Search Results (103)

Railway idle land offers a large and underused space for solar power, yet its economic value has not been evaluated. Revenue depends on the volatile System Marginal Price (SMP) and Renewable Energy Certificate (REC) markets, whose uncertainty cannot be fully captured by static discounted cash flow (DCF) analysis. This study asks whether solar development on Korea’s railway idle land is worthwhile over the long term, and at what scale it should proceed. It applies an integrated DCF and real options analysis (ROA) framework to a proposed 438 MW project on the Honam Line in southern Korea. Price volatility is estimated from monthly SMP and REC data with a geometric Brownian motion model, and the options to expand and to contract are valued on a binomial lattice. The DCF yields a marginal net present value of USD 3.2 million. The expansion option adds USD 172.5 million and is exercised in 67% of states, raising the total project value to USD 175.7 million. Rising panel efficiency and falling capital costs move the project firmly into feasibility. Therefore, scale flexibility turns a marginal project into a strongly positive one, supporting the large-scale deployment of solar on railway idle land. Full article

The increasing penetration of renewable energy has created an urgent need for economically competitive large-scale energy storage technologies. Conventional economic evaluations mainly focus on lifecycle costs while neglecting market participation, revenue diversification, and investment uncertainty. This study proposes an integrated lifecycle economic assessment framework combining discounted cash flow (DCF) theory, lifecycle cost analysis, multi-market revenue modeling, and uncertainty analysis. A revenue-adjusted indicator, termed Net Levelized Cost of Storage (Net LCOS), is introduced to quantify the actual economic competitiveness of energy storage technologies by incorporating revenues from energy arbitrage, ancillary services, and capacity remuneration. The proposed framework is applied to three representative large-scale energy storage technologies: pumped hydro storage (PHS), compressed air energy storage (CAES), and battery energy storage (BES). The results show that PHS exhibits the lowest levelized cost of energy (LCOE) (0.519 RMB/kWh) and the strongest economic robustness owing to its long service life and superior capital amortization capability. Incorporating multi-market revenues substantially improves the economic performance of all storage technologies. BES exhibits the largest reduction in Net LCOS, whereas PHS maintains the lowest Net LCOS and the strongest overall economic competitiveness. Sensitivity analysis identifies conversion efficiency, capital investment, capacity remuneration, and operational utilization as the dominant determinants of storage economics. The proposed framework extends a comprehensive approach for comparing large-scale energy storage technologies by integrating lifecycle costs, market revenues, and uncertainties, supporting investment decisions and electricity market design. Full article

Indonesia has committed to displacing diesel generation with solar PV and battery energy storage (PV-BESS) microgrids on its isolated island grids. However, no PV-BESS project on an isolated grid has reached financial close through the independent power producer procurement channel to date. Existing studies attribute this gap to implementation failure. This study proposes and tests an alternative hypothesis that the failure originates in the regulatory architecture itself. An Intent–Mechanism–Outcome analytical framework integrates directed qualitative content analysis of 30 regulatory instruments with discounted cash flow modeling of two island case studies (Sapudi, East Java, and Sabu, East Nusa Tenggara) under the actual tariff, procurement, and fiscal parameters governing private developers. The analysis identifies four interlocking regulatory mechanisms that collectively reproduce diesel dependence through tariff circularity, procurement asymmetry, fiscal channel asymmetry, and a BESS tariff penalty. Financial modeling produces debt service coverage ratios below the 1.30× lender covenant for PV-BESS with a 50% renewable energy share. Equity net present value is negative at the 12% required return, confirming that the projects cannot meet bankability thresholds on either a debt or equity basis. Only the smallest configuration (36–40% renewable share) clears bankability thresholds on either island, revealing a perverse regulatory selection mechanism that rewards minimal diesel displacement. The binding constraint is the tariff ceiling architecture, not technology cost or financing terms. Full article

This paper assesses the impact of uncertainty for investors on the economic valuation of medical innovation projects for orphan drugs or rare diseases. Conventionally, investor evaluation uses the deterministic discounted cash flow (DCF) method with an appropriate sensitivity analysis that captures some level of uncertainty. In healthcare, and particularly for rare diseases, the levels of uncertainty in financial outcomes (return on investment and net present value (NPV)) are broader than the ones normally captured by the DCF formula. Uncertainties include R&D costs, the approval process (level and timing) for obtaining reimbursement, sales, the production cost, and the failure probabilities of the clinical trial phases, to name a few. Additionally, there is not only one type of investor to consider, but different investors exposed to different levels of risk management of their investment. Our analysis tried to capture those two dilemmas (higher levels of uncertainty and different investor types) in two ways. One way was to identify a better method to enhance the different levels of uncertainty. The real option method of evaluation was proposed instead of DCF. For instance, the real option method better captures the uncertainty of the different phases of product development. The other way is to differentiate the investor types through their level of risk assessment perspectives. Small investors and start-up companies may see more benefit in applying the real option methodology to estimate their NPVs at different time points during product development. In summary, our evaluation identified various types of uncertainty when assessing an investment, along with methods to manage their effect on the economic/financial outcomes of medical innovations. Given the high uncertainty associated with early-stage drug development, such as orphan drugs for rare diseases, the real options approach is preferable to traditional DCF models. The analysis also showed that there is not just a single investor perspective to consider but specific perspectives that enhance the prime use of the real option methodology. Full article

This study aimed to develop a stand yield table for Acacia mangium plantations in Vietnam and to analyze their economic feasibility. Field measurements were conducted across 12 temporary sample plots established in A. mangium plantations aged 1–7 years in northeastern Vietnam, using a chronosequence sampling design. Based on this, regression analyses were performed to develop stand growth models for DBH, H, SD, BA, and V. NPV measures the absolute value of the investment at a specified discount rate (4.0%, 5.5%, and 7.0%), while IRR identifies the break-even discount rate. At rotation ages of 5–7 years, IRR values are negative—a mathematically valid result arising from non-conventional cash flow structures in which Norstrom’s sign-change criterion is not satisfied; positive IRR values (~4%) are obtained from rotation age 8 onward. A sensitivity analysis confirmed that increasing yields is the most effective strategy for increasing plantation viability. This study serves as a fundamental resource for plantation projects in Vietnam and emphasizes the necessity of management improvements to create sustainable plantation operations. Full article

Decarbonising the residential building sector requires not only technical solutions, but also financially viable delivery models. This paper examines the economic performance of Energy Plus Service (EPS) schemes applied to deep renovation projects under uncertainty, with particular attention to the role of project scale and market conditions. The analysis is based on a portfolio of 21 residential buildings in Northern Italy and combines a Discounted Cash Flow (DCF) model with Monte Carlo simulation. Key sources of uncertainty include renovation costs, post-retrofit energy performance, rental values, and electricity prices, allowing for the estimation of probabilistic Net Present Value (NPV) outcomes. The results show a clear impact of residential asset spatial scale on financial outcomes. Small projects are generally unprofitable, while medium-sized assets are highly sensitive to uncertainty. Larger projects, instead, display a much higher likelihood of positive financial outcomes. Sensitivity analysis indicates that financial performance is driven mainly by investment costs and rental income, while energy-related variables play a more limited role. The findings suggest that the viability of EPS models depends as much on market conditions as on technical performance, pointing to a potential misalignment between energy policy objectives and private investment incentives. Results suggest that projects approaching 160 m² are more likely to achieve a 50% probability of a positive NPV, indicating a potential scale threshold beyond which EPS schemes become significantly more bankable and below which aggregation or additional de-risking measures are likely to be required. Full article

Hydropower-dependent electricity systems, such as Ecuador’s, face critical supply disruptions during droughts: a vulnerability exemplified by the 2024 power outages. This study assesses the technical, economic and environmental feasibility of a 200.84 MWp grid-connected solar photovoltaic (PV) plant proposed for the Pacific Refinery site in Manabi, Ecuador, as a strategy to diversify the energy matrix and reduce hydrological risk. Using site-specific solar resource data (4.65 kWh/m²/day) and PVSyst simulations, the plant achieves an annual energy production of 295 GWh with a performance ratio (PR) of 85.3%. A discounted cash flow analysis over 25 years, assuming a 7% discount rate and an electricity price of 60 USD/MWh, yields a net present value (NPV) of 104.9 MUSD, an internal rate of return (IRR) of 62.2%, and a levelized cost of energy (LCOE) of 14.5 USD/MWh, well below current industrial tariffs in Ecuador. Sensitivity analysis confirms project viability under ±15% variations in investment cost, energy price, and solar resource. Over its lifetime, the plant avoids 1.83 Mt of CO₂ emissions, supporting national decarbonization goals. The results demonstrate that large-scale PV deployment in high-radiation, low-latitude regions can be highly profitable and contribute to energy sovereignty in hydropower-dependent systems. Furthermore, this study provides a replicable model for repurposing unused industrial land for renewable energy generation, offering actionable insights for policymakers and investors in developing economies. Full article

CO₂ enhanced coalbed methane recovery (CO₂-ECBM) using huff-and-puff technology has attracted increasing attention as a promising approach to enhance the productivity of low-productivity coalbed methane (CBM) wells while simultaneously enabling CO₂ storage. However, the economic feasibility of this method and the optimal soaking time remains unclear. In this study, an economic evaluation model for CO₂ huff-and-puff CBM projects was developed based on the discounted cash flow method, incorporating key factors such as CBM price, government subsidies, carbon trading price, CH₄ separation cost, and CO₂ purchase and injection costs. Three representative scenarios were designed to evaluate the impacts of policy support and market conditions. The effects of CO₂ injection volume and soaking time on net cash flow (NCF), net present value (NPV), and dynamic payback period (DPP) were systematically investigated. The results indicate that CO₂ huff-and-puff is economically viable for enhancing CBM recovery. Increasing CO₂ injection volume markedly improves CH₄ production and project revenue, but also leads to higher initial investment and a longer payback period. The economic performance exhibits a non-monotonic dependence on soaking time, with an optimal range that maximizes NPV. Specifically, the optimal soaking time ranges from 30 to 60 days for injection volumes up to 2000 t, and from 60 to 90 days for higher injection volumes. External economic factors exert a strong influence on project performance: higher carbon trading prices and lower CO₂ purchase costs markedly improve profitability, while government subsidies effectively increase net returns and shorten the payback period. In the absence of subsidies, higher injection volumes are required to maintain economic viability. Overall, this study provides a comprehensive economic evaluation framework for CO₂ huff-and-puff CBM projects, identifies key operational and economic parameters for optimizing project performance, and offers theoretical support for field-scale applications. Nevertheless, the economic evaluation is based on deterministic numerical simulation results and simplified market assumptions. Long-term operational risks and geological heterogeneity are not explicitly considered, which may limit the applicability of the results under field conditions. Full article

This study evaluates the economic feasibility of retrofitting split-type air-conditioning systems in a university administrative building in a hot-humid tropical climate in Colombia, addressing the need for cost-effective energy-efficiency strategies in such contexts. A measurement-calibrated building energy model was developed using DesignBuilder and EnergyPlus, and a baseline scenario with low-efficiency fixed-speed split units was compared against three retrofit scenarios with higher-efficiency units defined by market-available COP levels. A 10-year life-cycle cost (LCC) analysis was conducted using a discounted cash flow approach, incorporating investment costs, operation and maintenance expenses, electricity tariff escalation, and equipment performance degradation, complemented by a parametric sensitivity analysis. The results show that air-conditioning systems account for the majority of total building electricity consumption, and that retrofit scenarios reduce cooling energy use by approximately 45–53% relative to the baseline. All retrofit options yield lower life-cycle costs despite higher initial investments, achieving total LCC reductions of up to 30%. Sensitivity analysis indicates that the economic ranking of alternatives remains stable under significant variations in electricity prices. Overall, the proposed framework provides a robust and transferable approach for assessing HVAC retrofit strategies, supporting informed decision-making for energy and cost optimization in buildings located in tropical climates. Full article

Brazil possesses globally competitive offshore wind resources; however, financial viability is constrained by high capital expenditure (CAPEX) and industry risk. This study evaluates the investment feasibility of a 1 GW offshore wind project in northeast Brazil using a discounted cash flow (DCF) model. For the key parameter of CAPEX, a Baseline Case was established, assuming a 1.53% commodity price escalation from 2021 until the Financial Investment Decision (FID) date of 2027, and was sensitivity tested against an Optimistic Case, assuming 0% cost escalation and a Stress Case based on twice the commodity price escalation of 3.06% up to 2027. Each CAPEX Case was evaluated against 12 financing scenarios involving varying levels of public support through a blend of concessional debt and grants. Financial performance was measured using net present value (NPV) and Equity Internal Rate of Return (EIRR). Results indicate that project financial viability is achieved under the Baseline Case only with levels of grant funding and concessional debt that exceed realistic thresholds, unless PPA tariffs are raised by about 50% relative to current market benchmarks. The Optimistic Case is viable at current tariffs under more realistic financing structures but represents an unattainable degree of capital cost containment. The Stress Case is not viable at all without a doubling of current PPA tariffs. Sensitivity analysis further demonstrates that even the most promising financial scenarios are vulnerable to any shortening of the 20-year PPA contracting period, leading to greater merchant risk exposure. The paper concludes that catalysing Brazil’s nascent offshore wind market will therefore call for a combination of policy measures that: permit (and recoup) a transitional premium over current PPA prices; adopt structural measures to reduce associated CAPEX through local supply chain development; combine public and private sources of capital to soften financial terms; and incorporate price risk mitigation measures. Full article

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. Full article

This paper derives a closed-form expression for the expected discounted value of aggregate cash flows when arrival times follow a Poisson process but both the time horizon and the number of arrivals are finite. The result provides a tractable analytical formula for the expected discounted sum under simultaneous constraints on time and arrival counts. We show that the expression converges to the well-known infinite-horizon and infinite-arrival results as limiting cases. Numerical illustrations demonstrate the behavior of the formula under different parameter values. The result can be interpreted as the valuation of a discounted compound Poisson process with finite constraints and may be useful in stochastic modeling and risk-analysis applications. The proposed formula provides a simple analytical tool for evaluating discounted losses or revenues in finite risk portfolios. Full article

The contemporary green transformation of the economy is a strategic imperative for businesses, especially small and medium-sized enterprises (SMEs) operating in the energy market, forcing the integration of sustainable practices in decision-making processes, including investment efficiency assessment. Classic financial tools, such as the internal rate of return (IRR) and net present value (NPV), commonly used in the SME sector, do not always adequately account for environmental, regulatory, and social risks associated with green transformation, as—particularly in the case of IRR—they rely on the assumption of stable cash flows and do not incorporate regulatory uncertainty, environmental externalities, or ESG-related risks into discounting parameters. The aim of the study was to determine the impact of nominal and real discount rates, adjusted for a synthetic measure of green transformation, on investment decisions. The research methodology combines advanced multi-criteria decision-making techniques, specifically TOPSIS and CRITIC, with sustainable finance concepts, offering an innovative approach to investment decision-making in the SME sector. The study shows that integrating environmental factors, when treated as a risk component, increases the cost of capital and reduces the net present value, while maintaining the profitability of the analysed projects. Incorporating green components into the discount rate enhances valuation appropriateness and improves investment risk management, particularly under macroeconomic uncertainty. The main contribution of the study lies in linking a synthetic green transformation indicator with dynamic discount rate adjustment within a multicriteria framework, extending existing ESG-adjusted valuation models by enabling a more structured and data-driven incorporation of environmental transition risk. Full article

This paper presents a comprehensive life-cycle analysis of distribution transformers, based on realized measurements of the increased power losses as a result of their long-term service under real-world conditions. The study is based on aggregated measured data from extensive fleets of oil-immersed distribution transformers characterized by diverse designs, manufacturing vintages, and service lives. The evolution of no-load losses and short-circuit losses is analyzed as a function of operational duration, structural characteristics, and the specific technologies employed for windings and magnetic core construction. Statistical models describing the variation in these losses are presented, highlighting the limitations of the static assumptions commonly utilized in power distribution network planning. On this basis, an approximation of the time evolution of the transformer’s total power and energy losses is proposed as appropriate for implementation in a life-cycle analysis model. Furthermore, the impacts of thermal loading and abnormal operating conditions—such as unbalanced loads, frequent short circuits, and repeated overheating of the transformer oil—are analyzed as drivers of accelerated transformer aging. These effects are integrated into a unified life-cycle framework, enabling the quantitative assessment of loss variations and their associated operational expenditures (OPEX). A numerical example is provided to evaluate the cost-effectiveness of “repair vs. replacement” scenarios, utilizing a discounted cash flow analysis that incorporates a carbon component. The findings establish a methodological foundation for a broader assessment of technical condition and energy performance, identifying the optimal intervention point for repair or replacement to support decision-making for Distribution System Operators (DSOs) amidst increasing requirements for efficiency and decarbonization. Full article

Value investing remains grounded in intrinsic value estimation, margin-of-safety reasoning, and disciplined fundamental analysis, but its practical execution is increasingly constrained by the scale, heterogeneity, and velocity of modern financial information. Recent advances in artificial intelligence (AI), particularly large language models and automated information-extraction systems, create new opportunities to accelerate financial analysis; however, their outputs remain probabilistic, context-dependent, and potentially error-prone, making governance and verification essential. This article proposes an AI-assisted value investing framework that integrates automated extraction, valuation modeling, explainability, and human-in-the-loop (HITL) supervision into a unified decision-support architecture. The framework is organized into three layers: (i) a data layer for traceable extraction and normalization of structured and unstructured financial information; (ii) a modeling layer for automated key performance indicator (KPI) computation, forecasting support, and discounted cash flow (DCF) valuation; and (iii) an explainability and governance layer for traceability, verification, model-risk control, and analyst oversight. A central contribution of the paper is the operational characterization of prompt literacy as a determinant of analytical reliability, showing that structured, context-aware prompts materially affect extraction correctness, usability, and verification effort. The framework is evaluated through a case study using Rivanna AI on three large U.S. beverage firms—namely, The Coca-Cola Company, PepsiCo, and Keurig Dr Pepper—selected as a controlled, information-rich setting for comparative analysis. The results indicate that the proposed workflow can reduce end-to-end analysis time from approximately 25–40 h in a traditional manual process to approximately 8–12 h in an AI-assisted setting, including citation/source verification, unit and period reconciliation, and review of key valuation assumptions. Rather than eliminating analyst effort, AI shifts it from manual information processing toward verification, adjudication, and interpretation. Overall, the findings position AI not as an autonomous decision-maker, but as a governed reasoning accelerator whose effectiveness depends on structured human guidance, traceability, and disciplined validation. In value investing, a discipline traditionally grounded in labor-intensive fundamental analysis and disciplined intrinsic value estimation, AI introduces the potential to scale analytical coverage and accelerate evidence synthesis. However, AI systems in financial contexts are probabilistic, context-sensitive, and inherently dependent on human interaction, raising critical questions about reliability, governance, and operational integration. This article proposes a structured framework for AI-driven value investing that preserves the foundational principles of intrinsic value, margin of safety, and economic reasoning, while redesigning the analytical workflow through automation, explainability, and human-in-the-loop (HITL) supervision. The proposed architecture integrates three layers: (i) an AI-enabled data layer for traceable extraction and normalization of structured and unstructured financial information; (ii) a modeling and valuation layer combining automated KPI computation, machine learning forecasting, and discounted cash flow (DCF) valuation; and (iii) an explainability and governance layer ensuring traceability, verification, and model risk control. A central contribution of this work is the operational characterization of prompt literacy, namely the ability to formulate structured, context-aware requests to AI systems, as a critical determinant of system reliability and analytical correctness. Through a focused case study using an AI-assisted analysis platform (Rivanna AI) on three U.S. beverage firms, we provide evidence that structured prompt formulation can improve extraction consistency, reduce verification overhead, and increase workflow efficiency in a human-supervised setting. In this setting, analysis time decreased from a manual range of approximately 25–40 h to 8–12 h with AI assistance and HITL validation, while preserving traceability and decision accountability. The reported hour savings should be interpreted as conservative estimates from the initial deployment phase; additional efficiency gains are expected as operational maturity increases, driven by learning-economy effects. The findings position AI not as an autonomous decision-maker but as a probabilistic reasoning accelerator whose effectiveness depends on structured human guidance, verification discipline, and prompt-driven interaction. These results redefine the role of the financial analyst from manual data processor to reasoning architect, responsible for designing, guiding, and validating AI-assisted analytical workflows. Full article