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University of Pisa

Sant’Anna School of Advanced Studies

Master of Science in Economics

Long-run and short-run response of

Economic and Environmental indicators to

pandemics and other global shocks

Supervisor:

Dr Francesco Lamperti

By:

Rezvan Derayati

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Abstract

Global shocks, such as pandemics and wars, have an inevitable impact on the economic and environmental indicators. Consequently, the COVID-19 pandemic

has raised many questions about its cross-country effect on the shape

of economic recovery and its environmental sustainability. This research studies the short and long-term response of GDP per capita, CO2 emission per capita, and CO2 emission intensity to pandemics and other major global shocks such as wars and financial crises by utilizing the local projection approach. This study shows that for most analyzed countries, the end of a pandemic positively impacts both output and emissions in the short run, with the latter responding sharper than the former. However, a reverse response to pandemics in a few countries like the USA and Canada is observable, which contrasts with the

evidence emerging within Europe. also, we found that the end

of a war produces a similar impact as a pandemic on emission intensity, while for financial crises the evidence is mixed. The positive response of emission indicators to the end of a pandemic eventually leads us to conclude that the recovery from the current pandemic is likely to be brown for Europe, unless policy packages that are different from historical post-crisis interventions are undertaken. The European Green Deal appears promising in this respect.

Keywords: pandemics, wars, financial crises, GDP per capita, CO2 emission per capita, CO2

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Acknowledgments

This work would not have seen the light without the precious supervision of Pro-fessor Francesco Lamperti, to whom I reserve my sincere gratitude. Your insight-ful feedback pushed me to sharpen my thinking and brought my work to a higher level.

I dedicate this work to my family, Mr Vahid Zolfaghari, Dr Ehsan Derayati and my parents. I would like to appreciate your guidance and sympathetic ear. The little steps I achieved would not be possible without your support.

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Table of Contents

Abstract ... 2

Acknowledgments... 3

Introduction ... 11

Literature Review... 13

Materials and Methods ... 25

Data Consistency Check ... 27

Methodology ... 37

Results ... 42

Discussion ... 57

Effect of a Pandemic or War on GDP per capita ... 57

Effect of Financial crisis ... 60

The impulse response of CO2 emission per capita and CO2 intensity ... 60

The impulse response of a panel model ... 61

Study of the developing countries... 61

Conclusion ... 62

References ... 64

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Table of tables

Table 1 ... 26 Table 2 ... 27 Table 3 ... 28 Table 4 ... 28 Table 5 ... 32 Table 6 ... 34

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Table of figures

Figure 1 ... 15 Figure 2 ... 18 Figure 3 ... 20 Figure 4 ... 21 Figure 5 ... 22 Figure 6 ... 29 Figure 7 ... 30 Figure 8 ... 30 Figure 9 ... 31 Figure 10 ... 31 Figure 11 ... 36 Figure 12 ... 36 Figure 13 ... 36 Figure 16 ... 43 Figure 17 ... 43 Figure 18 ... 43 Figure 19 ... 44 Figure 20 ... 44 Figure 21 ... 44 Figure 22 ... 45 Figure 23 ... 45 Figure 24 ... 46 Figure 25 ... 46 Figure 26 ... 46 Figure 27 ... 47 Figure 28 ... 47 Figure 29 ... 47 Figure 30 ... 48 Figure 31 ... 49 Figure 32 ... 49 Figure 33 ... 50 Figure 34 ... 50 Figure 35 ... 51 Figure 36 ... 51 Figure 37 ... 52 Figure 38 ... 53

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Figure 39 ... 53 Figure 40 ... 54 Figure 41 ... 54 Figure 42 ... 55 Figure 43 ... 55 Figure 44 ... 56 Figure 45 ... 56 Figure 46 ... 59 Figure 47 ... 68 Figure 48 ... 69 Figure 49 ... 69 Figure 50 ... 70 Figure 51 ... 70 Figure 52 ... 71 Figure 53 ... 71 Figure 54 ... 72 Figure 55 ... 72 Figure 56 ... 73 Figure 57 ... 73 Figure 58 ... 74 Figure 59 ... 74 Figure 60 ... 75 Figure 61 ... 75 Figure 62 ... 76 Figure 63 ... 76 Figure 64 ... 77 Figure 65 ... 77 Figure 66 ... 78 Figure 67 ... 78 Figure 68 ... 79 Figure 69 ... 79 Figure 70 ... 79 Figure 71 ... 79 Figure 72 ... 80 Figure 73 ... 80 Figure 74 ... 80 Figure 75 ... 80 Figure 76 ... 81

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Figure 77 ... 81 Figure 78 ... 81 Figure 79 ... 82 Figure 80 ... 82 Figure 81 ... 82 Figure 82 ... 82 Figure 83 ... 83 Figure 84 ... 83 Figure 85 ... 83 Figure 86 ... 83 Figure 87 ... 84 Figure 88 ... 84 Figure 89 ... 84 Figure 90 ... 85 Figure 91 ... 85 Figure 92 ... 85 Figure 93 ... 85 Figure 94 ... 86 Figure 95 ... 86 Figure 96 ... 86 Figure 97 ... 86 Figure 98 ... 87 Figure 99 ... 87 Figure 100 ... 87 Figure 101 ... 88 Figure 102 ... 88 Figure 103 ... 88 Figure 104 ... 88 Figure 105 ... 89 Figure 106 ... 89 Figure 107 ... 89 Figure 108 ... 89 Figure 109 ... 90 Figure 110 ... 90 Figure 111 ... 90 Figure 112 ... 91 Figure 113 ... 91 Figure 114 ... 91

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Figure 115 ... 91 Figure 116 ... 92 Figure 117 ... 92 Figure 118 ... 92 Figure 119 ... 92 Figure 120 ... 93 Figure 121 ... 93 Figure 122 ... 94 Figure 123 ... 94 Figure 124 ... 95 Figure 125 ... 95 Figure 126 ... 96 Figure 127 ... 96 Figure 128 ... 97 Figure 129 ... 97 Figure 130 ... 98 Figure 131 ... 98 Figure 132 ... 99 Figure 133 ... 99 Figure 134 ... 100 Figure 135 ... 100 Figure 136 ... 101 Figure 137 ... 101 Figure 138 ... 102 Figure 139 ... 102 Figure 140 ... 103 Figure 141 ... 103 Figure 142 ... 104 Figure 143 ... 104 Figure 144 ... 105 Figure 145 ... 105 Figure 146 ... 106 Figure 147 ... 106 Figure 148 ... 107 Figure 149 ... 107 Figure 150 ... 108 Figure 151 ... 108 Figure 152 ... 109

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Figure 153 ... 109 Figure 154 ... 110 Figure 155 ... 110 Figure 156 ... 111 Figure 157 ... 111 Figure 158 ... 112 Figure 159 ... 112 Figure 160 ... 113 Figure 161 ... 113 Figure 162 ... 114 Figure 163 ... 114 Figure 164 ... 115 Figure 165 ... 115 Figure 166 ... 116 Figure 167 ... 116 Figure 168 ... 117 Figure 169 ... 117 Figure 170 ... 118 Figure 171 ... 118 Figure 172 ... 119 Figure 173 ... 119 Figure 174 ... 120

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Introduction

Climate change is one of the most deleterious phenomena in our era. It is primarily driven by Greenhouse gases, specifically CO2. A bi-directional causality exists between carbon emissions and economic growth[1]. Although economic activities and growth may increase environmental damage through prodigal resource use, at the same time, higher levels of development may also decrease ecological degradation.

Moreover, the start of pandemics has an undeniable negative effect on the economy. Specifically, it is believed that the COVID-19 pandemic causes a severe recession in the global economy, which is the worst global economic crisis since the Great Depression[2]. As we will see in the literature review, several studies have examined pandemics’ effect on GDP growth. Still, it can be informative to consider the pandemics’ consequences not only on economic indicators but also on environmental ones. As a result of the attempt to compensate for the economic loss, the CO2 can be emitted more compared with the pre-crisis condition[2]. Hence, there can be a correlation between pandemic and CO2 emission. To serve this assumption, investigating the behavior of CO2 emission in history will be helpful. It will eventually lead us to a reliable hint on whether the recovery from the current pandemic will be green or brown.

This dissertation investigates the short and long-term responses of GDP per capita, CO2 emission per capita, and CO2 intensity to the major global shocks, specifically pandemic shocks as exogenous shocks. Also, we check the existence of differences between the impact of pandemics and other major events such as wars, financial crises and major political events on these environmental and economic indicators by a macroeconometrical analysis.

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To do so, we will use the following data sets:

1) The first panel data, which contains 17 developed countries from 1870 to 2014, has been built based on merging the succeeding data resources: Jordà-Schularick-Taylor Macrohistory Database[3] & Carbon Dioxide In-formation Analysis Center (CDIAC)[4].

2) The second panel data covers 156 developed and developing countries be-tween 1950 and 2014. We built it by merging ensuing resources: PWT 9.1[5]& Carbon Dioxide Information Analysis Center (CDIAC)

Local Projection is the methodology applied in this study to calculate the impulse response of the economic and environmental indicators to a global shock.

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Literature Review

This part will review the literature on the impact of global shocks, specifically Pandemics and lockdown, on the economy and its environmental consequences at the country level. In this section, I will discuss the summary of works that have been done, but first, I provide a brief categorization of the literature that exists in this field:

• The long-run effect of pandemics on economic indices.

• The consequence of financial crisis on environmental indices - CO2 emis-sion.

• The effect of reducing economic activity – as one of the lockdown’s con-sequences- on the consumption of different energy resources.

• Socio-economic consequences of lockdown in developing countries. • The consequence of pandemic on sectoral economic activity in Asian

countries. Many studies have been done on India about the effect of lock-down on Indian's economy and energy usage as well as environmental indices such as CO2 emission and improvement of air quality. Our work will not cover sectoral analysis, but these works will complement this sec-tion and the analysis of Asian countries.

• Some articles discussed the predicted scenarios of the evolution of the cur-rent pandemic and further policy-making.

Jordà et al. (2020) studied the major pandemics using the rates of return on assets stretching back to the 14th century is explored in the paper "Longer-run economic consequences of pandemics" [6]. In this study, it is stated that the consequence of pandemic will remain for a long time, while real rates of return substantially depressed, which is in contrast with wars' impact. They asserted that this finding is congruous with the neoclassical growth model; in a pandemic, the capital is not

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destroyed as it happens in war, rather than pandemic causes labour shortage and/or move to greater precautionary savings.

In the same research line, Chudik et al. (2020) developed a threshold-augmented dynamic multi-country model designed to measure the pandemic's macroeconomic impacts [7]. The existence of threshold effects in the relationship between output growth and excess global volatility in most advanced economies and several emerging markets at the individual level shows the presence of threshold effects. Afterward, an estimation of a more general multi-country model has been performed. This model was enhanced with these threshold effects as well as long-term interest rates, oil prices, exchange rates, and equity returns to perform counterfactual analyses.

So far, we have looked into the impacts of pandemics on economic variables; now, we will review the studies of the impact of the COVID-19 pandemic on environmental indicators. Shan et al. (2020) investigated the effect of the COVID-19 pandemic and fiscal stimulus plan on the CO2 emission and the Paris agreement[8]. They asserted that the reduction in energy consumption and CO2 emission will occur because of the COVID-19 lockdowns. On the other hand, the countries are designing fiscal stimulus plan to recover the economy, and these policies can increase the global emission by 0.74% from 2020 to 2024. Although these policies can worsen the climate change issue, they might also lead to a zero greenhouse gas emission target. These outcomes directly depend on the investment strategy. In the “green” scenario and by investing in clean energy sectors and high-tech industries with low-carbon technologies, the emission will decline by 4.7%; in contrast, in the carbon-intensive scenario, the emission will increase 16.4%. Hence, the countries’ fiscal stimulus plans play a critical role in the post-covid environmental conditions.

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To further complete the picture of previous studies, we explore the research study on the effect of the political and financial crisis on environmental indicators, specifically the CO2 emission. It will be informative since pandemics can have a negative impact on economic activity, likewise the financial crises. And, as it is already mentioned, there can be a positive correlation between CO2 emission and economic activity. Petetrs et al. (2012) discussed the effect of the global financial crisis on CO2 emission in the paper "Rapid growth in CO2 emissions after the 2008–2009 global financial crisis" [9]. They have mentioned that the variations in GDP are smaller than variations in CO2 emissions over time. Since 1970, a negative growth of global GDP has been observed in one year (2009), while negative CO2 emission growth can be seen in ten disparate years. This study also asserted that in times of crisis, countries change their policy toward less energy-intensive activities. As stated in this paper, since the 1960s, the trajectory of global fossil fuel and industrial CO2 emissions has significantly changed (Figure 1).

Figure 1

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The oil crises in the 1970s had a severe impact on prices and changed the structure of energy production and consumption. These events substituted the global reliance on oil for natural gas, leading to less CO2 emission. Moreover, an emission reduction occurred in the early 1990 and 1997, owes to economic downturns and significant political events, not to change in energy consumption. The critical point is that the drop in emissions was long-lasting in the earlier economic crises. Still, the 2008 global financial crisis had a strong but transient impact on GDP decline and resulted in a short-lasting CO2 emission decline, as it can be seen in Figure 1, emission rebounded in 2010.

However, it is worth noting that the CO2 emission driven by fossil-fuel, during and after the COVID-19 pandemic can represent different trends due to a remarkable reduction in oil price during this current pandemic.

On the other hand, as reported by International Energy Agency (IEA) in their Global Energy Review 2020, the impact of the decline in economic activity on energy use is highly asymmetrical, and it depends on the specific energy use pattern[10]. This study suggests that each sector will react differently to the pandemic and select disparate energy consumption policies. It is important to note that traditional relationships between economic output and energy demand will vary due to the shock's characteristic. For energy usages like gas consumption for heating in a residential building, electricity usage for computing servers, and digital equipment, the consumption remained constant or even increased. In contrast, for cases like aviation fuel, a significant reduction was observed, even steeper than the GDP decline.

Moreover, the restrictions on mobility, social and economic activity for months cause a global recession. The represented scenario in this report determined the consequences of this recession on Energy usage. Based on this scenario, the economic and social activities in lockdown will open and resume only gradually. Despite macroeconomic policy efforts, the U-shaped economic recovery goes

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along with a considerable lasting loss of economic activity. Considering these situations, they foresaw a 6% decline in global GDP in 2020, which IMF longer outbreak case released in April confirmed this scenario. Like each pandemic, in this current situation, we face many uncertainties surrounding the economic point of view, such as the pandemic's behaviour, lockdown strategies, and the shape and speed of recovery as the pandemic fades. In this report, it is claimed that on the positive side, we can have V-shaped economic recovery through a limited period of lockdown which can have an effective suppression of the virus and then a gradual but speedy releasing of lockdown accompanied by effective and ambitious macro-financial policies. This outlook is broadly in line with the IMF baseline presented in April 2020. But as time goes by, we can observe that this optimistic viewpoint has not been realized. As mentioned in the report, more hazard risks were predicted, such as the possibility of more extended lockdown periods, reopening that may lead to spikes of infections, the second wave of the pandemic in the autumn/winter of 2020, and significant global supply chain disruptions. These possible risks are realized as many countries faced a third or even fourth peak of COVID-19 at least until March 2021.

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Figure 2 illustrates the path of recovery in three scenarios.

Figure 2

illustrative path of recovery in the three scenarios, Quarterly output gap (% of long-run GDP)[11]

It could be useful to check whether the lockdown has a homogeneous effect on environmental indices across different regions. Kumar & Managi (2020) answered this question for Indian regions in the paper "Does Stringency of Lockdown Affect Air Quality? Evidence from Indian Cities" [12]. This study shows that air quality improvement has ensued from particular actions, but it is not uniform across cities and pollutants. It is stated that regardless of the tangible positive effect of lockdown on weather's quality, there is still a gap until reaching the level of emission, which is determined by WHO standards. This outcome highlights the importance of environmental sustainability policy.

The diverse socio-economic consequences of pandemics on disparate regions is another aspect that should not be neglected. Bruckner & Mollerus (2020) mentioned the detrimental effects of Covid-19 for the least developed countries

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(LDCs) [13]. Since health systems in developing countries may be unable to cope with a precipitous increase in infections, they might also lack the resources to cope with lockdowns' socio-economic consequences. Although international communities have taken some policies to aid them, these actions do not seem sufficient to combat this pandemic's devastating impact. So, the Sustainable Development Goals (SDGs) by the 2030 deadline will be less likely to happen. Despite having the mentioned severe effect of a pandemic on the health sector in developing countries, its impact on economic indicators is more significant for developed countries. The following charts represent the economic impact of global shocks, specifically pandemic, on several countries and sectors.

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As shown in Figure 3, both advanced economies and emerging markets and developing economies are in a recession. Major economies have been significantly downgraded.

Figure 3

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Figure 4 shows the growth projection and effect of the pandemic on economic growth across regions all over the world.

Figure 4

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Figure 5 demonstrates how pandemics can have disparate consequences across different sectors in the Euro area.

Figure 5

The indictive current reduction in economic activity as a result of the lockdown in the Euro area, Share of total Gross Value-Added[11]

As previously discussed, several scenarios can be defined for the economic recovery of the COVID pandemic. In the paper "The Economic Impact of the COVID-19 Outbreak on Developing Asia," a range of scenarios and pandemic impacts are investigated for the People's Republic of China (PRC), some developing countries in Asia, and other continents as well as sectors within these economies. It is stated that the current shocks influence these countries through several ways, including "sharp declines in domestic demand, lower tourism and business travel, trade and production linkages, supply disruptions, and health effects". This study estimates a global impact of $77 billion to $347 billion or 0.1% to 0.4% of global GDP, with a moderate case estimate of $156 billion or 0.2% of global GDP[15]. To assess the magnitude of the outbreak's economic impact, we need to know the epidemic trend, which is mainly remained opaque. So, to reduce this ambiguity, a range of scenarios along with the impacts

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stemming from these scenarios should be investigated, and these scenarios should also be revised, as needed.

Following the previous studies, McKibbin & Fernando (2020) explored the devastating consequences of COVID-19 on the Chinese economy [16]. The massive uncertainty in different aspects such as the evolution of the disease and its economic influences complicates macroeconomic policy-making, and perhaps the outcome will not be reliable. Hence, this paper introduced seven scenarios about the evolution of pandemic using a modelling technique developed by Lee & McKibbin (2003) and extended by McKibbin & Sidorenko (2006). This technique assesses the response of macroeconomic outcomes and financial markets to each scenario in a global hybrid DSGE/CGE general equilibrium model.

The scenarios described in this paper showed that an outbreak can profoundly affect the global economy in a short time. They also demonstrated that investing more in the public health sector can reduce the costs in all economies. This reduction will be more significant for developed economies with less advanced health care systems and very condense areas.

In some studies, the economic consequences of pandemics have been analyzed by Input-Output (IO) model. Kanitkar (2020) demonstrated the use of a linear Input-Output model to estimate the economic losses in India due to COVID-19 in the paper "The COVID-19 lockdown in India: Impacts on the economy and the power sector" [17]. Based on this paper's results- depending on the length of lockdown- economic loss of about 10-31% of India's GDP is predicted. This method can be used to evaluate economic losses for other regions. The paper also discusses the pandemic consequences on the power sector’s demand and supply of electricity and CO2 emission. This paper's results indicate that depending on the lockdown duration, daily supply from coal-based power plants diminishes by 26%, leading to a possible reduction in emission of about 15–65 Mt CO2. It should

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be mentioned that in our research, we will not use the input-output model to analyze the consequence of pandemics on the economy and environment.

In a more general approach, Mandel & Veetil (2020) estimated the lockdown's cost in some sectors of the world economy[18]. In this paper, a multi sector disequilibrium model with buyer-seller relations between agents in different countries is developed. Applying this model makes it possible to study not only the direct cost emerging from lockdown but also the indirect cost which comes out from the reduction in available intermediate inputs. This model is calibrated to the world economy by exploiting input-output data for 56 industries in 44 countries, and it is stated that at the early stage of the pandemic, when the only country under lockdown was China, 7% of the world output diminished, while at the first peak of the pandemic when many countries were under a lockdown, 23% of the reduction was observed. Due to the relations of buyer-seller, as the pandemic spreads all over the world, the shock also spread across the world economy. The authors also investigated the process of economic recovery after lockdowns. They declare that the needed time for economic recovery will decrease by price flexibility and minor technological adaptations. They estimated that it would take one quarter after ending lockdowns to move towards the world economy's new equilibrium in an optimistic scenario. Since we have faced many partial and enduring lockdowns all over the world, this scenario does not seem attainable.

All in all, I have not seen a compact and comprehensive work on our selected topic for cross-country analysis in the economic as well as environmental consequences of the pandemic. This literature review helps to enlighten the framework of the work. Still, since this phenomenon has recently emerged, no similar study or report is done in our determined framework so far.

Our research will study the aggregation and also the country-specific response of GDP per capita, CO2 emission per capita, and CO2 emission intensity to pandemic

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shocks for several developed and developing countries by local projection methodology. Moreover, the effect of other global and regional major external shocks on these indicators will be investigated as control variables or independently.

Materials and Methods

After reviewing existing literature, this section covers the databases, the descriptive statistics of data and methodology.

In our study, three datasets are employed and merged with each other, to have the desirable data frames. So, two panel data have been built: the first one contains developed countries for an extended period of time. The second panel data includes more developed as well as developing countries for a shorter period -due to scarcity of data for initial years.

The first panel data, which contains 17 countries from 1870 through 2014, has been built by merging the succeeding data resources: Jordà-Schularick-Taylor Macrohistory Database [3] and Carbon Dioxide Information Analysis Center (CDIAC)[4]. This dataset encompasses the economic and environmental indica-tors for the countries in Table 1, with the following columns:

• Real GDP per capita (PPP)1

• GDP (nominal, local currency) 2

• Total CO2 emissions from fossil fuels and cement production (thousand metric tons of C)

• Emissions from bunker fuels (not included in the totals) • CO2 emissions per capita (metric tons of carbon)

1 Called rgdpmad on dataset 2 Called gdp on dataset

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• CO2 intensity for each country is also defined by the ratio of total CO2

emission3 divided by GDP per capita multiply by population.

Australia Belgium Canada Denmark Finland

France Germany Italy Japan Netherlands

Norway Portugal Spain Sweden Switzerland

United Kingdom United States

Table 1

All countries in the first panel data

The second panel data covers 156 countries for the time interval of 1950-2014. It has been built by merging the following resources: PWT 9.1[5] and Carbon Dioxide Information Analysis Center (CDIAC) using SQL server. This panel data contains the economic and environmental indicators of:

• Output-side real GDP at chained PPPs (in mil. 2011US$) 4

• Real GDP at constant 2011 national prices (in mil. 2011US$)5

• Total CO2 emissions from fossil-fuels Cement production (thousand metric tons of C)

• Emissions from bunker fuels (not included in the totals) • Per capita CO2 emissions (metric tons of carbon)

• CO2 intensity for this panel data is defined as the ratio of total CO2

emis-sion6to Output-side real GDP at chained PPPs. A limited number of

coun-tries were selected to have a more concentrated study, as shown in Table 2.

3 Total CO2 emission is defined as the sum of Total CO2 emissions from fossil-fuels and cement production and

Emissions from bunker fuels

4 Called rgdpo in original dataset 5 Called rgdpna in original dataset

6 Total CO2 emission is calculated as the sum of Total CO2 emissions from fossil-fuels and cement production

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Data Consistency Check

Since two different datasets were employed, we did a consistency check on eco-nomic indicators. In the first dataset, the variable is rgdpmad- which is real GDP per capita (ppp)- and in second panel data, we defined real GDP per capita by calculating the ratio of rgdpo- which is Output-side real GDP at chained PPPs (in mil. 2011US$)- to population. Then the resulting GDP per capita of these two panel data were compared. Not surprisingly, the values were not equal due to different chosen base years, definition and calculation methods, and so on. So, we checked the growth rate of GDP per capita by long average and log growth, and the results for both datasets were significantly close to each other. For exam-ple, from 1950 to 2014, the first panel data shows a 260.8 % increase in the USA’s GDP and based on the second panel data, this value is 260.3%. Hence, the ob-tained result based on these datasets will be reliable.

Table 2

Selected countries of the second panel data

In Jordà et al. (2020)[6], the wars were considered in the analysis to remove omitted variable bias. In our research, besides pandemics and wars, which were investigated in Jordà et al. (2020)[6], we also considered significant financial crises and significant political events to have a more comprehensive analysis. Time-series data were plotted to have a visual intuition about the data. First of all, the data were normalized to make the scale of different variables consistent with each other regardless of their initial scales and units. The applied normalization method is, considering data for the year 2000 as the base value and plots all the values accordingly. The previous pandemics, deadliest war, and

Continents Countries

Asia & Oceania China India Indonesia Iran Saudi Arabia Turkey New Zealand

Africa Egypt Morocco South Africa

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significant economic crisis or political events were determined as vertical lines to have a better historical vision. Table 3 contains the significant pandemics in history.

Pandemic End year Deaths

Black Death 1353 75000000

Great Plague of London 1666 100000

First Asia Europe Cholera Pandemic 1826 100000

Second Asia Europe Cholera Pandemic 1851 100000

Russia Cholera Pandemic 1860 1000000

Global Flu Pandemic 1890 1000000

Sixth Cholera Pandemic 1923 800000

Encephalitis Lethargic Pandemic 1926 1500000

Spanish Flu 1920 50000000

Asian Flu 1958 2000000

Hong Kong Flu 1969 1000000

H1N1 Pandemic 2010 203000

Table 3 Pandemics

Table 4 contains all significant events other than pandemics:

Event End year

World War I 1918 Sino-Japanese war 1945 World War II 1945

The Great Depression 1933

Korean War 1953 Vietnam War 1975

The International Debt Crisis 1982 Dissolution of The Soviet Union 1991 The East Asian Crisis 1999 The Great Recession 2009

Table 4

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As a sample, a combined figure for United States, United Kingdom, India, and China is plotted (Figure 6). For the sake of consistency, I used the second panel data for all countries mentioned above for the time interval between 1950 to 2014. Moreover, based on the first panel data, Italy (Figure 7), USA (Figure 8) are presented separately. Brazil (Figure 9) and Iran (Figure 10) are also plotted separately based on second panel data. The plots of other countries can be found in Appendix I. As it can be seen, different colors are used for various events (vertical lines) based on Table 3 and Table 4.

Figure 6

Normalized data based on the year 2000, along with significant events and pandemics (Table 3 and Table 4) for selected developed and developing countries

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Figure 7

Normalized data based on the year 2000, along with significant events and pandemics (Table 3 and Table 4 4) for Italy

Figure 8

Normalized data based on the year 2000, along with significant events and pandemics (Table 3 and Table 4 4) for the USA

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Figure 9

Normalized data based on the year 2000, along with significant events and pandemics (Table 3 and Table 4) for Brazil

Figure 10

Normalized data based on the year 2000, along with significant events and pandemics (Table 3 and Table 4 4) for Iran

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For the sake of clarity and comparison, the values of GDP per capita, CO2

emission per capita, and CO2 intensity, for the initial and final year are shown in Table 5.

GDP per capita

CO2 emission (thousand metric tons of C)

per capita CO2 intensity

1870 2014 1870x` 2014 1870 2014 Australia 3273.239 26817.47 0.09 4.42 0.03 0.16 Belgium 2691.523 25427.31 1.33 2.78 0.49 0.11 Canada 1694.525 28296.95 0.09 4.15 0.05 0.15 Denmark 2003.178 24091.6 0.17 1.87 0.08 0.08 Finland 1139.681 22796.94 0.01 2.47 0.01 0.11 France 1875.651 23503.82 0.36 1.35 0.19 0.06 Germany 1839.08 23583.27 0.51 2.53 0.28 0.11 Italy 1541.748 18809.77 0.03 1.53 0.02 0.08 Japan 737.3755 24560.64 0.000145 2.68 0 0.11 Netherlands 2755.225 26212.74 0.37 3.51 0.14 0.13 Norway 1360.14 30587.52 0.1 2.71 0.07 0.09 Portugal 975.04 14667.89 0.0014 1.29 0 0.09 Spain 1225.142 17586.86 0.05 1.59 0.04 0.09 Sweden 1345.063 27949.34 0.09 1.46 0.07 0.05 Switzerland 2875.65 27672.94 0.09 1.35 0.03 0.05 UK 3190.434 26831.24 1.99 1.98 0.62 0.07 USA 2444.644 34504.07 0.67 4.6 0.27 0.13 Table 5

The summary of utilized variables for the first panel data

As shown in Table 5, there is a significant growth in GDP per capita from 1870 to 2014 for all analyzed countries. Also, an incremental trend can be observed for CO2 emission per capita for all countries except the United Kingdom. This value has remained approximately constant for the UK. The greatest value for CO2 emissions per capita belongs to the US in 2014; however, Australia has the most considerable growth and stands the second rank for this variable. On the other hand, Portugal has the lowest value of CO2 emission per capita in 2014. In 1870,

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Japan had the lowest value of CO2 emissions per capita, and in contrast, the UK had the most significant value this year.

However, for CO2 intensity, which is calculated by dividing CO2 emission per capita by GDP per capita, an inconsistent trend is observed. The CO2 intensity has increased for some countries, namely, Australia, Canada, Finland, Italy, Japan, Norway, Portugal, Spain, Switzerland. On the other hand, the CO2 intensity declined in this period for Belgium, Denmark, France, Germany, Netherlands, Sweden, UK, and the US. Japan had the lowest value of CO2 intensity for the year 1870, while the lowest values for the year 2014 belong to Switzerland and Sweden. On the other hand, the greatest value for CO2 intensity for the year 1870 belonged to the UK, while the greatest one in 2014 was recorded for Australia. It is worth mentioning that an 88% reduction in CO2 intensity for the UK has happened within this time interval.

Table 6 contains the data of selected countries in our second panel data. Since there is a difference between the economic indices in the first and second panel data, the developed countries in the first panel data are also considered in this table based on their value in the second database to have a more comprehensive vision. We defined GDP per capita, CO2 emission per capita, and CO2 intensity for the second panel data to make two tables comparable.

From 1950 to 2014, for all the developed and developing countries in Table 6, considerable growth in GDP per capita can be observed. Also, CO2 emission per capita for all countries_ except the United Kingdom_ shows an incremental trend. The data of 2014 presents that Norway and Switzerland had the highest values for GDP per capita, respectively. On the other hand, India had the lowest GDP per capita between the countries above in 2014. In 1950, the highest value for GDP per capita belonged to the US and Switzerland, and in contrast, Egypt and India had the lowest value.

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GDP per capita CO2 emission (thousand metric

tons of C) per capita CO2 intensity

1950 2014 1950 2014 1950 2014 Australia 12283 46920.89 1.87 4.36 0.146 0.09 Belgium 7139.939 38166.57 2.44 2.79 0.338 0.06 Brazil 1509.882 15032.2 0.11 0.73 0.067 0.048 Canada 11186.32 45044.28 3.13 4.14 0.273 0.092 Colombia 3118.052 12769.83 0.17 0.51 0.056 0.038 Denmark 9305.479 43040.76 1.41 1.86 0.151 0.038 Egypt 626.4874 10579.69 0.13 0.62 0.202 0.057 Finland 5839.875 36899.66 0.45 2.47 0.077 0.065 France 6849.999 37070.69 1.32 1.34 0.19 0.034 Italy 4076.805 34683.63 0.25 1.54 0.06 0.043 India 841.3993 5434.344 0.05 0.48 0.059 0.087 Japan 2530.666 37741.94 0.34 2.65 0.132 0.069 Mexico 4487.799 17223.21 0.31 1.08 0.067 0.062 Morocco 1252.444 7502.529 0.08 0.49 0.06 0.064 Netherlands 7835.4 45789.12 1.5 3.54 0.178 0.059 New Zealand 10702.95 34784.37 1.32 2.27 0.115 0.06 Norway 8509.987 75652.12 0.75 2.63 0.083 0.034 Portugal 2649.24 25493.19 0.2 1.3 0.072 0.047 South Africa 5200.716 11925.56 1.27 2.5 0.235 0.206 Spain 3432.326 31177.77 0.37 1.58 0.094 0.044 Sweden 9136.641 41033.58 1.13 1.44 0.122 0.03 Switzerland 14172.44 63067.49 0.61 1.33 0.043 0.019 Turkey 3101.533 23675 0.13 1.26 0.041 0.052 UK 9354.296 37147.69 2.83 1.93 0.292 0.048 USA 14569.09 52504.05 4.5 4.61 0.306 0.086 China NA 11808.16 NA 2.03 NA 0.17 Iran NA 16011.7 NA 2.31 NA 0.14 Table 6

The summary of utilized variables for the second panel data

In 2014, for CO2 emissions per capita, India, Colombia, and Egypt showed the lowest value between these aforementioned countries. In contrast, Australia and the US have the highest amount of CO2 emission per capita; furthermore, it is

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worth mentioning that the US, India, and Japan emitted a tremendous amount of total CO2 emission in 2014. In the initial year, Morocco and Brazil recorded the lowest value for CO2 emission per capita, while, similar to 2014, the US emitted the most significant amount of CO2 per capita in 1950.

Unlike Table 5, for all countries excluding Turkey, there is a reduction in CO2 emission intensity between 1950-2014. Data shows the greatest decline for CO2 intensity for the UK and France by respectively 84% and 82% reduction. The most significant CO2 intensity is recorded for South Africa, China and Iran in 2014, while in 1950, South Africa occupied the fifth place after countries respectively, Belgium, the USA, the UK, and Canada. The lowest value for CO2 intensity in 1950 belongs to Turkey; however, in 2014, Switzerland had the lowest value of CO2 intensity.

In the following charts, an overview of the interest indices- GDP (output side) per capita, CO2 emission per capita, and CO2 intensity- is displayed from 1950 to 2014 for developed and developing countries mentioned above. In order to have comparability between data and avoid any measurement error or biasedness, Figure 11, Figure 12 and Figure 13 were plotted based on the data of the second panel data.

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Figure 12

Comparison of GDP per capita between Developed and Developing countries

1950 2014 0 10000 20000 30000 40000 50000 GDP per capita Developing Developed 1950 2014 0 0.5 1 1.5 2 2.5

CO2emission per capita

Developing Developed 1950 2014 0 0.05 0.1 0.15 0.2 CO2intensity Developing Developed Figure 11

Comparison of CO2 emission per capita between Developed and Developing countries

Figure 13

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Methodology

To calculate the economic and environmental indicators' response to a shock, we utilized the local projection methodology. Local projection approach estimates the autoregressive coefficients directly at each h-step ahead. Jordà introduced local Projection procedure to compute impulse responses without specification and estimation of the underlying multivariate dynamic system[19]. Local projection technique is a substitute for vector autoregressions (VARs), which is the traditional approach that estimates the propagating effect of structural shocks on the economic variables. Jordà (2005) affirmed that LPs have four advantages over VARs[19]. First, LPs can be easily estimated since they can be calculated based on simple linear regression techniques. Second, the estimated result by local projection is more robust to misspecification than the VARs approach. Third, the point or joint-wise analytical inference is easily conveyed. Forth, using LPs, compared with other techniques, makes the estimation of the highly non-linear experimentation much more convenient. Considering this explanation, we chose local projection as the technique to calculate impulse response to the shocks, since the methodology is more flexible in the case of misspecification and the obtained results will be more robust.

We utilized the lpirfs package in R to estimate impulse responses to a shock using LPs[20]. Philipp Adämmer, the author of the package, stated that there is no proper R package despite the thriving usage of this estimation methodology. Lpirfs is a flexible and fast package that can estimate and visualize IRF through LPs for a wide range of data sets. We used this package in our methodology to calculate the response of economic and environmental indicators to pandemic shocks.

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More specifically, we estimate linear impulse responses for an identified shock outside of the VAR for each country, using lp_lin_iv() function and estimate linear impulse responses for panel data by using lp_lin_panel() function in R. We investigated the response of GDP per capita, CO2 emission per capita, and CO2 intensity to, respectively, pandemic, war, and financial crisis from the year the shock ends to a future time 40 years later for developed and 20 years later for developing countries.

As the fundamental step for each part of the analysis, we chose lagged values of endogenous variable_ GDP per capita, CO2 emission per capita, or CO2 intensity_ as control variables. However, as mentioned in Jordà et al., 2020 [6], the estimation will be unbiased regardless of including or excluding these control variables. For the first stage of analysis on how the endogenous variables respond to a specific shock in the short and long term_ considering pandemic, war, or financial crisis as the shock variable_ we estimated the following equations:

𝐺𝐷𝑃𝑡+ℎ∗ = 𝛼ℎ+ 𝛽ℎ𝑆𝑡+ ∑ 𝜌𝑙ℎ𝐺𝐷𝑃𝑡−𝑙∗ 𝐿 𝑙=1 + 𝑒𝑡+ℎℎ ; ℎ = 1, … , 40; 𝐿 = 10 𝐶𝑂2𝑡+ℎ∗ = 𝛼ℎ+ 𝛽ℎ𝑆𝑡+ ∑ 𝜌𝑙ℎ𝐶𝑂2𝑡−𝑙∗ 𝐿 𝑙=1 + 𝑒𝑡+ℎℎ ; ℎ = 1, … , 40; 𝐿 = 10 𝐶𝑂2_𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑡+ℎ∗ = 𝛼ℎ+ 𝛽ℎ𝑆𝑡+ ∑ 𝜌𝑙ℎ𝐶𝑂2_𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑡−𝑙∗ 𝐿 𝑙=1 + 𝑒𝑡+ℎℎ ; ℎ = 1, … , 40; 𝐿 = 10

𝑆𝑡 refers to the specific shock in these equations, which is considered a dummy variable that is one if there is a shock ending in year 𝑡, and zero otherwise. The coefficient 𝛽ℎ represents the impact of the specified shock at time 𝑡 on the endogenous variable at time 𝑡 + ℎ.

In the next step, to remove the concerns about the presence of omitted variable bias, we estimated our model by considering other shocks as exogenous variables

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and their lagged values as controls in the model. For instance, we viewed pandemic as our interested shock (𝑆𝑡) and added financial crisis and wars as the other exogenous variables. We do so to ensure that the estimated values represent the main shock’s impact and avoid biased interpretation in our analysis. Hence, we calculated the impulse responses by adding the new control variables to our equations: 𝐺𝐷𝑃𝑡+ℎ∗ = 𝛼ℎ+ 𝛽ℎ𝑆𝑡+ 𝛾ℎ𝑜𝑡ℎ𝑒𝑟𝑠ℎ𝑜𝑐𝑘𝑡+ ∑ 𝜌𝑙ℎ𝐺𝐷𝑃𝑡−𝑙∗ 𝐿 𝑙=1 + ∑ 𝛿𝑙ℎ𝑜𝑡ℎ𝑒𝑟𝑠ℎ𝑜𝑐𝑘𝑡−𝑙∗ 𝐿 𝑙=1 + 𝑒𝑡+ℎℎ ; ℎ = 1, … , 40; 𝐿 = 10 𝐶𝑂2𝑡+ℎ∗ = 𝛼ℎ+ 𝛽ℎ𝑆𝑡+ 𝛾ℎ𝑜𝑡ℎ𝑒𝑟𝑠ℎ𝑜𝑐𝑘𝑡+ ∑ 𝜌𝑙ℎ𝐶𝑂2𝑡−𝑙∗ 𝐿 𝑙=1 + ∑ 𝛿𝑙ℎ𝑜𝑡ℎ𝑒𝑟𝑠ℎ𝑜𝑐𝑘𝑡−𝑙∗ 𝐿 𝑙=1 + 𝑒𝑡+ℎℎ ; ℎ = 1, … , 40; 𝐿 = 10 𝐶𝑂2_𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑡+ℎ∗ = 𝛼ℎ+ 𝛽ℎ𝑆𝑡+ 𝛾ℎ𝑜𝑡ℎ𝑒𝑟𝑠ℎ𝑜𝑐𝑘𝑡+ ∑ 𝜌𝑙ℎ𝐶𝑂2_𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑡−𝑙∗ 𝐿 𝑙=1 + ∑ 𝛿𝑙ℎ𝑜𝑡ℎ𝑒𝑟𝑠ℎ𝑜𝑐𝑘𝑡−𝑙∗ 𝐿 𝑙=1 + 𝑒𝑡+ℎℎ ; ℎ = 1, … , 40; 𝐿 = 10

Thus, in the case of exploring the consequences of a pandemic, the coefficients 𝛽ℎ and 𝛾ℎcorresponds to the impulse response of the GDP per capita, CO2 emission per capita and CO2 intensity at time 𝑡 + ℎ to, respectively, a pandemic and other shocks -war and financial crisis- in year 𝑡. We applied the same procedure for each shock and, afterwards, compared the results with the previous stage which we had not considered the other exogenous variables.

In the next step, we calculated the cumulative and non-cumulative impulse responses for our panel data which contains the economic and environmental indicators and the dummy variables representing major global shocks, namely pandemics, wars and financial crisis.

As was previously explained, we have two panel data; the first one encompasses the data of some developed countries from 1870 to 2014. The second one contains the data of several developed and developing countries from 1950 to 2014. Following the same approach as non-panel analysis, firstly, we only calculated each shock's impact without considering other exogenous shocks with contemporaneous or lagged impact. Afterwards, we added other shocks and their lagged values as control variables in the model. For estimating non-cumulative

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impulse response for panel data without considering other shocks as control variables, the regressions of

𝐺𝐷𝑃𝑖,𝑡+ℎ∗ = 𝑎𝑖ℎ+ 𝛽𝑖ℎ𝑆𝑡+ ∑ 𝜌𝑖,𝑙ℎ𝐺𝐷𝑃𝑡−𝑙∗ 𝐿 𝑙=1 + 𝜀𝑖,𝑡+ℎℎ ;ℎ= 1, … , 40; 𝐿 = 10 𝐶𝑂2𝑖,𝑡+ℎ∗ = 𝑎𝑖ℎ+ 𝛽𝑖ℎ𝑆𝑡+ ∑ 𝜌𝑖,𝑙ℎ𝐶𝑂2𝑡−𝑙∗ 𝐿 𝑙=1 + 𝜀𝑖,𝑡+ℎℎ ; ℎ = 1, … , 40; 𝐿 = 10 𝐶𝑂2_𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑖,𝑡+ℎ∗ = 𝑎𝑖ℎ+ 𝛽𝑖ℎ𝑆𝑡+ ∑ 𝜌𝑖,𝑙ℎ𝐶𝑂2_𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑡−𝑙∗ 𝐿 𝑙=1 + 𝜀𝑖,𝑡+ℎℎ ; ℎ = 1, … , 40; 𝐿 = 10 are calculated.

As it was explained, guaranteeing to avoid bias estimation, other shocks and their lags as control variables are included in the regression:

𝐺𝐷𝑃𝑖,𝑡+ℎ∗ = 𝑎𝑖ℎ+ 𝛽𝑖ℎ𝑆𝑡+ 𝛾𝑖ℎ𝑜𝑡ℎ𝑒𝑟𝑠ℎ𝑜𝑐𝑘𝑡+ ∑ 𝜌𝑖,𝑙ℎ𝐺𝐷𝑃𝑡−𝑙∗ 𝐿 𝑙=1 + ∑ 𝛿𝑖,𝑙ℎ𝑜𝑡ℎ𝑒𝑟𝑠ℎ𝑜𝑐𝑘𝑡−𝑙∗ 𝐿 𝑙=1 + 𝜀𝑖,𝑡+ℎℎ ; ℎ = 1, … , 40; 𝐿 = 10 𝐶𝑂2𝑖,𝑡+ℎ∗ = 𝑎𝑖ℎ+ 𝛽𝑖ℎ𝑆𝑡+ 𝛾𝑖ℎ𝑜𝑡ℎ𝑒𝑟𝑠ℎ𝑜𝑐𝑘𝑡+ ∑ 𝜌𝑖,𝑙ℎ𝐶𝑂2𝑡−𝑙∗ 𝐿 𝑙=1 + ∑ 𝛿𝑖,𝑙ℎ𝑜𝑡ℎ𝑒𝑟𝑠ℎ𝑜𝑐𝑘𝑡−𝑙∗ 𝐿 𝑙=1 + 𝜀𝑖,𝑡+ℎℎ ; ℎ = 1, … ,40; 𝐿 = 10 𝐶𝑂2_𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑖,𝑡+ℎ∗ = 𝑎𝑖ℎ+ 𝛽𝑖ℎ𝑆𝑡+ 𝛾𝑖ℎ𝑜𝑡ℎ𝑒𝑟𝑠ℎ𝑜𝑐𝑘𝑡+ ∑ 𝜌𝑖,𝑙ℎ𝐶𝑂2_𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑡−𝑙∗ 𝐿 𝑙=1 + ∑ 𝛿𝑖,𝑙ℎ𝑜𝑡ℎ𝑒𝑟𝑠ℎ𝑜𝑐𝑘𝑡−𝑙∗ 𝐿 𝑙=1 + 𝜀𝑖,𝑡+ℎℎ ; ℎ = 1, … , 40; 𝐿 = 10

Moreover, to estimate the cumulative impulse response, we defined (𝑦𝑖,𝑡+ℎ -𝑦𝑖,𝑡−1) as the endogenous variable. So, our equations will convert to the following ones: 𝐺𝐷𝑃𝑖,𝑡+ℎ∗ − 𝐺𝐷𝑃𝑖,𝑡−1∗ = 𝑎𝑖ℎ+ 𝛽𝑖ℎ𝑆𝑡+ 𝛾𝑖ℎ𝑜𝑡ℎ𝑒𝑟𝑠ℎ𝑜𝑐𝑘𝑡+ ∑ 𝜌𝑖,𝑙ℎ𝐺𝐷𝑃𝑡−𝑙∗ 𝐿 𝑙=1 + ∑ 𝛿𝑖,𝑙ℎ𝑜𝑡ℎ𝑒𝑟𝑠ℎ𝑜𝑐𝑘𝑡−𝑙∗ 𝐿 𝑙=1 + 𝜀𝑖,𝑡+ℎℎ ; ℎ = 1, … , 40; 𝐿 = 10 𝐶𝑂2𝑖,𝑡+ℎ− 𝐶𝑂2 𝑖,𝑡−1 ∗ = 𝑎 𝑖 ℎ+ 𝛽 𝑖ℎ𝑆𝑡+ 𝛾𝑖ℎ𝑜𝑡ℎ𝑒𝑟𝑠ℎ𝑜𝑐𝑘𝑡+ ∑ 𝜌𝑖,𝑙ℎ𝐶𝑂2𝑡−𝑙∗ 𝐿 𝑙=1 + ∑ 𝛿𝑖,𝑙𝑜𝑡ℎ𝑒𝑟𝑠ℎ𝑜𝑐𝑘 𝑡−𝑙∗ 𝐿 𝑙=1 + 𝜀𝑖,𝑡+ℎ ; ℎ = 1, … , 40; 𝐿 = 10 𝐶𝑂2𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑖,𝑡+ℎ∗ − 𝐶𝑂2𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑖,𝑡−1∗ = 𝑎𝑖ℎ+ 𝛽𝑖ℎ𝑆𝑡+ 𝛾𝑖ℎ𝑜𝑡ℎ𝑒𝑟𝑠ℎ𝑜𝑐𝑘𝑡+ ∑ 𝜌𝑖,𝑙ℎ𝐶𝑂2𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑡−𝑙 ∗ 𝐿 𝑙=1 + ∑ 𝛿𝑖,𝑙ℎ𝑜𝑡ℎ𝑒𝑟𝑠ℎ𝑜𝑐𝑘𝑡−𝑙∗ 𝐿 𝑙=1 + 𝜀𝑖,𝑡+ℎℎ ; ℎ = 1, … , 40; 𝐿 = 10

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For both panel and non-panel models, considering the selected data set, which includes different time intervals, we chose a different number of lags of variable as control and also estimated a different number of horizons. In the case of exploring the first panel data with the more extended period, we determined L=10 and h=1,..,40. So we defined ten lags as control variables and estimated 40 horizons. In another case, if the analysis from the second dataset, which encompasses a shorter time interval, was considered, we opted for L=5 and h=1,…,20. Thus, we added five lags as control variables to our regression and estimated 20 horizons.

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Results

This section presents the result of the impact of major global shocks, namely pandemic, war and financial crisis to GDP per capita, CO2 emission per capita and CO2 intensity.

The outcome of the analysis represents a meaningful behaviour. A similar reaction in response to pandemic and war can be observed. If a country responds positively to the end of a pandemic in the short run, the alike response to war would be noticed in the short run for that country and vice versa.

There is a significant similarity between responses to a pandemic for the UK, USA, Australia, and Canada. It is observable in all of these four developed countries that after the end of a pandemic, history shows that GDP and CO2 emission per capita are depressed in the short run. While, in the medium run, they represent an augmentation behaviour. In the same way as a pandemic, and as previously mentioned, the end of war causes a negative effect on GDP per capita and CO2 emission per capita of these four aforementioned countries in the short run. The obtained results for the USA and Canada are displayed in Figure 13, Figure 14, Figure 15, Figure 16, Figure 17, and Figure 18. The graphs for Australia and the UK are presented in the appendix.

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Figure 14

Response of GDP per capita, CO2 per capita and CO2 intensity to Pandemic for the USA

Figure 15

Response of GDP per capita, CO2 per capita and CO2 intensity to War for the USA

Figure 16

Response of GDP per capita, CO2 per capita and CO2 intensity to Financial crisis for the USA

Shock on GDP per capita Shock on 𝐶𝑂2 emission per capita Shock on 𝐶𝑂2 intensity

Shock on GDP per capita Shock on 𝐶𝑂2 emission per capita Shock on 𝐶𝑂2 intensity

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Figure 17

Response of GDP per capita, CO2 per capita and CO2 intensity to Pandemic for Canada

Figure 18

Response of GDP per capita, CO2 per capita and CO2 intensity to War for Canada

Figure 19

Response of GDP per capita, CO2 per capita and CO2 intensity to Financial crisis for Canada

Shock on GDP per capita Shock on 𝐶𝑂2 emission per capita Shock on 𝐶𝑂2 intensity

Shock on GDP per capita Shock on 𝐶𝑂2 emission per capita Shock on 𝐶𝑂2 intensity

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The other European countries’ responses to pandemic and war are opposite to the four countries mentioned above. As some examples, consequences of a pandemic, war, and financial crisis to GDP per capita and CO2 emission per capita for France, Italy and Sweden are provided in Figure 20, Figure 21, Figure 22, Figure 23, Figure 24, Figure 25, Figure 26, Figure 27, and Figure 28. Thus, converse to previous categories of countries, the specified economic and environmental indicators responded positively to the end of pandemics in the short run, and a declining trend is visible in the medium run. Furthermore, similar to pandemic’s consequence, the end of war leads to growth in GDP per capita and CO2 emission per capita in the short term for these countries.

Figure 20

Response of GDP per capita, CO2 per capita and CO2 intensity to Pandemic for France

Figure 21

Response of GDP per capita, CO2 per capita and CO2 intensity to War for France

Shock on GDP per capita Shock on 𝐶𝑂2 emission per capita Shock on 𝐶𝑂2 intensity

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Figure 22

Response of GDP per capita, CO2 per capita and CO2 intensity to Financial crisis for France

Figure 23

Response of GDP per capita, CO2 per capita and CO2 intensity to Pandemic for Italy

Figure 24

Response of GDP per capita, CO2 per capita and CO2 intensity to War for Italy

Shock on GDP per capita Shock on 𝐶𝑂2 emission per capita Shock on 𝐶𝑂2 intensity

Shock on GDP per capita Shock on 𝐶𝑂2 emission per capita Shock on 𝐶𝑂2 intensity

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Figure 25

Response of GDP per capita, CO2 per capita and CO2 intensity to Financial crisis for Italy

Figure 26

Response of GDP per capita, CO2 per capita and CO2 intensity to Pandemic for Sweden

Figure 27

Response of GDP per capita, CO2 per capita and CO2 intensity to War for Sweden

Shock on GDP per capita Shock on 𝐶𝑂2 emission per capita Shock on 𝐶𝑂2 intensity

Shock on GDP per capita Shock on 𝐶𝑂2 emission per capita Shock on 𝐶𝑂2 intensity

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Figure 28

Response of GDP per capita, CO2 per capita and CO2 intensity to Financial crisis for Sweden

The plots representing the indicators’ response to a shock for other developed countries of the first panel data are presented in the appendix.

The same analysis was performed for developing economies in our second panel data. The plots of GDP per capita, CO2 emission per capita and CO2 intensity response to a pandemic or financial crisis for developed countries are rendered in this part and the appendix. War is excluded from the analysis of the second dataset because from 1950 to 2014, no global war - which can be profoundly influencing these regions- has happened in the world. Figure 29 and Figure 30 display the response of our indicators to respectively a pandemic and financial crisis for China.

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Figure 29

Response of GDP per capita, CO2 per capita and CO2 intensity to Pandemic for China

Figure 30

Response of GDP per capita, CO2 per capita and CO2 intensity to Financial crisis for China

Although GDP per capita and CO2 emission per capita in China respond positively in the early years after the end of a pandemic_ four years_, the negative impact of the pandemic is observable after these initial years. Also, a financial crisis affects negatively from the very beginning on our economic and environmental indicators. The point that should not be neglected is that developing countries' available data does not seem long-lasting enough and does not contain many major pandemic events. Hence, it can be negatively impacting

Shock on GDP per capita Shock on 𝐶𝑂2 emission per capita Shock on 𝐶𝑂2 intensity

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the results. The discussion about the outcome will be elaborated on in the next chapter.

To have a broader vision about the behaviour toward the considered global shocks of each country in the short, medium and long term, we plotted the number of countries with a positive or negative response to each shock in each time slot in Figure 31, Figure 32, Figure 33, and Figure 34.

Figure 31

Comparison between the positive and negative response of GDP per capita of developed countries to a pandemic in each period

Figure 32

Comparison between the positive and negative response of GDP per capita of developed countries to war in each period

0 2 4 6 8 10 12

Positive Negative Positive Negative Positive Negative Short run Medium run Long run

GDP per capita response to Pandemic

0 2 4 6 8 10 12

Positive Negative Positive Negative Positive Negative Short run Medium run Long run

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Figure 33

Comparison between the positive and negative response of CO2 emission per capita of developed countries to a pandemic in each period

Figure 34

Comparison between the positive and negative response of CO2 emission per capita of developed countries to war in each period 0 2 4 6 8 10 12

Positive Negative Positive Negative Positive Negative Short run Medium run Long run

CO2 emission per capita response to Pandemic

0 2 4 6 8 10 12

Positive Negative Positive Negative Positive Negative Short run Medium run Long run

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As shown, for most of the analyzed countries, the end of a pandemic has a positive impact on the chosen economic and environmental indicators in the short run. Likewise, the end of the war will positively affect these indicators in the short run, and this impact will be augmented in the medium run.

Exploring how shocks affect environmental and economic indicators differently, each indicator's response to a shock is plotted in the same graph, and Figure 35, Figure 36, and Figure 37 contain the corresponding plots. As it is predictable, both GDP per capita and CO2 emission per capita respond in the same direction to a shock. Moreover, the noticeable point is that in most European countries, CO2 emission per capita responds more significantly to shocks than GDP per capita. In contrast, for the USA and Canada, the responses of these indicators to specific shock are not remarkably different from each other.

Figure 35

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Figure 36

Comparison between the mean of the impulse response of GDP per capita and CO2 emission per capita to War

Figure 37

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As described in the methodology chapter, the panel model is also investigated for developed countries.

Figure 38, Figure 39, Figure 40, Figure 41, Figure 42, and Figure 43 display the cumulative and non-cumulative impulse response of GDP per capita, CO2 emission per capita and CO2 intensity to global investigated shocks. We will expatiate the result of the panel model in the discussion chapter.

Figure 38

The cumulative impulse response of GDP per capita, CO2 per capita and CO2 intensity to Pandemic for panel model

Figure 39

The non-cumulative impulse response of GDP per capita, CO2 per capita and CO2 intensity to Pandemic for panel model

Shock on GDP per capita Shock on 𝐶𝑂2 emission per capita Shock on 𝐶𝑂2 intensity

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Figure 40

The cumulative impulse response of GDP per capita, CO2 per capita and CO2 intensity to war for panel model

Figure 41

The non-cumulative impulse response of GDP per capita, CO2 per capita and CO2 intensity to war for panel model

Shock on GDP per capita Shock on 𝐶𝑂2 emission per capita Shock on 𝐶𝑂2 intensity

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Figure 42

The cumulative impulse response of GDP per capita, CO2 per capita and CO2 intensity to financial crisis for panel model

Figure 43

The non-cumulative impulse response of GDP per capita, CO2 per capita and CO2 intensity to financial crisis for panel model

Shock on GDP per capita Shock on 𝐶𝑂2 emission per capita Shock on 𝐶𝑂2 intensity

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Discussion

This section plans to expound long-run and short-run response of GDP per capita, CO2 emission per capita, and CO2 intensity to major global shocks, namely pandemic, war, and financial crises. We investigated the consequences of these global shocks on the developed and developing countries, respectively, from 1870 and from 1950, using the Local Projection approach.

Effect of a Pandemic or War on GDP per capita

Our study shows that the end of pandemic leads to growth in GDP per capita for most analyzed developed countries in the short run. This behaviour is mainly observed in European countries. A pandemic does not usually destroy capital[6] and does not impact human capital structure. In general, the older people are more vulnerable to the pandemic’s consequences than the young ones, which are considered active force labours[21]–[23]. Although the Spanish flu pandemic has a unique feature between recent pandemics, the group of 20-40 years of age had one of the highest mortality, and this pernicious property of the Spanish flu is not well understood [24]. Considering these features of pandemics, the GDP per capita of most countries responds positively to the end of the pandemic.

Likewise, by ending the war, GDP per capita for those particular countries was augmented. However, investigating the reason for war’s effects requires more explanation. It is true that war causes capital destruction and negatively affects the labour force since it directly impacts the young generation. Still, we observe that end of a war can positively impact GDP per capita for most European countries.

However, all developed countries did not respond harmoniously to these global shocks. In contrast to most European countries, the end of a war and pandemic

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causes a negative impact on the GDP per capita of a few countries like the USA and Canada.

Having a more comprehensive analysis, we also investigated the impact of starting a war. In such a case, our study reveals that starting a war negatively impacts the GDP for most European countries and, in contrast, positively impacts the GDP per capita for the USA, Australia, and Canada.

Jordà et al. (2020) claimed that war has a negative impact on GDP per capita because the capital destruction and loss of lives during wars have a noticeable negative effect on labour productivity[6]. We challenge this general statement because they only investigated the impact of the end of the war on the behaviour of the UK GDP. Although we obtained the same result as theirs for the UK, our study also reveals the positive effect of the war's end on this economic indicator for most analyzed countries. So this point should not be overlooked that both the war or pandemic cause a diverse effect on different regions.

The time-series data confirms our results for the impact of war on GDP per capita. Along with our visualized data history for all the analyzed countries, including the USA, in Figure 8; Figure 44 presents the impact of the second world war as an example on the USA's GDP. As it can be observed, the USA's GDP enhanced during the wars. That growth during the war period was driven by government spending and accompanied by declines in consumption and investment compared to the pre-war trend [25]. The article” Economic Consequences of War on the US Economy” states that one of the USA’s economic benefits of war is higher GDP growth. This report shows that this growth has occurred throughout all of the most recent conflict periods, namely World War II, the Korean War, the Vietnam War, which we considered these war shocks in our analysis for the USA.

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Figure 44

History of the USA Growth[25]

Moreover, the geopolitical characteristics of the countries should not be neglected. Most European countries with a negative response to the start of the war, like Italy and France, located in the middle of “Battlefield.” Hence, they are more exposed to direct and negative consequences of the war. However, post world war || can be considered a period of remarkable economic growth and social progress in Europe. There is a paradox here for the western and Nordic European countries to show a positive response after those tremendous losses occurred by war. The following hypotheses explain this behaviour: First, the robust basis of economic recovery in Western Europe. Second, vital support for the reconstruction of European trade and cooperation, and the third, Allied support for reviving the German economy [26].

Another significant result of our study is that, for most European countries, in the very initial years after a war, the response of GDP per capita to the war shock remained negative, and after a lag, we can observe the increase in GDP per capita. It means that compared with a pandemic, the recovery from war occurs slower.

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Effect of Financial crisis

Despite that Financial crisis impact is not as explicit as war and pandemic’s due to lack of the occurrence of many significant events, the results show for almost all the countries that the financial crisis had a negative effect on GDP per capita and CO2 emission per capita in the short run and approximately similar to the other shocks, CO2 emission and GDP per capita respond in a similar direction to the financial crisis.

The impulse response of CO2 emission per capita and CO2

intensity

Analysis of CO2 emission per capita shows that this indicator and GDP per capita respond congruously to a shock. However, the impact of a pandemic on CO2 is starker (and more significant) than GDP per capita for most European countries, specifically the Nordic ones. On the other hand, these indicators respond to the same extent for the USA and Canada.

Since CO2 intensity is the ratio between the CO2 emission per capita and GDP per capita, the impulse response of CO2 intensity mainly depends on these two primary mentioned indicators. Although, as already mentioned, in most European countries, the short-run positive response of CO2 emission per capita to a pandemic is more significant than GDP’s response, each country has its specific features; so, we can not categorize the impulse response of CO2 intensity in the general way that we did for GDP and CO2 emission per capita’s responses. Thus, each country should be explained separately. However, we can say that CO2 intensity for the Nordic European countries responds positively to the pandemic in the short run. In contrast, for countries like Spain and Portugal, showing a positive impulse response of GDP per capita, the CO2 emission intensity, respectively, responds negative and neutral.

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