The economic value of fire damages in Tuscan agroforestry areas

Abstract

The Tuscan Region (Central Italy) spends about 12 million euros every year in the prevention and suppression of forest fires. In this context, this study aims to analyse the economic and environmental benefits derived from fire suppression activities. Starting from a case study of a real fire event in Tuscany, we simulated three hypothetical scenarios (with different fire durations) without fire extinction activities planned by using the open source software FARSITE. Benefits derived from fire extinction activities can be quantified as the avoided damage, which has been calculated through the estimation of the total economic value of forests not destroyed by fire thanks to the extinction action. The avoided damage is represented by the difference between values of forest areas burned by the real fire event and those burned by simulated fire. By providing an economic estimation of avoided damages, our results confirm that forest fire services and forest management have a high impact on both the economy and the environment.

Introduction

In the last few decades, changes in land use and society have led to a significant increase in the number of wildfires. This phenomenon has become an important socio-economic and environmental problem that requires great attention, especially in terms of prevention ([18]). Climate change plays an important role in the fire hazard, and a number of studies attempted to analyse this correlation ([25], [44]). In particular, climate change has a relevant role in the intensity of events, as drying and warming periods have proven to be important determinants for fire hazard. Cammelli & Angelsen ([9]) argue that frequency and extent of forest fires have been increasing over the last two decades in the Amazon. Tan et al. ([45]) analyse a large wildfire occurred in Fort McMurray, Canada, and related this event to extremely warm and dry weather conditions in spring due to climate change.

The development of urbanized areas and viability in wild and mountainous areas are the main factors of this phenomenon. Indeed, the trigger point of numerous fires is close to the edge of roads and highways ([30]).

Despite the increase in the number of fires, the surfaces covered by fire are progressively decreasing in extent. In Italy, in the decade between 1995 and 2005, 1,185,000 hectares (ha) of surface burned, while 765,000 ha were destroyed by fire in the previous decade (2006-2015), corresponding to a reduction of 35% ([34]). The reduction in the area covered by fires in recent years is, above all, the consequence of an improvement in firefighting organization both at regional and national level. In the case of Tuscany, the number of forest fires did actually increase from 2010 to 2015, but the wooded area covered by the fire significantly decreased. During this period, the number of forest fires rose from 243 to 303 per year, while the average area of individual events declined from 1.56 to 0.75 ha ([34]).

The regional administration of Tuscany invests almost 12 million euro per year in forest fire prevention and repression activities. Despite such significant financial commitment, both the extent of the damage caused to goods and that of the damage avoided thanks to fire prevention and repression are still unclear. Knowing the magnitude of such effects would allow better efficiency and effectiveness of investment planning policies.

Several studies in the literature aimed to assess the damage caused to agroforestry areas by fires ([3], [15]). This work focus on a methodology aimed to assess the potential evolution of fires in the absence of anthropogenic extinction and to evaluate the resulting avoided damages. Starting from a case study taken from a real event occurred in Tuscany, three different scenarios of wildfire were simulated excluding fire extinction activities. These simulations were implemented in a Geographical Information Systems (GIS) program using the open source software FARSITE ([20]) to simulate a fire event ([13]).

Avoided damages are related to forests and the ecosystem services they provide ([47]), and include benefits such as recreation and tourist functions, biodiversity conservation, timber production, carbon storage, and hydrogeological conservation. These benefits represent the Total Economic Value (TEV) of the forest and consider both private and public environmental functions. Many studies in the literature have quantified these functions ([46], [29], [10], [7]). In this study, we have calculated the TEV of the area burned during a real fire event and the TEV of areas destroyed by simulated fires. Fire extinction activities are planned only in the real event, so the difference between the TEV of simulated events and TEV of the real event represents the avoided damages due to fire extinction activities.

The goal of the paper is to quantify the avoided damage by forecast a burned area by simulating a fire event with FARSITE and calculate the corresponding TEV values.

Material and methods

Case study

The wildfire examined occurred in Verniano, Colle di Val d’Elsa, near Siena (Tuscany, Central Italy) during the period between July 11 and August 3, 2012 (Fig. 1). The affected area was 308.12 ha. The area is mostly hilly (66.5%), with some plains (about 8.4% of the territory) and major mountain ranges (25.1% of the region), and annual rainfall of about 600-700 mm.

Fig. 1 - The study area in Verniano, Tuscany (Central Italy).

Results

Simulated fires

Fig. 2, Fig. 3and Fig. 4show the results of the wildfire simulation model imposing different fire durations. The 3D images highlight how geomorphology and land use heavily affected the evolution of the fire; for matters of comparison, the boundary of the area interested by the real fire event is drawn in white, while the extent of the burned area obtained from the three simulations (Simulation 0: same duration of the real event; Simulation 1: +7 days; Simulation 2: +14 days) are depicted in red.

Fig. 2 - Extension of the area burned during the real fire event (in white) and that simulated under the “Scenario 0” (no fire suppression, same duration).

Fig. 3 - Extension of the area burned during the real fire event (in white) and that simulated under the “Scenario 1” (no fire suppression, +7 days duration).

Fig. 4 - Extension of the area burned during the real fire event (in white) and that simulated under the “Scenario 2” (no fire suppression, +14 days duration).

Conclusions and final remarks

The Tuscan Region spends about 12 million euros every year in the prevention and suppression of forest fires. In this context, this study has analysed the economic and environmental benefits derived from the activities of fire suppression.

Regarding the extinction activities, a monetary approach for the quantification of direct damage “avoided” to the environmental components and anthropic activities has been applied. In particular, the difference between the TEV of fire events simulated by FARSITE and the TEV of the real event represents the avoided damages due to fire suppression activities. For this reason, the fire growth simulation model is particularly important to define the surfaces preserved from fire thanks to the fire suppression intervention. The inclusion of different variables (e.g., land use, meteorological conditions, type of fuel) in the FARSITE models allowed to define the spatial and temporal dynamics of fire in a case study located in the province of Siena (central Italy).

It is important to highlight that the observed results are strictly related to the specific fire event occurred in the case study area, as each wildfire is obviously different. Nonetheless, the methodology applied in this study could be adopted in other contexts in order to map the fire events both in a specific area (municipality, province, region, etc), in a specific period (days, weeks, months, etc.) and/or under specific weather conditions.

All data used in this study have been georeferenced with a high level of detail (pixel resolution: 10 × 10 m) using a Geographical Information System. The use of high-resolution georeferenced data represents a new frontier in spatial territorial planning, as argued by Zandersen & Tol ([49]), Bernetti et al. ([6]), Baerenklau et al. ([4]), Bottalico et al. ([7]) and Cozzi et al. ([14]).

The main drawback of this study relies in the implicit assumption that the values estimated for each ecosystem function/service at each pixel can be added up together to quantify the TEV. This can be overcome in different ways in future studies. Nonetheless, the monetary quantifications of ecosystem services allowed to analyse fire damages from an economic and environmental point of view. Indeed, we highlighted the potential loss of the economic value of ecosystem services due to fire. Combining the information on the TEV in different fire scenarios based on simulations, we were able to quantify the damage avoided by fire suppression activities, which was equal to 14,537 euros year^-1 for the Simulation 0 scenario, i.e., 23% of TEV of the real condition.

The sensitivity analysis carried out allowed to forecast the future revenues that could be lost due to fire. This analysis provides different results using different temporal scenarios and different discount rates. The aim is to guide stakeholders towards an optimal planning decision.

In the case study, the fire suppression activities were characterized by a massive use of men and airplanes in the first four days to avoid fire propagation towards an inhabited area, and this justifies the high fire suppression costs. It is important to highlight how public goods have no effect on high suppression costs. The sequence of extinction activities and their intensity is listed in all operating manuals ([2]): first protect people, then things (e.g., houses, stables, agricultural outbuildings, etc.), and then forests. Consequently, they are attributed a significant economic and territorial protection value. It is important to stress that, despite the high annual costs of maintenance, the activities of fire prevention and suppression provide economic benefits related to the defense of environmental functions/services as well as private goods, like residential and touristic buildings and farms, which are fairly common in the rural areas of Tuscany.

Regarding the FARSITE model used in this study, the main weakness is the large amount of meteorological data necessary to apply the model to a specific area. However, the model does not consider the “management” choices of the coordinator of fire suppression operations, which deeply affects the fire extinction time and consequently the fire damage.

This study represents a first step to support the economic sustainability of fire extinction activities, and offers a useful basis to further improve the choice of correct planning strategies based on sustainable management of natural areas, as argued by Viccaro et al. ([48]) and Riccioli et al. ([39]). Future improvements of the methodology applied in this study should focus on the accumulation of the annual TEV of forest considering an accurate restoring time, which could be differentiated by the type of function and silvicultural system (coppice or hight forest). In this way, it will be possible to define the restoring process as a gradual function, whilst in our model the environmental functions/services are completely restored all at once at year 20. Moreover, an extension of the wildfires sampled and an improvement of the fuel model are desirable in future works.

References

(1) Adger WN, Brown K, Cervigni R, Moran D (1995). Total economic value of forests in Mexico. Ambio 24 (5): 286-296. Online | Gscholar

(2) Anonymous (2011). La lotta attiva agli incendi boschivi: manuale per la formazione di base dell’operatore di una squadra AIB. [Active forest firefighting: AIB Team Operator Basic Training Manual]. Regione Toscana - Giunta regionale, Direzione Generale, Centro stampa Giunta Regione Toscana, Florence, Italy, 1-81. [in Italian] Gscholar

(3) Arca B, Bacciu V, Duce P, Pellizzaro G, Salis M, Spano D (2009). Simulazione della propagazione degli incendi su vegetazione a macchia mediterranea [Simulation of the propagation of fires on mediterranean spot vegetation]. Collana Ricerca Trasferimento Innovazione, no. 8, Regione Toscana ed., Florence, Italy, pp. 79-84. [in Italian] Gscholar

(4) Baerenklau KA, Gonzàlez-Cabàn A, Paez C, Chavez E (2010). Spatial allocation of forest recreation value. Journal of Forest Economics 16: 113-126.

(5) Bernetti I, Alampi Sottini V, Marinelli N, Marone E, Menghini S, Riccioli F, Sacchelli S, Marinelli A (2013). Quantification of the total economic value of forest systems: spatial analysis application to the region of Tuscany (Italy). Aestimum no. 62, Firenze University Press, Florence, Italy, pp. 29-65. Gscholar

(6) Bernetti I, Marinelli A, Riccioli F (2011). L’allocazione spaziale del beneficio turistico-ricreativo del bosco [The spatial allocation of the recreational-tourist benefit of the forest]. Aestimum no. 59, Firenze University Press, Florence, Italy, pp. 87-104. [in Italian] Gscholar

(7) Bottalico F, Pesola L, Vizzarri M, Antonello L, Barbati A, Chirici A, Corona P, Cullotta S, Garfi V, Giannico V, Lafortezza R, Lombardi F, Marchetti M, Nocentini S, Riccioli F, Travaglini D, Sallustio L (2016). Modeling the influence of alternative forest management scenarios on wood production and carbon storage: a case study in the Mediterranean region. Environmental Research 144: 72-87.

(8) Boyle K, Bishop R (1987). Valuing wildlife in benefit cost analysis: a case study involving endan- gered species. Water Resources Research 23: 943-950.

(9) Cammelli F, Angelsen A (2019). Amazonian farmers’ response to fire policies and climate change. Ecological Economics 165: 106359.

(10) Chatzinikolaou P, Viaggi D, Raggi M (2015). The evaluation of ecosystem services production: an application in the Province of Ferrara. Bio-based and Applied Economics 4 (3): 235-259. Online | Gscholar

(11) Ciancio O, Corona P, Marinelli M, Pettenella D (2007). Metodologia per la valutazione economica dei danni da incendi boschivi [Methodology for the economic evaluation of forest fire damage]. Accademia Italiana di Scienze Forestali, Florence, Italy, pp. 5-127. [in Italian] Gscholar

(12) Civita M, De Maio M, Vigna B (1999). Una metodologia GIS per la valutazione della ricarica attiva degli acquiferi [A GIS methodology for the evaluation of active recharge of aquifers]. In: Proceeding of the Conference “III Convegno Nazionale sulla Protezione e Gestione delle Acque Sotterranee”. Parma (Italy) 13-15 Oct 1999. Pitagora, Bologna, Italy, pp. 1291-1303. [in Italian] Gscholar

(13) Corona P, Ferrari B, Cartisano R, Barbati A (2014). Calibration assessment of forest flammability potential in Italy. iForest - Biogeosciences and Forestry 7: 300-305.

(14) Cozzi M, Prete C, Viccaro M, Romano S (2019). Impacts of wildlife on agriculture: a spatial-based analysis and economic assessment for reducing damage. Natural Resources Research 28: 15-29.

(15) Di Renzo F, Fratini R, Marchi E (2012). Stima dei danni da incendio sui Monti Pisani [Estimate of fire damage in the Monti Pisani]. Sherwood 187: pp. 9-14. [in Italian] Gscholar

(16) Fagarazzi C, Bernetti I, Sacchelli S, Ciampi C (2009). I comparti forestale e di prima trasformazione del legno [The forest sector and first transformation of wood]. In: “Stima della potenzialità produttiva delle agrienergie in Toscana” (Bernetti I, Fagarazzi C, Sacchelli S, Ciampi C, Ragaglini G, Villani R, Triana F, Tozzini C, Bonari E eds). ARSIA - Regione Toscana ed., Florence, Italy, 43-76. [in Italian] Gscholar

(17) Fagarazzi C, Tirinnanzi A (2015). Strumenti per lo sviluppo di filiere biomassa energia di qualità: approcci operativi per garantire la sostenibilità ambientale e sociale [Tools for the development of quality biomass energy supply chains: operational approaches to ensure environmental and social sustainability]. Pacini ed., Lucca, Italy, pp. 1-294. [in Italian] Gscholar

(18) FAO (2011). Community-based fire management: a review. Forestry Paper, FAO, Rome, Italy, pp. 1-81. Gscholar

(19) Ferrini S (2002). La domanda di ricreazione all’aperto in parchi e riserve della Toscana [The demand for outdoor recreation in parks and reserves in Tuscany]. Aestimum no. 40, Firenze University Press, Florence, Italy, pp. 41-56. Gscholar

(20) Finney MA, Ryan KC (1995). Use of the FARSITE fire growth model for fire prediction in US National Parks. In: Proceeding of the Conference “The International Emergency Management and Engineering”. Sofia Antipolis (France) 9-12 May 1995. Society for Computer Simulations, S. Diego, CA, USA, pp. 183-189. Gscholar

(21) Finney MA (2004). Landscape fire simulation and fuel treatment optimization. In: “Methods for Integrating Modeling of Landscape Change: Interior Northwest Landscape Analysis System” (Hayes JL, Ager AA, Barbour JR eds). General Technical Report PNW-GTR-610, USDA Forest Service, Pacific Northwest Station, Portland, OR, USA, pp. 117-131. Online | Gscholar

(22) Finney MA (2006). An overview of FlamMap fire modeling capabilities. In: Proceeding of the Conference “Fuels Management: How to Measure Success”. Portland (OR, USA) 28-30 March 2006. Proceedings RMRS-P-41, USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO, USA, pp. 213-220. Online | Gscholar

(23) Fisher A, Raucher R (1984). Intrinsic benefits of improved water quality: conceptual and empirical perspectives. In: “Advances in Applied Micro-economics”, vol. 3 (Smith VK ed). JAI Press, Greenwich, UK, pp. 37-66. Gscholar

(24) Gallerani V, Viaggi D, Zanni G (2011). Manuale di estimo [Appraisal Manual] (2nd edn). McGraw-Hill, New York, USA, pp. 1-400. [in Italian] Gscholar

(25) Garbolino E, Sanseverino-Godfrin V, Hinojos-Mendoza G (2019). Describing and predicting of the vegetation development of Corsica due to expected climate change and its impact on forest fire risk evolution. Safety Science 88: 180-186.

(26) LAMMA (2020). Report metereologici [Metereological reports]. Web site. [in Italian] Online | Gscholar

(27) Malczewski J (2004). GIS-based land-use suitability analysis: a critical over-view. Progress in Planning 62 (1): 3-65.

(28) Marinelli A, Romano A (1997). La valutazione economica dei benefici e dell’impatto aggregato della caccia nella provincia di Firenze [The economic evaluation of the benefits and aggregate impact of hunting in the province of Florence]. Giunti ed., Firenze, Italy, pp. 1-280. [in Italian] Gscholar

(29) Marinelli A, Marone E (2013). Il valore economico totale dei boschi della Toscana [The total economic value of the forests of Tuscany]. Agricoltura e benessere, Franco Angeli ed., Milano, Itay, pp. 1-128. [in Italian] Gscholar

(30) Martínez J, Vega-Garcia C, Chuvieco E (2009). Human-caused wildfire risk rating for prevention planning in Spain. Journal of Environmental Management 90 (2): 1241-1252.

(31) Michieli I, Cipollotti B (2018). Trattato di estimo [Appraisal treaty]. Edagricole - New Business Media ed., Milano, Italy, pp. 1-720. [in Italian] Gscholar

(32) Nordhaus W (2007). Critical assumptions in the stern review on climate change. Science 317: 201-202.

(33) Pearce D (2001). The economic value of forest ecosystems. Ecosystem Health 7 (4): 284-296.

(34) Perelli F (2013). Rapporto incendi boschivi [Forest fire report]. Fare, Web site. [in Italian] Online | Gscholar

(35) Polelli M (2008). Nuovo trattato di estimo [New appraisal treaty]. Maggioli ed., Sant’Arcangelo di Romagna, Rimini, Italy, pp. 1-1087. Gscholar

(36) Riccioli F, Gabbrielli E, Casini L, Marone E, El Asmar JP, Fratini R (2019a). Geographical analysis of agro-environmental measures for reduction of chemical inputs in Tuscany. Natural Resources Research 28 (S1): 93-110.

(37) Riccioli F, Castiglione F, Casini L, El Asmar JP, Fratini R (2019b). Analysis of ecosystem services provided by forests: a case study in Southern Italy. Scienze Regionali, vol. 18 (3/2019), pp. 447-464. Gscholar

(38) Riccioli F, Marone E, Boncinelli F, Tattoni C, Rocchini D, Fratini R (2019c). The recreational value of forests under different management systems. New Forests 50 (2): 345-360.

(39) Riccioli F, Fratini R, Marone E, Fagarazzi C, Calderisi M, Brunialti G (2020). Indicators of sustainable forest management to evaluate the socio-economic functions of coppice in Tuscany, Italy. Socio-Economic Planning Sciences 70: 100732.

(40) Romano S, Fanelli L, Viccaro M, Di Napoli F, Cozzi M (2015). The effects of climate change on the multifunctional role of Basilicata’s forests: the effects induced on yield and CO₂ absorption. In: “The Sustainability of Agro-Food and Natural Resource Systems in the Mediterranean Basin” (Vastola A eds). Springer, Cham, Switzerland, pp. 191-207.

(41) Rothermel RC (1972). A mathematical model for predicting fire spread in wildland fuels. Intermountain forest and range experiment station. Research Paper INT-115, USDA Forest Service, Ogden, UT, USA, pp. 40. Online | Gscholar

(42) Srivas T, De Callafon RA, Crawl D, Altintas I (2017). Data assimilation of wildfires with fuel adjustment factors in farsite using Ensemble Kalman Filtering. Procedia Computer Science 108: 1572-1581.

(43) Sugihara NG, Van Wagtendonk JW, Shaffer KE, Thode AE, Fites-Kaufman J (2006). Fire in California’s ecosystems. University of California Press, Berkeley, CA, USA, pp. 1-568. Online | Gscholar

(44) Syphard AD, Rustigian-Ramsos H, Mann M, Conlisk E, Moritz MA, Ackerly D (2019). The relative influence of climate and housing development on current and projected future fire patterns and structure loss across three California landscapes. Global Environmental Change 56: 41-55.

(45) Tan X, Chen S, Gan TY, Liu B, Chen X (2019). Dynamic and thermodynamic changes conducive to the increased occurrence of extreme spring fire weather over western Canada under possible anthropogenic climate change. Agricultural and Forest Meteorology 265: 269-279.

(46) Tao Z, Yan H, Zhan J (2012). Economic valuation of forest ecosystem services in Heshui watershed using contingent valuation method. Procedia Environmental Sciences 13: 2445-2450.

(47) Viccaro M, Caniani D (2019). Forest, agriculture, and environmental protection as path to sustainable development. Natural Resources Research 28: 1-4.

(48) Viccaro M, Cozzi M, Fanelli L, Romano S (2019). Spatial modelling approach to evaluate the economic impacts of climate change on forests at a local scale. Ecological Indicators 106: 105523.

(49) Zandersen M, Tol RSJ (2009). A meta-analysis of forest recreation values in Europe. Journal of Forest Economics 15 (1-2): 109-130.

(50) Zhou T, Ding L, Ji J, Li L, Huang W (2019). Ensemble transform Kalman filter (ETKF) for large-scale wildland fire spread simulation using FARSITE tool and state estimation method. Fire Safety Journal 105: 95-106.