Driving biofuels from field to fuel tank.

Rising oil prices, fears of global warming, and instability in oil-producing countries have ignited the rush to produce biofuels from plants. The science is progressing rapidly, driven by favorable policies and generous financing, but many hurdles remain before cars and trucks run on "gasohol" or "grassoline."

Time-varying coefficient vector autoregressions model based on dynamic correlation with an application to crude oil and stock markets.

This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor's 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model.

Environment and economic risk: An analysis of carbon emission market and portfolio management.

Climate change has been one of the biggest and most controversial environmental issues of our times. It affects the global economy, environment and human health. Many researchers find that carbon dioxide (CO2) has contributed the most to climate change between 1750 and 2005. In this study, the orthogonal GARCH (OGARCH) model is applied to examine the time-varying correlations in European CO2 allowance, crude oil and stock markets in US, Europe and China during the Protocol's first commitment period. The results show that the correlations between EUA carbon spot price and the equity markets are higher and more volatile in US and Europe than in China. Then the optimal portfolios consisting these five time series are selected by Mean-Variance and Mean-CVAR models. It shows that the optimal portfolio selected by MV-OGARCH model has the best performance.

The hidden practices and experiences of healthcare practitioners dealing with fuel poverty.

BACKGROUND: Fuel poverty negatively impacts a population's health affecting life chances along the life course. Moreover, it represents a substantial inequality in the UK. Healthcare practitioners (HCPs) have a key role in identifying and supporting patients who are fuel poor. METHODS: A qualitative inquiry with District Nurses and General Practitioners, to explore their understanding and experiences of dealing with patients living in fuel poverty. RESULTS: Participants recognize fuel poverty by observing material cues. They perceive their relationship with the patient as pivotal to recognizing the fuel poor. Practitioners' sense of responsibility for their patients' social concerns is determined by their knowledge about the link to health outcomes. The services that they sign-post to are motivated by their experience dealing with the service, or their patients' experiences of the service. CONCLUSION: Participants' reliance on temporary material cues resulted in few experiences of recognition of the fuel poor. HCPs' perceptions of patient pride and the lack of personal relationship between doctor and patient presented barriers to identifying fuel poor patients. A limitation of this study is the small sample size of nine participants. These came from two professional groups, which afforded more depth of exploration, but may limit applicability to other professionals.

Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis.

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.

Day-ahead crude oil price forecasting using a novel morphological component analysis based model.

As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations.

Effect of temperature shock and inventory surprises on natural gas and heating oil futures returns.

The aim of this paper is to examine the impact of temperature shock on both near-month and far-month natural gas and heating oil futures returns by extending the weather and storage models of the previous study. Several notable findings from the empirical studies are presented. First, the expected temperature shock significantly and positively affects both the near-month and far-month natural gas and heating oil futures returns. Next, significant temperature shock has effect on both the conditional mean and volatility of natural gas and heating oil prices. The results indicate that expected inventory surprises significantly and negatively affects the far-month natural gas futures returns. Moreover, volatility of natural gas futures returns is higher on Thursdays and that of near-month heating oil futures returns is higher on Wednesdays than other days. Finally, it is found that storage announcement for natural gas significantly affects near-month and far-month natural gas futures returns. Furthermore, both natural gas and heating oil futures returns are affected more by the weighted average temperature reported by multiple weather reporting stations than that reported by a single weather reporting station.

WTI, Brent or implied volatility index: Perspective of volatility spillover from oil market to Chinese stock market.

This study investigates the impact of oil market uncertainty on the volatility of Chinese sector indexes. We utilize commonly used realized volatility of WTI and Brent oil price along with the CBOE crude oil volatility index (OVX) to embody the oil market uncertainty. Based on the sample span from Mar 16, 2011 to Dec 31, 2019, this study utilizes vector autoregression (VAR) model to derive the impacts of the three different uncertainty indicators on Chinese stock volatilities. The empirical results show, for all sectors, the impact of OVX on sectors volatilities are more economically and statistically significant than that of realized volatility of both WTI and Brent oil prices, especially after the Chinese refined oil pricing reform of March 27, 2013. That implies OVX is more informative than traditional WTI and Brent oil prices with respect to volatility spillover from oil market to Chinese stock market. This study could provide some important implications for the participants in Chinese stock market.

Asymmetric dynamic linkage between consumer sentiment, inflation expectations, and international energy prices: Evidence from time-frequency wavelet and nonlinear analysis.

This study investigates the relationship between consumer sentiment (CONS), inflation expectations (INEX) and international energy prices, drawing on principles from behavioral. We focus on Brent crude oil price and Henry Hub natural gas prices as key indicators of energy market dynamics. Based on the monthly data from January 2003 to March 2023, three wavelet methods are applied to examine the time-frequency linkage, while the nonlinear distributed lag model (NARDL) is used to verify the asymmetric impact of two factors on energy prices. The results highlight a substantial connection between consumer sentiment, inflation expectations and international energy prices, with the former in the short term and the latter in the medium to long term. Especially, these correlations are particularly pronounced during the financial crisis and global health emergencies, such as the COVID-19 epidemic. Furthermore, we detect short-term asymmetric effects of consumer sentiment and inflation expectations on Brent crude oil price, with the negative shocks dominating. The positive effects of these factors on oil prices contribute to observed long-term asymmetry. In contrast, inflation expectations have short-term and long-run asymmetric effects on natural gas price, and both are dominated by reverse shocks, while the impact of consumer sentiment on natural gas prices appears to be less asymmetric. This study could enrich current theories on the interaction between the international energy market and serve as a supplement to current literature.

Regional water implications of reducing oil imports with liquid transportation fuel alternatives in the United States.

The Renewable Fuel Standard (RFS) is among the cornerstone policies created to increase U.S. energy independence by using biofuels. Although greenhouse gas emissions have played a role in shaping the RFS, water implications are less understood. We demonstrate a spatial, life cycle approach to estimate water consumption of transportation fuel scenarios, including a comparison to current water withdrawals and drought incidence by state. The water consumption and land footprint of six scenarios are compared to the RFS, including shale oil, coal-to-liquids, shale gas-to-liquids, corn ethanol, and cellulosic ethanol from switchgrass. The corn scenario is the most water and land intense option and is weighted toward drought-prone states. Fossil options and cellulosic ethanol require significantly less water and are weighted toward less drought-prone states. Coal-to-liquids is an exception, where water consumption is partially weighted toward drought-prone states. Results suggest that there may be considerable water and land impacts associated with meeting energy security goals through using only biofuels. Ultimately, water and land requirements may constrain energy security goals without careful planning, indicating that there is a need to better balance trade-offs. Our approach provides policymakers with a method to integrate federal policies with regional planning over various temporal and spatial scales.

PM2.5 mass and species trends in Santiago, Chile, 1998 to 2010: the impact of fuel-related interventions and fuel sales.

UNLABELLED: Improving air quality in Santiago has been a high priority for the Chilean government. In this paper we examine trends of fine particulate matter (PM2.5) mass and species concentrations during the period 1998 to 2010 and explore the impact of fuel-related interventions and fuel sales on concentration changes. Smoothing spline functions were utilized to characterize and account for nonlinear relationships between pollutant concentrations and different parameters. Meteorology-adjusted PM2.5 concentrations were lower by 21.8 microg/m3 in 2010 compared to 1998. In this model, wind speed was the most important determinant of PM2.5 levels. A decrease in 24-hr average wind speed below 1.0 m/s was associated with a significant increase in daily PM2.5 levels, indicating a high sensitivity of PM2.5 concentrations to the accumulation of local emissions. The same regression model framework was applied to examine the trends of lead, bromine, and sulfur concentrations. Removal of lead and bromine from gasoline achieved dramatic decreases in their atmospheric concentrations. Nonetheless, both elements continue to persist, likely in the form of PbBrCl. The reduction of diesel sulfur content from 1,500 to 50 ppm corresponded to a 32% decrease in particulate sulfur levels. Lastly, a surge in PM2.5 was observed in 2005-2008. Further regression analyses suggested this was prompted by a rise in monthly petroleum-based fuel sales. IMPLICATIONS: In this paper, we elucidate meteorology-adjusted trends of PM2.5 mass and species concentrations in Santiago and assess the efficacy of fuel-related interventions, such as the removal of lead from gasoline and reduction of sulfur content in diesel. In addition, we explore the impact of fuel sales on PM2.5 trends. Given that fuel consumption is likely to increase further in this rapidly growing city, understanding its impact on PM2.5 trends can inform future air quality control efforts in Santiago.

Inquiring asymmetric effects of oil prices, money supply, and domestic debt on consumer prices: an empirical evidence from Pakistan.

Oil prices (OP) may play a significant role in determining inflation in any oil-importing economy and could have an asymmetrical effect as well. Thus, this paper aims to explore the asymmetric influence of OP, broad money supply (BMS), and domestic debt (DD) on the Consumer Price Index (CPI) in the oil-importing economy of Pakistan using the nonlinear autoregressive distributive lag (NARDL) methodology on an annual sample from 1980 to 2021. The long-run results show that increasing OP and BMS have a positive effect on CPI. Similarly, decreasing OP and BMS have a positive effect on CPI. So, increasing OP and BMS is raising price levels, and decreasing OP and BMS is reducing price levels. OP has a positive and symmetrical effect on CPI. However, the BMS has a positive but asymmetrical effect on CPI. Furthermore, the effect of decreasing BMS is found greater than increasing BMS. Moreover, the effect of DD on CPI is also found asymmetrical. The increasing DD has a positive effect, and decreasing DD has a negative effect on CPI. The most of short-run results follow the long-run results. However, energy usage shows a negative effect on CPI in the short run, which is insignificant in the long-run results. This study recommends controlling the money supply and oil prices to reduce consumer prices.

Effect of oil price shocks on output and prices: evidence from Saudi Arabia.

The primary objective of this article is to estimate how the spike in oil prices has impacted Saudi Arabia's output and other macroeconomic indicators. To address this question, the study utilizes a simple VAR estimation and VAR estimation with sign restrictions, specifically an oil demand shock and an oil supply shock, to determine the influence of oil price shocks. The study's findings are consistent with conventional wisdom and current research from oil-exporting countries. Consistent with all model specifications for both periods (monthly 2010q1 to 2020q2 and quarterly 1996q1 to 2020q2), the study finds that an oil price shock benefits the Saudi Arabian economy. The results indicate that a 10% increase in oil prices leads to a 2% increase in output, a 0.15 percentage point increase in the consumer price index, a 6% increase in export value, and a 3% increase in import value. The study highlights the importance of considering both oil supply and demand shocks in analyzing the impact of oil price fluctuations and concludes that the demand shock is more influential than the supply shock.