- Email: [email protected]
Decision-making tools for evaluation the impact on the eco-footprint and eco-environmental quality of green building development policy
Sustainable Cities and Society 23 (2016) 50–58
Contents lists available at ScienceDirect
Sustainable Cities and Society journal homepage: www.elsevier.com/locate/scs
Decision-making tools for evaluation the impact on the eco-footprint and eco-environmental quality of green building development policy Jiaying Teng a,∗ , Pengying Wang b , Xianguo Wu c , Chao Xu a a b c
School of Economics and Management, Jilin Jianzhu University, Changchun 130118, China Northeast Electric Power Design Institute CO., LTD., Changchun 130021, China School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan 430074, China
a r t i c l e
i n f o
Article history: Received 30 October 2015 Received in revised form 28 February 2016 Accepted 29 February 2016 Available online 3 March 2016 Keywords: Ecological system System simulation Strategy optimization
a b s t r a c t Building projects consume large amount of energy and resources, and emit solid waste and CO2 harmful to the eco-environment. In order to study the eco-environmental impact of green building development policies, a “Green Building Eco-environment (GBE)” model is constructed with the method of System Dynamics and implemented using the Vensim software. The model is used to simulate and evaluate the current state and future trend of variation of the eco-environmental impact of green building development in Wuhan during the years 2008–2050 under current green building development policies. Some policy factors are then adjusted in the simulation to determine the optimal green building development policies, under which the quality of the regional ecological environment in Wuhan would be improved most economically. © 2016 Elsevier Ltd. All rights reserved.
1. Introduction Green building is developed to alleviate the conflict between the rapid development of buildings and the deteriorating ecological environment (eco-environment). Building projects consume large amount of energy and resources, and emit solid waste and CO2 harmful to the eco-environment, so the eco-environmental impact of green building development needs more detailed and quantitative understanding. The degree of destruction of the eco-environment by a building project may be evaluated with “Building Eco-footprint” (Bin & Parker, 2012; Teng & Wu, 2014). an index based on the concept of Ecological Footprint (Eco-footprint), which is a simple, effective, and widely used index proposed by Mathis Wackernagel in the early 1990s (Li et al., 2010; Bin & Parker, 2012; Solis-Guzman, Marrero, & Ramirez-De-Arellano, 2013; Lawrence & Robinson, 2014; Shrestha, 2010). However, the interaction between green building development and the eco-environment system is dynamic and complex, which cannot be completely delineated by a single index. Moreover, other factors, such as the ecological carrying capacity, current green building development policies, and other indexes of the
∗ Corresponding author. Tel.: +86 043184566157. E-mail addresses: [email protected] (J. Teng), [email protected] (P. Wang), [email protected] (X. Wu), [email protected] (C. Xu). http://dx.doi.org/10.1016/j.scs.2016.02.018 2210-6707/© 2016 Elsevier Ltd. All rights reserved.
eco-environment, also play a role in the interaction. Therefore, developing a system dynamics model would be more suitable for analyzing the eco-environmental impact of green building development. System Dynamics (SD) studies the complex relationships between multiple factors in a system from macro-perspective and offers an effective method of modeling and simulation (Forrester, 1958; Richmond, 1998; Wolstenholme, 1990; Richardson & Otto, 2008). Nowadays, the SD method has become a popular technique in the research of system dynamics modeling and simulation. For instance, Shih and Tseng (2014) constructed a system model for estimating the economic benefits of energy-saving measures, and this model is used to study the economic benefits of different renewable energies consumed by buildings; with the SD theory, Yan (2006) built a giant system model of sustainable development in Chifeng, which was used to evaluate and forecast the current state and trend of variation of sustainable development in Chifeng and provide suggestions for policy makers; Dong and Liu (2013) built a system model for studying the strategies of large-scale development of green building. The SD method has been applied successfully by many researchers in various settings such as the sustainable development of energy (Blumberga et al., 2014), urban planning for carbon dioxide emissions (Fong, Matsumoto, & Lun, 2009), the carrying capacity of water resources (Feng, Zhang, & Luo, 2008), and construction performance (Wan, Kumaraswamy, & Liu, 2013; Han, Love, & Pena-Mora, 2013).
J. Teng et al. / Sustainable Cities and Society 23 (2016) 50–58
However, few scholars have treated green building and the eco-environment as a dynamic system, and there is a lack of direct report on system dynamics model for analyzing the ecoenvironmental impact of green building development. In this study, the SD method is used to construct a “Green Building Ecoenvironment (GBE)” model for simulating the current state and trend of variation of green building and ecological systems, and the impact of green building development on the eco-environment is analyzed. Such a model is expected to facilitate our understanding of the interaction between green building development and the eco-environment, and its pattern of variation. After this Green Building Eco-environment System Dynamics (GBE-SD) model is verified, the eco-environmental impact of green building development policies in Wuhan in 2008–2050 is simulated and analyzed, and then some policy factors are optimized with this model. Wuhan is one of the first branch of pilot cities in green building in China, and about 2.7% green buildings of China are located here, which makes Wuhan the best selection of this study. 2. Methodology 2.1. System Dynamics method System Dynamics (SD) provides a method of system analysis and simulation that quantitatively represents complex dynamic behaviors inherent in real-world systems, such as nonlinearity, hierarchy, and time-lag, using feedback models (Forrester, 1958; Richmond, 1998; Wolstenholme, 1990; Richardson & Otto, 2008). This method requires low-level data accuracy, but can describe complex, dynamic, high-order, and highly non-liner relationships among the factors in a huge system (Liang, 2008; Wang, 2010; Yuan et al., 2011; Zhang et al., 2014), so it is good for macro-perspective evaluation and forecast. There are five system dynamics software: Vensim, Professional DYNAMO, Stella, Ithink and Powersim. Compared to others, Vensim not only provides user-friendly interfaces for causal loop diagram design, stock-flow diagram construction, model quantification, result output visual display and policy test (Zhang et al., 2012), but also has wide application range and no fixed location. Therefore, the Vensim software provides an effective platform of implementing SD simulations. Vensim offers 4 types of variable expressions for SD models (seen in Fig. 1): (1) Level variables (L), representing the variables whose values change and accumulate over time; (2) Rate variables (R), representing the amount of the variation of level variables each year; (3) Auxiliary variables (A), assisting the transformation between level variables and rate variables; (4) Constant variables (C), which remain the same value over time. 2.2. Scope of study Green building interacts with the eco-environment in a dynamic and complex manner. This paper studies the ecological system affected by regional green building, in which “building” refers to “civil building”; the “region” is located in Wuhan, the capital of Hubei province in China, with an area of 8494 square kilometers. This study focuses on six types of policy, including construction land saving, energy saving, material saving, water saving, CO2
Fig. 1. Four types of variable expressions for SD models.
51
reduction and solid waste reduction policies. The whole policy presents the requirements of regional green building development. Take building CO2 emission saving policy as an example, it represents the proportion of the required annual reduction of CO2 emission to the amount emitted in the last year. The goal of the whole policy is to improve the eco-footprint and ecoenvironmental quality. 2.3. Process of study This study is composed of constructing a GBE-SD model and policy optimization, and three main steps (seen in Fig. 2) are included in this process: (1) Model construction and parameter quantification. Real-world observations and related data from Wuhan are used to find the influential factors in the GBE system, and the logical relationships among different factors are analyzed to design causal diagram and derive stock-flow model. A group of mathematical equations are obtained to quantitatively describe the relationships among different factors in the GBE system; (2) Model validation. The SD model is verified with two methods: comparison with historical data and model validation by the Vensim software. If the model result deviates from test data in either approach, identify the causes and modify the model until the validation requests are satisfied and the result can reflect actual behaviors; (3) Analyses of simulation results and policy optimization. Based on current green building development policies in Wuhan, the current state and trend of variation of the regional ecoenvironment are simulated and analyzed on the Vensim platform. Different policy plans are then simulated to find the optimal combination for sustainable development of green buildings in Wuhan with lower economical cost.
3. Model construction and validation The construction of the GBE-SD model includes SD model design, parameter quantification, and model verification. 3.1. SD model design 3.1.1. Causal diagram design Theoretically, green building has milder destruction to the eco-environment than traditional building; however, building construction and operation always consume energy, water, and materials, and also emit CO2 and solid waste, inevitably causing eco-environmental deterioration. The degree of such deterioration can be quantitatively evaluated with regional building eco-footprint (Teng & Wu, 2014); this index is also used to determine the major influential factors in a Green Building Ecoenvironment (GBE) system. The interaction between green building development and the eco-environment is complex, and a variety of factors need to be addressed simultaneously in the GBE system, such as those associated with the natural development rate of the eco-environment, energy, water, and materials consumptions, emission of CO2 and solid waste, the ecological carrying capacity, eco-footprint, and policies. These factors are also interconnected with and restricted by each other, and most of them are dynamic variables over time. Such factors in a GBE system are shown in the causal diagram in Fig. 3. Fig. 3 is an abstract and conceptual model of regional GBE system depicting the causal relationships between key factors. The “+” sign means positive causal loop, suggesting that the loop is
52
J. Teng et al. / Sustainable Cities and Society 23 (2016) 50–58
Fig. 2. Study flow.
strengthened over time, and the “−” sign has the opposite meaning. The (3), (27), (28), (29) and (32) factors are qualitative, and other factors are quantitative. Some factors in the diagram are further defined and interpreted as follows:
Factors (2), (4), (8), (9), (10), and (11) are all related to policy. Take factor (8) as an example, it represents the proportion of the required annual reduction of CO2 emission to the amount emitted in the last year;
Fig. 3. Causal diagram.
J. Teng et al. / Sustainable Cities and Society 23 (2016) 50–58
53
Fig. 4. GBE-SD model.
Factor (3) represents the mandatory intensity of land saving, energy saving, material saving, water saving, CO2 reduction and solid waste reduction policies; Factor (6) means the difference between the amount of annual construction land and that in the previous year. It could be negative or positive, representing annual increase or reduction of construction land. This factor and the regional construction land can promote each other, thus forming a positive loop; Factors (7), (12), (13), (16), and (20) are similar to factor (6), but refer to annual variation of water consumption, energy consumption, building material consumption, CO2 emission, and solid waste, respectively. Two factors, annual variation of construction land and the construction land promote each other and form a positive loop; Factor (19) represents the amounts of major materials consumed by regional buildings. Since a variety of materials are consumed in construction and it is difficult to collect real data, only five major materials, steel, cement, sand, gravel and coil, are counted in this study to simplify calculation. Factor (23) is used to quantify the degree of deterioration of the regional eco-environment; Factor (25) represents the operational costs of pollution treatment facilities for buildings (including residents);
Factor (26) means the highest threshold of destruction tolerable by the regional eco-environment; Factor (30) and (31) mean the speeds of ecological improvement and deterioration, respectively (Wang, 2009); Factor (33) represents the ratio of the quality of the regional ecoenvironment to its carrying capacity; 3.1.2. Stock-flow model The Stock-flow model of the GBE is based on the causal diagram in Fig. 3, and it is the final form of the GBE-SD model constructed in this paper (seen in Fig. 4). The Stock-flow model is expressed in equations (formulae) and computer codes (Yuan et al., 2011). The GBE-SD model built with the Vensim platform is used to quantitatively describe the relationships between major factors in the system. 3.2. Model quantification According to the variable type (seen in Fig. 1) of the factors in GBE-SD model (seen in Fig. 4), INTEG, LOOKUP and DELAY1 fuctions in Vensim are used to establish the mathematical equations relating different factors in the GBE-SD model (seen in Appendix A). The real data collected from official statistics website are used in the GBE-SD model quantification, such as National Bureau of
54
J. Teng et al. / Sustainable Cities and Society 23 (2016) 50–58
Fig. 5. Setting example of LOOKUP.
Statistics of the People’s Republic of China, Ministry of Environmental Protection of the People’s Republic of China, Hubei Statistical Yearbook, National Data, Statistical information of Wuhan, Wuhan Environmental Protection Bureau, and so on. Based on the real data, three methods of quantification are used to determine the initial values of the mathematical equations in Appendix A: (1) search official statistics and planning websites to obtain the initial values of the INTEG fuctions, carbon conversion factors of energy, and constant variables, such as the initial values of the mathematical Eqs. (1) and (23) in Appendix A; (2) use the Monte Carlo method to derive the coefficients of linear equations, and the eco-footprint conversion factors are quantified, such as the initial values of the mathematical Eqs. (32) and (7) in Appendix A; (3) combine expert assessment, on-demand computation and practical survy methods to quantify the LOOKUP fuctions, qualitative factors and the delay time in the DELAY1 fuctions, such as the initial values of the mathematical Eqs. (40) and (3) in Appendix A. Take the annual rate of variation of energy consumption LOOKUP as an example. This function is shown in Fig. 5, in which the independent variable is the year, and the dependent variable is the annual rate of variation of energy consumption. Additionally, qualitative factors are assessed on five levels: High[1,0.8), Relatively high[0.8,0.6), General[0.6,0.4), Relatively low[0.4,0.2) and Low[0.2,0]. The mathematical equations containing initial values determined with the above three approaches are shown in Appendix A. Before running the GBE-SD model on the Vensim platform, the simulation time is set to be 2008–2050. The simulation results in 2008–2014 are used to analyze the current state of the ecoenvironment in Wuhan, and those in 2015–2050 are used to study its future trend of variation. 3.3. Model validation Two model validation methods, comparison with historical data and the model validation function in Vensim, are used to verify the constructed SD model. 3.3.1. Comparison with historical data A quantitative factor, Regional Building Energy Consumption, in the GBE-SD model is taken as the object of model validation, and the errors between historical data and a preliminary simulation result
Fig. 6. The errors between historical data and a preliminary simulation result.
over time are shown in Fig. 6. It is generally considered (Liang, 2008; Zhang et al., 2014) that relative errors within ±10% are acceptable because building development is a complex activity and accurate simulation is almost impossible; moreover, the SD method is good at deriving consistent trend of variation with lower requirement of data accuracy. Fig. 6 shows that the average error of the GBE-SD model is within 3%, suggesting that the model is reasonable. 3.3.2. Model validation by Vensim Vensim has a model validation function which can be used to check the formulas, units and structures of an SD model. For the constructed model, this function shows the following results: (1) no warnings suggest that the formulas in the GBE-SD model are valid; (2) “Units Check” shows no errors; and (3) “Check Model” says okay. Therefore, both methods have verified that the constructed GBE-SD model (Fig. 4) can reflect the real system behaviors and conditions in Wuhan, and it can be used to analyze the current state and trend of variation of the eco-environment. 4. Simulation results and discussion The verified GBE-SD model (Fig. 4) is used to simulate the current state and future trend of variation of the eco-environment under current policies of green building development in Wuhan, and then numerical experiments are conducted to optimize the policies for the most economically efficient improvement of the eco-environment. 4.1. Analysis of the current state and future trend of variation of the GBE system The simulation results of main level variables and rate variables are shown in Fig. 7, with the results in 2008–2014 indicating the current state and those in 2015–2050 indicating future trend of variation. The curve 1 and curve 2 in Fig. 7a and b show that CO2 emmision and energy consumption by regional buildings tend to increase year by year. In the initial period of green building development, the cost of green technology is high and the technolgy is not mature enough to achive the theoretical effects, so the effect of green technology may be restricted by the economy, design and construction experiences, time delay, and so on. From 2008 to 2012, the annual variations of CO2 emission and energy consumption obviously fluctuate, and this fluctuation would continue increasing under current policies. By 2044, the annual variations of both
J. Teng et al. / Sustainable Cities and Society 23 (2016) 50–58
55
Fig. 7. Simulation results of main level and rate variables.
variables would start to decrease, suggesting that the CO2 emmision and energy consumptions of reginoal buildings would not have significant reduction in a short period. The water, material and land annually consumed by reginoal buildings are shown by the curve 3, 4 and 5 in Fig. 7a; all curves show the general trend of gradual decline with time. The curve 3, 4 and 5 in Fig. 7b show the annual variations of these variables, suggesting that the variation of water, material and land consumption would become steady by 2044, 2016 and 2020, respectively. The curve 6 in Fig. 7a and b show that the solid waste produced by regional buildings and its annual variation become steady by 2020 and 2030, respectively. These results suggest that current policies would have more significant effect on saving material and land and reducing solid waste. The curve 1 in Fig. 7c and d show that the eco-footpring of regional buildings have three stages of development: (1) fluctuation (2008–2013), (2) rapid decline (2014–2032) and (3) gradually steady stage (2033–2050). The continual decrease of eco-footprint suggests that the destruction of the eco-environment by regional buildings would be greatly reduced year by year, except for the fluctuations from 2008 to 2013, which is caused by the fluctuations of rate variables including the annual variation of CO2 and solid wast emission, and energy, material, water and land consumption. The curve 2 in Fig. 7c suggests that the quality of the ecoenvironment would continue to be improved after 2014. This variable is influenced by two rate variables, ecological deterioration and ecological improvement, which are shown by the curve 2 and 3 in Fig. 7d, respectively. The degree of ecological deterioration is influenced by the annual changes of eco-footprint and natural depletion rate, so it shows some fluctuations from 2008
to 2012 and increases after 2017 until becoming steady by 2032. The degree of ecological improvement slowly grows from 2008 to 2017, and starts to increase rapidly by 2026 untill becoming steady by 2049. The degree of ecological improvement is generally higher than that of ecological deterioration, so the eco-environment would be improved with the development of green buildings in Wuhan. The simulation results of main auxiliary variables are shown in Fig. 8. Curve 1 shows the level of development of the ecological system, which fluctuates before 2013, but rapidly increases at the rate of about 4% from 2014 to 2022, and then steadily increases at the
Fig. 8. Simulation results of main auxiliary variables.
56
J. Teng et al. / Sustainable Cities and Society 23 (2016) 50–58
Table 1 The scene settings of development policy optimizing experiment. Optimized object
Policy factor of CO2 emission reduction Policy factor of building energy saving Policy factor of construction land saving
Initial scheme
0.04 0.04 0.07
Scheme 1
2
0.08 0.08 0.07
0.12 0.12 0.07
rate of about 1% after 2023 until the level of development reaches a relatively high level of 0.8 Dmnl by 2044. This result suggests that the ecological system would be significantly improved under current policies. Curve 2 in Fig. 8 shows the eco-footprint per unit area, which plunges form 2008 to 2017, becomes steady after 2017, suggesting that green building development in Wuhan would significantly reduce the eco-footprint per unit area, and dramtically reduce the destruction of the eco-environment by building within a short time. Curve 3 in Fig. 8 also shows that the regional ecoenvironment index gradually increases with year, suggesting that the eco-environment in Wuhan would be improved year by year. 4.2. Policy factors—Simulation result and analysis The simulation results introduced above suggest that under current development policies, it would take about 30 years to improve the quality of the eco-environment in Wuhan to the level of 0.6
Optimized object
Initial scheme
Policy factor of building water saving Policy factor of building material saving Policy factor of solid waste emission reduction
0.04 0.02 0.18
Scheme 1
2
0.08 0.04 0.36
0.12 0.06 0.36
Dmnl. In order to find the policy factors (critical policy factors) capable of more efficiently improving the quality of the regional eco-environment, six poilicy factors are selected and adjusted in two test schemes, and the Scheme 1 shown in Table 1 is determined to be the optimal combination. The results are presented in Fig. 9a, which display how the six factors simultaneously influence the quality of the eco-environment. Table 1 shows the six policy factors which can indirectly influence the quality of the regional eco-environment. Alternation of a single factor can barely improve the quality of the eco-environment (Fig. 9c); however, simultaneously varying all the policy factors can achieve higher quality of the eco-environment. Fig. 9a, b, and d show that, compared with the original scheme, Scheme 1 and Scheme 2 can reduce the eco-footprint of reginoal buildings by 56% and 82%, respectively. Although Scheme 2 can bring greater reduction of eco-footprint, its effect on the quality of the eco-environment is similar to Scheme 1. Under both schemes, the quality of the eco-environment would reach a relatively high level of 0.7 Dmnl by 2048.
Fig. 9. Simulation results of simultaneously varying six policy indictors.
J. Teng et al. / Sustainable Cities and Society 23 (2016) 50–58
The result suggests that when the policy factors have reached a certain level (Scheme 1), they would not further improve the quality of the eco-environment. Scheme 1 have the policy factors with right levels, which can improve equal amount of the quality of the eco-environment with lower economic cost. 5. Conclusions This paper establishes a Green Building Eco-environment System Dynamic model (GBE-SD model) using the Vensim software. The current state and future trend of variation of the ecoenvironmental impact of green building development in Wuhan is simulated and analyzed. The GBE-SD model provides a new means of understanding green building ecological system and quantitatively analyzing the interaction between green building development and the ecoenvironment. Simulation results of the Wuhan-GBE show that under current green building development policies, the regional building CO2 emmision and energy consumption would not be significantly reduced until 2044. However, the regional building eco-footprint would decrease year by year, and the quality of the regional eco-environment would be continually improved and reach a general level of 0.6 Dmnl by 2050. Policy factor simulation indicates that when the selected policy factors have reached a certain level (Scheme 1), they would not further improve the quality of the eco-environment. For example, the maximal improvement of the quality of the sco-environment in Wuhan could be achieved if the annual reduction of CO2 emissions, energy and water resource consumption, construction land consumption, construction materials consumption and solid waste had reached 8%, 8%, 8%, 7%, and 36%, respectively, and the quality of the eco-environment in Wuhan would reach a higher level of 0.7 Dmnl by 2048. Appendix A. (1) Reginoal building CO2 emission = INTEG(Annual variations of CO2 emission,3.69363e + 006), Units: Ton (2) Reginoal building solid waste emission = INTEG(Annual variations of solid waste emission,1380), Units: Million tons (3) Annual variations of CO2 emission = Annual variations of energy consumption × 2.77 − Regional building CO2 emission × DELAY1(Policy factor of CO2 emission reduction,1), Units: Ton/year (4) Annual variations of solide waste emission = Annual variations of solide waste emission LOOKUP(Time) − Regional building solid waste emission × Policy factor of solid waste emission reduction, Units: Million tons (5) Annual variations of solide waste emission LOOKUP = ([(2008,1380)(2050,1381)],(2008,1380),(2011,1379.65),(2012, 1382.37), (2013,1381.55), (2030,1381) (2050,1381)), Units: Million tons (6) Annual variations of material consumption = Regional building material consumption × Annual rate of variation of material consumption(Time) − Regional building material consumption × DELAY1(Policy factor of building material saving,1), Units: Ton/year (7) Annual variations of eco-footprint = 0.00369 × Annual variations of solide waste emission + 5.34e − 007 × Annual variations of CO2 emission + 22.7 × Annual variations of material consumption + 8.78e − 007 × Annual variations of water consumption + 1.03e − 005 × Annual variations of energy consumption + 0.00144 × Annual variations of construction land, Units: ha/year
57
(8) Annual variations of water consumption = Regional building water consumption × Annual rate of variation of water consumption(Time) − Regional building water consumption × DELAY1(Policy factor of building water saving,1), Units: million tons/year (9) Annual variations of energy consumption = Regional building energy consumption × Annual rate of variation of energy consumption(Time) − Regional building energy consumption × DELAY1(Policy factor of building energy saving,1), Units: Tons of standard coal/year (10) Regional building material consumption = INTEG(Annual variations of material consumption, 380,661), Units: Ton (11) Regional building water consumption = INTEG(Annual variations of water consumption, 117,570), Units: Million tons (12) Eco-footpring of regional buildings = INTEG(Annual variations of eco-footprint,8.64137e + 006), Units: ha (13) The impact of Eco-footprint of regional buildings on Ecological deterioration LOOKUP = ([(−1.3e + 006,−0.06) − (1.7e + 006,0.28)],(−1.3e + 006,−0.06),(−200,000,−0.08),(−67,000, −0.04),(−15,630,−0.01),(−6000,−0.01),(20,0.03),(34,000,0.18), (1.7e + 006,0.28)), Units: Dmnl (14) Regional building energy consumption = INTEG(Annual variations of energy consumption,1.33347e + 007), Units: Tons of standard coal (15) Regional construction land = INTEG (Annual variations of construction land,152,653), Units: ha (16) Regional ecological carrying capacity = 1, Units: Dmnl (17) Regional eco-environment index = Quality of the ecoenvironment/Regional ecological carrying capacity, Units: Dmnl (18) The impact of Regional eco-environment index on Level of development of the ecological system LOOKUP = ([(0,0)−(1,0.88)],(0,0),(0.39,0.4),(0.42,0.6),(0.56,0.8),(0.7,0.85), (1,0.88)), Units: Dmnl (19) Quality of the eco-environment = INTEG (Ecological improvement-Ecological deterioration,0.4), Units: Dmnl (20) Mandatory level of regional green building development policy = Policy factor of CO2 emission reduction/1 + Policy factor of building energy saving/1 + Policy factor of construction land saving/1 + Policy factor of building water saving/1 + Policy factor of building material saving/1 + Policy factor of solid waste emission reduction/1, Units: Dmnl (21) Eco-footprint per unit area = Eco-footprint of regional buildings/(Total area of green buildings in this region × 10,000), Units: ha/m2 (22) Annual variations of construction land = Regional construction land × Annual rate of variation of construction land (Time) − Regional construction land × DELAY1(Policy factor of construction land saving,1), Units: ha/m2 (23) Policy factor of construction land saving = 0.07, Units: Dmnl (24) Policy factor of building material saving = 0.02, Units: Dmnl (25) Policy factor of building water saving = 0.04, Units: Dmnl (26) Policy factor of building energy saving = 0.04, Units: Dmnl (27) Annual rate of variation of construction lands LOOKUP = ([(2008,0.021) − (2050,0.075)],(2008,0.021),(2010, 0.024),(2020,0.065),(2050,0.075)), Units: Dmnl (28) Policy factor of CO2 emission reduction = 0.04, Units: Dmnl (29) Policy factor of solid waste emission reduction = 0.18, Units: Dmnl (30) Annual rate of variation of material consumption LOOKUP = ([(2008,0.024) − (2050,0.019)],(2008,0.024),(2009, 0.02),(2010,−0.04),(2011,0.227),(2012,−0.11),(2015,0.01), (2030,0.015),(2050,0.019)), Units: Dmnl (31) Annual rate of variation of water consumption LOOKUP = ([(2008,0.003) − (2050,−0.002)],(2008,0.003),(2009,
58
(32) (33)
(34)
(35)
(36)
(37)
(38) (39) (40)
J. Teng et al. / Sustainable Cities and Society 23 (2016) 50–58
0.0027),(2010,0.0014),(2011,−0.011),(2012,−0.0067),(2017, −0.002),(2030,−0.002),(2050,−0.002)), Units: Dmnl Environmental protection funds = 0.0159 × Regional GDP, Units: billion yuan The impact of Environmental protection funds on Ecological improvement LOOKUP = ([(62,0.02) − (1100,0.5)],(62,0.02), (500,0.08),(800,0.28),(999,0.48),(1000,0.5),(1100,0.5)), Units: Dmnl Level of development of the ecological system = The impact of Regional eco-environment index on Level of development of the ecological system LOOKUP(Regional eco-environment index), Units: Dmnl Ecological deterioration = Quality of the eco-environment × Natural depletion rate × The influence of Eco-footpring of reginoal buildings to Ecological deterioration LOOKUP(Annual variations of eco-footprint), Units: Dmnl Ecological improvement = Quality of the eco-environment × Natural recovery rate × The impact of Environmenprotection funds on Ecological improvement tal LOOKUP(Environmental protection funds), Units: Dmnl Natural increase rate LOOKUP = ([(2008,0.003)(2050,0.035)], (2008,0.00271),(2009,0.00348),(2010,0.00434),(2011, 0.00438),(2012,0.00618),(2013,0.0063),(2030,0.03), (2050,0.035)), Units: Dmnl Natural recovery rate = 0.05, Units: 1/Year Natural depletion rate = 0.08, Units: 1/Year Annual rate of variation of energy consumption LOOKUP = ([(2008,0.1) − (2050,0.08)],(2008,0.1),(2009,0.2), (2010,0.0025),(2011,0.2),(2012,0.19),(2015,0.16),(2020,0.14), (2025,0.13), (2030,0.12),(2050,0.08)), Units: Dmnl
References Bin, G., & Parker, P. (2012). Measuring buildings for sustainability: Comparing the initial and retrofit ecological footprint of a century home—The REEP House. Applied Energy, 93(SI), 24–32. Blumberga, A., Blumberga, D., Bazbauers, G., Zogla, G., & Laicane, I. (2014). Sustainable development modelling for the energy sector. Journal of Cleaner Production, 63, 134–142. Dong, Shixuan, & Liu, Yuming. (2013). Promotion strategy of green building large-scale development based on system dynamics. Jounal of Engineering Management, 06, 16–20. In Chinese. Forrester, J. W. (1958). Industrial dynamics: A major breakthrough for decision makers. Harvard Business Review, 36(4), 37–66. Fong, W. K., Matsumoto, H., & Lun, Y. F. (2009). Application of system dynamics model as decision making tool in urban planning process toward stabilizing
carbon dioxide emissions from cities. Building and Environment, 44(7), 1528–1537. Feng, L. H., Zhang, X. C., & Luo, G. Y. (2008). Application of system dynamics in analyzing the carrying capacity of water resources in Yiwu City China. Mathematics and Computers in Simulation, 79(3), 269–278. Han, S., Love, P., & Pena-Mora, F. (2013). A system dynamics model for assessing the impacts of design errors in construction projects. Mathematical and Computer Modelling, 57(9–10), 2044–2053. Li, D. Z., Hui, E., Leung, B., Li, Q. M., & Xu, X. (2010). A methodology for eco-efficiency evaluation of residential development at city level. Building and Environment, 45(3), 566–573. Lawrence, T. J., & Robinson, G. R. (2014). Reckoning perverse outcomes of resource conservation policies using the ecological footprint. Ecological Indicators, 41, 87–95. Liang, Huaiqing. (2008). Study for system dvaluation and simulation of energy and land saving housing development. Chongqing University. In Chinese. Richmond, B. (1998). An introduction to system thinking. In Stella software. Hanover, NH: High Performance Systems, Inc. Richardson, G. P., & Otto, P. (2008). Application of system dynamics in marketing: Editorial. Journal of Business Research, 61(11), 1099–1101. Solis-Guzman, J., Marrero, M., & Ramirez-De-Arellano, A. (2013). Methodology for determining the ecological footprint of the construction of residential buildings in Andalusia (Spain). Ecological Indicators, 25, 239–249. Shrestha, E. (2010). Evaluating the energy and carbon footprint of water conveyance system and future water supply options for Las Vegas Nevada. Las Vegas: University of Nevada. Shih, Y. H., & Tseng, C. H. (2014). Cost-benefit analysis of sustainable energy development using life-cycle co-benefits assessment and the system dynamics approach. Applied Energy, 119, 57–66. Teng, J. Y., & Wu, X. G. (2014). Eco-footprint-based life-cycle eco-efficiency assessment of building projects. Ecological Indicators, 39, 160–168. Wolstenholme, E. F. (1990). System enquiry—A system dynamics approach. Chichester, UK: John Wiley and Sons Ltd. Wang, Xue. (2010). Evaluation research on the coordinated development of real estate and economy in Harbin. Northeast Forestry University. In Chinese. Wan, S., Kumaraswamy, M., & Liu, D. (2013). Dynamic modelling of building services projects: A simulation model for real-life projects in the Hong Kong construction industry. Mathematical and Computer Modelling, 57(9–10), 2054–2066. Wang, Yang. (2009). Modeling and simulation of urban renewal based on system dynamics methodology. Huazhong University of Science and Technology. In Chinese. Yuan, H. P., Shen, L. Y., Hao, J. L., & Lu, W. S. (2011). A model for cost-benefit analysis of construction and demolition waste management throughout the waste chain. Resources, Conservation and Recycling, 55, 604–612. Yan, Qingqing. (2006). The research on the dynamic simulation of Chifeng’s sustainable development. Harbin University of Science and Technology. In Chinese. Zhang, Z., Lu, W. X., Zhao, Y., & Song, W. B. (2014). Development tendency analysis and evaluation of the water ecological carrying capacity in the Siping area of Jilin Province in China based on system dynamics and analytic hierarchy process. Ecological Modelling, 275, 9–21. Zhang, B., Yu, Z. H., Sun, Q., Li, S., Huang, M. X., & Wang, L. Q. (2012). The review of system dynamics and the related software. Environment and Sustainable Development, 02, 1–4. In Chinese.
Contents lists available at ScienceDirect
Sustainable Cities and Society journal homepage: www.elsevier.com/locate/scs
Decision-making tools for evaluation the impact on the eco-footprint and eco-environmental quality of green building development policy Jiaying Teng a,∗ , Pengying Wang b , Xianguo Wu c , Chao Xu a a b c
School of Economics and Management, Jilin Jianzhu University, Changchun 130118, China Northeast Electric Power Design Institute CO., LTD., Changchun 130021, China School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan 430074, China
a r t i c l e
i n f o
Article history: Received 30 October 2015 Received in revised form 28 February 2016 Accepted 29 February 2016 Available online 3 March 2016 Keywords: Ecological system System simulation Strategy optimization
a b s t r a c t Building projects consume large amount of energy and resources, and emit solid waste and CO2 harmful to the eco-environment. In order to study the eco-environmental impact of green building development policies, a “Green Building Eco-environment (GBE)” model is constructed with the method of System Dynamics and implemented using the Vensim software. The model is used to simulate and evaluate the current state and future trend of variation of the eco-environmental impact of green building development in Wuhan during the years 2008–2050 under current green building development policies. Some policy factors are then adjusted in the simulation to determine the optimal green building development policies, under which the quality of the regional ecological environment in Wuhan would be improved most economically. © 2016 Elsevier Ltd. All rights reserved.
1. Introduction Green building is developed to alleviate the conflict between the rapid development of buildings and the deteriorating ecological environment (eco-environment). Building projects consume large amount of energy and resources, and emit solid waste and CO2 harmful to the eco-environment, so the eco-environmental impact of green building development needs more detailed and quantitative understanding. The degree of destruction of the eco-environment by a building project may be evaluated with “Building Eco-footprint” (Bin & Parker, 2012; Teng & Wu, 2014). an index based on the concept of Ecological Footprint (Eco-footprint), which is a simple, effective, and widely used index proposed by Mathis Wackernagel in the early 1990s (Li et al., 2010; Bin & Parker, 2012; Solis-Guzman, Marrero, & Ramirez-De-Arellano, 2013; Lawrence & Robinson, 2014; Shrestha, 2010). However, the interaction between green building development and the eco-environment system is dynamic and complex, which cannot be completely delineated by a single index. Moreover, other factors, such as the ecological carrying capacity, current green building development policies, and other indexes of the
∗ Corresponding author. Tel.: +86 043184566157. E-mail addresses: [email protected] (J. Teng), [email protected] (P. Wang), [email protected] (X. Wu), [email protected] (C. Xu). http://dx.doi.org/10.1016/j.scs.2016.02.018 2210-6707/© 2016 Elsevier Ltd. All rights reserved.
eco-environment, also play a role in the interaction. Therefore, developing a system dynamics model would be more suitable for analyzing the eco-environmental impact of green building development. System Dynamics (SD) studies the complex relationships between multiple factors in a system from macro-perspective and offers an effective method of modeling and simulation (Forrester, 1958; Richmond, 1998; Wolstenholme, 1990; Richardson & Otto, 2008). Nowadays, the SD method has become a popular technique in the research of system dynamics modeling and simulation. For instance, Shih and Tseng (2014) constructed a system model for estimating the economic benefits of energy-saving measures, and this model is used to study the economic benefits of different renewable energies consumed by buildings; with the SD theory, Yan (2006) built a giant system model of sustainable development in Chifeng, which was used to evaluate and forecast the current state and trend of variation of sustainable development in Chifeng and provide suggestions for policy makers; Dong and Liu (2013) built a system model for studying the strategies of large-scale development of green building. The SD method has been applied successfully by many researchers in various settings such as the sustainable development of energy (Blumberga et al., 2014), urban planning for carbon dioxide emissions (Fong, Matsumoto, & Lun, 2009), the carrying capacity of water resources (Feng, Zhang, & Luo, 2008), and construction performance (Wan, Kumaraswamy, & Liu, 2013; Han, Love, & Pena-Mora, 2013).
J. Teng et al. / Sustainable Cities and Society 23 (2016) 50–58
However, few scholars have treated green building and the eco-environment as a dynamic system, and there is a lack of direct report on system dynamics model for analyzing the ecoenvironmental impact of green building development. In this study, the SD method is used to construct a “Green Building Ecoenvironment (GBE)” model for simulating the current state and trend of variation of green building and ecological systems, and the impact of green building development on the eco-environment is analyzed. Such a model is expected to facilitate our understanding of the interaction between green building development and the eco-environment, and its pattern of variation. After this Green Building Eco-environment System Dynamics (GBE-SD) model is verified, the eco-environmental impact of green building development policies in Wuhan in 2008–2050 is simulated and analyzed, and then some policy factors are optimized with this model. Wuhan is one of the first branch of pilot cities in green building in China, and about 2.7% green buildings of China are located here, which makes Wuhan the best selection of this study. 2. Methodology 2.1. System Dynamics method System Dynamics (SD) provides a method of system analysis and simulation that quantitatively represents complex dynamic behaviors inherent in real-world systems, such as nonlinearity, hierarchy, and time-lag, using feedback models (Forrester, 1958; Richmond, 1998; Wolstenholme, 1990; Richardson & Otto, 2008). This method requires low-level data accuracy, but can describe complex, dynamic, high-order, and highly non-liner relationships among the factors in a huge system (Liang, 2008; Wang, 2010; Yuan et al., 2011; Zhang et al., 2014), so it is good for macro-perspective evaluation and forecast. There are five system dynamics software: Vensim, Professional DYNAMO, Stella, Ithink and Powersim. Compared to others, Vensim not only provides user-friendly interfaces for causal loop diagram design, stock-flow diagram construction, model quantification, result output visual display and policy test (Zhang et al., 2012), but also has wide application range and no fixed location. Therefore, the Vensim software provides an effective platform of implementing SD simulations. Vensim offers 4 types of variable expressions for SD models (seen in Fig. 1): (1) Level variables (L), representing the variables whose values change and accumulate over time; (2) Rate variables (R), representing the amount of the variation of level variables each year; (3) Auxiliary variables (A), assisting the transformation between level variables and rate variables; (4) Constant variables (C), which remain the same value over time. 2.2. Scope of study Green building interacts with the eco-environment in a dynamic and complex manner. This paper studies the ecological system affected by regional green building, in which “building” refers to “civil building”; the “region” is located in Wuhan, the capital of Hubei province in China, with an area of 8494 square kilometers. This study focuses on six types of policy, including construction land saving, energy saving, material saving, water saving, CO2
Fig. 1. Four types of variable expressions for SD models.
51
reduction and solid waste reduction policies. The whole policy presents the requirements of regional green building development. Take building CO2 emission saving policy as an example, it represents the proportion of the required annual reduction of CO2 emission to the amount emitted in the last year. The goal of the whole policy is to improve the eco-footprint and ecoenvironmental quality. 2.3. Process of study This study is composed of constructing a GBE-SD model and policy optimization, and three main steps (seen in Fig. 2) are included in this process: (1) Model construction and parameter quantification. Real-world observations and related data from Wuhan are used to find the influential factors in the GBE system, and the logical relationships among different factors are analyzed to design causal diagram and derive stock-flow model. A group of mathematical equations are obtained to quantitatively describe the relationships among different factors in the GBE system; (2) Model validation. The SD model is verified with two methods: comparison with historical data and model validation by the Vensim software. If the model result deviates from test data in either approach, identify the causes and modify the model until the validation requests are satisfied and the result can reflect actual behaviors; (3) Analyses of simulation results and policy optimization. Based on current green building development policies in Wuhan, the current state and trend of variation of the regional ecoenvironment are simulated and analyzed on the Vensim platform. Different policy plans are then simulated to find the optimal combination for sustainable development of green buildings in Wuhan with lower economical cost.
3. Model construction and validation The construction of the GBE-SD model includes SD model design, parameter quantification, and model verification. 3.1. SD model design 3.1.1. Causal diagram design Theoretically, green building has milder destruction to the eco-environment than traditional building; however, building construction and operation always consume energy, water, and materials, and also emit CO2 and solid waste, inevitably causing eco-environmental deterioration. The degree of such deterioration can be quantitatively evaluated with regional building eco-footprint (Teng & Wu, 2014); this index is also used to determine the major influential factors in a Green Building Ecoenvironment (GBE) system. The interaction between green building development and the eco-environment is complex, and a variety of factors need to be addressed simultaneously in the GBE system, such as those associated with the natural development rate of the eco-environment, energy, water, and materials consumptions, emission of CO2 and solid waste, the ecological carrying capacity, eco-footprint, and policies. These factors are also interconnected with and restricted by each other, and most of them are dynamic variables over time. Such factors in a GBE system are shown in the causal diagram in Fig. 3. Fig. 3 is an abstract and conceptual model of regional GBE system depicting the causal relationships between key factors. The “+” sign means positive causal loop, suggesting that the loop is
52
J. Teng et al. / Sustainable Cities and Society 23 (2016) 50–58
Fig. 2. Study flow.
strengthened over time, and the “−” sign has the opposite meaning. The (3), (27), (28), (29) and (32) factors are qualitative, and other factors are quantitative. Some factors in the diagram are further defined and interpreted as follows:
Factors (2), (4), (8), (9), (10), and (11) are all related to policy. Take factor (8) as an example, it represents the proportion of the required annual reduction of CO2 emission to the amount emitted in the last year;
Fig. 3. Causal diagram.
J. Teng et al. / Sustainable Cities and Society 23 (2016) 50–58
53
Fig. 4. GBE-SD model.
Factor (3) represents the mandatory intensity of land saving, energy saving, material saving, water saving, CO2 reduction and solid waste reduction policies; Factor (6) means the difference between the amount of annual construction land and that in the previous year. It could be negative or positive, representing annual increase or reduction of construction land. This factor and the regional construction land can promote each other, thus forming a positive loop; Factors (7), (12), (13), (16), and (20) are similar to factor (6), but refer to annual variation of water consumption, energy consumption, building material consumption, CO2 emission, and solid waste, respectively. Two factors, annual variation of construction land and the construction land promote each other and form a positive loop; Factor (19) represents the amounts of major materials consumed by regional buildings. Since a variety of materials are consumed in construction and it is difficult to collect real data, only five major materials, steel, cement, sand, gravel and coil, are counted in this study to simplify calculation. Factor (23) is used to quantify the degree of deterioration of the regional eco-environment; Factor (25) represents the operational costs of pollution treatment facilities for buildings (including residents);
Factor (26) means the highest threshold of destruction tolerable by the regional eco-environment; Factor (30) and (31) mean the speeds of ecological improvement and deterioration, respectively (Wang, 2009); Factor (33) represents the ratio of the quality of the regional ecoenvironment to its carrying capacity; 3.1.2. Stock-flow model The Stock-flow model of the GBE is based on the causal diagram in Fig. 3, and it is the final form of the GBE-SD model constructed in this paper (seen in Fig. 4). The Stock-flow model is expressed in equations (formulae) and computer codes (Yuan et al., 2011). The GBE-SD model built with the Vensim platform is used to quantitatively describe the relationships between major factors in the system. 3.2. Model quantification According to the variable type (seen in Fig. 1) of the factors in GBE-SD model (seen in Fig. 4), INTEG, LOOKUP and DELAY1 fuctions in Vensim are used to establish the mathematical equations relating different factors in the GBE-SD model (seen in Appendix A). The real data collected from official statistics website are used in the GBE-SD model quantification, such as National Bureau of
54
J. Teng et al. / Sustainable Cities and Society 23 (2016) 50–58
Fig. 5. Setting example of LOOKUP.
Statistics of the People’s Republic of China, Ministry of Environmental Protection of the People’s Republic of China, Hubei Statistical Yearbook, National Data, Statistical information of Wuhan, Wuhan Environmental Protection Bureau, and so on. Based on the real data, three methods of quantification are used to determine the initial values of the mathematical equations in Appendix A: (1) search official statistics and planning websites to obtain the initial values of the INTEG fuctions, carbon conversion factors of energy, and constant variables, such as the initial values of the mathematical Eqs. (1) and (23) in Appendix A; (2) use the Monte Carlo method to derive the coefficients of linear equations, and the eco-footprint conversion factors are quantified, such as the initial values of the mathematical Eqs. (32) and (7) in Appendix A; (3) combine expert assessment, on-demand computation and practical survy methods to quantify the LOOKUP fuctions, qualitative factors and the delay time in the DELAY1 fuctions, such as the initial values of the mathematical Eqs. (40) and (3) in Appendix A. Take the annual rate of variation of energy consumption LOOKUP as an example. This function is shown in Fig. 5, in which the independent variable is the year, and the dependent variable is the annual rate of variation of energy consumption. Additionally, qualitative factors are assessed on five levels: High[1,0.8), Relatively high[0.8,0.6), General[0.6,0.4), Relatively low[0.4,0.2) and Low[0.2,0]. The mathematical equations containing initial values determined with the above three approaches are shown in Appendix A. Before running the GBE-SD model on the Vensim platform, the simulation time is set to be 2008–2050. The simulation results in 2008–2014 are used to analyze the current state of the ecoenvironment in Wuhan, and those in 2015–2050 are used to study its future trend of variation. 3.3. Model validation Two model validation methods, comparison with historical data and the model validation function in Vensim, are used to verify the constructed SD model. 3.3.1. Comparison with historical data A quantitative factor, Regional Building Energy Consumption, in the GBE-SD model is taken as the object of model validation, and the errors between historical data and a preliminary simulation result
Fig. 6. The errors between historical data and a preliminary simulation result.
over time are shown in Fig. 6. It is generally considered (Liang, 2008; Zhang et al., 2014) that relative errors within ±10% are acceptable because building development is a complex activity and accurate simulation is almost impossible; moreover, the SD method is good at deriving consistent trend of variation with lower requirement of data accuracy. Fig. 6 shows that the average error of the GBE-SD model is within 3%, suggesting that the model is reasonable. 3.3.2. Model validation by Vensim Vensim has a model validation function which can be used to check the formulas, units and structures of an SD model. For the constructed model, this function shows the following results: (1) no warnings suggest that the formulas in the GBE-SD model are valid; (2) “Units Check” shows no errors; and (3) “Check Model” says okay. Therefore, both methods have verified that the constructed GBE-SD model (Fig. 4) can reflect the real system behaviors and conditions in Wuhan, and it can be used to analyze the current state and trend of variation of the eco-environment. 4. Simulation results and discussion The verified GBE-SD model (Fig. 4) is used to simulate the current state and future trend of variation of the eco-environment under current policies of green building development in Wuhan, and then numerical experiments are conducted to optimize the policies for the most economically efficient improvement of the eco-environment. 4.1. Analysis of the current state and future trend of variation of the GBE system The simulation results of main level variables and rate variables are shown in Fig. 7, with the results in 2008–2014 indicating the current state and those in 2015–2050 indicating future trend of variation. The curve 1 and curve 2 in Fig. 7a and b show that CO2 emmision and energy consumption by regional buildings tend to increase year by year. In the initial period of green building development, the cost of green technology is high and the technolgy is not mature enough to achive the theoretical effects, so the effect of green technology may be restricted by the economy, design and construction experiences, time delay, and so on. From 2008 to 2012, the annual variations of CO2 emission and energy consumption obviously fluctuate, and this fluctuation would continue increasing under current policies. By 2044, the annual variations of both
J. Teng et al. / Sustainable Cities and Society 23 (2016) 50–58
55
Fig. 7. Simulation results of main level and rate variables.
variables would start to decrease, suggesting that the CO2 emmision and energy consumptions of reginoal buildings would not have significant reduction in a short period. The water, material and land annually consumed by reginoal buildings are shown by the curve 3, 4 and 5 in Fig. 7a; all curves show the general trend of gradual decline with time. The curve 3, 4 and 5 in Fig. 7b show the annual variations of these variables, suggesting that the variation of water, material and land consumption would become steady by 2044, 2016 and 2020, respectively. The curve 6 in Fig. 7a and b show that the solid waste produced by regional buildings and its annual variation become steady by 2020 and 2030, respectively. These results suggest that current policies would have more significant effect on saving material and land and reducing solid waste. The curve 1 in Fig. 7c and d show that the eco-footpring of regional buildings have three stages of development: (1) fluctuation (2008–2013), (2) rapid decline (2014–2032) and (3) gradually steady stage (2033–2050). The continual decrease of eco-footprint suggests that the destruction of the eco-environment by regional buildings would be greatly reduced year by year, except for the fluctuations from 2008 to 2013, which is caused by the fluctuations of rate variables including the annual variation of CO2 and solid wast emission, and energy, material, water and land consumption. The curve 2 in Fig. 7c suggests that the quality of the ecoenvironment would continue to be improved after 2014. This variable is influenced by two rate variables, ecological deterioration and ecological improvement, which are shown by the curve 2 and 3 in Fig. 7d, respectively. The degree of ecological deterioration is influenced by the annual changes of eco-footprint and natural depletion rate, so it shows some fluctuations from 2008
to 2012 and increases after 2017 until becoming steady by 2032. The degree of ecological improvement slowly grows from 2008 to 2017, and starts to increase rapidly by 2026 untill becoming steady by 2049. The degree of ecological improvement is generally higher than that of ecological deterioration, so the eco-environment would be improved with the development of green buildings in Wuhan. The simulation results of main auxiliary variables are shown in Fig. 8. Curve 1 shows the level of development of the ecological system, which fluctuates before 2013, but rapidly increases at the rate of about 4% from 2014 to 2022, and then steadily increases at the
Fig. 8. Simulation results of main auxiliary variables.
56
J. Teng et al. / Sustainable Cities and Society 23 (2016) 50–58
Table 1 The scene settings of development policy optimizing experiment. Optimized object
Policy factor of CO2 emission reduction Policy factor of building energy saving Policy factor of construction land saving
Initial scheme
0.04 0.04 0.07
Scheme 1
2
0.08 0.08 0.07
0.12 0.12 0.07
rate of about 1% after 2023 until the level of development reaches a relatively high level of 0.8 Dmnl by 2044. This result suggests that the ecological system would be significantly improved under current policies. Curve 2 in Fig. 8 shows the eco-footprint per unit area, which plunges form 2008 to 2017, becomes steady after 2017, suggesting that green building development in Wuhan would significantly reduce the eco-footprint per unit area, and dramtically reduce the destruction of the eco-environment by building within a short time. Curve 3 in Fig. 8 also shows that the regional ecoenvironment index gradually increases with year, suggesting that the eco-environment in Wuhan would be improved year by year. 4.2. Policy factors—Simulation result and analysis The simulation results introduced above suggest that under current development policies, it would take about 30 years to improve the quality of the eco-environment in Wuhan to the level of 0.6
Optimized object
Initial scheme
Policy factor of building water saving Policy factor of building material saving Policy factor of solid waste emission reduction
0.04 0.02 0.18
Scheme 1
2
0.08 0.04 0.36
0.12 0.06 0.36
Dmnl. In order to find the policy factors (critical policy factors) capable of more efficiently improving the quality of the regional eco-environment, six poilicy factors are selected and adjusted in two test schemes, and the Scheme 1 shown in Table 1 is determined to be the optimal combination. The results are presented in Fig. 9a, which display how the six factors simultaneously influence the quality of the eco-environment. Table 1 shows the six policy factors which can indirectly influence the quality of the regional eco-environment. Alternation of a single factor can barely improve the quality of the eco-environment (Fig. 9c); however, simultaneously varying all the policy factors can achieve higher quality of the eco-environment. Fig. 9a, b, and d show that, compared with the original scheme, Scheme 1 and Scheme 2 can reduce the eco-footprint of reginoal buildings by 56% and 82%, respectively. Although Scheme 2 can bring greater reduction of eco-footprint, its effect on the quality of the eco-environment is similar to Scheme 1. Under both schemes, the quality of the eco-environment would reach a relatively high level of 0.7 Dmnl by 2048.
Fig. 9. Simulation results of simultaneously varying six policy indictors.
J. Teng et al. / Sustainable Cities and Society 23 (2016) 50–58
The result suggests that when the policy factors have reached a certain level (Scheme 1), they would not further improve the quality of the eco-environment. Scheme 1 have the policy factors with right levels, which can improve equal amount of the quality of the eco-environment with lower economic cost. 5. Conclusions This paper establishes a Green Building Eco-environment System Dynamic model (GBE-SD model) using the Vensim software. The current state and future trend of variation of the ecoenvironmental impact of green building development in Wuhan is simulated and analyzed. The GBE-SD model provides a new means of understanding green building ecological system and quantitatively analyzing the interaction between green building development and the ecoenvironment. Simulation results of the Wuhan-GBE show that under current green building development policies, the regional building CO2 emmision and energy consumption would not be significantly reduced until 2044. However, the regional building eco-footprint would decrease year by year, and the quality of the regional eco-environment would be continually improved and reach a general level of 0.6 Dmnl by 2050. Policy factor simulation indicates that when the selected policy factors have reached a certain level (Scheme 1), they would not further improve the quality of the eco-environment. For example, the maximal improvement of the quality of the sco-environment in Wuhan could be achieved if the annual reduction of CO2 emissions, energy and water resource consumption, construction land consumption, construction materials consumption and solid waste had reached 8%, 8%, 8%, 7%, and 36%, respectively, and the quality of the eco-environment in Wuhan would reach a higher level of 0.7 Dmnl by 2048. Appendix A. (1) Reginoal building CO2 emission = INTEG(Annual variations of CO2 emission,3.69363e + 006), Units: Ton (2) Reginoal building solid waste emission = INTEG(Annual variations of solid waste emission,1380), Units: Million tons (3) Annual variations of CO2 emission = Annual variations of energy consumption × 2.77 − Regional building CO2 emission × DELAY1(Policy factor of CO2 emission reduction,1), Units: Ton/year (4) Annual variations of solide waste emission = Annual variations of solide waste emission LOOKUP(Time) − Regional building solid waste emission × Policy factor of solid waste emission reduction, Units: Million tons (5) Annual variations of solide waste emission LOOKUP = ([(2008,1380)(2050,1381)],(2008,1380),(2011,1379.65),(2012, 1382.37), (2013,1381.55), (2030,1381) (2050,1381)), Units: Million tons (6) Annual variations of material consumption = Regional building material consumption × Annual rate of variation of material consumption(Time) − Regional building material consumption × DELAY1(Policy factor of building material saving,1), Units: Ton/year (7) Annual variations of eco-footprint = 0.00369 × Annual variations of solide waste emission + 5.34e − 007 × Annual variations of CO2 emission + 22.7 × Annual variations of material consumption + 8.78e − 007 × Annual variations of water consumption + 1.03e − 005 × Annual variations of energy consumption + 0.00144 × Annual variations of construction land, Units: ha/year
57
(8) Annual variations of water consumption = Regional building water consumption × Annual rate of variation of water consumption(Time) − Regional building water consumption × DELAY1(Policy factor of building water saving,1), Units: million tons/year (9) Annual variations of energy consumption = Regional building energy consumption × Annual rate of variation of energy consumption(Time) − Regional building energy consumption × DELAY1(Policy factor of building energy saving,1), Units: Tons of standard coal/year (10) Regional building material consumption = INTEG(Annual variations of material consumption, 380,661), Units: Ton (11) Regional building water consumption = INTEG(Annual variations of water consumption, 117,570), Units: Million tons (12) Eco-footpring of regional buildings = INTEG(Annual variations of eco-footprint,8.64137e + 006), Units: ha (13) The impact of Eco-footprint of regional buildings on Ecological deterioration LOOKUP = ([(−1.3e + 006,−0.06) − (1.7e + 006,0.28)],(−1.3e + 006,−0.06),(−200,000,−0.08),(−67,000, −0.04),(−15,630,−0.01),(−6000,−0.01),(20,0.03),(34,000,0.18), (1.7e + 006,0.28)), Units: Dmnl (14) Regional building energy consumption = INTEG(Annual variations of energy consumption,1.33347e + 007), Units: Tons of standard coal (15) Regional construction land = INTEG (Annual variations of construction land,152,653), Units: ha (16) Regional ecological carrying capacity = 1, Units: Dmnl (17) Regional eco-environment index = Quality of the ecoenvironment/Regional ecological carrying capacity, Units: Dmnl (18) The impact of Regional eco-environment index on Level of development of the ecological system LOOKUP = ([(0,0)−(1,0.88)],(0,0),(0.39,0.4),(0.42,0.6),(0.56,0.8),(0.7,0.85), (1,0.88)), Units: Dmnl (19) Quality of the eco-environment = INTEG (Ecological improvement-Ecological deterioration,0.4), Units: Dmnl (20) Mandatory level of regional green building development policy = Policy factor of CO2 emission reduction/1 + Policy factor of building energy saving/1 + Policy factor of construction land saving/1 + Policy factor of building water saving/1 + Policy factor of building material saving/1 + Policy factor of solid waste emission reduction/1, Units: Dmnl (21) Eco-footprint per unit area = Eco-footprint of regional buildings/(Total area of green buildings in this region × 10,000), Units: ha/m2 (22) Annual variations of construction land = Regional construction land × Annual rate of variation of construction land (Time) − Regional construction land × DELAY1(Policy factor of construction land saving,1), Units: ha/m2 (23) Policy factor of construction land saving = 0.07, Units: Dmnl (24) Policy factor of building material saving = 0.02, Units: Dmnl (25) Policy factor of building water saving = 0.04, Units: Dmnl (26) Policy factor of building energy saving = 0.04, Units: Dmnl (27) Annual rate of variation of construction lands LOOKUP = ([(2008,0.021) − (2050,0.075)],(2008,0.021),(2010, 0.024),(2020,0.065),(2050,0.075)), Units: Dmnl (28) Policy factor of CO2 emission reduction = 0.04, Units: Dmnl (29) Policy factor of solid waste emission reduction = 0.18, Units: Dmnl (30) Annual rate of variation of material consumption LOOKUP = ([(2008,0.024) − (2050,0.019)],(2008,0.024),(2009, 0.02),(2010,−0.04),(2011,0.227),(2012,−0.11),(2015,0.01), (2030,0.015),(2050,0.019)), Units: Dmnl (31) Annual rate of variation of water consumption LOOKUP = ([(2008,0.003) − (2050,−0.002)],(2008,0.003),(2009,
58
(32) (33)
(34)
(35)
(36)
(37)
(38) (39) (40)
J. Teng et al. / Sustainable Cities and Society 23 (2016) 50–58
0.0027),(2010,0.0014),(2011,−0.011),(2012,−0.0067),(2017, −0.002),(2030,−0.002),(2050,−0.002)), Units: Dmnl Environmental protection funds = 0.0159 × Regional GDP, Units: billion yuan The impact of Environmental protection funds on Ecological improvement LOOKUP = ([(62,0.02) − (1100,0.5)],(62,0.02), (500,0.08),(800,0.28),(999,0.48),(1000,0.5),(1100,0.5)), Units: Dmnl Level of development of the ecological system = The impact of Regional eco-environment index on Level of development of the ecological system LOOKUP(Regional eco-environment index), Units: Dmnl Ecological deterioration = Quality of the eco-environment × Natural depletion rate × The influence of Eco-footpring of reginoal buildings to Ecological deterioration LOOKUP(Annual variations of eco-footprint), Units: Dmnl Ecological improvement = Quality of the eco-environment × Natural recovery rate × The impact of Environmenprotection funds on Ecological improvement tal LOOKUP(Environmental protection funds), Units: Dmnl Natural increase rate LOOKUP = ([(2008,0.003)(2050,0.035)], (2008,0.00271),(2009,0.00348),(2010,0.00434),(2011, 0.00438),(2012,0.00618),(2013,0.0063),(2030,0.03), (2050,0.035)), Units: Dmnl Natural recovery rate = 0.05, Units: 1/Year Natural depletion rate = 0.08, Units: 1/Year Annual rate of variation of energy consumption LOOKUP = ([(2008,0.1) − (2050,0.08)],(2008,0.1),(2009,0.2), (2010,0.0025),(2011,0.2),(2012,0.19),(2015,0.16),(2020,0.14), (2025,0.13), (2030,0.12),(2050,0.08)), Units: Dmnl
References Bin, G., & Parker, P. (2012). Measuring buildings for sustainability: Comparing the initial and retrofit ecological footprint of a century home—The REEP House. Applied Energy, 93(SI), 24–32. Blumberga, A., Blumberga, D., Bazbauers, G., Zogla, G., & Laicane, I. (2014). Sustainable development modelling for the energy sector. Journal of Cleaner Production, 63, 134–142. Dong, Shixuan, & Liu, Yuming. (2013). Promotion strategy of green building large-scale development based on system dynamics. Jounal of Engineering Management, 06, 16–20. In Chinese. Forrester, J. W. (1958). Industrial dynamics: A major breakthrough for decision makers. Harvard Business Review, 36(4), 37–66. Fong, W. K., Matsumoto, H., & Lun, Y. F. (2009). Application of system dynamics model as decision making tool in urban planning process toward stabilizing
carbon dioxide emissions from cities. Building and Environment, 44(7), 1528–1537. Feng, L. H., Zhang, X. C., & Luo, G. Y. (2008). Application of system dynamics in analyzing the carrying capacity of water resources in Yiwu City China. Mathematics and Computers in Simulation, 79(3), 269–278. Han, S., Love, P., & Pena-Mora, F. (2013). A system dynamics model for assessing the impacts of design errors in construction projects. Mathematical and Computer Modelling, 57(9–10), 2044–2053. Li, D. Z., Hui, E., Leung, B., Li, Q. M., & Xu, X. (2010). A methodology for eco-efficiency evaluation of residential development at city level. Building and Environment, 45(3), 566–573. Lawrence, T. J., & Robinson, G. R. (2014). Reckoning perverse outcomes of resource conservation policies using the ecological footprint. Ecological Indicators, 41, 87–95. Liang, Huaiqing. (2008). Study for system dvaluation and simulation of energy and land saving housing development. Chongqing University. In Chinese. Richmond, B. (1998). An introduction to system thinking. In Stella software. Hanover, NH: High Performance Systems, Inc. Richardson, G. P., & Otto, P. (2008). Application of system dynamics in marketing: Editorial. Journal of Business Research, 61(11), 1099–1101. Solis-Guzman, J., Marrero, M., & Ramirez-De-Arellano, A. (2013). Methodology for determining the ecological footprint of the construction of residential buildings in Andalusia (Spain). Ecological Indicators, 25, 239–249. Shrestha, E. (2010). Evaluating the energy and carbon footprint of water conveyance system and future water supply options for Las Vegas Nevada. Las Vegas: University of Nevada. Shih, Y. H., & Tseng, C. H. (2014). Cost-benefit analysis of sustainable energy development using life-cycle co-benefits assessment and the system dynamics approach. Applied Energy, 119, 57–66. Teng, J. Y., & Wu, X. G. (2014). Eco-footprint-based life-cycle eco-efficiency assessment of building projects. Ecological Indicators, 39, 160–168. Wolstenholme, E. F. (1990). System enquiry—A system dynamics approach. Chichester, UK: John Wiley and Sons Ltd. Wang, Xue. (2010). Evaluation research on the coordinated development of real estate and economy in Harbin. Northeast Forestry University. In Chinese. Wan, S., Kumaraswamy, M., & Liu, D. (2013). Dynamic modelling of building services projects: A simulation model for real-life projects in the Hong Kong construction industry. Mathematical and Computer Modelling, 57(9–10), 2054–2066. Wang, Yang. (2009). Modeling and simulation of urban renewal based on system dynamics methodology. Huazhong University of Science and Technology. In Chinese. Yuan, H. P., Shen, L. Y., Hao, J. L., & Lu, W. S. (2011). A model for cost-benefit analysis of construction and demolition waste management throughout the waste chain. Resources, Conservation and Recycling, 55, 604–612. Yan, Qingqing. (2006). The research on the dynamic simulation of Chifeng’s sustainable development. Harbin University of Science and Technology. In Chinese. Zhang, Z., Lu, W. X., Zhao, Y., & Song, W. B. (2014). Development tendency analysis and evaluation of the water ecological carrying capacity in the Siping area of Jilin Province in China based on system dynamics and analytic hierarchy process. Ecological Modelling, 275, 9–21. Zhang, B., Yu, Z. H., Sun, Q., Li, S., Huang, M. X., & Wang, L. Q. (2012). The review of system dynamics and the related software. Environment and Sustainable Development, 02, 1–4. In Chinese.