non-residential U-values
5. Calibration and Validation of the model outputs
The new modules of EDGE 2.0 have been subject to different validation tests and where needed calibrated to better represent the observed trends. Below we describe the validation tests for the Building stock module, followed by validation and calibration of the U-value investment module.
Building stock projections
The starting point of the building stock model is the relationship between income, population, population age and building stock. The derivative of the building stock over time then drives construction per year, subject also to demolition rates (equation 3 and 4 in the main manuscript). By running the model from 1945 the age distribution of the building stock in each year can be computed. Figure 4 and 5 and show the computed construction and vintages are compared to the European member states data [42].
Figure 4: 2014 projected vintages (model) compared to empirical vintages data (value) for different countries in Europe.
For countries outside of Europe time series construction data and vintages data was more difficult to find. The Japanese government official statics database contains
an-21
104 Chapter 3 Long term, cross-country effects of buildings insulation policies
Figure 5: Projected construction (model) compared to empirical construction data (data) for dif-ferent countries in Europe.
nual housings starts, documented from 1993 to 2018 [70]. The US government collected buildings energy data, including construction of new homes in the Buildings Energy Data Book. The last Buildings Energy Data Book was published in 2011 [71]. In ad-dition, the US energy information administration performed quite detailed household surveys which include also information on the buildings year of construction, see Figure 7. Both the Chinese and Russian government also have collected historical buildings construction data, but in this case expressed in floorspace as opposed to number of buildings. For data comparison in Figure 6 for both region the assumption is made that 1 household is equal to 70 m2. From the figures it can be seen that the model generally captures the order of magnitude and the overall patterns. The building life-time in the US is set to 120 years in the model for a better fit of the construction data, however still there the model overestimates compared to the collected data. In China the model shows a clear different trend then the data. This is the result of Chi-nese buildings policy. In cities until 1978, urban housing was allocated by the ChiChi-nese government as part of social welfare for urban households, which limited the growth.
While after 1978, a reform that has as aim to liberate the housing markets, triggered a strong wave of urban housing development[72].
Uvalue projections
U-value data in Europe is collected for two different years (2008 and 2014), as well as for the other model regions (see section 3), which allows to analyse and calibrate the model computation over time. Renovation rates, Window area, Implicit discount rate and the logit value in the multinomial logit are all model parameters that affect the U-value investment desicion. In this section we discuss how these parameters were deducted for the EDGE 2.0 model.
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Figure 6: Projected construction (model) compared to empirical construction data (data) in China (CHN), Japan (JPN), USA and Russia (RUS).
Figure 7: 2014 projected vintages (model) compared to empirical vintage data (data) for the USA.
Renovation rates
The rate of renovation is a fundamental parameter in the model, which is endogenously calculated following equation 14 in the manuscript. There is a wide spread in reported renovation rates in the literature. [73] studied renovation rates in France and finds values of 6% of glazed surfaces and 4% for opaque components being renovated over a period of 6 years. [41], in line with [73], outlines that despite scenario analyses commonly assuming renovation rates of 2.5-3%, in the reality rates are lower. Annual renovation rates resulting from dwelling stocks’ ownership turnover, or need for maintenance (therefore when energy efficiency measures could be readily introduced), range from 0.5-1.5% of the total stock. This is confirmed by the IEA, which indicate 106 Chapter 3 Long term, cross-country effects of buildings insulation policies
an annual 1% renovation rate. Considering that the EU EPBD has an objective of a 3% annual renovation rate, this would be a significant increase.
These different renovation rate sources do not contain any information about the intensity of renovation. The Zebra 2020 project collected data on major renovation rate equivalent [74], summarized in figure 8. Major renovation refers to a renovation where either the total cost exceeds 25% of the building value, or more than 25%
of the building envelope surface undergoes refurbishment. It is assumes that major renovations lead to final energy reductions of about 50-80%.
Figure 8: Equivalent major renovation rate [74]
In the model, renovation is either implemented for all opaque surfaces and/or for all windows. The model calculates very low renovation rates for windows and high for opaque surfaces, in accordance to [10], that find that windows replacement at for the benefit of energy conservation is rarely profitable, while this is not the case for opaque surfaces. The optimal U-value for opaque components generally equates to about 0.25 W/m2/K, which can be seen as a major renovation for all regions, and vintages.
Renlow and Renup (equation 14 in the manuscript) are used to calibrate the model renovation rates to average renovation rate for the considered countries depicted in 8.
The same equation 14 includes theP BTmax term, which represents the maximum amount of time that is given to the energy savings to pay back the initial investment cost. [18] assumes 30 years as the considered span of time over which the profitability of the investment is evaluated, while [75] indicates 25 years for this parameter. Based on these indications, in the model 30 years is assumed.
Windows area
As mentioned the model works on two different groups of surfaces (opaque and glazed).
The results from each computation are eventually aggregated in order to calculate the
3.8 Supplementary Information 107
envelope U-value, following equation 7. The share of windows compared to opaques surface is deducted on EU database for each region, which contain U-values of the separate components as well as the entire envelope. Especially for the newest vintages the windows share was often found to be higher than the conventionally assumed 25%.
Uenvelope=Uopaque·(1−Awindow) +Uwindow·Awindow (7) Implicit discount rates
The value of implicit discount rates are highly uncertain while playing an important role in the evaluation of building retrofit decisions. For instance, [76] states that applying a Monte Carlo simulation on Net Present Values, 60 % of their variance is explained by the discount rate, which was varied from 0 to 15%. Different studies assess what the levels are of discount rates that consumers implicitly assume. [77] states that investments in the building envelope show high rates of 10-30%, while indicates a 18%
median consumer discount rate for the same type of investments. The PRIMES model assumed a 17.5% household discount rate which then decreases over time in response to policy implementation [78]. The IPCC 4thassessment report, outlines that in developed countries, rates of around 4-6% are justified, while for developing countries this value could get to 12%. In this study discount rates are use as a calibration parameter.
In order to match historical data implicit discount rates and MNL lambda values are computed through auto-calibration. To obtain reasonable values, upper and lower limits of the implicit discount rates are respectively fixed to 3% and 15%. The regional results are shown in figure 9. The default value for the MNL logit parameterλwas set to 0.01 to reflect the market heterogeneity of technology choice. In some, in warmer climate regions, however, the data showed less heterogeneity (i.e. basically all houses have single glazed windows). In these regions the lambda value was set slightly higher, optimizing more for costs. Figure 10 outlines the computed values.
Figure 9: Computed implicit discount rates from the calibration process
From the figure it can be seen that the computed implicit discount rates seems to be reasonable, with rather rational values for European nations, which on average show levels of 5%. A noticeable exception is represented by Italy, which shows an implicit discount rate of 10%. In developing countries, the implicit discount rate is higher (i.e. closer to 15%), meaning that even if it would be convenient to insulate 108 Chapter 3 Long term, cross-country effects of buildings insulation policies
Figure 10: Computed logit parameters from the calibration process
for future savings, current expenditures are values more. The effect is that in these regions U-values are higher. These results are in accordance to the indications of [78].
The comparison of the model outcomes for 2010, with estimated/collected U-value data, is presented in figure 11. A general improvement of model results is reached by the auto-calibration. The only exception is represented by China, which both before and after calibration shows higher U-values compared to the actual data. As mentioned China presents a huge efficiency gap across rural and urban buildings [50].
Considering that most of the Chinese new construction will be placed in cities, in the model a correction which is specific for China is added, in order to reduce the new construction U-value.
Figure 11: Comparison between actual and computed U-valuesn
3.8 Supplementary Information 109
Future assumption for implicit discount rate and logit parameter
The development of country implicit discount rates over time is highly uncertain. Given that these parameters show relatively low values for developed countries, no future changes are assumed for these regions. For the developing regions, discount rates are assumed to linearly decrease with increased wealth, up to to 5% when their GDP per capita is equal to the European countries average in 2015. In addition, the logit parameter of developing countries (and Malta as well) is assumed to progressively decreased to 0.01 when the income levels of these regions reach 27500 $2005/cap. It is thus assumed as economies grow, technology markets in these countries start working as they now do in Europe.
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3.8 Supplementary Information 111
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