Question:
You are given the full text of a scientific research paper.

    Summarize the **research methods** used in the study in a way that is informative for policy audiences. Your response should:
    - Describe the overall study design, data sources, tools, and models used
    - Avoid unnecessary technical details or equations
    - Use accessible but precise language

    Write a short paragraph suitable for the “Scientific Research Study Methods” section of a policy brief.

    Scientific Research Paper:
    Nature Climate Change | Volume 14 | September 2024 | 936–942
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nature climate change
Article
https://doi.org/10.1038/s41558-024-02082-3
Removing development incentives in risky
areas promotes climate adaptation
Hannah Druckenmiller
  1,2,3,6
, Yanjun (Penny) Liao
  2,6, Sophie Pesek
  4,
Margaret Walls
  2 & Shan Zhang5
As natural disasters grow in frequency and intensity with climate
change, limiting the populations and properties in harm’s way will be
key to adaptation. This study evaluates one approach to discouraging
development in risky areas—eliminating public incentives for development,
such as infrastructure investments, disaster assistance and federal flood
insurance. Using machine learning and matching techniques, we examine
the Coastal Barrier Resources System (CBRS), a set of lands where these
federal incentives have been removed. We find that the policy leads to lower
development densities inside designated areas, increases development in
neighbouring areas, reduces flood damages and alters local demographics.
Our results suggest that the CBRS generates substantial savings for the
federal government by reducing flood claims in the National Flood Insurance
Program, while increasing the property tax base in coastal counties.
Limiting exposure to climate risks is an important aspect of adapta-
tion to climate change, particularly in coastal areas. As sea levels rise,
tidal flooding worsens, and coastal storms become more frequent and
severe, limiting the number of people and properties in harm’s way will
be key to managing climate damages.
In the United States, state and local governments are primarily
responsible for land-use and zoning decisions, but federal policies also
play important roles in shaping development. Federal investments in
roads, utilities and other infrastructure lay the groundwork for popu-
lation growth. Other government programmes, such as the National
Flood Insurance Program (NFIP), which offers flood insurance at sub-
sidized rates in most locations, and disaster assistance programmes,
which provide funding for disaster recovery, also affect location deci-
sions. By partially shifting disaster costs from property owners to the
government, these programmes reduce the financial disincentives to
development in risky areas.
Whether withdrawing some of these financial incentives would
curb development, lower the costs of disasters and help communities
prepare for climate change is unclear. Many factors affect development
decisions and federal incentives are only some of the factors at play.
Empirical research into this question is limited because few policy
experiments exist where a clear comparison can be made of ‘treatment’
settings, where incentives for development have been removed, and
‘control’ settings, similar areas where such incentives remain.
One such experiment does exist, however. The 1982 Coastal Barrier
Resources Act (CBRA) designated certain areas along the Atlantic and
Gulf coasts as a Coastal Barrier Resources System (CBRS). In these areas,
most new federal expenditures and financial assistance are prohibited.
This includes the prohibition of federal funding for new infrastructure,
disaster relief and flood insurance through the NFIP, among others. The
law intends to transfer the full cost of private development in these areas
from taxpayers to property owners. Besides removing federal incentives,
CBRS designations do not otherwise prohibit development. Because the
policy is both less restrictive and less costly than most land-use regula-
tions, it may offer an attractive option for managing development in
areas at high risk of natural disasters and climate change.
In this study, we leverage the CBRA to study the long-term eco-
nomic impacts of removing federal incentives in high-risk areas and
assess its efficacy as a land conservation and climate adaptation strat-
egy. We not only ask how effective the CBRA has been at discouraging
development within designated areas, but also assess the spillover
effects of the policy on neighbouring areas.
Received: 8 September 2023
Accepted: 5 July 2024
Published online: 5 August 2024
 Check for updates
1Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA. 2Resources for the Future, Washington, DC, USA.
3National Bureau of Economic Research, Cambridge, MA, USA. 4Energy and Resources Group, University of California, Berkeley, CA, USA. 5Department
of Economics, Old Dominion University, Norfolk, VA, USA. 6These authors contributed equally: Hannah Druckenmiller, Yanjun (Penny) Liao.
 e-mail: hdruck@caltech.edu

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by building footprints), a 41% reduction relative to control areas
(Fig. 3a). Using an alternative measure of development, we estimate
that CBRS units have 0.044, or 83%, fewer buildings per acre than con-
trol areas. The magnitude of these effects highlights the central role of
federal incentives in shaping development patterns along vulnerable
coastlines. Our results pass the standard synthetic control placebo
test (Extended Data Fig. 5) and are robust to the use of an alternative
research design (Suppmentary Section C).
Local officials may be concerned that by curbing development
levels, CBRS designations may adversely affect local property revenues.
Using property-level data from Zillow, we do not find evidence of such
an effect. We find that CBRS designations increase mean sales prices
and total assessed value per acre within designated areas, although
the estimates are not statistically significant. Still, the positive effect
on prices is consistent with prior evidence that, despite increasing
costs for landowners, coastal land-use regulations can increase local
property values by restricting supply and generating nature-based
amenities30. A higher assessed value per acre indicates that the lower
development densities are offset by higher average values per property.
We find no evidence of systematic differences in the characteristics of
properties in CBRS and control areas.
Finally, we examine the impact on local demographics. Because
CBRS designations transfer the costs of development and disasters
to state and local governments and property owners, the policy may
attract homeowners who are more able to bear these costs. Using
data from the American Community Survey, we find that CBRS des-
ignations increase median household income by US$15,000, or 19%,
relative to control areas, and increase the rent-to-income ratio by 2
p.p., or 6%. Thus, CBRS areas tend to attract more affluent residents
and have become less affordable for renters. Indeed, the CBRA raises
the barrier to entry in designated areas by, for example, necessitat-
ing self-insurance against floods31,32. Other land-use regulations,
such as the California coastal boundary zone, have led to similar
demographic shifts29.
The occupancy rates within CBRS areas are 14 p.p. lower than
those in control areas, suggesting a greater prevalence of second
homes or vacation rentals, consistent with policy resulting in greater
natural amenities. Finally, CBRS designations changed the racial
makeup of residents. While CBRS areas already contained more white
people than average coastal areas before designation, we estimate
that the policy increased the share of white people by 3% (2.9 p.p.)
and reduced the share of Black people by 29% (−1.8 p.p.) relative to
the control group.
A recent study employed a spatial regression discontinuity design
to compare long-term changes in development densities between
CBRS units and areas just outside their boundaries in five states1 (an
update of the authors’ earlier study that used cross-sectional data and
an ordinary least squares regression design2). The authors find lower
development rates by more than 75% in CBRS areas. However, the
regression discontinuity approach, which uses neighbouring areas for
comparison with the CBRS, is not appropriate if the CBRS boundaries
are based on development levels (Extended Data Fig. 1) and does not
allow for the estimation of spillover effects, which our results show
are important. Our study provides a comprehensive assessment of
the policy’s impact on flood damages, property markets and develop-
ment, sociodemographic outcomes, and local government finances.
We develop an alternative method for estimating the causal
effects of CBRS designations. We construct a control group to com-
pare with the CBRS treatment group using a spatial machine learning
technique known as regionalization in combination with a synthetic
controls research design. This procedure allows us to mimic the pro-
cess by which natural resource planners determined CBRS boundaries
based on geomorphic and development features (Extended Data
Fig. 2), thus identifying a set of coastal areas that could have been
selected for CBRS designation in 1982 but were not. To illustrate our
approach, we show one example of a CBRS treatment area and con-
structed control area, overlaid with parcel-level data on the value of
properties (Fig. 1).
Our analysis addresses several open questions about land-based
climate adaptation. First, it has long been suggested that federal incen-
tives play a role in encouraging development in risky areas3, yet quanti-
tative research on this question is limited4–8. We evaluate whether the
removal of these incentives on coastal lands has been a cost-effective
adaptation strategy. Second, by examining the spillover effects of
the policy on surrounding lands, we provide estimates of how natural
infrastructure affects coastal property values and flood damages, add-
ing to a large literature on the hazard protection and amenity value of
natural lands9–19. Third, our analysis sheds new light on how removing
federal incentives affects local government finances by providing the
first estimates of how the CBRA affects property tax revenues. Finally,
we show that CBRS designations led to demographic changes, adding
to the literature on residential sorting behaviours in response to envi-
ronmental conditions and land-use regulations20–29.
Identification of control regions
We measure the effects of removing federal financial incentives for
development by comparing outcomes in CBRS units and control areas
today. This approach relies on finding control areas that were statisti-
cally indistinguishable from treatment areas at the time of designation
in 1982, while measuring divergence in outcomes over the four decades
since, based on treatment status (Methods).
Our spatial machine learning and matching procedure identifies
a set of control regions that closely resemble CBRS treatment areas
before the policy is implemented (Table 1, Extended Data Table 1 and
Extended Data Fig. 3). Treatment and control areas share common
pre-trends in development densities (both inside the designated
units and in spillover areas) and are balanced across a wide array of
pre-treatment observable characteristics, including measures of land
cover, elevation, infrastructure density, proximity to urban centres,
income and flood risk. To illustrate the output of our procedure, we
plot examples of treatment and control units (Fig. 2) and map their
geographic distribution (Extended Data Fig. 4).
The effect of removing federal incentives within
the CBRS
We find that the CBRS has been effective at curbing development within
designated areas (Table 2). CBRS designations see 0.12 percentage
points (p.p.) less built-up surface area (the percent of land covered
Treatment
Control
Higher
Lower
Assessed value
Unit
Spillover
area
Each dot is
a property
Fig. 1 | Overview of research design. Left: an example CBRS (treatment) area,
with its spillover area (2 km buffer). Right: same, but for a control area. Properties
from ZTRAX, which comprises one set of outcomes studied here, are overlaid on
the maps as points. The colour of the points corresponds to the total assessed
value of the home.

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Heterogeneous effects on development
The efficacy of CBRS designations at detering development may
depend on surrounding natural and social systems, including state and
local policies. Here, we explore where CBRS designations are more and
less effective by estimating unit-specific treatment effects (Methods).
There is a wide distribution of individual treatment effects on
built-up surface area (Fig. 3b). Nearly 80% of the unit-level estimates
are negative, suggesting that CBRS designations prevent development
in most cases. However, the range of estimates is large. Comparing the
characteristics of the most and least effective units (Extended Data
Table 2), we find that more effective units tend to have lower built-up
areas at the time of designation and be larger in size. Natural resource
planners ought to consider these characteristics when making new
CBRS designations. However, we find no significant differences in
pre-treatment land cover, elevation, distance to coast or proximity to
urban centres. Additionally, the most effective and least effective units
are equally likely to be located on barrier islands or capes, suggesting
CBRS designations on these land forms do not have systematically dif-
ferent effects on development from those on the mainland.
The average treatment effect is negative in 10 out of 13 states
(Extended Data Table 3), with greater than 50% reductions in built-up
area attributable to the policy in Delaware, Georgia, Maine, North
Carolina, Rhode Island, South Carolina, Texas and Virginia. We estimate
positive average treatment effects in Alabama, Florida and New York.
These heterogeneous effects could reflect differences in state and local
policies, some of which reinforce CBRS designations by withdrawing
local funding in these areas, while others counteract CBRS designations
by offering increased subsidies to compensate for the withdrawal of
federal incentives. While we find no evidence of systematic differences
in pro-environment and pro-development leanings across effective and
non-effective units (Extended Data Table 2), prior case studies find the
CBRA can be less effective in areas with very high development pres-
sure33 and powerful growth coalitions34.
The spillover effects of removing subsidies on
nearby areas
We next examine whether removing federal subsidies within CBRS
designations has an impact on nearby, but untreated, areas. More
open space and natural lands in CBRS units might create benefits such
as amenity values and flood protective services. These benefits, in
turn, might encourage development and demographic changes in
nearby areas.
We estimate the impact of CBRS designation on nearby areas as a
function of distance to the unit boundary (Fig. 4). We find that removing
federal subsidies causes denser development, higher property sales
prices and higher assessed values per acre in neighbouring areas. The
effect on development densities is increasing in distance from the unit.
Within 1,000 m of CBRS units, we estimate an additional 0.03, or 4%
more, buildings per acre. Between 1,000 m and 2,000 m, the effect size
increases to 0.15 (+20%) and 0.19 (+47%) buildings per acre. The larger
spillover effects at greater distances can be explained by the CBRS
withdrawing infrastructure investment inside treated units: as trans-
portation and utilities are associated with network effects, we would
expect smaller increases in development closer to CBRS boundaries
due to limited supply of essential infrastructure.
In contrast, average sales prices and assessed values per acre are
highest closer to the CBRS units and decline with distance. Properties in
the 0–500 m band, for example, sell at a US$377,000 premium, or 77%
more than in control areas. Notably, the lower development densities
in built structures within CBRS units are more than offset by increased
development in neighbouring areas, indicating that the price increases
Table 1 | Covariate balance across CBRS and synthetic
control
CBRS
Control
SMD
(1)
(2)
(3)
Designated area
Wetland share
0.36
0.35
0.01
Barren share
0.23
0.22
0.03
Tree cover share
0.04
0.04
−0.01
Elevation (m)
1.60
1.78
−0.02
Pre-1982 buildings
3.22
3.42
−0.00
Community distance urban
population (millions)
1.54
1.67
−0.04
Median household income (thousand
US$)
34.12
35.49
−0.08
North Atlantic share
0.46
0.50
−0.08
South Atlantic share
0.31
0.24
0.15
Gulf Coast share
0.24
0.26
−0.05
Built-up surface 1960 (%)
0.03
0.03
−0.00
Built-up surface 1970 (%)
0.03
0.03
−0.00
Built-up surface 1980 (%)
0.04
0.04
−0.00
Spillover area
Flood zone A share
0.27
0.27
−0.01
Flood zone V share
0.28
0.27
0.07
Built-up surface 1960 (%)
0.19
0.21
−0.02
Built-up surface 1970 (%)
0.27
0.29
−0.02
Built-up surface 1980 (%)
0.38
0.39
−0.01
We assess the success of our procedure in identifying control areas by comparing the mean
characteristics of CBRS units (column (1)) and the synthetic control (column (2)). Column (3)
shows the standardized mean difference (SMD) between treatment and control areas using
synthetic control weights.
CBRS treatment unit
Matched counterfactual
Land cover, 1985
10 km
Developed
Cropland
Water
Wetland
Grass/shrub
Forest
Barren
Unit boundary
Fig. 2 | Example treatment and control areas. Here we show three CBRS units
(top panel) above counterfactual areas resulting from the three-to-one match
(bottom panel). Counterfactual units are constructed using a spatial clustering
algorithm based on land cover, elevation and distance to coast. Note that the
adjacent comparisons shown here are not directly used in estimation; we simply
provide them to develop the reader’s intuition for how the regionalization
algorithm functions. Instead, the matched regions serve as the donor pool of
counterfactual units used in the synthetic controls approach.

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in spillover areas cannot be explained by a supply contraction. Higher
property values near CBRS lands are consistent with a large hedonic
literature that shows that coastal amenities such as beach access and
hazard protection affect real estate markets13–18.
Higher assessed value per acre in spillover areas results from both
denser development (more houses per acre) and higher values per
property. Assessed values per acre are US$186,000 higher, or about
50% larger than the control group, in the 0–500 m distance band. The
magnitude of this effect decreases gradually with distance.
We next examine the impacts of the CBRS on flood damages in
spillover areas. We find economically large and statistically significant
negative impacts on overall damages from flooding, as measured by
flood insurance claims per acre (Fig. 4d). Annual insurance claims are
US$420 to US$760 less per acre (40–64% lower than in control areas).
Notably, treatment and control spillover areas have nearly identical
shares of land in floodplains (Table 1) and distributions of properties’
distances to the shoreline (Extended Data Fig. 6), suggesting that the
differences in flood damages is not caused by geographic differences.
The per-acre measure of flood damages is influenced by devel-
opment densities in flood zones and flood damages per property.
Consistent with the increased development in spillover areas, there
are more buildings per acre in high-risk flood zones of CBRS spillover
areas as compared with control spillover areas (Fig. 4f). However,
flood claims are US$19–27 lower per US$1,000 of coverage in treat-
ment areas (Fig. 4e), representing a 14–25% reduction in flood dam-
ages accounting for flood insurance uptake. In other words, the CBRS
provides flood protection at the property level. These flood protec-
tion services are probably generated by more natural lands inside the
units acting as barriers that dissipate and absorb flood waters. Indeed,
previous work establishes a link between wetlands and reductions in
flood damages9–12.
A back-of-the-envelope calculation shows that the original system
of CBRS units generates US$389 million per year in savings for the NFIP.
This figure represents approximately 19% of average annual NFIP claims
in Atlantic and Gulf coast counties over the period 2009–2021. The origi-
nal units make up only 0.46% of land areas in these counties. If we assume
the CBRS units added later along the Gulf and Atlantic coasts generate
similar benefits, the total saving in the current system (excluding the
Great Lakes) would be US$930 million per year in annual NFIP claims
generated from removing federal development subsidies on only 1% of
lands in these counties. This finding complements two existing studies
of the savings CBRA generates in federal post-disaster assistance35,36.
Table 2 | The direct effect of the CBRS within designated areas
Outcome
Estimate
Standard error
P value
Control mean
Relative effect (%)
N
Development densities
Built-up area (%)
−0.1195
0.071
0.0927
0.2881
−41
928
Buildings per acre
−0.0439
0.0088
8.7 × 10−7
0.0528
−83
460
Assessed values
Total assessed value (US$ per acre)
24,141
31,799
0.4481
78,518
+31
460
Property characteristics
Sales price (US$)
107,373
294,247
0.716
1,032,754
+10
106
Lot size (acres)
1.24
2.75
0.654
4.97
+25
98
Square footage
−132
814
0.872
4,120
−3
91
Bedrooms
0.04
0.22
0.863
3.31
+1
83
Population demographics
White (%)
2.88
1.19
0.0158
87.79
+3
453
Black (%)
−1.81
0.93
0.0526
6.29
−29
453
Median household income (US$)
15,105
3,462
1.6 × 10−5
78,105
+19
434
Median rent as percent of income
1.97
1.11
0.0759
31.07
+6
346
Occupied housing units (%)
−13.58
2.16
7.1–10
69.29
−20
452
The direct effects are estimated as weighted differences of outcomes inside CBRS and control units. The estimation uses synthetic control weights constructed from pre-trends in development
densities and pre-treatment measures of land use, geography and sociodemographic variables. Test statistics are based on a two-sided t-test. The relative effect is calculated as the percent
change from outcomes in the absence of treatment, as measured by the control group mean.
Non-efective
Efective
Distribution of individual treatment efects
−100
−50
0
50
100
0
5
10
15
Relative efect (% change)
−1.0
−0.5
0
0.5
1.0
Efect on built-up surface (p.p.)
Year
Year
0
10
20
30
40
50
Development densities in CBRS and control areas
1960
1980
2000
0
0.1
0.2
0.3
0.4
0.5
1960
1980
2000
−0.2
−0.1
0
0.1
0.2
Frequency
Frequency
Treatment – control
Built-up surface (%)
a
b
Fig. 3 | Effect of CBRS designations on development densities. a, Trends in
built-up surface area in treatment and control areas: left is the percent of land
area covered in built-up surface in treatment (solid blue line) and control (dashed
grey line) areas every 10 years between 1960 and 2010; right is the difference in
built-up surface over time (treatment minus control). b, The distribution of unit-
specific treatment effects: left is the estimate of the effect of CBRS designation on
built-up surface, measured in p.p.; right is the relative effect (top coded at 100%).

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Finally, we estimate the spillover effect of CBRS designations on
demographics (Fig 4g–i). The policy is associated with a large increase
in household income (over US$10,000), a decline in the share of occu-
pied housing units (5–8 p.p.), and an increase in the share of white
people (1.5–2.5 p.p.). These effects largely mirror the demographic
effects of the policy within the designations. Given the above evidence
that CBRS lands generate natural amenities and protection services,
these results are consistent with past findings on residential sorting in
response to environmental amenities20,37.
In the spillover areas, unlike in the CBRS, federal flood insurance
and disaster aid are still available. Thus the equity implications of
the policy are murky. The environmental benefits appear to flow
to wealthier households in both the CBRS and spillover areas. But
in the CBRS, those households also bear most of the costs of devel-
opment because federal infrastructure spending, disaster aid and
subsidized flood insurance are unavailable. The same is not true in
the spillover areas.
Fiscal impacts on counties
We calculate the effect of CBRS designations on property tax revenues
by combining our total assessed value estimates in both CBRS units and
their spillover areas with current average county property tax rates.
We find no evidence of a change in property tax revenues within CBRS
designations. However, we find a US$911 million per year increase in
revenues in spillover areas. This figure represents 2.5% of total local
property tax revenues in Atlantic and Gulf coast states.
Our findings are informative for coastal communities caught
between, on one hand, increasing disaster costs, and on the other, the
fiscal implications of limiting development. Our calculation suggests
that there is not necessarily a hard trade-off between the two objectives.
Rather, we show that curbing development in environmentally sensitive
areas can increase the property tax base by increasing development
densities and property values in nearby locations.
Discussion
Coastal communities are centres of economic development and home
to critical natural resources but face substantial threats from climate
change and human development. This paper investigates the efficacy
of one policy approach to limiting populations in harm’s way—with-
drawing the availability of federal financial assistance in risky areas.
We show that, in the case of the CBRA, this approach has been highly
effective at limiting development. Moreover, the resulting conservation
of natural lands generates environmental services in surrounding com-
munities, increasing property values and reducing flood damages. In
combination with analyses of the savings in federal disaster assistance
expenditures35, our findings provide a comprehensive assessment
useful for policymakers.
Methodologically, we develop a new approach for causal infer-
ence, using spatial machine learning and matching to construct a
comparable control group by mimicking the original CBRA designation
process. This approach tackles the central problem of non-random
assignment of treatment present in a broad class of place-based policies
−0.1
0
0.1
0.2
0.3
500
1,000
1,500
Distance to unit boundary (m)
Distance to unit boundary (m)
Distance to unit boundary (m)
Distance to unit boundary (m)
Distance to unit boundary (m)
Distance to unit boundary (m)
Distance to unit boundary (m)
Distance to unit boundary (m)
Distance to unit boundary (m)
2,000
0
250
500
500
1,000
1,500
2,000
0
100
200
300
500
1,000
1,500
2,000
−1,500
−1,000
−500
0
500
1,000
1,500
2,000
−40
−20
0
500
1,000
1,500
2,000
−0.05
0
0.05
0.10
0.15
500
1,000
1,500
2,000
0
5,000
10,000
15,000
20,000
25,000
500
1,000
1,500
2,000
−0.10
−0.05
0
500
1,000
1,500
2,000
0
0.02
0.04
500
1,000
1,500
2,000
Median houshold
income (US$)
Demographics
Occupied units (%)
White (%)
NFIP claims
per acre (US$)
Flood damages
NFIP claims per
US$1,000 coverage (US$)
Buildings per
acre in SFHA
Buildings per acre
Development and property values
Average sales
price (US$1,000)
Assessed value per
acre (US$1,000)
a
b
c
d
e
f
g
h
i
Fig. 4 | Spillover effects of the CBRS in neighbouring communities. a–i, Three
economic development outcomes (a–c), three flood insurance outcomes (d–f)
and three demographic outcomes (g–i) are shown. Dots are point estimates for
each 500 m band and the corresponding 90% (95%) confidence intervals are
shown in dark (light) blue. Confidence intervals are based on robust standard
errors. The sample size is N = 1,930 for all panels except b, where N = 852.
SFHA stands for “Special Flood Hazard Area”, the term used by the US Federal
Management Agency to describe areas with high flood risk.

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and could be applied to other settings where it is notoriously difficult
to establish causal effects.
Our results indicate that removing federal development incen-
tives can be a cost-effective option for preventing over-development
in risky areas while generating co-benefits. Still, programmes like the
CBRS are designed to pre-empt development in risky areas, not assist
in managed retreat. In areas where strategic relocation of people and
capital is deemed necessary, other policy interventions are likely to
be required.
Our findings have the potential to inform a number of ongoing pol-
icy discussions. The Strengthening Coastal Communities Act (S.5185)
would add approximately 292,000 acres to the CBRS. Our estimates
could serve as inputs into the cost–benefit analysis of this proposal.
More generally, our estimates can inform state- and local-level deci-
sions about zoning practices and government support for infrastruc-
ture development and repair in high-risk coastal areas. Notably, our
results apply to risky areas beyond just coastlines; a similar approach
to managing development could be taken in inland floodplains or in
wildfire-prone areas38.
Our study has important limitations. First, although we believe
our empirical approach represents a step forward, this is ultimately a
retrospective, quasi-experimental analysis. Second, our estimates do
not capture the full range of benefits and costs associated with CBRS
designation. Additional costs may include distortions to the spatial
allocation of economic growth39, while benefits may include wildlife
habitat, water filtration or recreation opportunities. Third, we are not
able to observe state and local policies that may either counteract or
reinforce CBRS designations. We find heterogeneous treatment effects
across states but are unable to attribute those differences to specific
policies. Our estimates should therefore be interpreted as the net
effect of CBRS designations, inclusive of state and local responses, to
the federal policy.
Online content
Any methods, additional references, Nature Portfolio reporting sum-
maries, source data, extended data, supplementary information,
acknowledgements, peer review information; details of author contri-
butions and competing interests; and statements of data and code avail-
ability are available at https://doi.org/10.1038/s41558-024-02082-3.
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Methods
The goal of our empirical strategy is to estimate the causal effect of
CBRS designations. The core challenge is identifying a set of appro-
priate counterfactual units to serve as ‘control’ areas for CBRS ‘treat-
ment’ areas. The CBRA encourages the conservation of biologically
rich, underdeveloped and hurricane-prone coastal areas. Therefore,
we cannot simply compare our outcomes of interest in CBRS units to
those in all other coastal areas; we must identify comparable areas that
could have been selected for CBRS designation but were not. We do so
using a novel procedure designed to mimic the process by which natural
resource planners defined CBRS boundaries. Intuitively, our method
for finding counterfactual treatment areas relies on finding locations
that are indistinguishable (to the algorithm) from CBRS lands at the
time of designation40,41.
Selection process and criteria for CBRS designations
We first describe the selection process for CBRS designations, as our
empirical approach relies on replicating it. The CBRS designation pro-
cess is described in detail in the 1982 Federal Register and in refs. 42,43,
where the US Fish & Wildlife Service (FWS) establishes a set of ‘defini-
tions and delineation criteria’. CBRS designations were then based upon
the application of these criteria to on-the-ground situations.
The first criteria for CBRS designations is that the land should be a
‘coastal barrier’, a class of low coastal land forms that protect landward
areas from tidal, wave or wind energies. For the purposes of CBRS des-
ignations, the definition of coastal barriers also includes all associated
aquatic habitats such as adjacent wetlands, marshes, estuaries, inlets
and nearshore waters. In addition to meeting this geological definition,
the CBRS requires coastal barriers to be ‘undeveloped’ in order to be
included in the system. Specifically, the delineation criteria state that
an area should be considered “only if there are few manmade structures
on the barrier or any portion thereof and these structures and man’s
activities on the barrier do not significantly impede geomorphic and
ecological processes”42.
According to the Federal Register, Reports to Congress and conver-
sations with programme officers at the FWS, natural resource planners
examined US Geological Survey (USGS) topographic maps and aerial
photographs to make the original CBRS designations based on just two
criteria: (1) adherence to the geologic definition of a coastal barrier,
which depends on elevation, land cover and location relative to the
shoreline; and (2) development levels.
Counterfactual construction
We now describe our procedure for identifying plausible control areas:
areas that could have been selected for CBRS designation in 1982 based
on the selection criteria, but were not.
CBRS boundaries do not follow traditional administrative bounda-
ries; they were hand drawn to follow geomorphic and development
features44. Our first step is to trace out potential counterfactual areas
using an automated procedure that closely resembles this process.
Importantly, we can observe close proxies for the information the
planners had available at the time of designation—aerial photographs
and topographic maps (Extended Data Fig. 2). We begin with 300 m
resolution gridded data on historical land cover, development levels,
elevation and distance to coast. Each cell of the raster represents a dis-
tinct observation that will be grouped into a region. We only consider
grid cells within 2 km of the coast. We exclude any cell that is 100%
water, within a CBRS unit (including both original units designated
in 1982 and all units designated since then) or an otherwise protected
area, and all grid cells within 2 km of a CBRS unit (to avoid selecting
control areas that may be ‘treated’ by spillover effects of CBRS units).
We then apply regionalization to group these pixels into spatially
contiguous areas that share similar attributes45. Regionalization is one
type of clustering—a machine learning technique that sorts observa-
tions into groups without any prior idea about what the groups are.
Clusters are delineated so that members of a group should be more
similar to one another than they are to members of a different group.
For example, observations in one group may have consistently high
scores on some traits but low scores on others. Regionalization is an
application of clustering to spatial data that can be used to provide
insights into the geographic structure of complex multivariate spatial
data. A ‘region’ is similar to a cluster, in the sense that all members of
a region have been grouped together, but a region also describes a
clear geographic area. That is, regionalization uses the same logic as
standard clustering techniques, but also requires connectivity: two
candidates can only be grouped together in the same region if a path
exists from one member to another member that never leaves the
region45 (see Supplementary Section B.2 for details).
Regionalization groups all coastal pixels into spatial clusters
(‘regions’). We limit our sample to only those regions that would have
met the basic requirements for inclusion into the CBRS by conducting
a three-to-one propensity score match between the regions and CBRS
units based on land cover, development levels and elevation. The algo-
rithm effectively acts as a natural resource planner would have—tracing
out low-elevation coastal lands that had high shares of wetlands and
beaches (barren), while avoiding highly developed areas. To illustrate
the results of this procedure, we show examples of CBRS areas beside
matched counterfactuals (Fig. 2). Notably, the adjacent comparisons
shown here are not directly used in estimation; we simply provide them
to develop the reader’s intuition for how the regionalization algorithm
functions. Instead, the matched regions serve as the donor pool for
counterfactual units.
Next we apply multi-unit synthetic controls, a technique designed
to reduce selection bias in observational studies by weighting the
control group to better match the treatment group prior to the inter-
vention46–49. We match on time trends in development densities, both
inside the designated units and in spillover areas, for the two decades
preceding the policy intervention (1960, 1970 and 1980). We also match
on a suite of covariates that we can observe around the time of designa-
tion in 1982, including measures of land cover, elevation, infrastructure
density, proximity to urban centres, income and flood risk in spillover
areas. This allows us to determine a set of weights that, on aggregate,
balances pre-trends in development densities and pre-treatment char-
acteristics across CBRS units and the control group. We can then use
the synthetic control to estimate what would have happened to the
CBRS units if they had not been treated by the policy.
Our empirical strategy requires the CBRS and control areas to
have been comparable at the time of designation, while allowing for,
and measuring, divergence in outcomes over the four decades since,
based on treatment status. We therefore limit our sample to the original
set of CBRS units designated in 1982, and all data used in the process
of generating counterfactuals comes from as close to this year as pos-
sible (and no later than the year 1990). Data sources and processing
techniques are described in detail in Supplementary Section B.1.
To illustrate the output of our procedure, we plot the locations of
all CBRS and counterfactual units included in our sample (Extended
Data Fig. 4). We include control areas in all 18 Atlantic and Gulf Coast
states, although four of these states (Maryland, New Hampshire, New
Jersey and Virginia) did not have any units in the original CBRS. Because
some states had a limited pool of undeveloped coastal areas in 1982,
our procedure does not require the distribution of control units to
match the distribution of treatment units across states. However, we
do balance treatment and control units across three broad regions—the
North Atlantic, South Atlantic and Gulf Coast—to avoid making inap-
propriate comparisons between, for example, coastal areas in Maine
and Mississippi.
We assess the success of our procedure for constructing coun-
terfactual areas by comparing the mean characteristics of the CBRS
treatment group and synthetic control (Table 1). Encouragingly, our
machine learning and matching procedure brings the observable

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characteristics of CBRS treatment and counterfactual areas into align-
ment. Column (3) shows that standardized mean difference (SMD)
between CBRS and counterfactual areas is at or below 0.1 (the com-
monly used threshold for accessing balance) across the 18 covariates.
Additional considerations for causal identification
Our approach to identification requires the assumption that not all
areas that meet the CBRS delineation criteria were designated as part
of the original system in 1982. This assumption is supported by the
fact that additional areas have been added to the CBRS over the past
40 years, with the most recent addition proposed in December 202250.
According to a 1988 Report to Congress51 and conversations with pro-
gramme officers at the FWS, not all eligible areas were designated as
part of the original system for two primary reasons. First, USGS quad-
rangles used in the inventory process did not show sufficient detail to
identify all qualifying undeveloped coastal barrier areas. Second, the
scientific definition of what qualifies as a ‘coastal barrier’ has evolved
over time. We argue that both of these factors are plausibly exogenous
to the housing market outcomes we analyse.
One additional concern is that local politics might have affected
which areas were designated as part of the CBRS. In particular, if local
governments were concerned about the negative impacts of CBRS
designation on property tax revenues, they may have objected to
CBRS designations in their jurisdiction. Reassuringly, no units were
removed from the original proposed set of designations after the public
comment period42, reflecting limited ability of localities to affect the
designation process. Additionally, we empirically test for the presence
of this type of selection in two ways. First, we test for differences in local
reliance on property tax revenues. Second, we test for differences in
the voting records of local members of Congress on environmental
issues, as measured by the League of Conservation Voters National
Environmental Scorecard, which records the voting records of all
members of Congress on major environmental legislation. We find no
meaningful differences in either of these measures across treatment
and control areas: local reliance on property tax revenues is 35.2% in
treatment areas and 35.9% in control areas (P value on difference = 0.61)
and environmentally friendly voting records (League of Conservation
Voters score) are 57.0 in treatment areas and 56.8 in control areas (P
value on difference = 0.96). These results are inconsistent with local
politics causing selection away from pro-development and towards
pro-environment areas.
Consideration of development in the designation process
It is worth emphasizing the consideration of development levels in the
CBRS designation process. The ‘any portion thereof’ language in the
definition of an ‘undeveloped’ coastal barrier is key because it means
that the statutory definition does not require an entire coastal barrier
to be designated as a CBRS unit; the FWS had the authority to include
only the portions of the barrier that were underdeveloped. In fact, the
boundary was often intentionally placed to exclude developed areas.
The Federal Register explains, “the side boundary is placed immediately
adjacent to the cluster of structures or the area with a full complement
of infrastructure indicating the end of the developed portion of the
coastal barrier”42. We show an example of this practice for a unit in
North Carolina, where developed areas were excluded from the CBRS
designation to satisfy the definition of an ‘undeveloped’ coastal barrier
(Extended Data Fig. 1a).
The central role of development levels in the delineation process
calls into question whether the causal effect of CBRS designation can be
identified through a spatial regression discontinuity design, as in ref. 1.
The core assumption of a spatial regression discontinuity—that land
located just within and just outside the boundary can be assumed to be
similar in all ways except for assignment to treatment—is problematic
in this setting because natural resource planners hand-selected CBRS
boundaries to avoid already developed areas. To see this, we calculate
the mean share of developed land just inside and just outside of CBRS
boundaries in 1985 using data from the USGS’s Land Change Moni-
toring, Assessment, and Projection (LCMAP) product52. We find that
around the time of designation in 1985, the share of developed land
was already 9.6 p.p. (95% confidence interval = 6.5 to 12.8) higher just
outside the boundary than just inside the boundary, representing a
more than doubling in development levels at the boundary. We define
‘just’ inside and outside using 100 m buffers, following ref. 1. We find
that there is a discrete jump in the share of developed land even as one
approaches the boundary (Extended Data Fig. 1b).
Our approach to identification, as described above, explicitly takes
into account the consideration of development levels in delineating
CBRS boundaries and is designed to be able to recover the spillover
effects of the policy on neighbouring areas. Indeed, we find that our
procedure does well in replicating the consideration of development
levels in drawing CBRS boundaries. Even before the policy was enacted,
development levels were 33% higher just outside than just inside CBRS
boundaries (within 100 m). We find a similar pattern among our syn-
thetic control areas, where development levels are 24% higher just
outside than just inside the boundary.
Outcomes
We examine two sets of outcomes related to the removal of develop-
ment subsidies: direct effects within CBRS units and spillover effects
in neighbouring communities. All outcomes are measured using the
most recent data available (2010 onwards) such that we capture the
long-term effect of CBRS designation. That is, we compare outcomes
today in treatment and control units under the assumption that these
two groups were comparable at the time of designation in 1982, and
test whether outcomes have diverged over the past four decades based
on treatment status.
Direct effects include impacts on development levels, land values,
and composition of the housing stock and population. We measure
development levels using two different data sources. The first measure
is the share of built-up surface area from the HISDAC-US Building Foot-
print Area (BUFA) database (https://dataverse.harvard.edu/dataverse/
hisdacus). Assembled from property assessment records, BUFA is a
gridded data product that estimates the sum of building areas at 250 m
spatial resolution every 10 years from 1900 to 2010. The key advantage
of this dataset is that we can observe the evolution of built-up area over
time, including in the decades before CBRS designation. Our second
measure of development densities is the number of structures per acre,
calculated from Microsoft Maps’ Building Footprint database (https://
github.com/microsoft/USBuildingFootprints). This dataset provides
approximately 130 million computer-generated building footprints
derived from satellite imagery for the entire United States. These data
offer higher spatial resolution than HISDAC-US, but only for a single
snapshot in time during the post-period.
We measure land values using property sales prices and assess-
ment values from the Zillow Transactions and Assessment Database
(ZTRAX) (https://www.zillow.com/research/ztrax/). The ZTRAX data-
set also provides information on the composition of the housing stock
(for example, lot size, year built, square footage, number of bedrooms).
Because outcome data from the ZTRAX Database (property sales prices
and characteristics) are incomplete—only about half of treatment and
control units have one or more parcels in the database—we calculate
a separate set of synthetic control weights for the ZTRAX outcomes
(Extended Data Fig. 3 and Extended Data Table 1). Missingness in
the ZTRAX database is described in Supplementary Section B.1 and
explored at length in ref. 53.
We evaluate the composition of the population using census
block-group-level 5-year estimate (2016–2020) from the American
Community Survey. Building footprints and ZTRAX data points are
assigned to CBRS units by intersecting the geocoded polygons and
points with CBRS boundaries. To calculate census observations for

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each CBRS area, we aggregate the values from all census block groups
that intersect a given CBRS unit using population weights calculated
from high-resolution (1 km) gridded population data.
We also examine the effects of CBRS areas on neighbouring com-
munities. To do so, we draw a 2 km buffer around each CBRS unit and
counterfactual area. When constructing spillover areas, we exclude
any area that is in a current CBRS designation or otherwise protected
area. For treatment spillover areas, if a geography falls within the 2 km
of multiple CBRS designations, we assign it only to the closest CBRS
unit. For counterfactual spillover areas, we exclude any area that is in
a spillover area (within 2 km) of a treated unit.
In addition to the outcomes described above, we measure the
impact of CBRS designation on flood damages in order to test whether
preserving natural lands provides protective services from flooding.
We measure flood damages using flood insurance claims from the NFIP,
the dominant insurer for flooding in the United States. Notably, home-
owners within CBRS areas are not eligible to participate in the NFIP, so
we do not look at the direct effect of CBRS designation on NFIP claims.
Because flooding is an infrequent event, we average annual flood claims
over the years 2009 to 2020. The most granular geographic identifier
available in the NFIP claims and policies data at the time of analysis was
the property census tract. To estimate the value of NFIP claims in each
CBRS spillover area, we aggregate the values from all census tracts that
intersect a given spillover area, weighting by the number of buildings
located in the Special Flood Hazard Areas designated by the US Federal
Emergency Management Agency (the high-flood-risk areas where flood
insurance is required for properties with federally backed mortgages).
The intuition behind this approach is that the distribution of NFIP
policies and claims over geographic space will probably resemble the
distribution of structures in areas with high flood risk.
We provide summary statistics for our outcome measures (Supple-
mentary Table B.1). Column 1 shows the mean values in counterfactual
areas and column 2 shows the mean values in CBRS units. All means are
weighted by the synthetic control weights used in estimation.
Estimating equations
Once we have constructed the synthetic control, we estimate
the effect of CBRS designations on built-up surface area using a
difference-in-differences framework. We use the weighted regression
Bit = β(Ti × POSTt) + λi + δPOSTt + γRi + ϵit
(1)
where i indexes the CBRS or control area and t indexes the time period.
B denotes the percent of land area covered in built-up surface, T is an
indicator for whether the area was treated by the CBRS and POST is an
indicator equal to one in the post-treatment period. The regression also
includes unit-level fixed effects (λi) and a control for whether the unit
is located on a barrier island or cape (R). We weight each observation
by the synthetic control weight. The error term ε captures unexplained
variations. The treatment effect of interest, β, captures any systematic
differences in built-up surface area caused by CBRS designation.
For all other outcomes, where outcome measures are not available
in the pre-treatment period, we estimate the direct effect of CBRS des-
ignation using a simple weighted regression that compares outcomes
in treatment areas today with outcomes in control areas today. The
estimating equation is
Yi = α + βTi + γRi + ϵi
(2)
where all variables are defined as in equation (1), except here Y cor-
responds to one of our other outcome variables. The regression is
weighted by the synthetic control weights. The treatment effect of
interest, β, captures any systematic differences between the outcomes
in CBRS and control areas today, under the identifying assumption that
these areas would have been comparable in the absence of treatment.
To estimate the spillover effects of CBRS designations on neigh-
bouring communities, we turn to a spatial lag model. This allows us
to capture heterogeneity in spillover effects by distance to the unit.
The estimating equation is:
Yi,b =
4
∑
b=1
βb1[B = b] × Ti + γRi + λbXi,b + ϵi,b
(3)
where the indicator 1[B = b] is equal to one if the observation falls within
distance band b relative to the CBRS or counterfactual unit boundary.
We use four distance bands, in increments of 500 m, out to a total
distance of 2 km. Because we match on built-up surface area in overall
spillover areas (not by distance band), we include distance-band level
controls for 1985 land cover, flood risk and share protected area (Xi,b).
The regression is weighted using the synthetic control weights.
Heterogeneous treatment effects
We next explore where CBRS designations are more and less effec-
tive by identifying individual counterfactual and treatment effects.
This represents a departure from the main analysis, where we pool all
treated units and find a set of synthetic control weights that balances
the pre-trends in development densities and mean characteristics of
treatment and control, on aggregate. We do not employ individual
treatment effects in the main analysis because it is not possible to
identify an appropriate counterfactual for each individual CBRS unit;
some units look very different from all other coastal areas in the United
States. For this part of the analysis, we use a paired-down set of match-
ing variables that only includes pre-trends in development densities
and pre-treatment land cover, elevation, proximity to urban centres
and region. We identify synthetic controls that match well along all
of these dimensions for 90 treatments units (55% of the full sample).
We evaluate heterogeneity in the effect of the CBRS on devel-
opment densities in three ways. First, we report the distribution of
treatment effects across the sample of 90 units for which we are able
to identify individual treatment effects. Second, we compute average
treatment effects by state. Third, we use a classification analysis to
compare the average characteristics of the most and least affected
units using two-sample t-tests. We define the least affected units as
those with a positive estimate of the individual treatment effect and
the most affected units as those with treatment effects, measured in
relative terms, in the most negative quartile (more than 67% reduction
in development densities).
Impacts on local property tax revenues
We calculate the approximate impact of CBRS designation on county
revenues from property taxes using the equation:
Rc = tc × (βAV
d Ac,d +
4
∑
b=1
βAV
b Ac,b)
(4)
where Rc denotes the estimated effect of CBRS designation on property
tax revenue in county c. The parameter βAV
d  is our estimated effect of
CBRS designation on total assessed value per acre within the CBRS unit
and βAV
b  is the estimated effect on assessed value within each distance
band b of the CBRS unit. We multiply these estimated effects by the
total acreage within the unit, Ac,d, and in each distance band, Ac,b, respec-
tively. Summing these values gives us an estimate of the effect of CBRS
designation on total assessed value within the county (the term in
parentheses). Notably, this estimate captures the direct effects within
CBRS units and the spillover effects on neighbouring properties, both
in number and value of developed properties. To estimate the effect
on property tax revenue, we then multiply by the county tax rate, tc.
This tax rate is derived from the Lincoln Institute of Land Policy Prop-
erty Tax Database (https://www.lincolninst.edu/data/significant-
features-property-tax/access-database/). The nominal tax rate is

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calculated by averaging municipal and school district taxes and adding
them to base county tax rates.
Impacts on NFIP payouts
To calculate the impact of CBRS designations on NFIP payouts in spillo-
ver areas, we multiply our point estimates of NFIP claims paid per acre
(Fig. 4) by distance band with the acres of land in each band. We then
sum over all CBRS units to calculate the total impact on NFIP payouts.
For savings within the CBRS units, we calculate the average claims per
acre across all counterfactual areas and multiply it by CBRS land areas.
Robustness to alternative research design
In Supplementary Section C, we present results based on overlap
weighting under a propensity score matching framework as an alter-
native approach and robustness check.
Data availability
All raw data used in this study are publicly available except the
property-level information on home values and characteristics from
the Zillow Transaction and Assessment Database (ZTRAX), which we
accessed through a licence available to researchers. Instructions for
accessing raw data are provided in Supplementary Section B.1. The pro-
cessed datasets are available at https://github.com/hdruckenmiller/cbra.
Source data are provided with this paper.
Code availability
The analysis code is available via GitHub at https://github.com/hdruck
enmiller/cbra. The analysis code and scripts are available via Zenodo
at https://doi.org/10.5281/zenodo.12199232 (ref. 54).
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Acknowledgements
This research was funded by the Lincoln Institute of Land Policy (H.D.,
Y.L. and M.W.). We thank T. BenDor, P. Mulder, A. Reilly, C. Taylor and
D. Wright for helpful comments and suggestions. Property assessment
and transaction data were provided by Zillow through the Zillow
Transaction and Assessment Dataset (ZTRAX). More information on
accessing the data can be found at https://www.zillow.com/research/
ztrax. The results and opinions are those of the author(s) and do not
reflect the position of Zillow Group.
Author contributions
H.D. and Y.L. conceived the study and contributed equally as first
authors. H.D., Y.L. and M.W. designed the analysis. H.D., Y.L., S.P. and
S.Z. constructed and analysed the data. All authors contributed to
writing and reviewing the manuscript.
Competing interests
The authors declare no competing interests.
Additional information
Extended data is available for this paper at
https://doi.org/10.1038/s41558-024-02082-3.
Supplementary information The online version
contains supplementary material available at
https://doi.org/10.1038/s41558-024-02082-3.
Correspondence and requests for materials should be addressed to
Hannah Druckenmiller.
Peer review information Nature Climate Change thanks Allan Beltran
and the other, anonymous, reviewer(s) for their contribution to the
peer review of this work.
Reprints and permissions information is available at
www.nature.com/reprints.

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Extended Data Fig. 1 | CBRS boundaries depend on development levels.
(a) Example of CBRS unit boundary for Topsail Unit (L06) in North Carolina55.
Developed areas are excluded from the system unit to meet the definition of an
‘underdeveloped’ coastal barrier. (b) Share of developed land within 100m of
CBRS boundaries in 1985 using LCMAP. Left side of the plot is just inside CBRS
boundaries and right side is just outside CBRS boundaries. Points show the
average share developed in 20m increments approaching the boundary.
Shaded areas show 95% confidence intervals. Panel a adapted with permission
from ref. 55, US Fish and Wildlife Service.

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Extended Data Fig. 2 | Comparison of maps used by CBRS planners and our
model. Example of input data for the Sandy Neck CBRS Unit (C09). The top row
shows the sources of information used by CBRS planners in making the original
designations, USGS quadrangles56 (top left) and aerial photographs57 (top right).
The bottom row shows two of the inputs into our model, land cover maps from
LCMAP (bottom left) and built-up surface area from HISDAC-US (bottom right).
The CBRS unit is outlined in yellow. Panels adapted with permission from:
top left, ref. 56, US Geological Survey; top right, ref. 57, US Geological Survey.

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Extended Data Fig. 3 | Trends in development densities in CBRS treatment
and control areas, compared across full and ZTRAX samples. Top panel shows
trends in built-up surface area in treatment and control areas in the full sample,
where the left plot shows the percent of land area covered in built-up surface in
treatment (blue) and control (dashed black) areas every 10 years between 1960
and 2010 and the right plot shows the difference (treatment minus control).
Same but in the subsample for of units with at least one ZTRAX transaction.

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Extended Data Fig. 4 | Geographic distribution of treatment and control
units. Panel a maps CBRS treatment units (blue) and constructed controls
(green). All treatment units receive a weight of one. The shading of the control
(green) points corresponds to the synthetic control weights, with lower weights
(less relevant observations) indicated by lighter shades and higher weights (more
relevant observations) indicated by darker shades. Panel b shows the distribution
of treatment and control units across states (left) and regions (right).

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Extended Data Fig. 5 | Placebo test for the effect of CBRS designations on
development densities. Left panel shows the trajectories of development densities
in true treated areas (blue) and the synthetic control (dashed black). Right panel
shows the difference in between treatment and control areas in blue. We then repeat
our estimation procedure 100 times, but each time, we create a placebo treatment
group by dropping the true CBRS units from the sample and randomly assigning
50 control units to have treatment status. The difference between the placebo
treatment and controls are shown by black lines (one for each run).

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Extended Data Fig. 6 | Average distance to the coast in spillovers of CBRS
and controls. We calculated the weighted average distance to the coast for all
properties within the 2km spillover areas of CBRS lands and control areas. We
then categorized these average distances into deciles. We conducted weighted
average t-tests for the distance from the coast between CBRS areas and controls
in each decile and found no statistically significant differences. This exercise
suggests that the lower flood damages observed in CBRS spillover areas as
compared to control spillover areas cannot be explained by differences in
distance to coast.

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Extended Data Table 1 | Covariate balance across CBRS and synthetic control in ZTRAX subsample
We assess the success of our procedure for identifying control areas by comparing the mean characteristics of CBRS units (column 1) and the synthetic control (column 2). Column 3 shows
the standardized mean difference (SMD) between treatment and control areas using synthetic control weights. This table is analogous to Table 1, but in the subsample of units that have at
least one ZTRAX transaction.

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Extended Data Table 2 | Characteristics of effective and noneffective CBRS units
This table compares the mean characteristics of effective (column 1, N = 23) and noneffective (column 2, N=19) CBRS units using individual treatment effects. We define effective units as those
with treatment effects, measured in relative terms, in the most negative quartile (greater than 67% reduction in development densities). We define noneffective units as those with a positive
treatment effect estimate. Column 4 shows the difference in means and column 3 reports the p-value on a two-sample, two-sided t-test of whether the difference in means is distinguishable
from zero.

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Extended Data Table 3 | Average effect of CBRS designations on development densities, by state
We estimate individual treatment effects for the subsample of 90 CBRS designations for which it is possible to identify an individual synthetic controls. Column (1) shows the average
treatment effects by state. Column (2) shows the relative effect, calculated as the percent change from outcomes in the absence of treatment, as measured by the control group mean.
Column (3) shows the number of CBRS designations used to calculate the state-level averages.

    Scientific Research Study Methods:

Answer: We compare outcomes in ‘treatment’ areas designated in a 1982 law as part of the Coastal Barrier Resources System (CBRS), where federal development incentives have been removed to ‘control’ areas that have similar characteristics but are not part of the CBRS. The control group is constructed using machine learning and matching techniques. Intuitively, the procedure mimics the process by which natural resource planners designated CBRS lands based on geomorphic and development features. This creates a set of control areas that are statistically indistinguishable from treatment areas in terms of land use, flood risk, development patterns and other factors in the years leading up to policy implementation. We analysed the long-term impacts of the policy on the amount of development and property values in the CBRS and on neighbouring lands up to 2 km from a CBRS boundary. We also estimated the policy’s effect on flood damages and property tax revenues.