Search Results (153)

Convertible bond financing has gained significant traction in China’s capital market, yet it poses financial risks, particularly for highly leveraged firms. This study investigates how corporate financial traits influence the decision to issue convertible bonds, challenging the direct applicability of Western theoretical frameworks in China’s unique institutional context. We employ a natural experiment design, constructing a binary logistic regression model to analyze data from Chinese A-share listed companies that issued convertible bonds, corporate bonds, seasoned equity offerings, or rights offerings between 2022 and 2023. Our results reveal a paradox: contrary to risk-transfer theory, firms with lower leverage exhibit a stronger propensity to issue convertible bonds. Instead, motives are driven by high profitability, operational inefficiencies, and robust operating cash flow generation—traits that align with signaling and backdoor equity theories. The study identifies China’s convertible bond market as a dual-track system where regulatory screening distorts classical motives while market frictions amplify the role of convertible bonds in resolving information asymmetry. We conclude with targeted policy implications for regulators and corporate treasurers to enhance market efficiency and governance. Full article

The scope of this topic stems from the change in insurance companies and the subsequent transition to IFRS 17. The new code came into force on 1 January 2023. Therefore, the purpose of this article is to compare the two standards in terms of methodology and process logic. To highlight the new aspects of the new standard and to present the author’s view that IFRS 17 provides more opportunities for timely action and intervention by company management in the processes and improvement of results compared to IFRS 4. To examine how the application of the standard has affected the strategy for recognising, measuring, and reporting liabilities under insurance contracts, as well as financial results in the insurance sector in China. The study uses a mixed approach, combining a comparison of IFRS 4 and IFRS 17 with examples illustrating actual practice in the sector to examine differences in accounting treatment. It cites examples from European and Asian traders to assess how things will develop in practice. Contribution: This study adds new evidence on the impact of IFRS 17 on value and profit management. Our study found that the new standard introduces a single model for measuring insurance contracts, which significantly increases transparency and comparability in financial statements. Furthermore, one of its most important findings is that, with the equalisation of the margin on contractual services and the recognition of profits over the entire term of insurance contracts, the balance sheets for all years will show more consistent reports of profits and losses. It also calls for attention to the challenges insurers met in developing cash flow discounting methods or putting the general measurement model into effect. Overall, the report found that search engine IFRS 17 has made comparability and transparency better while making suggestions to industry stakeholders about what problems came out when they were discovered afterwards. Full article

The Former General Electric Utility in Japan is a major participant in the electricity market. The integrated operational capabilities of these power companies have significant impacts on the stable development and sustainability of the power industry. This study evaluates the comprehensive operational capabilities of these power companies from 2003 to 2015 and analyzes the indicators that may affect their operational capabilities. Establishing an evaluation index system comprising five subsystems, namely profitability, management, solvency, growth, and scale, and optimizing it using principal component analysis. The Technique for Order of Preference by Similarity to Ideal Solution was utilized to calculate the relative closeness of each company, with a score representing the integrated operational capabilities. Furthermore, coupling coordination and grey correlation analyses were conducted to assess the internal coordination among subsystems and to identify critical drivers of sustainable performance. The results show that (1) the Kyushu Electric Power Company and Tohoku Electric Power Company have strong integrated operational capabilities. (2) The five evaluation subsystems of integrated operational capability during the period of 2003–2015, fluctuated between moderate and high levels. (3) The top 5 indicators with the highest average grey correlation are as follows: “Hydropower capacity factor”, “Operating cash flow to current liabilities ratio”, “Operating profit growth rate”, “Net profit growth rate”, “Total capital utilization”. This study contributes to the sustainable management of the electricity industry by providing a systematic and data-driven assessment framework. The findings offer practical insights for optimizing corporate governance, enhancing energy efficiency, and formulating policy measures that support the long-term sustainability and competitiveness of Japan’s power utilities. Full article

Agrivoltaic (AV) systems are increasingly recognized as a strategy to enhance sustainable land management, yet their application in post-conflict settings remains underexplored. This study addresses this gap by evaluating AV deployment in two Colombian municipalities located in PDET/ZOMAC regions, using an integrated framework that expands the conventional Water–Energy–Food (WEF) nexus into the Water–Energy–Food–Soil–Climate–Communities (WEFSCC) nexus. The research combined GIS-based site characterization, crop yield and water balance modeling (contrasting traditional irrigation with hydroponics), and photovoltaic performance simulations for 30 kW systems, under conservative and moderate scenarios. Economic analyses included Net Present Value (NPV), Internal Rate of Return (IRR), and Free Cash Flow (FCL), with sensitivity tests for crop prices, yields, tariffs, and costs. Results indicate that AV can reduce crop irrigation demand by up to 40%, while generating 17 MWh/month of electricity per site. Cabrera exhibited higher profitability than Pisba, explained by yield differences and site-specific energy outputs. Comparative analysis confirmed consistency with experiences in Africa and Europe, while emphasizing local socio-environmental benefits. Conclusions highlight AV systems as resilient tools for sustainable land management in Colombia’s post-conflict regions, with actionable implications for land-use regulation, fiscal incentives, and international cooperation programs targeting rural development. Full article

Customer loyalty is crucial for (while fueled by) business sustainability. Loyal customers advocate for a company’s offer and sustainable practices, while their steady support generates stable revenue stream, lower acquisition costs, and predictable cash flows that enable long-term business viability. Such a stable revenue stream is especially critical in periods of intense competition or macroeconomic disruption (e.g., COVID-19 pandemic) which profoundly influenced consumer behavior. In this context, the purpose of the current paper is to test the BG/NBD prediction model for its potential validation as a practical tool in estimating the buying behavior of customers of a self-service car washing company before and within the COVID-19 pandemic period. To achieving this, transaction data of the company’s customers was retrieved from the company’s internal information system and used as input for BG/NBD model. The model proved its effectiveness in estimating the total number of repeated transactions for the year 2020 based on the 2019 data at total customer base and at loyal customer level. Loyal customers were considered from the behavioral loyalty perspective only and defined as customers which used the company’s services at least once in both years. In the estimation of the repeated transactions frequencies, the model’s prediction accuracy increases with higher frequencies of loyal customers. Full article

A novel methodology for hybrid energy management in aquaculture is introduced, aimed at enhancing self-sufficiency and optimizing grid-related cash flows. Wind and solar energy generation are modeled using calibrated turbine performance curves and PVGIS data, respectively, with a photovoltaic capacity of 120 kWp. The system also incorporates a 250 kW small hydroelectric plant and a wood drying kiln that utilizes surplus wind energy. This study conducts a comparative analysis between HY4RES, a research-oriented simulation model, and HOMER Pro, a commercially available optimization tool, across multiple hybrid energy scenarios at two aquaculture sites. For grid-connected configurations at the Primary site (base case, Scenarios 1, 2, and 6), both models demonstrate strong concordance in terms of energy balance and overall performance. In Scenario 1, a peak power demand exceeding 1000 kW is observed in both models, attributed to the biomass kiln load. Scenario 2 reveals a 3.1% improvement in self-sufficiency with the integration of photovoltaic generation, as reported by HY4RES. In the off-grid Scenario 3, HY4RES supplies an additional 96,634 kWh of annual load compared to HOMER Pro. However, HOMER Pro indicates a 3.6% higher electricity deficit, primarily due to battery energy storage system (BESS) losses. Scenario 4 yields comparable generation outputs, with HY4RES enabling 6% more wood-drying capacity through the inclusion of photovoltaic energy. Scenario 5, which features a large-scale BESS, highlights a 4.7% unmet demand in HY4RES, whereas HOMER Pro successfully meets the entire load. In Scenario 6, both models exhibit similar load profiles; however, HY4RES reports a self-sufficiency rate that is 1.3% lower than in Scenario 1. At the Secondary site, financial outcomes are closely aligned. For instance, in the base case, HY4RES projects a cash flow of 54,154 EUR, while HOMER Pro estimates 55,532 EUR. Scenario 1 presents nearly identical financial results, and Scenario 2 underscores HOMER Pro’s superior BESS modeling capabilities during periods of reduced hydroelectric output. In conclusion, HY4RES demonstrates robust performance across all scenarios. When provided with harmonized input parameters, its simulation results are consistent with those of HOMER Pro, thereby validating its reliability for hybrid energy management in aquaculture applications. Full article

This paper develops an economic evaluation framework for pumped storage hydropower (PSH) projects based on real options, addressing the limitations of traditional economic evaluation methods that neglect investment flexibility and path dependence. The framework integrates an annual net cash flow model with an improved mean-reverting electricity price model to generate thousands of electricity price trajectories, while backward dynamic programming dynamically values abandonment options. The core innovation of this study lies in the dynamic pricing mechanism of abandonment options, which explicitly captures the flexibility of terminating projects under adverse conditions. A comparative analysis between the traditional NPV approach and the real options method reveals significant differences: the average NPV under base scenario is −38.35 million CNY, whereas option scenario yields an average NPV of 143.15 million CNY. The average value of real options is 181.5 million yuan, and it increases the average internal rate of return by 0.34%. These results demonstrate that incorporating real options prevents the underestimation of project value and provides more robust decision-making support under uncertainty, thereby offering methodological and policy insights for the investment appraisal of large-scale energy storage projects. Full article

This study distinguishes between observed, uncertain, and stochastic uncertain firm growth. Observed firm growth is measured via historical growth of fixed assets scaled by growth of sales revenue. Uncertain firm growth is the volatility of unobserved (estimated error terms) firm growth. The latter is simulated using nonuniform Monte Carlo to generate stochastic uncertain firm growth. The objective of this study is to examine the relationships among the firm specific, economic, and institutional factors that affect the uncertain and stochastic uncertain growth of a firm. The sample includes the nonfinancial firms listed in the DJIA30 and NASDAQ100, covering quarterly data from 1996Q1 to 2022Q4 for 121 companies. The results reveal that (a) sales growth, profitability, cash flow, and long-term financing help reduce a firm’s uncertain growth, (b) high involvement in exporting exposes firms to higher geopolitical uncertainty, (c) institutional quality (especially political stability and regulatory quality) paradoxically contribute to uncertain firm growth. This study contributes to related studies via offering perspectives to firm managers and policy makers about the factors that help manage the uncertainties of firm growth. Full article

The primary objective of this study is to assess the techno-economic feasibility of an innovative solar energy generation system with a rainwater collection feature to generate electrical energy and meet irrigation needs in agriculture. The proposed system is designed for an agricultural area (Gonyeli, North Cyprus) with high solar potential and limited rainfall. In the present study, global rainfall datasets are utilized to assess the potential of rainwater harvesting at the selected site. Due to the lack of the measured rainfall data at the selected site, the accuracy of rainfall of nine global reanalysis and analysis datasets (CHIRPS, CFSR, ERA5-LAND, ERA5, ERA5-AG, MERRA2, NOAA CPC CMORPH, NOAA CPC DAILY GLOBAL, and TerraClimate) are evaluated by using data from ground-based observations collected from the Meteorological Department located in Lefkoşa, Northern Cyprus from 1981 to 2023. The results demonstrate that ERA5 outperformed the other datasets, yielding a high R-squared value along with a low mean absolute error (MAE) and root mean square error (RMSE). Based on the best dataset, the potential of the rainwater harvesting system is estimated by analyzing the monthly and seasonal rainfall patterns utilizing 65 different probability distribution functions for the first time. Three goodness-of-fit tests are utilized to identify the best-fit probability distribution. The results show that the Johnson and Wakeby SB distributions outperform the other models in terms of fitting accuracy. Additionally, the results indicate that the rainwater harvesting system could supply between 31% and 38% of the building’s annual irrigation water demand (204 m³/year) based on average daily rainfall and between 285% and 346% based on maximum daily rainfall. Accordingly, the system might be able to collect a lot more water than is needed for irrigation, possibly producing an excess that could be stored for non-potable uses during periods of heavy rainfall. Furthermore, the techno-economic feasibility of the proposed system is evaluated using RETScreen software (version 9.1, 2023). The results show that household energy needs can be met by the proposed photovoltaic system, and the excess energy is transferred to the grid. Furthermore, the cash flow indicates that the investor can expect a return on investment from the proposed PV system within 2.4 years. Consequently, the findings demonstrate the significance of this system for promoting resource sustainability and climate change adaptation. Besides, the developed system can also help reduce environmental impact and enhance resilience in areas that rely on water and electricity. Full article

Previous studies on the relationship between climate policy uncertainty (CPU) and green technology innovation (GTI) generally belong to one of two opposing schools of thought: real options theory and growth options theory. This study proposes that, according to prospect theory, a nonlinear relationship exists between these variables. Using panel data from Chinese A-share listed companies spanning 2010–2022, we empirically test this hypothesis. Results indicate an inverted U-shaped relationship between CPU and GTI, with moderate levels of CPU facilitating optimal GTI. As CPU intensity strengthens, there exists an appropriate threshold at which GTI reaches its peak. More importantly, moderation analysis reveals that firms’ internal capabilities—including risk-bearing capability, cash flow, operational capability, and marketing capability—significantly outperform external government support in moderating the CPU-GTI relationship. These findings provide valuable implications for firms’ climate risk management and climate policymakers’ policy formulation in advancing sustainable development goals. Full article

Hydrogen is increasingly recognized as a key element of the transition to a low-carbon energy system, leading to a growing interest in accurate and sustainable assessment of its economic viability. Levelized Cost of Hydrogen (LCOH) is one of the most widely used metrics for comparing hydrogen production technologies and informing investment decisions. However, traditional LCOH calculation methods apply a single discount rate to all cash flows without distinguishing between the risks associated with outflows and inflows. This approach may yield a systematic overestimation of costs, especially in capital-intensive projects. In this study, we adapt a binary cash flow discounting model, previously proposed in the finance literature, for hydrogen energy systems. The model employs two distinct discount rates, one for costs and one for revenues, with a rate structure based on the required return and the risk-free rate, thereby ensuring that arbitrage conditions are not present. Our approach allows the range of possible LCOH values to be determined, eliminating the methodological errors inherent in traditional formulas. A numerical analysis is performed to assess the impact of a change in the general rate of return on the final LCOH value. The method is tested on five typical hydrogen production technologies with fixed productivity and cost parameters. The results show that the traditional approach consistently overestimates costs, whereas the binary model provides a more balanced and risk-adjusted representation of costs, particularly for projects with high capital expenditures. These findings may be useful for investors, policymakers, and researchers developing tools to support and evaluate hydrogen energy projects. Full article

This paper explores the effectiveness of an innovative FinTech risk-scoring model to predict the risk-appropriate return for short-term credit sales. The risk score serves to mitigate the information asymmetry between the seller of receivables (“Seller”) and the purchaser (“Funder”), at the same time providing an opportunity for the Funder to earn returns as well as to diversify its portfolio on a risk-appropriate basis. Selling receivables/credit to potential Funders at a risk-appropriate discount also helps Sellers to maintain their short-term financial liquidity and provide the necessary cash flow for operations and other immediate financial needs. We use 18,304 short-term credit-sale transactions between 23 April 2020 and 30 September 2022 from the private FinTech startup Crowdz and its Sustainability, Underwriting, Risk & Financial (SURF) risk-scoring system to analyze the risk/return relationship. The data includes risk scores for both Sellers of receivables (e.g., invoices) along with the Obligors (firms purchasing goods and services from the Seller) on those receivables and provides, as outputs, the mutual gains by the Sellers and the financial institutions or other investors funding the receivables (i.e., the Funders). Our analysis shows that the SURF Score is instrumental in mitigating the information asymmetry between the Sellers and the Funders and provides risk-appropriate periodic returns to the Funders across industries. A comparative analysis shows that the use of SURF technology generates higher risk-appropriate annualized internal rates of return (IRR) as compared to nonuse of the SURF Score risk-scoring system in these transactions. While Sellers and Funders enter into a win-win relationship (in the absence of a default), Sellers of credit instruments are not often scored based on the potential diversification by industry classification. Crowdz’s SURF technology does so and provides Funders with diversification opportunities through numerous invoices of differing amounts and SURF Scores in a wide range of industries. The analysis also shows that Sellers generally have lower financing stability as compared to the Obligors (payers on receivables), a fact captured in the SURF Scores. Full article

In the modern corporation, understanding sustainable cost management practices is essential for promoting economic resilience and resource efficiency. This study investigates how ownership structures influence the behavior of selling, and general and administrative (SG&A) costs during periods of sales fluctuations in South Korean firms, with particular attention to Chaebols. Drawing upon agency theory and corporate governance perspectives, we examine whether proxies for agency costs, namely, free cash flow, asset utilization ratios, and operating expense ratios, explain variations in SG&A cost responses to changes in revenue. Utilizing a panel dataset of 4279 firm-year observations from KOSPI-listed companies over the period 2011–2021, we employ Pooled Ordinary Least Squares (OLS), Fixed Effects, Random Effects, and Generalized Method of Moments (GMM) estimations to model SG&A cost behavior. The analysis incorporates regression-based interaction terms that capture asymmetric cost adjustments during sales declines, commonly referred to as cost stickiness. Our findings indicate that firms with concentrated ownership, such as Chaebols, exhibit significantly lower SG&A cost stickiness, reflecting stronger financial discipline and more efficient resource allocation. In contrast, firms with dispersed ownership demonstrate more pronounced cost stickiness, consistent with governance frictions and managerial discretion. These results emphasize the moderating role of ownership structure in cost behavior and highlight its implications for sustainable corporate governance. Our study contributes to the literature on cost management and financial sustainability by offering empirical insights from a distinctive institutional setting. Policy recommendations include enhancing internal controls, promoting transparent cost practices, and encouraging shareholder oversight to reinforce long-term efficiency. Full article

Calf weaning plays a fundamental role in the sustainability of cow-calf production systems. In South Africa, conventional weaning at six to nine months is widely practiced, but increasing climatic variability has highlighted early weaning as an adaptive strategy. To support producers in determining the optimal weaning age, we developed a Calf Weaning Decision-Support Framework through an extensive literature review. To ensure its practicality, we validated the framework using the Delphi technique, incorporating real-world insights from livestock experts. A two-round Delphi study was conducted with ten experts in livestock production and research, evaluating key factors influencing weaning age decisions. The study also used the Relative Importance Index (RII) to rank these factors based on expert consensus. The main findings showed strong agreement on productive factors, including weaning weight, conception rate, and dam body condition score alongside financial aspects that influence profitability, such as calf health and feeding expenses, as well as income generated at weaning. Experts identified three additional factors—cattle breed, enterprise cash flow needs, and veld type, emphasizing the need for flexible weaning strategies tailored to specific conditions. This study concludes that the expert-validated framework is a practical and adaptable tool, empowering South African beef producers to make informed, context-specific weaning decisions. Full article

The problem of using accounting semi-identity-based (ASI) models in Econometrics can be severe in certain circumstances, and estimations from OLS regressions in such models may not accurately reflect causal relationships. This dataset was generated through Monte Carlo simulations, which allowed for the precise control of a causal relationship. The problem of an ASI cannot be directly demonstrated in real samples, as researchers lack insight into the specific factors driving each company’s investment policy. Consequently, it is impossible to distinguish whether regression results in such datasets stem from actual causality or are merely a byproduct of arithmetic distortions introduced by the ASI. The strategy of addressing this issue through simulations allows researchers to determine the true value of any estimator with certainty. The selected model for testing the influence of the ASI problem is the investment-cash flow sensitivity model (Fazzari, Hubbard and Petersen (FHP hereinafter) (1988)), which seeks to establish a relationship between a company’s investments and its cash flows and which is an ASI as well. The dataset included randomly generated independent variables (cash flows and Tobin’s Q) to analyze how they influence the dependent variable (cash flows). The Monte Carlo methodology in Stata enabled repeated sampling to assess how ASIs affect regression models, highlighting their impact on variable relationships and the unreliability of estimated coefficients. The purpose of this paper is twofold: its first goal is to provide a deeper explanation of the syntax in the related article, offering more insights into the ASI problem. The openly available dataset supports replication and further research on ASIs’ effects in economic models and can be adapted for other ASI-based analyses, as the information comprised in the reusability examples prove. Second, our aim is to encourage research supported by Monte Carlo simulations, as they enable the modeling of a comprehensive ecosystem of economic relationships between variables. This allows researchers to address a variety of issues, such as partial correlations, heteroskedasticity, multicollinearity, autocorrelation, endogeneity, and more, while testing their impact on the true value of coefficients. Full article