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This work uses the vector-valued Bernoulli equation to build a nonlinear multi-variable grey Bernoulli model, which is available to describe the nonlinear relationship between the output variables. By using P a d e ´ approximation, the proposed model can be implemented with high time efficiency. Additionally, the Sine Cosine Algorithm is employed to determine the Bernoulli exponent, thereby enhancing prediction accuracy. To evaluate the predictive performance of the proposed model, three case studies using three real-world data sets with different features of predicting per capita household income, fuel prices and crude oil prices are carried out. The results are compared with three existing grey multi-input multi-output models. Experimental results demonstrate that the proposed model excels in handling nonlinear relationships between variables and has strong robustness against noise, consistently delivering lower error values, demonstrating superior predictive performance. • A nonlinear multi-variable grey Bernoulli model is proposed based on the Hadamard product. • Linear parameter estimation is carried out using the least squares method or the least norm method. • P a d e ´ approximation is used to compute the matrix exponential to improve the time efficiency. • The model is optimized using the Sine Cosine Algorithm. • The proposed model shows superior prediction performance compared with three other typical models. [ABSTRACT FROM AUTHOR]

During the development process of heavy oil with steam huff and puff, the wellbore undergoes alternating conditions of temperature rise and fall, leading to a significant decline in the strength of the cement sheath. This work studied the inhibition effect and mechanism of the composite material composed of mullite and basalt fiber on the strength decline of cement stone (the hardened body of cement slurry) under simulated wellbore temperature alternating conditions from 80 °C to 260 °C. The pore structure distribution of cement stone was investigated by mercury intrusion porosimetry (MIP), the microstructure and phase composition of cement stone was characterized by scanning electron microscope (SEM) and energy dispersive spectrometer (EDS), together with quantitative X-ray diffraction (XRD) analysis. After 4 rounds of curing under alternating temperature conditions, the compressive strength of cement stone containing mullite and basalt fiber composite material was 28.6 MPa at 260 °C, which was only 9.2 % lower than that at 80 °C. However, the compressive strength of the blank cement stone without composite materials decreased to 22.5 MPa at 260 °C, which was 20 % lower than that at 80 °C. After 4 rounds of alternating temperature cured, the harmful pores of the cement stone without composite materials increase, and the xonotlite flakes and stacks together. The cement stone added with mullite has a finer pore structure after 4 rounds of alternating temperature cured because mullite reacts with portlandite to form C-A-S-H gel. The xonotlite remains fibrous, and a small amount of new phase hibschite is formed. Furthermore, basalt fiber can react with C-A-S-H gel at high temperatures, and the two phases are closely combined to further improve the high-temperature resistance of cement stone. [ABSTRACT FROM AUTHOR]

This paper argues that energy efficiency is a potent shield against oil price uncertainty in an increasingly interconnected world fraught with geopolitical tensions. By reducing dependence on oil, enhancing economic resilience, and improving energy security, energy efficiency measures offer multifaceted benefits for both national economies and global stability. Specifically, a wavelet coherence analysis is conducted to study the response of global energy efficiency to geopolitical oil price uncertainty. Quantile-on-quantile and quantile regressions are additionally employed to separate the impacts of various geopolitical oil price risk quantiles on the quantiles of energy efficiency. These methods are utilized in the examination of global time-series data covering the timeframe 2004:Q1–2020:Q4. The wavelet coherence outcomes indicate a positive correlation between geopolitical oil price uncertainty and energy efficiency, particularly during the energy crisis of the 2000s and the COVID-19 pandemic. The results also reveal that geopolitical oil price uncertainty leads to energy efficiency, indicating that an upsurge in geopolitical oil price uncertainty causes energy efficiency to increase. Moreover, the results from quantile-on-quantile and quantile regressions affirm the predominantly positive effects of geopolitical oil price uncertainty on global energy efficiency. Our conclusion therefore is that through strategic investments, innovative policies, and international collaborations relating to energy efficiency, nations can fortify themselves against the destabilizing effects of geopolitical conflicts on energy markets. This would ensure a more sustainable and secure energy future for all. [ABSTRACT FROM AUTHOR]

In the evolving landscape of global energy, accurately forecasting oil prices plays a pivotal role in strategizing the transition to green energy. Traditional forecasting methods, though widely used, often fall short in capturing the multifaceted influences on oil price dynamics. This research introduces a multimodal deep learning approach that incorporates both time series oil price data and key economic indicators to enhance forecasting accuracy. By employing a combination of Long Short-Term Memory (LSTM) and Temporal Convolutional Networks (TCN), the model adeptly captures temporal patterns and economic influences. The findings not only demonstrate the model's superior performance over traditional methods but also underscore the profound impact of specific economic indicators on oil prices. [ABSTRACT FROM AUTHOR]

Crude oil prices play a critical role in the global economy, and accurately predicting their future values is of great importance. In this study, we evaluated the performance of three support vector machine (SVM) models - standard SVM, SMO-based SVM, and SGD-based SVM - in predicting crude oil prices based on daily, weekly, and monthly data, aligning with the Industry 4.0 paradigm, to enhance decision-making in the oil and gas sector. The results showed that all three models performed well, with high coefficient of determination R2 values and low MSE values across all versions of the dataset. The SMO and SGD-based models demonstrated particular advantages for larger datasets. Furthermore, SVMs are well-suited for modelling complex financial datasets and can handle non-linear relationships between variables. Our findings suggest that SVMs can provide effective models for predicting crude oil prices, with the SMO-based and SGD-based models showing potential for large datasets. [ABSTRACT FROM AUTHOR]

The detrending-moving-average-based multivariate linear regression has been recently introduced as a new tool to probe into multiscale dependent behavior between multivariate data. It well solves the dependence among the nonstationary variates while classic multivariate linear regression dose not. The real-world applications demonstrate its versatility and robustness. However, the statistical significance of this new model needs to be further improved mathematically not just empirically. Hence, the main motivation of this paper is to provide a solid theoretical basis for the latest regression framework. Specifically, four propositions and three theorems are deduced theoretically to support the statistical significance test of detrending-moving-average-based multivariate linear regression model. These propositions and theorems revolve around hypothesis testing of each partial regression coefficients and regression model. They can examine the statistical significance at every given scales. Moreover, we also resort to synthetic time series to verify the performances of the proposed statistical test procedure. By using those statistical tests, we investigate the multiscale interdependence between the crude oil price and the exchange rates among oil-importing and oil-exporting countries. Some interesting findings show that the dependent behavior between the oil price and exchange rates varies at various scales, which cannot be achieved from classical multivariate regression model. • Statistical properties are derived mathematically for the model of DMA-based MLR. • Artificial experiments are also conducted around the statistical test of new model. • The scale-dependent co-movement behavior is investigated between crude oil price and exchange rate by the proposed methods. • The co-movement behavior is compared before and during the oil bust. [ABSTRACT FROM AUTHOR]

The last decade witnessed a wide expansion of a fossil fuel divestment movement. Fossil fuel free (FFF) financial products—which are portfolios that exclude companies involved in the production of oil, gas, and coal—have proliferated, initiating a debate on their financial and ecological implications for economic agents. This paper examines whether and the extent to which these financial products are immune to fluctuations in the price of fossil fuels. To do so, we empirically assess the daily effects of changes in oil, gas, and coal prices on returns on fossil fuel free indices provided by S&P. While we find no evidence that the FFF indices underperforms their conventional counterparts on average, the results from our model further show that the returns on FFF portfolios have more exposure to movements in fossil fuel prices than those on mainstream portfolios. The insights from our estimations have important implications for investors and other economic agents. [ABSTRACT FROM AUTHOR]

Our paper examines and compares the influence of oil prices, international geopolitical risks, and government expenditures on Saudi Arabia's economic diversification during 1970–2020. We employ the bounds testing approach to cointegration to estimate the parameters of the Autoregressive Distributed Lag model to attain the paper's goal. Our results show that oil prices and international geopolitical risk harm the KSA diversification process in the short run. The destructive effect of oil prices continues in the long run, while the impact of the international geopolitical risk does not persist. On the other hand, government expenditures encourage the diversification attitude in the short and long run. This outcome highlights the capability of the KSA government to stimulate the diversification process. It is a positive sign of the state capitalism doctrine's impact on the KSA economic diversification mechanism. From a policy implication perspective, our paper suggests prioritizing economic diversification clearly on the KSA government's agenda. Simultaneously, strengthening the KSA institutional framework and developing new forms of the social contract are critical to motivating the diversification process. • Do oil prices, geopolitical risk, and government spending affect diversification. • We employ a time series autoregressive distributed lag model. • Oil prices and geopolitical risk discourage economic diversification. • Government expenditures encourage economic diversification. • The impact of oil prices and government expenditures prevails to the long run. [ABSTRACT FROM AUTHOR]

Existing research concerning determinants of climate change mitigation technology (CCMT) development mainly focuses on the role of climate policy, environmental awareness, or R&D intensity. What has been rarely noticed is that the oil price, as an indication of primary energy costs, can make countries with high energy intensity lean toward renewable energy consumption and invest more in the innovation activities of CCMT. Employing a panel investigation of 30 economies from 1990 to 2019, We find strong evidence that rising oil prices promote CCMT performance. The positive effect of increased oil prices on CCMT is imposed by lowering energy intensity, increasing renewable energy consumption, and promoting energy-technology R&D. Such an impact is more pronounced for CCMT related to energy generation and efficiency, greenhouse gas emissions, and transportation. To obtain more concrete policy implications, we also explore heterogeneities of this promoting effect across different levels of economic development and oil dependence. • Increased oil prices positively affect CCMT's share in green innovation. • A rise in oil prices spurs CCMT innovation related to energy, greenhouse gases, and transportation. • The effect of oil prices on CCMT's share in green innovation is more notable in oil-importing, high-income, and developed economies. [ABSTRACT FROM AUTHOR]

This research mainly focuses on investigating the role of the interactions between cash flows, oil price uncertainty, and geopolitical risk on corporate cash holdings across 42 countries. We also investigate whether such interactions may differ across firm and country characteristics. The main results show that the interactions between cash flows interacting with oil price uncertainty and geopolitical risk have a positive correlation with corporate cash holdings. Additionally, the nexus between the interactions and corporate cash holdings is more prominent for financially constrained firms, firms located in highly competitive industries, or Asia and Europe. The main policy implication based on our findings is that policymakers should exercise caution and take into account the effect of the uncertainties of oil price movement and geopolitical risk when designing policies related to corporate decision makings. [ABSTRACT FROM AUTHOR]

Recent surges in crude oil prices have motivated researchers to find an alternative sustainable fuel called biodiesel from various inedible oils with lower carbo impact on the environment. The research is performed in diesel engine fuelled with blends of biodiesel coupled with cerium oxide nanoparticles and hydrogen content so as to optimize various factors which are responsible for performance, vibration and emission characteristics. Multi-objective optimization is achieved by employing RSM which also examines prime input parameters (engine load, nanoparticle concentration, compression ratio, hydrogen blend percentage and ignition pressure) responsible in varying engine characteristics. Henceforth, blends of Water Hyacinth can be successfully applied in diesel engine with lower environmental impact and enhanced cost effectiveness. Experimentation is performed on the central composite rotating design (CCRD) matrix with 5-level factor. Engine load was applied between 0 and 100%, NPC varied between 0 and 80 ppm, CR ranges between 17 and 19, hydrogen blend percentage varies between 0 and 40% and at a maximum injection pressure of 240 bar. Pareto-optimal conditions achieved for input conditions of 28.68% biofuel blend, 87.88 engine load, 80 ppm NPC, compression ratio of 19 and 194.54 bar infusion pressure were BTE, BSEC, NOx, UBHC and vibration reduction are 33.57%, 0.2550, 461.3002 ppm vol., and 28.08 ppm vol. And 22.21%, respectively. • Effect of Load, Hydrogen Blend, NPC, IP & CR on engine performance is studied. • RSM based model to predict to optimize the engine performance is developed. • Pareto-optimal conditions for input are obtained for better engine performance. • Nanoparticles and hydrogen were amalgamated in different concentration. • Comparative analysis between generated outcomes and experimental values. [ABSTRACT FROM AUTHOR]

This paper aims to examine the relationship between oil prices and the indices of renewable energy and technology companies using Quantile Regression (QR.), Quantile-on-Quantile Regression (QQR), and Granger-Causality in Quantiles (GCQ) techniques. The results show strong evidence of an asymmetric effect at the sum of oil prices and technology companies' index on clean energy, indicating that the relationship is vital when these variables decrease than increase. The main finding of the QQR method demonstrates that the impact of oil prices and the technology stock index on clean energy is higher at the upper quantile than the lower quantile. We also establish bi-directional GCQ indicating the considerable change in oil price led to a change in renewable energy; besides, the outcome of the change in renewable energy does not support the causal link to the variation in oil prices across lower extreme quantile at 1%. Finally, our findings support the bi-directional causality from the tech companies market to the conventional and unconventional energy markets. Results identify the substantial role of renewable energy, with implications for global policy, decision-makers, and fund managers. [ABSTRACT FROM AUTHOR]

E10 is still the largest market for blending of ethanol in transportation fuels in the U.S. This paper analyzes the relationship between the input costs of transportation fuel blends (crude oil, wholesale gasoline) and the transportation fuel prices (E10) that U.S. consumers pay at the pump both in the short run and in the long run. The article employs a generalized spatial random-effects autoregressive-distributed lag model for E10 price using U.S. regional data. We estimate the short run elasticity of E10 price with respect to crude oil price as 0.28, and the long run elasticity as 0.42. In both the short run and the long run, the elasticities of E10 with respect to wholesale gasoline price are higher than the elasticities with respect to crude oil price; at 0.59 and 0.83. We find that the federal policy of E15 approval increased E10 prices. We do not find significant impacts of income or population on E10 prices. [ABSTRACT FROM AUTHOR]

The primary contribution of the current article is to use the SVAR methodology to study the dynamic responses of China's trade balance with its three largest partners – the U.S., Japan, and Korea – to shocks of oil supply, global demand, and oil-specific demand, controlling for world trade policy uncertainty. We discover that the transmission of oil price shocks relies on the type of shock and different trading partners. Thus, China would need to consider the various oil shock components as the key factors determining the dynamics of the bilateral trade flows with its major partners. We also reveal that world trade policy uncertainty plays an important role in fluctuating China's trade flows with the U.S., but not with Japan and Korea. When examining China–U.S. trade, therefore, it is important to take into consideration the uncertainty surrounding trade policies. Failure to do so may result in model misspecification in econometric models, which could lead to misleading conclusions. [ABSTRACT FROM AUTHOR]

Climate policy uncertainty needs to be considered urgently in view of the fact that climate policy is affecting energy and agricultural markets. This paper uses quantile connectedness to examine the dynamic spillover among climate policy uncertainty (CPU), crude oil prices (COP) and agricultural product prices (APP), further revealing the impact of climate policy on markets in both normal and extreme markets. The results show that there is a higher connectedness level at extreme quantile. Uncertain climate policy activities and major events affect system connectedness. Further analysis shows that, compared with the isolated role in the normal market (median quantile), CPUs in the extreme market (lower and upper quantile) become continuous shock receivers in the system. The risk spillover mainly focused on soybeans and corn, and COP increased the risk of spillover especially after the epidemic. By revealing the transmission mechanism of the price correlation between oil and agricultural products under the CPU framework, this can fully explain the linkage between them. In general, the stability of the oil market and agricultural product market is crucial to the formulation of climate policy. These conclusions provide significant enlightenment for policy-makers and investors coping with the high level of CPU. [ABSTRACT FROM AUTHOR]

This research aims to be guided decision-makers in future planning by estimating the tendency of data consistently. In this context, it is thought that the integration of the Autoregressive Distributed Lag-ARDL models, gathering the independent factors and their past effects as well as the past trend of the dependent variable, with fuzzy regression methods, would give more realistic results. To prove the correctness of this idea, the Fuzzy-ARDL method has been proposed and tested the superiority of the research on the projection of USAs' annual oil consumption data examined by researchers previously. For this purpose, raw data of crude oil import price, population, gross national domestic production (GDP) per capita, and oil production variables, previously compiled annually, have been considered independent variables. Then the proposed model has been benchmarked with the other promising models from the fuzzy regression literature. As a result, according to various Accuracy Measures values, it has been seen that the proposed model outperforms the other promising models. [ABSTRACT FROM AUTHOR]

This paper analyzes the dynamics of CO2 emissions over the last decade for a large group of 25 European countries, an issue that is at the center of the ecological transition project involving various commitments (COP21, COP26, G20 meetings, etc.). To this end, our model measures the repercussions of energy price shocks (oil, gas, coal) for carbon emissions, as well as changes in industrial production and sustainable development in the context of the coronavirus pandemic. Using annual data for 23 EU countries, together with Russia and the UK, our findings show that CO2 emissions reacted significantly to oil and coal price shocks and vary with industrial production cycles. We quantified this reaction while computing the related elasticities. Further, while a significant reduction in CO2 emissions was observed during the COVID-19 pandemic, the stepping up of investment in sustainable development and renewable energy consumption also had a negative impact on CO2 emissions. This suggests that the key driver to reducing the risk of climate change and lowering high carbon dioxide (CO2) emissions involves significant commitment to sustainable development. [ABSTRACT FROM AUTHOR]

This paper examines the nonlinear relationships between oil and inflation for twelve major global economies in the presence of important macroeconomic variables. Using the nonparametric quantile causality approach, we show strong evidence of causality-in-variance from inflation to changes in oil prices for all economies across different quantiles with the exception of Australia. On the other hand, evidence of a causality-in-mean from oil to inflation is identified for Australia, Canada, Greece, the UK, and the US and a causality-in-variance in the same direction for almost all economies, exhibiting a hump-shaped pattern across the quantiles. Results for partial and multivariate wavelet approaches show that the macroeconomic factors drive and accentuate the oil-inflation co-movements under all frequencies and over time. These results have significant implications for investors and policy makers. • We examine nonlinear relationships between oil and inflation for twelve major global economies. • Our work also considers important macroeconomic variables. • Evidence of causality-in-variance from inflation to changes in oil prices for all economies except Australia. • Evidence of a causality-in-mean and variance from oil to inflation for Australia, Canada, Greece, the UK, and the US. • Macroeconomic factors drive the oil-inflation co-movements across frequencies. [ABSTRACT FROM AUTHOR]

The share of renewable energy consumption in total electricity consumption is analysed for a sample of forty-nine countries worldwide from 1985 to 2017. The analysis follows a two-step approach. First, the club convergence model was used to find the convergent countries. Second, a panel vector autoregression (PVAR) model estimation was used to analyse the convergent countries. Club convergence analysis confirms that forty-two countries are convergent in renewable energy consumption in total electricity consumption. The subgroup convergence tests confirm the existence of seven subgroups and one non-convergent group. Of these eight groups, the first seven are convergent among their group members, but the eighth group (Norway, Switzerland, Trinidad and Tobago) is non-convergent. The PVAR revealed a dense Granger causal relationship among the variables share of renewable electricity consumption, fossil consumption, GDP, trade openness, economic complexity, financial development, and oil prices. In their interventions, policymakers should be aware that the share of renewable energy consumption in total electricity consumption is very interdependent. [ABSTRACT FROM AUTHOR]

Multi-source data is widely used in the field of energy future prices forecasting, the improvement of forecast ability and data screening are becoming the focus of current research. In this paper, two tree-based models (namely, Random Forest and XGBoost model) are employed to predict China's crude oil future prices. The empirical analysis confirms that Random Forest and XGBoost model have superior prediction performances than benchmark and the XGBoost model performs best. An important finding is that there is a time gap between investor information search and processing because the prediction performance within the time lags is obviously superior than that of the current period. [ABSTRACT FROM AUTHOR]

This paper collects monthly data for the United States from January 2005 to April 2021 and develops a time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR) model to investigate the time-varying relationship between climate policy uncertainty, oil prices, and renewable energy consumption. In addition, this paper discusses the dynamic impact of climate policy uncertainty on five types of renewable energy consumption. Our results show that the relationship between them is time-varying. In most periods, climate policy uncertainty positively affects oil prices in the short and medium term, and positively affects total renewable energy consumption in the short and long term. In addition, the impact of climate policy uncertainty on the consumption of the five types of renewable energy is heterogeneous. The results of the robustness tests indicate that our results are robust and our study has important implications for the development of relevant policies. [ABSTRACT FROM AUTHOR]

Many biotechnology applications tend to be for low production volumes and relatively high-value products such as insulin and vaccines. More difficult to perfect at scale are bioprocesses for high-volume products with lower value, especially if the target product is a reduced chemical such as a solvent or a plastic. Historically, industrial microbiology succeeded under special circumstances when fossil feedstocks were either unavailable or expensive. Inevitably, as these circumstances relaxed, bioprocesses struggled to compete with petrochemistry. Why try to compete? Fossil resources will be phased out in the coming decades in the struggle with climate change. To reach net-zero carbon by 2050 will require all sectors to transition, not only energy and transportation. This may herald a new opportunity for industrial bioprocesses with much better tools. Some of the issues around biotechnology in the first issue of Trends in Biotechnology remain issues today despite huge advances in technology. The Weizmann process and single-cell protein thrived, died, but may be on their way back. The bioeconomy envisages the gradual replacement of oil and gas, but the price of oil is very often a determining factor in the success of any competing technology. Net-zero carbon aspirations should be a huge opportunity for biotechnologies. The automation possible in the biofoundry allied to automated chemical synthesis seems to hold the key. Many of the hardest-to-abate industrial sectors cannot be decarbonized, but they might be 'defossilized'. Governments need to invest in bio-based manufacturing to bring them more quickly to the marketplace. [ABSTRACT FROM AUTHOR]

Due to the rise in the demand for semi-dairy beverages (vegetable fat in a de-fatted dairy matrix) as well as for fully plant-based formulations, there is a growing interest in better understanding how emulsification processes are influenced by raw material choices. Semi-dairy beverages, for example, utilize a wide variety of vegetable oils in their formulation. This contribution combines traditional emulsification experiments with high-resolution numerical drop breakup simulations to improve the understanding of how oil viscosity influences the emulsification process. It is concluded that the experimentally observed increase in drop diameter with oil viscosity is well predicted by the viscosity-corrected Kolmogorov Hinze theory. The numerical simulations are able to reproduce this trend and allow further insight into breakup mechanism; suggesting that it is the prolonging of the deformation phase that make more viscous oils more challenging to homogenize. From a beverage processing perspective, the homogenizing pressure (and energy cost) necessary to achieve the same physical stability needs to be 73% higher if using a typical high-viscosity vegetable oil than if using a typical low viscosity vegetable Oil. [ABSTRACT FROM AUTHOR]

This study examines how the rise in energy prices following the Russian invasion of Ukraine (which began on February 24, 2022) has spread to other sectors using the monthly input–output model (56 sectors in 44 countries). The estimated results demonstrate that if prices increase by 20% exclusively in Russia's mining and quarrying (M&Q) sector, there will be almost no global effect. However, if prices increase by 20% in every M&Q sector around the world, global prices (the weighted average across all sectors) will rise by 3.15%, damaging 6.83% of monthly GDP at the pre-invasion level as a surplus loss ($551,080 million per month). Because oil prices rose for almost five months from February 28 to August 3, 2022, the world suffered five times this damage, or 2.85% of annual GDP ($2.7 trillion). This scenario is roughly equivalent to a price change of 5 times in the Russian M&Q sector alone. • Two scenarios for +20% energy prices (fossil fuel) in Russian or global mining sector. • Input–output analysis in 56 sectors/44 countries to seek price rise and surplus loss. • If energy prices increase by 20% only in Russia, global overall prices rise 0.13%. • If increased 20% globally, overall prices rise 3.15% and surplus loses 6.83% of GDP. • To rise global energy price by 20%, the Russian energy price alone must be +5 times. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused turmoil in every aspect of life and may be prevalent for years. Therefore, this study aimed to determine whether the pandemic reflects oil prices in China. We utilized a model to simulate and examine the energy, economic, and environmental effects of COVID-19, which affects a wide range of industries and households. The impact of the pandemic is considered in terms of customer expectations and factor input changes. Based on these changes, we find that factor input changes are the primary cause of the economic recession. We further find a parallel relationship between CO2 emissions and economic downturn. In addition, a reduction in transportation has significantly influenced the Gross Domestic product (GDP), which plunged during the pandemic period by 0.49%. Transportation negatively influences industrial production, railway sector, and air transportation by 10.17%, 1.76%, and 1.53%, respectively. Based on these findings, this study proposes important policy implications. [ABSTRACT FROM AUTHOR]

The 12th Sustainable Development Goals (SDGs) highlighted the importance of ensuring sustainable consumption and production patterns. Researchers have found that energy subsidies may encourage overconsumption, misuse, and moral hazard problems. Hence, there is a need to rationalize inefficient energy subsidies. Consequently, the Malaysian government has been implementing a subsidy reformation program to improve energy efficiency and reduce fiscal burdens. This study aims to identify and compare the impacts of energy subsidies and oil prices on energy consumption across the agricultural, industrial and service sectors in Malaysia. It is critical for policymakers to better understand the role of energy subsidies in influencing energy consumption, especially the magnitude to which energy subsidies influence energy consumption, in order to structure a subsidy reform strategy which effectively addresses the energy overconsumption issue. In this study, a time series dataset covering the years of 1978 through 2018 is analyzed. The ARDL approach and Granger causality test were employed to determine meaningful statistical evidence and revealed four important findings. Firstly, energy subsidies have a positive relationship with energy consumption. Secondly, oil prices have a negative relationship with energy consumption and energy subsidies significantly interact with oil prices to affect energy consumption. This significant positive moderating effect suggests that energy subsidies may counteract the negative impact of oil prices on energy consumption. Thirdly, output has the greatest (positive) impact on energy consumption. And finally, energy subsidies drive higher outputs in the agricultural sector. [ABSTRACT FROM AUTHOR]

This paper investigates the hedge and safe-haven properties of green bonds (GBs) performing as a safeguard against oil price shocks and uncertainty, in comparison to the corresponding roles of gold, the 3-month European government bills, and the U.S. 3-Month T. bills. Oil price shocks are disentangled into oil supply, oil demand, and oil risk shocks based on the recent methodology of Ready (2018), generated in a framework that includes gold, oil shocks, and oil uncertainty (OVX). The ability of GBs to protect against oil price shocks and oil uncertainty is examined using the GARCH and quantile regression (QR) models. The results show that GBs are more appropriate than gold and conventional bonds (CBs) as hedging and safe haven tools against oil price shocks and oil uncertainty. Besides, the ability of GBs to protect against oil price shocks and uncertainty depends on whether the oil shocks are supply, demand, or risk shocks. Furthermore, we find that GBs serve as a strong hedge and/or a safe haven against structural oil shocks generally under bearish GBs market conditions. The European government bills and the US T. bills play the role of weak hedge and a safe haven asset against oil uncertainty and oil shocks. This role varies across quantiles (bear and bull market conditions). Relevant risk management implications are discussed. [ABSTRACT FROM AUTHOR]

The contribution of the present article is to explicitly consider the role of supply and demand shocks in the global oil market when analyzing the nexus between oil price fluctuations and macroeconomic activities – growth, inflation, and exchange rate – in an emerging economy, specifically Indonesia. For doing this, a structural vector autoregressive (SVAR) methodology is applied to monthly observations for 1998 through 2019. We show that the timing, magnitude, and even direction of the response of Indonesia's macroeconomy are likely to vary depending on the type of shock. We believe that this discovery provides important implications for empirical modeling and policy analysis. [ABSTRACT FROM AUTHOR]

Based on the TVP-VAR (time-varying parameter vector autoregressive) based extended joint connectedness methodology, this study analyzes the joint connectedness among BRICS's (Brazil, Russia, India, China, and South Africa) geopolitical risks and the US macro economy (interest rates, consumer price index, crude oil prices, stock prices, and gross domestic production) for return and volatility. Evidence suggests that shocks from geopolitical risks have a considerable impact on emerging economies. Emerging economies are closely linked to developed economies in today's globalized world. Hence, the geopolitical risk of BRICS countries may further impact developed economies. In this study, we performed a cross-sectional analysis of the relationships between geopolitical risks in BRICS nations and a developed economy (e.g., US macroeconomic indicators). Our results were significant in at least two ways. First, geopolitical risks in China and Russia considerably impact the return and volatility system. In contrast, geopolitical risks in China have fewer impacts on the US macro economy than in Russia, Brazil, and India. Second, the value of total joint connectedness during the COVID-19 period was greater than during the 2007–2008 global financial crisis. Third, the consumer price index, crude oil prices, and stock prices receive greater connectedness from the others. The results suggest that policymakers and investors should not pay excessive attention to geopolitical risk in China. Moreover, because the oil and stock markets are highly interdependent, risk spikes for investors who hold both oil and stock when facing high geopolitical risk. We recommend that investors reduce their simultaneous holdings of both assets in times of high geopolitical risk and consider alternative safe haven assets. [ABSTRACT FROM AUTHOR]

Using monthly data spanning from January 1997 to November 2022, this paper investigates the influence of global economic policy uncertainty (GEPU) and oil price uncertainty (OPU) on global economic activity using a structural vector autoregressive (SVAR) model. The findings from the baseline model reveal the adverse effects of both GEPU and OPU shocks on the global economy. The counterfactual analysis highlights that the negative impact of OPU shocks primarily stems from direct effects, while approximately half of the adverse impact of GEPU shocks is indirectly transmitted through amplifying OPU. Further examination of the indirect effects of these shocks uncovers that lagged effects primarily drive the indirect impact of OPU, whereas the indirect impact of GEPU is mainly driven by common shocks. These findings suggest a more substantial and independent impact of OPU shocks compared to GEPU shocks. Additionally, employing a time-varying vector autoregressive with stochastic volatility (TVP-VAR-SV) model, this paper explores the same dataset. The results of time-varying impulse responses illustrate a diminishing effect of GEPU shocks over the sample period, whereas the impact of OPU shocks initially decreases before subsequently increasing. • Distinct effects of GEPU and OPU shocks on global economic activity are studied. • The effects of OPU are larger and more independent than those of GEPU shocks. • The indirect effects of OPU and GEPU shocks stem from lagged effects and common shocks respectively. • The effects of GEPU shocks continues to diminish over time. • The adverse effects of OPU shocks undergoes a pattern of decreasing initially and then increasing. [ABSTRACT FROM AUTHOR]

This paper presents a novel model based on LSTM to predict future prices of WTI crude oil. The WTI price forecasting utilizes data on spot gold price, US 10-year bond yield, global economic activity, and US dollar index from January 1986 to May 2023. The model's performance is assessed using measures such as MAE, MSE, RMSE, MAPE, and R2 metrics. The results generated by the proposed new model are compared to those of the existing machine and deep learning methods, and it is observed that the new model performs better than the existing models in all statistical tests. The study further examined the decision-making processes of the model using SHAP analysis and assessed the individual contribution of each feature to the model's predictions. The correlation between the US Dollar Index and Gold prices and WTI crude oil prices is evident. The SHAP research has demonstrated that the model effectively captures complicated economic linkages and enhances the accuracy of forecasts. The results of this study enhance the development of models that are capable of predicting results, even in times of significant instability, such as economic crises. Using sophisticated data analytics and AI methods would improve the efficiency of energy market oversight. • Crude oil is an important commodity. • Prediction of crude oil price guides economic markets. • Machine learning is very important in price prediction and makes a significant difference to its alternatives. • Model's performance is assessed using measures such as MAE, MSE, RMSE, MAPE, and R2 metrics. [ABSTRACT FROM AUTHOR]

[Display omitted] • Green biodiesel production from Persian Lilac-Melia Azedarach fruits. • Application of green SC-CO 2 oil extraction method. • Using a new green catalyst synthesized from luffa sponge seeds. • Higher oil yield using SC-CO 2 (32%) than Ultrasonic and Soxhlet extraction methods. • Producing up to 94% of FAME (biodiesel) in just 3 hr. Considering the importance of finding new suitable and environmentally friend resources and methods for biodiesel production, the present work has investigated Supercritical Carbon dioxide (SC-CO 2) extraction as a new highly efficient and green oil extraction method for oil production from Persian Lilac-Melia Azedarach (PLMA) fruits and its conversion into biodiesel using a new green catalyst. The effects of pressure (22–30 MPa), temperature (40–80 °C) and extraction time (0–240 min) on supercritical carbon dioxide extraction were investigated and the results were compared with ultrasonic probe and Soxhlet extraction methods. The findings indicated that the total oil production rose with higher pressure and temperature, but a decrease was noticed at elevated temperatures. The most effective parameters for extracting PLMA oil were determined to be a pressure of 30 MPa, a temperature of 40 °C, and an extraction time of 180 min. The oil yield obtained by SC-CO 2 extraction method was 32 % while it was 30 % and 28 % for Ultrasonic probe and Soxhlet extraction methods, respectively. Finally, a new green catalyst was synthesized from sponge of luffa plant to convert the extracted oil into a green biodiesel. In the future outlook, the efficient oil extraction method including modified SC-CO 2 will be proposed for oil extraction from Melia azedarach fruits and the using of the modified catalyst for converting the oil to biodiesel. [ABSTRACT FROM AUTHOR]

To evaluate the effects of a negative oil price shock on individual labor market outcomes, we leverage the interaction of exogenous changes in global oil prices with predetermined geographical variation in oil dependency across Ecuadorian cantons. Difference-in-differences estimates show a significant decline in wages and non-labor earnings, moderate adjustments in working hours, but no impact on labor market participation following the resource bust. Decreased labor demand from public and private sectors in oil-producing areas, along with increased exposure of informal workers to oil-price volatility, serve as the main mechanisms explaining our findings. While we observe occupational sorting, we cannot exclude the possibility of homogeneous effects across socio-demographic groups, nor the existence of industry and geographic spillover effects. • We estimate the 2015 oil price bust effect on labor market outcomes in Ecuador. • Difference-in-Differences results show no impact on labor supply and unemployment. • Wages and non-labor earnings declined in oil-producing areas after the price bust. • The oil price bust induced occupational sorting from white to blue-collar jobs. • Job informality and business size are primary sources of heterogeneity. [ABSTRACT FROM AUTHOR]

This study investigates the lead-lag effect between crude oil futures and petroleum products from the view of price predictability. The findings of the Granger causality test provide evidence for the leading role of crude oil futures. Further empirical results show that crude oil futures demonstrate strong predictive power for petroleum products both in- and out-of-sample, and this predictability is a short-term phenomenon. During economic recessions, petroleum product prices exhibit stronger predictability, with out-of-sample R 2 reaching a maximum of 39.161 %. Additionally, the outstanding forecasting performance remains robust across a range of settings. Finally, two potential economic sources underlying the lead-lag relationship between crude oil and petroleum product markets are provided: the differential response speed to common energy market information and the perspective of raw material supply. • Explore the lead-lag effect between crude oil futures and petroleum products. • Short-term price predictability of petroleum products has been found both in- and out-of-sample. • Provide two potential economic sources underlying the superior predictability. • Further extension and robustness analyses confirm the outstanding forecasting performance. [ABSTRACT FROM AUTHOR]

The oil fraction will affect the aggregation behavior and structural strength of emulsion gels. In this study, the effect of the camellia oil (CO) fraction on the properties of emulsion gels stabilized by regenerated silk fibroin (RSF) was studied. The results showed that CO was essential for gel formation, with oil droplets incorporated into the RSF matrix as anchors to achieve rapid gelation of RSF. The gel hardness significantly increased from 20.03 to 53.35 g as the fraction of CO increased from 5 % to 25 %. The oxidation stability of the emulsion gels was also improved, and the peroxide value (POV) decreased from 2419.3 to 839.9 μmol/kg. As the oil fraction rose from 5 % to 25 %, the percentage of released free fatty acids decreased from 73.24 % to 59.49 % due to forming a more compact gel structure. In addition, the rheological results revealed that all emulsion gels had a shear-thinning behavior and good temperature stability in the range of 5 to 90 °C. This study provided a theoretical basis for preparing RSF-based emulsion gels, helps in the recycling of silk protein resources, and promotes the development of emulsion gel applications in the food industry. [Display omitted] [ABSTRACT FROM AUTHOR]

The Russo-Ukrainian conflict and its reverberations on energy markets have heightened concerns regarding energy security. Against the backdrop of fluctuations in international oil prices, oil-gas linkage pricing severely jeopardizes the security of the Chinese gas market. Clarifying the mechanism of high-risk spillover of international oil prices to Chinese LNG is essential for the safe operation of gas. We examine the dynamic spillover effects of Brent, WTI, Dubai crude oil, and Chinese LNG prices using the dynamic connectedness based on TVP-VAR. We innovatively establish the BART-QCA framework to screen the conditions and identify pathways that exacerbate the high-risk spillover of international oil prices to Chinese LNG. The study finds that: (1) Chinese LNG prices are net recipients from the three primary crude oil prices. (2) four pathways exacerbate the high-risk spillover, confirming the situational characteristics of high-risk spillover. (3) The increase in global economic policy uncertainty and China's high external dependence on gas are essential conditions for the high-risk spillover. (4) three pathways mitigate the high-risk spillover. A clear global economic policy situation, reasonable control of public opinion, and the weakening of the US dollar help alleviate high-risk spillover. Accordingly, new policies should achieve scenario-based governance through cross-market and multi-sectoral synergies. [Display omitted] • We employ the TVP-VAR-DY to scrutinize the dynamic spillover of Benchmark oil prices on Chinese LNG prices. • We pioneer BART-QCA framework for screening pathways exacerbating the high-risk spillover of oil prices into Chinese LNG. • Chinese LNG prices act as net receivers from the fluctuations in the world's benchmark crude oil prices. • Four pathways intensify the high-risk spillover of global oil prices into Chinese LNG prices. • Clarity in global economic policy, control of online opinion, and a weakened US dollar can alleviate high-risk spillovers. [ABSTRACT FROM AUTHOR]

A well-documented literature exists on the impact of global oil shocks on small open economies (SOEs). However, it still disregards the international financial (uncertainty) conditions. This study fills this gap by considering the conditions of global markets. We assess the macro-financial effects of global oil price shocks on oil-exporting and oil-importing emerging economies of BRIC by using the linear and non-linear SOE SVAR models. Documenting a novel amplification role of uncertain conditions, this study provides a clear message: international financial (uncertainty) conditions matter. We report three key results. First, negative oil price shocks tighten (loosen) financial conditions and induce a painful (beneficial) macroeconomic adjustment in BRIC oil exporters (importers). Second, two types of BRIC economies— oil-exporters and oil-importers— exhibit a divergent monetary policy response to common oil price shocks. Third, global financial (uncertainty) conditions amplify the macro-financial effects and make the monetary policy response to oil price shocks more aggressive. • We provide a novel amplification mechanism for oil shocks. • We estimate a time-varying threshold small-open economy SVAR model. • Global financial (uncertainty) conditions magnify the propagation of oil shocks. • BRIC monetary policy reacts more aggressively to such shocks under uncertain times. • Russia faces a challenging policy dilemma between financial and macroeconomic stability. [ABSTRACT FROM AUTHOR]

Crude oil price volatility prediction are important for energy policymaking and investment risk avoidance, and have attracted a great deal of global attention. In recent years, although some studies have proposed methods for prediction crude oil price volatility, most of them are point prediction, which are difficult to provide richer reference information for managers. Therefore, an improved hybrid interval prediction model is proposed. The hybrid model combines data feature extraction and quantification techniques, fuzzy information granulation(FIG), improved echo state network (IESN), and autoregressive integrated moving average model (ARIMA). The hybrid model is able to accurately portray the different features present in the volatility of crude oil spot price. The features are quantified in order to merge similar feature components and improve the prediction effect. On this basis, IESN, which possesses strong nonlinear learning ability, is used to predict high-frequency sequences, and ARIMA, which has strong linear carving ability, is used to predict low-frequency sequences and residual terms. By "divide and conquer", excellent prediction intervals are obtained. West Texas intermediate (WTI) crude oil spot and Brent crude oil spot are selected for analysis, and consider the influence of futures on interval construction. The effect of volatility intervals is validated from the pricing benchmark and time scale dimensions, respectively. The experimental results show that the proposed model has optimal performance in all scenarios. Take the performance of WTI on the daily scale as an example, its PICWC value is 0.8205. Compared with other benchmark models, it improves by 137.4504, 3.1118, 1.0506, and 0.4875, respectively. The proposed model provides a reliable new idea for crude oil spot volatility interval prediction. [ABSTRACT FROM AUTHOR]

To accurately forecast crude oil futures price and volatility, this article presents a novel deep learning combined model using secondary decomposition with West Texas Intermediate crude oil futures (WTI) and North Sea Brent crude oil futures (Brent) as examples. Firstly, a trend subsequence and several noise subsequences are obtained by decomposing the crude oil futures price or volatility using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the secondary decomposition is performed on the highest frequency noise subsequence using the variational mode decomposition (VMD). Secondly, the remaining subsequences obtained from CEEMDAN and the subsequences obtained from VMD are predicted separately using the BiGRU-Attention-CNN model. Finally, the predicted crude oil futures price or volatility is calculated by linearly integrating the predicted values of each subsequence. The empirical analysis shows that the novel combined model using secondary decomposition proposed in this paper has the best forecasting performance among many models, both in the comparison experiments and in the ablation experiments. The model is also shown to have good robustness by predicting the volatility at different maturities, varying the ratio of the training set and the test set for crude oil futures price prediction, and predicting the price of crude oil futures after extreme events. Overall, the novel combined forecasting model using secondary decomposition proposed in this paper can help countries grasp the direction of the crude oil market and improve national economic and political security. [ABSTRACT FROM AUTHOR]

The United Kingdom is among the countries experiencing a cost-of-living crisis believed to be influenced, at least in part, by the dynamics of the international oil and gas markets. To this end, this study aims to achieve to achieve two objectives. Firstly, the dynamic association between the UK's inflation and oil and gas prices is examined. Further, the study examines whether the response of the UK's inflation to energy price dynamics is (a)symmetric. This study adopts wavelet coherency to determine the dynamic co-movement between energy prices and inflation. In addition, the dynamic simulated autoregressive distributed lag model (DS-ARDL) is used to examine the dynamic response of inflation to energy price changes. The estimation results reveal a symmetric response of inflation to gas price shocks. Further, the response of inflation to oil price shocks is asymmetric. Interestingly, the effect of gas price dynamics passes more strongly to inflation than to oil price dynamics. These findings suggest that a more diversified energy mix could help prevent substantial energy price pass-through to inflationary pressures. • The (a)symmetric response of UK inflation to oil and gas price dynamics is examined. • Wavelet Coherency and DS-ARDL model are used for analysis. • The response of inflation to gas price shocks is symmetric. • The response of inflation to oil price shocks is asymmetric. • The effect of gas price dynamics passes more strongly to inflation than oil price dynamics. [ABSTRACT FROM AUTHOR]

This study aims to reveal the evolution mechanisms of the petroleum trade network by examining its dynamic evolution of structural dependence characteristics. We conduct an in-depth examination of the dynamic structural evolution of the global trade network by employing the TERGM and STERGM models, and utilizing oil trade data spanning the years 1997–2021. To understand the stability of oil network trade dependence, we incorporate the cascading failure and the ERGM models to simulate the impact of external supply crises on the structural dependency relations of the oil trade network. Our findings can be summarized as follows: first, reciprocal trade structures play a crucial role in building trade relationships, with countries emphasizing mutually beneficial partnerships. Second, with the increasing complexity of the global trade landscape, the stability and resilience of the oil trade network structure have experienced notable declines. Third, in response to external shocks, countries tend to diversify their oil trade relations to ensure energy supply security. The results suggest that the government should pay more attention to the oil trade dependency relationships. • The novel dynamic structural evolution mechanism of the GOTN is developed. • Explaining the structural dependence evolution based on TERGM and STERGM models. • Simulating the impact of supply crises by using integrate cascading failure model. • Countries break original trade hierarchy and actively seek diverse trade relations. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between unemployment and inflation in 29 African economies by re-examining the Phillips Curve hypothesis and evaluating the role of global oil prices in predicting inflation. Existing studies often use uniform-frequency variables, which can lead to biased results by omitting information from high-frequency series. We employ a mixed data sampling regression model to analyze inflation in selected African countries by regressing monthly inflation rates on annual unemployment rates and daily oil price returns. Our findings show that the Phillips Curve hypothesis holds in a few countries across Central Africa (Cameroon, the Central African Republic, Chad, and Republic of the Congo), East Africa (Rwanda), North Africa (Algeria), Southern Africa (Botswana and Malawi), and West Africa (Burkina Faso and Mauritania). In the other countries, the hypothesis does not hold, potentially indicating a predominance of cost-push inflation. Our analysis demonstrates that global oil prices impact inflation differently across these countries. Additionally, daily oil price data significantly enhance inflation forecasting compared to monthly data. Incorporating additional economic variables, such as exchange rates and interest rates, further improves forecast accuracy. We recommend targeted monetary policies to mitigate the impacts of oil price fluctuations and unemployment on inflation. • Reassesses traditional demand-side Phillips curve in Africa. • Integrates supply side, notably oil price, into Phillips curve. • MIDAS method exceeds uniform frequency in curve modeling. • Oil price inclusion improves curve's predictive capacity. • Results maintain robustness in all in/out-sample analyses. [ABSTRACT FROM AUTHOR]

This research examines the impact of oil prices, economic globalization, and inflation on the economic output in oil-producing countries in Latin America. We employ advanced time series techniques to achieve precision in findings and reliability of policy inferences. We use cointegration techniques with and without structural breaks because oil prices are highly volatile, and the economies analyzed are unstable in the face of political and social changes. Additionally, we performed a sensitivity analysis using standard quantile and the newly developed quantile-on-quantile regressions to measure the impacts of the covariates on real per capita output. Finally, we use causality Fourier techniques to identify the direction of causal relationships between the series. We found that the oil price, economic globalization, inflation, and output have a long-term equilibrium relationship in the presence of structural breaks. Likewise, quantile models show that the impacts of inflation, economic globalization, and oil prices on economic output are extensively non-linear across the quantile distribution. We found a negative connection between inflation and economic output in the surveyed territories. We propose that those responsible for energy policy in the countries studied should rethink crude oil policies to maximize the benefits of oil exploitation under stagnant globalization and growing inflation. • Effects of oil price, inflation and economic globalization on economic output are assessed. • Oil-rich nations in the Latin America (LA) are considered. • Advanced econometric model (i.e., quantile-on-quantile) is used. • Oil price and economic globalization accelerate economic growth/output in LA. • Inflation is observed to have negative effect on economic growth/output. [ABSTRACT FROM AUTHOR]

Gas flotation for oil–water mixtures is common practice in many industrial cleanup applications. Gas bubbles, typically air, are expected to come into contact with dispersed oil drops leading to the formation of compound drops that rise faster. Although this process is widely used, the details of the interaction and the capture conditions are not well understood. The present analysis discusses the necessary conditions for coalescence considering the fluid properties of the three phases. A critical time ratio is formulated considering the drainage and sliding times of the interaction; an engulfing parameter is proposed considering the surface tension of the three phases. Considering our experiments and the conditions typically found in applications, we argue that coalescence due to free interaction is unlikely to occur. [Display omitted] • The interaction between a droplet and a bubble is studied. • The engulfing parameter, E ∗ , and the interaction time ratio, T ∗ , were explored. • For coalescence, E ∗ must be greater than zero, and T ∗ must be greater than one. • Coalescence is unlikely to occur under ordinary experimental conditions. • Insights are offered to understand droplet-bubble interactions for applications. [ABSTRACT FROM AUTHOR]

In this paper, we used the Baidu feed index to quantify the valuable information from the search engine, and proposed a China's crude oil futures price forecasting model with feed index. The empirical analysis confirms that the feed index, which characterizes the investor attention of participants in China's crude oil futures market, provides valuable information for internet concerns and investor sentiment, and has significant impact on China's crude oil futures price forecasting. We found that the time lag between the search query results and the crude oil futures price changes, which mostly lies within the shorter time range. The forecasting model with the search engine data would produce forecasts with superior performance. [ABSTRACT FROM AUTHOR]

In this paper, a new crude oil price forecasting model using data embedding and Convolutional Neural Network has been proposed to predict more accurate crude oil price movement. It uses the time delayed embedding technique to transform the data into two-dimensional phase space. The Convolutional Neural Network model is used to extract the main data characteristics more effectively, taking advantage of the hierarchical feature extraction and modeling capability of CNN model. Empirical evaluation of the performance of the proposed TDE-CNN model has been conducted using daily price observations in West Taxes Intermediate market. Experiment results confirm that the TDE-CNN forecasting model produces forecasts with the improved forecasting accuracy. [ABSTRACT FROM AUTHOR]

This paper examines the impact of oil price and economic policy uncertainty on China's tourism market. We point out the shortcomings of previous works considering only one factor, and prove the necessity of considering both factors at the same time. The results show that the impacts of oil price and economic policy uncertainty on China's tourism market overlap significantly, their net impact is mainly concentrated in the medium-term and long-term, respectively, and it can explain the significant variation found in China's tourism stock indexes when they are considered simultaneously. Furthermore, among subsectors, hotels are more sensitive to economic policy uncertainty, and scenic spots are most affected by oil price. [ABSTRACT FROM AUTHOR]

This paper evaluates the impact of climate policy uncertainty on renewable and non-renewable energy consumption in the United States over the quarterly data from 2000Q1 to 2021Q3. Economic growth and crude oil prices are added to the energy consumption functions as control variables. The paper considers several approaches to model both renewable and non-renewable energy demand. It is found that crude oil prices promote non-renewable energy demand and climate policy uncertainty reduces it. Surprisingly, the impact of economic growth on non-renewable energy consumption is positive but insignificant. It is also observed that economic growth promotes renewable energy demand, and crude oil prices reduce it. Furthermore, climate policy uncertainty positively affects renewable energy demand in the long run. Some policy implications are provided for reducing non-renewable energy consumption and promoting renewable energy use in the United States through climate policy implementation. • We examine the impact of the Climate Policy Uncertainty (CPU) on renewable and non-renewable energy consumption. • We use the quarterly data from 2000Q1 to 2021Q3 in the United States. • The CPU reduces non-renewable energy demand. • The CPU positively affects renewable energy demand in the long run. • Promoting renewable energy use through climate policy implementation is discussed. [ABSTRACT FROM AUTHOR]

Being an environmentally friendly fuel obtained from rapeseed oil, biodiesel is used extensively in Europe. However, the dependence structure between global crude oil prices and the European prices of biodiesel and rapeseed oil is understudied and unclear. In this paper, we address this gap by utilizing asymmetric copulas and cross-quantilogram approaches on daily data. The results of the DCC-Student-t copula indicate that during bearish periods the conditional connectedness between crude oil prices and biodiesel (rapeseed oil) prices are stronger than during bullish periods, indicating increased co-movement with a decline in crude oil prices. The application of cross-quantilogram indicates that an increase in crude oil price positively influences biodiesel prices reflecting an asymmetric dependence structure among the assets. There is evidence of shifts in the dynamics of quantile dependency during periods of financial and economic turmoil. Overall, the results show a significant dependence between the global crude oil market and the European markets of biodiesel and rapeseed oil in specific periods and under specific market conditions, which have important implications for policymakers and investors. [ABSTRACT FROM AUTHOR]