System Dynamics Versus Agent-Based Modeling: a Review of Complexity Simulation in Construction Waste Management

sustainability

Article System Dynamics versus Agent-Based Modeling: A Review of Complexity Simulation in Construction Waste Management

Zhikun Ding, Wenyan Gong, Shenghan Li and Zezhou Wu *

Department of Construction Management and Real Estate, College of Civil Engineering, Shenzhen University, Shenzhen 518060, China; [email protected] (Z.D.); [email protected] (W.G.); [email protected] (S.L.) * Correspondence: [email protected]; Tel.: +86-755-2653-4286

Received: 29 June 2018; Accepted: 11 July 2018; Published: 16 July 2018

Abstract: The environmental impacts caused by construction waste have attracted increasing attention in recent years. The effective management of construction waste is essential in order to reduce negative environmental inﬂuences. Construction waste management (CWM) can be viewed as a complex adaptive system, as it involves not only various factors (e.g., social, economic, and environmental), but also different stakeholders (such as developers, contractors, designers, and governmental departments) simultaneously. System dynamics (SD) and agent-based modeling (ABM) are the two most popular approaches to deal with the complexity in CWM systems. However, the two approaches have their own advantages and drawbacks. The aim of this research is to conduct a comprehensive review and develop a novel model for combining the advantages of both SD and ABM. The research ﬁndings revealed that two options can be considered when combining SD with ABM; the two options are discussed.

Keywords: construction waste management; system dynamics; agent-based modeling

1. Introduction With the fast urbanization and rapid growth of construction activities, the amount of construction waste has been increasing signiﬁcantly in past years [1–3]. In the United States, the generation of construction debris was 530 million tons in 2013; cement concrete and asphalt concrete constituted the top two components [4]. In the European Union (EU), it was reported that construction waste represents approximately 25–30% of all EU waste; however, the recycling rate was very low [5]. In Hong Kong, the generation of construction waste also took a great proportion of total solid waste, representing about one third in 2014 and 2015, respectively [6]. It also encouraged the environmentally sound management of construction waste in China [2,7,8]. From the above statistics, it can be seen that there is an urgent need for effective construction waste management. So far, relevant waste management policies and strategies have been investigated, such as waste effective design [9], on-site sorting [10], barcode system [11], low waste technology [12], prefabrication [13,14], lean management [15], building information modeling [16], etc. The implementation of construction projects involves complexity and dynamics. Alvanchi, et al. [17] claimed that the modeling of construction systems aims to improve construction work performance by tracking the dynamic behavior of construction systems. From a systematics perspective, construction waste management (CWM) can be viewed as a complex adaptive system (CAS). A CAS is deﬁned as “a system in which a perfect understanding of the individual parts does not automatically convey a perfect understanding of the whole system’s behavior” [18]. It is a collection of individual agents that are free to act in ways that are not totally predictable [19]. However, the actions of agents are

Sustainability 2018, 10, 2484; doi:10.3390/su10072484 www.mdpi.com/journal/sustainability Sustainability 2018, 10, 2484 2 of 13 interconnected, which makes it is possible to learn from experiences and adapt to changes according to the external environment [20]. As CWM involves not only various factors (e.g., social, economic, environmental) but also different stakeholders (such as developers, contractors, designers, and governmental departments) simultaneously; thus, bearing the philosophy of complexity to investigate the routes of achieving effective CWM has great research potential. Scholars have investigated the adaptive characteristics of CWM systems in the literature. For example, Yuan and Wang [21] employed dynamic modeling to investigate the economic effectiveness of CWM. Ding, et al. [22] simulated the environmental performance of construction waste reduction management in China. Au, et al. [23] analyzed the impacts of government charges on the disposal of construction waste. System dynamics (SD) and agent-based modeling (ABM) are the two most commonly used approaches for investigating the complexity in CWM. Each approach has its particular advantages and drawbacks. For example, the method of SD, as a top–down approach, allows for convenient model construction and validation. On the contrary, ABM, as a bottom–up approach, allows for sophisticated interactions between agents and heterogenous state space [24]. Currently, the existing CWM studies were conducted independently without combining the advantages of the two approaches. However, the two approaches have great potential for combining with each other so as to be more powerful. The aim of this research is to thoroughly investigate the applications of SD and ABM in CWM studies, and to discuss the potential of combining the advantages of SD and ABM in order to comprehensively deal with CWM complexities. The rest of this paper is organized as follows. Firstly, the SD and ABM approaches are introduced in Section2. Then, the applications of these two approaches in CWM studies are reviewed and presented in Section3. Subsequently, a comparison of the two approaches is made, and discussions of the combination are made in Section4. Finally, conclusions are given in Section5.

2. Overview of System Dynamics (SD) and Agent-Based Modeling (ABM) Complexity science has attracted substantial attention from scholars in various disciplines, ranging from physics, economics, and computer science to social science [25–27]. It deals with the nonlinear relationships in a complex system, focusing on the characteristics and interaction rules among the components. To explore the complexity in a complex system, system dynamics (SD) and agent-based modeling (ABM) are the two most commonly used approaches.

2.1. System Dynamics (SD) System Dynamics (SD) is a top–down information feedback method that was proposed by Professor Forrester [28]. The essence of this method is the feedback structures with high order, multiloop, and nonlinearity. SD is a well-developed approach for visualizing, analyzing, and understanding complex dynamic feedbacks [29]. Diagramming tools, such as causal loop diagrams (CLDs) and stock–flow diagrams (SFDs), are used to capture the structure of a complex system [30]. CLDs are able to map the feedback structures of a complex system; they can show how the system is dynamically influenced by the interactions of all of the variables. A CLD consists of variables connected by arrows; the arrows denote the causal influences among the variables. Each causal link is assigned a polarity—either positive (+) or negative (−)—to indicate how the dependent variables are influenced by the independent variables. The important loops are highlighted by a loop identifier, showing whether the loops are positive (reinforcing) or negative (balancing). An illustrative CLD is presented in Figure1. A positive feedback illustrates that a change in any of the variables within the causal loop will eventually affect itself in a positive way, while a negative feedback means that a change on any variables within the causal loop will affect itself in a negative way [22]. SustainabilitySustainability2018 2018, 10, 10, 2484, x FOR PEER REVIEW 33 of of 13 13 Sustainability 2018, 10, x FOR PEER REVIEW 3 of 13

Figure 1. An illustrative causal loop diagram (CLD). FigureFigure 1.1. AnAn illustrativeillustrative causalcausal looploop diagramdiagram (CLD).(CLD). The CLDs can successfully describe the basic logical structures; however, in order to conduct a TheThe CLDsCLDs cancan successfullysuccessfully describedescribe thethe basicbasic logicalogicall structures;structures; however,however, inin orderorder toto conductconduct aa quantitative analysis, SFDs should be employed. SFDs can distinguish the nature of different variables quantitativequantitative analysis,analysis, SFDsSFDs shouldshould bebe employed.employed. SFDsSFDs cancan distinguishdistinguish thethe naturenature ofof differentdifferent and use integral or differential equations to represent the described information. Four building variablesvariables andand useuse integralintegral oror differentialdifferential equatiequationsons toto representrepresent thethe describeddescribed information.information. FourFour blocks are used to develop a quantitative SFD from a qualitative CLD: stock, flow, converter, buildingbuilding blocksblocks areare usedused toto developdevelop aa quantitatiquantitativeve SFDSFD fromfrom aa qualitativequalitative CLD:CLD: stock,stock, flow,flow, and connector [31], as shown in Figure2. Stocks create delays by accumulating the difference between converter,converter, andand connectorconnector [31],[31], asas shownshown inin FigureFigure 2.2. StocksStocks createcreate delaysdelays byby accumulatingaccumulating thethe the inflow and the outflow. By decoupling the rates of flows, stocks are the source of disequilibrium differencedifference betweenbetween thethe inflowinflow andand thethe outflow.outflow. ByBy decouplingdecoupling thethe ratesrates ofof flows,flows, stocksstocks areare thethe dynamics. Flows are the functions of the stock and other state variables and parameters [30]. The value sourcesource ofof disequilibriumdisequilibrium dynamicsdynamics.. FlowsFlows areare thethe functionsfunctions ofof thethe ststockock andand otherother statestate variablesvariables andand of a flow can be positive or negative. A positive flow is an inflow that will fill in the stock, while a parametersparameters [30].[30]. TheThe valuevalue ofof aa flowflow cancan bebe positivepositive oror negative.negative. AA positivepositive flowflow isis anan inflowinflow thatthat willwill negative flow is an outflow draining from the stock. A convertor has a utilitarian role in selecting fillfill inin thethe stock,stock, whilewhile aa negativenegative flowflow isis anan outflowoutflow drainingdraining fromfrom thethe stock.stock. AA convertorconvertor hashas aa the proper values and functions of the parameters in the model, and a connector is an information utilitarianutilitarian rolerole inin selectingselecting thethe properproper valuesvalues andand functionsfunctions ofof thethe parametersparameters inin thethe model,model, andand aa transmitter connecting elements [32]. connectorconnector isis anan informationinformation transmittertransmitter connectingconnecting elementselements [32].[32].

FigureFigureFigure 2.2. AnAn illustrativeillustrative stock–ﬂow stock–flowstock–flow diagram diagramdiagram (SFD). (SFD).(SFD).

A flow chart of the model development and simulation process is presented in Figure 3. From AA flow flow chart chart of of the the model model development development and and simulation simulati processon process is presented is presented in Figure in Figure3. From 3. thisFrom this figure, it can be seen that the basic procedure of an SD modeling covers the system analysis figure,this figure, it can beit can seen be that seen the that basic the procedure basic procedu of anre SD of modelingan SD modeling covers thecovers system the analysissystem analysis stage, stage, the establishment of the conceptual model stage, the establishment of the quantitative model thestage, establishment the establishment of the conceptual of the conceptual model stage, model the stage, establishment the establishment of the quantitative of the quantitative model stage,model stage, the model verification stage, and the model simulation stage. thestage, model the verification model verification stage, and stage, the modeland the simulation model simulation stage. stage. Sustainability 2018, 10, 2484 4 of 13

Sustainability 2018, 10, x FOR PEER REVIEW 4 of 13

Sustainability 2018, 10, x FOR PEER REVIEW 1. Explicit modeling purpose and system boundary 4 of 13 Systems Analysis 2. Put forward dynamic hypotheses 1. Explicit modeling purpose and system boundary Systems Analysis 2. Put forward dynamic hypotheses 3. Distinguish stock, flow, and auxiliary variables Conceptual Model 4. Establish causal relationships between variables Repeat in case 3. Distinguish stock, flow, and auxiliary variables Conceptual Model of failure 4. Establish causal relationships between variables Repeat in case Quantitative Model 5. Determine the parameter values of failure Quantitative Model 5. Determine the parameter values

Model Test 6. Test the validity of the structure

Model Test 6. Test the validity of the structure Pass the test

Pass the test 7. Model simulation Model Simulation 8. Simulation results analysis 7. Model simulation Model Simulation 8. Simulation results analysis

Figure 3. A flow chart of system dynamics (SD) modeling. Figure 3. A flow chart of system dynamics (SD) modeling. Figure 3. A flow chart of system dynamics (SD) modeling. 2.2. Agent-Based Modeling (ABM) 2.2. Agent-Based Modeling (ABM) 2.2. Agent-Based Modeling (ABM) In contrary to SD, agent-based modeling (ABM) is a bottom–up computational modeling approach.In contraryIn contraryBy tousing SD, to agent-basedABM, SD, agent-basedthe individual modeling modeling entities (ABM) (A in isBM) aa bottom–upCAS is a are bottom–up represented computational computational by discrete modeling modeling agents approach. that Byinteract usingapproach. ABM, autonomously By the using individual ABM, in a simulatedthe entities individual space in aentities CASto produce in are a representedCAS emergent are represented and by non-intuitive discrete by discrete agents outcomes agents that that interactat the autonomouslypopulationinteract autonomously level in a simulated[33]. The in interactions a space simulated to produce orspace communi to emergent producecations andemergent among non-intuitive and the non-intuitiveagents outcomes are made outcomes at according the population at the to a levelsetpopulation [ 33of ].predefined The level interactions “rules” [33]. The [34]. interactions or The communications rules or governing communi amongcations individual among the agents’ agents the agents behavior are are made made are according influentialaccording to toto a athe set of predefinedoutcomes/predictionsset of predefined “rules” “rules” [34 of]. ABM; The[34]. rulesThethus, rules governingit is governingnecessary individual individualto tightly agents’ coupleagents’ allbehavior behavior of the arerule-based are influential influential algorithms to the to the outcomes/predictionsat outcomes/predictionsall of the stages of of model ABM; of ABM; development. thus, thus, it isit necessaryis necessary to to tightly tightlycouple couple allall of the rule-based rule-based algorithms algorithms at at all of the stages of model development. all of theABM stages can of be model implemented development. by programming languages (e.g., C, Java, and Python) or specialized ABM can be implemented by programming languages (e.g., C, Java, and Python) or specialized toolkitsABM cansuch be as implemented NetLogo, Swarm, by programming and Repast. languagesGenerally, (e.g.,ABMC, follows Java, andan incremental Python) or specializedmodeling toolkits such as NetLogo, Swarm, and Repast. Generally, ABM follows an incremental modeling process by starting from a simple model to a complex model [35]. This process is illustrated in Figure toolkitsprocess such by as starting NetLogo, from Swarm, a simple andmodel Repast. to a comple Generally,x model ABM[35]. This follows process an is incrementalillustrated in Figure modeling 4. process4. by starting from a simple model to a complex model [35]. This process is illustrated in Figure4.

Figure 4. The process of agent-based modeling (ABM). FigureFigure 4. The process ofof agent-basedagent-based modeling modeling (ABM). (ABM). Sustainability 2018, 10, 2484 5 of 13

3. The Application of SD and ABM in CWM Studies Both SD and ABM have been used successfully to explore and understand CASs in different disciplines [36]. The applications of SD and ABM in CWM studies are presented as follows.

3.1. The Application of SD in CWM Research A CWM system involves various aspects: waste generation, reduction, reuse, recycling, transportation, etc. Several factors can affect the process of CWM, such as environment-related factors, economic-related factors, social-related factors, etc. There are interactions among these factors; thus, it is necessary to select an appropriate technique to simulate the complex relationships. SD can be used to investigate CWM systems, because it could not only precisely describe the cause–effect relationships among the quantitative variables, it could also properly deﬁne qualitative variables. A summary of SD application in CWM studies is shown in Table1. From Table1, it can be seen that the previous studies mainly focused on the general aspects of CWM, such as policy, the environment, the economy, society, and construction waste management issues. In terms of modeling software, iThink, Vensim, and Stella are the most popular.

Table 1. Construction waste management (CWM)-related studies based on system dynamics (SD).

Topic Selected Paper Software Main Focus Develop an SD-based model to determine an optimal charging system that [37] Vensim could reduce construction waste generation and maximize waste recycling. Assess the effectiveness of two policies (i.e., incentives and tax penalties) to Policy [38] Vensim evaluate how the government can influence the behavior of firms in terms of construction waste recycling. Evaluate the possible impacts arising from the application of prefabrication [39] Vensim on construction waste reduction. Develop an SD-based model for evaluating the environmental performance [40] iThink of CWM. Environment Simulate the environmental performance of construction waste [22] Vensim reduction management. Develop an SD-based model for quantitatively evaluating the social Society [41] iThink performance of CWM to provide insights for an effective promotion. Highlight the dynamics and interrelationships of CWM practices and [42] iThink analyze the cost–benefit of this process. Investigate the economic effectiveness of CWM and provide insightful [21] Vensim Economy recommendations for decision making. Analyze the cost and benefit of CWM from the standpoint of the contractor, [43] iThink and simulate the effects of economic compensations and penalties. Evaluate alternative types of recycling centers under different policy and Policy–economy [44] Vensim economy scenarios. Evaluate the impacts of two alternatives (i.e., recycling and disposing) Environment–economy [45] Stella for CWM. Develop an SD-based model by incorporating the relationships of major [46] Stella activities to assist practitioners in better understanding the complexity of CDW. Develop a simulation model that can better reveal the interrelationships of [47] iThink factors during the on-site waste sorting process. Develop a model of on-site construction waste management. [48] Stella The interconnections of the main activities are included in the model, with Management a focus on planning and management. Develop a simulation model integrating four sub-systems including [49] iThink construction waste generation, on-site waste sorting, waste landfill, and public filling. Propose a model that can serve as a decision support tool for construction [50] iThink waste reduction and provide a platform for simulating the effects of various reduction strategies. Examine the complexity of CWM by analyzing waste generation, [51] Stella transportation, recycling, landfilling, and illegal dumping. Sustainability 2018, 10, 2484 6 of 13

Although SD has been successfully employed in CWM research, some limitations were identified during application. Generally speaking, the SD model is based on the idea that all of the dynamics occur due to the accumulation of flows in stocks. Through a literature review, it was found that SD models did not provide an appropriate means to depict individual differences (e.g., various waste management participants’ preferences and waste collection levels). For instance, the SD model established by Yuan, et al. [42] only showed that the environmental awareness had an influence on waste generation and recycling. In other words, it was assumed that the interaction differences between related stakeholders (e.g., contractors, governments, designers) and the information distribution among them are homogeneous. However, in reality, the environmental awareness of different stakeholders is different; the environmental awareness among stakeholders may interact with each other. Thus, SD has a drawback that cannot provide full interpretations of how the microscopic stakeholders’ behavior would affect the emergent macro phenomena. It is necessary to explore a solution to investigate CWM more comprehensively.

3.2. The Application of ABM in CWM Research A CWM system involves various stakeholders, such as contractors, developers, transportation companies, recycling/landfilling centers, government departments, etc. These stakeholders are adaptable to the environment; they can communicate with the environment to change their behavior through continuous learning and the accumulation of experience. Hence, a bottom–up ABM method is capable of bridging the gap between microscopic behavior and macro phenomena in a CWM system [52]. Previous research using ABM primarily focused on the treatment alternatives of construction waste. Based on empirical data, Knoeri, et al. [53] presented an ABM of the Swiss recycled construction material market. It was found that raising construction stakeholders’ awareness of recycled materials in combination with small price incentives was most effective for promoting the use of recycled materials. Gan and Cheng [54] employed ABM to analyze the dynamic network of a backfill supply chain so as to maximize the backfill recovery and reduce the amount of construction waste to landfills. Ding, et al. [55] developed an ABM for simulating CWM measures to reveal interactions between the primary stakeholders and impacts on the environment. The construction waste generation and effects of various policies were investigated. In terms of the waste generated from demolition activities, Ding, et al. [56] compared the differences of environmental impacts between green demolition and traditional dismantling using the ABM method. From the above research, it can be concluded that ABM is more applicable to simulate a dynamic system due to its decentralization and fast reaction to unexpected disturbances. ABM has the advantage of direct one-to-one mapping between real and virtual agents in terms of parameter acquisition from experiments and model validation. In a CWM simulation model, agents are divided according to CWM stakeholders, such as construction enterprises, demolition companies, and transportation companies. Each agent has its own states, attributes, and behavior, allowing ABM to investigate system complexity and interactions from a lower individual agent level to emergent results at a higher level [57]. ABM generally focuses on micro-level interactions that may explain emergent patterns such as waste environmental performance at a system level. However, it ignores the feedback effect of various social and economic factors on the individuals. For example, in the ABM developed by Ding, et al. [55], the interactions between the agents and the economic and social environment were not considered.

4. Discussion From the above literature review, it can be seen that both SD and ABM have been successfully applied to investigate CWM-related studies. However, there are drawbacks for each approach. It is worthwhile to explore the possibility of combining the advantages of the two approaches. Sustainability 2018, 10, 2484 7 of 13

4.1. Comparison of SD and ABM Both SD and ABM can explore complexity problems in a CAS; however, there are differences between the two approaches: • SD is usually used to analyze problems from a macro and holistic-thinking perspective. It is a “top–down”Sustainability 2018, modeling10, x FOR PEER approach REVIEW that can avoid the limitations of one-sided thinking7 of 13 (e.g.,

the microBoth perspective) SD and ABM andcan explore help understand complexity problems the structure in a CAS; behind however, a complex there are differences phenomenon [58]. Anbetween SD model the two can approaches: be used to study a dynamic evolution process under different situations. The• philosophicalSD is usually foundationused to analyze is problems reductionism. from a macro Reductionism and holistic-thinking is a process perspective. of breaking It is a complex entities,“top–down” concepts, modeling or phenomena approach down that can into avoid their the smallestlimitations constituents; of one-sided thinking it can (e.g., transform the ideas into simplemicro forms perspective) [59]. However,and help understand SD is often the criticized,structure behind because a complex a complex phenomenon system [58]. cannot An be fully understoodSD model by just can dealingbe used withto study a single a dynamic discipline. evolution In process terms ofunder the diff CWMerent system, situations. SD The cannot give a profoundphilosophical explanation foundation of the is micro reductionism. behaviors Reductionism in the system, is a process because of itbreaking ignores complex the relationship entities, concepts, or phenomena down into their smallest constituents; it can transform ideas betweeninto the simple macro forms behavior [59]. However, and micro SD is behavior. often criticized, because a complex system cannot be • ABM providesfully understood a dynamic by just approach dealing with by a buildingsingle discipline. a virtual In terms system. of the It CWM follows system, a “bottom–up” SD procedurecannot that give emphasizes a profound explanation the spatial of the or micro social behaviors interactions in the system, between because individuals it ignores the and their environmentrelationship [60]. between The philosophical the macro behavior foundation and mi iscro syncretism. behavior. The importance of holistic analysis • ABM provides a dynamic approach by building a virtual system. It follows a “bottom–up” is emphasized; meanwhile, the composing parts are also involved [61]. ABM is an effective procedure that emphasizes the spatial or social interactions between individuals and their cross-scaleenvironment modeling [60]. method The philosophical that combines foundation time dimension is syncretism. with The space importance dimension, of holistic and bears the characteristicsanalysis is of emphasized; heterogeneity, meanwhile, space discretization,the composing parts time are discretization, also involved and[61]. discreteABM is an states [60]. Througheffective computer cross-scale simulation, modeling the method microscopic that combines mechanism time dimension of complex with macro space phenomenadimension, can be revealed.and However, bears the Wangcharacteristics and Deisboeck of heterogeneity, [62] claimed space thatdiscretization, ABM also time has discretization, some weaknesses. and Firstly, discrete states [60]. Through computer simulation, the microscopic mechanism of complex it is toomacro detailed phenomena to simulate can be overrevealed. a long However, period Wang because and Deisboeck of the large [62] claimed number that of ABM parameters also and rules, whichhas some makes weaknesses. parameter Firstly, identification it is too detailed difficult to simulate and requires over a long extensive period sensitivitybecause of the analyses to determinelarge the number prediction of parameters robustness. and rules, Secondly, which ABM makes is parameter sensitive toidentification small variations; difficult thus,and current ABM canrequires only extensive process asensitivity relatively analyses small to number determine of agents.the prediction Thirdly, robustness. ABM ignores Secondly, the ABM interactions betweenis sensitive agentsand to small macro variations; factors. thus, current ABM can only process a relatively small number of agents. Thirdly, ABM ignores the interactions between agents and macro factors. In summary, SD and ABM have their own advantages and disadvantages for analyzing complex In summary, SD and ABM have their own advantages and disadvantages for analyzing systems.complex SD focuses systems. on SD the focuses “flow” on relationshipsthe “flow” relationships and feedbacks and feedbacks that can that longitudinally can longitudinally simulate a system’ssimulate dynamic a system’s behavior. dynamic It is behavi appropriateor. It is appropriate to analyze to analyze the interactions the interactions between between different different elements and cumulativeelements and longitudinal cumulative effects.longitudinal However, effects. Ho spatialwever, factorsspatial factors are not are coverednot covered in thein the SD SD modeling process.modeling In contrast, process. ABM In contrast, considers ABM the considers spatial the interactions. spatial interactions. However, However, the feedback the feedback effect effect of various of various social and economic factors on agents is ignored. A more detailed comparison is social andpresented economic in Figure factors 5. on agents is ignored. A more detailed comparison is presented in Figure5.

Figure 5. Comparison between SD and ABM. Figure 5. Comparison between SD and ABM. Sustainability 2018, 10, x FOR PEER REVIEW 8 of 13

Sustainability 2018, 10, 2484 8 of 13 4.2. Combination of SD and ABM for CWM Research Considering the advantages and drawbacks of SD and ABM, a combination of the two 4.2.approaches Combination is essential of SD and in ABM order for to CWM overcome Research their limitations: (1) Considering SD models thecannot advantages consider and different drawbacks levels of SDof andaggregation; ABM, a combination however, ABM of the has two the approaches ability to is essentialcapture in ordera fine to level overcome of detail. their Thus, limitations: SD can have the highest abstraction level, and ABM can be (1) SDused models at lower cannot abstraction consider levels, different varying levels theof nature aggregation; and scale however, of elements ABM [63,64]. has the ability to (2) capture SD ignores a fine the level effects of detail. of heterogeneous Thus, SD can mixing have the; each highest stock abstractionconsists of level,homogeneous and ABM elements. can be usedThus, at distinctions lower abstraction within levels, the elements varying (i.e., the natureheterogeneities and scale of of any elements kind) [have63,64 to]. be modeled by (2) SDadding ignores new the stocks. effects However, of heterogeneous the heterogeneous mixing; each agents stock can consists be easily of es homogeneoustablished by using elements. ABM Thus,[65]. distinctions within the elements (i.e., heterogeneities of any kind) have to be modeled (3) by SD adding is equation-based, new stocks. However,and needs the quantified heterogeneous relationships agents can between be easily variables; established thus, by itusing is not ABMsuitable [65]. for complex systems with unknown structures. However, ABM can reasonably (3) SDrepresent is equation-based, complex systems and needs based quantified on a relationshipslimited number between of relatively variables; simple thus, it rules is not to suitable reveal foremergent complex behavior systems with[36]. unknown structures. However, ABM can reasonably represent complex systemsThe combination based on aof limited SD and number ABM has of relativelybeen attempted simple in rules other to disciplines. reveal emergent For instance, behavior Größler, [36]. et al.The [66] combination presented a of software-based SD and ABM integration has been of attempted SD and ABM in other that disciplines.investigated For supply instance, chain Größler,management. et al. [66 The] presented results showed a software-based that Vensim integration and RePast of can SD andbe used ABM simultaneously that investigated to solve supply the chaintechnical management. problem of The combining results showed the two that methods. Vensim In and the RePast same canvein, be it usedis feasible simultaneously to integrate to SD solve and theABM technical to solve problem complexity of combining problems the in the two CWM methods. field. In the same vein, it is feasible to integrate SD and ABMThere to solveare two complexity options to problems integrate in SD the and CWM ABM, field. as shown in Figure 6. The first option is the ABMThere method, are two which options is based to integrate on SD. SD This and option ABM, mo as showndels a certain in Figure number6. The firstof objects option on is thethe ABMmacro method,level (SD which part), is inside based which on SD. agents This are option modeled models at the a certain micro numberlevel (ABM of objects part). An on illustration the macro levelof the (SDABM part), method inside based which on agents SD for are CWM modeled is presented at the micro in levelFigure (ABM 7. The part). other An option illustration is the of SD the method ABM methodbased on based ABM. on SDIt models for CWM interactions is presented of agents in Figure at7 the. The macro other level option (ABM is the part), SD method and their based internal on ABM.structure It models at the interactions micro level of (SD agents part). at the An macro exampl levele of (ABM the SD part), method and their based internal on ABM structure is given, at the as microshown level in Figure (SD part). 8. An example of the SD method based on ABM is given, as shown in Figure8.

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FigureFigure 6. 6. TwoTwo optionsoptions for for combining combining SD SD and and ABM. ABM. Sustainability 2018, 10, 2484 9 of 13 SustainabilitySustainability 2018 2018, 10, 10, x, xFOR FOR PEER PEER REVIEW REVIEW 9 9of of 13 13

WasteWaste reduction reduction cultureculture + + BehaviorBehavior and and+ + DesignDesign EnvironmentalEnvironmental - - attitudeattitude to to waste waste modificationmodification awarenessawareness reductionreduction + + + + Waste reduction + + + - - ConstructionConstruction Waste reduction Industrialized + Industrialized reworkrework amount amount trainingtraining househouse design design WasteWaste management management on-siteon-site + + + + - - Management to Management to WasteWaste reduction reduction construction worker On-siteOn-site waste waste reduction reduction construction worker managementmanagement efforts efforts to to managementmanagement investment investment +reduce+reduce waste waste + + + + + + + + + + WasteWaste reduction reduction Waste recycle Waste recycle WasteWaste reduction reduction managementmanagement andand reuse reuse on-site on-site managementmanagement efforts efforts to to + regulation+ regulation - - reducereduce waste waste TotalTotal amount amount of of wastewaste generated generated - -EnvironmentalEnvironmental performanceperformance

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Attitude toward waste Attitude toward waste SourceSource reduction reduction reductionreduction behavior behavior+ + TechnicalTechnical level level of of AttitudeAttitude toward toward + + + + waste reduction intentionintention Source reduction demolitiondemolition waste reduction Source reduction + + + + behavior behaviorbehavior + + behavior Source reduction rate LevelLevel of of demolition demolition Source reduction rate Waste reduction + + WasteWaste reduction reduction wastewaste management management by design company Waste reduction + + by design+ company training SourceSource reduction reduction rate rate managementmanagement awarenessawareness + training byby demolition demolition company company investmentinvestment + + + + EnforceabEnforceabilityility of of + + + + + + + + + + lawslaws and and regulations regulations SourceSource reduction reduction On-site waste reduction On-site waste reduction Waste reduction EffectionEffection of of waste waste + + behaviorbehavior management efforts to Waste reduction PenaltyPenalty paid paid due due to to management efforts to management reductionreduction UnitUnit cost cost of of reduce waste management illegalillegal dumping dumping reduce waste investmentinvestment landfillinglandfilling DemolitionDemolition waste waste + + + + + + sourcesource reducted reducted by by + + + + designdesign company company WasteWaste reduction reduction DemolitionDemolition waste waste source source Perfection of laws reducted by demolition Perfection of laws managementmanagement reducted by demolition and regulations regulationregulation company and+ + regulations + + + + company + + BenefitBenefit BenefitBenefit

FigureFigureFigure 8. 8.8. SDSD SD method method based based based on on on ABM. ABM. ABM.

5.5.5. ConclusionsConclusions Conclusions ConstructionConstructionConstruction wastewaste waste hashas has greatgreat great negativenegative negative impactsimpacts impacts onon on thethe the environment.environment. environment. Recently,Recently, Recently, researchersresearchers researchers andand and practitionerspractitionerspractitioners havehave have paidpaid paid attentionattention attention toto to thethe the effective effective effective management management management ofof of construction constructi constructionon waste. waste. waste. ConstructionConstruction Construction wastewastewaste managementmanagement management (CWM)(CWM) (CWM) isis is aa a complexcomplex complex adaptiveadaptive adaptive systemsystem system thatthat that involvesinvolves involves notnot not onlyonly only variousvarious various factorsfactors factors (e.g.,(e.g., (e.g., social,social, economic,economic, andand environmental)environmental) butbut alsoalso differentdifferent stakeholdersstakeholders (such(such asas developers,developers, Sustainability 2018, 10, 2484 10 of 13 social, economic, and environmental) but also different stakeholders (such as developers, contractors, designers, and governmental departments) simultaneously. Thus, it is necessary to investigate the complexity of CWM based on complexity theories. This paper reviewed the SD and ABM applications in the ﬁeld of CWM. The ﬁndings revealed that the economic, social, and environmental aspects of CWM have been investigated. SD and ABM are totally different from each other: SD is a top–down modeling method that describes systems from a macro perspective, requiring knowledge of the system relations and causalities. ABM, on the contrary, is a bottom–up approach that models single acting entities of the system and the agents’ interactions during simulation in order to determine the macro behavior of a system. Both SD and ABM approaches have their advantages and drawbacks; thus, it is necessary to combine the two approaches in order to achieve a more powerful approach. Two options are proposed in this study for potential combination: namely, an ABM method based on SD, and an SD method based on ABM. Based on the proposed combinations, further studies can be conducted to comprehensively investigate the complexity in CWM-related systems.

Author Contributions: Conceptualization, Z.D.; Investigation, W.G. and S.L.; Methodology, W.G.; Writing-original draft, Z.D., W.G., S.L. and Z.W.; Writing-review & editing, Z.W. Funding: This research was funded by Shenzhen Government Basic Research Foundation for Free Exploration (JCYJ20170818141151733) and National Natural Science Foundation of China (51478267). Conﬂicts of Interest: The authors declare no conﬂict of interest.

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