GOVERNING ANTICIPATORY ACTION AND LOSS AND DAMAGE IN BANGLADESH: EVIDENCE FROM FLOOD AND COASTAL FRONTLINES

This paper examines governance effectiveness in anticipatory action and loss and damage across flood and coastal hazard contexts in Bangladesh. It uses a mixedmethods design that combines a household survey of 390 respondents in Kurigram and Bagerhat with Focus Group Discussions (FGDs), Key Informants’ Interviews (KIIs), and index-based analysis. Results indicate high early warning coverage across both sites (over 80 per cent), confirming the technical functionality of national forecasting and dissemination systems. However, pre disaster assistance remained limited (around 8 to 12 per cent), revealing a persistent gap between warning and action at the local government level. Regression findings show that institutional trust significantly increases the likelihood of receiving anticipatory support (OR = 1.67, p = .018), and that lack of such support is associated with higher loss severity (β = 0.42, p < .001). Loss patterns vary by context, with higher economic loss in flood prone Jatrapur and higher overall loss severity in coastal Southkhali (LDESI 3.92 vs 3.14, p < .01), driven largely by non-economic impacts related to health and salinity exposure. The findings indicate that governance effectiveness is shaped by legal authority, fiscal autonomy, and institutional legitimacy rather than forecasting capacity alone. The paper calls for strengthened pre disaster financing mechanisms, decentralised trigger-based decision making, and integration of non-economic loss into national assessment systems.

Governments, humanitarian agencies, and climate risk practitioners have

been rethinking disaster governance over the past decade in response to growing

climate risks.1 Traditional disaster management focused on emergency relief after

an event has struck, with limited mechanisms for preventive or pre-impact action.2

International policy frameworks have begun to challenge this model by promoting

anticipatory interventions that link scientific forecasting and pre-arranged financing

with predefined action triggers.3 The Sendai Framework for Disaster Risk Reduction

2015 to 2030 highlights the need to enhance disaster preparedness and reduce

disaster effects by strengthening governance systems that incorporate knowledge,

early warning, and risk information into proactive measures.4 This emphasis reflects

a broader shift toward risk governance that integrates pre-impact risk reduction with

anticipatory financing mechanisms.

Forecast-based financing and anticipatory action protocols have emerged as

operational tools to bridge the gap between early warning and timely interventions

that save lives and reduce losses. Anticipatory action is defined as planned and

financed action triggered by forecast information that enables actors to intervene

ahead of hazard impacts.5 The philosophy underlying these approaches is that

measurable prediction and pre-ordered resources create a window of opportunity for

action, often referred to as the forecast-to-action continuum of risk governance. The

integration of such mechanisms aims to reduce the compound effects of disasters

by enabling actors to act when scientific thresholds are met, rather than waiting for

impacts to expand.6

At the climate change policy level, recent decisions under the United Nations

Framework Convention on Climate Change have elevated the issue of Loss and

Damage. These decisions include an agreement to establish a dedicated financing

mechanism to support vulnerable developing countries facing climate impacts that

adaptation efforts cannot avert or adequately address. The decision at the twenty

seventh Conference of Parties to create a Loss and Damage Fund signalled a

recognition that impacts beyond existing adaptation capacity require specific policy

responses.7 Progress toward operationalising this fund was further advanced through

transitional processes leading up to the twenty eighth Conference of Parties, which

included provisions for financing urgent actions that help communities manage

residual risk.8

Improvements in forecasting technology, impact-based prediction models,

and early warning dissemination systems have expanded the evidence base for

anticipatory interventions.9 Yet governance arrangements that allow resources

to flow based on forecast thresholds remain underdeveloped in many national

systems.10 Forecast data may be available, but the legal and institutional mechanisms

that authorise pre-event fund allocation are often absent or unclear. In other words,

technical capacities for prediction have outpaced governance capacities for acting on

these predictions in a timely and transparent manner.11 This dynamic underlines the

need to reconceptualise both anticipatory action and Loss and Damage as governance

innovations that redefine authority, accountability, and resource allocation in risk

management systems, rather than as purely technical or humanitarian instruments.

Bangladesh has historically confronted recurrent climate hazards, including

cyclones, riverine floods, and storm surges. Over several decades, the country has

developed a structured disaster governance system that integrates national policies,

local institutions, volunteer networks, and community actors into an operational

apparatus for risk management.12 Early components of this system, such as the

Cyclone Preparedness Programme, demonstrate institutionalised roles for local

volunteers in early warning dissemination and evacuation, contributing to lowering

mortality in extreme events.13 The Standing Orders on Disaster 2019 provides

a comprehensive policy framework for disaster risk management in Bangladesh.

defines and distributes responsibilities across multiple tiers of government,

including Union Disaster Management Committees, Upazila Disaster Management

Committees, as well as district- and national-level bodies. However, governance

systems that excel in immediate crisis response do not automatically translate into

formal structures for anticipatory governance.14

Differences between flood and coastal contexts further illustrate governance

variation. Riverine flood systems benefit from predictable seasonal patterns that

enable early warning and preparation. Coastal regions face compound risks where

sudden onset cyclones interact with slow onset salinity processes, creating complex

governance demands.15 These differences highlight the need to examine governance

effectiveness across diverse hazard contexts.16

This study addresses these research gaps through a mixed-methods design.

It integrates quantitative survey data collected from 390 respondents with focus

group discussions and key informant interviews conducted across flood and coastal

governance frontlines in Bangladesh. The study develops a Governance Effectiveness

Index for anticipatory action and a Loss and Damage Experience Severity Index

to systematically measure and compare local experiences across hazard contexts.

The analysis examines how institutional readiness, predefined anticipatory action

triggers, and governance performance relate to observed loss outcomes. Economic

and non-economic dimensions of Loss and Damage are assessed through a composite

measurement framework, enabling comparison across flood and coastal settings.

Statistical analysis links governance failures and institutional constraints with

variations in loss severity, providing empirical evidence on how local governance

structures facilitate or hinder anticipatory action and shape residual risk outcomes.

This integrated approach positions the study to address the central research question

of the extent to which local governance arrangements influence anticipatory action

effectiveness and Loss and Damage mitigation across diverse hazard contexts in

Bangladesh.

• Existing literature on anticipatory climate governance highlights a transition from reactive disaster response to proactive risk management systems that integrate forecasting, finance, and institutional coordination.17 This shift reflects recognition that early warning systems alone do not reduce disaster losses unless they are linked with predefined actions, financial mechanisms, and institutional mandates. Studies in Bangladesh document significant progress in early warning dissemination and community preparedness. However, they also identify persistent institutional gaps in linking forecasts with timely financial action.18 Policy developments such as the National Early Action Protocol approved by the Ministry of Disaster Management and Relief in 2024 aim to formalise anticipatory action within national systems, though gaps remain in financing arrangements and operational mandates for pre event response.19 Forecast based financing pilots have demonstrated the effectiveness of early cash transfers in reducing asset loss, though these initiatives often remain project based and externally supported rather than embedded in routine public financial systems.20 As a result, technical forecasting capacity has advanced faster than institutional mechanisms that authorise and deliver anticipatory action. Governance research emphasises that local level implementation depends on administrative authority, coordination mechanisms, and trust between communities and institutions.21 The concept of governance effectiveness in disaster contexts has been examined through indicators such as transparency, responsiveness, accountability, and institutional coordination.22 These dimensions reflect how institutions process

risk information, allocate resources, and respond under uncertainty. Empirical studies show that institutional trust and perceived fairness influence household decisions related to evacuation, preparedness, and engagement with local authorities. Farid and Nasreen23 demonstrate that weak institutional communication and limited trust can reduce compliance with evacuation orders in cyclone prone areas. These insights support the use of composite indices that capture multiple dimensions of governance performance. The Governance Effectiveness Index developed in this study builds on this body of work by operationalising governance through household-level perceptions of early warning access, institutional trust, and the functional activity of Union Disaster Management Committees. It treats governance effectiveness as measurable through both institutional performance and public perception. This approach aligns with broader risk governance literature, which conceptualizes effectiveness as an outcome of both institutional design and societal perception. The Bangladesh context provides a distinct empirical setting where governance effectiveness can be examined across diverse hazard profiles. Riverine flood systems operate with relatively predictable seasonal patterns and established early warning channels, whereas coastal areas face compound risks that include cyclones, storm surge, and salinity intrusion.24 Slow onset processes such as salinity intrusion and riverbank erosion challenge governance systems that are structured around discrete events, exposing gaps in legal and financial instruments that address cumulative and irreversible impacts.25 This contrast illustrates a policy divide between sudden onset hazards with established response mechanisms and slow onset processes that remain weakly integrated into formal governance frameworks. Existing research has largely focused on forecasting accuracy, early warning infrastructure, and pilot interventions, with limited attention to how institutional arrangements shape anticipatory action and loss outcomes across hazard contexts.26 Research on Loss and Damage has expanded to include both economic and non-economic dimensions. Economic losses are typically measured through asset damage and income reduction, whereas non-economic losses include health impacts, displacement, social disruption, and psychosocial stress.27 Studies in Bangladesh highlight that non-economic impacts are particularly significant in coastal regions where salinity intrusion affects water access, health conditions, and livelihood stability.28 These impacts often develop over extended periods and remainunderrepresented in conventional damage assessments that prioritise visible and immediate losses. Recent work also identifies the interaction between governance conditions and loss outcomes, where limited institutional capacity and delayedresponse can intensify both economic and non economic impacts.29 Empirical evidence linking governance quality with measured Loss and Damage severity, however, remains limited, particularly at the local level.30 Composite indices have emerged as a method to capture the multidimensional nature of Loss and Damage in a systematic and comparable manner. Such approaches combine standardised indicators across economic and non-economic domains to generate aggregate measures of severity.31 The Loss and Damage Experience Severity Index developed in this study follows this approach by integrating incomebased loss estimates with reported impacts on health, displacement, education, and social wellbeing. The index structure reflects the need to account for both measurable economic loss and less tangible but persistent non-economic impacts that shape long term vulnerability. In parallel, the Governance Effectiveness Index provides a structured way to quantify institutional performance and link governance conditions with observed outcomes. The combined use of these indices responds to a gap in existing literature, where governance and loss dimensions are often studied separately rather than in an integrated analytical framework. Recent scholarship emphasises the value of mixed methods approaches in examining complex governance systems, where quantitative measurement alone cannot capture institutional dynamics and lived experiences.32 Integration of qualitative insights enables interpretation of statistical relationships through contextual explanations related to administrative practice, political incentives, and social structures. This study adopts this approach to connect index-based measurement with qualitative evidence from focus group discussions and key informant interviews. The analytical framework, therefore, links governance effectiveness with Loss and Damage outcomes across hazard contexts, providing empirical insight into how institutional arrangements shape anticipatory action and residual risk.

The conceptual framework presents the analytical relationship between governance effectiveness, anticipatory action, and Loss and Damage outcomes at the household level. It positions anticipatory action as the central mechanism through which governance systems influence the severity of disaster impacts. The framework begins with hazard context, which includes both sudden onset events such as floods and cyclones and slow onset processes such as salinity intrusion and riverbank erosion. These hazard types shape the timing, predictability, and nature of risk, which in turn influence the scope for anticipatory action. Governance effectiveness, measured through the Governance Effectiveness Index, represents the enabling institutional environment. It includes three core dimensions: access to early warning and lead time, institutional trust and transparency, and the activity and coordination of local disaster management committees. These elements determine whether forecast information is translated into actionable decisions at the local level. Governance effectiveness directly influences the extent and quality of anticipatory action. This includes forecast based financing, pre disaster assistance such as cash or relief, household level preparedness measures, and early evacuation or relocation. The framework assumes that stronger governance increases both the likelihood and timeliness of such actions. The outcomes of anticipatory action are captured through the Loss and Damage Experience Severity Index, which combines economic and non-economic impacts. Economic loss refers to income and asset damage, whereas non-economic loss includes health effects, displacement, livelihood disruption, and social stress. Effective anticipatory action is expected to reduce the overall severity of these impacts. The framework also incorporates mediating pathways and contextual moderators. Mediating pathways include actual receipt of pre disaster support, timely access to information, and household capacity to act. Contextual moderators include gender of household head, socio economic status, access to communication tools such as mobile phones, and distance from local government institutions. These factors shape how governance translates into action and how action influences outcomes.

4.1 Study Design and Study Area

This study employed a convergent mixed-methods design in which

quantitative and qualitative data were collected during the same research phase

and integrated at the interpretation stage. The design followed established mixed

methods scholarship that emphasises triangulation of numerical patterns with

institutional and experiential explanations in governance research.33 Quantitative

data were collected through a semi structured household questionnaire designed

to measure governance effectiveness, access to anticipatory action, and Loss and

Damage severity. The questionnaire was translated into Bangla to ensure clarity,

accessibility, and consistent understanding among respondents across study sites.

Qualitative data were collected in parallel to examine institutional practices, decision making authority, and governance constraints shaping observed outcomes. Two study areas were selected to capture contrasting climate risk governance contexts in Bangladesh. Jatrapur Union of Kurigram Sadar Upazila represented a riverine flood and erosion setting located along the Teesta and Brahmaputra river system. This area experiences recurrent monsoon flooding and riverbank erosion with relatively predictable seasonal patterns. Forecasts are usually issued several days in advance, which provides a defined governance window for anticipatory action. The site offered an empirical setting to assess whether predictable risk translates into timely institutional response.

Southkhali Union of Sharankhola Upazila in Bagerhat District represented

a coastal hazard context exposed to cyclones, storm surge, and salinity intrusion.

This area faces compound risks where sudden onset cyclone impacts interact with

slow onset salinity processes affecting agriculture, drinking water, and livelihoods.

Governance challenges in this context extend beyond early warning to the

management of residual impacts that exceed adaptation capacity.

The paired site design enabled comparison between predictable seasonal

risk associated with river flooding and complex compound risk associated

with coastal salinity and storm surge. This contrast supported analysis of how

governance effectiveness varies across hazard types and temporal risk profiles.

Both unions operate under the same national disaster management framework,

including Union Disaster Management Committees and Standing Orders on

Disaster, which strengthened comparability of governance structures across

sites.

4.2 Sampling and Data Collection

The quantitative component included 390 household respondents, with 195

respondents selected from each study site. Sample size determination followed the standard proportion estimation formula for cross-sectional surveys: Where Z=1.96 corresponds to a 95 per cent confidence level, p=0.5 represents

maximum variability, and d = 0.05 indicates the acceptable margin of error. The

calculated minimum sample was adjusted to support site wise and gender based

comparative analysis. A multi-stage stratified random sampling method was applied. In the first stage, household lists were obtained from Vulnerability Group registers maintained by Union Parishads, which include households classified as flood affected, cyclone affected, landless, or livelihood vulnerable. In the second stage, households were stratified by gender of household head and primary livelihood category. In the final stage, households were randomly selected proportionate to stratum size. Eligibility criteria required at least five years of residence and direct experience of a major hazard event after 2017. Qualitative data were collected in August of 2025 through six Focus Group Discussions and ten Key Informant Interviews. FGDs were gender segregated to capture differentiated governance experiences.

4.3 Analytical Strategy and Index Construction

Quantitative analysis followed a structured analytical sequence using standard

statistical techniques applied in governance and disaster research.34 Initial analysis

involved descriptive statistics, including frequencies, means, and standard deviations

for demographic variables, early warning access, warning lead time, anticipatory

support, and loss categories. Site wise and gender-based comparisons used Independent

Sample t tests for continuous variables and Chi square tests for categorical associations.

A Governance Effectiveness Index for Anticipatory Action (GEI) was constructed

to quantify institutional performance at the household level. The index comprised three

components: access to early warning, trust in local disaster institutions, and perceived

activity of Union Disaster Management Committees. Each component was measured

using Likert scale items, standardised using z score transformation, and aggregated with

equal weights. Higher GEI values represented stronger governance effectiveness.

A Loss and Damage Experience Severity Index (LDESI) measured

residual impacts beyond anticipatory action and adaptation. Economic loss was

operationalised as the percentage of annual household income lost due to hazard

events. Non-economic impacts included health disruption, displacement, education

interruption, and psychosocial stress. Economic loss contributed 60 per cent and

non-economic impacts contributed 40 per cent to the composite index based on

established loss and damage measurement approaches.35

Inferential analysis applied Binary Logistic Regression to identify

determinants of effective anticipatory action, with GEI categorised into effective and

ineffective governance outcomes. Independent variables included gender, education,

livelihood type, warning lead time, and institutional contact. Additional models

examined predictors of high LDESI scores. Qualitative data were coded thematically

using governance related categories and integrated with quantitative findings during

interpretation.

Qualitative analysis was conducted to explain and contextualise statistical

relationships observed in the quantitative data. Thematic coding was applied to FGD

and KII transcripts using predefined governance categories such as institutional

coordination, decision making authority, and access to support. This process enabled

identification of mechanisms that explain observed quantitative patterns, such as

the gap between warning dissemination and anticipatory action. Integration of

qualitative and quantitative findings was carried out at the interpretation stage of the

study. At this stage, qualitative evidence was used to validate, explain, and refine the

statistical results in accordance with mixed methods research principles.36

4.4 Quality Control and Ethical Considerations

For the quality control, enumerators were trained prior to data collection

through a structured orientation that covered survey objectives, questionnaire

content, ethical protocols, and field procedures. Training included mock interviews

and field testing to ensure consistency in data collection and accurate interpretation

of survey items. Special attention was given to translating technical terms into locally

understandable language to maintain reliability across respondents.

Ethical considerations were integrated throughout the research process.

Informed consent was obtained from all participants before data collection.

Respondents were informed about the purpose of the study, voluntary participation,

and confidentiality of their responses. No personally identifiable information was

recorded in the dataset. Data were stored securely and used only for research purposes.

4.5 Study Limitations and Future Research Directions

This study has several limitations. The sampling frame is based on

Vulnerability Group registers maintained by Union Parishads, which may introduce

selection bias by overrepresenting households already identified as vulnerable.

Households outside these registers may have different experiences of anticipatory

action and loss outcomes. The study also focuses on two unions representing distinct

hazard contexts. This site-specific design limits generalisability to other regions with

different socio ecological and governance conditions.

Future research can expand geographic coverage across multiple districts

and hazard types to improve external validity. Longitudinal designs can track

changes in anticipatory action and loss outcomes over time and across hazard events.

Future research can refine and validate the Governance Effectiveness Index and

the Loss and Damage Experience Severity Index by applying larger datasets and

alternative weighting structures. It can also extend the analysis through comparative

cross-country studies to examine how different governance arrangements influence

anticipatory action and residual risk outcomes.