Characterizing spatio-temporal patterns of social vulnerability to droughts, degradation and desertification in the Brazilian northeast

Journal Pre-proof Characterizing spatio-temporal patterns of social vulnerability to droughts, degradation and desertification in the Brazilian northeast Rita Marcia da Silva Pinto Vieira, Marcelo Francisco Sestini, Javier Tomasella, Victor Marchezini, Guilherme Reis Pereira, Alexandre Augusto Barbosa, Fabrícia Cristina Santos, Daniel Andrés Rodriguez, Flávio Rodrigues do Nascimento, Marcos Oliveira Santana, Francisco Carneiro Barreto Campello, Jean Pierre Henry Balbaud Ometto PII:

S2665-9727(19)30016-9

DOI:

https://doi.org/10.1016/j.indic.2019.100016

Reference:

INDIC 100016

To appear in:

Environmental and Sustainability Indicators

Received Date: 20 May 2019 Revised Date:

10 December 2019

Accepted Date: 11 December 2019

Please cite this article as: Vieira, R.M.d.S.P., Sestini, M.F., Tomasella, J., Marchezini, V., Pereira, G.R., Barbosa, A.A., Santos, F.C., Rodriguez, D.A., do Nascimento, F.R., Santana, M.O., Barreto Campello, F.C., Ometto, J.P.H.B., Characterizing spatio-temporal patterns of social vulnerability to droughts, degradation and desertification in the Brazilian northeast, Environmental and Sustainability Indicators, https://doi.org/10.1016/j.indic.2019.100016. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc.

CHARACTERIZING SPATIO-TEMPORAL PATTERNS OF SOCIAL VULNERABILITY TO DROUGHTS, DEGRADATION AND DESERTIFICATION IN THE BRAZILIAN NORTHEAST Rita Marcia da Silva Pinto Vieira1, Marcelo Francisco Sestini1, Javier Tomasella 2, Victor Marchezini 2, Guilherme Reis Pereira1, Alexandre Augusto Barbosa1, Fabrícia Cristina Santos1, Daniel Andrés Rodriguez5, Flávio Rodrigues do Nascimento3, Marcos Oliveira Santana4, Francisco Carneiro Barreto Campello4, Jean Pierre Henry Balbaud Ometto1 1

Instituto Nacional de Pesquisas Espaciais – INPE Caixa Postal 515 - 12245-970 - São José dos Campos - SP, Brasil {marcelo.sestini, rita.marcia, alexandre.barbosa, fabricia.santos, jean.ometto}@inpe.br, {[email protected]} 2

Centro Nacional de Monitoramento de Desastres Naturais – CEMADEN, Parque Tecnológico de São José dos Campos, Estrada Doutor Altino Bondensan, 500, São José dos Campos - São Paulo, 12247-016 {javier.tomasella, victor.marchezini @cemaden.gov.br} 3

Universidade Federal Fluminense Rua Miguel de Frias, 9 - Icaraí, Niterói - RJ, 24220900, Brasil {[email protected]} 4

Ministério do Meio Ambiente - Secretaria de Extrativismo e Desenvolvimento Rural Sustentável – SEDR, Departamento de Combate à Desertificação – DCD, Esplanada dos Ministérios, Bloco B, Sala 737 Brasília/DF - CEP: 70.068-900 {marcosoliveira.santana, francisco.campello}@mma.gov.br} 5

Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia, Universidade Federal do Rio de Janeiro, Av. Athos da Silveira Ramos, 149, Centro de Tecnologia - Bloco B, Sala 101 - Ilha do Fundão Caixa Postal 68506, Rio de Janeiro – RJ - CEP: 21941-909 {[email protected]}

Corresponding author: [email protected], [email protected] Instituto Nacional de Pesquisas Espaciais – INPE Caixa Postal 515 - 12245-970 São José dos Campos - SP, Brasil, Telephone +55 12 3208-7788.

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CHARACTERIZING SPATIO-TEMPORAL PATTERNS OF SOCIAL VULNERABILITY TO DROUGHTS, DEGRADATION AND DESERTIFICATION IN THE BRAZILIAN NORTHEAST

Abstract Examples of how environmental susceptibility has a direct impact on the social vulnerability of a population, by affecting both the social and cultural life quality are discussed for the Northeast of Brazil, which is considered to be the poorest region of the country. Several direct and indirect mechanisms associated to soil degradation/desertification are addressed, mainly in relation to the impact they cause to the livelihood such as income, immigration/emigration rate, and mortality. Our purpose is to provide, based on a social vulnerability index, a spatial-temporal analysis of the population dynamics in response to the effects of degradation/desertification and extended periods of drought. The results of this study demonstrated that social vulnerability is mostly dictated by social factors but marginally by environmental factors. This conclusion has an impact on federal public policies designed to reduce social inequality in the region. Keywords: desertification, environmental susceptibility; social vulnerability; Brazilian semi-arid, public policies.

24 25 26

1. Introduction

27

million hectares of land are lost every year to desertification and drought alone. In this

28

context, the UNCDD recommends the “strengthening of scientific and technical

29

cooperation networks, of monitoring indicators and of information systems at all levels,

30

as well as their integration, as appropriate, in worldwide systems of information” [76]

31

in order to plan measures to combat degradation/desertification and mitigate the effects

32

of droughts.

33

The UNCDD [76] defines desertification as land degradation in arid, semi-arid and dry

34

sub-humid areas resulting from various factors, including climatic variations and human

35

activities. Land degradation is a result of these interrelations between hazards and

36

vulnerabilities and “means reduction or loss, of the biological or economic

37

productivity and complexity of rain fed cropland, irrigated cropland, or range, pasture,

38

forest and woodlands resulting from land uses or from a process or combination of

39

processes, including processes arising from human activities and habitation patterns,

40

such as: soil erosion caused by wind and/or water; deterioration of the physical,

41

chemical and biological or economic properties of soil; and long-term loss of natural

According to the United Nations Convention to Combat Desertification [77], at least 12

2

42

vegetation” [76]. In general, vulnerability might be defined as an internal risk factor of

43

the subject or system that is exposed to a hazard and corresponds to its intrinsic

44

predisposition to be affected, or to be susceptible to damage [12]. Vulnerability is

45

closely tied to natural and manmade environmental degradation at urban and rural

46

levels. Thus, degradation, poverty and hazards are all expressions of environmental

47

problems and their materialization as disasters is a result of the social construction of

48

risk, brought about by the construction of vulnerability or hazard, or both

49

simultaneously [12, 50].

50

Brazil has differential vulnerability among its regions, which are explained by the levels

51

of investments and development distributed among the country’s regions and historical

52

patterns of development. Although the Brazilian Human Development Index reaches

53

0.73, the most socially vulnerable cities are concentrated in the North and Northeast

54

regions [29]. According to the DRIB-Index (Disaster Risk Indicators in Brazil), 1113

55

municipalities were classified as highly vulnerable, whereas 778 municipalities (69.9%

56

of this group) were concentrated in eight states, the majority in the North and Northeast

57

Regions [4].

58

Since this issue has not been well discussed by disaster risk research literature [50], this

59

study contributes to this debate [27, 41, 19] by analyzing the spatial patterns of

60

vulnerability of the Brazilian semi-arid to degradation/desertification in different

61

biomes, complementing previous studies involving physical susceptibility [81]. Our

62

main questions are: Do arid environments increase social vulnerability? How is the

63

spatial distribution of these vulnerabilities?

64

Assessing the spatial vulnerability of the population facing climate variability can serve

65

as a tool to plan adaptation measures, identifying differential impacts and abilities to

66

cope with and adapt to future risks associated with degradation/desertification. Maps

67

produced using this approach can guide targeting programs to reduce vulnerability and

68

could improve the efficacy of monitoring, warning and mitigation [27].

69

In this study, we explored the concepts, methods, materials and indicators used to

70

analyze vulnerability and proposed a Population Vulnerability to Degradation Index

71

(PVDI), which was applied to the Northeast of Brazil for the years 2000 and 2010. We

72

then examined the spatial-temporal patterns of social vulnerability in the region during

73

that period and explained the causes of the changes. Finally, we provided some

74

recommendations for integrated monitored and mitigation policies and some insights for

75

future research.

3

76

2. Concepts, materials and methods

77

To better understand the dynamics of the vulnerability process, it is necessary to analyze

78

the factors that lead to physical susceptibility to desertification, as well as the social

79

vulnerability associated with the process. Cardona [12] suggests a holistic way to

80

understand how vulnerability originates:

81

a) physical fragility or exposure: the susceptibility of a human settlement to be

82

affected by a dangerous phenomenon (such as drought) due to its location in the area of

83

influence of the phenomenon and a lack of physical resistance;

84

b) socio-economic fragility: the predisposition to suffer harm from the levels of

85

marginality and social segregation of human settlements, and the disadvantageous

86

conditions and relative weaknesses related to social and economic factors; and

87

c) lack of resilience: an expression of the limitations of access and mobilization

88

of the resources of human settlement, and its incapacity to respond when it comes to

89

absorbing the impact.

90

Consequently, the degree of vulnerability of a particular area can be estimated by a

91

composite index of indicators that define the physical and human dimensions and

92

attributes related to how an area responds to the pressure from these dimensions, and

93

whether it is capable of adapting to those pressures. Some vulnerability indicators can

94

be measured on the ground. In their absence, or when they are difficult to obtain, other

95

indicators can provide an approximate representation, such as: demographic structure,

96

livestock and crop production; drought risk, water resources availability; native species

97

and management; education, and income; among others [69, 60, 28,39]. In this type of

98

index, the factors related to the physical susceptibility and socioeconomic vulnerability

99

are combined to create a feedback mechanism: the same socioeconomic process that

100

causes soil degradation increases the exposure of the population to the negative effects

101

of the degradation, which is unable to adapt to new changes. The processes related to

102

physical and social vulnerability are dynamic and at the same time interconnected.

103

Sometimes, the indicators used to explain physical land use changes are also used to

104

understand socio-economic changes, and may even have the same weight in both cases

105

[26, 3, 20, 61, 58, 72]. The estimation of synthetic indices involves averages among

106

components, since this procedure is simple and easily reproducible and has been used in

107

several previous studies [15, 67, 68, 9, 55, 28].

4

108

Considering the cross-cutting character of desertification, the use of indicators related to

109

poverty, education, demographic structure, land tenure, type of productive activity, and

110

physical environmental conditions are generally considered in vulnerability

111

assessments. These indicators are related to resources available to the population, their

112

access to these resources and environmental factors, included in the categories of

113

exposure, sensitivity and adaptive capacity of the system analyzed. Depending on the

114

purpose of the vulnerability measure, a given variable may be included into one

115

category or another. This is the case, for instance, of the demographic profile, which can

116

be associated with either the sensitive or the adaptive capacity, depending on the

117

perspective in which it is analyzed [67, 68, 69, 28, 70, 21].

118

Although ranking municipalities according to their social vulnerability index to

119

degradation/desertification is important for public policies, analyzing its spatial

120

distribution is essential to define hotspots of vulnerability. The spatial analysis permits

121

identifying spatial groupings, in which the distribution of the values of a given attribute

122

presents a specific pattern associated with its geographical location and the dynamics

123

with its neighbors. This can be achieved through spatial indexes that identify and

124

measure the degree of association of a spatially distributed variable [22,10].

125

2.1 Study area and data

126

The Northeast Region of Brazil has low socioeconomic indicators, which are more

127

critical in rural areas and in the sertão (Interior areas) [45, 10]. Along with the Northern

128

Region, the Northeast is the poorest region of Brazil [47], which partly explains its

129

lower coping capacities/adaptability in response to an increased vulnerability to

130

droughts and degradation/desertification. Moreover, the Brazilian semi-arid region is

131

highly populated, with over 53 million inhabitants and a population density of about 34

132

inhabitants per km2 [33], and it is considered one of the most vulnerable areas to the

133

global climate changes of the next century in Brazil [35].

134

The study area is located close to the equatorial zone (1-21ºS, 32-49ºW), with a total

135

area of 1,797,123 km2 (approximately 20% of the Brazilian territory), of which 969,589

136

square kilometers are classified as the semi-arid region.

137

The region includes four biomes: the Amazon Forest (Tropical Forest), Savannah

138

(Cerrado), Steppe Savannah (Caatinga) and the Atlantic Forest (Figure 1). The Caatinga

139

is the dominant biome, covering approximately 62% of the whole area (MMA 2007),

140

and the vegetation is composed of thorny shrubs and small deciduous trees.

5

141

The region has two distinct soil sub-regions, which are directly related to climate. The

142

first sub-region covers most of the north and the coastal strip, with a humid climate,

143

predominantly forested and deep highly weathered soils and higher socioeconomic

144

levels. The second sub-region includes interior areas known as agreste and sertão and is

145

characterized by a semi-arid climate and shallow litholic soils, in which human

146

activities are developed precariously and without proper management of natural

147

resources, including inadequate land tenure, low-tech agriculture, extractivism,

148

predatory mining and poorly planned urbanization [1, 59, 47].

149

Figure 1

150

2.2 Components and indicators used in the PVDI

151

In this study, we are proposing a Population Vulnerability to Degradation Index –

152

PVDI, determined over a defined geographical space (in this case by municipality),

153

estimated by combining sub-indices that characterize degradation/desertification

154

(hazards) and the subsistence and sustainability conditions of the population, based on

155

natural, human and economic capital. The calculation of the PVDI index is based in the

156

methodology proposed by [28], which uses the seven major components equivalent to

157

those of Table 2. Each subcomponent equally contributes to the overall index. Since the

158

index is composed by a larger number of socio-economic data compared to physical

159

variables, it might be argued that prioritize socio-economic factors over climate issues,

160

in particularly aridity. However, the premise used in this work is the proposed index

161

should reflect the socioeconomic impact of climate change on local population and its

162

ability to adapt to those changes, rather than the risk of exposure to environmental

163

stresses. The categories related to vulnerability, shown in Table 1, were defined based

164

on literature and were expressed in the dimensions of exposure, sensitivity and adaptive

165

capacity [15, 67, 68, 82, 9,18, 3, 55, 83, 69, 8, 20, 28, 70, 21, 66, 46, 23, 79]. The

166

indicators and their respective sub-indexes related to vulnerability were selected and

167

derived from scientific and grey literature, and used different sources of data [30, 31,

168

33, 53, 9, 3, 7, 83, 55, 69, 6, 25, 28, 46, 16, 38, 74, 36, 29].

169

Table 1

170

Besides this, sub-indices related to physical-environmental component were obtained by

171

normalization of Environmental Quality Indices [81], which, in turn, were obtained

172

from multifactor analysis considering indicators related to climate, soil types and land

6

173

use cover [82, 35, 69, 62, 58, 60, 61, 59, 76, 71, 73]. Table 2 summarizes the sub-

174

indices corresponding to each sub component and the component used to generate

175

PVDI.

176

Table 2

177 178 179

2.3 Metrics used for the vulnerability indices

180

using equation 1:

Due to the large variety of data and scales, the sub-indices were normalized from 0 to 1

− −

=

(1)

181

Where Sd is the value of an indicator observed in a given municipality, d, Smin Smax

182

are, respectively, the minimum and maximum values of this indicator for the

183

municipality.

184

In some cases, the highest values of the Index Sd are associated with lower vulnerability

185

related to the higher values of HDI. The higher the HDI value, the lower the population

186

vulnerability because of better socio-economic conditions. Because of this, the IndexSd

187

was transformed to produce values between zero and one, which correspond to the

188

lowest and the highest vulnerabilities respectively, using the following equation: = 1−

(2)

189

In the case of indicators that include land tenure and property size, which range from 0

190

to 1 correspond, respectively, to farmers with no land ownership to owners of areas with

191

a size ≤ 2 ha and ≥100ha.

192

The physical and environmental sub-indices were normalized based on the quality

193

indices proposed by [80], ranging from low to high susceptibility to degradation /

194

desertification.

195

After standardizing the sub-indexes, each component indexes were obtained through

196

Equation 3:

197

=

∑

′

(3)

7

198

Where Md is the component referring to the municipality d, IndexSdi is a

199

subcomponent with normalized value (standardized) and n is the number of sub

200

components that define Md.

201

Finally, after the components of the index were calculated, the final value of PVDI was

202

obtained by averaging values of each component (Mdi), associated with the weights

203

(

204

equally to the overall index [28, 15, 55, 67, 68, 69, 70]:

) of each sub component (Equation 4), assuming that each component contributes

! =

∑ "# ∑ "#



(4)

205

2.4 Spatial analysis of PVDI

206

The values of PVDI were spatialized and classified in five intervals, ranging from very

207

low to very high. This procedure permitted the identification of the most critical areas

208

and the analysis of their context [28, 74]. To verify the presence of clusters and their

209

spatial distribution, measurements of spatial correlation were used. Outliers were

210

identified through indices that synthetically express the degree of association of a

211

variable of interest in a particular geographical area in relation to the weighted average

212

of neighboring values. The weight was obtained from the standardization of values,

213

based on the distance value and its neighbors. This analysis was done with the Moran

214

index as in previous studies [24, 78, 13, 49]. The Moran index shows the degree of

215

proximity and the spatial distribution, not only in the clusters, but also the transitional

216

areas between individual municipalities. These measures analyze the statistical

217

significance of clusters of a particular variable based on the assumption that the variable

218

analyzed follows a theoretical statistic distribution, generally a Normal Distribution [5,

219

48, 22, 88, 43, 10].

220

Moran's global index ranges from -1 to 1 whereas values near zero indicate lack of

221

spatial autocorrelation. This index can be broken down to a local level, and provides an

222

analysis of the degree of association of a particular neighborhood according to its

223

statistical significance. Spatial clustering indicators were obtained using the tools

224

Spatial Autocorrelation (Morans I) and Cluster and Outlier Analysis (Anselin Local

225

Morans I) available on the software ArcGis. Municipalities with statistically significant

226

correlation values, based on the p-value (p < 0.05) and assuming as the null hypothesis

227

that the variable is not spatially auto-correlated, were highlighted and classified into

8

228

four groups in relation to their neighborhoods: High-High (high attribute values

229

clusters); Low-Low (low attribute values clusters); High-Low and Low-High (transition

230

areas between significantly high or low values, respectively).

231

3. Results and discussion

232

The results showed that the physical characteristics of drylands do not necessarily imply

233

high social vulnerability, in the same way that low vulnerability is not a direct

234

consequence of a more humid climate regime. Factors linked to human, economic and

235

social aspects of communities have an important role in the better distribution of

236

resources and survival. This is explained by the fact that cooperation and reciprocity

237

enable access and participation in policy formulations that meet local needs. The

238

transition from a high social vulnerability situation, combined with environmental

239

degradation, to a condition of low vulnerability and sustainability depends on the

240

achievement of public policies through a partnership between government and civil

241

society.

242

Figure 2 indicates that the PVDI values varied between ≈0.16 and ≈0.57 over the study

243

area. While the location of the areas with high PVDI values were concentrated in the

244

Caatinga biome in 2000, in 2010 there was a shift towards the northwest of the study

245

area, including areas of the Cerrado and Amazon biomes.

246 247

Figure 2

248

Analyzing the figure above, we can observe that the north-northwest area specifically,

249

within the Amazonian and Cerrado biomes, socioeconomic components were

250

determinant in the increase of the PVDI due to the influence of socioeconomic

251

backwardness that prevails in many municipalities of the area [78]. In this particular

252

area, the percentage of rural population is among the highest of the whole study area

253

(higher than 35%). This demographic composition is reflected in the higher incidence of

254

poverty and lower human capital, i.e. low rates of education, health, and social

255

organization. Although an improvement in HDI values was identified between the years

256

2000 and 2010, with the education indicator showing the highest increase (0.250),

257

followed by life expectancy (longevity) and income, socio-economic indicators are still

258

the lowest of the whole study area, which contributes to the high PVDI values estimated

259

in 2010. Because the vulnerability indicator developed in this work requires IBGE

260

census data which is available until 2010, it is necessary to assess whether the severe

9

261

drought that affected the study region between 2012 and 2016 [17] have impacted

262

vulnerability indicators of municipalities located in the inner semiarid areas.

263

Nevertheless, it should be noted that the recent drought not only impacted the semi-arid

264

area but also more humid climate, where annual rainfall is around 1500 mm (IBGE,

265

1996), located on coastal and to the west of the study region. The growth of

266

deforestation in this region should also be highlightef, since it is associated with the

267

increase in the number of fires and intensive land use changes. In relation to fire, in the

268

year 2000, this region had 8,983 fire outbreaks, while this number rose to 28,897 in

269

2010, being the region with the highest number of fires in relation to the study site [34].

270

Regarding land use changes, about 20% of the Brazilian Amazon (760 square

271

kilometers of forest) has been cleared by 2014. The Cerrado biome is the worst when it

272

comes to human pressure, especially related to rural activities, in order to increase the

273

production of meat and grains for export [56]. Moreover, the regional space and the way

274

the territories were historically forged, amid cyclical droughts, have affected the

275

regional production and have caused strong negative impacts on the economy, society

276

and environment. The projections of IPCC (2014) in future climate change scenarios

277

suggest a decrease in water availability for agricultural irrigation and human use owing

278

to reductions in precipitation and increases in evapotranspiration. Such scenarios may

279

be indicative of the decline of agricultural production in the region, affecting the food

280

security of the rural population.

281

Figure 3 shows the spatial-temporal distribution of the Global Moran index obtained

282

with a correlation 0.296, p-value less than 0.01 and a z-score of 56.84.

283

Figure 3

284

The cluster maps (Figure 3) show that average and no significance values of

285

neighborhoods are distributed in the central part of the study area and around low value

286

clusters. As for the low PVDI values, they are concentrated in the south and southeast

287

zone of the study area, and may be related mainly to better physical environmental

288

conditions, which in turn, are allied to a favorable level of socio-economic indicators.

289

The northeast region (Rio Grande do Norte and Pernambuco states), also presents a low

290

PVDI (Figures 2 and 3) which can be attributed to more favorable conditions due to

291

fruit production, Brazil-nut production, higher monthly-average income per household

292

and lower poverty rate.

10

293

Since social capital refers to the social organization of elements such as networks,

294

norms and social trust that facilitate coordination and cooperation for mutual benefit

295

[54], including the coping capacities to deal with degradation/desertification, gender and

296

age characteristics of a society are also important to define social capital. Results

297

showed that the proportion of women as breadwinners is higher in urban than in rural

298

areas. However, when comparing the same relationship with the other regions of Brazil,

299

the proportion of women as breadwinners in rural areas is higher in the Northeast. This

300

fact leads to higher vulnerability due to the occurrence of a greater migration / mortality

301

of the male population [65, 11]. The higher vulnerability in this case is explained by the

302

fact that the female-headed rural families face difficulties in finding alternative income

303

sources, since they devote time to domestic and immediate subsistence activities.

304

Besides this, they face obstacles in obtaining befitting remuneration in the market due to

305

gender discrimination related to the cultural model in the society they live [84, 67, 68,

306

65, 28, 40].

307

The age imbalance is another factor that contributes to the greater vulnerability of the

308

region. The group of children under five in 1991 was 12.8%; in 2000, it fell to 10.6%,

309

reaching 8.0% in 2010. The elderly population increased from 5.1% in 1991 to 5.8% in

310

2000 and rose to 7.2% in 2010 [33]. The population ageing process increases

311

vulnerability since the elderly are more vulnerable to hazards such as heat waves [52]

312

and other creeping environmental changes [2].

313

Despite the higher proportion of elderly increasing social vulnerability, in certain

314

families this fact has the opposite effect, because the incomes from pensions and

315

retirement are the primary source of family income. However, this scenario can change

316

due to dynamic pressures such as global economic crisis and changes in national social

317

assistance policies. In the 1980s, for instance, unemployment among young people has

318

accelerated and the purchasing power of pensions and other fixed incomes of the elderly

319

has decreased [85]. In developing countries like Brazil, the dependency on pensions and

320

retirement has severe implications on public spending [53, 87, 65, 6, 11]. If the

321

imbalances in the pension system in Brazil and an acute financial crisis persist, the

322

national government might cut public spending of social assistance policies, which will

323

increase the social vulnerability to cope with threats and hazards [56, 51, 42].

324

Overall, the PVDI values and their spatial distribution proved to be consistent with the

325

areas with higher vulnerability, both at the individual and clusters levels. This result

326

indicates the existence of significant spatial autocorrelation of the PVDI in the study

11

327

region. The Moran cluster map (Figure 3) shows that the clusters corresponding to the

328

High-High neighborhood class is accompanied by PVDI values above 0.40 (Figure 2),

329

thus there is a strong concentration of municipalities with higher vulnerability.

330

However, other equally critical areas were not part of these classes. This may be

331

attributed both to the significant participation of human factors, which act to mitigate

332

the physical factor; and the fact that some sub-indexes contribute more than others,

333

affecting the final composite index. This last issue is reflected in the spatial analysis, in

334

which some municipalities may not have the most representative spatial correlation.

335

Although some studies [24, 13, 49] have found that it is possible to detect regionally

336

agglomerations and check trends using synthetic indexes, the index proposed in this

337

study has poor spatial correlation. In this study, the spatial regime analysis through

338

separate sub-indices revealed more consistent results, showing that sometimes the

339

analysis of all dimensions expressed in a single index will provide the corrected spatial

340

distribution.

341

The use of other indicators, such as quantity of jobs and services, diversity of

342

agricultural production, access to health facilities, irrigation and number of cisterns, can

343

provide more detailed and accurate results for the final index and should be considered

344

in future studies. It is also important to include indicators related to social capital, for

345

example, data about associations and cooperatives, among others.

346

We can conclude that the method offers a simple approach, with optimal results, which

347

help to understand the dynamics of issues related to vulnerability and degradation, as

348

well as its spatial distribution. It also provides guidance for planning of mitigation

349

measures, for the formulation of adequate policies that can minimize the risks and

350

effects of this phenomenon.

351

Finally, the development policies for the Northeast of Brazil have been focused on the

352

semiarid region. For instance, the Constitutional Fund for Northeast Funding (Federal

353

Law 7.827), which is an important source of low-cost credit in the region, prioritized the

354

population living in the semiarid area. Since this study suggests that the climate regime

355

is not determinant to the level of vulnerability, it is important to rethink public policies

356

to fulfill the main goal of the Fund, which is reducing regional and intraregional social

357

inequalities.

358

This study is the first approach carried out on a regional scale in the study area, which

359

derived and analyzed spatially a vulnerability index. By using indicators regularly

360

monitored and available from the Brazilian Institute of Geography and Statistics Brazil

12

361

(IBGE), it enables both a regional update and its generalizations for other regions of the

362

country. We also suggest that the analysis of the cluster map should take into account

363

future scenario maps of land use and climate change, integrating this data to projections

364

to identify future trends. Furthermore, it would be of great importance to use

365

discriminating methods to evaluate which variables are most sensitive to the

366

vulnerability in each case. This would avoid potential favoring of one variable over

367

another.

368

Acknowledgements

369

The authors are grateful the supported by FAPESP (Fundação de Amparo à Pesquisa do

370

Estado de São Paulo) grant 17/22269-2.

371

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Table 1 Major components and subcomponents comprising the Population Vulnerability to Degradation Index (PVDI)

Categories

System exposure

to environmental

pressures - is defined as the nature and degree to which a system is exposed to

Components / subcomponents

physical

and

environmental

/

Explanation of components/ subcomponents

aridity,

Unfavorable soil conditions, climate, inadequate

geomorphological characteristics, soil and rock types,

land management, occurrence of natural hazards,

vegetation type and land use.

increase population pressure

Source

Vieira, 2015 1

natural and human pressures

demographic structure by gender / demographic

Due to cultural aspects of the study area, men

Brazilian Institute of

structure involving gender (sex ratio, proportion of

migrate more than women. Although women are

Geography and

rural women and women as breadwinners).

responsible of caring for the family members,

Statistics - IBGE

their role is devalued by the market, making the

(2000 and 2010)

population more sensitive and less balanced.

System sensitivity to pressures - is the degree to which a system would be affected, either adversely or beneficially,

demographic

structure

of

senior

citizens

/

demographic structure involving age (aging index,

A high number of elderly people means a lower

Brazilian Institute of

number of individuals of working age.

Geography and

pensioner proportion)

Statistics – IBGE

by anthropogenic pressure

(2000 and 2010)

demographic structure / demographic structure

Very low population density is indicative of

Brazilian Institute of

regarding population

size and its distribution

migration and death. On the other hand, high

Geography

(Population density, growth rate, proportion of rural

density leads to an increased pressure on natural

Statistics – IBGE

and

population)

resources and in public services

(2000 and 2010)

Continue Adaptive capacity - is the ability of a system to adjust to climate change

HDI / Human Development Index at Municipality

Higher HDI represents a better response of the

João

level - IDHM

population to environmental pressures

Foundation

(including climate variability and

Pinheiro (2000

and 2010)

extremes), to take advantage of opportunities, and to cope with the consequences.

land tenure / type of ownership, size of the

Usually owners take better care of the land,

Brazilian

properties.

rather than tenants.

of Geography and

Institute

Statistics – IBGE Regarding the size of the properties, small

(2000 and 2010)

properties are generally unsustainable managed because of the owners lack access to capital and technology, and are not covered by insurance. These factors reduce the ability to adapt to impacts. Large areas, despite the higher impacts due to large-scale crops and pasture, are usually better managed.

extrativism and cattle / extrativism and cattle

Extractive activities and livestock raising, as

Brazilian Institute of

single or main economic activity, may represent

Geography

difficulties in adaptation, since they are very

Statistics – IBGE

sensitive to climatic changes and usually

(2000 and 2010)

and

associated with environmental impact due to soil compaction and overgrazing. 1

The sub-indices related to physical-environmental component were obtained by normalization of Environmental Quality Indices (Vieira et al, 2015), which, in turn, were obtained from multifactor analysis considering indicators related to climate, soil types and land use cover (Westing, 1995; Kosmas et al, 1999; Svenson, 2005; Sietz et al, 2006; Salvati et al, 2008; Santini, 2008; Santini et al, 2010; Salvati & Bajocco, 2011; Vale & Silva, 2011; Tesfa & Mekuriaw 2014; Travassos & Souza, 2014).

Table 2 Components and sub components for PVDI Components demographic structure and dynamics



Sub components Fraction of Rural Population

Sub Indices

(

Geometric Population Rate Growth (TCP) between 2000 and 2010(1)

r =

) × 100

P P

− 1 × 100

Demographic Density

demographic related to age/ working age population

Pensioner Ratio

Aging Index



"



(

'



"

) × 100



≥ 65

≤ 15

!



"

"



) × 100

demographic related to proportion and role of women (For the PVDI, the total proportion of women as breadwinners were analyzed separately in urban and rural areas, and then together)

Proportion of female rural population

land ownership

Property regime

Ownership varies from farmers with or without ownership. An area was considered economically active (size of a plot with economic value), when it used for activities such as horticulture, crops, livestock, etc. Frequency of types of ownership, defined by “class type of ownership “/” total types of ownership” (%) were considered, extrapolating the mode of the municipality.

Size of rural properties

Properties were divided into classes according to their size defined by the intervals in the range of <2ha to ≥100ha. The entire area was considered in the calculations and not only the area with economic activity. The frequency defined by “class property area” /” total property area classes” in percentage was extrapolated the mode of the municipality.

Herds

Cattle and goats total density

Extractivism

Average production of wood, charcoal and firewood

rural economic activity

Sex Ratio

(



Proportion of female householders

economic, educational and longevity conditions

Human Development Index( HDI) (2)

quality index (QI)

represented by the physical and environmental quality index

'

( (

+

.

'





, -





ℎ

" ℎ ) "

* ' '

× )

) × 100 "

) × 100

× 14

/0 = 12 /0 3 =1

Where Climate quality index (CQI) is provided by the aridity index; Environmental quality index

(EQI) is a result of multifactorial index, which integrates sub indices soil, geomorphology, geology and slope; Management quality index (MQI) which corresponds to the classes conservation units, livestock density, fire density and land use and land cover change.

(1) r = growth rate Pt = population at the end of the period under consideration P0 = population at the beginning of the period under consideration n = number of years of the period under consideration (considering the Census, 10 years) (2) Longevity considers life expectancy at birth; education considers school attendance and completion and income considers the income per capita sum.

Fig. 1. Location of the study area (left) and the main biomes (right)

Fig. 2 – Spatial distribution of the PVDI (Population Vulnerability to Degradation Index) for

the biomes in the Northeast of Brazil classified in 5 quantiles: dark red values indicates highest vulnerability, whilst pale shades of red are associated with lower vulnerability to degradation.

Fig. 3 – Spatial distribution of Moran Grouping for the Northeast of Brazil. Values in white

indicate that the spatial autocorrelation is statistically not significant; H-H indicates high attribute values clusters; H-L and L-H corresponds to transition areas between significantly high or low values, respectively, according to its surroundings; and L-L indicates low attribute values clusters.