Facilitating Brownfield Redevelopment Projects: Evaluation, Negotiation, and Policy

A risky project evaluation technique called the fuzzy real options analysis is developed to evaluate brownfield redevelopment projects. Other decision making techniques, such as multiple criteria analysis and conflict analysis, can be incorporated into fuzzy real options analysis to facilitate negotiations on brownfield redevelopment among decision makers (DMs). The value of managerial flexibility, which is important in negotiations and policy making for brownfield redevelopment, is overlooked when the traditional evaluation method, net present value (NPV), is employed. Findings of this thesis can be used to promote brownfield redevelopment, thereby helping to eliminate environmental threats and enhance regional sustainability. A brownfield is an abandoned or underutilized property that contains, or may contain, pollutants, hazardous substances, or contaminants from previous usage, typically industrial activity. Brownfields often occur when the local economy transits from industrial to service-oriented seeking more profit. Governments actively promote brownfield redevelopment to eliminate public health threats, help economic transition, and enhance sustainability. However, developers are reluctant to participate in brownfield redevelopment because they often regard these projects as unprofitable when using classic evaluation techniques. On the other hand, case studies show that brownfield redevelopment projects can be good business opportunities for developers. An improved evaluation method is developed in order to estimate the value of a brownfield more accurately. The main reason that makes the difference between estimates and ''actual'' values lies in the failure of the deterministic project evaluation tool to price the value of uncertainty, which leads to efforts to enhance the decision making under uncertainty. Real options modelling, which extends the ability of option pricing models in real asset evaluation, is employed in risky project evaluation because of its capacity to handle uncertainties. However, brownfield redevelopment projects contain uncertain factors that have no market price, thus violating the assumption of option pricing models for which all risks have been reflected in the market. This problem, called private risk, is addressed by incorporating fuzzy numbers into real options in this thesis, which can be called fuzzy real options. Fuzzy real options are shown to generalize the original model to deal with additional kinds of uncertainties, making them more suitable for project evaluation. A numerical technique based on hybrid variables is developed to price fuzzy real options. We proposed an extension of Least Squares Monte-Carlo simulation (LSM) that produces numerical evaluations of options. A major advantage of this methodology lies in its ability to produce results regardless of whether or not an analytic solution exists. Tests show that the generalized LSM produces similar results to the analytic valuation of fuzzy real options, when this is possible. To facilitate parameter estimation for the fuzzy real options model, another numerical method is proposed to represent the likelihood of contamination of a brownfield using fuzzy boundaries. Linguistic quantifiers and ordered weighted averaging (OWA) techniques are utilized to determine the likelihood of pollution at sample locations based on multiple environmental indicators, acting as a fuzzy deduction rule to calculate the triangle membership functions of the fuzzy parameters. Risk preferences of DMs are expressed as different ''ORness'' levels of OWA operators, which affect likelihood estimates. When the fuzzy boundaries of a brownfield are generated by interpolation of sample points, the parameters of fuzzy real options, drift rate and volatility, can be calculated as fuzzy numbers. Hence, this proposed method can act as an intermediary between DMs and the fuzzy real options models, making this model much easier to apply. The values of DMs to a brownfield can be input to the graph model for conflict resolution (GMCR) to identify possible resolutions during brownfield redevelopment negotiation among all possible states, or combinations of DMs' choices. Major redevelopment policies are studied using a brownfield redevelopment case, Ralgreen Community in Kitchener, Ontario, Canada. The fuzzy preference framework and probability-based comparison method to rank fuzzy variables are employed to integrate fuzzy real options and GMCR. Insights into this conflict and general policy suggestions are provided. A potential negotiation support system (NSS) implementing these numerical methods is discussed in the context of negotiating brownfield redevelopment projects. The NSS combines the computational modules, decision support system (DSS) prototypes, and geographic information systems (GIS), and message systems. A public-private partnership (PPP) will be enhanced through information sharing, scenario generation, and conflict analysis provided by the NSS, encouraging more efficient brownfield redevelopment and leading to greater regional sustainability. The integrated usage of fuzzy real options, OWA, and GMCR takes advantage of fuzziness and randomness, making better evaluation technique available in a multiple DMs negotiation setting. Decision techniques expand their range from decision analysis, multiple criteria analysis, to a game-theoretic approach, contributing to a big picture on decision making under uncertainty. When these methods are used to study brownfield redevelopment, we found that creating better business opportunities, such as allowing land use change to raise net income, are more important in determining equilibria than remediation cost refunding. Better redevelopment policies can be proposed to aid negotiations among stakeholders

NEGOTIATION-BASED RISK MANAGEMENT FOR PPP-BOT INFRASTRUCTURE PROJECTS

Ph.DDOCTOR OF PHILOSOPH

Simplified models for multi-criteria decision analysis under uncertainty

Includes abstract.Includes bibliographical references.When facilitating decisions in which some performance evaluations are uncertain, a decision must be taken about how this uncertainty is to be modelled. This involves, in part, choosing an uncertainty format {a way of representing the possible outcomes that may occur. It seems reasonable to suggest {and is an aim of the thesis to show {that the choice of how uncertain quantities are represented will exert some influence over the decision-making process and the final decision taken. Many models exist for multi-criteria decision analysis (MCDA) under conditions of uncertainty; perhaps the most well-known are those based on multi-attribute utility theory [MAUT, e.g. 147], which uses probability distributions to represent uncertainty. The great strength of MAUT is its axiomatic foundation, but even in its simplest form its practical implementation is formidable, and although there are several practical applications of MAUT reported in the literature [e.g. 39, 270] the number is small relative to its theoretical standing. Practical applications often use simpler decision models to aid decision making under uncertainty, based on uncertainty formats that `simplify' the full probability distributions (e.g. using expected values, variances, quantiles, etc). The aim of this thesis is to identify decision models associated with these `simplified' uncertainty formats and to evaluate the potential usefulness of these models as decision aids for problems involving uncertainty. It is hoped that doing so provides some guidance to practitioners about the types of models that may be used for uncertain decision making. The performance of simplified models is evaluated using three distinct methodological approaches {computer simulation, `laboratory' choice experiments, and real-world applications of decision analysis {in the hope of providing an integrated assessment. Chapter 3 generates a number of hypothetical decision problems by simulation, and within each problem simulates the hypothetical application of MAUT and various simplified decision models. The findings allow one to assess how the simplification of MAUT models might impact results, but do not provide any general conclusions because they are based on hypothetical decision problems and cannot evaluate practical issues like ease-of-use or the ability to generate insight that are critical to good decision aid. Chapter 4 addresses some of these limitations by reporting an experimental study consisting of choice tasks presented to numerate but unfacilitated participants. Tasks involved subjects selecting one from a set of five alternatives with uncertain attribute evaluations, with the format used to represent uncertainty and the number of objectives for the choice varied as part of the experimental design. The study is limited by the focus on descriptive rather than real prescriptive decision making, but has implications for prescriptive decision making practice in that natural tendencies are identified which may need to be overcome in the course of a prescriptive analysis

Options-based negotiation management of PPP–BOT infrastructure projects

10.1080/01446193.2017.1325962Construction Management and Economics3511-12676-69

Port capacity expansion under real options approach: a case study in Brazil

Investments in port container terminals are sensitive to uncertainties. Public investments in infrastructure have been significantly reduced in the last decade in developing countries. The Brazilian government infrastructure investment was only 1.85 % of GDP in 2019, representing the lowest level in the last fifty years. Nonetheless, the regulatory framework of the port sector in Brazil has undergone significant changes over time, increasing the number of private port container terminal leases. The expansion capacity of the private port facilities is strongly linked to the demand uncertainty, which impacts the financial return to the long run. In this scenario, the uncertainty of global cargo transportation can discourage infrastructure investments in this class of project in Brazil. To overcome these issues, the financial modelling applying real options approach is better suited than the traditional valuation methods based on Discounted Cash Flow (DCF) analysis. The present study aims to value flexibilities of anticipating, or postponing, or interrupting investments of an existing operational port terminal in Brazil with expansion capacity under the demand uncertainty. The financial decision to invest in a port expansion is modeled by an American option. The results demonstrate that the investor adds significant value to the project by having the possibility to postpone investments. The proposed model presents the contribution of optimizing the decision of sequential expansions of capacity in port terminals, at any time and according to scenarios' revelation. In addition, the model allows the government authorities to review lease contracts, considering the relevance of timing to invest in project expansion decisions. The proposed model can also be extended to other infrastructure projects in emerging economies

Risk Management for the Future

A large part of academic literature, business literature as well as practices in real life are resting on the assumption that uncertainty and risk does not exist. We all know that this is not true, yet, a whole variety of methods, tools and practices are not attuned to the fact that the future is uncertain and that risks are all around us. However, despite risk management entering the agenda some decades ago, it has introduced risks on its own as illustrated by the financial crisis. Here is a book that goes beyond risk management as it is today and tries to discuss what needs to be improved further. The book also offers some cases

Economic Appraisal of Investment Projects in Solar Energy under Uncertainty via Fuzzy Real Option Approach (Case Study: a 2-MW Photovoltaic Plant in South of Isfahan, Iran)

Investment in renewable energies especially solar energies is encountered with numerous uncertainties considering the increased dynamism in economic and financial conditions and makes investment in this field irreversible to a large extent, paying attention to modern methods of economic appraisal of such investments is highly important. A framework is provided in the current study in order to employ the real option theory in evaluation of photovoltaic plants comparing with traditional methods. To this end, first, uncertainty factors of these plants in Isfahan province (one of highly susceptible regions in Iran) are identified from the view point of experts and impact factor of each one on interests and expenses of the above plant will be evaluated in order to insert these parameters in the form of fuzzy numbers in the model for better coverage of uncertainty. Then, the project under study is evaluated through both traditional methods and fuzzy real option approach with the help of Black-Scholes model and the results are compared. The results disclosed that investment value in these plants is increased if real expansion and abandonment options are considered. As a result, the real option theory has a higher adequacy than the traditional methods for evaluation of projects

A Real option approach to valuating infrastructure investments

Thesis(Master) --KDI School:Master of Public Policy,2011A correct methodology for valuing an infrastructure investment is essential to both of two parties, government and private concessionaires, in order to allocate the project risks reasonably and fairly and makes the project successful. Real Option Analysis (ROA) can be a good approach for appropriately valuing an infrastructure investment because it can capture the "uncertainties of the project and flexible managerial strategies" (Dixit, and Pindiyck, 1994) during the investment horizon by using option pricing model. This study investigated the value of project using DCF method and ROA approach, the cause of the gap, and project value from ROA approach when adding government guarantees such as MRG and the option to abandon. Additionally, this paper identified how the project value from ROA approach would change when variable assumptions were adjusted. In conclusion, what these results can suggest to the policy makers was covered.masterpublishedby Hyuk Lee

Methodology for technology evaluation under uncertainty and its application in advanced coal gasification processes

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 273-287).Integrated gasification combined cycle (IGCC) technology has attracted interest as a cleaner alternative to conventional coal-fired power generation processes. While a number of pilot projects have been launched to experimentally test IGCC technologies, mathematical simulation remains a central part of the ongoing research efforts. A major challenge in modeling an IGCC power plant is the lack of real experience and reliable data. It is critical to properly understand the state of knowledge and evaluate the impact of uncertainty in every phase of the R&D process. A rigorous investigation of the effect of uncertainty on IGCC system requires accurate quantification of input uncertainty and efficient propagation of uncertainty through system models. This thesis proposes several uncertainty quantification methods which expand the sources of information that can be used for parameter estimation. Key features of these methods include the use of entropy maximization to translate subjective opinions to probability distribution functions, and a more flexible probability model that easily captures anomaly associated with small sample data. In addition, Bayesian estimation is extended to dynamic models. Aided by a computationally efficient algorithm, termed sequential Monte Carlo method, the Bayesian approach is shown to be an effective way to estimate time-variant parameters. Uncertainty propagation is performed using the deterministic equivalent modeling method (DEMM) which is based on polynomial chaos representation of random variables and probabilistic collocation algorithm. One major issue often overlooked in the analysis of IGCC models is to represent correlation in the input parameters. This thesis proposes the use of principal component analysis (PCA) to represent correlated random variables. The resulting formulation is the same as the truncated Karhunen-Lodve expansions. Explicit incorporation of correlation not only improves accuracy of the approximation but also reduces the overall computational time. A comprehensive study of the MIT-BP IGCC model is carried out to determine uncertainties of the key measures of performance and cost, including energy output, thermal efficiency, CO 2 emission, plant capital cost, and cost of electricity. Whenever possible, the probability distributions of input parameters are estimated based on realistic data. Experts' judgments are solicited if data acquisition is infeasible. Uncertainty analysis is conducted in a three-step approach. First, technology-related input parameters are taken into account to determine uncertainties of plant performance. Second, cost uncertainties are determined with only economic inputs in order to identify important economic parameters. Finally, the plant model is integrated with cost model and they are evaluated with the key technical and economic inputs identified in the previous steps. Our study indicates the property of coal feed has a substantial impact on the energy production of the IGCC plant, and subsequently on the cost of electricity. Immature technologies such as gasification and gas turbine have important bearing on model performance hence need to be addressed in future research.by Bo Gong.Ph.D