Wildfire Policy in Mediterranean France: How Far is it Efficient and Sustainable?

A new fire policy reinforcing aggressive fire suppression was established in Mediterranean France in response to the devastating wildfires of the 1990s, but to what extent this has changed fire activity yet remains poorly understood. For this purpose, we compared the number and location of ignitions and of burned areas between two 20‐year periods (1975–1994 vs. 1995–2014), in parallel to the changes in fuel covering, human activity promoting ignitions, and fire weather. The number of fires decreased almost continuously since 1975, but sharply after 1994, suggesting an effect of better fire prevention due to the new policy. But the major change in fire activity is a considerable reduction in fire size and burned areas after 1994, especially during summer and in the most fire‐prone places, in response to massive efforts put into fire suppression. These reductions have occurred while the covering by fuel biomass, the human pressure on ignition, and the fire weather index increased, thus making the study area more hazardous. Our results suggest that a strategy of aggressive fire suppression has great potential for counterbalancing the effects of climate changes and human activities and for controlling fire activity in the short term. However, we discuss whether such a suppression‐oriented approach is sustainable in the context of global changes, which cast new fire challenges as demonstrated by the devastative fires of 2003 and 2016. We advocate for a more comprehensive fire policy to come.     

Role of land-cover and WUI types on spatio-temporal dynamics of fires in the French Mediterranean area

This work aims at assessing, in the French Mediterranean area, the spatio-temporal trends of fires, including their causes, at fine scale (communities), comparing different periods between 1993 and 2017. These trends were compared to those of land-cover and wildland-urban interface (WUI) which were coupled with a spatial analysis of the ignitions in order to highlight the main drivers and preferential areas. Fire density was highly variable among communities, hotspots being located mostly close to big cities but spatially varying in time in contrast to fire occurrence and burned area. A decrease in the unknown cause proportion and a variation of the cause frequency were highlighted among periods, criminal fires being the most frequent and deleterious, especially before 2009, as well as those due to negligence during private activities, mostly after 2009. Land cover classes significantly varied among periods, artificialized and natural areas presenting a reversed trend compared with agricultural areas. Natural areas were the most affected by ignitions (60%), regardless of the period; this trend is slowly decreasing. WUI represented ∼30% of the study area, the different types varying spatially (denser clustered types mostly located in the South-East) and showed an increase over time, especially for both clustered types but with high variability among communities. Half of the ignitions occurred in WUI, with “very dense clustered” and “scattered” types being the most affected, especially in 2009. Better understanding the spatio-temporal evolution of fires and of their causes should allow refining the fire policies in terms of awareness raising, firefighting means, and land management.     

Risk Preferences in Strategic Wildfire Decision Making: A Choice Experiment with U.S. Wildfire Managers

Federal policy has embraced risa management as an appropriate paradigm for wildfire management. Economic theory suggests that over repeated wildfire events, potential economic costs and risas of ecological damage are optimally balanced when management decisions are free from biases, risa aversion, and risa seeking. Of primary concern in this article is how managers respond to wildfire risa, including the potential effect of wildfires (on ecological values, structures, and safety) and the likelihood of different fire outcomes. We use responses to a choice experiment questionnaire of U.S. federal wildfire managers to measure attitudes toward several components of wildfire risa and to test whether observed risa attitudes are consistent with the efficient allocation of wildfire suppression resources. Our results indicate that fire managers’ decisions are consistent with nonexpected utility theories of decisions under risa. Managers may overallocate firefighting resources when the likelihood or potential magnitude of damage from fires is low, and sensitivity to changes in the probability of fire outcomes depends on whether probabilities are close to one or zero and the magnitude of the potential harm.     

Forest Fire Risk Management and Public Participation in Changing Socioenvironmental Conditions: A Case Study in a Mediterranean Region

Under current conditions of accelerated socioenvironmental change in the Mediterranean forested landscapes, fire is one of the most critical and difficult risks to tackle within the region. This article summarizes the lessons learned from a project based on the participatory integration of qualitative local stakeholders' knowledge with expert GIS fire simulations carried out in the County of El Bages, Catalonia, Spain. First, in this article, a theoretical model--the forest fire circle--is presented in order to explain the reasons for the rise in the damage and frequency of forest fires in this Mediterranean area. Second, it describes the methodology developed and the stages followed during the project. Results show that: (1) the advocacy of old forest reactive management paradigm assumptions and practices based on uncontrolled forest succession can put vast wooded areas of the Mediterranean basin at critical risk; and (2) forest fire management approaches that ignore the crucial role of long-term prevention and local capacity building strategies have failed. In the final section, the content and the specific dimensions of the old reactive paradigm that has characterized forest fire risk management in Catalonia are discussed and contrasted with the possibly emerging preventative paradigm.     

Externalities in the wildland–urban interface: Private decisions,collective action,and results from wildfire simulation models for California

Property damage from wildfires occurs from spread into built-up areas, the wildland–urban interface. Fire spread occurs as embers from one burning structure ignite neighboring ones—but mitigation reduces the chances that fire spreads. In this study, we use a simulation model with realistic parameters for a neighborhood in California to illustrate patterns of marginal benefit from mitigation. We extend existing models of fire spread in two novel ways. We show how to describe the no-regulation equilibrium and social optimal levels of mitigation by incorporating data on a key factor, the distribution of house values in the community. We incorporate insurance in the model and show that it improves homeowner decision-making and insurance premium regulation. The fire spread simulations show that under plausible parameter values, there is a pattern in which mitigation's marginal benefit is low at low levels of community mitigation, rises to a maximum, and then falls quickly to a low level. We argue that the maximum marginal benefit is a guide to achieving optimal mitigation in a community. Owner mitigation decisions will depend on the distribution of house values in the neighborhood and other factors. In an illustration, we use the distribution of house values in a California community to illustrate the mitigation owners will choose under independent (Nash) investment decisions, and the efficiency-improving actions involving regulations or insurance premium subsidies that can lead to the social optimum.     

Modeling the Cost Effectiveness of Fire Protection Resource Allocation in the United States: Models and a 1980–2014 Case Study

The estimated cost of fire in the United States is about $329 billion a year, yet there are gaps in the literature to measure the effectiveness of investment and to allocate resources optimally in fire protection. This article fills these gaps by creating data‐driven empirical and theoretical models to study the effectiveness of nationwide fire protection investment in reducing economic and human losses. The regression between investment and loss vulnerability shows high R² values (≈0.93). This article also contributes to the literature by modeling strategic (national‐level or state‐level) resource allocation (RA) for fire protection with equity‐efficiency trade‐off considerations, while existing literature focuses on operational‐level RA. This model and its numerical analyses provide techniques and insights to aid the strategic decision‐making process. The results from this model are used to calculate fire risk scores for various geographic regions, which can be used as an indicator of fire risk. A case study of federal fire grant allocation is used to validate and show the utility of the optimal RA model. The results also identify potential underinvestment and overinvestment in fire protection in certain regions. This article presents scenarios in which the model presented outperforms the existing RA scheme, when compared in terms of the correlation of resources allocated with actual number of fire incidents. This article provides some novel insights to policymakers and analysts in fire protection and safety that would help in mitigating economic costs and saving lives.     

Big Data and Predictive Analytics in Fire Risk Using Weather Data

The objective of this article is to study the impact of weather on the damage caused by fire incidents across the United States. The article uses two sets of big data—-fire incidents data from the National Fire Incident Reporting System (NFIRS) and weather data from the National Oceanic and Atmospheric Administration (NOAA)—to obtain a single comprehensive data set for prediction and analysis of fire risk. In the article, the loss is referred to as “Total Percent Loss,” a metric that is calculated based on the content and property loss incurred by an owner over the total value of content and property. Gradient boosting tree (GBT), a machine learning algorithm, is implemented on the processed data to predict the losses due to fire incidents. An R² value of 0.933 and mean squared error (MSE) of 124.641 out of 10,000 signify the extent of high predictive accuracy obtained by implementing the GBT model. In addition to this, an excellent predictive performance demonstrated by the GBT model is further validated by a strong fitting between the predicted loss and the actual loss for the test data set, with an R² value of 0.97. While analyzing the influence of each input variable on the output, it is observed that the state in which a fire incident takes place plays a major role in determining fire risk. This article provides useful insights to fire managers and researchers in the form of a detailed framework of big data and predictive analytics for effective management of fire risk.     

Evaluating Lightning-Caused Fire Occurrence Using Spatial Generalized Additive Models: A Case Study in Central Spain

It is widely accepted that the relationship between lightning wildfire occurrence and its influencing factors vary depending on the spatial scale of analysis, making the development of models at the regional scale advisable. In this study, we analyze the effects of different biophysical variables and lightning characteristics on lightning-caused forest wildfires in Castilla y León region (Central Spain). The presence/absence of at least one lightning-caused fire in any 4 × 4-km grid cell was used as a dependent variable and vegetation type and structure, terrain, climate, and lightning characteristics were used as possible covariates. Five prediction methods were compared: a generalized linear model (GLM), a random forest model (RFM), a generalized additive model (GAM), a GAM that includes a spatial trend function (GAMs) and a spatial autoregressive model (AUREG). A GAMs with just one covariate, apart from longitude and latitude for each observation included as a combined effect, was considered the most appropriate model in terms of both predictive ability and simplicity. According to our results, the probability of a forest being affected by a lightning-caused fire is positively and nonlinearly associated with the percentage of coniferous woodlands in the landscape, suggesting that occurrence is more closely associated with vegetation type than with topography, climate, or lightning characteristics. The selected GAMs is intended to inform the Regional Government of Castilla y León (the fire and fuel agency in the region) regarding identification of areas at greatest risk so it can design long-term forest fuel and fire management strategies.     

Fire Risk Analysis for Nuclear Power Plants: Methodological Developments and Applications

A methodology to quantify the risk from fires in nuclear power plants is described. This methodology combines engineering judgment, statistical evidence, fire phenomenology, and plant system analysis. It can be divided into two major parts: (1) fire scenario identification and quantification, and (2) analysis of the impact on plant safety. This article primarily concentrates on the first part. Statistical analysis of fire occurrence data is used to establish the likelihood of ignition. The temporal behaviors of the two competing phenomena, fire propagation and fire detection and suppression, are studied and their characteristic times are compared. Severity measures are used to further specialize the frequency of the fire scenario. The methodology is applied to a switchgear room of a nuclear power plant.     

Which Fire to Extinguish First? A Risk‐Informed Approach to Emergency Response in Oil Terminals

The performance of fire protection measures plays a key role in the prevention and mitigation of fire escalation (fire domino effect) in process plants. In addition to passive and active safety measures, the intervention of firefighting teams can have a great impact on fire propagation. In the present study, we have demonstrated an application of dynamic Bayesian network to modeling and safety assessment of fire domino effect in oil terminals while considering the effect of safety measures in place. The results of the developed dynamic Bayesian network—prior and posterior probabilities—have been combined with information theory, in the form of mutual information, to identify optimal firefighting strategies, especially when the number of fire trucks is not sufficient to handle all the vessels in danger.     

Assessing Climate Change Impacts on Wildfire Exposure in Mediterranean Areas

We used simulation modeling to assess potential climate change impacts on wildfire exposure in Italy and Corsica (France). Weather data were obtained from a regional climate model for the period 1981–2070 using the IPCC A1B emissions scenario. Wildfire simulations were performed with the minimum travel time fire spread algorithm using predicted fuel moisture, wind speed, and wind direction to simulate expected changes in weather for three climatic periods (1981–2010, 2011–2040, and 2041–2070). Overall, the wildfire simulations showed very slight changes in flame length, while other outputs such as burn probability and fire size increased significantly in the second future period (2041–2070), especially in the southern portion of the study area. The projected changes fuel moisture could result in a lengthening of the fire season for the entire study area. This work represents the first application in Europe of a methodology based on high resolution (250 m) landscape wildfire modeling to assess potential impacts of climate changes on wildfire exposure at a national scale. The findings can provide information and support in wildfire management planning and fire risk mitigation activities.     

Assessing the Risk of Invasive Spread in Fragmented Landscapes

Little theoretical work has investigated how landscape structure affects invasive spread, even though broad-scale disturbances caused by habitat loss and fragmentation are believed to facilitate the spread of exotic species. Neutral landscape models (NLMs), derived from percolation theory in the field of landscape ecology, provide a tool for assessing the risk of invasive spread in fragmented landscapes. A percolation-based analysis of the potential for invasive spread in fragmented landscapes predicts that invasive spread may be enormously enhanced beyond some threshold level of habitat loss, which depends upon the species' dispersal abilities and the degree of habitat fragmentation. Assuming that invasive species spread primarily through disturbed areas of the landscape, poor dispersers may spread better in landscapes in which disturbances are concentrated in space, whereas good dispersers are predicted to spread better in landscapes where disturbances are small and dispersed (i.e., fragmented landscape). Assessing the risk of invasive spread in fragmented landscapes ultimately requires understanding the relative effects of landscape structure on processes that contribute to invasive spread--dispersal (successful colonization) and demography (successful establishment). Colonization success is predicted to be highest when >20% of the landscape has been disturbed, particularly if disturbances are large or aggregated in space, because propagules are more likely to encounter sites suitable for colonization and establishment. However, landscape pattern becomes less important for predicting colonization success if species are capable of occasional long-distance dispersal events. Invasive species are also more likely to persist and achieve positive population growth rates (successful establishment) in landscapes with clumped disturbance patterns, which can then function as population sources that produce immigrants that invade other landscapes. Finally, the invasibility of communities may be greatest in landscapes with a concentrated pattern of disturbance, especially below some critical threshold of biodiversity. Below the critical biodiversity threshold, the introduction of a single species can trigger a cascade of extinctions among indigenous species. The application of NLMs may thus offer new insights and opportunities for the management and restoration of landscapes so as to slow the spread of invasive species.     

Choosing What to Protect

We study a strategic model in which a defender must allocate defensive resources to a collection of locations, and an attacker must choose a location to attack. The defender does not know the attacker's preferences, while the attacker observes the defender's resource allocation. The defender's problem gives rise to negative externalities, in the sense that increasing the resources allocated to one location increases the likelihood of an attack at other locations. In equilibrium, the defender exploits these externalities to manipulate the attacker's behavior, sometimes optimally leaving a location undefended, and sometimes preferring a higher vulnerability at a particular location even if a lower risk could be achieved at zero cost. Key results of our model are as follows: (1) the defender prefers to allocate resources in a centralized (rather than decentralized) manner; (2) as the number of locations to be defended grows, the defender can cost effectively reduce the probability of a successful attack only if the number of valuable targets is bounded; (3) the optimal allocation of resources can be nonmonotonic in the relative value of the attacker's outside option; and (4) the defender prefers his or her defensive allocation to be public rather than secret.     

Crisis management. Putting out forest fires.

A crisis can hit any business. It can hurt cash flow, productivity, the public's faith, and the company's image. The adverse effects come in two major areas: public image and cash flow, both of which can be devastating. Lack of planning ensures maximum pain and the greatest chance of future collapse. A proactive stance can lessen the pain and give the best chance of recovery. Like a forest fire, a crisis can be sniffed early or it can destroy thousands of 'acres.' The best manager will have plans for crisis management and will be able to smell the smoke before the blaze consumes the forest.     

Improving fire risk communication between authorities and micro-entrepreneurs: A mental models study of Ghanaian central market fires

This study conceptualizes how fire management authorities can empower nonexpert public to participate in fire risk communication processes and increase their own responsibilities for managing fire preventive, protective and recovery processes effectively. Drawing narratives from 10 disaster management experts working at government institutions and nine micro-entrepreneurs operating self-sustaining businesses in different merchandized lines in Ghana, we analyzed the data thematically and explored new insights on mental models to generate a two-way fire risk communication model. The findings suggest that fire management authorities planned fire disasters at the strategic level, collaborated with multiple stakeholders, disseminated information through many risk communication methods, and utilized their capabilities to manage fire at the various stages of fire risk communication, but the outcomes were poor. The micro-entrepreneurs sought to improve fire management outcomes through attitude change, law enforcement actions, strengthened security and better public trust building. The study has implications for policymakers, governments, and risk communication authorities of developing countries to strengthen their fire disaster policies to minimize commercial fire incidents and address the damaging effects of fire on people's livelihoods, businesses, properties, and environments. Our proposed two-way fire risk communication model is a new theoretical lens for experts and the nonexpert public to assess each other's beliefs about risk information and manage fire risk communication effectively at all stages.     

ETHNIC DIVERSITY AND THE SPREAD OF CIVIL WAR

This paper theoretically and empirically investigates the relationship between local‐level ethnic composition and the spread of conflict. Cross‐country literature on conflict finds that ethnic diversity, and ethnic polarization in particular, are associated with greater incidence of conflict. However, the question remains as to where within ethnically diverse countries conflict begins and where and how it spreads. To study this question, I present a model in which local ethnic groups' decision to attack depends on three key variables: ethnic population shares, ethnic groups' weapons ratio, and the share of co‐ethnic successes in the battles that took place in the previous period. The model generates three predictions: conflict starts in ethnically homogeneous areas and only later spreads to ethnically heterogeneous areas; neighbor co‐ethnics' success increases subsequent probability of winning and may lead to attack; and greater ethnic diversity is associated with costlier conflict. I find strong support for these predictions using detailed municipal‐level data on attacks and ethnic polarization during the initial spread of the Bosnian Civil War. Moreover, my conflict model is able to predict the sequence of actual conflict outcomes with reasonably high accuracy. (JEL: D39, D74, J15, R12)     

Compressed Natural Gas Bus Safety: A Quantitative Risk Assessment

This study assesses the fire safety risks associated with compressed natural gas (CNG) vehicle systems, comprising primarily a typical school bus and supporting fuel infrastructure. The study determines the sensitivity of the results to variations in component failure rates and consequences of fire events. The components and subsystems that contribute most to fire safety risk are determined. Finally, the results are compared to fire risks of the present generation of diesel-fueled school buses. Direct computation of the safety risks associated with diesel-powered vehicles is possible because these are mature technologies for which historical performance data are available. Because of limited experience, fatal accident data for CNG bus fleets are minimal. Therefore, this study uses the probabilistic risk assessment (PRA) approach to model and predict fire safety risk of CNG buses. Generic failure data, engineering judgments, and assumptions are used in this study. This study predicts the mean fire fatality risk for typical CNG buses as approximately 0.23 fatalities per 100-million miles for all people involved, including bus passengers. The study estimates mean values of 0.16 fatalities per 100-million miles for bus passengers only. Based on historical data, diesel school bus mean fire fatality risk is 0.091 and 0.0007 per 100-million miles for all people and bus passengers, respectively. One can therefore conclude that CNG buses are more prone to fire fatality risk by 2.5 times that of diesel buses, with the bus passengers being more at risk by over two orders of magnitude. The study estimates a mean fire risk frequency of 2.2 x 10(-5) fatalities/bus per year. The 5% and 95% uncertainty bounds are 9.1 x 10(-6) and 4.0 x 10(-5), respectively. The risk result was found to be affected most by failure rates of pressure relief valves, CNG cylinders, and fuel piping.     

Determinants of strategic options in multinational market competition

This article conceptually explores the determinants of strategic options in multinational market competition. It draws on and integrates three research streams in strategy and international management: action and response in competitive rivalry; multipoint competition, and global competition and strategy. Specifically, it examines the various factors that determine whether and when a multinational corporation (MNC), while under attack from a rival MNC, will adopt the following responses: doing nothing, defending, counterattack, and total war. Those factors are organized into four categories: (1) types of attack, including price cut, introduction of new product, entry into new market, and advertising campaign; (2) awareness of interdependence of multinational markets, determined by market overlap, MNC strategy, and MNC organization; (3) motivation for globally coordinated moves, determined by the centrality of national markets, strategic importance of national markets, perceived importance of national markets, and perceived importance of rivals; and (4) feasibility of response, determined by entry barriers and diversity of national markets as well as resource heterogeneity and differential between rival MNCs. The effects of these four categories of factors on the choice of response are explored respectively. Research implications are offered in concluding remarks.     

Do actions reduce perceived risk? A longitudinal analysis of the relationship between risk perception and actions in response to forest disturbance in Colorado

As residents living in hazard-prone areas face on-going environmental threats, the actions they take to mitigate such risks are likely motivated by various factors. Whereas risk perception has been considered a key determinant of related behavioral responses, little is known about how risk mitigation actions influence subsequent perceived risk. In other words, do actions to prevent or mitigate risk reduce risk perception? This longitudinal study considers the dynamic relationships between risk perception and risk-mitigating behavior in the context of forest disturbance in north-central Colorado. Based on panel survey data collected in 2007 and 2018, the results provide a first look at changes in perceived forest risks as they relate to individual and community actions in response to an extensive mountain pine beetle outbreak. Analysis revealed that the perception of direct forest risks (forest fire and falling trees) increased, whereas indirect forest risk perception (concern on broader threats to local community) decreased across the two study phases. Higher individual or community activeness (level of actions) was associated with subsequent reductions in perceived forest fire risk, smaller increases in direct risk perception, and larger decreases in indirect risk perception. These findings contribute insights into the complex risk reappraisal process in forest hazard contexts, with direct implications for risk communication and management strategies.     

Increasing the board's involvement in strategy

For a long time, boards of directors have been considered weak, incapable of contributing to the financial success of their companies. This article proposes a way to revitalize boards by involving them in mapping corporate strategic directions. Building on recent changes in boardroom practices, it outlines eight conditions for an effective strategic contribution by boards and specifies areas of potential for an effective strategic contribution by boards and specifies areas of potential interest to directors. Taken together, these suggestions are expected to ensure continuous and disciplined contribution by boards to strategy and, ultimately, to effective corporate performance.