While real options valuation captures flexibility and uncertainty in oil projects, most studies on Production Sharing Agreements (PSAs) still use discounted cash flow methods. This study applies binomial lattices to evaluate Uganda's PSA using data from two oil fields. A robustness check using the two-factor Gibson-Schwartz model with stochastic convenience yield shows that incentive differences persist under richer price dynamics. We analyze the contract from both the International Oil Companies' (IOCs') and the government's perspectives, examining how optimal strategies vary with project parameters and oil price scenarios. The findings reveal potential conflicts, particularly due to the cost oil function and expected prices, highlighting the importance of contract design in aligning incentives for project realization.
This paper investigates low-carbon retrofit strategies for BF-BOF integrated steel plants under different budget levels, considering carbon price dynamics. An optimization model that incorporates carbon price is developed to select technology portfolios from 22 prevailing technologies and provide the retrofit strategies under high, medium, and low budget levels. The dynamics of carbon prices are incorporated based on expert forecasts and survey data, reflecting expected long-term trends and market volatility in China's carbon trading system. Under these budget scenarios, the economic and environmental performance of the resulting strategies is then evaluated in terms of their expected returns, annual CO2 reductions, and post-retrofit emission intensity. A process-level analysis further reveals that investment allocation and decarbonization characteristics vary across budget levels. Furthermore, robustness and sensitivity analyses confirm the stability of the main results under alternative parameter settings. These findings provide practical insights for steel enterprises to design adaptive low-carbon retrofit strategies that account for their investment capacities and the evolving carbon price.
Today, the defense industry is still supported by the state mechanism, and especially the defense industry enterprises face difficulties in accessing financial resources. At this point, in addition to the state financing of defense industry enterprises, their access to financing sources has also gained importance. In addition to the situation presented by the Information Barrier Model in the literature, where defense industry enterprises are financed by the government in the form of cost-plus or by banks through bank loans, this study considers a structure in which investors also finance enterprises. In this framework, the Information Barrier Model has been extended to include the strategies of investors, banks, and companies, and the model has been reinterpreted by classifying companies as experienced or inexperienced to include defense companies in developing countries rather than the high-low risk classification of companies in developed countries. The results suggest that policymakers can direct the financing of the defense industry between banks and potential investors by acting in a risk-return context.
The existing models face significant limitations, including an inability to adapt to changing project variables, potential generalization issues when using small datasets, and high computational costs for optimization. Accurately validating project cost remains a primary complexity. In this article, initially, input data is collected from online sources like Software Cost Estimation 1 (Dataset 1) and the SEERA Software Cost Estimation Dataset (Dataset 2). It fed into Hierarchical Multi-scale Dense-Recurrent Neural Network with Sparse Attention (HMDRSA) model. This HMDRSA architecture improves upon existing deep learning models by integrating a unique combination of hierarchical processing, multi-scale analysis, dense connections, and a sparse attention mechanism. This deep learning architecture is particularly well-suited for agile SCRUMBAN environments because its architecture is designed to capture complex project patterns efficiently, significantly reduce computational overhead, and improve generalization compared to traditional methods that struggle with dynamic project variables. Thus, the model aids in managing different Development Operations (DevOps) activities, helping to deliver software projects more quickly with higher quality. Finally, its performance is validated by comparing it with existing models. Experimental results on two public datasets demonstrate the model's effectiveness, achieving low error rates with an MAE of 3.01 and 3.89, and RMSE of 10.08 and 11.42.
