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This study evaluated the hypothesis that urban-tree planting increases neighborhood gentrification in Portland, Oregon. We defined gentrification as an increase in the median sales price of single-family homes in a Census tract compared to other tracts in the city after accounting for differences in the housing stock such as house size and number of bathrooms. We used tree-planting data from the non-profit Friends of Trees, who have planted 57,985 yard and street trees in Portland (1990–2019). We estimated a mixed model of gentrification (30 years and 141 tracts) including random intercepts at the tract level and a first-order auto-regressive residual structure. Tract-level house prices and tree planting may be codetermined. Therefore, to address potential endogeneity of tree planting in statistical modeling, we lagged the number of trees planted by at least one year. We found that the number of trees planted in a tract was significantly associated with a higher tract-level median sales price, although it took at least six years for this relationship to emerge. Specifically, each tree was associated with a $131 (95% CI: $53-$210; p-value=0.001) increase in tract-level median sales price six years after planting. The magnitude of the association between the number of trees planted and median sales price generally increased as the time lag lengthened. After twelve years, each tree was associated with a $265 (95% CI: $151-$379; p-value

Developing Detailed Shared Socioeconomic Pathway (SSP) Narratives for the Global Forest Sector

Planted forests are a rising share of total forests globally and an increasingly important source of timber product output, affecting national and global markets. We estimated econometric models of planted forest area by OECD and non-OECD country groups that control for economic, institutional and environmental policies likely to influence future changes in planted forest area. The models are then used to project planted forest area over next 55 years for 180 countries under five alternative scenarios of global socio-economic changes, represented in shared socioeconomic pathways (SSPs), adjunct products emerging from the Fifth Assessment of the Intergovernmental Panel on Climate Change (IPCC). By embedding key features of the SSP projections into a global forest sector model, we evaluate how planted forests lead to different global forest product market outcomes for each SSP, compared to corresponding outcomes where planted forests are not considered separately. Projected global planted forest area in 2070 ranges from 379 million ha (Mha) for SSP3 (a relatively poor and unequal world) to 475 Mha under SSP5 (a relatively wealthier and more equal world), representing respective increases of 46% and 66% compared to 2015. SSPs with the highest planted forest area increases have the lowest product prices (down by 12% by 2070, compared to SSP5 without planted forests) and higher global forest products production and consumption quantities (by as much as 3.3% by 2070, compared to SSP5 without planted forests). However, production does not increase in all countries by similar amounts, due to changes in relative advantages in production brought about by reduced product prices.

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.

The southern pine beetle (SPB) is among the leading biological agents killing southern pine species in the eastern United States. In light of recognized spatiotemporal autocorrelation in SPB outbreaks, we devise a spatiotemporal block bootstrapping method that can be applied to analyze spatiotemporally dependent infestations. We also identify the relevant risk determinants and evaluate their impacts on the frequency of SPB outbreaks. For example, we find forest type, climate, and natural disasters like storm and forest management are all significantly associated with SPB risks. Using the results of a statistical model, we design a county‐level group index insurance plan that generates estimates of actuarially fair premium rates for timber stands containing southern pine species. Given that no government‐provided compensation scheme for SPB epidemics currently exists, application of this new insurance product could reduce forest owners losses. Our study offers an approach to analyzing and protecting against risks of other destructive pests affecting the timber sector.

Forest resources are critical to environmental, economic, and social development, and there is substantial interest in understanding how global forest area will evolve in the future. Using an Environmental Kuznets Curve (EKC) model of total forest area that we updated using more recent data sets, we projected forest area through 2100 in 168 countries using variables including income, rural population density, and the size of the labor force under different world visions drawn from alternative Intergovernmental Panel on Climate Change socioeconomic pathways (SSPs). Results provided support for the existence of an EKC for total forest area, with rural population density negatively affecting forest area and labor force size positively affecting forest area. The projections showed modest and continuous increases in global forest area in all the SSPs, but varying trends for major world regions, which is consistent with the projected trends from the explanatory variables in each country. Aggregate global forest area is projected to increase by 7% as of 2100 relative to 2015 levels in SSP3, which predicts a future with the lowest rate of economic growth, and by 36% in SSP5, which is a future with the highest rate of economic growth and greater economic equality across countries. The results show how projections driven only by income produce biased results compared to the projections made with an EKC that includes rural population density and labor force variables.

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, UE SI

Downscaled climate projections need to be linked to downscaled projections of population and economic growth to fully develop implications for land, natural resources, and ecosystems for future scenarios. We develop an empirical spatiotemporal approach for jointly projecting population and income at the county scale in the United States that is consistent with neoclassical economic growth theory and overlapping labor markets and that accounts for labor migration and spatial spillovers. Downscaled projections generated for the five Shared Socioeconomic Pathways used to support global scenario analysis generally show growth focused around relatively few centers especially in the southeast and western regions, with some areas in the Midwest and northeast experiencing population declines. Results are consistent with economic growth theory and with historical trends in population change and convergence of per capita personal income across US counties.

Development of efficient forest wildfire policies requires an understanding of the underlying reasons behind forest fire occurrences. Globally, there is a close relationship between forest wildfires and human activities; most wildfires are human events due to negligence (e.g., agricultural burning escapes) and deliberate actions (e.g., vandalism, pyromania, revenge, land use change attempts). We model the risk of wildfire as a function of the spatial pattern of urban development and the abandonment/intensity of agricultural and forestry activities, while controlling for biophysical and climatic factors. We use a count data approach to model deliberately set fires in Galicia, N.W. Spain, where wildfire is a significant threat to forest ecosystems, with nearly 100,000 wildfires recorded during a thirteen-year period (1999e2011). The spatial units of analysis are more than 3600 parishes. Data for the human influences are derived from fine-resolution maps of wildlandeurban interface (WUI), housing spatial arrangements, road density, forest ownership, and vegetation type. We found wildfire risk to be higher where there are human populations and development/urbanisation pressure, as well as in unattended forest areas due to both rural exodus and a fragmented forest ownership structure that complicates the profitability of forestry practices. To better help direct management efforts, parameter estimates from our model were used to predict wildfire counts under alternative scenarios that account for variation across space on future landuse conditions. Policies that incentivize cooperative forest management and that constrain urban development in wildlands at hotspot fire locations are shown to reduce wildfire risk. Our results highlight the need for spatially targeted fire management strategies.

Municipal fire departments responded to approximately 53,000 intentionally-set fires annually from 2003 to 2007, according to National Fire Protection Association figures. A disproportionate amount of these fires occur in spatio-temporal clusters, making them predictable and, perhaps, preventable. The objective of this research is to evaluate how the aggregation of data across space and target types (residential, non-residential, vehicle, outdoor and other) affects daily arson forecast accuracy for several target types of arson, and the ability to leverage information quantifying the autoregressive nature of intentional firesetting. To do this, we estimate, for the city of Detroit, Michigan, competing statistical models that differ in their ability to recognize potential temporal autoregressivity in the daily count of arson fires. Spatial units vary from Census tracts, police precincts, to citywide. We find that (1) the out-of-sample performance of prospective hotspot models for arson cannot usefully exploit the autoregressive properties of arson at fine spatial scales, even though autoregression is significant in-sample, hinting at a possible bias-variance tradeoff; (2) aggregation of arson across reported targets can yield a model that differs from by-target models; (3) spatial aggregation of data tends to increase forecast accuracy of arson due partly to the ability to account for temporally dynamic firesetting; and (4) arson forecast models that recognize temporal autoregression can be used to forecast daily arson fire activity at the Citywide scale in Detroit. These results suggest a tradeoff between the collection of high resolution spatial data and the use of more sophisticated modeling techniques that explicitly account for temporal correlation.

This study reports the results from a 5 year simulation of forest thinning intended to reduce fire hazard on publicly managed lands in the western United States. A state simulation model of interrelated timber markets was used to evaluate the timber product outputs. Approximately 84 million acres (34 million hectares), or 66% of total timberland in the western United States, is publicly managed; of this 78 million acres (31.6 million hectares) are managed by the federal agencies. We considered three budget scenarios using a least-expensive highest-hazard area first policy. Our intention with this simulation is not to definitively answer questions about where or how to conduct treatments to reduce fire hazard on public lands but rather to begin to develop tools that can be used to inform such a policy debate. Considerable development of this tool is still needed before it will be useful for that purpose. Our initial simulations nonetheless provide insight into what might happen if available funds were allocated to the least-expensive highest-hazard areas across the west. Using assumptions of (1) an annual “subsidy” (payments for treatments), (2) the treatment costs, (3) the priority ranking by forest type, (4) fire hazard level, and (5) the wildland–urban interface (WUI) status, the simulation suggests that lodgepole pine (Pinus contorta), ponderosa pine (Pinus ponderosa), spruce (Picea spp.)–fir (Abies spp.) and Douglas-fir (Pseudotsuga menziesii) are projected to be major forest types treated in the West. A combination of our treatment ranking assumptions and the low total treatable WUI acres on public timberland caused the model to concentrate almost exclusively on all the WUI stands and non-WUI ponderosa pine forest type at the budget of $150 million and $300 million. With the further increase of budget, a large proportion of treated acres are lodgepole pine and spruce–fir forest types using the thin-from-below approach. About 41% of the volume removals are sawtimber for all the public timberland treated under the low budget scenario ($150 million/year), 58 for moderate budget ($300 million/year), 50 for the high budget scenario ($1500 million/year). Under the moderate budget case ($300 million a year), about 19% of the total wood removed is projected to come from trees less than 5-inches (12.7 cm) in diameter at breast height (dbh), and another 16% of the biomass is expected from trees 20-inches (50.8 cm) dbh and above. Published by Elsevier B.V.

Wildfires create damages in the wildland–urban interface (WUI) that total hundreds of millions of dollars annually in the United States. Understanding how fires are produced in built-up areas near and within fire prone landscapes requires evaluating and quantifying the roles that humans play in fire regimes. We outline a typology of wildfire production functions (WPFs) and empirically estimate three broad classes of WPFs: fire event (ignitions), fire aggregate extent, and a combination function of fire effect and aggregate extent (an intensity-weighted aggregate extent model). Our case study is Florida, which contains an abundance of both wildland and human populations. We find that socio-economic variables play statistically significant roles in all three estimated production functions. At the county level, we find that population and poverty are usually positively related to annual wildfire area and intensity-weighted fire area, while unemployment is negatively related to ignitions, area, and intensity-weighted wildfire area. Poverty is found to be negatively related to wildfire ignitions, while the number of police are correlated with fewer ignitions. These results suggest that managers and decision makers should be aware of socio-economic variables and consider them in their wildland fire management decisions in the wildland–urban interface. Our results also emphasize the importance of including such variables in statistical models of wildfire risk in the WUI.

Six Poisson autoregressive models of order p[PAR(p)] of daily wildland arson ignition counts are estimated for five locations in Florida (1994–2001). In addition, a fixed effects time-series Poisson model of annual arson counts is estimated for all Florida counties (1995–2001). PAR(p) model estimates reveal highly significant arson ignition autocorrelation, lasting up to eleven days, in addition to seasonality and links to law enforcement, wildland management, historical fire, and weather. The annual fixed effects model replicates many findings of the daily models but also detects the influence of wages and poverty on arson, in ways expected from theory. All findings support an economic model of crime. Copyright 2005, Oxford University Press.

Catastrophic shocks to existing stocks of a renewable resource can cause long-run price shifts. With timber, these long-run price shifts may be accompanied by a short-run price drop due to salvage. Hurricane Hugo damaged 20% of southern pine timber in the South Carolina Coastal Plain in 1989. To estimate the short- and long-run effects of the hurricane on the prices of timber stocks, we estimated an intervention model of the residuals of cointegration of South Carolina sawtimber and pulpwood stumpage prices with prices of similar products from other regions. Modeling revealed a 30% negative price spike due to salvage and a long-run enhancement effect, leading to prices that are 10% to 30% higher than they would have been had Hugo not occurred. Copyright 2000, Oxford University Press.

It is crucial for fire prevention policies to assess the spatial patterns of human-started fires and their relationship with geographical and socioeconomic aspects. This study uses fire reports for the period 1988-2006 in Galicia, Spain, to analyze the spatial distribution of human-induced fire risk attending to causes and underlying motivations associated with fire ignitions. Our results show that there are four distinctive types of municipalities in this region according to the incidence of intentional agricultural-livestock fires, pyromaniacal behavior, negligence, and unknown causes. They highlight that study of the spatial properties of the human causes and motivations of forest fire activity can provide valuable information for detecting the presence of non-random clusters of fires of various causes in particular locations, where fire management planning should be evaluated more in depth. © 2010 WIT Press.

Increasing severity of recent wildfires, storms, pest outbreaks, and biological invasions has intensified concern among governmental agencies, private enterprises, and the general public regarding the future of forest resources. Economic analysis can help decision-makers understand the causes and consequences of forest disturbances, as well as evaluate trade-offs, and set priorities. It is the premise of this book that similarities existing among forest disturbances permit the development of a unified framework for economic analysis. This book sketches out how this framework could be constructed, provides an overview and summary of current research in the economics of forest disturbances, and illustrates how economic theory and empirical methods can be applied to address specific disturbances. From an economic perspective, a forest disturbance can be defined as an event that interrupts or impedes the flow of goods and services provided by forest ecosystems that are desired by people. This definition parallels the ecological definition of a natural disturbance as " ... any relatively discrete event in time that disrupts ecosystem, community, or population structure and changes resources, substrate availability, or the physical environment" (White and Pickett 1985, p. 7). Although timber harvesting and land use change are forest disturbances according to this definition, in this book we address large-scale natural disturbances that can be mediated and modified by human actions. Examples of such large-scale natural forest disturbances occurring during the past century include the chestnut blight which largely eliminated chestnut trees from hardwood forests in the eastern United States, Hurricane Katrina which blew down large swaths of forest in the United States Southeast, and the 1988 fires in Yellowstone National Park and surrounding forest ecosystems that burned more than onequarter million hectares. Catastrophic disturbances affect both public and private land, and the management interventions applied to mitigate damages will vary depending on the

Natural disturbances in forests are driven by physical and biological processes. Large, landscape scale disturbances derive primarily from weather (droughts, winds, ice storms, and floods), geophysical activities (earthquakes, volcanic eruptions, even asteroid strikes), fires, insects, and diseases. Humans have always been affected by these processes and have invented ways to harness such processes or manipulate vegetation to enhance the values obtained from nature or reduce their negative impacts on human societies. For example, humans have cleared brush using fire to reduce pest! populations and encourage forage for animals (Pyne 1995). Historically, humans have relied on traditions, rules of thumb, and trial and error to predict how their actions may affect disturbance probabilities and characteristics. More recently, economic assessment tools have helped gauge the consequences of natural disturbances on forests . As the availability of science, technology, and environmental data have improved, scientists and economists have been able to quantify disturbances as production processes that emanate from a combination of biological, physical, and (or) human-initiated inputs. Ecologists have long recognized that disturbances lead to changes in ecological corrmlUnities, which subsequently affect human societies. Economists, on the other hand, have been focused on understanding how humans can intervene to alter both the frequency and severity of natural disturbances. Improving scientific and economic assessment tools, and experience using them, have in turn helped us to appreciate the many consequences of natural disturbances. The objectives of this chapter are to (1) define disturbances and their stages, (2) discuss how mathematical expressions of disturbance processes, disturbance production functions, may differ from the production functions defined in neoclassical economics, (3) identify the stages of disturbances, (4) provide a typology of production functions relevant to forest

Research shows that some categories of human-ignited wildfires may be forecastable, owing to their temporal clustering, with the possibility that resources could be predeployed to help reduce the incidence of such wildfires. We estimated several kinds of incendiary and other human-ignited wildfire forecast models at the weekly time step for tribal land units in the United States, evaluating their forecast skill out of sample. Analyses show that an autoregressive conditional Poisson model of both incendiary and non-incendiary human-ignited wildfires is more accurate out of sample compared with alternatives, and the simplest of the autoregressive conditional Poisson models performed the best. Additionally, an ensemble of these and simpler, less analytically intensive approaches performed even better. Wildfire hotspot forecast models using all model types were evaluated in a simulation mode to assess the net benefits of forecasts in the context of law-enforcement resource reallocations. Our analyses show that such hotspot tools could yield large positive net benefits for the tribes in terms of suppression expenditures averted for incendiary wildfires but that the hotspot tools were less likely to be beneficial for addressing outbreaks of non-incendiary human-ignited wildfires.

Humans cause more than 55% of wildfires on lands managed by the USDA Forest Service and US Department of the Interior, contributing to both suppression expenditures and damages. One means to reduce the expenditures and damages associated with these wildfires is through fire prevention activities, which can include burn permits, public service programs or announcements, outreach efforts to schools, youth groups and equipment operators, and law enforcement. Using data from 17 US Bureau of Indian Affairs tribal units, we modelled the effect of prevention programs and law enforcement on the number of human-caused ignitions. We also included weather and lagged burned area in our estimation of fixed-effects count models. The results show that prevention activities led to significant reductions in wildfires caused by escaped campfires, juveniles, fire-use (e.g. escaped debris burns) and equipment. Increased law enforcement resulted in fewer incendiary- and equipment-caused wildfires. Using average suppression expenditures by wildfire and our estimate of avoided wildfires per additional year of prevention, we estimate partial benefit–cost ratios of greater than 4.5 for all Bureau of Indian Affairs regions for the continuation of the prevention program.

We report daily time series models containing both temporal and spatiotemporal lags, which are applied to forecasting intentional wildfires in Galicia, Spain. Models are estimated independently for each of the 19 forest districts in Galicia using a 1999–2003 training dataset and evaluated out-of-sample with a 2004–06 dataset. Poisson autoregressive models of order P – PAR(P) models – significantly out-perform competing alternative models over both in-sample and out-of-sample datasets, reducing out-of-sample root-mean-squared errors by an average of 15%. PAR(P) and static Poisson models included covariates deriving from crime theory, including the temporal and spatiotemporal autoregressive time series components. Estimates indicate highly significant autoregressive components, lasting up to 3 days, and spatiotemporal autoregression, lasting up to 2 days. Models also applied to predict the effect of increased arrest rates for illegal intentional firesetting indicate that the direct long-run effect of an additional firesetting arrest, summed across forest districts in Galicia, is –139.6 intentional wildfires, equivalent to a long-run elasticity of –0.94.

We analyse the spatio-temporal structure of wildfire ignitions in the St Johns River Water Management District in north-eastern Florida. We show, using tools to analyse point patterns (e.g. the L-function), that wildfire events occur in clusters. Clustering of these events correlates with irregular distribution of fire ignitions, including lightning and human sources, and fuels on the landscape. In addition, we define a relative clustering index that summarizes the amount of clustering over various spatial scales. We carry our analysis in three steps: purely temporal, purely spatial, and spatio-temporal. Our results show that arson and lightning are the leading causes of wildfires in this region and that ignitions by railroad, lightning, and arson are spatially more clustered than ignitions by other accidental causes.