Review and Critical Analysis of International UHI Studies

places over simplified calculations to approximate highly complex phenomena, founded on
speculative assumptions and are not based on rigorous adapted, unified and detailed data
sampling, modeling and simulations. Potential sources of error (some identified by the authors)
are: estimates of radiative forcing (about 10% error); methodologies used to account for longer
term effects of CO2 on global climate aren’t factored; radiative forcing effect of increasing albedo
is estimated by the averaged cloud cover; estimates that the potential areas of urban surface
available to increase the albedo are 1%; gross estimation of the urban make‐up
(roof/pavement/land areas); ability to raise albedo by 0.1 in some areas may not be technically
feasible; and urban geometry isn’t factored. The study does indicate that as a result of a sample of
assumptions it makes, the CO2 offset can range from 30Gt to 100Gt. It is however, important that
the gross aggregations and assumptions made to derive offset values are addressed at the local
scale and then scalable methods are derived based on higher definition models and input data
before any realistic figures can emerge – therefore estimates as they stand are ball park figures to
give a feel for the range of benefits that could be attained through mass global mitigation
measure deployment upon the CO2 levels.
The models used to estimate the energy savings from the cool roofs did not always account for all
the parameters which may affect the energy consumption within a building/city. Several critical
parameters that contribute to the energy consumption apart from the roof albedo include: the
daily energy use patterns of the occupants, the energy efficiency of cooling systems (if present –
depends on climates and regions of the world e.g. in Europe A/C mainly found in commercial
buildings but not always present in residential buildings except often in Mediterranean climates),
and the insulation systems (grade and type) and materials used. Such parameters are important
considerations and are critical particularly when scale up of micro‐scale phenomena is used to
establish macro‐scale estimates. Limited attention was given to the control in cooling/heating
systems despite a mention of the challenge in regulating internal temperatures to ensure
occupant comfort and the reduction of A/C intensity (due to cool roofing) in assisting with
building temperature control. It is also important to note that in different climate types and
regions of the world occupant tolerance levels vary (costs associate with heating/cooling, nature
of the climate, culture, etc) and it appears that such considerations have been overlooked in most
studies.
Despite studies examining the costs and benefits associated with the use of cool roofs over
traditional roofs, several factors were not included in the financial analysis and thus could affect
the choice of roofing surface and viability of cool roofing materials. The factors that were not
accounted for include: roofing design and accessibility for installation and maintenance, cost
reductions associated with a decrease in the peak electric demand for cooling, extended life of
cool roofs (as they are less affected by thermal cycling), any incentive schemes for cool roofing,
and effectiveness in different climatic zones. Despite these shortfalls the early indication is that
the newer cool roof designs often have a slightly higher initial investment but have the potential
to be more attractive when examined on a life‐time basis.
From the papers that addressed the penalties in hot vs. cold climates the outcomes are often case
specific and not always easily transferrable to other climate types and cities. The mass
aggregation of a complex set of influential factors such as geographical location, building
orientation, time of year, efficiency of heating/cooling equipment, etc. are difficult to quantify.
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