Climate change and coastal resiliency of Suva, Fiji: a holistic approach for measuring climate risk using the climate and ocean risk vulnerability index (CORVI)

Abstract

Coastal cities are under severe threat from the impacts of climate change, such as sea-level rise, extreme weather events, coastal inundation, and ecosystem degradation. It is well known that the ocean, and in particular coastal environments, have been changing at an unprecedented rate, which poses increasing risks to people in small island developing states, such as Fiji. The Greater Suva Urban Area, the capital and largest metropolitan area of Fiji, is expected to be largely impacted by climate-related risks to its socio-economic, cultural, and political positions. In the face of these threats, creating a resilient city that can withstand and adapt to the impacts of climate change and promote sustainable development should be guided by a holistic approach, encompassing stakeholders from the government, the private sector, civil society organizations, and international institutions. This study assesses the risk profile of Suva city using an innovative risk information tool, the climate and ocean risk vulnerability index (CORVI), which applies structured expert judgment to quantify climate-related risks in data-sparse environments. Through comparative quantification of diverse risk factors and narrative analysis, this study identifies three priority areas for Suva’s future climate-resilient actions: development of climate risk-informed urban planning, harmonized urban development and natural restoration, and enhancing the climate resilience to the tourism sector.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11027-022-10043-4.

Introduction

Ocean and climate change in Fiji

The Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) identified widespread and rapid changes in the climate and ocean environments (IPCC 2021). Pacific Island countries (PICs) are at the forefront of climate change and at large risk from climate-induced hazards, such as tropical cyclones, rising sea levels, and coastal inundation. In the South Pacific region, for instance, the intensity of the strongest tropical cyclones is projected to increase, while the number of tropical cyclones are likely to decrease (Walsh et al. 2012). Coastal ecosystems, particularly coral reefs in PICs, are under acute stress from ocean warming and acidification. They are also under severe threat from other stressors such as unsustainable coastal development and marine pollution. In addition, climate change is projected to decrease global marine animal biomass, distribution, and stocks (IPCC 2019). Consequently, this would have an adverse effect on the income, livelihood, and food security of coastal and marine resource-dependent communities. A high concentration of infrastructure and people in coastal zones further exposes PICs to the impacts of climate change (Nurse et al. 2014).

Fiji, which consists of 332 islands including low-lying atolls, has a prominent coastline and thus is highly susceptible to the impacts of climate change such as sea-level rises and storm surges. Higher sea levels result in an increase in coastal flooding during storm surges. Economic impacts by extreme events are also a concern of significance. Estimated average asset losses caused by tropical cyclones account for 1.6 percent of Fiji’s GDP, and losses from the 100-year cyclones are estimated at around 11 percent of GDP, and adaptation costs for coastal protection is projected to account for up to 3 percent of Fiji’s GDP in the worst case scenario (Government of Fiji et al. 2017).

Approximately 75 percent of the Fijian population lives in coastal zones on Viti Levu Island (Swail et al. 2019). The ocean and coastal environments are intrinsically linked to the well-being, culture, and identity of the Fijian people (Hall 2017). Fishing and tourism are vital sectors of the Fijian economy with the fishery industry contributing about 1.8 percent of the country’s GDP and 7 percent of total export earnings (Ministry of Fisheries Fiji 2021), while pre-COVID tourism represented 38.9 percent of the GDP and 35.5 percent of employment (International Finance Corporation 2020). The increasing impacts of climate change threaten the capacity of the marine and coastal systems that support livelihoods, protect coastlines, and sustain Fiji’s economic progress (Holland et al. 2018). In response to these growing threats, Fiji has increased its presence in regional climate forums and assumed an increasingly active role in international policy debates on climate change and was actively involved in producing the 2015 Suva Declaration on Climate Change that calls for increased support for adaptation measures in the Pacific, especially under the United Nations Framework Convention on Climate Change (UNFCCC). A part of this declaration called for the inclusion in the Paris Agreement of a target “to limit global average temperature increase to below 1.5°C above pre-industrial levels in order to transition towards deep-decarbonization” (Pacific Islands Development Forum 2015). In 2016, Fiji became the world’s first country to ratify the Paris Agreement on climate change and the following year Fiji and Sweden co-chaired the Ocean Pathway Partnership, an international commitment to prioritize ocean issues within the UNFCCC (Government of Fiji 2016a). Fiji also held the presidency of the 23rd session of the Conference of Parties (COP 23) to the UNFCCC in 2017.

Domestically, the government of Fiji has created new policies and legislation to prioritize climate change. Fiji’s Parliament passed the Climate Change Act in September 2021: national legislation that creates institutional arrangements for the development and implementation of climate change policies in Fiji including the National Climate Change Policy (NCCP) and other relevant policies. Under the legislation the relevant the minister has the power to convene other ministries to support national efforts to mainstream climate change mitigation, adaptation, climate displacement, and planned relocation into decision-making processes. Part 13 of the Act is dedicated to oceans and aims to ensure that climate-related policies adequately capture ocean-related issues. The Act provides guidance on coordination of activities that increase the resilience of oceans by several means including through reduction of stressors such as pollution and waste and “by improving the resilience of marine ecosystems, mangroves, seagrasses and coral reefs, strengthening land-sea management and sustainably managing the marine resources within Fiji’s maritime boundaries” (Government of Fiji 2021 pp 472). The 2018–2030 NCCP is in line with the Climate Change Act and the Paris Agreement and provides clear guidelines as well as strategic actions to meet Fiji’s climate change adaptation and mitigation targets to ensure the delivery of priorities set out within the National Development Plan (NDP), Sustainable Development Goals (SDGs), National Adaptation Plan (NAP), and Fiji’s Low Emissions Development Strategy (LEDS) (Government of Fiji 2018a, 2018b).

Strengthening the resilience of coastal communities and cities to climate change is imperative for Fiji. To do this, the government will need to implement strategies to enhance coastal planning. It is also critical for Fiji—like many SIDS—to access additional climate finance to implement various coastal adaptation strategies. Although the total amount of global climate finance has been increasing each year, the current level of adaptation finance is wholly inadequate to support vulnerable coastal communities (UNEP 2021). Furthermore, it is still debated internationally whether risk mitigation and adaptation efforts are appropriately allocated to the areas most in need. It is widely acknowledged that climate change triggers not only environmental and ecological changes but also corresponding socio-economic and political risks with myriad interactions (Scheffran and Battaglini 2011). PICs have diverse geographies and economic and social structures, which increases the difficulty of efficiently allocating available finances and resources to climate risk management strategies (Kuruppu and Willie 2015). An integrated risk assessment is crucial for understanding and acting on multidimensional climate change impacts and supporting different kinds of adaptation and financial needs. This must be supported by robust datasets that assess climate and ocean risks to coastal cities.

However, the lack of historical and current environmental data in the Pacific poses significant challenges for decision-making and information management (Dookie et al. 2019) and is a critical roadblock to policy development (World Bank 2020). There is a distinct lack of centralized repositories for storage and retrieval of environmental data in the Pacific with most of it ending up in personal computers making environmental monitoring and reporting quite difficult (SPREP 2019). This is evidenced from the difficulties associated with climate modeling for the Pacific. The unavailability of up-to-date baseline data continues to impair the generation of local and regional projections for many small islands including those from the Pacific (IPCC 2022). Significant downscaling in computer models is needed to understand island vulnerability particularly for the dispersed countries in the Pacific which, due to this mode of extraction, can often misrepresent actual vulnerability (Nurse et al. 2014). This unavailability of baseline data and the resulting limitations with climate modeling continue to act as an impediment to policy-makers (Vousdoukas et al. 2020). To address the struggle with insufficient data in Fiji, several national policies focus on increasing data collection, analysis, storage, and sharing across all sectors. Fiji’s National Development Plan calls for strengthening of relevant organizational capacity to collate and report on natural resource and environment related data for a sound regulatory environment and sustainable development (Government of Fiji 2017). To better inform decision-makers in the midst of unavailability of data and information, an integrated approach to risk assessment is vital. This study contributes to the Fiji climate change assessment framework by producing a climate risk profile for GSUA, by employing the climate and ocean risk vulnerability index (CORVI) to conduct a city-level risk assessment. Utilizing a structured expert judgment methodology, CORVI combines empirical and expert survey data to measure ecological, financial, and political risks across 10 categories and 97 indicators. By incorporating climate risks and uncertainties into a holistic assessment, this study aims to inform policy-makers, development donors, partners, business owners, and investors on the critical areas that require investment prioritization and resource allocation to mitigate risk and enhance coastal resilience.

Study site and context

The study was conducted in the Greater Suva Urban Area (GSUA), which covers over 5000 ha and is located on the peninsula along the Suva-Nausori corridor (Fig. 1). The GSUA includes Suva city and three municipal towns, Lami, Nasinu, and Nausori, each overseen by a separate town council. The GSUA houses government buildings, ministries, and the headquarters of most business and finance institutions, as well as regional and global organizations. It is also Fiji’s primary transport hub, housing the Port of Suva and Nausori International Airport. The local municipalities and the central government share responsibility for the provision of infrastructure and basic services, with the national government responsible for providing water, sewerage, roads, power, and telecommunications (UN-Habitat 2012).

Fig. 1.

Map of the Greater Suva Urban Area (created by the author)

Increased urbanization poses many challenges for city officials and residents, including unemployment, poverty, squatter settlements, poor service, and connectivity (Phillips and Keen 2016). Land ownership is important for understanding these issues. In Fiji, there are three land types: iTaukei land (owned by native Fijians), freehold land (sole ownership), and state land (owned by the Fijian Government). iTaukei land makes up almost 72 percent of the total land area of the GSUA, with the remaining land divided between freehold and state ownership (TLTB 2018). In Suva, most of the land is iTaukei, making housing developments difficult (Bryant-Tokalau 2014). Moreover, the expiry of iTaukei land leases and non-renewal of these leases has negatively affected the sugar industry in the western division, causing many sugarcane farming families to migrate to GSUA in search of new employment (Whitbeck 2006). This influx combined with other incoming migrants seeking better life has resulted in a shortage of supporting infrastructure, which when coupled with a lack of affordable housing has led to the establishment of numerous squatter settlements located around the GSUA. In 2012, it was estimated that 17 percent of the population in GSUA lived in these informal settlements (UN-Habitat 2012); however more recently, rapid urbanization has increased this estimate to 20 percent (Phillips and Keen 2016). These squatters are usually among the poorest, with limited resources and assets. Furthermore, densely packed housing at squatter locations across Suva faces increased risk from fires and landslides (Koto 2011). The close distancing of the homes also creates a range of issues that affect the environment and health of the occupants due to inadequate supply of water, sewage and sanitation facilities, and poor waste collection and disposal.

Greater Suva Area Suva and its vulnerability to the climate and ocean

Recent years have seen growing concern over climate change in GSUA. Observed mean air temperatures in Suva have risen by 0.21℃ between 1942 and 2011, while warm nights have increased by 7.87 days per decade for the same period (PACCSAP 2015). Due to its location on the windward side of Viti Levu, most of the areas in GSUA experience heavy rainfall and are subject to increased cases of flooding. On the south-eastern slopes of Viti Levu that covers GSUA, the average annual rainfall is 3000 mm, which is much higher than that in the lowlands on the western side of the island with 1800 mm (PACCSAP 2015). Residents in the area face significant concerns associated with riverbank erosion, inundation, irregular water supply, change in rainfall, and flooding events (Lata and Nunn 2012).

Although projections of climate change in GSUA are limited, there are several available simulations that identify future climate trends in Fiji on a national scale. For example, Government of Fiji (2017) indicates that 1 in 10-year fluvial and pluvial flood in Fiji could increase by 13 percent and 15 percent by 2050, respectively, and 19 percent and 22 percent by 2100. The recent assessment by Knutson et al. (2020) reveals that the total number of tropical cyclones in the southwest Pacific is projected to decrease, but the intensity will possibly increase under a 2 °C global warming, which will result in loss of life and damage to buildings and infrastructure. Ocean acidification will also continue to increase due to the increasing oceanic uptake of carbon dioxide (Australian Bureau of Meteorology and CSIRO 2011).

Climate change exacerbates societal issues and poses a threat to the livelihoods of residents. For instance, extreme rainfall events will lead to inundation of the sewer pumping system. The sea-level rise will raise water tables as well as worsen storm surges, thereby contributing to coastal flood risks (Gingerich 2017). Lata and Nunn (2012) state that climate change impacts are resulting in several environmental stressors in the Rewa River Delta, located east of Suva. Increased flooding events, groundwater salinization, and subsequent reduction in the availability of seafood are already severely impacting the livelihood of communities along the river banks.

In addition, the impacts of climate change on informal settlements in coastal zones could be further exacerbated, especially if they engage in activities that harm the environment in a way that increases their own vulnerability (Swan 2021), such as removing mangroves for firewood or to create space for new housing (Chand 2021). These informal settlements are already susceptible to the adverse impacts of climate change, such as sea-level rise and coastal erosion; however when compounded by rapid urbanization (Davis and Quinn 2004) and unsustainable land use practices, their vulnerability increases. In Lami town, there are incidences of squatter households settling in flood prone areas, which increases their vulnerability to flood events.

In contrast to mangroves, coral reefs are coming under increased threat from mass bleaching events due to rising ocean temperatures, most notably in 2000. In 2000, bleaching caused the Suva barrier reef to lose approximately 30 percent of coral cover and 45 percent of coral colonies (Cumming et al. 2002). With increased temperatures projected for the Fiji Islands, bleaching thresholds are likely to be exceeded more frequently, resulting in an increasing number of bleaching events (Gingerich 2017). Mangroves, seagrasses, and coral reefs act as important natural coastal defenses that protect coastlines from violent storm surges and floods (Guannel et al. 2016), as such the continued loss of these environments can intensify the vulnerability of these coastal communities.

The majority of GSUA’s downtown area and shoreline is built on reclaimed land, which when combined with projections of sea-level rise around Fiji to be 0.63 m (0.28–0.98 m) by 2100 may result in approximately 6.2 percent of the shoreline infrastructure in Fiji being inundated (Merschroth et al. 2020). Since 1993, sea-level rise in Fiji has been at a rate of approximately 6 mm per year, which is greater than the global average (Gingerich 2017). Gravelle and Mimura (2008) identified areas where sea-level rise would cause flooding and inundation, with GSUA being one of the high-risk areas. Coastal wetlands in Suva are being adversely affected by industrialization, drainage alterations, landfill, nonpoint source pollution, and rapid urbanization (Davis and Quinn 2004), all of which are degrading coastal health in and around the GSUA. Aging infrastructure in GSUA is also vulnerable to the impacts of climate change and is insufficient to support the standard of life that its residents anticipate. To comprehensively assess the risks posed to the GSUA, decision-makers would need access to information that would enable them to prioritize resource allocation and make smart investments to increase GSUA’s climate- and ocean-related resilience.

Methodology

Introducing the climate and ocean risk vulnerability index (CORVI)

CORVI is a risk assessment methodology developed by the Stimson Center (Stuart et al. 2020). The method collects data across 10 categories, grouped under ecological, financial, and political risk. CORVI produces a holistic and comprehensive risk profile for a chosen coastal city (Fig. 2). This data is standardized on a 1–10 scale relative to 10–20 other coastal cities in a geographical region which share similar characteristics. The resulting information can then be used by decision-makers and investors to build resilience, by providing an assessment of climate risks to a coastal city and identifying problem areas where investments and resources need to be directed.

Fig. 2.

Diagram showing the risk groups and corresponding categories used the CORVI methodology (Stuart et al. 2020)

While empirical data on the impacts of climate and ocean risks have greatly improved, there remain several data gaps (Sumaila et al. 2020). In many cases, accessing comparative city-level data remains a considerable challenge. CORVI aims to help overcome this challenge by utilizing structured expert judgment (SEJ), a social science technique which aims to quantify risk when existing empirical data is limited (Stuart et. al. 2020). Through structured surveys, as well as a series of weighting procedures to ensure data is representative, the SEJ methodology allows researchers to quantify risks that might otherwise be impossible to study in a systematic fashion (Hemming et al. 2017). For example, SEJ has been used to analyze the contribution of sea ice to sea-level rise (Bamber et al. 2019). By combining pre-existing and expert survey data, the CORVI SEJ methodology allows researchers to collect data in areas that would otherwise be unavailable to researchers.

CORVI indicators were generally drawn from existing pools of indicators provided by the United Nations and other international donors and were supported by an advisory panel (Stuart et. al. 2020). To ensure that each indicator score provided a holistic risk rating, survey questions for each indicator were standardized and data collected across five indicator factors: (1) baseline, (2) past trend, (3) expected trend, (4) magnitude, and (5) impact (Fig. 3) (see Appendix for an example question). The results from this lead to a better understanding and prioritization of the issues most in need of action. Experts were allowed to fill one survey for each category that corresponded to their expertise.

Fig. 3.

CORVI indicator factors (Stuart et al. 2020)

CORVI has two notable features, a vector comparison and an area comparison. In the first feature, CORVI compares risk across multiple vectors, from financial costs to ecosystem degradation to political capacity. This allows city planners to compare risk factors across a range of sectors. Generally, risk factors surrounding a city are interlinked and often generate cascading impacts. For example, sea-level rise contributes to saltwater intrusion into groundwater systems, which increases the likelihood of old sewerage systems failing, and overflows, leaks, and pump failures can in turn lead to regional land and water contamination and health problems (Lawrence et al. 2020). The second feature of CORVI involves the production of a regional index which compares risk across coastal cities within an area to better illustrate relative risk dynamics and help international donors and investors to understand the risk scores of a city. For this purpose, data is collected on other coastal cities in the same region for comparison.

CORVI risk categories and indicators

The CORVI methodology collects data under three distinct risk groups, ecological, financial, and political, with each group separated into multiple categories. These categories have been defined by Stuart et al. (2020) and are summarized below.

Ecological risk

Ecological risk is composed of four categories: geology and water, climate, ecosystem, and fisheries. These categories are designed to identify the vulnerabilities of a coastal ecological environment to climate and ocean risks. The geology and water category focuses on the impacts of geological changes such as flooding, coastal erosion, sea-level rise, and landslides to coastal residents. The climate category assesses climate-related impacts, including both rapid-onset (e.g., tropical cyclones) and slow-onset events (e.g., droughts), which significantly impact the economic and social vulnerability in coastal cities. The ecosystem category identifies the health, coverage, and economic importance of surrounding ecosystems, such as mangroves, coral reefs, and seagrass beds, which provide numerous benefits for coastal residents, including safeguards against climate-related hazards. The fisheries category identifies the vulnerability of the fisheries sector of a country, by measuring the extent to which the community relies on fisheries as a source of income.

Financial risk

Financial risk comprises three categories: economics, major industries, and infrastructure. The economics category assesses how climate change impacts economic security. Likewise, the major industries category measures reliance of a city on major industries, including agriculture, fisheries, tourism, and shipping, and the degree to which this impacts the overall climate risk of the studied city. The infrastructure category examines the level of vulnerability and resilience of coastal infrastructure, including renewable energy development, access to electricity and water, and transportation. It will also measure the capacity of the city to accommodate squatter settlements and manage waste.

Political risk

Political risk includes three categories: social and demographic, governance, and stability. The social and demographics category explores how the demographic makeup of a city changes over time, along with trends in urbanization. Governance measures the quality and capacity of a government, combined with the ability of the government to respond to climate impacts. The stability category outlines factors that contribute to the present level of instability in a city that can be exacerbated by climate change. This includes reliance on climate-vulnerable industries for employment, incidences of civil unrest, and government stability.

Region selection

Geographically, PICs are among the most remote and dispersed nations in the world. They face numerous challenges in the form of rapid urbanization, small economies, limited financial and technical resources, and waste disposal and management. Additionally, coastal cities in PICs are exposed to sea-level rise and tropical cyclones increase in frequency and severity that worsen storm surge impacts (Hay et al. 2019). The coastal cities of Port Moresby in Papua New Guinea, Apia in Samoa, and Suva in Fiji have been identified as being highly susceptible to other coastal hazards, including flooding and erosion (Holland et al. 2018). In addition, Nouméa in New Caledonia and Apia in Samoa experience problems associated with saltwater intrusion, which adversely affects coastal agriculture (Berthe et al. 2014; Nicolini et al. 2016). The coastal vulnerabilities faced in these selected cities have led to ecosystem degradation, food security issues, and a lack of economic diversification.

To establish a regional baseline for the SEJ process, secondary data was collected on 13 coastal cities in the Pacific region (Fig. 4 and Table 1). This data is then used to evaluate the robustness of the survey answers through the SEJ process. The comparative approach also helps decision-makers understand a city’s climate risk in the context of other coastal cities within a region. Finally, this regional approach also allows for further CORVI assessments to be undertaken in the PIC region using the same secondary dataset.

Fig. 4.

Map showing the 13 selected countries and cities in brackets of the western Pacific region. The shaded areas surrounding each country represent territorial waters. Map data was created by the authors with Datawrapper (https://www.datawrapper.de/)

Table 1.

Populations and areas for selected cities used in this data was taken from World Population Review (https://worldpopulationreview.com/)

City	County	Population	Area (km2)
Suva	Fiji	93,970 (2017)	26.24
Avarua	Cook Islands	4,906 (2016)	-
Nouméa	New Caledonia	94,285 (2019)	45.7
Port Vila	Vanuatu	51,437 (2016)	23.6
Nuku’alofa	Tonga	23,221 (2016)	-
Apia	Samoa	37,391 (2016)	123.81
Nukunonu	Tokelau	80–120	5.5
Funafuti	Tuvalu	6,320 (2016)	2.4
Honiara	Solomon Islands	84,520 (2017)	22
Port Moresby	Papua New Guinea	364,145 (2011)	240
South Tarawa	Kiribati	63,439 (2020)	15.76
Yaren	Nauru	747 (2011)	1.5
Palikir	Federal State of Micronesia	6,647 (2010)	-

Data collection procedure

Data were collected according to the CORVI SEJ methodology (see Stuart et al. 2020 for details). This began with secondary data collection and was supplemented by surveys from experts who live in, or who have knowledge of, climate risk, and resiliency in GSUA. These data then underwent a weighting process and were subsequently assigned a risk score. A total of 94 CORVI indicators were adjusted to fit the regional context of the Pacific Region; however, 97 indicators from the 10 risk categories mentioned previously were ultimately used for the Suva assessment.

Secondary data collection

Secondary data were mainly collected from open-access global data sources (Table 2), with the data corresponding to 29 of the 97 indicators. Based on this, we developed a relative value for each indicator to check the robustness of the SEJ.

Table 2.

Origin of open-source data used in secondary data collection

Origin of open-source data	Source
FAOSTAT	UN Food and Agricultural Organization (2021) (https://www.fao.org/faostat/en/#home)
SDG Indicator	UN Statistics Division (2021) (https://unstats.un.org/sdgs/indicators/indicators-list/)
The Global Landslide Hazard Map	The Global Facility for Disaster Reduction and Recovery(2020) (https://www.geonode-gfdrrlab.org/layers/hazard:ls_arup)
EM-DAT International Disaster Dataset	Centre for Research on the Epidemiology of Disasters, Université catholique de Louvain (2009) (https://emdat.be/)
The Atlas of Ocean Wealth	The Nature Conservancy(2020) (https://oceanwealth.org/resources/atlas-of-ocean-wealth/)
World Atlas of Coral Reefs	UN Environment-World Conservation Monitoring Centre (2001) (https://www.unep-wcmc.org/resources-and-data/world-atlas-of-coral-reefs-2001)
World Atlas of Seagrass	UN Environment-World Conservation Monitoring Centre (2003) (https://www.unep-wcmc.org/resources-and-data/world-atlas-of-seagrasses)
The World Fact book	Central Intelligence Agency (2021) (https://www.cia.gov/the-world-factbook/)
World Population Prospects 2019	UN Population Division (2019) (https://population.un.org/wpp/)
World Urban Prospect 2018	UN Population Division (2018) (https://population.un.org/wup/)
ILOSTAT	International Labour Organization (2021) (https://ilostat.ilo.org/)
Global Debt Database	International Monetary Fund (2018) (https://www.imf.org/en/Publications/WP/Issues/2018/05/14/Global-Debt-Database-Methodology-and-Sources-45838)
World Travel and Tourism Council Economic Impact Reports	World Travel and Tourism Council (2019) (https://wttc.org/Research/Economic-Impact)
World Tourism Statistics	UN World Tourism Organization (2021) (https://www.e-unwto.org/toc/unwtotfb/current)
Sea Around Us Concepts, Design and Data	Pauly D., Zeller D., Palomares M.L.D. (Editors), (2020) (http://www.seaaroundus.org/data/#/eez)
Corruption Perceptions Index	Transparency International (2021) (https://www.transparency.org/en/cpi/2020/index/nzl)
INFORM Index for Risk Management Database	EU Science Hub (2016) (https://ec.europa.eu/jrc/en/scientific-tool/index-risk-management-inform)
International Institute for Democracy and Electoral Assistance Database	International Institute for Democracy and Electoral Assistance (2021) (https://www.idea.int/data-tools)
World Bank Open Data	The World Bank Group (2021) (https://data.worldbank.org/)
ADB Data Library	Asia Development Bank(2021) (https://data.adb.org/)

Survey data collection

Experts were identified to participate in surveys through a combination of desk-based research and interviews with local stakeholders from coastal urban environments. The surveys were conducted from February 2020 to March 2021. We employed hybrid surveys, a mix of paper-based, and online surveys, along with face-to-face interviews that were carried out in February 2020 and aligned with the distribution of the paper-based survey. The paper-based surveys were distributed to government officials, experts in international development institutions, and academic scholars, based on chain-referential sampling. Further to the in person visits, online surveys were also passed to the specified local experts, who were allowed to choose more than one risk category survey based on their expertise and preferences. Participants were asked to rate a series of different attributes (see Appendix for details). As a result, we gathered 83 survey responses in total: six for the geology and water category, 17 for climate, eight for ecosystem, five for the social and demographic category, 12 for economics, eight for major industry, eight for fishery, nine for infrastructure, six for governance, and four for the stability category.

Weighting process and risk scores

After data collection, the survey data were weighted using a two-step weighting process described by Stuart et al. (2020). Firstly, survey respondents were weighted by the extent of their understanding of the current situation of each risk. The percentage of variance is calculated by comparing their responses to the first question of the baseline value to the secondary data, where available. To ensure the credibility of the data, an expert’s answer was excluded if the variance was greater than 75 percent of the value. Within 75 percent of the value, responses are given a weighting of 1, and within 25 percent, responses are given a weighting of 2. To calculate each indicator score, the following procedures are applied: for factors (1) baseline and (2) past trend, survey data is only included if experts score within the top 25 percent variance. This is done to ensure survey data is not weighted more heavily than pre-existing secondary data. For factors (3) expected trend, (4) magnitude, and (5) impact, experts who scored within 25 percent of the value are weighted as 2, within 75 percent weighted as 1, and experts who score greater than 75 percent variance are excluded. Factors are then combined into an indicator score. Subsequently, the final risk score for each category can be calculated and is dependent on indicator relevance. As minimum criteria, indicators must have at least three expert surveys or a secondary data source to be included in a final risk category score. Measures of data confidence and indicator importance are then combined into an overall weighting. If secondary data are available, an indicator is weighted 1 or 0.5. Survey respondents were asked to identify the two most important and the two least important indicators for understanding risk in coastal cities, with the answers from this used to construct an importance weighting score.

Results and discussion

The scores, which are scaled from 1 to 10, for the ten risk categories for the GSUA are shown in Fig. 5. The scores are illustrative of relative risks levels and are combined with expert interviews and academic and gray literature to corroborate risk scores. Overall, the CORVI risk profile highlights GSUA’s high exposure to extreme climate-related events, which scored highest in the climate category. Major industries are the second highest scoring category, indicating that Fiji’s major industries are vulnerable to climate and ocean risks. For example, an increase in frequency of natural hazards, including sea-level rise, coastal inundation, and tropical cyclones pose a high risk to the tourism and food industry. Climate risks to the stability in GSUA were given the third highest score. In addition, similar levels of risk are also found in the geology and water category, which implies that GSUA is exposed to a great deal of risk from landslides and coastal erosion.

Fig. 5.

Overall category scores and the GSUA risk profile. Higher values indicate greater risks. The figure was created with Datawrapper (https://app.datawrapper.de/)

Ecological risk

Natural climate-related extreme events and slow-onset environmental degradation have put increased pressure on the coastal areas of Suva. The ecological risk category captures the environmental risks to GSUA within four sub-categories: geology and water, climate, ecosystem, and fisheries (Table 3).

Table 3.

The ecological risk scores given to Suva, separated by category and indicator

Category	Indicator	Score
Climate 7.23	Total number of hurricanes/tropical cyclones	8.78
	Number of droughts	8.40
	Frequency of daily rainfall	7.96
	Change in sea surface temperature	6.82
	Number of people affected by extreme weather events	6.59
	Number of extreme heat events	6.35
	Cases of vector-borne disease infections	6.07
	Number of flood events	5.90
Geology/water 5.88	Percent of landscape that is arable land	6.77
	Rate of coastal erosion	6.63
	Level of geophysical risk of landslides	6.63
	Projected change in sea-level rise	6.36
	Degree of saltwater intrusion in coastal aquifers	6.20
	Percent of metro area at risk of flooding	5.83
	Degree of soil salinity in arable lands	5.73
	Percent of bodies of water with high water quality	4.85
	Piped water supply continuity	3.47
Ecosystems 5.67	Percent of GDP protected by intertidal zones	7.35
	Level of coastal intertidal zone coverage	7.10
	Percentage of GDP protected by coral reefs	6.99
	Level of mangrove coverage	6.11
	Level of coral reef coverage	5.69
	Percent of GDP protected by sea grass beds	5.61
	Health of existing coral reefs	5.52
	Level of sea grass bed coverage	5.31
	Incidences of invasive species	5.20
	Percent of GDP protected by mangroves	5.19
	Rate of occurrence of harmful algal blooms	5.17
	Health of existing mangroves	5.02
	Health of existing sea grass beds	4.94
	Health of existing intertidal zone coverage	4.58
Fisheries 4.29	Nearshore fish stock status	5.90
	Percent of fisheries certified by MSC	5.81
	Number of incidents of foreign vessels fishing in EEZ	5.42
	Number of fisheries access agreements with foreign nations	5.15
	Offshore fish stock status	5.12
	Capacity of fisheries enforcement institutions	4.86
	Level of unreported catch estimate	3.66
	Fish consumption per capita	1.00

The climate category showed the highest score in the risk profile of GSUA, an average score of 7.23. In the indicators for this category, we noted a greater frequency of tropical cyclones (8.78), droughts (8.04), and daily rainfall (7.96) in comparison with other indicators. According to the EM-DAT natural disaster dataset1 a total of 15 tropical cyclones caused substantial damage to Fiji between 2004 and 2019, the highest number among the cities in the region. More recently, Cyclone Harold and Cyclone Yasa caused tremendous damage to Fiji in 2020 (Ahluwalia and Miller 2021). Fiji’s geographical features of steep-shelved coastlines and narrow fringing reefs, combined with other factors such as sea-level rise, astronomical tides, storm surges, and cyclone wind action, result in higher wind-driven waves that cause coastal flooding events (GFDRR 2018).

Under the geological and water category, average score of 5.88, most indicators show middle-high-risk levels. The rate of coastal erosion marks a relatively high score (6.63) among them because GSUA has been frequently hit by coastal flooding in recent years (January 2009, January and March 2012, and February 2016), causing substantial physical damage (Brown et al. 2017). Due to the geography of Viti Levu Island, observed climate-related extreme events are highly localized. The study that carried out hydrodynamic simulations shows that the northwestern side of the island, such as Nadi and Lautoka, is more exposed to tropical cyclones, than the southeast coast on which GSUA is located (McInnes et al 2014). Historically, GSUA has not experienced strong tropical cyclones mainly due to its location along the south eastern coast of Viti Levu. However, the gap between risk and perceived risk is considered as a barrier to the development of effective and sustainable adaptive strategies for climate change in Fiji (Lata and Nunn 2012). Meanwhile, the continuity of a piped water supply scored the lowest risk at 3.47.

In the ecosystem category, average score of 5.67, we found that all indicators show medium–high-risk levels. The highest score for this category was given to the percent of gross domestic product (GDP) protected by the intertidal zone (7.35), and the amount covered gives a score of 7.10, which indicates that experts believe the intertidal zone poses the greatest risk to the city. This score would increase with an increase in trade and maritime services. Following these is the risk to the percentage of GDP protected by coral reefs (6.99). The level of mangrove coverage also poses a relatively high risk (6.11), with Cameron et al. (2021) estimating Fiji’s mangrove coverage to be 65,243 ha, with an annual loss of 1135 ha at a rate of 0.11 percent between 2001 and 2018. Increased drought conditions may also reduce the flowering and fruiting of mangroves, and experts predict an expansion of the areas of upper intertidal salt flats currently found in the drier areas of the region (Waycott et al. 2011). Several experts identified a constant tension between urban expansion and the environment. The non-governmental organizations (NGO) interviewed also indicated a lack of confidence in government capacity to carry out environmental impact assessments (EIA).

Finally, the fishery category, average score of 4.29, is characterized by a wide range of risk levels, from low to medium to high. The highest scoring indicates the status of the nearshore fish stocks (5.90), with the declining health of nearshore fisheries identified by some experts. Small-scale nearshore fisheries are affected not only by climatic impacts but also by anthropological activities, including pollution, which is discharged into the coastal areas through the creeks that flow in the City (Suratissa and Rathnayake 2016). Low rates of MSC certification (5.81) and higher incidence of foreign vessels fishing in the exclusive economic zone (5.42) are also highlighted as medium risks. Dey et al. (2016) argued that natural resource management, such as marine protected areas (MPAs) and locally managed marine areas (LMMAs), is expected to expand the fish stock; however, Fiji’s current efforts are not sufficient to address current declining trends. Furthermore, while LMMA management in Fiji is popular, it does not cover a significant proportion of the ocean, and it is not a fully protected area as the prohibited zones are not formally regulated. While Fiji aims to protect 30 percent of its area within a national jurisdiction, implementation has been slow, especially due to it being a highly consultative process. Meanwhile, the CORVI implies that the risk of unreported catch is relatively low (3.66). In 2018, Fiji agreed on port state measures, a binding international agreement to prevent, deter, and eliminate illegal, unreported, and unregulated (IUU) fishing. Our results suggest that this effort has yielded some success.

Financial risk

Since 2010, Fiji’s economy has been growing with a GDP growth rate of 3.5 percent, and a stable average economic growth rate at 3.7 percent (ADB 2020). GSUA has been urbanized and is positioned as a hub for the Pacific Island region. Climate change has posed a threat to the economic growth of the city by undermining financial stability. Recently, the COVID-19 pandemic has exacerbated the damage to the economy of Fiji, with an estimated contraction of 15.7 percent during 2020, and the government forecasting a further decrease of 4.1 percent by 2022 (ADB 2021). The financial risk category offers a snapshot of current financial resilience of Suva to climate change, by scoring indicators across three sub-categories: economics, major industry, and infrastructure.

The major industry category has an average risk score of 6.63, which suggests that industries will be highly affected by the impacts of climate change. The indicator with the highest risk score was the percentage of the national economy based in the recreation and tourism industry (8.48) with diversity of lodging types (8.17) coming a close second (Table 4). Medium–high risks are identified for the percentage of the national economy based on port and shipping (7.15), offshore fisheries (6.16), and nearshore fishing industries (5.79), while agriculture marks a considerably lower risk of 3.96. The dominant industries in Fiji include tourism, sugar, and clothing. In particular, the recreation and tourism industries have served as primary drivers of the economic growth of Fiji and are the main source of foreign currency. The main industrial areas in Suva, i.e., manufacturing and food processing, are also vulnerable to the impacts of climate change as factories are usually located in coastal areas, while many other industries, such as boat repair or travel companies, will also likely be affected due to their proximity to coastal zones. The manufacturing industry in Fiji is highly dependent on natural resource use such as water and other locally sourced raw materials (Ministry of Strategic Planning, National Development & Statistics 2014). The intensification of climate change will affect Fiji’s natural resources in a variety of ways, and this is likely to have a detrimental effect on the manufacturing operations. The tourism sector had experienced stable growth prior to the global COVID-19 outbreak, with tourism earnings increasing by 1.5 times between 2012 and 2019 (Fiji Bureau of Statistics 2021). The GSUA, which entertains tourists with the historical heritage of Fiji, is one of the most popular tourist destinations. However, climate change will directly and indirectly impact tourism in the region, with coastal erosion and coral bleaching affecting the attractiveness of tourist destinations, combined with sea-level rise and extreme weather events posing a risk to coastal-based infrastructure (Jiang et al. 2012).

Table 4.

The financial risk scores given to Suva, separated by category and indicator

Category	Indicator	Score
Major industries 6.63	Percent of national economy based in tourism industry	8.48
	Diversity of lodging types	8.17
	Percent of national economy based in port and shipping industries	7.15
	Percent of national economy based in offshore fisheries	6.16
	Percent of national economy based in nearshore fishing industry	5.79
	Percent of national economy based in agriculture	3.96
Economics 5.61	Percent of GDP generated in coastal cities	7.66
	Market losses from extreme weather events	6.43
	Debt ratio ( percent of GDP)	6.42
	Income inequality	5.93
	Urban unemployment rate	5.51
	Level of informal economy	5.23
	National youth unemployment rate	5.00
	National unemployment rate	4.68
	National GDP per capita (purchasing power parity)	4.07
Infrastructure 5.07	Level of informal or unplanned settlement	6.68
	Level of shoreline development	6.63
	Level of housing damage from extreme weather events	5.75
	Percent of people living below 5 m above sea level	5.65
	Percent of low-income housing in relation to flood zones	5.50
	Degree of compliance for solid waste management procedures	5.15
	Level of water distribution infrastructure resilience	5.06
	Level of resilience for airports	4.95
	Renewable energy share in total energy consumption	4.73
	Level of commercial infrastructure damage from extreme weather events	4.61
	Proportion of wastewater safely treated	4.61
	Level of grid resilience	4.58
	Level of resilience for ports and shipping	4.45
	Level of resilience for roads	4.30
	Percent of population with adequate access to electricity	3.41

The results of this assessment indicate the future growth prospects of the port, shipping, and fishing industries. Kings Wharf in Suva is the main commercial port of Fiji, handling bulk and container freight for trade, and is key for maintaining the industrial presence of Suva in the national economy. One expert from the port operating company stated that while the port is vital for businesses in Suva, there are several challenges which affect its efficiency, such as insufficient human resources, fragmented initiatives without coordination and strategy, and lack of prioritization or central finance mechanism. However, the port and shipping sector have started proactively mitigating climate change impacts through a green stewardship program. For instance, plans are underway to establish garden areas in the port complex, and smart energy-efficient light-emitting diodes and water filter recycling tanks are being installed at the wharf operational areas. A new and more efficient electric powered incinerator was commissioned for use at the Port of Suva in 2019. The new incinerator has higher burning capacity and is more energy efficient as compared to the old diesel-powered incinerator which was in use since 1998. Additionally, Suva is one of the major fishery landing sites, and nearly half of the Fiji’s total landings from coastal commercial fisheries (about 5,500 tons per year) are made in Suva (FAO 2021). Fisheries in the city used to be under threat from foreshore reclamation projects and from anthropogenic biochemical pollution (Davis et al. 1999) but are now suffering from diminishing fish stocks, particularly near the shore. More recent natural resource management strategies, including marine protected areas (MPAs) and LMMAs in Fiji, are not large enough to have a meaningful impact in reversing these declining fish stocks (Dey et al. 2016).

The economics category (average risk score of 5.61; Table 4) highlights high-risk areas such as in the percentage of GDP generated in coastal cities (7.66). Medium–high scores were also identified in market loss from extreme weather events (6.43), high debt ratios (6.42), and income inequality (5.93). The CORVI assessment shows that the relatively high reliance of Fiji to economic activities in coastal cities will be a risk trigger. Manufacturing equipment and offices within the country are concentrated around coastal areas, which are mostly located within the GSUA. In addition, changing climate could increase pressures on goods and services provided by coastal ecosystems, which offer substantial economic value to the city. For instance, the economic value provided by the coastal ecosystems by fishing at Navakavu is estimated to generate net benefits of just over US$1,795,000 per year (O’Garra 2012). Market loss from extreme weather events is a significant concern in GSUA, as the economy of Fiji has been frequently affected by natural disasters, particularly tropical cyclones. The economic loss from Tropical Cyclone Winston in 2016, one of the most devastating cyclones in the history of the island, reached $1.4 billion, which reduced growth to 2.5 percent of GDP. It also caused inflation to rise to 5 percent, mainly due to a shortage of agricultural items (Reserve Bank of Fiji 2019). Although events that cause serious economic damage are not new phenomenon (for example, in 1997, tropical cyclones, drought, and El Nino devalued the Fijian dollar by 20 percent and caused a depression in sugar industry), recent record-breaking natural disasters occurring one after the other have led to intensifying losses from extreme weather events. For example, the estimated losses and damages from Cyclone Winston in 2016 were FJ$1.99 billion (US$ 900 million) (Government of Fiji 2016b).

In the infrastructure category (average risk score of 5.07; Table 4), we identified a higher risk from shoreline development (6.68) and informal or unplanned settlements (6.63). An expert from academia stated that industrial infrastructure based around coastal areas is being increasingly affected by climate change, which has led to a decrease in employment and business relocation and has resulted in increased risks and costs for businesses, particularly larger industries. In contrast, other experts frequently commented on the vulnerability of the city’s aging infrastructure. Designed in 1942, public infrastructure, including sewage, solid waste management, and transport, has not kept pace with rapid urban growth. Since the first Town Planning Scheme for Suva city was prepared in 1979, city-specific planning has become outdated. One expert mentioned that electrical infrastructure is the most vulnerable to the impacts of climate-related hazards, noting that other critical infrastructure, such as water and sewage, depend heavily on electricity and any disruption to their electricity supply would cripple the infrastructure of Suva. For example, erosion due to the floodwaters destabilizes the bases of power transmission towers and severe damage may follow when electrified equipment comes in contact with water, moisture, and dirt. Also, storm-force winds bring down power lines and cause massive damage to electrical infrastructure. According to an expert, in 2016, category 5 Tropical Cyclone Winston in Fiji severely damaged power lines, leaving about 80 percent of the population without power. In several communities, power was restored six months after the cyclone. In 2019, the Fijian government announced that it is in the process of updating its Town Planning Schemes for Suva city, which would be expected to address such identified infrastructure vulnerabilities. Some experts pointed out that there has been a boom in the number of squatter settlements in peri-urban areas surrounding Suva. Squatters in GSUA are considered to be the most vulnerable demographic group at risk from the impacts of climate change (Gravelle and Mimura 2008). Although the Fijian government enforced the Urban Growth Management Plan for the GSUA in 2006 with the aim of guiding strategic investments and expansion to safer lands, high-risk squatter areas are still located in low-lying areas, exacerbating the vulnerability of the poorest city residents. Another risk noted by experts was the location of many government offices in low-lying areas. According to the expert’s view, these organizations maintain extensive paper records that are often housed on the ground floor, and as such are at risk of being lost to flooding events. Fiji’s National Development Plan 2017 (NDP) mentions that the government is exploring options to decentralize some of its offices currently based in Suva to the western and northern divisions (Government of Fiji 2017).

Political risk

The political unrest has led economic instability in Fiji, as illustrated by the 2006 coup which resulted in a decline in development assistance for the following two years (Fletcher and Morakabati 2008; Schmaljohann and Prizzon 2014). Unstable political circumstances may undermine the effectiveness of foreign aid in coping with external shocks such as climate change (Chauvet and Guillaumont 2004). A growing body of literature shows that climate change could exacerbate social and political tensions (Schleussner et al. 2016; Abrahams 2020; Sofuoğlu and Ay 2020), while few studies have focused on the Pacific. The political risk category lays out vulnerability in the sub-categories of social and demographics, governance, and stability (Table 5).

Table 5.

The political risk scores given to GSUA, separated by category and indicator

Category	Indicator	Score
Stability 6.01	Percent of people employed in tourism	7.23
	Percent of people employed in port and shipping industries	6.40
	Percent of people employed in the commercial fishing industry nationwide	6.18
	Level of social tension	6.16
	Percent of people employed in agriculture	5.80
	Percent of people employed in artisanal and subsistence fishing	5.32
	Number of years that the current government structure has been in place	5.28
	Number of incidences of civil unrest or instability	4.72
Governance 5.19	Level of perceived transparency within government	5.66
	Capacity of ethics enforcement bodies	5.45
	Investment in climate resiliency development projects	5.41
	Civil society participation	5.35
	Rule of law	5.33
	Access to healthcare (HAQ)	5.14
	National climate adaptation plan	5.13
	Capacity of current disaster response	4.84
	Voter turnout	4.47
Social/demographics 4.80	Percent of urban population below 30 years of age	7.32
	Urban population density	6.96
	Percent of international migrants living in the country	6.96
	Percent of population below poverty line	6.82
	Urbanization rate	6.12
	Dependency ratio	5.67
	Percent of adult citizens living outside of the country	5.21
	Urban population	4.56
	Percent of population achieving proficiency in literacy and numeracy	4.20
	National population	1.00
	National population density	1.00

The risk levels of indicators in the stability category range from medium to medium–high and have an average score of 6.01. Relatively greater risks are situated in tourism (7.23). In 2018, the contribution of travel and tourism to employment for Fiji accounted for 35.3 percent of GDP (WTTC 2018). Climate change will induce changes to the basic conditions for coastal tourism, including climatic conditions, tourism resources, and coastal hazards (Weatherdon et al. 2016). Furthermore, natural disasters such as tropical cyclones have not only damaged tourism in Fiji by devastating infrastructure, but adverse publicity in the media has indirectly kept tourists away (Jayaraman et al. 2018).

The GSUA, which includes Suva city, Lami town, Nasinu town, and Nausori town, is administered by the four municipalities, respectively, headed by a government appointee known as a Special Administrator under the Local Government Reform (2008). Under the Local Government Act (Cap.125), the councils of the municipalities are mandated to observe, deliver, and enforce laws relating to urban management, including the maintenance of basic urban services such as public health, garbage collection, recreational areas, roads, and drainage systems (UN-Habitat 2012). However, the responsibilities between the national government and the local municipalities are sometimes blurred, for instance, building of new roads and the allocation of budgets. The municipalities also face the challenge of not being able to collect the council rates (revenues) from the population residing in informal settlements and the existing five iTaukei (Indigenous Fijian) villages within the GSUA (UN-Habitat 2012) although the need for provisions service is increasing.

The level of social tension is scored at 6.16. One possible main driver is the high level of informal or squatter settlement in GUSA as a result of rapid population growth and urbanization. Persisting customary land tenure and values, lack of livelihoods, limited financial accessibility, and growing inequality have kept this problem from being resolved and exacerbates social tensions (Phillips and Keen 2016). Ethnic divides could be another underlying factor of social instability in Fiji, particularly urban areas. Land ownership and access to natural resources are one of the primary reasons for inter-ethnic tensions with Indo-Fijians residing or farming on land leased by the indigenous Fijians who have a strong attachment to their land and fear expropriation by the government (Naidu et al 2013). De Vries (2002) investigated ethnic aspirations using a multi-stage regional sampling procedure, with most of the interviewees from the Suva Area. This study revealed strong differences between the two ethnic groups with the indigenous population having stronger ethnic supremacy aspirations and both groups aspiring to additional political power. These underlying ethnic tensions have erupted in the form of coups in the past with a constant fear in the public for the next one. While the impact of climate change on the social tensions is yet to be explored for the Suva region, the increased stress on natural resources and the loss of land due to climate change may give rise to conflicts especially considering inequality of access to these resources.

While not explicitly included in the CORVI assessment, impacts of climate change and unemployment issues in indigenous coastal areas influence culture and erode identity. This is highly applicable to indigenous Fijians who share strong attachment to their land and ocean. Eroding coastlines have contributed to migration/relocation, which causes some erosion of traditional hierarchies, values, and governance structures (McMichael and Powell 2021). Traditional governance structures are often essential for effectively managing coastal ecosystems, such as mangrove forests, coral reefs, and nearshore fisheries. These attributes are key contributors to resilience in many Melanesian cities such as Honiara, Solomon Islands, and Port Villa, where community-based approach to adaptation supplements the lack of institutional capacity (Trundle et al. 2019). The expiring native land lease and financial instability emanating from loss of farmland are the growing reasons for Indo-Fijians to migrate to cities in search of better livelihood and job security for their children.

We identified relatively low risks in the social/demographics category (average risk score of 4.80), which marks the highest score among the politically focused categories. Fiji has the second-largest population in the Pacific (UNFP 2014); however, population density is lower and less than half that of other small island nations, such as Nauru, Tuvalu, Kiribati, and Tonga. On the other hand, the percentage of the population living below the poverty line shows a medium to high level (7.32). Poverty has been one of the major issues in Fiji’s development plan. Through its impacts on agriculture, fisheries, and tourism, climate change may increase the levels of chronic and transitory poverty in the Pacific (Barnett 2011). Furthermore, we have identified a relatively lower dependency ratio, which is an age-population ratio of those not in the labor force. The dependent part is from ages 0 to 14 and 65 + , while those in the labor force are aged 15 to 64.

The governance category (average score of 5.19) shows that an insufficient level of perceived transparency within the government poses the greatest risk at medium–high level (5.66). Although the 2013 Constitution of the Republic of Fiji (CoRF) puts an emphasis on “good governance,” “transparency,” and “accountability,” there are some challenges in enhancing the governance of Fiji, such as incomplete accountability processes and a lack of coordination between governing institutions (Chohan 2017). The Open Budget Survey 2010, which assessed people’s access to their governments’ financial documents, placed Fiji second last in a list of 94 countries (International Budget Partnership 2020). Furthermore, the 2018 Freedom House Political Rights Score showed that Fiji scored the lowest among our targeted coastal countries. Fiji scored 24 and 23 in these surveys, respectively, while other countries recorded scores above 30 (Freedom House 2018). Transparency and accountability in government should be a critical issue for Fiji to enhance climate resilience, as failures in these areas could lead to the failed implementation of climate resilience projects (Tanner et al. 2009).

A medium risk of access to healthcare was identified (5.14). In 2018, the total health expenditure of Fiji was relatively low in the region, at 3.42 percent of GDP (Russell 2011; WHO 2018); however, there have been several attempts to integrate climate change and healthcare systems. Fiji has an approved national Climate Change and Health Strategic Action Plan 2016–2020, which is being implemented using limited resources and alongside the range of natural hazards impacting the island. Subsequently, the “Guidelines for Climate Resilient and Environmentally Sustainable Health Care Facilities” was launched in March 2021. These guidelines are a direct product of the Climate Change and Health Strategic Action Plan 2016–2020 and aim to build climate resilience throughout the health system and ultimately protect the Fijian population.

National climate adaptation planning also scored at a medium risk level (5.13). Fiji published its Comprehensive National Adaptation Plan in 2018, which incorporates a robust climate adaptation decision-making strategy and involves the relocation of communities severely affected by the impacts of climate change. Notably, the CORVI score shows that Fiji has an adequate capacity for disaster response (4.84), in which the Fiji National Disaster Management Office (NDMO) has played a major role. Recent severe tropical cyclones have enabled the Fijian military and police to gain experience in initial disaster response and coordination with donors and domestic stakeholders, including the NDMO, through cluster systems.

Discussion

Adaptation strategies for GSUA

The CORVI assessment shows that risks are concentrated in the climate, major industries, and stability of Suva. This information was used to develop the following action plans as priorities for climate-change adaptation strategies for the area.

Risk-informed urban planning

Risk-informed urban planning allows cities to reduce the risk to unprepared populations. The CORVI risk assessment highlights that Suva is exposed to severe climate-related disasters, such as tropical cyclones (8.78), droughts (8.40), and intense rainfall (7.96). Landslides are also a major geographical risk to people in vulnerable areas (6.77) and are commonly linked to intense rainfall events. There has also been a recent rapid increase in urban population under the age of 30 (7.32) exacerbating the expansion of informal and unplanned settlements (6.68). To combat these compounding risks, urban planning should be based on up-to-date hazard information. While GSUA is located on the south side of the island, this shelters the city from the impacts of extreme weather from the north. However, experts have expressed concerns that a lack of awareness among the city residents, disaster response, and urban planners to the potential impacts of climate change poses substantial risk. In addition to a lack of awareness of natural hazards, other contributing risk factors include a high population density, informal housing, and aging infrastructure. Above all, it should be recognized that risk-informed planning in GSUA, as a capital city or administrative center, would also have benefits for building resilience nationwide. For instance, interviewees mentioned that major government departments are situated along the coast and are highly susceptible to coastal flooding, which would present a risk of disruption and chaos of national governance, driven by localized extreme climate events.

Industrial deconcentration in coastal areas should also be considered. The CORVI findings show that Fiji’s economy relies on industrial activities in coastal cities (7.66) and the advancement of substantial shoreline developments (6.63). Relatively high climate risks, such as coastal erosion (6.63) and flooding events (5.83), have resulted in a highly vulnerable economy. Therefore, reconsidering the distribution of manufacturing equipment and offices is worthwhile. This entails a vulnerability assessment of coastal industrialized areas and transportation facilities to identify the most effective and adaptive production and supply network. Another point to consider could be the decentralization of industries and government agencies that are currently concentrated in the GSUA. Migrating these to other towns would also increase vertical integration, which is listed as a priority action area in the National Adaptation Plan.

Special planning is imperative to address these root causes and ensure a sustainable and resilient city. The recent NDP for Fiji has emphasized vulnerability assessment and climate change projections in infrastructure and urban planning. To achieve this, however, a modern climate-resilient metropolitan-level GSUA planning framework is needed to support and guide investments in basic infrastructure services, including informal settlements, to support an environmentally sustainable and inclusive urban environment. To adopt a truly integrated development approach as stated in Fiji’s NDP, there is a need to initiate consultations between various stakeholders including government, NGOs, the private sector, and the community. This will help to develop a consensus on how future disasters and stresses will impact GSUA visions, strategic development priorities, and the types of institutional reform, policies, and investments that will be required to address various shocks. While Fiji’s National Adaptation Plan highlights improved climate information services and management as one of its key components, there is a need for practices to incorporate developed information such as hazard maps and meteorological prediction systems into urban planning at the sub-national level. A risk-informed urban planning toolkit that includes concrete steps to utilize available information and resources to effectively plan climate-proof land use and urban developments in the city would be a suitable option. The potential leading organization could be the NDMO, who plays a substantial role in tackling the issues of climate change, emergency response, and relocation.

Harmonizing urban development and nature

Degradation of the coastal intertidal zone (7.10) and mangroves (6.11) are highlighted in the Suva risk profile. Although the government’s adaptation plan recognizes upgrading infrastructure as essential to build resilience in the city, the roles of key ecosystems that provide protection to communities, such as mangroves and coral reefs, are not clearly articulated. GSUA is rapidly developing with an increasing focus on coastal zones. This has contributed to the tension between urban development plans and environmental sustainability; for example, mangroves have been cleared to create space for new infrastructure. There is also a lack of appreciation of the cumulative climate impacts and the impact of such developments on a more regional scale. According to the Ministry of iTaukei Affairs and the Ministry of Land, urban planning and resilience measures must be sensitive to the land rights structure of Fiji, where there are three types of landowners: native or iTaukei land, free-hold land, and state-owned land. The study area included all three land types; as such, decision-makers must be mindful of this interplay between governmental and traditional governance structures when designing climate-oriented policies. While tensions between urban developments and ecological considerations will not end, several experts noted a lack of granularity in this debate. According to them, not all mangroves are equally important for protecting a city and supporting marine ecosystems. With the inclusion of mangroves on carbon markets and in selling carbon credits, Fiji is investing in the protection of these blue carbon sources, and this new initiative and direction has the potential to advance coastal protection and resilience. A blue carbon framework that identifies these highly critical ecosystems to guide a long-term strategy of supporting mangrove protection and restoration could be a way forward. A smart city program that promotes green growth initiatives is discussed in Fiji’s recent NDP; however, a new framework is needed to accelerate green infrastructure, financial innovations, and monitoring and evaluation mechanisms.

To overcome several of the challenges currently faced by the GSUA community, access to adequate blue/green climate-resilient infrastructure and financial resources is required. Exploring the best mix of green-gray infrastructure and nature-based solutions (NbS) should allow for developments that reduce physical risk, restore critical ecosystems, and improve access to climate finances. Good public private partnerships are necessary to enable economic growth for the region, which will improve the delivery of services to those who most need it. There is also an urgent need to create greater awareness on the issue of climate change among the local communities, especially in areas where mangrove and coral reef destruction is occurring, to enable residents to consider the long-term implications of their actions and to encourage behaviors that will increase their resilience to climate change impacts.

Climate-resilient tourism

Overcoming vulnerability stemming from high economic dependence on the recreation and tourism industries (8.48) is also important in building resilience. Tourism in Fiji is largely characterized by coastal activities such as diving, cruising, and other marine leisure tourism, which causes vulnerability to coastal disasters or erosion. Based on Fiji’s National Climate Change Policy, the government has promoted climate-resilient infrastructure, for instance, by enforcing building codes and promoting renewable energy technologies. Private sectors, including the tourism industry, are required to demonstrate alignment with Fiji’s enhanced NDC commitments in all their major infrastructure investments.

In addition to efforts to enhance the resilience of key social infrastructures such as airports and roads, the infrastructure associated with disaster risk management, including accommodation and resort facilities, should also be strengthened. In the face of the global pandemic, the Fijian government has emphasized that a more sustainable, inclusive, and resilient tourism sector is required as the foundation of COVID-19 recovery through long-term tourism diversification.

Furthermore, the CORVI assessment indicates that a large volume of the population engages in the tourism sector (7.23). It is therefore crucial to support the population employed in the tourism sector to sustain their livelihood in the case of a drop in tourism flow due to another disaster or crisis. One possible solution to scale up climate-resilient tourism is to diversify the use of the Environmental and Climate Adaptation Levy imposed on prescribed services offered to visitors, which was introduced by the Environmental Levy Act 2017. Currently, it is primarily utilized for environmental protection; there is potential to expand its purpose to climate change adaptation strategies, such as subsidizing the tourism sector to advance their disaster risk management and provide social protection to their employees.

Income diversification strategies are another possible way to strengthen resilience in the tourism sector. One thing that stands out after the COVID crisis in Fiji is that employees in the tourism industry must diversify their income sources. Income diversification has not received much attention previously, but as Fiji faces numerous challenges, there is a need to identify alternative and market-appropriate livelihood opportunities. The current health crisis and destructive cyclones in recent years have indicated a strong need for Fijians to diversify their income and strengthen food security through backyard gardening. These strategies will ensure economic resilience to the impacts of climate change.

Methodological limitation

In considering adaptation actions moving forward, future trends of climate risks are one of the key determinants. In the CORVI methodology, future projections are included in the risk score for each indicator through an experts’ judgment and the weighting process based on the empirical value. However, it does not fully explain expected impacts that future climate change may cause. Separating risk scores into risk ratings for past, current, and expected trends could garner further insights. However, a lack of data could be a limitation, pointing to the importance of surveying a wide range of experts across all categories.

Conclusions

Coastal resiliency in response to climate change is imperative for PICs. The diverse risk factors in Suva were examined from some of the perspective of the climate and ocean by utilizing a holistic risk assessment approach. CORVI, as a tool for informing decision-makers of city-level risk profiles, identified a priority for action toward resilience and sustainable development in GSUA. The largest climate and ocean risks were identified in areas relating to climate conditions, ecosystems, and major industries. Furthermore, subject matter experts noted both the physical threats of climate change to the city, as well as the impact of these physical changes on environmental, economic, and social systems. Based on the CORVI risk profile, we raised three recommended action areas: risk-informed urban planning, harmonizing urban development and nature, and climate-resilient tourism. These action areas would feed into adaptation and resilience-building actions under Fiji’s Climate Change Act. The development of integrated risk scenarios is one of the priority areas of the Bill, and thus, the CORVI risk assessment that provides a snapshot of comprehensive risk factors and their impacts on Suva would lead to adaptation efforts focusing in the right direction to respond to ocean and climate crises.

Finally, this study demonstrates the benefit of applying the CORVI methodology to PICs. Conducting additional CORVI cities risk profiles in the Pacific region would further increase the comparability of the regional dataset and provide decision-makers with a body of coastal city-level data which could provide greater insights into the climate and ocean risks these cities face.

Supplementary information

Below is the link to the electronic supplementary material.

Author contribution

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Nagisa Shiiba, Priyatma Singh, and Dhrishna Charan. The methodology was supervised by Jack Stuart. The advisory member includes Kushaal Raj (primary advisor), Arpana Pratap, and Miko Maekawa. The original draft of the manuscript was prepared by Nagisa Shiiba, Priyatma Singh, and Dhrishna Charan, and all authors commented on the previous versions of the manuscript.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

Not applicable.

Declarations

Ethics approval

As per Fiji’s research protocol, a research ethics approval was sought from the Ministry of Education, Heritage and Arts, Fiji, prior to conducting field activities.

Consent to participate

Participants were informed that they could withdraw their participation whenever they wanted and reassured that they would face no disbenefit from withdrawing their participation. Informants are deemed to have agreed to the publication of information by participating in surveys and interviews.

Consent for publication

Informants are deemed to have agreed to the publication of information by participating in surveys and interviews.

Conflict of interest

The authors declare no competing interests.