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2 Results: Effects of Greening on Ambient Temperatures

2 Results: Effects of Greening on Ambient Temperatures

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54



S. Benger et al.



4 Greening Scenario Analysis

4.1



Materials and Methods



The analysis described in Sect. 1.5 confirms that greening decreases temperatures.

This section quantifies the economic value of possible greening scenarios utilizing

the estimates shown later in Table 2.



4.1.1



Scenarios



This section considers increasing recreation areas. This is because recreation areas

have a higher affinity for urban areas, whose heatwave risks are high, rather than

forest and reserve areas. We assumed the following greening scenarios (hereafter,

we refer to recreation areas as green areas):

BAU (Business as usual) No greening.

Greening1

A scenario of increasing green areas in proportion to the

current amount of green area.

Greening2

A scenario of increasing green areas in higher risk

areas.

In scenario Greening2, districts whose heatwave risks are in the upper 10 % are

regarded as high risk districts, and a lower limit regulation for the green space ratio

is imposed for these districts. We tested three lower limits: 10 %

(Greening2_10 %), 20 % (Greening2_20 %), and 30 % (Greening2_30 %). On the

other hand, we also assume three Greening1 scenarios (Greening1_10 %;

Greening1_20 %; Greening1_30 %;) whose total increase in the green area is the

same as Green2_10 %, Green2_20 %, and Green2_30 %, respectively.



Table 2 Explanatory variables: residential sorting model

Category



Name



Description



Source



Price (pd)



Med_rent



Census 2011



District

(Xd,D)



Center_dist

Station_dist



Median mortgage repayment (AUD

per month)

Distance to Victoria Square

Distance to the nearest station public

transportations (km)

Distance to the ocean (km)

Ratio of recreation areas



Ocean_dist

Green

(Rec.)

Green

(other)

Temperature

Household

(Zh,H)



Income

Age



Government of

South Australia



Ratio of forest + reservation areas

Temperature January 9–16, 2014

(Fig. 1)

Income of householder (15 levels)

Age of householder (8 levels)



MODIS

Census 2011



Modeling Urban Heatwave Risk in Adelaide, South Australia



4.1.2



55



Model for Estimating Economic Value of the Greening Scenarios



Here, we quantified the economic value of each scenario. We need to consider at

least two factors: (i) the impact of greening on heatwave risk (temperature) mitigation, whose statistical significance was proved in through the analysis described

in Sect. 1.5, and; (ii) other possible impacts of greening on human amenity. Hence,

in this section, we quantified the economic value focusing not only on greening but

also its impact on temperature decrease.

In this economic value evaluation, we used the residential sorting model (Van

Duijn and Rouwendal 2013), which is an economic equilibrium-based residential

location choice model. The residential sorting model is a form of hedonic model,

which are widely used for evaluating the economic value of environmental

amenities (Kuminoff et al. 2010). These amenities are not explicitly traded in formal

markets, but are implicitly valued by consumers through choice of a location for a

home.

This model describes the probability that hth household selects sth district as

their residential location, uh,s, using Eqs. (7) and (8):

ud;h ẳ dd ỵ



X X

D



dd ẳ bpd ỵ q



!

bH;D Zh;H ZH ị Xd;D ỵ ed;h ;



7ị



H



X

d6ẳd 0



wd;d0 dd0 ỵ



X



bD Xd;D ỵ nd ;



8ị



D



where Zh,H is Hth household-level explanatory variables, Xd,D is Dth district-level

explanatory variables. They explain the residence location selection probability, ud,

h. pd denotes the standard housing price in the district d. wd,d′ measures the spatial

connectivity between districts d and d′. b, ρ, βH,D and βD are coefficients, and εd,h

and ξd are household-level and district-level disturbances, respectively.

Equation (7) describes the location choice behavior of each household, and Eq. (8)

describes the district level heterogeneity. Note that the second term in Eq. (8) is a

spatial autocorrelation term; it is known that an inclusion of this autocorrelation

effectively mitigates the omitted variables bias (i.e., the bias in parameter estimates

that is caused by factors that could not be introduced in the residential sorting

model, e.g., due to the difficulty of data acquisition).

We estimated the economic value of greening and its impact on temperature

decrease using the residential sorting model. Explanatory variables are as shown in

Table 2. Although Eq. (8) includes the cross-product of, not only Age and each Xd,

D, but also Income and each Xd,D, to avoid multicollinearity, the latter cross-product

terms are omitted. In other words, the explanatory variables of Eq. (8) consist of

Income and the cross-products of Age and each Xd,D, whereas explanatory variables

of Eq. (8) are all of Xd,D.



56



4.2

4.2.1



S. Benger et al.



Results

Estimation of WTP



Table 3 shows the estimated coefficients, which are used for the Willingness to Pay

(WTP) evaluation. The signs of the coefficients in the district-level model are

intuitively consistent; namely, green areas have positive impacts while temperature

increase has a negative influence. Among them, only Ocean_dist and the spatial

interaction term (ρ) are statistically significant.

H in

Agecent in Table 3 denotes the centered Age, which corresponds to Zh;H À Z

Eq. (7). The estimation result of the household-level model suggests that preferences for Cent_dist, Ocean_dist, Green(Rec.), Green(Other), and Temperature vary

significantly across ages..

WTPs of Green(Rec.) per 1 % increase in area, and Temperature per 1° increase,

which we consider in our scenario analysis, were evaluated based on the coefficient

estimates, by age class.

Finally, economic values of each greening scenario are evaluated. The WTP for

sth greening scenario in dth district (in terms of the greening itself), WTPgreen,d(s), is

evaluated by the following equation:

X

WTPgreen;d sị ẳ Dgd sị

wgreen;a hd;a ;

ð10Þ

a



where Δgd(α) is the increase of green area (Green(Rec)) in dth district under sth

scenario, wgreen,a is the WTP of ath age for an unit increase of green area (which

was estimated by the residential sorting model), and hd,a is the number of households categorized into ath age in dth district. Equation (10) simply sums up WTPs

of households in dth district. Likewise, the WTP of sth greening scenario in terms

of its impact on temperature decrease, WTPtemp,d(s), is evaluated by Eq. (11):

X

WTPtemp;d ðsÞ ¼ Dtd ðsÞ

wtemp;a hd;a ;

ð11Þ

a



where Δtd(α) is the decrease of green area (Green(Rec)) in dth district under sth

scenario, which is evaluated by [−3.81 × 10−2 (see, Table 1) × the increase of

green area], wtemp,a is the WTP of ath age for an unit increase (decrease) of temperature (which was estimated by the residential sorting model; see Table 3).

We summed up these WTPs across households in the Adelaide metropolitan

area, and total WTPs for each scenario are evaluated.



4.2.2



Valuation of Greening Scenarios and WTP



Table 4 summarizes estimated WTPs (WTP of each greening scenario minus WTP

of BAU scenario). This table suggests that Greening2 scenarios, which encourage



Modeling Urban Heatwave Risk in Adelaide, South Australia



57



Table 3 Estimates of the residential sorting model ()

Household-level model (Eq. 7)

Variables

Estimates

Intercept

Agecent ×

Agecent ×

Agecent ×

Agecent ×

Agecent ×

(Other)

Agecent ×

Income



Cent_dist

Station_dist

Ocean_dist

Green(Rec)

Green



−1.74

−1.70

−1.73

−6.08

−8.64

−7.00



×

×

×

×

×



10−4

10−5

10−4

10−3

10−3



District-level model (Eq. 8)

Variables

Estimates



−6542***

−1.77*

−0.17

−9.02***

−8.35***

−8.90***



Intercept

Cent_dist

Station_dist

Ocean_dist

Green(Rec)

Green(Other)



6.63

−1.42

−1.18

7.86

9.14

4.01



×

×

×

×

×

×



10−3

10−4

10−4

10−4

10−4

10−3



Temperature

−8.92 × 10−5

ρ

5.43 × 10−1

(interaction)

*,**,***Suggest statistical significances (1, 5, and 10 %, respectively)

Temperature



5.13 × 10−5

3.05 × 10−4



t-value



4.01***

163***



t-value

0.72

−0.24

−0.27

1.85*

0.19

1.16

−0.49

2.26**



Table 4 WTPs of greening scenarios (million AUD per year)

WTP



For greening

For temperature

decrease

Total



Scenario

Green1

10 %



Green1

20 %



Green1

30 %



Green2

10 %



Green2

20 %



Green2

30 %



136

14.5



307

25.2



480

32.7



282

27.2



632

37.0



984

42.7



151



332



512



309



669



1027



greening in high risk areas, have greater WTPs than Greening1 scenarios, not only

for temperature decrease but also for greening. As a result, total benefits of the

Greening2 scenarios are about twice those of the Greening1 scenarios. These results

verify that the Greening2 scenarios are likely to have the highest amenity for

Adelaide residents. The WTP evaluation result indicates that urban planning

implemented to achieve greening of the urban landscape will also have a measurable benefit for heatwave mitigation.

WTPs of Green(Rec) and Temperature, which we consider in our scenario

analysis, are evaluated based on the coefficient estimates, and summarized by age

class in Table 5. As expected, Green(Rec) has positive WTPs across all ages. In

addition, the elderly have greater WTP for Green(Rec) than younger people.

According to this analysis, the elderly demonstrate a higher preference (as measured by WTP) for living in areas with high proportions of green space. This

finding is consistent with other hedonic studies (e.g. Geoghegan 2002; Anderson

and West 2006), which found that older, higher income households have a greater

WTP for open/green space. In contrast, while Temperature has a negative WTP,

again as expected, our results show that younger people have a greater WTP for

temperature decrease. That is, younger people show a preference for living in cooler

areas of the city. The WTP figures for temperature increase in Table 5 are likely



58



S. Benger et al.



Table 5 Estimated willingness to pay (AUD per year)

Age class



Green_Rec (per 1 % increase)



Temperature (per 1 °C increase)



20–24

25–34

35–44

45–54

55–64

65–74

75–84

85–



20

34

52

71

89

107

126

144



−1281

−1179

−1044

−909

−773

−638

−503

−368



exaggerated as a result of individual neighborhood characteristics, but are nevertheless similar to the extensive hedonic pricing literature suggesting substantial

WTP for milder climates (Maddison 2003). While many of these effects are often

observed at more regional and global scales, which point to a strong relationship

between heat in particular and production and macroeconomic output (Heal and

Park 2013), they are clearly present at the city scale.



5 Discussion

Cities such as Adelaide are subject to external climate impacts which may be

beyond their control, but they can change their local climate through choices in

landuse planning and consumption that may influence heat flux, air quality, wind

speed and precipitation (Ruth and Baklanov 2012). Moderate climates can be a

consumption amenity affecting individual utility directly, along with the marginal

utility of types of consumption affected by climatic conditions such as using

neighbourhood parks and reserves (Park et al. 2015). Greening the urban environment facilitates the consumption of the ecosystem services and amenity that

green spaces provide while minimizing the risks associated with external shocks

such as heatwaves (Ruth and Franklin 2014).

A common response to increased temperatures is to increase installed air conditioning capacity, which has also been stimulated in recent times by decreasing

unit costs and rising incomes in Australia. All of the hotter states have very high

levels of residential air conditioning (ABS 2014). Air conditioner use forms a type

of adaptive capacity in the resilience equation, through reducing exposure to

extreme heat. Conversely, it also contributes to increased energy consumption and

greenhouse gas production (Ruth and Franklin 2014). The elderly are often

marginalized in terms of access to this amenity due to their low incomes and older

homes.

Increasing urbanization has been a feature of Australian life over the past century, a trend which is occurring throughout the world. This increases geographical



Modeling Urban Heatwave Risk in Adelaide, South Australia



59



concentration, creates hotspots of energy use and heat retention and generates a

matrix of urban diversity that results in pockets of social inequity and differing

access to the resources that a city provides. Human impacts on the environment are

greatest in cities but also the impacts of the environment are greatest due to the high

density of inhabitants and high value of assets (Ruth and Franklin 2014). The

numbers of elderly individuals continues to rise in many countries, and Adelaide, in

particular, has the highest proportion of elderly persons of any capital city in

Australia (ABS 2013). Concentrations of the vulnerable make it easier to respond to

their needs during emergencies but also require specialized approaches due to their

marginality.

Improved disaster management and emergency preparedness (Blanco and

Alberti 2010) is key to dealing with the threats posed by extreme heat events to

vulnerable members of society. The challenge for governments is to simultaneously

develop heatwave plans, as emergency planning and response policies, in the short

term and to develop longer term policies and plans to address exposure through

transport, urban planning and building design, changes to behavior and health

education (Bi et al. 2011). The provision of greater areas of green space in cities

forms an important component of urban planning approaches to facilitate cooler and

more climate resilient cities.

Most cities around the world will need to work toward development of the

policies and knowledge on how to manage the risks of climate change, which

include increased frequency and intensity of heatwave events, and build resilience

in their systems. Cities are social, economic and environmental systems and the

degree to which they can cope with climate induced risks defines their vulnerability

(Parry 2007). The components and people that comprise the urban system are not

affected equally and will have different vulnerabilities (Lundgren and Jonsson

2012). Addressing the needs of the vulnerable elderly is an important social consideration in most developed nations, but there is a need to also reduce their

exposure to environmental hazards such as heatwaves through better environmental

design in cities.

The concept of urban “liveability” has been a guiding principle for planners and

policymakers (Pacione 2003). Provision of green space and the amenities that it

provides is one part of the city environment which is a key element of liveability.

While this is due to a range of factors such as visual amenity, recreation opportunities, access to open space and others, green space also contributes to the resilience of cities through the moderating effects of cooler temperatures. A cooler

climate in turn reduces the risks to the urban population posed by extreme heat

events. Adelaide was fortunate to have a highly planned beginning which left the

city a legacy of green space, particularly in and around the city centre, which our

results show contributes to reduced heatwave risk. Expanding and consolidating

green space will form an important future requirement for planners while also

delivering urban inhabitants the features of the urban residential environment that

they desire and are willing to pay for.



60



S. Benger et al.



6 Conclusions

Planning for urban resilience under a future of climate change and increasing

temperatures will most likely prove a costly challenge for urban planners and policy

makers. Repurposing of urban land from industrial or residential to green space

must compete against the requirements for new land for high value urban residential

and commercial developments. Many cities around the world have developed

“organically” as economic and population growth have driven the need for

expansion of infrastructure, housing and industry. Green space in cities, where

present, has often been the legacy of foresight by planners and politicians in the

early stages of city evolution. Many cities, particularly in developing and emerging

economies, have only been able to integrate green space as part of the urban fringe.

It has long been recognized that social and environmental dynamics in cities shape

the character, and wellbeing, of the urban population, although different cities place

varying emphasis on their importance. Heatwave–prone cities such as Adelaide and

many others in Australia need to focus on utilizing the cooling effects of green

space and water bodies to improve urban resilience. Resilience, amenity and livability concepts are relatively new and implementing them in cities requires a range

of interventions and investment at multiple scales, while their economic value

remains contentious against the more measurable economic impacts of traditional

urban growth. It will require a transformative approach to anticipate future impacts

of global climate change and take difficult decisions and actions that will radically

alter the spatial arrangement of our cities.

Our research demonstrates, through hedonic spatial analysis, the greater WTP by

urban residents for residential proximity to green spaces, due in part to their temperature ameliorating effects as well as a range of other amenity benefits. This also

likely translates into higher land values and tax benefits to government for these

areas, along a with reduced health care cost burden. Vulnerable members of society

have less ability to pay for a desirable living environment and it therefore becomes

incumbent on city planners to deliver equitable outcomes for all members of urban

society. Greater provision of green space is one mechanism by which this may be

achieved, particularly in the face of more frequent and intense extreme heatwave

events. Issues of adaptation and resilience to extreme heat in cities will need to be

an important focus of both future research efforts and urban governance.



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Flood Risk Management in Cities

Daisuke Murakami and Yoshiki Yamagata



Abstract While bayside areas, which enjoy coastal natural environment, amenity,

scenic landscapes, and so on, are typically attractive residential areas, they are very

often vulnerable to flooding too. Unfortunately, flood risk is gradually increasing in

Asian cities. In particular, the Tokyo metropolitan area is known as a high-risk

metropolis. Building flood risk resilience while keeping the attractiveness of the

bayside area is a critical issue in Tokyo. The objective of this study is to analyze the

trade-off between benefits from the ocean and flood risk as a first step to increase

urban resilience. To quantify the trade-off, this study uses the hedonic approach. We

first review related hedonic studies and discuss which hedonic model is suitable to

apply in our analysis. Subsequently, we perform a hedonic analysis of condominium prices and quantify the benefits from ocean-related variables, including

ocean view and proximity to the ocean, and the negative effects from the flood risk.

Here, a spatial additive multilevel model is used. The analysis results reveal that the

flood risk is highly underestimated while the benefits from the ocean are appropriately evaluated in the target area.



Á



Á



Á



Á



Keywords Flood risk Trade-off Hedonic analysis Normalcy bias Yokohama



1 Introduction

A gradual increase of natural disaster risks is projected on the global scale (Pachauri

et al. 2014). Dettinger (2011) and Kundzewicz et al. (2014) projected that storms

are more and more frequent and severe in East Asia, North, Central, and Caribbean

America, while Hirabayashi et al. (2013) and Kundzewicz et al. (2014) projected an

increase of flood risks in Asia and some other areas. These regions include



D. Murakami (&) Á Y. Yamagata

Center for Global Environmental Research, National Institute

for Environmental Studies, 16-2, Onogawa, Tsukuba 305-8506, Japan

e-mail: murakami.daisuke@nies.go.jp

© Springer International Publishing Switzerland 2016

Y. Yamagata and H. Maruyama (eds.), Urban Resilience,

Advanced Sciences and Technologies for Security Applications,

DOI 10.1007/978-3-319-39812-9_4



63



64



D. Murakami and Y. Yamagata



megacities like Tokyo and New York and many growing cities, which are typically

in developing countries, and are vulnerable to disaster risks (OECD 2012). Building

resilience is increasingly important especially in these cities (Hammond et al.

2015).

Unfortunately, cities are not always resilient against disaster risks. Cities are

typically located in bayside/riverside areas where storm and flood risks are high,

because these areas are convenient for trading, agriculture, and so on. Furthermore,

inside these cities, people usually prefer living nearby the ocean (or rivers) that

enjoys coastal natural environment, amenity, and landscapes. Numerous studies

have empirically verified the significant attractiveness of bayside areas (e.g., Pompe

and Rinehart 1994; Jim and Chen 2009; Hamilton and Morgan 2010; Landry and

Hindsley 2011; Yamagata et al. 2015a, 2016).

It is important to increase the resilience of bayside cities while keeping their

attractiveness. However, policy making for that purpose is not necessarily

straightforward because of the trade-off between risks and other factors (e.g.,

Rascoff and Revesz 2002). For example, a land-use regulation to a high-risk area

might stop some economic activities, while an embankment construction, which

decreases flood risks, might destruct natural environment and obscure scenic ocean

views. Even compact city policy (see, Chapter “Urban Economics Model for LandUse Planning”), which is a popular policy toward sustainable development, does

not specifically consider disaster risks (OECD 2012). A city compaction can lower

the flood risk resilience if the policy concentrates people in a bayside area.

Unfortunately, urban structures are not necessarily adaptive to disaster risks. For

example, Fig. 1 shows the spatial distributions of population density (source:

Census 2010) and anticipated inundation depth (source: National Land Numerical

Information download service; URL: http://nlftp.mlit.go.jp/ksj-e/index.html) in the

central area of Tokyo. This figure suggests that the eastern area with large population density is also a high-risk area (note that this area also is known as a high-risk

area of liquefaction and earthquakes). Although the Tokyo Metropolitan



Fig. 1 Population density (left) and anticipated flood depth (right) in the 23 wards of Tokyo



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