Since the onset of the COVID-19 pandemic, energy price predictability has worsened. We evaluate the effectiveness of the two machine learning methods of shrinkage and combination on the spot prices of crude oil before and during the COVID-19 epidemic. The results demonstrated that COVID-19 increased economic uncertainty and diminished the predictive capacity of numerous models. Shrinkage methods have always been regarded as having an excellent out-of-sample forecast performance. However, during the COVID period, the combination methods provide more accurate information than the shrinkage methods. The reason is that the outbreak of the epidemic has altered the correlation between specific predictors and crude oil prices, and shrinkage methods are incapable of identifying this change, resulting in the loss of information.

Computable general equilibrium (CGE) models provide valuable insights into economy-wide impacts of anticipated future structural changes in the transportation sector, yet few CGE models offer detailed transportation representations. We use an enhanced Applied Dynamic Analysis of the Global Economy (ADAGE) CGE model to incorporate disaggregated transportation modes and technologies in on-road passenger and freight transportation. We assess the impacts of these inclusions on U.S. transportation patterns, energy consumption and greenhouse gas emissions. Simulating illustrative global oil price cases with and without transportation detail, we find subsector mode disaggregation and technology additions in a CGE model significantly alter the impacts of oil prices on global trade and freight patterns, energy consumption, and greenhouse gas (GHG) emissions. We find that: (1) alternative technologies are essential for capturing transportation sector impacts, (2) electrification may reduce emissions with electricity decarbonization, and (3) higher oil prices may hasten electrification.

Did Covid19 induce market turmoil impact the intraday volatility spillovers between energy and other ETFs?. To examine this, we first estimate the realized volatility of ETFs using the 5-min high-frequency data. Next, we employ time-varying parameter vector autoregressions (TVP-VAR). Finally, we utilize the wavelet coherence measure to test the time-frequency impact of COVID-induced sentiment on the spillovers by employing investors' psychological and behavioural factors. We find that oil and stock markets are net transmitters while currency, bonds, and silver markets are net receivers. The wavelet analysis embarked significant impact of media coverage and fake news index towards shaping investors' pessimism for their investments. We proposed useful implications for policymakers, governments, investors, and portfolio managers.

This study contributes to the existing literature on the relationship between oil market shocks and the green bond market by investigating the impact of the COVID-19 pandemic on their dynamic correlation. We first decompose the oil market shocks into components using a time-frequency framework. Then, we combine wavelet decomposition and quantile coherence and causality methods to discuss changes during the COVID-19 era. We observe positive effects of both supply-driven and demand-driven oil shocks on the green bond market at most quantile levels. However, supply-driven oil price changes play a major role. The results also indicate that long-term changes have a greater impact than short-term changes on the connection between oil and green bond markets. Nevertheless, the COVID-19 pandemic changed the nature of the causal relationship, as we observed no relationship under extreme market conditions during the pandemic era. We argue that the economic and social impacts of the COVID-19 pandemic have left investors focusing on the short-term substitution between oil and green bond markets.

Using U.S. data, we investigate how the COVID-19 pandemic influences oil price returns in an asset pricing framework. Unlike earlier studies, we consider a threshold model to allow for the possibility that COVID-19 risk may not play a role until it reaches a certain level. Based on WTI crude oil spot price data from January 2020 to December 2021, our findings show that oil returns significantly decline with the daily number of COVID-19 deaths but only if the daily death toll exceeds approximately 2100. In addition, a more severe COVID-19 pandemic can substantially increase the exposure of oil returns to various systematic risk factors, which has not been documented in previous literature.

This study analyzes the relationship between clean and dirty energy sources and energy metals during the COVID-19 pandemic. We document a sharp increase in connectedness after the COVID-19 pandemic, that is asymmetric at the lower and upper quantiles, with stronger dependence among the variables at the upper quantiles. Among the energy metals, cobalt is the least connected to the energy markets. Finally, our empirical results show a switch in the net connectedness indexes of energy metals and clean energy after January 2021. Our results have implication for investors and policy makers for energy and metal under various market conditions.

The COVID-19 pandemic caused severe economic contraction and paralyzed industrial activity. Despite a growing body of literature on the impacts of COVID-19 mitigation measures, scant evidence currently exists on the impacts of lockdowns on the economic and industrial activities of developing countries. Our study provides an empirical assessment of lockdown measures using 298,354 data points on daily electricity consumption in 396 sub-industries. To infer causal relationships, we employ difference-in-differences models that compare cities with and without lockdown policies and provide quantitative evidence on whether the long-term gain of lockdowns outweighs the short-term loss. The results show that lockdown policies led to a significant short-term drop in electricity consumption of 15.2% relative to the control group. However, the electricity loss under the no-lockdown scenario is 2.6 times larger than that under the strict lockdown scenario within 4 months of the outbreak. Discrepancies in the impacts among industries are identified, and even within the same industry, lockdowns have heterogeneous effects. The impact of lockdowns on small and medium-sized enterprises in developing countries is seriously underestimated, raising concerns about the distributional impact of subsidy measures. This study serves as a crucial reference for the government when facing public health emergencies and shocks to support better policies.

Power outages in the U.S. affect many firms' economic activity and are likely to result in downstream supply chain disruptions. This paper examines the impact of power disturbances in the supply chain for four industries: manufacturing, durable goods manufacturing, nondurable goods manufacturing, and total private industry. This indirect impact includes the losses at downstream firms that might not receive supplies on time due to disturbances at their suppliers' facilities. This is measured using observations of value added before and after power disturbances rather than the more common method of modeling an economy. This paper tests four hypotheses related to the effect of power disturbances with all four of them being supported by the model results. Further, using simulation we estimate the indirect costs of power disturbances. The results suggest that power disturbances have a statistically significant effect on gross domestic product (i.e., value added), particularly in manufacturing where power disturbances in the supply chain had a statistically significant effect. While this industry represents 12.8 % of private industry value added, it experiences 36.8 % of the supply chain losses due to power disturbances, as suggested by the results of the simulation. Power disturbances in the supply chain affected all four industries with nondurable goods being affected the most. This creates a significant disconnect between the stakeholder that invests in reliability in the power grid and the stakeholders that experience the bulk of losses.

Large energy infrastructure can imply special financing arrangements between governments in developing economies and investors or lenders. These arrangements can lead to economy-wide and sector-specific impacts which need to be considered in the project economic evaluation. By considering the case of the Bui Dam in Ghana, we use a macroeconomic approach to determine how the economic performance of critical energy infrastructure manifests during the construction, financing and operation phases. The analysis uses an integrated modelling framework that combines a Computable General Equilibrium (CGE) model of Ghana with a water balance model of the Lower Volta River Basin. The results highlight the importance of including indirect and induced effects, in addition to the direct effects from project operation, as they influence the scale and temporal evolution of the economic impacts. The collateral from the infrastructure loan agreement consisting in cocoa exports to China nearly doubles the project's positive GDP impact and has a significant multiplier effect over urban and rural household income compared to a standard commercial loan. We finish with a discussion of how the proposed investment-oriented modelling framework can contribute to ex-ante strategic assessments of proposed energy infrastructure in developing countries.

Prior research has shown that energy sector stock prices are impacted by uncertainty. The coronavirus (COVID-19) pandemic has given rise to widespread health and economic-related uncertainty. In this study, we investigate the magnitude and the timing of the impact of COVID-19 related uncertainty on returns and volatility for 20 national energy indices and a global energy index using ARCH/GARCH models. We propose a novel "overall impact of uncertainty" (OIU) measure, explained using a natural phenomenon analogy of the overall impact of a rainstorm, to gauge the magnitude and intensity of the impact of uncertainty on energy sector returns. Drawing upon economic psychology, COVID-19 related uncertainty is measured in terms of searches for information relating to COVID-19 as captured by Google search trends. Our results show that the energy sectors of countries further west from the outbreak of the virus in China are impacted to a greater extent by COVID-19 related uncertainty. A similar observation is made for net energy and oil exporters relative to importers. We also find that the impact of uncertainty on most national energy sectors intensified and then weakened as the pandemic evolved. Additional analysis confirms that COVID-19 uncertainty is part of the composite set of factors that drive energy sector returns over the COVID-19 period although its importance has declined over time.

The price jump behavior may bring tremendous challenges on risk management and asset pricing. This paper uses the BN-S test, the wavelet coherence method, and applies high-frequency data to explore whether and to what extent the COVID-19 pandemic impacts China's energy stock market jumps and its characteristics. The empirical results uncover the significant and heterogeneous interactions between the COVID-19 pandemic and China's energy stock market jumps across market specifications, investment horizons, and China/global pandemic tolls at different time scales. First, the oil stock market jumps were the most correlated with the pandemic, especially during the peak and re-deterioration phases. The pandemic played a positive and leading role in the short term (1-4 days length period) and long term (over 32 days length period). Second, the coal stock market jumps have similar characteristics to those of oil, but mainly show a negative correlation with the pandemic. Third, renewable energy stock market jumps were the least correlated, mainly showing a positive correlation in the short term and a negative correlation in the long term. In addition, the interaction characteristics of systemic co-jumps in different China's energy stock markets are also significant.

With many studies highlighting the heterogeneous impact of the COVID-19 pandemic on different commodity markets, this study provides evidence of quantile connectedness between energy, metals, and agriculture commodity markets before and during the COVID-19 outbreak. Since mean-based measures of connectedness are not necessarily suitable to measure connectedness in the crisis period, especially in the tails of the return distribution, thus in this study, we use the newly developed approach of quantile-based connectedness. The full-sample analysis results show that return shocks only propagate within the energy commodity group. The findings manifest that transmission of return spillovers is stronger in the left and right tails of the conditional return distribution. In addition, the results unveil that degree of tail-dependence between energy, metals, and agriculture commodities are time-varying. Meanwhile, our sub-sample analysis clearly shows that the commodity market return connectedness demonstrates a significant shift over time due to COVID-19 shocks. There is evidence of strong transmission of return shocks between energy, metals, and agriculture commodities during the COVID-19 fiasco. Finally, the results also illustrate that softs and livestock commodities hold significant diversification benefits for energy market investors.

This paper studies the connectedness among energy equity indices of oil-exporting and oil-importing countries around the world. For each country, we construct time-varying measures of how much shocks this country transmits to other countries and how much shocks this country receives from other countries. We analyze the network of countries and find that, on average, oil-exporting countries are mainly transmitting shocks, and oil-importing countries are mainly receiving shocks. Furthermore, we use panel data regressions to evaluate whether the connectedness among countries is influenced by economic sentiment, uncertainty, and the global COVID-19 pandemic. We find that the connectedness among countries increases significantly in periods of uncertainty, low economic sentiment, and COVID-19 problems. This implies that diversification benefits across countries are severely reduced exactly during crises, that is, during the times when diversification benefits are most important.

The ongoing COVID-19 pandemic has inspired an examination of the oil-gold prices nexus during four recent crises: the COVID-19 pandemic, the gold market crash, the European sovereign debt crisis, and the global financial crisis. Using daily data from May 2007-August 2021, we employ the nonlinear autoregressive distributed lag method to reveal five novel findings. First, this study contrasts with much of the literature, which infers that the relationship between oil and gold prices is strongly positive. Second, we find no oil and gold price relationship in the long term during all the crisis periods. Third, oil prices have substantially lost their power to predict gold prices in recent times and the oil-gold price linkage is not functional across all crisis periods. Fourth, in the short term, only negative Brent and negative West Texas Intermediate price changes cause positive gold price changes during the pandemic and gold market crash, respectively. Fifth, Brent prices have shown no link to gold prices before COVID-19. We argue that gold prices are less sensitive to oil prices than ever, and the uncertainty resulting from the COVID-19 crisis has attracted investors to gold. Our main findings hold under robustness analyses using fractional cointegration/integration models, lag length, and heteroskedasticity-consistent standard errors.

The COVID-19 pandemic has caused extreme economic fluctuations. However, the magnitude of the economic cost of this extreme event remains challenging to quantify. The impact of the COVID-19 pandemic on the economy is estimated through firm-level electricity consumption data from Hunan province, China. Specifically, a difference-in-differences (DID) model was employed to estimate the real economic costs. The results indicate that electricity consumption in Hunan Province dropped by 27.8% during the early stage of the COVID-19 pandemic. Manufacturing and the transportation industry suffered the most severe declines. Electricity consumption began to recover after the virus was controlled. We suggest that government departments should take full measures to prevent and control COVID-19 outbreaks and associated economic impacts, in conjunction with preparing for economic recovery, deploying targeted measures to support different industries in response to the heterogeneity COVID-19 pandemic impacts. The COVID-19 has changed people's living habits and brought a new direction, the Internet industry, of economic growth. Hunan Province needs to accelerate the digital empowerment of traditional industries, develop the Internet, 5G technology, and new digital infrastructure to offset the negative impact of the COVID-19 pandemic. Electricity consumption is an applicable index in estimate the real economic cost of extreme events.

In this paper the Covid-19 pandemic has been analysed from sustainability and climate change perspectives with the help of a recursive dynamic CGE model for India. The Covid-19 could have major long term impacts on GDP, household income, inequality, CO emissions, and carbon prices. Significant slowdown in labour intensive informal sectors such as construction and services, as well as in energy intensive and capital goods sectors, leads to adverse impacts on household income and inequality. Our analysis further suggests that climate policy consistent with the Paris Agreement target can complement the economic recovery process. Specifically, recycling of carbon tax revenues to investments could stimulate growth and employment, reduce inequality, and reduce carbon emissions, compared to a scenario without climate policy. Therefore, the need of the hour is to formulate and implement climate friendly recovery strategies.

Concerns about industry competitiveness and distributional impacts can deter ambitious climate policies. Typically, these issues are studied separately, without giving much attention to the interaction between the two. Here, we explore how carbon leakage reduction measures affect distributional outcomes across households within 11 European countries by combining an economy-wide computable general equilibrium model with a household-level microsimulation model. Quantitative simulations indicate that a free allocation of emission permits to safeguard the competitive position of energy-intensive trade-exposed industries leads to impacts that are slightly more regressive than under full auctioning. We identify three channels that contribute to this effect: higher capital and labour income; lower tax revenue for compensating low-income households; and stronger consumption price increases following from higher carbon prices needed to reach the same emissions target. While these findings suggest a competitiveness-equity trade-off, the results also show that redistributing the revenues from partial permit auctioning on an equal-per-household basis still ensures that climate policy is progressive, indicating that there is room for policy to reconcile competitiveness and equity concerns. Finally, we illustrate that indexing social benefits to consumer price changes mitigates pre-revenue-recycling impact regressivity, but is insufficient to compensate vulnerable households in the absence of other complementary measures.

Governments and advocacy groups have drawn attention to the precarious position of those members of society who are unable to attain an adequate level of energy services, i.e. the fuel poor. Concerns have also arisen about the ability of fuel poor individuals to adapt to the hardship recently brought about by the COVID-19 pandemic. This paper contributes to the literature by exploring empirically the link between fuel poverty and financial distress prior to and during the first wave the COVID-19 pandemic. The analysis is based on the most recent longitudinal, nationally representative survey of the United Kingdom, Understanding Society (UKHLS, Wave 10, January 2018-February 2020). After correcting for the effects of potential endogeneity in the variables of interest, our results identify a statistically robust relationship between fuel poverty indicators and self-reported measures of current financial distress, with stronger effects for subjective indicators. The fuel poverty indicators however exert only a limited influence on an individual's expectation of their future financial situation. Our analysis of the first wave of the COVID-19 pandemic also confirms that fuel poverty contributed to financial distress. Our main findings are robust to a suite of specification and sensitivity checks. Our results lead to recommend assessing measures which target fuel poverty on the basis of their potential indirect effect on financial distress.

Detecting the adverse effects of major emergencies on financial markets and real economy is of great importance not only for short-term policy reactions but also for economic and financial stability. This is the lesson we learnt from the COVID-19 pandemic. This paper focuses on the risk spillover effect of the COVID-19 on Chinese energy industry using a high-dimensional and time-varying factor-augmented VAR model. The results show that the net volatility spillovers of the pandemic remain positive to all underlying energy sectors during January to June of 2020 and February to April of 2021. For the former sub-period, the volatility spillover of the COVID-19 is not only the highest, but also lasts longest for oil exploitation sector, followed by the power and gas sectors. While for the latter sub-period, the COVID-19 has relatively higher volatility spillovers to the power, coal mining and petrochemical sectors. These findings suggest that the COVID-19 has significant risk spillover effects on Chinese energy sectors, and the effects vary among different energy sub-sectors and across different periods of time.

The COVID-19 pandemic damaged crude oil markets and amplified the consequences of uncertainty stemming from the Russia-Saudi Arabia oil price war in March-April of 2020. We investigate the impacts of the oil price war on global crude oil markets. By doing so, we use the daily futures and spot prices in three major crude oil markets - West Texas Intermediate, European Brent, and Oman - to perform a systematic analysis of the impacts of the oil price war on them. The event study method, a well-established analytical tool to measure the impacts of a given event on markets, is used in this study. The results indicate that information leakage plays an important role in the impacts of the price war. The outbreak of and truce following the price war have asymmetrical impacts on the markets; negative impacts generated by information leakage during the outbreak are generally more durable than the positive ones it generated during the truce. Furthermore, the magnitude of the impacts on futures markets is negatively correlated with the time-to-maturity of futures. Finally, negative crude oil prices affect West Texas Intermediate crude oil markets the most. Our findings generally show that market participants could perceive and assimilate market changes and adjust their expectations, which restrained the impacts that should have occurred within the oil price war.

This paper studies the impact of the outbreak of coronavirus disease 2019 (COVID-19) on the stock price crash risk of energy firms in China. We find that the stock price crash risk of energy firms significantly decreases in the post-COVID-19 period. We also find that firms that engage in more corporate social responsibility (CSR) activities are less exposed to stock price crash risk in the post-COVID-19 period than those that engage in less CSR activities. Finally, we show that the effect of COVID-19 on stock price crash risk is less severe for state-owned enterprises (SOEs) than for non-SOEs in the post-COVID-19 period. Our findings demonstrate China's economic recovery in the post-COVID-19 period and have policy implications for firms to improve their resilience to exogenous shocks.