Net Present Value Of Project Research Articles

Technoeconomic Evaluation of Electricity Generation from Concentrated Solar Power Technologies in Ghana

This work estimates the annual energy that could be generated from a concentrated solar power (CSP) plant. The optimal location used for this analysis was selected based on a set of multicriteria decision-making (MCDM) methods employed in an earlier research. The paper also determines the financial viability of implementing a CSP plant within the selected location. A 100 MW CSP plant for the said location was modelled and simulated using the System Advisor Model (SAM) software with data from the online database of the National Renewable Energy Lab (NREL) available from the SAM software. Using a solar multiple of 2.0 with a TES of 6 hours, the plant generated an estimated annual energy of 306.850 GWh with a capacity factor of 35.10% and gross-to-net conversion of 89.10%. The months with the highest generation were from November to March while July to September had the least generation. Generation begins from 8 am, rising to a peak around 12 pm to 4 pm and gradually declines into the night. Results from the financial analysis produced a net present value (NPV) of USD 156,287,433.72 after the plant life of 25 years, indicating profitability of the project. Results from the sensitivity analysis showed that the project NPV became negative only when the base case capital cost, electricity price, and revenue were, respectively, increased by 15%, reduced by 10%, and reduced by 13%.

Project NPV, Positive Externalities, Social Cost-Benefit Analysis— The Kansas City Light Rail Project

The Heartland Light Rail project represents Kansas City’s biggest infrastructural investment in decades. The ballot initiative for the light rail project was voted down three times until it was finally approved in November 2006. Using best estimates of construction costs, operating expenses and federal funding, I estimate the net present value (NPV) of the project to be negative $343 million. From a standard NPV perspective the Kansas City light rail transit (LRT) system is unlikely to break even. However, if the negative externalities of auto travel and the positive externalities associated with light rail are properly accounted for in a comprehensive social cost-benefit framework, investment in the Kansas City LRT system becomes an increasingly feasible option.

Methodology for calculating the net present value of investment project, taking into account the variability of determining the cash flow of capital investments

Subject. The article considers the variability of determining the cash flow of capital investments and methodology for calculating the net present value (NPV) of an investment project, taking into account the said variability. Objectives. The aim is to propose a methodology to calculate NPV, given the influence of variability in determining the cash flow from investing activities in terms of outflows, i.e. the required volume of investments to create new fixed assets. Methods. The study employs generally accepted scientific methods of information analysis and synthesis. Results. I analyzed modern domestic and foreign approaches and methods for calculating NPV of an investment project, formulated the concept of cash flow of capital investments, offered my own methodology for its variable determination. The methodology for NPV calculation covers three scenarios of investment project implementation: baseline, optimistic and pessimistic. The paper includes the impact analysis of variability of the determination of cash flow of capital investments on NPV of investment project. Conclusions. The paper defines the nature of the influence of variability in determining the cash flow of capital investments on the NPV of an investment project. The findings can be used to calculate and plan targeted indicators of investment performance (NPV), when evaluating and implementing investment projects, regardless of sources of financing.

Project Investment Plan Valuation Using Discounted Cash Flow Analysis (Case Study of LLP Compression Project Investment Plan at Tango Field, Mehacca Block)

This article performs a valuation analysis of LLP Compression in project investment plan at Tango Field in Mehacca Block. The LLP Compression investment project plan is currently under consideration for a Final Investment Decision (FID). The analysis is conducted by using quantitative methodology approach to evaluate 2 project scenario based on its cash flow. The outcome of financial valuation could assist decision maker in the company, in determining whether the project is economically profitable, whether it should be executed, what scenario can generate maximum economic profit for the company, and also what financial factor affecting LLP Compression Project cash flow that should be managed to avoid negative financial results and to forecast project likelihood of success. For DCF analysis, with company discount rate at 10.2%, the Net Present Value (NPV) result is 1.12 Million USD for Scenario 1 and 0.91 Million USD for Scenario 2. Internal Rate of Return (IRR) for both scenarios are higher than company’s discount rate. Payback Period for both scenarios are also the same in year 2026, or 2 years after production. Profitability Index (PI) for Scenario 1 is slightly higher than Scenario 1 amounting at 1.07, while PI for scenario 2 is 1.03. All of the parameters from DCF gives positive result more than expected return, with scenario 1 that provide better value than scenario 2. Monte Carlo simulation is also used to provide a likelihood of having a negative NPV. The result is that both scenarios have probability negative NPV amounting 28% for scenario 1 and 30% for scenario 2. Based on Monte Carlo Simulation result, scenario 1 has slightly lower probability of generating negative NPV than Scenario 2. Sensitivity analysis is also being used to look how large NPV project varies if the parameter inputs are changed. Four parameters tested with assumption change at ± 25% from original condition. Based on sensitivity analysis, it is observed that Gas Production, Gas Price are the most sensitive parameter for both scenarios of LLP Compression project. In summary, the LLP Compression Project investment plan is feasible to be executed for both scenarios, with scenario 1 provides higher economic profit than scenario 2, and it also generates slightly lower probability negative NPV than scenario 2. Since LLP Compression Project has probability of gaining negative NPV based on Monte Carlo Simulation Result, hence several strategies to minimize project risk exposure are required to be determined before project execution.

Multi-objective optimization of CO2 recycling operations for CCUS in pre-salt carbonate reservoirs

This study evaluates the potential of recycling carbon dioxide (CO2) in the associated gas in ultra-deepwater pre-salt carbonate reservoirs for Carbon Capture Utilization and Storage (CCUS). We performed compositional subsurface modeling of a hypothetical field for multi-objective optimization, aiming at minimizing carbon emissions whilst maximizing the project's Net Present Value (NPV). Although no absolute reduction in total CO2 emissions was met with economic improvement, all simulated CCUS scenarios yielded a lower carbon footprint oil, with 43.5% reduction in operational emissions and up to 25.5% increase in NPV. The best CCUS case, a tapered Water Alternating Gas (WAG) design, presented break-even oil price of 39.5 USD/STB and operational CO2 abatement cost of negative 94.8 USD/tCO2 abated, also showing significant reduction in number of squeeze treatments and cost of calcite scale management.

The Subordination of Shareholder and Intragroup Loans in Italy. Shifting Perspective in the Wake of the Covid-19 Emergency Legislation

Abstract 749 Amidst the COVID-19 outbreak, Italy as well as a number of other European states, including Austria, Germany and Spain, enacted emergency legislation suspending subordination of shareholders and intragroup loans in order to help companies stay afloat. While this measure was clearly prompted by extraordinary circumstances, it should be noted, however, that unlike jurisdictions which adopt automatic subordination, such as Germany and Spain, or where subordination is also based on quantitative criteria, such as Austria, subordination in Italy is based on flexible standards that may not strictly call for an emergency suspension. The COVID-19-related suspension of subordination provides the momentum for reassessing the scope of application of the subordination of shareholder and intragroup loans under Italian law. Although substantially unexplored in the international legal literature, subordination was introduced in Italy in 2003 and has now been applied for nearly twenty years. The system is quite sophisticated and also recognizes a variety of restructuring exemptions allowing priority to the loans. This paper examines subordination of shareholder and intragroup loans under Italian law, seeking to establish a workable construction of the standards of the excessive imbalance between debt and equity and of the reasonableness of a capital contribution on which the application of the remedy is predicated. Building also on relevant economic studies, the paper argues that the Italian subordination system has the potential to distinguish between desirable and undesirable shareholder and intragroup loans based on whether the loan is intended to finance positive net present value projects, restricting the latter while not deterring the former, so as to guarantee the goal of increasing the expected total value of the company and producing better satisfaction of creditors’ claims. 750

The Non-Tailing Sediment Waste Utilization Strategy at PTFI DMLZ Mine

Mining in PT. Freeport Indonesia (PTFI) produces tailing and non-tailing sediment waste. The purpose of the study is examining the financial feasibility analysis of managing non-tailings sediment waste through the conversion program of non-tailings sediment waste into concrete products and formulate the strategies of non-tailing sediment conversion program. Quantitative and qualitative methods are combined in this study. Capital budgeting method are used to examine financial feasibility analysis. IFAS, EFAS matrix and SWOT analysis are used for strategies formulation. Results of capital budgeting analysis have yielded a net present value (NPV) at a 9,4% discount rate of Rp12,1 billion for the conversion program of non-tailing waste, Net B/C result is greater than 1, suggesting that the project is viable from a financial standpoint. Sensitivity analysis has also demonstrated that the parameters with more significant influence on project NPV are concrete price, production cost, and sales volume. The non-tailings sediment conversion program is feasible because it provides greater economic, social, and environmental benefits when compared to non-tailings waste management using the landfilling method. The strategies that can be implemented to run the sediment waste utilization program are Strength- Opportunities strategies as follows: first is use a capital strong to access and apply the best technology, secondly increase the use of ready mix concrete made from non-tailing sediment waste for projects that require low Mpa concrete, the third, working with entities inside and outside PTFI to open the market for non-tailing sedimentary waste concrete products, and fourth utilizing good infrastructure and abundant sediment waste and fiber to diversify precast concrete products. Keywords: aggregates, capital budgeting, financial feasibility analysis, non-tailing waste, strategy, SWOT

Timing of transition from open-pit to underground mining: A simultaneous optimisation model for open-pit and underground mine production schedules

Generally, shallow deposits are mined through open-pit mining and where the deposits are extended to a substantial depth, there is a potential for a combination of open-pit and underground mining methods. In this case, mine planning and optimisation play a significant role in the decision-making. This paper proposes a mixed-integer programming model to obtain the optimal transition point and the transition period from open-pit to underground mining, maximizing the project net present value considering crown pillar placement and development cost. An implementing at a three-dimensional case study generates the optimal transition point and period and an optimal production schedule for both open-pit and underground operations. The implementation considers three scenarios: transition point only (scenario 1), transition point and period without production delay (scenario 2), and transition point and period with two-period production delay during the transition (scenario 3). The optimal transition point for scenario 1 is 315 m and for scenarios 2 and 3 is 360 m below the surface.

OPTIMIZATION OF GAS PIPELINE UTILIZATION FOR SECTION 2 PEMPING-TANJUNG UNCANG WITH THE PROVISION OF MINI LNG PLANT FOR KARIMUN REGENCY

Investment evaluation is a crucial part of investment decisions to measure will the project generate profit for the company. There is four Capital Budgeting technique used to measure this investment evaluation in this project Net Present Value (NPV), Internal Rate Return (IRR), Discounted Probability Index (DPI), and Payback Period (PBP). From the evaluation, it was obtained that a positive NPV of 280.649, an IRR of 8,10% greater than the WACC of 4,21%, while the DPI of 1.25 and PBP of 3,25 years was faster than the duration of the 5 (five) year contract. Monte Carlo simulation used 1.000 times to calculate Probability NPV<0 with result Probability NPV<0 in this project is 0,17% meanwhile probability NPV>0 is 99,83%.In the sensitivity analysis, it is found that the increase in the cost of capital and the duration of the agreement are factors that are sensitive to project feasibility. From the results of the above calculations, it can be concluded that Optimization Of Gas Pipeline Utilization For Section 2 Pemping – Tanjung Uncang With The Provision Of Mini LNG Plant For Karimun Regency is Eligible to be accepted.

Quantitative Policy Analysis for Sustainable Aviation Fuel Production Technologies

This paper quantifies the impact of different policy options on the economic viability of sustainable aviation fuel (SAF) production technologies. The pathways considered include isobutanol to jet from corn grain, hydroprocessed esters and fatty acids (HEFA) from inedible fats and oils, HEFA from palm fatty acid distillate, synthesized iso-paraffins from sugarcane, Fischer-Tropsch (FT) gasification and synthesis from municipal solid waste, and micro FT from wood residues. The policies considered include feedstock subsidies, capital grants, output based incentives, and two policies intended to reduce project risk. Stochastic techno-economic analysis models are used to quantify the policies’ impact on project net present value and minimum selling price of the middle distillate fuel products. None of the technology pathways studied are found to be financially viable without policy aid. The median total policy costs required for economic viability range from 35 to 337 million USD per production facility, or 0.07–0.71 USD/liter. Our results indicate that the cumulative impact of multiple policies, similar in magnitude to analogous real-world fuel policies, could result in economically viable SAF production.

An optimisation model for incentivising the development of marginal oil and gas fields amidst increasingly complex ownership patterns: UKCS case study

Many recent discoveries in the UKCS have been economically marginal and comparatively small such that they are unable to be developed on a standalone basis. Nevertheless, there is a proliferation of production facilities and transport infrastructure that can, on accepting a tariff, process hydrocarbons from these smaller-sized oil and gas fields, thereby contributing to the UK Government's strategy of maximising economic recovery (MER). However, the empirical dimensions of cost-sharing arrangements between a field owner (host field or hub) and tie-in (satellite) fields are not well examined in the literature although they influence long-term economics in matured oil provinces. In this paper, we develop a mixed integer programming optimisation model to analyse how these third-party access to infrastructure issues impact MER in the UKCS. A first-best situation with only one regional operator is empirically compared with the actual situation of multiple ownership. We then assess the impact of different infrastructure unbundling provisions via pipeline tariff choices and their interaction with the fiscal regime on the net present value (NPV) and other project metrics such as timings of hub and field shutdowns. We find that differences in field ownership, tariff choice and changes to the fiscal regime impact the overall NPV of field developments. Our results also show that having a progressive fiscal regime in a mature basin such as the UKCS is important to support continued operations under low oil prices while increasing the government's take at high prices. The cost-sharing effect shows that imposing strict cost-sharing rules does not change overall project valuations, even in a low oil price scenario. An additional contribution to the extant literature is that tariff determination could be based on cost-sharing rules that enjoin each field tieback to a hub to pay a split tariff, comprised of a fixed cost of service (access charge) and variable (marginal) costs. • Presents MIP model for analysing economic dependencies of infrastructure assets. • Ownership style, choice of tariffs, and fiscal regime impact overall field value. • Reducing marginal tax rates improves profitability, reflecting neutrality and progressivity. • Tariffs are decisive as to whether satellite or user fields are developed. • Tariff determination to maximise MER should be based on two-part cost-sharing.

Impact of financing and payout policy on the economic profitability of solar photovoltaic plants

This paper introduces an innovative comprehensive evaluation model for appraising an investment in a solar photovoltaic plant which encompasses both operational and financial management. We illustrate the intricate network of logical relations among technical (estimated) variables and financial (decision) variables and show that establishing transparent links between the former and the latter enhances the accuracy and soundness of the model. The results indicate that understanding the conceptual and formal relations of operating variables and financial decisions is necessary for correctly measuring shareholder value creation and making rational decisions, even for those projects (such as solar energy projects) where the operating, technical component is of paramount importance. We show how a firm's decision of replacing conventional energy with solar energy may be affected by managerial decisions regarding the firm's payout/retention policy and its financing policy to support the project. The model discloses insights on how to fine-tune the financing and distribution decisions in order to maximize the value creation for shareholders. We apply the model to a real-life photovoltaic project to be located in the province of Modena, in Northeast Italy, and quantify the effect of financial decisions on the project's net present value, showing that the financing and distribution policies may amplify or shrink the impact of changes in other inputs and may even revert an otherwise unprofitable project into a value-creating one. Finally, we allow operational variables as well as financial variables to change in order to measure their importance via the application of the Clean Finite Change Sensitivity Indices (Magni et al., 2020).

Monte Carlo Simulation in a Peruvian Highway

Highways and roads are important for nations' development and life quality. This is not different for Peru. A highway project called Daniel Alcides Carrión is expected to provide a solution to the over-employed Carretera Central road. This is a multimillionaire and important infrastructure project. Hence, it is important to evaluate the possible sustainability risks. In consequence, this study employed the Monte Carlo simulation for such a purpose. First, variables have been chosen and segregated into input and output. Variables like the initial investment, recurrent maintenance, periodical maintenance, savings in the operative cost of vehicles, and time savings employed the triangular distribution. Traffic growth and inflation rate employed the Pert distribution. The project's Net Present Value and Internal Rate of Return have been selected as output variables. Crystal Ball software has been employed to perform the Monte Carlo analysis. Consequently, this research found a high probability that the highway can become a profitable project due to its Net Present Value and Internal Rate of Return. Moreover, savings in operative costs of vehicles and traffic growth rate had positive impacts on the project's Net Present Value. However, the initial investment had a negative relationship with the output variable. Hence, the new highway should take prevision policies to maintain traffic flux. Thus, avoid closures that can have both human and natural sources. This study is the first in the Peruvian academic literature regarding highways risk analysis. Moreover, this study provides researchers, state officials, future highway managers, and users' valuable information to elaborate preventive measures to maintain the highway's social sustainability and increase its benefits.

A comprehensive techno-eco-assessment of CO2 enhanced oil recovery projects using a machine-learning assisted workflow

Carbon dioxide enhanced oil recovery (CO2-EOR) projects not only extract residual oil but also sequestrate CO2 in the depleted reservoirs. This study develops a machine-learning-based workflow to co-optimize the hydrocarbon recovery, CO2 sequestration volume and project net present value (NPV) simultaneously. Considering the trade-off relationships among the objective functions, support vector regression with Gaussian kernel (Gaussian- SVR) proxies are coupled with multi-objective particle swarm optimization (PSO) protocol and generate Pareto optimal solutions. Taking advantage of the high computational efficacy of the proxy model, economic uncertainties introduced by tax credits, capital costs and oil price are investigated by this study. The results indicate that the tax incentive policy (Section 45Q) plays a vital role in enhancing the economic returns of CO2-EOR projects, especially under the depression of crude oil market. The proposed workflow has been successfully implemented to optimize a water alternative CO2 (CO2-WAG) injection project in a depleted oil sand in the US. The optimization results yield an incremental oil production of 15.8 MM STB and 1.37 MM metric tons of CO2 storage in a 20-year development strategy, with the highest project NPV to be 205.6 MM US dollars.

Simulation-based priority rules for the stochastic resource-constrained net present value and risk problem

In this paper, the performance of activity-based and resource-based policies with newly designed priority rules for the stochastic resource-constrained project scheduling problem is considered. The aim of the paper is to maximize the expected net present value (eNPV) and to minimize the risk of the project's net present value by semivariance risk measurement. 16 (mainly cash flow-based) heuristic rules are considered, among which 10 rules are derived from the deterministic NPV literature which are referred to as direct priority rules, and 6 rules are developed based on simulation information. The performance of these rules in the mentioned objective functions has been evaluated on the benchmark data set project scheduling problem library. The priority rules have been ranked separately in maximization of eNPV and minimization of semivariance. The computational results on various test problems with 120 activities show that the top rank priority rules in optimizing each objective function under both policies are similar. Based on the obtained results, the priority rules which produce a schedule with a high eNPV, cause a schedule with high semivariance and this indicates the trade-off between eNPV and risk. The selection of the best priority rules is dependent on the risk-aversion level of the project manager. A risk-averse project manager is recommended to use the rule discounted cash flow weight based on activity duration in direct priority rules, and the rule statistical discounted cash flow weight at earliest finish time in simulation-based priority rules under both policies. But for a risk-preferent manager it is better to use the discounted cumulative cash flow weight at latest finish time in direct priority rules, and the statistical discounted cumulative cash flow weight at latest finish time in simulation-based priority rules under both policies.

A comprehensive techno-economic assessment of alkali–surfactant–polymer flooding processes using data-driven approaches

The objective of this work is to propose a machine-learning assisted multi-objective optimization protocol to design and optimize alkali–surfactant–polymer flooding processes in the presence of multiple technical and economic objective functions. Several universal multi-layer neural networks are trained, and they act as universal surrogate models of high-fidelity numerical simulator to evaluate the objective functions involved in the optimization workflow. The HLD-NAC equation is employed to model the microemulsion phase behavior of the crude oil/brine/surfactant system with the presence of alkali agents. The validity of the expert systems is confirmed via extensive blind testing applications with error margins of 9%, which includes the predictions of fluid production and pressure responses. A multi-objective optimization workflow is structured by coupling the expert systems with particle swarm optimizer, which employs Pareto optimum theory to carry comprehensive ASP injection assessments considering various technical and economic objective functions. Moreover, the proposed workflow enables the decision makers to comprehend the project risks by taking the inherent uncertainties from crude oil market into accounts. The robustness of the optimization protocol is verified by introducing a synthetic field case study, which validates the competence of optimizing ASP injection design considering project net present value as an objective function. A series of Pareto front solutions are generated when more objective functions such as chemical efficacies and water cut reductions are included. Subsequently, the workflow evaluates the project economic uncertainties from the fluctuation of oil price, which helps the decision makers investigate the project risks from technical and economic perspectives.

A Comprehensive Techno-eco-assessment of CO2 Enhanced Oil Recovery Projects Using a Machine-learning Assisted Workflow

Injection of CO2 gas to partially depleted oil reservoirs not only enhances hydrocarbon production but also take advances of the underground porous media for carbon storage purposes. Therefore, carbon utilization to enhanced oil recovery (EOR) projects become more and more attractive in the petroleum industry. Notably, the use of anthropogenic CO2 in EOR project could be challenging due to the capital and operational costs of carbon capture, transportation, and recycling. In the meantime, the US government offers encouraging tax policies, Section 45 Q, to oil field operators who inject a large volume of CO2 to produce oil. Thus, the project economics of the CO2-EOR project could be even more complex, especially when the oil price is low at this time. In this article, a robust machine-learning assisted CO2-EOR project optimization and design protocol is presented. The workflow aims at simultaneously optimizing the hydrocarbon recovery, carbon storage volume and project economics using a Pareto-front-based multiple-objective optimization (MOO) scheme. A solution repository containing various optimized development strategies will be structured. Moreover, the proposed workflow will investigate the project economics of the solutions by systematically considering impactive factors such as tax credits, project capital costs, oil prices, etc., and provide comprehensive recommendations to the field operators for decision-making purposes. This work employs data collected from the Farnsworth unit (FWU) in west Texas, US, as a field case to demonstrate the workflow. FWU is characterized as partially depleted oil sands undergoing water-alternative-CO2 (CO2-WAG) injection processes. A compositional numerical reservoir simulation model is established to investigate the fluid transportations dynamic of FWU. The numerical model is validated via a rigorous history-matching study using 55-year of primary and secondary (water flooding) recovery data, and 8-year of ternary recovery (CO2-WAG) data. It is worth emphasizing that the history-matched model successfully incorporates eight relative permeability curves to various spatial regions of the field. Such relative permeability curves are measured via laboratory investigations using representative core samples collected from the fields (hydraulic flow units). The history matched numerical model can be used to forecast the hydrocarbon production and CO2 storage volume using different CO2-WAG designs. In order to obtain finer optimization solutions, the field operational parameters, such as water injection rate, gas injection period, water injection periods, production well specifications, etc. of each individual well are considered as control parameters. In this way, the field operators can get detailed guidance to operate each individual well. However, the imposing of MOO requires computationally intensive procedures by totally relying on the high-fidelity numerical simulator, which engages the motivation to employ machine-learning-based proxies in the workflow. In this work, supported vector machine regression (SVR) combined with the Gaussian kernel is utilized to mimic the high-fidelity numerical model. The hyper-parameters of the SVR are optimized using Bayesian Optimization to achieve a better generalization performance. The SVR couples with Multi-objective Particle Swarm Optimization (MOPSO) protocol to structure the Pareto-front solution repository. To further eliminate the uncertainties introduced by the proxy error margins, a self-adapting scheme is integrated into the workflow to justify the Pareto-front solutions. This scheme will automatically add new sampling data to the training dataset for surrogate model development, making the optimization results more correct. Furthermore, this paper will carry comprehensive techno-eco-assessments of the optimized solutions considering the vital economic factors including the carbon capture and transporting costs, field operational cost, tax credits, oil price, etc. With the help of the proxy models, fast uncertainty analysis can be conducted to obtain stochastic results of the project net present value, rate of return and payback period. Field operators can make their decisions to design CO2-WAG projects based on both technical and economical perspectives.

Risk assessment modeling for knowledge based and startup projects based on feasibility studies: A Bayesian network approach

The start of any business requires investment. The risks involved in this pathway are one of the biggest barriers in each investment. Feasibility studies are one of the most common methods in analyzing an investment plan, but this method does not respond to the risk value of any plan. Therefore the present study aims to calculate the risk of a project through a feasibility study. To this end, Bayesian networks have attracted much attention as a powerful method for modeling decision making under uncertainty conditions in different domains. This paper presents a Bayesian network modeling framework that obtains the project risk by calculating uncertainty in net present value of projects. This model provides a powerful method for analyzing risk scenarios and their impact on the project success. This model can be used as a basis for assessing the risks of innovative projects whose feasibility study has been performed.

ЭФФЕКТИВНОСТЬ ИННОВАЦИОННОГО ПРОЕКТА ООО «ИНСТИТУТ «СТРОЙПРОЕКТ»

An innovative project of LLC «Institute Stroyproekt» was chosen as the object of research. The program of construction and installation works, the average annual production capacity of a construction organization, the effectiveness of personnel policy and administrative and economic costs, indicators of proceeds from product sales are linked by the authors with the efficiency of the economic activity of the enterprise and prof-itability indicators. The analysis summarizes the payback of the innovative project and the payback step, as well as the project's net present value and the internal rate of return. The article presents an assessment of the effectiveness of an innovative project using a discounted method for assessing investment and innovation projects. The authors noted that the innovation process, in addition to fundamental research, includes the determination of rational ways of practical implementation of development results through the implementation of research and development work and development work aimed at reengineering means of production, ma-terials, technologies and business processes, with the preparation of technical and economic justification and subsequent commercialization.

Positive Profits in Competitive Credit Markets With Adverse Selection

We provide theoretical foundations for positive lender profits in competitive credit markets with asymmetric information, where potential borrowers have scarce collateralizable assets. Strikingly, when some borrowers have negative net present value projects, an equilibrium always exists in which lenders make positive profits, despite their lack of ‘soft’ information and free entry of competitors. We then establish that greater access to collateral for borrowers reduces lender profits, and we relate our findings to the empirical evidence on micro-credit, payday lending, and, more broadly, retail and small business financing.