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date: 22 September 2021

Climate Change and Coastal Vulnerabilityfree

Climate Change and Coastal Vulnerabilityfree

  • Xiaoyu LiXiaoyu LiDepartment of Agricultural, Environmental, and Development Economics, The Ohio State University
  •  and Sathya GopalakrishnanSathya GopalakrishnanDepartment of Agricultural, Environmental, and Development Economics, The Ohio State University

Summary

The convergence of geophysical and economic forces that continuously influence environmental quality in the coastal zone presents a grand challenge for resource and environmental economists. To inform climate adaptation policy and identify pathways to sustainability, economists must draw from different lines of inquiry, including nonmarket valuation, quasi-experimental analyses, common-pool resource theory, and spatial-dynamic modeling of coupled coastal-economic systems. Theoretical and empirical contributions in valuing coastal amenities and risks help examine the economic impact of climate change on coastal communities and provide a key input to inform policy analysis. Co-evolution of community demographics, adaptation decisions, and the physical coastline can result in unintended consequences, like climate-induced migration, that impacts community composition after natural disasters. Positive and normative models of coupled coastline systems conceptualize the feedbacks between physical coastline dynamics and local community decisions as a dynamic geoeconomic resource management problem. There is a pressing need for interdisciplinary research across natural and social sciences to better understand climate adaptation and coastal resilience.

Subjects

  • Environmental, Agricultural, and Natural Resources Economics

Introduction

Dynamic coastal environments are continuously changing due to natural processes and human interventions that contribute to erosion and accretion of beaches, inlet formation, dune building, and changes in elevation on barrier islands. Geophysical coastal processes that affect shoreline positions directly influence coastal property values and impact local economies through tourism and resource extraction. Climate change intensifies changes in physical coastlines through increasing rates of sea-level rise and changing storm patterns. Humans respond to coastal change by building seawalls and other hard erosion-control structures, rebuilding eroding sections of the shoreline through beach renourishment, stabilizing inlets, and restoring wetlands and coastal vegetation. Human activities in the coastal zone, in turn, affect future environmental change, making developed coastlines tightly coupled coastal-economic systems.

Feedbacks between human and natural dynamics occur across multiple spatial and temporal scales (Werner & McNamara, 2007). Local adaptation decisions in one location can affect geophysical conditions at other locations along the shoreline (Gopalakrishnan et al., 2017; Williams et al., 2013). Acute episodic hazards, such as storms and flooding events, occur over relatively short timescales (days to months), whereas chronic effects of long-term seal-level rise unfold over longer time scales (decades). As economic and natural dynamics reinforce feedbacks over time, adaptation strategies that are effective for recovery from hazardous events in the short term can be maladaptive in the long run. The consequences of these feedbacks can be catastrophic in coastal areas with high property values (Nordhaus, 2010) and some of the highest population densities in the United States (Cohen, 2018; Crossett et al., 2013). Actions to stabilize property values in response to extreme storms can reinforce incentives to maintain development on the coast, which could increase vulnerability to future events.

The convergence of physical and economic forces in the coastal zone presents a grand challenge for resource and environmental economists. To inform climate adaptation policy, economists must draw from seemingly disparate lines of inquiry and multiple methodologies, including nonmarket valuation, quasi-experimental analyses, common-pool resource theory, and spatial-dynamic modeling of coupled coastal-economic systems. This article begins by reviewing key aspects of public goods and common-pool resources in the coastal zone and discusses the literature on valuing coastal amenities and risks as an input to inform policy analysis. The next section examines the coevolution of community demographics, adaptation decisions, and the physical coastline highlighting spillover effects and unintended consequences of adaptation that can lead to a development paradox, with a brief discussion on climate-induced migration and changes in community composition after natural disasters. Finally, the article discusses the geoeconomics of coastline change, highlighting both positive and normative modeling of coupled coastline systems, and concludes with an overview of future research to increase understanding of climate adaptation and coastal resilience.

Valuing Coastal Amenities and Risks

Coastal regions support a thriving economy with valuable housing markets and coastal ecosystem services. Densely populated coastal regions have some of the highest property values, with physical capital stocks estimated at more than $1.2 trillion (Nordhaus, 2010). Covering less than 10% of the total land area, coastal counties are home to 39% of the U.S. population and contribute to 45% of the U.S. gross domestic product (NOAA Office of Coastal Management, 2019). The coastal economics literature has focused extensively on recovering reliable estimates of the value of environmental assets and costs of environmental risks, but applications that examine dynamic feedbacks are limited (Gopalakrishnan et al., 2011; Landry & Hindsley, 2011; Li et al., 2021). Coastal housing markets respond to changes in environmental conditions, and market outcomes reveal peoples’ preferences and perceptions of coastal amenities and risks as well as expectations of potential climate impacts. Land use and development decisions are outcomes of balancing the benefits and costs of coastal amenities and risks that are accelerated by climate forcing. Coastal ecosystems represent a variety of natural capital stocks that contribute to local public goods, such as recreational amenity flows and storm protection, and global public goods, such as blue carbon sequestration in coastal mangroves and provision of marine biodiversity. This section reviews the empirical literature on values of climate risks, amenities, and ecosystem services embedded in economic behavior reflecting interactions between coastal resources and shoreline management decisions.

Coastal Amenities, Risks, and Housing Markets

Housing markets capitalize coastal amenities and risks, and signal the economic effects of climate change. Hedonic models (Rosen, 1974) are therefore commonly used to estimate the implicit value of coastal amenities. The hedonic price function decomposes the market price of a residential property into a bundle of attributes including structural characteristics (e.g., number of rooms), neighborhood characteristics (e.g., school quality), and environmental amenities to recover the marginal implicit price of each attribute.

Empirical estimates of the implicit price of coastal attributes consistently show that proximity to the shoreline and coastal wetlands (Brown & Pollakowski, 1977), beach width (Pompe & Rinehart, 1995b), and beach views (Earnhart, 2001; Milon et al., 1984; Shabman & Bertelson, 1979) increase property values. The estimated marginal values for beach width vary widely across locations and over time as summarized in Table 1, ranging between $192 to over $8,000 per additional foot of beach width (Gopalakrishnan et al., 2011; Landry et al., 2019). Proximity to the shoreline is an important factor in determining the spatial extent of benefits from coastal amenities. Empirical evidence suggests that capitalization of beach quality occurs within 300–2,000 feet from the shoreline, with a large portion of the amenity value accruing to oceanfront homes (Gopalakrishnan et al., 2011; Landry et al., 2019; Landry & Hindsley, 2011; Pompe & Rinehart, 1995a). The disproportional capitalization of amenities in nearshore property value highlights tradeoffs between the private benefits from shoreline protection and provision of public goods through increased access to coastal amenities and mitigation of storm damage. Coastal amenities are not pure public goods. For communities with private beaches, coastal open space becomes collective-owned and is managed through private investment in shoreline protection, which provides higher amenity value than public beaches. Properties in a gated coastal community receive a premium of 18.6% compared to similar houses in a nongated community (Pompe, 2008). When the value of coastal amenities dissipates with distance, it becomes difficult to disentangle the value of access to the amenity and the level or quality of the amenity. Quasi-experimental analyses that link high-resolution geospatial characteristics with housing transactions data have made progress in identifying the marginal value of ocean view and access to beach amenities (Bin et al., 2008; Dundas, 2017; Morgan & Hamilton, 2011).

Table 1. Representative Empirical Estimates of Beach Amenities

Method

Range of Value

Region

Period

Study

Panel A: Amenities

Beach Width

Hedonic: Oceanfront property value

$193–8,800/foot

North Carolina, South Carolina

1980s, 1990s, 2000s

Gopalakrishnan et al. (2011); Landry et al. (2019); Pompe and Rinehart (1995a)

Hedonic: Oceanfront undeveloped land value

$754/foot

South Carolina

1983–1990

Pompe and Rinehart (1995a)

Hedonic: Nearshore property value

$65–254/foot

South Carolina Georgia

1980s, 1990s

Pompe and Rinehart (1995a); Landry and Hindsley (2011)

Hedonic: Nearshore undeveloped land value

$165/foot

South Carolina

1983–1990

Pompe and Rinehart (1995a)

Random utility model: Consumer surplus

−$6 to −$11/person/visit for reduction to 75 feet; $3.1 million/year for 50% increase

Mid-Atlantic Region, California

1997, 2000

Parsons et al. (1999); Pendleton, Mohn, et al. (2012)

Travel cost model

-$5.00/adult/day for 75% reduction; $2.75/adult/day for 100% increase; %7 per trip for 100 feet increase

Delaware, North Carolina

2000s

Parsons et al. (2013); Whitehead et al. (2008)

Setback Width

Hedonic: Nearshore property value

$9–14/foot

Seattle

1969–1974

Brown and Pollakowski (1977)

Gated Beach Community

Hedonic: Nearshore property value

18.6% premium

Charleston, South Carolina

2002–2005

Pompe (2008)

Beach Access

Hedonic: Nearshore property value

−22.3–30.5% / mile from beach; −$29 to −$105/foot to nearest beach

South Carolina, Florida, North Carolina

1990s, 2000s

Pompe (2008); Morgan and Hamilton (2011); Bin et al. (2008)

Waterfront Location

Hedonic: Nearshore property value

−36.2% / first 500 feet away from coast; $14,232/lot for present value of amenity flow

Florida, Virginia

1950s, 1970s

Milon et al. (1984); Shabman and Bertelson (1979)

Conjoint survey: Compensating variation

$8,990–9,804

Fairfield, Connecticut

1994–1996

Earnhart (2001)

Hedonic: Property value

$141,022/house for soundfront; 114.6—131.3% premium for oceanfront

North Carolina South Carolina

1990s, 2000s

Bin et al. (2008); Pompe (2008)

Oceanview

Hedonic: Nearshore property value

$995–1,228/degree of viewshed

Florida, North Carolina

1990s, 2000s

Bin et al. (2008); Morgan and Hamilton (2011)

Hedonic: Accommodation price

€0.02–€0.04/night

Schleswig-Holstein, Germany

2003

Hamilton (2007)

Elevation

Hedonic: Nearshore property value

2% premium/meter

Massachusetts

2000–2010

Jin et al. (2015)

Panel B: Adaptation

Beach Nourishment

Hedonic: Oceanfront property value

13.4% premium

North Carolina

2008–2014

Qiu and Gopalakrishnan (2018)

Contingent valuation: WTP

$7.45–8.70/household

North Carolina, Georgia

1998–2012

Kriesel et al. (2005); Landry et al. (2020)

Contingent valuation: WTP

$2.5/household

Song-do Beach in South Korea

2014

Chang and Yoon (2017)

Conjoint survey: WTP

-$3.65–4.45/household

New Hampshire and Maine

2000

Huang et al. (2007)

Dunes

Hedonic: Nearshore property value

3.6% premium for oceanfront property; $83–142/foot of dune width

New Jersey, Georgia

1990s, 2000s

Dundas (2017); Landry and Hindsley (2011)

Dikes

Hedonic: Accommodation price

-€0.01–0.03/night

Schleswig-Holstein, Germany

2003

Hamilton (2007)

Seawall

Hedonic: Nearshore property value

10% premium

Massachusetts

2000–2010

Jin et al. (2015)

Erosion

Hedonic: Nearshore property value

-0.2% premium/meter

Massachusetts

2000–2010

Jin et al. (2015)

Parking Improvement

Travel cost model: Consumer surplus

$25/trip

Southeastern North Carolina

2003–2004

Whitehead et al. (2008)

Beach Management

Contingent valuation: WTP

$6–22/household/day for relocation, $0.09/household/day for shoreline armoring

Georgia, North Carolina

1998, 2012

Kriesel et al. (2005); Landry et al. (2020)

Note: WTP, willingness to pay.

When beaches are actively managed and maintained through allocations of common pool sand resources, beach width is endogenous in the evaluation of coastal amenities. Management agencies that engage in beach replenishment are required to evaluate projects via cost–benefit analysis in the feasibility, scoping, and planning stages. A benefit of beach sediment is the protection of coastal property from storm surge and flooding and is an important criterion for project justification and assessment conducted by federal agencies (Landry, 2011). Consistent estimates of the implicit price require appropriate instruments to control for measurement error, omitted variables, and endogeneity in the provision of coastal amenities. For beach width, suitable instrumental variables (IV) would be correlated with width but would not directly influence housing prices. Applications have used spatial variation in geological features such as distance to the continental shelf and the presence of scarps on the beach (Gopalakrishnan et al., 2011) and distance to the “depth of closure” (Landry et al., 2019) to purge potential endogeneity from beach width measures.1 Estimates reveal that marginal implicit prices with IV can be nearly five times the naïve estimates that assume exogenous beach width (Gopalakrishnan et al., 2011), although this finding is sensitive to the policy setting (Landry et al., 2019). Downward bias may also reflect inherent beach dynamics; as beach width changes continuously, width measured at a point in time in a hedonic framework introduces measurement error.

While households derive utility from recreational amenities, wider beach, and ocean view, coastal development also faces risks from storms, flooding, and coastal erosion. Land and housing markets capitalize climate risks that are reflected in risk-price gradients for storm and flood hazards, often estimated using flood zones as exogenous sources of risk information (Bin & Polasky, 2004; MacDonald et al., 2012; Rambaldi et al., 2013). The present value of flood insurance payments is a signal of flood risks (Bin et al., 2008; Gopalakrishnan et al., 2016). Quasi-experimental analyses that use the occurrence of storms to infer the effect of hazard risk on housing values find that natural disasters can increase price risk gradients in hazard-prone locations (Atreya et al., 2013; Bin & Landry, 2013). Price discounts dissipate over time, but hazard events and the designation of flood maps convey risk information even in locations that do not experience direct impacts of flooding and hazard-related damages (Hallstrom & Smith, 2005; Johnston & Moeltner, 2019). In a novel application of prospect theory in the context of demand for flood insurance, choice experiments conducted in a low-lying river delta region in the Netherlands showed that the willingness to pay (WTP) for flood insurance is high enough for it to be profitable in multiple scenarios of flood probability and insurance coverage, even when private flood insurance is not available (Botzen & Bergh, 2009, 2012).

While hedonic pricing is commonly used in the nonmarket valuation of coastal amenities and risks, studies that combine housing market responses with dynamic modeling, simulations, random utility models, and contingent valuation methods provide policy insight to evaluate alternative long-term scenarios. Coupled physical and economic models illustrate the effects of stochastic coastal storms, replenishment costs, erosion rates, and federal subsidies on property values in determining the optimal frequency of beach nourishment (McNamara & Keeler, 2013). Simulations based on spatial hedonic models of the impact of sea‐level rise on coastal real estate on the North Carolina coast have shown the value of residential property loss for mid‐range sea‐level-rise scenarios (a 16 cm increase by 2030 or a 46 cm increase by 2080), without discounting, can be $179 million in 2030 and as large as $526 million by 2080 (Bin et al., 2011).

Impact of Coastal Adaptation on Housing Markets

Coastal communities respond to climate risks with public and private adaptation investments to maintain amenity flows and reduce the risk and damage of natural hazards. Adaptation measures usually can be categorized into three options: protection, accommodation, and retreat (Oppenheimer et al., 2019). Protection includes the construction of hard structures (e.g., seawalls) and green infrastructures (e.g., vegetated dunes) that protect coastal development and mitigate risk while maintaining status quo land use patterns. Accommodation includes adaptation measures, such as flood proofing and increasing building elevation, to lower the impact or damage from coastal hazards. Retreat indicates abandonment of coastal land, usually due to excessive cost or environmental influences of potential protection. Housing markets capitalize the implicit value of mitigation and adaptation measures but empirical analyses that conduct ex-post policy evaluation of erosion control and hazard mitigation are scant (Gopalakrishnan et al., 2018).

Quasi-experimental studies that exploit spatial and temporal differences in adaptation investments and the occurrence of hazard events show that nearshore housing prices capitalize potential storm risk reduction from beach nourishment in North Carolina (Qiu & Gopalakrishnan, 2018) and the construction of vegetated dunes along the New Jersey coast (Dundas, 2017). In coastal Massachusetts, property values reflect a 10% premium for seawalls and a 2% premium for an additional meter in elevation (Jin et al., 2015). Though seawalls and other hard structures protect oceanfront property, they can accelerate erosion, leading to overall beach degradation (Pilkey & Wright, 1988), and exacerbate erosion in neighboring regions (Ells & Murray, 2012). Spatial externalities from deflecting wave energy that affects alongshore sediment movement and peer effects that influence private adaptation investments can further accelerate shoreline armoring (Beasley & Dundas, 2021). A comparison of alternative gray and green adaptation strategies illustrates the long-term effectiveness of nature-based adaptation, such as wetland restoration and beach renourishment, in reducing coastal risk and restoring environmental habitats (Reguero et al., 2018). Because shoreline retreat is not common in practice, empirical analyses of ex-post policy evaluation are challenging. Empirically based simulation that examines economic efficiency of beach nourishment, shoreline armoring, and shoreline retreat in terms of the recreational benefits, property value effects of beach management, and management costs suggests that the property losses under a retreat strategy are similar to forgone management costs (Landry et al., 2003).

Whereas community-level adaptation responses protect people and development along the coast, there are unintended consequences related to delaying proactive decisions and long-term adjustments, such as relocation and individual mitigation investments. Reduced erosion and flood risk to buildings and other structures close to the beach can encourage more construction and addition to the stock of beachfront housing (Keeler et al., 2018). Migration of young populations away from tornado-prone areas and to flood-prone areas also suggests that public investment in rebuilding and protecting flood-prone areas crowds out private self-protection (Boustan et al., 2012).

Recreation Demand and Coastal Environment

Coastal ecosystems foster demand for recreation and leisure services, supporting vibrant tourist economies along the shoreline. The coastal tourism economy both drives and responds to market forces and public policy. Recreational fisheries, outdoor activities, and ecosystem services provide critical support for coastal economies (Barbier, 2012; Barbier et al., 2011). Random utility models of recreation demand estimate beach visitors’ WTP for beach quality, beach width, and water quality (Parsons et al., 1999; Pendleton, Mohn, et al., 2012). A recreational site choice model in the Mid-Atlantic region shows that beaches that are too narrow (<75 feet wide) or too wide (>200 feet wide) command negative implicit prices, as they are likely crowded or have more restricted access to water (Parsons et al., 1999). Combining a panel survey of beach visitors with detailed geophysical characteristics of California beaches, studies have shown utility gained from increased beach width; the benefit is the largest for residents in counties closest to the beach, and a 50% increase in width of a currently narrow beach could generate consumer surplus of over $3 million per year (Pendleton, Mohn, et al., 2012).

The WTP for reduced congestion, improved beach quality, and wider beaches from beach renourishment projects is also evident in stated preference studies (Bell, 1986; Lindsay et al., 1992; McConnell, 1977; Oh et al., 2008; Shivlani et al., 2003; Silberman & Klock, 1988). A contingent valuation study that directly elicited the WTP for a beach restoration project in South Korea found that the value of beach restoration was $2.50 per household annually and total economic benefits could be up to $229.8 million (Chang & Yoon, 2017). However, studies have also found that the benefits of an erosion control program can be exaggerated when its negative effects are ignored, such as the adverse impact on wildlife habitat, induced erosion of neighboring beach, and that deterioration of water quality. Understanding of the negative effects reduces beach visitors’ support for erosion control (Huang et al., 2007).

Combining revealed preference and stated preference methods, travel cost estimates show a significant increase in recreation demand with increased beach width and improved access due to beach nourishment and parking improvement at North Carolina beaches (Whitehead et al., 2000, 2008). Analysis applying similar methods to study beach visitors’ preference for beach management alternatives has shown that, accounting for nonuse values of beaches, the median WTP for shoreline retreat ($22.20) is higher than for beach nourishment ($7.45) and shoreline armoring ($0.09; Landry et al., 2020). In comparison, estimates from a study of beaches in Georgia indicate the value of nourishment policy ranges between $8.18 and $8.70, and relocation ranges from $5.65 to $7.68 in Georgia beaches (Kriesel et al., 2005). Similarly, evaluation of Delaware beaches has shown a loss of about $5.00 per day for a three-fourths reduction in beach width, currently between 50 and 100 feet, and a gain of $2.75 per day from doubling the current width (Parsons et al., 2013).

It must be noted that revealed preference methods, such as travel cost models and hedonic pricing, are limited to measuring use values of coastal amenities and do not capture indirect benefits of beaches such as the existence, bequest, and option values provided by marine ecosystem services (Ariza et al., 2012), which is discussed in the next section.

Coastal and Marine Ecosystem Services

Coastal and marine ecosystems are increasingly threatened by the impacts of climate change and human activity (Brander et al., 2012; Chen et al., 2015; Crosby et al., 2016; Ratliff et al., 2015). Coastal landforms persist in a delicate balance between sediment supply, wave climate, sea-level and geophysical pressures and offering attractive environments that enable economic development based around tourism, recreation, and natural resources. Coastal wetlands include salt marshes, mangroves, coral reefs, and seagrass beds in places where land is rebounding from historic pressures. Coastal ecosystems contribute directly and indirectly to human well-being through ecological functions that produce ecosystem services and maintain habitats for marine flora and fauna. Along with consumptive use values like fisheries and nonconsumptive use values including recreation, transportation, and flood control, coastal ecosystems also provide vital nonuse values such as biodiversity and cultural diversity (Barbier, 2012).2 Reliable estimates of the value of coastal ecosystem services are a necessary input for cost–benefit analyses to evaluate policies to protect and maintain ecosystem services.

Valuing coastal ecosystem services requires the integration of ecological production functions, which is further complicated by the spatial and temporal feedbacks across different forms of natural capital (Barbier et al., 2011). The economic value of coral reefs reflected through tourism and recreational activities have been estimated at $1.26 million per year ($2,274 per hectare per year) in the Philippines (Samonte-Tan et al., 2007) and at $1,500 to $2,140 per hectare per year in Sri Lanka (Spurgeon, 2001). A study linking various sources of visitor and expenditure data, industry data, and social media data sets in a spatial model estimated that the value of coral reef adjacent and on-site tourism is about $35.8 billion globally every year and showed an uneven distribution of coral reef over the globe (Spalding et al., 2017). A dichotomous choice contingent valuation study to estimate nonuse values of the Tubbataha Reefs showed the mean WTP for reefs conservation, including bequest value, ranged from 233 to 437 Philippine pesos ($4.8–8.9; Subade & Francisco, 2014).

Coastal ecosystems, especially wetlands, serve a protective role by providing buffers to storm damage (Shepard et al., 2011). Combining hydrodynamics, storm simulation, and housing values, research shows that the presence of coastal marshes and vegetation can reduce storm surge levels and protect property (Barbier et al., 2013). The mitigation effect of coastal wetlands was also found in study of Texas coast where potential marsh area decreases induced by future sea‐level rise will increase flood heights and thus increase the required levee height by 12% and defense cost by 8% (Reddy et al., 2016).

The role of coastal ecosystems in blue carbon sequestration is especially noteworthy against the background of global warming (Himes-Cornell et al., 2018). Estimates indicate that 0.15–1.02 Pg (billion tons) of carbon dioxide, equivalent to 3%–19% of deforestation emissions and economic damages of $6–42 billion, are released annually at a global scale due to lost sequestration in the conversion of vegetated coastal ecosystems (Pendleton, Donato, et al., 2012). Policies can be targeted to preserve ecosystem services for mitigating climate change and biodiversity loss; in Indonesia it is estimated that the creation of marine protected areas reduced loss of mangrove forests by 14,000 hectares, which reflects avoided blue carbon emissions of approximately 13 million metric tons between 2000 to 2010 (Miteva et al., 2015).

Co-evolution of Development, Human Populations, and Risk Exposure in Coastal Habitats

Physical evolution of the coastline and landscape influences coastal economies. Coastal management practices are both influenced by natural processes and alter natural processes in ways that can enhance or reduce the habitability of sandy coastlines (e.g., Ells & Murray, 2012; Leatherman, 1979; Magliocca, 2008; Rogers et al., 2015). Understanding trends and feedbacks in the co-evolution of coastal and economic systems is essential to develop policies that can increase community resilience.

Trends and Projections of Coastal Population Growth

Coastal zones have always attracted human settlements and are regions of focused economic infrastructure and activity. Eight of the top 10 metropolitan regions in the world are located along coasts and 38% of the global population is estimated to live within 100 kilometers of a coast (Small & Cohen, 2004; Small & Nicholls, 2003; United Nations Atlas of the Oceans, n.d.). Low elevation coastal zones, the contiguous coast that is less than 10 meters above sea level, cover 2% of Earth’s surface area and contain 10% of the world’s population, which is projected to increase by more than 50% by 2030 to more than a billion people by 2060, even under the lowest growth rate scenario (McGranahan et al., 2007; Neumann et al., 2015). In the United States, coastal counties adjacent to the ocean, major estuaries, or the Great Lakes represent 10% of the total land area and support over 40% of the U.S. population with a population density (446 persons/mi2) over three times the national average (105 persons/mi2). Coastal communities have evolved in a consistent manner, with population growth of over 40% from 88.4 million in 1970 to 123.4 million in 2010 (Crossett et al., 2013; NOAA Office of Coastal Management, 2016).

Coastal regions face increasing natural hazards that make the coastal economic-geophysical system especially vulnerable to the impact of climate change. As coastal population continues to grow, higher exposure to climate-induced natural disasters has resulted in a dramatic increase in damage to lives and coastal infrastructure as illustrated by the steadily increasing number of billion-dollar natural disasters each year (NOAA National Centers for Environmental Information, 2021). Human responses to mitigate vulnerability alter regional patterns of coastline change, which ultimately affect future human coastline modifications. Therefore, understanding socioeconomic institutions, policy levers, and interaction between economic activity and geophysical processes that affect rates of development and human mobility along the coast is critical for sustainable management of coastlines.

Human Mobility and Changing Coastal Communities

Rising household incomes and demographic transitions of populations into retirement have increased the demand for recreational amenities provided by the coastal environment. Consequently, investments in coastal development have shifted to residential housing, commercial business, and public infrastructure. Economic, legal, and political institutions at multiple levels influence coastal development patterns and access to coastal natural resources. As discussed, housing prices encompass flows of value from coastal amenities (e.g., nice views, wide beaches) and disamenities (e.g., pollution, storm risks), which can be quantified in hedonic pricing models. The distribution of property values across locations along the coast then depends on economic agents who decide where to locate, sorting into different properties and markets according to their income and preference for amenities. Combining hedonic price functions with a description of the underlying utility maximizing process that stratifies heterogeneous agents into communities with varying levels of amenities, equilibrium sorting models explain how property prices will respond to policies that affect the provision of amenities (Bayer & McMillan, 2012; Bayer et al., 2016; Bayer & Timmins, 2007; Epple & Romer, 1991; Epple & Sieg, 1999; Klaiber & Phaneuf, 2010; Kuminoff et al., 2013; Tiebout, 1956). Because households respond to environmental change through housing location choices, external shocks to the community (e.g., through climate-induced hazards) not only affect property values but also induce changes in community size and demographic compositions (Banzhaf & Walsh, 2008, 2013; Epple et al., 2012).

Quasi-experimental analyses of natural hazards also provide empirical evidence of climate change–induced demographic change at the community level. An ex-post evaluation of the extent of damage caused by Hurricane Andrew in 1992 in Dade County, Florida, combined with decennial census demographic data indicated that areas with high damages experience faster population growth. However, the change in population also reflects a decrease in white renters and middle-income households and an increase in Hispanic homeowners and renters, and households with lower income (Smith et al., 2006). High-income households, with annual incomes over $150,000, were largely unaffected by the hurricane, suggesting that heterogeneity in hazard insurance and the ability to adapt drives human mobility along the coast. In the Gulf Coast, it was found that counties that experience higher hurricane damage had slower growth in population, indicating a withdrawal from risk. However, the study also found a larger decrease in white and young adult population relative to black and elderly residents in counties with lower poverty rates (Logan et al., 2016), further indicating that populations with fewer economic resources have fewer adaptation choices and face higher vulnerability.

A long-term study on the impact of natural disasters between 1920 and 2010 indicated that severe disasters that result in loss of life (25 or more deaths) increased out-migration rates at the county level by 1.5%, due to decreases in economic opportunities and labor demand and updated beliefs about future disaster risk (Boustan et al., 2020). For households that migrate to counties severely affected by natural disasters from nonadjacent states, the likelihood of owning their residence decreases by 3–5% (Sheldon & Zhan, 2019). Research on the long-run impact of the Netherlands flood in 1953 showed an immediate decrease in population growth, but in the long term, due to the flood protection program (Deltaworks), there was an increase in population in flood-prone areas (Husby et al., 2014). This research echoes a similar historical study of disasters in the United States that suggested that flood mitigation measures can attract more population to risky areas, which increases risk exposure and vulnerability (Boustan et al., 2012).

The Role of Government Investments in Coastal Adaptation and Critical Infrastructure

Coastal infrastructure critically affects trends in development and population growth in the coastal areas. Reliable access to energy grids, transportation, communication networks, and wastewater treatment affect development costs and the quality of life in communities. Critical infrastructure is vulnerable to the impact of climate change through flooding, coastal erosion, land subsidence, and saltwater intrusion (Azevedo de Almeida & Mostafavi, 2016; Nazarnia et al., 2020). Episodic extreme events can cause system failure and considerable damage to infrastructure. It is estimated that the energy infrastructure exposed to hurricane storm surge in the United States could increase by up to 67% by 2060 under the National Climate Assessment (NCA) high sea-level rise scenario (Bradbury et al., 2015). Furthermore, up to 5% of energy assets could be inundated by 2100 in major coastal cities such as New York City, Huston, Miami, and Los Angeles under the NCA intermediate-high scenario (Office of Electricity Delivery and Energy Reliability, 2014).

The optimal level of adaptation of coastal civil infrastructure in response to increasing climate change risks requires multidisciplinary efforts to find the right balance between the financial burdens of overinvestment and potential system failures caused by underinvestment. Studies of highway networks that demonstrate system resilience to random occurrences of extreme events can help direct adaptation resources to the most vital locations (Testa et al., 2015). Barrier island erosion and transgression affect the probability of failure for highway bridges between barrier islands and mainland due to changes in wind-setup and back-bay interactions, providing engineering pathways for increasing resilience of transportation infrastructure (Anarde et al., 2018). Financially sound resilience-improving measures such as redundancies between wastewater treatment plants and adaptive hazard mitigation practices can increase flood preparedness of coastal wastewater treatment plants to coastal flood risk (Karamouz et al., 2019). Establishing shoreline protection zones also affects human activities regarding population and housing, oil and gas infrastructure, transportation infrastructure, and water management infrastructure (Laska et al., 2005).

Coastal management in the United States has historically been funded through federal budget allocations; cumulative expenditures on beach renourishment projects alone exceed $7 billion (PSDS, 2015). An empirically grounded coupled model of the coastal-economic system indicated that federal subsidies for beach nourishment created a bubble in coastal real estate markets and the removal of nourishment subsidies could result in a significant decrease in coastal property values (McNamara et al., 2015). As the growing coastal population increases demand for shoreline management, state and local governments face increasing pressure to raise funds that can support investments in coastal adaptation (Qiu & Gopalakrishnan, 2018). Local municipalities have supported extensive development along the coast that increases tax revenues from high-valued oceanfront properties. As sea-level rise inundates coastal infrastructure and properties, municipalities face decreasing revenues but increasing adaptation and social welfare expenditures in the long term. Research in Massachusetts indicated increased vulnerability to sea-level rise as coastal municipalities faced increased fiscal costs of adaptation. The heterogeneity in the extent of sea-level rise impact also highlights regional inequality in vulnerability and adaptation capacity (Shi & Varuzzo, 2020). Local decision-making for beach nourishment is expected to increase and will impact costs and benefits occurring at local levels. To pay for nourishment, many towns along the U.S. coast use specially designated property taxes to fund beach nourishment. An agent-based model that evaluated the effect of decentralized nourishment costs on coastal management indicated that a targeted policy that imposes higher rates of taxes on near shore property owners, who receive the largest benefits from shoreline protection, can facilitate local investments in maintaining shoreline stabilization in the long term. (Mullin et al., 2019).

Coastal Development, Risk Exposure, and the Unintended Consequences of Adaptation

Unintended consequences of interactions between economic decisions and coastal processes can stem from a variety of human interventions on the coast, including beach nourishment, levee construction, and repair of roads and infrastructure. Actions to stabilize coastal development and property values after storms and other natural disasters also incentivize maintenance of existing configurations of communities, potentially increasing vulnerability to future events. Infrastructure investments to recover from episodic storm events can increase development, thereby placing more value at risk (Li et al., 2021; McNamara & Werner, 2008). Positive feedback between adaptation and value of development at risk accelerates public investment in shoreline protection and crowds out private defensive investments in locations that face high climate risks (Kousky et al., 2006). Research on the economic and geophysical drivers of adaptation decisions shows that housing stock and housing values positively affect community-level investments in shoreline protection (Beasley & Dundas, 2021; Qiu et al., 2020). Simultaneously, as regions recover from hurricanes, they tend to “build back bigger.” A study of five hurricane-affected regions along the U.S. Atlantic and Gulf coasts found that the average plan view footprint of individual residential units increased by 15–44% in 2017 compared to the prestorm footprint (Lazarus et al., 2018).

A detailed cross-sectional study of statewide coastal properties in Florida in 2010 showed that locations with beach nourishment had 1.5 times the total number of properties relative to zones without it. Housing density and housing units per kilometer of shoreline were higher, and the average property size was larger, indicating a positive feedback between nourishment and development that increases coastal vulnerability (Armstrong et al., 2016). Similarly, an analysis of spatial and temporal patterns of coastal development in North Carolina suggested that, controlling for geophysical and economic feedback that make neighboring property prices and adaptation investment endogenous, beach nourishment projects accelerate the development of nearshore residential properties (Li et al., 2021).

Climate-Induced Migration in Developing Countries

As climate change intensifies natural hazards, low-lying regions in developing countries with large populations face increased migration from the coast to urban inland centers (United Nations Environment Programme, 2016). Population projections with high-resolution demographic, socioeconomic, and climate data indicate that more than 140 million people in sub-Saharan Africa, South Asia, and Latin America are likely to be displaced by 2050 due to the chronic impacts of climate change, raising serious concern about the capacity of infrastructure and social support systems (Rigaud et al., 2018). Targeted investments and policies are necessary for migration to be a viable climate change adaptation strategy.

The impact of climate change—increased droughts, sea-level rise, more frequent storms, and an increase in the number of flooding events—affect migration decisions in combination with socioeconomic, political, and demographic factors. These effects alter future risk, opportunities for livelihood, and resource conflicts in disaster-prone locations (Black et al., 2011; Cattaneo et al., 2019). Studies consistently show a significant effect of climate variability on internal migration and displacement of population in developing regions of Africa, Asia, and South America (Berlemann & Steinhardt, 2017; Warner et al., 2010). The emergence of “hotspots” of climate-induced in-migration poses a significant challenge to climate-sensitive regions with limited infrastructural preparedness and a lack of social support. Analysis linking high-resolution climate data to migration in Kenya, Uganda, Nigeria, Burkina Faso, and Senegal has shown heterogeneous effects of variability in temperature and precipitation on migration (Gray & Wise, 2016). These findings challenge the generalizability of migration responses at global scales and suggest that migration may serve multiple objectives, including climate adaptation strategy and investment strategies, for households who can move to take advantage of beneficial agricultural conditions (Gray & Wise, 2016). Data from the Living Standards Measurement Study–Integrated Surveys on Agriculture from 2009 to 2014 for Ethiopia, Malawi, Tanzania, and Uganda show that climate-induced migration is most pronounced in urban areas, where climate effects are compounded by impact on local employment (Mueller et al., 2020). Evidence from India and the Philippines supported the hypothesis that migration opportunities can insure against climate-induced volatility in agricultural yield, and opportunities for employment, better information, and strong social networks enhance adaptive capacity of households that migrate (Jha et al., 2018; Yang & Choi, 2007). Empirical analysis with a nationally representative data set that tracked socioeconomic characteristics, environmental characteristics, and household migration decisions at the subdistrict level in coastal Bangladesh found that inundation due to sea-level rise has negligible effects on migration and agricultural production whereas gradual increases in soil salinity due to aquaculture drives internal migration of household members, after controlling for income losses (Chen & Mueller, 2018). An agent-based model of the migration dynamics in Bangladesh projected between 3 and 10 million internal migrants over the ensuing 40 years, who would move from the drought-prone western districts and areas vulnerable to cyclones and floods in the south toward northern and eastern districts (Hassani-Mahmooei & Parris, 2012).

Migration is an extreme form of adaptation to climate change. As the climate impacts hit the poorest and most climate vulnerable areas the hardest, research to understand migration patterns is critical for developing countries to plan and prepare for the demographic and socioeconomic changes induced by climate migration.

Geoeconomic Models of the Coupled Coastal-Economic System

Geophysical processes, such as alongshore sediment transport, local wave climates, sediment overwash and deposition, and sea-level rise, affect the stock of coastal resources. Even in the absence of climate change, physical and biological processes are constantly changing the coastal environment. Some coastal processes are fast and have immediate effects on humans whereas others unfold on time scales that are slow from the perspective of economic welfare. Geoeconomic models represent coastal resources as natural capital that generates a stream of services over time (Gopalakrishnan et al., 2016). Capital-theoretic models explore interactions between complex physical processes and economic decisions made by humans who depend on coastal resources (Landry, 2011; Smith et al., 2009) to evaluate efficiency and sustainability of resource allocation mechanisms.

Shoreline stabilization to protect coastal property and infrastructure, particularly through beach renourishment, the process of rebuilding an eroding section of the beach by dredging sand from another location (Dean, 2003), involves a choice of how often and how far out to build a beach. Nourishment is done periodically rather than continuously because it is characterized by high fixed costs—identifying sand borrow sites, mobilizing dredges, project engineering, and extensive permitting. The periodic nature of nourishment makes it analogous to the problem of managing forests, where forest managers choose how often to clear-cut and replant a forest stand (Gopalakrishnan et al., 2018; Smith et al., 2009). Nourishment can be represented as a periodic capital investment that provides benefits (amenity flows and storm protection), which are capitalized by coastal property values, and a beach manager chooses the optimal frequency of nourishment events to maximize net benefits. Investments in shoreline defense generate feedbacks that affect the rate of change in beach capital through accelerated erosion of the extended beach. As climate change accelerates the rate of coastline change, an important policy consideration for beach managers is whether beach replenishment is sustainable over long time horizons (Landry, 2011; McNamara & Keeler, 2013). A dynamic programming framework can also be used to evaluate long-run coastal adaptation decisions at the extensive margin. Determining the optimal timing of transition away from active shoreline defense to a policy of shoreline retreat, given assumptions about sea-level rise and costs and benefits, is essential to better adjust to long-term risks (Landry, 2011). Coupled models often use an agent-based framework to link human behavior with natural system dynamics. In a numerical model that couples the evolution of a barrier island with the development of a coastal resort community, McNamara and Werner (2008) found landscape patterns with periods of island stability and economic development alternating with periods of island migration and economic loss. These landscape patterns do not emerge from the dynamics of either the economic or coastal system alone, but from the feedbacks between them.

Spatial Dynamic Feedbacks in the Coupled Coastal Economic system

Coastal management decisions in one location can create spatial externalities that affect geophysical conditions and the flow of economic benefits in other locations along the coast. Stabilization of an eroding shoreline—either through beach nourishment or hard structures—can trigger striking coastline offsets in a matter of decades and affect long-term rates of shoreline change over surprisingly long distances up to tens of kilometers (e.g., Ells & Murray, 2012; Slott et al., 2010). Ignoring physical and economic implications of spatial-dynamic interaction between neighboring towns that implement localized stabilization policies can result in suboptimal outcomes relative to coordinated management of the coastline (Gopalakrishnan et al., 2017). In an extended coastline, when multiple towns make decentralized shoreline management decisions, spatial-dynamic feedbacks can lead to heterogeneous adaptation investments and the emergence of striking disparity in the distribution of net benefits among winners (free riders) and losers (suckers; Williams et al., 2013) and can effectively destabilize the entire coastal system (Lazarus et al., 2011).

Climate adaptation and poverty reduction are inextricably linked, making the global challenge more complex. An experimental study showed a collective-risk social dilemma to avert poverty- and climate-induced migration where the relatively rich participants try to prevent migration by the relatively poor. In the long run the game demonstrates a social welfare–reducing dilemma, where rich participants are willing to contributing to climate mitigation only when the poor experience climate extreme events that increase poverty, and the poor are willing to compensate some reduced effort by the rich toward the mitigation target only if the rich continue to contribute above a minimum threshold (Marotzke et al., 2020). Numerical results from geoeconomic modeling of the coupled coastline also show that geophysical characteristics can exacerbate challenges stemming from economic heterogeneity. Coordinated management can reduce economic inequality (represented by differences in average housing values across locations), but it often imposes higher costs on towns with lower baseline values, which suggests the need for spatially targeted subsidies when there is economic heterogeneity (Gopalakrishnan et al., 2017).

When human and natural systems are coupled, feedbacks can lead to emergent dynamics that are not related either to the human or the natural systems. If we know that human intervention influences the dynamic physical system, can we begin to think of optimally managing entire coastlines for a state or a country? In the generalized model, dynamics of beach evolution along a continuous coastline can be modeled as a spatially diffusive process with nourishment sand moving from regions of high concentration to low concentration (smoothing bumps). When an eroding beach is seen as a depleting natural resource, the beach manager has three choice variables—time of nourishment, quantity of nourishment, and the location of the nourishment project. Nourishment decisions at any given location affect future beach widths at all other locations. Coastal management can therefore be viewed as a multidimensional capital accumulation problem in resource economics. Because climate change can accelerate a number of natural coastal processes, more coastal processes become immediately relevant for human decision-making and questions of optimal capital accumulation.

Intensive and Extensive Margins in Coastal Adaptation

The discussion of shoreline stabilization via beach nourishment helps describe coastal adaptation as a geoeconomics problem. However, nourishment is not the only coastal adaptation strategy available to coastal managers, and it is not relevant to all coastlines. Coastal managers have a variety of management options, including the construction of hard structures to combat erosion, construction setbacks, land use restrictions, and ultimately retreat from the nearshore environment. Some of these tools are used in combination. For example, it is common in the U.S. Mid-Atlantic region to build a sea wall and nourish beaches on the seaward side of the wall.

Coastal engineering tools and the choice of adaptation strategies to pursue can be viewed as extensive margins of coastal climate adaptation. Systematic retreat from the coast, facilitated through buyout policies and aggregate outcomes of household migration decisions, also influence extensive margins of adaptation. Some of the first work on coastal climate adaptation in economics focused exclusively on the extensive margin of retreat (Yohe et al., 1995), assuming there was no intensive margin. Many communities along sandy coastlines in the eastern United States (especially the Atlantic and Gulf coasts) rely primarily on beach nourishment (often done in conjunction with shoreline armoring) as an erosion control strategy. Decisions of where and how often to nourish a beach are an intensive margin. Beach nourishment may become unsustainable as climate forcing intensifies, and communities may need to engage in increased use of hardened structures or retreat from the coast altogether (Ells & Murray, 2012; Slott et al., 2006).

Future Research Needs

Developed coastal environments are tightly coupled systems driven by feedbacks between human and natural dynamics that occur across multiple spatial and temporal scales, often accelerated by climate forcing (sea-level rise and changing storm patterns). With cascading long-term effects of shorter timescale dynamics, adaptation strategies to recover from hazardous events can be maladaptive in the long term. Similarly, spatial feedbacks imply that local adaptation decisions affect adjacent and sometimes distant communities. Advances in the literature on the geoeconomics of coastal management have furthered our understanding of the intensive and extensive margins of shoreline stabilization, but little is known positively or normatively about long-run implications of adaptation strategies that maintain developed coastlines and the inevitable transition to planned retreat from the shoreline in some locations. Understanding the dynamics of coupled human–natural coastal systems—and the mechanisms by which climate forcing, socioeconomic changes, and policy interventions affect these systems—will allow decision makers to steer the coastal system away from undesirable future states and toward sustainable community outcomes.

Research on the impact of coastal hazards on human mobility focuses on the short-term effects and disruptions associated with evacuation and dislocation. Understanding long-term migration into and out of coastal areas exposed to climate risk is needed to inform community adaptation responses and population redistributions as a consequence of climate change. Much work remains, including key questions about the impact of climate stressors on community composition: How is the demographic composition of coastal communities likely to change over long periods? Does hazard-induced migration increase income and racial segregation in coastal communities? Can feedbacks between adaptation decisions, residential development, and real estate prices and spatial and temporal trends in development result in coastal vulnerability hotspots?

Coastal climate adaptation poses a grand challenge not only due to the research needed to understand individual pieces of the coupled system but also because of the challenge of integration across subfields in environmental and resource economics. Geoeconomic models of the coupled coastal-economic system begin to bridge the gap between welfare-theoretic nonmarket valuation methods in environmental economics and capital-theoretic resource economics models to develop empirically grounded dynamic models of optimal shoreline management decisions. Within resource economics, coastline management has parallels to renewable resource problems in forest economics and spatial-dynamic resource management. Coastal climate adaptation involves policies at multiple spatial scales with tradeoffs along multiple margins. Rapidly increasing computational capacity and the availability of high-resolution data can advance coastal-economic modeling with increased geophysical and economic realism. Ultimately, examining tradeoffs along multiple margins with forward-looking models of coastal management will push new transdisciplinary approaches to understand and model complex adaptive systems.

Links to Digital Materials

Further Reading

References

Notes

  • 1. A scarp is a steep slope or miniature cliff, formed by wave action, in front of the berm on a beach.

  • 2. Barbier (2012) and Brander et al. (2007) provide comprehensive surveys of nonmarket valuation of coastal ecosystem services.