The application of optimization algorithms is crucial for analyzing oil and gas company portfolio and supporting decision-making. The paper investigates the process of optimizing a portfolio of oil and gas projects under economic uncertainty. The literature review explores the advantages of applying various optimizers to models that consider the mean and semi-standard deviations of stochastic multi-year cash flows and revenues. The methods and results of three different optimization algorithms are discussed: ranking and cutting algorithms, linear (Simplex) and evolutionary (genetic) algorithms. Functions of several key performance indicators were used to test these algorithms. The results confirmed that multi-objective optimization algorithms that examine various key performance indicators are used for efficient optimization in oil and gas companies. This paper proposes a multi-criteria optimization model for investment portfolios of oil and gas projects. The model considers the specific features of these projects and is based on the Markowitz portfolio theory and methodological recommendations for project assessment. An example of its practical application to oil and gas projects is also provided.

This paper aims to shed light on community-based disaster mitigation and the challenges encountered by using the Pangandaran coast as a case study, one of Indonesia’s disaster-prone areas. Observations, in-depth interviews, and documentation studies were used to collect data. The findings of this study indicate that community-based disaster mitigation is well realized, as evidenced by community early preparedness forums collaborating with the government to provide socialization and education to the community. However, disaster preparedness still faces challenges, including; since some of the mitigation objects are tourists, mitigation efforts need to be carried out sustainably while not following the budget they have; mitigation support devices and facilities such as damaged or missing signs for evacuation routes, temporary shelters, assembly point locations, and Early Warning System (EWS) devices whose number is still not optimal; lack of participation of hotels or restaurants in disaster mitigation, especially in engaging in preventive actions to minimize disaster risk. This situation is a challenge in itself for disaster mitigation management, moreover, Pangandaran Village must maintain its status as a “Tsunami Ready” village.

This study investigates the impact of corporate carbon performance on financing costs, focusing on S&P 500 companies from 2015 to 2022. Utilizing a fixed-effects regression model, the research reveals a complex U-shaped nonlinear relationship between carbon intensity (CI) and cost of debt (COD). The sample comprises 2896 firm-year observations, with CI measured by the ratio of Scope 1 and 2 greenhouse gas (GHG) emissions to annual sales. The findings indicate that companies with higher CI initially face increased COD due to heightened regulatory and operational risks. However, as CI falls below a certain threshold, further reductions in emissions can paradoxically lead to increased COD, likely due to the substantial investments required for advanced technologies. Additionally, a positive relationship between CI and cost of equity (COE) is observed, suggesting that shareholders demand higher returns from companies with greater environmental risks. These results underscore the importance of balancing short-term and long-term environmental strategies. The study highlights the need for corporate managers to communicate the long-term benefits of environmental efforts effectively to creditors and investors. Policymakers should consider these dynamics when designing regulations that incentivize lower carbon emissions.

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.

The transfer of knowledge and the preservation of traditions is passed down from generation to generation. The main objective of this study was to explore people’s knowledge of the gastronomic heritage of the Kisalföld regions through an analysis of the county’s (attendance to, decision-making and willingness to spend on food and beverages) taking place in the county, such as the Flavours of Szigetköz, the County Wines Festival, the Flavours of Rábaköz or Eszterházy Baroque Food Festival at Fertőd. A quantitative research was used to analyse the topic (N = 666), the sample is not representative and the selection of respondents was random. Data were collected between 1 September 2023 and 31 October 2023 using electronic questionnaires shared on Google Drive. Data were processed using SPSS 25.0 and MS Office Excel in addition to the descriptive statistical data (modus, median, standard deviation), correlation, and crosstabulation analyses. Important research questions of the study were whether the respondents’ place of respondents influences gastronomic awareness whether age determines willingness to travel to attend a gastronomy event, The most popular gastronomic event in the county was the Vegetables of Hanság Region (mean 3.35), and the least popular was the Szigetköz Flavours of Szigetköz festival (mean 3.01). The key finding of the study is that an essential aspect of sustainability for decision-makers is to know the characteristics of tourists (middle-aged female target group), to select and maximize the different program packages in the marketing of the offer, to distribute the traffic and to avoid mass tourism.

This study examined the labor regulations regarding the hours of work and rest for representative fishing countries (Norway) by the International Labor Organization (ILO) Convention C188—Work in Fishing, 2007. A dual comparative analysis with Norway is used to explore policy implications for the representation and protection of fishers’ labor standards in Korea. This study examined the possibility of synchronisation between national and international legislation on the hours of work and rest for fishers, with a particular focus on the Norwegian case. The objective is to identify policy enhancements related to the Korean Seafarers Act. This study looked in depth at the fatigue and well-being problems faced by Korean fishers working long times on various vessels. It is based on the results of a qualitative comparative study. To achieve the objectives, We proposed to ‘the name of the fishing vessel’, which are excluded from the protections afforded by the Seafarers Act and to clarify the regulations regarding the labor standards for them. This proposal will provide compensation and protection for Korean fishers’ labor rights. It aims to enhance labor conditions in line with ILO standards, harmonize national and international agreements to protect small-scale fisheries and contribute to the development of environmentally friendly propulsion technologies, such as hydrogen-fueled electric hybrids and LPG (Liquefied Petroleum Gas).