Threats from weather events, urbanization and resilience: A case study of a coastal geography in India

ABSTRACT

Urbanized coastal geographies with high population density but with low adaptive capacity are more exposed to threats of both rapid and slow onset weather events. Altered coasts along the Bay of Bengal is one such geography where general urbanization trend coupled with new policy driven tourism activity promotion is increasing exposure to frequent and intense disaster events. Current study takes a deeper dive into a 17 km long coastal stretch along Digha-Shankarpur-Mandarmoni in the Bay of Bengal in India. Community consultation using questionnaire-based survey is the primary source of evidence for this study. Community’s perception about threats from changing weather events and risks perceived by the coastal community engaged in various livelihood categories have been used to assess their resilience status, awareness of various local adaptive interventions and measures undertaken for local resilience building. With already predominant traditional agricultural and fisheries practices, promotion of tourism is altering the natural ecosystem faster through hard infrastructure expansion. The prevalent threats from weather events in the region lead to the loss of ecosystem services with adverse impacts on the communities whose livelihood are closely linked to the ecosystem service flows. In this context, how to build the resilience is the major research question. Nine impact indicators are used to assess risk using qualitative and quantitative methods. The analysis shows that individual and the community scale resilience increases with livelihood diversification, inclusive community engagement, polycentric governance structure, maintenance of diversity in ecosystem service flows, access to science based information on threats and opportunities, inclusion of local knowledge available with the communities in various livelihood categories for designing appropriate social protection measures. The study comes up with policy recommendations. It is important to avoid lack of long-term vision in the policies, enhance local institutional capacity through sector specific associations to deal with multiple threats quickly, overcome lack of awareness about preventive and soft adaptation measures, restoration actions, increase interaction and involvement of local stakeholders for local knowledge. Additionally, it is an urgent need to involve multiple even if sometimes conflicting scientific views on solutions vis-à-vis community wisdom for strengthening resilience.

KEYWORDS:

• Weather events
• Threat to livelihood
• Risks to livelihood
• Coastal ecosystems
• Coastal Urbanization
• Resilience

1. Introduction

Coastal ecosystems provide natural shoreline protection against storms and floods, and help in water quality maintenance (Mehvar et al. 2018). However, human intervention through uniform “urbanisation” strategies in coastal regions across all geographies needs to be revisited with changing threats from weather events evolving differently in different local socio-economic-ecological-demographic contexts under changed climate system (Pörtner et al. 2022). Certain ecosystem types are more vulnerable and less resilient to such threats. Coastal ecosystems are one of those (Pörtner et al. 2022). Multiple stresses arising from local to global scale drivers are also resulting in degradation of coastal environments (Turner et al. 1998; Crossland et al. 2005) (Martínez, Martínez et al. 2007); (Neumann et al. 2015). Coastal ecosystems account for substantial proportion of global primary production and are significant contributor to employment generation, food supply, GDP, international trade, human well-being. Coastal and marine ecosystems are sources of at least 20% of the protein consumed by around 3.3 billion people globally and are the provider of livelihood for 60 million people (Cooley et al. 2022). National exclusive economic zones, zones within 200 miles of the coast supply 90% of global fisheries (Turner et al. 1996) (IPCC, 2022); and approximately 38 million people are directly engaged in fisheries and trading of fish products across the world. Globally, approximately 50% of tourism is based on marine and coastal ecosystems (Northrop et al. 2022). Between 1890 and 1990 the number of international tourists turned almost double. It generated around 200 million jobs and approximately US$ 5 trillion income globally (Crossland et al. 2005).

It is expected that this rising trend in coastal urbanization will be more prominent in Asia, Africa, and South America. As per the latest Pörtner et al. (2022) report, many Asian cities have high exposure to risks of future flood events driven by population growth and urbanization. The IPCC report (2022) projected that coastal regions are likely to face higher risks (at least one order higher magnitude) by 2100 if substantial adaptation and global mitigation initiatives are not taken. The risks include sea level rise, flooding, coastal erosion and subsequent loss of public and private assets, lives, and ecosystems (Pörtner et al. 2022). In high emission scenarios across the global south including Central and South America, sub-Saharan Africa, and South Asia, 31 million to 143 million people are likely (Pörtner et al. 2022) to get displaced by 2050 as a result. For example, as per the assessment India is likely to transition to high risk with 1.5–2.0°C warming level leading to higher vulnerability of socio-ecological systems to physical climate change hazards. The impacts of cyclones and storm surges are found to be devastating for developing countries (Dasgupta et al. 2011). A chain effect is triggered by rise in sea surface temperature, which leads to higher intensity and frequency of cyclones and storm surges that along with sea level rise, increases chances of coastal flooding (Dasgupta et al. 2009, 2011). Changes in coastal morphology in some areas are considered to intensify the threat (Dasgupta et al. 2009). Apart from incidences of threat events, the socio-economic, economic, cultural, institutional, and political conditions of these less developed regions regulate the intensity and persistence of impacts (IPCC 2014).

Studies (Das et al. 2011; Chakraborty and Joshi 2016) have shown that the coastal districts of West Bengal in India are highly vulnerable to natural disasters, for example, risk of sea level rise, prone to cyclones and flooding. Pörtner et al. (2022) reports that Southeast Asian and Indian Ocean based fisheries sector is economically vulnerable due to the degradation and loss of coastal and marine ecosystems caused by climate change. These ecosystems also support the tourism sector (Northrop et al. 2022), which also is a vital source of livelihood in these regions. The Indian Ocean Tsunami of 2004 caused unprecedented changes in South and Southeast Asia. It was followed by extremely severe cyclonic storm in the Bay of Bengal, Sidr in 2007 that severely affected Bangladesh. In 2009 severe cyclonic storm Aila, in the Bay of Bengal adversely affected parts of coastal India and Bangladesh. The Pörtner et al. (2022) report also is clear that local context specific information will help building adaptation and resilience plans.

In this backdrop, this study takes a deep dive into Digha-Sankarpur-Mandarmoni coastal region, approximately 69 square kilometres of wide land-ocean interaction zone along 17 kilometres long coastline in India along the Bay of Bengal in the state of West Bengal. In this selected coastal geography population density is more than 3 times higher (Datta 2018) than all India coastal population density.

Digha-Sankarpur-Mandarmoni coastal belt is subject to frequent hazards like cyclonic storms, flooding, erosion and sea level rise (Jana et al. 2014; National Disaster Management Authority, India 2017). Additional human interventions (Bhattacharya et al. 2003; Roy et al. 2016) are also altering the ecosystem at various degrees. The region has been experiencing recent urbanization primarily driven by tourism expansion policy. The resilience of the ecosystem to weather shocks and ecosystem service dependent community’s social resilience are interlinked. Social resilience is the ability of communities to survive under social, political and environmental change induced external stresses and disturbances (Adger 2000) (Turner R. K. Turner et al. 1998); and it helps in sustaining community’s well-being. The novelty of this study lies in the case study approach to understand and analyse the risks, risk perception of the communities in Digha-Sankarpur-Mandarmoni coastal region in India. The existing literature (Ellis 2014); (Adger, Hughes, Folke, Carpenter, & Johan, Adger et al. 2005) (Walker et al. 2002; Biggs et al. 2015; Simonsen et al. 2015); either gives a broader perspective to the problem or focuses on other regions in the world. The existing studies on Digha- Sankarpur- Mandarmoni region (Bhattacharya et al. 2003; Jana et al. 2014) address other aspects of the problem. Hence for the contextual specific policy design it is important to understand the local dynamics. While carrying out this study availability of relevant secondary data turned out to be a major challenge to provide a policy guideline specific to the local context. The present study aims to fill this gap. The specific objectives of this study are to:

• Identify and measure livelihood category wise prevalent sources of risks and impacts based on primary survey of the coastal communities.

• Identify the drivers of resilience in this selected coastal geography.

• To come up with generalizable and specific recommendations for strengthening resilience in the similar contexts.

2. Methods

For measuring risk, the present study develops a method by considering different types of losses related to assets, health, life, infrastructure, livelihood caused by threats from weather events in the study area. This is in line with the literature, where ’risk’ is often represented in terms of outcomes or welfare losses like income loss, wealth loss due to additional expenses, income poverty, lower life expectancy, insufficient access to education etc (Morgan and Henrion 1990; UNDHA United Nations Department of Humanitarian Affairs 1992).., IPCC (IPCC 2001, 2007) conceptualizes risk as a combination of probability of occurrence of any hazardous incidence and the degree of impact. In the context of poverty, exposure to risk is considered as one of the aspects (Hoogeveen et al. 2004). It has been seen that, the extent of risk aversiveness of human society decides the degree of importance that is placed on interventions to preserve the ecosystem and the resilient components of the system (Kumar 2012).

This study adopts both qualitative and quantitative risk assessment methodologies by considering separate groups of impact indicators (Table 1). There are some common indicators which are evaluated using both quantitative metric and qualitative responses for risk assessment to minimize any possible bias in quantitative responses from the interviewees during the primary survey. The qualitative risk assessment uses some additional indicators which are not quantifiable and are used only in the qualitative risk assessment and are not included in the quantitative risk assessment. For the analysis primary data were collected from the Digha- Sankarpur- Mandarmoni region of West Bengal in India. 330 individuals across 13 livelihood categories were sampled through stratified random sampling method during 2013–2014. The quantitative and qualitative data were collected through questionnaire-based surveys¹ by using face-to-face interview method. Qualitative data were also collected by conducting focus group discussions with the different stakeholder groups engaged in agricultural and allied activities, fisheries, tourism, fisheries association, and the relevant local government departments.

Table 1. Impact indicators used in Qualitative and Quantitative Risk Assessment.

Qualitative Risk Assessment indicators	Quantitative Risk Assessment indicators
Degree of fixed asset loss (house, land, etc.)	Monetary value of fixed asset loss (house, land, etc.)
Degree of livelihood supporting asset loss (Shop/van/Boat, etc.)	Monetary value of livelihood supporting asset loss (Shop/van/Boat, etc.)
Degree of income loss	Amount of income loss
Degree of crop loss	Monetary value of crop loss
Degree of loss due to livelihood change	Monetary loss due to livelihood change
Level of health risk	Money spent to alleviate health risk
Risk to lives
Unavailability of drinking water
Level of loss of Public property

2.1. Qualitative risk assessment

In the qualitative risk assessment, exercise responses on the impact indicators are reported on qualitative response categories (Table 2). Corresponding to the original response categories, ordinal categorization is done to get numerical values of qualitative risk scores. The ordinal categorization is done by considering five-point Likert rating scale. The quantification of the mentioned categories is done by taking square of the numbers 0 to 4 to assign the values, that is, 0, 1, 4, 9, 16 to each response category.

Table 2. Qualitative risk assessment response categories.

Qualitative Response Category	Ordinal Categorization
Not relevant	0
No impact	1
Marginal Impact	4
Medium Impact	9
Very Significant Impact	16

First, the basic survey data of responses on risk impact indicators are ordinally categorized for the three types of threats namely coastal storms, sea water intrusion and coastal erosion. Then, Total Impact (TI) Scores of individual respondents for each of the threats are calculated using Equation 1(1) $T{I}_j=\hspace{0.17em}\sum _{i=1}^9{I}_{ji}$(1) .

In the next step, the TI scores corresponding to each of the threats are normalized using Equation 2(2) $NT{I}_j=\hspace{0.17em}\frac{T{I}_j-T{I}_{jmin}}{T{I}_{jmax}-T{I}_{jmin}}$(2) . Thus, Normalised Total Impact (NTI) scores are obtained for three threats from weather events. After getting NTI₁, NTI₂ and NTI₃ for all respondents, individual level aggregate risk (AR) scores are calculated using Equation 3(3) $AR=\hspace{0.17em}\sum _{j=1}^{3}NT{I}_j$(3) .

(1) $T{I}_j=\hspace{0.17em}\sum _{i=1}^9{I}_{ji}$(1)

(2) $NT{I}_j=\hspace{0.17em}\frac{T{I}_j-T{I}_{jmin}}{T{I}_{jmax}-T{I}_{jmin}}$(2)

(3) $AR=\hspace{0.17em}\sum _{j=1}^{3}NT{I}_j$(3)

In equation 1(1) $T{I}_j=\hspace{0.17em}\sum _{i=1}^9{I}_{ji}$(1) , TIj is total impact score of threat j from weather event, i is the number of impact indicators, Iji is Impact value of indicator i for threat j. Finally, livelihood category wise risk scores are derived by taking arithmetic mean of Aggregate Risk (AR) scores of individuals engaged in a particular livelihood category.

2.2. Quantitative risk assessment

The quantitative risk assessment indicators (Table 1) are built on respondents’ reporting of monetary values. It is done by calculating average annual monetary loss by averaging monetary loss values over 10 years. We have taken a recall period of 10 years during conducting primary survey. Quantitative Risk assessment at both individual and livelihood category levels for three threats from weather events are done in the following way.

First, primary data are collected from individual respondents across different livelihood categories to get information on monetary loss incurred for the six aforementioned impact indicators. Then Average Annual Monetary Loss (AAML) is estimated for all three threats from weather events at individual respondent level using Equation 4(4) $AAM{L}_j=\left(\frac{1}{10}\right)\sum _{i=1}^{6}TM{L}_{ji}$(4) . In this equation, TML_ji is the total monetary loss of ith impact indicator due to threat j from weather event by an individual over a period of 10 years.

Then, we calculate the overall AAML for each of the respondents to get overall risk impact of three threats from weather events, namely coastal storms, seawater intrusion and coastal erosion.

(4) $AAM{L}_j=\left(\frac{1}{10}\right)\sum _{i=1}^{6}TM{L}_{ji}$(4)

(5) $OverallAAML=\hspace{0.17em}\sum _{j=1}^{3}AAM{L}_j$(5)

Finally, we derive risk scores by livelihood category based on arithmetic mean of overall AAML values of individual respondents engaged in a particular livelihood category. In this way risk scores for 13 livelihood categories (Table 4) are derived from quantitative risk assessment exercise.

Table 3. Factors that help building resilience.

Broad aspects	Factors to analyse	Link to resilience building
Livelihood/income source diversity	Livelihood and income diversification	Positive
Link to Community level institutional support	Participation in community organization Engagement of community in carrying out economic activities	Positive
Inclusive and polycentric Governance structure	The nature of existing administrative structure and their roles	Positive
Ecosystem diversity	Ecosystem alteration status Change in supply of ecosystem services	Positive
Information access and Knowledge base	Awareness about threats Awareness about existing interventions Integration of local knowledge	Positive

Table 4. Adaptive and coping interventions in the study site.

Technological and Infrastructural	Financial and Economic	Regulatory and Governance	Knowledge Creation and Awareness Development
1. Early warning system (radio, television, newspaper, local announcements)	10. Financial assistance provided by the local administrative body in form of easy loans/aid	24. Evacuation of illegal hotels and restaurants built on the coast	30. Government provides scientific training for agriculture sector
2. Global Positioning System (GPS) and wireless installed in deep sea fishing trawlers and mechanized boats	11. Financial assistance provided by the panchayat in the form of easy loans/aid	25. Fine collection from illegal hotels and restaurants built on the coast	31. Government provides scientific training for fishery
3. Construction of embankments	12. Financial assistance provided by the other government bodies in the form of easy loans/aid	26. Beach cleaning organized by the local administrative body	32. Community awareness programs organized by Digha Science Centre (a central government entity for research and awareness development) to build community’s adaptive capacity
4. Improved infrastructure- roads	13. Financial assistance by non-governmental organizations (NGOs)	27. Rehabilitation programmes by the government	33. Community awareness programmes organized by the local administrative body to build community adaptive capacity
5. Improved infrastructure- electricity	14. Financial assistance by self-help groups (SHGs)	28. Hawker’s rehabilitation programme by the local administrative body
6. Improved infrastructure- healthcare	15. Loan from bank	29. Rehabilitation programmes by any NGO
7. Improved supply of drinking water	16. Loan from cooperative society
8. Afforestation programme by the forest department	17. Compensation/loan given by employer/owner
9. Sewerage treatment plant established by the local administrative body	18. Loan from friends/relatives/local money lender
	19. Crop damage compensation by government
	20. Crop insurance provided by the government
	21. Life insurance against disaster risk
	22. Government provides seed (free of cost/at low price)
	23. One time housing material aid by the Centre/State/Local government (Panchayat)

Source: Primary survey and literature review (Brooks and Adger 2005; Penalba and Elazegui 2011; Datta 2018).

2.3. Resilience assessment

We identified five drivers of resilience (Table 3) based on stakeholder perception mapping. Lack of resilience is identified (Adger et al. 2005) as the source of vulnerability against threats. The concept of resilience and its relation with vulnerability is not seen uniformly across literature. Resilience, in some literature (Ellis 2014), is conceptualized as opposite of vulnerability, whereas in some literature (Kaplan et al. 2009) resilience is considered to be a component of vulnerability and often associated with “adaptive capacity” and “coping capacity” (Dolan and Walker 2006; Gallopin 2006). According to Gallopin (2006), resilience can be seen as a component of adaptive and coping capacity of the concerned unit of analysis, on the other hand Walker et al. (2002) considers adaptive capacity as a certain aspect of resilience.

The components and determinants of resilience are context specific (Ellis 2014). For resilience assessment, it is required to identify relevant characteristics of the concerned system, dynamics of the system, shocks, and changes, understanding predominant issues of the system (Biggs et al. 2015). Also, identification of various effective interventions (e.g. technological, institutional, behavioural interventions) and experience of dealing with past events are considered to be relevant (Ellis 2014) for resilience assessment. Some of the determinants of resilience as mentioned in literature (Walker et al. 2002; Adger et al. 2005; Biggs et al. 2015) include mechanisms to cope with perturbations, patterns of resource use, polycentric governance, efficient management, development of knowledge and information access, maintenance of diversity in ecology, governance, and so on, strengthening networks and connectivity, participation of stakeholders in different stages of action.

Resilience of a society or community is related with the extent of risk-taking behaviour of a society or community (Ellis 2014). It depends on the way the community perceives risks and based on that it puts weight on practices, strategies or actions directed towards resilience. Researchers (Walker et al. 2002; Adger et al. 2005; Biggs et al. 2015; Simonsen et al. 2015) have specified attributes that resilient social-ecological system needs to possess along with drivers of resilience. That included instruments to cope with threats, diversification of resource use and activities, maintaining ecological diversity, strengthening networks, participatory governance, development of knowledge base, and so on. The assessment of resilience is not necessarily about quantification of resilience but about understanding the underlying process (Simonsen et al. 2015) of the system that we are concerned about.

For understanding the current resilience status in the context of the threats that the coastal community in our study site is exposed to, we focus on the some of the relevant factors associated with resilience building, which are mentioned in Table 3.

Income source diversification of individual respondents is measured by the Inverse Simpson Index of income diversity using the following equation.

(6) $InverseSimpsonIndex=1/({\displaystyle {\sum }_{i=1}^N{P}_i^2}),$(6)

where P_i = proportion of individual income generated by activity i to the total income, N = number of different income sources of an individual.

For assessing information access and awareness about possible interventions, 33 potential adaptive and coping actions were identified through literature review and field survey and validated through further stakeholder interaction in the study site (Datta 2018). These are listed in Table 4.

For different livelihood categories, awareness about various adaptive and coping interventions listed in Table 4 in the study site were estimated. Three response categories were defined for assessing the level of awareness. Those were, a= I know about it, b= I do not know about it, c= It does not matter to me. If a higher proportion of respondents chooses response category “a”, it implies that there is high degree of awareness about the chosen adaptive and coping interventions. For deriving overall livelihood category level percentages of choosing responses “a”, “b” or “c”, the following equation was used.

(7) $Avg\mathrm{\_}A{w}_{nr}=\hspace{0.17em}\frac{1}{33}\hspace{0.17em}{\sum }_{m=1}^{33}{P}_{mnr}$(7)

Here, Avg_Aw_nr is the average awareness percentage for livelihood category “n” for response category “r”; P_mnr is the percentage of respondents in livelihood category “n” selecting response “r”, where r = a or b or c.

3. Findings and discussion

The following livelihood categories (Table 5) and threats (Table 6) relevant for the study area are identified based on field survey.

Table 5. Livelihood categories studied.

Sl. No.	Livelihood categories studied
1	Agriculture
2	Aquaculture
3	Deep sea fishing in trawler
4	Fishing in mechanized boat
5	Fishing in manual boat
6	Fish drying
7	Hawking on and near beach
8	Shell crafting
9	Manual van driving
10	Motorized van driving
11	Horse riding on beach
12	Photography on beach
13	Hotels and resorts

Source: Primary Survey.

Table 6. Threats from weather-related events and direct human activities in Digha-Sankarpur-Mandarmoni coastal region.

Threats from weather events	Anthropogenic threats
Coastal storms	Wastewater discharge from hotels and restaurants leading to pollution of coast and seawater
Sea water intrusion during high tide
Coastal erosion
Flooding due to heavy rainfall	Driving motorized heavy vehicles on coast
Inadequate rainfall	Sand mining from coast

Source: Primary Survey.

3.1. Risk

In the context of the current study, individual level risks weather-related events are assessed through risk perception survey. The major threats and the associated risks in Digha-Sankarpur-Mandarmoni are shown in Table 7.

Table 7. Threats and associated risks in the study site.

Threats		Risks associated with outcomes
Coastal storms		Death, injury, and disruption to livelihood, impact on infrastructure in combination with a high dependency of people on critical services, e.g. electricity, water supply, health, and emergency services
Coastal erosion- Sea approaching closer resulting in Submergence of land into the sea		Loss of common-pool resources, private land, sense of place, and identity
Sea water intrusion during high tide		Disruption to livelihood, impact on infrastructure, insufficient access to irrigation water, loss of crop productivity
Flooding due to heavy rainfall		Injury, and disruption to livelihood Shortage of food supplies, and drinking water
Direct human activities	Pollution from wastewater discharge by hotels and restaurants	Loss of coastal ecosystem services
	Driving motorized heavy vehicles on the coast
	Sand-mining from the coast

Source: Primary Survey.

For risk assessment (Table 8), we take into account three major weather-related events : coastal storms, seawater intrusion during high tide and coastal erosion by which most of the respondents have been impacted in various ways (Figure 1, Figure 2 & Figure 3). We exclude direct human activity induced threats from this assessment since there was insufficient information on impact of loss of ecosystem services. Following the qualitative risk assessment methodology described in the earlier section, we assess risk of the identified livelihood categories.

Table 8. The overall qualitative and quantitative risk scenario.

	Mean	Median	Min.	Max.	Std. Deviation
Risk of Coastal Storm	26.74	0	0	121	33.65
Risk of Sea water intrusion	30.99	29	0	85	24.12
Risk of Coastal Erosion	21.83	16	0	88	19.70
Overall Risk (Qualitative)	0.83	0.74	0	2.25	0.50
Average annual monetary loss due to Coastal Storm (in Rs.)	10180	0	0	119000	21704.52
Average annual monetary loss due to Sea water intrusion and Coastal Erosion (in Rs.)	26362	170	0	2012000	189451.76
Overall Risk (Quantitative) (in Rs.)	36542	7075	0	2012000	190058.12

Source: Authors’ estimates based on the data from Primary Survey.

Figure 1. Percentage share of risks of individual threats in aggregate risk.

Source: Authors’ estimates based on the data from Primary Survey

Figure 2. Distribution of respondents across economic activities based on average annual monetary loss due to weather-related events .

Source: Authors’ estimates based on the data from Primary Survey

Figure 3. Distribution of respondents based on monetary loss incurred due to weather-related events.

Source: Authors’ estimates based on the data from Primary Survey

More than 20% of the respondents reported that because of sea water intrusion they have experienced very significant loss in case of public resources such as embankments, roads, and other hard infrastructure. A lesser number of respondents (11% of the respondents) reported very significant loss of private assets (e.g. livelihood assets (shop, boat, van), houses, land), but overall impact on health and lives were minimal due to sea water intrusion. Approx 50% of the respondents reported that they have faced some kind of losses (in terms of income, roads, and embankment loss) regardless of the degree of loss. 30% reported personal losses like fixed assets, livelihood assets which increased their indebtedness.

Coastal erosion has impacted less than 10% of the respondents in terms of unavailability of drinking water, migration, crop loss, health risk and asset loss other than fixed and livelihood al asset. More than 10% of the respondents have incurred very significant losses in terms of loss of roads, embankments, and other public resources due to coastal erosion while another 10% have suffered from fixed and livelihood supporting asset loss.

Sea water intrusion during high tide has overall higher risk impact across livelihood categories under tourism and fisheries sector. But the share of contribution of risk of seawater intrusion in aggregate risk is the highest for Hotels and Resorts, that is, they are more concerned about this threat. It has overall higher contribution to the aggregate risk.

Among coastal storms and coastal erosions, sector wise results show that coastal storms contributes higher to aggregate risk for fisheries sector, whereas that for coastal erosion it is in the tourism sector-based activities. It is also important to mention that certain tourism sector activities like hawking, motorized van driving and horse riding which involves higher investment in livelihood assets and are practiced near the coastline coastal storms contribute higher in in aggregate risk. Agriculture has almost similar shares of risks to three threats to aggregate risk. Aquaculture is also at higher risk due to coastal storms and coastal erosion.

Coastal erosion apart from its direct risk impact, has also an indirect effect since higher degree of coastal erosion increases risk of sea water intrusion during high tide. In this assessment, risk of sea water intrusion during high tide turned out to be the highest overall from both qualitative and quantitative risk assessment perspectives.

Variation in monetary loss due to weather events is the highest for fisheries activities, which is followed by tourism activities and agriculture, aquaculture. Also mean monetary loss due to weather-related threats is the highest for fisheries activities. Within the fisheries sector risk impact (monetary loss) is more for Deep Sea fishing in trawler. Within tourism sector, activities like horse riding and hawking on beach have higher risk impact through monetary loss. Among tourism-based activities: manual van driving, horse riding, hawking on beach and shell crafting are the riskiest livelihood in terms of risk from weather events. Larger proportion of people engaged in agriculture have faced medium to high loss due to weather-related threats than that of in aquaculture, whereas variation and range of monetary loss is much higher in case of aquaculture than agriculture.

3.2. Resilience

In the current study, we look into the factors as mentioned in Table 3 from our field interview-based observations and try to understand the current resilience status of the coastal ecosystem-based community in Digha-Sankarpur-Mandarmoni.

3.2.1 Livelihood diversity

Livelihood diversity emerges as an instrument to reduce income risk and develop resilience for informal sector livelihood. This phenomenon is more applicable for informal activities. We studied individuals engaged in 13 livelihood categories from tourism sector, fisheries sector, and agriculture. Many of the livelihood categories were informal as they are unregistered, except a few like hotels and resorts, trawler fishing, a part of hawker population, photography on beach, aquaculture.

Selection of respondents was based on primary livelihood of the respondents. Primary livelihood is the one which brings in the maximum share of income for the respondent. It was found, that among all the respondents more than 50% were having only one livelihood, that is, their primary livelihood. Around 40% of the respondent across all livelihood categories were engaged in two other secondary livelihood (either simultaneously or seasonally), apart from primary livelihood. The highest number of secondary livelihood of an individual was found to be 3 and it was the case for only less than one percent of the respondents.

All respondents from livelihood categories – deep sea fishing in trawler, Hotel and Resort (manager) and Horse riding on beach – had no secondary livelihood (Figure 4). This is because, almost all the respondents from Deep Sea fishing in trawler and Hotel and Resort (manager) were not local residents and also these jobs do not provide scope for allocating time for additional jobs. Managerial job of Hotels and Resorts are of full time engagements and the individuals in Deep sea fishing need to stay at the fishing harbour throughout the entire fishing season (almost 10 months).On the other hand, individuals engaged in shell crafting, fishing in manual and mechanized boat, motorized van driving, fish drying have overall higher engagement in secondary livelihood, as reflected by Inverse Simpson Index of Income Diversification (Figure 4). For shell crafters, the most common secondary livelihood is hawking on the beach, those engaged in fish drying also practice fishing as a secondary livelihood. Apart from that, other major secondary livelihood are traditional agriculture and daily labour. Some of the individuals engaged in seasonal primary livelihood also migrate to other states of the country and also to places within the state, during off season, to work as construction labour, work in restaurants, and so on.

Figure 4. Inverse Simpson Index of Individual Income Diversification.

Source: Authors’ estimates based on the data from Primary Survey

3.2.2. Community support

Community oriented livelihood practices are conducive for resilient system (Marschke and Berkes 2006). This community-oriented approach comes with knowledge and risk sharing opportunities, improved access to common resources. In the context of our study site, we tried to know how far individuals across different livelihood categories participated in community organizations like livelihood/sector-oriented associations and unions. Also, to know the status of community level engagement we included in the questionnaire the query, “Do there exist any community level association or group dealing with the concerned economic activity”? and if the response to that was “Yes”, then a follow up question was asked, “Are you a member of the association or group?”.

We found that (Table 9), certain livelihood categories like deep sea fishing in trawlers, fishing in mechanized boats and hotels and resorts had 100% membership in community organization. In Digha-Sankarpur-Mandarmoni region, it is a norm for all deep-sea fishermen to have membership of Fisherman’s Association. They are more regulated and formalized and have better access to technology, training facilities, livelihood supporting asset and life insurance, institutional loan and assistance in case of emergency and other disruptive situations. Also, all hotels and resorts are registered with Hotellier’s Association in the locality. Other livelihood within the fisheries sector such as fish drying and fishing in manual boat which are informal have lesser presence of community organization and even if exist participation is low.

Table 9. Distribution of livelihood categories based on type of primary activity.

Primary livelihood	Type of primary activity
	Activity carried out individually under organizational assistance	Activity carried out individually community support	Completely Group activity	Completely individual activity
	%	%	%	%
Agriculture	0	0	0	100
Aquaculture	0	0	0	100
Deep sea fishing in trawler	0	0	100	0
Fish drying	0	20	0	80
Fishing in manual boat	0	55.56	0	44.44
Fishing in mechanised boat	0	0	100	0
Hawking on and near beach	11.11	0	0	88.89
Horse riding on beach	0	0	0	100
Hotels and resorts	0	25	0	75
Manual van driving	0	0	0	100
Motorised van driving	0	0	0	100
Photography on beach	100	0	0	0
Shell crafting	0	75	0	25

Source: Authors’ estimates based on the data from Primary Survey.

Shell crafting is another livelihood which also has high participation in community organization. It is mainly practiced by women through formation of Self-Help Groups (SHG). These SHGs are another kind of community organization that is formalized (Dulhunty 2021) and have access to bank loans and other organizational facilities in carrying out the activity. Majority of hawkers, manual and motorized van drivers also have membership of unions and organization. But these are organized by the various local political parties so are not always united in approach due to political rivalry. Membership is voluntary unlike that of deep-sea fishing where it is compulsory. These unions have role in member conflict management, financial assistance, and other issues as and when arises. But the respondents engaged in agriculture, aquaculture, horse riding and photography on beach had no participation in any community organization. A common feature of these four livelihood categories is that they are migrants from other regions and are not local residents.

Respondents could be categorized into four types based on their engagement with the community organizations (a) Completely individually organized activity devoid of community engagement (b) Individual activity with community level organizational assistance, (c) activity initiated individually but carried out with community support and (d) group activity. Activities like agriculture, aquaculture, horse riding on beach, manual and motorized van driving, and majority of hawking on beach are of category (a) – completely individual activity. Photography on the beach is different from other activities since it is carried by an individual under supervision and assistance of photography studios which provide basic training and photography gears to the workers and get a contract share from the photographers on beach so are in category (b). Among all the livelihood categories, fisheries sector activities have significant role of community and is thus in category (c) and these activities are either complete group activity (e.g. deep sea fishing in trawler and fishing in mechanized boat), that is, of category (d) or carried out individually with community support (e.g. fishing in manual boat and fish drying)-category (c). Although shell crafting is mostly done by formation of SHGs, as already mentioned (category b), but some individuals also carry out the activity individually (category a).

3.2.3 Governance structure

There is a vital role of inclusiveness and polycentric governance structure in building resilience (Adger et al. 2005). The nature of existing administrative structure and their roles in decision making and policy implementation and inclusion of participation by local people/community level organizations are important. The existing policy governance structure as we found through stakeholder consultation in the study site is shown in Figure 5.

Figure 5. Governance structure in the study site.

Source: Primary Survey

Given the governance structure of Digha-Sankarpur-Mandarmoni study region, it has potential to operate involving various layers of people through multiple nodes. There are local government bodies operating with different set of strategies and target areas. But our observation is that they are sometimes working independently. It is observed that two different governing bodies have overlapping targets. In such situations efficient governance structure demands for designing collaborative action plan and implementation, it can be through dedicated task force which seems to be lacking in the study site. Often any department or governing body is not aware of the activities and strategies taken by other departments. All national, state and gram panchayats (village level institution) are democratically elected. But they can have different political affiliations which breed competition which may not always be healthy for local communities and for local developmental action to be carried out in tandem.

3.2.4 Ecosystem diversity

Ecosystem diversity is positively linked with the resilience of social-ecological systems. The study site being a coastal region is highly dependent on various ecosystem services. The major sectors, that is, tourism and fisheries are directly dependent on ecosystem services. Ecosystem alteration caused by climatic, geo-morphological and anthropogenic factors are widespread across the coastal stretch of the study site. It included erosion of coast and sand dunes, vegetation loss, loss of biodiversity, and so on. Ecosystem alteration immediately implies change in supply of ecosystem services including provisioning, cultural and recreational, supporting, and regulatory services (Adger et al. 2005).

Since our study is based on stakeholder’s perception analysis, so we tried to understand the state of ecosystem and service flows from the stakeholder’s perspective. For this exercise we focused on provisioning and recreational ecosystem services which the respondents from different livelihood categories could associate with in carrying out their livelihood activities. Agriculture and Fisheries sector activities were mostly dependent on provisioning ecosystem services whereas tourism sector activities were primarily dependent on recreational ecosystem services. For this, we asked questions like “Over the years did you experience any decline in availability of groundwater and/or surface water for irrigation”, “Over the years did you experience any decline in fish catch from the deep sea/coastal waters/estuaries” and so on.

The findings (Figure 6) show that more or less all the fisheries sector activities recognized that there is a decline in ecosystem services important for their livelihood. They identified changes in ecosystem services in terms of decline in fish stock in the sea, change in composition of fish species, rapidly eroding coast, changes in sedimentation pattern near the coast. As a result of these alterations, there has been a change in livelihood pattern too in the study site (Roy et al. 2021). Respondents practicing agriculture also were concerned about the changes in ground water availability, increased requirement for irrigation water for multi-cropping, change in soil quality, land productivity and so on. Among other livelihood, respondents practicing hawking on the beach and shell crafting were also aware about the ecosystem alteration and decline in ecosystem services such as: erosion of coast, rapid erosion of sand cover causing exposure of muddy soil on beach, loss of sand dunes and vegetation cover resulting in decline in recreational ecosystem services, reduced availability of seashells.

Figure 6. Percentage of respondents from each livelihood category reporting decline in ecosystem services.

Source: Authors’ estimates based on the data from Primary Survey

3.2.5 Information access and knowledge base

Access to information about the existing climatic scenario, predominant natural and anthropogenic threats, developing disaster preparedness, ways to cope and adapt with threats and possible alterations through traditional, emerging, and technological knowledge are significant aspects of building adaptive capacity and resilience (Adger et al. 2005).

From our study we found that, most of the respondents were aware of the prevalent of threats such as seawater intrusion during high tide and coastal erosion, although around 20% of the respondents were not aware of these threats (Figure 7). But the overall awareness about coastal storm was lesser, less than 50% of the respondents were aware of this most of them were from fisheries sector who get early warnings.

Figure 7. Percentage of respondents who are aware about major threats.

Source: Authors’ estimates based on the data from Primary Survey

The findings on awareness about the adaptive and coping interventions are derived by calculating the percentage of respondents within a particular livelihood category choosing responses, a= I know about it, b= I do not know about it, c= It does not matter to me. Under each livelihood category, higher the percentage figure for response “a”, higher is the awareness of the livelihood category.

In Figures 8, 9, and 10, the average percentage values have been shown for each of the 13 livelihood categories with respect to responses “a”, “b” and “c”, respectively. The calculations are done based on Equation 7(7) $Avg\mathrm{\_}A{w}_{nr}=\hspace{0.17em}\frac{1}{33}\hspace{0.17em}{\sum }_{m=1}^{33}{P}_{mnr}$(7) . It is found that in general, in all the livelihood categories in an average less than 80% of the respondent are aware of the interventions, except “Fishing in mechanized boat”, which has 100% awareness. Livelihood categories like “Horse riding on beach”, “Manual van driving” and “Hotels and Resorts” have less than 60% of the respondents on an average aware of the interventions. All these three livelihood categories belong to the tourism sector.

Further, the results showed that among different types of adaptive and coping interventions, awareness about the interventions under categories (Table 4) “Regulatory and Governance” and “Knowledge creation and awareness development” are very low, across all the livelihood categories. Whereas the awareness level for the “Technological and Infrastructural” intervention category is in general high across all livelihood categories.

Figure 8. Overall percentage of respondents having awareness about the interventions.

Source: Authors’ estimates based on the data from Primary Survey

Figure 9. Overall percentage of respondents who do not know about the interventions.

Source: Authors’ estimated based on the data from Primary Survey

Figure 10. Overall percentage of respondents reporting that the interventions do not matter to them.

Source: Authors’ estimates based on the data from Primary Survey

4. Conclusion

Various regional scale key risks that have been identified in IPCC 2022 report for Asia include flooding-related damages to human lives, health, infrastructure, especially in coastal cities and settlements, decline in coastal fishery resources. The same assessment report among many other constraints states with high confidence that adaptation planning and implementation becomes difficult due to lack of climate literacy at all levels and limited availability of local context specific information and data. The present study makes a modest attempt to fill this gap in the literature by selecting a coastal geography from global south. We used evidence collected through first hand structured interviews and focus group discussions with the people from various livelihood categories in the coastal study site of Digha-Sankarpur-Mandarmoni along the coast of Bay of Bengal in India. We get a comprehensive understanding of prevalent sources of risks and impacts for households in different livelihood categories. Sea water intrusion during high tide in the region is found to have greater overall risk across all livelihood categories compared to coastal storm and coastal erosion. However, it also became clear that indirectly coastal erosion increases risk of sea water intrusion during high tide. Because of sea water intrusion, they have experienced very significant loss in case of public resources such as embankments, roads and other hard infrastructure.

Among the major sectors in the study site, fisheries sector emerged as more risky than tourism sector due to various weather-related events. Direct dependence on ecosystem services is also higher in fisheries sector. Deep sea fishing in trawler users within fisheries sector and horse riding and hawking on the coasts among tourism sector came out as relatively more riskier. Average monetary loss due to weather-related threats is the highest for fisheries sector. There is more financial uncertainty in the case of aquaculture than agriculture. But the overall scenario of risk assessed through qualitative and quantitative methods shows that sea water intrusion is the most important driver of risk for newly emerging tourism sector which includes livelihood categories like hawking, horse riding, photography on beach, hotels and resorts, manual and motorized van driving in the study site. But the share of contribution of risk of seawater intrusion in aggregate risk is the highest for Hotels and Resorts, that is, they are more concerned about this threat. It has overall higher contribution to the aggregate risk. The human risk-taking behaviour too influence the decisions and choices made by the people engaged in the studied livelihood categories. The findings of Kumar (2012) are similar to the current study in this regard. It was observed that the fishing community is in general more risk averse than the tourism sector stakeholders.

Lack of resilience is identified (Adger et al. 2005) as the source of vulnerability against threats. The differences in characteristics of the livelihood categories define their level of resilience to the threats. The resilience status of individuals and households improve with livelihood and income diversification, which is also in line with global assessments (Pörtner et al. 2022) and are thus generalizable. Participation of households in local community institutions such as fisheries associations provide scope for better resilience. The drivers of resilience as identified based on this study show that efficient interventions to strengthen human and social capital, incentives and facilities for income diversification, governance, limiting ecosystem alteration and ecosystem restoration are also in line with literature (Walker et al. 2002; Adger et al. 2005; Biggs et al. 2015). These factors emerge as general conclusions for resilience building in coastal regions. The nature of existing administrative structure whether polycentric, awareness about the threats and relative impacts, access to existing public support programmes matter in resilience enhancement. Among these, in case of the tourism sector community-based institutions and engagement of community were mostly found to be missing in the study site as they are mostly driven by large investors. For example, in case of tourism and agricultural sector activities formation of community-based institutions would enable better access to information and knowledge sharing among small operators, regulating unsustainable practices within the community that pose threats to the community in the long term and getting better access to existing government facilities (e.g. insurance, formal credit, post disaster relief and so on) for improving resilience of economic activities.

At present, government interventions in the study site are more focused on hard engineering for infrastructure development and beach erosion prevention, beautification, promotion of tourism activities, and livelihood support without considering the aspect of resilience (Biggs et al. 2015). It has been found that for the existing policies there are several barriers to implementation, for example, lack of coordination among administrative departments, corruption at local administrative level and non-compliance in terms of coastal regulations related with permanent construction within the coastal regulatory zone and carrying out economic activities, lack of local level monitoring. There is evidence of violation of Coastal Regulation Zone (CRZ) regulation (MOEF 1991; Nayak 2002) by both formal and informal economic activities in the tourism sector. For pollution level monitoring and interventions there are issues like capacity and resource constraints, lack of dedicated guidelines, ad hoc monitoring depending on projects. These gaps in governance in terms of missing policies as well as barriers to implementation of relevant policies limit coastal resilience to changing frequency and intensification of threats. In controlling coastal erosion there is widespread construction of sea dykes, protective walls along the coastline which often does not consider traditional knowledge of the community related with storm protection and flooding and possible detrimental impacts on existing economic practices like fishing in manual and motorized boats. Local interview brought out the fact that while there are ports for trawlers, but embankments are making it difficult for local manually operated and motorboats for anchoring or parking.

Participation of stakeholders at different levels of decision-making including policy designing, implementation and MEL (monitoring, evaluation, and learning) is needed. Availability of recorded evidence of threats, incidences, frequency of events along with socio-economic data and future projections (Agnihotri 2022; The Print 2022) can help in future for better monitoring and policy shifts if necessary. Risks assessment in the context of direct human activity led threats could not be carried out in this study due to limitation of the data and lack of local awareness. Further, assessing the effectiveness of various policy interventions to reduce the identified risks in the region and to improve resilience needs a separate in-depth study by also analysing the politics of resilience.

Article highlights

• Rapid urbanization, promotion of tourism activities with low adaptation capacity are making coastal geographies more vulnerable and exposed to both rapid and slow onset weather events.

• Individual and community scale resilience improves through diversification of ecosystem services and livelihood opportunities, dissemination of science-based solutions, inclusive polycentric governance structure, local knowledge integration in designing of social protection measures.

• Enhanced institutional preparedness for managing multiple threats is an urgent need in coastal stretch along Digha-Shankarpur-Mandarmoni in the Bay of Bengal in India.

Ethics statement

The questionnaire involving human participants was reviewed and approved by the Project Ethics Committee of Global Change Programme- Jadavpur University. Written informed consent for participation was not required for this study.

Author contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Acknowledgments

The paper is based on part of the doctoral thesis of SD. The thesis was submitted, and the degree was awarded from the Department of Economics, Jadavpur University in 2019. The primary survey and data used in the study were collected during the APN project period 2013-2015. The authors acknowledge contributions at various stages from APN project partners and individual project members who were engaged during the project work in various capacities and feedback received from a number of Workshop, Seminar, Conference and Summer academy participants, Ph.D committee members, faculty members, and external examiners at the Department of Economics, Jadavpur University, reviewers of the article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The raw data supporting the conclusions of this article can be made available by the authors on request, based on discretion of the authors concerning the purpose and use of the requested data.

Additional information

Funding

The authors acknowledge the financial support received under the completed APN project (reference numbers: ARCP2012- 12NMY-ROY and ARCP2013-07CMY-ROY) for the period November 2012-March 2015. This provided the initial financial support for SD as a researcher in the project. Financial support was also received through the Global Change Programme of Jadavpur University for the period April 2015-March 2017. During April 2017-June 2018, as a registered Ph.D student, SD received the Swami Vivekananda Single Girl Child Scholarship for Research in Social Sciences (SVSGC) of University Grants Commission (UGC) of the Government of India. Additionally, the authors would like to thank the Munich Re Foundation for financially supporting the publication of this paper as well as for the organization of ‘World Risk and Adaptation Futures – Social Protection summer academy 2020’ which inspired this work. Also, the authors acknowledge the support provided under the SMARTS Center-PISCES project of SERD at the Asian Institute of Technology, Thailand at the final stage of substantial revision and publication of the work.

Notes

1. The questionnaire used for conducting the primary survey is available on request from the authors, and in Datta (2018).