Ssrn-id3241553 (1).pdf

Understanding green construction drivers for incentive structuring in the developing country Van Bastena,c; Igor Crévitsb; Yusuf Latiefa; Mohammed Ali Berawia* a

Department of Civil Engineering, Universitas Indonesia, Depok, Indonesia;

b

Laboratoire d’Automatique de Mécanique et d’Informatique industrielles et Humaines,

Université de Valenciennes et du Hainaut-Cambrésis, Le Mont Houy 59313, Valenciennes Cedex 9, France; cDepartment of Civil Engineering, Institut Teknologi Sumatera, Lampung, Indonesia Dr. Mohammed Ali Berawi is an associate professor in the department of civil engineering, faculty of engineering, Universitas Indonesia. Before joining the Faculty of Engineering at the University of Indonesia in 2008, Dr. Berawi was appointed Senior Lecturer at the Faculty of the Built Environment and Director of the Value Management Centre at the University of Malaya (2006-2008) and a lecturer in the School of Technology at Oxford Brookes University in the United Kingdom (2003-2006). Dr. Berawi was selected as the most outstanding lecturer/ researcher at the University of Indonesia (2009) and was a finalist for the UK Alumni Award (2008) and Toray Science and Technology Award (2014). His research leadership is reflected through his election as Editor-in-Chief of Value World, the journal of the Society of American Value Engineers (SAVE International) (2008-2014) and as Editor-in-Chief for the scopus indexed journal, International Journal of Technology (IJTech). Dr. Berawi has been involved in many national and international research collaborations and consultancies. He has been listed by Webometrics as one of the Top Scientists in Indonesia (2015-2017), and his biography featured in the 24th edition of Who’s Who in the World. *[email protected]

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Understanding green construction drivers for incentive structuring in the developing country This paper is to identify the driving factors that influence building consultant and contractor decision in green construction properties and model the building incentive using partial least square structural equation modelling (PLS-SEM) methods. Precisely, the study modelled the effects of uncertainty of economy condition, environment development, knowledge improvement, and regional policy on green construction implementation affecting building incentives in the developing country. The study was based on a survey of building stakeholder in construction phase in the capital city of Indonesia and the model was validated for reliability and validity. The PLS-SEM indicated that sustainable in green construction implementation has significant causal effects on incentives. Among these factors, selection of green construction technology was significant. Sustainable in green construction implementation was, however, found to have the most significant effect on internal and external green construction incentives. Keywords: green building; green construction; building incentive, developing country; uncertainty economy, environment development, knowledge improvement, regional policy

Introduction The building industry has an important role in reducing the negative impacts of urban infrastructure on environmental development. According to (Pheng Low, Gao, & Lin Tay, 2014), the building construction uses more than 40% of the world's energy and produces 30% of total greenhouse gas emissions on Earth (Qian, Fan, & Chan, 2016). Some countries in the world have made some efforts to reduce the negative impact of them through the concept of sustainable infrastructure or in buildings known as the concept of green building (Vyas & Jha, 2018). Green building buildings not only focus on environmental sustainability but also the social and sustainability of financing the life cycle of buildings (A. P. Chan, Darko, Ameyaw, & Owusu-Manu, 2016). Therefore, green building definition is the practice of increasing the efficiency of resource use in

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buildings to reduce negative impacts on the environment and human health throughout the phases of planning, construction, renovation, maintenance, operation, and demolition (Shazmin, Sipan, Sapri, Ali, & Raji, 2017; Vyas & Jha, 2018). Planning and construction of buildings is a significant part of the impact on the achievement of quality building construction environmentally friendly because the research was conducted by (González & Navarro, 2006) that this phase is a key success factor for integrating green technologies to reduce the negative impact on the environment both the construction phase and the operational phase. In these two critical phases, consultants and contractors capability have responsible toward the sustainability of building construction or known as green construction. The use of recycled materials, less water, less energy, and resource efficient techniques is an effort to be considered in the planning and construction phase of the building so that it can have an impact on improving the environmental performance and cost savings felt by the owners or users of the building in the operational phase. (Kats, 2003; Olubunmi, Xia, & Skitmore, 2016; Shazmin et al., 2017). Office building function is a type of building that has great potential in the application of the concept of green construction engineering because the construction of the building is still relatively high in energy usage that reaches 25% energy in the world (Higgins, Syme, McGregor, Marquez, & Seo, 2014). The concept of green construction provides an opportunity to improve project performance through appropriate green technologies application so that potentially cost savings management and speed up the work time (Guidry, 2004; Shazmin et al., 2017). However, most of developing countries are relatively very difficult to implement green construction concept with various reasons that hinder such as lack of public awareness, high risk in costs, lack of supportive implementation, resistance to change, inadequate knowledge and

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information, and negligence (Darko & Chan, 2016; Nguyen, Skitmore, Gray, Zhang, & Olanipekun, 2017). On the other hand, environmental attributes such as energy saving costs, and indoor environmental quality, become a benchmark for the assessment of green construction practices in general and appreciated only when the building is occupied and is in use. In fact, relatively a lot of stakeholder involvement that determines the success of green building before entering the operational phase includes the construction supply chain of builders, developers, planners, manufacturers, design (structure, landscape, and energy). Construction teams now need to consider the extra construction cost of a green building and the payback period for all factors such as the higher purchase and acquisition costs of green features and installations that conform to design specifications and higher labour costs (Geng, Dong, Xue, & Fu, 2012). Therefore, in almost all countries that have a relatively large amount of green building has been running a program of appreciation in the form of incentives to building stakeholders according to the role and performance in each phase of the building. Although some types of incentives have been modeled in some developed countries to increase the appeal of green construction implementation, there is no basic formula and incentive model definition to be implemented based on the characteristics of the country (Azis, Sipan, Sapri, & Ali, 2013; Shazmin, Sipan, & Sapri, 2016). It is important because the incentives format and formulation in each country are different. On the other hand, developing countries need that certainty for proper implementation of targets. Thus, the purpose of this study is to determine the key success factors of the green construction implementation in the context of uncertainty economic, environment development, knowledge improvement, and regional policy. After that, these critical success factors are tested to investigate the potential relationship between the variables

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in structuring an incentive model in the construction phase based on consultants and contractors view in developing countries, particularly in Indonesia. Several previous studies have reviewed the constraints of green construction implementation on the potential incentives partially in each aspect. Therefore, this study develops the integrated aspects of green construction toward buildings life cycle aspects. Uncertainty economic in response in the initial incremental cost of a green building which strongly depends on the country's situation (Alshamrani, 2017). Regional policies as the role of government to support the green construction concept implementation (Vyas & Jha, 2018), beside that environmental development aspect as a process of changing the needs and lifestyle of human, and the knowledge aspect to improve the awareness of the renewal technology and its implementation (Perini & Rosasco, 2013).

Background and hypothesis Barriers to green construction implementation Green construction is an approach that takes place in the building construction industry to achieve sustainable building agenda targets by reduce overall negative impacts of building construction on the environment through reducing greenhouse gas emissions, reducing pollution, conservation of resources through reuse and renewal programs materials, and decrease the amount of waste at all phases of building construction (Bohari et al., 2015). Therefore, as part of the life cycle of buildings, the construction phase has a role in sustainable infrastructure programs assessed through the achievement of environmentally friendly, social welfare, and economic viability (Uğur & Leblebici, 2017). In green construction implementation, stakeholders need extra cost in the construction process that is 4-11% depending on the target of certification achievement. On the other hand, a study was conducted by the World Green Building

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Council (WGBC) in 2013 showed that the cost of design and construction won't increase compared with standard construction if the building stakeholder implementation of financing strategy, green construction project management, and integrated environmental management strategy since the beginning of building development. More than fifty percent of construction cost components can be affected by incremental costs of green construction implementation such as building design engineering’s cost like green consultant fees, technology application fees including commissioning, cost of green feature materials, and the cost of green building certification process (Nurul Zahirah & Abidin, 2012). Strategy in management, personality skill, advanced materials, and technology knowledge is the integrated green construction component which green construction stakeholder have to have (Onuoha, Aliagha, & Rahman, 2018). In developed countries, the problems on the green construction implementation were the economic instability that leads interest rates increasing on or in other words green building investment with the concept of green construction becomes less attractive (Onuoha et al., 2018). In United States, green building design and construction is most driven by client demand, while in the United Kingdom and Japan, green construction is mandatory in government policy. On the other hands, building stakeholders in Italy apply the concept of green construction due to relatively high energy prices. (Ofek, Akron, & Portnov, 2018). In some developing countries, the risk of building investment is considered to be very high so that the building developers whose funding sources come from loans are subject to a high-interest rate of up to 18%. In addition, the low level of integration ability of green construction stakeholder often causes the failure of green construction implementation. Furthermore, it makes a low level of confidence in green construction investments. As a result, green finance products increase their lending rate by 25%

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(Onuoha et al., 2018). Developed countries anticipate the increase in price and interest on the loan by providing incentives for building that apply the concept of sustainable infrastructure. Unfortunately, as in the previous explanation, there are no indicators to determine the characteristics of the incentive model use that tend to vary in each country.

The impact of uncertainty economy Economic problems in building development, especially in green construction become an issue that affects the environmental sustainability and comfort for social activities (A. P. Chan et al., 2016; Meryman & Silman, 2004). Indicators that affect sustainability include green features prices still dependent on the regional technology and innovation, the availability of green investor or loans, the accuracy of the capital payback period, the operational cost of the feature, the project tax value, and the maintenance cost of the feature. (B. G. Hwang & J. S. Tan, 2012; Kibert & Kibert, 2008). Building certification has different cost values based on the complexity of the building and its duration. This technical matter is an example of how a project with green construction methods has the potential waste of cost when having an understanding of the concept of a sustainable environment (Geng et al., 2012). Based on these literature finding, the following can be hypothesized: H1: Uncertainty of economic growth negatively influences on sustainability of green construction implementation. H2: Uncertainty of economic growth positively influences on comfortability of green construction implementation. H3: Uncertainty of economic growth negatively influences on sustainability of green construction implementation.

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The influences of environment development Friendly environment as sustainable infrastructure achievement essentially opens great opportunities for increased efficiency and effectiveness of green construction technologies (Li et al., 2009; Wang, Hawkins, Lebredo, & Berman, 2012). Constraints in green construction implementation are the diversification condition of the building environment. Each building has different specifications and own uniqueness. In addition, Building's stakeholders find it difficult to identify green products and their implementation in an integrated manner. Therefore, there is no guidance to classify building conditions towards product code or standard establishment. Then the lack of ability to build stakeholders in the implementation of green construction as an example of planning and technicians do not understand well the process of greywater or how to recycle existing building materials. Building on these arguments, it is hypothesized that: H4: Environmental development positively influences on efficiency of green construction implementation. H5: Environmental development positively influences on comfortability of green construction implementation.

Knowledge of green building stakeholder The level of human knowledge has a significant influence on building community attitudes, lifestyle, behavior, and culture. It is similar to the green construction concept understanding where the building stakeholders habitual do not live in a friendly environment lifestyle or culture in building construction projects because unknown the concept clearly. Based on (B.-G. Hwang & J. S. Tan, 2012), without any support or interest from building stakeholders (owners, consultants, and contractors), it is

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infeasible to run projects using environmentally friendly construction technologies and practices. Technological innovation is difficult to accept due to low knowledge and curiosity. In technological and innovation, the manufacturing industry which providing green features has a high impact on the green construction process. Next is an understanding of the method of green construction through project planning, then the ability of the application by the project workers, and the ability of integration in the project team. Accordingly, this study posits that: H6: Knowledge improvement positively influences on sustainability of green construction implementation. H7: Knowledge improvement positively influences on manageability of green construction implementation.

The role of regional policy in green construction Building management and regional government have a high effect on the green construction implementation (Meryman & Silman, 2004). Building management awareness and ability to influence the building environment is one of the keys to success factor in green construction implementation when compared with the availability of advanced technology on green construction (Du, Zheng, Xie, & Mahalingam, 2014). Therefore, communication ability is an effective way to achieve successful implementation of green construction. The government also has a role in influencing building stakeholders in green construction implementation through persuasive and mandatory actions. The incentive is one of the persuasive ways, while the regional policy is the mandatory action. However, according to previous research, incentive implementation has a significant impact on the improvement in green construction implementation.

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In 2000 the Chinese State Government began to develop environmentally friendly policies through research funded directly by the Chinese Government. By 2015, China has reduced energy use by 16% and reduced carbon emissions by 17% (Gu, Zhu, & Gu, 2006; Mao, Shen, Shen, & Tang, 2013). Furthermore, China's policy direction is to continue to target energy efficiency building usage that is 30% in Year 2020 (Walsh, 2012). The assessment indicators on regional aspects in several countries in the world include the amount of energy savings, emissions reduction generated, water savings, and satisfaction of building occupants (Jeong et al., 2017). Consistent with literature, this study therefore postulate that: H8: Regional policy positively influences on efficiency of green construction implementation. H9: Regional policy positively influences on manageability of green construction implementation.

Incentives model in the context of green construction implementation Incentive is one of the persuasive ways, while the regional policy is the mandatory action. However, according to previous research, incentive implementation has a significant impact on the improvement in green construction implementation. Incentives are tools to encourage something to happen in a process. Therefore, incentives for green construction implementation is a step to encourage building stakeholders to perform environmentally friendly actions. In addition, incentives in green construction act as price stabilizers of green features, ensuring successful implementation of green construction, and increasing the number of green projects on infrastructure. Based on the literature above, some of hypothesis in incentive implementation on green construction are stated as:

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H10: Sustainability of green construction implementation positively influences on incentive model. H11: Efficiency of green construction implementation positively influences on incentive model. H12: Comfortability of green construction implementation positively influences on incentive model. H13: Manageability of green construction implementation positively influences on incentive model. Fig. 1 depicts the theoretical framework of the current study which empirically tested in the context of uncertainty economic, environment development, knowledge, and regional policy. It includes direct effect from dimensions of green construction towards incentives structure of building life cycle. The capital city of Indonesia tested this context as particular of the developing country.

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Figure 1. Conceptual Framework Materials and Methods Base on the literature above, there are 5 variables (economic uncertainty, environment development, knowledge improvement, regional policy, and green building implementation) observed in this study with 40 variable indicators were indicated. This study used the qualitative and quantitative method in developing a survey process to address local government perspective on green building incentive model particularly in the capital city of Indonesia. After that, semi-structured interviews allowing green building expert in Indonesia the freedom to actively engage in sharing their view in their own terms (Cohen & Crabtree, 2006; Galletta, 2013; Harrell & Bradley, 2009). Six experts have been successfully interviewed consisting of green building council, green building practitioners, and academics, to conduct constructive validation on this research. Fig. 2 describes the framework in all parts of this study.

Figure 2. Research Framework This second part, a pilot study was applied as the beginning of quantitative method implementation through the distribution of a structure self-administered questionnaire to 30 respondents as the minimum number of data process on SEM-PLS operation (Chin, 1998). All measures were rated on a six-point Likert-type scale, ranging from 1 (very low effect) to 6 (very high impact) without the moderate option.

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After the pilot study and validation process, the distribution of a structured selfadministered questionnaire to 84 respondent. The respondents come from the consultant, contractor, and building owner in the capital city of Indonesia which they are the responsibility to green building policy mandatory and implementation. The data collection was mostly the consultant and contractor staff which certified green professional. In addition, the building owner comes from 12 of 18 the owner of the green building in the capital city of Indonesia. Out of 100 distributed questionnaire, 84 operating responses were obtained which represent 84% response rate of all respondent.

Demographic information Based on the received questionnaire, Table 1 is created which majority of the responses were filled by males representing 78.57% and 21.43% were by females, with more than half of them being under the ages of 25 years old representing 3.57%, then the others being 25 to 35 years and above 50 years obtaining 35.71%, and 60.72% respectively. Regarding education, respondents who have a degree were 61.91% but those with master’s degree were 34.52% and PhD was 3.57%. With regards to working experience, respondents who have below 5 years were more with 59.52%, those having between 5 to 10 years were 19.05% then 21.43% represents those with 10 years and above respectively. Table 1 Respondent demographic features (90% response rate) Frequency

%

Female

18

21.43

Male

66

78.57

<25 years

3

3.57

25 - 35

30

35.71

>35 - 50

51

60.72

Degree

52

61.91

Variable Gender

Age

Educational level

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Working experience

Master

29

34.52

Ph.D.

3

3.57

Below 5 years

50

59.52

5 - 10 years

16

19.05

Above 10 years

18

21.43

Data Analysis This paper aims to examine and establish the purposes and impact of green building implementation on creating incentive structure in local government perspective. Partial least square structural equation modeling analysis (PLS-SEM) procedure were applied for the analyses of the proposed study model using the SmartPLS 3.0 software (Ringle, Wende, & Becker, 2015). According to the previous research, the analysis implemented a two-stage analytical method to test the measurement model or validity and reliability analysis and structural model or hypothesis testing (Hair Jr, Hult, Ringle, & Sarstedt, 2016). Structural Equation Modeling (SEM) is second-generation multivariate data analysis technique useful for theoretical model structures with “high complexity but low theoretical information” (Chin, 1997; Djimesah, Okine, & Mireku, 2018; Haenlein & Kaplan, 2004). The SEM helps researchers to visually study the relationships that exist among variables non-normal data, small sample sizes and uses formative indicators, the most prominent reasons for its application, and also allows the examination of more complex model structures or better handling of data inadequacies such as heterogeneity (F. Hair Jr, Sarstedt, Hopkins, & G. Kuppelwieser, 2014). The measures for latent constructs in reliability and validity were implemented in 2 phases. The first phase of convergent validity and discriminant validity analysis was checked by using the data from pilot study. Based on the test of load factor and AVE values, there are eight construct indicators that must be eliminated because they

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are below the parameter limit value. The eight indicators are Effi3, KSE1, KSE2, KSE5, RP1, Sust1, TK4, and TMI7. 32 of 40 variable indicators would be analysed as the results.

Results Measurement model Reliability analysis The reliability of the latent construct is tested by comparison of the amount of alpha and Cronbach composite reliability that both values must be greater than 0.70 (Chin, 1998; Joe F Hair, Ringle, & Sarstedt, 2011; Joseph F Hair, Sarstedt, Pieper, & Ringle, 2012). Table 2 indicates that the Cronbach’s alpha and composite reliability values for all constructs surpassed the threshold value of 0.70 except “sustainability” and “Comfort”, therefore establishing strong reliability among the measures but not for “sustainability” and “Comfort.” However, this construct couldn’t be deleted automatically before validity test was conducted. Table 2 Reliability and validity analysis Standardized Constructs

Cronbach’s

Composite

Indicators

AVE Loadings

Alpha

Reliability

Sust2

0.625

0.434

0.760

0.622

Sust3

0.923

Effi1

0.946

0.727

0.871

0.772

Effi2

0.806

Comf1

0.940

0.593

0.814

0.691

Comf2

0.706

Sustainability

Efficiency

Comfort

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Mang1

0.804

Mang2

0.906

Mang3

0.860

KSE1

0.808

KSE3

0.946

KSE4

0.740

ED1

0.751

ED2

0.524

ED3

0.639

ED4

0.545

ED5

0.508

TK1

0.934

TK2

0.710

TK3

0.923

RP2

0.083

RP3

0.403

RP4

0.853

TMI1

0.741

TMI2

0.792

TMI3

0.650

TMI4

0.685

TMI5

0.802

TMI6

0.797

TME1

0.885

TME2

0.921

0.822

0.893

0.736

0.788

0.873

0.698

0.712

0.734

0.360

0.850

0.895

0.742

0.864

0.460

0.299

0.847

0.883

0.558

0.940

0.956

0.845

Manageability

Economic uncertainty

Environment development

Knowledge improvement

Regional policy

Internal incentive

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TME3

0.949

TME4

0.920

External incentive

Convergent and discriminant validity Means of standardized factor loadings and Average Variance Extracted (AVE) is examined the convergent validity with a bootstrapping analysis of 500 subsamples. The result demonstrated that the standardized loadings of all measurement items, as presented in Table 2, were greater than of 0.60 with no cross-loadings except indicators ”ED2.” “ED4,” “ED5,” and “RP3” (Hair Jr et al., 2016). There were 24 of 36 significant variable indicators (p<.001) with strong confirmation of convergent validity, and that the measurement items were well loaded on their own constructs. Besides that, the convergent validity was also achieved when the AVE values of each construct in the model was found to be larger than 0.50 except indicator “RP” (Fornell & Larcker, 1981). Table 3 was the result of discriminant validity examination by comparing the shared variances between factors with individual factor AVE. All shared variances between factors in the model were lower than the square root of the individual factor AVE, confirming the satisfactory discriminant validity and that that the constructs were both conceptually and empirically dissimilar from each other. The internal incentive was found to have the strongest correlation with sustainable in green construction implementation (r=0.496, p<0.01), followed by comfort (r=0.496, p<0.01), and efficiency (r=0.297, p<0.01). Thus, each factor was statistically distinct from the other. Table 3 Inter-construct correlations and square root of the AVE along the diagonal Comf

Effi

ED

TK

KSE

Mang

TME

TMI

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RP

Sust

Comf

0.831

Effi

0.393

0.879

ED

0.282

0.287

0.600

TK

0.208

0.228

0.284 0.862

KSE

0.115

-0.051

0.181 0.412

0.836

Mang

0.445

0.596

0.428 0.113

-0.024

0.858

TME

0.112

0.181

0.147 0.247

0.256

0.139

0.919

TMI

0.318

0.390

0.235 0.297

-0.340

0.419

0.623

0.747

RP

0.049

0.224

0.182 0.281

0.133

0.218

0.193

0.396 0.547

Sust

0.460

0.297

0.173 0.117

0.182

0.285

-0.404

0.496 0.090

0.788

The GoF index for this study was 0.275, which indicates a medium Goodness-of Fit index (GoF > 0.36) and that the model has better explaining power in comparison with the baseline values defined above. Thus, the model provides adequate support to validate the PLS model globally.

Structural model In this section, a review was conducted on an evaluation of the intensity of the SEMPLS structural model as the objective of the study. R2 values measurements and the corresponding t-values have been performed at this stage to measure the significance of the indicator variables. Furthermore, the model was determined to have predictive relevance, as the cross-validated redundancy result (the Stone-Geisser test Q2) was 0.166, which is greater than 0. The R2 value for the endogenous variable was 0.342, which exceeded the minimum level of 10% suggested (Falk & Miller, 1992), signifying a strong explanatory power for the model (i.e. all independent variables accounted for

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40% of the total variance in green building internal incentive). Specifically, the results of the path coefficients and t-values were itemized as outlined in Table 4, whereby efficiency in green building implementation is seen to have a significant and positive link with green building external incentive, which is well within expectations (β11.2=0.376, T-value=2.161, P-value<0.031). Hence, H11.2 is therefore supported. In a similar vein, environment development had a significant influence on efficiency in green building implementation (β3=0.502, T-value=2.919, Pvalue<0.004), inferring that H3 is also retained. Further examination of the path coefficient shows that economic uncertainty is significant and positively relates to comfortability in green building implementation (β2=0.490, T-value=3.218, p<0.001), as posited by H3. Thus, H3 is reinforced. Besides that, the others hypothesis was found to be insignificant as depicted in Table 5. Table 4 Structural model analysis results Independent Hypothesis

Dependent

Path

T-

P-

Variable

Coeff.

Value

Values

Path Variable

Decision

H12.2

Comfort

->

Inc_Ext

-0.132 0.788

0.431

Not Supported

H12.1

Comfort

->

Inc_Int

-0.024 0.125

0.900

Not Supported

H11.2

Efficiency

->

Inc_Ext

0.094 0.687

0.493

Not Supported

H11.1

Efficiency

->

Inc_Int

0.143 1.085

0.278

Not Supported

H5

Envi_Dev

->

Comfort

0.270 1.507

0.132

Not Supported

H4

Envi_Dev

->

Efficiency

0.255 1.209

0.227

Not Supported

H7

Know

->

Manageable

0.056 0.305

0.761

Not Supported

H6

Know

->

Sustainable

0.050 0.307

0.759

Not Supported

H3

Unc_Eco

->

Envi_Dev

0.066 0.569

0.570

Not Supported

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H2

Unc_Eco

->

Comfort

0.181 1.071

0.284

Not Supported

H1

Unc_Eco

->

Sustainable

0.161 0.843

0.400

Not Supported

H13.2

Manageable

->

Inc_Ext

0.018 0.129

0.897

Not Supported

H13.1

Manageable

->

Inc_Int

0.230 1.376

0.170

Not Supported

H8

Reg_Pol

->

Efficiency

0.178 0.967

0.334

Not Supported

H9

Reg_Pol

->

Manageable

0.202 0.810

0.418

Not Supported

H10.2

Sustainable

->

Inc_Ext

0.431 3.614

0.000

Supported

H10.1

Sustainable

->

Inc_Int

0.399 3.115

0.002

Supported

Discussion This study assessed the impact of green construction in the context of economic uncertainty, environment development, knowledge, and regional policy on building incentive modeling. Next, the effect of building life cycle context towards green construction implementation was examined. Finally, the low efficiency of economic problems, building environment condition, knowledge improvement, and regional mandatory was not ascertained. The results support the previous research in obstacles to green construction implementation that burden in implementation was higher effect than economic impact. On the other hand, the obstacles of green construction implementation in Vietnam and Pakistan were lack of awareness (Azeem, Naeem, Waheed, & Thaheem, 2017). It’s different in Ghana where the barriers to green construction implementation were guidance, availability of competence team, and a low number of subsidies in green building funding (A. P. C. Chan, Darko, Olanipekun, & Ameyaw, 2018). Additionally, the finding from this quantitative research proves that project accessibility, sustainability of green open space in the project, and the availability of

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capital funding in green construction to cover the incremental cost in green construction implementation towards sustainability of green construction implementation in building project has a significant positive relationship with green building incentive modeling. Hence, H10 is maintained. In other words, when project accessibility, sustainability of green open space in the project, and the availability of capital funding becomes more positive, the level of green construction implementation amongst building stakeholder would also increase because they want to get the building incentives. In the previous factor analysis, there are several building incentives modeling that building stakeholders want such as increasing the building brand market value, optimization payback period, reduce the construction tax, gross floor area concessions, and expedited in building permit.

Conclusions This study's findings provide some important practical implications for research and green practice, as sustainability in green construction implementation and green building incentive real modeling continue to be important research issues. Notably, this research confirms that sustainability in green construction implementation factors is the most critical factor that could sway building stakeholder intention in building incentive. Consequently, the building stakeholder especially regional government should portray to the consumers that they play an imperative role in practicing a green environment in the competitive marketplace. Successfully explaining a green construction building to fortify their knowledge would help to better assist in their evaluation of sustainable infrastructure. Besides that, building stakeholder of green construction need to build an outstanding green construction implementation to attract green construction of current and potential stakeholder incentive, so as to easily differentiate the benefits of green construction from conventional building, particularly for Indonesia.

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The PLS empirical findings of this study offer academic contributions to the prevalent body of knowledge on structuring green building construction incentive in the context of economic problems, building environment condition, knowledge improvement, and regional mandatory. The empirical findings also add to the growing literature around green construction stakeholder. Although this study has yielded new insights into the subject matter mentioned above, it still has some limitations, which could be used as future research opportunities. Firstly, the public sample from the capital city of Indonesia, would not be representative of the population in Indonesia as a whole. Thus, extensive sampling is recommended to improve the generalizability of the findings.

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