## 1 Introduction
### Literature Review
#### 1.2.3 Risk Assessment in Energy Investments  
VaR and CVaR are metrics commonly used to assess losses in financial risk management approaches. The VaR offers investors an estimate of the loss their investment portfolio could face within a specified confidence level, as highlighted by [PERSON], Renault [41]. However, VaR has its limitations as it fails to consider losses that may surpass the predetermined confidence level noted by [42]. CVaR delves deeper into risk assessment than VaR; it offers a glimpse into the loss experienced beyond the VaR threshold by concentrating on the tail end of the loss distribution curve. This method broadens the horizon of risk evaluation; it helps overcome limitations associated with VaR analysis. In energy sector investments, where losses can be substantial due to factors such as policy shifts or significant market volatility, CVaR emerges as a tool [43]. Recent research has delved into utilizing VaR and CVaRs to evaluate risk in energy-hedging tactics [44]. By combining VaRs and CVaRs in their hedging approaches, as suggested by [PERSON] [45], companies can better fine-tune their strategies to strike a balance between expected returns and risk levels. In their study, [PERSON], [PERSON] [46] point out the fluctuations in energy market prices and advocate for incorporating CVaRs into hedging techniques to enhance risk management practices within the sector today. Integrating both VaR and CVaR approaches has transformed energy finance practices. Optimizationmodels have played a role in enhancing the management of uncertainties associated with energy investments. By enabling decision-makers to explore market scenarios through simulations, these models aid in developing portfolio strategies and making informed choices. [PERSON], [PERSON] [47] used stochastic programming to tackle energy production and usage uncertainties in their research study. They highlighted its efficiency in effectively managing energy costs and needs. Integrating dimensional models into optimization has further improved risk mitigation for funding energy resources. A concrete illustration is seen in the work of [PERSON] [48]; it created a model to maximize both advantages and financial gains from investments in renewable energy projects. This method offers a plan to tackle sustainability objectives effectively. The issue of energy financing presents hurdles, as emphasized by [PERSON] and [PERSON] [49], who underscore the significance of models for balancing priorities like maximizing profits and minimizing risks within the sector. [PERSON], [PERSON] [23] have broadened the application of stochastic models to incorporate option values into energy portfolios for flexible investment decisions. Their study shows that making decisions about energy investments involves balancing requirements with profitability, and this process depends on using objective frameworks effectively. [PERSON], [PERSON] [50] highlights the significance of optimization in safeguarding against market fluctuations by offering decision-makers portfolio strategies that are adjusted for risk and capable of performing in potential scenarios. Incorporating carbon credits and energy subsidies shapes the approaches for managing energy investments in today's energy finance sector. Carbon credits enable organizations to offset their emissions by backing projects [51]. According to [PERSON] [52] findings, carbon credits can notably enhance energy projects' risk-return profile, boosting their appeal. Various approaches to reducing risks may differ depending on the incentives offered, such as production tax credits and feed-in tariffs, as discussed by [PERSON] [53], who explored subsidies in the energy industry. Subsidies aim to reduce market fluctuations and make renewable energy projects more attractive for long-term investments. However, the sudden reduction or elimination of these subsidies could lead to uncertainty due to the nature of subsidy programs.