Vulenerability of Rural Households to Climate- Induced Shocks: The Case of the Chiro District, Eastern Oromia, Ethiopia

Vulenerability of Rural Households to Climate- Induced Shocks: The Case of the Chiro District, Eastern Oromia, Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Vulenerability of Rural Households to Climate- Induced Shocks: The Case of the Chiro District, Eastern Oromia, Ethiopia Feyera Jira This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6831483/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Ethiopia is highly vulnerable to climate change and variability, particularly climate-induced shocks, which exert significant pressure on the livelihoods of rural households that depend on natural resources. The extent of household vulnerability varies based on adaptive capacity, exposure, and sensitivity to climate risks. However, local and context-specific vulnerability assessments remain limited, particularly in the West Hararghe zone and the Chiro district, creating gaps in effective planning and intervention strategies. This study evaluates the vulnerability of rural households to climate change and climate-induced shocks in Chiro district, Eastern Oromia, Ethiopia. A descriptive research design was employed, integrating both quantitative and qualitative approaches. Data was collected from 300 randomly selected households across four kebeles using household surveys, key informant interviews, focus group discussions, and direct observations. The vulnerability assessment framework was based on the Intergovernmental Panel on Climate Change (IPCC) dimensions—exposure, sensitivity, and adaptive capacity—analyzed using the Principal Component Analysis (PCA) method by integrating with the Livelihood Vulnerability Index (LVI). The results indicate that households participating in the Productive Safety Net Program (PSNP) were 4.15% more vulnerable than non-participants due to their lower aggregate adaptive capacity for livelihood assets. Additionally, PSNP households exhibited higher exposure and susceptibility to climate-induced shocks and biophysical stressors. These findings highlight the need for targeted interventions that enhance off-farm and non-farm livelihood opportunities, expand access to credit, and promote gender equality and women’s empowerment to strengthen resilience against climate change​. Climate Analysis and Modeling Vulnerability Assessment Principal Component Analysis Exposure Sensitivity Adaptive Capacity Livelihood Vulnerability Index Climate Change Rural Households Productive Safety Net Program 1. INTRODUCTION Climate change, characterized by long-term shifts in temperature, precipitation, and atmospheric conditions, is predominantly driven by human activities that alter the global atmosphere [1]. Recent data indicate an unprecedented rise in global surface temperatures, with a 0.99°C increase between 2001 and 2020 compared to pre-industrial levels [1]. Developing nations, particularly those dependent on rainfed agriculture, face heightened vulnerability due to limited socioeconomic and financial resources, constraining their adaptive capacity [2,3]. Ethiopia, with its predominantly rainfed subsistence agriculture, ranks among the most climate-sensitive countries [4]. Economic constraints, inadequate education, outdated farming techniques, and weak infrastructure exacerbate household vulnerability [5]. Environmental degradation, deforestation, and increasing drought frequency further threaten rural livelihoods, while land mismanagement and soil erosion diminish agricultural productivity and food security [6,7]. In the Oromia National Regional State—particularly the West and East Hararghe Zones—climate variability has disrupted rainfall patterns and increased temperatures, adversely affecting crop yields [8]. Historical data (1986–2017) indicate mean minimum and maximum temperatures of 12.8°C and 27.2°C, respectively [9]. Similarly, Chiro district has experienced rising temperatures and declining rainfall (1980–2010), leading to land degradation and reduced agricultural output [10]. Despite these challenges, no empirical study has yet assessed rural households’ vulnerability in Chiro district, underscoring the need for localized research. Existing vulnerability assessments in Ethiopia have primarily focused on national or regional scales, overlooking local-level disparities [11]. Conflicting findings in spatial analyses further complicate vulnerability assessments—while some studies identify lowlands as most vulnerable [12], others highlight highland communities [13], suggesting that socioeconomic and livelihood factors play a critical role. Most prior studies have relied on the risk-hazard framework, emphasizing external climatic risks while neglecting intrinsic socioeconomic determinants [14]. To address this gap, this study employs Principal Component Analysis (PCA) within the IPCC’s exposure-sensitivity-adaptive capacity framework [15] to evaluate vulnerability in Chiro district. By providing a context-specific and data-driven assessment, this research aims to inform targeted policy interventions that enhance rural household resilience to climate-induced shocks Vulnerability Assessment Framework : The study utilizes the Intergovernmental Panel on Climate Change (IPCC) guidelines, applying the Principal Component Analysis (PCA) integrated with the Livelihood Vulnerability Index (LVI). LVI v - IPCC v = (Ex v -Ad v )*S V; where LVI-IPCC V is the LVI for the group of rural households, V as IPCC vulnerability framework; E xv , A dv , and S are the computed exposure, adaptive capacity, and sensitivity scores V, respectively. The LVI-IPCC value is between -1 (least vulnerable) to 1 (most vulnerable). 2. STUDY DESIGN & METHODOLOGY 2.1. Research Design This study employed a descriptive research design incorporating mixed research methods to enhance the reliability and validity of the findings. A quantitative approach was used to collect and analyze data related to climate-induced shocks, socioeconomic conditions, rainfall, and temperature trends. Meanwhile, qualitative methods were employed to gather insights on household and community perceptions of vulnerability to climate change and variability. The integration of both methods ensured a comprehensive understanding of the subject matter. 2.2. Study Area This study was conducted in Chiro district, located in Eastern Oromia, Ethiopia. The district lies between 8°50'47''N and 9°10'0''N latitude and 40°40'0''E and 41°10'0''E longitude. It covers a total area of 68,314.73 hectares (Chiro Agriculture and Natural Resource Office, 2023). Based on 2021 census data, the district has a population of 245,091 people, comprising 125,498 males and 119,593 females. The altitude of the district ranges from 1,500 to 3,060 meters above sea level, with undulating topography and mountainous terrain. Due to its steep slopes and limited vegetation cover, Chiro district is highly susceptible to erosion. Key environmental and socioeconomic challenges include drought, water shortages, soil erosion, flooding, scarcity of livestock forage, and limited livelihood diversification, all of which significantly threaten food security and sustainable development [ 16 ]. 2.3. Sampling A multistage sampling technique was adopted in this study. First, Chiro district was purposively selected from the 18 districts of the West Hararghe Zone due to its frequent exposure to climate change-induced shocks. Additionally, the district has been a focus of governmental and non-governmental (NGO) interventions, particularly through the Productive Safety Net Program (PSNP) over the past 18 years. In the second stage, four kebeles—Arba Rekete, Medhicho#2, Saro, and Baka Kubi—were selected using a simple random sampling method. The sample size was determined using [ 17 ] formula at a 95% confidence level with 5% precision (e): Where: n = sample size, N = total population, e = level of precision Applying this formula, a total of 300 households were selected, consisting of 150 PSNP beneficiaries and 150 non-PSNP households. These households were randomly sampled from the 1,200 households in the selected kebeles to ensure a representative dataset for analysis. 2.4. Data Sources and Collection Methods This study utilized both primary and secondary data sources. Primary data was gathered through focus group discussions (FGDs) and structured questionnaires, which were pre-tested before use. The questionnaires were designed to collect information on the socio-economic, biophysical, and institutional aspects of the study area, with household surveys serving as the main data collection method. A literature review was conducted to identify key components and sub-components relevant to vulnerability, ensuring alignment with the local context. Based on this review, survey questions were formulated to capture data on the forty-nine indicators used to calculate the Livelihood Vulnerability Index (LVI). Additional details on these sections are provided in annexed Table 1–3. The study population included household heads and district experts from the selected districts. The survey was conducted in Afaan Oromo, the local language, to facilitate better communication. Enumerators with relevant experience and language proficiency were trained to administer the survey. Prior to data collection, the questionnaire was reviewed to clarify any ambiguities. The data collection process took place between October 2022 and December 2022. Participants were required to sign consent forms before responding to the questionnaire or participating in interviews, ensuring ethical research practices. In addition to surveys, monthly time-series data on temperature and precipitation were obtained from the Ethiopian Meteorology Agency, covering the period from 1980 to 2021. A dataset spanning 42 years was used to analyze climate trends and assess the community’s vulnerability to climate-related shocks. Specific time-series data representing the Chiro district were extracted to provide localized insights. Furthermore, four FGDs were conducted in each selected kebele, with each group consisting of 6 to 9 members, resulting in a total of 32 participants across all kebeles. The discussions included religious leaders, local community leaders, women’s representatives, and youth. The author facilitated each FGD using a guided checklist. Additionally, key informant interviews were conducted with development agents, food security officials, CARE staff, and natural resource management experts. Both FGDs and key informant interviews followed a structured format, using pre-prepared questions and checklists. Field visits were also carried out to evaluate natural resource conditions and infrastructure availability within the study area. 2.5. Methods of Data Analysis The Livelihood Vulnerability Index (LVI), along with the IPCC-LVI, was used to evaluate the climate vulnerability of rural households depended on Ethiopia Productive safety net program (PSNP) in the Chiro district of Eastern Oromia, Ethiopia. The IPCC definition of livelihood vulnerability guided the calculation of LVI-IPCC. Microsoft Office Excel 2010 was utilized to estimate the LVI and create both the vulnerability spider chart and vulnerability triangle. The collected data was examined using both qualitative and quantitative approaches. The quantitative analysis, conducted with SPSS v23.0, evaluated 49 indicators. By integrating these methods, a more comprehensive insight into livelihood vulnerability in different contexts was achieved. Thematic analysis was applied to the qualitative data obtained from key informant interviews and focus group discussions (FGDs). Data organization and analysis were carried out using XLSTAT, MS Excel, and SPSS. Comprehensive Vulnerability Assessment: The study underscores the importance of a localized understanding of vulnerability, revealing that generalized national statistics may overlook critical regional dynamics. The LVI framework provides a nuanced view that integrates various socio-economic components to assess vulnerability accurately. This method ensures that policymakers can focus on tailored interventions that address specific local needs rather than applying a one-size-fits-all approach. Use of Principal Component Analysis (PCA) : The adoption of PCA to evaluate the interplay between exposure, sensitivity, and adaptive capacity offers methodological advancements in vulnerability assessments. By breaking down complex data into manageable components, the analysis reveals key indicators that policymakers should focus on to alleviate impacts on rural communities. 3. KEY FINDINGS 3.1. Demographic and Socioeconomic Characteristics of Households An analysis of 300 surveyed households revealed the gender distribution of headship was 57% male and 43% female. Most respondents had elementary education, with average household sizes of 5.7 members. The financial profiles indicated significant disparities in income and access to credit services between PSNP and non-PSNP households. Notably, PSNP participants had an average annual income of 24,285 ETB compared to 17,115 ETB for non-PSNP households. The demographic profile indicated that PSNP households had a higher dependency ratio and older average household heads than non-PSNP households. Access to credit and some economic resources was significantly better in PSNP households despite overall vulnerability. 3.2. Livelihood Vulnerability Analysis The LVI analysis highlighted critical disparities in vulnerability between PSNP and non-PSNP households. The PSNP cohort displayed higher exposure and sensitivity levels, attributed to economic reliance on agriculture and environmental degradation. 3.2.1. Exposure to Household Vulnerability Three decades of climate data indicated an upward trend in temperature, and significant fluctuations in rainfall patterns exacerbated vulnerability among rural households. PSNP beneficiaries faced greater exposure to climate risks compared to their non-PSNP counterparts, underscoring the urgent need for targeted climate adaptation strategies. Climate Variability as a Major Stressor : The research details that rural households are increasingly facing adverse climatic events such as droughts, floods, and pest outbreaks. The observed increase in temperature and decrease in precipitation correlate with reported declines in agricultural productivity, underscoring the critical need for adaptive agricultural practices tailored to predictability shifts in climate patterns. Climate Change Impacts : This study identifies how climate variability has significantly affected farmers in Eastern Oromia, particularly through increased temperatures and erratic rainfall. The observed average temperature rises, and variable precipitation patterns demand that rural households adopt more resilient agricultural practices to mitigate damage from climate extremes. Climate Trends in Chiro : From 1980 to 2021, the mean annual temperature increased significantly, with a statistical trend indicating a rise at a rate of 0.02°C per year. Rainfall showed variability with an average increase per year of 1.5 mm, indicating potential increases in extreme weather events. 3.2.2. Factors Influencing Sensitivity Vulnerability assessments revealed that PSNP households were significantly more sensitive to climate shocks due to factors like larger family sizes, higher dependency ratios, and poorer soil fertility. Key sensitivity indicators included household size, age, dependency ratios, soil fertility, and land erosion, with PSNP households exhibiting higher sensitivity levels. The analysis revealed that PSNP households had a higher sensitivity index (0.5222) compared to non-PSNP households (0.4358), indicating greater susceptibility to climate-induced shocks. 3.2.3. Adaptive Capacity Evaluation The adaptive capacity of households was influenced by five key assets: social, human, economic, physical, and natural capital. Non-PSNP households exhibited a higher adaptive capacity as indicated by better social networks and economic resources, essential for navigating climate-related challenges. Socio-Demographic Factors Influence Vulnerability : Data highlights that households with higher dependency ratios and female-headed households face amplified vulnerabilities due to socio-economic constraints. The gendered aspect of vulnerability suggests that empowering women through education and economic participation can be pivotal in improving overall household resilience to climatic shocks. Gender and Household Dynamics : The presence of female-headed households among the sample tends to correlate with higher vulnerability levels due to socio-economic factors. Initiatives aimed at empowering women and improving gender equity can improve adaptive capacity substantially. Livelihood Diversification : Encouraging off-farm and non-farm employment opportunities could reduce household reliance on agriculture, thereby decreasing vulnerability. This diversification is crucial to mitigate the effects of climate variability on crop yields and overall income stability. Education as a Lever for Change : The results emphasize the importance of education and vocational training in improving adaptive capacity. Educational programs that focus on climate-smart agriculture and resource management can sufficiently prepare communities for the uncertainties of climate variability. Natural Capital Importance : The influence of natural resources in supporting rural livelihoods was particularly noted, revealing that communities with more abundant resources demonstrate better resilience to climate impacts. Efforts to promote sustainable environmental practices are vital for maintaining this natural capital and enhancing community resilience. 3.2.4. Overall Vulnerability Assessment Climate Vulnerability in Ethiopia: Ethiopia is especially vulnerable to climate change, impacting rural households reliant on natural resources. Vulnerability varies based on adaptive capacity, exposure, and sensitivity to climate risks. The study concluded that PSNP households exhibited the highest vulnerability index, which can be attributed to their greater sensitivity and exposure coupled with lower adaptive capacity. In contrast, non-PSNP households demonstrated relatively better resilience, thanks to diversified economic activities and enhanced socio-economic conditions. Vulnerability Index Findings : The comparative analysis of vulnerability indices reveals that PSNP households exhibit significantly higher exposure and sensitivity levels, reinforcing the need for programs that address issues like asset distribution, recovery strategies, and resource availability in food production to bolster overall resilience. Higher Vulnerability Among PSNP Households : The analysis shows that participation in the PSNP does not significantly improve vulnerability status, exposing structural issues such as dependence on inadequate support networks. The findings raise questions about the effectiveness of safety net programmes in building long-term resilience and suggest that simply providing assistance without enhancing adaptive capacity is insufficient. Lack of Adaptive Capacity : The study finds that PSNP households have lower adaptive capacities due to factors such as limited access to financial services, education, and technology. As adaptive capacity increases with access to resources, enhancing financial inclusion can significantly promote resilience against climate-induced shocks. 4. SUMMARY, CONCLUSIONS & RECOMMENDATIONS Summary This research investigates the vulnerability of rural households in the Chiro District of Eastern Oromia, Ethiopia, to climate-induced shocks. Ethiopia faces significant challenges from climate change, particularly for rural populations dependent on agriculture. The study employs a mixed-methods approach, integrating quantitative and qualitative data collected from a sample of 300 households. It employs the Livelihood Vulnerability Index (LVI) to quantify vulnerability based on three dimensions: exposure, sensitivity, and adaptive capacity, as outlined by the Intergovernmental Panel on Climate Change (IPCC). The findings reveal that households involved in the Productive Safety Net Program (PSNP) exhibit greater vulnerability compared to non-PSNP households, largely due to lower adaptive capacity and increased exposure to climate shocks. The research highlights the urgent need for targeted interventions to enhance resilience and adaptive capacity in these communities. Conclusion This research highlights the pronounced vulnerability of rural households in Chiro district to climate-induced shocks, emphasizing the need for targeted interventions. The research highlights the effects of climate variability on the livelihoods of these households, focusing on the Productive Safety Net Program (PSNP) and using a Livelihood Vulnerability Index (LVI) alongside a framework from the Intergovernmental Panel on Climate Change (IPCC) to evaluate exposure, sensitivity, and adaptive capacity. The disparities found between PSNP and non-PSNP households indicate that while PSNP provides some economic benefits, it does not sufficiently bolster resilience against climate risks. Recommendations Enhancing Social and Human Capital : Investment in education and vocational training can empower households with skills necessary for climate adaptation and diversified income strategies. Strengthening Institutional Support : Expanding financial services and improving access to climate-related information could aid communities in making informed decisions regarding agricultural practices. Diversifying Livelihood Strategies : Promoting non-agricultural employment could reduce reliance on erratic weather patterns, thereby enhancing household resilience. Policy Integration : Incorporating climate risk management into local development plans would ensure a more systematic approach to building adaptive capacity within rural communities. Declarations This study was conducted in accordance with ethical standards and approved by the Institutional Review Board (IRB) of Haramaya University, Ethiopia. Informed consent was obtained from all participants prior to data collection. References Intergovernmental Panel on Climate Change (IPCC, 2023). Fifty-eighth session of the IPCC, Interlaken, Switzerland, 13–17 March 2023. 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Resilience: The emergence of a perspective for social–ecological systems analyses . Global Environmental Change, 16(3), 253-267. Costanza, R., de Groot, R., Braat, L., Kubiszewski, I., Fioramonti, L., Sutton, P., Farber, S., & Grasso, M. (2017). Twenty years of ecosystem services: How far have we come and how far do we still need to go? Ecosystem Services, 28, 1-16. Dasgupta, P. (2021). The Economics of Biodiversity: The Dasgupta Review . HM Treasury. Adger, W. N. (2003). Social capital, collective action, and adaptation to climate change . Economic Geography, 79(4), 387-404. Cutter, S. L., Boruff, B. J., & Shirley, W. L. (2003). Social vulnerability to environmental hazards . Social Science Quarterly, 84(2), 242-261. O’Brien, K., Eriksen, S., Nygaard, L. P., & Schjolden, A. (2007). Why different interpretations of vulnerability matter in climate change discourses . Climate Policy, 7(1), 73. Additional Declarations The authors declare no competing interests. Supplementary Files TablesRevised.docx FayeStudyarea2..tif Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6831483","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467188260,"identity":"911f6cdc-55e7-4cd1-ad50-a7c94e7fabe1","order_by":0,"name":"Feyera Jira","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIie2PMUvEMBiGUw5Sh+CtOTjwL1w5yC2l/SEuCYG6KA4uHTqkS24R/4mDi3OkkCnY9eCWFMHJ4VxEFzHt0C09R8E8w5fvhe/hJQAEAn+QWAwPRADMlKJl6kJUqykFqVGBzFpT9Ir4jTKs66STzbBOK/Ft8lJWp8vNVhFMTZvdbxvXUqXnXgWZdWI0REtDC0zLPX80zCm6uBIeJceXZCEgRBhQ7Vr2nCinRKLxKujsbfMlvp0y7yRm8pmTtjuiYESiWjoF89mKSZWR3bEWVNws6rteeY0sNZySnWuhE39BcfPwLj50jufXh6fPMstJe9HZQ5V6FQBOVm7oMbLhknrPe2LrRjXGfPI4EAgE/iU/MI9j4crHUrEAAAAASUVORK5CYII=","orcid":"","institution":"N/A","correspondingAuthor":true,"prefix":"","firstName":"Feyera","middleName":"","lastName":"Jira","suffix":""}],"badges":[],"createdAt":"2025-06-05 18:31:06","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6831483/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6831483/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84278663,"identity":"8b63a745-5579-4350-b93d-7a830eabe35d","added_by":"auto","created_at":"2025-06-10 06:10:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":769393,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6831483/v1/afd3852d-a78c-4d82-9b7b-c327954aa199.pdf"},{"id":84277955,"identity":"f32f9b8a-e0ae-4be2-bb79-7f865f7c0327","added_by":"auto","created_at":"2025-06-10 06:03:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":78452,"visible":true,"origin":"","legend":"","description":"","filename":"TablesRevised.docx","url":"https://assets-eu.researchsquare.com/files/rs-6831483/v1/bf381f60b2e4d9a286674f54.docx"},{"id":84277953,"identity":"c4d010d7-7b58-4822-a9c0-32621e8fb9ef","added_by":"auto","created_at":"2025-06-10 06:03:30","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":72561186,"visible":true,"origin":"","legend":"","description":"","filename":"FayeStudyarea2..tif","url":"https://assets-eu.researchsquare.com/files/rs-6831483/v1/1866cb5b2f42e65fa97f4959.tif"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eVulenerability of Rural Households to Climate- Induced Shocks: The Case of the Chiro District, Eastern Oromia, Ethiopia\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eClimate change, characterized by long-term shifts in temperature, precipitation, and atmospheric conditions, is predominantly driven by human activities that alter the global atmosphere [1]. Recent data indicate an unprecedented rise in global surface temperatures, with a 0.99\u0026deg;C increase between 2001 and 2020 compared to pre-industrial levels [1]. Developing nations, particularly those dependent on rainfed agriculture, face heightened vulnerability due to limited socioeconomic and financial resources, constraining their adaptive capacity [2,3].\u003c/p\u003e\n\u003cp\u003eEthiopia, with its predominantly rainfed subsistence agriculture, ranks among the most climate-sensitive countries [4]. Economic constraints, inadequate education, outdated farming techniques, and weak infrastructure exacerbate household vulnerability [5]. Environmental degradation, deforestation, and increasing drought frequency further threaten rural livelihoods, while land mismanagement and soil erosion diminish agricultural productivity and food security [6,7].\u003c/p\u003e\n\u003cp\u003eIn the Oromia National Regional State\u0026mdash;particularly the West and East Hararghe Zones\u0026mdash;climate variability has disrupted rainfall patterns and increased temperatures, adversely affecting crop yields [8]. Historical data (1986\u0026ndash;2017) indicate mean minimum and maximum temperatures of 12.8\u0026deg;C and 27.2\u0026deg;C, respectively [9]. Similarly, Chiro district has experienced rising temperatures and declining rainfall (1980\u0026ndash;2010), leading to land degradation and reduced agricultural output [10]. Despite these challenges, no empirical study has yet assessed rural households\u0026rsquo; vulnerability in Chiro district, underscoring the need for localized research.\u003c/p\u003e\n\u003cp\u003eExisting vulnerability assessments in Ethiopia have primarily focused on national or regional scales, overlooking local-level disparities [11]. Conflicting findings in spatial analyses further complicate vulnerability assessments\u0026mdash;while some studies identify lowlands as most vulnerable [12], others highlight highland communities [13], suggesting that socioeconomic and livelihood factors play a critical role.\u003c/p\u003e\n\u003cp\u003eMost prior studies have relied on the risk-hazard framework, emphasizing external climatic risks while neglecting intrinsic socioeconomic determinants [14]. To address this gap, this study employs Principal Component Analysis (PCA) within the IPCC\u0026rsquo;s exposure-sensitivity-adaptive capacity framework [15] to evaluate vulnerability in Chiro district. By providing a context-specific and data-driven assessment, this research aims to inform targeted policy interventions that enhance rural household resilience to climate-induced shocks\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVulnerability Assessment Framework\u003c/strong\u003e: The study utilizes the Intergovernmental Panel on Climate Change (IPCC) guidelines, applying the Principal Component Analysis (PCA) integrated with the Livelihood Vulnerability Index (LVI). LVI\u003csub\u003ev\u003c/sub\u003e - IPCC\u003csub\u003ev\u003c/sub\u003e = (Ex\u003csub\u003ev\u003c/sub\u003e-Ad\u003csub\u003ev\u003c/sub\u003e)*S\u003csub\u003eV;\u0026nbsp;\u003c/sub\u003ewhere LVI-IPCC\u003csub\u003eV\u003c/sub\u003e is the LVI for the group of rural households, V \u0026nbsp;as IPCC vulnerability framework; E\u003cem\u003exv\u003c/em\u003e, A\u003cem\u003edv\u003c/em\u003e, and \u003cem\u003eS\u0026nbsp;\u003c/em\u003eare the computed exposure, adaptive capacity, and sensitivity scores V, respectively. The LVI-IPCC value is between -1 (least vulnerable) to 1 (most vulnerable).\u003c/p\u003e"},{"header":"2. STUDY DESIGN \u0026 METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Research Design\u003c/h2\u003e\n \u003cp\u003eThis study employed a descriptive research design incorporating mixed research methods to enhance the reliability and validity of the findings. A quantitative approach was used to collect and analyze data related to climate-induced shocks, socioeconomic conditions, rainfall, and temperature trends. Meanwhile, qualitative methods were employed to gather insights on household and community perceptions of vulnerability to climate change and variability. The integration of both methods ensured a comprehensive understanding of the subject matter.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Study Area\u003c/h2\u003e\n \u003cp\u003eThis study was conducted in Chiro district, located in Eastern Oromia, Ethiopia. The district lies between 8\u0026deg;50\u0026apos;47\u0026apos;\u0026apos;N and 9\u0026deg;10\u0026apos;0\u0026apos;\u0026apos;N latitude and 40\u0026deg;40\u0026apos;0\u0026apos;\u0026apos;E and 41\u0026deg;10\u0026apos;0\u0026apos;\u0026apos;E longitude. It covers a total area of 68,314.73 hectares (Chiro Agriculture and Natural Resource Office, 2023). Based on 2021 census data, the district has a population of 245,091 people, comprising 125,498 males and 119,593 females. The altitude of the district ranges from 1,500 to 3,060 meters above sea level, with undulating topography and mountainous terrain. Due to its steep slopes and limited vegetation cover, Chiro district is highly susceptible to erosion. Key environmental and socioeconomic challenges include drought, water shortages, soil erosion, flooding, scarcity of livestock forage, and limited livelihood diversification, all of which significantly threaten food security and sustainable development [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Sampling\u003c/h2\u003e\n \u003cp\u003eA multistage sampling technique was adopted in this study. First, Chiro district was purposively selected from the 18 districts of the West Hararghe Zone due to its frequent exposure to climate change-induced shocks. Additionally, the district has been a focus of governmental and non-governmental (NGO) interventions, particularly through the Productive Safety Net Program (PSNP) over the past 18 years. In the second stage, four kebeles\u0026mdash;Arba Rekete, Medhicho#2, Saro, and Baka Kubi\u0026mdash;were selected using a simple random sampling method.\u003c/p\u003e\n \u003cp\u003eThe sample size was determined using [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e] formula at a 95% confidence level with 5% precision (e):\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\" height=\"88\" width=\"694\"\u003e\u003c/p\u003e\n \u003cp\u003eWhere: n\u0026thinsp;=\u0026thinsp;sample size, N\u0026thinsp;=\u0026thinsp;total population, e\u0026thinsp;=\u0026thinsp;level of precision\u003c/p\u003e\n \u003cp\u003eApplying this formula, a total of 300 households were selected, consisting of 150 PSNP beneficiaries and 150 non-PSNP households. These households were randomly sampled from the 1,200 households in the selected kebeles to ensure a representative dataset for analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Data Sources and Collection Methods\u003c/h2\u003e\n \u003cp\u003eThis study utilized both primary and secondary data sources. Primary data was gathered through focus group discussions (FGDs) and structured questionnaires, which were pre-tested before use. The questionnaires were designed to collect information on the socio-economic, biophysical, and institutional aspects of the study area, with household surveys serving as the main data collection method. A literature review was conducted to identify key components and sub-components relevant to vulnerability, ensuring alignment with the local context. Based on this review, survey questions were formulated to capture data on the forty-nine indicators used to calculate the Livelihood Vulnerability Index (LVI). Additional details on these sections are provided in annexed Table\u0026nbsp;1\u0026ndash;3.\u003c/p\u003e\n \u003cp\u003eThe study population included household heads and district experts from the selected districts. The survey was conducted in Afaan Oromo, the local language, to facilitate better communication. Enumerators with relevant experience and language proficiency were trained to administer the survey. Prior to data collection, the questionnaire was reviewed to clarify any ambiguities. The data collection process took place between October 2022 and December 2022. Participants were required to sign consent forms before responding to the questionnaire or participating in interviews, ensuring ethical research practices.\u003c/p\u003e\n \u003cp\u003eIn addition to surveys, monthly time-series data on temperature and precipitation were obtained from the Ethiopian Meteorology Agency, covering the period from 1980 to 2021. A dataset spanning 42 years was used to analyze climate trends and assess the community\u0026rsquo;s vulnerability to climate-related shocks. Specific time-series data representing the Chiro district were extracted to provide localized insights.\u003c/p\u003e\n \u003cp\u003eFurthermore, four FGDs were conducted in each selected kebele, with each group consisting of 6 to 9 members, resulting in a total of 32 participants across all kebeles. The discussions included religious leaders, local community leaders, women\u0026rsquo;s representatives, and youth. The author facilitated each FGD using a guided checklist. Additionally, key informant interviews were conducted with development agents, food security officials, CARE staff, and natural resource management experts. Both FGDs and key informant interviews followed a structured format, using pre-prepared questions and checklists. Field visits were also carried out to evaluate natural resource conditions and infrastructure availability within the study area.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. Methods of Data Analysis\u003c/h2\u003e\n \u003cp\u003eThe Livelihood Vulnerability Index (LVI), along with the IPCC-LVI, was used to evaluate the climate vulnerability of rural households depended on Ethiopia Productive safety net program (PSNP) in the Chiro district of Eastern Oromia, Ethiopia. The IPCC definition of livelihood vulnerability guided the calculation of LVI-IPCC. Microsoft Office Excel 2010 was utilized to estimate the LVI and create both the vulnerability spider chart and vulnerability triangle. The collected data was examined using both qualitative and quantitative approaches. The quantitative analysis, conducted with SPSS v23.0, evaluated 49 indicators. By integrating these methods, a more comprehensive insight into livelihood vulnerability in different contexts was achieved. Thematic analysis was applied to the qualitative data obtained from key informant interviews and focus group discussions (FGDs). Data organization and analysis were carried out using XLSTAT, MS Excel, and SPSS.\u003c/p\u003e\n \u003col\u003e\n \u003cli\u003e\u003cstrong\u003eComprehensive Vulnerability Assessment:\u0026nbsp;\u003c/strong\u003eThe study underscores the importance of a localized understanding of vulnerability, revealing that generalized national statistics may overlook critical regional dynamics. The LVI framework provides a nuanced view that integrates various socio-economic components to assess vulnerability accurately. This method ensures that policymakers can focus on tailored interventions that address specific local needs rather than applying a one-size-fits-all approach.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eUse of Principal Component Analysis (PCA)\u003c/strong\u003e: The adoption of PCA to evaluate the interplay between exposure, sensitivity, and adaptive capacity offers methodological advancements in vulnerability assessments. By breaking down complex data into manageable components, the analysis reveals key indicators that policymakers should focus on to alleviate impacts on rural communities.\u003c/li\u003e\n \u003c/ol\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. KEY FINDINGS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Demographic and Socioeconomic Characteristics of Households\u003c/h2\u003e \u003cp\u003eAn analysis of 300 surveyed households revealed the gender distribution of headship was 57% male and 43% female. Most respondents had elementary education, with average household sizes of 5.7 members. The financial profiles indicated significant disparities in income and access to credit services between PSNP and non-PSNP households. Notably, PSNP participants had an average annual income of 24,285 ETB compared to 17,115 ETB for non-PSNP households. The demographic profile indicated that PSNP households had a higher dependency ratio and older average household heads than non-PSNP households. Access to credit and some economic resources was significantly better in PSNP households despite overall vulnerability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Livelihood Vulnerability Analysis\u003c/h2\u003e \u003cp\u003eThe LVI analysis highlighted critical disparities in vulnerability between PSNP and non-PSNP households. The PSNP cohort displayed higher exposure and sensitivity levels, attributed to economic reliance on agriculture and environmental degradation.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Exposure to Household Vulnerability\u003c/h2\u003e \u003cp\u003eThree decades of climate data indicated an upward trend in temperature, and significant fluctuations in rainfall patterns exacerbated vulnerability among rural households. PSNP beneficiaries faced greater exposure to climate risks compared to their non-PSNP counterparts, underscoring the urgent need for targeted climate adaptation strategies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eClimate Variability as a Major Stressor\u003c/b\u003e: The research details that rural households are increasingly facing adverse climatic events such as droughts, floods, and pest outbreaks. The observed increase in temperature and decrease in precipitation correlate with reported declines in agricultural productivity, underscoring the critical need for adaptive agricultural practices tailored to predictability shifts in climate patterns.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eClimate Change Impacts\u003c/b\u003e: This study identifies how climate variability has significantly affected farmers in Eastern Oromia, particularly through increased temperatures and erratic rainfall. The observed average temperature rises, and variable precipitation patterns demand that rural households adopt more resilient agricultural practices to mitigate damage from climate extremes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eClimate Trends in Chiro\u003c/b\u003e: From 1980 to 2021, the mean annual temperature increased significantly, with a statistical trend indicating a rise at a rate of 0.02°C per year. Rainfall showed variability with an average increase per year of 1.5 mm, indicating potential increases in extreme weather events.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Factors Influencing Sensitivity\u003c/h2\u003e \u003cp\u003eVulnerability assessments revealed that PSNP households were significantly more sensitive to climate shocks due to factors like larger family sizes, higher dependency ratios, and poorer soil fertility. Key sensitivity indicators included household size, age, dependency ratios, soil fertility, and land erosion, with PSNP households exhibiting higher sensitivity levels. The analysis revealed that PSNP households had a higher sensitivity index (0.5222) compared to non-PSNP households (0.4358), indicating greater susceptibility to climate-induced shocks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Adaptive Capacity Evaluation\u003c/h2\u003e \u003cp\u003eThe adaptive capacity of households was influenced by five key assets: social, human, economic, physical, and natural capital. Non-PSNP households exhibited a higher adaptive capacity as indicated by better social networks and economic resources, essential for navigating climate-related challenges.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSocio-Demographic Factors Influence Vulnerability\u003c/b\u003e: Data highlights that households with higher dependency ratios and female-headed households face amplified vulnerabilities due to socio-economic constraints. The gendered aspect of vulnerability suggests that empowering women through education and economic participation can be pivotal in improving overall household resilience to climatic shocks.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eGender and Household Dynamics\u003c/b\u003e: The presence of female-headed households among the sample tends to correlate with higher vulnerability levels due to socio-economic factors. Initiatives aimed at empowering women and improving gender equity can improve adaptive capacity substantially.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLivelihood Diversification\u003c/b\u003e: Encouraging off-farm and non-farm employment opportunities could reduce household reliance on agriculture, thereby decreasing vulnerability. This diversification is crucial to mitigate the effects of climate variability on crop yields and overall income stability.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEducation as a Lever for Change\u003c/b\u003e: The results emphasize the importance of education and vocational training in improving adaptive capacity. Educational programs that focus on climate-smart agriculture and resource management can sufficiently prepare communities for the uncertainties of climate variability.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eNatural Capital Importance\u003c/b\u003e: The influence of natural resources in supporting rural livelihoods was particularly noted, revealing that communities with more abundant resources demonstrate better resilience to climate impacts. Efforts to promote sustainable environmental practices are vital for maintaining this natural capital and enhancing community resilience.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4. Overall Vulnerability Assessment\u003c/h2\u003e \u003cp\u003eClimate Vulnerability in Ethiopia: Ethiopia is especially vulnerable to climate change, impacting rural households reliant on natural resources. Vulnerability varies based on adaptive capacity, exposure, and sensitivity to climate risks. The study concluded that PSNP households exhibited the highest vulnerability index, which can be attributed to their greater sensitivity and exposure coupled with lower adaptive capacity. In contrast, non-PSNP households demonstrated relatively better resilience, thanks to diversified economic activities and enhanced socio-economic conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eVulnerability Index Findings\u003c/b\u003e: The comparative analysis of vulnerability indices reveals that PSNP households exhibit significantly higher exposure and sensitivity levels, reinforcing the need for programs that address issues like asset distribution, recovery strategies, and resource availability in food production to bolster overall resilience.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eHigher Vulnerability Among PSNP Households\u003c/b\u003e: The analysis shows that participation in the PSNP does not significantly improve vulnerability status, exposing structural issues such as dependence on inadequate support networks. The findings raise questions about the effectiveness of safety net programmes in building long-term resilience and suggest that simply providing assistance without enhancing adaptive capacity is insufficient.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLack of Adaptive Capacity\u003c/b\u003e: The study finds that PSNP households have lower adaptive capacities due to factors such as limited access to financial services, education, and technology. As adaptive capacity increases with access to resources, enhancing financial inclusion can significantly promote resilience against climate-induced shocks.\u003c/p\u003e "},{"header":"4.\tSUMMARY, CONCLUSIONS \u0026 RECOMMENDATIONS","content":"\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eSummary\u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis research investigates the vulnerability of rural households in the Chiro District of Eastern Oromia, Ethiopia, to climate-induced shocks. Ethiopia faces significant challenges from climate change, particularly for rural populations dependent on agriculture. The study employs a mixed-methods approach, integrating quantitative and qualitative data collected from a sample of 300 households. It employs the Livelihood Vulnerability Index (LVI) to quantify vulnerability based on three dimensions: exposure, sensitivity, and adaptive capacity, as outlined by the Intergovernmental Panel on Climate Change (IPCC). The findings reveal that households involved in the Productive Safety Net Program (PSNP) exhibit greater vulnerability compared to non-PSNP households, largely due to lower adaptive capacity and increased exposure to climate shocks. The research highlights the urgent need for targeted interventions to enhance resilience and adaptive capacity in these communities.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis research highlights the pronounced vulnerability of rural households in Chiro district to climate-induced shocks, emphasizing the need for targeted interventions. The research highlights the effects of climate variability on the livelihoods of these households, focusing on the Productive Safety Net Program (PSNP) and using a Livelihood Vulnerability Index (LVI) alongside a framework from the Intergovernmental Panel on Climate Change (IPCC) to evaluate exposure, sensitivity, and adaptive capacity. The disparities found between PSNP and non-PSNP households indicate that while PSNP provides some economic benefits, it does not sufficiently bolster resilience against climate risks.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n \u003cli\u003e\u003cstrong\u003eRecommendations\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eEnhancing Social and Human Capital\u003c/strong\u003e: Investment in education and vocational training can empower households with skills necessary for climate adaptation and diversified income strategies.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eStrengthening Institutional Support\u003c/strong\u003e: Expanding financial services and improving access to climate-related information could aid communities in making informed decisions regarding agricultural practices.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDiversifying Livelihood Strategies\u003c/strong\u003e: Promoting non-agricultural employment could reduce reliance on erratic weather patterns, thereby enhancing household resilience.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePolicy Integration\u003c/strong\u003e: Incorporating climate risk management into local development plans would ensure a more systematic approach to building adaptive capacity within rural communities.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cspan\u003eThis study was conducted in accordance with ethical standards and approved by the Institutional Review Board (IRB) of Haramaya University, Ethiopia. Informed consent was obtained from all participants prior to data collection.\u003c/span\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eIntergovernmental Panel on Climate Change (IPCC, 2023). Fifty-eighth session of the IPCC, Interlaken, Switzerland, 13\u0026ndash;17 March 2023.\u003c/li\u003e\n\u003cli\u003eSerdeczny, O., Adams, S., Baarsch, F., Coumou, D., Robinson, A., Hare, W., Schaeffer, M., Perrette, M., \u0026amp; Reinhardt, J. (2017). Climate change impacts in sub-Saharan Africa: From physical changes to their social repercussions. \u003cem\u003eRegional Environmental Change, 17\u003c/em\u003e(6), 1585\u0026ndash;1600. https://doi.org/10.1007/s10113-015-0910-2\u003c/li\u003e\n\u003cli\u003eIntergovernmental Panel on Climate Change (IPCC). 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Climate Policy, 7(1), 73.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"N/A","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Vulnerability Assessment, Principal Component Analysis, Exposure, Sensitivity, Adaptive Capacity, Livelihood Vulnerability Index, Climate Change, Rural Households, Productive Safety Net Program","lastPublishedDoi":"10.21203/rs.3.rs-6831483/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6831483/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eEthiopia is highly vulnerable to climate change and variability, particularly climate-induced shocks, which exert significant pressure on the livelihoods of rural households that depend on natural resources. The extent of household vulnerability varies based on adaptive capacity, exposure, and sensitivity to climate risks. However, local and context-specific vulnerability assessments remain limited, particularly in the West Hararghe zone and the Chiro district, creating gaps in effective planning and intervention strategies. This study evaluates the vulnerability of rural households to climate change and climate-induced shocks in Chiro district, Eastern Oromia, Ethiopia. A descriptive research design was employed, integrating both quantitative and qualitative approaches. Data was collected from 300 randomly selected households across four kebeles using household surveys, key informant interviews, focus group discussions, and direct observations. The vulnerability assessment framework was based on the Intergovernmental Panel on Climate Change (IPCC) dimensions—exposure, sensitivity, and adaptive capacity—analyzed using the Principal Component Analysis (PCA) method by integrating with the Livelihood Vulnerability Index (LVI). The results indicate that households participating in the Productive Safety Net Program (PSNP) were 4.15% more vulnerable than non-participants due to their lower aggregate adaptive capacity for livelihood assets. Additionally, PSNP households exhibited higher exposure and susceptibility to climate-induced shocks and biophysical stressors. These findings highlight the need for targeted interventions that enhance off-farm and non-farm livelihood opportunities, expand access to credit, and promote gender equality and women’s empowerment to strengthen resilience against climate change​.\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"Vulenerability of Rural Households to Climate- Induced Shocks: The Case of the Chiro District, Eastern Oromia, Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 06:01:50","doi":"10.21203/rs.3.rs-6831483/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"59782503-3818-462e-9697-a42f1e92c21b","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49790103,"name":"Climate Analysis and Modeling"}],"tags":[],"updatedAt":"2025-06-10T06:01:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-10 06:01:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6831483","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6831483","identity":"rs-6831483","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}