The value of cool roof as a strategy to mitigate urban heat island effect: A contingent valuation approach

Accepted Manuscript The value of cool roof as a strategy to mitigate urban heat island effect: A contingent valuation approach

Li Zhang, Hiroatsu Fukuda, Zhonghui Liu PII:

S0959-6526(19)31435-0

DOI:

10.1016/j.jclepro.2019.04.338

Reference:

JCLP 16694

To appear in:

Journal of Cleaner Production

Received Date:

06 March 2019

Accepted Date:

25 April 2019

Please cite this article as: Li Zhang, Hiroatsu Fukuda, Zhonghui Liu, The value of cool roof as a strategy to mitigate urban heat island effect: A contingent valuation approach, Journal of Cleaner Production (2019), doi: 10.1016/j.jclepro.2019.04.338

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Character Count: 6313

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The value of cool roof as a strategy to mitigate urban heat

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island effect: A contingent valuation approach

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Li Zhang 1, Hiroatsu Fukuda 2*, and Zhonghui Liu 3

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Japan, [email protected] 2

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Department of Architecture, the University of Kitakyushu, Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, Department of Architecture, the University of Kitakyushu, Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, Japan, [email protected]

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Department of Architecture, the University of Kitakyushu, Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, Japan, [email protected]

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* Correspondence: [email protected]; Tel: +91-090-1369-8821

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Abstract:

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The urban heat island effect in Beijing is significant, which has become a serious environmental problem posed to Beijing

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citizens. As an important measure to alleviate the urban heat island effect, cool roof is recommended in relevant Evaluation

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Standard for Green Building of China. In order to elicit policy implications, this paper investigates Beijing residents'

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willingness to pay for promoting cold roofs to alleviate the urban heat island effect and its determinants. This research

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applied double-bounded discrete choice format and face to face interview to elicit public’s willingness to pay. 841 Beijing

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households were randomly selected and interviewed. For 242 (29%) respondents refuse to pay, the spike model was

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introduced to process the data for it has been proved to outperform the conventional model in dealing with zero responses.

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The average annual willingness to pay was computed as 1510.854 Chinese Yuan, which is 220.562 US dollars per household.

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In addition, In terms of the determinants of residents’ willingness to pay, other than conventional demographic

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characteristics, we added and estimated covariance with regard to environmental knowledge and the theory of planned

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behavior. The result indicated that social norm, perceived behavior control, and previous knowledge of urban heat island

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effect and cool roof are statistical significantly related with residents’ willingness to pay. This research suggests that timely

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information disclosure with regard to urban environment management and pro-environment education are urgently needed

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in promoting public participation in alleviating the urban heat island effect.

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Keywords: urban heat island; public participation; contingent valuation; cool roof; willingness to pay

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1. Introduction

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The increasing urban population has imposed a heavy burden on the urban environment and climate. According to

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United Nations world population prospect, over half of the world's population currently lives in cities, while 2.5 billion

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more people are projected to move to cities by 2025 (United Nations, 2017). In China, the urban population has tripled from

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1978 to 2010. With the expansion of the city, the urban climate and its impact on human health is becoming increasingly

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important. During the urban expansion, the original natural vegetation was replaced by artificial impermeable materials,

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such as conventional asphalt and concrete, which affects the thermal environment of the urban surface and changes the heat

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and moisture exchange between the surface and the atmosphere, and forms special meteorological phenomenon. Urban heat

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island (UHI) effect, which is, the significant difference of temperature between urban and suburban area (Santamouri et al.,

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2013). This phenomenon reduces the thermal comfort of urban residents significantly, increases energy consumption in

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summer and worsens air quality (Stafoggia et al., 2008; Xu et al., 2018).

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Beijing city has been developed rapidly during the past decades and the UHI effect is significant. A long-term measured

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weather dataset from 1961 to 2014 by Cui et al. (2017) has indicated that the UHI effect in Beijing is significant, with an

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urban-to-rural temperature difference of up to 8°C during the winter nighttime, Ge et al. (2016) reported the UHI intensity

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of Beijing fluctuated from 5.37 ℃ to 9.27 ℃ from 1991 to 2011. UHI effect has been one of the main environmental problem

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posed to Beijing citizens.

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Replacing conventional roofs with high albedo materials to reduce the absorption of solar radiation has become an

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important mean to alleviate the UHI effect (Santamouris, 2014). Cool roof is defined as the roof with high solar reflectance

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(ability to reflect sunlight, spectrum 0.3–2.5 μm) and high thermal emittance (ability to emit thermal radiation, spectrum 4–

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80 μm) (Gao et al., 2014). The effectiveness of this approach has been tested in situ and simulated with different urban

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scales in China.

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Jiang (2012) indicated that if the roof solar reflectance was increased from 0.18 to 0.82, the surface temperature of a

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dormitory in Guangzhou province can be reduce by 10-15K in summer afternoon. Study on a natural ventilated factory in

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southern China in the summer of 2011 indicated that white coating can reduce the outer surface temperature by as much as

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17 degrees (Gao et al., 2014). An experimental research of Yang (2014) in Beijing indicated that cool roof can reduce

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building roof surface temperature by as much as 17 degrees by increasing solar reflectance form 0.20 to 0.80.

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As one important UHI effect mitigation method, cool roofs are also encouraged in relevant building energy efficiency

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standards (China, 2006). In 2010, China's Ministry of Housing and Urban-Rural Development (MOHURD) and the US

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Department of Energy (DOE) formed a cool roof working group, aiming at evaluating the potential value of applying cool

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roofs in China. Lawrence Berkeley National Laboratory, USA, together with Chongqing University of China and

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Guangdong Academy of Building Research, China, conducts general research on the science and policies of cool roof within

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the US-China Clean Energy Research Center Building Energy Efficiency Consortium.

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The role of government and the public sector in promoting new techniques in dealing with UHI effect has been found

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in numerous studies (Gao et al., 2014; Synnefa and Santamouris, 2012), while the policy makers needs information

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associated with the value of cool roof mitigating UHI effect. The benefit of UHI mitigation, which is a non-traded product,

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has no market price. Due to the difficulty of measuring the economic value of the benefits of the UHI effect mitigation,

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evaluating the willingness to pay (WTP) has become a common measurement of its economic value (Laitila, 2004). WTP

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refers to the maximum amount a consumer will pay for a specific utility, or to avoid undesirable things. In our research, it

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refers to the effect of cool roof in UHI effect mitigation. Moreover, supporting public participation is considered as essential

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part of sustainable development strategy of China (China, 1994). However, due to China’s governance structure, individual

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participation in urban environmental issues is relatively rare. The study of Huang (2015) have shown that the urban

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environment deterioration is widely concerned by the public, which may become an important opportunity to promote

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public participation in urban environmental governance. In addition, There has been increasingly concerned about the

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factors affecting individual pro-environmental behavior among the world's environmental policy makers, which could

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contribute to a more effective environmental policies (Simões, 2016). Therefore, the acceptance of residents in promoting

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cool roof for mitigating the UHI effect, especially the WTP, should be fully concerned.

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In a hypothetical market, WTP can be elicited with contingent valuation method (CVM) (Mitchell, 1989). CVM is part

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of a wider family of statement preference method, which is a survey based economic valuation method. The contingent

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valuation method is based on respondents’ responses instead of observable market behavior, it refers to direct questioning

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of people to elicit the WTP. The CVM is able to obtain the total economic value of a specific public good which has no

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market value, and it is considered to be the only method available for the assessment of non-use value of a specific

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environmental goods and services (Venkatachalam, 2004). Nowadays, it has been widely applied in fields like

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environmental resource management, cultural goods evaluation, health risk reduction, public policy as well as many other

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fields (Baranzini et al., 2010; Longo, 2012; Santagata and Signorello, 2000; Spash et al., 2009).

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A considerable numbers of researchers have investigated the public WTP for mitigating UHI effect or urban heat

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waves. Zhang et al. (2016) used the CVM to find the WTP for the protective measures of heat waves provided by the market

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and the government. The result indicated that the annual WTP accounts for 40 Chinese yuan (CNY). In addition, they also

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reported that the WTP is correlated with the factors of gender, income, district, heat wave experience, chronic non-

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communicable disease, and air conditioner ownership. Kim et al. (2016) applied the choice experiment to assess the WTP

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for mitigating UHI effect with urban forest. The derived WTP are 56.88-76.59 US dollars for every increase of the urban

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forest by 1m2. Ihara et al. (2011) evaluated the WTP to avoid heat disorders caused by UHI effect. Morawetz and Koemle

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(2017) applied CVM method to estimate the WTP for trees and fountains as measures against UHI effect in Vienna, Austria,

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with limitation of the research method discussed. To the best of our knowledge, in this strand of research no studies have

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explored the social acceptance and WTP for the main technologies for UHI effect mitigation along with its determinants.

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In this study, we assessed Beijing household’s willingness to pay for the promotion of cool roof to mitigating the UHI

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effect. In comparison with previous studies, there are two elements that differentiate this research with the others. The first

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point is that we combined the double-bounded discrete choice (DBDC) format with spike model to process zero-response

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samples, which has been proved to outperform the conventional model in processing zero-response samples (Kristrom,

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1997). The second is that we extended the determinants of WTP. In addition to conventional social-economic factors, we

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added and estimated covariates associated with environmental knowledge and the theory of planned behavior (TPB) (Ajzen,

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1991; Vicente-Molina et al., 2013), which may provide new evidence for unstanding individual’s pro-environment behavior.

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The rest of this research is organized as follows: In Section two, the study methodology is presented. A model of WTP

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estimation with spike model is described in section three. Section four reported and result along with its discussion. Section

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five summarizes the main conclusion and provides policy implications.

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2. Research method

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2.1. Method of Assessing the WTP

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CVM has been widely used to evaluating non-market value (Mitchell, 1989). There are no limitation on the objects to

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be assessed. CVM is superior to other non-market evaluation method for it can capture the non-use value or existence value.

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The environment goods of our research is the effect of cool roof in alleviating the UHI effect, as explained before.

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Direct questioning is the main feature of CVM, while it is also the source of its possible bias. Some scholars has

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questioned about the practicality and reliability of CVM. Regarding this, the blue ribbon National Oceanic and Atmospheric

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Administration (NOAA) points out that related bias can be eliminated by technical means, and CVM could provide reliable

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quantitative evaluation results. NOAA has proposed several guidelines to ensure the reliability of relevant CVM research

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(Arrow, 1993).

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2.2. The Design of Survey

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The CVM field survey was conducted from July 10th -August 5th of 2018. A total of 1050 Beijing households was interviewed and the final valid responses was 841, the response rate was 80%.

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A CVM survey can be conducted through face-to-face interview, mail survey, telephone interview, and online research.

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The limitation of mail survey is that the response rate is low, which may lead to representative bias. The telephone interview

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can provide only limited information to respondents. The reliability of online survey is still controversial. This research

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applied face to face interview, which is also recommended by the NOAA guidelines (Arrow, 1993).

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A total of 10 interviewers were involved and were divided into five groups, each group was consisted of one interview

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and one supervisor (Franceschi and F. Vásquez, 2011). Before the onsite interview, each interviewer has received some

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training, including how to explain the purpose of the experiment, the related rights of respondents, and how to answer

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possible questions that respondents might ask. We selected 39 residential blocks of Beijing randomly. In each block 20-30

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households were chosen. To derive reliable decision making, only 18-70 years old respondents were selected as decision

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maker of each household and interviewed in this survey.

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The survey instrument consists of five parts. The first part was the introduction section, explaining the general

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background information, including the definition of UHI effect, the current situation of UHI effect in Beijing, the hazard of

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UHI effect, and the effect of cool roof in alleviating the UHI effect. The rights of each respondent was introduced before

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the interview. The second part contained the information of individual’s motivation and behavior. The third part contained

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questions relating to the respondents’ previous knowledge (previous knowledge of UHI effect and cool roof). The fourth

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and fifth part comprised the socio-economic characteristic and demographic characteristic, respectively (Fig.1). The WTP

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question is: If Beijing government is going to replace 10% of building roof of Beijing into cool roof (approximately 20

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million m2) , is your household willing to pay a certain amount by increasing the personal income tax for the next 7 years?

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Then, the SPSS 24 and R version 3.5.3 were applied to conduct descriptive analysis and estimate the determinants for WTP.

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At last, policy implications was proposed.

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Fig. 1. Questionnaire and measurement items.

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2.3. WTP Elicitation technique

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Four WTP elicitation techniques are currently in use:

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1. Bidding format: The questioner proposes WTP value and keeps making higher or lower bids until the maximum

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amount the respondent is willing to pay is identified.

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2. Payment card format: Respondents selected the most acceptable options from a number of predetermined prices

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3. Open-ended format: Respondents report the maximum WTP directly.

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4. Dichotomous choice format (DC): Respondents were asked about whether to accept or reject a randomly assigned

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bid.

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The DC format was chosen to obtain WTP. The NOAA blue-ribbon panel’s report also recommend this elicitation

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technique (Arrow, 1993). In previous researches, the single-bounded dichotomous choice (SBDC) and double-bounded

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dichotomous choice (DBDC) is mostly used. SBDC is a one-time DC question while DBDC contains two questions (Soon

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and Ahmad, 2015).

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Four bid combinations were set, which is (100/200/400) (400/800/1500) (800/1500/3000) (1500/3000/5000) CHY.

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The middle figure is the initial bid, the first element is the lower bid while the third element is the higher bid. If the initial

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bid was rejected by the respondent, the lower bid will be presented, or otherwise, the higher bid will be presented. US dollar

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(USD) 1.0 was approximately equal to CHY 6.68 with the current exchange rate.

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2.4. Payment Vehicle

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Respondents may feel confused when asked directly about WTP, and a payment medium can help reveal the true

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payment intention. This payment medium is usually referred to as payment vehicle. In previous CVM studies, the most

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commonly used payment vehicle includes taxes, funds, donation, ticket fee, etc. According to Carson et al. (2001), a

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payment vehicle with mandatory feature can effectively reduce the action of free-riding and over-pledging of respondents.

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In addition, respondents should be familiar with the payment vehicle. Therefore, we choose tax as the payment vehicle of

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this research. Compare with other types taxation, Beijing citizens are more familiar with personal income tax. The

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acceptance of payment vehicle was tested before the main survey.

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At last, payment frequency and payment duration should be decided. According to Egan et al. (2015), we chose annual

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fee as the payment frequency. The payment duration of this research is 7 years, which is the minimum expected duration of

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benefits from a cool roof.

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2.5. Determinants of WTP

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Over the past few decades, individual responsibility for environmental protection and personal pro-environment

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behavior have received more and more attention (Simões, 2016). The theory of planned behavior (TPB) is a theory that

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links one’s belief, perceived resources, and behavior. According to the original TPB model, the most proximal predictors

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of behavior are behavioral intentions, and these intentions are partially influenced by the following: (a) attitudes: an

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individual’s positive or negative appraisal towards the behavior option; (b) The subjective norms: an individual's perception

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of particular behavior, which is influenced by pressure of related social groups (e.g., family members, classmates, friends);

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and (c) perceived behavioral control: an individual's perceived resources, time, money in performing the specific behavior

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(Ajzen, 1991). The TPB assumes that attitudes, perceived behavioral control, and subjective norms can help us better

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understand and predict individual’s pro-environment behavior, in our case, the behavior of paying for UHI effect mitigation.

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Environmental knowledge is defined as “knowledge and awareness about environmental problem and possible solutions

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to those problems”(Zsóka et al., 2013). Numbers of studies have shown that people with rich environmental knowledge are

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more inclined to take environmental behavior, and a lack of environmental knowledge will limit people's pro-environmental

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behavior (Kaiser and Fuhrer, 2003; Mobley et al., 2010; Oğuz et al., 2010). The study of Kennedy et al. (2009) indicates

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that 60% of respondents felt that their environmental behaviors often limited by lack of relevant knowledge.

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In this research, other than conventional covariance used in previous studies (age, gender, income, et al.) determinants

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with regard to TPB theory and environmental knowledge were added.

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3. DBDC plus spike model

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3.1. The conventional DBDC-CVM model

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In DBDC survey, if the respondents rejected the initial bid, he/she would then be presented with a lower bid, or

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otherwise, be presented with a higher bid. Possible responses includes “yes-no”, “yes-yes,” “no-no”, and “no-yes”. The

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𝑌𝑌 𝑁𝑁 𝑁𝑌 binary-valued indicator variables were 𝐼𝑌𝑁 𝑖 ,𝐼𝑖 ,𝐼𝑖 ,𝐼𝑖 , respectively.

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𝐼𝑌𝑁 𝑖 (ith respondent’s answer was ‘yes-no’)

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𝐼𝑌𝑌 𝑖 (ith respondent’s answer was ‘yes-yes’)

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𝐼𝑁𝑁 𝑖 (ith respondent’s answer was ‘no-no’)

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𝐼𝑁𝑌 𝑖 (ith respondent’s answer was ‘no-yes’)

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𝐺𝐶(𝐴 ;γ) is a cumulative distribution function (cdf) that refers to WTP. γ is the parameter to be estimated and A is

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the value of bid. 𝐴𝑖 refers to the initial bids, while 𝐴𝑢𝑖 (𝐴𝑖 < 𝐴𝑢𝑖) is the higher bid presented after the initial bid and 𝐴𝑑𝑖 is

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the lower bid presented after the initial bid. The log-likelihood function is as follows: 𝑁

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ln 𝐿 =

∑ {𝐼

𝑌𝑌 𝑖 ln

𝑢 𝑁𝑌 𝑑 𝑁𝑁 𝑑 [1 ― 𝐺𝐶(𝐴𝑢𝑖;γ)] + 𝐼𝑌𝑁 𝑖 ln [𝐺𝐶(𝐴𝑖 ;γ) ― 𝐺𝐶(𝐴𝑖;γ)] + 𝐼𝑖 ln [𝐺𝐶(𝐴𝑖;γ) ― 𝐺𝐶(𝐴𝑖 ;γ)] + 𝐼𝑖 ln 𝐺𝐶(𝐴𝑖 ;γ)}

𝑖=1

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Formulating 1 ― 𝐺𝐶( .) as logistic cdf and combining this with γ = (a,b) yields:

ACCEPTED MANUSCRIPT 𝐺𝐶(𝐴𝑖;γ) = [1 + exp (𝑎 ― 𝑏𝐴)] ―1

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𝐶 + is the mean WTP, where C can be both positive or negative. The mean WTP is 𝐶 + = 𝑎/𝑏. 3.2. Spike model

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Zero responses often appear in CVM studies (in our case it is 29% of respondents), ignoring which may raise zero

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responses bias. The spike-model provides one approach to mitigate this possible bias without compromising the analysis.

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It was originally proposed for SBDC-CVM data by (Kristrom, 1997), which takes into account a spike at zero that is the

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truncation, at zero, of the negative part of the WTP distribution. Then it was adjusted for DBDC-CVM data by Yoo and

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Kwak (2002), which indicates that the overall results of the spike model outperforms the conventional DBDC-CVM model

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significantly. In recent years, the spike model has been applied in CVM researches with regard to energy policy making,

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historical land conservation, and urban sustainable development (Kim et al., 2017; Kwon et al., 2018; Lim and Yoo, 2014).

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The respondents with “no-no” response were asked with a following-up question to distinguish positive WTP samples

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𝑁𝑁𝑌 from real zero samples. For each respondent i, 𝐼𝑁𝑁 and 𝐼𝑁𝑁𝑁 , as follows: 𝑖 can be classified into 𝐼𝑖 𝑖

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𝐼𝑁𝑁𝑌 = 1(ith respondent’s answer was “no-no-yes”) 𝑖

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𝐼𝑁𝑁𝑁 = 1(ith respondent’s answer was “no-no-no”) 𝑖

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The log-likelihood function for the spike model takes the form: 𝑁

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ln 𝐿 =

∑ {𝐼

𝑌𝑌 𝑖 ln

𝑢 𝑁𝑌 𝑑 𝑁𝑁𝑌 [1 ― 𝐺𝐶(𝐴𝑢𝑖;γ)] + 𝐼𝑌𝑁 [ln 𝐺𝐶(𝐴𝑑𝑖;γ) ― 𝐺𝐶(0 ;γ)] + 𝑖 ln [𝐺𝐶(𝐴𝑖 ;γ) ― 𝐺𝐶(𝐴𝑖;γ)] + 𝐼𝑖 ln [𝐺𝐶(𝐴𝑖;γ) ― 𝐺𝐶(𝐴𝑖 ;γ)] + 𝐼𝑖

𝑖=1

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In which

{

[1 + exp (𝑎 ― 𝑏𝐴)] ―1 𝐺𝐶(𝐴 ;θ) = [1 + exp (𝑎)] ―1 0

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𝑖𝑓𝐴 > 0 𝑖𝑓𝐴 = 0 𝑖𝑓𝐴 < 0

The spike is defined by [1 + 𝑒𝑥𝑝(𝑎)] ―1 . The average mean WTP can be computed as 𝐶 + = (1/𝑏)ln [1 + exp (𝑎)].

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4. Results

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4.1. Data

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Table 1 explains the distribution responses for each bid combinations. A total of 242 respondents gave “NNN” responses,

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suggesting that it is suitable for applying the spike model to deal with the zero response samples in this study. The proportion

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of “Yes” response to the initial bid declines as the magnitude of the bid increases. A total of 132 (62%) respondents were

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willing to pay 200 CHY, while 85 (41%) respondents were willing to pay 3000 CHY.

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Table 1. Distribution of Responses

ACCEPTED MANUSCRIPT 　Bid combination

YY

YN

NY

NNY

NNN

SUM

400/200/100

72(34%)

60(28%)

10(5%)

18(8%)

51(24%)

211(100%)

1500/800/400

39(19%)

50(24%)

15(7%)

35(17%)

71(34%)

210(100%)

3000/1500/800

32(15%)

54(26%)

19(9%)

36(17%)

69(33%)

210(100%)

5000/3000/1500

52(25%)

33(16%)

16(8%)

58(28%)

51(24%)

210(100%)

sum

195(23%)

197(23%)

60(7%)

147(18%)

242(29%)

841(100%)

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The definitions, mean values, and standard deviations of variables are included in Table 2. Of all the variables, gender,

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age, family size, education level, and residential area were all available from the Beijing Statistical Office. The variables of

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gender, age and family size were closed to the official data of the whole population, while the gap between the education

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level of our samples and official data was comparatively big, indicating a limitation of our sampling. The possible reason

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is that the most people who refused to be interviewed had low education level. For the socioeconomic factors of the

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respondents were not significantly different from the general except for education, we consider that our sample is suitable

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for estimating WTP of the whole population.

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Table 2. Sample statistics and definition of variables Variable

Mean

Dev

Census

Gender (Male=1, female=0)

0.49

0.5

0.5

Education (Have a college degree =1,Others=0)

0.52

0.5

0.36

Age (More than 55=3,30-55=2,<30=1)

1.81

0.99

1.84

Family size (More than 3 members=1, others=0)

0.36

0.48

0.31

Residence (Living in urban central area =1, Living in

0.6

0.49

0.59

Income (More than 4000=1,less than 4000=0)

0.71

0.45

Job (The respondent has a job currently=1, Others=0)

0.78

0.41

Children (Raising a child younger than 12 currently=1,

0.52

0.5

0.85

0.36

urban suburban area = 0)

others=0) Attitude (Regarding UHI effect mitigation as important=1, Others =0)

ACCEPTED MANUSCRIPT Subjective norm (People that important for respondent

0.83

0.38

0.39

0.49

0.72

0.45

0.40

0.49

0.84

0.37

would support his/her pro-environment behavior=1, Others=0) Knowledge of UHI (Know well about the UHI effect =1, others =0) Perceived behavioral control (Have enough resources for participating in UHI mitigation activities=1, Others=0) Knowledge of cool roof (Know well about the cool roof = 1, Others=0) Health (Health condition is good=1,Others=0)

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4.2 Descriptive analysis

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Although the concept of UHI effect has been discussed for a long time in the academic field, it is still relatively new to

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the public, especially in developed context like China. Therefore, it is important to understand how the public get knowledge

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about the UHI effect. The interview results showed that about 40% (331 respondents) of the respondents had never heard

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of the UHI effect, which may partially because the UHI effect is relatively difficult to be recognized comparing with other

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urban environmental problems (sandstorms, haze, flood, etc.), thus unlikely to raise public awareness. Among the

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respondents that understand the knowledge of the UHI effect, online inquiry was the main channel by which to learn about

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the UHI effect, with 24% (199 respondents) of the respondents selected that option. 17% (140 respondents) of the

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respondents got information from the other people (family, friends, teachers, community members, etc.), and Television

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ranked forth (16%). In addition, 12 respondents get related information from newspapers, 7 people from pro-environment

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pamphlet and 7 people from the publicity board. The promotion of knowledge with regard to UHI effects needs to be

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strengthened, and various information dissemination programs are needed to improve citizens’ understanding of UHI effect

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(Fig.2).

ACCEPTED MANUSCRIPT

16%

Television Online

40%

Other people Newspaper 24%

Community publicity board Pamphlet No

1%

17%

1%

243 244

1%

Fig.2. Public information sources of the UHI effect

245

In promoting public participation in UHI effect mitigation. More than 80% of respondents reported that government

246

should enhance transparency in urban environmental management, including the disclosure of urban environmental

247

monitoring information (42%) and the use of environmental protection fund (40%). 11% of respondents preferred

248

establishing a rewarding mechanism to stimulate individual pro-environmental behavior, and 5% respondents preferred

249

legal services offered by the government with regard to public supervision and public funding for urban environment issues

250

(Fig. 3).

Environmental monitoring data disclosure

11%

2% 5%

42%

Disclosure the use of environment protection funds Establishing a reward mechanism for proenvironment action

39%

Providing environmental protection legal services Others

251 252 253

Fig.3. Suggestions for government for promoting citizens’ participation UHI effect mitigation.

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4.3. Estimation results

255

The estimation result without covariance are presented in Table 3. Maximum likelihood estimation function was applied

256

to estimate the parameter. The spike is 0.297, which is similar to the zero responses (29%) provided in Table 1, which

257

means the estimation data fits our data well. The Wald statistic rejected the null hypothesis that the estimated parameter are

258

zero since the p-value is less than 0.01.

259

The mean annual WTP was estimated to be 1510.854 CHY (220.56 USD) per household. The t-value is 26.747, thus

260

the result is statistically significant at the 1% level. We also obtained the 95% and 99% confidence intervals for the estimate,

261

using Krisky and Robb’s parametric bootstrapping method approach with 5000 replications (McConnell, 1990).

262

Table 3. Estimation result of the model

263

Variables Coef t values Constant 0.721 11.677 Bid 0.001 202.520 Spike 0.327 24.065 MTP 1510.854 26.747 95% confidence interval 1395.888 1624.057 99% confidence interval 1368.400 1663.974 Wald statics 41055.180 Log-likelihood -1463.826 Notes: The unit of mean willingness to pay (MTP) is CHY. *p<0.1, **p<0.05, ***P<0.01

264

4.4. Estimation results with covariates

p values 0.000*** 0.000*** 0.000*** 0.000***

0.000***

265

Independent variables was divided into three groups, and therefore three estimation models are established (Table 4).

266

In model one, only eight demographic attributes were included. According to Kaiser and Fuhrer (2003) and Kollmuss and

267

Agyeman (2010) People who has a deeper knowledge of environment issues and the remedies are more likely to take actions

268

to protect the environment. Variables with regard to environmental knowledge were added in model two (previous

269

knowledge of UHI effect and previous knowledge of cool roof). Model three included attributes with regard to personal

270

belief and perceived resources, which is inspired by the theory of planned behavior (Ajzen, 1991). This study applied the

271

partial correlation coefficients analysis prevent common method bias, while the result reported no obvious outliers.

272

In terms of demographic attributes, all of the three models indicated that females are more likely to fund for cool roofs,

273

while the education level and presence of children are also positively related to the likelihood of paying for cool roofs. In

274

model two and model three, respondents aged over 55 are less willing to pay. In model one and model two, health condition

275

has significant impact on respondents’ WTP. As for variables with regard to environmental knowledge, model two and

276

model three indicated that WTP increases with an increase in knowledge with regard to UHI effect and cool roof. The

ACCEPTED MANUSCRIPT 277

results presented in model 3 suggest that respondents’ perceived resources in contributing for cool roof construction and

278

pro-environmental social norm have a positive and significant effect on WTP, while there is no significant linkage between

279

respondents’ attitude and WTP.

280

Table 4. Estimation Result with Covariance Variables

Model 1 Coef -0.404

p values 0.220

Model 2 Coef -0.623

281

Constant Demographic attributes Gender -0.232 0.095* -0.370 Income 0.230 0.214 0.020 Age 30 0.291 0.104 0.104 Age 55 -0.465 0.116 -0.505 Residence 0.067 0.653 -0.090 Job 0.149 0.444 0.203 Family size -0.230 0.157 -0.118 Education 0.398 0.024** 0.476 Presence of children 0.518 0.013** 0.485 Healthy 0.751 0.000*** 0.690 Environmental knowledge Knowledge of UHI 0.597 Knowledge of cool roof 0.861 Belief and perceived resources Attitude Perceived behavior control Subjective norm Bid 0.001 0.000*** 0.001 spike 0.287 0.000*** 0.290 MTP 1134.087 0.000*** 1188.687 95% confidence interval 1049.926 1102.802 99% confidence interval 1026.087 1075.894 Wald statistic 98616.240 0.000*** 103666.490 Log-likelihood -1497.585 -1468.078 Notes: The unit of MTP is CHY, *p<0.1, **p<0.05, ***p<0.01

282

4.5.Discussion of the results

p values 0.065*

Model 3 Coef -1.275

p values 0.001***

0.009*** 0.917 0.572 0.095* 0.551 0.302 0.475 0.009*** 0.024** 0.000***

-0.260 -0.074 0.040 -0.517 -0.023 0.131 -0.148 0.423 0.576 0.116

0.074* 0.699 0.828 0.095* 0.882 0.514 0.379 0.051* 0.003*** 0.563

0.000*** 0.000***

0.369 0.666

0.024** 0.000***

0.192 1.173 0.417 0.001 0.290 1240.286 1154.656 1120.939 103074.778 -1423.276

0.302 0.000*** 0.039** 0.000*** 0.000*** 0.000***

0.000*** 0.000*** 0.000***

0.000***

0.000***

283

The annual mean WTP obtained with no covariates was chosen for calculating the total WTP since the setting of

284

covariates may affect the mean WTP. The mean annual WTP is 1510.854 CHY (220.563 USD), which accounted for 1.2%

285

of the disposable income of Beijing household. The WTP of each hectare of cool roof is 0.76 CHY, which is in line with

286

previous study with regard to Beijing household’s WTP for UHI mitigation techniques (Zhang et al., 2019). According to

287

the official data, the number of households in Beijing amounts to 5.38 million at the survey time. Expanding the mean

288

annual WTP value to the population of Beijing, Beijing households are willing to pay 8.128 billion CHY (1.187 billion

ACCEPTED MANUSCRIPT 289

USD) for promoting the construction of cool roof for UHI effect mitigation. The corresponding 95% and 99% WTP intervals

290

are 7.510 - 8.738 billion CHY (1.096 – 1.276 billion USD) and 7.335 – 8.952 billion CHY (1.071 – 1.307 billion USD),

291

respectively.

292

Due to governance mode and cultural reasons, public participation in urban environmental management is rare. The

293

result of our research that may offer new evidence for promoting public participation in UHI effect mitigation. Descriptive

294

analysis indicated that the majority of residents have great expectation of government affairs openness with regard to urban

295

environment management, which reflects that the transparency and credibility of government should be enhanced. For the

296

determinants of WTP, we added the covariates of attitude, perceived behavior control, social norms, and previous

297

knowledge with regard to UHI effect and cool roofs to the conventional ones. The results indicated that residents' previous

298

knowledge, perceived resources in participating in UHI mitigation activities, and social norms were statistically significant

299

with the likelihood of “yes” response to a given bid, which implies the necessity of publicity and education with regard to

300

UHI mitigation.

301

5. Conclusions

302

Beijing has experienced rapid urban expansion in the past few decades and is now experiencing severe UHI effect.

303

The deterioration of the urban thermal environment is mainly due to the fact that the negative externalities of urban

304

expansion have long been overlooked. The original natural vegetation was replaced by artificial impermeable materials,

305

which worsen the urban thermal environment. In addition, the production activities, urban transportation, and residential

306

life require fuel, which emitted a large amount of heat to the urban environment.

307

As one of the important goals of achieving the sustainability of urban development, improving the urban thermal

308

environment has received extensive attention of the Chinese government. New technologies with regard to UHI mitigation

309

have been promoted and cool roof is one of them.

310

To provide evidence for the related policy making, this paper assesses the economic benefits of promoting cool roofs

311

for mitigating the UHI effect. 841 households in Beijing were interviewed in 2018. DBDC format and spike model is

312

adopted to obtain residents’ WTP and reveal the determinants. The results show that most respondents are willing to pay

313

for the cool roofs. Average annual WTP amounts to 1510.854 CHY (220.563 dollars) per household and the total WTP is

314

8.128 billion CHY (1.187 billion USD). As for the determinants of WTP, other than conventional socio-economic variables,

315

our findings indicated that respondents’ previous knowledge, social norm, and perceived resources has great influence on

316

people's pro-environmental behavior. At last, timely information disclosure with regard to urban environment management

317

and related environmental education are necessary for promoting public participation.

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Bases on these conclusions, policy implications were provided as follows:

319

1. Beijing residents have great willingness to pay for mitigating the UHI effect, a special environmental fund might

320

be established to tap the potential source of money for improving urban thermal environment.

321

2. More CVM research on mitigating the UHI effect is necessary for these studies can provide a new evidence for the

322

formulation of relevant policies.

323

3. In order to improve residents' participation in urban environmental improvement, timely disclosure of related

324

information is necessary, especially the use of environmental protection funds.

325

4. The enhancement of knowledge of UHI effects is urgent. Therefore, various information carriers should be

326

encouraged to promote the dissemination of knowledge related to UHI effects.

327

Appendix A. Notation and abbreviation list UHI

Urban heat island

MOHURD

China's Ministry of Housing and Urban-Rural Development

DOE

US Department of Energy

NOAA

National Oceanic and Atmospheric Administration

TPB

the theory of planned behavior

CHY

Chinese Yuan

CVM

Contingent valuation method

USD

US dollars

WTP

Willingness to pay

MTP

Mean willingness to pay

DC

Dichotomous choice format

SBDC

Single-bounded dichotomous choice

DBDC

Double-bounded dichotomous choice

cdf

Cumulative distribution function

328 329

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Volume 1.

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Highlights: 1. Willingness to pay for cool roofs for mitigating urban heat island effect is estimated. 2. The annual mean willingness to pay 1510.854 Chinese Yuan. 3. TPB theory and environmental knowledge can greatly explain pro-environment behavior. 4. Government credibility and education is important in promoting public participation.