Contingent valuation (CV) is a survey-based method frequently used for placing monetary values on environmental goods and services not bought and sold in the marketplace. CV is usually the only feasible method for including passive use considerations in an economic analysis, a practice that has engendered considerable controversy. The issue of what a CV study tries to value is first addressed from the perspective of a policymaker and then the controversy over the inclusion of passive use is taken up in more detail. The major issues and positions taken in the technical debate over the use of CV are summarized from a user's perspective. Key design and implementation issues involved in undertaking a CV survey are examined and the reader is provided with a set of factors to examine in assessing the quality of a CV study.

In the early 1990's the attention of economists was captured by empirical evidence suggesting rising income levels in developing countries could be good rather than bad for the environment. This evidence drove a stake into the heart of those opposing growth on environmental grounds. Ultimately, the view that income growth by itself eventually will be good for the environment also appears to be wrong because a causal relationship between income and environmental quality cannot be demonstrated. The original empirical estimates appear fragile at best compared to the use of more representative datasets, higher quality data, and more appropriate econometric techniques. More plausible stories revolve around good government, effective regulation and diffusion of technological change. These factors tend to be related in a diffuse manner with higher income and suggest it is likely, but not inevitable, that a society will chose to reduce pollution levels as it becomes wealthier.

Our results suggest that the anticipated path of China's Carbon Dioxide (CO2) emissions has dramatically increased over the last five years. The magnitude of the projected increase in Chinese emissions out to 2015 is several times larger than reductions embodied in the Kyoto Protocol. Our estimates are based on a unique provincial level panel data set from the Chinese Environmental Protection Agency. This dataset contains considerably more information relevant to the path of likely Chinese greenhouse gas emissions than national level time series models currently in use. Model selection criteria clearly reject the popular static environmental Kuznets curve specification in favor of a class of dynamic models with spatial dependence.

An international agreement to phase out the most potent greenhouse gas is both warranted and feasible.

Contingent valuation (CV) has become one of the most widely used non-market valuation techniques. CV's prominence is due to its flexibility and ability to estimate total value, including passive use value. Its use and the inclusion of passive use value in benefit-cost analyses and environmental litigation are the subject of a contentious debate. This paper discusses key areas of the debate over CV and the validity of passive use value. We conclude that many of the alleged problems with CV can be resolved by careful study design and implementation. We further conclude that claims that empirical CV findings are theoretically inconsistent are not generally supported by the literature. The debate over CV, however, has clarified several key issues related to nonmarket valuation and can provide useful guidance both to CV practitioners and the users of CV results.

This dissertation studies three distinctive aspects of environmental economics. Chapter 1 examines the impact of smoke from fires on agriculture production of the two main U.S. cash crops: corn and soybeans. Linking smoke plume maps derived from satellite images with county-level information on corn and soybean yields, I use a panel data approach to estimate exposure to smoke plumes treating their exact frequency, timing, and location in any year as exogenous shocks. Exposure to one more day of smoke, on average, reduces yields of corn and soybeans by 0.31% and 0.23%, respectively. To help put these results in an economic context for corn and soybeans, a 10% increase in smoke relative to 2019 results in an annual loss of almost $1 billion. Chapter 2 explores the interaction relation of temperature and precipitation with number of outdoor recreation trips. Using detailed information on outdoor recreation trips in England over a four-year period, I use a semi-parametric response surface approach to examine the interaction relation. I found that although daily visits increase with temperature and decrease with rain, these gradients only have small variations across rain or temperature. Interaction of the two variables plays a small role in outdoor recreation. Chapter 3 examines how the introduction of ridesharing services such as Uber and Lyft into the U.S. urban market influences trip choice decisions. Using data from the 2009 and 2017 National Household Travel Surveys, I show that the longer Uber and Lyft have been in an urban market, the greater the increase in the 2017 survey trips that were made using taxi/rideshare services relative to the 2009 survey benchmark. This increase is driven by an upward shift that is more pronounced for short and longer distance trips than for middle distance trips and is also more pronounced on weekdays relative to weekends. Ridesharing services are shown to be a substitute for short haul bus trips, but a complement with longer rail trips.

These essays study environmental policy and regulation, ecosystem service valuation, and the economic impacts of climate change. Chapter 1 explores the role of coastal wetlands in reducing property damage during tropical cyclones impacting the U.S. and estimates the economic value of this protective service. Chapter 2 investigates the effectiveness of a large-scale green stimulus measure in China: a major sales tax cut for greener vehicles. Chapter 3 studies the role of extreme weather in time-use decisions in China.

Chapter 1 examines the economic consequences of expanding housing supply in productive urban cities and analyzes how residential sorting plays a role in forming a new market equilibrium. Using the newly released 2013-2017 American Community Survey data, I construct an economic model system that includes the models characterizing household residential location choices and their simultaneous spatial interactions with local labor markets, housing markets, and urban amenities across geographical areas in California. I find that, in an open economy with agglomeration effects, the positive residential sorting largely undoes what the housing legislation aims to achieve and reduces the quality of urban amenities in productive cities.

Chapter 2 documents the relationship between climate amenities and locational choices in retirement. Using data from 2017 release of the American Community Survey, I construct a household residential location choice model and value climate amenities from the trade-offs among housing cost, climate amenities, and other locational attributes in a metropolitan statistical area (MSA). The results show that values of climate amenities vary with household demographic characteristics, and older households with a higher retirement income and disability have a higher marginal willingness to pay for a favorable climate. Using projected climate data, I find that over 2\% of retired households would relocate in response to this level of climate change.

Chapter 3 investigates how the residential real estate market, the second-largest asset market, in the U.S. has been fundamentally changed by the advent of online real estate websites. Using data on over 50,000 completed transactions obtained from Zillow, we first look at how the availability of the Zestimate influences both listing and sales prices. The factors influencing the listing realtor's decision to hold an open house are examined, as is the role such an open house has on the sales price and sales timing. Empirical results suggest that Zestimates play an important and complex role in driving the sales process and that holding an initial open house substantially increases sales price and decreases time on the market.

This chapter is both a commentary on and extension of the Carson and Groves (2007) (hereafter CG) article reprinted in this volume. The substantial attention the paper has received has been enormously gratifying. Reception of CG has largely been positive with little if any substantive criticism directed toward it; and, there are many papers now being presented at conferences that are testing or relying on various aspects of it.

Our remarks are organized into a series of short sections. The first points out that the main purpose of CG was to extend the revealed preference paradigm to cover some types of survey responses. The second notes that CG provides the theoretical foundation that some critics of contingent valuation (CV) had argued was missing. The third takes the concepts of “hypothetical” and “hypothetical bias” head on and argues that these concepts are, for the most part, ill-defined or simply wrong and have done enormous damage to clear and careful thinking about the nature of the response to stated preference questions. The fourth examines the properties of cheap talk which is often proposed as a way to reduced hypothetical bias. The fifth provides some elaboration on CG and the issue of how to interpret information extracted from preferences questions. The sixth poses an answer to the often asked question: Is a single binary discrete choice (SBC) question always the best elicitation format for a researcher to use? The seventh provides some elaboration on the payment card elicitation format, which in recent years has seen resurgence. The eighth turns to an examination of some of the properties of the now widely used discrete choice experiment. The ninth considers the usefulness of economic experiments to help determine the performance of preference elicitation formats. The last section addresses the relationship between CG and the behavioralist critique of neoclassical economics with a focus on the different-answers-to-the-same-underlying-question issue.

Chapter 1 of this dissertation seeks to understand the sources and nature of measurement errors in temperature variables, and how they can influence climate impact estimations. Measurement error in temperature variables is usually assumed to be “classical'' i.i.d. normal and “small''. This type of measurement error leads to attenuation bias, that is often small enough to be ignored. We show, however, that measurement errors in temperature variables are often large, distinctly non-normal, and vary systematically across space and time. The divergence between our empirical results and conventional wisdom stems from the fact that the construction of the temperature variables involves a series of steps, each of which introduces a distinct source of measurement error. This work is the first to formally characterize sources of measurement error in temperature variables and how they interact. Simulation results are provided that illustrate the influence of these sources of measurement errors on climate impact estimates. We further propose a correction method that can be used to obtain consistent estimates of the parameters of interest under the conditions identified.

In Chapter 2, I zoom in to examine one particular source of measurement error: measurement error induced by using coarse data to approximate daily mean temperature. Much of the historical records from weather stations around the world reports on minimum and maximum temperature and this practice is still followed by most stations. The minimum and maximum are averaged to approximate daily mean temperature, the exposure variable most commonly used in climate impact studies. Empirically, I find substantive differences between daily mean temperatures constructed using hourly temperature data and daily minimum and maximum temperature. This single source of measurement error can easily result in a 5 to 10\% bias in estimates of the impact of daily mean temperature on an output measure of economic interest, with the direction of the bias dependent on the location and season of the year.

Chapter 3 investigates climate impact models from another perspective: the representation of climate variables. The “bin” regression model has been put forward as a flexible semi-parametric method for representing a climate variable and it has emerged as the workhorse approach for empirical work (e.g., Deschênes and Greenstone, 2011). Our work is the first to formally explore econometric properties of the bin regression approach. We show that, although the bin regression approach often produces reasonable results, that the approach produces consistent parameter estimates only under very stringent and highly unlikely assumptions about the true data generating procedure. Problems with the bin regression approach are likely to be most severe in the tail bin categories, where most policy interest with respect to climate change impacts lies. We propose alternatives to bin model for the climate change impacts that produce consistent estimates and generally have better efficiency properties.