A Study on the Driving Factors of Carbon Neutralization Behavior in Construction Enterprises Based on a Structural Equation Model

Abstract

The carbon-neutral behavior of building construction companies is a key issue in carbon-neutral research worldwide. However, little is known about the willingness of construction firms to segregate and recycle construction waste. After studying a large amount of literature, this study finally focused on combining various statistical methods based on constructing a structural equation model of the drivers of carbon-neutral behaviors of construction enterprises to collect and analyze the attitudes and opinions of building construction enterprises on resource utilization management. The results of the study show that the willingness of construction enterprises to manage the separate disposal and utilization of construction waste is mainly influenced by perceived usefulness, perceived ease of use, and perceived risk drivers. The degree of influence was 0.36 for perceived usefulness, 0.26 for perceived risk, and 0.24 for perceived ease of use, with increasing project revenue having the greatest influence on firms’ willingness; resource cost having the least degree of influence on firms’ willingness; and subjective norms and perceived behavioral control having a non-significant influence on behavioral intentions. It further suggests that an increase in project revenue and a decrease in project cost may motivate construction firms to implement carbon-neutral projects. This study may provide a theoretical framework and research direction for construction enterprises to formulate policies for the classification and disposal of construction waste and recycling management.

1. Introduction

Global warming has become a trend, and it is a very necessary measure to change the current global warming problem through carbon neutralization. Carbon in carbon neutral refers to some natural resources composed of carbon such as oil, wood, coal, etc. The more such resources are consumed, the more carbon dioxide there is in the atmosphere, which will cause global warming, which will have a great impact on people’s lives, and it will also bring a lot of environmental problems as well as social problems. General Secretary Xi Jinping said at the United Nations General Assembly in 2020 that China needs to achieve carbon peak by 2030 and strive to achieve carbon neutrality by 2060, so it can be seen that carbon peak and carbon neutrality have become a very important link in the layout of China’s ecological civilization construction [1]. Rapidly growing cities in emerging countries have become major contributors to the generation of 3 billion tons of construction and demolition waste annually [2,3]. In these cities, a large portion of the waste is landfilled or dumped illegally and indiscriminately in urban areas, leading to material waste and inefficient land use. As the populations, physical sizes, and economies of these cities continue to expand in the coming decades (UNDESA, 2018), the associated environmental problems and land-use conflicts will only intensify in the absence of appropriate management practices [4]. As the protagonist of carbon dioxide emissions, cities occupy an important position as the main body to realize the goal of carbon neutrality. Cities are composed of buildings, and the carbon emissions from the construction industry affect the total carbon emissions in the city to a certain extent (Figure 1). In the process of realizing the goal of carbon neutrality in the city, the “carbon peak and carbon neutrality”link of the construction industry is crucial [5].

As an indispensable part of the carbon emissions of the construction industry, most of the related studies have been conducted from the perspectives of policy-making, industrial chain, and stakeholder game theory. There are relatively few studies on the willingness of the construction industry to separate and dispose of recycled waste. By studying the relevant literature, it is found that due to the lack of technology, economy, and environmental awareness, construction waste is still disposed of by open piles and simple landfills, wasting a large amount of land and causing serious environmental pollution [6]. If appropriate methods are adopted, these wastes can be recycled and even used to construct new buildings. After a comprehensive analysis, we will explore the intrinsic driving force of the willingness to manage waste in the construction industry, so as to achieve the goal of encouraging enterprises to actively implement the reuse of construction waste and break through the bottleneck of enterprise development [7].

This paper firstly elaborates on the related theories involved through a literature search, puts forward theoretical hypotheses, and sets the carbon-neutral behavioral coefficients of construction enterprises for the research objectives; secondly, it describes the structural equation model, including the conceptual model, formulaic equations, advantages, and disadvantages, etc.; thirdly, it carries out the exploratory factor analysis, goodness-of-fit test, and the fitting path analysis of different indexes through the empirical research; and lastly, it discusses and draws the final conclusions on the results.

The construction industry, as one of the industries with more serious carbon emissions in China, has become one of the main directions for the future development of the construction industry by searching for a realization path to achieve carbon neutrality. This study aims to identify the main factors affecting construction waste disposal, analyze the intention of construction enterprises to dispose of construction waste, and explore the relationship between the influencing factors.

2. Relevant Theories

2.1. Main Theoretical Basis

Through a literature search, this study adopted the Theory of Planned Behavior [8,9] as the main theoretical basis because the model is applicable to identify the drivers ofwaste separation and disposal in construction companies. The Theory of Planned Behavior includes five basic variables: attitudes, subjective norms, perceived behavioral control, behavioral intentions, and behaviors (Figure 2).

Attitude refers to the positive or negative emotions an individual displays when taking a particular action. Subjective norms refer to the social pressures that an individual feels when taking a particular action [10]. Perceived behavioral control refers to the assessment of the difficulty of taking a particular action based on the individual’s own abilities, combined with his or her past experiences, and the recognition of resistance to the action [11]. If the attitude is more positive, subjective norms are evident, and the ability to perceive and control the behavior is strong, then the individual’s will to act is stronger, and vice versa.

2.2. Theoretical Assumptions

Through a literature survey on the worldwide classification and disposal of construction waste, combined with the Theory of Planned Behavior, it was deduced that in the implementation of waste classification and disposal at construction sites, there are important high-frequency factors, which can be divided into four elements: perceived usefulness, perceived ease of use, perceived risk, and perceived willingness to act. The three factors of perceived usefulness, perceived ease of use, and perceived risk are all based on the construction company’s perception of carbon-neutral-related technologies, while the willingness factor judges its own acceptance of the resource. In order to explore the potential drivers of waste segregation and disposal in construction companies, the following five hypotheses were proposed for testing [12,13,14].

H1. 

Perceived usefulness has a significant positive effect on the willingness of construction firms to implement separate waste disposal.

H2. 

Perceived ease of use has a significant positive effect on the action of implementing waste separation and disposal in construction firms.

H3. 

Perceived risk is negatively related to the willingness of construction firms to implement waste separation and disposal.

H4. 

Subjective norms are positively related to the willingness of construction companies to act.

H5. 

Perceived behavioral control is positively related to behavioral willingness.

2.3. Screening of Carbon-Neutral Behavioral Factors in Construction Enterprises

Behavioral factors are constructed according to the theoretical assumptions and the opinions of industry experts in the interviews. It includes perceived usefulness, perceived ease of use, perceived risk, subjective norms, perceived behavioral control, and behavioral willingness (

Table 1. Indices of carbon neutrality behavior factors for CE.

Variable	Observation Variables	Source
Perceived usefulness	Reduced tax penalties	[15,16,17,18,19]
	Corporate strategic management concepts
	Increased project revenue
	Government financial subsidies
Perceived easeof use	Policy support	[20,21,22,23]
	Technical support
	Type of closed-loop industrial chain operation
	Reasonable reusable product system
Perceived risk	Consumer concerns	[24,25,26,27,28]
	Extension of project duration
	High recycling costs
Subjective norms	Pressure from mandatory government regulations	[23,29,30,31]
	Peer pressure on environmental issues
	Social environmental pressure
	Market demand pressure
Perceived behavioral control	Sufficient financial, material, and human resources	[21,28,32,33]
	Enterprise operations and management methodology
	Past project experience
Behavioral willingness	Willingness to participate in carbon neutrality behavior	[23]
	Willingness to strengthen carbon neutrality behavior
	Willingness to expand carbon neutrality behavior

Note: This paper directly utilizes search systems to obtain literature information. They are mainly the China Knowledge Network (CNKI) and the Wanfang database.

).

2.4. Structural Equation Modeling

2.4.1. Conceptual Modeling

Currently, structural equation modeling SEM has become one of the effective methods for identifying and verifying causes and influencing factors in both academia and industry, and its main advantage lies in its ability to quantify causes that cannot be directly measured (endogenous latent variables and exogenous latent variables), its ability to accurately measure and gauge the relationship between endogenous latent variables and exogenous latent variables, the relationship between endogenous latent variables and endogenous latent variables, the relationship between exogenous latent variables and exogenous latent variables, and the relationship between latent variables and corresponding observed variables [31]. It can accurately measure the relationship between endogenous latent variables and exogenous latent variables, between endogenous latent variables and endogenous latent variables, between exogenous latent variables and exogenous latent variables, and between latent variables and their corresponding observational variables (measurement variables). It can also reasonably rank and screen the influencing factors and causes, ultimately identify the influencing factors and causes, and determine the degree of influence of the influencing factors and causes on the dependent variable, the significance of the effect, and the direction of the effect. Structural equation modeling mainly includes two main models, i.e., the measurement model and structural model, and the composition structure of structural equation modeling SEM is shown in Figure 3. Among them, the measurement model mainly characterizes and measures the relationship between latent variables (specifically including endogenous latent variables and exogenous latent variables) and measurable variables; the structural model mainly expresses and measures the causal relationship between latent variables [34].

The modeling steps of structural equation modeling mainly include constructing the initial structural equation model, determining the relationship between endogenous latent variables and extrinsic latent variables as well as the relationship between latent variables and explicit variables, estimating the path coefficients through AMOS software, and calculating the goodness-of-fit indexes of the structural equation model (the main key goodness-of-fit indexes include specifically the chi-square statistic to the ratio of the degrees of freedom, RMSEA, CFI, TLI, and the specific formulas are shown below in Equations (1)–(4)). Set the correction path and correction index of the structural equation model, construct the structural equation model again, and estimate the path coefficients again until the corresponding goodness-of-fit indexes of the structural equation model reach the specified standards, etc.

$$PDF=Max[\frac{{\chi }^2-df}{N-1},0]$$

(1)

$$RMSEA=Sqrt(\frac{PDF}{df})$$

(2)

$$TLI=NNFI=\frac{({\chi }_N^2/d{f}_N)-({\chi }_T^2/d{f}_T)}{({\chi }_N^2/d{f}_N)-1}$$

(3)

$$CFI=1-\frac{\max ({\chi }_T^2-d{f}_T,0)}{\max ({\chi }_N^2-d{f}_N,0)}$$

(4)

where $E$ denotes the regeneration covariance matrix; $S$ denotes the sample covariance matrix; $I$ denotes the unit matrix; $N$ denotes the sample capacity; $p$ is the number of observed variables; $df$ is the degrees of freedom; $d{f}_N$ is the degrees of freedom of the dummy model; and $d{f}_T$ is the degrees of freedom of the theoretical model [35].

2.4.2. Limitations of the Method

① Subjectivity of model setting: the model setting of SEM depends on the subjective experience and theoretical assumptions of the researcher, and there may be selective bias.

② Uncertainty of model parameter estimation: the results of SEM are affected by the estimation error of model parameters, so the results may have some uncertainty.

③ Limitations of model complexity: the complexity of the SEM model is limited by factors such as sample size and data type, and if the model is too complex, it may lead to overfitting.

④ Limitations of model assumptions: The model assumptions of SEM include linear relationships between variables, normal distribution, random errors, etc. These assumptions may not be in line with the actual situation, so the results of SEM need to be interpreted with caution [36].

3. Empirical Research

3.1. Modeling

Structural equation modeling describes the factors and channels affecting the willingness of enterprises to implement construction waste separation and disposal and provides a theoretical framework for the development of construction waste separation and disposal management policies (Figure 4). The hypothesized six candidate variables of perceived usefulness, perceived ease of use, perceived risk, subjective norms, perceived behavioral control, and behavioral willingness affect the behavioral drivers of construction firms’ sorting and disposal behavior of reusable waste. There are 21 observed variables in the model.

3.2. Questionnaire Design and Data Collection

3.2.1. Questionnaire Design

Based on the Theory of Planned Behavior and Technology Acceptance Model, as well as the literature on the behavior of waste separation and disposal in construction enterprises, a questionnaire on the drivers of waste separation and disposal in construction enterprises was designed. And based on the conceptual model, the questionnaire on Drivers of Separate Disposal Behavior of Consumer Electronic Reusable Waste in China was designed using a five-level Likert scale method. The survey used an online tool to distribute and collect the questionnaires. A total of 268 questionnaires were collected. Among them, 247 questionnaires were valid, with a valid recovery rate of 92.2%.

In the process of questionnaire design, five independent variable factors of perceived usefulness, perceived ease of use, perceived risk, subjective norms, perceived behavioral control and one dependent variable factor of behavioral intention were extracted, and the questionnaire questions were designed according to these six factors. The questionnaire is divided into two parts: the first part mainly collects information about personal characteristics, such as gender, age, length of service, education, and nature of the enterprise. The second part is the core part of the questionnaire, which investigates the factors influencing the management of carbon-neutral behavioral utilization in construction. Respondents were mainly given five different levels of choices to answer the questions, namely, “Totally disagree”, “Disagree”, “Unsure”, “Agree”, and “Totally agree”. In particular, there were four sub-questions measuring the perceived usefulness dimension under question Q8, four sub-questions measuring the perceived ease of use dimension under question Q9, three sub-questions measuring the perceived risk dimension under question Q10, four sub-questions measuring the subjective norms dimension under question Q11, three sub-questions measuring the perceived behavioral control dimension under question Q12, three sub-questions measuring the behavioral intention dimension under question Q12, and three sub-questions measuring the behavioral intention dimension under question Q13.

3.2.2. Data Collection

Advanced computer software such as Python 3.10 was used to distribute the links, and professional questionnaire distributors and government agencies were commissioned to distribute the questionnaires [37,38]. The attribute characteristics of valid samples were counted. After the original questionnaire was completed, relevant experts were first invited to appraise the original items to check whether the item setting of each variable could achieve the purpose of the survey and whether the item expression was easy for the undertaker to understand correctly. In order to accurately express the questionnaire items, we went to the construction site to improve the wording and expression of the questions according to the actual situation of the construction unit. The “Research on the Willingness of Construction Enterprises to Carbon Neutral Behavior” questionnaire consists of two parts: the first part mainly collects the basic information of the respondents, and the second part, which is the core part of the questionnaire, investigates the influencing factors of the carbon-neutral behavior of construction enterprises.

3.2.3. Sample Characteristics

Table 2. Sample attribute characteristics of the questionnaire on waste separation and disposal behavior of construction enterprises.

Variable	Option	Frequency	Percentage (%)
Gender	Male	160	64.8
	female	87	35.2
Age	under 25	45	18.2
	25–35	95	38.5
	36–45	75	30.4
	above 45	32	12.9
Working years	less than 3 years	41	16.6
	3–5 years	56	22.7
	6–10 years	81	32.8
	11–15 years	50	20.2
	more than 15 years	19	7.7
Education	specialty	61	24.7
	undergraduate	142	57.5
	graduate student	36	14.6
	doctor	8	3.2
Enterprise nature	private enterprise	102	41.3
	state-owned enterprise	89	36
	foreign-funded enterprises	56	22.7
Years of establishment	less than 5 years	78	31.6
	5–10 years	100	40.4
	10–15 years	53	21.5
	more than 15 years	16	6.5
Enterprise location	eastern region	186	75.3
	western region	61	24.7
Degree of understanding	understand	78	31.6
	Heard about it but do not understand.	135	54.7
	Never knew about it.	34	13.7

Note: “Frequency” in the table refers to the number of times the option appeared in the questionnaire.

shows the distribution of sample attribute characteristics of the questionnaire on influencing factors of carbon-neutral behavior in construction enterprises. From the questionnaire data, it can be found that the respondents are mostly male, accounting for 64.8% of the total data. There is an uneven distribution of men and women in the construction industry, and the management and construction teams are still dominated by men, which is in line with the actual situation. This shows that the survey is representative to a certain extent. Among the respondents, 38.5% were aged between 25 and 35, followed by 30.4% aged between 36 and 45. It can be seen that the construction workforce is gradually becoming younger. In terms of years of working experience, 32.8% of the respondents had worked for 6–10 years. In terms of education, undergraduates accounted for the largest proportion of the total sample, 57.5%, and postgraduates accounted for 14.6%. It can be seen that the personnel of construction enterprises have a high educational background. In terms of enterprise type and qualification, there are 102 private enterprises, accounting for 41.3% of the total. In the eastern part of the city, where the economy is relatively developed, about 31.6% of the respondents know about the classified disposal of construction waste, while in the western part of the city, where the economy is relatively underdeveloped, only 13.7% of the respondents have heard of the classified disposal of construction waste. State-owned enterprises (SOEs) are more willing to participate in the management of carbon-neutral behavioral utilization of construction sites because large enterprises are more influenced by national policies. There is a potential psychological motivation for SOEs to respond positively to the government’s call to conserve natural resources.

3.3. Reliability and Validity Tests

3.3.1. Reliability and Validity Test

Questionnaire: The raw data collected need to be checked for reliability and validity, and the reliability and validity of the survey results are analyzed using SPSS20.0 software and AMOS20.0 so as to obtain consistent and valid data. Only data that reflect the reasonableness of the questionnaire after the reliability and validity analysis will be used.

The results show that the overall Cronbach’s alpha of the questionnaire is 0.851 (

Table 3. Potential variable characteristics and reliability test results.

Potential Variables	Number of Measurement Items	Observed Variable	Cronbach’s Alpha
Perceived usefulness	4	PU1–PU4	0.856
Perceived easeof use	4	PEU1–PEU4	0.886
Perceived risk	3	PR1–PR3	0.813
Subjective norm	4	SN1–SN4	0.798
Perceived behavior control	3	PBC1–PBC3	0.895
Behavioral willingness	3	BW1–BW3	0.857

PU1 = Reduce tax penalties; PU2 = Enterprise strategic management philosophy; PU3 = Increase project revenue; PU4 = Government financial subsidies; PEU1 = Policy support; PEU2 = Technical support; PEU3 = Closed-loop industrial chain operation mode; PEU4 = Rational renewable product system; PR1 = Consumer concerns; PR2 = Extension of construction period; PR3 = High resource cost; SN1 = Government mandatory regulatory pressure; SN2 = Environmental protection pressure from other companies in the same industry; SN3 = Social environmental pressure; SN4 = Market demand pressure; PBC1 = Sufficient financial, material, and human resources; PBC2 = Enterprise operation and management mode; PBC3 = Existing project experience; BW1 = Willing to try carbon-neutral behavior; BW2 = Willing to increase the intensity of carbon neutrality; BW3 = Willing to improve carbon neutrality.

), which indicates that each observed variable represents the latent variable, and the overall data of the scale are reliable with a high reliability value, which indicates that each question of the questionnaire reflects the wishes of the construction companies.

3.3.2. Exploratory Factor Analysis

Exploratory factor analysis was performed on the 21 measurements using SPSS20.0 software. The principal component analysis with Oblimin rotation was used to obtain 6 common factors, and the orthogonal rotation coefficient matrices are shown in

Table 4. Factor load matrix after PU orthogonal rotation.

Observed Variable	Factor 1
Enterprise strategic management concept (PU2)	0.883
Increase project income (PU3)	0.871
Government financial subsidy(PU4)	0.865
Reduce tax penalty(PU1)	0.812

PU = Perceived usefulness.

,

Table 5. Factor load matrix after SNorthogonal rotation.

Observed Variable	Factor 2
Environmental protection pressure of the same industry (SN2)	0.875
Government mandatory regulatory pressure(SN1)	0.865
Social environmental pressure(SN3)	0.863
Market demand pressure(SN4)	0.785

SN = Subjective norm.

,

Table 6. Factor load matrix after PEU orthogonal rotation.

Observed Variable	Factor 3
Closed-loop industrial chain operation mode (PEU3)	0.894
Reasonable regeneration product system(PEU4)	0.881
Government support (PEU1)	0.826
Technical support (PEU2)	0.790

PEU = Perceived easeof use.

,

Table 7. Factor load matrix after PR orthogonal rotation.

Observed Variable	Factor 4
Duration extension(PR2)	0.872
High resource cost (PR3)	0.863
Consumer doubts(PR1)	0.852

PR = Perceived risk.

,

Table 8. Factor load matrix after BW orthogonal rotation.

Observed Variable	Factor 5
Willing to try classified disposal management(BW1)	0.845
Willing to improve classified disposal management level (BW3)	0.826
Willing to increase classified management(BW2)	0.810

BW = Behavioral willingness.

and

Table 9. Factor load matrix after PBC orthogonal rotation.

Observed Variable	Factor 6
Enterprise operation management mode(PBC2)	0.865
Existing project experience (PBC3)	0.850
Sufficient financial, material, and human resources (PBC1)	0.793

PBC = Perceived behavior control.

.

3.3.3. Model Fit Goodness-of-Fit Test

The model fit obtained using AMOS 20.0 software is shown in

Table 10. Model fit index.

Statistic	Value	Standard	Fit or Not
RMSEA	0.036	<0.08	yes
CFI	0.978	>0.9	yes
TLI	0.975	>0.9	yes

, where root mean square error of approximation (RMSEA) = 0.036 < 0.08, comparative fit index (CFI) = 0.978 > 0.90, Tucker–Lewis index (TLI) = 0.975 > 0.90, indicating that the model is acceptable.

In Figure 5, Q8 is used to measure four dimensions of perceived usefulness, Q9 is used to measure four dimensions of perceived ease of use, Q10 is used to measure three dimensions of perceived risk, Q11 is used to measure four dimensions of subjective norms, Q12 is used to measure three dimensions of perceived behavioral control, and Q13 is used to measure three dimensions of behavioral willingness.

The calculation results (Figure 5) show that the values of commonly fitted indicators meet the requirements. The analysis results show that the management intention of enterprises to separate construction waste for disposal and utilization is mainly influenced by “perceived usefulness”, “perceived ease of use”, and “perceived risk”. The degree of influence, in descending order, is 0.36 for “perceived usefulness”, 0.26 for “perceived risk”, and 0.24 for “perceived ease of use”, with “increasing project revenue” in “perceived usefulness” having the greatest influence on the path of the firm’s intention. However, “Subjective Norms” (0.12) and “Perceived Behavioral Control” (0.14) have no significant effect on behavioral intention.

By analyzing the reliability of the data of the factors in the questionnaire, combined with Table 3, it can be seen that Cronbach’s alpha of perceived usefulness is 0.856, a high reliability value, indicating that questions Q8-1, Q8-2, Q8-3, and Q8-4 are consistent with the variable of perceived usefulness; and that Cronbach’s alpha of perceived ease of use is 0.886, a high reliability value, indicating that Questions Q9-1, Q9-2, Q9-3, and Q9-4 are also consistent with the perceived ease of use variable; Cronbach’s alpha for perceived risk is 0.813, a high reliability value, indicating that questions Q10-1, Q10-2, and Q10-3 are consistent with the perceived risk variable; and Cronbach’s alpha for subjective norms is 0.798, a high reliability value, indicating that questions Q11-1, Q11-2, Q11-3, and Q11-4 are consistent with subjective norms; Cronbach’s alpha for perceived behavioral control is 0. Cronbach’s alpha for behavioral willingness is 0.857, a high reliability value, indicating that questions Q13-1, Q13-2, and Q13-3 are consistent with behavioral willingness variables.

4. Final Path Analysis

The significance of the standardized path coefficient estimates is shown in

Table 11. Significant level of model fitting path coefficient.

Assumed Path	Standard Deviation Estimate	Estimate	Critical Ratio	Research Hypothesis(H)	Inspection Results
Perceived usefulness vs. Perceived easeof use	0.176	0.226	2.776	H2	Not established
Behavioral willingness vs. Perceived easeof use	0.326	0.258	5.491	H1	Established
Behavioral willingness vs. Perceived risk	−0.236	−0.212	−4.096	H3	Established
Behavioral willingness vs. Subjective norms	0.181	0.167	3.075	H4	Not established
Behavioral willingness vs. Perceived behavior control	0.176	0.182	2.931	H5	Not established

.

The result of the analysis of hypothesis H1, the path coefficient of behavioral willingness and perceived ease of use, is 0.326, p < 0.001. The test result is acceptable, and the hypothesis can be verified, which means that perceived ease of use has a significant positive effect on behavioral willingness. It was found that increasing program income (PU3) had the greatest effect on perceived usefulness, followed by financial subsidies (PU4), while reducing penalties (PU1) had the least effect on perceived usefulness.

For hypothesis H2, the path coefficient of perceived usefulness on perceived ease of use is 0.176, p = 0.005 > 0.001, which indicates that the test result is unacceptable and disproves the hypothesis that perceived ease of use has no significant effect on perceived usefulness.

For hypothesis H3, the coefficient of perceived risk on behavioral intention is −0.236, p < 0.001. The test result is realistic and verifies the hypothesis that perceived risk has a significant negative effect on behavioral intention. It can be seen that cost increase (PR3) has the greatest effect on perceived risk, followed by time delay (PR2).

For hypothesis H4, the path coefficient of subjective norms on behavioral intentions is 0.181, p = 0.003 > 0.001. the test result seems to be untrue and the hypothesis is disproved, indicating that subjective norms have a small effect on behavioral intentions.

For hypothesis H5, the path coefficient of perceived behavioral control on behavioral willingness is 0.176, p = 0.004 > 0.001, which indicates that the test results are not real and the hypothesis is overthrown, suggesting that the influence of perceived behavioral control on behavioral willingness is not obvious.

Through the above comprehensive analysis, the management willingness of construction enterprises in the separate disposal and utilization of construction waste is mainly influenced by perceived usefulness, perceived ease of use, and perceived risk drivers. The degree of influence is, in order, 0.36 for perceived usefulness, 0.26 for perceived risk, and 0.24 for perceived ease of use. Project revenue, financial subsidies, reduced penalties, increased costs, and time delays all have an impact on the carbon-neutral behavior of the enterprise, and the degree of influence is varied, with increased project revenue having the greatest influence on the willingness of the enterprise; resource costs having the least; subjective norms and perceived behavioral control had insignificant effects on behavioral intentions. It further suggests that the increase in project revenue and the decrease in project cost may motivate construction companies to implement carbon-neutral projects.

5. Discussion and Conclusions

5.1. Discussion

The influencing factors such as project income, financial subsidies, penalty reduction, cost increase, and time delay derived from the study correspond to the research results of previous scholars, which indicates that the research results of this paper are more reasonable. This paper uses structural equation modeling to explore and analyze the drivers of carbon-neutral behavior in construction companies in five aspects: attitude, subjective norms, perceived behavioral control, behavioral intention, and behavior. Although some scholars have studied the issue [39,40,41], most of them have started from a single perspective and take theoretical analysis as the main research method;thus, the research process and results are too one-sided. Compared with the research content of other scholars in this field, this paper focuses on the above aspects of construction waste disposal and thus explores the specific impact of carbon-neutral driving factors of the research, which is more comprehensive.,At the same time, the questionnaire survey and structural equations combined with the methodology used in this field have a certain degree of innovation.

5.2. Conclusions

In this study, a comprehensive analytical model was developed to explore the endogenous motivation of construction firms to participate in construction waste recycling, through which construction firms are urged to actively implement construction waste reuse to break their development bottles. Through the study, it was concluded that perceived ease of use has a significant positive effect on behavioral willingness, and perceived risk has a significant negative effect on behavioral willingness These can reduce the carbon emissions generated by the construction industry in terms of waste by improving the behavioral willingness to dispose of construction waste.

There are a lot of factors affecting “carbon neutrality” in the construction industry other than those studied in this paper, such as carbon offsetting mechanismsthat can be considered: collection of green construction planting requirements for on-site tree-planting projects, thus realizing forest carbon offsetting [42]; construction of additional water conservancy and hydropower projects, realizing carbon offsetting at the engineering level; deepening the details related to carbon offsetting in each link on the existing green construction requirements; details related to carbon offset, etc. It is also possible to consider strengthening the electrification of on-site engineering machinery to reduce the proportion of petroleum fuels; optimizing power and heat transmission methods and routes to reduce energy loss in the transmission project; strengthening the thermal insulation function of the building to reduce energy loss; and establishing a financial trading market for carbon sinks in the construction industry to constrain the carbon emission behaviors of the construction enterprises from the angle of interests [43]. Numerous factors such as these can make the evaluation model more refined. However, in connection with management’s willingness to include construction waste placement in the building industry, the perceived usefulness, perceived risk, perceived ease of use, and the specific factors involved in influencing these perceptions such as increased project income, financial subsidies, and increased costs will provide some reference to the research in the direction of the management willingness of construction waste placement in the building industry.

This study fits in with the “14th Five-Year Plan Guidelines for the Comprehensive Utilization of Bulk Solid Waste in China” which clearly states that by 2025, the recycling capacity of bulk solid waste will be greatly improved, the scale of recycling will continue to expand, and the urban ecological environment will continue to improve. We believe that the establishment of a dynamic mechanism within construction companies for construction waste recycling will go a long way toward achieving carbon neutrality.