Incorporating Knightian uncertainty into real options analysis: Using multiple-priors in the case of rail transit investment

This paper revisits the real option investment problem from a ‘Knightian’ perspective of uncertainty. We examine the decision to invest in rail transit infrastructure (i.e., transport improvement) by treating population scale and the attitudes of decision-makers or social planners as sources of risk and ambiguity. An α-maxmin multiple-priors expected utility framework is developed to solve for the option value of rail transit investment under Knightian uncertainty. We find that the threshold for investment varies with the ambiguity attitudes (i.e., pessimism or optimism) of decision-makers regarding future population dynamics, and show that option value can in fact either increase or decrease with uncertainty subject to κ-ignorance and ambiguity. We also underline the effect of transport improvement on traffic congestion under various states of nature, and determine the specific population scale ranges for which investment is warranted under (1) risk and (2) Knightian uncertainty. These dynamics are illustrated in a numerical application adapted from a Chinese rail transit initiative specific to the Xiamen region.     

Towards improved port capacity investment decisions under uncertainty: a real options approach

ABSTRACT

Port activity plays an important role in facilitating international trade. Sufficient capacity is indispensable for a port to attract flows to a region and retain them. The capacity decision is the result of a trade-off between investment and waiting costs. Traditional methods to value expansion projects do not deal adequately with managerial flexibility in the face of uncertainty from different sources in the complex port environment. In this paper, real options (RO) models are identified as an alternative method to making project valuations and investment decisions, as they attribute the correct value to managerial flexibility under uncertainty. In order to be able to build and use such RO models for port capacity investment decisions, the sources and implications of uncertainty in the port and the different RO model specifications need to be understood. To this end, both the literature about uncertainty in the port context and the literature about real options models are reviewed in order to provide researchers who want to build their own decision-making models, with the necessary knowledge of both fields. The review makes clear that the complex interactions in and competition between the logistics chains and their actors coming together in ports have significant impacts on port capacity. Uncertainty is also caused by uncertain international trade flows and changes in legislation following new technologies and environmental impacts. An analysis of the components of some general RO models shows how the options of flexible output, investment size and timing are valued by RO models in a setting with demand uncertainty. Moreover, the review presents researchers with insights in how to deal with cooperative and competitive interactions in the chain, time to build, cyclical markets and legislation changes. It also shows how to value the expansion and the phased investment options. The new insights resulting from this review are subsequently combined in a framework that serves as a guideline to build RO models for port capacity investments. Finally, an exemplifying application of the framework is used to build an actual port capacity investment decision model.     

Heavy-duty trucking and the energy efficiency paradox: Evidence from focus groups and interviews

Theory suggests that profit maximizing firms have an incentive to incorporate cost-effective technologies into their products. However, simple net present value calculations comparing upfront costs of fuel-saving technologies to future savings suggest this is not always the case. This puzzle is commonly referred to as the “energy efficiency paradox.” A growing number of empirical studies examine why households may under-invest in energy efficiency. Fewer studies examine similar undervaluation by businesses. We explore investment decisions within the heavy-duty trucking sector for fuel-saving technologies via focus groups and interviews to gain insight into what factors might explain apparent underinvestment in fuel-saving technologies. We find some evidence that market failures related to lack of information about technology performance and network externalities contribute to slow adoption of some technologies. However, information about new technologies for tractors seems to generate limited spillovers. There is also some evidence of split incentives between owners and drivers, though companies have invested in a variety of technologies and approaches in an attempt to address these effects. Other factors important in trucking investment decisions that are not classic market failures include tradeoffs between fuel economy and other valued truck attributes, as well as uncertainty and risk associated with new technologies if decision-makers are loss averse.     

The effects of federal transit subsidy policy on investment decisions: The case of San Francisco's Geary Corridor

In the United States, federal funding for public transit often accounts for a large proportion of a local agency's budget, especially for capital investments. For this reason, local governments can be expected to plan a portfolio of projects that maximize federal contributions. This study examines the financial effects of federal transit subsidy policy on local transit investment decisions. Data from a System Planning Study for the Geary Corridor in San Francisco are used as an illustration. It is found that federal transit subsidy policy provides financial incentives for local decision-makers to select capital-intensive investment options that may not be efficient or effective. While federal financial incentives are not the only factor influencing local investment decisions, some reform of the current subsidy policy may be necessary to reduce the incentive for ineffective use of public resources.     

Does uncertainty make cost-benefit analyses pointless?

Cost-benefit analysis (CBA) is widely used in public decision making on infrastructure investments. However, the demand forecasts, cost estimates, benefit valuations and effect assessments that are conducted as part of CBAs are all subject to various degrees of uncertainty. The question is to what extent CBAs, given such uncertainties, are still useful as a way to prioritize between infrastructure investments, or put differently, how robust the policy conclusions of CBA are with respect to uncertainties. Using simulations based on real data on national infrastructure plans in Sweden and Norway, we study how investment selection and total realized benefits change when decisions are based on CBA assessments subject to several different types of uncertainty. Our results indicate that realized benefits and investment selection are surprisingly insensitive to all studied types of uncertainty, even for high levels of uncertainty. The two types of uncertainty that affect results the most are uncertainties about investment cost and transport demand. Provided that decisions are based on CBA outcomes, reducing uncertainty is still worthwhile, however, because of the huge sums at stake. Even moderate reductions of uncertainties about unit values, investment costs, future demand and project effects may increase the realized benefits infrastructure investment plans by tens or hundreds of million euros. We conclude that, despite the many types of uncertainties, CBA is able to fairly consistently separate the wheat from the chaff and hence contribute to substantially improved infrastructure decisions.     

Incorporating uncertainty and risk in transportation investment decision-making

This paper presents a framework for addressing uncertainty and risk for large-scale transportation investments involving public–private participation. Demand, fare/toll and demand responsive costs are considered in the uncertainty analysis. Uncertainty analysis provides information on economic feasibility of the project. A set of relaxation policies is proposed to form various Ownership, Tenure and Governance (OTG) strategies reflecting the nature and level of participation by the public and private entity. A Monte Carlo Simulation-based Value at Risk is used to quantify risk. Finally, a methodology is proposed to integrate uncertainty and risk. The framework is tested on the proposed multibillion dollar Detroit River International Crossing connecting the cities of Detroit in the USA with Windsor in Canada. The analysis provides insights to probable outcomes for this transportation infrastructure investment under different OTG scenarios.     

Examining transport futures with scenario analysis and MCA

Climate change is a global problem and across the world the transport sector is finding it difficult to break projected increases in carbon dioxide (CO₂) emissions; there are very few contexts where deep reductions in transport CO₂ emissions are being made. A number of research studies are now examining the potential for future lower CO₂ emissions in the transport sector. This paper develops this work to consider some of the wider sustainability impacts (economic, social and local environmental) as well as the lower CO₂ transport impacts of different policy trajectories. Hence the central argument made is for an integrated approach to transport policy making over the longer term - incorporating scenario analysis and multi-criteria assessment (MCA) - to help assess likely progress against a range of objectives.The analysis is based on work carried out in Oxfordshire, UK. Different packages of measures are selected and two scenarios developed which satisfy lower CO₂ aspirations, one of which also provides wider positive sustainability impacts. A simulation model has been produced to help explore the strategic policy choices and tensions evident for decision-makers involved in local transport planning. The paper argues for a ‘strategic conversation’ (Van der Heijden, 1996) at the sub-regional and city level, based upon future scenario analysis and MCA, discussing the priorities for intervention. Such an approach will help us examine the scale of change and trade-offs required in moving towards sustainable transport futures.     

Traffic forecasts under uncertainty and capacity constraints

Traffic forecasts provide essential input for the appraisal of transport investment projects. However, according to recent empirical evidence, long-term predictions are subject to high levels of uncertainty. This article quantifies uncertainty in traffic forecasts for the tolled motorway network in Spain. Uncertainty is quantified in the form of a confidence interval for the traffic forecast that includes both model uncertainty and input uncertainty. We apply a stochastic simulation process based on bootstrapping techniques. Furthermore, the article proposes a new methodology to account for capacity constraints in long-term traffic forecasts. Specifically, we suggest a dynamic model in which the speed of adjustment is related to the ratio between the actual traffic flow and the maximum capacity of the motorway. As an illustrative example, this methodology is applied to a specific public policy that consists of suppressing the toll on a certain motorway section before the concession expires.     

Robust network pricing and system optimization under combined long-term stochasticity and elasticity of travel demand

Network pricing serves as an instrument for congestion management, however, agencies and planners often encounter problems of estimating appropriate toll prices. Tolls are commonly estimated for a single-point deterministic travel demand, which may lead to imperfect policy decisions due to inherent uncertainties in future travel demand. Previous research has addressed the issue of demand uncertainty in the pricing context, but the elastic nature of demand along with its uncertainty has not been explicitly considered. Similarly, interactions between elasticity and uncertainty of demand have not been characterized. This study addresses these gaps and proposes a framework to estimate nearest optimal first-best tolls under long-term stochasticity in elastic demand. We show first that the optimal tolls under the deterministic-elastic and stochastic-elastic demand cases coincide when cost and demand functions are linear, and the set of equilibrium paths is constant. These assumptions are restrictive, so three larger networks are considered numerically, and the subsequent pricing decisions are assessed. The results of the numerical experiments suggest that in many cases, optimal pricing decisions under the combined stochastic-elastic demand scenario resemble those when demand is known exactly. The applications in this study thus suggest that inclusion of demand elasticity offsets the need of considering future demand uncertainties for first-best congestion pricing frameworks.     

Port investments on coastal and marine disasters prevention: Economic modeling and implications

Located along shorelines, seaports are highly vulnerable to coastal and marine natural disasters largely due to climate change. Damage caused by disasters can be prevented or alleviated if sufficient investments are made in a timely manner. However, despite a wide range of investment options and well-developed engineering expertise, port investment on disaster prevention remains a challenging task involving great complexities. This paper develops an integrated economic model for the analysis of disaster-prevention investments at a “landlord” port. It simultaneously considers the uncertainty of disaster occurrence and associated return of prevention investments, the information accumulation and related investment timing, and the benefit spillovers of investment among stakeholders. Our analysis shows that the timing of port investments depends on the probability of disasters. Immediate investment is optimal for disasters with very high probability, while investment should be postponed if such a probability is very low. Optimal timing for cases of intermediate probability cannot be determined analytically, as it is influenced by other factors such as discount rate, information accumulation and efficiency of investments. Positive spillovers between a port and its tenants lead to under-investment, which can be corrected by coordination between stakeholders. However, since there are risks of “overinvestment” (the marginal benefits of investments are zero ex post if there is no disaster), regulatory intervention is not always optimal when the regulator does not have a good understanding of disaster probability distribution. Therefore, scientific research would bring significant economic and strategic value to policy, planning and investment decisions.     

Transport policy scenario-building

Long-term planning decisions must be as robust as possible to withstand such changes in the policy-making environment. This calls for a new approach for identifying how policies might fare under different types of conditions and for identifying robust no-regrets strategies. This paper outlines a systematic and logical framework for the development of policy scenarios and shows how scenarios can be used in the analysis of future policy options. The article discusses how scenario analysis can be used to identify, anticipate and assess policy options for the future in an environment of uncertainty, mainly using examples of transport policy in Europe.     

Uncertainty analysis of the large zone economic module of the simple,efficient, elegant,and effective model (SE3M) of land use and transportation

Integrated land use and transportation forecasting models are used to assist decision-makers in the policy analysis and infrastructure capital improvement selection process. These models are typically given precise, point-estimate inputs that are mathematically linked, through a series of submodels, to forecasted model outputs. These point-estimate inputs represent an unrealistic level of precision and a growing body of research is focusing on statistical techniques to model uncertainty in model inputs and parameters and tracking the effects of this uncertainty through the various submodels to the model outputs. This paper presents an uncertainty analysis of the Large Zone Economic Module (LZEM) of the Simple, Efficient, Elegant, and Effective Model (SE³M) of land use and transportation. Three case-study implementations of the model are used to obtain a reasonably sound approximation of how uncertainty affects LZEM outputs: Guam, Puerto Rico, and Oahu, Hawaii. These case studies were the subject of an early transferability study with SE³M and were selected based on both their insularity and diverse physical, economic, and demographic geographies. The findings of this research demonstrate that LZEM has a robust framework, with the potential to estimate error both in the positive and negative direction under uncertain input/parameter conditions.     

The effect of uncertainty on US transport-related GHG emissions and fuel consumption out to 2050

The future of US transport energy requirements and emissions is uncertain. Transport policy research has explored a number of scenarios to better understand the future characteristics of US light-duty vehicles. Deterministic scenario analysis is, however, unable to identify the impact of uncertainty on the future US vehicle fleet emissions and energy use. Variables determining the future fleet emissions and fuel use are inherently uncertain and thus the shortfall in understanding the impact of uncertainty on the future of US transport needs to be addressed. This paper uses a stochastic technology and fleet assessment model to quantify the uncertainties in US vehicle fleet emissions and fuel use for a realistic yet ambitious pathway which results in about a 50% reduction in fleet GHG emissions in 2050. The results show the probability distribution of fleet emissions, fuel use, and energy consumption over time out to 2050. The expected value for the fleet fuel consumption is about 450 and 350 billion litres of gasoline equivalent with standard deviations of 40 and 80 in 2030 and 2050, respectively. The expected value for the fleet GHG emissions is about 1360 and 850 Mt CO₂ equivalent with standard deviation of 130 and 230 in 2030 and 2050 respectively. The parameters that are major contributors to variations in emissions and fuel consumption are also identified and ranked through the uncertainty analysis. It is further shown that these major contributors change over time, and include parameters such as: vehicle scrappage rate, annual growth of vehicle kilometres travelled in the near term, total vehicle sales, fuel economy of the dominant naturally-aspirated spark ignition vehicles, and percentage of gasoline displaced by cellulosic ethanol. The findings in this paper demonstrate the importance of taking uncertainties into consideration when choosing amongst alternative fuel and emissions reduction pathways, in the light of their possible consequences.     

A heuristic model of bounded route choice in urban areas

There is substantial evidence to indicate that route choice in urban areas is complex cognitive process, conducted under uncertainty and formed on partial perspectives. Yet, conventional route choice models continue make simplistic assumptions around the nature of human cognitive ability, memory and preference. In this paper, a novel framework for route choice in urban areas is introduced, aiming to more accurately reflect the uncertain, bounded nature of route choice decision making. Two main advances are introduced. The first involves the definition of a hierarchical model of space representing the relationship between urban features and human cognition, combining findings from both the extensive previous literature on spatial cognition and a large route choice dataset. The second advance involves the development of heuristic rules for route choice decisions, building upon the hierarchical model of urban space. The heuristics describe the process by which quick, ‘good enough’ decisions are made when individuals are faced with uncertainty. This element of the model is once more constructed and parameterised according to findings from prior research and the trends identified within a large routing dataset. The paper outlines the implementation of the framework within a real-world context, validating the results against observed behaviours. Conclusions are offered as to the extension and improvement of this approach, outlining its potential as an alternative to other route choice modelling frameworks.     

Institutional issues in planning for more uncertain futures

Future travel demand has always been difficult to estimate. Recent trends of a slow down or stagnation in traffic growth combined with substantial demographic, economic, and technological shifts further complicate that task. This poses a significant planning challenge given that decision-making is often based on the benefits of infrastructure investments which accrue over periods as long as 60 years. In contrast to the changing ideas around what mobility in the future will look like and the types of demand it may need to service, the practice of forecasting future travel demand remains largely unchanged as do the decision-making processes which flow from this. Alternative approaches to thinking about futures such as scenario planning exist but have had more limited deployment in the transport sector. This paper explores the institutional issues surrounding the purpose, practice and barriers to changing the approaches of forecasting and decision-making through an exploration of the state of practice in the UK drawing on interviews with 23 practitioners. Drawing on Hall’s work on policy change, the research finds that there is a strong policy paradigm built around the relationships between transport investment and traffic growth. This has strengthened since the recession with an increased focus on the relationship between transport and job creation. The forecasting approaches in use today are an important part of a complex decision-making apparatus reflective of specialized policy arenas like transport. Challenges in acknowledging, representing and communicating uncertainty are identified leaving a growing tension between planning visions and planning practice. We conclude by reflecting on events which may stimulate a broader reframing of how we plan for transport futures whilst embracing key uncertainties.

     

Real option analysis for environmental compliance: LNG and emission control areas

A wide array of technical and operational solutions is available to shipowners in order to comply with existing and upcoming environmental regulation within Emission Control Areas (ECAs). Liquefied Natural Gas (LNG) is a promising alternative since it offers potential cost savings in addition to ensuring compliance with ECA regulation. But investment to retrofit existing vessels to be able to use LNG carries significant upfront costs, and a high degree of uncertainty remains on the differential between the prices of LNG and conventional maritime fuels, as well as on the availability of LNG and the reliability of its supply chain. New technologies such as LNG inherently carry substantial risk and an ill-chosen investment strategy may have irreversible consequences that could jeopardise the future of the shipping company. One important question is whether interested owners should invest in LNG now to comply with ECA rules in 2015 and reap the benefits of lower LNG prices, or whether it would be advisable to wait until some of the uncertainty is resolved.While traditional discounted cash flow techniques are unable to account for the value of managerial flexibility linked, for example, to the possibility of deferring an investment, real option analysis can be used to analyse such cases. The paper discusses the optimal time for investment in LNG retrofit and takes specific account of the value of an investment deferral strategy versus the advantages obtainable from the immediate exploitation of fuel price differentials. Through the use of a real option model the paper shows that there is a trade-off between low fuel prices and capital expenses for investment in LNG retrofit. The development in LNG is critically dependent on its future price as well as the reduction in capital costs and ship retrofitting costs. In this respect, policy makers can play a critical role in providing support to advance technical knowledge, maintain LNG prices at favourable levels and in avoiding ambiguity on regulation.     

Coping with uncertainty: an expedition into the field of new transport technology

Many decisions taken by mankind are rational only to a limited extent. This holds for individual travel behaviour, but also for long-range strategic decisions on transport systems or transport technology. In any decision problem, coping with uncertainty is the most critical element. The introduction of new transport technology is surrounded by uncertainty. For example, there is uncertainty about the pace and extent of adoption of new technology and there is uncertainty about the impact of new technology in terms of increased sustainability or increased efficiency. This article maps uncertainty surrounding new transport technology and identifies ways in which to deal with uncertainty in policy making. The findings will be illustrated with electric vehicles, particularly with two specific strategies to deal with uncertainty: interactive technology watching and experimentation in a market niche. The paper concludes with a discussion of success factors that influence the outcomes of such strategies.     

Preferences for travel time under risk and ambiguity: Implications in path selection and network equilibrium

In this paper, we study the preferences for uncertain travel times in which probability distributions may not be fully characterized. In evaluating an uncertain travel time, we explicitly distinguish between risk, where the probability distribution is precisely known, and ambiguity, where it is not. In particular, we propose a new criterion called ambiguity-aware CARA travel time (ACT) for evaluating uncertain travel times under various attitudes of risk and ambiguity, which is a preference based on blending the Hurwicz criterion and Constant Absolute Risk Aversion (CARA). More importantly, we show that when the uncertain link travel times are independently distributed, finding the path that minimizes travel time under the ACT criterion is essentially a shortest path problem. We also study the implications on Network Equilibrium (NE) model where travelers on the traffic network are characterized by their knowledge of the network uncertainty as well as their risk and ambiguity attitudes under the ACT. We derive and analyze the existence and uniqueness of solutions under NE. Finally, we obtain the Price of Anarchy that characterizes the inefficiency of this new equilibrium. The computational study suggests that as uncertainty increases, the influence of selfishness on inefficiency diminishes.     

Valuation of road projects with uncertain outcomes

This is a practical implementation of a multicriteria methodology developed to assess the risk involved in investment or policy projects. It is based on the specification of a non-linear, but piecewise linear, additive utility function, where the partial utility functions are specified as recursive exponential functions of only one curvature parameter. This allows for a reduction of the quantity of information necessary to build the utility function in a context of uncertainty. It is introduced in an aggregation model whereby the partial utility functions are built separately through a specific questioning process. The methodology is applied to the selection of road infrastructure projects of the Walloon Region in Belgium with several experts and civil servants from the Transport Ministry whose individual utility functions are derived. Software called MUSTARD is used, which stepwise and interactively helps the decision-makers to formulate the problem, build the criteria's distributions and state their preferences. The projects are ranked according to their rate of return computed on the basis of the projects' certain equivalent money values.     

Evaluation of the policies in British local transport plans with regard to the promotion of rail freight

Over the last couple of decades there has been a trend in Western Europe and North America towards making the railway industry more able to compete successfully with road for freight haulage through deregulation and/or structural change. This trend has been drive, partly, by a concern to produce more commercially viable railway industries, but also by concerns to reduce the environmental impacts of road haulage through modal shift. The latter in particular has led to the development of public policies supportive of rail freight, particularly at the national or international levels. This paper reports on qualitative research that analysed public policy aimed at promoting rail freight in Britain but, unusually, the focus was on local rather than national policy. The research was an investigation of the approach of local authorities to the encouragement of rail freight as evidenced through their Local Transport Plans. The paper concludes that in Britain there has been a significant amount of local policymaking for rail freight and that although national policy matters are of overriding importance, the role of local policy is significant and can be developed further.