Revenue Risk Research Articles

Electricity arbitrage for mobile energy storage in marginal pricing mechanism via bi-level programming

The rapid growth of renewable energy is integrated into the distribution system. The creation of both new market mechanisms and investment models has critical effects on the economics and security of the distribution market. Mobile energy storage has been used to increase the resilience of distribution grids due to their advantages in mobility and flexibility, which offer electricity arbitrage options for merchant investments. This paper presents a bi-level optimization framework based on location marginal pricing settlement of mobile energy storage financial rights revenue in active distribution systems. In the developed framework, the upper-level problem determines the optimal capacity, routing, and dispatching of mobile energy storage to maximize revenue in the liberalized electricity market. The lower-level problem performs the joint optimization of energy and reserve market clearing as well as renewable energy capacity optimization based on the alternating current optimal power flow model. The non-convexity of the line flow and location marginal pricing is linearized via second-order cone relaxation and Karush-Kuhn-Tucker. The bi-level programming is reformatted as mathematical programming with equilibrium constraints. Further, the revenue risk due to renewable energy uncertainty is measured via conditional value-at-risk. Finally, the effectiveness of the optimization framework and solution method is verified using the modified IEEE-33 test system. The results show that mobile energy storage promotes renewable energy and reduces distribution network costs by 2.3%.

Assessing the fiscal implications of changes in critical minerals’ demand in the low-carbon energy transition

This study introduces the Fiscal Revenue Risk Index (FRRI), a novel metric designed to assess the impact of market fluctuations in critical minerals due to the materialization of climate-related transition risks on countries highly dependent on these resources. Focusing on minerals essential for renewable energy technologies, such as lithium, cobalt, nickel, and rare earth elements, our analysis evaluates how shifts in global demand might influence government revenues, providing crucial insights for policymakers. The FRRI methodology involves several key steps, including assessing fiscal revenue dependency, estimating price volatility, computing minerals’ production changes for future transition scenarios, and integrating these factors into a weighted risk model. This approach allows the computation of aggregate risk scores across various minerals and scenarios, reflecting a country’s exposure to fiscal risks. The study findings indicate that the demand for critical minerals could grow up to sevenfold over the coming decades, significantly impacting fiscal revenues in emerging markets and developing economies (EMDEs). While these countries stand to benefit from increased exports, they also face challenges related to supply reliability and price volatility. Overall, the FRRI is essential for identifying economic risks and opportunities related to the low-carbon transition in EMDEs.

Risk Transmission in Low-Carbon Supply Chains Considering Corporate Risk Aversion

In order to study the impact of risk aversion characteristics of enterprises on supply chain risk transmission, the risk aversion utility function is introduced, and the risk elasticity coefficient is used to construct a supplier-dominated low-carbon supply chain risk transmission model. Simulation analysis is conducted to investigate the transmission of emission reduction and revenue risks caused by internal and external contingent risk factors. The study reveals that under conditions of market demand uncertainty, the risk transmission effect is unaffected by the risk aversion characteristics of members in the low-carbon supply chain. While the risk-aversion characteristics of suppliers can decrease their own profit risk, they have a negative effect on the profit risk of manufacturers and the emission reduction risk of supply chain nodes. There exists a critical threshold for the impact of the risk-aversion degree of suppliers on their own emission reduction risk transmission effect. When this threshold is exceeded, the emission reduction risk decreases with increasing risk aversion intensity of suppliers, and vice versa. The risk aversion characteristics of manufacturers can weaken the negative effect of supplier risk aversion on the fluctuation risk of manufacturer profits, but they exacerbate the emission reduction risk transmission effect of manufacturers under asymmetric information influence. The findings have important theoretical and practical implications for supply chain risk management.

Bidding Strategy for Wind and Thermal Power Joint Participation in the Electricity Spot Market Considering Uncertainty

As the proportion of new energy sources, such as wind power, in the electricity system rapidly increases, their participation in spot market competition has become an inevitable trend. However, the uncertainty of clearing price and wind power output will lead to bidding deviation and bring revenue risks. In response to this, a bidding strategy is proposed for wind farms to participate in the spot market jointly with carbon capture power plants (CCPP) that have flexible regulation capabilities. First, a two-stage decision model is constructed in the day-ahead market and real-time balancing market. Under the joint bidding mode, CCPP can help alleviate wind power output deviations, thereby reducing real-time imbalanced power settlement. On this basis, a tiered carbon trading mechanism is introduced to optimize day-ahead bidding, aiming at maximizing revenue in both the electricity spot market and carbon trading market. Secondly, conditional value at risk (CVaR) is introduced to quantitatively assess the risks posed by uncertainties in the two-stage decision model, and the risk aversion coefficient is used to represent the decision-maker’s risk preference, providing corresponding strategies. The model is transformed into a mixed-integer linear programming model using piecewise linearization and McCormick enveloping. Finally, the effectiveness of the proposed model and methods is verified through numerical examples.

Do Weather Derivatives Mitigate the Revenue Risk of Farmers?—The Case of Tongliao, Inner Mongolia, China

This research probes the potential of weather derivatives as tools for mitigating the variability of crop yields due to climatic uncertainties in China. Centered on Tongliao City in Inner Mongolia, the study exploits a long short-term memory (LSTM) network to dissect and simulate 32 years of local precipitation data, thereby achieving a simulation of high reliability. Further exploration through a multiple linear regression model confirms a marked positive relationship between rainfall amounts and maize yields. By combining precipitation put options and the total revenue function for farmers, mathematical derivations yield specific expressions for optimal trading quantities and risk hedging efficiency. The research findings show that, using an assumption of a maize price that is 3 CNY/kg, when farmers purchase around 6.22 precipitation put options they can achieve 67.9% risk hedging efficiency. This highlights the significant role of precipitation put options under specific conditions in reducing the risk of decreased maize yields due to reduced precipitation. However, in practical markets, variations in maize prices and the price change unit (λ) are inevitable. Through further analysis, this study reveals that as these factors change, the optimal trading quantity and hedging efficiency also undergo varying degrees of adjustment. The investigation lays a theoretical groundwork for the practical application and empirical validation of weather derivatives within China’s agrarian sector. However, the study underscores the necessity of a holistic approach to market dynamics to refine hedging strategies. Future decision making must integrate market fluctuations, and adopting transparent pricing mechanisms is critical for enhanced risk management and the advancement of sustainable agricultural practices.

Financial contracts for differences: The problems with conventional CfDs in electricity markets and how forward contracts can help solve them

Contracts for differences are widely seen as a cornerstone of Europe's future electricity market design. This paper is about designing such contracts. We identify the dispatch and investment distortions that conventional CfDs cause, the patches used to overcome these shortcomings, and the problems these fixes introduce. We then propose an alternative contract we call “financial” CfD. This hybrid between conventional CfDs and forward contracts mitigates revenue risk to a substantial degree while providing undistorted incentives. Like conventional CfDs, it is long-term and tailored to technology-specific (wind, solar, nuclear) generation patterns but, like forwards, decouples payments from actual generation. The proposed contract mitigates volume risk and avoids margin calls by accepting physical assets as collateral.

Optimal Scheduling of Virtual Power Plant Considering Revenue Risk with High-Proportion Renewable Energy Penetration

Distributed power supplies have gradually become a new trend in power supply development, but access to a large number of distributed energy sources has a certain impact on the stable operation of the power grid. A virtual power plant (VPP) can integrate a variety of distributed power sources for coordination and optimization; thus, it can effectively solve the difficulties faced by a distributed energy grid connection and promote the complementarity of energy sources. However, renewable energy often has a degree of volatility and randomness when distributed, which can bring certain risks to the operation of the VPP. In order to consider the risks brought by renewable energy, an optimal scheduling model of the VPP, based on an improved generative adversarial network (GAN) and the conditional value at risk (CVaR), was proposed to measure the relationship between the benefits and risks. Firstly, the uncertainty of new energy is analyzed, and wind power and photovoltaic scenarios are generated by the improved GAN; then, typical scenarios are generated by the k-medoids method. Finally, based on the CVaR, the optimal scheduling model of the VPP is established to study the effect of risk weight on VPP revenue. The results show that the model can effectively measure the relationship between the benefits and risks and can provide some references for the VPP to make reasonable operational decisions with different risk preferences.

Using Consumer Tweets to Improve Revenue Risk Assessments in Consumer-Oriented Industries

SUMMARY This article summarizes the study of Rozario, Vasarhelyi, and Wang (2022), which examines the use of consumer tweets in improving the prediction and error detection performance of preliminary analytical procedures for the revenue account for firms that belong to consumer-oriented industries. They find that consumer tweets about product or brand interest increase the prediction and error detection ability of analytical procedures compared to analytical procedures that do not include it. These results suggest that this new source of external nonfinancial information is incrementally informative to auditors in developing assessments for the risk of misstated revenue in the planning stage of the audit. The findings of this study have important implications that may be relevant to the audits of other financial statement accounts.

The impact of risk perception and preference on farmland transfer-out: Evidence from a survey of farm households in China

This study examines the impact of farmers' risk perceptions on their decision to transfer-out farmland, as well as the variations in this influence based on different risk preferences. The research utilizes survey data from 1389 farmers in rural China. By expanding the decision-making model for farmer households within the context of risk perception, this paper analyzes the equilibrium point of farmers' decision-making using mathematical models. Employing the Probit and Tobit models with instrumental variable methods, we discovered that farmers' perceptions of land revenue risk, land use risk, and non-farm employment risk significantly hindered the decision and rate of farmland transfer-out. Notably, the inhibitory effect decreased progressively. Moreover, risk preference was found to moderates the negative impacts. However, it is worth noting that risk perception remains a crucial factor impeding farmland transfer, primarily due to the prevalence of neutral or risk-averse risk preferences among most farmers. This study presents a novel perspective and explanatory framework for development of the farmland circulation market, aiming to overcome existing challenges from a risk-oriented standpoint. Consequently, it is essential for the government to carefully address all risks associated with farmland transfer, enhance the income guarantee system for farmers post-transfer, and offer non-farm employment training and job opportunities to facilitate the healthy and rapid development of the agricultural land transfer market.

Nonprofit Revenue Strategy and Downside Risk: Applying Portfolio Theory and Extreme Value Theory

The risk of revenue instability is a concern for any nonprofit. Existing research leads to the well-known strategy of equalizing revenues across sources to reduce revenue volatility. This study offers several expansions to this strategy. First, rather than focusing solely on deviations from mean revenue, I incorporate extreme revenue loss, or downside risk, as it threatens organizational survival and occurs more frequently than expected. Second, while revenue equalization is indifferent to the type of revenue source, I incorporate portfolio diversification that seeks a negative correlation between sources to avoid simultaneous losses. Using 2008 to 2012 financial data, results of fixed-effects regression suggest that portfolio diversification can reduce revenue volatility and downside risk. Moreover, the relationships between financial flexibility, growth potential, and revenue risk are more nuanced compared to existing research focused solely on deviation risk. The results can help nonprofits consider incorporating downside risk in revenue portfolio management to enhance financial security.

How flexible are energy flexibilities? Developing a flexibility score for revenue and risk analysis in industrial demand-side management

With rising and increasingly volatile energy prices, demand-side management (DSM) is becoming attractive within industry to optimize flexible electricity demand and to remain competitive. Despite economic and ecological benefits for companies, DSM implementation is not yet widespread. High effort in flexibility audits and complex optimization models to quantify DSM’s economic potentials represent initial barriers in practice, raising the question whether there is a simple way to analyze economic DSM potential as an initial indicator of successful DSM implementation that requires little data on flexibility characteristics. To address this need, this study develops a flexibility score that describes the degrees of freedom of individual energy flexibility measures (EFMs), i.e. how flexible a flexibility measure is. The flexibility score is tested and validated in a risk-return analysis on 46 real-world industrial EFMs from Germany optimized with integer linear programming (ILP). Analyzing Germany’s Day-Ahead and Intraday markets, the results show an increase in expected revenues and a simultaneous decrease in revenue risk with an increasing flexibility score, outlining DSM investments as attractive. Due to its ease of use, we see industrial companies, aggregators, energy service providers, and (energy) consultancy agencies as target users of the flexibility score. Policymakers can use the flexibility score to identify and design subsidies for energy flexibility investments.

Financial Hazard Prediction Due to Power Outages Associated with Severe Weather-Related Natural Disaster Categories

Severe weather conditions not only damage electric power infrastructure, and energy systems, but also affect millions of users, including residential, commercial or industrial consumers. Moreover, power outages due to weather-related natural disasters have been causing financial losses worth billions of US dollars. In this paper, we analyze the impact of power outages on the revenue of electric power suppliers, particularly due to the top five weather-related natural disasters. For this purpose, reliable and publicly available power outage events data are considered. The data provide the time of the outage event, the geographic region, electricity consumption and tariffs, social and economic indicators, climatological annotation, consumer category distribution, population and land area, and so forth. An exploratory analysis is carried out to reveal the impact of weather-related disasters and the associated electric power revenue risk. The top five catastrophic weather-related natural disaster categories are investigated individually to predict the related revenue loss. The most influencing parameters contributing to efficient prediction are identified and their partial dependence on revenue loss is illustrated. It was found that the electric power revenue associated with weather-related natural disasters is a function of several parameters, including outage duration, number of customers, tariffs and economic indicators. The findings of this research will help electric power suppliers estimate revenue risk, as well as authorities to make risk-informed decisions regarding the energy infrastructure and systems planning.

Climate change adaptation and its impacts on farm income and downside risk exposure

Multiple previous reports have established that climate change disproportionately impacts smallholder farmers in developing countries. This study investigates the impact of climate change adaptation, defined by farmers’ decisions to adopt the improved practices to mitigate or reduce the effects of climate change, on crop revenue and revenue risk exposure. We employ the control function approach in an endogenous switching regression framework to account for selection bias. Using the household survey data from Nepal, we find that climate change adaptation positively affects crop revenue and revenue risk reduction. Specifically, climate change adaptation leads to a 21.6% increase in farm revenue and a 6.4% reduction in downside risk exposure, which are robust to several specifications. Counterfactual analysis shows the considerable heterogeneities in the outcomes among adapters and non-adapters. In particular, adapting farm households realize substantial and distinguishable gains in revenues and declines in risk levels relative to their non-adapting peer households. Our findings imply that adapting to climate change can be an effective management practice to mitigate the risks associated with climate change and increase resilience. • We explore the impact of climate change adaptation on crop revenue and revenue risk exposure in Nepal. • The study employs the control function approach in an endogenous switching regression framework to account for selection bias. • Climate change adaptation positively affects crop revenue and revenue risk reduction. • Adapting farm households realize substantial and distinguishable gains in revenues and declines in risk levels relative to their non-adapting peer households. • Adapting to climate change can be an effective management practice to mitigate the risks associated with climate change and increase resilience.

A farmer-centric agricultural decision support system for market dynamics in a volatile agricultural supply chain

PurposeIn a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.Design/methodology/approachThe present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.FindingsEmpirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.Practical implicationsAs a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.Originality/valueBased on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

Unintended environmental benefits of crop insurance: Nitrogen and phosphorus in water bodies

Agricultural policies without explicit environmental goals can indirectly affect the natural environment through its effect on farmer input use behavior. For example, the highly-subsidized crop insurance program in the United States (US), while developed to protect farmers against yield and revenue risks, also has the potential to influence fertilizer and land use decisions, which can then impact the extent of excess nitrogen and phosphorus that can run-off and pollute nearby water bodies. This study utilizes county-level panel data from 1989-2015 to directly evaluate the impact of crop insurance participation on nitrogen and phosphorus concentration in waterways. Results from linear panel fixed effects (FE) models suggest that counties with higher crop insurance participation tend to have lower nitrogen concentrations in its water bodies, but the effects are small. In contrast, we do not find a consistent statistically significant crop insurance effect on phosphorus concentrations. Findings based on alternative estimation techniques and other empirical specifications generally support our baseline FE model results. We posit that the modest crop insurance effects may be due to two competing mechanisms — the moral hazard effect of crop insurance (i.e., reducing fertilizer use), being counteracted by the incentive to bring in riskier crops or marginal land to production (i.e., increasing fertilizer use).

Independent power producers and deregulation in an island based small electricity system: The case of Papua New Guinea

The small island economy of Papua New Guinea (PNG) is facing severe electricity shortages and is therefore turning to implementing broader power sector reforms as a vehicle to attract private capital and investments in electricity generation. This study, based on a case-study approach, revisits the reform progress and plans in the electricity sector of PNG alongside the development and integration of IPPs in its small power system. Lessons of reform experiences and IPPs integration are drawn from three other smaller systems of Nepal, Nicaragua and the Northern Territory of Australia including stakeholder consultations, which includes two IPPs of PNG. We find a widening gap between reform ‘theory’ and ‘practice’ in the PNG power sector. Cost reflective pricing is implemented while cost recovery is never achieved by the vertically integrated state-owned utility and the insolvency of this state-owned single buyer poses the greatest perceived revenue risk to the IPPs. This lack of revenue reimbursement to the IPPs by the single buyer is a barrier towards attracting private capital into electricity generation. We recommend that strong political will and strengthening of institutional arrangements are urgent reform measures to attract private capital in power generation as political instability continue.

Research on PPP Enterprise Credit Dynamic Prediction Model

The debt default risk of local government financing vehicles (LGFVs) has become a potential trigger for systemic financial risks. How to effectively prevent hidden debt risk has always been a hot issue in public-private partnership (PPP) financing management research. In recent years, machine learning has become more and more popular in the study of enterprise credit evaluation. However, most scholars only focus on the output of the model, and do not explain in detail the extent to which variables affect the model and the decision-making process of the model. In this paper, we aim to apply a better credit rating method to the key factors and analysis of LGFV’s default risk, and analyze the decision-making process of the model in a visual form. Firstly, this paper analyzes the financial data of LGFVs. Secondly, the XGBoost-logistic combination algorithm is introduced to integrate the typical characteristics of PPP projects and construct the credit evaluation model of LGFVs. Finally, we verify the feasibility of the model by K-fold cross validation and performance evaluation. The results show that: (1) net worth, total assets, operating income, and return on equity are the most critical factors affecting the credit risk of LGFVs, asset-liability ratio and tax revenue are also potentially important factors; (2) the XGBoost-logistic model can identify the key factors affecting the credit risk of LGFVs, and has better classification performance and predictive ability. (3) The influence of each characteristic variable on model decision can be quantified by the SHAP value, and the classification decision visualization of the model improves the interpretability of the model.

Enhanced Weather-Based Index Insurance Design for Hedging Crop Yield Risk.

This study proposes an optimization-based weather-yield model to reduce the basis risk of weather-based index insurance. This weather-yield model helps us capture the growing season's monthly variation as it involves monthly explanatory weather indices. In addition, it can capture additional extreme weather effects by including extreme cooling or heating weather indices. This study presents an innovative machine learning framework incorporating optimization approaches to ensure the parsimony of weather index models and the accuracy of crop yield predictions, which can be integrated into the conventional policy design and pricing process. The advantages of this modeling approach and the effectiveness of weather index-based insurance based on this approach in reducing basis risk and revenue risk are demonstrated by applying county-level yield data for mid-season rice in the Anhui province, China.

Risk Analysis of Public–Private Partnership Waste-to-Energy Incineration Projects from the Perspective of Rural Revitalization

In China, more and more waste-to-energy (WTE) incineration plants are being delivered through public–private partnership (PPP) schemes in rural areas, which are focused on rural revitalization. These can not only deal with the ever-increasing solid waste but also provide renewable energy and thus benefit local societies. However, they usually endure a lot of risks due to long concession periods and complex contractual relationships. This research investigates the risk management of PPP WTE incineration projects from the perspective of rural revitalization. First, a preliminary list of 36 risks was derived based on a literature review. Second, a focused group discussion with eight experts was held to obtain the final list of 36 risk factors, taking into account rural revitalization. Third, a structured questionnaire survey was conducted to consult the risk frequency and risk severity. A total of 100 valid questionaries were collected. Finally, risk analysis and discussion were provided on the basis of the survey. The top 10 risks are rural appearance, payment risk, local employment, local economic development, local government succession, operation cost overrun, waste supply, construction cost overrun, revenue risk and price change risk. It indicates that (1) PPP WTE incineration projects can effectively promote rural development; (2) government authorities play a significant role in the sustainable development of PPP WTE incineration projects; and (3) the risk preference of rural PPP WTE incineration projects is social, economic and environmental risks.

Contributions and challenges of healthcare financing towards universal health coverage in Ethiopia: a narrative evidence synthesis

BackgroundHigh burden of healthcare expenditure precludes the poor from access to quality healthcare services. In Ethiopia, a significant proportion of the population has faced financial catastrophe associated with the costs of healthcare services. The Ethiopian Government aims to achieve universal health coverage (UHC) by 2030; however, the Ethiopian health system is struggling with low healthcare funding and high out-of-pocket (OOP) expenditure despite the implementation of several reforms in health care financing (HCF). This review aims to map the contributions, successes and challenges of HCF initiatives in Ethiopia.MethodsWe searched literature in three databases: PubMed, Scopus, and Web of science. Search terms were identified in broader three themes: health care financing, UHC and Ethiopia. We synthesised the findings using the health care financing framework: revenue generation, risk pooling and strategic purchasing.ResultsA total of 52 articles were included in the final review. Generating an additional income for health facilities, promoting cost-sharing, risk-sharing/ social solidarity for the non-predicted illness, providing special assistance mechanisms for those who cannot afford to pay, and purchasing healthcare services were the successes of Ethiopia’s health financing. Ethiopia's HCF initiatives have significant contributions to healthcare infrastructures, medical supplies, diagnostic capacity, drugs, financial-risk protection, and healthcare services. However, poor access to equitable quality healthcare services was associated with low healthcare funding and high OOP payments.ConclusionEthiopia's health financing initiatives have various successes and contributions to revenue generation, risk pooling, and purchasing healthcare services towards UHC. Standardisation of benefit packages, ensuring beneficiaries equal access to care and introducing an accreditation system to maintain quality of care help to manage service disparities. A unified health insurance system that providing the same benefit packages for all, is the most efficient way to attain equitable access to health care.