What Drives Green Housing Construction? Evidence from Switzerland

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What Drives Green Housing Construction?

Evidence from Switzerland

Marco Salvi

and Juerg Syz

October 15, 2010

Abstract

Switzerland boasts the arguably highest density of green properties in the world. In 2008, more

than 15% of total new construction received the Swiss energy building label Minergie. The

spatial distribution of these green buildings, however, is highly heterogeneous. In some regions

more than half of the new dwellings are built according to the Swiss green building standard. In

others, this share is still negligible. The purpose of this paper is to identify the determinants of

the distribution of green housing.

For 2'571 Swiss municipalities we compute the green building share of new residential

buildings. We collect data for several variables measuring demographic, geographic, social,

cultural, and political aspects that according to our hypothesis may influence green building

activity. We use count regression to estimate the impact of these variables on the demand for

green buildings.

We find that differences in income levels and cultural affiliation between Swiss municipalities

account for the largest part of the variation in green building activity. The impact of

homeowners stance on environmentalism is highly significant but less important., Government

subsidies do not seem to trigger additional green housing activity.

Keywords Green buildings, Residential Housing, Minergie, Switzerland.

Zrcher Kantonalbank and Eidgenssische Technische Hochschule, Department of Architecture, Zrich.

[email protected]

Diener Syz Real Estate, Zollikon and Shangai, and Universitt Zrich, Zrich. [email protected]

* We are indebted for helpful comments and research assistance with Andrea Horehjov, Julie Neeser

and Andreas Brhl. We thank Erika Meins and Philippe Thalmann for their precious help and

encouragements. The views expressed in this paper, as well as its errors, are those of the authors.

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1. Introduction

The Swiss property market is an ideal playground to examine the determinants of the

demand for green properties. Indeed, Switzerland has one of the highest densities of

energy-efficient buildings in the world (Salvi et al., 2010). By mid 2010 more than

16000 new and retrofitted buildings had received the Swiss green building label

Minergie. In 2008, roughly 15% of newly constructed buildings successfully

completed the Minergie certification process. This paper draws on data collected from

the Swiss market to investigate the question of who builds green houses and why.

Previous research has shown that homeowners do value the expected future cost

savings generated by investments in energy-efficient buildings. Several authors have

documented the incentive effects of higher energy prices on the demand for energy-

efficient technologies, see, e.g., Hausman, (1979), Beresteanu and Li (2008), and Linn

and Klier (2008). However, this paper argues that more moderate utility bills alone do

not explain the demand for green properties.

We observe that the spatial distribution of green buildings in Switzerland is highly

heterogeneous. In some cities more than half of the new constructions are built

according to the Swiss green building standard. In other regions this share is negligible,

suggesting that there are more subtle drivers of the demand for green buildings thanenergy cost savings. To detect these drivers, we investigate the determinants of green

housing activity that lead to regional clusters in Switzerland. Our approach is closely

related to Kahn and Vaughn (2009) who study clusters of LEED (Leadership in Energy

and Environmental Design) registered buildings and hybrid cars in the United States.

However, in their paper, these authors analyze just 10000 registered LEED buildings

(765 of them certified) scattered across the United States. In this paper we explore a

market where the density of certified green buildings is by two orders of magnitude

higher. Moreover, thanks to its decentralized political structures and to the intensive

use of direct democratic instruments at the federal, cantonal and municipal level,

Switzerland offers an ideal situation for studying the impact of environmentalism and

government subsidies on green building activity. We use a unique data set, including

all newly built, Minergie labeled residential buildings in Switzerland. We relate the

green housing density in Swiss municipalities to corresponding demographic,

geographic, social, cultural, and political attributes. We include a measure of

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environmentalism based on voting data as well as government subsidies offered at the

level of the 26 Swiss cantons. We find that among all investigated variables

differences in income across municipalities account for the largest part of the

explained variation in green building activity. Linguistic affiliation, as a proxy for

cultural norms, turns out to have a strong impact on the regional distribution of

Minergie residential buildings as well. The influence of political affiliation, as measured

by voting data, is statistically significant but less important. Government subsidies for

green buildings do not appear to have any positive impact on the clustering of green

properties.

The paper is organized as follows. In the next section, we describe the characteristics

of the Swiss green building standard Minergie and its rapid propagation. We also

document the large regional differences of green building activity. In Section 3, we

develop six hypotheses for potential drivers leading to the observed regional clusters

of green housing activity. We present the regression results with regard to the

correlates of green housing adoption in Section 4. We draw our conclusions in Section

5.

2. Green Buildings in Switzerland

2.1 The Swiss green building standard Minergie

Minergie is the leading eco-label for energy-efficient buildings in Switzerland [1]. A

non-profit private association, Minergie is supported by its members, which include

the federal government, the cantons, schools, companies, individuals, and various

associations. Minergie offers both eco-labeling and eco-certification. Third parties,

usually a cantonal authority, certify Minergie buildings. There are three levels of

Minergie building certifications. The basic Minergie certification is used broadly for

new and retrofitted buildings. To attain the standard, the building must achieve a

reduction of at least 25% in general energy consumption in comparison to the average

conventional building. In addition to this requirement, fossil-fuel consumption has to

be less than half of that of the average conventional building. Minergie-P is a stricter

certification that requires very low energy consumption and is especially demanding

with regard to heating energy demand. This standard broadly corresponds to the

German Passivhaus standard. Finally, Minergie-ECO involves an additional certification

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that verifies the use of environmental-friendly building materials. In this paper we do

not differentiate between the sub-labels as the basic Minergie label covers 95% of the

certified buildings [2]. The list of the certified buildings is publicly available on the

Minergie website. Real estate agents routinely advertise the presence of the label as a

part of the sale process.

As with other green building labels, the implicit assumption of Minergie is that the

energy consumption of a dwelling is a function of its building standard. Energy

consumption estimates are based on the characteristics of the materials applied and

used to assess whether a new or retrofitted building qualifies for the Minergie label. As

of today (2010), the requirements of the basic Minergie standard described above set

a limit of 38 kWh per square meter of floor area and year. This corresponds roughly to

the lower bound of the energy rating B of the European Energy Performance of

Buildings Directive (EPBD) [3]. The use of active ventilation is mandatory to obtain

certification.

Since its launch in 1998, the Minergie label has been quite successful. In principle,

properties of all types be it office buildings or residential housing can be certified if

they meet the label's criteria. In practice, however, private homeowners are at the

forefront of green building activity in Switzerland, as most green properties belong toresidential owner-occupiers and private owners of residential multi-family buildings.

As of August 2009, 11555 or 91% of all certified buildings are residential buildings,

whereof 68% are single-family and 32% multi-family homes. Of the 9% of non-

residential units, about 70% are owned by the public sector. Schools, sports facilities or

office buildings make up the larger part of this category. Because of the predominant

share of residential buildings, we focus our analysis on this segment [4]. Table 1

summarizes the distribution of certified buildings by property type.

Table 1. Minergie certified buildings in Switzerland as of Mid 2009

Number of Minergie certified

buildings

Percentage of total

Single-family homes 7810 62%

Multi-family homes 3745 29%

Others, whereof 1101 9%

Administration offices 432

Schools 308

Sport facilities 73

Total 12656 100%

Source: see Data Appendix

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The number of new Minergie buildings tripled between 2004 and 2009. While at the

beginning of this period only 5% of the new buildings received the label, this

proportion increased to 15% in 2008. Figure 1 shows the number of Minergie certified

new residential buildings and their share of total new residential buildings since 1998.

However, new construction represents only a small part of the total built stock. Hence,

only about 1% of the existing Swiss buildings have been certified so far. Nevertheless,

to the best of our knowledge, the rate of green buildings is higher in Switzerland than

in comparable countries. Indeed, Minergies penetration rate in Switzerland is roughly

280 times higher than LEEDs rate in the United States, where it represents the most

widely used green building label [5]. In England, the number of residential buildings in

the energy efficiency rating bands A and B represented only 0.3% of the housing

stock in 2008 (UK Department for Communities and Local Government, 2010).

Insert Figure 1 about here [Figure 1. Number of Minergie certified new residential

buildings and their share of total new residential buildings, 1998 to 2008]

2.2 The spatial distribution of green housingThe geographical distribution of Minergie buildings in Switzerland is highly clustered.

Most Minergie houses are located in the northern and northeastern part of the

country, as well as in the cities of Bern and Geneva. To compare green building activity

between municipalities, we divide the number of Minergie certified new buildings by

the number of total residential buildings constructed between 1998 and 2008. Figure 2

presents a map of the spatial distribution of this share. The city of Zurich stands out

with a share of approximately 20%, followed by regions in the agglomeration of Zurich.

Alpine touristic resorts such as Davos and Zermatt also stand out for their high share of

energy-efficient buildings. In these resorts roughly one new building in ten has

received the Minergie label. At the other end of the scale, the cantons of Ticino and

Jura and the area around the lake of Geneva exhibit very low green housing activity.

There, the share of new green buildings accounted for less than 2%.

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Insert Figure 2 about here [Figure 2. Minergies share of new residential buildings in

Swiss regions, 1998 to 2008]

On first inspection, the share of Minergie certified houses seems to mirror the

linguistic regions in Switzerland. In the German-speaking part, every fifth new

residential building completed in 2008 obtained the Minergie certification, while in

French-speaking Romandy and in the Italian-speaking region only one in 12,

respectively one in 14 did. On the other hand, French-speaking Geneva tops the list of

green building activity among Swiss cities, as shown in Table 2. The heterogeneous

distribution of green building activity raises the question of the drivers of the demand

for green buildings. We address this question in the remainder of the paper.

Table 2: Recent green construction activity in Switzerlands largest cities (2004-2008)

Rank City Percentage of all new

buildings

Number of Minergie certified new

buildings

1 Geneva 34.5% 39

2 Zurich 33.2% 249

3 Bern 19.8% 26

4 Winterthur 15.1% 97

5 Lucerne 14.7% 22

6 Basel 8.5% 9

7 St. Gall 8.4% 18

8 Lugano 3.1% 10

9 Lausanne 1.2% 4


3. The drivers of green building activity

3.1 Hypothesis development

Even though at least one Minergie building is present in more than half of the Swiss

municipalities, the share of green buildings varies widely across the municipalities. We

develop six hypotheses to explain this heterogeneity. Our list of likely correlates of

green housing activity includes demographic, geographic, social, cultural, and political

aspects [6].

3.1.1 Income: If green buildings are superior goods, their demand will be strongly

positively related to income [7]. We test the hypothesis that Minergie buildings are

more likely in richer municipalities.

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H1. Green building demand increases with income.

3.1.2 Age: Some researchers have argued that the willingness to pay for

environmentally friendly goods declines with age (Hersch and Viscusi, 2005). We test

this hypothesis by including the municipalities age distribution in our regressions.

H2. Green building demand is negatively related to age.

3.1.3 Cultural norms: At a more general level, cultural differences may be important in

addressing environmental problems (Milton, 1996). This may also influence green

building activity. As a multilingual country, Switzerland has natural cultural boundaries

within its national borders. We investigate whether the different linguistic regions vary

in their affinity towards green housing.

H3. Green building demand varies with linguistic affiliation.

3.1.4 Geography: The energy consumption of a building depends crucially on heating

demand, which is a function of the difference between internal and external

temperature (MacKay, 2008). Outside temperature may thus affect the demand for

energy-efficient buildings. Unfortunately, average local temperature and heatingdegrees data is only available for the limited number of communities in which a

meteorological station is located. In Switzerland, however, differences in temperature

are strongly correlated with altitude, which can be easily obtained for every

community [8]. We thus include the average height above sea level as a proxy for this

demand driver.

H4. Green building demand is positively related to altitude.

3.1.5 Government subsides: We are interested in measuring the impact played by

governmental subsides on regional green building activity. The Swiss cantons have a

wide discretion when fixing the amount of the subsidies to be granted to energy

efficient buildings. As a result, payments for Minergie certified new buildings vary

considerably from canton to canton. In 2008, the canton of Bern paid subsidies totaling

CHF 2.2 million while 11 out of the 26 Swiss cantons, including the canton of Zurich,

did not make any subsidy payments. Among those cantons which supported Minergie

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new buildings, payments ranged from CHF 3'700 per building in canton Ticino to CHF

31'340 in the canton of Valais. We use the average subsidy payment per new Minergie

building in each canton to investigate the effectiveness of governmental programs.

H5. Green building demand is positively related to government subsidies.

3.1.6 Environmental activity: Environmentalists tend to be more likely to purchase

green products and may be willing to pay more for environmentally friendly products

(Kotchen and Moore, 2007 and 2008, Kahn and Vaughn, 2009). We suppose that

municipalities with a large share of the population supportive of green ideas are more

likely to have a higher share of green buildings. We measure environmentalism based

on revealed preference political data.

H6. Green building demand is positively related to the degree of

environmentalism in the municipality.

3.2 Measuring Environmentalism

Following Kahn (2007) and Kahn and Vaughn (2009), we construct two indicators of

environmentalism at the municipal level. The basic rationale is that people vote withtheir feet to find the community that provides their optimal bundle of environmental

public goods and taxes (Banzaf and Walsh, 2008). In Switzerland, voters have the

additional opportunity to decide directly on the bundle of environmental public goods.

Through ballot initiatives and referenda at the federal, cantonal, and municipal level,

they can propose or refuse particular changes in the legislation. At the municipal level,

voters are routinely asked to vote on the financing of local infrastructure projects such

as schools or new office buildings for the administration. The outcome of the voting on

these initiatives can be informative about the voters stance on environmental issues.

We base our first indicator, the green index, on the results of five federal initiatives

on environmental issues, listed in Table 3. All five federal initiatives were rejected,

most of them by a wide margin [9]. To construct our first aggregate measure of

environmentalism, we run a factor analysis on the voting results of the federal

initiatives. The correlation of the pro-environmental vote across the five initiatives is

high. The bivariate correlation coefficients range between 0.25 and 0.75. The 2008

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federal initiative Right of appeal of NGOs (Verbandsbeschwerdeinitiative) has the

lowest correlation with other initiatives. In the factor analysis it receives the lowest

factor loading (0.177) [10]. The factor loadings of the other initiatives range between

0.23 and 0.32.

Table 3: Federal initiatives on general environmental issues in the period 2000-2008

Ballot title Year Main aim Yes votes, % Participation

rate, %

"Cut traffic by half" 2000 Reduction of road traffic by half

over a 10-year period

21.3 42.3

"Solar cent" 2000 Introduction of a tax of CHF 0.005

per kWh on non-renewable energy

sources. Half of the tax ear-

marked for solar power uses

31.3 44.7

"Steering tax on non-

renewable energy"

2000 Introduction of a Pigou-tax of CHF

0.02/kWh on non-renewable

energy sources

44.5 44.9

"Electricity without

nuclear power"

2003 Gradual abandonment of nuclear

energy

32.7 49.7

"Right of appeal for

NGOs"

2008 Curtailing of the rights of

environmental organizations to

appeal construction projects

33.0 47.4


By this account, the list of the greenest communities in Switzerland closely matches

the list of the main cities with Zurich, Geneva and Basel among the 5% of the Swiss

municipalities with the highest green index values. Of the ten largest Swiss cities, only

Lugano, situated in the Italian-speaking canton Ticino, does not appear in the 10% of

municipalities with the highest green index score.

The second measure of local environmentalism is based on the results of the 2007

election for the Swiss National Council, Switzerland's lower house of parliament [11].

We count the percentage of votes cast at the municipal level for the Green Party (GPS)

and the 2004 founded Green Liberal Party (GLP). Environmental issues and the

promotion of renewable energies are at the core of both parties platforms.

Accordingly, the GPS endorsed all pro-environment initiatives listed in Table 3. Both

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the GPS and the GLP recommended the rejection of the Right of appeal of NGOs-

Initiative. On social issues the GPS is in general allied with left wing parties, whereas

the GLP is positioned at the center of the political spectrum. In the 2007 elections, the

GLP won 1.4% of the popular vote nationwide and 3 out of 200 seats. The GPS won 9.6

% of the votes and 20 seats. Unsurprisingly, green parties are strongest in the main

urban areas. Six out of the ten largest Swiss cities belong to the 5% communities with

the highest share of green parties votes. Again, Lugano stands out among this group

with a share of only 4.6% of green votes, lower than the median at 8.5%. The

Spearman rank-order correlation between the two measures of environmentalism is

0.36.

The green parties did not run for election in the smaller, mostly rural constituencies.

Hence, this direct measure of environmentalism is not available for nine out of 26

cantons. Data on federal initiatives, however, is available for all municipalities. We test

both measures of environmentalism in the following regression analysis.

4. Empirical results

4.1 Model selection

We test the six hypotheses stated in section 3 with data available at the municipallevel. We run count regressions on the number of Minergie-labeled properties built

between 1998 and 2008 in each municipality. We take into account the nonnegative

integer-valued aspect of the dependent variable. Specifically, we assume that the

conditional expectation ofyi, the number of Minergie buildings in municipality i, is

( ) )exp(,| iiiiiii xxyE +== , (1)

where xi is the vector of covariates and is an heterogeneity factor

independent of xi. Taking the exponential ensures that the mean parameter is

nonnegative; adding

i= exp

i( )

i allows for unobserved heterogeneity between municipalities

that is not fully accounted for by the covariates [12]. It can be shown (Winkelmann,

2000) that the distribution ofyi conditional onxiand i is Poisson distributed with

g y i( )= P Yi = y i | x i,i)( ) =eii

y i

y i!, i = 0, 1, 2, ... . (2)

The heterogeneity factor, i , can be integrated out of this conditional distribution

under the assumption that it is gamma distributed. This solution is called the negative

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binomial model. It is more general than the Poisson regression. Its use is widespread

because, unlike the standard Poisson model, the conditional variance can exceed the

conditional mean. As such, it can accommodate over-dispersion resulting from

neglected unobserved heterogeneity.

In contrast to standard logit regression, the use of count regression allows us the take

directly into account the fact that in 40.5% of all municipalities no Minergie house has

been built between 1998 and 2008. Zero-inflated count models provide a simple way

of modeling so-called excess zeros (Winkelmann, 2000, p. 109). We thus explicitly

model the production of zero counts by specifying a Bernoulli trial that has g(yi) as

outcome with probability i, or zero otherwise. This gives rise to a zero-inflated

negative binomial model (ZINB), where the probability of a non-zero event depends on

the characteristics of a municipality zi, specified as

i= F

i= F zi( ), (3)

where the link function Fis a logistic function. The standard estimator for the negative

binomial model is the maximum likelihood estimator. Estimates of the coefficient

vectors and are found by minimization of the corresponding log-likelihood

function (Winkelmann, 2000). We next present estimates for various specification of

the ZINB model. In Section 4.3 we discuss the estimation results for simpler models,

such as the Poisson model, the negative binomial model and the zero-inflated Poisson

model.

4.2 Regression results

In the base model, the number of Minergie houses is regressed on several covariates

related to the hypotheses developed in Section 3. The covariates include the share of

residents in four age classes, the average altitude in the municipality, the majority

language spoken in the municipality, two indicators of environmentalism, the share of

residents in each of three income brackets, the amount of subsidies for Minergie

buildings and the total number of new buildings in the municipality. Again, we refer to

the Data Appendix for the data sources and the exact definition of the variables.

Table 4 presents summary statistics. Between 1998 and 2008 an average of 3.7

Minergie buildings and about 70 new buildings were completed in each municipality

[13]. We notice that there is not much variation in the population distribution by age.

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The median Swiss municipality is situated at an altitude of 613 meters above sea level.

In the 25% richest communities, at least 42% of the tax income payers are in the

highest income bracket.

Table 4: Descriptive statistics for the 2571 Swiss municipalities

Variable Description Mean Std P25 Median P75

AGE_0_19 Share of population less

than 20

0.252 0.042 0.227 0.254 0.280

AGE_20_39 Share of population aged

20 to 40

0.271 0.038 0.250 0.273 0.294

AGE_40_59 Share of population aged

40 to 60

0.281 0.033 0.261 0.280 0.301

AGE_60_99 Share of population over

60

0.196 0.052 0.161 0.190 0.222

ALTITUDE Altitude above sea level

[km]

0.804 0.481 0.484 0.613 0.930

DGERMAN German speaking

municipality [yes=1]

0.607 0.488 - 1.000 1.000

GREEN_IND1 Share of green parties

votes (*)

0.088 0.094 0.057 0.127 0.161

GREEN_IND2 Green index 0.000 1.000 -0.686 -0.069 0.576

INCOME_LOW Share of taxpayers in low

income class 0.290 0.100 0.222 0.270 0.330

INCOME_MID Share of taxpayers in mid

income class 0.410 0.060 0.385 0.420 0.450

INCOME_HIGH Share of taxpayers in high

income class 0.300 0.100 0.232 0.290 0.360

MIN_BUILD Number of Minergie

buildings in municipality

3.718 10.322 - 1.000 3.000

MIN_GRANT Cantonal subsidy per

Minergie building

[1000 CHF]

8.687 10.497 - 4.416 10.936

NEW_BUILD Number of new buildings

in municipality

68.963 97.551 13.000 38.000 89.000

(*) Only available for 2219 municipalities. Source: see Data Appendix

The first column of Table 5 presents our base case estimation results for the vector of

parameters in equation (1). In this specification, we use the ZINB model and the

green index as indicator for environmentalism [14]. The coefficients for the green

index, the income variables, and the language affiliation are highly significant. To

illustrate the economic effect of the estimated coefficients, we report the change in

the expected number of Minergie buildings per municipality that is associated with a

given change of a covariate. Thus, for each of the covariates in xi, we compute

at the median and at the third quartile of the distribution of the covariate

and report the relative change in the expected number of Minergie buildings in the

)exp( ix

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second column. The table further displays the estimation results using the green

parties share of votes instead of the green index as indicator for environmentalism, as

well as estimation results based on cantonal fixed effects (columns 3 and 4). As a

robustness exercise we also report the results of a regression with cantonal fixed

effects (column 5).

Table 5: Main Estimation Results, ZINB Model

Estimation results using the

green index

Estimation results using

green parties share of

votes

Estimation results

using cantonal fixed

effects

Parameter Estimate Effects Estimate Effects Estimate

Intercept -4.011(0.789)

- -5.043(0.815)

- -4.021(0.861)

Number of new

buildings in

municipality

0.664

(0.034)***

0.403 0.630

(0.035)***

0.414 0.611

(0.034)***

Green index 0.179

(0.032)***

0.122 n/a n/a 0.205

(0.036)***

Green parties share

of votes [%]

n/a n/a 3.608

(0.688)***

0.155 n/a

Share of taxpayers in

mid income class

3.960

(0.783)***

0.120 4.458

(0.891)***

0.134 4.195

(0.793)***


high income class

4.251

(0.495)***

0.324 4.103

(0.552)***

0.320 3.136

(0.524)***

Share of population

aged 20 to 40

2.619

(1.168)*

0.058 3.418

(1.265)**

0.075 3.112

(1.203)**

Share of population

aged 40 to 60

0.277

(1.259)

0.006 0.451

(1.335)

0.009 1.343

(1.342)

Share of population

over 60

1.835

(0.860)*

0.061 2.956

(0.860)***

0.091 3.074

(0.893)***

Altitude above sea

level [km]

0.142

(0.079)

0.046 0.414

(0.091)***

0.191 0.045

(0.097)

German speaking

municipality [yes=1]

0.566

(0.085)***

0.762 0.529

(0.093)***

0.697 0.656

(0.136)***

Subsidy per Minergie

building (cantonallevel)

-0.008

(0.004)*

-0.053 -0.015

(0.004)***

0.125 n/a

Observations 2516 2219 2516

Notes: * significant at 5%; ** significant at 1%; *** significant at 0.1%.

Percentage difference in the value of the expected count of Minergie buildings computed at the median

and at the third quartile of the given covariate. All other covariates are held constant at their median

value.

Per capita taxable income has a decisive impact on the number of Minergie buildings in

a municipality. Other things being equal, an increase in the proportion of taxpayers in

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the highest income bracket from 29.0% (the median) to 36.0% (third quartile) is

associated with a 32.4% increase in the number of Minergie buildings. A share of

42.9% of taxpayers in the highest income bracket corresponding to the 90th

percentile, not shown in Table 4 is associated with an increase of 74% of Minergie

constructions.

High levels of environmentalism as measured by the green index are associated

with somewhat higher Minergie residential building densities. A move from the

median to the third quartile of the index is followed by an increase of the green

building density by 12.2%. A further move to the 90th percentile is associated with an

increase of 27.5% in Minergie constructions. Substituting this measure of

environmentalism with the green parties' voting share does not substantially alter the

results (Column 3 of Table 5). Raising the green voting share from 12.7% to 16.1%

which again corresponds to a move from the median vote to the third quartile leads

to an increase in the expected number of green buildings by 15.5%. A move to the 90th

percentile raises this effect to 24.4%.

Although the demographic structure of a municipality does affect the demand for

Minergie buildings, its impact is relatively small. It is further difficult to interpret. The

density of Minergie buildings increases with the share of 20- to 40-years-old residentsand with the share of residents over 60 but is insensitive to the share of 40- to 60-

years-old [15].

The altitude, as a proxy for heating degree days, has a positive impact on the number

of green buildings, but its statistical significance is quite sensitive to the model

specification. It is highly significant in the model of Column 3, Table 5, which is based

on a smaller sample. This is due to the fact that the cantons where the GPS did not run

in the 2007 election are in majority located in the Swiss Alps and do not have a large

share of green buildings.

In both the base and the alternative specification, we find a weakly negative

correlation between the size of subsidy payments and the number of new Minergie

buildings This correlation is even lower when we exclude from the base case

regression those cantons which do not grant any subsidies (coefficient of -0.013,

standard error 0.0054). Hence, we do not think that subsidy payments triggered a

significant number of Minergie certifications. We conjecture that the payments were

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too small in relation to the extra cost associated with the Minergie certification. The

extra cost is estimated at 5% to 10% of conventional construction cost, i.e. CHF 25000

to CHF 50000 for a typical CHF 500000 construction, while the median subsidy

payment was CHF 4416.

The language affiliation strongly correlates with the number of Minergie buildings.

Minergie building density for German-speaking municipalities is 76.2% higher than for

comparable French-, Italian-, or Romansh-speaking municipalities. These results

suggest that cultural norms may exert influence on environmental choice that is

different from the choices expressed by political affiliation. Alternatively this difference

may simply reflects more extensive marketing activities of the Minergie association in

the German-speaking region. However, the case of the bilingual (German and French)

Canton of Valais further hints at a different sensitivity towards green building issues

across the language border. The share of green buildings in the German-speaking part

of the Canton is roughly ten percentage points higher than in the southern, French-

speaking part, although they share a similar economic environment and the same

cantonal laws.

We thus perform a robustness exercise and limit the estimation of the fixed effects

model (Column 5, Table 5) to the cantons of Bern, Fribourg, Valais and Graubnden,the only multilingual cantons. These cantons belong to the largest in terms of the

number of municipalities. They make up 1'063 of the 2'561 observations in the

national sample. In this setting, which includes cantonal fixed effects, the estimated

parameter for the linguistic region is determined solely by intra-cantonal variation in

green housing construction. We obtain a parameter for the German-speaking indicator

variable of 0.781 (standard error 0.142). This is even larger than the national estimate

of 0.566. As a further test, we then split the national sample in two, the first sub-

sample containing all 1'561 German-speaking, the second containing only the French,

Italian- and Romansh speaking municipalities (N=1'110). We then run the ZINB count

regression with the base case specification (Column 1, Table 5). The results of the

estimation are listed in Table 6.

While many of the variables lose statistical significance, the coefficients of the most

significant ones do not change very much, when compared to the pooled results of

Table 5. Indeed, they are similar across the two distinct regional samples. Finally, we

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note that neither income, demographic nor a lower share of votes for green parties

can possibly explain the large differences in the Minergie density between Western

Switzerland and the German-speaking part of the country [16].

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Table 6: Count Regression Estimates for the Linguistic Regions

Estimation results in non-

German-speakingmunicipalities

Estimation results in

German-speakingmunicipalities

Parameter Estimate Estimate

Intercept -3.086

(1.759)

-3.954***

(0.922)

Number of new

buildings in

municipality

0.732

(0.084)***

0.649

(0.038)***

Green index 0.137

(0.072)*

0.187

(0.038)***


mid income class

2.155

(1.281)

5.150

(0.037)***


high income class

3.679

(0.873)***

4.989

(0.663)***

Share of population

aged 20 to 40

3.699

(2.490)

2.317

(1.354)

Share of population

aged 40 to 60

-0.539

(2.665)

-0.017

(1.495)

Share of population

over 60

1.091*

(1.976)

1.399

(0.972)

Altitude above sea

level [km]

0.147

(0.176)

0.138

(0.094)

Subsidy per Minergie

building (cantonal

level)

-0.014*

(0.006)

-0.005

(0.094)

Observations 1110 1561

Log Likelihood -1186 -3490

See notes in Table 5.

4.3 Sensitivity analysis

In Table 7 we compare fit statistics for different count regression models. The base

case is the ZINB model, as applied for the results in Table 5. We compare these results

with the ones of less specific models, i.e. the zero-inflated Poisson model (ZIP), the

negative binomial model (NegBin) and the standard Poisson model.

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Table 7: General Fit Statistics of Alternative Model Specifications

ZINB ZIP Neg Bin Poisson

Log-likelihood -4687 -6844 -4838 -8277

AIC 9405 13715 9699 16575

Alpha 0.781 - 1.120 -

Standard Error 0.042 - 0.052 -

Observations 2571 2571 2571 2571

With regard to the overall fit, the ZINB model, closely followed by the negative

binomial regression model, achieves the lowest absolute value of the log-likelihood

function. In both models, there is evidence of over-dispersion as indicated by the

rejection of the test that the conditional mean is equal to the variance. If this were the

case, the coefficient would be zero and the negative binomial model would reduce to

the standard Poisson. Figure 3 plots the difference between the actual and the

expected probabilities for the different statistical models and reveals that both the ZIP

model and the Poisson model do not fit the data well. This is most evident in the range

between zero and ten Minergie buildings per municipality, which covers 87% of the

observations. Notice however that all models do well at larger numbers of Minergie

buildings per municipality. For brevity, we do not report the estimated coefficients of

the alternative model as they do not display significant variations from the results in

Table 5.

Insert Figure 3 about here [Figure 3. Difference between observed and predicted

frequencies of Minergie building counts for Poisson, negative binomial, ZINB, and ZIP

models]

5. Policy implications and conclusion

In recent years, there has been a global surge in interest in energy-efficient buildings.

In Switzerland, green building construction has been largely left to the initiative of

private property investors and owner-occupiers. Their willingness to incur both the

certification costs and the significantly larger costs associated with higher energy-

efficiency standards has supported the Minergie label.

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As Switzerland boasts one of the highest densities of green buildings, it offers a

congenial environment to examine the determinants of green building activity. This

paper presents one of the first empirical analyses of what drives the demand for green

housing. The heterogeneous spatial distribution of green buildings in Switzerland

allows us to examine the impact of a comprehensive series of municipality level

attributes on green housing density. We develop and test six hypotheses to explain

this heterogeneity, including demographic, geographic, social, cultural, and political

aspects. We find that differences in income levels and linguistic affiliation account for

the largest part of the systematic variation in green building activity across the

municipalities. The impact of environmentalism, as measured by voting data, is

statistically significant but less important.

We pay particular attention to the effectiveness of government subsidies granted by

15 of the 26 Swiss cantons. Our empirical results show that higher subsidy payments

for new Minergie buildings are not associated with a larger number of certifications.

As the median subsidy payment accounts for just about a tenth of the extra building

cost associated with the Minergie certification, we conjecture that the subsidies are

too small to trigger green construction. Accordingly, other factors must drive the

decision to build green, the most obvious being the private benefits of a Minergiecertification. These benefits likely include the improved building quality and comfort,

as well as a hedge against rising energy prices. Ideology, while not decisive, does

contribute somewhat to green building activity.

One possible conclusion of our work is that that policy makers should consider giving

up subsidies to new Minergie dwellings, as most of these buildings would probably

have been built anyway. Alternatively, the efficiency of the policies could be improved

by focusing on the marginal projects, for example retrofits, which are much less likely

to fulfill the Minergie standard.

In contrast to Kahn and Vaughn (2009), our results suggest that the willingness to incur

the extra cost is predominately related to income levels rather than to environmental

ideology. As such, they are amenable to an interpretation related to the environmental

Kuznets curve, the observation that environmental quality often appears to improve as

income grows beyond a certain level. Indeed, the claim that pollutants involving very

dispersed externalities such as carbon emissions related to energy-inefficient

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buildings could have no turning point is still actively discussed (Galeotti et al., 2006).

Our results would argue against this claim.

We conjecture that the strong correlation of green buildings with linguistic affiliation is

a result of the higher awareness of the Minergie label in the German-speaking part of

Switzerland and of the varying affinity towards green technology among different

cultural groups.

The demand for green housing is likely to be the result of complex attitudes and

actions involving public good aspects (a better environment) and private benefits

(higher building quality). As shown by Delmas and Grant (2008) for the case of organic

wine, the decision to eco-certify and label a product additionally involves subtle

informational issues, both on the producers and the consumers side. For the case of

green buildings, it would be interesting to follow this lead to address the issue of price

discrimination against the renters and buyers of green property. This is left to further

research.

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Notes

1. See Salvi et al. (2010) for an overview of the green building labels available in Switzerland.

2. All Minergie related figures are based on data and publications of the Verein Minergie, see

http://www.minergie.ch/publications.478.html.

3. Directive on the energy performance of buildings (EPBD, Directive 2002/91/EC of the European

Parliament and Council). The limit of the energy bound A is set at 32 kWh/a. The energy band B

corresponds to an annual energy consumption between 32 and 65 kWh/a.

4. The focus on residential properties is also dictated by the limits of the Swiss construction statistics.

Annual new constructions data is available only for housing.

5. As of the beginning of 2009, LEED had approximately 2,000 certified units. Minergie, in the roughly 40

times smaller Swiss market, counted 7 times more (Beyeler et al., 2009).

6. We give the exact description of the data sources and discuss the issues related to the construction of

the variables in the data appendix.

7. This proposition, however, is disputed. See, e.g., Kristrm and Riera (1996) for evidence of an income

elasticity of environmental improvements less than one.

8. The OLS regression of the heating degree day index of 44 locations in Switzerland on the respective

altitude has a R2

of 0.96.

9. Note that for the initiative "Right of Appeal of NGOs" (Verbandsbeschwerdeinitiative) the 'no' votes

signals support for environmental issues.

10. The wording of the initiative may have confused many voters. In an exit poll one third of the voters

recognized to have cast a vote against their true voting intentions (GfS Bern, 2008). Although the

unintended yes and no votes have approximately leveled out each other, the deviation from the true

voting intention may partially explain the lower correlation of this initiative to the other initiatives.

11. Elections for the National Council are held every four years. Each of the 26 cantons is a constituency.

The number of deputies of each constituency depends on the population of the canton.

12. If we do not allow for individual heterogeneity, we obtain the standard Poisson regression model.

13. As of 2008, the average Swiss municipality had 2'945 residents.

14. For the sake of a clear exposition we do not tabulate the coefficients of the logistic model in Table 5.

15. The reference category is the share of residents aged 0 to 19.

16. Unfortunately, we cannot further differentiate the impact of language affiliation. Both the Italian- and

the Romansh-speaking communities are almost completely located within the boarders of a single

canton, Ticino for the former and Graubnden for the latter. They are thus nearly collinear with the

cantonal fixed effects or with the subsidy variable.

17. The mean share of the green parties votes is 9.6% in the French-speaking and 9.2% in the German-

speaking municipalities. The mean share of the higher income category is 29.9% and 30.0%,

respectively.

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Banzhaf, S. H. and Walsh, R. P. (2008), Do People Vote with Their Feet? An Empirical Test of

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Beresteanu, A. and Li, S. (2008), Gasoline Prices, Government Support, and the Demand for

Hybrid Vehicles in the U.S., working paper, Duke University, Durham, January.

Beyeler, F., Beglinger, N. and Roder, U. (2009), Minergie: the Swiss Sustainable Building

Standard, Innovations, Vol. 4 No. 4, pp. 241-244.

Delmas, M. A. and Grant, L. E. (2008), Eco-labeling Strategies: The Eco-premium Puzzle in the

Wine Industry, American Association of Wine Economists Working Paper #13, March.

Galeotti, M., Lanza, A. and Pauli, F. (2006), Reassessing the Environmental Kuznets Curve for

CO2 Emissions: a Robustness Exercise", Ecological Economics, Vol. 57 No. 1, pp. 152-163.

GfS Bern (2008), Analyse der eidgenssischen Abstimmungen vom 30. November 2008",

available at:

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analysen/081130d.html#3 (accessed August 1st, 2010).

Hausman, J. A. (1979), Individual Discount Rates and the Purchase and Utilization of Energy-

using Durables, Bell Journal of Economics, Vol. 10, pp. 33-54.

Hersch, J. and Viscusi, K. (2005), The Generational Divide in Support for Environmental

Policies: European Evidence, NBER Working Paper #11859, December.

Kahn, M.E. (2007), Do Greens drive Hummers? Environmental Ideology as a Determinant of

Consumer Choice,Journal of Environmental Economics and Management, Vol. 54 No. 2,

pp. 129-145.

Kahn, M. E. and Vaughn. R. K. (2009), Green Market Geography: The Spatial Clustering of

Hybrid Vehicles and LEED Registered Buildings, The B.E. Journal of Economic Analysis &

Policy, Vol. 9 No. 2 (Contributions), Article 2.

Kotchen, M. and Moore, M. (2007), Private Provision of Environmental Public Goods:

Household Participation in Green-electricity Programs, Journal of Environmental

Economics and Management, Vol. 53, pp. 1-16.

Kotchen, M. and Moore, M. (2008), Conservation Behavior From Voluntary Restraint to a

Voluntary Price Premium, Environmental and Resource Economics, Vol. 48, pp. 195-210.

Kristrm, B. and Riera, P. (1996), Is the Income Elasticity of Environmental Improvements Less

Than One?, Environmental and Resource Economics, Vol. 7, pp. 45-55.

Leire, C. and Thidell, A. (2005), Product-related Environmental Information to Guide Consumer

Purchases A review and Analysis of Research on Perceptions, Understanding and Use

Among Nordic Consumers,Journal of Cleaner Production, Vol. 13, pp. 1061-1070.

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Linn, J. and Klier, T. (2009), The Price of Gasoline and the Demand for Fuel Efficiency: Evidence

from Monthly New Vehicles Sales Data, FRB of Chicago Working Paper No. 2009-15,

August.

MacKay, D. J. C. (2008), Sustainable Energy Without the Hot Air, available at:

http://www.withouthotair.com/download.html (accessed August 1st, 2010).

Milton, K. (1996), Environmentalism and Cultural Theory: Exploring the Role of Anthropology in

Environmental Discourse, Routledge, London, UK.

Salvi, M., Horehajova, A. and Neeser, J. (2010), Der Minergie-Boom unter der Lupe, Zrcher

Kantonalbank, available at: http://www.ccrs.uzh.ch/index.php/publikationen (accessed

August 1st, 2010)

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http://www.communities.gov.uk/publications/housing/ehs200809headlinereport

(accessed 1 August 2010).

Winkelmann, R. (2000), Econometric Analysis of Count Data, Springer-Verlag, Berlin.

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Data Appendix

The Data Appendix provides additional information on the data sources and discusses

some issues related to the construction of the variables used in the paper. All data are

available at the municipality level with the exception of the government subsides to

Minergie buildings, available at the cantonal level only. The number of political

municipalities in Switzerland steadily decreased from 2899 at the beginning of the

year 2000 to 2596 at the end of 2009. Our cross-section consistently distinguishes

between 2571 municipalities. Municipalities that merged during the investigated time

period are added together for the full time period.

Political voting results

The first indicator (GREEN_IND1) is based on a factor analysis of the results of five

federal initiatives on environmental issues, listed in Table 3. Out of the 44 national

initiatives submitted to the vote between 2000 and 2009, we identified five issues that

were suitable to characterize the voters sentiments towards environmentalism. As

detailed in the main text, we also use results of the most recent (2007) election for the

Swiss National Council (GREEN_IND2). Election and voting data at the municipal level

can be downloaded at the site of the Swiss Federal Office of Statistics.

http://www.bfs.admin.ch/bfs/portal/de/index/themen/17/03.html

Altitude

The average altitude of municipalities (ALTITUDE) is extracted from the RIMINI public

use map of the Swiss Federal Office of Topography.

http://www.swisstopo.admin.ch/internet/swisstopo/de/home/products/downloads/h

eight/rimini.html

Income

The share of residents subject to the Federal income tax in seven income brackets is

from the Swiss Federal Tax Administration, 2006. We merged it to three classes, CHF 0

to 40000 (INCOME_LOW), 40000 to 75000 (INCOME_MID), and above 75000

(INCOME_HIGH).

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http://www.estv.admin.ch/dokumentation/00075/00076/00701/index.html?lang=de#

sprungmarke0_8

Minergie data

Minergie provides address data for all certified new and retrofitted buildings, including

the year of certification. We obtain 11555 new residential buildings (MIN_BUILD) that

were certified from 2001 to 2008.

http://www.minergie.ch/list-of-buildings.html

Linguistic affiliation

The language spoken by the majority of the residents in a municipality is from the 2000

decennial census. Each municipality is assigned to one of the four official languages in

Switzerland, i.e. German, French, Italian, and Romansh. In the regressions we

distinguish German speaking (DGERMAN) from non-German speaking municipalities.

http://www.bfs.admin.ch/bfs/portal/de/index/infothek/lexikon/bienvenue___login/bl

ank/zugang_lexikon.topic.1.html

Demographical dataThe share of the population in four age classes, 0 to 19 (AGE_0_19), 20 to 39

(AGE_20_39), 40 to 59 (AGE_40_59) and over 60 (AGE_60_99) is from the 2000

decennial census.

http://www.bfs.admin.ch/bfs/portal/de/index/infothek/lexikon/bienvenue___login/bl

ank/zugang_lexikon.topic.1.html

New Residential Buildings Construction

The number of new residential buildings per municipality between 1998 and 2008

(NEW_BUILD) is from the construction statistics of the Swiss Federal Office of

Statistics.

http://www.bfs.admin.ch/bfs/portal/de/index/infothek/onlinedb/superweb/presentat

ion_generale.html

Government subsidies

25
http://www.minergie.ch/list-of-buildings.htmlhttp://www.bfs.admin.ch/bfs/portal/de/index/infothek/lexikon/bienvenue___login/blank/zugang_lexikon.topic.1.htmlhttp://www.bfs.admin.ch/bfs/portal/de/index/infothek/lexikon/bienvenue___login/blank/zugang_lexikon.topic.1.htmlhttp://www.bfs.admin.ch/bfs/portal/de/index/infothek/lexikon/bienvenue___login/blank/zugang_lexikon.topic.1.htmlhttp://www.bfs.admin.ch/bfs/portal/de/index/infothek/lexikon/bienvenue___login/blank/zugang_lexikon.topic.1.htmlhttp://www.minergie.ch/list-of-buildings.html


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The average grant for a new Minergie building in each canton (MIN_GRANT) is from

the Swiss Federal Office of Energy. The data covers the period 2003 to 2008. For 2001

to 2003, the share of funding allocated to new Minergie buildings is available at the

national level only. We allocated this sum to the cantons in proportion to their share of

payments between 2004 and 2008.

http://www.bfe.admin.ch/dokumentation/publikationen

26
http://www.bfe.admin.ch/dokumentation/publikationenhttp://www.bfe.admin.ch/dokumentation/publikationen


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Figure 1. Number of new Minergie residential buildings and their share of total new

residential buildings, 1998 to 2008

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Figure 2. Minergies share of new residential buildings in Swiss regions, 1998 to 2008

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Figure 3. Difference between observed and predicted frequencies of Minergie building

counts for Poisson, negative binomial, ZINB, and ZIP models

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0 1 2 3 4 5 6 7 8 9 10 11 12 13

Minergie buildings per municipality (counts)

Differencebetweenpredictedandobservedfrequencies,

percentagepoints

POISSON

NEGBIN

ZIP

ZINB

Documents

What Drives Green Housing Construction? Evidence from Switzerland