Regional
Resilience
Trust Funds
An Exploratory Analysis
for the New York
Metropolitan Region
A Report by Jesse M. Keenan
for The Fourth Regional Plan
October 2017
© President and Fellows of Harvard College.
Information for reproducing excerpts from this working paper can be directed to the principal
investigator at Harvard University, Graduate School of Design, 407 Gund Hall, 48 Quincy Street,
Cambridge, MA., 02138 or by contacting jkeenan@gsd.harvard.edu.
The opinions expressed represent the opinions of the principal investigator and not those of the
Regional Plan Association, Graduate School of Design, Harvard University or any of the persons,
entities, or organizations providing support to, or affiliated with, these aforementioned entities. The
findings and conclusions of this report are solely the responsibility of the principal investigator.
The principal investigator acknowledges that he has been involved in a professional and academic
capacity in a number of the subject geographies, jurisdictions and projects. Any unacknowledged
investigator biases are the sole responsibility of the principal investigator.
Regional Resilience Trust Funds:
An Exploratory Analysis for the New York Metropolitan Region
Jesse M. Keenan
Abstract
This paper explores the legal and financial viability of a series of state trust funds designed to
provide financial products to support interventions advanced in the name of climate change
adaptation and resilience in the New York Metropolitan Region. The subject financial model,
known as a Regional Resilience Trust Fund (RRTF), would be governed by a Regional Resilience
Commission (RRC) made up of political appointees from the states of New York, New Jersey and
Connecticut who would serve as stewards of each state’s respective trust fund. This paper evaluates
the proposition that the RRTF could be feasibly capitalized by a surcharge on certain regulated
insurance lines (Proposition A). Second, based on the assumed validity of the First Proposition,
the paper evaluates the proposition that the RRTF could sustainably support a range of financial
products, including grants, low-cost loans, non-recourse loans and market-rate loans that could
accommodate 100% of the states’ unmet resilience needs, as defined by existing disaster resilience
plans (Proposition B). The findings of this research support an affirmation of the legal and financial
feasibility of the RRTF pursuant to Proposition A. Consistent with Proposition B, this paper
provides evidence in support of a sustainable portfolio strategy for products supporting a range
of potential projects, from short-term community resilience planning to long-term infrastructure
finance. With the exception of Connecticut, the modeled assumptions of the RRTF could not
support an affirmation of the proposition that a RRTF could fulfill 100% of the unmet resilience
needs of the states. However, the findings do support an alternative proposition that the RRTF
may be able to accommodate a significant portion of unmet resilience needs. This paper provides
a broader strategic understanding of how products and portfolios can be designed to operate in the
uncertainties associated with climate change.
Keywords
Climate Change, Finance, Resilience, Adaptation, Insurance, Trust Funds
Peer Review Version
Keenan, J.M. (2017). Regional Resilience Trust Funds: An Exploratory Analysis for Leveraging
Insurance Surcharges. Environment Systems and Decisions. doi: 10.1007/s10669-017-9656-3
Table of Contents
I.
II.
III.
IV.
V.
VI.
VII.
VIII.
IX.
X.
Introduction............................................................................ ..............................................1
Resilience and Adaptation Finance........................................ ..............................................1
Research Design and Methodology.....................................................................................4
Governance: Regional Resilience Commission..................... .............................................4
Trust Fund
a.
Organization.............................................................................................................6
b.
Capitalization...........................................................................................................7
c.
Operations and Underwriting.................................................................................10
d.
Products..................................................................................................................12
e.
Example Projects....................................................................................................13
Portfolio Modeling
a.
Portfolio Model Setup and Results........................................................................17
b.
Bond Leverage Analysis........................................................................................18
Conclusions........................................................................................................................19
Bibliography.......................................................................................................................21
List of Tables and Figures..................................................................................................28
Appendix............................................................................................................................29
made up of political appointees from the states
of New York, New Jersey and Connecticut (the
“States”), who would serve as stewards of each
State’s respective RRTF. The broader intent of
the RRTF is to both catalyze and define public
and private sector investments in resilience and
adaptation.
I. Introduction
The New York Metropolitan Region
(“NYMR”) faces an uncertain future in light
of the impacts of climate change (Horton, et
al., 2015, 2016). These wide ranging impacts
from extreme heat (Knowlton, et al., 2007;
Rosenthal, Kinney & Metzger, 2014) to sea
level rise (Hallegatte, 2013; Kemp, et al., 2017)
challenge the conventional utility of single
jurisdiction resources and strategies (Vella, et
al., 2016). As such, some policy makers and
scholars have called for a regional approach
to resilience planning and development that
benefits from an aggregation of greater political
and economic capital than could otherwise be
mustered by individual jurisdictions in isolation
(Lebel, et al., 2006; Jacobs, et al., 2016; Peng,
et al., 2017). In particular, existing funding
mechanisms for resilience and adaptation
are highly irregular and are largely reliant
on philanthropic and federal post-disaster
sources (Adeniyi., Perera & Collins, 2016).
Those sources of financing that do exist are
often programmatically too rigid to address a
variety of processes and co-benefits necessary
for effective and comprehensive adaptation
planning and administration (LePore, 2016).
The central research question of this paper
is whether the RRTF could serve as a viable
model for financing local and regional projects
planned and designed in the advancement of
resilience and adaptation. Specifically, this
paper evaluates the proposition that the RRTFs
could be feasibly capitalized by a surcharge on
certain regulated insurance lines as originally
proposed under the Bloomberg administration
in New York City (City of New York, 2013)
(“Proposition A”). Second, based on the
assumed validity of the First Proposition,
the paper evaluates the proposition that the
RRTF could sustainably support a range of
financial products, including grants, low-cost
loans, non-recourse loans and market-rate
loans that could accommodate 100% of the
States’ unmet resilience needs, as defined by
current resilience planning (“Proposition B”).
Propositions A and B are evaluated through a
mixed methods research design grounded in
initial semi-structured interviews and focus
groups that helped shape the underlying
research question and subsequent propositions,
which are evaluated further through structured
interviews, legal and programmatic textual
reviews, and portfolio simulation analysis.
The relevance of this research is defined by
the unchartered waters for designing and
evaluating resilience and adaptation financing
models in the U.S.. Should Propositions A and
B be affirmed, it would represent a potentially
significant step forward in a broader discourse
as to the viability of advancing innovation in the
financing of climate resilience and adaptation.
In response to these constraints,
the Regional Plan Association proposed
the development of a regional governance
organization for the NYMR, known as the
Regional Resilience Commission (“RRC”).
The RRC would serve as regional entity to
facilitate the pooling of resources and to
coordinate multijurisdictional climate change
planning. This paper explores the legal and
financial viability of a series of state trust
funds designed to provide financial products to
support interventions advanced in the name of
climate change adaptation and resilience. The
subject financial model, known as a Regional
Resilience Trust Fund (“RRTF”), would be
governed by the RRC, which itself would be
1
outcomes and not necessarily regimented
processes for obtaining those outcomes (Long,
2014; Wildlife Conservation Society, 2017b).
However, most of these adaptation funds
are more or less grant programs, wherein the
utilization of debt and equity instruments has
been a controversial and unsettled debate
largely relegated to a limited number of private
and public sector actors, such as sovereign
wealth funds (Atteridge, 2009; Stadelmann,
Michaelowa, & Roberts, 2013; Fenton, et al.,
2014; Pauw, et al., 2016).
II. Resilience and
Adaptation Finance
Financing relating to climate change planning and interventions falls into one of several
categories that are defined more by the beneficiary of the resources than they are by the
process of underwriting or delivering such resources. First, adaptation trust funds have been
utilized to advance adaptation mainstreaming
within conventional international development
channels as a mechanism for developed countries to offset the impacts of climate change on
developing countries (Müller, 2009). The most
prominent mechanism is the Adaptation Fund
developed pursuant to the 2001 Kyoto Protocol
(Adaptation Fund, 2017). For instance, Africa
contributes less than 4% to global greenhouse
gases, but annual adaptation costs are expected
to reach 1.5% to 3% of annual gross domestic product (GDP) by 2030 (Reddy, Zhanje &
Taylor, 2011). These funds primarily operate
within existing government programs and are
highly institutionalized and capital intensive
in their modes of delivery (Hortsmann, 2011).
Largely for this reason, the Adaptation Fund
has been challenged for its ability to efficiently and equitably reach vulnerable populations
(Persson & Remling, 2014; Stadelmann, 2014).
In addition, as funds like the Green Climate
Fund mobilize on a global scale, there are also
renewed debates as to the appropriate allocations for investing in mitigation versus adaptation (Fridahl & Linnér, 2016).
A third category of trust funds most
relevant to evaluating the feasibility of the
RRTF model relates to the financing of
resilience and risk mitigation measures. This
category has perhaps been the most ripe for
dual public and private sector engagement
given the emerging methodological capacity
to measure avoided costs (Vajjhala, 2016). In
a domestic context, conventional cost-benefit
analyses have consistently shown that the
benefits of risk mitigation outweigh the costs
on average “by about four times the costs in
terms of avoided and reduced losses” (Mechler,
2016, p. 2123; see generally, Multihazard
Mitigation Council, 2005). However, avoided
cost evaluations are often based on cost benefit
analyses (“CBA”) that are methodologically
limited in their capacity to accommodate
qualitative data that cannot easily or reliably
be reduced to a monetary value (Liu, et al.,
2016). CBA’s are also limited in their ability
to utilize quantitative data from which there is
a limited probabilistic basis to draw correlative
inferences between mitigation and avoided
losses or undefined benefits associated with
an indeterminate definition of general system
resilience (Knight-Lenihan, 2016).
A second category of adaptation trust funds
relates to conservation biology and ecology.
The most notable of these domestic funds is
the Climate Adaptation Fund managed by
The Wildlife Conservation Society (Wildlife
Conservation Society, 2017a). Despite its
limited size, this fund has a broad scope where
the evaluation of incoming projects is based on
a set of open criteria that speak to measured
Therefore, while a CBA may work well
in terms of risk mitigation within a welldefined parameters of a closed system (e.g.,
flood mitigation), it arguably under accounts
2
for costs and benefits associated with the
resilience of complex and open systems, such
as communities and cities (Mechler, et al.,
2014; André, et al., 2016). As such, many
have called for CBA-driven underwriting to
be augmented by a variety of decision support
tools including costs-effectiveness analysis
(“CEA”), multi-criteria analysis (“MCA”),
real options approaches (ROA”), and robust
decision making (“RDM”), which does not
focus on economic optimization but instead
looks across a wide array of uncertain futures
for the most robust, effective and socially and
environmentally optimal outcomes (Watkiss,
et al., 2015; Ellen, et al., 2016). While
probabilistic risk may lend itself to economic
analysis within the context of risk mitigation
(i.e., avoided losses), resilience investments
are much more challenging to evaluate. Given
the deep uncertainty or lack of probabilities
associated with many impacts of climate
change, resilience and adaptation frameworks
that focus solely on avoided costs are limited
in their expected value functions.
80% of observed activities and investments
were motivated by secondary factors (BerrangFord, Ford & Paterson, 2011). Therefore, one
could infer that adaptation is often motivated
not only by climate change but also by the
opportunity to capture co-benefits defined
by secondary considerations. A version of
this value-add perspective was popularized
by the resilience “dividend” advanced by the
Rockefeller Foundation (Brown, 2012; Rodin,
2014).
Constructive examples of funds or pools of
funds within either risk mitigation and/or valueadd driven resilience frameworks are relatively
scarce. From an international perspective, the
most notable example is the Global Resilience
Partnership financed by the Rockefeller
Foundation, USAID and Sweden. In addition,
there are a very limited number of domestic
prototypes for pooling funds. Examples
include the State of Washington’s Floodplains
by Design (Floodplains by Design, 2016)
and natureVest’s D.C. Green Infrastructure
Fund (natureVest, 2016). There are also a few
notable federally driven programs for resilience
oriented retrofitting, including the PropertyAssessed Clean Energy (PACE) financing and
Water Infrastructure Finance and Innovation
Program at the Environmental Protection
Agency (EPA)(White House, 2016). Overall,
according to the U.S. Climate Resilience
Toolkit, there are just thirteen (n=13) funds or
grant programs available to support resilience
and adaptation in the U.S. in both the public
and private sectors (USCRT, 2016). Despite the
lack of financing resources and conduits, the
need for resilience finance programs is more
relevant than ever considering the pending
Federal Emergency Management Agency
(FEMA) rule for imposing a disaster deductible
on states (FEMA, 2017). Unfortunately, none
of the existing programs represent a portfolio
approach based on a dedicated revenue source,
and only a handful of programs have leveraged
While the aforementioned resilience and
risk mitigation perspective have focused
on the internal risk management and the
implications of avoided costs, there is another
sub-category of resilience financing that looks
to the value-add benefits that are both internal
and external to the underlying investment.
This perspective builds off a body of work in
strategic adaptation that looks at the costs and
benefits of a range of strategies including: (i)
no regrets; (ii) reversible/flexible; (iii) cheap
safety margins; (iv) reduced decision horizon;
and, (v) and co-benefits synergies (Hallegatte,
2009; Keenan, 2015). These strategies have
expanded the limitations of conventional risk
mitigation models wherein benefits only accrue
in the event of an extreme event (Tanner, et al.,
2016). This value-add perspective is consistent
with empirical research in climate adaptation
that suggests, in at least one case, that nearly
3
finance products. As such, there are no freestanding funds or programs that represent
analog models that can directly speak to the
modeled parameters of the RRTF.
Finally, existing state trust funds were evaluated
to identify critical elements for investment
management, asset management, auditing
requirements, fiscal oversight and governance.
Research was not conducted to evaluate the
socio-political preferences that would speak
to the viability of passing the laws necessary
to authorize the formation and capitalization
of the RRTF. In evaluating Proposition B, a
quantitative portfolio analysis was utilized
to simulate the operational parameters of the
RRTF with and without bond leverage (Liesiö,
Mild, & Salo, 2008; Liesiö & Salo, 2012). The
preliminary results of the portfolio modeling
were shared with selected interviewees in
order to calibrate, validate and further qualify
the results. This underlying methodology will
be explained in more detail in the subsequent
section dedicated to portfolio modeling.
III. Research Design
and Methodology
The research design for this paper is based
on a mixed methods approach developed over
the course of approximately twenty-four (24)
months (Creswell, 2013). Initial scoping on
the broader theme of resilience and adaptation
finance occurred over the course of eighteen
(18) months by researchers operating under the
Climate Change Working Group of the Fourth
Regional Plan promulgated by the Regional
Plan Association. The central research question
and the general parameters of the RRC and the
RRTF were developed through a combination
of semi-structured interviews (Galletta,
2013), focus groups (Eliasson, 2000), and
textual reviews of academic literature (Hart,
1998), gray literature (Gray, 2013), finance
programs and applicable laws and regulations
(Goldsmith & Vermeule, 2002). The total
number of formal semi-structured interviews
was twenty-five (n=25). Transcripts of each
interview were subsequently produced and
shared with interviewees to ensure the accuracy
of statements.
IV. Governance: Regional
Resilience Commission
A normative exploration of the potential
governance mechanisms of the RRTF model
is central to understanding the broader utility
of the evaluated Propositions. While the details
of the normative development of the RRC are
beyond the purview of this paper, it is useful
to briefly frame the underlying prospective
organizational structure of the entity. The RRC
would be a single administrative unit chartered
by the legislatures of New York, New Jersey
and Connecticut. Pursuant to the interstate
compact clause of the U.S. Constitution (Art.
I, Sec. 10), compacts between states require the
consent of Congress. While the case of Virginia
v. Tennessee, 148 U.S. 503 (1893) qualified this
consent requirement to matters where states
would increase their power though a compact,
the congressional consent of the Port Authority
of New York and New Jersey (1921) likely
provides a strong precedent for the necessity
of congressional approval in light of the intent
In evaluating Propositions A and B, two
distinct methodologies were utilized. For
Proposition A, legal research was conducted to
evaluate the legality of an insurance surcharge
that would be hypothetically imposed by
each State’s legislature, as well as the lawful
incorporation of the investment vehicle as a
public benefit corporation. Thereafter, research
was conducted to evaluate prior surcharges,
as well as the total capitalization necessary
to meet documented unmet financial needs
relative to existing climate change planning.
4
of Directors”). Each appointee would have a
staggered term of four years. At any given time,
each State must have at least one (1) appointee
who serves as a designated representative who
is otherwise qualified as a scientist whose
expertise relates, in part, to climate change.
In addition to a professional staff of public
finance professional, actuarists, scientists,
ecologists and engineers, the RRC board of
directors is supported by an gubernatorial
appointed advisory board made up of an equal
number of representatives from each of the
following categories: (i) community advocacy
organizations; (ii) environmental advocacy
organizations; (iii) municipal and county
officials; and, (iv) private sector commercial
enterprise (collectively, the “Advisory Board”).
of the each state’s RRTF to have the authority,
if necessary, to issue revenue bonds based on
the insurance surcharges. However, because
the RRC would not be issuing the bonds, it
is debatable whether congressional consent
would be required.
As a technicality, the governance of the
RRTF could be shared through a Memorandum
of Understanding between a third-party asset
or portfolio manager and the RRC. While it is
also conceptually possible that a RRTF could
operate independent from the RRC, the RRC
offers an opportunity to independently define
the public benefits associated with resilience
and adaptation investments. For instance, the
governance of the RRC could be made up
up of four (4) gubernatorial appointees from
each of the States for a total of twelve (12)
members of the board of directors (the “Board
Even though each state administers
and manages its own RRTF, the RRC is
Figure 1: Hypothetical Relationship between Regional Resilience Trust Funds and Regional Resilience Commission
Regional Resilience Commission (RRC)
Reserve
Investments
Loan Proceeds
Bond Debt Service
Loan Proceeds
Bond Debt Service
Loan Proceeds
Bond Debt Service
Connecticut
Regional Resilience
Trust Fund (RRTF)
Loan Repayments
5
Asset Manager
(Third-Party)
Bond Proceeds
Bond
Grantees Local
Investors
Borrowers
Insurance
Surcharge
Investment Earnings
Loan Repayments
Reserve
Investments
Bond Proceeds
Bond
Grantees Local
Investors
Borrowers
Asset Manager
(Third-Party)
New York
Regional Resilience
Trust Fund (RRTF)
Investment Earnings
Loan Repayments
Reserve
Investments
Bond Proceeds
Investment Earnings
Reserve Funds
New Jersey
Regional Resilience
Trust Fund (RRTF)
Insurance
Surcharge
Reserve Funds
Asset Manager
(Third-Party)
Reserve Funds
Insurance
Surcharge
Bond
Grantees Local
Investors
Borrowers
a. Organization
Life Insurance Guaranty Fund (NY INS § 7501,
et seq.) and Property and Casualty Security
Funds (NY INS § 7601, et seq.). New Jersey
has a similar fund known as the Surplus Lines
Guaranty Fund, but the fund has not utilized its
statutory authority to impose a surcharge since
1993 (NJ Rev Stat § 17:22-6.73). New York and
New Jersey (for workers compensation only)
operate under a pre-assessment model wherein
a surcharge is only assessed when the net asset
value of the fund dips below a certain amount.
For property and casualty lines, Connecticut
and New Jersey have a similar fund organized
as an association that is financed by direct
assessments to member insurance companies
based on their proportional market share of
lines of coverage, which are subject to actual
instances of insolvency each year (C.G.S. §
38a-866)1. One critical issue identified in the
course of this research related to the equity and
practicality of imposing a surcharge on an entire
State versus imposing the surcharge on select
counties that fall within the NYMR for each of
the States. At issue is the extent to which coastal
NYMR counties cross-subsidize landlocked
non-NYMR counties who are arguably less
vulnerable to the effects of sea level rise and
storm surge. In addition, while the States do
not have a history of utilizing surcharges for
the external financing of investments deemed
to be in the broader public benefit, other states
such as Kentucky (K.R.S. § 136.392) (e.g., law
enforcement) and Mississippi (M.S.C. § 83-3437)(e.g., reinsurance / general fund) have had
more liberal applications of the surcharge.
The States each have a history of extending
surcharges for the purposes of capitalizing trust
funds or public benefit corporations. In New
York, these surcharges capitalize funds whose
legislative intent is to protect consumers in the
event of an insolvency. These funds include the
The organizational structure of each of
the RRTFs would be based on the respective
laws of incorporation in each of the States.
In New York, the RRTF would be a Public
Benefit Corporation authorized under the state
constitution (NY Const.. art. X, § 5; NY BCL
charged with independently underwriting and
approving all grants and loans (collectively,
the “Product(s)”), including those Products
that are expended entirely within any one of
the States, as represented in Figure 1. Figure
1 represents just one of multiple potential
options for structuring the governance of the
RRC. States would be obligated to allocate an
equal fixed minimum percentage of each state’s
RRTF annual portfolio allocations for Products
to be designated for projects that are regional
in nature, as defined by those projects whose
impacts extend beyond any single State. The
investment criteria of the RRC would prioritize
the allocation of Products for those projects
that have the potential to promote regional
resilience and/or adaptation. For example,
with the approval of the Advisory Board, the
Board of Directors would have to have at least
six (6) votes to approve any given Product.
Without the approval of the Advisory Board,
the Board of Directors would require nine (9)
votes to approve any given project. The intent
of the RRC is to help guide and incentivize
a regional effort to plan and mainstream
resilience and adaptation within public and
private investments. In so doing, the RRC has
the opportunity to set benchmarks for planning,
design, operations and performance that inure
to a broad array of projects that may or may not
benefit from the RRC’s Products.
V. Trust Fund
Connecticut H.B. 5518 (2016) proposed a surcharge on net direct premium for fund operating budgets for local
firefighting services. The bill has since been tabled.
1
6
particularly relevant given the lack of empirical
evidence for demonstrating the existence of
true win-win, no-regrets strategies (Preston,
Mustelin, & Maloney, 2015).
§1702(e)). As this legal entity is not considered
a state agency and would have its own fiscal
obligations separate from the state (Wein v.
State, 39 N.Y.2d 136 (1976)), it may or may
not require direct state oversight provided
by the New York Public Authorities Control
Board (NY PBA § 50, et seq.). However,
interviewees noted that such direct oversight
may add additional compliance costs to
operations that may not be necessary in light
of the gubernatorial appointees to the RRC,
which could theoretically possess a controlling
percentage of the RRTF’s shares. New Jersey
has a similar entity known as a Benefit
Corporation, but with the added requirement
for the transparency of the measurement and
reporting of the public benefits (NJ Rev Stat
§ 14A:18-1(2013), as well as requirements for
evaluating the effects of any such investments
or actions across a wide array of considerations
from the global environment to a domestic
workforce (NJ Rev Stat § 14A:18-6 (2013).
b. Capitalization
The question as to what is the optimal
size—in terms of capitalization—required
to adequately capture unmet resilience and
adaptation needs is central to the development
of the RRTF model. In particular, the answer
to this question is critical to establishing
a rational legislative intent likely required
to justify a surcharge on particular lines of
insurance. This paper makes the assumption
that the size of each State’s RRTF would be
benchmarked to an amount approximately
equal to the unmet financial needs identified in
each of the State’s Community Development
Block Grant—Disaster Recovery (“CDBGDR”) amended Action Plans (“Action Plans”).
Table 1 shows the range of unmet needs based
on existing assessments derived following
Hurricane Sandy. The CDBG-DR numbers are
not an ideal proxy for unmet needs because
they include recovery, mitigation and resilience
expenditures. As such, the categories identified
in Table 1 do not necessarily reflect the
underwriting criteria and/or funding priorities
of the RRC. While these estimates are not
necessarily a precise proxy for the total amount
of unmet resilience needs for each local
jurisdiction within the NYMR, they represent
the only consistent approximation based on the
existing capacity of the relevant jurisdictions
to engage in climate change and disaster risk
mitigation planning. An additional logic for
using these numbers as an initial capitalization
benchmark is that there is a potential downside
politically and economically to overcapitalizing
the fund if there are not a sufficient number
of projects that would qualify as advancing
resilience and/or adaptation. This “absorptive
capacity” problem has been observed to be a
Finally, Connecticut has a similar
Benefit Corporation, but with the additional
requirement that the benefits be derived
pursuant to a “third-party standard” (C.G.S. §
33-1351(15)). While New Jersey has a similar
third-party requirement, Connecticut requires
a more formal standard development process
to govern the determination of public benefit
(Id.). Given the lack of standards development
in resilience and adaptation, these requirements
may present a potential barrier, as internal
underwriting and stewardship requirements
promulgated by the RRC may or may not
qualify as a third-party standard. In addition,
absent a win-win, no-regrets co-benefits
strategy, resilience and adaptation investments
represent a potential existential challenge to the
notion of recognized public benefits because
many or most of the benefits may not arise until
a point in time where their utility is recognized
contemporaneous with the occurrence of
climate change or an extreme event. This is
7
later phase of resilience planning can develop
the appropriate projects.
constraint for adaptation funds across the globe
(Müller, 2009). To that end, while interviewees
highlighted the opportunity to issue bonds
based on the potential revenue from insurance
surcharge, an initial survey of potential projects
in the NYMR suggested that this bonding
capacity might not need to be utilized until a
Based on the assumption relating to unmet
needs, the question remains as to what is the
optimal insurance surcharge for each State.
This question should be contextualized with
the variable term to which a surcharge does
or does not sunset (i.e., expire) based on
prevailing legislative preferences. NYC’s
Special Initiative for Recovery and Resiliency
(SIRR) originally proposed a hypothetical
1.5% surcharge (City of New York, 2013).
As the SIRR report noted, “[t]his surcharge
would translate to just over a dollar a month
for a homeowners’ insurance policy with a
$1,000 annual premium” (Id., p. 405). Based
on a sample of insurance premiums for the
mean household value in the NYMR, this cost
burden would be closer to $2 per month. Table
2 provides a sensitivity analysis for a projected
annual revenue generated from a range of
surcharges for each state based on recent
historical rates of growth for property and
casualty lines found in Appendix Table 2. Based
on this information, it would take a number of
years to reach a capitalization roughly equal
to the present value unmet needs of the States.
Table 1: Estimates of State Unmet Resilience Needs
New Jersey Unmet / CDBG-DR
Flood Hazard
$4,955,329,131
$5,607,534,587
Energy
$2,639,620,426
Water/ Wastewater
$3,708,313,761
Transportation
$236,548,191
Community Facilities
$225,406,264
Debris Removal and Dredging
$17,372,752,360
Total
Source: New Jersey Department of Community Affairs (2016)
New York City Unmet / CDBG-DR
Housing
Business
Infrastructure
Other City Services
Coastal Resiliency
Total
Source: City of New York (2016)
$2,381,944,000
$2,309,000,000
$2,409,070,000
$571,467,000
$1,952,463,000
$9,623,944,000
Connecticut Unmet / CDBG-DR
Infrastructure
Housing
Economic Revitalization
Mitigation
Planning
Total
Source: Connecticut Department of Housing (2016).
$151,600,000
$259,407,500
$10,797,888
$27,758,056
$25,000,000
$474,563,444
Table 2: Sensitivity Analysis for Projected Revenue from State Insurance Surcharges
($ in thousands)
Surcharge Revenue (Low, 0.5%)
New York
New Jersey
Connecticut
2018
$72,255
$31,190
$15,594
2019
$75,771
$32,918
$16,353
2020
$79,458
$34,519
$17,149
2021
$83,324
$36,199
$17,984
2022
$87,379
$37,961
$18,859
2023
$91,631
$39,808
$19,776
2024
2025
$96,090 $100,766
$41,745 $43,776
$20,739 $21,748
2026
$105,669
$45,907
$22,806
2027 10 Yr Total Proj. CAGR
$110,811 $903,154
4.87%
$48,140 $392,363
4.87%
$23,916 $194,924
4.87%
2022
2023
$174,758 $183,262
$75,921 $79,616
$37,717 $39,553
2024
2025
$192,180 $201,532
$83,490 $87,553
$41,477 $43,496
2026
$211,338
$91,813
$45,612
2027 10 Yr Total Proj. CAGR
$221,622 $1,806,309
4.87%
$96,281 $784,726
4.87%
$47,832 $389,847
4.87%
2022
2023
$262,137 $274,893
$113,882 $119,424
$56,576 $59,329
2024
2025
$288,270 $302,298
$125,235 $131,329
$62,216 $65,243
2026
$317,008
$137,720
$68,418
2027 10 Yr Total Proj. CAGR
4.87%
$332,434 $2,709,463
$144,421 $1,177,089
4.87%
$71,748 $584,771
4.87%
($ in thousands)
Surcharge Revenue (Medium, 1.0%)
New York
New Jersey
Connecticut
2018
$144,510
$62,780
$31,189
2019
2020
2021
$151,542 $158,916 $166,649
$65,835 $69,039 $72,398
$32,707 $34,298 $35,967
($ in thousands)
Surcharge Revenue (High, 1.5%)
New York
New Jersey
Connecticut
2018
$216,764
$94,170
$46,783
2019
2020
2021
$227,312 $238,374 $249,973
$98,753 $103,558 $108,597
$49,060 $51,447 $53,951
Source: Adapted from data from, Insurance Information Institute (2007-2015).
*2018 Figures based on 2015 property and casualty insurance premium projection based on a 9 year historical average adjusted for inflation.
8
financial transaction taxes, etc...). With this in
mind, future analysis of alternative sources of
capitalization would need to consider the extent
to which such sources would be considered
stable and consistent enough to float revenue
bonds.
For instance, assuming a 1.5% surcharge, an
unlevered RRTF operating over the course of
ten years would only accommodate 21.9% of
New York’s and 7.8% of New Jersey’s unmet
needs adjusted for inflation over the same
period. However, it would cover 96.3% of
Connecticut’s unmet needs. Pursuant to Table
3, the surcharge necessary to meet the unmet
needs within one decade are equal to 5.33% in
New York, 14.67% in New Jersey, and 1.16% in
Connecticut. Under this scenario, a New Jersey
homeowner could see a cost burden of a little
under $20 a month. Given the relatively large
surcharges or lengthy surcharge sunset periods
for New York and New Jersey, leverage from
revenue bonds offers an alternative scenario. By
contrast, Connecticut could arguable proceed
with no bond leverage. Of course, insurance
surcharges are just one of many different
options for funding an RRTF (e.g., impact fees,
Table 4 represents possible revenue
streams with bond leverage based on three
different surcharge rates. These gross figures
do not represent the operations and returns
of the RRTF, which will be discussed in
the following section. As such, assuming
a 1.5% surcharge, a levered (1x) RRTF
operating over the course of 20 years would
accommodate 25.9% of New York’s, 9.6% of
New Jersey’s and 116.3% of Connecticut’s
unmet needs adjusted for inflation over the
same period. Therefore, with the exception
of Connecticut, leverage only marginal fills
Table 3: Revenue Scenarios to Fulfill CDBG-DR Unmet Needs with 10-Year Sunset
Surcharge Rate
New York
New Jersey
Connecticut
($ in thousands)
Unmet Needs (CDBG-DR)
New York
New Jersey
Connecticut
5.33%
14.67%
1.16%
$9,623,944
$11,765,218
$474,563
Surcharge Revenue
($ in thousands)
2018
$769,942
$928,537
$36,757
New York
New Jersey
Connecticut
2020
2021
2022
2023
2024
2025
2026
2027 10 Yr Total
2019
$931,105
$976,414 $1,023,927 $1,073,753 $1,126,003 $1,180,796 $9,623,944
$846,697
$887,899
$807,408
$976,425 $1,026,784 $1,079,740 $1,135,427 $1,193,986 $1,255,565 $1,320,320 $1,388,414 $1,460,021 $11,765,218
$474,563
$53,679
$56,663
$59,813
$50,852
$40,957
$43,234
$45,637
$48,174
$38,800
Source: Connecticut Department of Housing (2015); New Jersey Department of Community Affairs (2016); City of New York (2016).
*2018 Figures based on 2015 property and casualty insurance premium projection based on a 9 year historical average adjusted for inflation.
**New Jersey's CDBG-DR unmet needs do not include energy projects (n= $5,607,534,587), as they would prospectively fall under the mandate of the New Jersey
Energy Resilience Bank.
Table 4: Sensitivity Analysis for Projected Capitalized with Bond Leverage by State
Surcharge Revenue (Low, 0.5%)
($ in thousands)
2017 Bond Revenue (PV)
$1,361,917
2018
$69,309
2019
$69,719
2020
$70,131
2021
$70,537
2022
$70,954
2023
$71,373
2024
$71,795
2025
$72,211
2026
$72,638
2027
$73,067
2028
$68,712
2029
$68,687
2030
$68,671
2031
$68,656
2032
$68,640
2033
$62,153
2034
$61,757
2035
$61,362
2036
$60,970
2037
$60,572
New Jersey
$618,053
$29,895
$29,857
$29,819
$29,777
$29,739
$29,701
$29,663
$29,622
$29,584
$29,546
$29,508
$29,467
$29,429
$29,392
$29,354
$34,167
$34,451
$34,738
$35,027
$35,315
Connecticut
$301,404
$14,995
$15,120
$15,245
$15,371
$15,499
$15,628
$15,758
$15,888
$16,020
$16,153
$14,660
$14,639
$14,620
$14,602
$14,583
$14,563
$14,544
$14,525
$14,507
$14,486
New York
Surcharge Revenue (Medium, 1.0%)
New York
New Jersey
Connecticut
($ in thousands)
2017 Bond Revenue (PV)
$2,723,834
2018
$138,618
2019
$139,438
2020
$140,262
2021
$141,075
2022
$141,908
2023
$142,747
2024
$143,591
2025
$144,423
2026
$145,276
2027
$146,135
2028
$137,423
2029
$137,374
2030
$137,343
2031
$137,312
2032
$137,280
2033
$124,307
2034
$123,513
2035
$122,725
2036
$121,941
2037
$121,144
$1,236,107
$59,791
$59,714
$59,638
$59,554
$59,478
$59,403
$59,327
$59,327
$59,168
$59,092
$59,017
$58,934
$58,859
$58,784
$58,709
$68,333
$68,902
$69,476
$70,055
$70,630
$602,809
$29,989
$30,239
$30,491
$30,741
$30,998
$31,256
$31,516
$31,775
$32,040
$32,306
$29,319
$29,278
$29,241
$29,203
$29,166
$29,125
$29,088
$29,051
$29,014
$28,973
Surcharge Revenue (High, 1.5%)
New York
New Jersey
Connecticut
($ in thousands)
2017 Bond Revenue (PV)
$4,085,751
2018
$207,928
2019
$209,156
2020
$210,393
2021
$211,612
2022
$212,862
2023
$214,120
2024
$215,386
2025
$216,634
2026
$217,915
2027
$219,202
2028
$206,135
2029
$206,061
2030
$206,014
2031
$205,967
2032
$205,920
2033
$186,460
2034
$185,270
2035
$184,087
2036
$182,911
2037
$181,717
$1,854,160
$89,686
$89,572
$89,457
$89,331
$89,218
$89,104
$88,990
$88,865
$88,752
$88,638
$88,525
$88,401
$88,288
$88,175
$88,063
$102,500
$103,353
$104,214
$105,082
$105,946
$904,213
$44,984
$45,359
$45,736
$46,112
$46,496
$46,883
$47,274
$47,663
$48,059
$48,460
$43,979
$43,917
$43,861
$43,805
$43,749
$43,688
$43,632
$43,576
$43,521
$43,459
Source: Adapted from data from, Insurance Information Institute. (2007-2015); State of New York (2016); Moodys (2016).
*2018 Figures based on 2015 property and casualty insurance premium projection based on a 9 year historical average adjusted for inflation
**Based on hypothetical 1x bond leverage.
9
the conduit for identifying, evaluating and
underwriting projects and Products. Although
the asset manager would have the ultimate
fiduciary obligation to ratify projects referred
from the RRC, the asset manager would not
have the authority to independently originate
Products. However, the asset manager would
retain the authority to develop new Products;
to alter and approve amended terms of
existing projects; and, to refrain or discontinue
certain types of Products that undermine
the sustainability and stability of the RRTF
portfolio. For instance, interviewees suggested
that a certain fixed percentage of grants is
desirable to advance resilience and adaptation
planning efforts in a pre-design phase. For
instance, as will be discussed in the next section,
allocations in favor of grants and non-resource
loans may vary year-to-year depending on the
broader performance of the portfolio defined in
terms of reinvestment roll-over, default rates,
interest rates, deployment lag and various other
portfolio considerations.
the gaps over 20 years—assuming no other
investments are made to substantively reduce
unmet needs and that the needs themselves
would otherwise hold constant. In present
value terms, a bond issuance based on a 1.5%
surcharge in 2017 would account for 42.4%
of New York’s, 15.76% of New Jersey’s and
190.5% of Connecticut’s current unmet needs.
Again, CDBG-DR unmet needs may not be
representative of the true unmet needs for
adaptation to climate change. These numbers
may be overestimated based on recovery in
certain areas (e.g., housing) and underestimated
in other areas (e.g., aging infrastructure). An
additional challenge that will be discussed in
the portfolio modeling section of this paper
is the ability to translate this revenue into
products that support projects that may or
not be in a pipeline sufficient to rollover the
investment capital of each State’s RRTF.
Again, the absorptive capacity of the
system to supply eligible projects suggests that
leverage may not be immediately desirable.
In theory, the amount of unmet needs will
grow over time. However, the estimated rates
of growth above and beyond various rates of
capital and project inflation are unknown.
The time function for capitalization is based
on the previously cited prevailing legislative
preference to either sunset a surcharge or only
utilize it to maintain either a minimum floor
net asset valuation (NAV) or a minimum level
of liquidity for the fund. In the alternative, the
legislature could maintain a lower surcharge
rate that does not sunset. Under this scenario,
the utilization of leverage through revenue
bonds could offer the opportunity to amplify a
lower surcharge over a longer period of time.
Beyond the conventions of portfolio
and asset management, there rests a more
fundamental question as to how one defines
and underwrites investments that advance
resilience and adaptation. In particular,
resilience knowledge can be divided into
categorical variations that include ecological,
socioecological, engineering, urban, disaster,
and community variants (Brand & Jax, 2007;
Matyas & Pelling, 2015; Meerow, Newell
& Stults, 2016). To this end, across scales
of time and space, as well as actor or object
orientation, resilience may be simultaneously
viewed as a positive, negative and/or neutral
intervention (Carpenter, et al., 2001; Klein,
Nicholls, & Thomalla, 2003; Olsson, et al.,
2015). Resilience is not an objective good and
the benefits of resilience investments may be
subjectively evaluated and unevenly distributed.
Despite the proliferation of resilience policies
in the Obama Administration (Keenan, 2016),
c. Operations and Underwriting
As previously noted, the RRTF would
prospectively have a separate asset manager
for each State and the RRC would serve as
10
down model that mainstreams these concepts
into existing matters of public administration
and service provision (e.g., The Adaptation
Fund). This top-down model uses existing
metrics modified to account for the marginal
costs and returns for mitigating hazards,
reducing exposure and reducing vulnerability.
Conventional methods such as CBA, CEA and
ROA are more appropriate given the existing
known parameters of performance, risk and
uncertainty. The other model is based on a loose
framework generated from bottom-up local
actors who are engaged in scalable prototypes
and experiments (e.g., Wildlife Conservation
Society). The evaluation of these projects are
often based on MCA and RDM methods that
can capture the qualitative innovation that has
little to no historical precedent to support a
probabilistic assessment. Both of these delivery
models are generally based on a specific type
of resilience known as ‘disaster resilience’
(Davidson, et al., 2016).
a lack of operationalizable guidelines and
metrics has thwarted the development of both
public administration (Larkin, et al., 2015) and
private sector finance (White House, 2016).
As resilience was originally a descriptive
and not a normative concept (Holling, 1973;
Gunderson, 2000), there have been numerous
lines of research advanced by scholars and
practitioners in disaster risk reduction who
have attempted to use indicators as proxies
for measuring the post-disaster resilience of
specific communities and jurisdictions (Cutter,
2016). However, much of the empirical
research that has attempted to validate these
indicators has either come up short or has
identified somewhat self-evident indicators for
explaining recovery, such as housing income
and tenure (Burton, 2015).
By contrast, within closed and engineered
systems (i.e., infrastructure), resilience is
relatively well defined and forms the basis for
many practices in process, systems and civil
engineering (Gilbert, 2010; Menoni, et al., 2012;
Ayyub, 2014). However, what unites finance
with various other applications of resilience,
including engineering resilience, is the struggle
to accommodate either deep uncertainty or
ignorance as to the nature or depth of either
probabilistic or non-probabilistic events—
particularly low probability, high impact events
(Hallegatte, et al., 2012). While the occurrence
of many physical phenomenon of climate
change are probabilistic, many more impacts
are not. This presents a short-term biasing in
everything from insurance to the pricing of the
future values associated with risk mitigation
investments. As previously referenced, the
uncertainty associated with value-add aspects
of resilience and/or adaptation are equally as
challenging in methodological terms.
There are two perspectives on disaster
resilience. The basic definition suggests that
multi-hazard resilience is “reactively [oriented]
through resistance, relief and recovery
approaches” (Id., p. 27). More advanced and
integrated definitions of disaster resilience
expand reactionary performance characteristics
to precautionary capacities developed through
interdisciplinary perspectives that cut across
environmental, community and infrastructural
perspectives (Zhou, et al., 2010).
One
opportunity
to
develop
performance
benchmarks is to build off of the analytical
standards developed by the U.S. Commerce
Department at the National Institute of
Standards and Technology (NIST). The NIST
Community Planning Guides (NIST Guides)
have set a benchmark for expanding disaster
resilience within both the community and
infrastructure domains (NIST, 2015a, 2015b).
The analyses found in these standards could be
organized by infrastructure systems and could
There are two perspectives on how to
underwrite and deliver resilience and adaptation
investments. One perspective is based on a top11
provide a basis for additional weighting within
a MCA model. One weakness in the NIST
Guides is that they do not contain metrics for
environmental resilience. Recent interviews
with EPA researchers confirmed that resilience
metrics are woefully underdeveloped. By
example, in FEMA’s broad interagency review
of resilience metrics, they could identify only
one environmental resilience metric (FEMA,
2016).
that is often open, unbounded and difficult to
measure or observe (Carpenter, et al., 2012).
While this perspective may bias material
investments over social investments, one is
obligated to clearly articulate the costs and
benefit to a degrees the narrows the parity
between cost burden and beneficiaries. In
addition, the broader assumption is that the
specific resilience of particular projects will
collectively work to advance general resilience.
Interviewees highlighted a number of keys
aspects for focusing resilience sufficiently
enough to evaluate the performance of any given
prospective project. First, a prospective project
should be specific and precise with regard to
who or what will be the beneficiaries of the
project, as well as what set of risks or hazards
are addressed by the project. In addition, the
project should be just as transparent about what
the project will not address. By extension, the
project should identify conflicts or opportunity
costs with other actors or objects that are
directly or indirectly engaged or impacted by
the project. Interviews consistently reinforced
the necessity to understand the trade-offs and
path dependencies associated with resilience
and/or adaptation. For instance, how will this
resilience investment (e.g., risk mitigation) limit
my options (e.g., ROA) to adapt in the future?
Appendix Table 3 contains a checklist that
attempts to capture a range of unweighted
criteria for assessing the nature of any given
project’s resilience or adaptive capacity
(“Checklist”). Whether one utilizes an approach
such as a scorecard or an index, the process
often distills to a matter of weighting for each
criteria. As such, each State will likely have a
different weighting depending on not only its
unmet needs, but also its local preferences. The
Checklist builds off of the work of the National
Security Council and the author for distilling
disaster resilience within the parameters of
capabilities identified with the framework of
the National Preparedness Goals developed
pursuant to Presidential Homeland Security
Directive 8 (HSPD-8)(U.S. Department of
Homeland Security, 2011). This Checklist
and the respective capabilities or capacities
represent merely a starting point. Additional
capacities may include a range of performance
criteria relating to the environment, for instance,
including water storage, water filtration,
toxics remediation or radiant cooling. Based
on data from the interviews, the intent should
be to use these as inclusive and not exclusive
evaluation criteria. It can be argued that the
most successful project evaluation process will
be one that looks at specific resilience within
a top-down mainstreaming that is reinforced
by bottom-up local experimentation based on
novel and innovative capacities.
Second, the range of potential hazards
should be extended beyond the conventions of
flooding and heat to also include human-caused
and technological hazards. This represents an
opportunity to capture a variety of co-benefits
within other domains such as national security,
public safety or public health. Third, the project
level performance should be based on specific
resilience and not general resilience. Specific
resilience is focused on a specific object or a
specific system or organization with clear and
articulated performance measures, boundaries
and metrics (Nelson, 2011). General resilience
focuses on the resilience of a broader system
12
grants but are able to accommodate specific
requirement relating to federal income tax
to which some borrowers may be sensitive.
The next class of loans are fixed and variable
rate mid-cap, below-market loans with terms
ranging from 5 to 25 years. These concession
loans are intended to serve a variety of purposes,
including gap financing or permanent financing
where traditional infrastructure products cannot
efficiently scale-down. The final class of loans
relates to fixed and index-adjusted variable rate
loans with 5 and 10 year terms. This class of
loans is primarily intended to help gap finance
larger infrastructure projects or finance critical
risk mitigation interventions that are difficult to
finance with conventional products. A portfolio
allocation optimization analysis in the following
section will highlight optimal allocations based
on a survey of existing loan rates and terms.
The immediately following section offers some
example projects that might benefit from one of
the foregoing Products.
d. Products
Given the diversity of potential eligible
projects, delivery models and underwriting
methodologies, it can be argued that that the
RRTF’s Products must be flexible enough to
accommodate changing financial and fiscal
circumstances. However, an infinity array
of flexible Product terms is not possible
for effective and sustainable portfolio
management. This paper assumes several
Product types that form the basis for portfolio
modeling in the following section. These
Products include grants, which are estimated to
account for a minimum of 10% of a portfolio’s
allocation. These grants can be utilized to
advance everything from project level climate
change planning to education and training. The
portfolio could also include soft loans that are
non-recourse and bear a 0% interest rate or
are indexed to inflation or the weighted cost
of capital. Soft loans have a similar intent to
13
Project 1: Small Grant
Borrower: State of New York, Department of
Environmental Conservation
Amount: $500,000
Type: Planning Study
This project would utilize a small grant of $500,000
for a planning study of the coastline of New York.
The state agency could leverage the grant with
federal funding through the U.S. Department of
Housing and Urban Development Sustainable
Communities Regional Planning Grant. The grant
would be disbursed in the first year of operation and
would require a 20% funding matching from the
state. The study would take upwards of three years
and would include: (i) a coastal area typology study;
(ii) an inventory of potential adaptation strategies for
existing green infrastructure; (iii) adaptive
management processes for science informed
decision making in local jurisdictions; (iv) case
studies of existing resilience and adaptation
projects; and, (v) education and outreach materials
for engaging coastal communities in the face of
extreme events and climate change.
Project 2: Large Grant
Borrower: New Jersey Sports and Exposition
Authority
Amount: $15,000,000
Type: Brownfield Remediation
This project would utilize a large grant of
$15,000,000 to offset eligible projects costs
for remediating toxic chemicals from land in
the Meadowlands that is highly vulnerable to
flooding and inundation with sea level rise. In
partnership with local jurisdictions and
property owners, the authority would
leverage funds from the U.S. Environmental
Protections Agency’s Brownfield Grant
Program and the New Jersey Hazardous
Discharge Site Remediation Fund. The term
of the project would be 10 years and the
grant would be based on allowable expenses
in the first 5 years. In addition to cleanup
activities, the grant would help support
adjacent site assessments, ecological
adaptation strategies for local habits, and
community planning and training.
Project 3: Large Grant
Borrower: Norwalk Department of Public Works /
Stamford Office of Operations / Fairfield County,
Connecticut
Amount: $1,000,000
Type: Green Infrastructure Design and Maintenance
Training
This project would develop programs to train
municipal and county public works personnel to
design and maintain green infrastructure that
serves a dual hazard mitigation purpose. The
project team is based on a collaboration with
academic institutions, including the University of
Connecticut, Yale University, Rutgers University, and
the State University of New York, Stony Brook.
Project funding could be leveraged from several
federal sources, including the U.S. Department of
Housing and Urban Development’s Green
Infrastructure and the Sustainable Communities
Initiative and the US EPA’s Clean Water Act Section
319 grant program. With a project term of 3 years,
the grant would require a 10 % funding match and
would be disbursed in the first year.
Project 4: Concession Loan
Borrower: Local Town, New Jersey
Amount: $30,000,000
Type: Managed Housing Relocation
Finance Program
This project is based on a program to
help finance the relocation of
low-to-moderate income households
whose properties are in highly
vulnerable geographies subject to
the risk of subsidence, storm surge
and relative sea level rise. The
program would help finance the
disposition of existing properties and
the acquisition of in-land properties
that were previously foreclosed
(“REO Asset(s)”). Local governments
and REO Asset managers would
contribute capital allocations to a
holding company that would be
capitalized in part by the RRTF. This
would allow for a lowerassessment
on the fair market value of foreclosed
homes and would allow risk to be
shifted off the balance sheet of banks
based on a fixed pre-negotiated
return. Highly vulnerable disposed
properties would be cleared, cleaned
and deeded to a land conservation.
The program’s initial term would be
limited to 20 years and the
concession loan would be disbursed
based quarterly in an amount equal
to the sum of mortgages provided to
cover the acquired homes. The
mortgages would be held by the
holding company in a REMIC trust
whose A tranches are held by the
RRTF and whose B pieces are held by
the holding company. The net result
is that relocated households have a
lower barrier to entry to in-land
housing markets and neighborhoods
with previously foreclosed properties
get an injection of social and financial
capital.
Project 5: Concession Loan
Borrower: GRID Alternatives (non-profit)
Amount: $5,000,000
Type: Photovoltaic (PV) Installation in Public and Senior
Housing
This projects supports the assessment, design and
installation of PV systems on public and senior housing
facilities. The project serves the co-benefits of climate
mitigation, as well as the benefits of increasing the
passive survivability of facilities supporting highly
vulnerable populations. With climate change, extreme
heat and power disruptions represent critical hazards
for impacting human health. With an aging society,
passive survivability is a potentially important part of
community resilience. In conjuncture with existing
energy efficiency subsidies, this concession loan
provides the capital necessary to bring the levelized
cost of energy to within the means of financially
strapped housing operators. The loan terms would be
15 years with 3.5% interest rate, which would otherwise
serve as an effective hedge on increased energy
costs. However, in the event of a power outage, the
value of lives potentially saved defies monetization.
Project 6: Prime Rate Loan
Borrower: Nassau County Department of
Public Works
Amount: $100,000,000
Type: Gap Loan for Bay Park Water
Reclamation Facility
This project would provide the gap financing
necessary to help the borrower accommodate
an $830 million renovation to the plant
designed to mitigate and manage the risks
associated with storm surge, increased deluge
events, and relative sea level rise. In particular
the loan would support the funding of: (i) the
upgrading of power and back-up systems; (ii)
the elevating of chemical tanks and electrical
controls; (iii) the installing of new pumping
systems; and, (iv) the development of
dual-purpose public spaces that promote the
physical
resiliency
and
environmental
sustainability of the adjacent neighborhood.
This loan helps finance the increased marginal
costs associated with resilience and adaptation
measures and operations of the facility.
Project 7: Prime Rate Loan
Borrower: Lower Manhattan Property Cooperative
(non-profit)
Amount: $350,000,000
Type: Infrastructure Finance for Multi-Purpose Flood
Protection
This project would provide supplemental efforts to the
ongoing city led effort to fortify Lower Manhattan. The
borrower is a public-private non-profit cooperative
corporation whose members are property owners, building
owners, large tenants, Con Edison and the New York City
Economic Development Corporation. The members of the
association would contribute additional working capital and
resources to the association whose mission is to develop
block and district level infrastructure improvements that
complement the Lower Manhattan Coastal Resiliency
Project. As aging commercial office buildings are replaced,
this source of funding helps finance coordinated lot and
block improvements that synchronize with the district level
waterfront improvements. Eligible improvements would be
limited to those interventions in energy distribution, water
management and public space that inure to the resilience
of public and private operations in the district.
Project 8: Prime Rate Loan
Borrower: New Jersey Transit
Amount: $100,000,000
Type: NJ TransitGrid
This project builds off existing U.S. Department of
Transportation and state financial commitments
to enhance the energy resilience of NJ Transit
operations in the NYMR. The project would
provide additional financing for developing an
innovative micro-grid that accommodate a
variety of extreme events from heat to flooding.
With an increasing stressed and aging transit
system, the project seeks to increase reliability
and reduce down time through the intelligent
management of distributed and renewable
energy sources. This includes the development
of more energy efficiency local generation
capacity to support the system. Aside from the
core infrastructure improvements, the financing
could support consumer communications for
re-routing when service is altered, as well as
contingent operations planning and operations
redundancy for extreme events.
10 year sunset on the surcharge. The result
for grant allocations was rounded up to 10%.
Thereafter, based on a sample of returns and
terms of each product type, mean and standard
deviations were normally distributed using a
Monte Carlo method to calculated randomized
rates of return (Glasserman, 2013). This
method is based on an iterative sample rate
(n=1,000) to calculate the distribution. Pairs
of products, not including grants and nonrecourse soft loans, were then simulated and
correlated for a 2-loan portfolio using Sharp
regression (Sharpe, 1994; Goetzmann, et al.,
2007). The regression simulates different
weights of the pair to isolate an optimal weight
strength, which is graphically represented
in Appendix Figure 1 as the highest Sharpe
ratio (Greenwood, Seasholes & Biery, 2015).
The Sharpe ratio is understood to equal the
required return minus the risk free return over
volatility (Dowd, 2000). Again, the transient
portfolio of loans is subject to 1,000 sample
Monte Carlo simulation. The overall results
of this Sharpe analysis provided the basis for
the optimal allocations identified in Table 5,
which are based on a constructed weighting of
each transient portfolio of pairs over the entire
product offering. However, it should be noted
that each run of the portfolio model would
dictate a slightly different output than what is
represented in Table 5. Therefore, Table 5 is
merely an approximation based on a limited
number of model simulation runs.
VI. Portfolio Modeling
a. Portfolio Model Setup and Results
Based on the range of unleveraged
capitalization potentially available from the
State insurance surcharges identified in the
preceding sections, the research design dictated
that a secondary step is to develop an optimal
allocation of aforereferenced product types.
To optimize a portfolio allocation, interest
rates and terms for comparable products were
researched for each of the States, as well as
across the country. Interest rate spreads were
developed for each product and were modeled
relative to recent and anticipated trends in
various interest rate structures. Debt product
interest rates were then compared with rates
and terms of recent (i.e., 18 months) bond
issuances to provide greater sensitivity for
how each State’s RRTF may be underwritten
by the market. For instance, while various New
York revenue and general obligation bonds
are relatively stable, the bond market in New
Jersey has been comparatively volatile with
long-term pension and infrastructure liabilities
underscoring a broader financing capacity.
An initial step in the portfolio engineering
was to model a fixed percentage of grants in
order to ascertain what percentage allocation—
relative to assumed parameters of the products—
would provide a sustainable portfolio with a
Table 5: Optimal Portfolio Weighting and Product Allocations
Products
Term (Yr) Return Type Financial Return σ Financial Return Volatility Loan Weights Total Weight Financial Return MC
Grants
Soft Loans
Concession Loans
5
Market Loans (Fixed)
10
15
20
25
5
Market Loans (CPI Indexed)
10
5
10
Weighted Portfolio Return 5.46%
Loan Portfolio Volatility 0.1102
Rg
Rs
Rc
X
X
2.18%
X
X
0.0041
X
X
0.1892
X
X
2.96%
3.37%
2.75%
3.63%
3.55%
Rx
5.00%
7.00%
Rxi
6.77%
8.77%
Years to Revolve 8.15
0.0108
0.0123
0.0114
0.0050
0.0150
0.0250
0.0280
0.0380
0.3203
0.4460
0.3138
0.1410
0.3000
0.3571
0.4131
0.4329
5.04%
1.53%
5.84%
28.55%
12.39%
14.08%
9.60%
20.00%
18
10.00%
90.00%
X
X
2.17%
3.41%
2.78%
3.64%
3.64%
5.04%
6.85%
6.64%
8.83%
Using the portfolio weights (i.e., product
allocations) in Table 5, cash flows for each
instrument are weighted and adjusted for
projected inflation (CPI-U) over a period of 20
years. For each loan, it is assumed that there is
a rollover of the investment capital without any
reinvestment risk. This represents a significant
methodological limitation for evaluating the
performance of any RRTF because of the
long lead time for the planning, designing
and permitting of infrastructure projects.
Therefore, the ability of any given RRTF to
rollover investments via larger infrastructure
debt products is dependent on the extent to
which the RRC and local jurisdictions can
develop a pipeline of resilience infrastructure
interventions. The answer to this outstanding
concern could have significant impact on rates,
terms and volatility of the portfolio. However,
under the fixed assumptions presented in Table
5, the portfolio could potentially achieve a
weighted portfolio return of 5.46% and could be
entirely self-sustained in 8.15 years. Therefore,
if the surcharge were to sunset in 10 years, the
RRTF could continue to operate independently
without any additional surcharge revenue. If a
RRTF is not able to deploy capital consistently
and timely enough to meet the weighted
return, then lower returns would mean a longer
revolving period that may extend beyond the
current estimates. As such, a 10 year sunset
may be insufficient to achieve independent
operations. In addition, a 10% allocation to
grants is merely a modeled assumption. A
portfolio could operate with a higher percentage
allocation for grants. However, the greater the
percentage of grants, the longer it takes for
the portfolio to be independently sustainable
in its operations relative to its reliance on an
insurance surcharge.
revenue from the surcharges and the initial
operations of the unlevered portfolio suggested
that the revenue would be insufficient for
New York and New Jersey. As such, the third
step of the research design was to model the
RRTFs with (1x) and without (0x) leverage.
The estimates for leverage and coupon rates
were based on a survey of recent issuances
(e.g., 18 months) over various bond types in
each of the States as reported by Moodys and
the States themselves. This survey provided
a blended rate (mean) and standard deviation
for the bond market assumptions for each of
the RRTFs. Again, each of these assumptions
varies depending on the underlying relative
performance of each State’s bond market.
Appendix Table 4 provides gross revenue
and Appendix Table 5 provides net revenue
from a balanced portfolio after payments
to the bold holders. A balanced portfolio is
inclusive of both reserve investment returns
and portfolio returns. Appendix Table 6 and
7 extend the analysis of a balanced portfolio
without and without leverage over the course
of 20 years and discount the cash flow to 5%.
This discount rate is an approximation on the
levered weighted average cost of capital, plus
investment and reinvestment risk. Based on the
data memorialized in the tables, leverage in a
1.5% surcharge scenario with no sunset would
yield an additional $1.8 billion over 20 years
for New York; $999 million for New Jersey;
and, $455 million for Connecticut.
Table 6 highlights some additional
sensitivity for surcharges in terms of what
would be allocated to loans and grants. Based
on a 1.5% surcharge and a straight-line
allocation, approximately $20 million in New
York, $9 million in New Jersey and $4 million
in Connecticut could be allocated for grants
every year. Over all, pursuant to Table 7, the
impact of leverage is more pronounced as the
surcharge rate increases. However, under the
existing assumptions for leverage and bond and
b. Bond Leverage Analysis
Although the existing metric for unmet
needs is somewhat problematic, the unlevered
19
Table 6: Sensitivity Analysis for Projected Bond Revenue Allocations
Table 7: Sensitivity Analysis for Net Impact of Bond Leverage
($ in thousands)
Cash flow invested as grants
Cash flow invested as loans
($ in thousands)
Surcharge Revenue (Low, 0.5%)
Loans
Grants
New York
New Jersey
Connecticut
$1,225,725
$556,248
$271,264
$136,192
$61,805
$30,140
Surcharge Revenue (Medium, 1.0%)
Loans
Grants
New York
New Jersey
Connecticut
$2,451,451
$1,112,496
$542,528
$272,383
$123,611
$60,281
Surcharge Revenue (High, 1.5%)
Loans
Grants
New York
New Jersey
Connecticut
$3,677,176
$1,668,744
$813,792
$408,575
$185,416
$90,421
10%
90%
Surcharge Revenue (Low, 0.5%)
1x Leverage
0x Leverage
New York
New Jersey
Connecticut
$1,977,440
$923,722
$445,079
$1,359,332
$590,543
$293,378
Surcharge Revenue (Medium, 1.0%)
1x Leverage
0x Leverage
New York
New Jersey
Connecticut
$3,954,879
$1,847,443
$890,158
$2,718,664
$1,181,086
$586,757
Surcharge Revenue (High, 1.5%)
1x Leverage
0x Leverage
New York
New Jersey
Connecticut
$5,932,319
$2,771,165
$1,335,237
$4,077,996
$1,771,629
$880,135
*Discounted to 5% over 20 years.
portfolio models are useful for deriving
optimal allocations based on potentially
impactful products, there is a major unknown
in the ability of local actors to plan, design
and permit projects in a timely manner to take
advantage of the RRTF’s opportunity to rollover
investment capital. This rollover risk represents
a significant uncertainty in the operations of a
RRTF. If a RRTF is not able to deploy funds at
a higher return rate than the baseline reserve
return rate (e.g., federal bonds), then a RRTF
may be indefinitely reliant on a surcharge
beyond a sunset term. This may or may not be
a handicap, as a large capital reserves could be
useful for promoting resilience efforts following
the probabilistic occurrence of an extreme
event. However, the uncertainties associated
with extreme events provides little guidance
for portfolio management of an RRTF.
product rates and terms, the total capitalization
of the RRTFs would not be able to address the
State’s unmet needs. If the unmet needs hold
constant and are adjusted for inflation over
twenty years, under a 1.5% surcharge scenario
with 1x leverage, New York and New Jersey
would only be able to accommodate 37.6%
and 14.3% of their respective unmet needs.
VII. Conclusions
The findings of this paper support
an affirmation of the feasibility for the
development and operation of an RRTF model
pursuant to Proposition A. These findings
include references to historical precedents
for insurance surcharges; an existing legal
and organizational capacity for public benefit
funds; and, a portfolio model that could operate
independently within a hypothetical and likely
politically convenient 10 year sunset of an
insurance surcharge. However, significant
challenges remain for not only sourcing
eligible resilience and adaptation projects but
also developing metrics to underwrite and
define resilience and adaptation. While this
paper offers some constructive evaluation
perspectives and methodologies relating to risk
mitigation and disaster resilience, other areas
such as environmental resilience are much
less developed. In addition, while the existing
Pursuant to Proposition B, the findings
of this paper suggest that under the current
assumptions (e.g., 1.5% surcharge, 1x leverage)
the RRTFs could not accommodate 100% of the
unmet resilience needs in New York and New
Jersey. However, under these assumptions,
Connecticut could very well accommodate
well beyond its current documented resilience
needs. The baseline data for unmet resilience
needs is based on CDBG-DR reporting that is
somewhat problematic as it conflates recovery
20
resilience and adaptation analysis that sets a
new benchmark for professional practices.
At the same time, this paper highlights the
reality of the necessity and the opportunity to
finance the marginal costs of resilience within
conventional projects. The necessity rests in the
practical acknowledgment that free-standing
resilience and adaptation projects are limited in
number and are challenging to design, finance
and permit in a sufficient volume to justify the
scale of capital aggregated by an RRTF.
and resilience and does not clearly articulate
future resilience needs that may arise by virtue
of either increased exposure or increased
risk—or, both. Existing unmet needs are
unlikely to be entirely accommodated through
increased leverage. With increased leverage
comes increased risk, and bond rates and terms
would reflect this dynamic. Future research
needs to evaluate not only more precise metrics
for unmet and future needs, but also the risks
and opportunities associated with increasing
premiums and/or decreasing market share
by virtue of the manifestation of hazards that
result in a lack of insurability.
While the current framing of resilience
practices are primarily oriented towards
disasters and risk mitigation, there is much
room for advancement in community
resilience, economic adaptation and adaptive
environmental management. To this end,
the RRTF model is simply a conduit that
financially
incentivizes
more
resolute
analytical processes that are inclusive of a
broader array of considerations from social
equity to environmental justice. Without such
an incentive, it can be argued that the only
other motivation for the pooling of collective
resources will be based on the shame and
indignity of a post-disaster response. In this
light, this paper provides a partial affirmation
that the RRTF model represents a potential
innovation that dictates that not all commons
are predisposed to tragedy.
The RRC and the RRTFs reflect an
opportunity for process innovation that offers
the potential to not only manage risks, but also
to capture opportunities associated with climate
change. Whether it is co-benefits between public
health and infrastructure or workforce training
and environmental conservation, the broader
adaptation of society is dependent on resources
allocations in both the public and private sector.
The RRTF model offers a novel approach for
not only funding resilience and adaptation
interventions, but also for the identification
and evaluation of such interventions. Because
of the open and competitive nature of project
selection under the RRC, a broad array
of potential projects may seek to advance
21
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29
IX. List of Tables and Figures
1. Figure 1: Hypothetical Relationship between Regional Resilience Trust Funds and Regional
Resilience Commission
2. Table 1: Estimates of State Unmet Resilience Needs
3. Table 2: Sensitivity Analysis for Projected Capitalization Range for State Insurance Surcharges
4. Table 3: Revenue Scenarios to Fulfill CDBG-DR Unmet Needs with 10-Year Sunset
5. Table 4: Sensitivity Analysis for Projected Capitalized with Bond Leverage by State
6. Table 5: Optimal Portfolio Weighting and Product Allocations
7. Table 6: Sensitivity Analysis for Projected Bond Revenue Allocations
8. Table 7: Sensitivity Analysis for Net Impact of Bond Leverage
9. Appendix Figure 1: Sharpe Ratio Portfolio Optimization
10. Appendix Table 1: List of Interviewees
11. Appendix Table 2: Property & Casualty Insurance Premiums Written by State, 2006-2015
12. Appendix Table 3: RRTF Resilience Underwriting Check-List
13. Appendix Table 4: Gross Revenue from Balanced Portfolio from Bond Issue
14. Appendix Table 5: Net Revenue on Balanced Portfolio from Bond Issue
15. Appendix Table 6: Net Investments with 1x Leverage
16. Appendix Table 7: Net Investments with 0x Leverage
30
X. Appendix
Appendix Figure 1: Sharpe Ratio Portfolio Optimization
0.40
0.35
R8
0.30
R7
Sharpe Ratio
0.25
R6
0.20
R5
R7-Rxi10 Portfolio Optimal
0.15
R4
R5-Rx10 Portfolio Optimal
R3-Rc25 Portfolio Optimal
R6-Rxi5 Portfolio Optimal
R4-Rx5 Portfolio Optimal
0.10
R3
R1
R2
0.05
R2-Rc20 Portfolio Optimal
R1-Rc15 Portfolio Optimal
Rc5-Rc10 Portfolio Optimal
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Rc Weight
31
0.9
1.0
1.1
1.2
1.3
1.4
1.5
Appendix Table 1: List of Interviewees
Name
Affiliation
Chen, Chen
Civitenga, Peter
Clark, Anthony
Davies, Craig
Firth, John
Gimont, Stan
Grunwaldt, Alfred
Kaniewski, Daniel
Kao, Andrew
Koh, Jay L.
Laven, Chuck
Levaggi, Marcia
Lindberg, Mark
Liu, Tony
McFadden, Marion
Medlock, Samantha
Murdock, Sarah
Ollikainen, Mikko
Poliquin, Brent
Roy, Arghya Sinha
Seville, Aleka
Shafer, Julie
Starkman, Kendall
Swann, Stacy
Vajjhala, Shalini
Wilson, Steven R.
University of Notre Dame
AIR Worldwide
Conneticut Green Bank
University of Cambridge
Acclimatise
U.S. Department of Housing and Urban Development
Inter-American Development Bank
AIR Worldwide
AIR Worldwide
Siguler, Guff & Company
Forsyth Street
Adaptation Fund
Margaret Cargill Foundation
Siguler, Guff & Company
Enterprise Community Partners
U.S. Office of Management and Budget
The Natural Conservancy
Adaptation Fund
AIR Worldwide
Asia Development Bank
Twenty Four Seven Consulting
Bank of the West
Twenty Four Seven Consulting
Climate Finance Advisors
Re:Focus Parnters
Inter-American Development Bank
32
Appendix Table 2: Property & Casualty Insurance Premiums Written by State, 2006 -2015
New York
Homeowners Multiple Peril
Commercial Multiple Peril
Farmowners Multiple Peril
Fire
Allied Lines
Inland Marine
Ocean Marine
Boiler and Machinery
Earthquake
Private Crop
Total, Nominal
Total (2015 Dollars)*
Growth Rate, Nominal
2006
$3,627,091
$3,180,652
$33,066
$806,746
$468,609
$913,226
$599,118
$82,534
$37,786
$9,748,828
$11,461,498
2011
$4,519,844
$3,119,458
$35,788
$746,028
$459,309
$1,048,911
$454,701
$89,064
$35,965
2012
$4,725,048
$3,310,734
$36,787
$759,213
$501,327
$1,150,991
$447,849
$99,538
$40,883
$10,152,560 $10,133,263 $10,143,943 $10,509,068
$11,176,478 $11,195,065 $11,026,007 $11,073,348
4.14%
-0.19%
0.11%
3.60%
$11,072,370
$11,430,351
5.36%
2008
$4,096,975
$3,131,076
$34,822
$797,810
$470,253
$987,741
$513,898
$87,213
$32,772
2009
$4,238,743
$3,096,679
$33,935
$767,144
$447,712
$978,460
$450,468
$90,297
$29,825
2010
$4,357,145
$3,035,189
$34,902
$736,578
$432,166
$985,589
$440,482
$87,413
$34,479
2013
$4,925,004
$3,562,364
$38,249
$812,122
$567,790
$1,288,601
$446,304
$103,017
$44,211
2014
$5,110,113
$3,711,941
$39,591
$849,193
$593,643
$1,400,482
$452,773
$106,112
$50,597
$11,787,662 $12,314,445
$11,993,099 $12,329,062
6.46%
4.47%
2015
$5,220,744
$3,706,915
$41,296
$820,460
$653,181
$1,510,929
$406,485
$117,613
$53,503
$28
$12,531,154
$12,531,154
1.76%
New Jersey
Homeowners Multiple Peril
Commercial Multiple Peril
Farmowners Multiple Peril
Fire
Allied Lines
Inland Marine
Ocean Marine
Boiler and Machinery
Earthquake
Private Crop
Total, Nominal
Total (2015 Dollars)*
Growth Rate, Nominal
2006
$1,696,424
$1,314,336
$2,739
$324,053
$216,633
$410,067
$117,068
$35,347
$15,731
2008
$1,877,038
$1,256,621
$2,882
$320,259
$218,352
$417,190
$129,710
$38,194
$12,062
2009
$1,957,270
$1,204,386
$2,855
$333,534
$219,554
$378,611
$125,855
$38,842
$12,147
2010
$2,007,475
$1,172,881
$2,846
$335,752
$220,117
$385,998
$120,655
$36,938
$12,770
2011
$2,093,434
$1,201,258
$2,322
$349,831
$229,828
$400,398
$121,728
$40,125
$13,969
2012
$2,230,734
$1,284,139
$2,392
$377,147
$256,475
$435,714
$131,395
$41,077
$15,209
2013
$2,391,724
$1,379,336
$2,375
$407,562
$304,375
$471,649
$138,011
$45,331
$16,687
2014
$2,479,828
$1,434,577
$2,544
$387,830
$343,522
$507,068
$133,880
$44,717
$19,339
$4,132,398
$4,858,376
$4,272,308
$4,703,184
3.39%
$4,273,054
$4,720,801
0.02%
$4,295,432
$4,668,940
0.52%
$4,452,893
$4,691,989
3.67%
$4,774,282
$4,928,639
7.22%
$5,157,050
$5,246,928
8.02%
$5,353,305
$5,359,659
3.81%
2015
$2,556,089
$1,424,250
$2,622
$372,581
$334,645
$550,979
$135,249
$47,948
$19,597
$27
$5,443,987
$5,443,987
1.69%
2006
$913,479
$581,031
$3,021
$103,629
$98,349
$209,174
$52,335
$14,584
$5,867
2008
$1,037,569
$548,684
$3,557
$112,028
$91,637
$218,427
$54,115
$14,570
$5,152
2009
$1,065,532
$519,668
$3,795
$121,308
$89,901
$213,434
$48,112
$15,477
$5,140
2010
$1,107,784
$512,310
$4,068
$120,031
$92,696
$193,758
$50,844
$15,115
$5,526
2011
$1,146,334
$532,001
$4,300
$131,540
$102,863
$216,538
$50,301
$16,173
$6,900
2012
$1,221,067
$578,155
$4,680
$132,374
$109,365
$243,015
$49,374
$17,700
$7,089
2013
$1,308,798
$612,146
$5,051
$139,986
$120,763
$249,951
$51,652
$19,370
$6,720
2014
$1,379,750
$635,253
$5,493
$142,656
$124,768
$254,596
$52,181
$18,559
$8,428
2015
$1,408,185
$638,210
$5,894
$130,236
$111,834
$293,180
$87,409
$21,316
$8,277
$1,981,469
$2,329,573
$2,085,739
$2,296,092
5.26%
$2,082,367
$2,300,565
-0.16%
$2,102,132
$2,284,922
0.95%
$2,206,950
$2,325,451
4.99%
$2,362,819
$2,439,211
7.06%
$2,514,437
$2,558,259
6.42%
$2,621,684
$2,624,796
4.27%
$2,704,541
$2,704,541
3.16%
Connecticut
Homeowners Multiple Peril
Commercial Multiple Peril
Farmowners Multiple Peril
Fire
Allied Lines
Inland Marine
Ocean Marine
Boiler and Machinery
Earthquake
Private Crop
Total, Nominal
Total (2015 Dollars)*
Growth Rate, Nominal
*Adjusted for inflation using the annual Consumer Price Index for All Urban Consumers (CPI-U); complete data is not available for 2007.
33
Appendix Table 3: RRTF Resilience Underwriting Check-List
Abstract Information
Borrower/ Grantee:
Agency:
Product Type:
Borrower:
Guarantor:
Project Term:
Finance Term:
Community Partnerships:
Government Partnerships:
Jurisdictions:
Project Type
Buildings (new)
Buy-out
Communications
Ecological Conservation/Restoration
Green Infrastructure
Intelligence Capacity
Reference Standards
Resilience Planning
Social Network Development
Vulnerability Assessment
Buildings (retrofit)
Climate Change Planning
Community Planning
Energy
Hazard Mitigation Planning
Infrastructure
Research & Development
Resilience Personnel
Social Service Delivery
Training Course
Review Questionnaire
Proprietary Information
Is the submission publicly available?
If yes, explain:
Was this submission developed with public input?
If yes, explain:
Is the submission compliant with existing codes and standards?
If yes, explain:
Does the submission help advance a new reference standard?
If yes, explain:
Is the submission sponsored by a public authority?
If yes, explain:
Has the submission been reviewed by a public authority?
If yes, explain:
Is the submission specific to extreme events?
If yes, explain:
Does the submission offer potentially generalizable findings/practices?
If yes, explain:
Does the submission offer potentially scalable findings/practices?
If yes, explain:
Does the submission address private sector benefits?
If yes, explain:
Does the submission address public sector benefits?
If yes, explain:
Has the submission been informed by on-the-ground practice or experience?
If yes, explain:
34
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Has the submission been endorsed by resilience or advocacy organizations?
If yes, explain:
Can the outcomes of this submission be measured with existing capacities?
If yes, explain:
Yes
No
Yes
No
Categorization
Geographic Scope (state, county, municipal, district, block, lot)
Region
Population:
Geography:
Hazards and Impacts
Natural
Algal Bloom
Collateral Hazards
Earthquake: Ground Deform.
Glacial Melt
Landslide
Rapid Sea Level Rise
Severe Convection/ Winds
Storm Surge
Vector Borne Disease
Winter/Ice Storm
Technological
Air Traffic Suspension
Dam Failure
Hazmat Release (Chemical)
Road Failure
Underground Fire
Water Contamination
Human-Caused
Active Shooter(s)
Biological Attack
Cyber Attack
Fisheries Depletion
Metal Theft
Sector
Public
Defense
Emergency Services
Energy: Nuclear
Mass Transportation
Social Services
Water
Private
Agriculture
Critical Manufacturing
Energy: Nuclear
Financial Services
Information Technology
Urban
Coastal
Suburban
Riverine
Exurban
Interior(Non-Riverine)
Rural
Mountainous
Animal Disease
Drought
Extreme Temperatures
Hurricane/Typhoon
Pandemic (Human)
Salt Water Intrusion
Sinkholes/Subsidence
Tornado
Volcanic Eruption
Avalanche
Earthquake: Shaking
Flood
Invasive Species
Permafrost Melt
Sea Level Rise (Mean Proj.)
Space Weather
Tsunami
Wildfire
Bridge Failure
Fuel Shortage
Mine Accident
Track Failure
Urban Conflagration
Communications Failure
Hazmat Release (Radiological)
Pipeline Failure/ Explosion
Transportation Accident
Utility Interruption
Aircraft as Weapon
Chemical Attack
Drone as Weapon
Food/Water Contamination
Nuclear/ Radiological Attack
Arson
Civilian Disturbance
Explosive Devices
Mass Migration
Disaster Management
Energy: Coal & Natural Gas
Financial
Monetary System
Solid Waste
Telecommunications
Education
Energy: Hydroelectric & Dams
Highway Transportation
Public Health
Water & Wastewater
Other:
Chemical
Education
Energy: Coal & Natural Gas
Food Distribution
Logistics
Construction
Defense Industrial Base
Energy: Green
Healthcare
Mining
35
Sector cont’d
Private cont’d
Pharmaceuticals
Solid Waste
Telecommunications
Non-Profit
Aging & Elderly
Cultural Affinity
Industry Association
Political Advocacy
Social Assistance
Real Estate
Scientific/Tech R&D
Transportation
Recycling
Water & Wastewater
Other:
Community Advocacy
Environmental Stewardship
Mental Health & Substance Abuse
Professional Association
Urban Planning
Community Recovery
Food Bank
Philanthropy
Religious
Other:
Strategy (select all that apply)
Social Resilience
Access / Functional Needs Assessments
Community Network Capacity
Communications Models
Economic Development
Post-Recovery Shelter & Housing
Technical & Design Resilience
Building Codes & Standards
Risk Standards & Thresholds
Urban Design
Material Resilience
Composite Innovation in Materials
Materials Performance Standards
Thermal Dynamic Materials
Infrastructure Resilience
External Intelligence Systems
Impact Assessments
Internal Intelligence Systems
System Redundancy
Organizational Resilience
Business Continuity Planning
Data Backup & Security
Internal Intelligence Systems
Workforce Training
Ecosystems Resilience
Agriculture
Water
Community Impact Assessments
Community Planning
Cultural Preservation
Long-term Housing & Community Development
Public Health
Engineering Techniques & Analysis
Sustainable Systems
Zoning & Land Use Planning
Low-Technology Solutions
Nano-Materials
Facility Guidance & Training
Innovation in Risk Management
Operations & Scenario Planning
Critical Systems Design
External Intelligence Systems
Remote Workforce
Fisheries
Wildlife
Climate Change
Climate Change Mitigation
Climate Change Adaptation
Planning Phases
Determine Options, Goals & Objectives
Plan Implementation, Testing & Maintenance
Planning Team and Stakholder Identification
Plan Development
Plan Preparation
Understand Context, Risks & Impacts
Primary Actor
Private Sector Enterprise
Other
Advocacy Organizations
State & Local Government
36
Capacity Building
Access Control & Identity Verification
Actor & Stakeholder Identification
Agricultural Extension System
Civil Court System Services
Climate & Weather Services
Critical Transportation Services
Cultural Resource Preservation
Displaced Persons Registry
Emergency Financial/Funding Capacity
Emergency Procurement System
Environmental Response/Health Services
Fire Management and Suppression
Government Affairs Services
Interdiction and Disruption
Logistics and Supply Chain Management
Long-term Vulnerability Reduction
Mass Care Services
Mental Health Counseling
Natural Resource Preservation
Operational Communications
Operational Planning
Potable Water Distribution Services
Private Sector Cost-Benefit Analysis
Public Health, Healthcare, & EMS
Redundant Infrastructure Systems
Risk Management Expertise
Screening, Search, & Detection
Situational Assessment
Social Service Delivery
Strategic Food Supplies
Strategic Planning
Supply Chain Management
Volunteer System Mobilization
Accounting & Auditing Systems
Agency Benefit-Cost Analysis
Chief Resilience Officer / Staff
Claims Adjusting
Community Advocacy Mobilization
Cross-Jurisdictional Professional Capacity
Cybersecurity
Economic Recovery Strategies
Emergency Healthcare Delivery
Employment Training
Fatality Management Services
Forensics and Attribution
Intelligence and Information Sharing
Legal Planning
Long-term Housing Planning
Marketing & Public Communications
Mass Search and Rescue Operations
Mutual Assurance Agreements
On-scene Security, Protection, & Law EnforceOperational Coordination
Physical Protective Measures
Primary Healthcare Delivery
Property and Engineering Inspections
Public Information and Warning
Risk and Disaster Resilience Assessment
Risk Management for Protection Programs and Activity
Short-term Shelter Providers
Social Network Development, Resourcing & Maint.
Strategic Building Materials Supplies
Strategic Medical Supplies
Supply Chain Integrity and Security
Threats and Hazard Identification
37
Appendix Table 4: Gross Revenue from Balanced Portfolio from Bond Issue
Surcharge Revenue (Low, 0.5%)
($ in thousands)
New York
2018
$169,438
2019
$169,438
2020
$169,438
2021
$169,438
2022
$169,438
2023
$169,438
2024
$169,438
2025
$169,438
2026
$169,438
2027
$169,438
2028
$169,438
2029
$169,438
2030
$169,438
2031
$169,438
2032
$169,438
2033
$169,438
2034
$169,438
2035
$169,438
2036
$169,438
2037
$169,438
New Jersey
$76,893
$76,893
$76,893
$76,893
$76,893
$76,893
$76,893
$76,893
$76,893
$76,893
$76,893
$76,893
$76,893
$76,893
$76,893
$76,893
$76,893
$76,893
$76,893
$76,893
Connecticut
$37,498
$37,498
$37,498
$37,498
$37,498
$37,498
$37,498
$37,498
$37,498
$37,498
$37,498
$37,498
$37,498
$37,498
$37,498
$37,498
$37,498
$37,498
$37,498
$37,498
Surcharge Revenue (Medium, 1.0%)
($ in thousands)
New York
2018
$338,875
2019
$338,875
2020
$338,875
2021
$338,875
2022
$338,875
2023
$338,875
2024
$338,875
2025
$338,875
2026
$338,875
2027
$338,875
2028
$338,875
2029
$338,875
2030
$338,875
2031
$338,875
2032
$338,875
2033
$338,875
2034
$338,875
2035
$338,875
2036
$338,875
2037
$338,875
New Jersey
$153,785
$153,785
$153,785
$153,785
$153,785
$153,785
$153,785
$153,785
$153,785
$153,785
$153,785
$153,785
$153,785
$153,785
$153,785
$153,785
$153,785
$153,785
$153,785
$153,785
Connecticut
$74,996
$74,996
$74,996
$74,996
$74,996
$74,996
$74,996
$74,996
$74,996
$74,996
$74,996
$74,996
$74,996
$74,996
$74,996
$74,996
$74,996
$74,996
$74,996
$74,996
Surcharge Revenue (High, 1.5%)
($ in thousands)
New York
2018
$508,313
2019
$508,313
2020
$508,313
2021
$508,313
2022
$508,313
2023
$508,313
2024
$508,313
2025
$508,313
2026
$508,313
2027
$508,313
2028
$508,313
2029
$508,313
2030
$508,313
2031
$508,313
2032
$508,313
2033
$508,313
2034
$508,313
2035
$508,313
2036
$508,313
2037
$508,313
New Jersey
$230,678
$230,678
$230,678
$230,678
$230,678
$230,678
$230,678
$230,678
$230,678
$230,678
$230,678
$230,678
$230,678
$230,678
$230,678
$230,678
$230,678
$230,678
$230,678
$230,678
Connecticut
$112,494
$112,494
$112,494
$112,494
$112,494
$112,494
$112,494
$112,494
$112,494
$112,494
$112,494
$112,494
$112,494
$112,494
$112,494
$112,494
$112,494
$112,494
$112,494
$112,494
*Gross revenue before bond payments.
Appendix Table 5: Net Revenue on Balanced Portfolio from Bond Issue
Surcharge Revenue (Low, 0.5%)
($ in thousands)
Net Revolving Amount*
$1,033,105
2018
$97,183
2019
$93,667
2020
$89,980
2021
$86,113
2022
$82,059
2023
$77,807
2024
$73,348
2025
$68,672
2026
$63,768
2027
$58,626
2028
$53,234
2029
$47,580
2030
$41,650
2031
$35,432
2032
$28,911
2033
$22,072
2034
$14,901
2035
$7,381
2036
-$504
2037
-$8,774
New Jersey
$514,475
$45,503
$43,975
$42,373
$40,694
$38,932
$37,085
$35,148
$33,116
$30,986
$28,752
$26,410
$23,953
$21,377
$18,676
$15,843
$12,872
$9,757
$6,490
$3,064
-$529
Connecticut
$241,553
$21,904
$21,145
$20,349
$19,515
$18,639
$17,722
$16,759
$15,750
$14,692
$13,582
$12,418
$11,198
$9,918
$8,576
$7,169
$5,693
$4,145
$2,522
$820
-$965
New York
Surcharge Revenue (Medium, 1.0%)
New York
($ in thousands)
Net Revolving Amount*
$2,066,209
2018
$194,366
2019
$187,334
2020
$179,959
2021
$172,226
2022
$164,117
2023
$155,613
2024
$146,695
2025
$137,344
2026
$127,537
2027
$117,253
2028
$106,468
2029
$95,159
2030
$83,300
2031
$70,863
2032
$57,821
2033
$44,145
2034
$29,803
2035
$14,763
2036
-$1,009
2037
-$17,548
$1,028,951
$91,005
$87,950
$84,747
$81,387
$77,864
$74,170
$70,296
$66,233
$61,972
$57,505
$52,819
$47,906
$42,754
$37,351
$31,685
$25,744
$19,513
$12,979
$6,127
-$1,058
$483,105
$43,807
$42,290
$40,698
$39,029
$37,279
$35,443
$33,519
$31,500
$29,384
$27,164
$24,837
$22,396
$19,836
$17,152
$14,338
$11,386
$8,290
$5,044
$1,641
-$1,929
New Jersey
Connecticut
Surcharge Revenue (High, 1.5%)
New York
($ in thousands)
Net Revolving Amount*
$3,099,314
2018
$291,549
2019
$281,000
2020
$269,939
2021
$258,339
2022
$246,176
2023
$233,420
2024
$220,043
2025
$206,015
2026
$191,305
2027
$175,879
2028
$159,703
2029
$142,739
2030
$124,949
2031
$106,295
2032
$86,732
2033
$66,217
2034
$44,704
2035
$22,144
2036
-$1,513
2037
-$26,322
$1,543,426
$136,508
$131,925
$127,120
$122,081
$116,796
$111,255
$105,443
$99,349
$92,959
$86,257
$79,229
$71,860
$64,131
$56,027
$47,528
$38,616
$29,270
$19,469
$9,191
-$1,587
$724,658
$65,711
$63,434
$61,047
$58,544
$55,918
$53,165
$50,278
$47,251
$44,076
$40,747
$37,255
$33,594
$29,755
$25,728
$21,506
$17,079
$12,436
$7,567
$2,461
-$2,894
New Jersey
Connecticut
*After Bond Payments.
Appendix Table 6: Net Investments with 1x Leverage
Surcharge Revenue (Low, 0.5%)
($ in thousands)
Net Investment in PV 2017 Bond Revenue
$1,977,440
$1,225,725
2036
-$199
2037
-$3,305
New Jersey
$923,722
$556,248
$43,336
$39,887
$36,604
$33,474
$30,500
$27,670
$24,976
$22,408
$19,969
$17,647
$15,437
$13,333
$11,332
$9,429
$7,618
$5,894
$4,254
$2,695
$1,212
-$199
Connecticut
$445,079
$271,264
$20,861
$19,179
$17,578
$16,053
$14,603
$13,222
$11,909
$10,658
$9,468
$8,336
$7,259
$6,233
$5,258
$4,330
$3,447
$2,607
$1,808
$1,047
$324
-$363
2018
$185,110
2019
$169,917
2020
$155,456
2021
$141,672
2022
$128,573
2023
$116,105
2024
$104,240
2025
$92,935
2026
$82,189
2027
$71,964
2028
$62,233
2029
$52,967
2030
$44,158
2031
$35,776
2032
$27,802
2033
$20,212
2034
$12,996
2035
$6,131
New York
2018
$92,555
2019
$84,959
2020
$77,728
2021
$70,836
2022
$64,286
2023
$58,053
2024
$52,120
2025
$46,467
2026
$41,095
2027
$35,982
2028
$31,117
2029
$26,483
2030
$22,079
2031
$17,888
2032
$13,901
2033
$10,106
2034
$6,498
2035
$3,066
Surcharge Revenue (Medium, 1.0%)
New York
New Jersey
Connecticut
($ in thousands)
2036
-$399
2037
-$6,609
$1,847,443
$1,112,496
$86,672
$79,773
$73,207
$66,948
$61,000
$55,339
$49,951
$44,817
$39,937
$35,293
$30,874
$26,665
$22,664
$18,857
$15,235
$11,787
$8,509
$5,390
$2,424
-$398
$890,158
$542,528
$41,721
$38,358
$35,157
$32,105
$29,205
$26,445
$23,818
$21,315
$18,936
$16,672
$14,518
$12,466
$10,515
$8,660
$6,894
$5,213
$3,615
$2,095
$649
-$727
Net Investment in PV 2017 Bond Revenue
$5,932,319
$3,677,176
2018
$277,665
2019
$254,876
2020
$233,184
2021
$212,508
2022
$192,859
2023
$174,158
2024
$156,360
2025
$139,402
2026
$123,284
2027
$107,946
2028
$93,350
2029
$79,450
2030
$66,237
2031
$53,664
2032
$41,703
2033
$30,319
2034
$19,494
2035
$9,197
2036
-$598
Net Investment in PV 2017 Bond Revenue
$3,954,879
$2,451,451
Surcharge Revenue (High, 1.5%)
New York
($ in thousands)
2037
-$9,914
New Jersey
$2,771,165
$1,668,744
$130,007
$119,660
$109,811
$100,423
$91,501
$83,009
$74,927
$67,225
$59,906
$52,940
$46,311
$39,998
$33,997
$28,286
$22,853
$17,681
$12,763
$8,085
$3,635
-$598
Connecticut
$1,335,237
$813,792
$62,582
$57,537
$52,735
$48,158
$43,808
$39,667
$35,727
$31,973
$28,404
$25,008
$21,777
$18,699
$15,773
$12,989
$10,341
$7,820
$5,423
$3,142
$973
-$1,090
*Discount Rate 5%.
Appendix Table 7: Net Investments with 0x Leverage
Surcharge Revenue (Low, 0.5%)
($ in thousands)
Net Investment in PV 2017 Bond Revenue
$1,359,332
$-
2036
$67,216
2037
$67,121
New Jersey
$590,543
$-
$29,895
$29,857
$29,819
$29,777
$29,739
$29,701
$29,663
$29,622
$29,584
$29,546
$29,508
$29,467
$29,429
$29,392
$29,354
$29,313
$29,276
$29,238
$29,201
$29,160
Connecticut
$293,378
$-
$14,852
$14,833
$14,814
$14,793
$14,774
$14,755
$14,737
$14,716
$14,697
$14,678
$14,660
$14,639
$14,620
$14,602
$14,583
$14,563
$14,544
$14,525
$14,507
$14,486
Net Investment in PV 2017 Bond Revenue
$2,718,664
$-
2018
$137,628
2019
$137,453
2020
$137,277
2021
$137,084
2022
$136,909
2023
$136,735
2024
$136,560
2025
$136,368
2026
$136,194
2027
$136,021
2028
$135,847
2029
$135,656
2030
$135,483
2031
$135,310
2032
$135,138
2033
$134,947
2034
$134,775
2035
$134,603
New York
2018
$68,814
2019
$68,726
2020
$68,639
2021
$68,542
2022
$68,455
2023
$68,367
2024
$68,280
2025
$68,184
2026
$68,097
2027
$68,010
2028
$67,924
2029
$67,828
2030
$67,741
2031
$67,655
2032
$67,569
2033
$67,474
2034
$67,388
2035
$67,302
Surcharge Revenue (Medium, 1.0%)
New York
New Jersey
Connecticut
($ in thousands)
2036
$134,432
2037
$134,242
$1,181,086
$-
$59,791
$59,714
$59,638
$59,554
$59,478
$59,403
$59,327
$59,243
$59,168
$59,092
$59,017
$58,934
$58,859
$58,784
$58,709
$58,626
$58,551
$58,477
$58,402
$58,320
$586,757
$-
$29,704
$29,666
$29,628
$29,586
$29,548
$29,511
$29,473
$29,432
$29,394
$29,357
$29,319
$29,278
$29,241
$29,203
$29,166
$29,125
$29,088
$29,051
$29,014
$28,973
Net Investment in PV 2017 Bond Revenue
$4,077,996
$-
2018
$206,442
2019
$206,179
2020
$205,916
2021
$205,626
2022
$205,364
2023
$205,102
2024
$204,841
2025
$204,552
2026
$204,291
2027
$204,031
2028
$203,771
2029
$203,484
2030
$203,224
2031
$202,965
2032
$202,706
2033
$202,421
2034
$202,163
2035
$201,905
Surcharge Revenue (High, 1.5%)
New York
New Jersey
Connecticut
($ in thousands)
2036
$201,648
2037
$201,364
$1,771,629
$-
$89,686
$89,572
$89,457
$89,331
$89,217
$89,104
$88,990
$88,865
$88,752
$88,638
$88,525
$88,401
$88,288
$88,175
$88,063
$87,939
$87,827
$87,715
$87,603
$87,480
$880,135
$-
$44,555
$44,499
$44,442
$44,379
$44,323
$44,266
$44,210
$44,148
$44,091
$44,035
$43,979
$43,917
$43,861
$43,805
$43,749
$43,688
$43,632
$43,576
$43,521
$43,459
*Discount Rate 5%.
38
Credits
Harvard University Staff:
Jesse M. Keenan, Graduate School of Design (Principal Investigator / Author)
Anna Ponting, Kennedy School of Government and Harvard Business School (Researcher)
Anurag Gumber, Kennedy School of Government (Researcher)
Elias Logan, Graduate School of Design (Designer)
Regional Plan Association Staff:
Robert Freudenburg, Vice President, Energy and Environment
Lucrecia Montemayor, Deputy Director, Energy and Environment
Emily Korman, Research Analyst, Energy and Environment
Image Credits
Project 1: Symbolon; Project 2: Iconathon; Project 3: Maxim Kulikov; Project 4: Dilon Choudhury;
Project 5: Luis Prado; Project 6: Gan Khoon Lay; Project 7: Adrian Coquet; Project 8: Bob Holzer,
Michael Bundscherer.
Acknowledgments
The research supporting this paper was conducted pursuant to a sponsored research grant provided
to the Harvard University Graduate School of Design by the Regional Plan Association.
The RPA research team would like to thank Nick Shufro, Joyce Coffee and Dan Tunstall for their
support, dedication and contributions to the research. The Harvard research team would like to
thank Rosetta Elkin, Diane Davis and Mohsen Mostavi for their support of resilience research.
40
Harvard University
Graduate School of Design
RP A
Regional Plan Association
J.M. Keenan