Search Results (388)

This study aims to develop an optimal underwriting strategy for affordable (H1 and M1) and premium (L1 and M2) electric vehicles (EVs), balancing financial risk and sustainability commitments. The research is motivated by regulatory pressures, risk management needs, and sustainability goals, necessitating an adaptation of traditional underwriting models. The study employs a modified Delphi method with industry experts to identify key risk factors, including accident risk, repair costs, battery safety, driver behavior, and PCAF carbon impact. A sensitivity analysis was conducted to examine premium adjustments under different risk scenarios, categorizing EVs into four risk segments: Low-Risk, Low-Carbon (L1); Medium-Risk, Low-Carbon (M1); Medium-Risk, High-Carbon (M2); and High-Risk, High-Carbon (H1). Findings indicate that premium EVs (L1 and M2) exhibit lower volatility in underwriting costs, benefiting from advanced safety features, lower accident rates, and reduced carbon attribution penalties. Conversely, budget EVs (H1 and M1) experience higher premium fluctuations due to greater accident risks, costly repairs, and higher carbon costs under PCAF implementation. The worst-case scenario showed a 14.5% premium increase, while the best-case scenario led to a 10.5% premium reduction. The study recommends prioritizing premium EVs for insurance coverage due to their lower underwriting risks and carbon efficiency. For budget EVs, insurers should implement selective underwriting based on safety features, driver risk profiling, and energy efficiency. Additionally, incentive-based pricing such as telematics discounts, green repair incentives, and low-carbon charging rewards can mitigate financial risks and align with net-zero insurance commitments. This research provides a structured framework for insurers to optimize EV underwriting while ensuring long-term profitability and regulatory compliance. Full article

This paper develops a unified framework for mathematical finance under general semimartingale models that allow for dividend payments, negative asset prices, and unbounded jumps. We present a rigorous approach to the mathematical modeling of financial markets with dividend-paying assets by defining appropriate concepts of numéraires, discounted processes, and self-financing trading strategies. While most of the mathematical results are not new, this unified framework has been missing in the literature. We carefully examine the transition between nominal and discounted price processes and define appropriate notions of admissible strategies that work naturally in both settings. By establishing the equivalence between these models and providing clear conditions for their applicability, we create a mathematical foundation that encompasses a wide range of realistic market scenarios and can serve as a basis for future work on mathematical finance and derivative pricing. We demonstrate the practical relevance of our framework through a comprehensive application to dividend-paying equity markets where the framework naturally handles discrete dividend payments. This application shows that our theoretical framework is not merely abstract but provides the rigorous foundation for pricing derivatives in real-world markets where classical assumptions need extension. Full article

In recent years, carbon dioxide (CO₂) emissions have increased at the fastest rates ever recorded. This is a trend that contradicts global efforts to stabilise greenhouse gas (GHG) concentrations and prevent long-term climate change. Over 90% of global transport relies on oil-based fuels. The continued use of diesel and petrol raises concerns related to oil costs, supply security, GHG emissions, and the release of air pollutants and volatile organic compounds. This study explored electric vehicle (EV) charging networks by assessing environmental impacts through GHG and petroleum savings, developing dynamic pricing strategies, and forecasting infrastructure needs. A substantial dataset of over 259,000 EV charging records from Palo Alto, California, was statistically analysed. Machine learning models were applied to generate insights that support sustainable and economically viable electric transport planning for policymakers, urban planners, and other stakeholders. Findings indicate that GHG and gasoline savings are directly proportional to energy consumed, with conversion rates of 0.42 kg CO₂ and 0.125 gallons per kilowatt-hour (kWh), respectively. Additionally, dynamic pricing strategies such as a 20% discount on underutilised days and a 15% surcharge during peak hours are proposed to optimise charging behaviour and improve station efficiency. Full article

California’s SB 1014 (Clean Miles Standard) mandates ridehailing fleet electrification to reduce emissions from vehicle miles traveled, posing financial and infrastructure challenges for drivers. This study employs a mixed-methods approach, including expert interviews (n = 10), group discussions (n = 8), and a survey of full- and part-time drivers (n = 436), to examine electric vehicle (EV) adoption attitudes and policy preferences. Access to home charging and prior EV experience emerged as the most statistically significant predictors of EV acquisition. Socio-demographic variables, particularly income and age, could also influence the EV choice and sensitivity to policy design. Full-time drivers, though confident in the EV range, were concerned about income loss from the charging downtime and access to urban fast chargers. They showed a greater interest in EVs than part-time drivers and favored an income-based instant rebate at the point of sale. In contrast, part-time drivers showed greater hesitancy and were more responsive to vehicle purchase discounts (price reductions or instant rebates at the point of sale available to all customers) and charging credits (monetary incentive or prepaid allowance to offset the cost of EV charging equipment). Policymakers might target low-income full-time drivers with greater price reductions and offer charging credits (USD 500 to USD 1500) to part-time drivers needing operational and infrastructure support. Full article

This study examines how contract structures influence coordination and innovation incentives in dual-channel telecommunications supply chains. We consider a setting where a mobile network operator (MNO) supplies services both directly to consumers and indirectly through a mobile virtual network operator (MVNO), which competes in the retail market. Using a game-theoretic framework, we evaluate how different contracts—single wholesale pricing, revenue sharing, and quantity discounts—shape strategic decisions, particularly in the presence of investment spillovers between parties. A key coordination problem emerges from the externalized gains of innovation, where one party’s investment generates value for both participants. Our results show that single wholesale and revenue sharing contracts often lead to suboptimal investment and profit outcomes. In contrast, quantity discount contracts, especially when combined with appropriate transfer payments, improve coordination and enhance the total performance of the supply chain. We also find that innovation led by the MVNO, while generally less impactful, can still yield reciprocal benefits for the MNO, reinforcing the value of cooperative arrangements. These findings emphasize the importance of contract design in managing interdependence and improving efficiency in decentralized supply chains. This study offers theoretical and practical implications for telecommunications providers and policymakers aiming to promote innovation and mutually beneficial outcomes through well-aligned contractual mechanisms. Full article

Bamboo residues represent an abundant, renewable biomass feedstock that can be converted into hard carbon—an emerging anode material for sodium-ion batteries. This study presents a detailed techno-economic analysis of hard carbon production from bamboo residues across China’s ten most bamboo-rich provinces. Regional feedstock availability was estimated from provincial production statistics, while average transportation distances were derived using a square-root-area-based approximation method. The process includes hydrothermal pretreatment, acid washing, carbonization, graphitization, and ball milling. Material and energy inputs were estimated for each stage, and both capital and operating expenses were evaluated using a discounted cash flow model assuming a 15% internal rate of return. The resulting minimum selling price of bamboo-derived hard carbon ranges from 14.47 to 18.15 CNY/kg. Assuming 10% of bamboo residues can be feasibly collected and processed, these ten provinces could collectively support an annual hard carbon production capacity of approximately 1.04 million tons. The results demonstrate that bamboo residues are a strategically distributed and underutilized resource for producing cost-competitive hard carbon at scale, particularly in provinces with existing bamboo industries and supply chains. Full article

This study conducts a techno-economic evaluation of an all-electric energy station in China. It assesses the system’s feasibility and sustainability. The all-electric energy station integrates multiple components: chillers, air-source heat pumps, electric boilers, water thermal storage, and gas boilers. These components work together to deliver comprehensive cooling and heating services. The research compares this system with an integrated electricity-gas system. It analyzes performance across three key areas: economic benefits, environmental impact, and energy utilization efficiency. The results show significant advantages for the all-electric energy station. Economic analysis reveals that the net present value (NPV) of the all-electric energy station is positive, the internal rate of return (IRR) is high, and the payback period is significantly shorter compared to traditional systems. Sensitivity analysis highlights that the discount rate and initial investment are the most influential factors affecting NPV, while cooling prices present substantial revenue optimization potential. The all-electric configuration exhibits greater sensitivity to parameter variations, underscoring the importance of strategic risk management. Additionally, the all-electric energy station excels in environmental protection. Carbon emissions are reduced by 11.5% compared to conventional systems. As renewable energy increases in the grid, indirect carbon emissions will decrease further. The all-electric energy station demonstrates strong economic feasibility. It plays a crucial role in achieving carbon neutrality and promoting green energy development. This study provides valuable insights for future regional integrated energy systems. Full article

Although impulsive and hedonic purchasing behaviors may seem to contradict sustainability principles, there are unexplored opportunities through which social media platforms and influencers can redirect these impulses toward sustainable actions. Young consumers, increasingly concerned about the ecological impact of their choices, can be encouraged to adopt responsible and sustainable buying behaviors when these are promoted attractively, enjoyably, and emotionally satisfyingly through social media. This research investigates how social media communication influences hedonic and impulsive purchasing behavior in the Romanian clothing market. In the context where social media is one of the main sources of information and influence for consumers, the research analyzes several determining factors of the purchase decision. Price reductions and the use of credit cards are highlighted as elements that facilitate spontaneous and hedonic targeted purchases, while the attractiveness of clothing items and the need felt play an important role in terms of the desire to buy. In addition, sources of information (such as reviews) have a major impact on consumers’ perceptions and their purchase intentions. Additionally, the study investigates factors such as overall shopping experience and its influence on consumer loyalty. It is approached from two perspectives: attitudinal loyalty, reflected in the preference for brands promoted on social media, and behavioral loyalty, expressed through repeat purchases. The results show that social media acts as an accelerator for hedonic and impulsive buying behaviors, prompting consumers to react quickly to stimuli such as discount campaigns or personalized recommendations. The conclusions highlight the importance of adopting digital marketing strategies that capitalize on the consumers emotional need while also strengthening brand loyalty. These perspectives can guide companies in the clothing industry to adapt their promotion methods to the specifics of the Romanian market and the consumer behavior. Full article

This study investigates two pricing strategies within a vendor-buyer supply chain system under cap-and-trade regulation, emphasizing demand sensitivity to market price and green technology investment. The findings reveal that quantity discounts significantly enhance profitability across the supply chain by encouraging buyers to place larger orders, thereby benefiting vendors, buyers, and end consumers. A novel profit-sharing parameter is introduced to foster sustainable and mutually beneficial relationships between supply chain participants. A search algorithm is developed to determine the optimal solutions by using the profit-sharing mechanisms. The analysis yields three key insights: first, a critical cap threshold is identified, enabling supply chain participants to make informed strategic decisions based on the value of the cap; second, another critical cap threshold is derived to assist governments in setting feasible emission limits that incentivize vendors to invest in green technology—caps below this threshold may discourage such investments; third, a reasonable return on investment (ROI) benchmark is established to guide vendors in adopting effective green technology strategies. Numerical examples and sensitivity analyses are conducted to illustrate the theoretical framework and validate the findings. Full article

As China’s voluntary greenhouse gas emission reduction mechanism undergoes institutional revitalization, the accurate valuation of carbon assets such as China Certified Emission Reductions (CCERs) becomes increasingly critical for effective climate finance and sustainability-oriented investment. This study proposes an integrated value assessment model for CCERs that combines Long Short-Term Memory (LSTM) neural network-based carbon price forecasting with both the discounted net cash flow method and the Black–Scholes option pricing framework. Applying this model to a wind power project, the study found that the practical value of CCERs, derived from verified emission reductions, significantly exceeds their market option value, underscoring the economic and environmental viability of such projects. By distinguishing between the realized and potential values of carbon credits, this research offers a comprehensive tool for carbon asset valuation that supports corporate carbon management and policy development. The framework contributes to the growing literature on sustainable finance by aligning carbon asset pricing with long-term climate goals and enhancing transparency in carbon markets. Full article

This paper investigates the competition between two types of bike-sharing services, particularly at bus stops, subway stations, and residential areas. Two types of shared bicycle travel choice models are constructed. A shared bicycle operator attracts users by implementing discount incentives, and the comfort levels of riding the two types of shared bicycles are different. The equilibrium fares, potential user demand, and operator profits under joint profit maximization, price competition, and potential user demand competition scenarios are derived, and the competitive results under the three scenarios are compared. The results show that, in the potential user demand competition, the difference in potential demand between the two operators is largest; in the joint profit maximization scenario involving shared bicycle operators, the difference in potential user demand is smallest. In all competitive scenarios, higher operating costs and costs in lowering comfort loss for the shared bicycle operators will increase fares; the substitution level between the two types of shared bicycles has a positive impact on potential user demand, and the higher the substitution level, the better the effect of discounts in attracting users. Full article

This paper proposes a novel robustness framework for studying the unit elasticity of intertemporal substitution (EIS) assumption based on the Perron-Frobenius sieve estimation model by Christensen, 2017. The sieve nonparametric decomposition is a central model that connects key strands of the long run risk literature and recovers the stochastic discount factor (SDF) under the unit EIS assumption. I generate various economies based on Epstein–Zin preferences to simulate scenarios where the EIS deviates from unity. Then, I study the main estimation mechanism of the decomposition as well as the time discount factor and the risk aversion parameter estimation surface. The results demonstrate the robustness of estimating the average yield, change of measure, and preference parameters but also reveal an “absorption effect” arising from the unit EIS assumption. The findings highlight that asset pricing models assuming a unit EIS produce distorted parameter estimates, caution researchers about the potential under- or over-estimation of risk aversion, and provide insight into trends of misestimation when interpreting the results. I also identify an additional source of failure from a consumption component, which demonstrates a more general limit of the consumption-based capital asset pricing model and the structure used to estimate relevant preference parameters. Full article

This study examines whether Chief Executive Officers (CEOs) exercise a greater proportion of their exercisable options in response to firm-specific stock price bubbles. For a sample of U.S. firms from 1992 to 2021, the study identifies stock price bubble periods using the Generalized Sup Augmented Dickey-Fuller (GSADF) method. A bubble is a statistical measure that detects an ex-post firm-specific stock price exuberance that creates abnormally high variation in stock prices arising from changes in discount rates, R&D and market liquidity. If executives have private information and can infer firm-specific bubbles, they are likely to exercise a greater proportion of their exercisable stock options during bubbles to benefit from their firms’ stock price exuberance. Using data aggregated at the CEO-year level, we find that executives are prone to exercising a larger portion of their vested stock options during market bubbles, with the aim of monetizing on the exuberance in the firm’s stock price. They leverage their expertise and their acquired price-sensitive private information to identify these bubbles. We also find that CEOs’ option exercise activity increases as the duration of the bubble increases to capture the price momentum. Full article

This paper explores optimal consumption and investment strategies for agents facing mortality risk within a complete financial market. Departing from traditional frameworks, we leverage state-dependent utility theory, discounted by the state–price process, to compare consumption streams and utilize life insurance as a strategic hedging instrument. To model the ability of insurance companies to hedge the mortality risk of consumer pools, we introduce the concept of life insurance completeness, allowing individuals to achieve optimal consumption even in scenarios involving negative wealth. Our model relaxes the stringent integrability conditions commonly imposed in the literature, offering a more economically grounded approach to valuation and hedging. We derive a general solution to the optimization problem using martingale techniques under minimal assumptions, demonstrating that life insurance primarily serves as a mortality risk hedge rather than a bequest motive. This perspective resolves longstanding theoretical and empirical challenges, notably the annuity puzzle, by illustrating that optimal consumption and investment, in the absence of labor income, do not necessitate annuities or other life insurance policies. Our key contributions include (1) extending valuation frameworks to encompass prepaid insurance and less restrictive integrability criteria, (2) establishing life insurance completeness for effective mortality risk hedging, (3) demonstrating the feasibility of optimal consumption under negative wealth and state-dependent preferences, and (4) offering a resolution to the annuity puzzle that aligns with empirical observations. Full article

The COVID-19 pandemic potentially affected stock prices in two non-mutually exclusive ways: discount rates and cash flows. This paper focuses on the latter and analyzes it through the lens of an asset-pricing model. It shows how workplace resilience and financial resilience interacted and significantly affected asset prices. The model-based equity premium increases with the probability of a disaster. The results suggest the significant amplification of workplace resilience by financial resilience. Specifically, the dividend growth of low-resilience firms is significantly more responsive to workplace flexibility and suffers more severely than that of high-resilience firms. Full article