR is a large multi-country, spatial-temporal model that allows for a high degree ofinterdependence and dynamics. Notably, it can control for unobserved global or foreign shocks that
affect each country. This approach is critical for assessing the impact of COVID-19 because of the
global scale of its spread and the associated economic effects. The spread of the virus that first hit
China induced significant economic disruptions in Asia, Europe, North America and beyond. While
the spread of the virus itself reduced domestic economic activities in each country, it also disrupted
the global supply chain (Ivanov, 2020), which in turn amplified the negative economic effects acrosscountries. GVAR takes into account such cross-country dependencies and dynamic macroeconomic
effects. For reliable estimation of such a large dimensional model, we apply GVAR to a sufficientnumber of time-series observations across countries that cover a major part of the world economy.
A related strand of literature investigates the relationship between energy consumption and growth;
see Kahouli (2019) for an extensive survey. Many of these studies employ a single country analysis,
adopting VAR models and vector error correction (VEC) models (e.g. Bloch, 2012; Akpan andAkpan, 2012; Bozkurt and Akan, 2014), due to the relatively small number of observations (annual
data since 1960). Recently, studies using panel data analysis have emerged in the literature. Omri andKahouli (2014) and Saidi and Hammami (2015), among others, use cross-country panel data with arelatively large number of countries (around 60) for 22-23 years of observations. They estimate shortdynamic panel data models with generalised method of moments (GMM). Cores and Sanders (2013)
and Antonakakis et al. (2017) employ panel VAR models whilst Shahbaz et al. (2013) employ the
autoregressive distributed lag (ARDL) bounds testing approach using longer panel data with around40 observations. These panel data studies permit country heterogeneity only in a limited way without
controlling for global common shocks or spatial dependence despite both of these featuring prominently
in the recent panel data econometrics literature. Notably, our econometric approach can address all
1

See Allan et al. (2020) for a proposition of such recovery packages.

1

of these issues. Thus it is suitable for studying the intermediate-term economic impact of COVID-19,
which must take into account the different transmission channels of the economic effects of the virus
across countries, over time.
In this paper, the model makes use of a unique quarterly data set of coal, natural gas and oil
consumption, output and exchange rates, including global fossil fuel prices, for 32 countries spanningthe period 1984Q1-2014Q4.2 To the best of our knowledge, no such compiled quarterly disaggregatedconsumption data set is publicly available for the period of our interest, apart from an annual data
set provided by British Petroleum (BP) and the World Bank, which is typically used in the literature
to date. According to the BP data for the year 2018, 81% of the global CO2 emissions due to fossil
fuel combustion was released by the 32 countries in our sample. To predict CO2 emissions, it isimportant to decompose the fossil fuel sources into coal, natural gas and oil because of the different
CO2 intensity among fuel sources. Furthermore, among the fossil fuels, the share of coal consumption
within emerging economies is much higher than in advanced economies. Our forecasts indicate how
CO2 emissions evolve in the emerging and advanced economies as the fuel mix changes across countries
within these groups.
In order to assess the impact of the global economic shock from the COVID-19 spread, we produce
conditional forecasts of coal, natural gas and oil consumption, for the advanced and the emerging
economies, separately, conditional on GDP forecast trajectories (scenarios) over eight quarters. These
trajectories are based on the IMF forecasts for 2020Q1-2021Q4 published in IMF (2020b), the World
Economic Outlook, April 2020. IMF (2020b) publishes the quarterly forecast figures, while the IMF(2020c) update does not. We compare the results of three scenarios to identify the effects of: (0)no outbreak; (1) one virus spread in 2020Q1-Q2; (2) two waves of virus spread in 2020Q1-Q2 and2021Q1-Q2. We assess the effects on the above two country groups individually as well as on the world
as a whole.
Another main objective of this paper is to investigate the effect of changes in the relative fossil
fuel prices on output and consumption. Carbon pricing policies impose costs to the combustion offossil fuel. For example, as coal is more carbon intensive than natural gas, an introduction of, ortightening of, carbon pricing policy should increase the relative (end-user) price of coal. The effect ofsuch (end-user) relative price changes on output is critical when assessing the trade-off between the
reduction of emissions and the negative effect on the economy, which can be an important issue during
this time of depression.
The above counterfactuals will allow us to assess the following for each country group (e.g. advanced and emerging): (i) relative emission resilience to the negative economic shock; (ii) relative
sensitivity of emissions to carbon pricing; and (iii) relative robustness of output to carbon pricing.
Related policy implications could be useful for the country- or regional-level long-term emissions targets beyond their intended nationally determined contributions under the Paris Agreement.
We find that fossil fuel consumption and CO2 emissions are expected to return to their pre-crisis
levels, and even exceed them, within the two-year horizon despite large reductions in the first quarter
following the outbreak. Our forecasts anticipate more robust recovery and growth for the emerging
economies than for the advanced. Recovery to pre-crisis levels is expected even if another wave of
pandemic takes place within a year. We argue that this result may be due to (i) the IMF forecasts
predicting GDP recovery for the advanced economies in the two-year period, with faster recovery forthe emerging economies, and (ii) limited responsiveness of fossil fuel consumption to income changes,
as indicated in existing empirical studies.
With regard to fuel prices, they display a sharp decline over the first quarters before exhibiting
recovery. Importantly, different fuels have different trajectories of price recovery. The coal and oil
prices first increase then drop sharply before recovering in the second year. The fuel prices are expected
to return to their pre-crisis level within the two-year period, but the price recovery may be slower if asecond pandemic wave occurs within a year. Turning to the effect of carbon pricing, our counterfactual
analysis suggests that (i) a permanent rise in the price of coal relative to natural gas and oil will reducefossil fuel consumption in advanced and emerging economies by a similar magnitude; (ii) the negative
effects on GDP are smaller than those on coal consumption; and (iii) the magnitude of the negative
GDP effect on the emerging economies is only around half of what the advanced economies experience.
2

The end date of 2014Q4 is due to data not being available primarily for natural gas for a number of countries inour sample, and also to the large time delay in providing data extensions at the primary source level, mainly for thisvariable.

2

These results suggest that, while emerging economies appear to be more resilient to carbon pricing
policy, such policy is as effective in reducing emissions for these economies as it is for the advanced
ones.
Our research is not the first to employ the GVAR model in the area of energy economics. Cashin etal. (2014) apply a GVAR model for 38 countries/regions over the period 1979Q2–2011Q2 to show thatthe economic consequences of a supply-driven oil-price shock are very different from those of an oildemand shock driven by global economic activity. Mohaddes and Pesaran (2016) develop a GVAR-oilmodel that integrates a model for the global oil market within that of the global economy to identify
country-specific oil-supply shocks. Mohaddes and Pesaran (2017), using a global VAR model for 27
countries/regions over the period 1979Q2 and 2013Q1, find that a fall in oil prices lowers interest rates
and inflation in most countries and increases global real equity prices. Mohaddes and Raissi (2019) use
a similar GVAR model to Cashin et al. (2014) to investigate the global macroeconomic consequencesof falling oil prices due to the oil revolution in the USA. We are not aware of any GVAR analysis that
focuses on energy consumption of fossil fuels (at the country level) and related CO2 emissions.
The paper is organised as follows. Section 2 describes the data. Section 3 discusses the econometricmodel and estimation method. Section 4 explains the implementation of the conditional forecasting,conditional on the GDP scenarios after the COVID-19 spread. Section 5 discusses the experimentassociated with a higher relative coal price. Section 6 concludes. Details of the data sources, the
GVAR model, and additional empirical results can be found in Appendices A-C.

2

Data

In order to assess the impact of the global economic shock due to the COVID-19 spread on world CO2
emissions, we consider a multi-country model that captures complex spatial-temporal interdependencies of the global economy and energy consumption. We employ a unique quarterly data set of coal,natural gas and oil consumption, output and exchange rates, including global fossil fuel prices, for 32
countries spanning the period 1984Q1-2014Q4. The countries included in the analysis are listed inTable 1. According to the BP data for the year 2018, China, India and the US emitted 28.7%, 15.7%
and 7.6% of CO2 , totaling 51.9% of the 81% of world CO2 emissions released by these 32 countries
as mentioned earlier. Because coal is cheaper relative to oil and natural gas, it is the main primaryenergy source for many emerging economies, including China and India. In view of this, we explicitly
use coal, natural gas and oil consumption data for each of the countries.
Following the IMF, we partition the countries into two groups, the advanced economies and theemerging market and developing economies, as shown in Table 1. To be concise, we refer to the lattergroup simply as ‘emerging economies’. Within the advanced economies, we also consider the subset
EU+ , which consists of ten EU countries plus Norway and Switzerland. We include these two countries
due to their historical close relationship to the EU. This group has been severely hit by the pandemic
and is subject to the most stringent carbon pricing policy.
We analyse spatial-temporal interactions between eight variables. Let coalit , gasit and oilit represent the logarithms of per capita consumption of coal, natural gas and crude oil (in Mtoe) for country
i in quarter t. The logarithm of the associated global prices, pcoalt , pgast and poilt , are based on theAustralian coal price (US dollars per mt), European Natural Gas price (US dollars per mmbtu) and
Brent crude oil (US dollars per bbl), respectively, which are all normalised to 100 in 2014Q4. The
remaining two domestic variables are economic variables, gdpit and epit , which are the logarithms ofthe (real) gross domestic product and the real exchange rate in terms of US dollars for country i inquarter t, respectively. Details of the construction and sources of the data are available in Appendix
A.1. The consumption data were first tested for seasonality. Those series that exhibited significant
seasonal effects were subsequently temperature adjusted and/or seasonally adjusted. In some cases
where temperature adjustment induced spurious volatility, only seasonal adjustment was performed.
Temperature adjustment is based on the use of heating and cooling degree days as well as their 30-year
average between 1981 and 2010 (the ‘climate normals,’ see Elkhafif, 1996, and Won et al., 2016, forrecent applications). The procedures for performing temperature and seasonal adjustment as well as
for assessing seasonal effects can be found in Appendix A.2, together with a table summarising therequired adjustments to the consumption series for each country. Energy prices as well as the economic
variables (in the case of ep the underlying Consumer Price Indices) were seasonally adjusted where3Table 1: 32 countries and associated groups within the sample
Advanced Economies
EU+
Austria
Australia
Belgium
CanadaFranceJapan
Finland
Korea
Germany
New Zealand
Italy
Singapore
Netherlands
United States
NorwaySpain
SwedenSwitzerland
United Kingdom

Emerging Economies
Argentina
Brazil
China
Chile
India
Indonesia
Malaysia
MexicoPhilippines
South Africa
Saudi Arabia
ThailandTurkeyrequired.

3

Modelling Framework

We employ the global vector autoregressive (GVAR) modelling framework of Pesaran et al. (2004),
which is further developed in Dées et al. (2007). GVAR is based on a multi-country model that links
country-specific models in a coherent manner using time series and panel techniques. The ultimate
aim of the GVAR approach is to construct a VAR model formed of a global variable vector, which
consists of the endogenous variables of all the countries. Define the ki ×1 vector of energy consumption
and economic variables of country i in quarter t by xit = (coalit , gasit , oilit ; gdpit , epit )0 , as well as the
md × 1 vector of energy prices by dt = (pcoalt , pgast , poilt )0 . Stacking xit for i =P0, 1, ..., N for our 320000
countries yields the k × 1 global variable vector, xt = (x0t , x1t , ..., xN t ) with k = N
j=0 ki , and country
0 taken as the numeraire country, here the US. The pth -order GVAR model of our 155 × 13 global
variable vector and energy prices at t, yt = (x0t , d0t )0 , is given by
yt = c0 + c1 t + C1 yt−1 + ... + Cp yt−p + εt , t = 1, ..., T ,(1)where contemporaneous correlations in the error term are permitted. The GVAR model of equation(1) is a large model that, despite its simple overall structure, allows for a rich set of dynamics including
a high degree of interdependencies. It is not directly estimable due to the curse of dimensionality andpossible existence of cointegrated variables. To avoid these problems, estimation and specification of
the GVAR model involves two-steps. In the first step, a vector error correction model for the domestic
variables, xit , is estimated for each i, augmented with the global variables, dt , and the foreign variablesof country i, x∗it , which are specified below. Based on these parameter estimates as well as those ofan estimated model for dt , in the second step the estimated version of equation (1) is obtained. The
two-step estimation approach is explained in detail below.

3.1

The country-specific VARX* models

Consider the following VARX*(pi , qi ) structure for the ith country-specific modelxit = ai0 + ai1 t + Φi1 xi,t−1 + ... + Φipi xi,t−pi + Λi0 x∗it + Λi1 x∗i,t−1 + ... + Λiqi x∗i,t−qi ,+ Ψi0 dt + Ψi1 dt−1 + ... + Ψiqi dt−qi + uit ,3

See Table B.1(ii) in Appendix B.1.

4

(2)

for i = 0, 1, ..., N where xit and dt are the ki × 1 vector of domestic variables and the md × 1 vector
of common global variables, respectively, given above, and x∗it is a ki∗ × 1 vector of foreign variables.
The foreign variables
across all domestic variables in the model
PN
PN are constructed as weighted averages
wsuch that x∗it =
w
x
,
where
w
=
0
and
ii
j=0 ij = 1, and can be considered as proxies
j=0 ij jt
for unobserved common factors. These variables are expected to ‘soak up’ most of the cross-section
correlation leaving only a modest degree in the estimated residuals. The weights, wij , are computed
here based on the trade relationship (average of imports and exports) of the individual countries with
their corresponding trading partners.4 The common global variables in each country model are treatedsimilar to the foreign ‘star’ variables, which includes sharing the same lag order, qi .
As discussed in Pesaran et al. (2004), GVAR allows for interactions among different countries
through three separate but interrelated channels:
1. Contemporaneous dependence of xit on x∗it and on its lagged values, where as mentioned earlier
the star variables can be considered as proxies for common unobserved factors such as, forexample, the diffusion of technological progress or global upheaval in the case of COVID-19.
2. Dependence of the country-specific variables xit on common global variables dt and on its lagged
values, which are the global fuel prices in our context.
3. Nonzero contemporaneous dependence of shocks in country i on the shocks in country j, measured via the cross-country covariances, Σij = cov(uit , ujt ) for i 6= j. Such ‘residual’ interdependencies (after global unobserved factors have been taken into account) could be due, forexample, to policy and trade spillover effects.
In the presence of possible I(1) variables and cointegration the corresponding vector error correction form of equation (2), the VECMX*, assuming for expositional purposes that pi = qi , can be
written as
Xpi −1∆xit = ci0 − αi β 0i [ezi,t−1 − γ i (t − 1)] + Λi0 ∆x∗it + Ψi0 ∆dt +Γz,ij ∆ezi,t−j + uit ,
(3)
j=1

0 z
0
0
0
∗
where zit = (x0it , x∗0i,t−1 = (zi,t−1 , dt−1 ) , αi is a ki ×ri matrix of rank ri and β i is a (ki +ki +md )×ri
it ) , e0 0matrix of rank ri . By partitioning β i as β i = (β 0ix , β 0ix∗ , β 0id )0 conformable to z̃it = (x0it , x∗0it , dt ) , theri error correction terms defined by the above equation can be written as
β 0i (ezit −γ i t) = β 0ix xit + β 0ix∗ x∗it + β 0id dt − β 0i γ i t,which allows for the possibility of cointegration both within xit and between xit , x∗it and dt , andconsequently across xit and xjt for i 6= j.
For the estimation of (2) the foreign and common global variables are treated as weakly exogenous
with respect to the long run parameters αi and β i of the model, an assumption testable by thedata. This assumption implies that there are no feedback effects from the domestic variables to thesevariables in the long-run without precluding short-term interactions between the two. It is effectively
this assumption that allows the GVAR model to decouple and for the countries to be estimatedindividually, conditional on the foreign and global variables. Further details on the variables includedin the GVAR model are found in Appendix B.1.
The rank of the cointegrating space for each country model is computed using Johansen’s (1991)
trace statistic as set out in Pesaran et al. (2000) for models with weakly exogenous I(1) regressors. Thisway, the number of cointegrating relations of each country, ri , the speed of adjustment coefficients,
αi , and the cointegrating vectors β i for each country model are obtained. Conditional on a given
estimate of β i , the remaining parameters in equation (3) are consistently estimated by ordinary leastsquares (OLS). From the estimated VECMX* models we can then recover the estimated version ofequation (2).
Unit root tests applied to our variables suggested that on the whole these are I(1). The lag ordersof the individual country VARX* models were selected using the Akaike information criterion (AIC)
setting a maximum for pi and qi of 3 and 2, respectively. The choice of these maximum values was
based on the available number of observations and the desire to reduce serial correlation. The testable
4

In principle any type of weights could be used including different weights for different variables, as well as weightsthat change over time, as long as they satisfy the ’smallness’ condition given in Pesaran et al. (2004).

5

assumption of weak exogeneity of the foreign and global variables was supported by the data. Weakexogeneity test results for all countries, along with the individual country lag orders, number of cointegrating relations and other related output can be found in Section S.2 of the supplementary material.
The construction of the weight matrix is based on a three year average of the trade relationships
between the countries over the years 2008-2010.

3.2

Modelling the global prices

While estimation of the individual country VECMX* models in the presence of the weakly exogenous
regressors does not require separately specifying a model for the global prices, for the purpose of
forecasting in what follows this is required.
The modelling procedure for the global prices proceeds in two steps. Since the global prices, dt ,were found to be I(1) in order to allow for the possibility of cointegration, in the first step the following
error correction model that includes a restricted intercept is estimatedP d −1∆dt = −αd β 0d [dt−1 − µ] + pj=1
Γd,j ∆dt−j + η t ,(4)where αd and β d are md × rd vectors, and rd denotes the number of cointegrating relations. Using
the trace statistic of the Johansen cointegration approach one cointegrating relationship was foundamong the global prices, with pd = 3 selected based on the AIC and no remaining serial correlation.
The cointegrating vector, β d , was estimated subject to one overidentifying restriction which was
d d,t−1 = β̂ 0d [dt−1 − µ̂] be the estimated error correction term which is
supported by the data. Let EC
given byd d,t−1 = 0.267coalt−1 + 1.000gast−1 − 1.000oilt−1 − 1.347.
EC(5)
In the second step the error correction specification in equation (4) is augmented with additional
feedback effects computed as a weighted average of the domestic variable vector, xit . Specifically,
d d,t−1 + Ppe−1 Θd,j ∆dt−j + Pqe−1 Θx,j ∆e∆dt = c + δ EC
(6)xt−j + η t .
j=1
j=1
P
d d,t−1 is taken as given (estimated in the first step), x
et = Nei xit is a k × 1 vector ofwhere ECi=0 w
PN
ei = 1, which are computed based on PPP-GDP
feedback effects, with the weights w
ei such that i=0 w
figures averaged over the years 2008-2010.
We further allow for separate lag orders, namely pe` and qe` with ` = 1, 2, 3, to be selected by the
AIC for each of the individual price equations using a maximum lag order of 3 for both. Having
estimated equation (6) the VAR form for dt is given byet−1 + ... + Λx,eq x
et−eq + η t .
dt = µ0 + µ1 t + Φd,1 dt−1 + ... + Φd,ep dt−ep + Λx,1 x(7)

Combining the estimated versions of models (2) and (7), the estimated GVAR model of equation(1) in terms of yt is obtained. Details of solving for the GVAR model based on equations (2) and (7)can be found in Appendix B.2.

4

Conditional forecasts of fossil fuel consumption and carbon emissions

In what follows, we produce forecasts for coal, natural gas and oil consumption conditional on threedifferent eight-quarter horizon GDP growth rate paths (scenarios) for the advanced and emerging
economies. These trajectories are based on the forecasts published in the IMF World Economic
Outlook. We consider three GDP scenarios (Figure 1). Scenario 0 is the GDP forecast publishedby the IMF in January 2020, IMF (2020a), i.e., without the effect of the economic shock from the
COVID-19 pandemic. Period zero corresponds to 2019Q4 and the value is normalised to 100. Scenario
1 is identical to the updated IMF forecast in April 2020 (IMF, 2020b, Figure 1.6, p.9), which clearlyshows the effect of the economic shock from COVID-19’s hit to Asia during January-February 2020
and to Europe and the US during March-April 2020. This trajectory asserts that the world economy
returns to the long-run growth path in around 2020Q4, in the absence of a second outbreak. Scenario

6

Scenario 0

Scenario 1

Scenario 2

Figure 1: Three GDP scenarios with and without the global economic shock from COVID-19

2 assumes a second wave of pandemic around 2021Q1 and Q2 with an associated decline in GDP
growth, which is slightly smaller in magnitude than that of the first wave.
In order to assess the effects of the global economic shock due to COVID-19, we implement conditional forecasting based on the growth rate paths of GDP indicated by the above scenarios. Technicaldetails of this undertaking are explained in the next sub-section. In particular, we compute the eightquarter GDP trajectories corresponding to the above Scenarios 0-2 for each country of our model
starting from the 2014Q4 GDP values, by applying the implied GDP changes of these scenarios,
which are different for advanced and emerging economies.
A few remarks are warranted on this method of constructing the conditioning GDP scenarios. First,
due to the availability of our data set ending at 2014Q4 and not 2019Q4, the estimated GDP trend andhypothesised GDP trend in the advanced and emerging groups may be slightly different.5 Second,even though the GDP growth rates would differ across individual countries, we assume a common
GDP growth trend across countries within each of the two groups following the available publishedIMF forecasts. This assumption may be innocuous to the extent that our primary aim is to assess theimpact of the (almost) simultaneous spread of COVID-19 to the advanced and emerging economies bycomparing the conditional forecasts under Scenario 0 to those under Scenarios 1 and 2. We partiallyaddress this concern by further comparing the conditional forecasts with the unconditional forecasts
from the GVAR model (1) in Appendix C.1. Finally, the IMF published revised GDP forecasts on
24 June 2020 (IMF, 2020c; Figure 1), in which the drop in 2020Q2 is larger for both the advanced
and emerging groups compared to their April 2020 forecasts (IMF, 2020b). Unfortunately, unlike IMF(2020b), the revised quarterly forecast figures are not publicly available and we could not update our
exercise. However, because the IMF’s (2020c) revised GDP forecast falls between our Scenario 1 andScenario 2 forecasts, it is likely that the forecasts of fossil fuel consumption and CO2 emissions under
this revised Scenario 1 would be lower than those under our entertained (IMF, 2020b) Scenario 1 but
higher than those under Scenario 2. Therefore, the general results in this paper would still hold withthe revised scenario in IMF (2020c).

4.1

Conditional forecasting: method

Consider the estimated GVAR(3) model given by
yt = ĉ0 + ĉ1 t + Ĉ1 yt−1 + Ĉ2 yt−2 +Ĉ3 yt−3 + ε̂t ,(8)t = 1, ..., T . Note that the estimation sample ends at 2014Q4. We produce conditional forecasts frommodel (8) for each quarter T + h, for h = 1, 2, ..., 8 conditional on the IMF forecasted GDP growth
paths of 2020Q1-2021Q4 implied by each of the Scenarios 0-2.
The GDP growth paths are applied to the gdp variable of all countries in model (8) starting fromthe last quarterly observation of our sample, T . This results in eight quarterly values for the gdp
variable for every country: namely, gdpi,T +j , j = 1, ..., H(= 8) (two different sets of values for theadvanced and emerging economies). These can be written compactly as
SyT +j = gT +j , j = 1, 2, ..., H,
5

As an alternative the GDP trend of 2014Q4 could be used.

7(9)where S is a suitably defined (N +1)×(k +3) matrix with 1 in the position of gdpi,T +j for each countryi, 0 elsewhere; gT +j is a (N + 1) × 1 vector that contains the values for the gdp variable corresponding
to quarter T + j for a given scenario; and H denotes the conditioning horizon, which is equal toeight (the same as the forecast horizon). The forecast for every quarter, T + h, is then obtained byconditioning on all eight quarterly values for gdp defined by (9) across all countries simultaneously.
The conditional point forecasts of yT +h are given byµ∗h = E yT +h IT , SyT +j = gT +j , j = 1, 2, ..., H , for h = 1, 2, ..., H,(10)where IT is the information set at time T . In deriving the expectations it is assumed that conditioning
on the GDP growth paths does not affect the GVAR parameters, Ci , i = 1, 2, 3 and the covariancematrix, Σε , associated with εt , which is also assumed to be jointly normally distributed.
We further define the unconditional point forecasts of yT +h given byµh = E (yT +h |IT ) , for h = 1, 2, ..., H.

(11)

While these forecasts condition on the information set IT , we define them as unconditional to distinguish them from the conditional forecasts in (10), which in addition condition on the gdp values given
by SyT +j = gT +j , j = 1, 2, ..., H.
We use the latter forecasts to construct the difference, δ h = µ∗h − µh . Obtaining the probability
distribution of this difference then allows us to compute the probability, for example, that consumption
is lower in view of Scenarios 1 and 2, and to check whether these probabilities are in line with thedifferences of the point forecasts obtained between Scenarios 1-0 and 2-0. It is the probabilities
associated with δh that are given in Appendix C.1 referred to earlier. Further technical details related
to the derivation of the conditional and unconditional forecasts, as well as the difference between thetwo, are available in Section S.3 of the supplementary material.

4.2

Conditional forecasting: results

In this subsection we report the conditional forecast results of the total amount of energy consumption
and CO2 emissions for the different groups (see Table 1) under the different GDP scenarios.
4.2.1

Energy consumption

The conditional forecasts of the total amount of energy consumption are summarised in Figure 2. The
horizontal axis represents the quarters over the two-year forecast horizon and the vertical axis reports
the forecasts of the amount of consumption. We first focus on the results for Scenario 0 (no COVID-19
effects). In the initial period (the observed 2014Q4 data point), the EU+ and the advanced groups
have a similar composition of energy consumption. In 2014Q4 the shares of coal, natural gas and oilfor the advanced economies are 18.2%, 36.8% and 45%, respectively. For the EU+ countries, perhaps
reflecting their stringent emission policy, the coal share is smaller (13.7%) and the natural gas shareis larger (40.1%). In contrast, coal is the major energy source in the emerging group, which accounts
for 57.2% of the total. Under Scenario 0, total fuel consumption in the emerging economies rises
much faster than in the advanced over the eight-quarter horizon. This reflects the different average
GDP growth rate of the two groups. The fuel mix also exhibits a different pattern of change forthese two groups. For the advanced economies, the share of coal and oil decreases by 1.0% and 0.8%,
respectively, across the two-year horizon, which is compensated by a 1.8% rise in the share of naturalgas. For the emerging group, the share of coal increases by 1.8% over the horizon.
We next turn to the results of Scenario 1 which assumes a one-time spread of COVID-19. The EU+
group appears to be hit much harder than the advanced group as a whole in the first quarter, displaying
large negative drops in oil and natural gas consumption, simultaneously. Advanced economies as awhole follow a similar, but less pronounced pattern compared to EU+ . This is followed by fast recoveryof consumption in the subsequent quarter(s), and a further up and down movement in consumption.
This implies that the observed effects of the COVID-10 outbreak in the first year may continue during
the second forecast year even though patterns from this GDP scenario are stable at the end of thefirst year. The emerging economies exhibit a notable negative hit in the first quarter, but then start
growing at a similar pace as under Scenario 0. Under Scenario 2, which assumes a resurgence of the8coronarivus, the consumption patterns in EU+ are similar over the first three quarters. The plungein the fourth quarter is deeper and the recovery in the subsequent quarter is much weaker than in
Scenario 1. A similar observation applies to the advanced economies. For emerging economies, thesecond wave of negative GDP shocks push down consumption in the fifth quarter, which drags down
consumption growth.
Scenario 0

Scenario 1
EU+

Scenario 2

Advanced

Emerging

World

Figure 2: Forecasts of consumption of coal, natural gas, and oil by country group (annualised, in
Mtoe)

9

4.2.2CO2 emissions

Based on the forecasted energy consumption levels, we estimate the amount of emissions due to fossil
fuel combustion. We use the simple emission factors which were used in the BP report. Specifically,a tonne of oil equivalent (toe) coal, natural gas and oil is converted to 3.96, 2.35 and 3.07 tonnes of
CO2 .6
As expected, the emissions forecasts are qualitatively similar to the consumption forecasts as theformer are a scaled version of the latter, weighted by the above emission factors. To save space,
the emission forecasts are reported in Figure C.2 in Appendix C.2. Here we focus on investigating
the effect of COVID-19’s negative economic shock on emissions for the different country groups. We
measure the effect by the change of each fuel sourced emissions implied by Scenario 1 or 2 (with a
COVID-19 shock) over Scenario 0 (without the shock), which is reported in Figure 3. The figuresin the first row report the difference between the emissions in Scenario 1 and 0. For the advanced
economies, the Scenario 1 GDP shock reduces emissions due to oil use across all eight quarters, whilst
emissions due to coal and natural gas decrease and then increase continuously over the quarters. It
is clear that oil is the largest contributor to the reduction of emissions. Table 2 reports the changesof the emissions for Scenarios 1 and 2 against Scenario 0. On average, over the eight quarters, theScenario 1 GDP shock reduces emissions by 2.8% in advanced economies. On the other hand, in theemerging economies, apart from the drop in the first quarter, the emissions in the rest of the quarters
are greater in Scenario 1 than in Scenario 0. The increase in emissions is mostly due to higher coalconsumption. The average change in emissions for Scenario 1 over 0 is 1.7% (see first panel of Table 2).
Consequently, despite the massive 4.5% decrease of world emissions in the first quarter, the average
emission changes over the two-year horizon, shown in the same panel of Table 2, is -0.1%, which is verysmall. This is because the decreases in emissions in advanced economies are offset by the increases inemissions in emerging economies. We next turn to the second row of Figure 3 and the second panel ofTable 2, which report the change in emissions for Scenario 2 over 0. Due to the second negative shock
to GDP, emissions are more negatively affected compared to Scenario 1, particularly in the second
forecast year. This reduces the growth in emissions in both the advanced and the emerging economies.
The average changes in the advanced and in the emerging economies are -3.2% and 0.0%, respectively.
Consequently, the average world emission changes is -1.4%.
Why does our analysis forecast relatively small effects of COVID-19 on fossil fuel consumption
and CO2 emissions over a two-year period? Several factors can explain the result. First, although
the IMF (2020b) forecasts predict a large and immediate negative impact on GDP across countries
that is unforeseen in the recent history, they indicate that the advanced economies’ output recovers
to the pre-pandemic level in the two-year period, while the emerging economies’ output recovers evenquicker. Second, existing cross-country studies on energy demand indicate that energy consumption
may not be highly responsive to income changes.7 Figure 1 indicates a decline in GDP by more than
5% for the emerging economies under Scenario 1 relative to Scenario 0 in the first quarter; and morethan a 10% decline for the advanced economies in the second quarter. Table 2, on the other hand,suggests that CO2 emissions decline by a smaller magnitude in both country groups in the initialquarters. These observations are consistent with the income elasticity estimates in the literature. As
the income level increases over the later quarters, the fossil fuel consumption catches up, resulting ina fast recovery of CO2 emissions.
To sum up, under Scenario 1 where the COVID-19 shock negatively affects the world economy
in early 2020 but not in late 2020 to 2021, advanced economies will struggle to restore their energy
consumption growth to the no COVID-19 levels until the end of 2021. In contrast, the emerging
economies may recover faster from the drop in early 2020 and consume more energy than for the casewithout COVID-19. Consequently, total emissions in the world during 2020-2021 may not be affectedmuch by the COVID-19 shock. However, if the world economy is further negatively affected by asecond outbreak of COVID-19, then energy consumption in advanced and emerging economies will godown further. As a result, the world CO2 emissions level could be slightly less than that under the no6

We found that this simple method produces larger emission estimates compared to the BP estimates by 8.0%-10.1%,
which is in line with BP’s note. See Appendix A.3 for the comparison of our estimates with BP’s and related details.
7
Huntington et al. (2019) reviews income elasticity estimates for liquid fuels in the literature, indicating a wide rangeof estimates that average at around less than 1. They find little evidence that countries with higher income levels havelower income responses.

10

COVID-19 scenario.

Advanced

Emerging
Scenario 1 - 0

WorldScenario 2 - 0

Figure 3: Difference in CO2 emissions (annualised in MtCO2 )Table 2: Changes in CO2 emissions under Scenerios 1 and 2

Horizon
1
2
3
4
5
6
7
8
Average

4.3

EU+-18.9%
-1.1%
12.6%
-1.3%
4.5%
-6.7%
-5.2%
0.6%
-1.9%

Scenario 1 to 0
Advanced Emerging
-5.8%
-3.5%
2.6%
2.4%
-5.4%
2.0%
-3.3%
3.5%
0.8%
2.3%
-3.3%
2.0%
-3.3%
2.4%
-2.8%
2.8%
-2.6%
1.7%

World
-4.5%
2.5%
-1.3%
0.5%
1.6%
-0.3%
0.0%
0.5%
-0.1%

EU+
-19.1%
-0.5%
15.1%
-0.1%
-2.5%
-9.7%
-1.6%
-0.3%
-2.3%

Scenario 2 to 0
Advanced Emerging
-5.6%
-3.7%
2.6%
2.0%
-5.2%
1.0%
-2.0%
2.7%
-1.8%
-1.7%
-3.0%
0.0%
-6.7%
-0.3%
-3.8%
0.3%
-3.2%
0.0%

World
-4.6%
2.3%
-1.7%
0.6%
-1.7%
-1.3%
-3.0%
-1.4%
-1.4%

Forecasting fossil fuel prices after the COVID-19 spread

We have seen that the global negative shock due to COVID-19 can have different impact on fuelconsumption, and hence CO2 emissions, in different countries. This will result in a subsequent sharp
fall in fuel prices. Our model takes into account (short-term) feedback effects of the domestic variableson the fuel prices as seen in equation (7). The conditional forecast of prices using the GDP shock
scenarios are given in Figure 4. As this figure indicates (and as the evidence partially supports), under
Scenario 1 the prices of coal and oil first increase then drop sharply, and exhibit a gradual recovery
11

Scenario 0

Scenario 1

Scenario 2

Figure 4: Conditional forecasts of fossil fuel prices conditional on GDP scenarios after the COVID-19
spreadthereafter. The drop of the natural gas price starts in the third quarter. Under Scenario 2, over the
first three quarters the prices move similarly to those under Scenario 1, but from the fourth quarter
onwards coal and natural gas prices exhibit a sharp rise-and-drop. Importantly, the exogenous shocks
can change the relative prices of coal, natural gas and oil, which would affect the future consumptionof these fuels.

5

The effect of changes in the fossil fuel prices on fuel consumption
and output

Our analysis so far indicates that the effect of COVID-19 on the global CO2 emissions would be close
to zero over the two-year time horizon though the fossil fuel prices are expected to have sizeablefluctuations (5% or more) during this period. In particular, it shows that (i) an exogenous shock canchange the relative prices of fossil fuels; and (ii) COVID-19 will not alleviate the urgency to reduce
more CO2 emissions globally. In this section, we investigate the effect of changes in the relative pricesof coal, natural gas and oil, due to carbon pricing, on fuel consumption and output by applying
conditional forecasting to our GVAR model.
Since coal is the most carbon intensive among the three fossil fuels, we implement a counterfactual
experiment that raises the coal price. We use the unconditional forecast prices as the benchmark. We
can regard this experiment as one of investigating the effect of the introduction of carbon pricing tocoal or the effect of a coal price increase due to market forces, or a mix of the two. For this purpose we
restrict the GVAR model so that contemporaneous and lagged feedback from the domestic variables(energy consumption, GDP and exchange rates) to the fossil fuel equation are shut off and do notconfound the interpretation of the experiment. Under this restriction, given the value of the prices,
forecasts are produced from the following modelxt = b̂0 + b̂1 t + F̂1 xt−1 + F̂2 xt−2 + F̂3 xt−3 + Υ̂0 dt + Υ̂1 dt−1 + Υ̂2 dt−2 + v̂t ,(12)where the estimated coefficients embody the internal and external linkages and dynamics resulting
from the estimation of the country-specific models in Section 3.1 (for further details see Section B.2of the Appendix). What we define as the unconditional forecast in what follows refers to the forecasts
from the above model for xt , with the given global prices obtained as forecasts from the estimated
VAR(3) representation of model (4) that includes the error-correction term given in (5). Under thecounterfactual scenario, the forecasts for xt are obtained subject to an added hike to the coal price.
In order to describe long-run effects, we consider forecasts over forty quarters.
Figure 5 reports the unconditional and conditional forecasts. Each panel displays average percapita log fuel consumption and per capita log GDP for each country group. The solid lines are
the unconditional forecasts and the dashed lines are conditional on (12.5%) higher coal prices. The
first panel shows that coal consumption in the advanced economies declines over the horizon, whereas
it increases in the emerging economies. The second panel indicates that natural gas consumption
increases worldwide, but much faster in the emerging economies. The third panel shows that oil
12

Coal

Natural gas

Oil

GDP

Figure 5: Forecasts of fuel consumption and GDP for benchmark and higher coal price cases

EU+

Advanced

Emerging

Figure 6: Difference in forecasts of fuel consumption and GDP between benchmark and higher coalprice cases

consumption in the advanced economies stays almost constant over 10 years while it increases rapidly
in the emerging economies. Finally, GDP grows faster in the emerging economies than in the advanced
economies as illustrated in the last panel. These trajectories are remarkably similar under the scenario
with higher coal prices. However, the negative effects of the higher coal price appear to be larger for
EU+ and the advanced economies than for the emerging ones.
In order to assess the effect of the higher coal price more clearly, we report the difference in
consumption and GDP with and without the 12.5% coal price increase in Figure 6. The properties
13

Table 3: The forecast elasticity of fossil fuel consumption and GDP per capita with respect to coal
price
EU+
coal
gas
oil GDP
Average Annual Change-3.1% -3.1% -1.5% -2.0%
s.e.(autocorrelation robust)
0.9% 0.4% 0.2% 0.2%
t-ratio-3.4-7.8-9.8 -10.5
p-value
0.000 0.000 0.000 0.000

Average Annual Changes.e.(autocorrelation robust)t-ratiop-value

coal
-2.4%
0.7%
-3.4
0.000

Advancedgasoil-2.3% -1.4%
0.3% 0.1%-8.7 -12.4
0.000 0.000

Average Annual Changes.e.(autocorrelation robust)t-ratiop-value

coal
-2.3%
0.5%
-4.8
0.000

Emerging
gas
oil-0.4% -0.8%
0.3% 0.1%-1.3 -10.1
0.091 0.000

GDP
-1.7%
0.2%
-10.0
0.000

GDP
-0.9%
0.1%
-9.8
0.000

Note: The average annual change refers to the annualized average (over 40 quarters) of the quarterly change
due to a 100% increase in the relative price of coal.

of the differences in consumption and GDP for the baseline and for the higher coal price are similar
for EU+ and the advanced economies, but the former group is more sensitive to the coal price hike.
Eventually the higher coal price decreases energy consumption and GDP and the gap under thetwo scenarios widens. While natural gas consumption increases in the initial two years, it decreases
thereafter.
Table 3 reports the ‘elasticity’ of fossil fuel consumption and GDP per capita due to a 100%
increase in the price of coal. The elasticities are small across the board. For example, the estimatedcoal price elasticity of coal consumption per capita is between -3.1% and -2.3%. This magnitude ofprice elasticity is within the range of estimated price elasticity of energy demand in the literature.8
For the emerging economies, the negative effect of the higher coal price on coal consumption issimilar to that of the advanced economies, but the effects on GDP, oil and natural gas consumption
are much smaller. In particular, the elasticity of natural gas consumption is not statistically different
from zero. The results also indicate that, relative to the elasticity of coal, the negative impact of ahigher coal price on GDP is smaller.
Overall, the above simulation reveals that a substantial increase in the coal price can significantly
reduce fossil fuel consumption across the world; the effects on GDP are smaller than those on coalconsumption, and the negative effect on GDP due to a higher coal price is more limited in the emerging
than in the advanced economies.

6

Concluding remarks

In this paper we assess the impact of the global economic shock from COVID-19 on global fossil fuelconsumption and CO2 emissions over a two-year horizon. For this purpose, we employ the global vector
autoregressive (GVAR) model. The model captures cross-sectional and time series interdependencies
across countries, and is thus suitable for studying the global impacts of COVID-19.
8
According to a meta analysis by Huntington et al. (2019), estimated short-run price elasticity of oil demand varies
substantially in the literature, averaging at -0.07 for developing countries and -0.11 for OECD countries among thereviewed studies. Note that our estimate is not the price elasticity of energy demand, so it is not directly comparable tothe elasticity estimates in the literature.

14

The analysis based on data for 32 countries, which generate 81% of the global CO2 emissions due
to fossil fuel use, indicates that the negative effect of COVID-19 on global fossil fuel consumption, andhence CO2 emissions, is large in the first quarter but limited over the two-year horizon. The COVID19 shock negatively affects the world economy in the first quarter after the pandemic occurs, but it
is expected to recover in about a year. The advanced economies will struggle to restore their energy
consumption growth by the end of the two years to the level under the counterfactual scenario without
COVID-19. On the other hand, the emerging economies may recover from the drop in early 2020 rather
quickly and consume more energy than for the case of no COVID-19 outbreak. Consequently, total
emissions in the world during 2020-2021 may not be affected much by the COVID-19 shock. However,if the world economy is further negatively affected, such as by a second wave of the coronavirus, energy
consumption in advanced and emerging economies will further go down, and emissions could be muchless than in the case of no COVID-19 shock.
What will happen if these economies adopt carbon pricing which increases the relative price of coal?
Our conditional forecast analysis reveals that higher coal prices lead to lower fossil fuel consumption
with less-than-proportional decreases in GDP. In particular, the impact on fossil fuel consumption
and GDP is smaller for the emerging economies.
Overall, our GVAR analysis indicates that the pandemic and the resulting economic shut down
will not lead to a sizeable reduction in CO2 emissions over a two-year time horizon. Thus COVID-19
would not provide countries with a reason to delay climate-change mitigation efforts. Fossil fuel priceincreases due to carbon pricing will slow down GDP growth. However, our conditional forecast analysis
with a higher coal price demonstrates that the effect on GDP is limited compared to its impact on
fossil fuel consumption.
Due to lower output under the pandemic and scaled-down industrial activities, the EUETS Allowance price dropped sharply in mid March 2020 though in the last three months since then it has
been increasing to recover to the pre-crisis level. Our analysis implies that continued efforts to reduce
CO2 emissions, through an expanded introduction of carbon pricing in other countries, may not be
too costly in terms of the size of the GDP impacts. As the effects on GDP are expected to be more
limited in the emerging than in advanced economies, global application of carbon pricing may not leadto concerning distributional impacts across countries with different income levels.
With regard to the impact of the economic shock from COVID-19 on the advanced versus emerging
economies, which is revealed by our analysis, the following caveats are in order: our analysis does notstudy the effects on each country separately; stringent financing restrictions in emerging economies may
drag down their recovery from the pandemic’s negative economic effects (IMF, 2020b). Considering a
global model that can reflect differences in sovereign credits across countries is left for future research.
Our analysis also demonstrates differential impacts of slower GDP growth due to COVID-19 on
different fossil fuel sources in different parts of the world. The simulation associated with a higher
coal price also indicated different effects on natural gas and oil consumption. In the context of climatechange mitigation, it would be useful to consider the impact on renewable energy integration, which
this paper does not address due to data availability. Future research could address how COVID-19’s
negative economic shocks influence the speed of renewables integration.

Appendix A: Further Description of the Data
A.1

Data Sources

A.1.1

Macroeconomic variables, PPP-GDP and trade data

Real GDP and the real exchange rate for all countries, as well as PPP-GDP figures and the trade datafor construction of the trade matrix (used in computing the foreign variables of the GVAR model) weretaken from the GVAR 2016 Vintage available at https://sites.google.com/site/gvarmodelling/data.
This is an updated version of the 2013 Vintage, updated by Kamiar Mohaddes and Mehdi Raissi. The
PPP-GDP data are from the World Development Indicator database of the World Bank. The trade
data is from the IMF Direction of Trade statistics constructed based on the average of Exports andImports (c.i.f.) at the annual frequency. Further details including the source of the macroeconomic
variables for each country can be found there.

15

A.1.2

Population

Population data are from the World Bank website https://data.worldbank.org/indicator/SP.POP.TOTL
available at the annual frequency. The annual data were interpolated to obtain the quarterly values
using the approx function in R selecting the method “linear.”

A.1.3

Energy consumption

Energy consumption data were obtained from Oxford Economics (https://www.oxfordeconomics.com/)
whose provider is the International Energy Agency (IEA). The Lisman and Sandee (1964) method
was used for interpolation where required.
Data for coal consumption (domestic demand, annualised, Mtoe) for all countries are constructedfrom the IEA World Energy Balances service, Summary and Extended Energy Balances databasewhich contains annual data. Quarterly values were interpolated from the annual series.
Data for natural gas consumption (domestic demand, annualised, Mtoe) for all OECD countries
are constructed from the IEA Natural Gas Monthly service, Natural Gas Balance database. Monthly
figures were summed to obtain the quarterly values. Quarterly data for non-OECD countries wereinterpolated from annual data which were constructed from the IEA World Energy Balances service,
Summary and Extended Energy Balances database.
Data for oil consumption (domestic demand, annualised, Mtoe) for all OECD countries, and nonOECD countries over the period 1991-2014, are from the IEA Monthly Oil Service. Monthly figures
are summed to obtain the quarterly data. Quarterly values for non-OECD data for the period 1991Q12014Q4 were interpolated from the annual data of the IEA World Energy Balances service.

A.1.4

Global energy prices

The coal, natural gas and oil quarterly prices are computed from the monthly prices obtained fromthe World Bank Commodity Price Data (‘Pink Sheet’ Data).

A.1.5

Temperature data

Daily temperature data from monitoring stations across the countries of interest were extracted fromthe Global Historical Climatology Network- Daily (GHCN) hosted by the National Centers for Environmental Information (NCEI) of the National Oceanic and Atmospheric Administration (NOAA)
over the period 1981-2014. GHCN daily is an integrated database of daily climate summaries fromland surface stations across the globe that have been subjected to a common suite of quality assurance
reviews. It contains records from over 100,000 stations in 180 countries and territories. NCEI provides numerous daily variables, including maximum temperature (Tmin) and minimum temperature(Tmax), total daily precipitation, snowfall, and snow depth. Both the record length and period ofrecord vary by station and cover intervals ranging from less than a year to more than 175 years.
For each monitoring station, all available data that fulfilled the following criteria were retained:(i) both Tmax and Tmin were available for each day and (ii) no quality assurance or quality control
issues were identified for either Tmax or Tmin. For those countries with missing data over the period
of interest, namely Canada, Germany, Singapore and UK, data from the monitoring stations of the top
three largest metropolitan areas based on population were included. Similarly for the US, data fromthe monitoring stations of the top three largest states in terms of population were included namely
California, Texas and New York.

A.2

Temperature and Seasonal Adjustment

A.2.1

Construction of HDD and CDD

The daily temperature data were converted from degree Fahrenheit to Celsius and heating and coolingdegree days, HDDiτ d and CDDiτ d respectively, were construted for each country i and each day d of16each year τ as follows

18 ◦ C − mean daily temperature of country i0, if 18◦ C > mean daily temperature of country imean daily temperature of country i − 21 ◦ C
0, if 21 ◦ C < mean daily temperature of country i

HDDiτ d =CDDiτ d =where mean daily temperature was computed as (Tmin+Tmin)/2.
The corresponding quarterly data for HDDit and CDDit for t = 1, 2, ..., T were constructed bytaking the average daily HDDiτ d and CDDiτ d values for each of the four quarters. Missing valuesof no more than six consecutive values were encountered for HDD for Germany and for CDD for
Germany, Indonesia and Malaysia. These gaps were filled using a simple linear interpolation method.

A.2.2

Temperature adjustment

Let yit be quarterly energy consumption for country i. Following common practice (see for example
Elkhafif, 1996) temperature adjustment of energy consumption was performed as follows:
1. Run the regression based on the quarterly data
yit = b0i + b1i HDDit + b2i CDDit + εit , t = 1, 2, ..., T.

(A.1)

Equation (A.1) may consist of only HDDit or CDDit see Table A.2.
2. Construct the correction factor as
CORRiτ q = b̂1i (HDDiτ q − N HDDiq ) + b̂2i (CDDiτ q − N CDDiq )where τ is the year (τ = 1984, 1985, ..., 2014) and q is the quarter (q = Q1, Q2, Q3, Q4),
N HDDiq = the 30 year average of HDDi for the q th quarter (1981-2010)
N CDDiq = the 30 year average of CDDi for the q th quarter (1981-2010),
are climate normals, typically computed over a three consecutive ten-year period the most recent
being 1981-2010, see Won et al. (2016) for a discussion of climate normals.
3. The temperature adjusted consumption series is then(yit )ta = yit − CORRiτ q .

A.2.3

Seasonal adjustment

Joint significance of seasonal components was tested for all consumption and price series based on theprocedure described in Section A.2.4. For consumption, seasonal effects were not significant for anyof the annual series namely all coal series and the natural gas non-OECD series. Significant seasonal
effects were found for all natural gas OECD series with the exception of Norway and for all oil series
with the exception of Indonesia9 and Malaysia. No temperature and/or seasonal adjustment was
performed for the natural gas series of Norway and the oil series of Malaysia nor for the coal andnon-OECD natural gas series. For prices, only the natural gas prices exhibited seasonal effects andwas seasonally adjusted.
To seasonally adjust the data we start from log(y) (where y here is either the original consumption
series or the temperature adjusted one, or the natural gas price series) and take the first difference,∆ log(y). This is seasonally adjusted using the X-12 quarterly seasonal adjustment method under the
additive option to obtain ∆ log(y)sa . Then using the first observation of the raw series log(y) (levels,
not seasonally adjusted) the seasonally adjusted log changes, ∆ log(y)sa , are cumulated to obtain thelog adjusted series log(y)sa . Finally, the seasonal adjusted level series, (y)sa , is obtained by taking the
exponential of log(y)sa .
9For the oil series of Indonesia we did peform seasonal adjustent despite the non-significant finding of the seasonality
test as the high volatility of the series appeared to obscure the clear seasonal pattern in the less volatile part of the series

17

Table A.2 summarises all adjustments performed to the energy consumption series for each country. It also includes information on the available HDD and CDD series for each country, with a‘no’ indicating that the corresponding series was zero throughout the sample period 1984-2014 andwas therefore not included in the temperature adjustment procedure (if performed), yes indicatingotherwise.
Table A.2: Summary of adjustments to energy consumption of each country
Coal
Natural Gas
Oil
HDD
Argentina
original data original datasayesAustralia
original datata & sata & sayes
Austria
original datata & sasa
yes
Belgium
original datata & sasa
yesBraziloriginal data original datasayesCanadaoriginal data
sa
sa
yes
Chile
original data
sa
sa
yes
China
original data original datasayesFinlandoriginal datata & sata & sayesFranceoriginal datata & sasa
yesGermany
original datasata & sayes
India
original data original datasayesIndonesia
original data original data
sa
no
Italy
original datasata & sayesJapanoriginal datasata & sa
yes
Korea
original datasa
yes
Malaysia
original data original data original datanoMexicooriginal data
sa
sa
yes
Netherlands
original data
sa
sa
yes
New Zealand original datata & sasa
yes
Norwayoriginal data original datasayesPhilippinesoriginal datasanoSaudi Arabia
original datasa
yesSingapore
original datasanoSouth Africa original data
sa
yes
Spain
original datasata & sayes
Swedenoriginal datata & sayes
Switzerland
original datata & sasa
yesThailandoriginal data original datasanoTurkeyoriginal data
sa
yes
UK
original datasata & sayesUSoriginal datata & sata & sa
yes

CDD
yes
yes
yes
yes
yes
yes
yes
yes
no
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
no
yes
yes
yes
yes
yes
no
yes
yes
yes
yes
yes

Note: ‘original data’ signifies that the no temperature and/or seasonal adjustment was performed on the corresponding
series. ‘ta’ and ‘sa’ signifies temperature adjusted and seasonally adjusted respectively. For the oil series any ‘ta’
and/or ‘sa’ for non-OECD countries was performed over the period 1991Q1-2014Q4 as prior to 1991 the series areinterpolated from annual data. ‘no’ for any HDD or CDD series signifies that all values for the corresponding series were
zero throughout the sample for the associated country and was therefore not included in the temperature adjustment
procedure (if performed), ‘yes’ signifies otherwise.

A.2.4

Assessing the joint significance of seasonal effects

To assess the joint significance of the seasonal components for a series y we consider its natural
logarithm denoted by log(y), and use the following procedure:
1. Let S1 , S2 , S3 and S4 be the usual seasonal dummies, such that Si , i = 1, 2, 3, 4, takes the valueof 1 in the ith quarter and zero in the remaining three quarters.
2. Construct S14 = S1 − S4 , S24 = S2 − S4 , S34 = S3 − S4 .
3. Run a regression of ∆ log(y) (where the lower case denotes the natural logarithm of the corresponding variable) on an intercept and S14 , S24 , S34 . Denote the OLS estimates of S14 , S24 andS34 by a1 , a2 and a3 .

18

4. Asses the joint significance of the seasonal components by testing the hypothesis that a1 = a2 =a3 = 0 using the F-statistic.
5. In cases where the null hypothesis was rejected at the 10% level, seasonal adjustment was
performed on the log-difference of the original series using the X-12 procedure as describedabove.

A.3

Comparison of Consumption and Emissions Data with BP Data

Our CO2 emissions were calculated by applying the single emission factor used by BP10 to each of
the coal, natural gas and oil series. These emission factors are based on standard global average
conversion factors compiled on the basis of average carbon content: coal at 94,600 kg CO2 per TJ(3.96 tonnes per tonne of oil equivalent); natural gas at 56,100 kg CO2 per TJ (2.35 tonnes per tonneof oil equivalent); and oil at 73,300 kg CO2 per TJ (3.07 tonnes per tonne of oil equivalent).
We compared our CO2 emission data with published BP data, bp-stats-review-2019-consolidated-dataset-panel-format.xlsx. The comparison is based on 2014Q4 annualised consumption and the2014 BP data file11 for the four country groups of Table 1: (i) EU+ , (ii) Advanced, (iii) Emerging,(iv) Total. The CO2 emissions and consumption of coal, natural gas and oil per Mtoe is given in thetable that follows.
Table A.3: CO2 emissions and consumption of coal, natural gas and oil per Mtoe(a) Our consumption(b) BP consumption(c) Emission Factors(d) CO2 (a)×(c)(e) CO2 (b)×(c)(f) BP CO2(g) log((d)/(e)) (%)(h) log((e)/(f)) (%)(i) log((d)/(f)) (%)(a) Our consumption(b) BP consumption(c) Emission Factors(d) CO2 (a)×(c)(e) CO2 (b)×(c)(f) BP CO2(g) log((d)/(e)) (%)(h) log((e)/(f)) (%)(i) log((d)/(f)) (%)

coal
851.1
866.5
3.96
3370.23431.3(i) Advanced
gasoil
1725.1 2108.9
1201.9 1935.7
2.35
3.07
4054.1 6474.4
2824.5 5942.6

-1.8%

36.1%

coal
164.3
165.7
3.96
650.8
656.2

(iii) EU+
gas
oil
480.5
553.5
288.5
517.4
2.35
3.07
1129.2 1699.2
677.9 1588.5

-0.8%

51.0%

8.6%

6.7%

total
4685.14004.1
13898.6
12198.3
11023.9
13.0%
10.1%
23.2%

total
1198.3971.63479.12922.62651.6
17.4%
9.7%
27.2%

coal
2683.72596.03.9610627.510280.2
3.3%

coal
3534.83462.5
3.9613997.7
13711.5
2.1%(ii) Emerging
gas
oil
533.4
1472.5597.4
1431.32.35
3.07
1253.44520.71403.94394.2-11.3%

2.8%

Total (i) + (ii)gasoil
2258.53581.51799.33367.02.35
3.07
5307.5 10995.1
4228.4 10336.8
22.7%

6.2%

total
4689.64624.816401.6
16078.4
14988.2
2.0%
7.0%
9.0%

total
8628.830300.2
28276.726012.1
6.9%
8.3%
15.3%

Note: The difference between our CO2 emission estimates (d) and those of BP (f) for total emissions is 15.3%. This
discrepancy is wider for advanced economies (23.2%) than for emerging economies (9.0%). This difference can be
decomposed into two parts: that from converting energy consumption into emissions (h) and that associated with thecalculation of consumption (g). Our simple conversion method using the factor (c) tends to overestimate emissions by
7.0%-10.1%, which is in line with BP’s note according to footnote 11 below. The other source of discrepancy related
to consumption calculation is negligible for coal and moderate for oil, but substantial for natural gas. The difference
between our natural gas consumption figures (a) and those of BP (b) are reported in (g) as 36.1%, -11.3%, and 22.7%
10
These are the emission factors that BP used to estimate carbon emissions from energy consumption prior to revising
their process for the 2016 edition of the Statistical Review.2016.
11
While these figures are based on BP’s revised methodology for constructing CO2 emissions since 2016, it is mentioned
in their note that applying their emission factors we use here would result in CO2 emissions about 8% higher than thosederived from their revised methodology.

19

for the Advanced, Emerging and Total groups, respectively. Since natural gas is far more used by the advanced group,
the discrepancy in emission estimates due to the difference in energy consumption is larger for this group: these (g) are
13%, 2.0% and 6.9% for advanced, emerging and total, respectively.

Appendix B: Further Description and Results of the GVAR Model
B.1

Country-specific Variables

Table B.1(i) Summary of domestic and foreign variables included in the country-specific models
Variables
GDP per capita
Coal consumption
Nat. gas consumption
Oil consumption
Real exchange rate
Coal priceNat. gas price
Oil price

All Countries Excluding US
Endogenous Weakly Exogenous
gdpit
gdp∗it∗coalitcoalit
∗
gasit
gasit
∗
oilit
oilit
epit
pcoaltpgast
poilt

Endogenous
gdpU S,tcoalU S,tgasU S,toilU S,t-US
Weakly Exogenous
gdp∗U S,t
∗
coalU
S,t
∗
gasU S,t
∗
oilU
S,t
∗
epU S,tpcoaltpgast
poilt

Table B.1(ii) Composition of country variables
Variables
GDP per capita
Real exchange rate
Coal consumption
Nat. gas consumption
Oil consumption# Countries
32
31
30
27

Excluding: US
Excluding: Saudi Arabia and Singapore
Excluding: Korea, Philippines, South Africa,
Sweden, Turkey

32

Note: The excluded consumption series had at least some part of the series equal to zero.

B.2

Solving for the GVAR model

We solve for the GVAR model in terms of yt = (x0t , d0t )0 , using the the estimated VECMX* models (2) and the global price model (7). We initially obtain the global model associated with theindividual country equations given
variable vector
P by (2), expressed in terms of the k0 × 1∗0 global
0 (2) can be expressed
xt = (x00t , x01t , ..., x0N t )0 with k = N
k
.
To
this
end,
by
setting
z
=
(x
,
x
)
it
it
it
i=0 i
in terms of zit as follows
Gi0 zit = ai0 + ai1 t + Gi1 zi,t−1 + ... + Gipi zi,t−pi+ Ψi1 dt−1 + ... + Ψiqi dt−qi + uit ,(B.1)

where Gi0 = (Iki , −Λi0 ) and Gij = (Φij , Λij ), for j = 1, ..., pi . Then using the identity zit = Wi xt , fori = 0, 1, ..., N, where Wi are the link matrices defined by the trade weights wij , (B.1) can be written
as
Gi0 Wi xt = ai0 + ai1 t + Gi1 Wi xt−1 +... + Gipi Wi xt−pi + Ψi0 dt ++ Ψi1 dt−1 + ... + Ψiqi dt−qi + uit .

(B.2)

The individual models in (B.2) are then stacked to yield the model for xt given by
G0 xt = a0 + a1 t + G1 xt−1 + ... + Gp xt−p + Ψ0 dt + Ψ1 dt−1 + ... + Ψq dt−q + ut ,

20

(B.3)

where p = max(pi ), q = max(qi ), and
G0j W0 G1j W1

Gj = 
..

.


Ψ0s

 Ψ1s  , Ψs =  ..  ,

 . 




j = 0, 1, ..., p;
s = 0, 1, ..., q,ΨN s
GN j WN





u0t
a01
a00
 u1t  a11  a10 a0 =  .  , a1 =  .  , ut =  .  .
.
.
.
 . 
 . 
 . 
uN t
aN 1
aN 0


P
f t ,12 and assuming that p = pe = qe = q for ease of exposition, (B.3)
et = N
Setting x
ei xit = Wxi=0 w
and (7) can be written in terms of yt = (x0t , d0t )0 as
H0 yt = h0 + h1 t + H1 yt−1 + ... + Hp yt−p + ζ t ,

(B.4)

where

H0 =

Hj =



0md ×k

−Ψ0
Imd

Gj
fj
Λxj W

Ψj
Φdj



G0


, h0 =a0µ0, j = 1, ..., p,






a1
, h1 =
,
µ1


ut
ζt =
.
ηt

Assuming that H0 is invertible, premultiplying (B.4) by H−1
0 we arrive at the GVAR modelyt = c0 + c1 t + C1 yt−1 + ... + Cp yt−p + εt ,

(B.5)

−1
−1
with cj = H−1
0 hj , j = 0, 1, Cj = H0 Hj , j = 1, ..., p, and εt = H0 ζ t , which is the model
used for forecasting. In general, the lag order of yt will be determined by the maximum lag order max(max(p, pe), max(q, qe)). In our case the final estimated GVAR is of order 3. Related output on
the stability of model (B.5) can be found in the supplementary material.13
For the purpose of forecasting when the global prices are taken as given (and no feedback effects
are considered) as in the experiment of Section 5, forecasts are based on the modelxt = b0 + b1 t + F1 xt−1 + ... + Fp xt−p + Υ0 dt + Υ1 dt−1 + ... + Υq dt−q + vt ,

(B.6)

−1
which follows from model (B.3) above with bj = G−1
0 aj , j = 0, 1, Fj = G0 Gj , j = 1, ..., p, Υj =−1
−1
G0 Ψj , j = 0, 1, ..., q and vt = G0 ut , where Gj j = 0, 1, ..., q, with p and q equal to 3 and 2
respectively. Note that the dynamic properties of model (B.6) differ from those of model (B.5), withdifferences in the number of cointegrating relations for certain countries required to obtain a stable
solution.

Appendix C: Additional Empirical Results
C.1

Conditional Forecast Probabilities

There are discrepancies between the estimated GVAR model and the assertions implied by the forecast
scenarios, mainly due to the difference in the estimation periods. To address these, we compare theconditional forecasts based on the implied GDP changes in Scenarios 0 to 2 with the unconditional
12

f are the same for all variables.
Note that for expositional simplicity we are assuming that the PPP-GDP weights in W
In practice, as in our case, these can be somewhat different if, for example, a certain variable is excluded from theestimation of the country-specific models for certain countries (see Table B.1(ii)), as the weights then get re-weighted inorder to sum to one. The same holds true for the trade weights in Wi .
13
All GVAR related output in this paper has been obtained using the GVAR Toolbox 2.0 of Smith and Galesi (2014)
with modifications and additions to the existing functions.

21

Scenario 0

Scenario 1
EU+

Scenario 2

Advanced

Emerging

World

Note: The vertical axis measures the probability that consumption under the conditional forecast exceeds that under
the unconditional forecast in deviation from 0.5.

Figure C.1: Conditional forecast probabilities of changes in relative consumption by country group22

forecasts obtained from the estimated GVAR model. Figure C.1 reports the probabilities of positive
consumption of the difference between the conditional and unconditional forecast of coal, natural gas,and oil by country group. The reported probabilities are the deviations from 0.5. When the figure ispositive (negative), the chance of the conditional forecast of consumption being larger (smaller) thanthe unconditional forecast is more than 50/50. For Scenario 0, all figures are positive and show anincreasing trend over time. For the advanced and EU+ countries, the figure is not very large, well
below 0.2. On the other hand, the figures for the emerging economies are mostly larger than 0.2.
These results suggest that, on average, the scenario growth rate is mostly higher than the estimatedrate, and the discrepancy is higher for the emerging economies. With this in mind, we turn to theconditional forecast probability for Scenario 1 (against the unconditional forecast). For the EU+ andadvanced countries, in most quarters the chance of having negative consumption is higher. The chanceof negative consumption is the highest in the first quarter with a high chance maintained over the
first year, easing somewhat towards the second half of the year. For the emerging economies, after
the first quarter’s negative figure, chance evens out in the second and third quarters, with the figures
exceeding 0.1 thereafter and rising, mainly led by coal and oil. Hence, for emerging economies the
Scenario 1 effect of COVID-19 on consumption is limited to the first quarter. The result for the world
is a combination of that of advanced and emerging economies. The effect of COVID-19 on the worldin Scenario 1 is largely pronounced in the first quarter, with small negative effects in quarters two
until four, and consumption starting to grow thereafter, in the second year. Turning to the results for
Scenario 2, compared to Scenario 1 the figures for EU+ and advanced countries are similarly negative
during the first year but much more negative during the second year. In Scenario 2, the positive
figures for the emerging economies are much smaller, especially in the second year. Consequently, theworld figures are mostly negative.

C.2

Forecasts of CO2 Emissions

The conditional forecasts of the total amount of CO2 emissions for the different country groups issummarised in Figure C.2. The horizontal axis represents the quarters over the two-year forecasthorizon and the vertical axis reports the forecasts of the amount of emissions. See the discussion in
Section 4.2.

23

Scenario 0

Scenario 1
EU+

Scenario 2

Advanced

Emerging

World

Figure C.2: Forecasts of CO2 emissions due to fossil fuel use by country group (annualised in MtCO2 )References
Allan, J. Donovan, C. Ekins, P., Gambhir, A., Hepburn, C., Reay, D., Robins, N., Shuckburgh, E.,
Zenghelis, D., (2020). A net-zero emissions economic recovery from COVID-19. Working Paper No.
20-01, Oxford Smith School of Enterprise and the Environment.
Akpan, G.E., Akpan, U.F. (2012). Electricity consumption, carbon emissions and economic growthin Nigeria. International Journal of Energy Economics and Policy, 2, 292-306.
Antonakakis, N., Chatziantoniou, I., Filis, G., (2017). Energy consumption, CO2 emissions, andeconomic growth: An ethical dilemma. Renewable and Sustainable Energy Reviews, 68, 808–824.
24

Bloch, H., Rafiq, S., Salim, R. (2012). Coal consumption, CO2 emission and economic growth inChina: Empirical evidence and policy responses. Energy Economics, 34, 518-528.
Bozkurt, C., Akan, Y. (2014). Economic growth, CO2 emissions and energy consumption: The Turkishcase. International Journal of Energy Economics and Policy, 4, 484-494.
BP p.l.c. (2019). BP Statistical Review of World Energy 2019, https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html
Cashin, P., Mohaddes, K., Raissi, M., Raissi, M. (2014) The differential effects of oil demand andsupply shocks on the global economy. Energy Economics, 44, 113–134.
Dées, S., di Mauro, F. Pesaran, M.H., Smith, L.V. (2007). Exploring the international linkages of theeuro area: A global VAR analysis. Journal of Applied Econometrics, 22, 1– 38.
Elkhafif, M.A.T. (1996). An iterative approach for weather-correcting energy consumption data.
Energy Economics, 18, 221-230.
Huntington, H. G., Barrios, J. J., Arora, V. (2019). Review of key international demand elasticities
for major industrializing economies. Energy Policy, 133, 110878.
IMF (2020a). World Economic Outlook Update, January 2020.
IMF (2020b). World Economic Outlook, April 2020.
IMF (2020c). World Economic Outlook Update, June 2020.
Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: A simulationbased analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation ResearchPart E: Logistics and Transportation Review 136.
Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector
autoregressive models. Econometrica, 59, 1551-1580.
Kahouli, K., (2019). Does static and dynamic relationship between economic growth and energy
consumption exist in OECD countries? Energy Reports, 5, 104-116.
Le Quere, C., Jackson, R.B., Jones, M.W., Smith, A.J.P., Abernethy, S., Andrew, R.M., De-Gol1,
A.J., Willis, D.R., Shan, Y., Canadell, J.G., Friedlingstein, P., Creutzig, F., Peters, G.P., (2020).
Temporary reduction in daily global CO2 emissions during the COVID-19 forced confinement, Nature
Climate Change, https://doi.org/10.1038/s41558-020-0797-x
Lisman, J., Sandee, J. (1964). Derivation of quarterly figures from annual data. Journal of the Royal
Statistical Society (Series C), 13, 87-90.
Liu, Z., Deng, Z., Ciais, P., Lei, R., Feng, S., Davis, S.J., Wang, Y., Yue, X., Lei, Y., Zhou, H. et al.,(2020). COVID-19 causes record decline in global CO2 emissions, arXiv preprint arXiv:2004.13614.
Mohaddes K., Pesaran M.H. (2016). Country-specific oil supply shocks and the global economy: acounterfactual analysis. Energy Economics, 59, 382-399.
Mohaddes K., Pesaran M.H. (2017). Oil prices and the global economy: Is it different this timearound? Energy Economics, 65, 315-325.
Mohaddes K., Raissi, M. (2019). The U.S. oil supply revolution and the global economy. Empirical
Economics, 57, 1515-1546.
Omri, A., Kahouli, B., (2014). Causal relationships between energy consumption, foreign direct
investment and EG: Fresh evidence from dynamic simultaneous equations models. Energy Policy, 67,
913–922.
Pesaran, M. H., Schuermann, T., Weiner, S.M. (2004). Modelling regional interdependencies using a
global error-correcting macroeconometric model. Journal of Business and Economics Statistics, 22,
129-162.
Pesaran, M.H., Shin, Y., Smith, R.J. (2000). Structural analysis of vector error correction models
with exogenous I(1) variables. Journal of Econometrics, 97, 293-343.

25

Saidi, K., Hammami, S. (2015). The impact of CO2 emissions and economic growth on energy consumption in 58 countries. Energy Reports, 1, 62-70.
Shahbaz, M., Khan, S., Tahir, M.I., (2013). The dynamic links between energy consumption, economic growth, financial development and trade in China: Fresh evidence from multivariate framework
analysis. Energy Economics, 40, 8–21.
Smith, L.V., Galesi, A., (2014). The GVAR Toolbox 2.0. Available at: https://sites.google.com/site/
gvarmodelling/gvar-toolbox/
Won, S.J., Wang, X.H., Warren, H.E. (2016). Climate normals and weather normalization for utility
regulation. Energy Economics, 54, 405-416.

26

An Online Supplement forAssessing the Impact of COVID-19 on Global Fossil Fuel Consumption and CO2 Emissions

L. Vanessa Smith
University of York

Nori Tarui
University of Hawaii

Takashi Yamagata
University of York

June 2020

S.1

Introduction

This supplement is organised as follows: Section S.2 provides a selected set of results related to theestimated GVAR(3) model given in (8) and the associated country-specific models. Section S.3 gives
the technical details associated with the GVAR forecasting discussed in Section 4.1 of the main paper.

S.2

Country-Specific and GVAR results

The estimated GVAR(3) model in yt = (x0t , d0t )0 , where xt is the global variable vector and dt is thevector of global prices, has 155 endogenous variables (3 of which correspond to the global prices), 108
stochastic trends (2 corresponding to the global prices) and 47 cointegrating relations (1 corresponding
to the global prices). All the roots of the global VAR model in the 32 countries either lie on or insidethe unit circle. The moduli of the largest non unit eigenvalue is 0.949. The lag orders for the domestic
variables, pi , and foreign variables, qi , are selected based on the Akaike criterion with pmax i = 3
and qmax i = 2. The individual country models are estimated subject to reduced rank restrictions
as described in Section 3.2 and the cointegrating relations obtained are based on the trace statistic
at the 95% critical value.14 For estimation, x∗it are treated as “long-run forcing” or I(1) weakly
exogenous with respect to the parameters of the conditional model. This assumption can be tested
by regressing x∗it on the error correction terms for country i and testing whether these terms are
statistically significant. A selected set of results related to the estimated GVAR(3) and associatedcountry-specific models are given in Tables S.2(i)-S.2(iv) and Figure S.2(i). Additional results areavailable upon request.

14

The number of cointegrating relations is determined for the case where unrestricted constants and restricted trend
coefficients are included in the individual country error correction models.

S1

Table S.2(i): VARX* orders and number of cointegrating relationships
Country
ARGENTINA
AUSTRALIA
AUSTRIABELGIUM
BRAZILCANADACHILE
CHINA
FINLANDFRANCEGERMANYINDIA
INDONESIA
ITALY
JAPAN
KOREA
MALAYSIAMEXICONETHERLANDSNEW ZEALANDNORWAYPHILIPPINESSAUDI ARABIASINGAPORE
SOUTH AFRICA
SPAIN
SWEDENSWITZERLAND
THAILAND
TURKEYUNITED KINGDOMUSAVARX*(pi , qi )
pi
qi
3
1
3
1
3
2
3
2
3
2
3
2
2
2
3
1
3
1
3
1
2
1
3
2
3
1
3
2
3
2
3
2
3
1
3
2
3
2
1
1
3
2
3
2
3
1
1
1
2
1
3
2
3
2
3
1
3
1
3
2
3
1
3
1

# Cointegrating
Relationships
1
1
2
1
1
3
2
1
1
2
2
2
3
2
2
1
3
1
0
2
1
2
0
1
0
2
0
1
2
1
2
1

Note: The lag orders for the domestic variables, pi , and foreign variables, qi , are selected based on the Akaike criterionwith pmax i = 3 and qmax i = 2. The individual country models are estimated subject to reduced rank restrictions
as described in Section 3.2 and the cointegrating relations obtained are based on the trace statistic at the 95% criticalvalue. The number of cointegrating relations was reduced for certain countries based on the performance of the persistence
profiles of the GVAR.

Figure S.2(i): Persistence profiles

S2

Table S.2(ii): Number of rejections of the null of parameter constancy per variable across thecountry specific models at the 1 percent level
Alternative
Test Statistics
P KsupP KmsqNrobust-N
QLR
robust-QLR
MW
robust- M W
AP W
robust- AP W

coal
1(3.3)
1(3.3)
4(13.3)0(0.0)
7(23.3)1(3.3)
5(16.7)1(3.3)
7(23.3)2(6.7)

Domestic Variables
gas
oil
gdp
2(7.4)
2(6.3)
4(12.5)2(7.4)
1(3.1)
5(15.6)2(7.4)
3(9.4)
0(0.0)
1(3.7)
0(0.0)
0(0.0)
9(33.3) 7(21.9) 6(18.8)0(0.0)
2(6.3)
0(0.0)
5(18.5) 6(18.8)3(9.4)
0(0.0)
2(6.3)
1(3.1)
9(33.3) 8(25.0) 6(18.8)0(0.0)
3(9.4)
0(0.0)

ep
1(3.2)
0(0.0)
0(0.0)
0(0.0)
2(6.5)
1(3.2)
1(3.2)
0(0.0)
2(6.5)
1(3.2)

Numbers(%)
10(6.6)9(5.9)
9(5.9)
1(0.7)
31(20.4)
4(2.6)
20(13.2)4(2.6)
32(21.1)6(3.9)

Note: The test statistics P Ksup and P Kmsq are based on the cumulative sums of OLS residuals, N is the Nyblom test for
time-varying parameters and QLR, M W and AP W are the sequential Wald statistics for a single break at an unknown
change point. Statistics with the prefix robust denote the heteroskedasticity robust version of the tests. All tests are
implemented at the 1% significance level.

Table S.2(iii) Contemporaneous effects of the foreign variables on their domestic counterparts inthe country-specific models
Country
ARGENTINA
AUSTRALIA
AUSTRIABELGIUM
BRAZILCANADACHILE
CHINA
FINLANDFRANCEGERMANYINDIA
INDONESIA
ITALY
JAPAN
KOREA
MALAYSIAMEXICONETHERLANDSNEW ZEALANDNORWAYPHILIPPINESSAUDI ARABIASINGAPORE
SOUTH AFRICA
SPAIN
SWEDENSWITZERLAND
THAILAND
TURKEYUNITED KINGDOM
USA

coal
0.31 [0.23]-0.25 [0.23]
0.39 [0.22]
1.35† [0.34]
0.36† [0.13]
0.46† [0.14]
1.32† [0.43]
0.04 [0.04]
0.37 [0.22]
0.01 [0.12]
0.23† [0.06]
0.00 [0.04]
0.24 [1.13]
0.82† [0.15]
0.20† [0.05]
0.38 [0.23]
0.76 [0.78]
0.58† [0.26]
0.32† [0.16]
0.65 [0.49]
0.39 [0.33]
0.11 [0.23]-0.04 [0.13]
0.12 [0.16]
1.09† [0.34]
0.76 [0.55]
0.20 [0.32]
0.23† [0.11]-0.27 [0.17]
0.06 [0.03]

gas
0.08† [0.04]-0.01 [0.49]
0.04 [0.13]
0.00 [0.16]-0.03 [0.08]
0.37† [0.15]-0.39 [0.55]
0.01 [0.04]
0.05 [0.20]
0.60 [0.31]
0.27 [0.17]-0.09 [0.07]-0.12 [0.17]
0.70† [0.14]
0.10 [0.22]
0.06 [0.05]
0.22 [0.14]
0.60† [0.18]-0.16 [0.34]
0.23 [0.82]
0.03 [0.09]-0.93† [0.43]
0.12 [0.13]
0.05 [0.14]
0.09 [0.06]
0.39† [0.15]
0.29 [0.24]

oil
0.54 [0.31]
0.26 [0.15]
0.39† [0.15]
1.08† [0.27]-0.07 [0.21]
0.62† [0.17]
0.20 [0.45]-0.14 [0.25]
0.54† [0.22]
0.51† [0.10]
1.09† [0.27]
0.11 [0.20]
1.59 [0.88]
0.43† [0.14]
0.78† [0.20]
0.46 [0.26]
0.08 [0.32]
0.18 [0.16]
0.12 [0.16]
0.39 [0.34]
0.89† [0.39]
0.49 [0.29]-0.28 [0.18]
0.52† [0.24]
0.18 [0.27]
0.68† [0.13]
1.19† [0.28]
0.74† [0.24]
0.58† [0.25]
0.42 [0.34]
0.62† [0.14]
0.28† [0.12]

gdp
0.39 [0.37]
0.38† [0.12]
0.95† [0.13]
0.94† [0.16]
0.29 [0.39]
0.50† [0.09]
0.78† [0.28]
0.65† [0.31]
1.52† [0.23]
0.70† [0.06]
1.30† [0.15]
0.75 [0.40]
0.18 [0.25]
0.92† [0.13]
0.40† [0.19]
0.27 [0.24]
0.91† [0.14]
0.66† [0.23]
0.41† [0.11]
0.36† [0.15]
0.65 [0.35]
0.42 [0.22]
0.37 [0.24]
1.52† [0.22]
0.34 [0.10]
0.20† [0.08]
1.19† [0.17]
0.78† [0.17]
0.93† [0.34]
0.99 [0.59]
0.42† [0.10]
0.48† [0.08]

Note: White’s heteroskedastic-robust standard errors are given in square brackets. † denotes statistical significance atthe 5% level.

S3

Table S.2(iv): F Statistics for testing the weak exogeneity of the country-specific foreign variables
and global prices
Country
ARGENTINA
AUSTRALIA
AUSTRIABELGIUM
BRAZILCANADACHILE
CHINA
FINLANDFRANCEGERMANYINDIA
INDONESIA
ITALY
JAPAN
KOREA
MALAYSIAMEXICONETHERLANDSNEW ZEALANDNORWAYPHILIPPINESSAUDI ARABIASINGAPORE
SOUTH AFRICA
SPAIN
SWEDENSWITZERLAND
THAILAND
TURKEYUNITED KINGDOM
USA

F testF(1,96)F(1,91)F(2,76)F(1,91)F(1,105)
F(3,80)F(1,96)F(2,90)F(1,96)F(2,90)F(2,90)F(2,81)
F(3,94)F(2,95)F(2,104)
F(1,106)
F(3,94)F(1,96)F(0,97)F(1,87)F(2,95)F(2,96)F(0,98)F(0,98)F(1,97)F(2,95)F(0,98)F(1,96)F(2,90)F(1,88)F(2,95)F(1,80)

coal∗
0.01
1.63
1.05
0.31
1.21
0.37
0.28
1.75
1.74
0.09
0.47
1.47
0.61
0.87
0.40
0.00
1.18
1.19
0.57
0.06
1.39
0.31
1.60
0.65
0.88
1.14
0.80
2.31

gas∗
1.18
0.13
0.21
4.61
0.00
1.17
1.15
0.24
0.06
0.11
3.99†
2.76
0.67
0.81
0.06
1.22
1.18
0.01
0.40
3.39
1.56
1.13
2.48
0.76
0.16
1.82
1.00
0.01

oil∗
0.12
0.10
0.39
0.03
0.20
1.01
1.22
0.77
3.25
0.29
0.11
0.37
0.26
1.25
0.83
0.35
0.95
1.15
0.14
2.02
0.34
0.40
0.69
0.08
0.42
1.10
0.11
0.09

gdp∗
1.40
1.17
1.46
2.00
0.70
0.08
0.06
0.02
2.43
0.03
0.33
1.34
0.33
0.81
0.03
0.51
0.25
1.15
0.68
1.49
0.49
0.52
0.00
3.09
0.05
0.10
0.11
0.17

ep∗
0.28

pcoal
0.23
0.41
2.23
2.43
0.12
0.67
1.28
0.33
0.16
0.45
1.88
1.34
0.34
0.18
0.22
0.85
0.98
0.41
0.06
0.08
1.23
0.00
0.32
0.01
0.30
0.22
1.30
2.68

pgas
0.14
1.42
2.55
2.07
0.92
2.13
0.44
0.00
0.01
2.25
0.97
2.22
0.23
1.03
2.74
0.13
0.40
1.26
0.30
0.34
0.30
0.03
0.54
0.43
0.26
0.01
0.91
0.00

poil
1.23
0.08
0.21
0.44
0.06
0.39
0.34
0.01
1.67
0.73
4.56†
0.87
0.52
0.30
1.39
0.00
0.54
2.01
1.58
2.63
2.33
0.75
0.07
0.41
2.35
0.11
3.60†
1.74

Note: These F statistics test zero restrictions on the coefficients of the error correction terms in the error-correction
regressions for the country-specific variables. The lag orders for the domestic variables and those for the foreign andglobal price variables were selected based on the Akaike criterion with the maximum value for both set to two. Increasing
the lag orders further reduced the number of statistically significant outcomes. † denotes statistical significance at the
5% level.

S.3

Forecasting using the GVAR

S.3.1

Unconditional forecasts

The unconditional forecasts based on the estimated GVAR(3) model in (8) are computed recursivelybyµh = ĉ0 + ĉ1 (T + h) + Ĉ1 µh−1 + Ĉ2 µh−2 + Ĉ3 µh−3 , for h = 1, 2, ..., H.
(S.1)
with initial values µ0 = yT , µ−1 = yT −1 and µ−2 = yT −2 , where h is the forecast horizon.
Equivalently expressed in terms of the GVAR(1) companion form we have

 

 
 

C1
C2
C3
yt
yt−1
b0 + b1 tεt
 yt−1  =  Ik+md +  0 ,
00   yt−2  + 
00
Ik+md 0
yt−2
yt−3
0
0
or
Yt = CYt−1 + Dt + Et ,(S.2)

where recall k + md is the total number of domestic variables and global prices in the GVAR model.
HenceP
Ph−1 ``
YT +h = Ch YT + h−1(S.3)`=0 C DT +h−` +`=0 C ET +h−` ,andyT +h = F YT +h ,
S4

(S.4)

whereF=

Ik+md

0(k+md )×(k+md )0(k+md )×(k+md )



.

(S.5)

Conditional on the initial values, YT , the point forecasts µh equivalently to (S.1) are given by
P
`
(S.6)
µh = F Ch YT + h−1`=0 F C DT +h−` .

S.3.2

Conditional forecasts

Under joint normality of the GVAR shocks we have that yT +h |IT is normally distributed
yT +h |IT ∼ N (µh , Ωhh ),where µh = E (yT +h |IT ) and Ωhh is given by
h−1
XC` ΣC0` F 0 ,

(S.7)


Σε 0 0Σ =  0 0 0 ,
0 0 0

(S.8)

Ωhh = F

`=0

with



and Σε =Cov(εT +h−i ), and C and F are given by (S.2) and (S.5), respectively. The covariance of εt
can be estimated using the residuals of (8) as Σ̂ε = T1 ε̂t ε̂0t .
Furthermore we haveyT +h = µh + ξ T +h ,
(S.9)
where
ξ T +h =Ph−1``=0 F C ET +h−` ,with ξ T +h |IT normally distributed as N (0, Ωhh ), and ET +h−` is defined by (S.2).
From (9) and (S.9) it follows thatSξ T +j = gT +j − Sµj for j = 1, 2, ..., H,(S.10)and setting qT +j = gT +j − Sµj ∀j, (S.10) can be written as(IH ⊗S)ξ H = qH ,where ξ H = (ξ 0T +1 , ξ 0T +2 , ..., ξ 0T +H )0 and qH = (q0T +1 , q0T +2 , ..., q0T +H )0 .
Under joint normality of the shocks and surpressing the dependence of ξ T +h on H we haveE ξ T +h IT , SyT +j = gT +j , j = 1, ..., H= E ξ T +h |IT , (IH ⊗S)ξ H = qH
= (s0hH ⊗Ik+md )ΩH (IH ⊗S0 )[(IH ⊗S)ΩH (IH ⊗S0 )]−1 qH ,where shH is a H × 1 selection vector with unity as its hth element and zeros elsewhere, and ΩH is
the (k + md )H × (k + md )H matrixΩ11 Ω12 · · · Ω1H Ω21 Ω22 · · · Ω2H ΩH =  .
(S.11)..
..  .
..
 ..
.
.
. 
ΩH1 ΩH2 · · · ΩHH
The diagonal elements of ΩH , that is {Ωii }H
i=1 , are given by (S.7), while the off-diagonal elements canbe expressed asPi−1 `F ( `=0
C ΣC0` )C0(j−i) F 0 , i < j
P
Ωij =
0`
0
(i−j)
`
FC
( j−1
`=0 C ΣC )F , i > jwhere Σ is defined by (S.8).
Hence, the conditional point forecasts are given byµ∗h = µh + (s0hH ⊗Ik+md )ΩH (IH ⊗S0 )[(IH ⊗S)ΩH (IH ⊗S0 )]−1 qH .
S5

S.3.3

Probability distribution of the difference between conditional and unconditional forecasts

Let yT∗ +h be the values of yT +h conditional on a given GDP growth path scenario given byyT∗ +h = µh + ξ ∗T +h , h = 1, 2, ..., H,
where µh is given by (S.1) and ξ ∗T +h is the random variable defined by the probability distribution ofξ T +h conditional on (IH ⊗S)ξ H = qH , that isξ ∗T +h =Ph−1``=0 F C ET +h−` | (IH ⊗S)ξ H = qH .

(S.12)The difference between yT∗ +h and yT +h is given byδ T +h = yT∗ +h − yT +h = ξ ∗T +h − ξ T +h .
The mean difference isE (δ T +h |IT ) = µ∗h − µh .
The variance V (δ T +h | IT ) is given by
V (δ T +h | IT ) = V ξ ∗T +h | IT + V ξ T +h | IT− Cov ξ ∗T +h , ξ T +h | IT − Cov ξ T +h , ξ ∗T +h | IT ,(S.13)

V ξ ∗T +h | IT = Ω∗hh ,(S.14)

where15
with(S.15)Ω∗hh = Ωhh − (s0hH ⊗Ik+md )ΩH (IH ⊗S0 )[(IH ⊗S)ΩH (IH ⊗S0 )]−1 (IH ⊗S)ΩH (shH ⊗Ik+md ).
To obtain Cov ξ ∗T +h , ξ T +h | IT , recall that conditional on IT the (k + md ) × 1 random
 vector∗∗∗
ξ T +h defined by (S.12) is dependently distributed with mean µh and variance V ξ T +h | IT = Ω∗hh .
1/2

Let S0H = ΩH (IH ⊗S0 ) and note that (S.15) can alternatively be expressed as
1/2

1/2

1/2

Ωhh − Ω∗hh = (s0hH ⊗Ik+md )ΩH PH ΩH (shH ⊗Ik+md ),where PH = S0H (SH S0H )−1 SH is a projection matrix which is symmetric, idempotent and positive
semi-definite. Hence, Ωhh − Ω∗hh is a positive semi-definite matrix.
Under the assumption that16

(S.16)Cov ξ T +h , ξ ∗T +h | IT = V ξ ∗T +h | IT ,using this result in (S.13) the variance of δ T +h is derived as
V (δ T +h | IT ) = Ωhh − Ω∗hh ≥ 0,
which is a positive semi-definite matrix. Hence,
δ T +h | IT ∼ N (µ∗h − µh , Ωhh − Ω∗hh ).

(S.17)

References
Pesaran, M.H., Smith, L.V., Smith, R.P. (2007). What if the UK or Sweden had joined the Euro in
1999? An empirical evaluation using a global VAR. International Journal of Finance and Economics,
12, 55-87.
15
16

The dependence of Ω∗hh on H is suppressed here.
For a similar assumption and related argument see Pesaran et al. (2007).

S6

S.3.3

Probability distribution of the difference between conditional and unconditional forecasts

Let yT∗ +h be the values of yT +h conditional on a given GDP growth path scenario given byyT∗ +h = µh + ξ ∗T +h , h = 1, 2, ..., H,
where µh is given by (S.1) and ξ ∗T +h is the random variable defined by the probability distribution ofξ T +h conditional on (IH ⊗S)ξ H = qH , that isξ ∗T +h =Ph−1``=0 F C ET +h−` | (IH ⊗S)ξ H = qH .

(S.12)The difference between yT∗ +h and yT +h is given byδ T +h = yT∗ +h − yT +h = ξ ∗T +h − ξ T +h .
The mean difference isE (δ T +h |IT ) = µ∗h − µh .
The variance V (δ T +h | IT ) is given by
V (δ T +h | IT ) = V ξ ∗T +h | IT + V ξ T +h | IT− Cov ξ ∗T +h , ξ T +h | IT − Cov ξ T +h , ξ ∗T +h | IT ,(S.13)

V ξ ∗T +h | IT = Ω∗hh ,(S.14)

where15
with(S.15)Ω∗hh = Ωhh − (s0hH ⊗Ik+md )ΩH (IH ⊗S0 )[(IH ⊗S)ΩH (IH ⊗S0 )]−1 (IH ⊗S)ΩH (shH ⊗Ik+md ).
To obtain Cov ξ ∗T +h , ξ T +h | IT , recall that conditional on IT the (k + md ) × 1 random
 vector∗∗∗
ξ T +h defined by (S.12) is dependently distributed with mean µh and variance V ξ T +h | IT = Ω∗hh .
1/2

Let S0H = ΩH (IH ⊗S0 ) and note that (S.15) can alternatively be expressed as
1/2

1/2

1/2

Ωhh − Ω∗hh = (s0hH ⊗Ik+md )ΩH PH ΩH (shH ⊗Ik+md ),where PH = S0H (SH S0H )−1 SH is a projection matrix which is symmetric, idempotent and positive
semi-definite. Hence, Ωhh − Ω∗hh is a positive semi-definite matrix.
Under the assumption that16

(S.16)Cov ξ T +h , ξ ∗T +h | IT = V ξ ∗T +h | IT ,using this result in (S.13) the variance of δ T +h is derived as
V (δ T +h | IT ) = Ωhh − Ω∗hh ≥ 0,
which is a positive semi-definite matrix. Hence,
δ T +h | IT ∼ N (µ∗h − µh , Ωhh − Ω∗hh ).

(S.17)

References
Pesaran, M.H., Smith, L.V., Smith, R.P. (2007). What if the UK or Sweden had joined the Euro in
1999? An empirical evaluation using a global VAR. International Journal of Finance and Economics,
12, 55-87.
15
16

The dependence of Ω∗hh on H is suppressed here.
For a similar assumption and related argument see Pesaran et al. (2007).

S6