Future Oil Research Articles

Soil erosion susceptibility maps and raster dataset for the hydrological basins of North Africa

Soil erosion in North Africa modulates agricultural and urban developments as well as the impacts of flash floods. Existing investigations and associated datasets are mainly performed in localized urban areas, often representing a limited part of a watershed. The above compromises the implementation of mitigation measures for this vast area under accentuating extremes and continuous hydroclimatic fluctuations. To address this deficiency, we use the Revised Universal Soil Loss Equation to map surface erosion, providing the first insight into the decadal impacts of land degradation, which are largely unconstrained on North Africa’s continental scale. We generate soil erosion maps for the major hydrological basins of North Africa using Google Earth Engine and multiple hydroclimatic and land use datasets, covering 5.8 million square kilometers. The generated geospatial dataset integrates land use, soil erodibility, slope, vegetation cover, and land practices. The resulting product is an expansive and publicly available Soil erosion susceptibility maps and rasters dataset (SESMAR). This dataset is a crucial step toward understanding the drivers of soil erosion in this vast, poorly characterized area as well as its potential to be used for future soil conservation campaigns for both agricultural and urban planning. We validate SESMAR using the Global Rainfall Erosivity Database (GloREDa) and the European Soil Data Centre (ESDAC) datasets as well as published peer-reviewed reports across 20 watersheds, demonstrating a robust agreement in assessing the average annual soil loss values and soil erosion classes in local areas covered by independent study teams. Our continental maps show commendable accuracy, supporting scientists, practitioners, and policymakers in their efforts for more resilient land management practices across North Africa to mitigate rising hydroclimatic extremes.

The tail risk premium in the oil market

This paper studies tail risk and its option-implied risk compensation in the crude oil market. We identify economically large premia for upside and downside tail risks that significantly forecast crude oil futures returns. These premia are also reflected in the convenience yield for physical oil, which amplifies the predictive power for spot returns. Oil tail risk premia are not spanned by aggregate uncertainty measures, suggesting that shifts in market-specific risk attitudes contribute to commodity price volatility and return predictability.

Machine learning combined with geodetector to predict the spatial distribution of soil heavy metals in mining areas.

An accurate understanding of the spatial distribution of soil heavy metals (HMs) is crucial for the effective prevention of soil pollution and remediation strategies. Traditional machine learning models often overlook the spatially stratified heterogeneity inherent to environmental data, which can impair predictive accuracy. Therefore, we combined the Geodetector model (GDM) with machine learning models. The factor detection results were used to screen covariates to consider the local spatial heterogeneity of model features. The interaction detection results were used to construct spatially stratified covariates to consider the spatially stratified heterogeneity of model features. The results showed that covariate screening largely avoided the introduction of redundant features. The constructed spatially stratified covariates improved the predictive performance of the model (both the R-squared (R2) and root mean square error (RMSE) of different models were optimized). Among these, the XGB model exhibited the best performance. Analysis of the factors influencing Pb and Cr revealed that the interaction between pH and NDVI was the main determinant of Pb spatial distribution (q=0.3516, XGB Importance Score=93). In contrast, the interaction between DEM and pH (q=0.7156, XGB Importance Score=121) as well as the distance to waste piles (q=0.6390, XGB Importance Score=66), were the main driving factors for the spatial distribution of Cr. The current work provides an improved approach for interrogating the factors that influence HM distribution in soil. This study offers valuable insights into the spatial distribution of soil HMs. The proposed methodology can be applied in future soil pollution assessments and environmental management strategies, thus contributing to more precise pollution prevention and remediation efforts.

Developing soil quality indices to investigate degradation impacts of different land use types in Northern Ethiopia.

Assessing soil quality is imperative to diagnose soil functioning and recognize inappropriate soil managements. However, the absence of defined indicators and their scoring methods and lack of universally accepted soil quality indexing frameworks complicate soil quality assessment, given soil systems complexity and diversity caused by variation in soil formation factors. This study was aimed to assess soil quality of three land use managements (CL, cropland; GR, grassland; SL, shrubland) in Northern Ethiopia, using two data sets (Total data set (TDS) and minimum data set (MDS)), linear and non-linear indicator transformation techniques and three indexing scenarios (Additive (SQIa), nemoro (SQIn) and weighted additive (SQIw)). To screen MDS and TDS members, one-way analysis of variance and principal component analysis were applied. Twenty-two soil indicators responsible to changes in land use were screened as members of TDS. Four indicators (silt, mean weight diameter, visual evaluation of soil structure, and exchangeable calcium) were selected to establish MDS. The study results demonstrated a clear impact by land use managements to SQ in the study area. In all cases, CL soils scored significantly lower SQI values than those of GL and SL. Moderate (III) and low (IV) quality grade soils have the largest spatial share in the area. Soil organic carbon and structural stability were found to be the most important indicators explaining soil quality and its spatial variability. Overall, it was demonstrated by the study that soil quality deterioration is a serious issue particularly for soils in CL and GL, suggesting the essentiality to consider revisions and improvements in soil management. A positive and significant correlation was demonstrated among SQIs, revealing that all SQIs were sensitive to measure land use management impacts to soil quality. However, SQI was estimated and differentiated better when using non-linear indicator transformation and MDS indicator selection approach in the SQIw (SQI_NLw_MDS) than other SQIs, and thus was suggested for future soil quality studies in areas of similar soil and climate environments.

Core transcriptome network modulates temperature (heat and cold) and osmotic (drought, salinity, and waterlogging) stress responses in oil palm

Oil palm (Elaeis guineensis) yield is impacted by abiotic stresses, leading to significant economic losses. To understand the core abiotic stress transcriptome (CAST) of oil palm, we performed RNA-Seq analyses of oil palm leaves subjected to drought, salinity, waterlogging, heat, and cold stresses. A total of 19,834 differentially expressed genes (DEGs) were identified. Cold treatment induced the highest number of DEGs (5,300), followed by heat (4,114), drought (3,751), waterlogging (3,573), and, lastly, salinity (3096) stress. Subsequent analysis revealed the CAST of oil palm, comprising 588 DEGs commonly expressed under drought, salinity, waterlogging, heat, and cold stress conditions. Function annotation of these DEGs suggests their roles in signal transduction, transcription regulation, and abiotic stress responses including synthesis of osmolytes, secondary metabolites, and molecular chaperones. Moreover, we identified core DEGs encoding kinases, ERF, NAC TFs, heat shock proteins, E3 ubiquitin-protein ligase, terpineol synthase, and cytochrome P450. These core DEGs may be potential key modulators that interplay in triggering rapid abiotic stress responses to achieve delicate equilibrium between productivity and adaptation to abiotic stresses. This comprehensive study provides insights into the key modulators in the CAST of oil palm, and their potential applications as markers for selecting climate-resilient oil palms or opportunities to develop future climate resilient oil palm using genome editing.

Application of the Hydrocarbon Generation Potential Method in Resource Potential Evaluation: A Case Study of the Qiongzhusi Formation in the Sichuan Basin, China

Global recoverable shale gas reserves are estimated to be 214.5 × 1012 m3. Estimation methods for shale gas resources, such as volumetric, analog, and genetic approaches, have been widely used in previous studies. However, these approaches have notable limitations, including the substantial effect of rock heterogeneity, difficulties in determining the similarity of analog accumulations, and unsuitability for evaluating high-mature–overmature source rocks. In the Qiongzhusi Formation (Є1q) of the Sichuan Basin, China, extensive development of high-mature–overmature shales has led to significant advancements in conventional and unconventional shale gas exploration. This progress highlights the need for the development of an integrated evaluation system for conventional and unconventional resources. Hence, this study uses the whole petroleum system theory and an improved hydrocarbon generation potential method to analyze the distribution patterns of hydrocarbon generation, retention, and expulsion during various stages of oil and gas accumulation in the Є1q. In addition, it assesses the resource potential of conventional and shale oil and gas. Hydrocarbon generation and expulsion centers are favorable exploration targets for conventional oil and gas, primarily located in the central and northern regions of the Mianyang—Changning rift trough, with an estimated resource potential of 6560 × 1012 m3. Hydrocarbon retention centers represent promising targets for shale oil and gas exploration, concentrated in the central Mianyang—Changning rift trough, with a resource potential of 287 × 1012 m3. This study provides strategic guidance for future oil and gas exploration in the Є1q and offers a methodological reference for integrated resource assessments of conventional and unconventional oil and gas systems of high-mature–overmature source rocks in similar basins worldwide.

The older, the better: a comprehensive survey of soil organic carbon under commercial oil palm plantations

Soil conditions of croplands are a frequent topic of scientific research. In contrast, less is known about large-scale commercial plantations of perennial crops such as oil palm. Oil palm is a globally important tropical commodity crop which contributes to both food and energy security due to its exceptional productivity. However, oil palm crops are associated with short lifecycles and high nutrient demands, which may disproportionately affect soil health. With the goal of exploring baseline soil properties in commercial oil palm plantations, we evaluated data from two large-scale soil surveys carried out in 2014/2015 and 2018/2019 across more than 400 fields located throughout Peninsular Malaysia. We examined variation in field-measured soil quality indicators with a focus on soil organic carbon content at three depths (0–15 cm, 15–30 cm, 30–45 cm) and investigated links with spatial covariates, including plantation age. We found SOC contents to be low (1.6–2%) across the sampled locations with limited correlation with spatial predictors employed in soil organic carbon modelling. Furthermore, we found that immature and young mature plantations, which consisted of fields that were re-planted as part of a 20-year-long oil palm rotation, were characterised by significantly lower soil organic carbon content than the mature plantations. This suggests that management practices should target younger oil palm plantations for soil organic conservation measures to increase the overall baseline SOC content, which will subsequently accumulate over the plantation’s lifespan. We further provide recommendations for future soil sampling efforts, which could increase the robustness of collected data and facilitate their use for soil monitoring through modelling approaches involving, for example, digital soil mapping.

Modeling the tail risk of crude oil futures using a quantum approach

This study proposes a quantum approach to measure the tail risk of crude oil futures. Based on the quantum harmonic oscillator (QHO) model, we first model the log return process of crude oil futures. From the equilibrium distribution of QHO model, we calculate the well-known tail risk measures such as Value-at-Risk (VaR) and Expected Shortfall (ES); we confirm that this quantum approach provides precise estimates of in-sample and out-of-sample VaR and ES for crude oil futures. The findings suggest the QHO model for log return process outperforms the geometric Brownian motion for price process and the historical simulation approach. The excellence of QHO model in explaining the tail risk of crude oil futures can be attributed to its fast angular frequency and high probability allocation in excited states. Crude oil market participants can use this study’s analytical framework to precisely measure and manage potential risks in the marketplace. Regulatory authorities can evaluate the degree of crude oil market instability through the current market state of crude oil futures characterized by the QHO model parameters while devising and revising crude oil market regulations. Future research can examine quantum models with (1) different assumptions, e.g., position-dependent diffusion and time-varying equilibrium return, and (2) different potential functions, e.g., infinite and finite square well.

The Effect of Pre–Triassic Unconformity on a Hydrocarbon Reservoir: A Case Study from the Eastern Mahu Area, Northwestern Junggar Basin, China

Unconformities are of significant interest to petroleum geologists because of their crucial roles in influencing reservoir quality and controlling oil and gas migration. This study investigates the impact of unconformities on a reservoir within a prolific oil–gas-bearing zone between the Middle Permian and Lower Triassic strata in the northwestern Junggar Basin, utilizing thin sections, well logging data, seismic profiles, and geochemical analyses. The results reveal a well-developed three-layer unconformity structure characterized by a thick weathered clay layer, which acts as an effective caprock for hydrocarbons. The diagenetic evolution of the Lower Wuerhe Formation in the northwestern Junggar Basin consists of an initial stage of compaction followed by a subsequent stage of dissolution and cementation. Four key factors, including low argillaceous content in sandstone and conglomerate, diagenetic compaction, zeolite dissolution and cementation, and clay mineral infill, have played a crucial role in influencing the reservoir characteristics of the Lower Wuerhe Formation. In addition, the development of unconformities promotes atmospheric freshwater leaching, which enhances the dissolution of the underlying reservoir while developing an extensive network of strike-slip faults that improve connectivity within hydrocarbon reservoirs. This process facilitates both vertical and lateral migration of hydrocarbons along hard rock layers, which allows the unconformity to breach into the overlying conglomerate reservoirs. The results of this study suggest that the reservoir in proximity to the unconformity surface often exhibits high porosity and rich hydrocarbon content, offering valuable insights for future oil and gas exploration and development.

Forecasting volatility in Chinese crude oil futures: insights from volatility-of-volatility and Markov regime-switching approaches

This study aims to improve the prediction ability of realized volatility in the Chinese crude oil futures market by characterizing the volatility of volatility (VOV) and its jump components, as well as the Markov regime-switching feature. We extend the HAR-DJI-GARCH model to include the continuous and jump volatility of volatility while incorporating the Markov regime-switching feature through the MS-GARCH framework, thus offering a novel approach for capturing the intricate, nonlinear behaviour of crude oil futures volatility. Model parameters are estimated by improving the maximum likelihood approach, and the performance of the proposed model is compared to that of other models via out-of-sample R2, the CW test, the MCS test and various robustness checks. The empirical findings suggest that the incorporation of VOV, particularly jump information, alongside Markov regime switching significantly enhances the predictive power for the volatility of the Chinese crude oil futures market.

Dynamic downscaling of climate projections using WRF-CCSM4: future soil temperature predictions for the Arabian Peninsula and Kuwait

This study employs the Weather Research and Forecasting (WRF) model to dynamically downscale climate projections from the Community Climate System Model version 4 (CCSM4) using 12 km and 4 km high grid resolutions. The performance of the WRF-CCSM4 configuration is evaluated against observational data from the automated weather observing systems of Kuwait (AWOSK). The analysis focuses on future predictions for maximum soil temperature during the summer months from May to September for the period 2050–2060. The findings indicate a projected increase in average soil temperatures of 1–3°C across the Arabian Peninsula and Kuwait. Notably, the results demonstrate that the 4-km high-resolution WRF domain, is more effective framework for accurate weather and climate predictions in this region. These insights underscore the importance of high-resolution modeling in understanding and mitigating the impacts of climate change, particularly in arid environments like Kuwait and the broader Arabian Peninsula.

Future soil moisture will slowdown the advancement of ecosystem productivity in the Northern Hemisphere.

Future soil moisture will slowdown the advancement of ecosystem productivity in the Northern Hemisphere.

Applicability of soil health assessment dominated by biological indicators in facility agriculture

The minimum data set (MDS) is widely used for soil health index (SHI) assessment, with the selection of indicators being crucial. However, biological indicators are less frequently used compared to physical and chemical indicators. This study aimed to establish a biologically-centered MDS for assessing soil health in tomato facility agriculture in Ningbo. Using biochemical, microbiological, and nematode data from 60 continuous cropping soils, we identified key indicators for the MDS. Principal component analysis revealed a strong correlation between the MDS and the total data set (TDS), with the final MDS including available phosphorus, electrical conductivity, β-glucosidase, urease, bacterial Chao1 index, bacterial Shannon index, Ralstonia solanacearum, nematode Shannon index, structure index, and channel index. Random forest modeling showed that the predicted SHI had a high fit with the MDS-SHI, with bacterial and nematode Shannon indices being the most influential predictors, emphasizing the critical role of biological communities in maintaining soil health. Furthermore, the study identified three distinct phases in soil health during long-term continuous cropping: healthy (0–10 years), sub-healthy (11–15 years), and recovery (16–20 years), demonstrating the soil's potential for self-recovery. By incorporating biodiversity as a core component, this biologically-focused MDS provides a more comprehensive and precise method for evaluating soil health in facility agriculture. The findings suggest that this MDS could inform future research and applications in similar agricultural systems. Moreover, the results highlight the vital role of biodiversity in agroecosystem stability, suggesting that future soil health assessments should place greater emphasis on biological indicators to better capture the complexity of soil functions.

Spatial Assessment of Soil Erosion Risk Using RUSLE and GIS in Dhumuga Watershed, Ambo, Ethiopia

Soil erosion has become a critical problem leading to land degradation and environmental risks globally. To grasp the rates of soil loss and identify the main factors driving these issues, it is vital to examine the specific impacts of soil erosion across different locations. Therefore, between 2021 and 2023, a research initiative was undertaken to assess, rank, identify, and map sections of the watershed that are particularly susceptible to soil erosion. The RUSLE components for factors R, K, L, S, C, and P were integrated using the ArcGIS 10.4.1 spatial analyst's raster calculator tool to calculate and create maps that illustrate the risk and intensity of soil erosion in the Dhumuga watershed. The Dhumuga watershed was categorized into five groups based on average annual soil loss: 0–5 ton/ha<sup>-1</sup> year<sup>-1</sup> (very slight), 5–10 ton/ha<sup>-1</sup> year<sup>-1</sup> (slight), 10–20 ton/ha<sup>-1</sup> year<sup>-1</sup> (moderate), 20–50 ton/ha<sup>-1</sup> year<sup>-1</sup> (high), and > 50 ton/ha-1 year-1 (very high). The assessment of soil erosion severity was influenced by factors such as rainfall, soil type, DEM, land use, and land cover, employing the GIS-based RUSLE equation. The spatial risk of soil erosion was sorted into five categories based on severity, with 11.58% of the area categorized as very high risk (>50 ton ha<sup>-1</sup> year<sup>-1</sup>), and 54.2% in the very low to low-risk category. On average, the watershed yielded an annual sediment production of up to 13.94 tons/ha/year, which is within an acceptable range. Considering these research findings, GIS-based analyses can be utilized to pinpoint areas at risk of soil erosion and identify vulnerable zones, offering crucial insights for future soil conservation and model enhancement.

Visibility graph analysis of crude oil futures markets: Insights from the COVID-19 pandemic and Russia-Ukraine conflict

Visibility graph analysis of crude oil futures markets: Insights from the COVID-19 pandemic and Russia-Ukraine conflict

Climate risk perception and oil financialization in China: Evidence from a time-varying Granger model

Financialization has introduced a new dimension to the oil market by incorporating various financial instruments and market participants. This study thus firstly measures the oil financialization by using daily data on Shanghai crude oil futures and Shanghai composite index. The results identify a lower level of oil financialization in China, implying that the oil market is independent on the stock market. Moreover, the exogenous events volatile the degree of oil financialization. We then estimate the impact of climate risk perception on oil financialization using a time-varying Granger model. Novel evidence finds negative and significant time-varying coefficients of climate risk perception on oil financialization, indicating that the effect of climate risk perception is non-constant over time. The empirical results reinforce the implications of the bounded rationality decision and signal theory.

Dynamic spillovers between Shanghai crude oil futures and China's green markets: Evidence from quantile-on-quantile connectedness approach

This paper provides a preliminary investigation of the dynamic relationship between crude oil and different green assets by examining the spillover effects between the Shanghai crude oil futures (INE), carbon emissions trading (CET), ESG stocks (ESG), clean energy stocks (CSI) and green bonds (GB) markets. The innovation of this paper is its adoption of the quantile-on-quantile connectedness approach to analyze the spillover effects that occur in these markets across various quantiles and provide more accurate and comprehensive empirical results. Our results can be summarized as follows. First, CSI and ESG are positively correlated with INE during the sample period, while CET and GB are negatively correlated with INE. Second, CET has strong potential to hedge against oil shocks during normal market states, while GB shows better hedging performance during extreme market states. Third, regardless of the quantile level, ESG and CSI play the role of primary information transmitters in the network, while GB and CET act more as net spillover recipients. These findings help oil investors to realign their portfolios for risk avoidance and provide policymakers with a reference for formulating policy measures.

Examining dynamics: Unraveling the impact of oil price fluctuations on forecasting agricultural futures prices

This research aims to explore the complex dynamics governing the correlation between oil futures prices and Chinese agricultural futures prices, with a specific emphasis on unveiling the crucial role played by oil futures prices in predicting the trajectory of agricultural futures prices. The study utilizes the Vector Error Correction-Dynamic Conditional Correlation-Multivariate Generalized Autoregressive Conditional Heteroskedasticity (VEC-DCC-MGARCH) model to dissect the interplay among oil, soybean, and corn price series. Additionally, this study integrates the innovative Spatio-temporal Information Recombination Hypergraph Neural Network (STIR-HGNN) model to analyze how oil futures prices contribute to improving the accuracy of forecasting agricultural product prices. Findings indicate numerous connections between oil prices and agricultural futures prices, highlighting the significant role of oil prices in forecasting agricultural futures price movements. The empirical insights derived from this study serve as a valuable compass for futures market participants, urging them to leverage these findings to refine and optimize their market strategies, enhancing their capacity to navigate and capitalize on the intricate complexities inherent in these interconnected markets.

Integrating sentiment information for risk prediction: the case of crude oil futures market in China

AbstractThis paper incorporates investor sentiment indexes into the traditional standard heterogeneous autoregressive (HAR) model to improve its power on predicting crude oil futures risk. Using the 5-min high-frequency trading data to construct the daily realized volatility, the original and revised HAR models are used for in-sample regression and out-of-sample forecasting on a daily, weekly, and monthly basis. The results show that the sentiment indexes and the search trend contain incremental information for forecasting the realized volatility of INE crude oil futures in the short and medium term. The search volume is the best indicator for weekly risk forecasting of INE crude oil futures. No robust index can improve the performance of HAR-type model on long-term risk prediction. This paper thus highlights that market participants should select appropriate strategies to minimize risk when volatility is at stake for their decisions.

Crude Oil Markets Volatility Forecasting: A Novel Deep Learning Hybrid Model

ABSTRACTTo the national economy, increasing the forecasting accuracy of realised volatility (RV) on crude oil futures markets is of critical strategic importance. However, the RV of crude oil futures cannot be accurately predicted with a single model. For this study, we adopt a hybrid model which combines gated recurrent unit (GRU) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to forecast the RV of crude oil futures. Moreover, back propagation neural networks (BP), Elman neural networks (Elman), support vector regression machine (SVR), autoregressive model (AR), heterogeneous autoregressive model (HAR), and their hybrid models with CEEMDAN are adopted as comparisons. In general, this article demonstrates the superiority of the CEEMDAN‐GRU model in RV forecasting from several aspects: for both evaluation criteria, CEEMDAN‐GRU achieves the highest RV forecasting accuracy in emerging and developed crude oil futures markets; furthermore, the empirical results are robust to alternative realised measures and training sets of different lengths.