A system dynamics-based environmental benefit assessment model of construction waste reduction management at the design and construction stages

Journal of Cleaner Production 176 (2018) 676e692

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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

A system dynamics-based environmental benefit assessment model of construction waste reduction management at the design and construction stages Zhikun Ding a, b, Menglian Zhu a, Vivian W.Y. Tam a, c, *, Guizhen Yi d, Cuong N.N. Tran c a

College of Civil Engineering, Shenzhen University, PR China Smart City Research Institute, Shenzhen University, PR China Western Sydney University, School of Computing, Engineering and Mathematics, Locked Bag 1797, Penrith, NSW 2751, Australia d Land Management Center of Baoan District, Shenzhen, PR China b c

a r t i c l e i n f o

a b s t r a c t

Article history:

Construction waste generation and its environmental impact reduction have become an urgent issue to be solved with the acceleration of urbanization process in China. However, limited research has been conducted to cover both the design stage and the construction stage such that the overall construction waste reduction outcome could be comprehensively assessed. Based on interview data and literature review, Vensim software was used to build a two-stage environmental benefit assessment system dynamics (SD) model which covered construction waste reduction management subsystem, waste generation and disposal subsystem, and environmental benefit assessment subsystem. Simulation results highlight that the reduction management can reduce 40.63% of waste generation. In the meantime, the reduction management achieves good environmental benefits including the reduction of greenhouse-gas emissions of 12,623.30 kg, saving waste landfill of 3901.05 m3 and reducing the use of public vacant site for the illegal dumping of 688.42 m3. The simulation results demonstrate that the dynamic model could assess the environmental benefits of construction waste reduction effectively at the design and the construction stage. This research can provide insight to the design and construction professionals for waste reduction measures such as prefabricated components application, reduced design modification at the design stage, on-site sorting and material reuse at the construction stage, and to provide references for governments in assessing the reduction management outcomes of construction projects and the environmental benefits. © 2017 Elsevier Ltd. All rights reserved.

Keywords: Design and construction stage Construction waste minimization Environmental benefits System dynamics

1. Introduction With the rapid development of urbanization and acceleration of urban renewal, enormous construction, renovation and demolition activities are conducted in China resulting a high-speed growth of construction and demolition waste generation (Ding et al., 2015). Construction waste have been identified as the one of the major problems in the construction industry (Park and Tucker, 2016; Udawatta et al., 2015) and most construction waste is delivered to suburban or rural areas for landfills in China, which has become an urgent problem due to its adverse effect on the environment (Ding

* Corresponding author. Western Sydney University, School of Computing, Engineering and Mathematics, Locked Bag 1797, Penrith, NSW 2751, Australia. E-mail address: [email protected] (V.W.Y. Tam). https://doi.org/10.1016/j.jclepro.2017.12.101 0959-6526/© 2017 Elsevier Ltd. All rights reserved.

et al., 2017). Construction waste (CW) not only consumes land resources but also causes ecological environmental damages such as the destruction of the city natural landscape, soil and water pollution (Coelho and Brito, 2012). Similar problems occur worldwide (Wang et al., 2014, 2015). How to reduce the CW generation and prevent the “garbage siege” phenomenon has become an important issue for governments around the world. From the perspective of sustainable development, effective waste management must focus on generating sources and the implementation of waste reduction management (Tan, 2011). In recent years, increasing number of researchers paid additional attention to the influence of design on the CW reduction. Construction waste management has been implemented at different levels, but the design phase is limited, especially the implementation of waste minimization by design (Dickerson, 2016;

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Li et al., 2015). A large number of studies have indicated that a large part of the CW generation is due to improper design (Jaillon and Poon, 2014; Osmani et al., 2008b; Wang et al., 2014, 2015) and ineffective construction process (Faniran and Caban, 1998; Gangolells et al., 2011; Jaillon et al., 2009; Wang et al., 2010). According to Innes (2004), 33% of CW is caused by improper design. Baldwin et al., (2009) stated that CW management should focus on the source reduction. In order to reduce CW, designers should consider waste reduction during their design process (Baldwin et al., 2009). Currently, waste management research at the design stage focused on the waste generation cause analysis, new wastereduction technologies, designers’ attitudes, and the constraints of waste minimization design (Li, 2013). Previous studies mostly concentrated on the construction process to avoid waste generation and increase on-site waste reuse. The implementation of effective waste management at the construction stage to reduce CW generation plays an important role in practice. The contractor's C&D waste management performance significantly contributes to the construction and demolition waste minimization (Wu et al., 2017). Ding et al., (2016a) developed the SD model of CW reduction management at the construction stage and the simulation results showed that the source reduction is an effective waste reduction measure which reduced 27.05% of the total waste generation. In order to reduce the construction and demolition waste, Ajayi drew on series of semi-structured focus group discussions with experts from the design and construction companies, and qualitative methods were used to explore the design of waste efficient building projects (Ajayi et al., 2017). In the meantime, Poon et al. (2001) showed that waste recycling and waste reduction measures at the construction stage can significantly improve the recovery, waste utilization and waste reduction. In construction projects, proper waste management is a complex process and requires systematic thinking and analysis (Ding et al., 2016; Zuo and Zhao, 2014). Many researchers have conducted CW management studies based on SD methods and demonstrated the importance of SD in CW management research ~ amora et al., 2008; Wang and Yuan, 2009). (Lei and Xing, 2004; Pen However, previous studies focused on either design stage or construction stage only. Different factors dynamically interacting with each other at the design and construction stages influence waste reduction outcomes. A single stage oriented analysis for either design or construction stage can break the interactive factors across the two stages and cannot fully reveal the waste reduction effect. Therefore, the aim of the paper is to build an integrated two-stage dynamic assessment model which synthesizes the design and construction factors affecting waste reduction and assesses waste reduction environmental benefits. Construction waste reduction system with a specific focus on waste reduction is a source reduction system in the general context of construction waste management which includes waste generation subsystem, waste disposal subsystem etc. (Ding et al., 2017). The CW reduction management at the design and construction stages is not independent. From a system perspective, the change at the design stage will affect the waste reduction behavior at the construction stage (Ding et al., 2016a; Osmani et al., 2008a). The whole system is not a simple accumulation of system elements. If the system is viewed in isolation, it is unable to appreciate the dynamic relationship among the system elements. The application of system dynamics provides a way to systematically analyze the structure of CW reduction management system and the dynamic relationship among the system elements. In this paper, the key factors of CW reduction management at the design and construction stages were identified by literature review and interview. Based on SD, an environmental benefit

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assessment model of CW reduction management at the two stages is developed to reveal how the dynamic relations among the factors and their variations influence the waste reduction management and the environmental impacts. 2. System dynamic and applications in construction waste management studies SD is a subject of information feedback system. It is an integration of system theory, control theory and information theory, and provides a way of understanding and solving system problems (Forrester, 1970). System dynamics modeling is a method of analyzing complex systems by using system analysis tools such as system environment, system function and structure. A SD model is to simulate a real world system and the purpose of simulation is to extract the real system structure and the key variables instead of replicating the actual system. Modeling should be problem oriented with clear purposes. The basic process of SD modeling includes three steps: (1) model building, (2) model validation, and (3) scenario analysis (Glasshusain et al., 2000). CW management system is a complex system involving many stakeholders (such as engineers and designers) and many components (such as waste, sorting, recycling, landfill, illegal dumping etc.). Engineers are the on-site construction professionals including project managers, workers etc. while designers are referred to the design professionals such as architects. SD provides a powerful tool for investigating the dynamic association among the system stakeholders and components (Yuan et al., 2011). Tam et al. (2014) applied SD to explore the complexity of CW management system in Shenzhen and analyzed the dynamic correlation between the generation and waste disposal (including waste generation, recycle, landfill and illegal dumping, etc.). Hao et al. (2008) proposed the CW management SD model which depicted the process of CW generation and provided decision-making tools for better implementation of waste management. Li (2013) developed a SD evaluation model of CW minimization at the design stage. Simulation results showed that the waste reduction at the design stage can effectively reduce waste generation and bring environmental benefits. 3. An environmental benefit assessment model of construction waste reduction management Based on a literature review (Ding et al., 2016a; Li, 2013), the model is divided into three subsystems: (1) two-stage waste reduction management subsystem, (2) waste generation and disposal subsystem, and (3) two-stage waste reduction management environment benefit assessment subsystem. Before model simulation, the relationship among the variables and the parameter values in the SD model needs to be defined. There are three methods to determine the parameter values i.e. the literature review, system dynamics specific function (table functions) and interview data. 3.1. Two-stage construction waste reduction management subsystem Design and construction stages are the most important periods of construction projects. Design provides the architecture for construction and construction turns design into an entity. Therefore, design and construction are connected with each other. Effective waste management at either stage can directly affect the outcomes for the next stages. The variables of design and construction stages can cause variations of waste generation and disposal which, in turn, are directly reflected in the environmental benefit

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assessment. On the contrary, the results of environmental benefits assessment of waste reduction will also affect the other two subsystems through a feedback loop. Due to the limited research about the waste management relations between design and construction stages, the dynamic relations between the two stages are developed according to literature review and semi-structured interview. Interview can help identify the important factors of CW reduction at the design and construction stages. Convenience sampling based on researchers’ industry social networks was used to select the respondents. Interviews were conducted with 5 engineering design professionals at the design stage and 7 construction professionals including 3 project managers, 2 construction workers, 2 material managers at the construction stage. All interviewees have more than 5 years of working experience in the industry. The interview questions such as what were the waste reduction measures at the design and construction stage respectively, how the design stage affected waste reduction at the construction stage etc. were investigated. The interview data were analyzed independently by two authors with content analysis method. The inductive content coding process was carried out to identify key variables across the design and construction stage. The interview data coding should continue until the two independent coding results were consistent with each other. Based on the interview data analysis, one of the main impacts of the design stage to the construction stage is improper design resulting in construction rework and material waste generation. Secondly, the prefabricated components at the design stage will greatly reduce the on-site wet-trade waste generation. Therefore, the key variables at the design stage affecting waste generation at the construction stage are “wet-trade activities” and “construction

rework”. Fig. 1 shows the causal-loop diagram of the two-stage waste reduction management subsystem. Seven causal loops are presented and analyzed which could reveal the feedback mechanisms inherent in the CW reduction management system. All seven feedback loops are found to be positive. Considering the feedback loop R1 (Environmental performance/ Waste reduction management regulation/ Waste reduction management investment/ Steel scaffolding, Metal formworks, Prefabricated component/ On-site waste reduction management efforts to reduce waste/Waste reduction management efforts to reduce waste/ Total amount of waste generated/ Environmental performance) as an example, one change in any variables within the causal loop can directly affect each other in a positive way. The CW reduction regulations have been well developed for the design and construction stages in Mainland China. Designers and contractors have to increase resources for waste reduction. These resources for reducing on-site CW generation include purchasing low-waste-generation products such as prefabricated components, reusable metal formworks, steel scaffolding. Waste reduction not only can bring significant environmental benefits but also further encourage regulation refinement for the design and construction stages. The same logic is applied to the feedback loops, R2, R3, R4, R5, R6 and R7. Based on the causal-loop diagram of the two-stage waste reduction management subsystem, all the key variables that affect CW reduction management are identified. Vensim software was used to convert the causal-loop diagram to a stock-flow diagram. For example, the mathematical equation corresponding to causal path “Environmental performance/ Waste reduction

Environmental awareness

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+ + + On-site sorting

+ + - + waste reduction + On-site management efforts to + reduce waste

Environmental - performance

+

+ ++ Waste recycle and reuse on-site Waste reduction management regulation

Fig. 1. The causal-loop diagram of the two-stage waste reduction management subsystem.

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management regulation” is “Waste reduction management regulation ¼ (1 þ Effect of environmental performance)* Initial value of construction waste regulation” in the stock-flow diagram, in which the relation between environmental performance (EP) and effect of environmental performance (EEP) was determined according to SD table functions. The stock-flow diagram is shown in Fig. 2. All descriptions of the variables are listed in Appendix A.

each other in a positive way. When a variety of CW reduction measures are taken at the design and construction stages, the effect of CW reduction management can gradually be accumulated and it will reflect in the amount of CW generated. Therefore, the total amount of waste reduction will be increased. In contrast, the amount of recycle and reuse waste will be reduced. These two types of waste reduction can reduce the landfills. The reduction of waste generation reduces the cost of waste disposal and transportation as well as the rate of illegal waste dumping. Hence, the amount of illegal waste dumping will be reduced. The reduced amount of waste is equivalent to reducing the energy and resource consumption of building materials, the amount of greenhouse-gas emissions during production and transportation, and landfilling space requirements. It is conducive to improve the CW reduction management of environmental benefits at design and construction stages, and further promoting the waste reduction management measures. Based on the causal-loop diagram of the waste generation and disposal subsystem, all the key variables that affect CW reduction management are identified. Vensim software was used to convert the causal-loop diagram to a stock-flow diagram. The stock-flow diagram is shown in Fig. 4. All descriptions of the variables definitions are listed in Appendix B.

3.2. Waste generation and disposal subsystem CW disposal can be identified as on-site recovery, recycling, sorting, landfill and illegal dumping. According to the interview data, a large amount of on-site CW comes from cutting and processing of material. Most of the waste material can directly be reused or reused after processing such as steel. Some toxic waste generated during construction such as chemical material could cause great damage to human health and environment. Hence, they must be collected and processed by qualified chemical treatment companies. However, chemical waste accounts for a very small proportion which will not be covered in this research. Based on the analysis of main influencing factors in the waste generation and disposal subsystem, the causal-loops are shown in Fig. 3. Five causal loops are presented and analyzed which could reveal the feedback mechanisms inherent in the CW generation and disposal subsystem. The five feedback loops are all positive. Providing the positive feedback loop, R1 (Environmental performance/ Waste decrease rate/Waste reduction management efforts to reduce waste/ Total amount of waste reduction/ Recycle and reuse waste/ Environmental performance)as an example, any variable changes within the causal loop can affect

3.3. Environmental benefit assessment subsystem The environmental benefit is defined as the benefit of reducing the environmental burden caused by the CW reduction, the production of construction material, the transportation of CW and the reduction of landfills. There are many types of environmental

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impacts caused by the construction process such as energy and resource consumption, wastewater discharge, noise and dust pollution. According to Gao (2012) and interview data, five environmental benefit assessment indicators of waste reduction

management at the design and construction stages were selected which include (1) resource consumption, (2) energy consumption, (3) greenhouse-gas emissions, (4) land space occupation of waste landfill, and (5) public vacant space occupation.

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Based on the above analysis, the causal-loop diagram of the twostage CW reduction environmental benefit assessment subsystem is shown in Fig. 5. Six causal loops are presented and analyzed which could reveal the feedback mechanisms inherent in the subsystem. The six feedback loops are all positive. Considering the positive feedback loop, R1 (Environmental performance/ Waste reduction management efforts to reduce waste/ Total amount of waste reduction/ Landfilling waste decrease; Public landfilling waste decrease/ Land space saving from landfilling; Land space saving from public landfilling/ Land space saving from landfilling reduction/ Environmental performance) as an example, any variable changes can directly affect each other. After taking a variety of CW reduction measures at the design and construction stages, CW reduction management will gradually accumulate and improve the amount of CW reduction. This not only can reduce the quantity of waste transported to landfills and public landfill site but also can reduce the amount of illegal dumping and land space occupation. It is conducive to improve the environmental benefits of construction waste reduction management at the design and construction stages, and further promoting the implementation of waste reduction management measures. Based on the above causal-loop diagram, the stock-flow diagram of the environmental benefit assessment subsystem is established in Fig. 6. The interpretations of the environmental benefit assessment variables are listed in Appendix C.

3.4. An environmental benefit assessment model of waste reduction management at the design and construction stages An integrated SD model is developed based on the connections among the variables in the subsystems of the environmental benefit assessment model of waste reduction management at the design and construction stages. The structural relations among various subsystems and the variables are shown in Fig. 7.

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4. Simulation of environmental benefit assessment for construction waste reduction management 4.1. Model parameter Some parameters in the model are obtained from the available research data, publications, governmental reports and some relevant official websites. The units, values and sources of the quantitative variables in the model are shown in Table 1. SD contains a series of specific functions such as time functions, table functions, and hypothesis functions. Many formulas in the system dynamics model involve nonlinear functions, Y ¼ f ðXÞ, which are specified in an analytical manner (Sterman, 2000). Table functions are used to represent nonlinear variables which are not suitable for algebraic description. In practice, some variables may not be able to have available statistics data, but through the system analysis, it is found that there are some associations among these variables. According to SD theory, table functions could be used to describe the dynamic correlation among these variables. Some model parameters need to be determined according to some empirical project data. In order to collect the data, face-toface interviews were conducted with project designers and contractors. The parameter values of reduction measures were collected for project design and construction stage. The model parameters are shown in Table 2. The selected project was a new residential community and it is ongoing in Shenzhen, China. There were high-rise residential buildings, primary school, kindergarten, community sports facilities and other facilities in the project. The land area was 587,744.50 m2 and the total of construction areas was about 449,000 m2. The construction period was expected to be 30 months and the average of construction area is 14,967 m2 per month. Waste management approaches such as design for recycled materials, on-site waste sorting, reuse, waste reduction management regulations, etc. were adopted in the project.

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+ + + + + Environmental performance

Fig. 5. A causal-loop diagram of environment benefits assessment subsystem.

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4.2. Model validation 4.2.1. Boundary-adequacy test Boundary-adequacy test is performed for evaluating the model boundary's suitability and determining the boundary's range. Model boundary charts and subsystem diagrams are the most common tools (Sterman, 2000). The model boundary was defined according to literature review (Ding et al., 2016a; Li, 2013) and interview data. After examining all the variables in the SD model, it

is found that each of these variables is related to the model and has a significant impact in evaluating the CW reduction management performance. 4.2.2. Structure assessment test Model structure evaluation is used for evaluating the model logic and its consistence with the practical situation (Qudrat-Ullah and Seong, 2010). The model structure was developed according to the causal-loop diagram in Figs. 2, 4 and 6 which were based on a

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Table 1 Quantitative variable values. Variable

Value

Unit

Source(s)

Waste generation indicator Recycling and reused rate On site sorting rate Landfill rate Public landfill rate Illegal dumping coefficient Unit land loss from landfills space by CW landfill Unit waste landfill energy consumption Unit waste landfill resource consumption Unit waste landfill CO2 emissions Unit construction material production energy consumption Unit construction material resource consumption potential Unit construction material production CO2 Unit construction materials transportation energy consumption Unit construction material transportation resources consumption Unit construction materials transportation CO2 emissions

0.037 0.36 0.88 0.45 0.45 0.15 0.68 4.143*E4 7.78*E19 6.172*E4 0.5593 2.001*E17 1.868 1.429 0.2684*E17 2.126*E3

t/m2 e e e e e m3/t kgce/t 1/t kg/t kgce/t 1/t kg/t kgce/t 1/t kg/t

(Bureau, 2011) (Yuan, 2011) (Li, 2013) (Li, 2013) (Li, 2013) (Li, 2013) (Yuan, 2011) (Gao, 2012) (Gao, 2012) (Gao, 2012) (Gao, 2012) (Gao, 2012) (Gao, 2012) (Gao, 2012) (Gao, 2012) (Gao, 2012)

Table 2 Model parameter values for reduction measures. Reduction measures

Initial value

Reduction measures

Initial value

Prefabricated component Reduce design changes Designed for recycling of materials Industrial design Initial value of education background Designer's capacity Designer behavior attitude Waste reduction culture within design institute Large scale metal template Metal scaffolding

0.056 0.139 0.149 0.167 0.365 0.33 0.232 0.31 0.139 0.253

Waste management on-site On-site sorting Waste recycle and reuse on-site Waste reduction culture within construction institute Regulation implement supervision Waste reduction management regulation Waste reduction management investment Waste reduction training Environmental awareness Regulation implement supervision

0.315 0.25 0.311 0.34 0.038 0.195 0.05 0.167 0.398 0.226

comprehensive literature review and waste reduction management practice. Therefore, the model structure is logical and closely consistent with the practical situation.

4.2.3. Dimension consistency test Dimension consistency is one of the most basic tests for verifying the consistence of the units in the dynamic model equations (Sterman, 2000). SD software provides the function of dimension consistency test. For example, Vensim can check the equation

dimension by clicking “Units check” under “model” menu. The dimension consistency test result is shown in Fig. 8. “Units are A.O.K.” meant that the SD model passed the dimension consistency test.

4.2.4. Extreme condition test Extreme condition test is used for verifying the model behavior under extreme conditions which can be carried out in two main ways: (1) by direct inspection of the model equations, and (2) by

OK

Fig. 8. Dimension consistency check results.

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simulation results (Sterman, 2000). The SD model must be guaranteed to be logically consistent with the actual projects under extreme parameter values implying that the model structures are reasonable. Taking the illegal dumping rate as an example, the normal value is within the range of 0 and 1. In the literature, the illegal dumping rate was 0.15, which is used for testing whether the model behavior is reasonable under extreme conditions, and the values of 0, 0.15, 0.5 and 1 are simulated to observe the illegal dumping waste (IDW) volume changes. The simulation results are shown in Fig. 9. When the illegal dumping rate is 0, the total amount of illegal dumping is 0 ton, as shown in line 4. When the illegal dumping rate is 1, the total amount of illegal dumping waste has reached the maximum value, as shown in line 1. Under the two extreme conditions, the amount of illegal dumping waste is still logical, and the simulation results are consistent with the practical situation. The other variables in the model are also similarly and successfully verified. 4.2.5. Sensitivity analysis The model sensitivity analysis is used to analyze the values of some key variables and observe the model behavior. For example, by changing the value of on-site sorting, the effect of waste reduction at the construction stage can be observed. The range of the on-site sorting is between 0 and 1 and the variation covered [0, 0.25, 0.5, 0.5, 0.75, 1] as shown in Fig. 10. The figure highlights that the implementation of on-site sorting can improve the effect of waste reduction at the construction stage. This is also consistent with previous studies on waste reduction management i.e. the use of on-site sorting can effectively reduce the waste generation and bring environment benefits. In the same vein, the other key variables are tested and passed the sensitivity analysis. 4.2.6. Comparison of simulation results with analytical data In order to test whether the simulation results were consistent with the practical situation, the study adopted the comparative test method (Li, 2013). Since the project is ongoing, the real construction waste data are not available. Alternatively, the simulation results were compared with analytical data which were computed according to the waste generation index in Shenzhen “Construction Waste Emission Reduction Technical Specifications”. Then, the model was run under the baseline scenario, and the simulation results were generated. The error was calculated by comparing simulation results and the analytical data according to the following equation.


ei ¼

yim  b y im



b y im

Fig. 9. Extreme condition test results.

Fig. 10. Sensitivity analysis.

yim , b y im represent the analytical value and simulated value of ith variable in the mth month with i ¼ 1, 2 … n, n is the number of variables, At the design and construction stage, the initial values of waste reduction measures are 0 (No waste reduction measures), and the simulated waste production is about 16098.5 tons. According to the Shenzhen “Construction Waste Emission Reduction Technical Specifications”, the analytical waste generation is 16613.4 tons, with an error of 3.1% which is less than 5%. It could be concluded that the SD model was well verified by the analytical data. From Fig. 11 the base run line 1 and the model simulation result line 2(Initial value ¼ 0) fit each other quite well.

5. Simulation results and discussions 5.1. Simulation results Fig. 12 shows that the total amount of waste generated, waste reduction and waste generated after waste reduction. It can be seen that the total amount of waste generated, the total amount of waste reduction and the total amount of waste generated after reduction management are increasing during the simulation periods. The total amount of waste reduction is increasing while the total amount of waste generated after the reduction management is increasing at a decreasing rate. It is indicated that the implementation of waste reduction measures at the design and construction stages can significantly reduce waste generation. Table 3 describes the waste generated before and after taking waste reduction measures. When no reduction measures were

Fig. 11. Comparison of simulation results with analytical data.

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Fig. 12. Waste generation and reduction.

Table 3 Comparison of waste before and after taking reduction measures. Variable

The total amount The total amount The total amount of waste generated after waste of waste of waste reduction reduction generated

Production 16613.4 (ton) Percentage 100% (%)

6749.22

9864.16

40.63%

59.37%

taken, the total amount of construction waste generation is 16613.4 tons. At the design and construction stage after taking reduction measures, the total amount of reduced waste is 6749.22 tons, accounting for 40.63% of the total amount of waste generated. The total amount of construction waste generation after waste reduction management is 9864.16 tons. Therefore, reduction measures at design and construction stage can reduce construction waste by 40.63%, effectively decreasing the urban environmental impacts of construction waste. Fig. 13 shows the total amount of waste reduction for five different options during simulation periods. The total amount of landfill waste reduction, the total amount of public landfilling waste reduction, the total amount of on-site sorting waste reduction, the total amount of recycle and reuse waste reduction and illegal dumping reduction are increased during simulation periods among which the amount of on-site sorting waste reduction is the largest.

Fig. 13. Total waste reduction.

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Table 4 describes the reduction in the total amount of waste from different disposal methods after taking a series of reduction measures at design and construction stage. The amount of all different waste disposals are reduced after taking the reduction measures, such as the amount of landfill waste decreased from 7476.02 tons to 4438.87 tons and the amount of waste disposal improvement was 40.63%. Fig. 14 depicts the environmental impact of waste reduction. Saving of resource depletion potential, saving of energy consumption, reduction of global warming emissions, land space saving from landfilling reduction and public space saving from illegal dumping shows a sharp upward trend. It can be concluded that the reduction measures at the design and construction stages can achieve significant environmental benefits. Table 5 summarizes the waste reduction simulation results at the design and construction stages. It is provided intuitive and comprehensive indices for decision-making in assessing the waste reduction benefits at the design and construction stages. 5.2. Scenario analysis 5.2.1. Single stage scenario analysis In this scenario analysis, no waste reduction measures at both the design and construction stage is set as a reference case (Base run). The construction waste reduction measures (such as prefabricated etc.) are initially set to 0 (Case2) and evaluating waste reduction management environmental benefit at the design stage. The design stage waste reduction measures (such as reducing design changes etc.) are initially set to 0 (Case3) and evaluating waste reduction management environmental benefits at the construction stage. The total amount of waste reduction and its environmental benefits were analyzed in the three cases. As shown in Fig. 15, when the initial value of waste reduction measures at the construction stage is set to 0, the total amount of waste reduction is reduced from 6749.22 tons to 3242.74 tons. The improvement of construction waste reduction was reduced by 19.52%. When the initial value of waste reduction measures at the design stage is set to 0, the total amount of waste reduction is reduced from 6749.22 tons to 4021.38 tons and the construction waste reduction improvement will be reduced by 24.21%. It can be seen that the reduction effect and environmental benefits brought by the implementation of waste reduction management at a single stage are limited. Therefore, waste reduction management at both the design and construction stage should be combined to achieve effective waste reduction outcomes. 5.2.2. Single factor analysis In the scenario analysis, the key variables, “prefabricated components”, “reduced design modification” at the design stage, “construction on-site sorting” and “construction on-site material reuse” at the construction stage are selected. Scenario 1 (S1): Prefabricated components This scenario analyzes the impact on the total amount of waste reduction and environmental benefits with the increasing use of prefabricated components. In the case study, the initial values of prefabricated variable are 0.056 (Base run), 0.5 (Case 2) and 0.75 (Case 3), respectively. By increasing the prefabricated adoption rate, the amount of waste reduction and environmental benefits could be observed. The simulation results are shown in Fig. 16. When the initial value of the prefabricated variable is increased, the improvements of the environmental benefits and the total waste reduction are consistent. When the initial value of the prefabricated components was adjusted to 0.5, the total of waste reduction was increased from 6749.22 tons to 8890.4 tons, and the construction waste reduction

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Table 4 The total reduction of waste disposal in different ways. Variable

Landfill waste

Public landfill waste

Sorting waste in construction site

Recycling and recycling of waste

Illegally dumped waste

Production (ton) Reduce total (ton) Quantity after reduction (ton) Improvement (%)

7476.02 3037.15 4438.87 40.63%

6645.35 2699.69 3945.66 40.63%

14619.8 5939.31 8680.46 40.63%

5980.81 2429.72 3551.1 40.63%

2492.01 1012.38 1479.62 40.63%

Fig. 14. Environmental benefit.

improvement increased by 53.51%. When the initial value of the prefabricated components was adjusted to 0.75, the total amount of waste reduction was increased to 9663.1 tons, and the construction waste reduction improvement increased by 58.16%.The findings are also consistent with Wang et al. (2015). Scenario 2 (S2): Reduced design modification: This scenario analyzes the impact on the total amount of waste reduction and environmental benefits with the reduction of design modification. In the case study, the initial value of reduced design modification variable is 0.239 (Base run) and assuming reduced design modification variable value are 0.5 (Case 2) and 0.75 (Case 3). When the initial value of the reduction of the design change was adjusted to 0.5, the total of waste reduction was increased from 6749.22 tons to 8213.47 tons, and the construction waste reduction improvement increased by 49.44%.When the initial value of the reduction of the design change was adjusted to 0.75, the total amount of waste reduction was increased to 9546.5 tons, and the construction waste reduction improvement increased by 57.46%.It means decreasing

Fig. 15. Total amount of waste reduction in single stage scenario analysis.

Fig. 16. Simulation results of the total amount of waste reduction under S1.

Table 5 Design and construction of the main benefits of waste reduction. Main benefit index

Waste generation Total waste generation Total waste reduction Waste disposal Landfill waste reduction Public landfill waste reduction Classification of construction sites to reduce the total amount of waste separation sorting Reducing the total amount of material used in construction site Reduce the total amount of illegal dumping of waste Environmental benefit Total saving of resource consumption Total energy consumption Total reduction in greenhouse gas emissions Waste heap size The total reduction of illegal dumping of public vacant space occupied

Improved quantity after reduction

Improvement

Units

Value

t t

16,613.4 6749.22

e 40.63%

t t t t t

3037.15 2699.69 5939.31 2429.72 1012.38

40.63% 40.63% 40.63% 40.63% 40.63%

1 kgce kg m3 m3

1.55E-10 1,4349.5 1,2623.3 3901.05 688.42

40.63% 40.63% 40.63% 40.63% 40.63%

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the reduced design modification adoption rate can be observed the amount of waste reduction and environmental benefits. Reduced design modification scenarios simulation results are shown in Fig. 17, when changing the initial value of the reduced design modification, the improved rate of the environmental benefits and the total of the waste reduction is consistent. The results are also supported by Poon et al., (2004). Scenario 3 (S3): Construction on-site sorting: This scenario mainly analyzes the impact on the total amount of waste reduction and environmental benefits with the increase of on-site sorting. In the case study, the initial value of on-site sorting variable is 0.25 (Base run) and assuming prefabricated variable value are 0.5 (Case 2) and 0.75 (Case 3). When the initial value of on-site sorting was adjusted to 0.5, the total of waste reduction was increased from 6749.22 tons to 8734.41 tons, and the construction waste reduction improvement increased by 52.57%. When the initial value of on-site sorting was adjusted to 0.75, the total amount of waste reduction was increased to 9557.2 tons, and the construction waste reduction improvement increased by 57.53%. It means increasing the on-site sorting adoption rate for improving the amount of waste reduction and environmental benefits. Construction on-site sorting scenarios simulation results are shown in Fig. 18, when changing the initial value of the on-site sorting, the improved rate of the environmental benefits and the total of the waste reduction is consistent. Similar findings could also be found in Ding et al. (2016b). Scenario 4 (S4): Construction on-site material reuse: This scenario mainly analyzes the impact on the total amount of waste reduction and environmental benefits with the increase of on-site material reuse. In the case study, the initial value of on-site sorting variable is 0.311 (Base run) and assuming prefabricated variable value are 0.5

687

Fig. 18. Simulation results of the total amount of waste reduction under S3.

(Case 2) and 0.75 (Case 3). When the initial value of on-site material reuse was adjusted to 0.5, the total of waste reduction was increased from 6749.22 tons to 8094.27 tons, and the construction waste reduction improvement increased by 19.93%. When the initial value of on-site material reuse was adjusted to 0.75, the total amount of waste reduction was increased to 8716.18 tons, and the construction waste reduction improvement increased by 29.14%. It means increasing the on-site material reuse adoption rate can improve waste reduction and environmental benefits. Construction on-site material reuse scenario simulation results are shown in Fig. 19. When changing the initial value of the on-site material

Fig. 17. Simulation results of the total amount of waste reduction under S2.

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Fig. 19. Simulation results of the total amount of waste reduction under S4.

5.2.3. Multivariate analysis scenario In the practical situation, the management of construction waste reduction is simultaneously affected by multiple variables. The influences among different variables are complicated. Therefore, it is necessary to carry out comprehensive scenario analysis of the multiple influencing variables. The value of each variable is shown in Table 6. The simulation results of scenario analysis are shown in Fig. 20, Tables 7 and 8. At the design and construction stages, the effect of the construction waste reduction is not the outcome aggregation of each single variable but the synthesis of multiple variables. The simulation results of Cases 2 and 3 show that the reduction outcomes in the multiple variable scenarios are significantly higher than other variable scenarios. The effect of waste reduction can be reached between 62.13% and 71.49%.

maximum benefits of waste reduction. This study is consistent with the research of Wang et al. (2015). Previous studies showed design strategies play the significant role in the waste construction reduction procedure. Any design modification may lead to producing waste because of demolition of structure parts (Poon and Jaillon, 2002). In this study, besides studying improvement in design stage, more analyses are implemented further during the construction phase with the utilization of material on-site sorting as well as material recycling. Results from the model simulation show that waste product may be reduced efficiently with the multiple measures during design and construction stages. By the model, every project participant will acknowledge the benefit of construction waste management not merely outcomes reduction in construction site but also to the surrounding environment of the project. This will lead to reduce the frequency of transportation, improve the quality of landfill usage and ease the alarming climate change by greenhouse-gas emissions reduction which released during the building material production phase.

6. Discussions

7. Conclusion

Based on the four waste reduction measures i.e. the “Prefabricated”, “Reduce design change”, “Construction on-site sorting”, and “Construction materials on-site reuse”, the simulation results show that their individual reduction outcomes are different. The reduction effect of multiple measures is more significant than that of any single measure scenario. The improvement is much larger than any single measure scenario, which brings better environmental benefits. This shows that applications of any single waste reduction measure are limited while multiple waste reduction measures must be implemented simultaneously to achieve the

Previous research on CW reduction management mainly concentrated on the construction stage but increasing number of researchers began to acknowledge the importance of CW prevention at the design stage. The dynamic interconnections of CW reduction management between the design and construction stages were also ignored in the current literature. Based on literature review and empirical data, this paper developed a SD model for environmental benefit assessment of CW reduction management and took the residential projects in Shenzhen as an example in which SD simulation was implemented.

reuse, the improved rate of the environmental benefits and the total waste reduction is consistent.

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Table 6 The value of each variable in different scenarios. Scenario

Prefabricated components

Reduced design modification

Construction on-site sorting

Construction on-site material reuse

Base run Case 2 Case 3

0.056 0.5 0.75

0.239 0.5 0.75

0.25 0.5 0.75

0.311 0.5 0.75

Fig. 20. Simulation results of the total amount of waste reduction under S5.

Table 7 Simulation results of the multivariate scenarios. Scenario

Base run

Total amount of waste reduction The total savings in resource consumption Total energy savings Total GHG emissions reductions Saving waste heap volume Illegal dumping occupied public vacant space to reduce the total

Case2

Case3

Reduction

Improvement

Reduction

Improvement

Reduction

Improvement

6749.22 1.56E-10 14971.6 12624.2 4130.52 688.42

40.63% 40.63% 40.63% 40.63% 40.63% 40.63%

10321.2 2.39E-10 22894.57 19304.93 6316.39 1052.73

62.13% 62.13% 62.13% 62.13% 62.13% 62.13%

11876.7 2.75E-10 26345.52 22214.80 7268.48 1211.41

71.49% 71.49% 71.49% 71.49% 71.49% 71.49%

Table 8 Multivariate scenarios simulation results. Scenario

Base run

Case 2

Total waste

Reduction

Improvement

Reduction

Improvement

Reduction

Improvement

Total amount of waste generated

16613.4

6749.22

40.63%

10321.2

62.13%

11876.7

71.49%

Simulation results showed that the total amount of waste generated was 16,613.40 tons, and the total amount of waste reduction of 6749.22 tons accounting for 40.63% of the total amount

Case 3

of waste generated at the design and construction stages, which effectively reduce the waste generation. As a result, the waste reduction management at the design and construction stages can

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bring about the following environment benefits: the total resource consumption saving was 1.54913E-10, the total energy consumption saving 14349.5kgce, the total green house gas emission reduction 12,623.30 kg, waste landfill volume saving 3901.05 m3, occupied public vacant space saving from illegal dumping 688.42 m3 as well as decreased illegal dumping activities. With the accelerated process of urbanization, urban land space has become increasingly scarce. So the occupation reduction of land resources can alleviate the problem of land shortage in urban space. Effective CW reduction management can greatly reduce the CW transportation, landfill and greenhouse-gas emissions during the production of building material and alleviate the increasingly severe global warming problem. This research demonstrated that environmental benefit assessment model can help designers and contractors appreciating the significance of CW reduction measures, policies and actions at the design and construction stages. The developed model in this paper can help construction project stakeholders to appreciate the environmental benefits of construction waste reduction management such as reducing rework and adopting multiple measures simultaneously. Governments should motivate the industry to consider the design and construction process as a whole from the perspective of waste reduction. For example, engineering, procurement and construction (EPC) may be encouraged to integrate the two

stages. Moreover, this model can provide insights to the design and construction professionals for waste reduction practices and references for governments in assessing the reduction management outcomes. Future research can further explore the CW management issues by covering the demolition stage such that the dynamic waste management could be applied across a project life cycle.

Acknowledgement This research was conducted with the support of the National Science Foundation for Young Scholars of China (Grant No. 71202101); Social Science Research Support Grant (No.17QNFC34), Shenzhen University; , Ministry of Education of P.R.C; Scientific Planning Research Grant (No. 2009-K4-17, No. 2011-K6-24), Ministry of Housing and Urban-Rural Development of P.R.C.

Appendix A. The list of two-stage construction waste reduction management subsystem variables

No. Abbreviation Variable name

No Abbreviation

Variable name

1 2 3 4 5 6 7 8 9 10 11

MF IVMF EMF SS IVSS ESS WRMR IVCWR EWR WRMI IVWRMI

40 41 42 43 44 45 46 47 48 49 50

Waste reduction culture within construction institute Initial value of waste reduction culture within construction institute Effect of waste reduction culture within construction institute Construction worker's attitude to waste reduction Effect of construction worker's attitude Variation of on-site waste reduction management efforts to reduce waste On-site waste reduction management efforts to reduce waste Effect of construction workers environmental awareness Waste reduction culture within design institute Initial value of waste reduction cultural within design institute Effect of waste reduction culture within design institute

12 13 14

EWRMI WRT IVWRMT

15 16 17 18

EWRMT EA IVEA ECWEA

19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39

EDEA WRInc IVWRI EWRI PVEM RIS IVRIS ERIS WMOS IVWMOS EWMOS MCW IVMCW EMCW OSS IVOS EOS WRROS IVWRRO EWRRO ECWEA

Metal formworks Initial value of metal formworks Effect of metal formworks Steel scaffolding Initial value of steel scaffolding Effect of steel scaffolding Waste reduction management regulation Initial value of construction waste regulation Effect of waste regulation Waste reduction management investment Initial value of waste reduction management investment Effect of waste reduction management investment Waste reduction training Initial value of waste reduction management training Effect of waste reduction management training Environmental awareness Initial value of environmental awareness Effect of construction workers' environmental awareness Effect of designer's environmental awareness Waste reduction incentive Initial value of waste reduction incentive Effect of waste reduction incentive Promotion via external mechanism Regulation implement supervision Initial value of regulation implement supervision Effect of regulation implement supervision Waste management on-site Initial value of waste management on site Effect of waste management on site Management to construction worker Initial value of management to construction worker Effect of management to construction worker On-site sorting Initial value of on-site sorting Effect of on-site sorting Waste recycle and reuse on-site Initial value of waste recycle and reuse on-site Effect of waste recycle and reuse on-site Effect of construction workers environmental awareness

WRCWCI IVWRCC EWRCWCI CWAWR ECWA VOSWRMERW OSWRMERW ECWEA WRCWDI IVWRCDI EWRCWDI

51 DBAWR 52 EDAWR 53 PC

Designer's behavior and attitude to waste reduction Effect of designer's attitude to waste reduction Prefabricated component

54 55 56 57

IVPC EPC EB IVEB

Initial value of prefabricated component Effect of prefabricated component Education background Initial value of education background

58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78

EDB DC IVDC EDC FDM IVFDM EFDM DRM IVDRM EDRM VDERW DERW CRAD ECRAD VWAD EWTMD WRMERW IVIHD IHD EIHD EEP

Effect of education background Designer's capacity Initial value of designer's capacity Effect of designer's capacity Fewer design modification Initial value of fewer design modification Effect of fewer design modification Design for recycled materials Initial value of design for recycled materials Effect of design for recycled materials Variation of design efforts to reduce waste Design efforts to reduce waste Construction rework amount decrease Effect of construction rework amount decrease Wet trade amount decrease Effect of wet trade amount decrease Waste reduction management efforts to reduce waste Initial value of industrialized house design Industrialized house design Effect of industrialized house design Effect of environmental performance

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Appendix B. List of waste generation and disposal subsystem variables

No. Abbreviation Variable name

No Abbreviation Variable name

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44

TAWG WGI BAPM WI TAWR WR WDR LW LWI LWD LR LWR PLW PLR PLWI PLWR PLWD OSW OSWR OSWI OSWD OSR

Total amount of waste generated Waste generation index Building area of the project monthly Waste increase Total amount of waste reduction Waste reducing Waste decrease rate Landfilling waste Landfilling waste increase Landfilling waste decrease Landfilling rate Total amount of landfilling waste reduction Total amount of public landfilling waste Public Landfilling rate Public landfilling waste increase Total amount of public landfilling waste reduction Public landfilling waste decrease Total amount of on-site sorting waste On-site sorting waste reduction On-site sorting waste increase On-site sorting waste decrease On-site sorting rate

IDW IDWI IDWR IDWD IDR RRW RRWI TARRWD RRWD RRR TAWGRM WGPMRM TALWRM LWPMRM TAPLWRM PLWPMRM TAOSWRM OSWPMRM TAIDWRM IDWPMRM TARRWRM RRWPMRM

Illegal dumping waste Illegal dumping waste increase Total amount of illegal dumping waste reduction Illegal dumping waste decrease Illegal dumping rate Total amount of recycle and reuse waste Recycle and reuse waste increase Total amount of recycle and reuse waste decrease Recycle and reuse waste decrease Recycle and reuse rate Total amount of waste generated after waste reduction management Waste generated per month after waste reduction management Total amount of landfilling waste after waste reduction management Landfilling waste per month after waste reduction management Total amount of public landfilling waste after waste reduction management Public landfilling waste per month after waste reduction management Total amount of on-site sorting waste after waste reduction management On-site sorting waste per month after waste reduction management Total amount of illegal dumping waste after waste reduction management Illegal dumping waste per month after waste reduction management Total amount of recycle and reuse waste after waste reduction management Recycle and reuse waste per month after waste reduction management

Appendix C. List of environmental benefit assessment subsystem variables No.

Abbreviation

Variable name

No

Abbreviation

Variable name

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

SRDP SRDPT SRDPMS URDPT RUDPMP ISRDP RGWE RGWET UGWET SECMS UGWEMP IGWE SEC SECMS UECMP SECT UECT IEC LSSLR

Saving of resource depletion potential Saving of resource depletion potential from transportation Saving of resource depletion potential from material saved Unit resource depletion potential of transportation Unit resource depletion potential of material production Impact of saving of resource depletion potential Reduction of global warming emissions Reduction of global warming emissions from transportation Unit global warming emissions of transportation Saving of energy consumption from material saved Unit global warming emissions of material production Impact of global warming emissions Saving of energy consumption Saving of energy consumption from material saved Unit energy consumption of material production Saving of energy consumption from transportation Unit energy consumption of transportation Impact of energy consumption Land space saving from landfilling reduction

20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

LSSPL LSSL UFACW ILSS EP RDSLR URDL ESLR UECL GWESLR UGWEL RGWEMS IGWED IRS IES PSSIDR PSSID IPSSID

Land space saving from public landfilling Land space saving from landfilling Unit floor area of construction waste Impact of land space saving Environmental performance Resource depletion saving from landfilling reduction Unit resource depletion of landfilling Energy saving from landfilling reduction Unit energy consumption of landfilling Global warming emissions saving from landfilling reduction Unit global warming emission of landfilling Reduction of global warming emission from material save Impact of global warming emission decrease Impact of resource saving Impact of energy saving Public space saving from illegal dumping reduction Public space saving from illegal dumping Impact of public space saving from illegal dumping

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