This study was conducted in Beng district, located in Oudomxay province, northern Lao People’s Democratic Republic (Lao PDR), as shown in Figure 1 . The province is mountainous, with altitudes ranging from 300 to 1800 meters, and comprises seven districts and 471 villages over an area of 15,370 km². It has a population of approximately 308,000 people, most of whom rely on subsistence agriculture as their primary livelihood. The main crops include upland and lowland rice, supplemented by maize, vegetables, and non-timber forest products. Oudomxay is prone to climate-related hazards such as droughts, flash floods, and erratic rainfall, which increasingly threaten agricultural productivity, food security, and household income. The climate profile includes a warm temperate climate with distinct wet and dry seasons. The rainy season spans May to October, while the dry season lasts from November to February. Rainfall patterns in the past decade have shown increased variability, with significant dry spells and extreme precipitation even during the dry season—demonstrating clear signs of climate change impacts on local ecosystems and communities. Given these conditions, Beng district was selected as the study area for assessing household-level vulnerability to climate change, using the Livelihood Vulnerability Index (LVI) and its adaptation based on the IPCC framework (LVI-IPCC).

The data used in this study were obtained from the Mekong Sentinel Landscape Project, an initiative of the World Agroforestry Centre (ICRAF), which aimed to understand socio-economic and environmental dynamics across the Mekong region. The project collected household-level data from Laos, China, and Thailand between 2014 and 2016. This study specifically focused on the 2016 dataset for Lao PDR, from which 450 households were selected across 15 villages in Beng district (30 households per village). The selected villages were: Huaylor, Lai Gna, Mang, Muangho, Nalai, Namet, Namtouy, Nangoua, Napa, Pangtho, Phiahua, Phonsa, Siengle, Tharmun, and Yor. The data were originally collected using a structured household questionnaire covering livelihood activities, resource access, health, education, social capital, and exposure to climate hazards. For the purpose of this study, 22 relevant sub-indicators were extracted from the dataset to construct both the LVI and LVI-IPCC metrics.

The Livelihood Vulnerability Index (LVI) provides a composite measure of household vulnerability by aggregating multiple indicators into a single index. This index captures the multidimensional nature of vulnerability through seven major components, each composed of several sub-indicators (22 in total). These components were selected based on their theoretical relevance, empirical validation in previous vulnerability assessments ( Hahn et al., 2009 ), and contextual applicability to rural communities in northern Laos. The rationale for the selection and structuring of indicators is shown in Table 1 .

LVI aggregates indicators into a single index, reflecting the degree of livelihood vulnerability. It consists of seven major components, each comprising multiple sub-indicators (22 in total):

Modifications and Implications

During the operationalization of the LVI in this study, minor contextual adaptations were made to certain indicators to reflect local realities. For instance:

The “access to health care” indicator was modified to include both distance to the nearest clinic and availability of traditional healers, as these are widely used in remote Lao villages.

“Migration support” was expanded to include seasonal labor migration to neighboring countries, reflecting a significant coping mechanism in northern Laos.

“Access to safe drinking water” was further disaggregated into dry-season and wet-season availability to better represent seasonal vulnerabilities.

While these modifications enhance the cultural and geographical relevance of the indicators, they may slightly affect comparability with LVI studies conducted in other regions or countries. However, transparency in indicator definitions and consistent internal methodology ensure that results remain internally comparable across villages in the study area and valid for cross-sectional analysis. Importantly, these adjustments allow for more accurate interpretation of results and the formulation of context-specific adaptation strategies.

Each sub-indicator was standardized using min-max normalization:

$Inde{x}_{sv}=\frac{S_v-S_{\min }}{S_{\max }-S_{\min }}$

where: $S_v$ is value for the sub-indicator in village v.

$S_{\max }$, $S_{\min }$ is minimum and maximum values of that sub-indicator across all villages.

Each major component score $M_v$ was calculated by averaging its normalized sub-indicators:

$M_v=\frac{1}{n}\underset{i=n}{\overset{n}{{\displaystyle \sum }}}Inde{x}_{svi}$

Finally, the overall LVI was calculated as a weighted average of all seven components:

$LV{I}_v=\frac{{{\displaystyle \sum }}_{i=1}^7{W}_i*M_{vi}}{{{\displaystyle \sum }}_{i=1}^7{W}_i}$

where: $W_i$ is weight of each component (equal for all components);

$M_{vi}$ is score of the $i$ component for village $v$;

LVI scores range from 0 (least vulnerable) to 0.5 (most vulnerable).

The LVI-IPCC adapts the LVI to align with the IPCC’s definition of vulnerability as a function of Exposure (E), Sensitivity (S), and Adaptive Capacity (A):

$Vulnerability=\left(E-A\right)\times S$

Each of the 7 LVI components was assigned to one of the three IPCC contributing factors as illustrated in Table 2 .

For each IPCC factor, component scores were averaged using:

$C{F}_v=\frac{{\displaystyle {\sum }_{i=1}^n{W}_i\ast M_{vi}}}{{\displaystyle {\sum }_{i=1}^n{W}_i}}$

where $C{F}_v$ is the value of Exposure, Sensitivity, or Adaptive Capacity for village $v$.

Adaptive capacity indicators were inverted (where appropriate) to reflect that higher values indicate lower vulnerability (e.g., education level, income diversity).

The resulting LVI-IPCC score ranged from −1 (least vulnerable) to +1 (most vulnerable).

To explore gender-specific patterns of vulnerability, the study performed a crosstab analysis using SPSS. The LVI scores and select sub-indicators were disaggregated by the gender of the household head (male vs. female), allowing for an assessment of differences in exposure, sensitivity, and adaptive capacity between male- and female-headed households. This analysis complemented the main findings and supported the study’s aim of identifying vulnerability disparities across demographic groups.

The overall LVI scores ranged from 0.327 to 0.476, with Mang village showing the highest vulnerability (0.476), followed by Huaylor (0.465) and Napa (0.448) as demonstrated in Table 3 . In contrast, Namtouy exhibited the lowest vulnerability (0.327). Figure 2 presents a spider diagram comparing the most and least vulnerable villages.

The Livelihood Vulnerability Index (LVI) scores across the 15 surveyed villages in Beng District ranged from 0.327 (least vulnerable) in Namtouy to 0.476 (most vulnerable) in Mang, within the possible scale of 0 (least vulnerable) to 0.5 (most vulnerable). This distribution indicates a moderate to high level of vulnerability across all villages, with some variation in the magnitude and drivers of vulnerability. Mang exhibited the highest LVI score (0.476), primarily due to a critically high sociodemographic profile score (0.845), suggesting an aging or less-educated population with reduced adaptive capacity. Elevated scores in natural disasters and climate variability (0.460) and food security (0.633) further contributed to Mang’s high vulnerability. Huaylor (LVI = 0.465) also ranked among the most vulnerable, driven by extremely high livelihood strategies vulnerability (0.978), indicating poor income diversification and a dependence on climate-sensitive livelihoods. Meanwhile, Phiahuanam, Phonsa At, and Lai Gnai exhibited moderate vulnerabilities (LVI ≈ 0.399 - 0.411), but with distinct patterns such as high food insecurity and limited water access. At the lower end of the spectrum, Namtouy (0.327), Sienglea (0.364), and Nangoua (0.368) displayed comparatively lower vulnerability levels. These villages had consistently low scores in key components such as water access, health, and social networks, reflecting relatively better access to services and support. However, even these villages exhibited moderate exposure to natural disasters and climate variability, indicating district-wide climatic sensitivity. Component-level analysis revealed that livelihood strategies, natural disasters and climate variability, and food security were the most influential contributors to overall vulnerability. Conversely, water access and health generally presented lower vulnerability scores across the sample. These findings underscore the multidimensional and spatially variable nature of livelihood vulnerability, and suggest that targeted interventions such as improving income diversification, strengthening disaster preparedness, and enhancing food security are essential to building resilience in Beng District.

The Livelihood Vulnerability Index-IPCC (LVI-IPCC) was calculated by regrouping the seven primary indicators into three IPCC-defined components: adaptive capacity, sensitivity, and exposure.

To align with the IPCC framework, sub-indicators under adaptive capacity were inverted, for instance, female-headed households were recoded as male-headed, and similar adjustments were made for all relevant variables as shown in Table 4 .

According to the calculation of vulnerability based on the Intergovernmental Panel for Climate Change (IPCC), contributing factors (Adaptive capacity, Sensitivity and Exposure as shown in Table 5 and Figure 3 ) show that Mang village demonstrated the highest vulnerability score (0.033), followed by Huaylor (0.006).

Crosstab analysis reveals that female-headed households consistently face higher levels of vulnerability compared to male-headed households across multiple indicators as illustrated in Table 6 . Notably, 26.1% of female-headed households experienced food shortages in the past year, compared to 16.2% of male-headed households. Female-headed households were also more likely to receive government assistance (12.4% vs. 7.2%), borrow from microfinance institutions (42% vs. 38.9%), and purchase agricultural seeds (32.2% vs. 18%). Furthermore, 18.8% of female heads reported facing rising food prices, significantly more than the 4.8% of male heads. In terms of climate impact, 43.5% of female-headed households reported crop loss due to drought, compared to 28.7% of male-headed households. These findings underscore the heightened vulnerability of female-headed households to food insecurity, financial stress, and climate-related shocks.

This study utilized secondary data from the Mekong Sentinel Landscape household survey to calculate the Livelihood Vulnerability Index (LVI). While the dataset offered a useful basis for analysis, the selection and adaptation of indicators were constrained by data availability, limiting comparability with other LVI studies. Key variables such as income trends, land tenure, and local adaptation practices were either absent or insufficiently detailed, affecting the comprehensiveness of the vulnerability assessment. Sampling bias is a further concern. As noted by Hahn et al. (2009) , a survey may underrepresent households with migrant or working adults, skewing results toward more sedentary populations. This could distort indicators related to livelihood strategies and adaptive capacity. Future studies should consider randomized sampling, follow-up visits, and oversampling of vulnerable groups to improve representativeness. Moreover, the cross-sectional nature of the data limits insights into temporal dynamics. Longitudinal studies and repeated surveys are essential to track changing vulnerability and adaptation over time. Finally, combining quantitative assessments with qualitative methods—such as key informant interviews and focus group discussions—would enrich context-specific understanding and improve the relevance of policy recommendations.

Based on the study’s findings, the following recommendations are proposed for policymakers, development partners, and local stakeholders:

Given that this study employed the LVI-IPCC framework, a widely recognized tool that synthesizes elements of the Sustainable Livelihoods Framework with the IPCC’s earlier conceptualization of vulnerability as a function of exposure, sensitivity, and adaptive capacity, it is important to acknowledge recent developments in climate risk assessment. The IPCC’s Sixth Assessment Report (AR6) advances a more integrated risk-based framework, emphasizing the interaction of hazards, vulnerability, and exposure within broader socio-ecological systems. While the LVI-IPCC continues to offer practical utility for community-level, indicator-based vulnerability assessments, future research would benefit from aligning with the AR6 paradigm. This includes incorporating a more nuanced analysis of socio-institutional drivers of risk, accounting for non-linear and compounding effects of climate hazards, and recognizing feedbacks between system components. Such integration would enhance the conceptual robustness and policy relevance of LVI-based assessments in the context of evolving global climate frameworks.