It is estimated that more than 1.3 million hectares of forest are destroyed by wildfires in Europe each year (

According to data provided by the Galician Institute of Statistics (

The wildfire risk depends on several climatological, social or environmental factors, which could be modified by public policies at short or medium term. Nevertheless, decision-making is characterised by the presence of dynamic risk factors (

In order to study wildfires in depth, it is necessary to be aware of the currentsituation of the agro-forestry areas in Galicia. Certain areas have scattered populations with a constant rural depopulation and continuing migration of young people to more highly populated areas (

Galicia contains different climatic areas, resulting in an uneven availability of biomass that can be burnt (

To this end, the proposed model must be simple, structured and easy to standardise so that it can be easily updated (

Until now, several methods have been used to identify wildfire risk factors. Some studies have used various explanatory variables in order to explain the reasons why some areas are more heavily affected than others, although they do not quantify the described relationships and/or support their arguments in a quantitative way (

In some earlier work, researchers have studied the error term to identify geographical and temporal trends (

Other papers explore the possible relationship between wildfires and a specific group of variables (

Several international studies analyze the problem of fires from a spatial context. For example,

The aim of this study is to extend previous analyses using current data and taking into account the impact of socio-economic factors, land cover, and climatology using spatial analysis. Thus, econometric models have been developed to analyse the possible influence of socioeconomic factors on the risk of wildfires. The Ordinary Least Square (OLS) and econometric models for counted data are used to identify these socioeconomic factors. Random effects (RE) and Fixed Effects (FE) are also estimated to assess the presence of spatial patterns. Other methods, such as the Moran’s I and LISA statistics, are included to determine whether wildfire occurrence shows spatial patterns. We expect the present results can help to improve public policy focussing on exploring spatial and temporal impacts on fire occurrence.

This research starts by explaining the data and methods used. In the next section, the results are described and discussed, and then it follows a section in which the main conclusions of the research are summarized and policy implications are provided.

Data have been gathered from 2001 to 2010. The most up-to-date data available from the 19 forest districts established by the Galician regional government were collected (

The explanatory variables are shown in

Wildfires data were recorded from the Galician Forest Districts. On the other hand, meteorological data were recorded directly by the weather stations, and such data had to be linked and extrapolated to the District level. Finally, the agro-forestry data are mainly recorded by Geographic Information Systems (GIS). Thus, a shape-file with the Galician Forest Districts was designed adding the municipality limits obtained from the National Geographic Institute (

The data for the climatic variables were collected from

The density per hectare is used to describe the population structure. Therefore, the total population divided by the total Forest District area is used to calculate this variable. Both data were recorded from

The Third Spanish National Forest Inventory (NFI3), the Corine Land Cover and the Livestock Census were the main sources to gather information about the agro-forestry situation (

The wildfire variables were also obtained from the

Graphs and statistics are useful in order to identify the spatial patterns in Galician wildfires. The first one involves the representation of the data to identify the temporal trends and the heterogeneity between the Galician forest districts. Then, in case of existing temporal trends, these could be identified showing differences of each entity´s mean value. Another alternative is to represent the data for each year by a graph. The independent years could register more or less spatial differences.

The Moran’s I statistic (

The Moran’s I statistic takes into account the number of geographical areas (N); the analyzed areas (_{j,i}). Then, the Moran’s I statistic could be expressed by the

According to the definition of the weight matrix, the relations between close forest districts are included. Therefore, the closest neighbors to each polygon are identified with this matrix. Mathematically, the weight matrix could be expressed as _{ij} represents the spatial matrix of adjacent polygon (

The spatial relationships in this matrix can be used with different contiguity interpretations. In other words, if a regular grid is designed, the weight matrix could be constructed according to four spatial relationships: linear (

The LISA statistics can be developed from the Moran’s I statistics (_{i} represents the normalized value of the selected variable in respect to the mean and J_{i} is all polygons (districts) next to

In order to analyse the relationship between the previous variables and wildfires in Galicia, a baseline lineal regression estimated by OLS was used. In this baseline estimation the coefficients are controlled by the heterogeneity of each district through the Huber-White correction of standard errors (

With this common specification, two independent equations were estimated. The first model used the ratio of forest-burned area in each forest district as the dependent variable, and the second specification modelled the number of wildfires. The independent variables in both models include socio-economic factors represented by X_{jit}, (mainly population structure, territorial features, economic information and agroforestry data for each forest district), climatology represented by X_{kit} (including the variables of average maximum temperature and average monthly precipitation); and finally, the vector X_{hit} represents the dummy yearly indicators.

Using the Box-Cox test, the functional form of the _{it}) assumes a normal distribution in order to estimate the parameters β and θ.

As such, if the estimation of θ is close to zero, then the best specification to be used would be the log-lineal model. However, if the respective statistics are significant and close to one, a lineal model should be used.

Since, the number of wildfires is a counted data variable, the Poisson Regression Model (PRM) shown on

Given that count data can exhibit overdispersion (^{2}). In this case if α=0, then the variance is equal to the mean and there is no overdispersion; and thus, the PRM can be a suitable model.

On the other hand, if the coefficient α is different from zero, then the number of wildfires should be estimated by a Negative Binomial Regression model (NBRM). This model is more general than the PRM and should prove to have a better goodness of fit in case of overdispersion (

In order to interpret the coefficients of the previous model, the use of the Incidence Rate Ratio (IRR) is recommended as its results are easier to interpret (

In this setting, two different models could be used to analyse the error term: FE and RE. The FE represented by the _{it}) of the _{i}) and another error term (τ_{it}).

In the following RE models, the previously fixed term (v_{i}) is now random. The specification of this model is equal to _{i} and different variance from zero (Var(v_{it})≠0). These unobservable factors are used for the OLS model but also for the MRP (

The Hausman test (H) is used to select between RE and FE models. The specification of this test is shown in

The spatial patterns of the number of wildfires and burned-forest area ratio can be observed with graphical displays. The variation of the burned-forest area ratio by year is represented in the

A weight matrix should be constructed to develop the spatial statistics. As stated earlier, the direct Queen contiguity is selected to analyse the relationship between districts (

The Moran’s I and LISA statistics are used to analyse the spatial patterns of wildfires and the yearly burned-forest area ratio in Galicia.

The LISA statistic represents the various significant spatial patterns as follows (

High-High (H-H): a particular forest district and their neighborhoods have high values. This type of relationship is represented by the red color.

High-Low (H-L): a particular forest district has high values and their neighborhoods have lower values. This type of relationship is represented by the pink color.

Low-High (L-H): is similar to the previous category, but in this case the forest district has high values and their neighborhoods have lower values. This type of relationship is represented by the sky-blue color.

Low-Low (L-L): the forest district and their neighborhoods have low values. This type of relationship is represented by the blue color.

The remaining values are represented by a grey color because these entities have a random relationship (

In order to analyze the number of wildfires, the LISA statistics are shown in

All previous graphs and statistics show the existence of relevant spatial patterns and temporal trends. Therefore, these should be included in the econometric model for both dependent variables. The temporal trends are included in the empirical models by using dummy variables for each year, considering 2001 as the baseline year. On the other hand, in order to correct for spatial patterns in the research, data are set according to a panel of forest districts and controlling the heterogeneity by district through standard errors correction. The spatial patterns are also analyzed using FE and RE models.

In order to specify the most suitable econometric model to analyse the evolution of the burned-forest area ratio, a Box-Cox test was estimated (

Following the results displayed in

The dummy variables determine significant effects over several years. A positive trend is identified from 2002 to 2006. The majority of dummy variables are significant and positive with respect to the 2001 year. However, after 2006, the trend is clearly negative and significant for all years. These results are robust across the econometric selected models.

The climatological variables show in particular the importance of rainfall in order to reduce the burned-forest area ratio. This variable is significant carrying a negative effect in the causes of wildfire occurrence. Thus, the average effect of rainfall on burned-forest areas ratio is -0.643, when the precipitation changes by one unit over time and between districts. However, the maximum temperature has a positive effect, although this variable is not significant in order to predict the burned area. The small variability in this variable may be responsible for this finding.

In terms of socioeconomic variables, the ratio of the equines and the number of agricultural cooperatives have both a negative and significant effect on the burned-forest ratio area for the OLS and RE models. Their effects show that if the value changes over time and between districts by one unit, then the average effect of equine radio stock and the number of agricultural cooperatives over the burned-forest area will respectively decrease by a factor of -0.385 and -0.555.

On the other hand, the density of

For the purpose of determining the functional form for the regression of wildfires, the Box-Cox test does not provide conclusive evidence of the superiority of any functional form. However, in order to compare the number of wildfires with the regression of the burned-forest area ratio, the logarithmic model is selected. Also, this functional form is estimated to allow for comparability between all regressions.

Following the results displayed in

Some variables, such as the ratio of equines and the agricultural cooperatives do not have a significant relationship with the number of wildfires during 2001-2010, are not significant in the assessment of the wildfires using the OLS models. However, the rest of the socioeconomic variables, are significant and have positive effects over the wildfires occurrence.

The number of wildfires is modelled by count data models. Therefore, overdispersion should be studied in order to select the best econometric model. Taking into account the results of

Analysing the effects of the yearly variables, temporal trends are found according to the NBMR results, both with FE or RE. In this way, since 2006, it is observable that the wildfire occurrence diminishes with respect 2001. However, the NBRM also detects a significant growth in wildfires in 2005 with respect to the baseline year. The OLS and NBMR models, with or without RE, demonstrate the presence of temporal trends in Galician wildfires.

Furthermore, the estimator of summer rainfall is significant and carries a negative effect on the number of wildfires (0.994). According to the estimation with RE, if this independent variable changes over time and between districts by one unit, then the average effect of the average summer rainfall over the number of wildfires is significant (0.944). Otherwise, the average of the maximum temperature during the summer is not significant to explain the wildfires according with the NBMR models.

In the NBMR models, the ratio of natural pasture,

In addition, socioeconomic variables are significant in the NBRM with RE. Nevertheless, the remaining variables have different impacts on wildfire occurrence. The agricultural cooperatives and population density have a positive relationship with the occurrence of Galician wildfires. If these previous variables increase by one point, the rate of the number of wildfires would be expected to increase by a factor of 1.015 and 1.180, respectively, while holding all other variables constant. Furthermore, the ratio of equines has a negative relationship with wildfires occurrence.

The summer average rainfall is significant in order to predict the wildfires occurrence. This is explained by the absence of raining, given that this increases the wildfire risk. Nevertheless, the summer maximum temperature is only significant in the OLS results.

Spatial patterns and temporal trends can be observed with graphical data representation. Furthermore, the spatial dependence of wildfires can also be determined by spatial statistics. Various econometric models are employed to assess the impact of socio-economic, climatic and geographical variables, as well temporal and spatial effects. Following the econometric models employed, and in particular those from RE models, the number of wildfires and the affected area ratio are estimated for each Galician forest districts in 2010. The estimations portrayed in

In terms of the econometric results, it was found that the agro-forestry features are important factors given that the land cover is conditioned by this activity. The type of forest plantation, the livestock used in the farms or the land assigned to agricultural activity influences the wildfire occurrence. The ratio of equines is slo important in order to reduce the wildfire occurrence (

We also find that the

In general terms, the population density is important in order to predict the wildfire occurrence. However, in some of the empirical models, results are not conclusive. For example, in the OLS with RE, this variable is not significant when explaining the burned-forest area ratio. The same happens in the NBRM when analyzingthe wildfire number. In the remaining models, the population density is positively related to the occurrence of wildfires. This result is explained by the progressive migratory flow from the rural to urban areas. Then, the wild land-urban density around to main areas is increasing. In addition, the new residents are not involved in the agricultural sector and they are not involved with forest production (

The types of forest covers are represented by the ratio of

Unexpectedly, the protected areas influence positively the occurrence of wildfires. This result may show the general rejection towards having protected lands in rural areas. This result could also imply an inadequate public policy to manage these areas against wildfires (

This research provides evidence characterizing the wildfire occurrence in the agricultural sector in relation to the climatic conditions, the forest cover, the social context, and time and spatial patterns. A relevant finding is that the forest species and the farming systems condition the wildfire risk. Hence, public policies may mitigate the factors that affect the wildfire risk. In this way, the presence of equines and extensive agricultural practices should be promoted in order to reduce the wildfire risk.

According to the main results, some guidelines could be developed as a reference for regional and local governments to help in the fight of wildfires. In particular, public policies could regulate the quality and quantity of woodland made available, as well as the plantation of different species. These regulatory agencies should also consider the geographical and spatial differences in the occurrence of wildfires in order to formulate better forest policies, and deal with possible “contagion” effects across districts.

Finally, we should remark that the current research has some limitations. In particular, additional variables would be desirable by employing more geographical disaggregated data, such as roads and infrastructures. Unfortunately, such data are not currently available, although, they are expected to be in the near future. In spite of that, many of the obtained conclusions could be applicable to other similar European areas, especially in depopulated rural areas. In particular, these main results could be implemented in the French Mediterranean basin (

Following

In some cases, climatological data were not available for all of the time periods and/or forest districts. The unavailable data had to be supplemented with those from other forest districts according to the climatic areas established by