In this research, we sought to improve the temporal and spatial understanding of the causes of forest land wildfires in Galicia by analyzing the importance of socioeconomic variables— and natural variables—over the wildfire ignitions and hectares burned during 2001–2015. We used clustering to analyze the spatial dimension and regression analysis of panel data to investigate the temporal dimension. The cluster analysis allowed us divide the region interterritorially into four areas, according to its socio-economic behavior only. The panel data analysis showed rural abandonment, neglect of the environment, aging deprivation and low investment in prevention and infrastructure as determinants of wildfire ignitions
Recent wildfire research suggests a need for improved analysis of biophysical and socioeconomic factors in wildfire occurrence and area burned. Such analysis can aid policymakers and managers in understanding wildfire dynamics within regions and differences between territories (Ganteaume et al. 2013; Costafreda-Aumedes, Comas, and Vega-Garcia 2017). Scholars have repeatedly used cluster analysis as an attempt to identify and characterize spatial patterns of fire regimes. In Spain Moreno and Chivieco (2013) describe four fire regimes in terms of the density and seasonality of fire activity; and Montiel and Molina (2016) identify five land-based fire scenarios –connecting fire regimes and territorial dynamics- on a national and regional scale. For Portugal, Parente et al (2016) identify two types of fire regimes driven by climate and vegetation. Although the variables analyzed to a greater extent have to do with climatic or biophysical factors, all authors agree on the importance of the human factor for better understanding fire regimes and optimizing fire prevention/mitigation policy measures.
In fact, the present study is in line with other works that have used the socioeconomic variables for spatial fire segmentation: In this regard, Chas-Amil et al (2010) analyze the spatial distribution of human-induced fire risk attending to causes and underlying motivations associated with fire ignitions in Galicia and encounter four distinctive types of municipalities according to the incidence of intentional agricultural-livestock fires, pyromaniacal behavior, negligence, and unknown causes; Gaither et al (2011) examine the association between wildland fire risk and social vulnerability in six states in the southeastern U.S. and conclude that poorer communities in the southeast with high wildland fire risk may be at a more significant disadvantage than more affluent, high fire risk communities in these states. — Ferrara et al (2019) demonstrate, for the South of Italy, that characteristic wildfire attributes (frequency, intensity, and severity) are systematically higher in socioeconomic contexts characterized by rural poverty, unemployment, and deregulated urban expansion. Sousa et al (2021) findings confirm that mainland central Portugal has a low potentiality index –and the subsequent rural abandonment and lacking human activities, such as agriculture- is one of the main factors facilitating fire spread in this region.
In addition, as some other scholars analyzing the influence of multiple socioeconomic factors on fire frequency and size at regional scales, we have resourced to the use of statistical and econometric approaches, including the panel data strategy (Michetti and Pinar 2013; Costafreda-Aumedes, Comas, and Vega-Garcia 2017; Padli, Habibullah, and Baharom 2018; Mercer and Prestemon 2005). In the first place, factor analysis splits the set of previously selected variables into various dimensions; this demonstrates the relation between certain variables andthe existence of different socioeconomic components. These factors, similar to the dimensions described in the relevant literature (Adger, 2006; Chas-Amil et al., 2015; FAAS, 2016; Authors, 2021), allow us to analyze the region with higher precision. In the second place, cluster analysis has made it possible to unearth geographical differences within the region; these geographical differences have proven to be very useful for fire management, as the variables within the factors affect the number of ignitions and the hectares burned differently. Finally, our two panel data sets enabled us to take advantage of both the spatial and temporal variation in fire and socioeconomic data.
The research timeframe used in our study is more significant than most offered by different works related to Galicia: 2001–2009 used by Barreal et al. ( 2011), 2006 used by Balsa and Hermosilla (2013), and 2001–2010 used by Loureiro and Barreal (2015). Only Prestemon et al (2019) were able to cover a slightly more extended period (16 years from 1999 to 2014). Regarding the unit of spatial analysis, some scholars have used smaller research units such as parishes to analyze the influence of human variables on wildfire patterns (Chas-Amil et al. 2015); however, since parishes are not considered as local entities (Ministerio de Política Territorial 2022), those macroeconomic variables that are measured at the municipal level and used in the models presented in this article (such as GDP, GDP per capita and debt ratio) are not available at the parish level.
Concerning the dependent variable ‘Number of wildfires,’ the importance of the land and environmental dimensions is remarkable. Ignitions turn out to be directly related to an increase in available fuel load and, in turn,n unveil the existence of ill-managed lands (Moreira et al. 2011). The presence of farms and livestock has also been suggested as an explanation for ignitions in rural areas (Ganteaume et al. 2013); in this sense, the results confirm the direct relationship between these variables and the number of ignitions. In addition, together with the relevant literature, this paper has considered the aging of the rural population as one of the primary causes of ignitions through traditional fire-use practices (Grala et al. 2017) The existing relation between economic deprivation and ignitions prominent in Cluster 3 and 4, corroborates that the economic dimension plays an important role in wildfire occurrence. Nevertheless, the behavior of the unemployment rate shows contradictory findings: in Mercer and Prestemon (2005), it is considered as a proxy for economic activity, so there is a positive relationship between the number of ignitions and the unemployment rates; the negative relationship between unemployment and ignitions shown by this research could be explained because the unemployment variable is part of the ‘Population’ factor, and so it describes the features of urban centers, where the number of ignitions is much lower.
As far as the dependent variable ‘Hectares burned’ is concerned, the effects of the independent variables are slightly different, as it is related to the fire’s behavior, that is, its intensity and spread (Calviño-Cancela et al. 2017). For this reason, the hectares burned are affected mainly by land-related or biophysical factors, at the expense of the economic and population variables (Balch et al. 2017). Our results corroborate these previous findings, showing a significant impact of the meteorological variables and land-related variables in the hectares burned. The remarkable and positive relationship between road density and hectares burned can be explained by the importance of roads in the spread of a wildfire; this finding confirms the results of some pieces of research. In contrast, roads have also proven to help stop the spread of wildfires in some regions (Ganteaume et al. 2013). Additionally, as it has already been pointed out by other authors (Grala et al. 2017; Cattau et al. 2020), to explain the characteristics of fire better, it is still necessary to consider the interaction between climate, land, and socioeconomic factors; this is reflected on the results of this research, where the population and its socioeconomic characteristics are statistically significant. In this respect, land abandonment, one of the main problems in rural areas, directly affects the quantity of fuel available, as it increases the presence of shrubs and grassland, along with non-managed forest areas (Vega-García and Chuvieco 2006; Padli, Habibullah, and Baharom 2018; Arellano-Pérez et al. 2018).
Improved knowledge of both temporal and spatial dimensions of wildfire determinants is necessary for designing wildfire risk mitigation and prevention policies adapted to different regions (Oliveira et al. 2017; Costafreda-Aumedes, Comas, and Vega-Garcia 2017). So, Chas-Amil et al (2015) explain that policies that incentivize cooperative forest management and constrain urban development in wildlands at hotspot fire locations reduce wildfire risk in Galicia. Canadas (2016) points out for the Portuguese case how the same legislation has very different effects depending on the human variables for each region; and Blas and Lourenço (2019) show how in the Spanish-Portuguese forestry sector, places belonging to different countries –but included in the same cluster- show identical fire behavior.
Indeed, Galicia is an example of how the common regulation (i.e. the 2007-currently-enforce Law on Prevention and Defence against Forest Fires) has been ineffective, not achieving the objective of eradicating (or even reducing) the recurrent summer fires in the region. Focused on extinction rather than prevention, the regulation has generated a complicated political-administrative and territorial tangle. Despite all this, there is room for hope, as a new Law will come into force in 2022. The new law has been drafted through a participatory process involving all the relevant stakeholders, such as specialists, professionals, and policymakers (Osbodigital 2022). The underlying principles of this new law seem to be aligned with the results and conclusions of our research, as it focuses on pillars such as socio-demographic changes, preventive actions on the territory or the promotion of initiatives for the recovery of forest uses", “addressing the sensitization and awareness of citizens” (Xunta de Galicia 2022).
Eradicating and reducing the number of wildfires becomes easier when there is better knowledge of their causes (Keeley and Syphard 2018). In this respect, the greater understanding of the region's fire behavior provided by our article will undoubtedly contribute to a better and more effective design of the specific measures for applying this coming Law.