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This research entails the simulation of three possible power scenarios for Kenya from 2015 to 2035 using low emissions analysis platform (LEAP). These scenarios represent the unfolding future electricity generation that will fully satisfy the demand while considering the following: energy security, power generation cost, and impacts on the environment. These scenarios are reference scenario (RS), coal scenario (CS), nuclear scenario (NS), and more renewable scenario (MRS). The findings obtained reveals that the most sustainable scenario while comparing the costs was found to be the coal scenario with a net present value (NPV) of$30 052.67 million though it has the highest greenhouse gases (GHGs) emissions. However, the more renewable scenario (MRS) has the least GHGs emissions but is found to be the most expensive scenario to implement with an NPV of $ 30 733.07 million. The paper provides in‐depth analysis of energy demand scenarios, the demand, transformation, emission, and cost analysis conducted using a low emissions analysis platform (LEAP) to determine the best possible scenario suitable for the Kenya energy space. Upon comparison of scenarios, it is realized that the coal scenario is the most favorable, the added plants size is moderate with the maximum plant capacity added being 300 MW.

The problem regarding the optimal integration of efficient reactive power compensation in radial and meshed distribution networks using fixed-step capacitor banks and distribution static compensators (D-STATCOMs) is addressed in this research paper by proposing a master–slave optimization methodology. Radial and meshed distribution topologies are considered for the grid structure while including variable active and reactive demand curves. An economic analysis is performed, considering the net present value of the optimization plan, as well as the costs of energy losses and the capacitor banks’ acquisition, installation, and operation. In the case of the D-STATCOMs, an annualized costs analysis is presented. In the master stage, the discrete version of the generalized normal distribution optimization (GNDO) algorithm selects the nodes and the sizes of the capacitor banks. In the slave stage, the successive approximations power flow approach is implemented. Numerical results in the IEEE 33-bus grid (with both radial and meshed topologies) and the IEEE 85-bus grid (with a radial configuration) demonstrated the proposed master–slave optimization’s effectiveness in minimizing the project’s expected net present value for a planning period of five years. Moreover, a simulation in the IEEE 69-bus grid under peak operation conditions showed that the GNDO approach is an excellent optimization technique to solve the studied problem when compared to combinatorial and exact optimization methods. In addition, numerical validations considering D-STATCOMs in the IEEE 85-bus grid confirmed the effectiveness and robustness of the GNDO approach in addressing problems associated with optimal reactive power compensation in medium-voltage distribution systems.

The cost of road construction consists of design expenses, material extraction, construction equipment, maintenance and rehabilitation strategies, and operations over the entire service life. An economic analysis process known as Life-Cycle Cost Analysis (LCCA) is used to evaluate the cost-efficiency of alternatives based on the Net Present Value (NPV) concept. It is essential to evaluate the above-mentioned cost aspects in order to obtain optimum pavement life-cycle costs. However, pavement managers are often unable to consider each important element that may be required for performing future maintenance tasks. Over the last few decades, several approaches have been developed by agencies and institutions for pavement Life-Cycle Cost Analysis (LCCA). While the transportation community has increasingly been utilising LCCA as an essential practice, several organisations have even designed computer programs for their LCCA approaches in order to assist with the analysis. Current LCCA methods are analysed and LCCA software is introduced in this article. Subsequently, a list of economic indicators is provided along with their substantial components. Collecting previous literature will help highlight and study the weakest aspects so as to mitigate the shortcomings of existing LCCA methods and processes. LCCA research will become more robust if improvements are made, facilitating private industries and government agencies to accomplish their economic aims.

BackgroundWhile patient groups, regulators, and sponsors are increasingly considering engaging with patients in the design and conduct of clinical development programs, sponsors are often reluctant to go beyond pilot programs because of uncertainty in the return on investment. We developed an approach to estimate the financial value of patient engagement.MethodsExpected net present value (ENPV) is a common technique that integrates the key business drivers of cost, time, revenue, and risk into a summary metric for project strategy and portfolio decisions. We assessed the impact of patient engagement on ENPV for a typical oncology development program entering phase 2 or phase 3.ResultsFor a pre-phase 2 project, the cumulative impact of a patient engagement activity that avoids one protocol amendment and improves enrollment, adherence, and retention is an increase in net present value (NPV) of$62MM ($ 65MM for pre-phase 3) and an increase in ENPV of$35MM ($ 75MM for pre-phase 3). Compared with an investment of $100,000 in patient engagement, the NPV and ENPV increases can exceed 500-fold the investment. This ENPV increase is the equivalent of accelerating a pre-phase 2 product launch by 2½ years (1½ years for pre-phase 3).ConclusionsRisk-adjusted financial models can assess the impact of patient engagement. A combination of empirical data and subjective parameter estimates shows that engagement activities with the potential to avoid protocol amendments and/or improve enrollment, adherence, and retention may add considerable financial value. This approach can help sponsors assess patient engagement investment decisions.

Background Deployment of remote and virtual clinical trial methods and technologies, referred to collectively as decentralized clinical trials (DCTs), represents a profound shift in clinical trial practice. To our knowledge, a comprehensive assessment of the financial net benefits of DCTs has not been conducted. Methods We developed an expected net present value (eNPV) model of the cash flows for new drug development and commercialization to assess the financial impact of DCTs. The measure of DCT value is the increment in eNPV that occurs, on average, when DCT methods are employed in comparison to when they are not. The model is populated with parameter values taken from published studies, Tufts CSDD benchmark data, and Medable Inc. data on DCT projects. We also calculated the return on investment (ROI) in DCTs as the ratio of the increment in eNPV to the DCT implementation cost. Results We found substantial value from employing DCT methods in phase II and phase III trials. If we assume that DCT methods are applied to both phase II and phase III trials the increase in value is $20 million per drug that enters phase II, with a seven-fold ROI. Conclusions DCTs can provide substantial extra value to sponsors developing new drugs, with high returns to investment in these technologies. Future research on this topic should focus on expanding the data to larger datasets and on additional aspects of clinical trial operations not currently measured.

This article presents a comprehensive approach to assessing the economic efficiency of investments in energy-saving measures specifically for public utility enterprises. This study contributes to the theoretical and practical justification for using efficiency evaluation criteria, such as net present value (NPV), return on investment (ROI), and internal rate of return (IRR) for energy projects. Analysis revealed that the highest electricity consumption occurs in the winter period—approximately 246,923 kWh when using 90 W lamps operating 16 h per day. In the summer period, with an average daylight duration of 8 h, consumption decreases to 31,298 kWh. This difference is due to the influence of temporal and seasonal factors, highlighting the need for a comprehensive assessment of energy-saving measures’ effectiveness across different times of the year. Furthermore, a methodology for calculating and utilizing the payback ratio was proposed, according to which, by reducing lamp wattage from 90 W to 60 W and operating hours from 16 to 8 h, companies can reduce electricity costs to 21,076 kWh in the summer period, demonstrating potential savings of 1.5 to 2 times. This study also proposes specific financing schemes for energy efficiency projects, enabling the more accurate assessment of needs and the optimization of energy consumption under limited budget conditions and high environmental requirements.

The transition toward carbon-neutral cities and sustainable infrastructure requires massive capital mobilization, yet traditional static valuation models like discounted cash flow (DCF) systematically undervalue green projects due to high initial capital expenditures and long-term uncertainty. To address this critical gap in sustainable finance, this study proposes a novel Artificial Intelligence–Blockchain–Multiple Real Options (AI-MRO) integrated framework. This model aligns infrastructure profitability with Environmental, Social, and Governance (ESG) criteria and United Nations Sustainable Development Goals (SDGs), specifically SDG 11 (Sustainable Cities), SDG 13 (Climate Action), and SDG 9 (Industry, Innovation, and Infrastructure). The core approach integrates AI-based probabilistic forecasting for carbon footprint optimization and cash flow prediction, MRO-based operational flexibility assessment, and blockchain-based smart contracts (Security Token Offerings, STOs) to ensure transparent green finance governance and social inclusion. Through empirical validation at Singapore’s Punggol Digital District (PDD)—a flagship smart city project featuring a district-level smart grid reducing 1700 tonnes of CO2 and generating 3000 MWh of solar energy annually—this model successfully captured investment resilience (Extended Net Present Value, ENPV > 0) even in crisis scenarios where conventional DCF models failed. The results demonstrate that integrating digital twins and AI-driven ESG metrics structurally reduces the risk premium and amplifies the strategic value of sustainable investments. This study represents a substantial methodological contribution toward data-driven, automated, and transparent governance, offering a scalable financial framework for global net-zero infrastructure development.

This study focused on assessing the financial efficiency of investing in a horizontal shower heat exchanger. The analysis was based on net present value (NPV). The research also examined the possibility of using artificial neural networks and SHapley Additive exPlanation (SHAP) analysis to assess the profitability of the investment and the significance of individual parameters affecting the NPV of the project related to installing the heat exchanger in buildings. Comprehensive research was conducted, considering a wide range of input parameters. As a result, 1,215,000 NPV values were obtained, ranging from EUR −1996.40 to EUR 36,933.83. Based on these values, artificial neural network models were generated, and the one exhibiting the highest accuracy in prediction was selected (R2 ≈ 0.999, RMSE ≈ 57). SHAP analysis identified total daily shower length and initial energy price as key factors influencing the profitability of the shower heat exchanger. The least influential parameter was found to be the efficiency of the hot water heater. The research results can contribute to improving systems for assessing the profitability of investments in shower heat exchangers. The application of the developed model can also help in selecting appropriate technical parameters of the system to achieve maximum financial benefits.

The use of solar photovoltaic (PV) generation and battery energy storage (BES) systems in commercial buildings has been increasing significantly in recent years. Most of these systems, however, are designed to solely minimize the investment and operation costs. With the increasing concerns about high-impact low-probability (HILP) events, such as natural disasters, and their impact on power system resilience, there is a substantial need to integrate outage risks in power system infrastructure planning problems. This paper examines the impact of various electricity rates on the feasibility of PV and BES systems in commercial buildings for financial and resilience purposes. Simulation studies are conducted using the Renewable Energy Integration & Optimization (REopt) decision support software to optimize the size of solar PV and BES systems for both financial and resilience purposes, considering different combinations of geographic locations, load profiles, electricity rates, and outage durations. The feasibility assessment is conducted by analyzing and comparing the net present value (NPV) for each combination of parameters.

Industrial projects are plagued by uncertainties, often resulting in both time and cost overruns. This research introduces an innovative approach, employing Reinforcement Learning (RL), to address three distinct project management challenges within a setting of uncertain activity durations. The primary objective is to identify stable baseline schedules. The first challenge encompasses the multimode lean project management problem, wherein the goal is to maximize a project’s value function while adhering to both due date and budget chance constraints. The second challenge involves the chance-constrained critical chain buffer management problem in a multimode context. Here, the aim is to minimize the project delivery date while considering resource constraints and duration-chance constraints. The third challenge revolves around striking a balance between the project value and its net present value (NPV) within a resource-constrained multimode environment. To tackle these three challenges, we devised mathematical programming models, some of which were solved optimally. Additionally, we developed competitive RL-based algorithms and verified their performance against established benchmarks. Our RL algorithms consistently generated schedules that compared favorably with the benchmarks, leading to higher project values and NPVs and shorter schedules while staying within the stakeholders’ risk thresholds. The potential beneficiaries of this research are project managers and decision-makers who can use this approach to generate an efficient frontier of optimal project plans.