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The increasing requirement for distillates, accompanied by higher quantities of heavy crude oil in world production, has positioned gas oil hydrocracking as one of the most significant processes in refineries. In the petrochemical industry, hydrocracking is an essential process that converts heavy hydrocarbons into lighter and more valuable products such as LPG (liquefied petroleum gas), diesel, kerosene, light naphtha, and heavy naphtha. This method uses hydrogen and a catalyst to break down the gas oil feedstock through hydrogenation and hydrocracking reactions. However, the gas oil hydrocracking process faces significant technical, economic, and financial obstacles that must be overcome to reveal its full potential. In this study, a computer-assisted technical–economic evaluation and an evaluation of the technical–economic resilience of the gas oil hydrocracking process at an industrial scale was carried out. Twelve technical–economic and three financial indicators were evaluated to identify this type of process’s current commercial status and to analyze possible economic performance parameter optimizations. The economic indicators listed include gross profit (GP), profitability after taxes (PAT), economic potential (EP), cumulative cash flow (CCF), payback period (PBP), depreciable payback period (DPBP), return on investment (ROI), internal rate of return (IRR), net present value (NPV), annual cost/revenues (ACR), break-even point (BEP), and on-stream efficiency at the BEP. On the other hand, the financial indicators proposed by the methodology are earnings before taxes (EBT), earnings before interest and taxes (EBIT), and earnings before interest, taxes, depreciation, and amortization (EBITDA). The technical–economic resilience of the process was also evaluated, considering the costs of raw materials, the market prices of the products, and processing capacity. The gas oil hydrocracking plant described, with a useful life of 20 years and a processing capacity of 1,937,247.91 tonnes per year, achieved a gross profit (GP) of USD 58.97 million and a return after tax (PAT) of USD 39.77 million for the first year, operating at maximum capacity. The results indicated that the process is attractive under a commercial approach, presenting a net present value (NPV) of USD 68.87 million at the end of the last year of operation and a cumulative cash flow (CCF) of less than one year⁻¹ (0.34 years⁻¹) for the first year at full processing capacity, which shows that in this process, variable costs have more weight on the economic indicators than fixed costs. Full article

This study estimates the efficiency of 19 tertiary hospitals in Taiwan using a two-stage analysis of Data Envelopment Analysis (DEA) and TOBIT regression. It is a retrospective panel-data study and includes all the tertiary hospitals in Taiwan. The data were sourced from open information hospitals legally required to disclose to the National Health Insurance (NHI) Administration, Ministry of Health and Welfare. The variables, including five inputs (total hospital beds, total physicians, gross equipment, fixed assets net value, the rate of emergency transfer in-patient stay over 48 h) and six outputs (surplus or deficit of appropriation, length of stay, the total relative value units [RVUs] for outpatient services, total RVUs for inpatient services, self-pay income, modified EBITDA) were adopted into the Charnes, Cooper and Rhodes (CCR) and Banker, Charnes and Cooper (BCC) model. In the CCR model, the technical efficiency (TE) from 2015–2018 increases annually, and the average efficiency of all tertiary hospitals is 96.0%. In the BCC model, the highest pure technical efficiency (PTE) was in 2018 and the average efficiency of all medical centers is 99.1%. The average scale efficiency of all medical centers was 96.8% in the BBC model, meaning investment can be reduced by 3.2% and the current production level can be maintained with a fixed return to scale. Correlation coefficient analysis shows that all variables are correlated positively; the highest was the number of beds and the number of days in hospital (r = 0.988). The results show that TE in the CCR model was similar to PTE in the BCC model in four years. The difference analysis shows that more hospitals must improve regarding surplus or deficit of appropriation, modified EBITDA, and self-pay income. TOBIT regression reveals that the higher the bed-occupancy rate and turnover rate of fixed assets, the higher the TE; and the higher number of hospital beds per 100,000 people and turnover rate of fixed assets, the higher the PTE. DEA and TOBIT regression are used to analyze the other factors that affect medical center efficiency, and different categories of hospitals are chosen to assess whether different years or different types of medical centers affect operational performance. This study provides reference values for the improvable directions of relevant large hospitals’ inefficiency decision-making units through reference group analysis and slack variable analysis. Full article

Forecasting has become essential in different economic sectors for decision making in local and regional policies. Therefore, the aim of this paper is to use and compare performance of two linear models to predict future values of a measure of real profit for a group of companies in the fashion sector, as a financial strategy to determine the economic behavior of this industry. With forecasting purposes, Exponential Smoothing (ES) and autoregressive integrated moving averages (ARIMA) models were used for yearly data. ES and ARIMA models are widely used in statistical methods for time series forecasting. Accuracy metrics were used to select the model with best performance and ES parameters. For the real profit measure of the financial performance of the fashion sector in Colombia EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) was used and was calculated using multiple SQL queries. Full article

Corporate social responsibility (CSR) is one of the main drivers of corporate reputation. Many studies show that CSR can positively affect financial performance (FP) and vice versa. However, the relationship between FP and CSR depends on the type of industry in which the company operates, and there is little research regarding the energy sector in this area. The basis of empirical research in this study is slack resource theory which argues that financial performance is the cause of corporate social performance. This paper aims to analyze if financial performance affects corporate social responsibility adoption in energy sector companies. In order to achieve this goal, the study specifically examines the relationship between selected financial performance indicators and CSR adoption. Analyzing an international sample of 219 companies from thirty-two countries for 2020, we observed the statistically significant relations between financial performance and the implementing of the CSR strategy of the energy industry companies. The Return on Assets measure (ROA) and the Earnings Before Interest and Taxes measure (EBIT) were significantly higher among companies implementing the CSR strategy. The Enterprise Value to earnings before interest, taxes, depreciation, and amortization ratio (EV EBITDA) was lower among companies that adopted CSR. We did not confirm that the Return on Equity measure (ROE), Beta coefficient, and EBITDA per Share correlated with CSR adoption. Our research had implications for firms’ investment policies in social initiatives and highlighted the relation between the financial performance and CSR initiatives of the energy sector companies. Full article

With a proactive loan policy to raise construction funds, a large number of toll freeways have been built in Mainland China in the past three decades. However, it brought about a long-term heavy debt burden for most provincial governments. To ensure financial sustainability of toll freeways, an accurate and appropriate debt risk evaluation has become necessary. This research aims to explore debt risk factors and calculate the overall debt risk levels of toll freeways using the grey approach. Debt risk factors were identified as belonging to five categories—debt scale, debt structure, debt management, external environment, and solvency—and three new debt risk factors were added for specific concern of toll freeways—toll revenue, free cash flow, and earnings before interest, tax, depreciation, and amortization (EBITDA) margin. Debt risk levels of toll freeways in 29 provinces in Mainland China were evaluated by the proposed method and classified into three groups–low debt risk, medium debt risk, and high debt risk according to grey possibility degree ranges. Calculation results show that six provinces have low debt risk, 10 provinces have medium debt risk, and 13 provinces have high debt risk. Additionally, some specific policies to reduce toll freeway debt risk were provided based on the evaluation findings. Full article