Spatio-Temporal Wildland Arson Crime Functions

Wildland arson creates damages to structures and timber and affects the health and safety of people living in rural and wildland urban interface areas. We develop a model that incorporates temporal autocorrelations and spatial correlations in wildland arson ignitions in Florida. A Poisson autoregressive model of order p, or PAR(p) model, is estimated for six high arson Census tracts in the state for the period 1994-2001. Spatio-temporal lags of wildland arson ignitions are introduced as dummy variables indicating the presence of an ignition in previous days in surrounding Census tracts and counties. Temporal lags of ignition activity within the Census tract are shown to be statistically significant and larger than previously reported for non-spatial variants of the PAR(p) model. Spatio-temporal lagged relationships with current arson that were statistically significant show that arson activity up to a county away explains arson patterns, and spatio-temporal lags longer than two days were not significant. Other variables showing significance include weather and wildfire activity in the previous six years, but prescribed fire and several variables that provide evidence that such activity is consistent with an economic model of crime were less commonly significant.Resource /Energy Economics and Policy,

Nonlinear Models of Exchange Rate Pass-Through in International Forest Product Markets

Forest Products, International Price Linkages, Exchange Rate Pass-Through, Vector Error Correction Models (VECM), Thresholds, International Relations/Trade, Research Methods/ Statistical Methods,

Copula-Based Nonlinear Models of Spatial Market Linkages

Replaced with revised version of paper 06/28/11.Spatial Market Linkages, Copula Models, State-dependence, Forest Products, Research Methods/ Statistical Methods,

Projecting global and regional outlooks for planted forests under the shared socio-economic pathways

There is rising global interest in growing more trees in order to meet growing population, climate change, and wood energy needs. Using recently published data on planted forests by country, we estimated relationships between per capita income and planted forest area that are useful for understanding prospective planted forest area futures through 2100 under various United Nations Intergovernmental Panel on Climate Change-inspired Shared Socio-economic Pathways (SSPs). Under all SSPs, projections indicate increasing global planted forest area trends for the next three to four decades and declining trends thereafter, commensurate with the quadratic functions employed. Our projections indicate somewhat less total future planted forest area than prior linear forecasts. Compared to 293 million ha (Mha) of planted forests globally in 2015, SSP5 (a vision of a wealthier world) projects the largest increase (to 334 Mha, a 14% gain) by 2055, followed by SSP2 (a continuation of historical socio-economic trends, to 327 Mha, or an 11% gain), and SSP3 (a vision of a poorer world, to 319 Mha, a 9% gain). The projected trends for major world regions differ from global trends, consistent with differing socio-economic development trajectories in those regions. Our projections based on empirical FAO data for the past 25 years, as well as those by other researchers, suggest that achieving the much more ambitious global planted forest targets proposed recently will require exceptional forest land and investment supply shifts.Peer reviewe

ECONOMICALLY OPTIMAL WILDFIRE INTERVENTION REGIMES

Wildfires in the United States result in total damages and costs that are likely to exceed billions of dollars annually. Land managers and policy makers propose higher rates of prescribed burning and other kinds of vegetation management to reduce amounts of wildfire and the risks of catastrophic losses. A wildfire public welfare maximization function, using a wildfire production function estimated using a time series model of a panel of Florida counties, is employed to simulate the publicly optimal level of prescribed burning in an example county in Florida (Volusia). Evaluation of the production function reveals that prescribed fire is not associated with reduced catastrophic wildfire risks in Volusia County Florida, indicating a short-run elasticity of -0.16 and a long-run elasticity of wildfire with respect to prescribed fire of -0.07. Stochastic dominance is used to evaluate the optimal amount of prescribed fire most likely to maximize a measure of public welfare. Results of that analysis reveal that the optimal amount of annual prescribed fire is about 3 percent (9,000 acres/year) of the total forest area, which is very close to the actual average amount of prescribed burning (12,700 acres/year) between 1994-99.Resource /Energy Economics and Policy,

Net reductions or spatiotemporal displacement of intentional wildfires in response to arrests? : evidence from Spain

Research to date has not examined how the impacts of arrests manifest across space and time in environmental crimes. We evaluate whether arrests reduce or merely spatiotemporally displace intentional illegal outdoor firesetting. Using municipality-level daily wildfire count data from Galicia, Spain, from 1999 to 2014, we develop daily spatiotemporal ignition count models of agricultural, non-agricultural and total intentional illegal wildfires as functions of spatiotemporally lagged arrests, the election cycle, seasonal and day indicators, meteorological factors and socioeconomic variables. We find evidence that arrests reduce future intentional illegal fires across space in subsequent time periods.This research was partly funded by Project ECO2017–89274-R MINECO/AEI/FEDER, UES

Spatial patterns of social vulnerability in relation to wildfire risk and wildland-urban interface presence

Wildfires have greater impacts on socially vulnerable communities. Identifying these vulnerable communities and enhancing understanding of what influences their susceptibility to wildfires can guide the design of spatially targeted strategies in preparedness, mitigation plans, and adaptation strategies. This paper investigates the heterogeneous spatial coincidence of social vulnerability and wildfire risk in Galicia (Spain) at the municipality level. Results show that socioeconomic status, rates of dependence on social programs, and household unit characteristics are factors that contribute the most to social vulnerability. In general, municipalities with the highest proportion of their area in the Wildland-Urban Interface (WUI) have the lowest social vulnerability. Within Galicia, locations with high social vulnerability and high wildfire risk are spatially concentrated in the south and tend to be low-population density communities, often in remote locations and with relatively high percentages of elderly people. Our findings provide an empirical foundation for wildfire management planning that accounts for the spatial distribution of vulnerable communitiesThis research was supported by project ECO2017-89274-R (MINECO/AEI/FEDER, UE)S

Predicting cannabis cultivation on national forests using a rational choice framework

Government agencies in the United States eradicated 10.3 million cannabis plants in 2010. Most (94%) of these plants were outdoor-grown, and 46% of those were discovered on federal lands, primarily on national forests in California, Oregon, and Washington. We developed models that reveal how drug markets, policies, and environmental conditions affect grow siting decisions. The models were built on a rational choice theoretical structure, and utilized data describing 2322 cannabis grow locations (2004–2012) and 9324 absence locations in the states\u27 national forests. Predictor variables included cannabis market prices, law enforcement density, and socioeconomic, demographic, and environmental variables.We also used the models to construct regional maps of grow site likelihood. Significant predictors included marijuana street price and variables associated with grow site productivity (e.g., elevation and proximity to water), production costs, and risk of discovery. Overall, the pattern of grow site establishment on national forests is consistent with rational choice theory. In particular, growers consider cannabis prices and law enforcement when selecting sites. Ongoing adjustments in state cannabis laws could affect cultivation decisions on national forests. Any changes in cannabis policies can be reflected in our models to allow agencies to redirect interdiction resources and potentially increase discovery success

Nonlinear exchange rate pass-through in timber products: the case of oriented strand board in Canada and the United States

We assess exchange rate pass–through (ERPT) for U.S. and Canadian prices for oriented strand board (OSB), a wood panel product used extensively in U.S. residential construction. Because of its prominence in construction and international trade, OSB markets are likely sensitive to general economic conditions. In keeping with recent research (e.g., Al-Abri and Goodwin, 2009; Larue et al., 2010), we examine regime–specific ERPT effects; we use a smooth transition vector error correction model. We also build on work by Nogueira, Jr. and Leon-Ledesma (2011) and Chew et al. (2011) in considering ERPT asymmetries associated with a measure of general macroeconomic activity. Our results indicate that during expansionary periods ERPT is modest, at least initially, but during the recent financial crises ERPT effects were quite large

Predicting cannabis cultivation on national forests using a rational choice framework

Government agencies in the United States eradicated 10.3 million cannabis plants in 2010. Most (94%) of these plants were outdoor-grown, and 46% of those were discovered on federal lands, primarily on national forests in California, Oregon, and Washington. We developed models that reveal how drug markets, policies, and environmental conditions affect grow siting decisions. The models were built on a rational choice theoretical structure, and utilized data describing 2322 cannabis grow locations (2004–2012) and 9324 absence locations in the states\u27 national forests. Predictor variables included cannabis market prices, law enforcement density, and socioeconomic, demographic, and environmental variables.We also used the models to construct regional maps of grow site likelihood. Significant predictors included marijuana street price and variables associated with grow site productivity (e.g., elevation and proximity to water), production costs, and risk of discovery. Overall, the pattern of grow site establishment on national forests is consistent with rational choice theory. In particular, growers consider cannabis prices and law enforcement when selecting sites. Ongoing adjustments in state cannabis laws could affect cultivation decisions on national forests. Any changes in cannabis policies can be reflected in our models to allow agencies to redirect interdiction resources and potentially increase discovery success