Understanding of livelihood vulnerability to cilimate change evidence from quang ninh province viet nam

Review 62 137 – 147 2021 Understanding Livelihood Vulnerability to Climate Change: Evidence from Quang Ninh Province, Vietnam Van Hong Thi Ha1, Nguyen Bang Nong2* 1, 2Vietnam Academy

Journal homepage: http://gatrenterprise.com/GATRJournals/JBER/vol6_2021_issue2.html

J Bus Econ Review 6(2) 137 – 147 (2021)

Understanding Livelihood Vulnerability to Climate Change: Evidence

from Quang Ninh Province, Vietnam

Van Hong Thi Ha1, Nguyen Bang Nong2*

1, 2Vietnam Academy of Social Sciences, 1 Lieu Giai, Ba Dinh, 11106, Hanoi, Vietnam

ABSTRACT

Objective – Vietnam is one of the world's most severely affected countries by climate change The consequences of

climate change reduce the goal of poverty alleviation and sustainable development of the country Quang Ninh is a coastal province with vigorous development in industry and tourism and is the leading province in Provincial Competitiveness Index (PCI) in Vietnam in recent years

Methodology/Technique – However, for many years, Quang Ninh province has suffered many negative impacts of

climate change Based on empirical evidence, the article assesses the vulnerability in people's livelihoods under the impact of climate change in Quang Ninh province by using the Livelihood Vulnerability Index (LVI) developed by Hahn, Riederer, and Foster

Findings – The paper also assesses livelihood vulnerability based on the Intergovernmental Panel on Climate Change

(IPCC.) The research results show that the components of responding well to climate change are not acceptable

Novelty – The study also shows that there should be different policies, strategies, and reduction components to improve

the capacity to respond to climate change to ensure sustainable development goals

JEL Classification: Q01; Q56

Keywords: Climate Change, Resources, Livelihood Vulnerability, Sustainable Development, Vietnam

Reference to this paper should be made as follows: Ha, V.H.T; Nong, N.B (2021) Understanding Livelihood

Vulnerability to Climate Change: Evidence from Quang Ninh Province, Vietnam, J Bus Econ Review, 6(2), 137–147

https://doi.org/10.35609/jber.2021.6.2(3)

1 Introduction

Vietnam is one of the world's most severely affected countries by climate change Among 84 developing countries in coastal areas and affected by sea-level rise, Vietnam ranks first in severe consequences related to GDP growth and agricultural production (World Bank, 2016.) According to (Maplecroft, 2014), Vietnam is

in 30 hazardous countries in the world Climate change can reduce agricultural production by about 12% in the Red River Delta and 24% in the Mekong Delta (World Bank, 2010.) Under the impact of sea-level rise in

2070, Vietnam will be reduced the diversity of fisheries and degraded their quality, causing adverse impacts

on the fisheries sector (VMNRE, 2016)

_

* Paper Info: Revised: July 16, 2021

Accepted: September 30, 2021

Climate change is increasingly evident in Vietnam Since 1958-2014, the average temperature in Vietnam has increased by about 0.62 Celsius, double the global average (Nguyen et al., 2013; IPCC, 2007.) Sea level along the coast of Vietnam's sea has increased by about 3.50mm/year Annual rainfall decreased in the North and increased in the South, causing droughts to behave differently in different climates (VNMNRE, 2016.) The consequences of climate change reduce the country's ability to achieve sustainable development goals, including poverty reduction

Livelihood vulnerability is the degree to which people and systems cannot cope with climate change and social contexts (Adger et al., 2009) ; (Dow, 1992) ; IPCC, 2014; (O’BRIEN et al., 2007) Livelihood injuries also affect food supply, infrastructure development, health, water use, and ecosystems (IPCC, 2007.)

Based on the analytical framework of (Hahn, M B., Riederer, A M., & Foster, 2009) and IPCC (2001), the paper assesses the livelihood vulnerability of people in the Van Don district, Quang Ninh province, under the impact of climate change in the past ten years (2010-2019.)

Quang Ninh province is the leading province in Vietnam in the Provincial Competitiveness Index (PCI) in recent years (Malesky, E., Phan, T N., & Pham, 2021) The province's per capita income reached 2,712 USD per person per year, 1.2 times higher than the national average (Vietnam General Statistics, 2020) In addition, Quang Ninh province has developed tourism in recent years (Nong& Ha, 2021) The survey site of the article is in Ha Long commune, Van Don district, Quang Ninh province This area is one of three special economic zones of Vietnam The survey sample of the article consists of 200 households, based on a systematic random sampling process, based on the overall list of Ha Long commune (Bryman, 2016) ; (Bernard, 2013) The research team also conducted in-depth interviews, focus groups, and observations The climate change’s context in Quang Ninh province increased in temperature from 22.9 Celcius in 1980

to 24.9 Celcius in 2019 From 2010-2019, Quang Ninh has had more than 93 disasters from nature (People's Committee of QuangNinh Province, 2010; Quang Ninh Statistic Office, 2010-2020) In the years 2011, 2016,

2017, and 2019, Quang Ninh had more than ten storms and unusual natural disasters in the province (Quang Ninh Statistic Office, 2010-2020.) In the past ten years (2010-2019), the total damage from natural disasters was more than $266 million US dollars each year In addition, severe storms have entered this region, such as Son Tinh (2012,) Haiyan (2013), and Mirinae (2016.) Many people and local leaders in the Van Don district still remembered that during the 2016 storm, almost 80% of the oyster farming area of people in the sea was wiped out (Alwang et al., 2001)

2 Literature review

The background researches on climate change's impact on people's lives can be mentioned as the study of (Watts, M J., & Bohle, 1993), (Blaikie et al., n.d.), (Kelly & Adger, 2000) These authors showed a correlation between environmental change and social dynamism to increase the environment's resilience to disasters However, many other studies, such as those of (Adger et al., 2009) and Cutter et al (2000), also showed that population structure features can or cannot adapt to climate changes For example, (Dulal, H B., Brodnig, G., Thakur, H K., & Green-Onoriose, 2010) demonstrated that due to a lack of capital, poor households are less able to adapt to the effects of climate change On the other hand, research by (McElwee,

2010) showed that social dimensions in terms of gender, religion can adapt to climate change in Vietnam (Aryal et al., 2014)

One of the first assessments of livelihoods at the household level begins with the five sources of capital (natural, social, financial, physical, and human capital) that (Chambers, R., & Conway, 1992) proposed in

1992 Many international organizations, such as those of (DFID (Department for International Development)., 1999), (FAO (Food and Agriculture Organization of the United Nations), 2009), and UNDP (2017), currently use these capitals in evaluating projects related to livelihoods

From some of the above background studies, the two most used indicators are IPCC (2001) and (Hahn, M B., Riederer, A M., & Foster, 2009) in evaluating climate change vulnerability to livelihood

In the study in Mozambique, (Hahn, M B., Riederer, A M., & Foster, 2009) showed indicators of social networks and household characteristics most vulnerable to the effects of climate change (Shah et al., 2013)

in Trinidad and Tobago showed that livelihood strategies, health care, access to clean water, and land ownership impacted the ability of households to adapt to climate change (Tewari et al., 2010) in Bihar showed that the level of education, diversity of livelihoods, and expansion of households' social networks coped with the negative impacts of climate change (Panthi et al., 2015) in Nepal research indicated that livelihood strategies and access to food are the most important causes of people's vulnerability to climate change (Simane et al., 2016), in their analysis of the vulnerability of climate change in Ethiopia, showed that the wealth, technology, and infrastructure profiles were strong determinants of vulnerability In the Ghana study, (Adu et al., 2018) also found that social networks and livelihood strategies were essential indicators that households were vulnerable to extreme weather (Ding et al., 2018) showed that social capital and natural capital were two essential indicators that affect people's livelihoods to adapt to negative climate phenomena (Sujakhu et al., n.d.) showed that financial resources, household health, and livelihood strategies affected the capacity to adapt to climate change (Majid et al., 2019) combined GIS to assess livelihood vulnerability showed that households living along rivers were vulnerable to climate change like a flood Overall, studies on livelihood vulnerability show a diverse picture of communities affected by climate change worldwide (Bernstein, L., Bosch, P., Canziani, O., Chen, Z., Christ, R., & Riahi, 2008) (Bang Nong, N., & Ha, 2021) (Pandey & Jha, 2012)

3 Research methodology

The formulation for LVI developed for this study is based on the livelihood vulnerability analysis technique developed by (Hahn, M B., Riederer, A M., & Foster, 2009) including seven major components (Socio-Demographic Profile (SDP), Livelihood Strategies (LS), Social Networks (SN), Health (H), Access to Food (F), Access to Water (W), Natural Disaster and Climate Variability (NDCV).) In addition, each major component includes sub-components (see more at Table 1.)

Since each sub-component is measured using a different scale, it was necessary first to standardize each index Therefore, the following equation is adapted from the Human Development Index (UNDP, 2007; (Hahn, M B., Riederer, A M., & Foster, 2009);

𝑖𝑛𝑑𝑒𝑥𝑠𝑣𝑑= 𝑠𝑣𝑑 − 𝑠𝑚𝑖𝑛

𝑠𝑚𝑎𝑥− 𝑠𝑚𝑖𝑛 (1)

wheresvd denotes the original subcomponent for the research area and smin and smax denote the subcomponent's minimum and maximum values determined using data from the research area For instance, the percent of households reporting in their community was set between 0 and 100 After standardization, the sub-components were averaged to determine the value of each major component using Equation 2:

𝑀𝑣𝑑=

𝑆𝑣𝑑𝑖 𝑛

𝑖=1

𝑛 (2)

where Mvd equals one of the seven components for the research area (SDP, LS, SN, H, F, W, or NDCV), indexSvdi represents the subcomponents, indexed by i, that make up each principal component, and n is the number of sub-components in each component After calculating the values for the seven significant components of the research area, they were averaged using Equation 3 to obtain the research area's LVI.: LVIvd=

∑7 WMi

i=1 Mvdi

∑7 WMi

i=1

which can also be explained in Equation 4 as below:

𝐿𝑉𝐼𝑣𝑑= 𝑊𝑆𝐷𝑃 𝑆𝐷𝑃𝑣𝑑+ 𝑊𝐿𝑆𝐿𝑆𝑣𝑑+ 𝑊𝑆𝑁𝑆𝑁𝑣𝑑+ 𝑊𝐻𝐻𝑣𝑑+ 𝑊𝐹𝐹𝑣𝑑+ 𝑊𝑊𝑊𝑣𝑑+ 𝑊𝑁𝐷𝐶𝑉𝑁𝐷𝐶𝑉𝑣𝑑

where LVIvd is the Livelihood Vulnerability Index for the research area vd, and the weightage of the seven major components, WMi, determined by the number of sub-components that make up each major component, contribute equally to the overall LVI (Sullivan, 2002) ; (Hahn, M B., Riederer, A M., & Foster, 2009) Thus, in this study, the LVI is scaled from 0 (least vulnerable) to 0.5 (most vulnerable)

The LVI-IPCC approaches utilise household level primary data to measure the sub-components Exposure (NDCV), adaptive capacity (SDP, LS, and SN), and sensitivity (H, F, W) are critical components of the LVI-IPCC that contribute to vulnerability All three central components represent in Equation 5 (Hahn, M B., Riederer, A M., & Foster, 2009):

𝐶𝐹𝑣𝑑=

∑𝑛 𝑊𝑀𝑖

𝑖=1 𝑀𝑣𝑑𝑖

𝑛

𝑖=1

(5)

where CFvd is an IPCC-defined contributing factor (exposure, sensitivity, or adaptive capacity) for the research area vd, Mvdi is the major components for the research area vd, indexed by i, WMi is the weightage

of each significant component, and n is the number of major components in each contributing factor (Hahn,

M B., Riederer, A M., & Foster, 2009) After calculating exposure, sensitivity, and adaptive capacity, the three contributing variables were combined using Equation 6:

where LVI-IPCCvd is the LVI for the research area vd expressed using the IPCC vulnerability framework,

e is the calculated exposure score (equivalent to NDCV major component,) a is the adaptive capacity score (equivalent to SD, LS, SN,) and s is sensitivity score (equivalent to H, F, and W) (Hahn, M B., Riederer, A M., & Foster, 2009) The LVI-IPCC is adjusted from -1 (least vulnerable) to 1 (most vulnerable)

Table 1 Major components and sub-components comprising the livelihood vulnerability index developed for Van Don

district, Quang Ninh province, Vietnam

• Socio-Demographic Profile:

- Dependency ratio1: The population under 15 and over 65 years of age to the population

- Percent of female-headed households2: Percentage of households where the primary adult is female

- Multidimensionally poor and near poor households3: Percentage of households in which category according to the

criteria of Quang Ninh province

• Livelihood Strategies:

- Households that report family members who work outside of the community4: Percentage of households with at least one family member who works primarily outside the community

- Households dependent mainly on fishery5: Percentage of households that report only fishery as a primary source of income

- Average livelihood diversification index6: The number of livelihood activities (+1) reported by a household, e.g., A household that farms raise animals and collect natural resources, will have a Livelihood Diversification Index = 1/(3 + 1) = 0.25

• Health:

- Average time to health facility (minutes)7: The average time it takes the households to get to the nearest health facility

- Households having family members with chronic illness8: Percent of households with family members with chronic illness

- Family member who had to miss work or school due to illness9: Percentage of households where a family member could not work or attend school in the last two weeks

- Headed-households well-being10: Health status of head of household

• Social Networks:

- Households supported in the past 12 months11: Percentage of households receiving help in the past 12 months

- Households borrowed or lent money in the past 12 months12: Percentage of households borrowing or lending money in the past 12 months

- Households that have not to belong to an official or unofficial organisations13: Percent of households that have not

to belong to any official or unofficial organisations

• Food:

- Households dependent mainly on the fishery for food14: Percent of households dependent on the fishery for food

- Households could not buy food in the past 12 months15: Percentage of households unable to buy food in the past 12 months

- Average Fishery Diversity Index16: The inverse of the number of fishes grown by a household (+1) E.g., A

household that grows oyster, clam, groupers, and mantis shrimp will have a Fishery Diversity Index = 1/(4 + 1) = 0.20

• Water:

- Households using hygienic water (%)17: Percentage of households using hygienic water (%)

- Households that utilise natural water sources in daily life18: Percentage of households that used well and/or rain in their daily lives

- Households with water sources contaminated with salt and alum19: Percentage of households with water sources contaminated with salt and alum

• Natural Disasters and Climate Variability:

- Households that were not warned of impending natural disasters20: Percent of households that did not warn about the pending natural disasters

- Households with an injury or death resulting from the most severe natural disaster21: Percentage of households affected by the most severe natural disaster in the last decade (2010-2019)

- Damages related to housing22: Percentage of households with housing-related damage (loss of house, damage, area

of house incarcerated, house landslide) in the past ten years (2010-2019)

- Damages related to crops and livestock23: Percentage of households with losses related to crops and livestock in the past ten years (2010-2019)

- Fishery-related damages24: Percentage of households with fish-related damage in the past ten years (2010-2019)

- 10-year average temperature (2010-2019)25: 10-year average temperature (2010-2019)

- Average annual rainfall for ten years (2010-2019)26: Average annual rainfall from 2010-2019

- Number of floods, drought, and cyclone events in the past ten years (2010-2019)27: The average number of floods, drought, and cyclone events in the past ten years (2010-2019)

Notes: 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 20, 21, 22, 23, 24Adapted from Hahn et al (2009); 3, 25, 26, 27Quang Ninh Statistic Office, 2010-2020; United Nations, 2015; 15, 19UNDP, 2017; 17, 18United Nations, 2015; UNDP, 2017

4 Results

The LVI of Quang Ninh province is relatively high, with a measurement index of 0.384 This result is consistent with many previous reports by the international research agencies, the Government of Vietnam, and Quang Ninh province In the LVI, social networks (0.487) and access to food (0.432) are the most vulnerable components (Figure 1) in which sub-components about helping (0.660) and borrowing are very high (0.550.) The research results reflect the reality of coastal residents' livelihoods under the influence of many storms, seriously hurting aquaculture households in the sea and leading to people having to borrow and borrow money continuously to rebuild their livelihoods, invest in production, or buy food for the family (Table 2.)

In the context of climate change, households with only one livelihood activity based on fishing or aquaculture (0.460) are more vulnerable than households with multiple livelihoods (0.314) Furthermore, access to clean water for coastal residents is alarming due to the constant rain and storms The rate of poor households is meager in the study area (7%), but the dependency rate is very high, significantly affecting the vulnerability in sustainable livelihoods of households under the impact of poverty Research results show that SDP has a relatively high injury index (0.321) in LVI The health (H) index is also relatively high in the LVI (0.327,) in which the distance from home to the medical facility has the highest index (0.727) (Table 2.)

Table 2 Livelihood Vulnerability Index sub-component values and minimum and maximum subcomponent values for

Van Don district, Quang Ninh province, Vietnam

Major

component Subcomponents Units

The maximu

m value

in the province

The minimu

m value

in the province

Index for sub-component

s

Index for major componen

t

Socio-Demographi

c Profile

0.321

Multidimensionally poor and near-poor

Livelihood

Strategies

Households that report family members

Households dependent mainly on fishery Percent 100 0 0.460 Average livelihood diversification index 1/#livelihood

Health

0.327

Households having family members with

Family member who had to miss work or

Social

Networks

Households supported in the past 12

0.487 Households borrowed or lent money in

Households that have not belong to any

Food

Households dependent mainly on the

0.432 Households could not buy food in the past

Water

0.387

Households that utilise natural water

Households with water sources

Natural

Disasters and

Climate

Variability

Households that were not warned of

0.419

Households with an injury or death resulting from the most severe natural

disaster

Average annual rainfall for 10 years Millimeters 2.367,60 1.499,10 0.380 Number of floods, drought, and cyclone

The LVI-IPCC approach also gives similar results with an index of 0.022 The vulnerability triangle shows that storms and weather change significantly impact the LVI-IPCC index (0.419) (Figure 2.) Meanwhile, people's adaptation strategies are pretty low (0.361) (Table 3.) That shows that the weak ability to adapt to

climate change of coastal residents in Quang Ninh

Figure 1 The vulnerability spider diagram of the LVI's

major components for the Van Don district, Quang Ninh

province, Vietnam

Figure 2 Vulnerability triangle diagram of the LVI– IPCC index' contributing factors for Van Don district, Quang Ninh province, Vietnam

Table 3 Components contributing factors for the Inter government Panel on Climate Change (IPCC) for Van Don

district, Quang Ninh province, Vietnam

Adaptive Stretegies

0.361

Sensitivity

0.377

5 Conclusions

The research results show that two different indicators conclude that the livelihood vulnerability of coastal residents under the impact of climate change in the past ten years in Quang Ninh province is quite significant The capital of coastal residents plays an important role in responding and adapting to climate change The components that make up the financial capital are quite diverse due to the context of economic restructuring

to industry, commerce, and services; at the same time, the process of forming a special economic zone also creates opportunities for people to increase this capital through a variety of livelihood activities Natural capital, and physical capital is also very good that should be mentioned Meanwhile, social capital and human capital are quite low When negative effects of climate change occur, such as storms, plus planning problems, land acquisition for local infrastructure development, then people need skills and capacity to adapt to this change

0 0.1 0.2 0.3 0.4 0.5

Socio-Demograp…

Livelihood Strategies

Health

Social Networks Food

Water

Natural

Disasters…

Adaptive Capacity

Sensitivity Exposure

0.32 0.34 0.36 0.38 0.4 0.42

While the aquaculture-based livelihood strategy brings good income to the people, the number of storms and tropical depressions severely impacts people's livelihoods In addition, at that time, the social network, financial capital, and access to food were not very good Since then, these factors reduce the ability of people

to adapt to climate change at the study site

The current research results are also limited due to the small sample size, but partly reflect the judgment of international organizations, the Government of Vietnam, and Quang Ninh province in identifying the negative impacts of climate change on people's lives.

Acknowledgements

We gratefully thank the Vietnam Academy of Social Sciences for financial support in this research

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