The Relationship Between Climate Change and the Poverty Conditions of the Chota Valley’s Afro-Ecuadorian Population and Their Mitigation Actions

Abstract

This study analyzes the relationship between climate change and poverty in Chota Valley’s Afro-Ecuadorian communities. Using a mixed-methods approach—quantitative data from a household survey and qualitative insights from a focus group—the research explores how climate variability affects income, knowledge, and adaptive actions. The findings reveal that while most residents are aware of climate change, their understanding stems from lived agricultural experiences rather than formal education. The Proxy Means Testing (PMT) index shows that higher poverty levels correlate with greater awareness and adaptation efforts. Moreover, climate change has prompted crop substitutions (e.g., to mango), temporarily improving economic conditions. However, the study concludes that sustained resilience requires enhanced education and community-led adaptation strategies. These findings highlight the intersection of environmental vulnerability and social inequality, emphasizing the need for targeted policies and local engagement in climate action.

1. Introduction

Climate change is a phenomenon that causes a gradual increase in the planet’s global temperature, as shown in certain models of development and human activities, which have not considered the fragility of ecosystem processes or the finitude of natural resources. The phenomenon can be easily perceived in any place when comparisons are made of what the climate was like years ago and the current weather. Only there is it evident that the climate is no longer the same; there are extreme variations in rainfall regimes, with prolonged periods of drought, increases in local temperature, and even decreases in natural water flows.

The main effects of climate change include decreased crop productivity, rapid disappearance of mountain glaciers, extinction of species of flora and fauna, and reductions in rainfall in certain areas and increases in others. In the Republic of Ecuador, the Ministry of the Environment has identified problems such as an increase in extreme weather events, an increase in sea level and beach erosion, a decrease in annual runoff, a decrease in snow-capped glaciers, an increase in the areas where it transmits dengue and other tropical diseases, loss of biodiversity, decrease in agricultural areas and a greater need for water for irrigation and human consumption [1].

Some actions that contribute to a reduction in the new environmental conditions generated by climate change constitute mitigation, which represents the effort made to reduce the amount of greenhouse gases produced by human activities; for instance, avoiding the burning of fossil fuels, firewood or crop stubble.

These measures do not allow us to see immediate changes, so adaptation is necessary, which is the set of efforts that are made to minimize the vulnerability and damage that climate change can have on ecosystems and the lives of populations, allowing natural and human systems to adjust to new climatic conditions. Adaptation does not mean resigning but being informed, organizing, and making decisions to face the adverse effects of climate change [2]. These adaptation measures allow communities and ecosystems to be resilient, that is, capable of facing adversity; being able to recover and achieve sustainability over time; and adapting to conditions to react to climate change, whose incidence occurs slowly but causes very severe damage.

While global climate change has been extensively documented, there remains a critical need to understand its localized impacts on vulnerable populations. In this study, we focus on the Afro-Ecuadorian communities of the Chota Valley, addressing specific knowledge gaps related to poverty conditions, adaptive actions, and socio-environmental resilience.

In the Chota Valley, this research sought to investigate community-based adaptation, which is based on knowing their needs, perceptions, knowledge, and experiences. It was proposed to reflect on vulnerability to climate change and disasters to then induce work to improve living conditions and overcome poverty conditions, with emphasis on rights-, generational-, ethnic-, and gender-based approaches.

From this perspective, vulnerability to climate change, that is, the degree to which the ecosystem and the communities can withstand the alterations and effects, depends not only on the phenomenon itself but also on factors of social inequity.

Poverty is a phenomenon tied to humans’ existence, behavior, and power relations. Adam Smith [3] defined poverty as “…a lack of those necessities which the custom of a country makes it indecent, both for the wealthy and for the lower class, to lack them”; however, the quantification of poverty, despite having been a concern, was not visible until 1885, when Charles Booth [4] carried out work that documented the life of the working class in London and produced a poverty map of the city.

Some methodologies have been used to measure poverty, including monetary approaches and unsatisfied basic needs (UBNs) [5], as well as multidimensional methods. The most widespread are undoubtedly monetary ones, where measuring poverty by income (expenditure) has become generalized due to its inclusion in household surveys and simplicity of interpretation; however, this approach may overlook critical aspects of poverty, particularly in diverse contexts like Ecuador, where equivalent income measures can provide more accurate insights into household poverty levels [6]. Income poverty measurement has been consolidated as the basis for policy decision-making [7,8].

Measuring poverty using UBNs considers how the state covers infrastructural needs regarding health, education, and basic services to households. In Ecuador, its use is linked to the first phase of the targeting of social programs (geographical targeting) and mainly to the allocation of resources to the Decentralized Autonomous Governments (GADs).

However, poverty has a multidimensional character; it can be thought of as more than the possibility of having monetary resources to satisfy needs as a set of “capacity deprivations” of people to be able to develop their lives in the best possible way, despite not having been measured in that way [9].

Recent studies have further highlighted the differentiated impacts of climate change on marginalized communities, emphasizing the need for context-specific adaptation strategies (e.g., [10,11]; United Nations Development Programme [UNDP], 2022). Incorporating this emerging evidence strengthens the understanding of how socio-economic vulnerability and environmental change interact in the Chota Valley.

This study makes a novel contribution to the literature on climate change, poverty, and Afro-descendant populations in Latin America by providing empirical evidence from the Chota Valley, a region with high socio-environmental vulnerability that has been scarcely addressed in prior research. Unlike previous studies, we integrate a mixed-methods approach that combines perception analysis with quantitative modeling to identify the socioeconomic and cultural determinants of local climate action. By focusing on an Afro-descendant community in a rural Andean context, this research expands the geographic and socio-cultural scope of climate change studies in Latin America, offering insights for targeted adaptation and poverty reduction policies.

A Look at the Chota Valley

The ancestral territory of the Chota Valley, Concepción, and Salinas, located in the Mira River Basin [12], comprises a group of localities located on the border between the provinces of Imbabura and Carchi [12]. Figure 1 shows the location of the Chota Valley study area.

The population that inhabits the Chota Valley is eminently Afro-Ecuadorian. The presence of black people in Ecuador dates back to colonial times, specifically to the 17th century, when they were brought from Africa to work as slaves. They were taken to the Chota Valley by the Jesuits and Mercedarians to work in the mines and on the sugar cane plantations [13].

The main economic activity of the people of the Chota Valley has traditionally been agriculture, with its main products being eggs, beans, tomatoes, avocados, cucumbers, and other vegetables. At present, although it is true that most people continue to dedicate themselves to agriculture, the star product is mango. In other words, there has been a substitution of products that may have been caused by climate change.

To better identify the Afro-Ecuadorian population, and, in particular, those of the Chota Valley, the Integrated System of Social Indicators of Ecuador (SIISE) has defined seven socio-cultural areas: (1) North Coast, made up of the provinces of Esmeraldas and Manabí; (2) Chota Valley, formed by the provinces of Carchi and Imbabura; (3) Pichincha, formed only by the province of the same name; (4) North Amazon, formed by the provinces of Napo, Sucumbíos and Orellana; (5) Central-South Coast, made up of the provinces of Guayas, Los Ríos, El Oro and Loja; (6) Central-South Sierra, made up of the provinces of Azuay, Bolívar, Cañar, Cotopaxi, Chimborazo and Tungurahua; and (7) rest of the country, made up of the provinces of Morona Santiago, Pastaza, Zamora Chinchipe, Galapagos and the Undelimited Zones.

According to the classification in

Table 1. Afro-Ecuadorian population by sociocultural area in Ecuador. Source: according to the INEC Censuses 2001 and 2010.

Sociocultural Area (*)	Afro-Ecuadorian Population		Total Population		Percentages
	2001	2010	2001	2010	2001	2010
North Coast	183,113	251,562	1,571,248	1,802,814	11.7%	14.0%
Chota Valley	24,783	26,262	496,983	546,535	5.0%	4.8%
Pichincha	78,621	128,327	2,388,817	2,745,575	3.3%	4.7%
North Amazon	10,884	14,631	294,627	398,799	3.7%	3.7%
Central-South Coast	275,452	428,210	4,889,810	5,286,021	5.6%	8.1%
Center-South Sierra	23,700	39,997	2,170,103	2,401,944	1.1%	1.7%
Rest of the country	7456	7172	345,020	366,074	2.2%	2.0%
Total	604,009	896,161	12,156,608	13,547,762

(*) To be comparable, Santo Domingo in Pichincha and Santa Elena in the Central-South Coast area were also included in 2010.

, the Chota Valley population, understood as the Afro-Ecuadorian population of the provinces of Imbabura and Carchi [14], in 2001 was 24,783 people, while in 2010, it was 26,262 people, representing 5% and 4.8% of the total population of the sociocultural area, respectively. This indicates that the Chota Valley population has not undergone drastic percentage changes; that is, there are no external or internal factors that affect the natural population growth.

2. Theoretical Framework

2.1. Poverty

In general terms, poverty refers to the inability of people to live a tolerable life [15]. Among the aspects that compose it are leading a long and healthy life, having an education and enjoying a decent standard of living, in addition to other elements such as political freedom, respect for human rights, personal security, access to productive and well-paid work, and participation in community life. However, given the natural difficulty of measuring some constituent elements of “quality of life”, the study of poverty has been restricted to the quantifiable (Although there is a growing literary trend in anthropology and sociology that studies poverty in qualitative terms, there are also efforts to unite and cross-reference methods. For more details, see https://www.trentu.ca/ids/faculty-research/q-squared-working-papers, accessed on 1 November 2023) and general material aspects of it.

The term “poverty” has different meanings in the social sciences. Spiker et al. [16] identified eleven possible ways of interpreting this word: need, standard of living, insufficient resources, lack of basic security, lack of entitlements, multiple deprivation, exclusion, inequality, class, dependency, and unacceptable suffering. All these interpretations are mutually exclusive, although several of them can be applied at the same time, and some may not be applicable in all situations.

In Latin America, two methodological approaches are preferably used to measure and characterize poverty: the indirect method called the “income method” or “poverty lines” and the direct method of “social indicators”, whose most widespread modality in recent years has been UBNs.

As is known, both methods respond to different conceptual approaches, to the point that “they do not really constitute alternative ways of measuring the same thing, but rather represent two different conceptions of poverty” [17]; these conceptions are based, in one case, on the notion of the capacity to satisfy essential needs and, in the other—the direct method—on the observation of the real consumption of people in relation to certain conventions on minimum needs. However, the two methods are of great interest and contribute significantly to diagnosing poverty.

The main source of information on unsatisfied basic needs (UBNs) is the population and housing censuses, while the estimates using poverty lines are made based on household surveys. In both cases, naturally, there is a methodological scheme and a certain operational matrix. However, their specific application usually goes through variants that are not always fully explained by the usual limitations regarding information. This means that in some cases, the results of studies that apparently use the same method and the same data sources, especially under the poverty lines approach, differ in the magnitude of estimated poverty (sometimes to a considerable extent) to the obvious bewilderment of analysts and public opinion. Such discrepancies affect the credibility and technical reliability of these measurements, call into question the evaluations of the level and evolution of poverty, and make international comparisons difficult [18].

The consumption or income method is an indirect method that classifies as poor those people who do not have sufficient resources to satisfy their basic needs (they are below the poverty line). It measures the standard of living based on the income or consumption of individuals or households. Although consumption (or income) reflects the ability to satisfy material needs, it does not necessarily reflect the standard of living achieved over time, nor those needs that do not directly and immediately depend on money (for example, the provision of public services).

“Poor” is defined as a person who belongs to a household whose per capita consumption in a given period is less than the value of the poverty line. The poverty line is the monetary equivalent of the cost of a basic basket of goods and services per person per period (generally a fortnight or month).

The UBN method, or social indicators, directly measures poverty; it defines a household (or person) as poor when it has not been able to satisfy a group of needs classified as basic priorities, which are generally related to access to education, health, nutrition, housing, urban services, and employment opportunities [18].

Ecuador has adopted the definition of poverty using UBNs of the Andean Community (CAN), which is composed of five “dimensions”, as described below:

• The dwelling has inadequate physical characteristics;
• The house has inadequate services;
• The household has high economic dependence;
• In the home, there are children who do not attend school;
• The household is critically overcrowded.

A household (or person) is considered to be in a poverty situation if it maintains one of the dimensions, or in a situation of extreme poverty if it presents two or more.

2.2. Climate Change and Poverty

The relationship between climate change and poverty is not a recent issue. Historically, it is known that according to geographic location, people with significant resources can choose the “best lands”. Therefore, in cold places, such as the Ecuadorian highlands, rich people choose to live in the lower part because it is warmer and they have the most productive land, while the rest must settle in the upper part. In contrast, in warm places, wealthy people choose the highest places that are ventilated, while the rest must settle in the lower part. Not being able to choose both the site for their housing and the land for production makes poor people highly vulnerable to climate change.

Climate change intensifies global poverty by disproportionately impacting resource-limited populations with low adaptive capacity. Mortality from climate-related events during the 2010s was 15 times higher in highly vulnerable regions than in those with very low vulnerability, evidencing the unequal burden of climate impacts [19].

From a theoretical perspective, the relationship between climate change reflected in temperature and poverty measured through income has been proposed as an inverted U shape, as shown in Figure 2. If indeed this inverted U relationship exists between temperature and income, then poor countries will be more sensitive to climate change than rich countries because the slope of this relationship is steeper at the extremes; a small change in temperature at the level where the slope is relatively flat would cause a small change in income, while a small change at the extremes could cause large changes in income [20].

The vulnerability of people living in poverty to climate change is because they generally have limited access to basic services, such as drinking water, sewage, roads, and telecommunications, due to the costs of providing them as a result of their area of residence. Likewise, they suffer a higher disease incidence because higher humidity and temperature levels stimulate the spread of infectious diseases. Finally, they receive a low income, or in many cases, do not receive one.

In the long term, the impact of climate change on poverty will be seen in phenomena such as food security, productivity, and the very viability of the world’s agricultural ecosystems.

According to Sánchez et al. [21], in reference to the pronouncements of the Food and Agriculture Organization of the United Nations (FAO),

“…changes in agricultural production models, derived from climate change, will affect food security in two ways. First, the food supply locally and globally will be affected. In many low-income countries, with limited financial capacity to trade and relying largely on their own production to meet their food needs, it may be impossible to make up for the decline in local supply without increasing their reliance on food aid. Second, all traditional forms of agricultural production will be affected and the ability to access food will be reduced.

It is important to mention that in addition to agricultural production, other processes of the food system are equally important with respect to food security and poverty, such as the processing, distribution, acquisition, preparation and consumption of food. With climate change, the risk of damage to transport by storms and distribution infrastructure increases with the consequent disorganization in food production chains. In addition to the above, current projections for 2030 show that the share of groceries in the average expenditure of a family will continue to increase, due, among other factors, to the growing scarcity of water, land and fuel that exert progressive pressure on food prices generating higher levels of poverty”.

In short, although the climate can have various effects on individuals, it is considered that with a general warming of the atmosphere, the greatest impact will manifest as an increase in mortality and a decrease in the well-being levels of the population; that is, existing poverty and vulnerability will be strengthened and the difficulties faced by the poorest inhabitants of the planet to improve their development possibilities will increase. Notably, the effects of climate change will be especially devastating for populations living in developing countries since these economies have few economic, human, and technical resources and weak or non-existent institutions to deal with the effects of climate change. Therefore, this presents a serious obstacle to eradicating poverty and influences the search for solutions to the main problems, such as universal education, health, and food [22].

To the extent that countries and communities are unable to effectively adapt to climate change, poverty reduction will become increasingly difficult, and existing adaptation “deficits” could widen into significant adaptation “gaps”. The artificial difference created between adaptation and development in the areas of politics and negotiations must be put aside, and investment in climate resilience must be made in such a way that it allows for the identification and implementation of policy instruments that can effectively counteract the poverty-inducing impacts of climate change [23].

3. Methodology

3.1. General Research Design

This study adopts a sequential explanatory mixed methods design [24], which combines quantitative and qualitative techniques to analyze the relationship between climate change and poverty in Afro-Ecuadorian communities in the Chota Valley. The quantitative phase allowed us to identify general patterns and statistical relationships, while the qualitative phase delved deeper into the meanings, experiences, and local contexts that explain these patterns. This methodological triangulation strengthens the internal and external validity of the study [25].

3.2. Transition from the Theoretical Framework to the Methodological Approach

Based on the theoretical framework that establishes poverty as a phenomenon linked to the existence, behavior, and power relations of human beings (Adam Smith [3]), as well as a multidimensional phenomenon [26], and climate change defined as a risk multiplier that disproportionately affects vulnerable populations [19], a methodological strategy was designed to operationalize these concepts in the specific context of the Chota Valley. Thus, we moved from abstract conceptualization to concrete measurement through the following dimensions:

• Poverty: Measured through the Proxy Mean Index (PMT), constructed from observable variables of housing, access to services, and human capital.
• Climate Change: Assessed through objective climate variables (temperature, precipitation) and subjective perceptions of the population regarding its impacts and adaptation strategies.
• Relationship between the two: Analyzed using econometric models that relate climate variables to agricultural income, and logit models that explore how the level of poverty (PMI) relates to knowledge and actions in the face of climate change.

The proposed methodological flowchart is presented in Figure 3.

3.3. Study Population and Sampling

The study focused on the Afro-Ecuadorian population of the Chota Valley, a region with high socio-environmental vulnerability and historical dependence on agriculture. The sampling frame included 37 localities in the provinces of Imbabura and Carchi.

Quantitative Sample: A probabilistic sample of 224 households was selected, with a confidence level of 95% and a sampling error of 6.5%. The selection was carried out in two stages: (1) random selection of 12 localities, and (2) systematic selection of households within each locality, with probability proportional to size. This design allows the results to be generalized to the study universe.

Qualitative sample: A focus group was conducted with 10 farmers from the locality of Chalguayacu, Ambuquí canton. Participants were selected through purposive sampling with the support of local leaders, seeking to include people with direct experience in agriculture and a perception of climate change. The sample size was determined by the principle of information saturation [27]. Appendix A.1 details the characteristics of the quantitative and qualitative samples.

3.4. Data Collection

Data was collected between 2014 and 2017 using two main instruments:

• EVCH Survey (Chota Valley Survey): A structured questionnaire administered to heads of households, which collected information on:
  • Sociodemographic characteristics of the household and its head.
  • Housing conditions and access to basic services.
  • Perception, knowledge, and actions regarding climate change.
  • Agricultural income and production strategies.
• Focus group: A semi-structured guide facilitated discussion around five thematic areas:
  • Perception and knowledge of climate change.
  • Impacts on agricultural production.
  • Water availability and irrigation practices.
  • Perception of living standards and economic changes.
  • Adaptation strategies and willingness to act.

3.5. Data Analysis

The analysis was carried out in two phases, corresponding to the quantitative and qualitative components, in order to subsequently integrate their findings.

3.5.1. Quantitative Analysis

Quantitative analysis includes calculating the Proxy Means Testing (PMT) index, developing an econometric model to find the relationship between climate variables and income, and finally, logit regression to find relationships between PMT and knowledge and adaptation to climate change.

Construction of the PMT Index

The PMT index is constructed from a set of observable and verifiable variables that describe the structural conditions of households, such as housing characteristics, access to services, ownership of durable goods, educational level of the head of household, and household composition. Each variable is classified as ordinal or nominal according to its nature and is then recoded to preserve the order in the former and maintain neutrality in the latter.

To assign numerical values to the categories, a Categorical Principal Component Analysis (CATPCA) is applied, which allows for optimal quantification of the categorical variables by maximizing the explained variance. This analysis yields coefficients that represent the relative contribution of each variable, which are used as weights to calculate the raw score for each household. This score is obtained by multiplying the quantified value of each variable by its weight and adding the results.

If differentiated sub-indices are calculated—for example, one for housing characteristics and another for personal characteristics—they are integrated using a weighted average, using the explained variance, or an equitable distribution as the criterion. Finally, the raw score is rescaled to a scale of 0 to 100, where 0 indicates maximum deprivation and 100 indicates no deprivation. The validity of the index is verified by reviewing the explained variance, the consistency of the weights, and the correlation with external measures of economic well-being, thus ensuring that the PMT adequately represents the socioeconomic condition of households.

A step-by-step guide to constructing the PMT is provided below:

• Selection of variables. Observable and verifiable variables are chosen that capture structural aspects of household well-being, such as housing quality, access to services, ownership of durable goods, educational level of the head of household, and household composition. This selection was based on theoretical criteria and empirical evidence of correlation with income or consumption.
• Definition of the measurement level. Each variable is classified as ordinal (when there is a natural order in its categories) or nominal (when there is no intrinsic order). This distinction is necessary for the analysis to assign quantifications consistent with the nature of each variable.
• Recoding of categories. The original categories are transformed into values that preserve the order in the ordinal variables and maintain neutrality in the nominal variables. This allows the optimal quantification algorithm to process the information correctly.
• Optimal quantification (optimal scaling). Using CATPCA, each variable category is assigned an optimized numerical value that maximizes the variance explained in the dataset, respecting the order restrictions in the ordinal variables.
• Weight extraction. From the CATPCA results, coefficients were obtained that indicate the relative contribution of each quantified variable to the first component. These coefficients function as “weights” to calculate the score for each household.
• Calculation of the raw score. For each household, the quantified value of each variable is multiplied by its weight, and the results are added together. This raw score reflects the relative position of the household on the axis defined by the first component.
• Integration of sub-indices. If sub-indices are calculated (for example, one for housing characteristics and another for personal characteristics), these are combined using a weighted average, where the weights are defined according to criteria of explained variance or equal importance.
• Rescaling of the score. The raw score is transformed to a scale of 0 to 100, where 0 represents the greatest deprivation and 100 the absence of deprivation. This rescaling facilitates interpretation and comparison between households.
• Validation and diagnosis. The internal consistency of the index is evaluated by reviewing the explained variance, the variable weights, and the correlation with independent measures of economic well-being, ensuring that the index is a valid representation of socioeconomic status.

A Categorical Principal Component Analysis (CATPCA) was applied to 10 housing variables and 6 human capital variables to construct a proxy poverty index scaled from 0 to 100 using SPSS version 23 [28]. The first principal component, retained for index construction, explained nearly 85% of the total variance. This index synthesizes the structural welfare conditions of households.

Econometric Modeling

According to Lopez-Feldman [29], existing studies on the impacts of climate change on inequality and poverty within a country have used general equilibrium models and social accounting matrices [25] or cross-sectional regressions and aggregate data at the subnational level [20]; however, as Mideksa [30] points out, an ideal study on income distribution as an indicator of poverty should be based on income distribution at the household or individual level.

Lopez-Feldman [29] proposed two procedures to estimate the relationship between climate variables and income.

The first consists of directly establishing the relationship between total income and climate variables, as proposed [20]. In this procedure, it is not necessary to detail the transmission channels of climate change that affect income, and therefore, it is sufficient to analyze total income; however, this approach is not without criticism, since several authors argue that the possible impacts of climate change should include extreme events and indirect impacts [26]. The second is to make estimates that are not based on total income, but on farm income, assuming that this is where the greatest direct impacts are expected, especially when the unit of analysis is a rural household. This does not mean that the only impact of climate change on households is due to changes in farm income; however, this is the clearest and most studied direct transmission channel. In the first part of this paper, an approach to this procedure is followed under the assumption that the Chota Valley is clearly rural and that all households surveyed were engaged in agriculture, either as a primary or secondary economic activity.

The methodology used in the first part of this study sought to capture the relationship between climate variables and agricultural income through cross-sectional regressions with household-level microdata. The proposed econometric model is a non-linear relationship between farm income and climatic variables (temperature and precipitation) controlled by household characteristic factors, which could affect agricultural production levels. The econometric equation proposed in this study takes the following form:

To analyze the relationship between climate variables and income, the following equation was estimated using Ordinary Least Squares (OLS):

$$y_i=\alpha +{\beta }_{1}temperatur{e}_i+{\beta }_{2}temperatur{e}_i^2+{\beta }_{3}precipitatio{n}_i+{\beta }_{4}precipitatio{n}_i^2+\gamma Z_i+u_i$$

(1)

where

$y_i$: log of household (agricultural) income per capita i;

$temperatur{e}_i$: Average temperature in the home location i;

$precipitatio{n}_i$: Monthly average precipitation per quarter (taking the central month as a proxy for the quarter, that is, February, May, August, and November);

$Z_i$: Home features vector i;

$u_i$: Error term.

The estimation was performed using ordinary least squares. Importantly, Equation (1) has been adequate to econometrically model the relationship between agricultural income and climate in developing countries [17]; furthermore, there are no alternative studies in the literature that suggest other functional forms or other methods of econometric estimation of said relationship using cross-sectional data. The model is estimated only for agricultural households (that is, those households that devoted most of their time or resources to agricultural production during the year).

Logit Models

To assess the relationship between poverty (PMT) and knowledge about climate change, as well as the relationship between poverty and adaptation actions, logistic regression models were estimated.

The mathematical expression proposed to calculate the probability of knowledge of climate change and adaptation actions is:

$$P\left[Y=1|X_1,X_2,\dots ,X_k\right]={\displaystyle \frac{e^{{\beta }_0+{\beta }_1{x}_1+{\beta }_2{x}_2+\cdots +{\beta }_k{x}_k}}{1+e^{{\beta }_0+{\beta }_1{x}_1+{\beta }_2{x}_2+\cdots +{\beta }_k{x}_k}}}$$

(2)

where

$Y$ is a binary dependent variable (with two possible values: 0 and 1).

$\left(X_1,X_2,\dots ,X_k\right)$ is a set of k independent variables that are observed to predict or explain the value of $Y$.

To do this, we seek $P\left[Y=1|X_1,X_2,\dots ,X_k\right]$ to determine from the construction of the model $P\left[Y=1|X_1,X_2,\dots ,X_k\right]=p(X_1,X_2,\dots ,X_k;\mathit{\beta }$), where

$p\left(X_1,X_2,\dots ,X_k;\mathit{\beta }\right):{ℝ}^k\underset{link function}{\to }\left[0,1\right]$ depends on a vector of parameters

$\mathit{\beta }=({\beta }_0,{\beta }_1,\dots ,{\beta }_{\mathrm{k}})$.

For the case study, referring to climate change knowledge, the variables are defined as follows:

Y: Climate change knowledge;

$X=\left(X_1,X_2,\dots ,X_k\right)$: Vector of independent variables, including the PMT variable;

$P\left[Y=1|X_1,X_2,\dots ,X_k\right]$: Probability of climate change knowledge explained by the PMT poverty rate and household characteristics variables;

$\mathit{\beta }=({\beta }_0,{\beta }_1,\dots ,{\beta }_{\mathrm{k}})$: Regression coefficients (parameters to be estimated).

As a result, probabilities of climate change awareness and adaptation actions based on poverty were obtained.

3.5.2. Qualitative Analysis

Within this qualitative component, the focus group technique was used, which consists of a guided conversation among a small group of participants, moderated by a facilitator and based on a previously designed thematic guide. This technique is especially useful for exploring shared perceptions, identifying consensus and dissent, and generating contextual inputs that allow interpretation and nuance of the quantitative findings [31]. In this case, the focus group provided insight into how communities in the Chota Valley interpret climate change, how it affects their agricultural production, and what adaptation strategies they implement, providing qualitative explanations for patterns previously identified in the statistical data. The complementarity between the methods lies in that the quantitative results provide a generalizable view of trends and associations, while the focus group offers depth and descriptive richness to interpret such findings, incorporate dimensions not captured in the surveys, and propose policy recommendations more sensitive to the local context [32].

Focus group transcripts were analyzed by thematic analysis following the six-phase framework proposed by Braun and Clarke [33]: (1) familiarization with the data, (2) generation of initial codes, (3) search for themes, (4) review of themes, (5) definition and naming of themes, and (6) production of the report, following an inductive process. Emergent codes were identified, grouped into categories, and synthesized into central themes that explain community perceptions and coping strategies. ATLAS.ti version 23 software was used to manage the coding and analysis. The identified core themes explain community perceptions and coping strategies. Subsequently, qualitative results were used to triangulate, contextualize and explain in depth the quantitative findings; thus, for example, statistics on crop substitution were enriched with farmers’ narratives on their decision-making process in the face of climate variations.

3.6. Ethical Considerations

The study had the informed consent of all participants. The format approved by the Vice Rectorate of Research of the Universidad Central del Ecuador was used, which complies with national protocols and the Declaration of Helsinki. The confidentiality and anonymity of the data collected were guaranteed and used only for research purposes.

4. Empirical Results and Discussion

4.1. Quantitative Empirical Results

This section shows the results from the descriptive analysis of the variables and the analysis of the relationships between poverty and climate change variables obtained from Equations (1) and (2).

4.1.1. Descriptive Analysis of the EVCH Variables

The descriptions of the variables of the housing and household, the household head, and perceptions regarding climate change are shown.

Housing and Household Variables

The descriptions of the housing and household variables are shown in

Table 2. Descriptive statistics of housing and household characteristics from the EVCH survey, including type of dwelling, construction materials, and access to services.

Type of Housing	House/villa	62.5%
	Mediagua	33.5%
	Apartment/room in each tenancy/ranch–shack–covacha	4.0%
House Roof	Concrete/slab/cement	40.2%
	Zinc	42.0%
	Asbestos/Eternit	11.6%
	Tile/other	6.2%
House Walls	Concrete/block/brick	64.7%
	Rustic block/brick	29.5%
	Adobe/wood	5.8%
Apartment Floor	Cement/brick	46.4%
	Ceramic/tile/vinyl	36.2%
	Stave/parquet/plank/floating floor	12.9%
	Board/untreated plank/cane/dirt	4.5%
State of the Roof of the House	Okay	54.0%
	Regular	41.1%
	Bad	4.9%
Condition of the Walls of the House	Okay	52.7%
	Regular	40.6%
	Bad	6.7%
State of the Apartment Floor	Okay	53.6%
	Regular	42.4%
	Bad	4.0%
Access to Housing	Paved or paved road/street	89.3%
	Cobbled/ballasted/dirt road/path	10.7%
Cooking Fuel	Gas	97.8%
	Wood/coal/electricity/no cooking	2.2%
Excreta Disposal Type	Toilet/toilet and sewer	98.2%
	Toilet/toilet and septic tank/does not have	1.8%
Type of Toilet Service	Home exclusive	89.7%
	Shared with others	10.3%
Location of the Toilet	Inside the house	80.7%
	Outside the house	19.3%
Water Source	Public network	98.2%
	Other source by pipeline/river slope or ditch	1.8%
Water Source Location	Inside the house	79.9%
	Outside the house	20.1%
Economic Situation of the Household Compared with 2014	It has gotten a lot worse/It has gotten a little worse	82.1%
	It remains the same	12.5%
	It has improved a little/It has improved a lot	5.4%

.

In total, 62.5% of the dwellings are of the house/villa type, while 33.5% are of the mediagua type.

In total, 40.2% of the houses have a concrete/slab/cement roof, while 42% have a zinc roof and 11.6% have an asbestos/Eternit roof.

The walls of the houses are mostly concrete/block/brick, 64.7%, while 29.5% are rustic block/brick.

The floors of the houses are cement/brick in 46.4% of the cases, followed by ceramic/tile/vinyl in 36.2% and stave/parquet/plank/floating floor in 12.9%. The state of the roof is good in 54% of cases, while in 41.1%, it is regular. Likewise, the state of the floor is good in 53.6% of cases, while in 42.4%, it is regular. Similarly, the condition of the walls is 52.7% good and 40.6% fair.

Access to housing is through paved or cobbled roads/streets in 89.3% of cases.

In total, 97.8% of homes cook with gas.

In total, 98.2% of households have a toilet/toilet and sewage system, 89.3% of households have an exclusive toilet service for the household and 80% of households have toilets inside the house.

Household water is obtained from the public network in 98.2%, and in 79.9% of cases, the water supply is inside the home.

When asked about a comparison of the economic situation of households relative to the year 2014, 70.1% answered that it had gotten much worse, while 12.1% said that it had gotten a little worse.

Head of Household Variables

Table 3. Descriptive statistics of household head variables from the EVCH survey, including age, gender, education level, and employment status.

Sex	Man	58.04%
	Woman	41.96%
Marital Status	Married	37.50%
	Single	19.20%
	Widower	16.96%
	Free union	14.29%
	Separate	7.59%
	Divorced	4.46%
Ethnic Self-Definition	Afro-Ecuadorian/Black/Mulatto	73.22%
	Half-blood	25.00%
	Montubio	0.89%
	Indigenous	0.45%
	White	0.45%
Knows How to Read and Write	Yes	86.16%
	No	13.84%
Level of Instruction	Primary	66.52%
	None	15.63%
	Middle school or high school	7.14%
	Secondary	6.25%
	University superior	2.23%
	Basic education	1.34%
	Basic ed. for adults	0.45%
	Non-university higher	0.45%
Years of Schooling of the Person for the Population aged 15 or over	0	15.63%
	1 to 5	26.34%
	6	41.07%
	7 and over	16.96%
Employment	Yes	76.79%
	No	23.21%

shows the descriptions of the variables of the household head.

In total, 58% of households are headed by a man and 42% by a woman.

In total, 37.5% of household heads are married, 19.2% single, 17% widower, and 14.3% live in a free union.

In total, 73.2% of household heads identify themselves as Afro-Ecuadorian, while 25% as mestizo.

In total, 86.2% of household heads know how to read and write; however, 66.5% only have primary education.

In total, 41% of household heads have 6 years of schooling, that is, they completed primary school. Furthermore, 76.8% of household heads say they have a jshows the summary statistics of the households and their members.

Table 4. Summary statistics of household composition and member characteristics from the EVCH survey.

Variable	Obs.	Mean	Std. Dev.	Min.	Max.
Household Size	224	3027	1599	1	8
Household Head’s Age	224	53,295	16,564	18	92
Head’s Schooling	224	5366	3795	0	17
People in the Household with High School Education	224	0.830	1163	0	5

shows a summary of the characteristics of the surveyed households.

Households have sizes ranging from 1 to 8 members; on average, the number of members is 3.03.

The age of the household head varies between 18 and 92 years. On average, the age of the household head is 53.3 years.

The years of schooling of the household head vary from 0 to 17 years; on average, the value is 5.36 years.

The number of people in the household with secondary education varies from 0 to 5. On average, the number of people in the household with secondary education is 0.83.

Climate Change Variables

This section that investigates climate change is divided into two parts: the first regarding knowledge of causes and consequences of climate change, and the second on actions, measures and adaptation regarding climate change.

Knowledge of Causes and Consequences of Climate Change

Table 5. Respondents’ knowledge of the causes and consequences of climate change, measured through the EVCH survey.

Have you heard of climate change?	Yes	93.3%
	No	6.3%
	NS	0.4%
According to you what is climate change? (Non-exclusive responses)	Change in weather conditions	78.1%
	Change in planting season	37.1%
	Lack or excess of rain	23.7%
	Presence, absence of pests	18.8%
	Disappearance of water sources	14.7%
	Disappearance of plant/animal species	11.2%
On a scale of 1 to 10, your opinion regarding the problem of climate change. (1 means that it is not a serious problem; 10 means that it is a very serious problem)	From 1 to 5	19.6%
	From 6 to 9	51.8%
	10	28.6%
How concerned are you about climate change?	Nothing/Little	31.7%
	Quite/Very/Extremely	63.8%
	NS/NR	4.5%
How does climate change make you feel?	Impotence	20.5%
	Indignation	31.3%
	Fear	23.7%
	Interest	7.1%
	Fault	1.3%
	Indifference	10.7%
	NS/NR	5.4%
Thinking about the causes of climate change, which of the following sentences best describes your opinion? Climate change is caused…	Mainly by natural processes	30.4%
	Mainly due to human activity	27.2%
	Both by natural processes and by human activity	33.0%
	I’m not sure climate change really exists	4.0%
	NS/NR	5.4%
To what extent do you agree with the following statements: (options totally agree, plus quite agree) (Non-exclusive responses)	To fight climate change, it is necessary for each person to reduce their energy consumption	58%
	To fight climate change, we all need to give up some comforts	58.9%
	Thanks to science, it will be possible to combat climate change without changing our way of life	53.1%
	Institutions should spend the money on other things, instead of in the fight against climate change	60.7%
When do you think the effects of climate change will begin to be felt in the Chota Valley?	We are already feeling the effects	85.7%
	Between 5 and more than 50 years	5.8%
	Never	3.6%
	NS/NR	4.9%
What would be the degree of affectation of the effects of climate change on the following areas? Score from 1 to 10 (1 is very low; 10 is very high) (Options 6 to 10) (Non-exclusive responses)	Water (effects on its availability and management)	52.2%
	The weather	57.1%
	Natural environment (effect on flora, fauna, forest areas, etc.)	61.6%
	Agricultural production (quantity)	66.1%
	Agricultural production (change of crops)	66.5%
	Population health	58.9%
	The economic well-being of the population	54.9%
	The migration	32.1%

shows the descriptions regarding the knowledge of causes and consequences of climate change.

In total, 93.3% of those interviewed state that they know of, have heard of or have experienced climate change.

When asked about what they understand by climate change, 78.1% associate it with changes in weather conditions, 37.1% associate it with a change in the planting season, 23.7% associate it with a lack or excess of rains, 18.8% associate it with the presence or absence of pests, 14.7% relate it to the disappearance of water sources and 11.2% associate it with the disappearance of plant/animal species.

In total, 80.4% of people consider that climate change is a serious problem (categories from 6 to 10), and of these, 26.8% (category 10) think that it is a very serious problem.

In total, 63.8% of those surveyed are concerned about climate change, while 31.7% of people are not concerned at all.

As for the feeling caused by climate change, 31% are outraged, 23.7% feel fear, 20.5% feel impotence and 10.7% feel indifference.

For 30.4% of people, climate change is due to natural processes and 27.2% believe that it is caused by human activity, while 33% have a mixed opinion, that is, climate change is due to natural processes and human activity; 4% answered not knowing what climate change is about.

In total, 58% of people agree that to fight climate change, it is necessary for each person to reduce their energy consumption; 58.9% agree to give up some comforts; 53.1% of people believe that thanks to science, it will be possible to combat climate change without changing our way of life; and finally, 60.7% think that institutions should spend money on other things instead of fighting climate change.

Regarding the question about the time in which climate change effects will begin to be felt in the Chota Valley, 85.7% responded that they are already feeling it now, while 5.7% said that it will begin to be felt in a time between 5 and 50 years and 3.6% say never.

Regarding the impact of climate change on some environmental factors, the informants responded as follows:

Regarding water’s availability and management, 52.2% state that climate change affects them. Regarding the weather, 57.1% believe that climate change is affecting it. Regarding the natural environment, i.e., flora, fauna and forest spaces, 61.6% believe that climate change has an effect. When looking at agricultural production, 66.1% of people agree that climate change has an effect on agriculture, particularly on the amount harvested, which is much less than previously, and 66.5% state that climate change has effects on agriculture, where, above all, it has forced them to abandon traditional crops, such as ovo or beans, and switch to mangoes. A total of 58.9% of those surveyed accept that climate change affects the population’s health, while 54.9% state that climate change affects the population’s economic well-being. What is new in the case of the Chota Valley is that it forced them to change their crops, and this has improved the economic conditions of many families. Climate change has not greatly affected the migration of Chota Valley’s population, where 67.9% of those surveyed answered that the effect is low.

According to the results of the survey, those who must fight against climate change are the national government (45.1%), local governments (4%) and environmental groups (1.3%). The respondents do not attach responsibility to companies or the industry, to the community itself or to each person, nor is there a collective feeling that we all must fight against climate change.

When asked about the importance of increasing the use of renewable energy, such as hydraulic and wind energies, in the future, 84.9% consider that it is very important to change from fossil energies to renewable energies.

Actions Against Climate Change and Adaptation Measures

Table 6. Actions and adaptation measures against climate change reported by households in the EVCH survey.

Who is responsible for the fight against climate change in Ecuador	National government	45.1%
	Local Governments/Business and Industry/The Community/Environmental Groups/Yourself	14.3%
	All of them	33.9%
	None/NS/NR	6.7%
To what extent do you consider it important to increase the amount of renewable energy used, such as hydro, solar, wind in the future	Very important/Fairly important	84.8%
	Not very important	7.1%
	Nothing important/NS	8.0%
Indicate your degree of agreement with the implementation in the Chota Valley of measures against climate change aimed at: (Options total agreement, plus quite a lot of agreement)	Develop and implement an adaptive model in the face of climate change (saving water, garbage collection, less use of fungicides, etc.)	83.1%
	Prepare society for the extreme risks arising from climate change (awareness campaigns, energy efficiency, etc.)	82.6%
	Integrate pilot projects of good practices, innovative and that serve as an example of adaptation to climate change in municipal or parish management (improve storage and management of water, uses of clean energy, waste management, etc.)	83%
	Conserve the biodiversity of the Chota Valley and the services that nature makes available to us (air purification, pest control, renewable energy, etc.)	82.6%
Have you personally taken any action to combat climate change in the last 6 months?	Yes	69.2%
	No	25.4%
	NS	5.4%
Which of the following actions apply to you, if any (Non-exclusive responses)	Regularly walks, rides a bike	68.3%
	Try to reduce your consumption of disposable items such as plastic bags, tubs, straws, whenever you can	26.3%
	Try to reduce your waste and regularly separate it for recycling	23.7%
Do you think you could personally contribute more to the fight against climate change?	Yes	65.6%
	No	34.4%
Why don’t you do it?	For comfort	6.3%
	Because they don’t know what to do	27.2%
	Because those who have to act are companies and governments	0.4%
	NS	0.4%
Thinking about the future, what would you think if the following situations happened in the Chota Valley in 15 years? (Non-exclusive responses)	That a large part of the houses had systems to generate their own energy	80.4%
	That a large part of the houses has water reuse systems	84.4%
	That there are exclusive bicycle lanes	81.7%
	That at least 50% of the energy consumed in the valley be of renewable origin	82.1%
	Let families sort the trash	86.6%

shows the descriptions regarding actions against climate change and adaptation measures.

When asked who is responsible for the fight against climate change in Ecuador, the most common answer is the national government with 45.1%. The responsibility of local governments, industry and oneself reaches 14.3%. The shared responsibility between all the actors is 33.9%.

In total, 84.8% of those surveyed consider it important to increase the amount of renewable energy used, such as hydraulic, solar and wind energies. To 7.1%, it does not seem very important, and to 8%, it does not seem important at all or they do not know.

When asked if they agree with developing and implementing an adaptive model to face climate change (saving water, garbage collection, less use of fungicides, etc.), 83.1% said yes.

Regarding the implementation of measures aimed at preparing the Chota Valley society for the extreme risks derived from climate change (awareness campaigns, energy efficiency, etc.), 82.6% agreed.

With respect to integrating pilot projects of good, innovative practices that serve as an example of adaptation to climate change in municipal or parish management (improving water storage and management, clean energy uses, waste management, etc.), 83% of the population agreed.

In total, 82.6% of those surveyed agreed to conserve the biodiversity of the Chota Valley and the services that nature makes available to us (air purification, pest control, renewable energy, etc.).

When asked about having personally taken measures to fight climate change in the last 6 months, 69.2% said yes.

Concerning the actions that people take, the first option is to regularly walk or travel by bicycle, with 68.3%, followed by trying to reduce the consumption of disposable items, such as plastic bags, tubs and straws, whenever they can, with 26.3%, and try to reduce their waste and regularly separate it for recycling, with 23.7%; the remaining options have very low percentages.

In total, 65.6% of those surveyed agreed that they could personally contribute more to the fight against climate change; 27.2% responded that they do not contribute because they do not know what they can do.

Regarding the approach to the next 15 years of various situations in the Chota Valley, the answers were as follows: they agreed that most houses had systems to generate their own energy (80.4%), most houses had water reuse systems (84.4%), there are exclusive paths for bicycles (81.7%), at least 50% of the energy consumed in the valley be of renewable origin (82.1%), and that families classify garbage (86.6%).

4.1.2. Poverty and Climatic Variables

According to the proposed methodology, a regression model was developed that related poverty and climatic variables.

Variable Selection

According to the theoretical model described in Equation (1), the variables used are as follows:

$temp$—The average temperature in each data collection location (taking the central month as a proxy for the quarter, that is, February, May, August and November);

$tem{p}^2$—The temperature of each locality squared;

$prec$—Monthly average precipitation per quarter (taking the central month as a proxy for the quarter, that is, February, May, August and November);

$pre{c}^2$—Average monthly precipitation squared;

$Z_i$—Household characteristics vector.

Within the variables vector $Z_i$, depending on the model, the following are incorporated: years of schooling, experience, sex, ethnicity, people in the household with at least secondary education, and interactions between temperature and precipitation.

The data for the study period were obtained from the website accuweather.com [34] and freemeteo.org [35]. The statistics of the variables are shown in

Table 7. Quarterly climate variables (temperature and precipitation) for the Chota Valley, derived from freemeteo.org and national meteorological data.

Variable	Mean	Typical Deviation
First-trimester temperature (°C)	26.13	0.30
Second-trimester temperature (°C)	25.99	0.22
Third-trimester temperature (°C)	25.40	0.25
Fourth-trimester temperature (°C)	26.43	0.25
First-quarter rainfall (mm/month)	175.98	0.25
Second-quarter rainfall (mm/month)	171.09	0.47
Third-quarter precipitation (mm/month)	110.24	6.04
Fourth-quarter precipitation (mm/month)	189.73	0.97

.

The Regression Model

The regression model proposed in Equation (2) was run using three models: The first one considers only the climatic variables: temperature and precipitation. The second model includes the climatic variables temperature and precipitation, as well as the household characteristics variables. The third model considers all the variables of Model 2 and the interactions between climatic variables.

Linear regression was performed using ordinary least squares to find the relationships between climatic variables and poverty. To ensure better results, the natural logarithm of the per capita income was used as the dependent variable and those corresponding to the three models described were used as independent variables. The presence of squared variables explains the non-linearity of the relationships between the variables. The control characteristics vector Z includes variables such as sex, age, education of the household head, number of household members with secondary education and ethnicity (dummy). The inclusion of these variables seeks to control the impact on income due to the household characteristics.

The results are shown in

Table 8. Regression results examining the relationship between poverty and climatic variables in the Chota Valley.

Model 1
Variable	Coefficient	Estimated t-Value	90% Confidence Interval	p-Value
Second-quarter temperature	51.6886	1.8236	4.8606, 98.5166	0.0696
Second-quarter temperature 2	−1.0048	−1.8069	−1.9235, −0.0861	0.0722
Third-quarter temperature	−20.8233 *	−1.994	−38.0755, −3.5710	0.0474
Third-quarter temperature 2	0.4251 *	1.9791	0.0702, 0.7800	0.0491
Fourth-quarter temperature 2	0.0139 *	2.4644	0.0046, 0.0232	0.0145
Third-quarter precipitation	−2.9446	−1.8141	−5.6261, −0.2630	0.0711
Third-quarter precipitation 2	0.1314	1.7865	0.0099, 0.2529	0.0755
Fourth-quarter precipitation 2	0.0303 *	2.2252	0.0078, 0.0528	0.0271
R-squared		0.0854
N. of cases		224
* p < 0.05, ** p < 0.01
Model 2
Variable	Coefficient	Estimated t-Value	90% Confidence Interval	p-Value
Third-quarter temperature	−31.0000	−1.9777	−56.2758, −4.8416	0.0514
Third-quarter temperature 2	0.6186	1.9552	0.0920, 1.1451	0.0541
Fourth-quarter temperature 2	0.0242 **	2.8753	0.0102, 0.0382	0.0052
Third-quarter precipitation	−6.7624 **	−2.6867	−10.9515, −2.5732	0.0088
Third-quarter precipitation 2	0.3052 **	2.6607	0.1143, 0.4962	0.0094
Fourth-quarter precipitation 2	0.0377	1.9594	0.0057, 0.0697	0.0536
Schooling of household head	0.0959	1.9555	0.0143, 0.1775	0.0541
N. of members with secondary education	−0.1091	1.7857	−0.2107, −0.0074	0.0780
R-squared		0.2750
N. of cases		97
* p < 0.05, ** p < 0.01
Model 3
Variable	Coefficient	Estimated t-Value	90% Confidence Interval	p-Value
Fourth-quarter temperature 2	−2.0282 *	−2.2549	−3.5252, −0.5312	0.0269
Fourth-quarter precipitation 2	−4.3076 *	−2.261	−7.4785, −1.1366	0.0265
Schooling of household head	0.0959	1.9555	0.0143, 0.1775	0.0541
N. of members with secondary education	−0.1091	−1.7857	−0.2107, −0.0074	0.0780
Fourth-quarter temperature * Fourth-quarter precipitation	5.9756 *	2.2636	1.5819, 10.3693	0.0263
R-squared		0.2750
N. of cases		97
* p < 0.05, ** p < 0.01

.

Model 1 shows that only the third-quarter and fourth-quarter temperature coefficients and fourth-quarter precipitation are significant at 90%; that is, the effects on income of the climatic variables corresponding to the first and second quarters are not significant.

Model 2 shows that third-quarter and fourth-quarter temperature coefficients and fourth-quarter precipitation and the variables years of schooling of the household head and people in the household with secondary education are significant at 90%, which indicates that the household characteristics have a greater effect than the climatic variables on income. In addition, the variables temperature at the third quarter and precipitation at the fourth quarter are significant at 99%.

Model 3 indicates that the climatic variables fourth-quarter temperature and fourth-quarter precipitation are significant al 95%. Likewise, the variables years of schooling of the household head and number of people with secondary education mainly explain the income behavior. Additionally, it is observed that the fourth-quarter interaction temperature*precipitation is significant at 95%. Therefore, it has a greater effect on income.

The marginal effects of the variables are shown in

Table 9. Marginal effects of poverty and climatic variables from the regression model.

Variable	Model 1		Model 2		Model 3
	dy/dx	z	dy/dx	z	dy/dx	z
First-trimester temperature squared	−0.00320	−1.42	−0.00035	−0.09
Second-quarter temperature squared	0.00445	0.69	0.01069	0.95	0.42153	−0.92
Third-trimester temperature	−15.13343	−1.91	−21.55744	−1.54
Third-quarter temperature squared	0.31771	1.9	0.44864	1.52	0.02281	1.14
Fourth-quarter temperature squared	0.00319	0.8	0.00725	0.89	−2.02754	−1.91
First-quarter temperature squared	−0.00036	−0.56	−0.00022	−0.22	−0.00005	−0.05
Second-quarter precipitation squared	−0.00016	−0.94	−0.00007	−0.21	−0.00905	−0.94
Third-quarter precipitation	0.05445	0.36	−0.12426	−0.46
Third-quarter precipitation squared	−0.00026	−0.38	0.00056	0.45	0.00124	1.04
Fourth-quarter precipitation	29.21960	1.93	46.22595	1.38
Fourth-quarter precipitation squared	−0.07659	−1.92	−0.12128	−1.38	−0.04306	−1.92

.

The marginal effects of Model 1 explain, with a confidence level of 90%, that a 1 °C increase in temperature in the third quarter has a negative influence on agricultural production, and therefore, on income; likewise, a one-millimeter increase in rainfall in the fourth quarter, which is normally drier, positively affects agriculture, and therefore, household income. The rest of the climatic variables are not significant and have no effect on agricultural production, and therefore, on income.

The Model 2 climatic variables do not cause significant effects on agricultural production, and therefore, on household income, with a confidence level of 90%. With lower confidence levels (86% and 81%), it can be mentioned that there is a negative influence of temperature in the third quarter and a positive effect of rainfall in the fourth quarter on income.

Temperature’s negative effect on income in the third quarter is verified with a confidence level of 90%; likewise, a negative effect of fourth-quarter rainfall squared can be observed and is simply an element of balance of the positive effect shown in the two previous models.

4.1.3. The Proxy Poverty Index

Although questions were asked about household income, on many occasions, a direct inquiry without the detail that an income and expenditure survey could have or a survey to evaluate consumption, such as the ECV Living Conditions Survey, can lead to bias. This is why a proxy poverty index, regularly called the “Proxy Means Testing” (PMT) index, was calculated. The PMT index represents observable and verifiable characteristics of households and serves as an approximation of their well-being. The PMT index is commonly used in situations where verifiable earnings data are not available. In our case, this index was calculated with the aim of having an alternative value for the poverty measure.

Variable Selection

The variables used to calculate the PMT index came from the EVCH survey. There are two groups of variables: the characteristics of the household and the people.

The household characteristics variables, recoded for the calculation, are given in

Table 10. Recoded household characteristics variables used in the calculation of the Proxy Means Test (PMT) index.

Variable	Categories
Predominant material on the floor	1 Untreated board/plank–cane–soil–other, which one
	2 Cement/brick
	3 Stave/parquet/plank/floating floor–ceramic/tile/vinyl–marble
Location of the bathroom	1 Outside the home but on the lot/outside the home, lot or land
	2 Inside the house
Main material of the ceiling	1 Zinc/palm/straw/leaf–other, which one
	2 Asbestos/tile
	3 Concrete/slab/cement
State of the floor of the house	1 Bad
	2 Fair
	3 Good
Water supply location	1 Outside the home but on the lot/outside the home, lot or land
	2 Inside the house
Road access to the house	1 Path–river/sea–other
	2 Cobbled–ballast/soil street
	3 Road/street paved or cobblestone
Bathroom type	1 Latrine–does not have
	2 Toilet/toilet and cesspool
	3 Toilet/toilet and septic tank
	4 Toilet/toilet and sewer
Main source of household water	1 Delivery car/tricycle–well/river, spring or ditch/other, which one
	2 Other sources by pipeline
	3 Public network–battery/pool or public key
Predominant material of the walls	1 Adobe/tapia–wood–bahareque–cane or mat–other, which one
	2 Block/rustic brick–asbestos/cement/fibrolit
	3 Concrete/block/brick
Housing type	1 Room in a tenancy house–mediagua–ranch/shack/covacha–other, which one
	2 House/villa–apartment

.

Likewise, the recoded person variables are presented in

Table 11. Recoded individual-level variables used in the PMT index calculation.

Variable	Categories
Number of illiterate people in the household	1 One or more
	2 There are no illiterate people
Years of schooling of the household head	1 Up to 6 years
	2 Between 7 and 12 years
	3 13 years and over
	4 Under 5 years
Number of people from 5 to 17 years old in the household who do not attend school	1 One or more
	2 No children aged 5–17/everyone attends
Number of people aged 5 to 17 employed	1 One or more
	2 There are no children between the ages of 5 and 17 employed
Children under 6 years old in the household	1 Two or more
	2 One
	3 There are no children in that age range
Number of people aged 65 and over in the household	1 Two or more
	2 One
	3 There are no older adults

.

The PMT Index

The technique used was categorical principal component analysis (CATPCA). This procedure simultaneously quantifies the categorical variables while reducing the dimension of the data.

The goal of principal component analysis is to reduce an original set of variables into a smaller set of uncorrelated components that represent most of the information found in the original variables. The technique is most useful when numerous variables prevent an efficient interpretation of the relationships between objects (subjects and units). By reducing the dimension, a few components are interpreted instead of many variables.

Standard principal component analysis assumes linear relationships between numerical variables. In contrast, the optimal scaling method allows variables to be scaled at different levels. Categorical variables are optimally quantified on the specified dimension. As a result, non-linear relationships between variables can be modeled [28].

The technique used allows the calculation of two indexes, one of the household characteristics (from 10 variables) and another of the people characteristics (from 6 variables). Finally, using principal components, the PMT index is calculated. The PMT index, like the component index, has been expressed on a scale ranging from 0 to 100 points, where 0 represents total deprivation, while 100 represents no deprivation.

Table 12. Statistics of the partial indexes and the overall PMT index.

Variable	Obs	Mean	Std. Dev.	Min	Max
Household characteristics index	224	69.19	15.32	0	100
People index	224	38.04	18.29	0	100
PMT index	224	53.05	19.30	0	100

shows statistics of the component and PMT indexes.

Figure 4 shows that the PMT index meets the required statistical conditions; that is, its shape fits a normal distribution and the nucleus of the distribution is the most dense.

The objective of calculating this index was to use it as an independent variable in the relationships between climate change knowledge and poverty.

4.1.4. Poverty and Climate Change Knowledge

This section seeks to establish relationships between poverty expressed as the PMT index and climate change knowledge.

Variable Selection

The variables used to establish the relationships with poverty embodied in the PMT index are those that inquire about climate change knowledge. To measure the knowledge of causes and consequences of climate change, the variables represented in

Table 13. Recoded variables of knowledge of the causes and consequences of climate change (CC).

Do you know of, have you heard of or have you experienced CC?	1 Yes
	0 No, do not know
What do you mean by CC?	1 Change in weather conditions
	0 Other
What do you mean by CC?	1 Change in planting time
	0 Other
What do you mean by CC?	1 Lack or excess of rain
	0 Other
What do you mean by CC?	1 Presence/absence of pests
	0 Other
What do you mean by CC?	1 Disappearance of water sources
	0 Other
What do you mean by CC?	1 Disappearance of species
	0 Other
Is CC a problem?	1 Very serious
	0 Nothing serious
To what extent are you concerned about CC?	1 Concerned/very concerned about CC
	0 Little or not at all worried about CC
When talking about CC, what feelings does it provoke?	1 Helplessness/indignation/fear/interest/guilt
	0 Indifference
Climate change is caused by…	1 Natural processes/human activity
	0 Does not exist
How far is it?	1 Agree to reduce energy consumption
	0 I do not agree to reduce energy consumption
How far is it?	1 Agree to give up comforts
	0 I do not agree to give up comforts
How far is it?	1 Agree that science will help fight CC
	0 I do not agree that science will help fight CC
To what extent do you think…	1 Institutions must spend in the fight against CC
	0 Institutions should NOT spend in the fight against CC
When do you think the effects of CC will begin to be felt?	1 Short-term effects
	0 Long-term effects or never
What is the degree of effect on the water?	1 High impact of CC on water
	0 Low affectation of the CC on the water
What is the degree of impact on the weather?	1 High impact of CC on weather
	0 Low impact of the CC on the weather
What is the degree of impact on the natural environment?	1 High impact of the CC on the natural environment
	0 Low impact of the CC on the natural environment
What is the degree of effect on agricultural production (quantity)?	1 High impact of CC on the amount of agricultural production
	0 Low impact of CC on the amount of agricultural production
What is the degree of effect on agricultural production (change of crops)?	1 High impact on CC that drives crop change
	0 Low impact on CC that drives crop change
What is the degree of effect on the health of the population?	1 High impact of CC on health
	0 Low effect of CC on health
What is the degree of impact on the economy?	1 High impact of CC on the economy
	0 Low impact of CC on the economy
What is the degree of effect on migration?	1 High impact of CC on migration
	0 Low impact of CC on migration
Who should fight CC?	1 Those who must fight against CC are the government/companies
	2 Those who must fight against CC are the people
	3 Everyone must fight against CC
Considers it important to increase the amount of renewable energy.	1 Important to increase renewable energy
	0 Slightly or not at all important to increase renewable energy

were proposed.

From this list of variables, those that are effectively related to poverty were chosen.

To determine the variables that explain climate change knowledge, which are categorical, as a function of poverty, Cramer’s V coefficient matrix (equivalent to the correlation matrix) was calculated. This analysis allows for eliminating redundant variables (those whose coefficient is greater than |0.5|) and variables with very low explanatory power (coefficient less than |0.1|). The final matrix with the four selected variables is shown in

Table 14. Cramér’s V coefficient matrix for variables of climate change knowledge.

	PMT	C1	C2	C3	C4
PMT	1
C1	0.1318	1
C2	0.157	0.3767	1
C3	0.1755	0.1686	0.406	1
C4	−0.1272	0.3501	0.1587	−0.1134	1

where PMT: Proxy poverty index. C1: Knows about, has heard about or has experienced CC. C2: What do you mean by CC? Change in weather conditions. C3: What do you understand by CC? Change in the planting season. C4: To fight CC, it is necessary for each person to reduce their energy consumption.

.

The Regression Model

According to the methodology proposed in Equation (2), logit-type regressions were conducted that allowed for determining the relationships between climate change knowledge and poverty.

The logit regression between climate change knowledge as a function of the PMT index and household characteristics variables when the question “knows, has heard about or experienced climate change” was evaluated as a dependent variable did not yield significant results. When evaluating climate change knowledge understood as the change in the planting season, the results are significant, which, from a practical point of view, makes sense because it is a simpler and clearer form of climate change knowledge for informants. The two additional variables that explain climate change knowledge are the education of the household head and the number of people with secondary education in the household. The results of the regression are shown in

Table 15. Regression results assessing the relationship between climate change knowledge and selected socio-economic variables.

Logistic regression				Number of obs	=	224
				LR chi2 (3)	=	13.06
				prob > chi2	=	0.0045
Loglikelihood = −58.412319				Pseudo-R2	=	0.1006
Change in Planting Season	Coef.	Std. Err.	z	p value	[95% Confidence Interval]
PMT index	0.044680	0.016209	2.76	0.006	0.01291	0.07645
Scholarship	0.117339	0.057898	2.03	0.043	0.00386	0.23082
People with high school	0.573630	0.261232	2.2	0.028	0.06162	1.08564
Constant	−3.191815	1.122963	−2.84	0.004	−5.39278	−0.99085

Source: EVCH. Own elaboration.

.

Climate change knowledge, understood as the change in the planting season, is related to poverty. The higher the PMT index, the greater the climate change knowledge. Similarly, the variables education of the household head and people with secondary education contributed.

The LR chi² coefficient ensures that the model variables are adequate to explain the probability of climate change knowledge. Individually, the regression coefficients are significant at 95%. Additionally, by calculating the classification statistics, the model correctly predicts 70.1% of cases.

As a result of the model, the average probability of climate change knowledge in the Chota Valley is 62.39%. The marginal effects of

Table 16. Marginal effects from the regression on climate change knowledge determinants.

Marginal effects after logit
y =	Pr (climate change knowledge) (predict)
	0.62386
Variable	dy/dx	Std. Err.	z	p value	[95% Confidence Interval]		X
PMT index	0.01048	0.00378	2.77	0.006	0.00307	0.01790	38.75810
Scholarship	0.02753	0.01353	2.04	0.042	0.00102	0.05405	7.38144
People with a high school education	0.13461	0.06082	2.21	0.027	0.01541	0.25381	1.91753

show that a change in one point in the PMT index contributes 1.0105 times to climate change knowledge. Similarly, a change in one year in the education of the household head contributes 1028 times the climate change knowledge, and an increase of a person with a secondary education in the household contributes 1135 times to climate change knowledge. The results of this regression are shown in Table 16.

With the model described, if a person had an index of 90 (not poor) and had 15 years of schooling, the probability of climate change knowledge would be 97.56%, while if a person had an index of 30 (poor) and 2 years of schooling, their probability of knowing about climate change would be 37.36%.

4.1.5. Poverty and Actions, Measures, and Adaptation Against Climate Change

This section seeks to establish relationships between poverty expressed as the PMT index and actions, measures taken and adaptation against climate change.

Variable Selection

The variables used to establish the relationships with poverty embodied in the PMT index are those that inquire about actions, measures and adaptation against climate change. To measure actions, measures and adaptation against climate change, the variables represented in

Table 17. Recoded Variables of actions against CC to evaluate the relationship with poverty.

Actions Against Climate Change
Start up an adaptive model against CC in the Chota Valley.	1 Adaptive model agreement versus CC
	0 No agreement on adaptive model versus CC
Prepare society for the risks derived from CC.	1 Agreement to prepare society for CC risks
	0 I do not agree to prepare society for CC risks
Integrate pilot projects of good adaptation practices against CC.	1 Agreement to integrate pilot projects of good practices against CC
	0 I do not agree to integrate pilot projects of good practices against CC
Conserve the biodiversity of the Chota Valley.	1 Agreement to conserve biodiversity of the Chota Valley
	0 No agreement to conserve biodiversity of the Chota Valley
Has personally taken any measure or action against the CC.	1 If you have personally taken action against CC
	0 Has not personally taken action against CC
What actions?	1 Regularly walks/rides a bike
	0 Other actions against CC
They believe that they could contribute to a greater extent in the fight against CC.	1 If you can contribute more against CC
	0 Cannot contribute further against CC
Why do you not do it?	1 No because they do not know what to do
	0 For other reasons
What do you think about future homes having systems to generate their own energy?	1 Very good or good
	0 Bad/very bad/NS/NR
What do you think about future homes having water reuse systems?	1 Very good or good
	0 Bad/very bad/NS/NR
What do you think about in the future, there will be exclusive bicycle lanes?	1 Very good or good
	0 Bad/very bad/NS/NR
What do you think about in the future, at least 50% of the energy consumed in the Chota Valley is renewable?	1 Very good or good
	0 Bad/very bad/NS/NR
What do you think about families sorting garbage in the future?	1 Very good or good
	0 Bad/very bad/NS/NR

were proposed.

From this list of variables, those that are effectively related to poverty were chosen.

To determine the variables that explain actions, measures and adaptation against climate change as a function of poverty, which are categorical, Cramer’s V coefficient matrix was calculated. The final matrix with the two selected variables is shown in

Table 18. Cramér’s V coefficient matrix for variables of actions against climate change.

	PMT	A1	A2
PMT	1
A1	0.1352	1
A2	0.1415	0.3393	1

where PMT: Proxy poverty index; A1: Agreement to develop and implement an adaptive model against CC (saving water, garbage collection, less use of fungicides, etc.) in the Chota Valley; A2: People believe that they could contribute to a greater extent to the fight against CC.

.

The Regression Model

Based on the logit regression between the actions against climate change as a function of the PMT index, the variables—years of education of the household head and number of people in the household with secondary education—are not significant; the regression results are shown in

Table 19. Regression results assessing the relationship between climate change actions and selected socio-economic variables.

Logistic regression				Number of obs	=	224
				LR chi2 (3)	=	3.97
				prob > chi2	=	0.0463
Loglikelihood = −60.654332				Pseudo-R2	=	0.0317
Implement an Adaptive Model in the Face of Climate Change	Coef.	Std. Err.	z	p value	[95% Confidence Interval]
PMT index	−0.02482	0.01244	−2.00	0.046	−0.0492	−0.0004
Constant	3.70035	0.72512	5.10	0.000	2.2791	5.1216

.

The model shows that actions, measures, and adaptation against climate change, understood as “implementing an adaptive model in the face of climate change”, is related to poverty. It is surprising that in this model, a higher PMT index increases the lack of interest in acting on climate change.

The LR chi² coefficient ensures that the PMT index variable is adequate to explain the probability of actions against climate change. Individually, the regression coefficients are significant at 95%. Additionally, by calculating the classification statistics, the model correctly predicts 91.96% of cases. Notably, the negative sign of the PMT index variable coefficient indicates that there is an inverse relationship between poverty and actions against climate change.

As a result of the model, the average probability of undertaking actions against climate change in the Chota Valley is 92.65%. The marginal effects in

Table 20. Marginal effects from the regression on climate change action determinants.

Marginal effects after logit
y =	Pr (actions against climate change) (predict)
	0.92654
Variable	dy/dx	Std. Err.	z	p value	[95% Confidence Interval]		X
PMT index	−0.00169	0.00079	−2.13	0.033	−0.00324	−0.00014	46.95490

show that a change in one point in the poverty index causes the actions against climate change to decrease by 1.0017 times.

According to the model, if a person had an index of 90 (not poor), the probability of actions against climate change would be 81.24%, while if a person had an index of 30 (poor), their probability of actions against climate change would be 95.05%.

4.2. Qualitative Empirical Results

Focus Group

Through a qualitative analysis, an approximation was sought to the feelings and reactions of people regarding the research topics, particularly climate change knowledge and actions, measures, and adaptation against climate change. These topics were addressed using one of the qualitative techniques suitable for our purposes, namely, the focus group. From this perspective, a question guide was designed, the sample and groups of invited people were chosen, the focus group was carried out, the results were systematized and interpreted, and an attempt was made to complement and contrast the quantitative results.

The focus group formed in the town of Chalguayacu, Ambuquí canton, comprised farmers selected through contact with local leaders. The group showed significant interest in the topic and a great willingness to discuss and present their criteria. They spoke enthusiastically and showed informed opinions on climate change. Most had at least a secondary school education and had experienced climate change in their own way.

Two of the participants were leaders of groups of fruit producers from the Chota Valley.

Repeated topic in the conversation: The transition that they have had to make from traditional products, such as eggs, prickly pears, and cucumbers, towards the massive production of mangoes.

Consensus: The climate has changed and is reflected in the planting and harvesting seasons, as well as in the gradual reduction in the production of traditional crops, which has prompted them to look for other products that are more productive, which is why they are now dedicated to mango cultivation.

Disagreements: In general, there were no differentiated positions in the group on the issues addressed.

Changes in opinion in the group: There were no changes in opinion caused by the group interaction.

Several opinions on a topic by the same participant: Each participant had a defined position on the topics, which they held throughout the group discussion.

Presence of dominant participants: There were two participants, leaders of the farmers in the area, who took the initiative to respond and, in some way, formed the basis of the interaction. This does not mean that the others did not speak, but that they complemented each other.

Figure 4 shows the word cloud that represents the thinking of the group participants regarding climate change from the perspective of people dedicated to agriculture.

What stands out is the clear relationship between climate change and the type of products they are dedicated to, mainly mangoes. The mango is the star product to which they have migrated, leaving aside traditional products, such as beans, ovo, cucumber, and avocado, due to the decrease in production, precisely caused by climate change.

The full transcript of the focus group is shown in Appendix A.2.

The questionnaire addressed five thematic areas: knowledge and perception of climate change, changes in agricultural production, water availability and irrigation, perception of living standards, adaptation strategies, and willingness to act.

The analysis was conducted using a thematic coding approach [33], identifying response patterns and shared narratives among participants. Inductive analysis was applied, respecting local language and examples to ensure cultural validity.

Regarding the key findings on knowledge and perception of climate change, participants recognize climate change as a phenomenon that directly affects agricultural production and attribute its impacts to variations in precipitation, temperature, and pests. One participant noted: “There are times when it seems like it doesn’t rain, and when there is a lot of water, our crops burn.” Empirical rather than technical knowledge is observed, with explanations based on direct agricultural experience.

Regarding the impacts on agricultural production, there are three fundamental aspects: Loss of traditional crops: Beans, tomatoes, and eggplants show drastic drops in yield (“before we used to harvest 40 boxes, now 7 is lucky”). Crop change as a survival strategy: Mangoes have replaced more vulnerable crops, improving income and reducing the workload. Pests and diseases: Greater need for fumigation, with side effects such as the disappearance of bees (“they died because of what we fumigated”). This productive change is seen in part as a result of climate change and in part as a rational economic response.

Concerning water availability and irrigation, two important issues are highlighted: Reduction in water sources (“before there were quite a few water sources, now there are only a few”). Greater dependence on artificial irrigation, especially for mangoes and ovo, with almost permanent irrigation practices to improve fruit size and weight.

With regard to the Perception of the standard of living, there is a perceived improvement in well-being since the switch to mangoes, thanks to higher incomes and less physical effort. The productive reconversion is associated with “turning the tables” in economic terms. However, limitations on access to land to expand crops persist.

In terms of adaptation strategies and willingness to act, the following stand out: Productive adaptation related to the switch to more resistant crops and the use of organic fertilizers. Limited geographic mobility due to high territorial dependence (“we have nowhere else to go, we were born here and we have to die here”). Willingness to take environmental measures regarding the use of cardboard packaging instead of plastic, although motivated more by market forces than by environmental awareness. Finally, conditional resilience is understood as openness to diversify crops if more profitable and adapted options appear, but is limited by land availability.

In general, the focus group reveals an adaptive process driven by both climate pressure and economic incentives. The conversion to mangoes illustrates local economic resilience but also exposes vulnerabilities: dependence on a single crop, biodiversity degradation (disappearance of bees), and increasing water pressure.

In terms of poverty, climate change has had an indirect effect: by affecting traditional crops, it forced the community to transform itself productively. This transformation improved incomes, but it still faces risks from climate or market shocks that affect mangoes.

Figure 5 graphically shows the word cloud of the conversation held in the focus group. It can be noted that the word magician is one of the most frequent, as well as climate and climate change, fertilizers.

4.3. Discussion

The results of this research show a significant relationship between the climate change effects and poverty among the Afro-Ecuadorian population of the Chota Valley. In particular, it confirms that the most socioeconomically vulnerable households are also the most exposed to extreme weather events, with less capacity to adapt and limited access to institutional protection mechanisms. This reality reflects how climate change acts as a risk multiplier, deepening structural poverty and territorial exclusion, in line with previous studies [19,36,37].

From a mixed approach, the triangulation of quantitative data (econometric models and PMT index) and qualitative data (focus group) allowed for a more comprehensive understanding of the phenomenon, offering a replicable framework for analyzing similar contexts and highlighting the importance of integrating local perspectives into global debates. It was identified that climatic variables, particularly third-quarter temperatures and fourth-quarter rainfall, have a significant impact on agricultural income. This is consistent with previous studies indicating that climate change disproportionately affects rural communities dependent on agriculture [19,36]. The forced transition to more resistant crops, such as mangoes, illustrates an insufficient adaptation strategy to underlying problems, such as soil degradation and water scarcity, which, while temporarily improving incomes, could prove unsustainable in the face of worsening climate effects.

One of the most relevant findings was the apparent contradiction between poverty levels and interest in adaptive actions: poorer households showed a greater willingness to adapt, although with limited resources to do so, while households with better economic conditions were not interested in acting against climate change. Furthermore, although awareness of climate change among the respondents was high (93.3%), it was based on empirical perceptions rather than formal education, which could limit the effectiveness of long-term adaptive decisions.

The study has some limitations that should be considered. First, the use of climate data from secondary sources—Accuweather and Freemeteo—could introduce biases, as they do not capture local microclimatic variations. Second, although the study gathered valuable information through interviews and participatory workshops, its cross-sectional design and the short period analyzed (2014–2017) limit the ability to assess the long-term effects of climate change on households. Additionally, the sample focused on the Chota Valley region, so the results may not be directly applicable to other rural communities with different contexts. Finally, key aspects, such as migration dynamics and the impact of public policies, were not explored in depth, which represents an area of opportunity for future studies.

Despite these limitations, this study makes a significant contribution to the existing literature by offering empirical evidence from a territorial and ethnic–cultural perspective on how climate change deepens poverty and inequalities in vulnerable rural communities, in line with global findings [19,23].

5. Conclusions

Poverty, inadequate infrastructure, the lack of basic services, social disorganization, and ignorance of climate change are key factors in determining how climate change affects a social group. For this reason, the ability of communities to adapt is closely linked to local social and economic development, confirming the findings of Hanna et al. [38] in their study on pollution.

Climate change, seen as the change in temperature in the different quarters of the year, shows a relationship with production, and as the families are eminently dedicated to agriculture, the effect on income is evident. Model 1, developed by only considering climatic variables, shows negative effects of temperature in the third quarter and positive effects of precipitation in the fourth quarter.

Model 2, which considers climatic variables and household characteristics, shows that the characteristics of the people influence the household income more than the climatic variables do. This result shows that the education levels of the head and other members of the household are important in generating income for the families of the Chota Valley that dedicate themselves almost exclusively to agricultural activities. It can be inferred that access to education ensures access to information that improves cultivation techniques and procedures that lead to better income.

Model 3, which considers climatic variables, household characteristics, and interactions between climatic variables, shows that only the climatic variables of the fourth quarter have effects on income, and the educational characteristics of the household members are verified as determining factors in the income behavior.

The construction of a PMT proxy poverty index based on 10 household variables and 6 individual variables contributes to a better identification of the poverty level of the Chota Valley population.

When discussing the relationship between poverty and climate change knowledge, the descriptive statistics show encouraging results (93.3% say they know); however, this “knowledge” is related to understanding climate change, not for what it is, but for the consequences that are perceived, with the main ways of understanding being the changes in weather conditions and the planting season.

The variables that explain climate change knowledge, understood as the change in weather conditions, are poverty (PMT index), the education level of the household head, and the number of people with at least secondary education in the household. The marginal effect of each explanatory variable is positive concerning the increase in climate change knowledge. Therefore, an increase of one point in the PMT index contributes 1.01 times to climate change knowledge, an increase of one year in the schooling of the household head causes climate change knowledge to increase by 1.03 times, and an increase of one person with a secondary education in the household increases climate change knowledge by 1.13 times.

The variable that explains the actions, measures, and adaptation against climate change, understood as the “Agreement to develop and implement in the Chota Valley an adaptive model against CC (Saving water, garbage collection, less use of fungicides, etc.)”, is poverty. The variables of schooling characteristics of the head and other members of the household are not significant in this case. The marginal effect of the PMT index on actions, measures and adaptation against climate change yields a “new” unexpected result: a one-point increase in the PMT index contributes to a 1.025 times decrease in actions against climate change. If this result can be extrapolated, it means that non-poor people are not interested in acting against climate change.

In summary, the results of knowledge and actions against climate change in the Chota Valley could show that poor people lack knowledge about the phenomenon, and with that knowledge, they would be willing to fight and adapt to climate change, while non-poor people have the knowledge but are averse to adaptation.

The qualitative investigation showed that the people of the Chota Valley have experienced climate change in their own way regarding changes in the weather, planting and harvesting times; the disappearance of water sources, the low productivity of their products; and the need to replace them with mangoes.

Economically, the people of the Chota Valley see climate change as the impetus towards migration to the production of more profitable fruits, particularly mangoes. However, they do not think about the long-term effects, where radical changes can occur that cause their current star product to no longer be produced, which happened with ovos or cucumbers.

When asked about the changes in the living conditions of the Chota families between 2014 and 2017, they all said that they had improved due to the transition to mango production.

6. Recommendations

Although the results of this study show relative public support for climate change adaptation measures, the literature emphasizes that such support does not always automatically translate into concrete actions [39]. To close this gap, it is necessary to articulate strategies that mobilize citizens and strengthen the operational capacity of local governments, as they are the actors closest to the territorial and social realities of communities [40].

Community participation is a key factor in ensuring the legitimacy, relevance, and sustainability of adaptation plans. Experiences in various contexts have demonstrated that involving the population from the diagnostic phase through to implementation enhances the quality of interventions and fosters social ownership of the measures [41,42]. Tools such as participatory budgeting, deliberative workshops, community risk mapping, and the co-creation of adaptive solutions have proven effective in transforming support into active engagement.

For their part, local governments require technical, financial, and regulatory support to turn intentions into executable action plans. This includes specialized training for municipal staff, access to up-to-date climate data, participatory planning tools, and financial resources from international or national climate funds [19]. Coordination through inter-municipal networks and joint work with non-governmental organizations and the private sector allows for the sharing of best practices and the optimization of resources.

In this regard, the following actions are proposed: (i) establish local adaptation committees with diverse representation of stakeholders; (ii) develop technical training programs for municipal officials and community leaders; (iii) create decentralized climate financing mechanisms; and (iv) strengthen multilevel governance to ensure consistency between national policies and local actions. The implementation of these measures would help transform the public support identified in this study into concrete interventions that increase territorial resilience to climate change.

Future research directions include: (1) Conducting a study that directly measures precipitation and temperatures at each study location, enabling a more precise analysis of pure agricultural income to develop a Ricardian model [17] of land value in the Chota Valley. (2) Since climate change is an ongoing issue and will remain so, a study based on the one conducted in the Chota Valley could be expanded to a regional or national level.