Techno-economic and environmental assessment of photovoltaic–battery microgrids across diverse U.S. states

• Bio-methanol hits fossil parity in 2030 s, staying 3-times cheaper than e-methanol. • 10% IRR requires e- and bio-methanol selling prices of >1713 €/t and 955 €/t. • FuelEU and EU ETS enable premiums for bio-methanol; e-methanol needs support. • Reliability of European renewable methanol supply remains policy-dependent. Renewable methanol is a key option for decarbonizing European industrial sectors, particularly the chemical industry and maritime transport. Despite growing project announcements, large-scale deployment remains limited due to high production costs and investment risks. This study assesses the techno-economic feasibility of renewable methanol production in Europe by comparatively evaluating e-methanol, bio-methanol and import pathways within a harmonized modelling framework. Levelized cost of methanol, net present value and internal rate of return calculations are applied across multiple regional scenarios for Germany, Sweden and Portugal, explicitly accounting for policy boundary conditions under the European Emissions Trading System and FuelEU Maritime. Across all scenarios, bio-methanol approaches cost parity with conventional methanol by around 2030 and consistently outperforms e-methanol in terms of cost levels and economic robustness. E-methanol remains substantially more expensive under current conditions, while imports become competitive only after 2040. In the German Baseline Scenario, selling prices of 1079 €/t for bio-methanol and 2169 €/t for e-methanol are required to achieve a target internal rate of return of 10 %. Although regulatory frameworks increase willingness to pay for renewable methanol in shipping, they are insufficient to enable low-risk e-methanol investments without further cost reductions. Overall, the results indicate that renewable methanol deployment in Europe remains structurally conditioned by cost competitiveness and input cost dynamics. Bio-methanol represents the most viable near-term pathway, whereas e-methanol depends on substantial hydrogen cost reductions and sustained policy support to achieve economic viability.

Predicting blue carbon sequestration in Sundarban coastal mangroves: A spatially explicit approach with INVEST and machine learning to advance climate resilience and UN SDG-aligned nature-based climate solutions.

As energy systems transition toward decentralization and decarbonization, residential prosumers are playing an increasingly vital role in grid stability and energy market participation. However, the economic viability of investing in battery energy storage systems (BESS) and joining virtual power plant (VPP) schemes remains uncertain, particularly under real-world operating conditions where battery dispatch is optimized for grid objectives rather than prosumer benefit. Although a growing body of research examines VPP optimization and grid-level services, relatively limited attention has been given to evaluating the economic feasibility of prosumer participation using empirical operational data.. This study addresses that gap by using empirical data from a residential VPP trial involving 750 Australian households to evaluate the economic performance and dispatch characteristics of residential battery systems under real VPP control. A clustering analysis is used to characterize battery operational patterns, and a techno-economic framework assesses investment viability across twelve scenarios that vary electricity tariff structures and battery degradation assumptions. Financial performance is evaluated using payback period, net present value, and internal rate of return. The results show that VPP participation combined with government incentives can reduce the payback period by up to five years and improve annual energy bill savings by approximately 20 percent. However, under low tariff conditions and without targeted incentives, residential battery adoption remains economically challenging. These findings highlight the importance of aligning VPP incentive structures with prosumer economic viability to enable sustained participation and realize broader system-level benefits.

Strategic framework for sustainable centralisation of wastewater treatment plants: integrating economic and environmental analysis.

Abstract The accelerating consumption of electrical and electronic equipment (EEE) has led to rapid growth in waste electrical and electronic equipment (WEEE), which is currently the fastest-growing waste stream worldwide. E-waste contains valuable resources such as gold, copper, and rare earth elements, but also hazardous substances including lead and cadmium, making its sustainable management a critical environmental and economic challenge. This study applies a PRISMA-guided systematic review of 122 peer-reviewed studies (2014–2024) combined with quantitative analysis of e-waste generation, material recovery potential, life-cycle environmental impacts, and techno-economic feasibility for the Indian context. Globally, e-waste generation reached approximately 62 Mt in 2022, yet less than half was formally recycled, leaving an estimated USD 45 billion in recoverable materials unreclaimed annually. In India, approximately 4.14 Mt of e-waste was generated in 2022, yet only about 1.4% entered formal recycling systems. Population-scaled estimates indicate that Uttar Pradesh, Maharashtra, Bihar, West Bengal, and Madhya Pradesh together account for nearly 45% of India’s total e-waste generation. Material composition analysis indicated a potential recovery of approximately 57,000 tonnes of critical and precious metals, with an estimated market value of USD 4.9 billion. Life-Cycle Assessment (LCA) revealed environmental trade-offs between recycling pathways: pyrometallurgical recovery emitted about 25% more greenhouse gases but required 18% less energy than hydrometallurgical processing. Techno-economic evaluation using Net Present Value (NPV) and Benefit–Cost Ratio (BCR) indicators showed stronger financial feasibility for hydrometallurgical recovery, with an estimated national NPV of USD 1.2 billion and a BCR of 1.45 under baseline assumptions. The findings suggest that hydrometallurgical or hybrid recovery strategies, supported by stronger Extended Producer Responsibility (EPR) enforcement, integration of informal recycling actors, and investment in advanced recycling infrastructure, could significantly enhance resource recovery while reducing environmental impacts, thereby supporting the transition toward a circular economy-based e-waste management system in India. The results represent comparative assessments based on published datasets and secondary sources and therefore highlight general trends and policy implications rather than exact national operational estimates. Graphical Abstract

Alfalfa (Medicago sativa) is a crucial legume as fodder for livestock, especially in high Andean regions. Despite its growing national importance, production remains limited by agroecological and climatic factors, mainly altitude. In this context, this study estimated the profitability and risks of alfalfa production in the district of San Andrés de Tupicocha, located in the highlands of Lima, Peru. To this end, surveys were conducted with producers during the year 2024, collecting information on yields, costs and risk perceptions. Profitability was analyzed using a stochastic Monte Carlo simulation of the Net Present Value (NPV), complemented by statistical tests comparing two zones with different altitudes and agroecological conditions. On average, the NPV/ha was S/ 159,555; with S/ 134,832 in the high zone (up to 4000 m.a.s.l) and S/ 188,286 in the low zone (up to 2500 m.a.s.l). The high zone showed a higher probability of economic losses, mainly due to frost and heavy rains. These findings suggest that, in order to improve productive efficiency and mitigate risks, it is necessary to implement strategies differentiated according to altitude and local agroecological characteristics. This differentiation will allow optimizing land use and available resources, favoring the sustainability of alfalfa production in the district.

Pediatric subspecialists in academic medicine earn less than adult subspecialists, but whether this disparity has changed over time is unclear. We compared the net present value (NPV) of lifetime earnings between pediatric and adult subspecialists in 2014 and 2024 and evaluated changes in the relative earnings differential. We conducted a retrospective economic analysis using Association of American Medical Colleges Faculty Salary Reports from 2014 and 2024. Salaries at the 25th, 50th, and 75th percentiles were extracted across academic ranks for pediatric and adult subspecialties and converted to 2024 dollars. Lifetime earnings were modeled through retirement at age 67 using a 3% real discount rate and three promotion pathways (baseline, plateaued, and no promotion). The relative earnings differential was defined as (Adult NPV - Pediatrics NPV)/Pediatrics NPV × 100. Across subspecialties, adult subspecialists in academic medicine had higher lifetime NPVs than pediatric subspecialists in both years. In 2024, lifetime NPVs for pediatric subspecialists ranged from $4.9 million to $7.6 million, compared with $5.3 million to $9.4 million for adult counterparts (50th percentile, baseline promotion pathway). The average relative earnings differential widened from 15.7% in 2014 to 20.2% in 2024. Disparities were amplified at higher salary percentiles and with plateaued or absent promotion, reaching 41.5% in a no-promotion pathway at the 75th percentile. From 2014 to 2024, the lifetime earnings gap between pediatric and adult subspecialists in academic medicine widened, with potential implications for pediatric workforce sustainability.

This project consists in the development of an exfoliating soap from olive leaves and olive pits. Its aim is the hydration and hygiene of the user’s skin, while also being able to execute mechanical exfoliation. To ensure the customers’ needs are satisfied, the Chemical Product Design (CPD) approach was followed. The production process consists of three main steps: extraction of oleuropein and hydroxytyrosol from olive leaves, grinding of olive pits, and the mixture process. With 9 tons of olive pits and 260 tons of olive tree leaves, it is possible to produce 1.8 million soaps annually. An economic analysis was performed to evaluate the product’s viability, and it was found that the product sales would break profit from the second year on, meaning that the payback time is equal to 2 years. Specifically, the NET Present Value is 2.3 million euros, and the Internal Rate of Return is 24 %.

Achieving Great Britain’s 2050 net-zero target requires strategic integration of hydrogen into the national energy system. This study evaluates the system-wide impacts of hydrogen blending (0–100%) using a bi-level optimisation framework that combines long-term cooperative investment planning with short-term operational Optimal Power and Gas Flow (OPGF) simulation. The strategic layer models infrastructure investment decisions under a cooperative game-theoretic structure, where system value is allocated among electricity, hydrogen production, and storage technologies using the Shapley-value payoff mechanism. Contrary to traditional centralised cost-minimisation models, our findings demonstrate that a cooperative planning structure identifies superior transition pathways. Comparative results reveal that at 100% hydrogen penetration, the cooperative framework reduces total system CO2 emissions by 31%, lowers operational costs by 26%, and decreases total electricity supply requirements by 8% relative to centralised planning. Furthermore, the cooperative approach significantly enhances economic resilience, yielding a more robust Net Present Value (NPV) across all blending levels compared to centralised planning, while ensuring project profitability at lower blending thresholds (20%) where traditional models remain loss-making. Simulation results indicate that hydrogen blending up to 20% maintains operational stability with manageable increases in operational cost. Full hydrogen conversion (100%) increases peak electricity supply requirements by approximately 30% relative to low-blending scenarios due to electrolysis-driven load expansion and conversion losses. The findings demonstrate that hydrogen blending represents a viable transitional pathway when supported by integrated infrastructure development and cooperative stakeholder coordination, enabling a more efficient and economically sustainable phased progression towards Great Britain’s 2050 net-zero target.

Purpose: This study presents an integrated data-driven framework for optimising gas injection strategies in enhanced oil recovery processes. Design/Methodology/Approach: The proposed framework uses Long Short-Term Memory (LSTM) to model nonlinear temporal dependencies. The key operational and reservoir variables, such as gas injection rate (GIR), bottom-hole pressure, separator pressure, reservoir temperature, tubing inner diameter, gas-oil ratio (GOR), and gas composition, are considered. In the proposed method, after preprocessing, feature selection is done using the Sequential Forward Selection (SFS) method. Then, the Whale Optimisation Algorithm (WOA) was employed to optimise injection strategies and tune LSTM hyperparameters. The objective is to maximise the net present value (NPV) subject to operational constraints. Research Limitation: Uncertainties and changing reservoir conditions may limit the generalisability of the proposed framework without further real-time adaptation. Findings: The results demonstrate that the hybrid WOA–LSTM framework outperforms LSTM and GRU models in both prediction accuracy and economic evaluation. In the multivariate scenario, the model's RMSE is 2.22, MAE is 1.09, accuracy is 97.20%, and NPV is $27.42 million. The results confirm the effectiveness of integrating metaheuristic optimisation and deep learning to enhance production forecasting and decision-making. Practical Implication: It can enable oil field operators to improve the production efficiency and maximise economic returns while respecting the operational constraints. Social Implication: Optimising gas injection resources reduces waste and enhances energy efficiency. In air pollution, the proposed model reduces hydrocarbon production and improves air quality. Originality/Value: This study introduces a novel hybrid framework that combines LSTM-based forecasting with WOA for prediction. The model offers a powerful solution for complex reservoir management problems.

Abstract This study assesses options for operating low-carbon freight rail systems in the United States, considering a specified rail corridor in Southern California (between the Port of Los Angeles and Barstow, California) as a case study. It considers the net present value (NPV) of rail freight operation over the course of a 20–30 year locomotive lifespan using five propulsion technologies: diesel, biodiesel, hydrogen, batteries, and catenary. It considers three scopes of analysis for calculating NPV: private NPV without the impact of climate change mitigation policies, termed private scope; private NPV after considering the impact of low-carbon fuel standard credits and tax credits for batteries, biofuels, and hydrogen production, termed credit scope; and the social NPV considering the positive spillovers from rail freight into the economy, and the cost of GHG emissions and criteria pollution, termed social scope. For the private scope of calculation, it finds a competitive NPV for electrification with catenary and batteries compared to diesel, hydrogen, or biodiesel operation. For the credit and social scopes of calculation, it finds that electric operation with either catenary or batteries are cost-competitive with operation with diesel. It finds a low or negative abatement cost of GHG emissions for rail electrification with batteries or catenary, respectively. This study discusses the reasons that US railroads remain opposed to electrification, how this barrier can be overcome, and proposes policy solutions.

Fisheries around the world serve more than 3.3 billion as a major source of animal protein and about 10 % of worldwide livelihood but almost 35% of the measured fish stocks are currently overexploited based on the Food and Agriculture Organization. This paper will discuss the role of sustainable methods of harvesting and habitat preservation strategies to balance economic development with sustainable conservation of the ecosystem in the long run. The issue that is discussed is the constant trade-off between maximization of revenues in the short-term and the decreasing biological productivity of marine ecosystem, which poses a threat to both the biodiversity and economic stability. The study has adopted a mixed methods design to combine bioeconomic modeling, catch-per-unit-effort (CPUE) and spatial habitat evaluation on three coastal fisheries over 10 years. Scenarios involved the comparison of traditional open access harvesting with regulated systems that involved catch quota systems, seasonal restrictions, selective gear technologies, and marine protected areas (MPA) of 20-30% of important habitats. Economic (net present value, employment rates) and ecological (spawning stock biomass, species diversity index) indices were compared. Findings suggest that application of science based catch limits led to a 28% drop in the rate of stock depletion and a 35% rise in spawning biomass after a period of five years. Varying gear usage reduced bycatch by 22% and MPAs increased adjacent fishery yields by 17% as a result of spill over effects. Regulated fisheries were found to have 14% higher long-term net present value than the open access models, even though it was initially reduced by 8% of the short-term profits. The results indicate that a combination of sustainable harvesting and habitat protection can be used to both improve ecological performance and financial payoffs. Policies that couple conservation incentives and market responses are critical to achieve fisheries productivity, biodiversity conservation and livelihoods of the coastal community in the context of increased global demand.

ABSTRACT Marginal oil wells, often abandoned due to low pressure and declining productivity, still contain significant untapped reserves that could support energy security and local economies. This study investigates nanofluid‐enhanced oil recovery (EOR) as a cost‐effective and scalable solution for revitalizing such wells. Silica‐ and cobalt ferrite (CoFe 2 O 4 )‐based nanofluids, stabilized with sodium dodecyl sulfate (SDS), were synthesized and tested on sandstone and carbonate cores under simulated reservoir conditions. Core flooding experiments demonstrated an incremental oil recovery of 14%–18% at an optimal concentration of 0.05 wt%, driven by interfacial tension reduction (32 to 6 mN/m) and wettability alteration (115°–45°). Economic analysis showed a recovery cost of $5.30/barrel and a breakeven oil price of $34, with a return on investment up to 2.4×. A Techno‐Socio‐Economic Viability Index (TSEVI) indicated strong socio‐economic benefits, including 60%–75% employment retention and positive net present value. Overall, nanofluid EOR presents a technically efficient, economically viable, and socially beneficial approach for extending the productive life of marginal oilfields.

Abstract - This research investigates the financial soundness and stability of Chennai Metro Rail Limited for the five-year period 2021 to 2025. The study uses secondary data given from annual reports and financial statements. The methodology section of this research discusses the following: financial statements, ratio, trend, and DuPont analyses, Altman Z-score, and investment appraisal techniques like Net Present Value (NPV), Payback Period, and the Accounting Rate of Return (ARR), Discounted cash Flow. The findings point to significant asset growth and sustained investment in infrastructure, which demonstrates expansion. The profitability, however, is inconsistent with negative returns during the majority of the years, and a slight increase in 2024. The cash flow analysis indicates a negative operating cash flow, which suggests that the core operations of the firm are weak, however, a positive cash flow from financing activities indicates that the firm is reliant on external funding. Investment analysis shows a negative NPV in the majority of the years considered, which suggests that the firm is making low return investments. The firm is in a capital-intensive business, and therefore the findings show that the firm operates in a low return environment, and as a result of this, operational efficiency, cost control, financial planning and overall government support, the firm needs to make improvements to secure longer-term financial stability and sustainability. Key Words: Financial Soundness, Financial Stability, Ratio Analysis, DuPont Analysis, Altman Z-Score, Chennai Metro Rail Limited.

Characterizing multiscale fracture networks in shale gas reservoirs remains challenging, while the limited applicability of conventional continuum-based models and insufficient multi-objective coordination often lead to low efficiency in development optimization. To address these issues, this study proposes a production history matching and multi-objective collaborative optimization framework for shale gas horizontal wells based on an equivalent fractal fracture (EFF) model. By integrating fractal theory with intelligent optimization techniques, a multiscale equivalent fractal permeability tensor is constructed, forming a hybrid machine-learning framework that combines physics-based fractal constraints with data-driven learning for efficient representation of complex fracture networks. Microseismic event clouds were converted into continuous fracture-density and fractal-geometry descriptors through denoising, temporal alignment, and spatial interpolation, and these descriptors were mapped to the equivalent fractal fracture model to dynamically update key flow parameters for history matching and parameter inversion. On this basis, a multi-objective collaborative optimization strategy is developed to achieve simultaneous time-varying fracture characterization and dynamic regulation of development parameters. Comparative results indicate that the EFF-based approach yields a production prediction error of 6.8%, slightly higher than the 4.2% obtained using discrete fracture network (DFN) models, while requiring only one-eighteenth of the computational time. Using the net present value (NPV) as the unified objective function, constraints are imposed on bottom-hole flowing pressure, flowback rate and system switching time for optimization. With the optimized pressure drop being more uniform and the gas saturation distribution being more balanced, it is verified that “EFF + NPV” can achieve the coordinated optimization of “production capacity—decline—cost” and enhance the development efficiency.

Impacts of the operational phase on the life cycle cost of permeable pavements.

ABSTRACT When designing railways in earthquake-prone regions, it is generally difficult to bypass all the active fault zones in the study area. Therefore, the fault-crossing risk is a crucial factor that should be considered during the early railway alignment design stage. However, little attention has been paid to this topic in the literature. To this end, a probabilistic fault-crossing risk assessment model and a methodology for seismic railway alignment optimisation are proposed in this paper. First, a Probabilistic Fault-Crossing Risk Analysis (PFCRA) model, which combines three probabilistic analysis modules for fault displacement hazard, railway structural fragility and infrastructure monetary loss, is constructed particularly for fault-crossing railway planning and design. Then, this PFCRA model is integrated with a previous least-cost model to construct a cost-risk Net Present Value (NPV) model for railway alignment optimisation. To solve this NPV model, a particle swarm algorithm is improved with a tailored Monte Carlo simulation to estimate and optimise the construction costs and possible fault-crossing risks concurrently for railway alignments. Lastly, a representative real-world railway example is employed as the case study for the developed method based on four groups of testing experiments.

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.

The search for profitable and sustainable alternatives for production in the Amazon directs attention to Livestock–Forest Integration Systems (iLF), which may present, depending on the species used, high economic viability. Thus, the objective of this study was to evaluate the economic feasibility of establishing different hybrids of Eucalyptus spp. in a Livestock–Forest Integration system (iLF). The study was conducted at Fazenda União, located in the municipality of Pimenta Bueno, state of Rondônia, in an area of nine hectares. Three scenarios were proposed: scenario 1 (C1) and scenario 2 (C2), with rotations of four years and seven years, with the assortment destined for energy biomass, and scenario 3 (C3), ten years, destined for structural timber. The program SisILPF_Eucalipto was used to estimate production. The economic criteria used were: Benefit–Cost Ratio (B/C); Net Present Value (NPV); and Internal Rate of Return (IRR). A sensitivity analysis was performed by applying three different annual interest rates: 7.44%, 8.50%, and 12.90%. The establishment cost was R$3,211.28 ha⁻¹, with estimated production for C1, C2, and C3 of 81.14 st ha⁻¹, 184.86 st ha⁻¹, and 261.43 st ha⁻¹, respectively. Thus, the economic evaluation criteria applied to all scenarios were viable, regardless of the interest rate used, with C3 demonstrating higher economic returns. Therefore, these analyses provide support for the development of this type of enterprise in the region and state, being important to motivate producers, identifying potential returns and encouraging the adoption of silvicultural practices.