Lisø PhD Dissertation Manuscript - NTNU

Building economics of climate change
step. Such a structure will arise if there are fixed
costs associated with starting up a conversion project.
Assume K = 0, and that a conversion is considered in
period 1. Consider the states where a conversion is profitable
in period 2. As long as K = 0 this can be done
without incurring new fixed conversion costs. A conversion
today will not reduce the profitability of future conversions.
To put the argument the other way around: as
long as there is sufficient economies of scale in undertaking
a conversion (K < 0), the uncertainty could make
it profitable to postpone a conversion until one learns
more about the range and nature of climatic impacts of
the future.
In addition, the existence of states where the building
will be scrapped (in period 2) will reduce the profitability
of period 1 conversion. The presence of economies
of scale in conversions and/or possible future climates
(or states), where a building will be scrapped, leads to
a (partial) decrease in the propensity to undertake
conversion activities.
Some short remarks on the scrapping strategy
In a myopic framework, the scrapping criterion is quite
simple. If the net value of the building in use exceeds the
termination value it should not be scrapped. The termination
value will typically equal the value of the lot in
alternative use minus demolition costs. The difference
between the net value in use and the termination value
can be termed a quasi-rent. Alfred Marshall introduced
the concept of the quasi rent, in order to distinguish
between the rent on produced capital in the long and
the short run (Førsund, 1984). Note that a positive
quasi rent suffices for inducing the owner to keep her
building in use. To induce an owner to set up a new
building a rent that also yields (at least) a normal return
on invested capital is needed.
In a dynamic setting with different possible climate
regimes, the quasi rent criterion gets a little more complex.
Under such a framework, it must be taken into
account that scrapping today kills the option to scrap, or
convert, tomorrow. This value can be positive partly
because future lot values are uncertain, and can consequently
increase the value of ‘continued ordinary use’ of
the building. Consequently, the uncertainty of climate
change can reduce the amount of buildings that are
scrapped in period 1. In our simple three-period
model, this will lead to an increased scrapping activity
in period 2.
Empirical studies of how the quasi rents in different
vintages of the building stock are affected under different
climate change scenarios could provide important
insight into the climate vulnerability of a society (see
Førsund, 1981).
Further work
773
This paper is embedded within the ongoing NBI
Research & Development Programme ‘Climate 2000’
(Lisø and Kvande, 2004). Our ambition is to use the
theoretical apparatus developed here as a tool for
empirical analysis of potential implications of climate
change on the building stock and on the behaviour of
decision makers. A narrow-minded technical approach
to this question could be to measure and predict the
climatic strains that hit the building stock and evaluate
how this affects the state and performance of buildings
and building enclosures.
However, stakeholders react towards information on
changed climatic strains hitting the building stock, and
they adapt. The framework of this paper should help to
understand these adaptations, the interdependencies
between them, and the effects on the state and performance
of buildings. At later stages of the project, we will
characterize different vintages and classes of the building
stock according to which kind of adaptation one
should expect using these tools. Camilleri et al. (2001)
stress that their research on impacts of climate change
on building performance in New Zealand shows large
differences between classes of buildings and locations.
This calls for an approach where impact studies treat a
diversity of classes and vintages of buildings, and take
their heterogeneity into consideration.
Operation and use of a building involves a multiplicity
of stakeholders. In our model, this is simplified down
to one single agent – the owner. In reality owners,
tenants and insurance companies share the responsibility
of the climatic strains that a building is exposed to. In
effect, there exists co-ordination problems arising out of
the fact that some costs of adaptations are borne by
some of the agents, while the gain of them are collected
by others: Why shall I prepare my roof for a potentially
more severe climate if the insurance covers a collapse?
We find it especially important to investigate the role
of insurance. How will the menu of insurance contracts
be affected by climate change? What about their price?
Will increased risks be pooled or should one expect
higher occurrences of separating insurance contracts
(see Rothschild and Stiglitz, 1976)? Questions like this
could, and should, be treated within behaviour models
of the type presented in this paper. Changed conduct
by insurance companies will change the conduct of
other stakeholders, probably in a way that reduces the
adverse impacts of climate change in the built environment.
Hence, a more efficient risk sharing can reduce
aggregate risk exposure.
The focus on insurance leads us to another feature
not treated in this paper: Damage to buildings. The best
way to incorporate building damage into the model is