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---

### Key empirical findings on energy and crises
- Lockdowns during the COVID-19 pandemic caused global energy demand to decline; according to the International Energy Agency (IEA, 2020a), energy demand dropped by 25 percent on average per week in nations with a full lockdown, and 18 percent in those in partial lockdown.
- Domestic electricity/private consumption rose by 40 percent during lockdowns (Broom, 2020).
- Global use of renewable energy in all sectors increased by 1.5 percent in 2020; the share of renewables in electricity demand increased in many regions (International Energy Agency 2020b).
- Recessions are associated with a permanent decline in energy demand stemming from lower GDP and a decline in energy intensity (energy per output).
- Recessions are associated with a medium-term increase of about 2 percentage points in the share of renewable energy and a corresponding decline in the share of dirty energy.
- Supportive policy in the form of more stringent environmental protection regulation can amplify the effect of recessions on renewables to produce a near doubling—about 4 percentage points—in the share of renewables in total energy.

### Mechanisms and interpretation
- Lower demand during recessions leads to excess electricity supply and power plant shutdowns, especially among older, higher marginal cost coal-based plants.
- Two opposing forces during crises:
  - Crisis-induced disruption in financing may reduce innovation in new energy because research and development is highly procyclical.
  - Recessions may strengthen incentives for firms to improve efficiency, leading to "creative destruction"—faster obsolescence of outdated units and reallocation toward newer, more efficient technologies.
- Creative destruction framework (Schumpeter 1939, 1942): recessions can act as a “time of cleansing,” accelerating permanent structural change by concentrating shutdowns among less productive units and facilitating entry of more efficient producers.

### Historical evidence and examples
- The Great Depression caused permanent structural change in the U.S. motor vehicles industry—shutdowns concentrated in smaller, less productive craftsmanship plants, while mass-production plants survived and new mass-production plants entered the industry (Bresnahan and Raff, 1991, 1993).
- After the Global Financial Crisis: Jaeger (2020) documents U.S. solar electricity generation increased over 30 times from 2008 to 2015, and wind generation increased over three times.
- Energy shock-induced crises (1970s and 1980s oil crises) contributed to major improvements in renewable energy production and energy efficiency (Peters et al., 2012).
- Introduction of standards and charges for Sulphur oxides in Japan during the 1960s and 1970s resulted in reductions in levels of these pollutants and significant technological innovation.
- Policy examples: the Climate Change Levy (CCL) in the United Kingdom (introduced in 2001) had a strong negative impact on energy intensity and electricity use; Spain’s support for R&D and technological innovation raised investments in environmental protection and renewables.

### Data sources and empirical framework
- Primary energy and subcomponents data from Our World in Data sourced from BP Statistical Review of World Energy; additional energy consumption from the SHIFT data portal; electricity consumption and mix supplemented from the EMBER global electricity dashboard.
- Coverage:
  - Overall primary energy and subcomponents: 176 countries, 1965–2019.
  - Electricity and energy mix (fossil fuels, nuclear, renewables): 172 countries, 1985–2019.
- Robustness check: International Energy Agency’s World Energy Balances (around 130 countries, 1980–2016).
- Economic data: IMF World Economic Outlook, World Bank World Development Indicators, Penn World Tables.
- Environmental policy variables: Environmental Policy Stringency Index (EPS) dataset of the OECD (28 OECD and six BRICS countries), 1990–2015; EPS scaled from 0 (not stringent at all) to 6 (very stringent); breakdown by instrument type (market-based and non-market-based).
- Baseline shock: recessions defined as periods of negative real GDP growth; additional shocks: financial crises (Laeven and Valencia, 2020), five pandemics (SARS 2003; H1N1 2009; MERS 2012; Ebola 2014; Zika 2016), peaks-to-troughs growth slowdowns (Harding-Pagan algorithm), and changes in (log) GDP.
- Dynamic estimation method: local projection methods (Jordà, 2005). Smooth transition autoregressive model used to test heterogeneity by environmental protection regimes with normalized EPS as transition variable.

### Key empirical results — levels and magnitudes
- After five years following recessions:
  - Primary energy use is around 10 percent below pre-recession trend.
  - Coal demand down by around 5 percent.
  - Oil demand down by around 8 percent.
  - Electricity demand declines by around 7 percent.
- After a major recession (five years after episode):
  - Share of electricity from coal goes down by about 1 percent.
  - Share of renewables increases by almost 2 percent.
- Electricity production from renewables is resilient to recessions, so renewables’ share rises despite lower demand.
- Intensity of renewables (renewable energy as a share of GDP) increases durably after recessions.
- Within renewables:
  - Solar and hydro receive boosts.
  - Results for wind are less robust.
  - Virtually no change in share of nuclear in total energy.

### Quantitative estimates from EPS analysis and Table 1 (exact values preserved)
- Recession coefficients (share of renewables in electricity generation, with robust standard errors clustered at the country level):
  - 1 year: 0.0138** (standard error 0.00606)
  - 5 years: 0.0136* (standard error 0.00669)
  - 8 years: 0.0174*** (standard error 0.00530)
- Overall EPS (Lag 1):
  - 1 year: 0.00290 (standard error 0.00449)
  - 5 years: 0.0277*** (standard error 0.00569)
  - 8 years: 0.0485*** (standard error 0.00982)
- Market EPS (Lag 1):
  - 1 year: 0.00507** (standard error 0.00242)
  - 5 years: 0.0239*** (standard error 0.00635)
  - 8 years: 0.0378*** (standard error 0.00837)
- Non-market EPS (Lag 1):
  - 1 year: 0.00427 (standard error 0.00423)
  - 5 years: 0.0207*** (standard error 0.00483)
  - 8 years: 0.0343*** (standard error 0.00625)
- Sample and fit (selected columns):
  - Observations: 708, 692, 627; Number of countries: 33; R-squared values: 0.609, 0.601, 0.500.
- Significance notation: *** p<0.01, ** p<0.05, * p<0.1.

### Robustness checks and alternative shocks
- Financial crises:
  - Total electricity use declines by around 6 percent after five years.
  - Share of renewables increases by a little over 1 percent.
  - Financial crises decrease primary energy demand by around 8 percent while boosting renewables (solar, wind, hydro).
- Pandemics:
  - Impact on energy mix takes longer to develop and is weaker; household demand increases partially offset lower business demand; net effect: positive impact on energy mix with an increase in share of renewables.
- Peak-to-trough growth slowdowns:
  - Baseline results generally hold but changes in energy mix are somewhat weaker and less statistically significant, particularly for solar and wind.
- Additional robustness:
  - Results robust to using eight lags and to adding two and eight leads of the shock variable.
  - Controlling directly for lagged GDP growth, including time dummies, and controlling for country-specific time trends: main results continue to hold.
  - Additional controls (population growth, change in urbanization, credit growth, investment growth, changes in manufacturing share, export growth) do not affect main results.
  - Bias-adjusted treatment estimator (Oster, 2019) applied; results similar to baseline.
  - Placebo test (randomly assigning recession dates) shows no significant effects.

### Heterogeneity by country characteristics and environmental policy
- Advanced economies vs emerging market and developing economies:
  - Share of renewables rises strongly in advanced economies.
  - Effects weaker and quantitatively smaller in emerging market and developing economies.
- Environmental Policy Stringency (EPS) effects:
  - Smooth transition results: high EPS regimes see larger increases in share of renewables after recessions (around 4 percentage points), roughly double the average 2 percentage point increase; low EPS regimes: effect not statistically significant.
  - A unitary increase in EPS can lead to medium-term increases of 3-5 percentage points in the share of renewable energy.
- Market-based and non-market-based EPS:
  - Both instrument types associated with increases in renewables after a recession.
  - Market-based measures include taxes on emissions, trading schemes, feed-in tariffs.
  - Non-market measures include emission limits, fuel standards, R&D subsidies, public investment.
  - Specific measures tied to larger shifts: higher emission and fuel standards, feed-in tariffs, and trading schemes (green certificates, white certificates).

### Policy implications and recommendations
- Recessions present a policy window for fostering a greener recovery through well-designed environmental policies and complementary measures to protect vulnerable households.
- Supportive measures that can amplify recession-driven shifts toward greener energy include:
  - Emission and fuel standards
  - Taxes on pollutants
  - Trading schemes for carbon
  - R&D subsidies and public investment in renewables
  - Feed-in tariffs and renewable energy certificates
- Carbon pricing and stringent climate policies can encourage renewable demand and help meet ambitious climate targets.
- Political economy considerations: mitigation policies should be designed to address opposition and distributional impacts.
- Timely and properly designed green stimulus measures can generate economic growth, create jobs and bring environmental benefits, but involve trade-offs among economic, environmental and social objectives.

### Annex: figures and estimation notes (summary)
- Multiple figures estimate impulse response functions using equation (1) and local projections (Jordà, 2005) with samples varying by outcome:
  - Impact of recessions on growth in energy use: sample of 176 countries, 1965–2019.
  - GDP growth and level after recession: sample of 180 countries, 1982–2019.
  - Changes in level of renewables after recession: sample of 172 countries, 1985–2019.
  - Energy intensity after recession: sample of 172 countries, 1985–2019.
  - Capacity utilization and investment during recessions: data sources include Federal Reserve, Bloomberg, UNEP, IRENA.
  - Impact of financial crisis, pandemics, growth slowdowns, GDP growth, and various robustness checks: panels with samples of 176 and 172 countries over varying periods; graphs show response and 95 and 90 percent confidence bands; X-axis: years after the event with t=0 the year of the recession.

*Source: Annex I. Figures — wpiea2021284-print-pdf*

### Annex I. Figures .......................................................................................................

### Annex I. Figures

### Key empirical findings on energy and crises
- Lockdowns during the COVID-19 pandemic caused global energy demand to decline; according to the International Energy Agency (IEA, 2020a), energy demand dropped by 25 percent on average per week in nations with a full lockdown, and 18 percent in those in partial lockdown.
- Domestic electricity/private consumption rose by 40 percent during lockdowns (Broom, 2020).
- Global use of renewable energy in all sectors increased by 1.5 percent in 2020; the share of renewables in electricity demand increased in many regions (International Energy Agency 2020b).
- Recessions are associated with a permanent decline in energy demand stemming from lower GDP and a decline in energy intensity (energy per output).
- Recessions are associated with a medium-term increase of about 2 percentage points in the share of renewable energy and a corresponding decline in the share of dirty energy (see Figure 3).
- Supportive policy in the form of more stringent environmental protection regulation can amplify the effect of recessions on renewables to produce a near doubling—about 4 percentage points—in the share of renewables in total energy.

### Mechanisms and interpretation
- Lower demand during recessions leads to excess electricity supply and power plant shutdowns, especially among older, higher marginal cost coal-based plants.
- Two opposing forces during crises:
  - Crisis-induced disruption in financing may reduce innovation in new energy because research and development is highly procyclical.
  - Recessions may strengthen incentives for firms to improve efficiency, leading to "creative destruction"—faster obsolescence of outdated units and reallocation toward newer, more efficient technologies.
- Creative destruction framework (Schumpeter 1939, 1942): recessions can act as a “time of cleansing,” accelerating permanent structural change by concentrating shutdowns among less productive units and facilitating entry of more efficient producers.

### Historical evidence and examples
- Bresnahan and Raff (1991, 1993): The Great Depression caused a permanent structural change in the U.S. motor vehicles industry—shutdowns concentrated in smaller, less productive craftsmanship plants, while mass-production plants survived and new mass-production plants entered the industry.
- Evidence from the Global Financial Crisis and subsequent years: Jaeger (2020) documents U.S. solar electricity generation increased over 30 times from 2008 to 2015, and wind generation increased over three times.
- Peters et al. (2012): Energy shock-induced crises (e.g., 1970s and 1980s oil crises) contributed to major improvements in renewable energy production and energy efficiency.
- Policy examples: the Climate Change Levy (CCL) in the United Kingdom (introduced in 2001) had a strong negative impact on energy intensity and electricity use; Spain’s support for R&D and technological innovation raised investments in environmental protection and renewables.

### Policy implications
- Policy can amplify recession-driven shifts toward greener energy; examples of supportive measures include:
  - Emission and fuel standards
  - Taxes on pollutants
  - Trading schemes for carbon
  - R&D subsidies and public investment in renewables
- Stringent environmental protection regulation interacts with recessions to produce significantly larger increases in the share of renewables.

*Source: Annex I. Figures — wpiea2021284-print-pdf*

### Introduction of standards and charges for Sulphur oxides in Japan during the 1960s and 1970s resulted in

### wpiea2021284-print-pdf - Introduction of standards and charges for Sulphur oxides in Japan during the 1960s and 1970s resulted in

### Introduction and context
- Introduction of standards and charges for Sulphur oxides in Japan during the 1960s and 1970s resulted in reductions in the level of these pollutants and significant technological innovation.
- Downturns provide opportunities to step change public spending on environmental policies and work through public investment backlogs (Bowen and Stern (2010)).
- Timely and properly designed green stimulus measures can generate economic growth, create jobs and bring environmental benefits, but involve trade-offs among economic, environmental and social objectives (Agarwal et. al., 2020).

### Data sources and coverage
- Primary energy and subcomponents data (oil, coal, etc.) from Our World in Data sourced from BP Statistical Review of World Energy; additional energy consumption from the SHIFT data portal; electricity consumption and mix supplemented from the EMBER global electricity dashboard.
- Coverage:
  - Overall primary energy and subcomponents: 176 countries, 1965–2019.
  - Electricity and energy mix (fossil fuels, nuclear, renewables): 172 countries, 1985–2019 (key focus).
- Robustness check: International Energy Agency’s World Energy Balances (around 130 countries, 1980–2016); main results continue to hold.
- Economic data: IMF World Economic Outlook, World Bank World Development Indicators, Penn World Tables.
- Environmental policy variables: Environmental Policy Stringency Index (EPS) dataset of the OECD (28 OECD and six BRICS countries), 1990–2015; EPS scaled from 0 (not stringent at all) to 6 (very stringent); breakdown by instrument type (market-based and non-market-based).

### Empirical framework and identification
- Baseline shock: recessions defined as periods of negative real GDP growth; additional shocks: financial crises (Laeven and Valencia, 2020), five pandemics (SARS 2003; H1N1 2009; MERS 2012; Ebola 2014; Zika 2016, severity via infections per capita), peaks-to-troughs growth slowdowns (Harding-Pagan algorithm on annual real GDP and per-capita GDP), and changes in (log) GDP.
- Dynamic estimation method: local projection methods (Jordà, 2005); equation (1) estimates impulse response functions for h=0,1,2...
- Main energy variable 푒푖,푡+ℎ: log of energy use (terawatt-hour) or share of different sources in total electricity.
- Growth slowdown indicator 푠푖,푡; country-fixed effects 푢푖 included; baseline excludes time dummies to avoid absorbing global crises, but time dummies and country-specific time 트렌드푖 included as robustness checks.
- Smooth transition autoregressive model (Granger and Terävistra, 1993) used to test heterogeneity by environmental protection regimes using weighting function F(z) (equation (2)), where z is normalized EPS (zero mean, unit variance). Coefficients 휃ℎL and 휃ℎH capture impacts in low and high EPS regimes.

### Key empirical results — Energy use and intensity
- Recessions associated with a significant and permanent decline in energy use.
- After five years following recessions:
  - Primary energy use is around 10 percent below pre-recession trend.
  - Coal demand down by around 5 percent.
  - Oil demand down by around 8 percent.
  - Electricity demand declines by around 7 percent.
- Growth-rate slowdown is temporary; growth rates return to trend after around three years.
- Energy intensity (energy per unit of output) declines durably after a recession:
  - Initial decline in energy use mirrors output decline (no immediate significant change in intensity), but energy intensity declines over time as output recovers.

### Key empirical results — Energy mix and renewables
- After a major recession (five years after episode):
  - Share of electricity from coal goes down by about 1 percent.
  - Share of renewables increases by almost 2 percent.
- Electricity production from renewables is resilient to recessions (level of renewables remains), resulting in higher shares of renewables despite overall lower energy demand.
- Intensity of renewables (renewable energy as a share of GDP) increases durably after recessions.
- Mechanisms:
  - Renewables (hydro, wind, solar) have very low marginal costs and often receive grid priority, insulating them from lower demand.
  - Older, high-marginal-cost coal plants are first to be shut down when capacity utilization falls.
  - Recovery investment favors newer renewable technologies rather than restarting obsolete coal plants.
- Within renewables:
  - Solar and hydro receive boosts.
  - Results for wind are less robust.
  - Virtually no change in share of nuclear in total energy.

### Robustness checks and alternative shocks
- Financial crises:
  - Total electricity use declines by around 6 percent after five years.
  - Share of renewables increases by a little over 1 percent.
  - Financial crises decrease primary energy demand by around 8 percent while boosting renewables (solar, wind, hydro).
- Pandemics:
  - Impact on energy mix takes longer to develop and is weaker.
  - Household demand increases (lockdowns, work from home) partially offset lower business demand.
  - Net effect: positive impact on energy mix with an increase in share of renewables.
- Peak-to-trough growth slowdowns (Harding-Pagan):
  - Baseline results generally hold: decline in overall energy demand and increase in share of renewables.
  - Changes in energy mix somewhat weaker and less statistically significant, particularly for solar and wind.
  - Explanation: prolonged slow growth implies longer periods of lower investment, weakening creative-destruction channel.
- Share of renewable energy is counter-cyclical when looking at impulse responses to GDP growth (inverted).
- Alternative specifications:
  - Results robust to using eight lags and to adding two and eight leads of the shock variable.
  - Controlling directly for lagged GDP growth, including time dummies, and controlling for country-specific time trends: main results continue to hold.
- Additional controls included to address omitted variable bias:
  - Population growth, change in urbanization, credit growth, investment growth, changes in the share of manufacturing in total value added, export growth — inclusion does not affect main results.
- Bias-adjusted treatment estimator (Oster, 2019) applied; results similar to baseline.
- Placebo test (randomly assigning recession dates) shows no significant effects — supporting parallel trends assumption.

### Heterogeneity by country characteristics and environmental policy
- Advanced economies vs emerging market and developing economies:
  - Share of renewables rises strongly in advanced economies.
  - Effects weaker and quantitatively smaller in emerging market and developing economies.
  - Reasons: earlier sample start (1985) when non-hydro renewables were concentrated in advanced economies; resource constraints and lower environmental policy stringency in emerging economies.
- Environmental Policy Stringency (EPS) effects:
  - Baseline results hold in restricted sample used for EPS analysis (33 countries, 1990–2015).
  - Table 1: Recession coefficients indicate positive and statistically significant effects on renewable share at 1 year, 5 years, and 8 years (e.g., Recession 0.0138** at 1 year; 0.0136* at 5 years; 0.0174*** at 8 years in column (1)-(3) baseline).
  - A unitary increase in EPS can lead to medium-term increases of 3-5 percentage points in the share of renewable energy.
  - Smooth transition results (Figure 16):
    - High EPS regimes see larger increases in share of renewables after recessions (around 4 percentage points), roughly double the average 2 percentage point increase.
    - Low EPS regimes: effect not statistically significant.
- Market-based vs non-market-based EPS:
  - Both market and non-market EPS associated with an increase in renewables after a recession.
  - Market-based measures include taxes on emissions, trading schemes, feed-in tariffs.
  - Non-market measures include emission limits, fuel standards, R&D subsidies, public investment.
  - Specific measures: higher emission and fuel standards, feed-in tariffs, and trading schemes (green certificates, white certificates) are associated with larger shifts toward renewables after recessions.

### Quantitative highlights from Table 1 (selected exact values preserved)
- Recession coefficients:
  - 1 year: 0.0138** (standard error 0.00606)
  - 5 years: 0.0136* (standard error 0.00669)
  - 8 years: 0.0174*** (standard error 0.00530)
- Overall EPS (Lag 1):
  - 1 year: 0.00290 (standard error 0.00449)
  - 5 years: 0.0277*** (standard error 0.00569)
  - 8 years: 0.0485*** (standard error 0.00982)
- Market EPS (Lag 1) (selected columns):
  - 1 year: 0.00507** (standard error 0.00242)
  - 5 years: 0.0239*** (standard error 0.00635)
  - 8 years: 0.0378*** (standard error 0.00837)
- Non-market EPS (Lag 1) (selected columns):
  - 1 year: 0.00427 (standard error 0.00423)
  - 5 years: 0.0207*** (standard error 0.00483)
  - 8 years: 0.0343*** (standard error 0.00625)
- Sample and fit:
  - Observations: 708, 692, 627 in various columns; Number of countries: 33; R-squared values: 0.609, 0.601, 0.500 (selected columns).
- Note: Dependent variable is the share of renewables in electricity generation. Robust standard errors clustered at the country level. *** p<0.01, ** p<0.05, * p<0.1.

### Policy-relevant conclusions
- Growth slowdowns (recessions, pandemics, financial crises) are associated with:
  - Permanent increase in energy efficiency and decline in energy intensity of output.
  - Disproportionate reduction in dirty energy and a shift toward renewables.
- Effects stronger in advanced economies and in the presence of stronger environmental policies.
- Both non-market-based policies (emission and fuel standards, R&D incentives, public investment) and market-based measures (trading schemes, renewable energy certificates, feed-in tariffs) can effectively boost transition to renewables.
- Policy implications:
  - Recessions present a policy window for fostering a greener recovery through well-designed environmental policies and complementary measures to protect vulnerable households.
  - Carbon pricing and stringent climate policies can encourage renewable demand and help meet ambitious climate targets.
  - Political economy considerations matter; mitigation policies should be designed to address opposition and distributional impacts.

*Source: IMF Working Paper — Creative Destruction during Crises (sections III–VII, figures and tables as provided).*

### Annex I. Figures

### Annex I. Figures

### Impact of recessions on energy use
- Figure: "Impact of recessions on growth in energy use"
- Estimation details:
  - Sample of 176 countries over the period 1965–2019
  - Estimated using equation (1)
  - Graph shows the response and 95 and 90 percent confidence bands
  - X-axis: years after the event, with t=0 is the year of the recession

### GDP growth and level after recession
- Figure: "GDP growth and level after recession"
- Estimation details:
  - Sample of 180 countries over the period 1982–2019
  - Estimated using equation (1)
  - Graph shows the response and 95 and 90 percent confidence bands
  - X-axis: years after the event, with t=0 is the year of the recession

### Changes in level of renewables after recession
- Figure: "Changes in level of renewables after recession"
- Estimation details:
  - Sample of 172 countries over the period 1985–2019
  - Estimated using equation (1)
  - Graph shows the response and 95 and 90 percent confidence bands
  - X-axis: years after the event, with t=0 is the year of the recession

### Energy intensity after recession
- Figure: "Energy intensity after recession"
- Estimation details:
  - Sample of 172 countries over the period 1985–2019
  - Estimated using equation (1)
  - Graph shows the response and 95 and 90 percent confidence bands
  - X-axis: years after the event, with t=0 is the year of the recession

### Capacity utilization and investment during recessions
- Figure: "Capacity utilization and investment during recessions"
- Data sources cited on figure:
  - Board of Governors of the Federal Reserve System (US), Federal Reserve Bank of St. Louis
  - Bloomberg; UNEP; FS-UNEP Collaborating Centre
  - The International Renewable Energy Agency (IRENA)

### Impact of financial crisis
- Figure: "Impact of financial crisis"
- Estimation details (multiple panels referenced):
  - Sample of 176 countries over the period 1965–2019 (some panels)
  - Sample of 172 countries over the period 1985–2019 (other panels)
  - Estimated using equation (1)
  - Graphs show the response and 95 and 90 percent confidence bands
  - X-axis: years after the event, with t=0 is the year of the recession

### Impact of pandemics (cases/population)
- Figure: "Impact of pandemics (cases/population)"
- Estimation details:
  - Sample of 176 countries over the period 1965–2019 (some panels)
  - Sample of 172 countries over the period 1985–2019 (other panels)
  - Estimated using equation (1)
  - Graphs show the response and 95 and 90 percent confidence bands
  - X-axis: years after the event, with t=0 is the year of the recession

### Impact of growth slowdown (peak to trough)
- Figure: "Impact of growth slowdown (peak to trough)"
- Estimation details:
  - Sample of 176 countries over the period 1965–2019 (some panels)
  - Sample of 172 countries over the period 1985–2019 (other panels)
  - Estimated using equation (1)
  - Graphs show the response and 95 and 90 percent confidence bands
  - X-axis: years after the event, with t=0 is the year of the recession

### Impact of per capita growth slowdown (peak to trough)
- Figure: "Impact of per capita growth slowdown (peak to trough)"
- Estimation details:
  - Sample of 176 countries over the period 1965–2019 (some panels)
  - Sample of 172 countries over the period 1985–2019 (other panels)
  - Estimated using equation (1)
  - Graphs show the response and 95 and 90 percent confidence bands
  - X-axis: years after the event, with t=0 is the year of the recession

### Impact of GDP growth
- Figure: "Impact of GDP growth"
- Notes:
  - Impulse response functions to changes in GDP growth (inverted)
  - Sample of 176 countries over the period 1965–2019 (some panels)
  - Sample of 172 countries over the period 1985–2019 (other panels)
  - Estimated using equation (1)
  - Graphs show the response and 95 and 90 percent confidence bands
  - X-axis: years after the event, with t=0 is the year of the recession

### Controlling for GDP growth directly (additional results)
- Figure: "Controlling for GDP growth directly (additional results)"
- Estimation details:
  - Sample of 176 countries over the period 1965–2019 (some panels)
  - Sample of 172 countries over the period 1985–2019 (other panels)
  - Estimated using equation (1)
  - Graphs show the response and 95 and 90 percent confidence bands
  - X-axis: years after the event, with t=0 is the year of the recession

### Time dummies (additional results)
- Figure: "Time dummies (additional results)"
- Estimation details:
  - Sample of 176 countries over the period 1965–2019 (some panels)
  - Sample of 172 countries over the period 1985–2019 (other panels)
  - Estimated using equation (1)
  - Graphs show the response and 95 and 90 percent confidence bands
  - X-axis: years after the event, with t=0 is the year of the recession

### Time trends (additional results)
- Figure: "Time trends (additional results)"
- Estimation details:
  - Sample of 176 countries over the period 1965–2019 (some panels)
  - Sample of 172 countries over the period 1985–2019 (other panels)
  - Estimated using equation (1)
  - Graphs show the response and 95 and 90 percent confidence bands
  - X-axis: years after the event, with t=0 is the year of the recession

*Source: IMF Working Papers — Annex I. Figures (figures and notes as presented).*

### References

### References

### Energy–growth and energy policy literature
- Al-Iriani, M.A., 2006. “Energy–GDP relationship revisited: an example from GCC countries using panel causality,” Energy policy, vol.34(17), pp.3342-3350.
- Jakovac, P., 2018. “Causality between energy consumption and economic growth: Literature review,” In Proceedings of the 5th International Conference on Education and Social Sciences, Istanbul.
- Lee, C.C., 2006. “The causality relationship between energy consumption and GDP in G-11 countries revisited,” Energy policy, vol.34(9), pp.1086-1093.
- Sharma, S.S., 2010. “The relationship between energy and economic growth: Empirical evidence from 66 countries,” Applied energy, vol.87(11), pp.3565-3574.
- Soytas, U. and Sari, R., 2003. “Energy consumption and GDP: causality relationship in G-7 countries and emerging markets,” Energy economics, vol.25(1), pp.33-37.
- Stern, D.I., 2000. “A multivariate cointegration analysis of the role of energy in the US macroeconomy,” Energy economics, vol.22(2), pp.267-283.
- International Energy Agency, 2020a. “19. Exploring the impacts of the COVID-19 pandemic on global energy markets, energy resilience, and climate change 2020,” Retrieved from: https://www.iea.org/topics/covid-19
- International Energy Agency, 2020b. “Sustainable Recovery,” World Energy Outlook Special Report, Retrieved from: https://www.iea.org/reports/sustainable-recovery#Whiel
- International Energy Agency, 2020c, “Green stimulus after the 2008 crisis,” IEA, Paris, Retrieved from: https://www.iea.org/articles/green-stimulus-after-the-2008-crisis
- UNEP, 2009. “The global financial crisis and its impact on renewable energy finance, Retrieved from: verfügbar unter http://sefi. unep. org/fileadmin/media/sefi/docs/publica-
tions/Study_Financial_Crisis_impact_0n_RE_. pdf, zuletzt geprüft am, 21, p.2010.
- Union of Concerned Scientists, 2017. “Barriers to renewable energy technologies,” Retrieved from: https://www.ucsusa.org/resources/barriers-renewable-energy-technologies

### Creative destruction, productivity, and firm/industry dynamics
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### Crises, reform, and political economy
- Alesina, A., Ardagna, S. and Trebbi, F., 2006. “Who adjusts and when? The political economy of reforms,” IMF Staff Papers, vol.53(1), pp.1-29.
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### Climate, emissions, and COVID-19 impacts
- Le Quéré, C. et al. 2020. Temporary Reduction in Daily Global CO2 Emissions during the COVID-19 Forced Confinement,” 10 Nature Climate Change, vol.647.
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### Innovation, environmental policy, and abatement costs
- Baldwin, E., Cai, Y. and Kuralbayeva, K., 2020. “To build or not to build? Capital stocks and climate policy,” Journal of Environmental Economics and Management, vol.100, p.102235.
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- Martin, R., Wagner, U.J. and de Preux, L.B., 2011. “The impacts of the Climate Change Levy on business: evidence from microdata,” National Bureau of Economic Research.
- Cullen, J.A. and Mansur, E.T., 2017. “Inferring carbon abatement costs in electricity markets: A revealed preference approach using the shale revolution,” American Economic Journal: Economic Policy, vol.9(3), pp.106-33.
- Organisation for Economic Co-operation and Development and Ash, M., 2010. “Taxation, innovation and the environment,” OECD.

### Methods, econometrics, and modeling references
- Granger, C.W.J. and Teräsvirta, T., 1993. “Modelling Nonlinear Economic Relationships,” Oxford University Press.
- Johansen, L., 1959. “Substitution versus fixed production coefficients in the theory of economic growth: a synthesis,” Econometrica: Journal of the Econometric Society, pp.157-176.
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- Sheshinski, E., 1967. “Balanced growth and stability in the Johansen vintage model,” The Review of Economic Studies, vol.34(2), pp.239-248.
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- Altonji, J.G., Elder, T.E. and Taber, C.R., 2005. “An evaluation of instrumental variable strategies for estimating the effects of catholic schooling,” Journal of Human resources, vol.40(4), pp.791-821.
- Bourcet, C., 2020. “Empirical determinants of renewable energy deployment: A systematic literature review,” Energy Economics, vol.85, p.104563.

### Additional empirical studies and sectoral evidence
- Jalles, J.T., 2019. “Crises and emissions: New empirical evidence from a large sample,” Energy Policy, vol.129, pp.880-895.
- Cullen, J.A. and Mansur, E.T., 2017. “Inferring carbon abatement costs in electricity markets: A revealed preference approach using the shale revolution,” American Economic Journal: Economic Policy, vol.9(3), pp.106-33.
- Martin, R., Wagner, U.J. and de Preux, L.B., 2011. “The impacts of the Climate Change Levy on business: evidence from microdata,” National Bureau of Economic Research.
- Le Quéré, C. et al. 2020. Temporary Reduction in Daily Global CO2 Emissions during the COVID-19 Forced Confinement,” 10 Nature Climate Change, vol.647.

*Working Paper No. [WP/YYYY/###]*

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_Source: https://www.imf.org/-/media/files/publications/wp/2021/english/wpiea2021284-print-pdf.pdf_