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We present a weekly structural Vector Autoregressive (VAR) model of the US crude oil market. Exploiting weekly data we can explain short-run crude oil price dynamics, including those related with the COVID-19 pandemic and with the Russia’s invasion of Ukraine. The model is set identified with a Bayesian approach that allows to impose restrictions directly on structural parameters of interest, such as supply and demand elasticises. Our model incorporates both the futures-spot price spread to capture shocks to the real price of crude oil driven by changes in expectations and US inventories to describe price fluctuations due to unexpected of variations of above-ground stocks. Including the futures-spot price spread is key for accounting for feedback effects from the financial to the physical market for crude oil and for identifying a new structural shock that we label expectational shock. This shock plays a crucial role when describing the series of events that have led to the spike in the price of crude oil recorded in the aftermath of Russia’s invasion of Ukraine.

We develop a Bayesian Structural VAR (SVAR) model to study the relationship between different kinds of energy shocks and inflation dynamics in Europe. Specifically, we include in our specification two separate energy markets (oil and natural gas) and two target macroeconomic variables, measuring inflation expectations and the realized headline inflation. Our results demonstrate that, during the last year, inflation in the Euro area is more affected from energy price shocks, particularly those coming from the natural gas sector. The high peaks of the Eurozone inflation are mainly associated with gas consumption demand shocks and, to a lesser extent, to oil and gas supply shocks.

The identification of the effects of climate shocks on economic growth is central to design effective policies aiming at managing the future global climate change challenge. In this study, we investigate the effects of temperature and precipitation shocks on economic growth across different countries by means of a new methodology, namely a Bayesian Structural Global VARX model. This setup accommodates economic interpretation of the shocks and accounts for crosssectional spillovers among countries, as well as endogeneity of the climate variables with respect to the economy. Results show a high degree of heterogeneity, with some economies positively and some others negatively affected by climate shocks. In contrast with a consistent strand of the literature, according to which hot and poor countries bear the heaviest burden, we show that climate shocks may have severe effects for the economic growth of rich and cold countries as well. Furthermore, accounting for trade interdependence across countries, we document that, in response to unexpected temperature and precipitation shocks, some economies benefit from interconnections, while some others are damaged, depending on some key structural characteristics as the import-export mix, the relevant trade partners network and the level of economic development.

We study the relationship between the risk of default and Environmental, Social and Governance (ESG) factors using Supervised Machine Learning (SML) techniques on a cross-section of European listed companies. Our proxy for credit risk is the z-score originally proposed by Altman (1968). We consider an extensive number of ESG raw factors sourced from the rating provider MSCI as potential explanatory variables. In a first stage we show, using different SML methods such as LASSO and Random Forest, that a selection of ESG factors, in addition to the usual accounting ratios, helps explaining a firm's probability of default. In a second stage, we measure the impact of the selected variables on the risk of default. Our approach provides a novel perspective to understand which environmental, social responsibility and governance characteristics may reinforce the credit score of individual companies.

The aim of this paper is to analyze the connectedness between renewable energy (RE) sectors, the oil & gas sector and other assets using time-scale spillover approach. We find that the RE bioenergy firms are the most connected to oil & gas firms and oil prices. The bond market transmits spillover to the RE sectors, while it receives spillover from the oil & gas sector. Moreover, short-run connectedness drives the dynamic total connectedness. Since changes in bond rates mainly spillover to RE firms and not to oil & gas firms, policy makers should also be aware that changes in interest rates may impact the societal transition to a RE based energy system. Since a shock that increases connectedness in the short run will deter investors from investing in RE assets, it is important for climate policy makers to develop policies that reduce the effect of increased connectedness on RE investments.

The aim of this paper is to analyze the relationship between different types of uncertainty and stock returns of the renewable energy and the oil & gas sectors. We use the quantile regression approach developed by Koenker and d’Orey (1987; 1994) to assess which uncertainties are the potential drivers of stock returns under different market conditions. We find that the bioenergy and the oil & gas sectors are most sensitive to uncertainties. Both sectors are affected by financial, euro currency, geopolitical and economic policy uncertainties. Our results have several policy implications. Climate policy makers can prioritize policies that support bioenergy in order to reduce the potentially negative effects of uncertainties on bioenergy investment. Investors aiming to diversify their portfolio should be aware that many uncertainties are common drivers of bioenergy and oil & gas returns, the connectedness between assets of these energy types could therefore increase when uncertainty increases.

We study the impact of human capital and the level of education on the pollution-income relationship controlling for income inequality in 17 OECD countries. By applying an innovative approach to country grouping, based on the temporal evolution of income inequality and clustering techniques to feature the annual value of the Gini Index on disposable income from 1987 to 2015, we have estimated panel data models by distinguishing between low and high levels of income inequality country clusters. Robustness checks and endogeneity tests are further performed considering as the discriminant factor the income inequality affecting the countries in the sample. The findings highlight the role of the educational level and years of schooling in validating the EKC hypothesis. We recommend that this variable should not be neglected in future EKC studies. Therefore, any EKC theory should also acknowledge a new EKC model specification that we named the Educational EKC.

The aim of this paper is to investigate how major net oil exporter economies react to oil price shocks. We contribute to the literature by considering, at the same time, the possible nonlinearity and asymmetry of this relationship with respect to sign, size and causes of the oil price shocks, as well as the state of the economy in which the shocks occur. We apply a Threshold Structural VAR approach, characterized by a separation of the observations into different regimes based on a threshold variable, to model time series non-linearities. We use the economic activity as the threshold variable, as it divides economic development in two regimes under which we expect the effects of oil price shocks to differ. First, We find that the effects of oil price shocks on oil exporting economies greatly depend on the underlying cause of the shocks as well as the state of the economy. Second, we find little evidence of asymmetric response of output to the sign of oil price shocks. Our main findings warn decision makers in the area of macroeconomic planning that, when making decisions based on the oil price, the underlying causes of its variations as well as the state of the economy in which the oil price shocks occur have to be considered.