Introduction

The impacts of climate change are intensifying and reshaping both ecosystem and socio-economic conditions worldwide1,2,3, with developing countries facing significant vulnerability challenges at the community level4,5,6. Among the most pressing hazards, including floods, riverbank erosion, salinity intrusion, waterlogging, landslides, droughts, and extreme heat, are increasing in frequency and intensity over time7,8,9,10,11. The consequences of these hazards threaten livelihoods, disrupt ecosystem services, damage infrastructure, and jeopardize cultural heritage12,13,14,15,16.

Bangladesh offers a critical case study for understanding these impacts, as it is disproportionately affected by climate change due to structural vulnerabilities, limited adaptive capacities, and high dependence on climate-sensitive sectors17. Bangladesh is considered a global hotspot for climate-induced hazards due to its geographical position and climatic characteristics18,19,20. The country experiences recurrent floods due to the confluence of three major rivers—the Ganges, Brahmaputra, and Meghna—while salinity intrusion in the southern region is exacerbated by rising sea levels and erratic rainfall patterns21,22,23,24,25. Coastal and riverine areas, which are densely populated, are disproportionately affected by hazards26,27,28. Agriculture- and fishery-dependent families in these areas face significant risks and experience continuous disruptions29,30. Beyond measurable economic impacts, local people face significant non-economic losses, including declines in social cohesion, health problems, and cultural disintegration31,32,33,34. Such non-economic losses are harder to quantify but are substantial because they affect resilience and long-term adaptation35,36,37,38,39. While global attention focuses on measuring direct hazard-induced economic losses, equally critical climate-driven non-economic losses remain largely unexplored, especially in hazard-prone countries like Bangladesh, where comprehensive datasets are lacking. This lack of data hinders the development of well-rounded policy frameworks to address climate-driven challenges.

Existing studies have highlighted the economic loss and damage (L&D) associated with climate hazards, including declines in agricultural yields, recovery and displacement costs, and infrastructure damage6,40,41,42. For example, riverbank erosion leads to reconstruction costs, forced migration, and loss of arable land, resulting in economic burdens43,44. Salinity intrusion reduces freshwater availability and crop productivity45,46,47,48,49,50. However, there remains a gap in addressing non-economic L&D, despite clear evidence of their critical importance. Due to flood and riverbank erosion and the associated displacement, the erosion of cultural heritage and indigenous knowledge is well-known; yet such studies are few and far between. Furthermore, these studies provide a fragmented understanding by considering individual hazards without exploring the interconnected nature of their overlapping compound impacts. Due to the diverse nature of multi-hazards, it is necessary to understand the economic and non-economic impacts, particularly in hazard-prone countries like Bangladesh. The complex interactions between social and ecological vulnerabilities compound the severity of both economic and non-economic impacts. A significant shortcoming in the current literature is that most previous studies have not focused on specific hazards and their associated L&D51,52,53,54,55. While hazard-specific research offers valuable insights, it often fails to account for overlapping impacts when multiple hazards occur within a specific geographic and socio-economic context. This limitation can lead to an incomplete understanding of the cumulative risks faced by communities. For example, in the coastal regions of Bangladesh, the simultaneous occurrence of salinity intrusion and waterlogging exacerbates agricultural losses and heightens water insecurity. Similarly, floods and riverbank erosion often occur simultaneously, leading to displacement and infrastructure damage. The absence of interactive frameworks limits our understanding and hampers the design of effective, context-specific policy interventions. Understanding this interconnectedness is crucial for academics, policymakers, and practitioners to develop effective strategies that address climate-induced vulnerabilities and cascading risks.

This study addresses these knowledge gaps by critically investigating six major hazards—floods, riverbank erosion, salinity, waterlogging, landslides, and drought & extreme heat—are interrelated and associated with economic and non-economic L&D in hazard-prone areas of Bangladesh. Specifically, the objectives are: (1) to understand the locally predominant hazards; (2) to explore the hazard-specific economic and non-economic L&D; and (3) to establish relationships between hazards and the corresponding L&D. By employing a mixed-method approach for data collection, including quantitative analysis such as cluster analysis (CA) and multivariate analysis of variance (MANOVA), as well as qualitative methods like focus group discussions (FGD) and key informant interviews (KIIs) with thematic analysis, this study provides a comprehensive understanding and valuable insights into the impacts of hazards. Mixed methods are well-suited to capture the complex dimensions of climate hazards and their effects on communities. While the quantitative methods (survey, CA and MANOVA) provide statistically significant patterns and groupings, qualitative methods (FGD, KII) help to dig down into socio-cultural dimensions by providing rich content, context specific insight and lived experiences.

Findings from this study will not only serve the climatic purpose of Bangladesh, but it also has broader implications to similar climate-vulnerable countries, particularly Least Developed Countries (LDCs). As this study illustrates the compound and interconnected nature of climate risk, it will contribute to the global discussion of L&D, allocation of climate finance and context-specific adaptation strategies aligned with UNFCCC’s frameworks like the Paris Agreement and the Warsaw International Mechanism on Loss and Damage.

Results

Demographic profile

This study found that 70%, 37%, 42%, 67%, and 44% of respondents in Bagerhat, Kurigram, Rajshahi, Rangamati, and Sunamganj, respectively, depended on fishing or farming as their primary occupation. Specifically, 70% relied on fishing, while 37%, 42%, 67%, and 44% were engaged in farming. According to the survey, the average income in Bagerhat, Kurigram, Rajshahi, Rangamati, and Sunamganj was approximately 9000, 10,000, 15,000, 9000, and 8000 BDT, respectively. In samples, approximately 59%, 48%, 47%, 29%, and 66% of respondents were found to be illiterate in Bagerhat, Kurigram, Rajshahi, Rangamati, and Sunamganj, respectively. The average family size in Bagerhat, Kurigram, Rajshahi, Rangamati, and Sunamganj was 5, 5, 6, 6, and 6, respectively. The study pinpointed that most respondents in the five areas rely on fishing or agriculture, earning between 8000 and 15,000 BDT, with a high rate of illiteracy (29–66%), and average family sizes varying from 5 to 6 members. The households are more susceptible to current and future hazards which depend more on natural resources like fisheries, lower level of education, or income as they have limited access to weather forecasts, disaster training, and resilience to cope with future hazards56.

Locally predominant hazards

All the study locations, including Sunamganj, Bagerhat, Kurigram, Rangamati, and Rajshahi, were found to be affected by drought and extreme heat. According to the survey, 85%, 96%, and 87% of respondents reported vulnerability to flood hazards in Bagerhat, Kurigram, and Sunamganj, respectively. Approximately 56% and 98% of respondents stated that Bagerhat and Kurigram are highly vulnerable to riverbank erosion, respectively. About 87% of respondents indicated that Rangamati is highly vulnerable to landslides. Additionally, 97% and 94% of respondents in Bagerhat reported their vulnerability to salinity hazards and waterlogging problems, respectively. According to findings, drought and severe heat touch all research sites, with high levels of reported vulnerability to floods (85–96%), riverbank erosion (56–98%), landslides in Rangamati (87%), and in Bagerhat, high risks from salinity (97%) and waterlogging (94%).

Linkages between climatic hazards and economic and non-economic loss and damage

Figure 1 shows how waterlogging and riverbank erosion are each linked to economic and non-economic L&D. Figure 1a, the dendrogram of waterlogging and economic L&D variables, reveals two distinct clusters: Cluster 1 and Cluster 2. Cluster 1 is further divided into two sub-clusters: Cluster 1a and Cluster 1b. Cluster 1a is composed of waterlogging, decrease in development opportunities, loss of existing income, decrease in agricultural yield, damage to agricultural land, costs associated with migration, loss of employment opportunities, infrastructure damage, damage to energy infrastructure, less productive land, and community experiences of the cost of relief and recovery.

Fig. 1: The dendrogram classifies economic and non-economic loss and damage (L&D) variables related to waterlogging and riverbank erosion into several groups.
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Panels (a) and (c) show the economic L&D variables, whereas panels (b) and (d) represent for non-economic L&D variables.

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Figure 1b, the dendrogram of waterlogging and non-economic L&D variables reveals two distinct clusters: Cluster 1 and Cluster 2. Cluster 2 is further divided into two sub-clusters: Cluster 2a and Cluster 2b. Cluster 2b consists of waterlogging and the deterioration of community and social networks, loss of natural appearance or scenic landscapes, prompting family members to migrate, loss of cultural traditions or sites, loss of indigenous knowledge, decrease in the quality of ecosystem services, and difficulty accessing clean water.

Figure 1c, the dendrogram of riverbank erosion and economic L&D variables, reveals two distinct clusters: Cluster 1 and Cluster 2. Cluster 2 is further divided into two sub-clusters: Cluster 2a and Cluster 2b. Cluster 2b is associated with riverbank erosion and costs related to migration, decrease in development opportunities, loss of existing income, loss of employment opportunities, business interruption costs, and community costs associated with relief and recovery.

Figure 1d, the dendrogram of riverbank erosion and non-economic L&D variables reveals two distinct clusters: Cluster 1 and Cluster 2. Cluster 2 forms a group with riverbank erosion and impacts such as hindering children from going to school, damage to school supplies and books, impacts on the capacity to travel, loss of natural appearance or scenic landscapes, impacts on sense of identity, decrease in resource control, harm to cultural or traditional practices, damage to historical land, loss of cultural traditions or sites, and changes in dietary practices.

Similarly, Figure 2 illustrates the linkages between salinity and landslide, respectively, with their associated economic and non-economic L&D. Figure 2a, the dendrogram of salinity hazards and economic L&D variables, reveals three distinct clusters: Cluster 1, Cluster 2, and Cluster 3. Cluster 1 is composed of salinity, decrease in development opportunities, loss of existing income, decrease in agricultural yield, costs associated with migration, damage to agricultural land, and healthcare expenses.

Fig. 2: The dendrogram classifies economic and non-economic loss and damage (L&D) variables related to salinity and landslide into several groups.
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Panels (a) and (c) show the economic L&D variables, whereas panels (b) and (d) represent for non-economic L&D variables.

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Figure 2b, the dendrogram of salinity and non-economic L&D variables, reveals two distinct clusters: Cluster 1 and Cluster 2. Cluster 1 is further divided into two sub-clusters: Cluster 1a and Cluster 1b. Cluster 1a groups salinity with harm to cultural or traditional practices, loss of cultural traditions or sites, loss of indigenous knowledge, decreased quality of ecosystem services, harder to access clean water, loss of natural appearance and scenic landscapes, family migration, displacement, changes in dietary practices, impacts on sense of identity, damage to school supplies and books, loss of sense of security and safety, decrease in biodiversity, and hindrance of children from going to school.

Figure 2c, the dendrogram of landslide and economic L&D variables, reveals two distinct clusters: Cluster 1 and Cluster 2. Cluster 2 is further divided into two sub-clusters: Cluster 2a and Cluster 2b. Cluster 2a is grouped with landslides, infrastructure damage, decrease in agricultural yield, irreversible loss of natural resources, decrease in ecosystem services, damage to agricultural land, and costs associated with migration.

Figure 2d, the dendrogram of landslide and non-economic L&D variables reveals two distinct clusters: Cluster 1 and Cluster 2. Cluster 1 was divided into two sub-clusters: Cluster 1a and Cluster 1b. Cluster 1b consisted of landslide and impacts on sense of identity, impacts on capacity to travel, loss of cultural traditions or sites, decrease in resource control, change in dietary practices, suffered fatalities, and prompted family members to migrate.

Furthermore, Fig. 3 demonstrates how flood and drought & extreme heat are each linked to economic and non-economic L&D. Figure 3a, the dendrogram of flood and economic L&D variables, reveals two distinct clusters: Cluster 1 and Cluster 2. Cluster-2 is divided into two sub-clusters, Cluster-2a and Cluster-2b. Cluster-2a is further divided into two sub-clusters, Cluster-2ai and Cluster-2aii. Cluster-2b is composed of flood, business interruption costs, infrastructure damage, disruption of communication systems, decrease in development opportunities, community experience costs associated with relief and recovery, and costs associated with migration. Cluster-2aii is grouped with a decrease in agricultural yield, damage to agricultural land, loss of money related to farming, loss of existing income, and loss of employment opportunities.

Fig. 3: The dendrogram classifies economic and non-economic loss and damage variables related to flood and drought & extreme heat into several groups.
figure 3

Panels (a) and (c) show the economic L&D variables, whereas panels (b) and (d) represent for non-economic L&D variables.

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Figure 3b, the dendrogram of flood and non-economic L&D variables, reveals two distinct clusters: Cluster 1 and Cluster 2. Cluster 2 was divided into two sub-clusters: Cluster 2a and Cluster 2b. Cluster 2a is composed of suffered fatalities, deterioration of social connections, detrimental impacts on physical health, deterioration of community and social networks, disruption of daily activities, loss of indigenous knowledge, damage to school supplies and books, and the migration of family members.

Figure 3c, the dendrogram of drought and extreme heat and economic L&D variables reveals two distinct clusters: Cluster 1 and Cluster 2. Cluster 2 was grouped with drought and extreme heat, and a decrease in the productivity of fisheries, harm to natural resources, decrease in ecosystem services, decrease in agricultural yield, damage to agricultural land, loss of a significant amount of money, less productive land, decreases in development opportunities, and extreme temperatures harming farming tools.

Figure 3d, the dendrogram of drought and extreme heat and non-economic L&D variables reveals two distinct clusters: Cluster 1 and Cluster 2. Cluster 2 is associated with drought and extreme heat, as well as difficulties in accessing clean water, impacts on the capacity to access traditional water sources, a decrease in biodiversity, and impacts on the health of ecosystems or biodiversity, and a decline in the quality of ecosystem services.

Effects of climatic hazards on economic and non-economic loss and damage

From Table 1, MANOVA identified significant differences between the perceived waterlogging and economic L&D variables. The results of MANOVA revealed the effects of waterlogging on perceived loss of existing income, decreases in development opportunities, damage to agricultural land, community experiences of the cost of relief and recovery, damage to energy infrastructure, loss of money related to farming, and less productive land.

Table 1 Results of the one-way MANOVA analyzing waterlogging hazards in relation to economic and non-economic loss and damage (L&D) risk factors
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From Table 1, according to the MANOVA analysis, significant differences were found between perceived waterlogging and non-economic L&D variables. A significant effect of waterlogging was found on the perceived loss of cultural traditions or sites, loss of natural appearance or scenic landscapes, changes in dietary practices, disruption of daily activities, prompting family members to migrate, and deterioration of community and social networks.

From Table 2, the MANOVA analysis yielded significant differences between riverbank erosion and economic L&D. The effects of riverbank erosion were found on the loss of existing income, decrease in agricultural yield, irreversible loss of natural resources, decrease in development opportunities, infrastructure damage, damage to agricultural land, damage to energy infrastructure, healthcare expenses, significant loss of money, additional restoration costs, and loss of employment opportunities.

Table 2 Results of the one-way MANOVA analyzing riverbank erosion hazards in relation to economic and non-economic loss and damage (L&D) risk factors
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From Table 2, similarly, MANOVA revealed significant effects of riverbank erosion on non-economic L&D variables, including impacts on sense of identity, decrease in resource control, emotional and psychological distress, loss of cultural traditions or sites, destruction of open or recreational places, and impacts on capacity to travel.

From Table 3, regarding the MANOVA analysis, this study found significant differences between salinity and economic L&D variables. The effects of salinity were evident in the loss of existing income, decrease in agricultural yield, decrease in tourism revenue, decrease in the productivity of fisheries, health issues and economic losses, costs associated with migration, decreases in development opportunities, property damage, community experiences related to the cost of relief and recovery, business interruption costs, loss of money related to farming, and less productive land.

Table 3 Results of the one-way MANOVA analyzing salinity hazards in relation to economic and non-economic loss and damage (L&D) risk factors
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From Table 3, similarly, MANOVA analysis revealed significant differences between salinity and non-economic L&D variables, including suffered fatalities, loss of cultural traditions or sites, loss of indigenous knowledge, impacts on sense of identity, decrease in the quality of ecosystem services, loss of natural appearance or scenic landscapes, damage to historical land, hindrance to children going to school, changes in dietary practices, disruption of daily activities, and impacts on the capacity to access traditional water sources.

From Table 4, the MANOVA analysis revealed a significant difference between landslide and economic L&D variables. The effects were found on the decrease in ecosystem services, decreased value of property, irreversible loss of natural resources, decrease in agricultural yield, decreases in development opportunities, infrastructure damage, property damage, damage to agricultural land, costs incurred related to evacuation and resettlement, disruption of communication systems, costs associated with migration, and community experiences the cost of relief and recovery.

Table 4 Results of the one-way MANOVA analyzing landslide hazards in relation to economic and non-economic loss and damage (L&D) risk factors
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From Table 4, similar effects of landslides were found on non-economic L&D variables, including suffered fatalities, detrimental effects on mental health, loss of cultural traditions or sites, decreases in biodiversity, deterioration of community and social networks, impacts on sense of identity, decrease in the quality of ecosystem services, decrease in resource control, harm to cultural or traditional practices, weakened social structure, loss of natural appearance or scenic landscapes, damage to historical land, displacement, deterioration of social connections, changes in dietary practices, disruption of daily activities, impacts on capacity to travel, and prompting family members to migrate.

From Table 5, as per the MANOVA results, the present study found significant differences between flood and economic L&D variables. The significant effects of flood were observed on disruption of communication systems, business interruption costs, damage to energy infrastructure, property damage, decreases in development opportunities, and a decrease in tourism revenue.

Table 5 Results of the one-way MANOVA analyzing flood hazards in relation to economic and non-economic loss and damage (L&D) risk factors
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From Table 5, similarly, MANOVA revealed significant effects of flooding on non-economic L&D variables, including suffered fatalities, detrimental effects on mental health, deterioration of community and social networks, decrease in the quality of ecosystem services, damage to historical land, displacement, difficulty in accessing clean water, changes in dietary practices, disruption of daily activities, prompting family members to migrate, and impacts on the capacity to access traditional water sources.

From Table 6, the MANOVA analysis showed a significant difference between drought and extreme heat and economic L&D variables. The effects included loss of existing income, decrease in agricultural yield, decline in ecosystem services, reduced property value, irreversible loss of natural resources, health issues and economic losses, decrease in development opportunities, damage to agricultural land, damage to energy infrastructure, harm to natural resources, loss of money related to farming, less productive land, and extreme temperatures damaging farming tools.

Table 6 Results of the one-way MANOVA analyzing drought & Extreme heat hazards in relation to economic and non-economic loss and damage (L&D) risk factors
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From Table 6, similar effects of drought and extreme heat were found on non-economic L&D variables, including detrimental effects on physical health, decreases in biodiversity, a decline in the quality of ecosystem services, detrimental impacts on mental health, loss of natural appearance or scenic landscapes, impacts on the health of ecosystems or biodiversity, difficulty in accessing clean water, changes in dietary practices, and reduced capacity to access traditional water sources.

Results from FGDs and KIIs

The FGDs and KIIs provide insights into the socio-economic and non-economic impacts associated with climate hazards, enriching the qualitative findings and complementing them through thematic analysis. Participants highlighted permanent displacement and loss of land due to riverbank erosion as significant economic losses and a major concern. Farmers spoke about losing agricultural land and being forced into informal work outside their communities or in urban areas. One farmer shared, ‘The river not only took away our land but also our dignity. My family now lives in a temporary shelter, and I work as a rickshaw puller to feed my children and other family members.’ Similarly, many fishers reported declining opportunities in fish-related livelihoods, which forced many of them to abandon fishing. One of the fishers stated, ‘The water in the surrounding area is too salty, and the freshwater fish are already gone. Now we have no choice but to go to the nearest city to work as day laborers.’ These stories aligned with the survey findings, identifying floods, riverbank erosion, and salinity intrusion as significant factors reducing livelihood opportunities in the affected communities.

Non-economic losses were equally substantial as economic losses; for instance, health and well-being were found to be highly affected. A health worker reported an increase in uterine diseases among women in the coastal belt of Bangladesh due to high salinity in drinking water and the water used for daily activities. Meanwhile, FGD participants highlighted heat-related illnesses caused by high temperatures, particularly affecting elderly people and children. Another critical theme was education, as riverbank erosion and floods forced local people to migrate. Participants shared how these hazards led to financial constraints and migration, causing children to drop out of school. A mother stated, ‘My son was in class five when we moved from our area to here. During that time, there was no school nearby. He is now working with his father on the farm.’ Emotional and social losses were found to be prevalent, including the loss of cultural heritage due to displacement, which uprooted families from their communities and ancestral lands. One elderly person lamented, ‘We used to celebrate Eid together in our community. Now we are scattered across different areas in this district, and it no longer feels the same.’ A faded traditional social support system has led to weakened community bonds. A local leader reported, ‘Locals used to help each other during floods, but now they are living in different areas and struggling themselves to provide help.’

Participants highlighted the efforts of local communities to adapt and build resilience against the impacts of climate change, despite the challenges and constraints. KIIs with NGOs and government officials highlighted innovative solutions, such as the widespread use of saline-tolerant crops, the restoration of mangrove forests along the coast, and raising awareness to address funding constraints. A government official stated, ‘We have identified some saline-tolerant crop species, but they are not widely adopted due to funding constraints and a lack of awareness among local farmers.’ Additionally, there are challenges with the cultivation process and periods. As an adaptation strategy, local women save money to cope with climate crises and financial hardships, highlighting a gendered impact that is evident. A participant stated, ‘We started pooling or borrowing money from our neighbors to help each other in our community, which is considered a way to survive.’ These findings highlight the complex interconnections of climate hazards. Insights into these interrelations are essential to addressing economic and non-economic L&D while promoting sustainable adaptation approaches.

Discussion

This study systematically investigated the economic and non-economic L&D associated with six predominant hazards across the country, including waterlogging, flood, riverbank erosion, salinity, landslides, and drought & extreme heat. Using CA and MANOVA, the findings provide critical insights into the commonalities, overlaps, and unique factors characterizing these hazards. The results not only highlight vulnerabilities but also demonstrate hazard-specific impacts, which can be instrumental in tailoring interventions effectively.

As a form of economic L&D, this study found that a decrease in agricultural yield, damage to agricultural land, and costs associated with migration were linked to all types of climatic hazards. A farmer stated, “We had no choice but to migrate because, in the last consecutive years, floods damaged our crops, and we had no savings to survive.” A decrease in development opportunities, loss of existing income, and infrastructure damage were associated with waterlogging, riverbank erosion, salinity, floods, and landslides. One of the key informants from the local government officials reported “We build the roads and embankments almost every year, which is why our budget is stretched thin.” Loss of employment opportunities and community costs associated with relief and recovery were linked to waterlogging, riverbank erosion, floods, and salinity. Furthermore, floods, drought, extreme heat, and salinity disrupt local business operations and degrade ecosystem services, adversely affecting both the local economy and livelihoods dependent on ecological resources such as fishing. A respondent reported, “Many fishers have left their fishing profession because of the decreasing trend in fish production and have migrated to cities.”

Likewise, landslides and waterlogging damage energy infrastructure that cause power outages and other infrastructure-related issues and disrupt essential services, such as education and healthcare. One of the schoolteachers said, “We cannot properly teach our students because, during the monsoon, we face frequent power outage problems.” Drought and extreme heat were found to be associated with reduced land productivity and extreme temperatures that damaged farming tools. Agriculture officers reported that adopting coping strategies, such as using heat-resistant crops, is important, but many farmers remain highly vulnerable due to limited resources. Floods and riverbank erosion directly influence monetary losses by disrupting economic stability and imposing recovery expenses on affected communities. FGDs revealed that affected families often use their savings and fall into debt to recover from flood-driven business losses. A small businessman stated, “After the flood, I had to borrow money to rebuild my business. It will take at least two years to repay the loan.”

Non-economic L&D; force migration of family members, cultural tradition or site loss, and the loss of indigenous knowledge were associated with waterlogging, riverbank erosion, salinity, landslides, floods, drought, and extreme heat. Displaced communities reported, “In our community, our traditional festivals have faded away because our people are now scattered across different areas of the country.” The loss of natural beauty and scenic landscapes, along with difficulties in accessing clean water, was observed to be associated with waterlogging, riverbank erosion, salinity, landslides, and floods. One participant claimed, “We travel long distances for drinking water, and it’s still not enough” Impacts on the sense of identity and disruptions to children’s education were associated with floods, salinity, landslides, and riverbank erosion. A mother shared, “When we moved from our previous place to this one, we did not receive support for our children’s education. There is no school here, so my kids had to drop out.”

Moreover, waterlogging, salinity, and floods were found to be associated with a decrease in the quality of ecosystem services and the deterioration of social or community networks. These results demonstrate that these hazards lead to multifaceted problems, impacting both social and environmental systems. During the KIIs with community leaders, participants reported that among the affected, particularly the displaced community, social cohesion weakens over time as displaced people are forced to rebuild social connections in new locations. Floods and landslides were associated with fatalities. During FGDs with affected communities who have lost their loved ones due to disasters, a participant reported, “We lost our neighbors because of the landslide; we have never been the same since.” The irreversible loss of natural resources, changes in dietary practices, and damage to historical lands were associated with landslides, salinity, and riverbank erosion, respectively and contribute to the destruction of natural resources and cultural heritage, while many individuals are forced to change their regular dietary practices due to environmental challenges. A female participant reported, “Due to high salinity in the soil, we can no longer grow our traditional vegetables, and we eat only whatever we can afford, even if it is not nutritious.”

Interestingly, all the results of this study highlighted the crucial need for specific policy interventions to reduce climatic hazards and their associated economic and non-economic L&D in a climate vulnerable country like Bangladesh. Most of the existing policies and L&D frameworks are focused on rapid impacts and tangible economic consequences and compensations. However, the study found that the intangible, non-economic L&D such as cultural and traditional losses, mental trauma, disruption of education, and the loss of identity are often either ignored or remained unarticulated.

First, a hazard and context specific integrated multi-hazard risk management framework is needed to address the compound vulnerabilities across the country due to overlapping nature of climate hazards and ensure adaptation in development planning. One size does not fit for all, for example flood-prone areas need housing and shelter support, while the areas facing waterlogging and salinity problems require integrated water and land use policy. Similarly, to reduce fatalities from sudden-onset events like floods and landslides, community-based preparedness programs and strengthened early warning systems are crucial. In contrast, long-term plans like promotion of climate-resilient agricultural practices, such as adopting drought-tolerant and saline-resistant crops, are more suited for slow-onset hazards like salinity, drought, and heat waves.

Second, it’s crucial to intergrade the non-economic dimensions of L&D into local, national, and global climate and disaster management policies and frameworks. Planners and policymakers should count non-economic L&D with the same priority as tangible ones and incorporate appropriate strategies to tackle those issues. This could include temporary learning centers and mobile schools for displaced children, mental health and psychosocial support to address the psychological impacts of hazards, particularly for vulnerable groups such as children and women.

Third, to address the disproportionate impacts of climatic hazards on women and girls, gender-responsive policies are needed, such as targeted health services and disaster preparedness training, ensuring their participation in planning processes. However, institutional constraints—including lack of coordination, financial limitation, and unavailability of data weaken policy effectiveness at the local level. Interministerial coordination and digitalization of the services can reduce these barriers.

Fourth, to manage climate migration issues both internally and externally (cross-border), an effective national climate migration policy is needed, focusing on livelihood opportunities and safe migration pathways for migrants. On the other hand, vocational training opportunities and livelihood diversification programs should be prioritized to address employment disruptions and business interruptions caused by hazards like salinity and floods. Mentioned hazards are mostly linked with internal migration both permanent and seasonal, more projects like Khuruskol Asrayan Project can be adopted to provide better housing and livelihood at near their place of origin rather than coming to megacity Dhaka or divisional cities such as Chattogram, Khulna.

Fifth, Bangladesh can feed its economic and non-economic vulnerabilities and experiences into UNFCCC’s mechanisms like Santiago Network and Loss and Damage Fund by aligning local vulnerability assessment indicators with Global Stocktaking process. By this way, developing countries like Bangladesh will get more room at UNFCCC’s climate finance negotiations for addressing both their economic and non-economic L&D.

Finally, to reduce the financial burden on affected communities, it is necessary to establish dedicated funds for disaster relief and recovery, or Bangladesh Climate Change Trust Fund should allocate and mobilize resources on the projects related to L&D. An innovative climate bond is needed to ensure the mobilization of domestic and international resources for supporting the effective implementation of large-scale resilience-building initiatives. Country’s financial infrastructure improvement and proper regulation will support the companies to operate. On the other hand, tax benefits will attract more investors to this sector. This comprehensive policy approach can be useful in enhancing resilience and supporting a sustainable development approach by reducing vulnerabilities.

The study is limited to focusing on the nature and types of L&D associated with specific hazards rather than quantifying their monetary value or conducting cost benefit analysis of alternative adaptation options. As research revealed the compound impacts of multi-hazards and associated L&D across the country, it illustrated the connection among hazards and impacts in place of disaggregated statistical analysis. Furthermore, it included the cross-sectional nature of the study, which limited the temporal analysis of climate data and L&D progression. Comprehensive further research could address the longitudinal gap and temporal analysis of this study.

In conclusion, the present study provides a comprehensive analysis and understanding of how six major climate hazards—waterlogging, flood, riverbank erosion, salinity, landslide, and drought & extreme heat—are associated with economic and non-economic L&D in Bangladesh. This study provides details on commonalities and hazard-specific impacts using CA and MANOVA, which are critical for designing targeted interventions. Though the study is grounded in Bangladesh and the findings only highlighted the local context, but the conceptual frameworks, research methodology and policy actions provide broader insights for similar vulnerable countries; particularly LDCs and SIDS (Small Island Developing States). The primary contribution of this study is to identify and address these hazard specific vulnerabilities that require context-specific and multi-sectorial policy interventions, which are both important and urgent. To mitigate economic L&D, investing in disaster-resistant infrastructure, fostering livelihood diversification, and promoting climate-resilient agricultural practices are critical and imperative. Similarly, to address non-economic L&D, it is critical to rebuild social cohesion, preserve cultural heritage, and ensure access to clean water and education at the community level. To reduce the disproportionate impacts on vulnerable communities, this study highlighted the importance of integrating mental health into disaster response, implementing gender-responsive policies, and adopting ecosystem-based adaptation initiatives such as mangrove forest restoration. As these are currently underrepresented both in national and global climate discussions, this research emphasizes the importance of addressing overlapping vulnerabilities and migration challenges through the development of multi-hazard risk management frameworks and national climate migration policies. By understanding the pattern of hazard specific L&D, this study revealed a shift from reactive emergency relief-based policies and actions to a long-term proactive inclusive approach. This kind of shifting should be aligned with global L&D discourse such as Santiago Network on Loss and Damage (SNLD), Warsaw International Mechanism (WIM) and Loss and Damage Fund (LDF). This study’s ground evidence and actionable recommendation contribute to both academic and climate discourse and mobilize resources for tackling climate-driven vulnerabilities and supporting large-scale resilience-building initiatives in vulnerable countries like Bangladesh.

Methods

In this section, methods are broadly illustrated under the sub-headings; Study Area (geographical scope), conceptual framework development (interlinkage between hazards, their economic and non-economic impacts), research design (how and which technique and tools are used), data collection and data analysis.

Study area

This study was conducted in Bangladesh, which is located between 20°30’ to 26°38’ north latitude and 88°04’ to 92°44’ east longitude57. Geographically, Bangladesh is highly vulnerable due to its location and climate-related hazards, which have continuously exposed people to significant risks, leading to substantial L&D under unprecedented climate change impacts. To represent this vulnerability, the study was carried out in five different districts—Kurigram (northern region), Bagerhat (southwestern region), Sunamganj (northeastern region), Rajshahi (northwestern region), and Rangamati (southeastern region)—selected based on their climate-induced risks. The locations of these study areas are shown in Fig. 4. These regions are highly exposed to multiple climate-driven hazards, including droughts, floods, extreme heat, cyclones, waterlogging, landslides, salinity intrusion, and riverbank erosion. The impacts of these hazards have severely affected socio-economic, environmental, and social systems, increasing vulnerabilities over time. The repeated occurrence of these disasters has led to loss of lives, destruction of infrastructure, and escalating economic and non-economic damages. Over the past few decades, thousands of people have been forced to leave their homes due to these recurring hazards. Given the intensifying effects of climate change, it is crucial to assess how localized threats accelerate economic and non-economic L&D in the context of specific hazards.

Fig. 4
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The map shows different study areas across Bangladesh.

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Conceptual framework development

The main idea behind this study is encapsulated in the concept of Developing Relationships and Policy Frameworks to Address the Economic and Non-Economic L&D of Local Climatic Hazards. This study aims to analyze the complex linkages between local climatic hazards, the resulting economic and non-economic L&D, and the policy implications that should be considered. This proposed framework consists of three main components: hazard exposure, loss and damage assessment, and policy response.

The first component, hazard exposure, involves identifying primary hazards in specific regions—such as floods, droughts, cyclones, riverbank erosion, and landslides—and analyzing their effects across different geographic and socio-economic contexts. To identify both tangible and intangible types of L&D, exploring and understanding exposure are crucial and fundamental parts of this study.

The second component, loss and damage assessment, systematically evaluates both economic impacts (e.g., income loss, property damage, and reduced agricultural output) and non-economic impacts (e.g., psychological trauma, displacement, cultural disruption, and biodiversity loss). The conceptual framework illustrates how different types of hazards contribute to varying forms of L&D, emphasizing both direct and complex interactions between economic and non-economic factors. This component is essential for recognizing the full spectrum of climate-induced L&D, beyond conventional economic metrics.

The third component, policy response, focuses on developing comprehensive frameworks that integrate insights from hazard-specific L&D assessments into practical strategies. This involves evaluating multi layers: local, national, and, where relevant, international existing policies for gaps in addressing non-economic losses and proposing new or revised policies that incorporate socio-cultural and psychological dimensions of L&D. These may include locally led adaptation plans, inclusion of L&D in national climate policies, particularly non-economic L&D, and alignment with global frameworks like the UNFCCC’s Warsaw International Mechanism (WIM), Santiago Network on Loss and damage (SNLD) and Loss and Damage Fund (LDF). The goal is to promote a holistic approach to disaster risk reduction and climate adaptation.

Finally, the framework establishes the relationships between hazards, L&D, and policy interventions while organizing these elements in a structured and interactive format. This conceptual framework gives a more comprehensive illustration of those three components all together, whereas existing studies merely focused on either hazard impacts or policy solutions. It enhances understanding of how local climatic hazards impact L&D and demonstrates how targeted policy frameworks can mitigate these effects. Ultimately, the study aims to develop effective, context-specific strategies to strengthen resilience and sustainability in hazard-prone vulnerable communities in Bangladesh. Moreover, the facts and insights generated here can inform broader global discussions on climate resilience in other similar vulnerable regions; particularly in developing countries.

Research design

This study employed a mixed-method approach for data collection, incorporating both quantitative and qualitative techniques to capture climate-hazard-specific L&D across Bangladesh. To ensure the representations of each geographical region of the country, five study locations (Kurigram- riverine floodplain, Bagerhat- saline-prone coastal area, Sunamganj- hoar basin, Rajshahi- drought prone barind tract, and Rangamati- landslide prone hill tract) were selected based on their past exposure data and climate vulnerability.

To strengthen the reliability and validity of the findings, this research design enabled triangulation by combining numerical data with lived experiences. For the quantitative component, a household questionnaire survey was conducted to gather household-level data related to climate hazard exposure and their economic and non-economic impacts. The qualitative component included FGDs and KIIs to capture in-depth insights into the communities. These tools were utilized to understand social, cultural and psychological dimensions that are often difficult to quantify but critical in assessing complex concepts of Non-Economic Loss and Damage (NELD) such as loss of identity, social cohesion, and emotional distress. The integration of both methods ensured a comprehensive understanding of the research problem as the findings were blended for interpretation that provided a pathway for future research directions.

Data collection

For data collection, both qualitative and quantitative methods were used, including household questionnaire surveys, FGDs, and KIIs. Before designing the questionnaire, an extensive literature review was conducted on economic and non-economic L&D. A semi-structured questionnaire was developed, consisting of four sections: (1) demographic information, (2) local climatic hazards, (3) economic loss and damage, and (4) non-economic loss and damage. These sections contained 11, 18, 27, and 29 questions, respectively, incorporating open-ended, binary, and five-point Likert scale questions. After the initial development, the questionnaire was pilot-tested in the field to ensure consistency, clarity, and reliability. Finally, a total of 523 surveys were conducted across five study locations using a simple random sampling technique. The questionnaire was administered by trained enumerators through face-to-face interviews in Bengali, with each interview lasting approximately 30 min. All respondents voluntarily participated in the survey.

To gain deeper insights into local climatic hazards and their economic and non-economic impacts, purposive sampling technique was used for both FGDs and KIIs to ensure the diversity and representation across gender, age, occupation and income groups (e.g., farmer, student, fisher, religious leader) Before final data collection, learning from the pilot-test incorporated and resonated local cultural nuances in both the formation of questions and facilitation. Three FGDs were carried out in each location, totaling 15 discussions, with 6 to 10 participants in each session. These discussions included individuals from diverse social backgrounds, allowing them to share their experiences and perceptions regarding climate hazards. All questions were open-ended, fostering interactive discussions in Bengali. A trained moderator facilitated the discussions, ensuring active engagement from all participants. A research assistant was present to take notes and record non-verbal cues. Ethical guidelines were strictly followed to ensure data security and participant confidentiality.

Similarly, KIIs were conducted with 15 experts who have knowledge of economic and non-economic losses, adaptation strategies, and climate hazards. Participants included community leaders, academics, disaster management officials, and practitioners. The interviews were semi-structured and conducted in Bengali, allowing flexibility for the participants to elaborate on key issues. Each interview lasted 30 to 40 min and was conducted by a trained interviewer. Important points were documented in real-time, and ethical guidelines were strictly followed to ensure confidentiality and data security.

Data analysis

After data collection, all data were coded and checked before performing statistical analysis, and then organized accordingly for further analysis. CA was conducted to examine the relationships among climate hazards and economic and non-economic L&D based on similar characteristics. This analysis was performed in two ways: first, linking climatic hazards (e.g., salinity) with economic L&D (e.g., migration-associated costs), and second, linking climatic hazards with non-economic L&D. Validity of hierarchical clustering was assessed by inspecting the dendrogram, which provided a visual representation of how observations were grouped. The existing obvious segregation and ordering in the dendrogram verified that there were valid and distinct clusters, indicating the appropriateness of the clustering solution. To assess the effects of climatic hazards—including drought, extreme heat, floods, riverbank erosion, salinity, waterlogging, and landslides—on economic and non-economic L&D, a Multivariate Analysis of Variance (MANOVA) was conducted. MANOVA’s primary assumptions of multivariate normality, homogeneity of variance-covariance matrices, and multicollinearity were tested before analysis.

All recorded audio and video data were transcribed to identify patterns of climate hazard-induced economic and non-economic L&D, as well as community responses. The verbatim transcriptions of informants’ audio statements were translated from Bengali into English. Finally, thematic analysis was conducted to identify key patterns and insights related to the economic and non-economic L&D caused by climatic hazards.

Both FGDs and KIIs were used as part of a triangulation approach to validate the findings from the survey and enhance the reliability of the study. The qualitative information from the KIIs and FGDs was integrated with quantitative findings to provide a description of observed patterns and context and to verify key results. According to the questionnaire survey, all climate hazard-related variables were considered independent variables, while economic and non-economic L&D were considered dependent variables.