This research focused on coastal tourism destinations in Pesisir Barat Regency, Lampung Province, including Pisang Island Beach, Krui Beach, Tanjung Setia Beach, Mandiri Beach, Walur Beach, Way Jambu Beach, and Melasti Beach Figure 1. Pesisir Barat Regency is a prominent tourist area, as designated in the Lampung Province Regional Tourism Development Master Plan 2012 (Government, 2012). These locations were selected due to their high risk of multi-hazards, evident in the 2022 Indonesian Disaster Risk Index (B.N.P.B., 2022), and their vulnerability to tsunamis ((Amirudin et al., 2021); (Pratiwi & Fitri, 2021)). Furthermore, these areas are potentially impacted by megathrust earthquakes originating from the active Mentawai-Pagai, Enggano, and Sunda-Banten Strait subduction zones, with maximum magnitudes of 8.9, 8.8, and 8.8, respectively (Center, 2017). Notably, Pesisir Barat Regency's disaster resilience has not yet been assessed by Indonesia’s National Disaster Management Agency (BNPB) Figure 1.

The location of the coastal tourism destinations, namely Pulau Pisang Beach, Krui Beach, Walur Beach, Mandiri Beach, Tanjung Setia Beach, Way Jambu Beach, and Melasti Beach, was identified through field surveys and interviews with local managers. Each beach has starting and ending points that were marked using GPS to ensure the coverage of the administrative area of each beach. The coastal line data used to calculate the beach length was obtained from the Geospatial Information Agency (BIG).

The administrative location information along the coastline of each destination was used as the basis for analyzing the resilience level of each destination. As is known, a tourism destination is a space with physical and administrative boundaries that define its management, where tourists spend at least one night (Cooper, 2020). (Hall et al., 2018) stated that a tourism destination is a subnational spatial unit determined by visitors, services such as accommodation and transportation used by tourists and residents, and regional governance that regulates services and sustainability. Based on these definitions, the unit of analysis used in this study is a tourism destination with its administrative management area Table 1.

Resilience is the ability of a system, community, or society exposed to hazards to withstand, absorb, adapt, and recover from the impact of hazards efficiently and promptly, including through the preservation and restoration of its essential structures and functions (U.N.I.S.D.R., 2009). The application and improvement of resilience in the tourism sector enables it to maintain its sustainability. (Birkmann, 2013) explains that building resilience is a key strategy for social, community, individual, or social-ecological systems to adapt and thrive in a dynamic environment and socio-economic conditions. Additionally, increasing adaptability from the learning process due to disruption is needed to increase the ability to defend against disruption (Cutter et al., 2008).

Resilience is key to achieving the Sendai Framework for Disaster Risk Reduction, the Paris Agreement on the Framework Convention on Climate Change, and the Sustainable Development Goals (Cutter, 2016). Given that resilience is crucial for enhancing the capacity of tourism destinations to cope with climate change and disruption, measuring resilience in these destinations is necessary. This assessment aims to determine the initial resilience levels, identify areas for improvement, and ensure that resilience improvement programs are both appropriate and effectively implemented.

A quantitative methods approach was used to assess the level of resilience using the disaster resilience of place (DROP) model (Cutter et al., 2008)(Cutter et al., 2010)(Scherzer et al., 2019). The DROP model assesses the capacity of communities by evaluating social, economic, institutional, infrastructure, community, and environmental factors to cope with and recover from disasters. This approach is particularly relevant for tourism destinations as the DROP model assesses the level of place-based resilience that considers the local context of an area.

(Basurto-Cedeño & Pennington-Gray, 2018) explained that the use of models in assessing the level of resilience in destinations must be applicable at different scales, considering that tourism destinations have varying regional scales. The use of this model will help identify the specific vulnerabilities and strengths of a destination. Quantitative data, including demographic statistics, economic indicators, institutional, community, infrastructure, and environmental data, are utilized to map the measurable attributes and contextual factors of resilience classification in tourism destinations. This approach supports the development of tourism destinations that are sustainable and resilient in the face of disasters.

This study employs a rigorous approach to indicator selection and data pre-processing, utilizing the Baseline Resilience Indicators for Communities (BRIC) as an operational framework derived from the Disaster Resilience of Place (DROP) model developed by (Cutter et al., 2008)(Cutter et al., 2010). The selection of these models is particularly relevant for coastal tourism destinations in Indonesia, which face multiple hazards such as tsunamis, wave abrasion, and climate change impacts. Several indicators, including social, economic, institutional, infrastructure, and community capital, serve as key measures of a destination's ability to withstand and recover from disasters.

However, to better fit the context of coastal tourism destinations, this study incorporated additional variables related to tourism dependency (e.g., the number of hotels and accommodations), infrastructure accessibility (e.g., road density and evacuation routes), and environmental vulnerability (e.g., flood, landslide, tsunami, earthquake, wave abrasion, extreme weather). (Scherzer et al., 2019) emphasized the importance of rigorous indicator selection, prioritizing theoretical relevance and data availability. The final indicator set was developed by combining variables and indicators based on (Cutter et al., 2010) and (Scherzer et al., 2019) resulting in a framework encompassing social, economic, institutional, infrastructure, community, and environmental resilience variables, ensuring relevance for assessing resilience in coastal tourism destinations Table 2.

At the initial stage, 43 potential indicators for assessing tourism destination resilience were identified from the previous studies ((Cutter et al., 2010); (Scherzer et al., 2019)) and adapted to the specific characteristics of the destinations. A selection process was conducted to evaluate data completeness. Indicators with inadequate or inconsistent data were eliminated, such as incomplete data acquisition and the unavailability of data obtained from the collection process, including the percentage of insured buildings, disaster management budget, and the percentage of buildings aged before 1970 and after 1994.

The selection process resulted in 40 indicators that met the criteria for data completeness and relevance to the research objectives. The data of selected indicators were obtained directly from relevant stakeholders through field surveys and interviews. The data collection process involved engaging with local disaster management officers, tourism practitioners, tourism community representatives, and other institutions, ensuring that the indicators reflected locally observed conditions and destination capacities Table 2. Data used in this research were quantitative and collected directly from field observations, government reports, and institutional data provided by relevant stakeholders, including local disaster management agencies and tourism authorities. This grounded, field-based quantitative approach ensured the relevance and applicability of each indicator to the real-world disaster resilience landscape of the study area.

Furthermore, during the analysis process, the measurement scale of several indicators was reversed to maintain consistent analysis, where a higher value consistently represents a greater level of resilience, namely the percentage of individuals with disabilities, the rate of female employees, the distance to hospitals, and the distance to airports Table 2. Additionally, some indicators exhibited identical values across the destinations, such as Indonesian language mastery, Health Insurance, disaster mitigation plan documents, the Gini index, the percentage of the population covered by disaster mitigation plans, and the frequency of hazards. Consequently, these indicators were not processed further as they could not undergo min-max transformation. Subsequently, the selected indicators that met the criteria were normalized using the min-max transformation method to ensure consistency across variables. This approach generated an indicator framework that is not only theoretically sound but also practically applicable, providing a robust foundation for resilience analysis in tourism destinations.

Indicator scores were normalized using the Min-Max rescaling method (Equation 1). This normalization standardized scores between zero (0) and one (1), facilitating relative performance comparison. This approach also involved reversing the contribution order for indicators, with higher values indicating lower resilience ((Cutter et al., 2010) ;(Scherzer et al., 2019)). Subsequently, indicators within each resilience sub-domain were summed and averaged to create a sub-index score (Equation 2). Finally, the normalized sub-index scores were aggregated to produce a composite resilience score (BRIC score) (Equation 3). Disaster resilience assessments, informed by this approach, can significantly contribute to the development of strategies for enhancing tourism destination resilience to disaster risks.

(1)

(2)

(3)

The final tourism destination resilience class was determined by analyzing the BRIC score and its corresponding standard deviation. A low resilience class was assigned to destinations with a standard deviation below 1.5, a medium resilience class with a standard deviation between -0.5 and 0.5, and a high resilience class with a standard deviation exceeding 1.5 (Cutter et al., 2010). Subsequently, the resilience class of each tourism destination was visualized using Geographic Information System (GIS) software to provide an overview of the spatial distribution of resilience across Pesisir Barat Regency, Lampungthe study area.

Each sub-index was analyzed based on specific indicators that describe the adaptive capacity and vulnerability of each destination. Based on social dimensions, the proportion of the population under 55 years old ranged from 43.32% in Pulau Pisang to 90.62% in Walur, indicating demographic disparities that may affect adaptive capacity. Access to communication technology, as reflected by mobile phone usage, also varied significantly, from 28.76% in Pulau Pisang to 60.99% in Walur. The economic variable revealed disparities in employment and land ownership. The percentage of the working population was highest in Melasti (36.20%) and lowest in Pulau Pisang (18.17%). Female labor force participation ranged from 20.20% to 26.51%, while land ownership exceeded 95% in all destinations.

Institutional resilience demonstrated the most pronounced variation among all dimensions. Although disaster mitigation documents were reportedly available across all destinations, their availability was solely based on administrative records provided by the Regional Disaster Management Agency (BPBD) of Pesisir Barat Regency. Community involvement in disaster preparedness activities also varied considerably, ranging from as low as 4.21% in Mandiri Beach to as high as 44.74% in Krui Beach, indicating differing levels of social mobilization and disaster awareness capacity across communities. The community dimension, particularly the percentage of tourism workers trained in disaster response, revealed significant gaps. Most destinations reported minimal participation (e.g., 7.69%), with only Krui achieving a significantly higher figure (46.15%). Access to community support facilities such as legal advisory firms and communication stations remained limited across sites.

In terms of infrastructure and accommodation, the number of shelters, health facilities, hotels, and schools varied sharply. For example, hospital bed availability ranged from 0 to 0.02 beds per 1,000 people, and shelter availability reached a high of 70% in Krui but was nonexistent in most other destinations. Accessibility, measured by proximity to hospitals and airports, also indicated stark contrasts. Lastly, the environmental dimensions highlighted spatial disparities in hazard exposure and land use. Flood-free areas ranged from 50.84% in Walur to 100% in Pulau Pisang, while tsunami risk zones varied considerably, with tsunami-free areas as low as 1.20% in Walur and as high as 80.79% in Mandiri. Open space availability also differed significantly, from 1.07 hectares in Pulau Pisang to 23.97 hectares in Way Jambu.

The resilience sub-index includes the highest, lowest, and average scores across social, economic, institutional, community, infrastructure and accommodation, and environmental variables. This reflects a destination's ability to withstand and recover from various potential disasters and crises. The obtained scores indicate a high level of preparedness in handling changes and crises, strengthening overall community and environmental resilience. A detailed explanation of each variable’s score is presented in Table 3.

The total sub-index results highlight how disparities in institutional strength, community capacity, and infrastructure quality drive overall resilience gaps among the destinations. Krui Beach ranks highest across nearly all dimensions, particularly institutional (0.93) and infrastructure (0.90), illustrating how integrated governance and investment in disaster-ready facilities directly enhance destination resilience. These findings align with (Putera et al., 2025), who emphasize the importance of decentralized, participatory governance in enabling effective tourism disaster management. Conversely, Walur Beach and Way Jambu Beach, despite showing relatively strong performance in the social dimension (0.88 and 0.44, respectively), significantly face challenges in institutional, community, and infrastructure aspects. The combination of weak local governance, limited community engagement, and inadequate basic services reflects the governance fragmentation challenges (Valentina et al., 2025). These deficiencies hinder the ability to prepare for, respond to, and recover from natural disasters and crises, thereby placing destinations at greater risk amid escalating climate change threats, particularly in coastal tourism areas.These deficiencies hinder the ability to prepare for respond to and recover from natural disasters and crises, placing these destinations at greater risk amidst escalating climate change threats, particularly in coastal tourism destinations.

Furthermore, the economic resilience scores, ranging from 0.27 to 0.63 across all destinations, suggest a widespread dependency on traditional or undiversified sectors. This economic vulnerability aligns with the broader literature emphasizing the role of economic diversification and local capacity building in reducing exposure and accelerating recovery (Saputra et al., 2024). Interestingly, the environmental sub-index demonstrates relatively balanced performance among the destinations (0.47 to 0.69), indicating a more uniform distribution of ecological conditions, such as natural buffers and hazard exposure levels. However, this also highlights a key insight: strong environmental conditions alone do not guarantee resilience without the support of governance, infrastructure, and community systems (Cutter et al., 2010). The results show that destinations with better social, economic, and institutional capacities tend to have higher resilience levels. Krui Beach, with the highest scores in most sub-indices, exemplifies effective governance, adequate infrastructure, and strong community capacity. However, destinations such as Melasti Beach and Way Jambu exhibit significant gaps across multiple dimensions, requiring targeted improvements. These results support the findings of (Scherzer et al., 2019), who argue that a multidimensional approach is essential for comprehensively assessing community resilience. Addressing one dimension in isolation is insufficient; only by integrating social, economic, institutional, community, and infrastructural aspects can a destination build meaningful adaptive capacity. Integrating risk mitigation, community empowerment, and infrastructure strengthening is key to enhancing tourism destination resilience. Consistent with this, further research by (Basurto-Cedeño & Pennington-Gray, 2018) suggests that exploring the relationship between environmental sub-indices and a destination’s adaptive capacity can lead to more holistic resilience strategies. Collectively, the findings underscore the interdependence of resilience dimensions and the necessity of holistic interventions. Enhancing disaster resilience requires not only investing in infrastructure but also strengthening institutions, mobilizing communities, and embedding sustainability in economic and tourism development policies.

The result of the resilience sub-index assessment and the total resilience score will result in a resilience score ranking in tourism destinations. Overall, the resilience score ranking can be influential in providing an overview of how resilient destinations are to survive and adapt in the face of disasters, as well as how they can strengthen their capacity to recover faster and more effectively after a disaster occurs. The higher the total resilience index score, the better prepared the destination is for potential disasters. The ranking of resilience scores per destination can be seen inFigure 2.

Standardized resilience scores reveal substantial disparities in the resilience profiles of the seven coastal tourism destinations Figure 3. Krui Beach stands out with the highest total resilience score (2.090) due to it consistently performing strongly across all dimensions, most notably institutional (2.022), economic (1.978), and infrastructure and accommodation (1.950). This finding indicates a robust governance structure, a diversified economic base, and a well-developed infrastructure network. Such conditions are critical for post-crisis recovery, as also emphasized by (Scherzer et al., 2019), who argue that resilient destinations demonstrate strength across multiple, interconnected domains. The case of Krui also supports insights from (Traskevich & Fontanari, 2021), who highlight that multi-level institutional support and infrastructure investments enable tourism areas to maintain adaptive functionality under systemic stress. In contrast, Way Jambu Beach ranks lowest (–0.826), as it exhibits negative scores across all dimensions, particularly in economy (–1.152) and infrastructure (–0.973). This suggests significant structural vulnerabilities and limited capacity to respond to and adapt to disasters. These weaknesses reflect findings by (Valentina et al., 2025), who emphasize that fragmented local governance, lack of coordination, and limited disaster management integration often hinder resilience development in rural or peripheral coastal areas.

Moreover, such institutional deficits parallel the governance and planning barriers identified by (Holladay, 2018), who emphasized the importance of proactive disaster risk governance in sustaining tourism operations. Similarly, Melasti Beach and Pisang Island Beach display low resilience scores, primarily driven by weak institutional capacity and limited environmental safeguards. These destinations illustrate the concern raised by (Calgaro et al., 2014) and (Valentina et al., 2025), who found that destinations with minimal institutional engagement and limited investment in environmental risk reduction struggle to maintain operational continuity during and after disasters. Furthermore, such systemic weaknesses mirror the internal gaps in leadership, absorptive capacity, and knowledge-sharing mechanisms identified by (Saputra et al., 2024) as critical barriers to adaptive resilience. The observed low scores also resonate with (Mazilu et al., 2024), who argue that inadequate ecosystem-based planning in tourism zones compromises both resilience and long-term sustainability.

Interestingly, Tanjung Setia Beach performs relatively well in environmental resilience (1.703), yet significant deficiencies in institutional and community engagement keep its overall resilience low. This underscores the importance of a balanced and multidimensional approach to resilience building, where strong performance in one domain cannot fully offset weaknesses in others ((Cutter et al., 2010); (Scherzer et al., 2019)). The observed imbalances reaffirm findings from (Holladay, 2018), who argued that resilient destinations require coordinated investment in both ecological safeguards and institutional frameworks. Though moderately resilient (0.443), Mandiri Beach demonstrates potential for improvement, especially if it enhances institutional readiness and infrastructure accessibility. Its community score (0.933) indicates high local engagement, which can serve as a foundation for capacity-building initiatives. This finding supports the work of (Putera et al., 2025), who emphasized the importance of empowering local communities as a pillar of disaster resilience in tourism areas. Likewise, (Traskevich & Fontanari, 2021) noted that community-based adaptation can enhance both social cohesion and long-term sustainability. These findings highlight the necessity for targeted interventions in destinations with low resilience. Specifically, stakeholders must strengthen governance, improve infrastructure, and foster inclusive community participation.

Furthermore, Krui Beach's strong performance offers a benchmark for best practices that can inform policy replication in similar contexts. As (Khater & Faik, 2025) and (Mazilu et al., 2024) suggest, integrating ecosystem-based adaptation with participatory governance mechanisms provides a strategic pathway toward resilient and sustainable tourism destinations in the face of escalating climate risks. Destinations that demonstrate higher levels of these capacities are better positioned to achieve long-term resilience. These results underscore the importance of inclusive governance, active local engagement, and continuous institutional development in coastal tourism planning. The findings also support (Cutter et al., 2010), who argue that a multidimensional approach is essential for designing holistic adaptation strategies in tourism destinations. Strengthening social, economic, institutional, community, infrastructure, and environmental dimensions emerges as a crucial step in enhancing destination resilience against potential risks. Given these findings, targeted interventions are necessary to address the weakest dimensions in low-resilience destinations, including institutional capacity-building, improved access to essential services, and better utilization of environmental assets to support adaptive strategies.