Optimized Multivariate Adaptive Regression Splines for Predicting Crude Oil Demand in Saudi Arabia

This paper presents optimized linear regression with multivariate adaptive regression splines (LR-MARS) for predicting crude oil demand in Saudi Arabia based on social spider optimization (SSO) algorithm. The SSO algorithm is applied to optimize LR-MARS performance by fine-tuning its hyperparameters. The proposed prediction model was trained and tested using historical oil data gathered from different sources. The results suggest that the demand for crude oil in Saudi Arabia will continue to increase during the forecast period (1980–2015). A number of predicting accuracy metrics including Mean Absolute Error (MAE), Median Absolute Error (MedAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and coefficient of determination ( R 2 ) were used to examine and verify the predicting performance for various models. Analysis of variance (ANOVA) was also applied to reveal the predicting result of the crude oil demand in Saudi Arabia and also to compare the actual test data and predict results between different predicting models. The experimental results show that optimized LR-MARS model performs better than other models in predicting the crude oil demand.

Peramalan Penjualan Olein Curah di Perusahaan Pengolahan Kelapa Sawit Menggunakan Double Moving Average

Bulk olein is one of the products produced by Palm Oil Processing Company. Bulk cooking oil controls 75 percent of the production market share in Indonesia and about 77.5 percent of households in Indonesia use bulk cooking oil because the price is cheaper than packaged cooking oil. Demand for olein in the future is predicted to be continued to increase, so it is necessary to estimate future sales so that production activities become more effective and efficient. The method used in this study is the double moving average (DMA), which is one of the forecasting methods with data that has a trend. The calculation will be done by comparing the result of 3 moving, 4 moving and 5 moving. Forecasting error is calculated using mean absolute percentage error (MAPE). The calculation results show that the average MAPE from DMA with 5 moving has the smallest value. To verify these results, an analysis of the processed data was carried out, namely looking for data with the furthest distance from the linear line, namely t3 data and t7 data. The data is omitted in data processing and then the MAPE error value is recalculated. The results obtained are that DMA with 3 moving results have the smallest error, which is 11.863 percent. For this reason, the chosen forecasting calculation is a double moving average with 3 moving.

The Multiple Impacts of the COVID-19 Pandemic on China’s Oil Security and the Rising Green Opportunities

The COVID-19 pandemic has seriously challenged the global oil market, and coronavirus-induced oil prices crash, oil demand decline and global economic recession affect China’s oil supply as well. China has high oil vulnerability due to its rising oil import dependency which aggravates Beijing’s concerns about oil security, despite at a time of the pandemic-induced oil oversupply. This study uses the SWOT analytical model to identify the strengths, weaknesses, opportunities and threats in China’s oil sector, and the changes in opportunities and threats caused by the COVID-19 pandemic. The pandemic has brought multiple impacts to China’s oil security. Results from the analysis show that the existing opportunities such as oil investments in the Belt and Road Initiative (BRI) and domestic upstream opening-up have been weakened; new threats that the uncertainty over global oil demand-supply and decrease in global upstream investments have emerged; opportunities that an increase in domestic strategic petroleum reserve (SPR) and low-carbon development are rising amid the pandemic. Notably, the COVID-19 pandemic has demonstrated the vulnerability of the global oil market to systemic risks and accelerated the transition to renewable energy.

The Multiple Impacts of the COVID-19 Pandemic on China’s Oil Security and the Rising Green Opportunities

The COVID-19 pandemic has seriously challenged the global oil market, and coronavirus-induced oil prices crash, oil demand decline and global economic recession affect China’s oil supply as well. China has high oil vulnerability due to its rising oil import dependency which aggravates Beijing’s concerns about oil security, despite at a time of the pandemic-induced oil oversupply. This study uses the SWOT analytical model to identify the strengths, weaknesses, opportunities and threats in China’s oil sector, and the changes in opportunities and threats caused by the COVID-19 pandemic. The pandemic has brought multiple impacts to China’s oil security. Results from the analysis show that the existing opportunities such as oil investments in the Belt and Road Initiative (BRI) and domestic upstream opening-up have been weakened; new threats that the uncertainty over global oil demand-supply and decrease in global upstream investments have emerged; opportunities that an increase in domestic strategic petroleum reserve (SPR) and low-carbon development are rising amid the pandemic. Notably, the COVID-19 pandemic has demonstrated the vulnerability of the global oil market to systemic risks and accelerated the transition to renewable energy.

Analyzing global competitiveness of Indonesian palm oil

Indonesia is the largest producer and exporter of palm oil in the world vegetable oil market. However, Indonesia export share had decreasing trend and there is an emerging competition from South America and African palm oil producer. Another issue is a non-competitive strategy from European countries for limiting palm oil import. This study aims to seek the pricing behavior of Indonesian exporter in the world palm oil market. This study shows Indonesian exporter apply Local Currency Price Stabilization Strategy to European countries but more competitive behavior to other importing countries. The LCPS strategy to European countries might be induced by more elastic palm oil demand on that region. The opposite with India and China that might have less elastic palm oil demand schedule. However, Indonesian exporter should have more awareness of future competition in the palm oil market and its pricing strategy, especially to emerging demand from India and China.