Estimating Private Incentives for Wildfire Risk Mitigation: Determinants of Demands for Different Fire-Safe Actions

In this article we develop a general conceptual model of a property-owner’s decision to implement actions to protect his property against wildfire threat. Assuming a prospective-utility maximizing decision maker, we derive a system of demand functions for fire-safe actions that characterizes factors affecting individual decision making. We then empirically estimate the demands for various fire-safe actions functions using survey data of property owners facing a wildfire threat in Nevada. We find that the probability of individuals implementing some fire-safe action increases with value of the residence, previous experience with wildfire, the property being used as the primary residence, positive attitude towards wildfire management methods on public lands, and connectedness of community members. A lower probability of implementing fire-safe actions is found for those who value pristine nature and privacy that nature provides.Risk and Uncertainty,

Wildfire Prevention and Mitigation: The Case of Southern Greece

The summer of 2007 was the worst wildfire season ever recorded in Greek contemporary history with approximately 270,000 hectares of land burned throughout the country. The area most severely hit was the Peloponnesian state of Elia. Econometric analysis with the use of primary and secondary data was carried out in an attempt to disentangle the effects of a variety of factors in the spread of the fire. The findings identified villages in low altitudes and steep slopes as the ones most vulnerable to the risk of wildfire. Wind speed played a significant role in exacerbating the blazes. As far as human factors are concerned population density was negatively associated with wildfire spread. In addition, the more olive groves were found within the boundaries of a village the less damage the settlement was found to have sustained. Finally, participation of local people in fire abatement efforts was significant in reducing wildfire risk. We conclude that public policy should consider a more holistic approach to wildfire management; one that would incorporate the “human-fire” interactions more thoroughly and balance the importance of ecological variables and social parameters in both wildfire prevention and mitigation

Dynamic factor analysis for panel data: A generalized model

I develop a generalized dynamic factor model for panel data with the goal of estimating an unobserved index. While similar models have been developed in the literature of dynamic factor analysis, my contribution is threefold. First, contrary to simple dynamic factor analysis where multiple attributes of the same subject are measured at each time period, my model also accounts for multiple subjects. It is therefore applicable to a panel data framework (i.e. multiple attributes for multiple subjects observed over time). Second, it estimates an unobserved index for every subject for every time period, as opposed to previous work where a single unobserved index was estimated for all subjects for every time period. Third, I address the complexity of the model by developing a novel iterative estimation process which we call the Two-Cycle Conditional Expectation-Maximization (2CCEM) algorithm. The 2CCEM algorithm is flexible enough to handle a variety of different types of datasets. The model is applied on a panel measuring attributes related to the operation of water and sanitation utilities. The goal is to estimate a dynamic benchmarking index that will capture the financial and operational performance of these utilities

Dynamic Factor Analysis for Short Panels: Estimating Performance Trajectories for Water Utilities

We develop a dynamic factor model for panel data with a short time dimension (i.e. n<15). Unlike most of the work in the DFM literature where one common factor is estimated for a group of cross sectional units, our interest lies in the estimation of a latent variable for each cross sectional unit at every point in time. This difference increases the computational challenges of the estimation process. To facilitate estimation we develop the “Two-Cycle Conditional Expectation-Maximization” (2CCEM) algorithm which is a variant of the EM algorithm and it’s extensions (Dempster et al. 1977; Meng and Rubin 1993; Liu and Rubin 1994). Initially, the latent variable is estimated (first cycle) and then the dynamic component is incorporated into the estimation process (second cycle). The estimates of each cycle are updated with information from the estimates of the previous cycle until convergence is achieved. We provide simulation results demonstrating consistency of our 2CCEM estimator. One of the advantages of this work is that the estimation strategy can account for multiple cross sectional units with a short time dimension, and is flexible enough to be used in different types of applications. We apply our model to a dataset of 853 water and sanitation utilities from 45 countries and use the 2CCEM algorithm to estimate performance trajectories for each utility

A Generalized Dynamic Factor Model for Panel Data: Estimation with a Two-Cycle Conditional Expectation-Maximization Algorithm

We develop a generalized dynamic factor model for panel data with the goal of estimating an unobserved index. While similar models have been developed in the literature of dynamic factor analysis, our contribution is threefold. First, contrary to simple dynamic factor analysis where multiple attributes of the same subject are measured at each time period, our model also accounts for multiple subjects. It is therefore suitable to a panel data framework. Second, our model estimates a unique unobserved index for every subject for every time period, as opposed to previous work where a temporal index common to all subjects was used. Third, we develop a novel iterative estimation process which we call the Two-Cycle Conditional Expectation-Maximization (2CCEM) algorithm and is flexible enough to handle a variety of different types of datasets. The model is applied on a panel measuring attributes related to the operation of water and sanitation utilities

Racial, ethnic, and income disparities in air pollution: A study of excess emissions in Texas

Objective: Excess emissions are pollutant releases that occur during periods of startups, shutdowns or malfunctions and are considered violations of the U.S. Clean Air Act. They are an important, but understudied and under-regulated, category of pollution releases given their frequency and magnitude. In this paper, we examine the demographic correlates of excess emissions, using data from industrial sources in Texas. Methods: We conduct two complementary sets of analyses: one at the census tract level and one at the facility level. At the census tract level, we use a multinomial logit model to examine the relationships between racial, ethnic, and income characteristics and the incidence of excess emissions. At the facility level, we first estimate a logit model to examine whether these characteristics are associated with facilities that emit excess emissions, and then, conditional on the presence of excess emissions, we use ordinary least square regression to estimate their correlation with the magnitude of releases. Results: Across our analyses, we find that the percentage of Black population and median household income are positively associated with excess emissions; percentage of college graduate, population density, median housing value, and percentage of owner-occupied housing unit are negatively associated with excess emissions. We, however, have not found a clear and significant relationship between the percentage of Hispanic population and excess emissions

Tying enforcement to prices in emissions markets: An experimental evaluation

We present results from laboratory emissions permit markets designed to investigate the transmission of abatement cost risk to firms' compliance behavior and regulatory enforcement strategies. With a fixed expected marginal penalty, abatement cost shocks produced significant violations and emissions volatility as predicted. Tying the monitoring probability to average permit prices effectively eliminated noncompliance, but transmitted abatement cost risk to monitoring effort. Tying the penalty to average prices reduced violations, but did not eliminate them. Some individuals in these treatments sold permits at low prices, presumably in an attempt to weaken enforcement. While tying sanctions directly to prevailing permit prices has theoretical and practical advantages over tying monitoring to prices, our results suggest that tying sanctions to prices may not be as effective as predicted without additional modifications

I Want In On That: Community-level Policies for Unconventional Gas Development in New York

We investigate geospatial and socio-demographic attributes that explain differences in community-level policies affecting unconventional gas development (UGD) in New York. We examine local policy decisions (i.e., municipal bans, moratoria, and pre-emptive resolutions supporting development) through ordered probit models and middle-inflated and zero-inflated ordered probits to account for communities without UGD policies and estimate a spatial ordered probit to address spatial correlations between communities’ decisions. Our findings suggest that New York communities near Pennsylvania UGD are more likely to sup-port UGD. Communities that are predominantly Democrat or have more citizens who have bachelor’s de-grees are more likely to adopt policies opposing UGD