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.
This paper investigates the development of a Robotic Process Automation (RPA)-based prototype for automated invoice data extraction and its potential implications for financial data analysis. We address the challenge of manual invoice processing by utilizing Microsoft's Power Automate and AI Builder tools. The prototype employs Optical Character Recognition (OCR) and Natural Language Processing (NLP) to extract relevant financial data from invoices received via email and populate an Excel workbook. Achieving a 96% accuracy rate in data extraction is assessed in the light of technical reliability and economic feasibility within an integrated performance-cost framework. The limitations of the prototype are discussed, and avenues for future development are proposed, including integration with accounting software via eXtensible Markup Language (XML) conversion.
Given the large investment in public transit systems worldwide as emphasized, it is of interest for policy makers to ask how the benefits of public transportation in urban areas compare with the associated costs. Using published data from eighty-one urbanized areas (UAZs) in the United States, we present a copula-based methodology for analyzing benefit-to-cost ratios of public transit systems under uncertainty. To describe the research motivation, we discuss the limitations of the existing probabilistic cost-benefit analysis (CBA) approaches, namely, Gaussian assumptions, linear correlation, and tail dependencies. This article provides a robust approach to evaluating how well transit systems compare across different urban areas and extends the probabilistic CBA models to incorporate nonlinear dependence.
This study focuses on the techno-economic feasibility analysis of a 60-MW wind farm in the Agadez and Tahoua regions for the generation of electricity that can be injected into the power grid. The study is based on wind data provided by the National Meteorological Agency (DMN) of Niger. These data, covering an eleven-year period (2009 to 2019), were statistically processed to determine wind characteristics at the hub height of the wind turbines. Six available wind turbine models were selected, namely G128/4500, V126/3000, E115/3000, E115/2500, E92/2350, and E82/2050. The calculation model proposed by Borowy and Salameh was used to determine the power generated by these wind turbines and to evaluate their performance based on the capacity factor and annual energy output for both regions. An economic analysis was then conducted to estimate the LCOE. The results demonstrate the economic and environmental viability of the project for the V126/3000 turbines. A total capital cost for the implementation is estimated to $191.5 million at the Agadez station ($281.4 million at the Tahoua station), as well as average annual maintenance costs of $2.39 million at the Agadez station ($3.52 million at the Tahoua station). The average LCOE was estimated approximately at $0.094/kWh and $0.130/kWh at Agadez and Tahoua stations respectively, with a payback period of about 8 years at the Agadez station and 11 years at the Tahoua station. From an environmental perspective, the project will save approximately 145,015.2 and 149,982 metric tons of CO2 respectively at Agadez and Tahoua Stations. These results could provide strong basis for policymakers for leveraging renewable energy deployment in Niger.
The necessary expansion of carbon credit markets, a vital economic tool for climate mitigation, is severely constrained by operational inefficiencies, high intermediary costs, and debilitating verification delays. These persistent structural weaknesses undermine the system's economic scalability and deter broad market participation, particularly from smaller project developers. This study employs a Design Science Research (DSR) approach to engineer and validate a novel, modular blockchain-based tokenization framework. We frame this shift from credits to infrastructure, reconceptualizing credits as traceable financial instruments rather than simple commodities. The system integrates automated issuance, peer-to-peer trading, and permanent retirement via four dedicated smart contract modules, thereby promoting procedural rigor. Prototype validation, including the analysis of computational demands and deployment gas usage, demonstrates the framework's potential to significantly reduce administrative friction and verification latency. By replacing costly manual processes with transparent, rule-based execution, this architecture enhances operational efficiency, mitigates systemic risks such as fraud and double claims, and lowers barriers to entry, thereby promoting the long-term economic viability and scalability of global carbon finance.
Studying green finance's impact on green innovation in energy-intensive enterprises clarifies its role in industrial green transformation and offers policy implications for optimizing resource allocation and mitigating innovation resource crowding-out. Based on Chinese listed energy-intensive firms from 2014 to 2023 under the carbon emission reduction goals, this paper finds that green finance and green innovation inputs/outputs in China's energy-intensive manufacturing grew significantly, but more enterprises resorted to greenwashing. Green finance investment diverted technological innovation resources, reduced innovation output with time lag, and this negative effect was more obvious in developed regions and state-owned enterprises. Mechanism analysis shows the fund reservoir and short-term wealth effects inhibited their correlation, with innovation resource invasion and technology introduction inhibition as mediating effects. This indicates Chinese managers often regard green finance investment as a speculative profit channel, leading to fraud and squeezing out enterprise innovation resources. Thus, it is critical to enhance green fund specificity and build a green finance effectiveness evaluation system to curb greenwashing.
We develop a differential game model to investigate the green R&D efforts of industrial firms under a data economy. Data can be used by firms to improve production efficiency, but the production activities of firms will generate pollution as a side product. Thus, an environmental tax can be adopted by the government to control pollution and maximize social welfare. Interestingly, we find that the relationship between data economy and pollution can be either positive or negative, depending on the relative importance of data for industrial firms adopting digital technologies. In fact, as digital technology progresses, data can serve as an important factor of production that either substitutes or complements capital investment of firms, and this will then greatly affect the incentive for firms to make green R&D efforts for pollution control purposes. As a result, it is suggested that the government should carefully consider the impacts of data economy and optimally adjust the environmental tax.
In this study, a novel theoretical framework is provided for the potential effect of smart contracts on reducing transaction costs in financial markets to increase corporate investment. The theoretical model considered in this paper is based on Williamson's transaction cost approach and considers how an important blockchain technology such as smart contracts can induce corporate investment through its potential transaction cost reduction effect. The findings obtained show that smart contracts minimize transaction costs and increase future growth opportunities for firms to corporate investment. This study sheds new light on smart contract-based finance and its impact on business investment within the Williamson's transaction cost framework.
This study addresses the problems in the expropriation process of immovable properties and emphasizes the need to revise valuation methods to resolve disputes. By comparing the effects of variable attributes under the influence of market data on expropriation cost determination, the focus is on comparing models using modern approaches with classical methods. The study's main objective is to eliminate the difference between expropriation and real estate valuation and create a model that reaches the closest result to the real value. In this context, six different models were created using different parameters. The models were analyzed using Artificial Neural Networks, Gaussian Process Regression, M5 Tree, Multivariate Adaptive Regression Analysis, Support Vector Machines, and Least Squares Support Vector Machines. It is concluded that the expropriation cost estimation should not only take into account the change in money over time but also the changes in building characteristics and construction cost index rates.
This article evaluates the economic feasibility and sustainability of integrating electric bicycles (e-bikes) into urban mobility systems, considering four key cost dimensions: capital, operating, infrastructure, and externalities. A global dataset spanning 2012-2022 was used to forecast e-bike demand through 2030 via a dual-scenario model, combining Winters's exponential smoothing and population-based projections. Capital and operating costs were derived from industry data, while a linear programming model optimized infrastructure requirement. Externality benefits, particularly greenhouse gas (GHG) emission reductions, were quantified using U.S. Environmental Protection Agency (EPA) valuations. Net social cost (NSC) was assessed through discounted cash flow analysis over a 7-year horizon. Sensitivity analyses were conducted on purchasing cost, insurance, interest rates, and carbon pricing to determine key factors influencing feasibility. Results indicate that while initial costs are significant, externality savings substantially offset expenditures under favorable conditions. Policy interventions such as subsidies, infrastructure investments, and regulations are recommended to support adoption. The findings provide critical insights for policymakers and urban planners pursuing low-carbon, equitable transport solutions.
The valuation of investment projects and decisions depending on the price of a specific commodity is an important branch of financial economics. Many models for valuing investment projects and real options assume that the commodity price follows a geometric Brownian motion and that the cash flow rate varies linearly with the commodity price. This article presents explicit formulas for project values as functions of commodity price and time for three distinct types of cash flow rates and formulas for project values as functions of commodity price and reserve. It also includes formulas for options to expand or contract an investment project. The derived explicit formulas enable significantly faster and more accurate valuation of projects and options than previously used numerical methods. Additionally, they provide new theoretical insights by demonstrating how project value is influenced by model parameters and identifying conditions under which the resulting value is close to the traditional net present value.
This study investigates the interplay between investor attention deficits, internal information risks, and sentiment in driving market volatility and hindering sustainable investments in Iran's energy and technology sectors. Drawing on behavioral finance and sustainable finance theories, we address a critical gap in emerging market literature by examining how cognitive biases and informational asymmetries amplify instability in the Tehran Stock Exchange (TSE). Employing a mixed-methods design, we analyze historical TSE data (2015-2023), conduct semi-structured interviews with 30 investors and project managers, and apply advanced models including dynamic panel regression, GARCH, structural equation modeling, and deep neural networks (DNNs). Findings reveal that attention deficits significantly heighten volatility, with an Attention Deficit Index exhibiting strong positive correlations to short- and long-term fluctuations. Internal risks, such as incomplete disclosures and managerial turnover, double the odds of market inefficiency. Investor sentiment mediates 48% of attention deficits' effects on volatility, while DNNs (89% accuracy) uncover nonlinear interactions between biases and risks. Results demonstrate that behavioral inefficiencies outweigh macroeconomic drivers (e.g., exchange rates, oil prices) in fostering instability, underscoring the need for enhanced transparency and education. This research offers novel insights for policymakers to bolster sustainable transitions aligned with SDGs, extending behavioral models to sanctioned economies.
This paper presents a real-world case study that exposes violations of economic reasoning in a typical timeshare sales pitch. The case is based on an actual offer made to the author and is designed to help students apply core principles of engineering economics-such as present value, discounting, economic equivalence, and capital recovery-in a context that is both accessible and engaging. Students are asked to compare the cost of hotel stays over time with the upfront and ongoing costs of a timeshare, both with and without financing. The analysis reveals that the timeshare offer is economically inferior under reasonable assumptions, despite appearing attractive when future hotel costs are presented without discounting. The case also includes sensitivity analyses of time horizon, interest rates, inflation assumptions, and resale value. It concludes with a discussion of behavioral factors that may explain why such offers remain persuasive despite poor economic fundamentals. Used successfully in undergraduate engineering economics courses for over two decades, this case provides a replicable and pedagogically rich framework for teaching sound financial decision-making.
The cost of risk, a central concept of the Decoupled Net Present Value (DNPV) method, quantifies risks in monetary terms in a consistent and transparent manner. Within the DNPV framework, the cost of risk represents the monetary compensation investors receive for bearing identified investment risks. One of those risks-the risk of permanently stopping at any stage of a project cycle-is ubiquitous and yet not explicitly included in standard financial valuation methods with a customary practice of incorporating all risks in a single variable (i.e., the discount rate), and thus are not equipped to deal with individual risks. Novel general expressions are derived to calculate the cost of risk due to permanent shutdown during the initial sequential investment and revenue-generating phases. The permanent shutdown occurs due to a random event described by a binomial distribution. Closed-form solutions for simplified cases are derived from the general expressions to illustrate how the cost of risk varies with time during the investment and revenue-generation phases. A simple example of an investment in the development of a pharmaceutical drug is presented to illustrate how the derived expressions can be used to effortlessly complement a standard financial valuation with the proposed risk-based valuation.
We develop a stylized model of the ideation-evaluation process to study how to allocate a fixed budget between the generation of ideas and the evaluation of their quality. We study three versions, differentiated by our assumptions about the prior distribution of idea quality and the structure of information gathering. We prove that interior allocations are always optimal and analytically derive optimal allocations for small-budget instances. We explore larger instances through numerical simulation, finding that it is always optimal to allocate 38-75% of the budget to ideation, and, in general, more ideation is optimal when prior quality is low.
The payback period is unambiguously defined for conventional investment projects, projects in which a series of cash outflows is followed by a series of cash inflows. Its definition for nonconventional projects is more challenging, since their balances (cumulative cash flow streams) may have multiple break-even points. Academics and practitioners offer a few contradictory recipes to manage this issue, suggesting to use the first break-even point of the balance, the last break-even point of the balance, or the break-even point of the modified cumulative cash flow stream, representing the moment of time in which the cumulative cash inflow exceeds the total cash outflow. In this note, we show that the last break-even point of the project balance is the only definition of the payback period consistent with a set of economically meaningful axioms. An analogous result is established for the discounted payback period.
This paper investigates the cash-flow bullwhip (CFB) phenomenon, which refers to the amplification of financial volatility across supply chains, using simulation modeling across multiple network topologies. While prior studies confirmed CFB empirically and analytically, most focused on simple serial chains. Here, we extend the analysis to serial, convergent, divergent, con-div, and general network structures, incorporating both the traditional inventory bullwhip effect (BWE) and financial dynamics. Designed experiments test how stochastic procurement and payment lead times and information sharing influence CFB under both steady-state and demand-shock conditions. Results show that lead-time variability, demand forecasting, and information sharing significantly shape CFB propagation, with effects most pronounced upstream. Convergent networks dampen CFB the most, up to 78-86% lower variance than other topologies, while divergent networks amplify it fastest. Information sharing reduces mean CFB and BWE by roughly 70 and 60%, respectively, with benefits evident even at the first upstream tier. Exponential smoothing produces lower CFB and forecast errors than stochastic financial analytics. Under demand shocks, the rate of CFB amplification increases by 2-3.4 times, highlighting how operational and financial dynamics interact to shape supply chain resilience.
Circular economy aims to reduce waste, enhance resource efficiency, and support sustainable development. However, small and medium-sized enterprises (SMEs) often struggle to adopt existing indicators due to limited resources and complex data demands. This study proposes CLEVER (Company-Level production Elements Value and Effectiveness Ratio), a novel indicator assessing the circular value and performance of energy, resources, and materials. CLEVER is simple, low-cost, and easy to apply in SME operations. An empirical study from the textile industry indicates that CLEVER improves total weighted solid waste and wastewater decreased by up to 47.3%, and overall waste reduction reached 40%. CLEVER can serve as a benchmark and implementation tool for SMEs and has strong potential as part of annual ESG report.
Corporate social responsibility (CSR) is vital in reducing financing costs. It is also a crucial component of global governance. This study is the first to examine the relationship between CSR and financing costs in developed and developing countries via a meta-analysis. It analyses 167,276 data points from empirical studies published between 2010 and 2022. The results revealed that the effect of good CSR has stronger impacts on (1) reducing financing costs in developing than developed countries; (2) the cost of equity is more significant in developed than developing countries; and (3) the cost of debt is more significant in developing than developed countries. This article studies CSR's heterogeneous impact on financing costs under different economic development stages. It provides useful ideas to firms when they need to make financial decisions and CSR tactics to enhance sustainability.
In environments with weak external monitoring, investment inefficiency persists due to agency conflicts and information asymmetry. This study examines the effect of environmental, social, and governance (ESG) ratings on corporate investment efficiency in non-financial listed firms in South Africa using a panel dataset of 2921 firm-year observations from 2010 to 2023. Employing fixed effects, instrumental variable regressions, and mediation models, the analysis reveals that higher ESG ratings are significantly associated with lower levels of investment inefficiency. This effect is particularly stronger in firms with weaker governance quality, where ESG substitutes are inadequate for internal monitoring. Furthermore, the findings show that ESG ratings reduce underinvestment by lowering information asymmetry and curbing overinvestment by constraining agency-driven resource misuse. The heterogeneity analysis demonstrates that ESG-driven efficiency gains are amplified in non-state-owned firms, younger firms, and nonpolluting industries, where competitive market pressures, proactive strategic signaling, and a stronger reliance on stakeholder trust enhance the role of ESG ratings in improving capital allocation. These findings underscore that ESG ratings function not only as a strategic signal of stakeholder alignment but also as a mechanism for addressing structural inefficiencies in capital allocation. This study provides a foundation for refining sustainability-oriented investment policies in emerging markets, where ESG frameworks are nascent but financially important.
