The Intergovernmental Panel on Climate Change (IPCC) defines vulnerability as the degree to which geophysical, biological, and socio-economic systems are susceptible to, and unable to cope with adverse impacts of climate change (Houghton 1996). As these impacts at local scales are uncertain, vulnerability assessments have become necessary (Adger et al. 2004) to increase the understanding of climate-sensitive systems and to inform the specification of targets and allocation of funds. Other uses for assessments are to prioritize political and research efforts, to develop and to implement adaptation strategies (Füssel and Klein 2006); and to evaluate program/policy effectiveness in data-scarce regions (Adger et al. 2004; Pandey and Jha 2011).
The evolution of vulnerability assessments has revealed trends toward interdisciplinary analyses of the consequences of climate change, as well as integration with adaptation, environmental degradation, agricultural productivity, food security, population growth, and conflict research (Füssel and Klein 2006; Schilling et al. 2012).
Though there is no uniform methodology to assess vulnerability and its components (i.e. exposure, sensitivity and adaptive capacity), all vulnerability assessments require a detailed contextual understanding of the relevant systems and of the multiple stressors (Füssel 2010). Assessments have been conducted at different geographic scales, such as watersheds, rural areas (Eakin 2005), regions, countries, or worldwide (Pachauri et al. 2014). These assessments have made use of a wide array of technical tools such as local-level case studies (O’Brien et al. 2004; Sutanta et al. 2013), resilience indicators (Brenkert and Malone 2005), bio-economic models (Schilling et al. 2012), general equilibrium models (Parry et al. 2004; Tol 2002), and cross-sectional studies (Mendelsohn et al. 2006). The selection of the appropriate approach depends on the research or policy questions addressed, the urgency of the threat, the geographical and temporal scope of the analysis, the reliability of future climate impact projections, the level of previous knowledge, and the availability of data, expertise, and other resources (Füssel and Klein 2002, 2006; Füssel 2010).
Regardless of the assessment approach, challenges arise when the regions analyzed are communities that suffer from food, health and environmental insecurity, poverty, economic inequalities, weak governance, deficient infrastructure and education, lack of access to appropriate resources, or poor capacity to deal with extreme events (Bele et al. 2013). These issues are likely to determine the sensitivity of communities to climate change (Watson et al. 1998). For example, poverty and inequality are associated with poor quality housing, which is easily damaged by floods or storms, and with the formation of vulnerable and marginalized groups that lack the financial resources for adaptation, and may be forced to settle in climate-exposed areas (Adger et al. 2004). Thus, finding good-quality socioeconomic data at local scale and harmonizing them with climatic variables is a significant aspect of vulnerability research.
Hence, the purpose of this paper is to assess vulnerability to climate change at a local level in Ecuador. We rely on the statistical behavior of climatic and socioeconomic variables where the weighting and aggregation mechanism of the composite vulnerability indicator is similar to Iyengar and Sudarshan ( 1982) and Deepa et al. ( 2013). Rather than assuming the variables contribute equally to the composite indicator, our assessment approach defines the weights or contributions of each variables with respect to the aggregated variability of all variables across cantons (i.e. the political-administrative division of Ecuador, see the Additional file 1). Thus, through normalization, this approach mitigates the dominance of variables with large variance. Furthermore, it allows aggregation of variables to a measure of the overall variability that represents the multi-dimensionality of vulnerability (Leichenko and O’Brien 2002). We use a large number of economic, social, and environmental variables to construct the composite vulnerability indicator, and a contribution of this paper is the use of empirical orthogonal functions (EOF) and reanalysis datasets to incorporate the long-run and spatial patterns of the climatic variables.
We assume that exposure to climate change affects the sensitivity of cantons where communities respond given their adaptive capacity. Thus, we separate the composite indicator into three components: (1) exposure: the condition of disadvantage due to the position or location of a subject, object or system at risk; (2) sensitivity: the degree of internal ability of a subject, object or system to meet a threat and receive a possible impact due to the occurrence of an adverse event; and (3) adaptive capacity: the ability of a system, community or society exposed to hazards to cope, absorb, and recover from the effects of an adverse event effectively and in a timely manner, considering the preservation and restoration of its essential basic structures and functions (Antwi-Agyei et al. 2012; Houghton 1996; Ionescu et al. 2008; Luers et al. 2003; Martin et al. 2012; O’Brien et al. 2004).
The paper is organized as follows: " Methods" section describes both the modelling framework to construct the composite vulnerability indicator, and the data. " Results and discussion" section presents the results. " Conclusions" section concludes.
We choose Ecuador as a case study because socioeconomic data are readily and publicly available. Also the cantons in this country show a large degree of heterogeneity in terms of development and climatic conditions. We map vulnerability indicators at the canton level and show how different factors that shape vulnerability vary within Ecuador. The dataset includes 221 cantons of the continental territory of Ecuador, and other settlements such as Manga del Cura, El Piedrero and Las Golondrinas, which, at the time the data were collected, did not belong to any province.
Figure 1 shows the results of the EOF analysis. Each panel shows the first principal component (PC) and the share of variance explained. Temperature, wind velocity, and relative humidity show inter-annual variation, which may be interpreted as seasonal variation. Thus, correlating the temporal variation with the corresponding PC, at each location, quantifies the link between these climatic variables and the seasonal variations. Also, it is noticeable that these variables in western Ecuador show a better representation of the seasonal variation because the boundaries of this area include the significant climatic influences from the Pacific Ocean. The PCs of temperature, wind velocity, and relative humidity in eastern Ecuador represent the seasonal variation but with small perturbations as in this region there are significant orographic and vegetation differences that affect the behavior of these variables. Additionally, the PCs do not show a strong representation of extreme events such as El Niño or La Niña.
EOF/PC analysis of climatic indicators. Fraction of variance explained (FOV) for each climate indicator is greater than 51 %
The EOF analysis of the monthly variation of precipitation resulted in a PC that did not show positive correlations. The monthly variation of precipitation is highly dispersed. For example, Northern Ecuador shows different values from the South because of the greater influence of the Intertropical Convergence Zone. This adds to the existing geophysical differences between the Eastern and Western zones. Hence, for EOF analysis of precipitation we use the yearly variation to obtain the PC at the geographic location of each of the cantons. As it may be observed in the first PC, both in Eastern and Western Ecuador, there is a strong signal at the occurrence period of events such as El Niño and La Niña in 1981 and 1984. The PCs explain 51.4 and 58.9 % of the yearly variation and result in positive correlations with the geographic locations of the cities.
For the beta distribution, the parameter
Plot of the composite vulnerability indicator
Table 2 shows the calculated weights of each indicator, that is, their contribution to the formation of the composite vulnerability indicators. The largest weight corresponds to the institutional capacity. For the relative ordering we observe that the climatic indicators are among the ten with the largest contributions. Temperature has the second largest contribution, then wind velocity has the third, relative humidity the sixth; and, precipitation the tenth. Other indicators with large contributions are the proportion of households with sewage treatment service; the proportion of households with garbage collection service; the proportion of population working in agriculture, hunting or fisheries; the proportion of households owning agricultural land; and, the proportion of households receiving water through a pipe system.
Calculated weights of the vulnerability indicators
| Institutional capacity | 0.0484 | Proportion of houses with exclusive sanitary facilities | 0.0239 |
| Temperature | 0.0476 | Proportion of population 65 years or older | 0.0236 |
| Wind velocity | 0.0466 | Proportion of crowded households | 0.0230 |
| Proportion of households with sewage treatment service | 0.0321 | Average number of children per household | 0.0224 |
| Proportion of households with garbage collection service | 0.0319 | Proportion of households with land phone service | 0.0218 |
| Relative humidity | 0.0317 | Illiteracy rate | 0.0206 |
| Proportion of population working agriculture, hunting or fisheries | 0.0305 | Proportion of population less than 5 year old | 0.0203 |
| Proportion of households on agriculture owning land | 0.0297 | Average time in business | 0.0202 |
| Proportion of households receiving water through piped system | 0.0292 | Proportion of population with mobile access | 0.0202 |
| Precipitation | 0.0282 | Average business revenues | 0.0191 |
| Proportion of population not under social security coverage | 0.0279 | Proportion of population with any permanent disability | 0.0181 |
| Proportion of households living on owned house | 0.0272 | Proportion of population with private health insurance | 0.0168 |
| Proportion of households below poverty line | 0.0271 | Proportion of households with electricity service | 0.0168 |
| Proportion irrigated land/total agricultural land | 0.0258 | Proportion of population affected by disasters | 0.0149 |
| Proportion of population with internet access | 0.0258 | Minimum distance to large town | 0.0145 |
| Proportion of households with access to computer | 0.0256 | Tax revenues per capita | 0.0125 |
| Net rate high school attendance | 0.0255 | Funds for environmental protection per capita | 0.0118 |
| Proportion of households with proper sanitary facilities | 0.0253 | Population per non-medical doctor | 0.0106 |
| Employment rate | 0.0252 | Population density | 0.0102 |
| Proportion of households where head is female | 0.0240 | Number of patients per hospital bed | 0.0099 |
| Proportion of population working in own business | 0.0240 | Average energy consumption (Kwh/annum) | 0.0095 |
In turn, the indicators with the lowest contribution are funds for environmental protection per capita, population per non-medical doctor, population density, number of patients per hospital bed, and average energy consumption. Intermediate values appear for some of the sensitivity indicators such as the proportion of population with no social security coverage (11th), the proportion of households living below the poverty line (13th), the proportion of irrigated agricultural land with respect to total agricultural land (14th), the proportion of households with proper sanitary facilities (18th), and the proportion of households where the head is a female (20th).
Figure 3 shows the vulnerability categories. The cantons with the highest vulnerabilities are located to the northwest of Ecuador, in the province of Esmeraldas, to the Midwest, in the provinces of Manabi and Los Rios, and to the South, in the provinces of Loja and Morona Santiago. In addition, most of the cantons in the Amazonian region are either in the highly or very highly vulnerable categories. The least and less vulnerable cantons are located to the central region of the country, mainly in the provinces of Pichincha; others are in the South, in the province of El Oro. The least vulnerable cantons are Guayaquil, Ecuador’s largest city, and, Quito, Ecuador’s capital. These are followed by Cuenca, the third largest city in the country, and other mid-size cities such as Riobamba, Esmeraldas, Machala, Ambato, Loja, and Santo Domingo where populations range between 190,000 and 505,000 inhabitants. The correlation between the composite vulnerability indicator and population size is −0.5. This result points to the fact that larger cantons are not significantly less vulnerable.
Vulnerability to climate change by Canton
Other smaller cantons, such as Mejia (pop. 81,335) and Marcelino Maridueña (pop. 12,033), are also in the less vulnerable categories. Arguably the proximity of Mejia to Quito and of Marcelino Maridueña to Guayaquil implies that the development facilities and infrastructure of large cities spill over to the adaptive capacity of surrounding smaller cantons. This situation may also explain the low vulnerability of other cantons such as Samborondon and Duran with respect to Guayaquil; Pasaje and Santa Rosa with respect to Machala; and Rumiñahui with respect to Quito. The situation does not, however, apply to cantons such as Salitre and Nobol, which despite their close proximity to Guayaquil (on average 20 miles), appear as highly vulnerable.
In the category of moderately vulnerable cantons we find that Quevedo, Latacunga, and Babahoyo (where population size is at least 153,000 inhabitants) are not different, in terms of vulnerability, from other much smaller cantons such as Calvas (pop. 28,185), Macara (pop. 19,018), San Pedro de Huaca (pop. 7624), and Chaguarpamba (7161). This indicates that there is no clear pattern between population size and vulnerability to climate change. In the category of vulnerable cantons we find Portoviejo (pop. 280,029), which is one of the 10 most populated cantons in the country, as well as smaller cantons like Guaranda (pop. 25,001), Salinas (pop. 28,650), and Quilanga (pop. 4337).
In the category of highly vulnerable cantons we have El Empalme (pop. 64,789), Santa Elena (pop. 30,920), Pallatanga (pop. 12,000) and Gualaquiza (pop. 7409). For the very highly vulnerable category we find mid-size cantons such as Lago Agrio (pop. 57,727), Pedro Carbo (pop. 31,337), Olmedo (pop. 4870), and Aguarico (pop. 1024), which is the canton with the highest vulnerability index.
Figures 5, 6 and 7 shows the confidence intervals at 95 % level for selected indicators across vulnerability categories. For the climatic indicators we find that temperature and, to some extent, precipitation determine higher exposure in the very highly vulnerable category. Though in this category the confidence interval for precipitation overlaps with those in the moderately to highly vulnerable categories; temperature is significantly different compared with the rest of the vulnerability categories.
Descriptive results of selected indicators by vulnerability group. Filled square represents the average and filled rectangle represents upper and lower limits of the 95 % confidence interval
Descriptive results of selected indicators by vulnerability group. Filled square represents the average and filled rectangle represents upper and lower limits of the 95 % confidence interval
Descriptive results of selected indicators by vulnerability group. Filled square represents the average and filled rectangle represents upper and lower limits of the 95 % confidence interval
For the adaptive capacity indicators we find that agricultural land tenure (i.e. the proportion of households owning agricultural land with respect to total agricultural land) is not significantly different between the moderately vulnerable to the very highly vulnerable categories. In these groups on average 71 % of farmers own the land where they work. This figure increases to 85 % for the less vulnerable category. There is, however, a significant difference between the less and very highly vulnerable groups. In addition, for the proportion of the population working in agriculture, silviculture or hunting, 25.53 % work in cantons in the less vulnerable category. This figure increases to 52.35 % in the rest of the vulnerability categories.
The indicator for institutional capacity is highest for the moderately vulnerable category, and lowest for the highly and very highly vulnerable categories. Significant differences may appear in confidence intervals using levels slightly lower than 95 %. Public funds per capita for environmental protection are highest for the less vulnerable category (32.18 US dollars) but there is wide variation across cantons. For the rest of the categories funds per capita are on average 12.45 US dollars.
The proportion of households with access to garbage collection service is highest in the less vulnerable category (82.08 %). This is significantly different compared with the moderately vulnerable (64.43 %), the vulnerable (56.89 %), and the highly and very highly vulnerable (45.51 %) categories. Similarly, on average the proportion of households with sewage treatment service is 81.55 % in the less vulnerable category but is 43.67 % in the very highly vulnerable category. A similar result is observed for the proportion of households receiving water through a public piped system: the proportion is 67.54 % in the less vulnerable category, 33.95 % in the highly vulnerable category, and 27.8 % in the very highly vulnerable.
Regarding human capital as part of adaptive capacity, there is high variation in the proportion of population affected by natural disasters, where the average is 16.54 %. No significant differences are found across vulnerability categories. In addition, less than 10 % of the population across all vulnerability categories has private health insurance: 55 % of the population is not under social security coverage.
There is also a high variation on the minimum distance to a large town in the less vulnerable category and the confidence intervals overlap across all categories.
Regarding the sensitivity indicators, adult illiteracy rate is significantly higher in the very highly vulnerable category (13.12 %), and lower in the less vulnerable (4.99 %) and moderately vulnerable (8.75 %) categories. For the remaining categories, the illiteracy rate averages 10.01 %. For crowded households, this proportion is lowest for the less vulnerable category (14.19 %) and is significantly different for the rest of the vulnerability categories (highly vulnerable 23.33 % and very highly vulnerable, 27.93 %). Population density is significantly larger in the less vulnerable category (180 inhabitants per square km) compared with the very highly vulnerable category. However, there is a high variation where no pattern is identified and confidence intervals overlap across all categories.
Earlier work has documented the extensive range of tools to assess climate change vulnerability at local levels. The use of any tool depends on the research questions to be answered. Vulnerability assessments, through the indicators approach, provide an overview of the socioeconomic, climatic, and geophysical determinants for a canton, or any other geographic unit, that are vulnerable to climate change. In this paper we use an approach that enables the relative ordering of the cantons in the continental territory of Ecuador. Our aim is to make full use of the climatic and non-climatic features, and construct additional indicators related to exposure, sensitivity, and adaptive capacity to climate change (Pachauri et al. 2014). Our approach allows us to capture the multi-dimensionality of vulnerability in a comprehensive framework (Leichenko and O’Brien 2002).
To construct the composite vulnerability indicator we use a large number of variables. Thus, in order to attain a meaningful and theoretically consistent vulnerability indicator, we normalize the variables and calculate the weights based on their relative variability. The weights represent their contribution to the formation of the composite indicator.
As it was not possible to apply standard modelling procedures to climatic data, because of the data’s unavailability or coarse resolution, we use a reanalysis strategy where the EOF incorporates spatial and temporal patterns. The composite indicator is bounded between 0 and 1 and takes the form of ratios, thus we assume it follows a beta distribution. It may be argued that this assumption does not apply to every dataset and context, but if a priori we assumed a normal distribution we would lose generality and incur potential specification errors if asymmetries arose. By assuming a beta distribution we leave open the possibility of approximating the beta to a normal distribution in case the parameters
The institutional capacity of the cantons has the largest contribution to the formation of the composite indicator. Though we did not observe significant differences across the vulnerability categories, we argue that the availability of environmental- and climate-change-related policies improve the adaptive capacity. These policies serve to anticipate the impacts of climate change and form potential responses. However, this indicator does not capture the nature and extent of those policies. For this we use as a proxy the funds per capita allocated to environmental protection. Results showed that there is a wide variation in the less vulnerable category, thus we cannot reach a conclusion that funds are significantly larger for the cantons in this category. However, getting allocation of funds in general from the central government is often the outcome of an intense lobbying process that depends on the relative economic and political importance of each canton. As the less vulnerable category includes some of Ecuador’s largest cantons (e.g. Guayaquil and Quito) along with others that are much smaller (e.g. Mejía and Marcelino Maridueña), we argue that correcting by economy and population size, both institutional capabilities and greater funds for environmental development can determine whether a canton is less vulnerable to climate change.
Earlier work has shown that variables such as the proportion of households with sewage treatment service, with garbage collection service, and with access to water through piped system indicate both poverty levels and development (Baker 2004; Feitelson and Chenoweth 2002). Thus, we found that those cantons in the least vulnerable category are also those with higher coverage of these public services. In addition, the proportion of population working in agriculture, hunting or fisheries and the proportion of households on owning agricultural land are associated with local economic capabilities and opportunities. Results show that these indicators are among the ten largest contributions to the composite indicator. That is, vulnerability in Ecuador is also influenced by the concentration of economic activities related to agriculture, which limits other economic activities and the ability of people to shift in response to reduced agricultural income that may result from adverse climatic conditions. Some implications of these results arise: first, land tenure promotes economic development and reduces vulnerability as economic deprivation, in an adverse climatic event, is partially resolved by selling land assets. In addition, as agricultural work does not necessarily demand academic qualifications or schooling, this affects human capital accumulation and implies reduced adaptive capacity.
For cantons in the highly and very highly vulnerable categories, at least 50 % of the population works in agriculture, hunting or fisheries. With a weaker contribution to the composite indicator, the proportion of agricultural land that is irrigated has the fourteenth largest contribution. To this we add that 64 % of Ecuador’s population (9.3 million individuals) lives in cantons that are considered highly and very highly exposed. Thus, small holder and subsistence farmers will suffer impacts of climate change that will be locally specific and hard to predict. Hence, the variety of crop and livestock species produced by any one household, and their interactions in production and marketing, will increase the complexity both of the impacts and of subsequent adaptations (Morton 2007). In addition, approximately 20 % of the population (2.9 million individuals) live in cantons with high and very high vulnerability mainly because of the limitations of adaptive capacity.
Some of the largest cantons in Ecuador (Quito, Guayaquil, and Machala) show a very high adaptive capacity, given their development infrastructure, which may spill into surrounding cantons. However, it is not possible to conclude that there is a strong correlation between population size and adaptive capacity because mid-size cantons such as El Empalme and Santa Elena are categorized as highly vulnerable. Furthermore, we cannot generalize that proximity to the largest cantons will guarantee coverage of public services, for example, Salitre and Nobol, which, despite their close proximity to Guayaquil (on average 20 miles), appear as highly vulnerable.
For policy purposes, particular attention should be directed to the low coverage of health services, private health insurance, and social security. Though coverage may have increased since data collection, it may not be at a high enough level to guarantee adequate adaptive capacity in most of the cantons analysed.
Some limitations are worth mentioning. First, we do not include the Galapagos Islands, as their climatic features are different from Ecuadorian continental territory. Second, though we do not intend to compete with other vulnerability approaches and have not developed a formal comparison test with PCA, a research path would be to assess how different weighting of indicators influence interpretation and to identify the potential links to planning, prioritization, decision-making, and monitoring over time, given the dynamic nature of vulnerability (Bele et al. 2013). Third, we did not take a particular focus on agriculture or rural areas. Future economic or demographic impact analysis would certainly complement the extent of the implications of this study.
MF gathered the data, coded the model in STATA and wrote the draft of the paper. SB administered the workshops with experts and stakeholders, harmonized part of the dataset and reviewed the literature. WR estimated the empirical orthogonal functions and carried out all the GIS analysis. All authors read and approved the final manuscript.
Santiago Bucaram and Willington Renteria would like to acknowledge support from the Interamerican Development Bank contract RG-T1872/ATN/SF/12471-RG. We thank Suzie Greenhalgh, Chin-Hsien Yu, Florian Eppink and Varvara Vetrova for comments on previous versions of this paper.
The authors declare that they have no competing interests.