Search Results (103)

The rise in energy prices due to global uncertainties and risks is accelerating the transition to renewable energy in countries. It is expected that embracing energy diversity instead of dependence on a single energy source, such as oil, will curb energy-related increases in inflation. In this study, the impact of the transition to renewable energy on inflation is investigated using the energy diversification index. For this purpose, the Chinese economy is analyzed with the Augmented ARDL method. According to the long-term results of the analysis covering the 1991–2023 period, the effect of energy diversity on inflation is negative. The study also examined the effect of composing an energy portfolio consisting of various renewable energy sources rather than a single renewable energy source on inflation. According to the results obtained, renewable energy diversity has a negative effect on inflation, too. As a result, inflation is expected to decrease as renewable energy diversification and overall energy diversification increase. In sum, inflation can be expected to fall when authorities increase both renewable energy diversity and overall energy diversity instead of solely depending on oil or any renewable energy source. Full article

Energy raw materials are the basis of the economic system. From this emerges the need to examine in more detail how various uncertainty indices interact with the dynamic of spillover connectedness among energy markets. The TVP-VAR model is used to investigate connectedness among US, European, and Indian oil and gas markets and the S&P carbon allowances Eua index. Following this, the wavelet decomposition technique is used to capture the dynamic correlations between uncertainty indices (GPR and VIX) and connectedness indices. First, the results indicate that energy market spillovers are time-varying and crisis-sensitive. Second, the time–frequency dependence among uncertainty indices and connectedness indices is more marked and can change with the occurrence of unexpected events and geopolitical conflicts. The VIX index shows a positive dependence on total dynamic connectedness in the mid-long-term, while the GPR index has a long-term effect only after 2020. The analysis of the interdependence among the connectedness of each market and the uncertainty indices is more heterogeneous. Political tensions and geopolitical risks are, therefore, causal factors of energy prices. Given their strategic and economic importance, policy makers and investors should establish a risk warning mechanism and try to avoid the transmission of spillovers as much as possible. Full article

This paper examines the changing role of emissions trading systems (ETSs) within the macro-financial framework of energy markets, emphasizing price dynamics and systemic spillovers. Utilizing monthly data from seven ETS jurisdictions spanning January 2021 to December 2024 (N = 287 observations after log transformation and first differencing), which includes four auction-based markets (United States, Canada, United Kingdom, South Korea), two secondary markets (China, New Zealand), and a government-set fixed-price scheme (Germany), this research estimates a panel vector autoregression (PVAR) employing a Common Correlated Effects (CCE) model and augments it with machine learning analysis utilizing XGBoost and explainable AI methodologies. The PVAR-CEE reveals numerous unexpected findings related to carbon markets: ETS returns exhibit persistence with an autoregressive coefficient of −0.137 after a four-month lag, while increasing inflation results in rising ETS after the same period. Furthermore, ETSs generate spillover effects in the real economy, as elevated ETSs today forecast a 0.125-point reduction in unemployment one month later and a 0.0173 increase in inflation after two months. Impulse response analysis indicates that exogenous shocks, including Brent oil prices, policy uncertainty, and financial volatility, are swiftly assimilated by ETS pricing, with effects dissipating completely within three to eight months. XGBoost models ascertain that policy uncertainty and Brent oil prices are the most significant predictors of one-month-ahead ETSs, whereas ESG factors are relevant only beyond certain thresholds and in conditions of low policy uncertainty. These findings establish ETS markets as dynamic transmitters of macroeconomic signals, influencing energy management, labor changes, and sustainable finance under carbon pricing frameworks. Full article

This paper revisits the international finance trilemma by analyzing how different monetary policy objectives and exchange rate regimes shape the transmission of global risk shocks. Using a structural vector autoregressive model with exogenous variables (SVARX), we examine the monetary policy responses and exchange rate fluctuations of ASEAN+4 economies—China, Japan, Korea, and Hong Kong—to external shocks including U.S. monetary policy changes, oil price fluctuations, global policy uncertainty, and financial risk during 2010–2022. Economies are grouped according to their trilemma configurations: floating exchange rates with free capital flows, fixed exchange rates, and capital control regimes. Our findings broadly support the trilemma hypothesis: fixed-rate economies align with U.S. interest rate movements, capital control economies retain greater monetary autonomy, and open, floating regimes show partial responsiveness. More importantly, monetary responses vary by global shock type: U.S. monetary policy drives the most synchronized policy reactions, while oil price and uncertainty shocks produce more heterogeneous outcomes. Robustness checks include alternative model specifications, where global shocks are treated as endogenous, and extensions, such as using Japan’s monetary base as a proxy for unconventional monetary policy. These results refine the empirical understanding of the trilemma by showing that its dynamics depend not only on institutional arrangements but also on the nature of global shocks—underscoring the need for more tailored and, where possible, regionally coordinated monetary policy strategies. Full article

The scaling up of carbon capture, utilization, and storage (CCUS) deployment is constrained by multiple factors, including technological immaturity, high capital expenditures, and extended investment return periods. The existing research on CCUS investment decisions predominantly centers on coal-fired power plants, with the utilization pathways placing a primary emphasis on storage or enhanced oil recovery (EOR). There is limited research available regarding the chemical utilization of carbon dioxide (CO₂). This study develops an options-based analytical model, employing geometric Brownian motion to characterize carbon and oil price uncertainties while incorporating the learning curve effect in carbon capture infrastructure costs. Additionally, revenues from chemical utilization and EOR are integrated into the return model. A case study is conducted on a process producing 100,000 tons of methanol annually via CO₂ hydrogenation. Based on numerical simulations, we determine the optimal investment conditions for the “CO₂-to-methanol + EOR” collaborative scheme. Parameter sensitivity analyses further evaluate how key variables—carbon pricing, oil market dynamics, targeted subsidies, and the cost of renewable electricity—influence investment timing and feasibility. The results reveal that the following: (1) Carbon pricing plays a pivotal role in influencing investment decisions related to CCUS. A stable and sufficiently high carbon price improves the economic feasibility of CCUS projects. When the initial carbon price reaches 125 CNY/t or higher, refining–chemical integrated plants are incentivized to make immediate investments. (2) Increases in oil prices also encourage CCUS investment decisions by refining–chemical integrated plants, but the effect is weaker than that of carbon prices. The model reveals that when oil prices exceed USD 134 per barrel, the investment trigger is activated, leading to earlier project implementation. (3) EOR subsidy and the initial equipment investment subsidy can promote investment and bring forward the expected exercise time of the option. Immediate investment conditions will be triggered when EOR subsidy reaches CNY 75 per barrel or more, or the subsidy coefficient reaches 0.2 or higher. (4) The levelized cost of electricity (LCOE) from photovoltaic sources is identified as a key determinant of hydrogen production economics. A sustained decline in LCOE—from CNY 0.30/kWh to 0.22/kWh, and further to 0.12/kWh or below—significantly advances the optimal investment window. When LCOE reaches CNY 0.12/kWh, the project achieves economic viability, enabling investment potentially as early as 2025. This study provides guidance and reference cases for CCUS investment decisions integrating EOR and chemical utilization in China’s refining–chemical integrated plants. Full article

This study explores how energy price inflation affects supply chain pressures under different levels of political uncertainty. Using local projection impulse–response functions, we examine the effects of oil price shocks under two regimes: one with above-average levels of political uncertainty and another with below-average uncertainty. While previous research has focused on the direct macroeconomic impacts of oil price shocks, particularly on firm costs and consumer prices, this study highlights the effects of these shocks on supply chain disruption as a whole. Our findings indicate that heightened political uncertainty significantly amplifies the impact of oil price shocks on supply chain pressures, causing notable and persistent disruptions. Conversely, when political stability is high, the response of supply chains to the same shocks is minimal, suggesting that a stable political environment fosters greater resilience in supply chains. Full article

This study examines the interplay between oil price volatility, sustainability-driven initiatives, and the MSCI UAE Index, highlighting the challenges that oil-dependent economies face in balancing financial stability with sustainability transitions. Using a dataset of 2707 daily observations from 2014 to 2024, we applied linear regression, ARCH, GARCH, and TARCH models to analyze volatility dynamics across two key periods: the 2014–2016 oil price collapse and the 2019–2023 phase marked by the COVID-19 pandemic and increasing sustainability efforts. Our findings indicate that oil price fluctuations significantly impact the MSCI UAE Index, with GARCH models confirming persistent volatility and TARCH models revealing asymmetrical effects, where negative shocks intensify market fluctuations. While the initial sustainability policy announcements contributed to short-term volatility and investor uncertainty, they ultimately fostered market confidence and long-term stabilization. Unlike previous studies focusing solely on oil price volatility in emerging markets, this research integrates sustainability policy announcements into financial modeling, providing novel empirical insights into their impact on financial stability in oil-exporting economies. The findings suggest that stabilization funds, dynamic portfolio strategies, and transparent regulatory policies can mitigate oil price volatility risks and enhance market resilience during sustainability transitions, offering valuable insights for investors, policymakers, and financial institutions navigating the UAE’s evolving economic landscape. Full article

This study examines the relationship between stock prices, energy prices, and climate policy uncertainty using 11 sectoral stocks in the U.S. market. The evidence confirms that rising prices of energy commodities positively affect not only the energy and oil sector stocks but also create spillover effects across other sectors. Notably, all sectoral stocks, except Real Estate sector, show resilience to increases in crude oil and gasoline, suggesting potential hedging benefits. In addition, the findings reveal that sectoral stock returns are generally negatively affected by several types of uncertainty, including climate policy uncertainty, economic policy uncertainty, oil price uncertainty, as well as energy and environmental regulation-induced equity market volatility and the energy uncertainty index. These adverse effects are present across sectors, with few exceptions. The evidence reveals that the feedback effect between changes in climate policy uncertainty and changes in oil prices has an adverse impact on stock returns. Omitting these uncertainty factors from analyses could lead to biased estimates in the relationship between stock prices and energy prices. Full article

This paper examines the impact of various uncertainty channels on stock market returns in Saudi Arabia, with a focus on the Tadawul All Share Index (TASI). It examines factors such as Saudi-specific Geopolitical Risk, Global Oil Price Uncertainty, Climate Policy Uncertainty, and U.S. Monetary Policy Uncertainty. Using monthly data from November 1998 to June 2024 and the Local Projections (LP) methodology, the study examines how these uncertainties impact market returns across various time horizons, taking into account potential structural breaks and nonlinear dynamics. Our findings indicate significant variations in the market’s response to the uncertainty measures across two distinct periods. During the first period, geopolitical risks have a strong positive impact on market returns. Conversely, the second period reveals a reversal, with negative cumulative effects, suggesting a shift in risk–return dynamics. Oil Price Uncertainty consistently exhibits a negative impact in both periods, highlighting the changing nature of oil dependency in the Saudi market. Additionally, Climate Policy Uncertainty is becoming more significant, reflecting increased market sensitivity to global environmental policy changes. Our analysis reveals significant asymmetries in the effects of various uncertainty shocks, with Monetary Policy Uncertainty exhibiting nonlinear effects that peak at intermediate horizons, while commodity-related uncertainties exhibit more persistent impacts. These findings, which remain robust across various tests, offer critical insights for portfolio management, policy formulation, and risk assessment in emerging markets undergoing substantial economic changes. Full article

This study employs asymmetric quantile regression to investigate the asymmetric impact of WTI crude oil prices and economic policy uncertainty (EPU) on stock market returns from May 2014 to December 2024 in oil-importing (China, India, Germany, Italy, Japan, USA, and South Korea) and oil-exporting (Saudi Arabia, Russia, Iraq, Canada, and the United Arab Emirates) countries. The findings reveal that an increase in oil prices significantly impacts the returns of all countries. For oil-importing countries, an increase in oil prices consistently exhibits a positive impact, with insignificant effects in lower and medium quantiles and significant effects in higher quantiles. Conversely, a decrease in oil prices generally decreases stock market returns across all quantiles. This study offers valuable insights for investors to manage risks and improve the predictability of oil price fluctuations. It also provides strategies and policy implications for capitalists and decision-makers. By addressing contemporary issues and using up-to-date data, the study supports financial institutions and portfolio managers in formulating effective strategies. Full article

This article presents a methodology for risk assessment and management in the development of hard-to-recover hydrocarbon reserves. The proposed methodology integrates Monte Carlo simulation and fuzzy logic methods, which allows for the consideration of both quantitative stochastic risks (e.g., fluctuations in oil prices, variability in costs, and production volumes) and qualitative uncertainties (e.g., environmental, social, and technological risks) that are traditionally difficult to formalize. The approach facilitates the incorporation of uncertainties associated with complex field developments and aims to improve managerial decisions through comprehensive risk assessment. The article elaborates on the theoretical aspects of the proposed methodology, including risk identification stages, the formalization of qualitative data using fuzzy logic, and the application of the Monte Carlo method for integrating various risk categories. The results confirm the potential of this methodology as a tool to enhance the resilience and economic efficiency of projects involving the development of hard-to-recover hydrocarbon reserves. Full article

This study investigates the multifractal characteristics of the tanker freight market from 1998 to 2024. Using multifractal detrended fluctuation analysis (MF-DFA) and multifractal detrending moving average (MF-DMA), we analyze temporal correlations and volatility, revealing subtle differences in multifractal features before and after 2010. We further examine the influence of key external factors—including economic disturbances (the 2008 financial crisis), technological innovations (the 2014 Shale Oil Revolution), supply chain disruptions (the COVID-19 pandemic), and geopolitical uncertainties (the Russia–Ukraine conflict)—on market complexity. Building on this, a predictive framework is introduced, leveraging the Baltic Dirty Tanker Index (BDTI) to forecast Brent oil prices. By integrating multifractal analysis with machine learning models (e.g., XGBoost, LightGBM, and CatBoost), our framework fully exploits the predictability from the freight index to oil prices across the above four major global events. The results demonstrate the potential of combining multifractal analysis with advanced machine learning models to improve forecasting accuracy and provide actionable insights during periods of heightened market volatility. On average, the coefficient of determination (R²) increases by approximately 62.65% to 182.54% for training and 55.20% to 167.62% for testing, while the mean squared error (MSE) reduces by 60.83% to 92.71%. This highlights the effectiveness of multifractal analysis in enhancing model performance, especially in more complex market conditions post-2010. Full article

This study analyzes the crude oil supply in 2030 and 2050, comparing it with demand scenarios from the UN Intergovernmental Panel on Climate Change and the International Energy Agency. It focuses on the oil under production or development as of today (or the supply already under contract), and the oil frontiers. For that, it firstly evaluates a database of over 107,000 assets to identify and classify recoverable oil volumes through 2050. By comparing the supply and demand, this study identifies scenarios requiring production declines or, in opposition, the development of new projects and exploratory frontiers. The focus is on 2030 and 2050, which are key milestones in the global climate agenda. As an original contribution, the analysis also identifies how oil supply regions position themselves regarding oil quality, production costs, and the GHG emission intensity of the oil offered. As the second contribution, this study develops the probability assessment of recoverable resources to evaluate a typical oil frontier, analyzing how global climate scenarios could affect the probability of approving a deepwater offshore project. The findings show that cumulative oil consumption by 2050 may range from 600 billion to 1 trillion barrels, with marginal supply costs between US$28/bbl and US$44/bbl. The findings indicate that the frontier project lacks economic attractiveness in scenarios limiting the increase in the global surface temperature (GST) below 1.5 °C with no or limited overshoots. However, assuming a smooth price decline trajectory from today to 2050, the project exhibits high profitability and returns across all the scenarios. This suggests that the industry might remain inclined to approve new projects, even amid potential energy transition scenarios, driven by favorable short- and medium-term returns despite long-term uncertainties. Full article

Inflation rate is considered undesirable in the modern globalized world due to its adverse and long-lasting impacts. The Kingdom of Saudi Arabia (KSA, hereafter) has also experienced inflationary pressure during the last few years, specifically post-COVID-19. However, the empirical literature on the determinants of inflation is indeed very scarce in the context of KSA. Amid this backdrop, this research paper aims to figure out the true determinants of inflation by focusing on the role of supply chain disruptions and global uncertainty by focusing on KSA. Quantitative data were collected from credible sources on a monthly basis for the period of 1998M01 to 2024M02 and were analyzed through the “Autoregressive Distributed Lag Model (ARDL)”. Our findings indicate that inflation in KSA is positively impacted by supply chain disruptions, global uncertainty, inflation spillovers from the United States, and money supply in the long run. Similarly, in the short run, only money supply, supply chain disruptions, and global uncertainty are responsible for the prevailing inflation rate in KSA. Moreover, the real effective exchange rate is positively and significantly linked with inflation only in the long run. Furthermore, positive shocks in oil prices cure inflation, while negative shocks in oil prices accelerate inflation in the short run. Our results are expected to shape policy formulation regarding the management of the inflation rate in KSA significantly. Full article

While there is a large body of literature on oil uncertainty-equity prices and/or returns nexus, an associated important question of how oil market uncertainty affects stock market bubbles remains unanswered. In this paper, we first use the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator (MS-LPPLS-CI) approach to detect both positive and negative bubbles in the short-, medium- and long-term stock markets of the G7 countries. While detecting major crashes and booms in the seven stock markets over the monthly period of February 1973 to May 2020, we also observe similar timing of strong (positive and negative) LPPLS-CIs across the G7, suggesting synchronized boom-bust cycles. Given this, we next apply dynamic heterogeneous coefficients panel databased regressions to analyze the predictive impact of a model-free robust metric of oil price uncertainty on the bubbles indicators. After controlling for the impacts of output growth, inflation, and monetary policy, we find that oil price uncertainty predicts a decrease in all the time scales and countries of the positive bubbles and increases strongly in the medium term for five countries (and weakly the short-term) negative LPPLS-CIs. The aggregate findings continue to hold with the inclusion of investor sentiment indicators. Our results have important implications for both investors and policymakers, as the higher (lower) oil price uncertainty can lead to a crash (recovery) in a bullish (bearish) market. Full article