Project Investment Decision Research Articles

Investment in CCUS under technical uncertainty considering investor's risk aversion: An exotic compound real-options approach

Carbon capture, utilization, and storage (CCUS) technology is effective and value-adding solution for reducing emissions. However, the development and commercialization of these technologies are challenging due to high investment costs and several uncertainties. This study develops a novel comprehensive real-options-based model to evaluate investment in CCUS projects considering the technical risk and the investor's risk aversion. This study proposed an exotic compound real options model that combines American and barrier options. First, applying the Poison process, the technical risk is explicitly modeled. Secondly, the investor's risk aversion is defined as a barrier level for the barrier option part of the proposed model. Thirdly, the value of the project is evaluated through the exotic compound real option. Finally, we assess the economic viability of the project under multiple scenarios. The results of implementing the model for a real case show that the integrated technical risk assessment and the barrier option appropriately address investors' risk aversion. Furthermore, the comparison indicates that the proposed compound real options model is more effective than the traditional NPV (Net Present Value). Regarding policymaking, the results reveal that setting an appropriate carbon tax that considers the costs of carbon capture would be more beneficial. Further, the model provides investors helpful guidance to make proper investment decisions for CCUS technology projects under uncertainties.

ESG-Driven Investment Decisions in Photovoltaic Projects

As global climate change intensifies and environmental awareness increases, investing in renewable energy has become a primary economic and social development priority. Photovoltaic (PV) projects, as a clean and sustainable energy technology, have garnered significant attention due to their notable environmental and economic benefits. However, traditional investment evaluation methods such as net present value (NPV) analysis fail to adequately capture the flexibility and future uncertainties inherent in PV project investments. This paper presents a case study analysis proposing a delay option model that incorporates environmental, social, and governance (ESG) factors, providing a more scientific and flexible investment decision framework for PV projects. The case study results indicate that considering ESG factors significantly enhances the investment value of PV projects. This model not only provides comprehensive support for PV project investment decisions but also underscores the importance of establishing stringent carbon trading markets and policy incentive mechanisms to promote the widespread adoption and sustainable development of renewable energy projects.

Sequential investment decisions for mining projects using compound multiple volatility real options approach

Managers evaluating investment decisions in mining projects have reported using the net present value approach (NPV). However, NPV has the drawback of collapsing variations from uncertainties in future project cash flows into a fixed expected project value today. Ignoring future uncertainties and contingencies can lead managers to make incorrect up-front binary decisions, such as investing in or abandoning the project now. The real options analysis approach (ROA) instead captures variations from uncertainties as volatilities in project value and allows flexibility in investment decisions to be contingent on information as it is revealed. Managers we consulted agreed that ROA is superior to NPV in principle, but too complex to apply in practice. The advanced mathematics and restrictive assumptions required by analytical ROAs make them especially impractical for real-world projects exposed to multiple uncertainties. Furthermore, real-world projects are often multistage and involve valuing a compound option, which is an option on an underlying option, for sequential investment decisions. Extant numerical compound ROAs have been applied to such projects but suffer from various drawbacks. Amongst other issues, they combine variations from multiple uncertainties into a consolidated volatility of project value. This conceals the impact of each source of uncertainty, resulting in inaccurate project valuation and incorrect investment decisions. We present an innovative compound multiple volatility real options approach (C-MVR) to value a multistage project while accommodating separate volatilities of project value arising from multiple uncertainties for making sequential investment decisions. The resultant value is termed the compound enhanced net present value. Implementing C-MVR for a real coal mining project demonstrates how other ROAs can be seen as inaccurate simplifications that produce erroneous investment decisions. C-MVR provides a rigorous and versatile approach that can be applied to many investment decisions across various industries.

Project Investment Decisions: Comparison of Nonmonetary Benefits

Project Investment Decisions: Comparison of Nonmonetary Benefits

The Comparison of Benefits in the Project Investment Decision

The Comparison of Benefits in the Project Investment Decision

An improved capacitance-resistance model for analysing hydrogen production with geothermal energy utilisation

The integration of geothermal and hydrogen energy through advanced predictive modeling not only enhances energy production efficiency but also contributes significantly to the global transition towards renewable energy sources. This study commences with the establishment of a geothermal hydrogen production model using Aspen Plus, incorporating physical constraints, derived from the Capacitance Resistance Model (CRM) model. Refinements to Long Short-Term Memory (LSTM) neural networks based on historical data and CRM constraints tailor them for geothermal reforming-based hydrogen production. Production analysis indicates that the hybrid CRM-LSTM model adeptly predicts heat and hydrogen production, improving system performance. The approach demonstrates superior accuracy, with hydrogen production predictions deviating by less than 2%. The comparison highlights the hybrid model’s advantage in handling nonlinear characteristics but it also shows the hybrid model requires longer training times. Sensitivity analysis reveals significant implications for investment decisions, with CRM predicting a 26-year cost recovery period under standard conditions, potentially underestimating actual outcomes by over eight months. Such discrepancies underscore the importance of accurate predictive models in guiding investment decisions for sustainable energy projects. This model contributes to achieving sustainable development goals by integrating geothermal and hydrogen energy, advancing the transition towards renewable and environmentally friendly energy sources.

Research on optimal investment decision of distribution network projects under the construction of the new power system

Abstract This article establishes a ranking model of distribution network projects to make optimal investment decisions. The project optimization ranking model consists of the LCC model and the project comprehensive evaluation model. When constructing the evaluation model of distribution network projects, the evaluation index system is first set, and then the weight allocation for each index is calculated based on the G1-antientropy weight comprehensive integrated weighting method, so as to calculate the comprehensive benefit of the unit cost of a project for optimal sorting. Taking a distribution network planning project in Sichuan Province as an example, the empirical analysis is conducted, and the ranking results are obtained by using the above optimization ranking model for distribution network projects depending on the comprehensive benefit index of unit cost of the project A-E, which confirms the reasonability and practicability of the decision-making system and realizes the optimization of investment decisions for distribution network projects.

Measures for Investment Risk Management of Enterprise Technological Innovation Projects

Enterprise technological innovation project investment risk management refers to the enterprise taking a series of measures to manage and control possible investment risks when making technological innovation project investment decisions. Investment risks in technological innovation projects refer to various risks that may have a negative impact on investment due to the uncertainty of technological innovation and changes in the market environment. This article, from an industry professional perspective, analyzes and discusses measures for investment risk management of corporate technological innovation projects.

Design and Implementation of an Intelligent Metro Project Investment Decision Support System

The construction of subway projects involves tight engineering cycles, multiple technical challenges, and complex coordination among various stakeholders. Due to the influence of uncertain factors during the construction process, the investment in subway project construction exhibits non-linear changes over time. Investment decision-making is the process through which the investment entity determines its investment activities. For typical investment entities, project investment decision-making primarily entails analyzing and evaluating proposed engineering projects based on investigation, analysis, and argumentation, ultimately deciding whether to invest. With the widespread application of information technology (IT) across various fields, decision support systems (DSS) have emerged to enhance the decision-making capabilities of enterprise management. This article designs an intelligent subway project investment DSS, leveraging data mining (DM) technology to integrate DSS with a data warehouse (DW).

Cost Monitoring and Management Algorithms in the Construction Process of Building Projects

Construction project cost estimation is an essential activity in construction-based fields; it offers a valuable source for the project feasibility studies and the design plan comparisons, and the accuracy can directly influence project investment decisions. A successful implementation of project cost forecasts makes it easier to control and manage project costs. Therefore, this paper presents the Ladybug Reptile Search Algorithm_ Deep Maxout Network (LRSA_DMN) (LRSA_DMN) to estimate construction costs. Additionally, LRSA is included to correct DMN weights. Furthermore, Bootstrap scheme is used to augment data and use this data to input the DMN for cost estimation. In addition, features are selected using a Weighted Correlation Coefficient to choose the best features for further processing. The test results show that LRSA_DMN performs better regarding MSE, RMSE, and Accuracy by 0.092, 0.371, and 0.907, respectively

Preference disaggregation analysis for sorting problems in the context of group decision-making with uncertain and inconsistent preferences

Preference disaggregation analysis is effective for inferring a preference model of decision makers from decision examples. Although multiple criteria sorting problems are often handled within the context of group decision-making, it has not been well studied how to estimate a collective sorting model for a group with uncertain and inconsistent preferences through preference disaggregation. To this end, this paper proposes a threshold-based value-driven preference disaggregation method to solve multiple criteria sorting problems involving multiple decision-makers. We allow decision-makers to reduce the cognitive effort of preference expressions by providing possible classes of reference alternatives or pairwise comparisons between them through uncertain expressions. In addition, we develop a two-stage inconsistency identification and elimination procedure that includes dealing with contradictory preference information provided by a single decision maker and managing disagreement among a group of decision makers. A collective preference model consisting of an additive value function and value thresholds is estimated by linear programming. The proposed method is demonstrated by a practical example of financial investment decisions for government-funded projects. The results show that dealing with uncertain and inconsistent preferences can improve the accuracy of preference disaggregation.

Using Monte Carlo Simulation to Support Project Investment Decisions Under Uncertainty: Case of Jordan Phosphate Mines Company

Using Monte Carlo Simulation to Support Project Investment Decisions Under Uncertainty: Case of Jordan Phosphate Mines Company

Strategic and flexible LNG production under uncertain future demand and natural gas prices

The expectation in global demand for liquified natural gas (LNG) remains bullish in the coming years. Despite the unprecedented impact of the COVID-19 pandemic, and the oil price wars between OPEC and Russia in 2020, causing oversupply and falling prices, the LNG markets continue to demonstrate flexibility and resilience in delivering the needs of different sectors, whilst helping achieve the emissions targets. This is attributed to the high competitiveness amongst LNG producers and suppliers, providing greater confidence for medium-to-long term demand. However, the uncertainties in the current outlook for the return of demand and price growth in the post-COVID period pose difficulty for new liquefaction project investment decisions in the pre-Investment Decision Phase (pre-FID). Accordingly, the consideration of new production and selling strategies is needed in the early design stages of projects to cope with the shift in buyers' sentiments favouring increased reliance on spot and short-term uncontracted volumes, as well as incorporating additional flexibility into long-term contracts. In this study, the economic valuation of the flexible Air Product's AP-X liquefaction technology was investigated considering the modelling of price volatilities, using the mean-reverting jump-diffusion pricing model and Monte Carlo simulation, assuming different demand level scenarios in the high-income Asia Pacific markets based on historical trends. The results clearly demonstrate that embedding flexibility within an LNG production system allows producers and suppliers to diversify selling strategies, and take advantage of the lucrative market conditions when demand and prices increase, and hedge against market risks when demand and prices are low.

From risks to rewards: A comprehensive guide to sustainable investment decisions in renewable energy using a hybrid facial expression-based fuzzy decision-making approach

There are some risks in renewable energy investments, such as legal, technology and financial issues. Hence, appropriate actions should be taken to minimize these risks. However, each of the measures to be taken for the management of risks creates new costs for businesses. Therefore, to ensure the financial sustainability of the projects, measures for the most important risks should be taken at the first stage. Because of this situation, it is vital to make a priority analysis for the risks faced by renewable energy projects so that it can be possible to increase the effectiveness of risk management. Accordingly, in this study, it is aimed to examine the risks and rewards regarding sustainable investment decisions for renewable energy projects. In this scope, a novel model is generated by integrating different techniques. In this process, the evaluations of six different experts are taken into consideration. First, missing evaluations for the sustainable investment decisions in renewable energy are estimated with neuro decision-making and collaborative filtering. Secondly, the weights of the risk factors of sustainable energy investments are computed with neuro quantum spherical fuzzy (QNSLF) DEMATEL with golden cut. Thirdly, reward alternatives for sustainable decision making are analyzed with Neuro QNSLF TOPSIS with golden cut. This study contributes to literature by determining the most important risks for renewable energy projects by an original decision-making model. Another important novelty of this study is that a new technique called neuro decision-making has also been developed. In this technique, the facial expressions of the experts who evaluate are taken into consideration. It is defined that technological changes have the greatest significance. Additionally, governmental incentives are found as the most essential reward to improve sustainable investments in renewable energy. Thus, for the effective management of these risks, appropriate actions should be taken. It is also obvious that the competitiveness of companies that fall behind this new technology will decrease. In this context, these enterprises need to take the necessary measures to manage the technology risk to be sustainable in the long term.

Application Analysis of Internal Rate of Return Capital Budgeting Method in Project Investment Decision-Making

Capital budgeting is crucial for a company to make the right investment decision. Among the typically used evaluation methods, the net present value and the internal rate of return are the mostly commonly used and preferred. This paper aims to focus on IRR, providing a comprehensive view of IRR that includes its basic characteristic, advantages and disadvantages when using it as the investment creation, as well as the improvements of its modified version MIRR, there will also be a comparison between the NPV and IRR to illustrate their difference. The study could enable the audience a comprehensive understanding of IRR in project appraisal, and whether to choose IRR or NPV as the investment criterion under different circumstances of the project or base on their own preferences. This research can provide more comprehensive and more realistic economic evaluation decision-making information, making the project economic evaluation more scientific.

How Can Comprehensive Goal Setting Enhance Project Investment Decisions?

Managers continuously make decisions on investing in projects that aim to enhance organizational performance. Poor project investment decision making results in lost benefits (rejecting a good project) and inefficiency (approving a suboptimal project). Whereas decisions regarding what project proposals to approve heavily rely on their stated goals, such goals are frequently unrealistic. This article aims at enhancing project investment decision making by extending decision comprehensiveness theory to the project goal-setting context. In an initial survey study, we found empirical support that a comprehensive goal-setting process enhances decision quality under uncertainty. In a second longitudinal study, we found that comprehensive goal setting further enhances project investment decision making above and beyond other established goal-setting dimensions (e.g., SMART goals), and eventually improves project performance. Last, we found that extensive consultation with key project stakeholders is the most effective comprehensive goal-setting practice. Based on research and practice, we describe what should comprise an effective goal-setting consultation process. This article extends goal-setting theory by suggesting that in addition to established characteristics, the goal-setting process under uncertainty should also be comprehensive.

Risk and Impacts Associated with Marine Carbon Sink Projects: An Example from Guangdong, China

He, Y.; Hu, Y.; Xie, S.; Li, C.; Xu, M., and Chen, X., 2023. Risk and impacts associated with marine carbon sink projects: An example from Guangdong, China. Journal of Coastal Research, 39(2), 246–256. Charlotte (North Carolina), ISSN 0749-0208. This paper aims to measure the risk and impact of the marine carbon sink project in Guangdong, China. This measurement increases the investors' willingness to participate in development and operation of such projects, and promotes the transformation of a low-carbon economy and society. This research employs triangular fuzzy number and Structural Equation Model to develop the risk measurement model of the marine carbon sink project. The net present value and risk equivalent methods measure the impact of risk factors on the project value and investment decision of the representative project in China. The results showed that: (1) there are certain risks in developing marine carbon sequestration projects, including policy, natural, market, and human behavior risks. The comprehensive risk value is 0.444. (2) Among the different types of marine carbon sink risks, the large ones are those caused by storm surges, invasion of alien species, and unknown ownership of marine carbon sink. (3) The risk of marine carbon sequestration reduces the profitability of its projects. (4) Compared with the traditional net present value method, the risk equivalent method is more in line with the development and operation of marine carbon sink projects. Based on this, combined with the risk characteristics and potential factors of marine carbon sequestration projects, and to promote their sustainable development, this paper has the following suggestions: (1) Strengthen policy evaluation and regulation, and formulate policies and measures in line with the development stage of marine carbon sequestration trading. (2) Strengthen the early warning of natural disasters and formulate targeted disaster prevention, mitigation, and relief plans. (3) Strengthen the dynamic supervision of marine carbon sequestration transactions and formulate emergency plans for market transaction risks. (4) Improve public participation and establish a reward and punishment mechanism for human behavior risk prevention and control.

Entropy-Based Fuzzy TOPSIS Method for Investment Decision Optimization of Large-Scale Projects.

Investment of large-scale projects must consider various factors, such as economic conditions and investment environment when making decisions. In large-scale project investment problems, almost 90% of them are completed in a multiobjective context. To this end, the authors of this paper have proposed an entropy-based fuzzy TOPSIS synthesis method to assist in the decision-making optimization of large-scale project investment. Firstly, in accordance with the background of the development of multiattribute decision-making method (MADM), closely related to large-scale project investment, the related research works were studied, and the relevant methods are sorted out. Then, the improved vague-entropy-weight based fuzzy TOPSIS (VEWF-TOPSIS) method, entropy and interval language intuitionistic fuzzy sets based TOPSIS (EILIF-TOPSIS) method, and information entropy attribute importance based intuitionistic fuzzy TOPSIS (IEAI-IF-TOPSIS) method were introduced, and a synthesis MADM method that comprehensively combines the above three methods was proposed. Finally, a numerical case was constructed to properly show the effectiveness of the method proposed in the present work, and the conclusions were put forward for providing ideas and methods for large-scale project investment decision-making. It is of great significance that the proposed framework would make large-scale project investment decisions more reasonable and practical, which enriches the research methods of MADM problems to a certain extent and can provide reference for the actual large-scale project investment decision-making problems.

A novel TOPSIS method with decision-theoretic rough fuzzy sets

The TOPSIS method follows a basic principle, that is, the better the alternative, the farther the alternative is to the pessimistic ideal solution, and the closer the alternative is to the optimistic ideal solution. However, in the decision-making process, there may be some alternatives whose ranking cannot be directly judged from this perspective. Therefore, researchers have proposed some sorting formulas, which can accomplish the overall sorting of alternatives. But, these formulas can only be used to deal with the sorting of alternatives. To expand the application scope and decision prospect of the TOPSIS method, we introduce decision-theoretic rough fuzzy sets, and design a novel TOPSIS method that simultaneously deals with the sorting and classification of alternatives. Using the basic principle of the TOPSIS method to acquire the optimistic and pessimistic ideal distances, we regard these two distances as two fuzzy concepts, here their features are opposite, and utilize the newly defined decision-theoretic rough fuzzy set models to obtain the corresponding decision areas. Then, according to these two distances, we analyze connection between the ordering of alternatives and the total loss of alternatives and summarize the sorting rules when alternatives are classified into the same decision area and the sorting rules when alternatives are classified into different decision areas. Further, the concept of joint decision area is defined. Meanwhile, we summarize the sorting rules when alternatives are classified into the same joint decision area and the sorting rules when alternatives are classified into different joint decision areas. Using these rules, we introduce the detailed process of the novel TOPSIS method and analyze the rationality of the method. Subsequently, we test the feasibility of our method with project investment decision example and illustrate its superiority and stability through comparative analysis and parameter test. Besides, we conduct simulation experiment to further verify the similarities and differences of the sorting function of the traditional TOPSIS method and our method.

Application of Financial and Economic Evaluation Methods in Project Investment Decision

Application of Financial and Economic Evaluation Methods in Project Investment Decision