Deep Learning-Based Economic Forecasting for the New Energy Vehicle Industry

Recently the issues of insufficient energy and serious air pollution around the world have been rising. Henceforth, there is a need to carry out a research of new energy. Soon, new energy vehicles will be the mainstream trend, which can not only reduce the burden of consumers due to rising fuel prices but also solve the air pollution problem caused by the exhaust emissions of fuel vehicles. With the rapid development of science and technology, deep learning continues to make breakthroughs, and, in the field of economy with huge information data, we have more powerful weapons available to predict and research important economic data with infinite value, which can not only provide reference information to policy makers but also help enterprises and even economic markets to develop more healthily and sustainably. Therefore, this article uses deep learning algorithms to forecast and analyze the new energy industry, starting from the financial information released by new energy vehicle companies in their annual reports, in order to make basic judgments and help policy makers and enterprises in the new energy vehicle industry.

Bubble Identification in the Emerging Economy Fuel Price Series: Evidence from a Generalized Sup Augmented Dickey–Fuller Test

In the recent past, the world in general and Pakistan in particular faced a drastic fuel price change, affecting the economic productivity of the country. This has drawn the attention of empirical researchers to analyze the abrupt change in fuel prices. This study takes a lead and investigates for the first time, in the literature related to Pakistan, the presence of multiple fuel price bubbles, with the purpose of knowing if the price driver is due to demand or it is exuberant consumer behavior that prevails and contributes to a sudden boom in fuel price series. The empirical analysis is performed through a recently proposed state-of-the-art generalized sup ADF (GSADF) approach on six commonly used fuel price series, namely, LDO (light diesel oil), HSD (high-speed diesel), petrol, natural gas, kerosene, and MS (motor spirit). The bubble analysis for each of the six fuel price series is based on monthly data from July 2005 to August 2020. The findings provide evidence of the existence of multiple bubbles in all series considered. Specifically, four bubbles are detected in each of the kerosene and natural gas price series, whereas three bubbles are noted in each of the HSD, LDO, petrol and MS price series. The maximum duration of occurrence of bubbles is of 12 months for kerosene. The date-stamping of the bubbles shows that the financial crisis of 2008 contributed to the emergence of bubbles that pushed oil prices upward and caused a depreciation in the national currency.

Extreme weather events and high Colombian food prices: A non-stationary extreme value approach

Given the importance of climate change and the increase of its severity under extreme weather events, we analyze the main drivers of high food prices in Colombia between 1985 and 2020 focusing on extreme weather shocks like a strong El Ni˜no.We estimate a non-stationary extreme value model for Colombian food prices. Our findings suggest that perishable foods are more exposed to extreme weather conditions in comparison to processed foods. In fact, an extremely low precipitation level explains only high prices in perishable foods. The risk of high perishable food prices is significantly larger for low rainfall levels (dry seasons) compared to high precipitation levels (rainy seasons). This risk gradually results in higher perishable food prices. It is non linear and is also significantly larger than the risk related to changes in the US dollar-Colombian peso exchange rate and fuel prices. Those covariates also explain high prices for both perishable and processed foods. Finally, we find that the events associated with the strongest El Ni˜no in 1988 and 2016 are expected to reoccur once every 50 years.

Proses Fermentasi pada Produksi Bioetanol Dedak Padi dengan Hidrolisis Enzimatis

Meningkatnya harga bahan bakar dan menurunnya cadangan bahan bakar fosil memaksa untuk mencari sumber-sumber energi yang murah sebagai biofuel, seperti bioetanol dapat mengurangi efek negatif dari penggunaan bahan bakar fosil yang tidak terbarukan. Bioetanol dapat terbuat dari biomassa yang mengandung gula, pati dan selulosa. Dedak padi merupakan hasil samping dari penggilingan padi dan menyumbang ±11% dari berat padi. Dalam hal ini dedak padi karbohidrat yang cukup tinggi untuk diolah menjadi bioetanol. Sementara untuk meningkatkan kadar etanol, proses terpenting dalam produksi bioetanol adalah proses fermentasi. Tujuan dari penelitian ini yaitu pengoptimalan proses fermentasi meliputi pH dan waktu pada pembuatan bioetanol dari ekstrak dedak padi dengan penambahan urea dan NPK sebagai sumber nutrisi untuk pertumbuhan saccharomycess cerevisiae. Penelitian ini menggunakan dedak padi yang dihidrolisis, kemudian difermentasi menggunakan saccharomycess cerevisiae dengan penambahan nutrisi urea dan NPK, dan di distilasi untuk dimurnikan. Pengoptimalan pH dan waktu pada proses fermentasi serta penambahan nutrisi urea dan NPK diharapkan mampu meningkatkan kadar bioetanol yang dihasilkan. Hasil data dihitung dengan menggunakan metode perhitungan faktorial desain untuk mengetahui variabel paling berpengaruh. Rising fuel prices and declining fossil fuel reserves force to find cheap energy sources as biofuels, such as bioethanol can reduce the negative impact of using non-renewable fossil fuels. Bioethanol can be produced from biomass containing sugar, starch and cellulose. Rice bran is a by product of rice milling and accounts for ±11% of the weight of rice. In this case the carbohydrate rice bran is high enough so that it can be processed into bioethanol. Meanwhile, to increase ethanol content the most important process in manufacture of bioethanol is fermentation process. The purpose of this study is to optimize the fermentation process including pH and time in manufacture of bioethanol from rice bran extract with addition of urea and NPK as a source of nutrients for growth saccharomycess cerevisiae. This study used rice bran which was hydrolyzed, then fermented using saccharomycess cerevisiae with addition of urea and NPK nutrients, and then distilled to be purified. Optimizing pH and time in the fermentation process as well as adding urea and NPK nutrients are expected to increase levels of bioethanol produced. The results of the data are calculated using the design factorial calculation method to determine the most influential variable.

Vulnerability and resilience of the road transport industry in Poland to the COVID-19 pandemic crisis

The research aims to examine the vulnerability and resilience of road transport enterprises in Poland to a crisis caused by the COVID-19 pandemic. In theory, we refer to the Schumpeterian perspective of creative destruction. In the empirical analysis, survey data on 500 transport companies randomly selected from the database were used. We estimated partial proportional odds models to show the factors responsible for the enterprises' vulnerability and resilience to unforeseen shock. The perspective refers to the total sample size and the division into two subgroups: micro and small and medium enterprises. To justify the results, we calculated a set of statistical indicators and tests. These models enable separating enterprises according to the vulnerability level. Transport enterprises occurred significantly vulnerable to the COVID-19 crisis, particularly the demand shock. The only factor that influenced resilience was the decrease in fuel prices, which allowed a cost reduction. The crisis showed that government aid was helpful in the short run, particularly for micro and small enterprises. The medium-sized enterprises were more resilient than micro and small ones. We formulated several recommendations to help transport enterprises to adjust in the medium term.

The role of fuel prices in spatial price transmission between horticultural markets: empirical analysis from a developing country

This article aims to analyze how fuel prices impact spatial price transmission between two Chilean horticultural wholesale markets. We implement a regime-dependent VECM where price transmission parameters depend on dynamics imposed by a stationary exogenous variable (fuel price). We identified two price transmission regimes characterized by different equilibrium relationships and short-run adjustment processes. This implies that fuel prices affect price transmission elasticities and intermarket adjustment speeds. Our results show increasing marketing costs as farm to market distance grows. This impact depends on each product’s attributes. Highlights This article analyzes the effect of fuel prices on the price transmission mechanism between the most relevant Chilean horticultural wholesale markets. A regime-dependent Vector Error Correction Model where price transmission parameters depend on fuel price was implemented. Clear evidence of the role played by fuel prices for in horizontal price transmission between the wholesale markets considered in this study was found. This situation supports the idea that regardless of quantities traded in regional markets, the major effect of price adjustment is a result of the high demand, distances and market concentration of a central market. This impact depends on each product’s attributes.

Wind assisted propulsion: Economic and ecological considerations

Wind propulsion is well known and has propelled ships for millennia. However, with the advent of fossil-fuel powered ships, wind energy lost its importance for cargo transport almost 100 years ago. Rising fuel prices and a society being more aware of the consequences of CO2 emissions fuels the revitalization of this energy source and a variety of wind assisted propulsion systems are on the market today. Key factors for success of wind assisted propulsion are discussed and a case study for a multipurpose vessel is used as illustration.