Using real options, this paper formulates an optimal stopping model for applying pest control measures when the density of a pest population varies randomly. A delay between successive pesticide applications is introduced to analyze the farmer’s expected marginal cost of reentry. This model is applied to the control of a foliar pest of apples via a pesticide, and solved numerically. A sensitivity analysis shows that the pest density that should trigger pesticide use can vary significantly with the pest density volatility. Incorporating pest randomness into simple decision rules may thus help better manage the chemicals applied to soils and crops

This paper presents a theory of harvesting that allows for partial harvests and accounts for the risk of extinction, for biological assets with size-dependent stochastic growth. The harvesting decision is formulated as a disinvestment problem in continuous time and generalized Faustmann formulas are derived. The probability of extinction is then analyzed for a wide class of growth functions. An illustration based on the logistic Brownian motion shows that both optimal biomass at harvest and harvest size do not vary monotonically with uncertainty. More generally, this paper illustrates the importance of properly accounting for barriers in stochastic investment problems. (C) 2003 Elsevier B.V. All rights reserved.

This paper presents a methodology for deriving the closed-form density of diffusions restricted to finite intervals with reflecting or absorbing barriers. Bounded diffusions are useful, for example, in finance, resource economics, or industrial organization. Results are derived for popular diffusions. (C) 2004 Elsevier B.V. All rights reserved.

The growth of electronic waste (e-waste) is of increasing concern because of its toxic content and low recycling rates. The e-waste recycling infrastructure needs to be developed, yet little is known about people’s willingness to fund its expansion. This paper examines this issue based on a 2004 mail survey of California households. Using an ordered logit model, we find that age, income, beliefs about government and business roles, proximity to existing recycling facilities, community density, education, and environmental attitudes are significant factors for explaining people’s willingness to pay an advanced recycling fee (ARF) for electronics. Most respondents are willing to support a 1% ARF. Our results suggest that policymakers should target middle-aged and older adults, improve programs in communities with existing recycling centers or in rural communities, and consider public-private partnerships for e-waste recycling programs.

Environmental studies of motor vehicles typically focus on air pollution or noise, but ignore water pollution. In this paper, we examine some of the impacts of motor vehicle transportation on non-point source and on groundwater pollution. Our estimates of the present value of costs for cleaning up leaking underground storage tanks and for controlling highway runoff for major arterials range from $45 billion to $235 billion, which is at least as much as noise damages. Our review of applicable measures suggests that effective policies should combine economic incentives, information campaigns, and enforcement measures, coupled with preventive environmental measures.

We analyze the impact of population uncertainty on the socially optimum timing of a congestion-relief project in a linear monocentric city with fixed boundaries, where congestion pricing cannot be implemented. This project requires time to bear fruit but no urban land. Under certainty, we show that utility maximization is roughly equivalent to a standard benefit-cost analysis (BCA). Under uncertainty, we derive an explicit optimal threshold for relieving congestion when the urban population follows a geometric Brownian motion. If the time to implement the project is short, we show analytically that deciding on the timing of congestion relief based on a BCA could lead to acting prematurely; the reverse holds if project implementation is long and uncertainty is large enough.

This dissertation proposes a methodology applicable worldwide to assess key health, climate and electricity system impacts of high penetrations of variable renewable energy (VRE), such as wind and solar energy, in the production of electricity. Three primary questions are addressed: (1) what are the health benefits and control costs of tightening emission standards for particulate matter (PM); (2) what are the health, climate and electricity grid impacts of high penetrations of VRE; and (3) is a 100% Renewable Portfolio Standard (RPS) electricity grid technically feasible in Northeast (NE) Brazil, and what are the associated health and climate benefits? The methodology I developed to answer these questions combines highly resolved spatial and temporal electricity grid simulation (via Plexos), atmospheric dispersion of power plant emissions (via CALPUFF), and human health impacts estimation (via BenMAP). The methodology is validated extensively over detailed case studies in NE Brazil, a region that has exceptionally high VRE and hydroelectric potential. Results for Question (1) indicate that when tightening emission standards, the health benefits outweigh the control costs by at least 50 times, even in a relatively clean region. Results for Question (2) show that health and climate benefits exceed US$267 million/yr and US$1.2 billion/yr respectively if NE Brazil transitions to a 45% VRE instead of a 30% VRE penetration in 2030. For Question (3), I find that a 100% RPS is not feasible using only wind, solar PV and hydroelectric resources in NE Brazil unless ~13% of demand can be flexibly imported and ~23% of generation can be flexibly exported; otherwise additional types of generation, storage and load balancing technology would need to be deployed. For this case, the health and climate benefits are at least US$433 million/yr and US$2.4 billion/yr respectively if NE Brazil transitions to a 100% RPS instead of a 30% VRE penetration in 2030.

Environmental studies of motor vehicles typically focus on air pollution or noise, but ignore water pollution. In this paper, we investigate the costs of reversing some of the environmental impacts of motor vehicle transportation on surface waters and groundwater. Our estimates of the cost of cleaning-up leaking underground storage tanks range from $6.5 billion to $19.6 billion, while control costs for highway runoff from major arterials in the United States are an order of magnitude larger (from $45.3 billion to $249 billion, all in 2005 $). Some causes of non-point source pollution were unintentionally created by regulations or could be addressed by changing the design of motor vehicles. Effective clean-up policies should emphasize prevention, coupled with public education, enforcement, and economic incentives. In general, preventing water pollution from motor vehicles would be much cheaper than cleaning it up.

Using a real options framework in continuous time, we examine two problems associated with the management of exotic pests under uncertainty. First, we analyze the optimal timing of investing in a control action. Given fixed control costs, we obtain a closed-form solution for when to control an exotic pest whose density varies randomly. Second, we define an analytical framework for finding the optimal expenditures for obtaining the relevant bioeconomic data. Our numerical results, based on a wide range of bioeconomic parameters, highlight the importance of accounting for uncertainty and irreversibility when developing simple rules to manage exotic pests. (c) 2004 Elsevier B.V. All rights reserved.