Download Commentary of Adaptation Cost-Curves

Commentary of Adaptation Cost-Curves:
With reference to the paper “Research to identify potential low-regrets
adaptation options to climate change in the residential buildings sector” (Davis
Langdon 2011): background paper to the 2nd report of the UK Climate Change
Committee’s Adaptation Sub-Committee
Nicola Ranger
Grantham Research Institute on Climate Change and the Environment
11th July 2011
Summary
This commentary aims to appraise the usefulness of the adaptation cost-curve
methodology for decision making, with specific reference to its application in the
paper “Research to identify potential low-regrets adaptation options to climate
change in the residential buildings sector” by Davis Langdon (2011), which is one
input to Chapter 5 of the 2nd report of the Climate Change Committee’s Adaptation
Sub-Committee (ASC). Further work will extend this appraisal to other adaptation
cost-curves available in the literature.
The comments provided below focus on the relevance of these methodologies and
findings for adaptation planning and decision making by householders and
government today and how the work could be extended to increase its utility for these
purposes.
I conclude that the cost-curve methodology provides a useful approach to presenting
information on the cost-benefit of different adaptation options and their effectiveness
and the paper presents some useful findings; however there are several constraints
on the application of these findings to decision making. To summarise, the most
significant issues in terms of the application of this research are the lack of generality
of the findings and the non-independence of measures within the cost-curves; these
issues are most acute for the flood case, but may not be a significant problem for the
water case. It is also important to note that the cost-curves presented provide only
one input to this decision making; they contain only a small set of the information
needed by a decision maker.
The lack of consideration of climate change in the paper is a significant missed
opportunity. While it should not affect the specific conclusions outlined in Chapter 5
of the ASC’s 2nd report, it has two important consequences:
• For new build properties, it could lead missed opportunities to identify and
build-in appropriate adaptation to cope with climate change cheaply and
easily today; specifically, those measures that are not cost-beneficial in the
current climate, but become so when climate change is considered.
Identifying such measures is particularly important for buildings, which have a
long lifetime and high sunk costs; not considering climate change in decisions
today risks locking-in maladaptation and consequently, unnecessary
additional costs and risks to homeowners in the future.
• The method only looks at adaptation options implemented today and provides
no information about the timing and circumstances under which measures
should be implemented in the future with climate change. Adaptation is a
continuous process and this should be reflected in adaptation planning today.
Treating adaptation as a ‘one-off’ could also lead to maladaptation. This was,
for example, demonstrated in the Thames Estuary 2100 project.
Overview of Davis Langdon (2011)
The paper applies the ‘cost-curve’ methodology to identify ‘low-regrets’ adaptation
options within the residential buildings sector associated with three types of hazards:
flood, water stress and overheating. ‘Low-regrets’ measures are defined in the paper
as having a cost-benefit ratio of less than one today (i.e. a partial definition1).
The paper is not a standalone piece of work and should be read alongside Chapter 5
of the ASC’s 2nd report, which outlines the objectives of the work and provides an
analysis of its findings and implications for policy. I understand the objectives of the
analysis to be:
1. to investigate the utility of the cost-curve methodology for adaptation planning
2. to inform government policy on adaptation planning related to low-regrets
options in the building sector.
The range of adaptation options considered is limited to those that can be
implemented within the home and (largely) to those applicable to existing buildings
as well as new buildings. Cost-curves show the cost-benefit ratio of measures versus
their total economic benefit (in terms of a defined metric) and are presented for both
a societal perspective and the householder perspective. Cost curves differentiate
between applications to new builds and existing buildings (during repair or retrofit).
Each hazard is studied for a specific region (as results are location-specific). Findings
are aggregated over the regions and time (to 2020 and 2050). No representation of
climate change is included in the cost-curves presented in the paper.
General usefulness of the cost-curve methodology
The cost-curve method provides a useful approach to presenting information on the
economic cost-benefit of different adaptation options and their effectiveness (i.e. the
reduction in economic losses). Clearly, this information is only one input to an
adaptation decision and further information will be required.
In addition, typically, as in this study, the definition of economic costs and benefits is
limited to direct monetary costs and benefits, and no attempt is made to quantify nonmonetary2 or indirect3 costs and benefits. This may lead a decision maker to
undervalue the impacts of an adaptation measure. Similarly, cost-curves provide a
representation of the ‘technical potential’ of adaptation measures; this may be an
overestimate of their benefits as it takes no account of behavioural issues (e.g.
improper usage or inadequate maintenance over time) and risks of failure.
Lack of generality of cost-curves
A deficiency of the method (as applied) for informing national-level decision making is
that the cost-curves are dependent on the scenario (i.e. the climate change scenario,
but also scenarios, such as land-use changes over time), the location (including
building types in this case), and assumptions about societal adaptation. This means
that it can not be assumed that the findings from the cost curves are generally
applicable and therefore, limits their usefulness for decision making.
1
In an adaptation context, the definition of ‘low-regrets’ requires that measures are robust
under plausible climate change scenarios. In some cases, the definition to broadened further,
for example, having minimal trade-offs with other objectives or strong co-benefits. None of
these decision factors are considered in this paper.
2
For example, the benefits of increased comfort levels in the home in the overheating case.
3
Indirect costs and benefits includes, for example, a valuation of the trade-offs and cobenefits.
This problem will be particularly acute for the flood cost-curves. In this case, the
cost-benefit and effectiveness will be highly sensitive to location (i.e. the hazard level
in the region of the property), the attributes of the specific building, and importantly,
the other adaptation measures already in place both within the home and at a
community level. For example, if the property is in a town that is protected by a flood
wall, the economic benefits of household measures will decline significantly. For this
reason, I conclude that the findings are more relevant to rural properties.
For the flood case, the cost-curves could be made less location-specific (and
therefore, more useful to planning) by showing the findings for different levels
of inundation water depth within the home, rather than AEPs.
The findings for the water case should be more generally applicable for
national/regional policymaking (though for householders, there will be some
differences in the economics of measures depending on individual water use). The
findings for overheating will be location specific, though less so than for flood;
however, I would expect differences between urban and rural locations.
Independence/non-independence of measures within the cost-curve
It is not clear from the cost-curves whether measures are substitutes or complements
–i.e. should they be applied together or should one be selected? The way that costcurves are constructed (i.e. with measures sitting along the x-axis in order of
increasing cost-benefit ratio) suggests that they can be combined to increase overall
effectiveness up to the point that the cost-benefit ratio equals 1), but in some cases
this is not correct, for both practical and economic reasons.
In terms of the economic issues here, in the calculations, different measures are
treated as independent (i.e. they are treated as if they were substitutes), when in
reality this may not be the case. For example, household resistance and resilience
measures for flood are normally complementary – the homeowner will usually employ
a set of measures. However, this is not reflected in the calculations and this leads to
an overestimate of the economic benefits of the measures. For example, if the
household flood resistance measures are put in place, this will decrease the
economic benefits of the resilience measures.
The problem of non-independence will be most acute for the flood case. It
should not be a problem for the water case, as in this case most measures act
independently (using one measure will not effect the economic benefits of
other measures). It could have some effect in the overheating case, but this
should be quite small (some further modelling work here could be beneficial).
Lack of representation of climate change and its implications
The representation of climate change in the paper is disappointingly poor. In
adaptation, the term ‘low-regrets’ usually refers to a measure that is cost-beneficial
under most plausible future climate change scenarios. However, no attempt is made
to quantify how measures will perform under a range of plausible climate change
scenarios. Indeed, none of the results shown in the paper include any climate
change. Considering climate change is particularly important for the buildings sector
because buildings are long-lived (in some cases, lasting for more than 100 years)
and so are potentially vulnerable to a changing climate.
However, I agree with the conclusion of Chapter 5 of the ASC’s report that for the
specific case explored, those measures that are shown to be cost-beneficial today
will likely remain so if climate change were included. This is simply because most of
the measures shown to be cost-beneficial today have: (a) a short-lifetime of less than
about 15 years and/or (b) very low or zero costs.
But, not including climate change in an assessment of adaptation for new build
properties could be a significant oversight as it could lead to future underadaptation and consequently, unnecessary additional costs and risks to
homeowners in the future. For those measures that do have longer lifetimes and
higher costs (e.g. structural measures), incorporating climate change could in some
cases make them cost-beneficial. This information is particularly important to identify
for new build properties, where it is significantly cheaper and easier to build-in
adaptation at the development stage rather than retrofit later on and therefore there
may be justification for ‘over-adapting’ the building today for current climate such that
it can cope with future climate. The paper does not consider this.
A further disadvantage of this approach is that it only looks at adaptation
options implemented today and provides no information about the timing and
circumstances under which measures should be implemented in the future –
this is a particularly important question for planning retrofits to existing properties,
which may take several years to implement. Adaptation is not a one-off – it is a
continuous process – and several previous academic and policy papers have
demonstrated the importance of reflecting this in adaptation planning today. For
example, the Thames Estuary 2100 (TE2100) project considers both measures that
need to be implemented today and explores the timing of adaptation decisions over
the coming decades. As a result, TE2100 provided information on potential future
investment needs and made recommendations on appropriate strategies to review
adaptation options over time to ensure opportunities are not missed; this approach
would be useful in this case.
Range of measures included
The study (largely) includes only ‘structural’ or ‘physical’ adaptation measures. It
ignores behavioural measures and other ‘non-structural’ measures such as
insurance. To make an informed choice, it is important to have information on all the
available adaptation options.
In addition, the adaptation measures considered are relatively small-scale. In
locations at high current or future risk, more significant adaptations might become
low-regrets (e.g. structural changes to buildings).
Additional issues for informing decision making
The paper presents cost-curves from two perspectives, the homeowner perspective
and the societal perspective. Each of the comments above will be relevant in both of
these cases. In addition:
Societal perspective: informing national-level decision making
This perspective aims to inform adaptation policy, including for example, how public
investments should be prioritised and how policies should be designed to incentivise
appropriate individual (autonomous) adaptation. The cost-curves presented provide
one input to this decision making, but they contain only a small set of the information
needed by a decision maker interested in managing risk at the societal level. In
particular, she will most likely need to consider a broader range of adaptation options
than just those applicable to residential building. For example, to identify the
appropriate measures to manage flood risk, she will need to compare the costeffectiveness of measures in the buildings sector to other measures, such as flood
defences, risk transfer initiatives, or land-use planning. For flood risk management,
community flood protection measures (such as dykes) can often be more cost-
beneficial and effective than household measures in urban areas (whereas
household measures tend to be most desirable in areas of low population and asset
concentration). Similarly, for the water sector, efficiency at the household level will
need to be compared with other demand reduction measures (e.g. reducing leakage)
and supply-based measures.
Considering this broader range of measures outside of the building sector is
particularly important in adaptation to climate change, where it is unlikely that
adaptations in the building sector alone could meet societal adaptation needs.
An exception to this could be overheating, where residential adaptations are likely to
be the most significant contributor to long-term risk reduction.
The ‘societal’ decision maker will also probably need to consider more factors than
just the (direct) economic cost-benefit ratio and (direct) economic effectiveness of
measures. Firstly, she may consider a broader range of decision criteria, such as the
number of people at risk and the protection of strategically important assets (e.g.
major economic centres, such as London, or critical infrastructure). She is also likely
to need to consider the broader co-benefits and trade-offs of measures with other
objectives, such as the co-benefits of passive cooling within the home for emissions
reductions, and the trade-offs involved in land-planning policy. Importantly, there may
be fixed budgets constraints. None of these additional factors are captured in the
cost-curves methodology.
Homeowner perspective
The fact that cost-curves are location specific (and building specific) is a crucial
disadvantage for homeowners and particularly for the flood case. The utility of costcurves for homeowners could be improved by generating cost-curves for different
location types and circumstances.
In addition, while the metrics used to convey the effectiveness of measures seem
useful from a policymaking perspective (e.g. economic benefits), other metrics might
be more useable from the householder perspective, for example, the payback period
of the investment (for water and flood) or the reduction in number of days in which
indoor temperatures reach uncomfortable levels (for overheating).
The cost-curves also do not account for the risk aversion of different homeowners.
The decision maker is assumed to be risk neutral and while this may be appropriate
for public sector decision making, it is not for individual decision making. For
homeowner decision making, a welfare-based decision framework that reflects
individual risk aversion might be more appropriate.
Future work: increasing the utility of the flood case
For most of the comments made above, the interpretation of findings from the flood
case proves most difficult. The findings could be quickly made easier to generalise by
presenting different cost-curves for different levels of water inundation within the
home, rather than AEPs. This is because AEPs are highly dependent on the specific
circumstances of the property and its location.
In addition, flood must be treated stochastically; to calculate the total economic
benefits of a flood measure one must aggregate the benefits of all possible flood
depths within the building weighted by their probability. This approach was not used
in the report and so the findings may be incorrect (note that this may also be true for
overheating). The aggregation of benefits could increase the estimated effectiveness
of measures and lower their cost-benefit ratio.
Leading on from this, to develop a cost-curve for a region or the UK, cost-curves for
individual flood depths and building types should be combined with data on the
frequencies of flooding at each location (i.e. a PDF of flood depths at each location,
taking account of adaptation measures). Such data is available at a national level.