An analysis on behaviors of real estate developers and government in sustainable building decision making

Xie, Xiuli; Liu, Yisheng; Hou, Jing Article An analysis on behaviors of real estate developers and government in sustainable building decision making Journal of Industrial Engineering and Management (JIEM) Provided in Cooperation with: The School of Industrial, Aerospace and Audiovisual Engineering of Terrassa (ESEIAAT), Universitat Politècnica de Catalunya (UPC) Suggested Citation: Xie, Xiuli; Liu, Yisheng; Hou, Jing (2014) : An analysis on behaviors of real estate developers and government in sustainable building decision making, Journal of Industrial Engineering and Management (JIEM), ISSN 2013-0953, OmniaScience, Barcelona, Vol. 7, Iss. 2, pp. 491-505, https://doi.org/10.3926/jiem.1042 This Version is available at: https://hdl.handle.net/10419/188614 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc/3.0/ Journal of Industrial Engineering and Management JIEM, 2014 – 7(2): 491-505 – Online ISSN: 2013-0953 – Print ISSN: 2013-8423 http://dx.doi.org/10.3926/jiem.1042 An Analysis on Behaviors of Real Estate Developers and Government in Sustainable Building Decision Making Xiuli Xie, Yisheng Liu*, Jing Hou School of Economics and Management, Beijing Jiaotong University (China) [email protected], *Coresponding author [email protected], [email protected] Abstract: Purpose: The Chinese government takes measures to promote the development of green building (GB). But until 2013, there are only few green buildings in China. The real estate developers are skeptical in entering GB market, which requires theories to explain developers and government’s behaviors. Design/methodology/approach: In this study, we attempt Evolutionary game theory and System dynamics (SD) into the analysis. A system dynamics model is built for studying evolutionary games between the government and developers in greening building decision making. Findings and Originality/value: The results of mixed-strategy stability analysis and SD simulation show that evolutionary equilibrium does not exist with a static government incentive. Therefore, a dynamical incentive is suggested in the SD model for promoting the green building market. The symmetric game and asymmetric game between two developers show, if the primary proportion who choose GB strategy is lower, all the group in game may finally evolve to GB strategy. In this case and in this time, the government should take measures to encourage developers to enter into the GB market. If the proportion who choose GB strategy is high enough, the government should gradually cancel or reduce those incentive measure. -491- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.1042 Research limitations/implications: an Evolution Analysis and System Dynamics Simulation on Behaviors of Real Estate Developers and Government could give some advice for the government to promote the green building market. Keywords: green building, evolutionary game, system dynamics, real estate market 1. Introduction With the development of modernization and urbanization, building energy consumption has steadily increased in China, also causing negative impacts on the environment. The implementation of building energy efficiency standards can reduce a large amount of pollutants emissions, like Carbon footprint emission (Pilar, 2013), and increase the GDP at the same time (Liu & Geoffrey, 2009). The Chinese government takes measures to promote the development of green building, including the formulation of laws and regulations, incentive policies, assessment system, etc. (Sauer & Siddiqi, 2009). In 2008 china begin to adopt a system of green labeling for new constructed residential and business building. The system, called ‘Green Building Label’, has been publicized in the county. But until 2012, only 661 building projects were awarded the Green Building Label (Figure 1). As indicated in Figure 2, from 2008 to 2012 ，the proportion of Green Building in Floor Space under Construction increased, but no more than 0.29%. Figure 1. The number of Chinese Green Building label from 2008 to 2012 -492- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.1042 Figure 2. The proportion of Green Building in Floor Space under Construction in China It is widely believed that the additional investment on Green building can promote energy savings, water savings, and healthier indoor environments, also should return price premium for the Green certified buildings. Some studies suggest that the additional investment on GB should be well paid off by higher selling prices. the Green Mark certified buildings in the Singapore housing market are substantial price premium (Deng, Li & Quigley, 2012). The intangible effects of the label itself seem to play a role in determining the value of green buildings in the marketplace (Eichholtz, Kok & John, 2009; 2010). The label still yields positive effects on a building’s value. Real estate developers are profit driven and working in a competitive environment. There are so many benefits developing GB, but why real estate developers are skeptical in entering the GB market, which requires alternate theories to explain their behaviors. Researchers also apply game theory to explain and analyze a wide array of phenomenon in the field of energy efficiency. Game theory was used to study incentive measures or behaviors that in green building (energy efficiency) areas. In this paper, the market behavior of government and developers are discussed based on the principle of evolutionary game theory. We use SD model to simulation the behavior and analyze the affection factors of real estate developers in GB investment. Game theory has been widely used as a methodology to explain human interactions. So far, the most widely used model in Evolutionary Game Theory is “Replicator Dynamics Model” (Taylor & Jonke, 1978) which is proposed by Taylor and Jonker. The real onset of the theory can be dated to two seminal books in the early 1980s: Smith’s Evolution and the Theory of Games (Smith, 1982), which introduced the concept of evolutionary stable strategies (ESS), and Axelrod’s The Evolution of Cooperation (Axelrod, 1984), which opened up the field for economics and social sciences. Hirshleifer proposed the conception of evolutionary equilibrium (EE) and tried to describe the evolutionary stability (Hirshleifer, 1982; and Friedman, 1998) gave more detailed conditions and application on EE. -493- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.1042 Queena K. Qian attempt incorporating transaction cost economics (TCE) and game theoretical frameworks into the analysis of the real estate developer’s behaviors to explain why real estate developers are skeptical in entering the BEE market (Qian, Chan & Choy., 2013). Kim and Kim proposed an SD model for a mixed-strategy game between police and driver and carried out a qualitative analysis on its simulation result (Kim & Kim, 1997). Wang, Cai & We built an SD model to simulate an evolutionary game between the government that manages environmental pollution and the firms that contaminate during their production processes (Wang, Cai & Zeng, 2011). 2. Evolutionary game between government and developers We suppose that such a game can be used to describe the interaction between government and the developers. Government hopes the developers to build more green building. Developers have two strategies, namely, build GB or normal building (NB). Government has two choices, “valid incentive (Incentive)” and “invalid or no incentive (N-incentive)”. The incentive measures including financial subsidy, tax incentives, and other government incentive. Table 1 presents the simplified payoff matrix of game players. A better strategy for the developers is NB when the government choose Incentive (c1>a1) or the NB when N-incentive (b1>d1). Similarly, Incentive is the better choice for the government when developers choose NB (a2>b2). But N-incentive is the better strategy when developers choose GB (d2>c2). Supposing the mixed-strategy of the government is x, where x is the probability with which the government chooses to incentive. Let y is the proportion of developers choose to build normal building. Ug is the government’s average expected payoff, and Ud is the developer’s average expected payoff. Ug=x [ y a2+ ( 1−y ) c2 ] + ( 1−x ) [ y b2+ ( 1−y ) d2 ] (1) Ud=y [ x a1+ ( 1−x ) b1 ] + ( 1−y ) [ x c1+ ( 1−x ) d1 ] (2) Strategies The government Incentive N-incentive Developers GB a1,a2b1, b2 NB c1, c2d1, d2 Table 1. The payoff matrix of government and developers -494- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.1042 With the greatest expected utilities, we can get the Nash equilibrium state(x*, y*): x∗=b1−d1 c1−a1+b1−d1 (3) y∗=d2−c2 a2−b2−c2+d2 (4) The government’s Replicator dynamic function (Fudenberg & Maskin, 1990) is: dx dt =F ( x ) =y ( 1−y ) [ ( a1−c1−b1+d1 ) x+b1−d1 ] (5) dy dt =G ( y ) =x ( 1−x ) [ ( a2−b2−c2+d2 ) y+c2−d2 ] (6) X= ( F(x) G(y) ) =0 , gives the systemic stable equilibriums: X1= ( 0 0 ) , X 2= ( 0 1 ) , X 3= ( 1 0 ) , X 4= ( 1 1 ) X5= ( x∗ y∗ ) = ( b1−d1 c1−a1+b1−d1 d2−c2 a2−b2−c2+d2 ) This article uses Vensim PLE 6.0b to build the SD model of evolutionary game. Figure 3 shows the simplified model. The SD model mainly consists of four flow variables, two rate-changing variables and eight external variables. Table 2 presents the meanings of the external variables in the SD model. This SD model’s initial value assumed are INITIAL TIME = 0, FINAL TIME = 10000, TIME STEP = 0.5. -495- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.1042 Figure 3. SD model of evolutionary game Variables for payoff in Table 1 External variables in SD model Value assumed a1Developer payoff for NB with incentive 1 b1Developer payoff for NB with no incentive 4 c1Developer payoff for GB with incentive 3 d1Developer payoff for GB with no incentive 2 a2Government payoff for incentive with NB 4 b2Government payoff for no incentive with NB 1 c2Government payoff for incentive with GB 2 d2Government payoff for no incentive with GB 3 Table 2. Meanings of external variables in SD model The equilibrium state in the model can be calculated by applying the Equation 3 and Equation 4 to the payoffs of Table 2. We get the following probabilities for the behavior of game players: x*=0.5, y*=0.25. Figure 4 shows that no one of the game players will change his decision when his initial value is in stable point X1, X2, X3, X4, X5. Figure 4 shows that the proportion of developers in GB strategy will increase from 1% to 100%, when the government makes a perfect incentive (x=1). -496- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.1042 Developers’ behaviours Government’ behaviours Figure 4. Developers and government’ behavior in evolutionary game(X = X1,X2,X3,X4,X5) x = 1 y = 0.01 Figure 5. Developers and government’ behavior in evolutionary game(x =1) We suppose that the government acts with the probability x = x * while the firms act with the probability y ≠ y*. Figure 6 shows the simulation results when the government acts with the probability x = x *, while the lines 1 and 2 represent the firms acting with the probabilities y=0.1 and y = 0.9, respectively. We can see from Figure 6 the probability of NB is fluctuate, is instable. -497- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.1042 y = 0.1 y = 0.9 Figure 6. Developer’ behavior in evolutionary game when x = 0.5 Figure 7 shows SD model for automatic/dynamical incentive management system, which including in c1 is calculated as follows： Incentive(y) = (1 – y) × k (7) In this function, Incentive(y)≥0, k is basic value of incentive, including financial subsidy, tax incentives, etc. When y reduced, Incentive(y) increase, this means if the proportion of developers in building GB decline, the government will gradually increase its value of incentive measures. Similarly, if there are more developers in building GB, the government will gradually reduce its value of incentive measures. Compared Figure 6 and Figure 7, we can see dynamical incentive is more effective than a static incentive. Figure 7 implies that under the dynamical incentive, developing normal building can be restrained to a very low level. Fixed incentive y=0.9 Dynamical incentive y=0.9 Figure 7. Behaviors of developer in different incentive mechanisms when x=0.5 -498- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.1042 Qian, Q.K., Chan, E.H. & Choy, L.H. (2013). How transaction costs affect real estate developers entering into the building energy efficiency (BEE) market? Habitat International, 37, 138-147. http://dx.doi.org/10.1016/j.habitatint.2011.12.005 Sauer, M., & Siddiqi, K. (2009). Incentives for Green Residential Construction. Construction Research Congress 2009, 578-587. http://dx.doi.org/10.1061/41020(339)59 Smith, J.M. (1982). Evolution and the Theory of Games. Cambridge: Cambridge University Press. http://dx.doi.org/10.1017/CBO9780511806292 Taylor, P., & Jonker, L. (1978) . Evolutionary Stable Strategies and Game Dynamics. Mathematical Biosciences, 40, 145-156. http://dx.doi.org/10.1016/0025-5564(78)90077-9 Wang, H., Cai, L., & Zeng, W. (2011). Research on the evolutionary game of environmental pollution in system dynamics model. Journal of Experimental & Theoretical Artificial Intelligence, 23(1), 39-50. http://dx.doi.org/10.1080/0952813X.2010.506300 Journal of Industrial Engineering and Management, 2014 (www.jiem.org) Article's contents are provided on a Attribution-Non Commercial 3.0 Creative commons license. Readers are allowed to copy, distribute and communicate article's contents, provided the author's and Journal of Industrial Engineering and Management's names are included. It must not be used for commercial purposes. To see the complete license contents, please visit http://creativecommons.org/licenses/by-nc/3.0/. -505-