Earth's transient climate response (TCR) quantifies the global mean surface air temperature change due to a doubling of atmospheric CO2 concentration after 70 years of a compounding 1% per year increase. TCR is highly correlated with near-term climate projections, and thus of relevance for climate policy, but remains poorly constrained in part due to uncertainties in the representation of key physical processes in Earth System Models (ESMs). Within state-of-the-art ESMs participating in the Coupled Model Intercomparison Project (CMIP6), the TCR range (1.1 degrees C-2.9 degrees C) is too wide to offer useful guidance to policymakers. Similarly, the sixth report of the Intergovernmental Panel on Climate Change, while not solely reliant on ESMs for its TCR assessment, produced a very likely range of 1.2 degrees C-2.4 degrees C. To complement earlier, ESM-based, estimates, we here present a new TCR estimate of 2.17 (1.72-2.77) degrees C (95% confidence interval), derived based on a statistical relationship between surface air temperature and observational proxies for its main drivers, i.e. changes in atmospheric greenhouse gases and aerosols. We show that, within uncertainty, this method correctly diagnoses TCR from 20 CMIP6 ESMs if the same input variables are taken from the ESMs that are available from observations. This increases confidence in the new observation-based central estimate and range, which is respectively higher and narrower than the mean and spread of the estimates from the entire ensemble of CMIP6. Many ESM-based estimates tend to produce TCRs lower than the observational range reported here. Our findings suggest that a misrepresentation of the aerosol cooling effect could be the cause of this discrepancy. Further, the revised TCR estimate suggests a downward revision of the remaining carbon budgets aligned with the overarching goal of the Paris agreement.

Traditional economic models of climate change impacts rely on annual mean temperatures, overlooking crucial interactions between temperature and precipitation. Using a 59-year panel of country-level data, we show that incorporating these interactions reveals substantial heterogeneity in economic impacts across countries. Under a high-emission scenario, we project global economic production to decline by 58% by 2100 when accounting for interactive effects, compared to 75% without them. The interactions capture adaptation to local climates, with 80% of the countries experiencing reduced damages when including temperature-precipitation interactions compared to models without them. These countries generally experience temperatures above their optima, and higher precipitation trends are associated with greater damage mitigation. However, in cold regions traditionally expected to benefit from warming, projected wetting trends reduce these benefits and in some cases convert them to damages. These findings demonstrate that incorporating climate interactions moderates extreme projections and is essential for accurate assessment of climate change's economic impacts.

The recent climate crisis has solemnized renewable energy for emission reduction and halting an environmental disaster globally. However, renewable energy technologies are severely dependent on the import of critical minerals and elements that can be severely impacted by energy policy uncertainties (ERPU). Germany highly relies on renewables for powering economic activities and is the largest renewable energy producer in the European Union (EU) countries. However, German renewable energy generation heavily depends on imported critical minerals and rare earths. Therefore, the primary objective of this study is to examine how ERPU drives mineral-dependent renewable energy production in Germany from 1996M1-2018M12 by utilizing the Quantileon-Quantile (QQR) regression method as a baseline estimator. Quantile regression (QR) is also used to check for robustness. Empirical outcomes denote that ERPU can pose significant risks for cobalt-graphite-, copper-, nickeland rare earths-driven renewable energy production. However, the harmful impact of ERPU on nickel-driven renewable energy production is relatively limited. In addition, QR results majorly confirm the QQR results. Therefore, policymakers in Germany should focus on developing alternative energy policies to ease the undesired impacts of rising ERPU on renewable energy production.

This study employs a quantile moments approach to examine how economic policy uncertainty (EPU), geopolitical risk (GPR), and climate risks affect commodity return volatility. By incorporating interaction effects, we show that models ignoring these interactions underestimate volatility by up to 35% during stress periods. The analysis reveals varied effects across different volatility regimes, with transition climate risk intensifying market volatility particularly during turbulent times, whereas physical climate risk exhibits a mitigating effect. These findings offer valuable implications for risk management and policy coordination in commodity markets, highlighting the importance of considering interaction effects both normal and volatile market conditions.

Using an event study methodology, we investigate how unexpected political events affect climate-sensitive sectors. We find that events related to climate change policy have significantly impacted returns. The clean energy sector benefitted from the Paris Agreement, Climategate, and Fukushima since these events increased climate change awareness and favor toward policies related to reducing the impact of climate change. For the utilities, energy-intensive, and transport sectors, these events imply increased transition-related political and market risks, which should be compensated. Events weakening climate change policy are associated with positive abnormal returns for the fossil energy sector. We further find that stock market investors are quick to adapt to new information related to climate change. Policymakers should be aware of such events' impact on the stock market because the investors are likely to price in both climate risk and expectation about sectors' growth.

In this study, we investigate the dynamic relationship between return and liquidity in the Brent and the West Texas Intermediate (WTI) oil markets. The research utilises daily oil price and volume data and monthly macroeconomic data from January 1, 1996 to April 28, 2023 obtained from the Energy Information Association (EIA), the Organisation for Economic Co-operation and Development (OECD), the Federal Reserve Economic Data (FRED), investing.com, and the International Monetary Fund (IMF). We employ the ARMAX(1,1)-aDCC-GARCH-t(1,1) model to capture time-varying associations between return and liquidity. Our findings reveal a significant impact of speculation on the return-liquidity relationship, which is more persistent in the WTI market. Furthermore, we observe a pattern between the Brent and WTI markets during the study period, which the heterogeneous trader hypothesis can explain. These insights hold implications for policymakers aiming to enhance the crude oil market’s stability, as well as for market traders in developing trading and risk management strategies.

This study investigates the hedge and safe-haven possibilities with bitcoin, gold and crude oil in different equity markets in the presence of time-varying market inefficiency. Our results indicate that periods of market inefficiency for the Bitcoin, gold and crude oil price positively influence their function as a hedge asset for the equity markets of Japan, China, the US, Europe and emerging countries. In addition to contributing to the discussion on the factors which affect the functioning of safe-haven assets, the empirical findings of this study further highlight the importance of market efficiency as a market microstructure feature. These results have important implications for investors seeking to manage risk through diversification across different asset classes.

We examine the relationship between the top five cryptos and the U.S. S&P500 index from January 2018 to December 2021. We use the novel General-to-specific Vector Autoregression (GETS VAR) and traditional Vector Autoregression (VAR) model to analyze the short-and long -run, cumulative impulse-response, and Granger causality test between S&P500 returns and the returns of Bitcoin, Ethereum, Ripple, Binance and Tether. Additionally, we used the Diebold and Yilmaz (DY) spillover index of variance decomposition to validate our findings. Evidence from the analysis suggests positive short-and long-run effects of historical S&P500 returns on Bitcoin, Ethereum, Ripple, and Tether returns--and negative short-and long-run effects of the historical returns of Bitcoin, Ethereum, Ripple, Binance, and Tether on S&P500 returns. Alternatively, evidence suggests a negative short-and long-run effect of historical S&P500 returns on Binance returns. The cumulative test of impulse-response indicates a shock in historical S&P500 returns stimulates a positive response from cryptocurrency returns while a shock in historical crypto returns triggers a negative response from S&P500 returns. Empirical evidence of bi-directional causality between S&P500 returns and crypto returns suggest the mutual coupling of these market. Although, S&P500 returns have high-intensity spillover effects on crypto returns than crypto returns have on S&P500. This contradicts the fundamental attribute of cryptocurrencies for hedging and diversification of assets to reduce risk exposure. Our findings demonstrate the need to monitor and implement appropriate regulatory policies in the crypto market to mitigate the potential risks of financial contagion.

Global warming has slowed economic growth and aggravated global economic inequality, affecting individual wellbeing in a wide-ranging aspects. Quantifying these historical impacts is critical for informing climate change mitigation and adaptation and achieving a more equitable economic development. This paper extends existing literature by exploring the effects of precipitation on economic growth. Based on a panel of 169 countries over the period 1961-2019, we demonstrate a statistically significant non-linear effect of precipitation on economic growth, such that output is maximized at around 2.03 metres of annual total precipitation. Despite of the significant sensitivity of precipitation, we find its impacts are relatively small and are completely overwhelmed by the effects of temperature. We examine the historical marginal effects of climate change and find realized temperature has lowered the annual global growth rate by 0.31 percentage points per year on average, whereas realized precipitation has increased the annual economic growth by roughly 0.01 percentage points. Furthermore, we highlight that countries endowed with different climate conditions exhibit substantially different reactions to historical climate change. For example, Europe and Central Asia countries have benefited both from temperature rising and precipitation fluctuations; while adverse impacts are observed for both factors in African countries. These findings suggest the importance of precipitation for countries with vulnerable ecosystems and inform the possibility of incorporating precipitation in economic development projections under future climate trajectories.

In this paper I document a positive relation between the volatility of liquidity and expected returns. Specifically, I analyze the relationship between the idiosyncratic volatility of market liquidity and the returns of the five largest cryptocurrencies by market capitalization. I find that the correlation between liquidity volatility and returns is overall significantly positive, but highly time-varying. This implies that investors demand a premium for a high variation in liquidity volatility. I furthermore find that the correlation between returns and the level of liquidity is mostly positive, thus, when liquidity is low, expected returns are high. The results corroborates results from other financial markets.

This paper assesses the effects of investors' lottery-seeking behavior on expected returns in the Norwegian equity market, a relatively small equity market dominated by the energy industry. We use the MAX factor defined as maximum daily return over the previous month as the proxy of investors' preference for lottery-like stocks. Despite evidence from recent literature that MAX has a negative relationship with the expected returns in other developed European markets, we find that the relationship is generally insignificant in Norway; however, it becomes more nuanced when we control for the state of the oil market. The dominance of firms related to the oil industry, which have experienced tremendous growth over the last couple of decades, masks the effect to a large extent. Conditional regressions show that the MAX effect is only significant in the Norwegian stock market when the oil market is in the bearish state.

This paper examines time-varying market efficiency in the crude oil spot market using a recently derived measure of market efficiency: the Adjusted Market Inefficiency Model (AMIM). Analysing efficiency in the crude oil market, and its response to significant events within the global financial and commodity market, we identify that the Brent market is on average more efficient than the West Texas Intermediate (WTI). We also find that the WTI market is persistently inefficient during financial crises, with high volatility of the efficiency in such periods. In addition to confirming the adaptive market hypothesis, this study offers a new perspective by highlighting the non-uniform response of efficiency in similar markets to global events.

Earth’s transient climate response (TCR) quantifies the global mean surface air temperature change due to a doubling of atmospheric CO2, at the time of doubling. TCR is highly correlated with near-term climate projections, and thus of utmost relevance for climate policy, but remains poorly constrained. Within state-of-the-art Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP6), the TCR range (1.1 -2.9oC is much too wide to offer useful guidance to policymakers on remaining carbon budgets aligneded with the Paris agreement goals. To address this issue, we here present an observation-based TCR estimate of 1.9-2.7oC (95% confidence interval). We show that this method correctly diagnoses TCR from 22 CMIP6 ESMs if the same variables are taken from the ESMs as are available from observations. This increases confidence in the new estimate and range, which are higher and narrower, respectively, than those of the CMIP6 ensemble.

The environmental sustainability of bitcoin is making waves in the empirical literature, yet, no study has thus far examined the financial determinants of bitcoin energy consumption and carbon footprint. Here, we use novel estimation methods comprising dynamic ARDL simulations and general-to-specific VAR to examine steady-state effects, cumulative impulse-response, and counterfactual shocks of bitcoin trade volume on bitcoin energy bitcoin carbon footprint to ensure genuine causal inferences. We observed an increase in bitcoin trade volume spur both carbon and energy footprint by 24% in the long-run, whereas a dynamic shock in trade volume escalates bitcoin energy and carbon footprint by 46.54%.

Using a data set on climate risk constructed by Google BERT (AI) algorithm, fine-tuned by Kölbel et al. (2022), we demonstrate that physical climate risk is materialized in US stock markets. This premium is positive, both statistically and economically significant (1.5% to 2.7% annually), consistent, and cannot be explained by industry variation or other well- known risk factors. We also find no consistent premium related to the climate transitional risk across different industries. The physical climate risk premium is more salient after the Paris Agreement (3.2% to 4.2% annually), possibly due to increased investor attention to climate-related issues.

We investigate the relationship between hybrid tail covariance risk (HTCR) and expected return over the last four decades. Despite a significant positive HTCR-expected return relationship in Bali et al. (2014) , we find that this relationship is not significant at least during average market conditions. However, if we control for market regime the relationship starts to appear. We find a strong link between market volatility and the relationship between HTCR and expected returns. We analyze this relationship during two market regimes, calm and noisy, depending on the return and return-volatility. We find that these market regimes pose as a catalyst to HTCR pricing in the cross-section of expected returns because HTCR-expected return relationship exists only during the calm regime and it ceases to exist during the noisy regime. Firm level cross-sectional regressions show significant positive relation (no relation) between HTCR and expected returns during calm (noisy) regime even after controlling for other relevant priced factors.

Abstract This study applies statistical methods to interpolate missing values in a data set of radiative energy fluxes at the surface of Earth. We apply Random Forest (RF) and seven other conventional spatial interpolation models to a global Surface Solar Radiation (SSR) data set. We apply three categories of predictors: climatic, spatial, and time series variables. Although the first category is the most common in research, our study shows that it is actually the last two categories that are best suited to predict the response. In fact, the best spatial variable is almost 40 times more important than the best climatic variable in predicting SSR. Furthermore, the 10‐fold cross validation shows that the RF has a Mean Absolute Error (MAE) of 10.2 Wm−2 and a standard deviation of 1.5 Wm−2. On the other hand, the average MAE of the conventional interpolation methods is 21.3 Wm−2, which is more than twice as large as the RF method, in addition to an average standard deviation of 6.4 Wm−2, which is more than four times larger than the RF standard deviation. This highlights the benefits of using machine learning in environmental research.

The green bond market develops rapidly and aims to contribute to climate mitigation and adaptation significantly. Green bonds as any asset are subject to transition climate risk, namely, regulatory risk. This paper investigates the impact of unexpected political events on the risk and returns of green bonds and their correlation with other assets. We apply a traditional and regression-based event study and find that events related to climate change policy impact green bonds indices. Green bonds indices anticipated the 2015 Paris Agreement on climate change as a favorable event, whereas the 2016 US Presidential Election had a significant negative impact. The negative impact of the US withdrawal from the Paris agreement is more prominent for municipal but not corporate green bonds. All three events also have a similar effect on green bonds performance in the long term. The results imply that, despite the benefits of issuing green bonds, there are substantial risks that are difficult to hedge. This additional risk to green bonds might cause a time-varying premium for green bonds found in previous literature.

Downward surface solar radiation (SSR) is a crucial component of the Global Energy Balance, affecting temperature and the hydrological cycle profoundly, and it provides crucial information about climate change. Many studies have examined SSR trends, however they are often concentrated on specific regions due to limited spatial coverage of ground based observation stations. To overcome this spatial limitation, this study performs a spatial interpolation based on a machine learning method, Random Forest, to interpolate monthly SSR anomalies using a number of climatic variables (various temperature indices, cloud coverage, etc.), time point indicators (years and months of SSR observations), and geographical characteristics of locations (latitudes, longitudes, etc). The predictors that provide the largest explanatory power for interannual variability are diurnal temperature range and cloud coverage. The output of the spatial interpolation is a 0:5° ×0:5° monthly gridded dataset of SSR anomalies with complete land coverage over the period 1961-2019, which is used afterwards in a comprehensive trend analysis for i) each continent separately, and ii) the entire globe.The continental level analysis reveals the major contributors to the global dimming and brightening. In particular, the global dimming before the 1980s is primarily dominated by negative trends in Asia and North America, while Europe and Oceania have been the two largest contributors to the brightening after 1982 and up until 2019.

We show that the level of market-efficiency in the five largest cryptocurrencies is highly time-varying. Specifically, before 2017, cryptocurrency-markets are mostly inefficient. This corroborates recent results on the matter. However, the cryptocurrency-markets become more efficient over time in the period 2017-2019. This contradicts other, more recent, results on the matter. One reason is that we apply a longer sample than previous studies. Another important reason is that we apply a robust measure of efficiency, being directly able to determine if the efficiency is significant or not. On average, Litecoin is the most efficient cryptocurrency, and Ripple being the least efficient cryptocurrency.