From Policy to Practice: A Systems Approach to Green Building Advancement for Regional Sustainability

Abstract

Green buildings play a pivotal role in advancing urban sustainability and fostering a green economy. This study constructs an integrated model to examine the key drivers of green building development and their influence on urban greening. Data were gathered from 403 stakeholders across Shandong Province, China, and analyzed using Structural Equation Modeling through SPSS and AMOS. The results demonstrate that policy, market demand, technological advancement, and long-term economic benefits are critical drivers, among which policy exerts the most significant influence, while economic viability serves as a fundamental incentive for market adoption. Quantitatively, the SEM path coefficients indicate that policy (β ≈ 0.56) and market forces (β ≈ 0.55) exert the strongest effects, followed by technology (β ≈ 0.49) and economic factors (β ≈ 0.38). The study further identifies eight specific sub-factors—including streamlined approval processes and enhanced regulatory measures—and proposes nine targeted strategies to foster sustainable and economically viable building practices. These findings align with several Sustainable Development Goals, such as affordable and clean energy, sustainable cities and communities, and climate action, offering a strategic framework to guide policymakers and industry stakeholders toward synergistic achievement of environmental and economic objectives.

1. Introduction

The significance of green buildings in urban greening initiatives is profound, as they substantially enhance both urban ecological systems and societal progress through the integration of sustainable architectural practices and natural elements. Their design and architecture contribute essential green spaces to urban areas, including green roofs, eco-walls, and courtyards. These features not only enhance the aesthetic appeal of urban environments but also support biodiversity by creating vital habitats and augmenting urban green oases. Consequently, green building is an indispensable component of urban greening efforts and is crucial for creating modern cities that are pleasant to live and work in [1]. The United Nations Environment Program has articulated clear goals and strategies for enhancing green building development through its Global Action Plan for Green Buildings. This plan underscores the pivotal role of green buildings in combating climate change and reducing energy consumption and environmental degradation, advocating for collaborative efforts among governments, businesses, and the public [2]. The European Union’s Building Energy Efficiency Directive mandates member states to establish and uphold stringent energy efficiency standards for buildings and to advance the green transformation within the construction sector. The Chinese government has promulgated the Opinions on Promoting Green Development in Urban and Rural Construction, delineating precise objectives for the erection of high-quality green buildings and the execution of strategies aimed at achieving carbon peak and carbon neutrality in the construction industry [3].

The green building progression has been disparate. In regions with lower economic development, the advancement of green building has been significantly impeded by financial and technological constraints [4]. This geographical disparity constrains the widespread adoption and implementation of green building on a national scale [5]. Numerous scholars have conducted extensive research on green building, encompassing diverse facets such as energy-efficient technologies [6,7], assessment criteria [8], construction methodologies [9], cost–benefit analyses [10], and the environmental ramifications of green building [11]. These investigations contribute to the advancement and integration of green building technologies and the enduring progress of the construction sector. Green building evolution should not solely concentrate on the physical edifice itself but should also comprehensively consider external influences such as social and economic impacts [12]. Pivotal influencers’ elucidation and developmental trajectories for green building at a macroscopic level lacks definitive clarity. This research centers on this quandary, disseminates surveys to stakeholders involved in sustainable building projects in China, establishes structural equation models, and conducts quantitative analyses utilizing SPSS 27 and AMOS 28 software. It endeavors to discern the principal inhibiting factors that curtail the development of green buildings and to propose suitable trajectories for accelerating the proliferation of green buildings, thereby advancing urban greening objectives. Therefore, the aim of this study is to integrate green building models with urban greening, identify the key drivers of urban greening, and develop targeted strategies that contribute to sustainable urban development.

2. Literature Review and Research Hypothesis

In order to better understand how green building practices contribute to urban greening, this study seeks to address the following core question: What are the key driving factors that influence the extent to which green buildings promote urban greening in different regional contexts? More specifically, the study investigates how four dimensions—namely, policy support, long-term economic benefits, market development, and technological advancement—affect the role of green buildings in advancing urban greening. These questions are further specified and tested through four corresponding hypotheses within the structural equation model.

2.1. Policy and Green Building

Policies fostering the development of green buildings advocate for the incorporation of energy-efficient materials, advanced green technologies, and renewable energy sources [13]. This approach significantly diminishes the environmental impact associated with both the construction and operational phases of buildings [14]. Green structures are frequently designed incorporating elements such as green roofs and vertical gardens. These features not only enhance the aesthetic appeal of urban environments but also contribute to improved air quality, noise pollution reduction, and increased biodiversity within urban settings [15]. Furthermore, the enactment of these policies has reinforced ecological standards within urban planning and architectural design. Green building directives typically mandate that new constructions adhere to specific environmental criteria [16], thereby encouraging developers and architects to integrate sustainable practices from the initial stages of design [17]. As these policies become more widespread and implemented, the principles of green building are increasingly applied across various facets of urban development [18], fostering more ecologically balanced and sustainable urban configurations [19]. For instance, urban planning might designate additional public green spaces or establish continuous ecological corridors to preserve ecosystem integrity and functionality [20]. Moreover, green building policies serve to heighten public awareness and engagement in environmental conservation [21]. Through targeted education and outreach efforts, a broader segment of the population begins to acknowledge the environmental and health advantages of green buildings, leading to greater support for and active participation in urban greening projects [22]. This surge in societal consciousness forms the foundation for achieving sustainable urban development and acts as a pivotal driver for advancing enduring environmental policy transformations [23]. However, the effectiveness of such policies may vary significantly across regions with differing levels of governance capacity, institutional support, and public awareness. In highly developed urban centers, policy enforcement tends to be stronger, while in less developed inland cities, the implementation may face practical barriers. This regional disparity influences how green building policies actually contribute to urban greening. Therefore, we establish Hypothesis 1.

H1. 

The enactment of targeted green building development policies can promote urban greening.

2.2. Economic and Green Building

The enduring economic viability of green buildings significantly enhances urban greening efforts by boosting energy efficiency and diminishing maintenance expenditures [24]. This economic advantage is predominantly manifested in green structures through energy and water conservation, the durability of construction materials, and enhanced comfort for occupants [25]. Green buildings decrease reliance on conventional energy sources by optimizing the utilization of natural illumination and ventilation, leveraging natural resources such as sunlight and wind [26]. Regarding water management, these buildings often incorporate rainwater harvesting and recycling systems, which not only lessen dependence on municipal water supplies but also are cost-effective to operate and maintain, thus reducing total operational expenses [27]. In terms of material selection, green buildings typically prefer materials that are durable and require minimal maintenance. This choice not only prolongs the structure’s lifespan but also minimizes the environmental impact associated with maintenance activities [28]. Additionally, the design of green buildings prioritizes occupant comfort, which elevates the desirability of these buildings for residential or commercial use [29]. A comfortable living or working environment curtails the energy demands of occupants. Optimal indoor air quality and ample natural lighting can significantly enhance occupant health and overall quality of life, factors that directly influence the market value of a building [30]. Consequently, the long-term economic performance of green buildings not only lowers operational and maintenance costs but also elevates living standards and operational efficiency, benefits that collectively support and advance urban greening initiatives [31]. Nevertheless, in less economically developed regions, the high initial costs and limited financial incentives can hinder the adoption of green building practices, thus weakening the long-term economic argument. Contrasts between economically vibrant coastal cities and resource-constrained rural or inland areas highlight the need to tailor economic strategies regionally. Therefore, we establish Hypothesis 2.

H2. 

Long-term economics of green buildings can promote urban greening.

2.3. Market and Green Building

As global attention to climate change intensifies and the pursuit of sustainable development goals escalates, the green building market is experiencing unprecedented growth opportunities [32]. The surge in demand for green buildings has spurred an increase in the supply of related products and services [33]. With the widespread adoption and cost reduction of green building products and technologies, more construction projects can seamlessly integrate these green elements, thus promoting the construction of green spaces at the urban level [34]. The expansion of the green building market has attracted the attention of more investors and developers. Recognizing the potential and long-term benefits of the green building market, investors and developers are increasingly willing to fund projects that adhere to environmental standards [35]. This investment extends beyond new projects to include the greening of existing buildings [36]. Consequently, as capital inflows increase, urban greening projects also receive more financial support, leading to broader implementation [37]. As the green building market matures, the awareness and acceptance of green buildings among consumers and businesses are also increasing [38]. This enhanced recognition helps to create a favorable market environment, positioning green buildings as a trend rather than an exception [39]. Such a market environment not only drives the construction of more green buildings but also encourages the adoption of broader urban greening measures, such as urban parks, street greening, and ecological design of public spaces [40]. The promising prospects of the green building market, driven by potential market demand growth and financial investment, bring more green spaces to cities while also promoting environmental awareness and the adoption of green lifestyles [41]. However, regional differences in market maturity, consumer demand, and private investment lead to uneven adoption of green building initiatives across China. For example, first-tier cities have more robust green building markets, whereas third- and fourth-tier cities often lack investor confidence and sufficient awareness, limiting market-driven greening efforts. Therefore, we establish Hypothesis 3.

H3. 

Promising green building market can promote urban greening.

2.4. Technology and Green Building

The design philosophy of green buildings emphasizes harmonious coexistence with the natural environment, which directly promotes the integration of architecture and greening [42]. Green buildings utilize advanced technologies such as smart water resource management systems, which efficiently utilize and recycle rainwater and greywater [43]. These systems not only reduce dependency on urban water supply systems but also provide necessary water resources for urban greening [44]. With such technology, urban greening is no longer limited by water resource shortages and can be implemented in a broader area. Furthermore, energy management technology is a crucial component of green buildings [45]. For example, the use of solar panels and wind energy systems provides buildings with energy, reducing reliance on fossil fuels [46]. This self-sufficient energy solution alleviates the burden on urban energy infrastructure, reduces environmental pollution, and creates healthier environmental conditions for urban greening [47]. The popularization and application of green building technologies have driven the development and use of environmentally friendly materials, such as recycled and locally sourced materials [48]. The use of these materials reduces the negative environmental impact of construction and motivates the entire construction industry towards more sustainable practices [49]. As the application of sustainable building materials becomes the norm, it also sets new standards and expectations for urban greening [50]. The application of these technologies not only enhances the functionality of the buildings themselves but also contributes to the sustainable development of the entire city [51]. Nonetheless, access to advanced technologies remains uneven across regions. Urban centers with strong R&D capabilities and government funding can more easily deploy and benefit from cutting-edge green technologies, while rural or underdeveloped areas may lack the infrastructure and expertise to implement such systems effectively. This discrepancy affects the practical contribution of technology to urban greening. Therefore, we establish Hypothesis 4.

H4. 

Advanced technologies for green buildings can promote urban greening.

3. Research Design

3.1. Measurement Indicators Selection

Table 1. Latent variables and Observed Variables.

Latent Variables	Serial	Observed Variables	Sources
Green building Policy	P1	Reasonableness and feasibility of relevant policies and standards	[15,16,18,20,22,23]
	P2	Green building program rewards and incentives
	P3	Restrictions on conventional buildings and guiding green building development
	P4	Development and implementation of green building technologies
	P5	Stringency in approving and regulating green building projects
	P6	Government sustainability awareness
Green building Economic	E1	Research inputs for green buildings	[26,27,29,30,31]
	E2	Green building incremental costs
	E3	Green building economic benefits
	E4	Economic development level
Green building Market	M1	Market demand	[32,34,35,38,40]
	M2	Consumer willingness to pay
	M3	Public environmental protection awareness
	M4	Industry chain integrity
	M5	Public awareness of green buildings
Green building Technology	T1	Green technology maturity	[42,44,45,48,49]
	T2	Green building design and construction level
	T3	Green technology exchange
	T4	Green building operational effects
	T5	Green building technology materials R&D and application capacity
Urban Greening	G1	Green Coverage Ratio	[5,7,12,13,21,36,46]
	G2	Biodiversity
	G3	Air Quality Improvement
	G4	Public Engagement and Satisfaction
	G5	Sustainable Management and Maintenance

delineates measurement indicators associated with each latent variable. Policy dimension encompasses 6 elements, including rationality and feasibility of policy standards. Economic dimension comprises 4 factors, such as incremental cost associated with green building initiatives. The market dimension integrates 5 variables, including consumer willingness to pay. Technology dimension entails 5 factors, such as green technologies maturity level. Green building development dimension involves five factors, including green buildings market share, etc. All variables were assessed using a 5-point Likert scale, where 1 score signifies strong disagreement and 5 score denotes strong agreement. The use of the 5-point Likert scale is justified as it effectively captures respondents’ subjective perceptions and attitudes, which are essential for latent constructs such as policy support, market awareness, and technological capability. The Likert-type items were treated as continuous variables in the SEM analysis, consistent with established methodological practice for large-sample structural modeling. All variables share the same scaling structure; therefore, additional normalization was unnecessary, ensuring comparability and stability of parameter estimation.

3.2. Questionnaire Distribution and Collection

The research objectives were established through consultations with stakeholders involved in green building projects across China and through collaboration with alumni networks previously engaged with our research team. We provided these organizations, along with their qualified senior and mid-level managers, with the necessary contact information to enable their participation in the survey process. Verbal informed consent was obtained from all respondents prior to data collection. All research activities complied with relevant institutional guidelines and adhered to the ethical principles of the Declaration of Helsinki, which emphasize individual autonomy, voluntary engagement, confidentiality, and the freedom to withdraw at any stage. Although initially intended for biomedical research, the Declaration now serves as a global benchmark for ethical conduct in studies involving human participants, including social science investigations [26,35]. Its application in this research guarantees adherence to international ethical norms and reinforces the integrity of questionnaire administration and data handling.

The survey data were primarily collected from stakeholders involved in green building projects across various cities in Shandong Province, which has been at the forefront of green building development in China. This regional focus provides a representative geographic context for the study. Among the 403 valid respondents, 41.7% were from government or public-sector institutions, 33.3% from industry (including design institutes, construction enterprises, and green building technology firms), and 25.0% from academia and research institutions. Regionally, participants were drawn from multiple cities including Jinan, Qingdao, Yantai, Weifang, and others, reflecting both urban centers and smaller municipalities.

In the preliminary stage, we communicated the purpose, methodology, data collection schedule, and frequency of the study to senior executives, emphasizing both confidentiality and the voluntary nature of participation. These leaders were provided with detailed guidelines for identifying suitable team members for inclusion in the survey. Our multilevel sampling framework incorporated both strategic management staff (e.g., department directors, project managers) and operational personnel (e.g., engineers, site supervisors, and technical specialists). This classification was essential, as the two groups vary in decision-making authority, policy interpretation, and technical execution, potentially shaping distinct perceptions and response tendencies. Including representatives from both levels enhanced the accuracy and representativeness of the results [38]. Targeted demographic groups included project leaders, technical coordinators, and green building professionals, all of whom play key roles in project governance, design implementation, and possess a comprehensive understanding of green building practices. Using a two-stage sampling process, we first identified qualified organizations and subsequently conducted random selection of participants within those entities. To improve sample diversity, we further accounted for variations in project scale and regional distribution of participants across China. To minimize response bias across sectors, we ensured that no single organization contributed more than five participants, and we avoided clustering responses from the same department. During data collection, anonymized survey links were distributed individually rather than group-wide to reduce peer influence, and respondents were unaware of others’ participation status, further mitigating potential conformity bias. To augment data collection process efficiency, we established milestones to monitor survey completion rates, issuing reminders to participants who had yet to submit their responses. The survey period from 6 January to 8 April 2023, a period of 92 days.

To ensure content clarity and reliability of responses, we conducted a pilot study prior to formal survey rollout. This pilot involved 20 participants and was designed to test the logical flow, clarity of language, and appropriateness of question items. Based on participant feedback, we revised ambiguous terms, adjusted the length of the questionnaire, and added explanatory notes to enhance comprehension. These refinements minimized response bias and improved data quality in the final round [20]. Furthermore, preliminary reliability and validity tests were conducted based on pilot data. The Cronbach’s α values for each latent construct exceeded 0.70, indicating acceptable internal consistency. Additionally, KMO values for all latent factors ranged from 0.745 to 0.823, confirming sampling adequacy and supporting the suitability of factor analysis. These results helped establish the robustness of the instrument prior to full-scale deployment.

To reduce bias and respondent fatigue, which could undermine data validity in lengthy surveys, a pilot test was implemented. This initial stage evaluated the clarity, relevance, and comprehensiveness of the questionnaire. Insights from the pilot phase guided the revision of question design and structure, improving precision and data quality. The final version was administered to 488 participants through the Questionnaire Star platform. After excluding records with contradictory responses, missing data, or repetitive submissions within short intervals, 403 valid questionnaires were retained, yielding a response rate of 82.6%.

Table 2. Questionnaire basic statistics.

Title	Options	Frequency
Gender	Female	211
	Male	192
Educational level	Primary and below	16
	Middle school	76
	Undergraduate and above	134
	Vocational college	177
Unit	Proprietor	36
	Government	7
	Construction enterprise	145
	Supervision enterprise	49
	Design enterprise	76
	Operation enterprise	53
	Universities/Research institutes	37
Working experience	0–2 Years	40
	3–5 Years	124
	6–10 Years	135
	10–15 Years	75
	>15 Years	29
Career	Professional and technical staff	356
	Ph.D. Students	10
	Government staff	15
	Scientific researcher	6
	Other	16
Total		403

presents the sample’s descriptive characteristics. Participants’ professional experience was divided into five categories, ranging from less than one year to over fifteen years, reflecting typical phases of career advancement and knowledge accumulation in the green building industry. Practitioners with over six years of experience generally exhibit extensive project-level expertise, while those with 0–2 years represent the early-career cohort. This classification aligns with industry conventions and facilitates analysis of experience-driven perceptual differences [16,22].

To verify the suitability of the statistical techniques employed—such as structural equation modeling and reliability assessment—we first evaluated the adequacy of the sample size. With 403 valid observations and a model comprising fewer than 50 measured variables, the sample-to-parameter ratio surpasses the widely accepted 10:1 benchmark. In addition, a post hoc power analysis conducted using G*Power 3.1 indicated that this sample size was sufficient to detect medium effect sizes (f² = 0.15) with a power level above 0.80 at α = 0.05, thereby satisfying the fundamental criteria for statistical reliability and robustness. These findings confirm that the analytical procedures applied in this study are both valid and methodologically sound. Regarding the reported influence order among latent dimensions (Policy > Market > Technology > Economy), we conducted an additional robustness check using sensitivity analysis. By systematically varying model parameters and re-running the structural model under alternative specifications (e.g., split-sample by sector, bootstrapping confidence levels), the dominance of the policy factor remained consistent, thereby reinforcing the statistical validity of this conclusion.

4. Data Analysis and Results

As shown in Figure 1. The SEM analysis followed a structured procedure, indicator selection, survey distribution and data collection, followed by reliability and validity testing, and Pearson correlation analysis. Confirmatory factor analysis was then conducted to assess model structure, before evaluating convergent and discriminant validity. Finally, SEM analysis was performed and results were reported.

4.1. Sample Descriptive Statistics

The questionnaire results investigating factors affecting green building development were analyzed using descriptive statistics. Through SPSS software, the mean, extreme values, and S.D. of each observed variable were computed, as detailed in

Table 3. Questionnaire basic statistics.

Serial	Sample Size	Min	Max	Average	S.D.
P1	403	1	5	3.271	1.149
P2	403	1	5	3.252	1.155
P3	403	1	5	3.273	1.191
P4	403	1	5	3.351	1.150
P5	403	1	5	3.361	1.160
P6	403	1	5	3.314	1.166
E1	403	1	5	3.335	1.176
E2	403	1	5	3.315	1.151
E3	403	1	5	3.332	1.174
E4	403	1	5	3.334	1.137
M1	403	1	5	3.323	1.169
M2	403	1	5	3.371	1.117
M3	403	1	5	3.364	1.196
M4	403	1	5	3.365	1.198
M5	403	1	5	3.411	1.182
T1	403	1	5	3.352	1.159
T2	403	1	5	3.342	1.146
T3	403	1	5	3.265	1.177
T4	403	1	5	3.334	1.201
T5	403	1	5	3.271	1.180
G1	403	1	5	3.272	1.245
G2	403	1	5	3.288	1.183
G3	403	1	5	3.365	1.226
G4	403	1	5	3.212	1.193
G5	403	1	5	3.261	1.226

. These calculations illustrate the central tendency and data dispersion, providing a data distribution essential comprehensive understanding for underpinning subsequent reliability and validity assessments.

4.2. Questionnaire Reliability and Validity Analysis

The reliability assessment mainly examines the internal consistency of data collected through the questionnaire scales, measuring how well the items within each construct align with one another. The evaluation uses Cronbach’s alpha coefficient, ranging from 0 to 1, to indicate the degree of consistency. A higher alpha represents more dependable results, reflecting stronger data stability. Specifically, an alpha above 0.9 denotes excellent reliability; between 0.8 and 0.9 indicates good reliability; between 0.7 and 0.8 reflects acceptable reliability; and below 0.7 suggests insufficient reliability, implying the questionnaire should be revised or discarded. The SPSS software was applied to test the reliability of valid responses. Cronbach’s alpha values were calculated for five latent variables to verify internal consistency. Further testing involved recalculating alpha after sequentially removing each observed item. If alpha increased following removal, the excluded item had weak correlation with others and warranted elimination. As shown in

Table 4. Sample data reliability test results.

Latent Variables	Observed Variables	Cronbach’s α After Deleting Item	Cronbach’s α	Reliability
Green building Policy	P1	0.837	0.862	Reliable
	P2	0.845
	P3	0.866
	P4	0.849
	P5	0.868
	P6	0.879
Green building Economic	E1	0.802	0.881	Reliable
	E2	0.897
	E3	0.896
	E4	0.825
Green building Market	M1	0.876	0.879	Reliable
	M2	0.888
	M3	0.885
	M4	0.884
	M5	0.850
Green building Technology	T1	0.851	0.852	Reliable
	T2	0.862
	T3	0.854
	T4	0.839
	T5	0.847
Urban Greening	G1	0.853	0.851	Reliable
	G2	0.862
	G3	0.854
	G4	0.840
	G5	0.845

, the overall questionnaire alpha was 0.863, while the five latent variables yielded coefficients of 0.826, 0.881, 0.879, 0.852, and 0.851, all exceeding the 0.7 threshold, confirming satisfactory reliability. Moreover, since all adjusted alpha values were lower than the original ones, no item modification was required.

The internal consistency of the scale was assessed using Cronbach’s α coefficient. The formula for Cronbach’s α is given below:

$$\mathrm{Cronbach}’\mathrm{s} \alpha = {\displaystyle \frac{k}{k-1}}\left(1-{\displaystyle \frac{{\displaystyle {\sum }_{i=1}^k} {\sigma }_i^2}{{\sigma }_T^2}}\right)$$

(1)

where k is the number of items, σ_i² is the variance of each item, and σ_T² is the total variance of the scale. A higher α value indicates better internal consistency.

The purpose of validity evaluation is to determine whether the questionnaire items effectively reflect the constructs that the study aims to measure, ensuring that each item accurately conveys the intended research concept. To assess the adequacy of data for factor analysis, the KMO statistic and Bartlett’s test of sphericity are commonly applied. A dataset is considered suitable when the KMO value exceeds 0.6, indicating adequate inter-variable correlation, and the p-value is below 0.05, confirming statistical significance. Exploratory factor analysis (EFA) is then conducted to verify whether the extracted factor structure aligns with theoretical expectations by identifying common underlying dimensions. In this study, the validity of the questionnaire data was analyzed using SPSS software. The results, summarized in

Table 5. KMO and Bartlett’s test table.

KMO Value		0.945
Bartlett sphericity test	Approximate chi-square	4863.52
	Degree of freedom	421
	Significance level	0.005

, report a KMO value of 0.945, demonstrating strong correlations among variables, and a p-value of 0.005, well below the 0.05 threshold, confirming that the dataset was appropriate for EFA and met validity requirements.

The maximum variance rotation method was applied to extract common factors with eigenvalues greater than one. After rotation, factor analysis was performed, and the corresponding outcomes are summarized in

Table 6. Total variance explained table.

Factor Number	Characteristic Roots			R2 Before Rotation			R2 After Rotation
	CR	R2	Accumulated Value	CR	R2	Accumulated Value	CR	R2	Accumulated Value
P1	8.517	34.069	34.069	8.517	34.069	34.069	3.804	15.215	15.215
P2	2.347	9.390	43.458	2.347	9.390	43.458	3.344	13.377	28.592
P3	1.989	7.954	51.413	1.989	7.954	51.413	3.282	13.128	41.720
P4	1.771	7.085	58.498	1.771	7.085	58.498	3.263	13.054	54.773
P5	1.732	6.930	65.428	1.732	6.930	65.428	2.664	10.655	65.428
P6	0.652	2.606	68.034	-	-	-	-	-	-
E1	0.593	2.372	70.406	-	-	-	-	-	-
E2	0.576	2.302	72.708	-	-	-	-	-	-
E3	0.539	2.157	74.865	-	-	-	-	-	-
E4	0.536	2.144	77.009	-	-	-	-	-	-
M1	0.498	1.993	79.001	-	-	-	-	-	-
M2	0.476	1.903	80.904	-	-	-	-	-	-
M3	0.468	1.874	82.778	-	-	-	-	-	-
M4	0.442	1.769	84.547	-	-	-	-	-	-
M5	0.433	1.731	86.278	-	-	-	-	-	-
T1	0.419	1.675	87.953	-	-	-	-	-	-
T2	0.393	1.572	89.525	-	-	-	-	-	-
T3	0.389	1.557	91.082	-	-	-	-	-	-
T4	0.360	1.439	92.522	-	-	-	-	-	-
T5	0.348	1.393	93.914	-	-	-	-	-	-
G1	0.339	1.356	95.271	-	-	-	-	-	-
G2	0.327	1.307	96.578	-	-	-	-	-	-
G3	0.310	1.239	97.817	-	-	-	-	-	-
G4	0.282	1.126	98.944	-	-	-	-	-	-
G5	0.264	1.056	100.000	-	-	-	-	-	-

. The procedure grouped the 25 observed indicators into five main components, which together explained 65.428% of the total variance. Since this value exceeds the 50% benchmark, it demonstrates that the identified factors effectively capture the overall structure of the dataset.

The scope of information extraction for each factor was carefully examined, showing that the analysis identified five distinct components, all with eigenvalues greater than one. After rotation, the squared multiple correlations (R²) for these components were 15.215%, 13.377%, 13.128%, 13.054%, and 10.655%, resulting in a cumulative R² of 65.428%. The rotation was performed using the varimax method, designed to clarify the associations between variables and latent factors.

Table 7. Exploratory factor analysis results.

Factor	Components
	1	2	3	4	5
P1	0.724
P2	0.738
P3	0.742
P4	0.789
P5	0.763
P6	0.748
E1		0.733
E2		0.761
E3		0.770
E4		0.783
M1			0.749
M2			0.762
M3			0.757
M4			0.790
M5			0.746
T1				0.789
T2				0.794
T3				0.781
T4				0.750
T5				0.735
G1					0.767
G2					0.760
G3					0.725
G4					0.746
G5					0.761

presents the extent of information captured by each factor and its correspondence with the observed indicators. All items displayed commonality values above 0.5, indicating strong linkages between the measured variables and extracted components, thereby confirming the factors’ effectiveness in representing the underlying information structure.

Prior to model estimation, essential statistical assumptions were tested to ensure data suitability for SEM analysis. Normality was assessed using skewness and kurtosis statistics, with all values falling within the acceptable range (|skewness| < 2, |kurtosis| < 7), indicating approximate univariate normality [16]. Multicollinearity was also examined by calculating Variance Inflation Factors (VIF), with all VIF values well below the commonly used threshold of 5, confirming the absence of serious multicollinearity among observed variables.

To assess the robustness and stability of parameter estimates, a nonparametric bootstrapping procedure was conducted with 2000 resamples using AMOS 24.0. The bootstrap standard errors and confidence intervals were examined, and all critical paths remained statistically significant (p < 0.01), supporting the consistency and reliability of the model [41]. This procedure also verified that the model’s fit and estimated coefficients were not unduly sensitive to sampling variation, thereby strengthening the credibility of the findings.

4.3. Correlation Analysis

Correlation analysis was conducted to examine the relationships between the green building development matrix scale and the four influencing dimensions—policy, economy, market, and technology. The Pearson correlation coefficient was applied to quantify the strength and direction of these associations. The analytical outcomes are summarized in

Table 8. Pearson’s correlation coefficient table.

Facets	Average	S.D.	Policy	Economic	Market	Technology	Urban Greening
Green building Policy	3.301	0.919	1
Green building Economic	3.329	0.940	0.394 **	1
Green building Market	3.366	0.948	0.379 **	0.376 **	1
Green building Technology	3.310	0.954	0.352 **	0.378 **	0.397 **	1
Urban Greening	3.273	0.987	0.449 **	0.399 **	0.445 **	0.435 **	1

** p < 0.01.

.

4.4. Confirmatory Factor Analysis

CFA was performed on five latent constructs comprising twenty-five measurement items. The analysis utilized 403 valid responses, exceeding the ten-to-one ratio of samples to variables, indicating an adequate and balanced dataset. Factor loadings were used to express the strength of association between each latent construct and its corresponding observed indicator. All standardized loading coefficients were greater than 0.6 in absolute value and statistically significant, reflecting strong relationships between indicators and their respective constructs. The detailed empirical outcomes are presented in

Table 9. Factor loading table.

Latent Variables	Observed Variables	Unstandardized Factor Loading	S.E.	CR	p	Standardized Factor Loading
Green building Policy	P1	1.000	-	-	-	0.730
	P2	1.006	0.072	13.958	0.000	0.730
	P3	1.032	0.074	13.890	0.000	0.726
	P4	1.027	0.072	14.318	0.000	0.749
	P5	1.064	0.072	14.711	0.000	0.769
	P6	1.042	0.073	14.328	0.000	0.749
Green building Economic	E1	1.000	-	-	-	0.745
	E2	0.938	0.072	13.078	0.000	0.714
	E3	1.021	0.074	13.844	0.000	0.762
	E4	0.945	0.071	13.305	0.000	0.727
Green building Market	M1	1.000	-	-	-	0.711
	M2	1.021	0.073	13.963	0.000	0.760
	M3	1.118	0.078	14.253	0.000	0.778
	M4	1.107	0.078	14.107	0.000	0.769
	M5	1.058	0.077	13.703	0.000	0.745
Green building Technology	T1	1.000	-	-	-	0.768
	T2	0.988	0.064	15.371	0.000	0.767
	T3	1.044	0.066	15.855	0.000	0.789
	T4	1.030	0.067	15.293	0.000	0.763
	T5	0.946	0.067	14.211	0.000	0.713
Urban Greening	G1	1.000	-	-	-	0.775
	G2	0.899	0.061	14.821	0.000	0.734
	G3	0.954	0.063	15.221	0.000	0.751
	G4	0.959	0.061	15.774	0.000	0.776
	G5	0.959	0.063	15.300	0.000	0.755

. These findings demonstrate that the observed measures effectively capture their underlying factors, confirming the soundness of the measurement model and ensuring a reliable basis for subsequent structural modeling.

4.5. Convergent and Discriminant Validity

In

Table 10. Convergent and discriminant validity value.

Facet	AVE	Policy	Economic	Market	Technology	Urban Greening
Green building Policy	0.549	0.741
Green building Economic	0.557	0.395	0.746
Green building Market	0.572	0.384	0.379	0.756
Green building Technology	0.574	0.377	0.376	0.403	0.758
Urban Greening	0.573	0.465	0.413	0.451	0.442	0.757

, the Average Variance Extracted (AVE) values for all five latent constructs exceeded 0.5, indicating strong convergent validity within the dataset. The computed square roots of AVE for the constructs—policy, economy, market, technology, and green building development—were 0.741, 0.746, 0.756, 0.758, and 0.757, respectively. Each of these figures surpasses the highest absolute correlation among corresponding constructs, confirming that the variables demonstrate solid discriminant validity and effectively differentiate between the measured dimensions.

4.6. Structural Equation Model Analysis

Based on the preceding analyses, the structural equation model (SEM) was established in accordance with the proposed hypotheses, as illustrated in Figure 2. To ensure the model’s robustness, it was necessary to refine the structure by eliminating non-significant paths between observed indicators and latent constructs within both the measurement and structural components. After systematically reorganizing the dataset in a single iteration, the path coefficients were computed. The standardized estimates for hypotheses H1, H2, H3, and H4 were 0.56, 0.38, 0.55, and 0.49, respectively—all exceeding the 0.2 threshold, indicating meaningful relationships. Moreover, the coefficients between latent variables and their respective observed measures ranged from 0.71 to 0.78, demonstrating that the indicators reliably capture their underlying constructs and providing strong support for the model’s overall validity.

Model adequacy was evaluated through a range of absolute and incremental fit indices, including IGF (Incremental Goodness-of-Fit), RMSEA (Root Mean Square Error of Approximation), RMR (Root Mean Square Residual), ICF (Incremental Comparative Fit Index), INF (Incremental Normed Fit Index), INNF (Incremental Non-Normed Fit Index), ITL (Tucker–Lewis Index), IAGF (Adjusted Goodness-of-Fit Index), IIF (Incremental Fit Index), IPGF (Parsimonious Goodness-of-Fit Index), and IPNF (Parsimonious Normed Fit Index). Generally, index values exceeding 0.90 (or 0.50 for parsimonious measures) denote an acceptable model fit. Additionally, parameter estimations were examined using Standard Error (S.E.), Standard Deviation (S.D.), and Critical Ratio (CR), where CR values greater than ±1.96 signify statistical significance.

The standardized model was validated and analyzed using AMOS 24.0 software. The results indicated a satisfactory model fit (χ²/df = 1.775, CFI = 0.967, TLI = 0.958, RMSEA = 0.042 [90% CI: 0.031–0.053], SRMR = 0.036). All indices met the conventional criteria (CFI and TLI > 0.90, RMSEA and SRMR < 0.08), demonstrating that the proposed model provided an adequate representation of the observed data. Specifically, the CFI and TLI are incremental fit indices that compare the specified model to a baseline (null) model: values ≥ 0.95 are often considered indicative of very good fit, with values ≥ 0.90 acceptable in many applied studies. The RMSEA is an absolute bad-fit index reflecting the discrepancy per degree of freedom while adjusting for model complexity; values ≤ 0.05 suggest close fit, values up to 0.08 indicate reasonable fit. The SRMR measures the standardized residuals between observed and predicted covariances, with values < 0.08 commonly regarded as acceptable. Justification for these thresholds is grounded in methodological literature [31,40], for example, while the χ² test is sensitive to sample size, use of multiple fit indices (incremental + absolute) provides a more balanced evaluation. Moreover, it is recognized that strict cut-offs may vary by model complexity, sample size, and indicator type, and thus values meeting or exceeding the guidelines above indicate that our model demonstrates robust structural validity. In our case, with CFI = 0.967 and TLI = 0.958 both above the higher benchmark of 0.95, and RMSEA = 0.042 well below 0.05, we conclude that the model fit is very good, rather than only marginally acceptable.

The findings validate all four hypotheses, demonstrating that policy, economic conditions, market dynamics, and technological advancement act as key determinants of green building development and thereby contribute to urban sustainability. Among these, policy (0.56) and market (0.55) exhibit the most pronounced influences, indicating that governmental initiatives and investment motivation serve as primary catalysts for advancing green urban transitions. This outcome underscores the importance of implementing targeted regulatory mechanisms, enhanced financial incentives, and market-oriented strategies in regions where green progress remains limited. For professionals, the established framework functions as a diagnostic tool to evaluate project viability and identify priority domains for capital allocation or technological innovation. Consequently, this research delivers both theoretical insights and practical guidance for planners, decision-makers, and stakeholders committed to fostering environmentally sustainable urban development.

Regarding the effects of policy, economic factors, market conditions, and technological progress on green building development, as presented in

Table 11. Structural Path Estimates and Significance Levels.

Path	Estimate	S.E.	CR (Z)	p
Green building Policy → Urban Greening	0.556	0.045	7.120	0.000
Green building Economic → Urban Greening	0.384	0.042	5.681	0.000
Green building Market → Urban Greening	0.547	0.046	6.979	0.002
Green building Technology → Urban Greening	0.494	0.045	6.626	0.000

, the corresponding standardized path coefficients are 0.494, 0.547, 0.384, and 0.556, respectively. All paths are statistically significant at the 0.01 level (p < 0.01), indicating that each dimension has a notable and positive influence on the advancement of green construction practices. Therefore, hypotheses H1 through H4 are empirically supported.

5. Discussion

5.1. Developing Green Buildings Can Significantly Promote Urban Greening

The advancement of green buildings as a pathway to urban greening is shaped by policy, market dynamics, technological development, and economic conditions, with their relative influence following the order: policy > market > technology > economy.

Strengthening the implementation of green building policies is crucial to fostering urban ecological transformation. By formulating regulations and standards specific to sustainable construction, governments provide a strategic framework that guides industry practices [52]. These directives establish environmental criteria across the stages of design, construction, and operation, while promoting the integration of renewable energy, energy-efficient systems, and eco-friendly materials, thereby stimulating technological advancement. Furthermore, financial incentives, such as tax reliefs and subsidies, help offset the higher costs of sustainable construction and improve its market competitiveness. The creation of green building certification and evaluation systems recognizes exemplary projects and accelerates overall industry progress [53]. In addition, through public outreach and educational initiatives, governments raise social awareness and foster public endorsement, cultivating a supportive environment that facilitates the broader adoption of green buildings and advances urban sustainability.

These findings echo global trends in sustainable construction, particularly within the European Union. For instance, the EU’s Energy Performance of Buildings Directive (EPBD) mandates nearly zero-energy buildings (nZEB) and emphasizes lifecycle energy performance, aligning with the policy-driven approach observed in China. Similarly, the European Green Deal integrates green buildings as a cornerstone for achieving climate neutrality by 2050. Both frameworks reflect the importance of top-down policy mechanisms in driving technological innovation and market adaptation, findings that our model corroborates.

The market’s impact is substantial, though marginally lower than that of policy. As public awareness and acceptance of green buildings continue to grow, corresponding market demand rises accordingly. This upward trend generates expansive business opportunities and stimulates innovation in sustainable construction technologies. The competitive environment drives firms to consistently improve the performance and efficiency of green buildings to meet evolving consumer expectations [54]. Such competition not only accelerates technological progress within the industry but also enhances overall quality standards, thereby reinforcing the commercial viability of advancing urban greening through green building development.

Moreover, our findings can be contextualized within international certification systems such as LEED (U.S.), BREEAM (UK), and DGNB (Germany), all of which serve as market-based tools to align consumer preferences with environmental performance. The policy–market–technology dynamic revealed in our model is reflected in how these certifications influence both public perception and private investment. For example, LEED-certified buildings in the U.S. and BREEAM projects in Europe often receive market premiums and policy-based incentives, indicating the global applicability of the interaction pathways identified in this study.

Advanced technologies embedded in green buildings form the foundation for achieving urban sustainability, though their impact ranks below policy and market influences. The development and application of such technologies depend heavily on institutional guidance and governmental support. Broad implementation becomes feasible only when sufficient policy incentives are in place. Meanwhile, market demand acts as a driving force for technological progress, as enterprises are encouraged to invest in research and development once market maturity reaches a certain level.

The economic dimension exerts a comparatively smaller effect on urban greening advancement. The high upfront costs of constructing and operating green buildings can restrict their rapid diffusion, particularly in financially constrained regions where technical capacity is limited. Nevertheless, their long-term economic advantages—including energy efficiency, emission reduction, and resource conservation—gradually emerge over time [55]. In the short run, economic assessments often emphasize immediate cost–benefit considerations, which may temporarily overshadow broader environmental gains.

In summary, the implementation of green building policies is the most influential factor in promoting urban greening, due to its capacity to guide and regulate, thus creating an optimal environment for development. The green building market is the second most influential factor, driving technological innovation and progress. The inherent advancements in green building technologies are market-driven, and their development and application depend on policy support and market demand. Although economic factors are important, their short-term impact is minimal, requiring time and sustained effort to realize the long-term benefits of green buildings, thus exerting a weaker influence on the advancement of urban greening. Recent studies have highlighted that integrating green buildings into smart city strategies—such as digital infrastructure and coordinated planning can further enhance urban sustainability. For example, European cities like London and Amsterdam demonstrate how such integration redefines urban development models. Although this study centers on China, referencing global smart city practices offers useful comparative insight [56].

Comparatively, China’s model emphasizes centralized governance, regulatory mandates, and infrastructure-led urbanization, which differ from the more decentralized, market-enabling policy tools prevalent in Europe. However, both systems underscore the necessity of aligning certification mechanisms, public awareness, and investment incentives to realize green transitions. Thus, lessons from the Chinese context—particularly the importance of vertical policy alignment and multilevel stakeholder coordination—offer valuable reference for emerging economies where institutional capacities may resemble China’s more than Europe’s.

Although this research verifies that green buildings play a crucial role in advancing urban greening, future efforts should tackle ongoing sustainability issues such as the urban heat island phenomenon, emissions from building upkeep, and long-term ecological stability. Incorporating climate-adaptive design features—including green roofs and high-reflectance materials—can help alleviate localized temperature increases, while low-carbon materials and life-cycle management in maintenance practices can effectively reduce indirect emissions. Moreover, integrating urban greening initiatives with the United Nations Sustainable Development Goals (e.g., SDG 11: Sustainable Cities and Communities and SDG 13: Climate Action) ensures that green building development not only enhances urban livability but also aligns with global environmental and climate objectives. Future studies should further investigate approaches to reconcile immediate urban greening priorities with the long-term sustainability of green building systems across diverse urban settings.

5.2. Pathways for Promoting Urban Greening Through Green Buildings

Among the four dimensions, the policy component demonstrates the strongest influence, with approval efficiency, regulatory enforcement, and governmental commitment to sustainability yielding an impact coefficient of 0.76. Within the economic domain, financial performance of green buildings and research investment emerge as key drivers, registering coefficients of 0.75 and 0.73, respectively. Regarding market dynamics, public awareness of environmental protection and integration across the industrial supply chain show the highest influence, each marked at 0.77. For technological aspects, the rate of green technology diffusion and technological maturity exhibit notable importance, with coefficients of 0.78 and 0.77. Future advancement of urban greening through green building development can be strengthened by emphasizing these strategic directions, which closely align with the ESG (Environmental, Social, and Governance) framework. From an environmental perspective, green buildings lower emissions and enhance resource efficiency; from a social standpoint, they improve air quality and create healthier, more livable communities; and from a governance angle, mechanisms such as green certification systems and performance accountability ensure transparency and policy adherence. Collectively, these strategies foster the transition toward low-carbon urban systems by embedding green infrastructure within city cores, reducing reliance on fossil fuels, and expanding carbon-absorbing green spaces. Consequently, the proposed pathways advance both building-level sustainability goals and broader urban ecological objectives.

5.2.1. Policy Field

These policy strategies directly contribute to SDG 11 (Sustainable Cities and Communities) and SDG 16 (Peace, Justice and Strong Institutions), aiming to address institutional and procedural barriers to green building implementation. Compared to prior studies that primarily emphasized technical standards, this study highlights the governance dimension as a crucial policy lever, especially in accelerating approval, supervision, and institutional learning.

(1)

Optimize the Approval Process.

Governments are advised to streamline the green building approval process by establishing dedicated channels to expedite administrative procedures [17]. Clear standards and guidelines should be developed to ensure transparency, fairness, and efficiency. Implementing information-sharing mechanisms can prevent redundant reviews and improve coordination across departments.

(2)

Enhance Supervision.

A robust supervisory framework is essential to ensure the quality and safety of green building projects. This includes regular inspections, random checks, and responsive remediation measures. Enforcement mechanisms should include stricter penalties for violations, which can serve as effective deterrents [21].

(3)

Deepen Commitment to Sustainable Development.

Long-term policy commitments should include explicit green building objectives, integrated into government evaluation systems. International cooperation is also essential, particularly in learning from global best practices and adapting advanced green building technologies [57]. Promoting pilot projects and public engagement initiatives can strengthen both political and social support for green transformation.

5.2.2. Economic Field

Aligned with SDG 8 (Decent Work and Economic Growth) and SDG 9 (Industry, Innovation and Infrastructure), the economic strategies in this study extend beyond traditional subsidy mechanisms, highlighting the importance of financial innovation and research investment in promoting long-term economic sustainability of green buildings.

(1)

Enhance Economic Returns.

A comprehensive incentive system should be established, encompassing financial subsidies, tax benefits, and green financing tools [58]. Financial institutions should be encouraged to develop specialized products for green building investments. Economic performance assessments of green buildings can help reduce investment uncertainty and attract private capital.

(2)

Increase Scientific Research Investment.

Governments should allocate dedicated funds to support research and development in green building technologies. Strengthening the industry–academia–research collaboration can accelerate the commercialization of innovations [59]. Partnerships with international research bodies will also help globalize and standardize green technologies.

5.2.3. Market Field

These market-driven strategies contribute to SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action). Unlike previous works focusing mainly on consumer-side incentives, this study underscores the importance of system-wide collaboration and participatory governance across the entire market chain.

(1)

Enhance Public Environmental Awareness.

Educational campaigns and media outreach should be employed to raise public awareness and acceptance of green buildings [60]. Sharing successful case studies through digital platforms can enhance trust. A participatory mechanism should be introduced to involve the public in project decision-making, construction, and oversight.

(2)

Promote Industry Chain Coherence.

It is vital to promote synergy across the green building industry chain, including developers, material suppliers, and technology providers. An industry-wide platform for information exchange should be established [61], enabling better coordination and efficient resource allocation, thus improving overall competitiveness.

5.2.4. Technical Field

The technical recommendations align with SDG 7 (Affordable and Clean Energy) and SDG 17 (Partnerships for the Goals). This study contributes to the literature by emphasizing technological maturity and cross-regional knowledge exchange as key pathways to scale up innovation.

(1)

Foster Green Technology Exchange and Cooperation.

A national-level platform should be created for the exchange of green building technologies, facilitating collaboration among companies and research institutes. Hosting technical forums and international exchanges can help absorb and adapt global innovations [62].

(2)

Advance Green Technology Maturity.

Increasing investments in R&D will help improve the performance and adaptability of green building technologies [63]. Establishing a third-party assessment and certification system will ensure reliability and encourage adoption. Demonstration projects can serve as testing grounds for scalable deployment.

6. Conclusions

This study rigorously examines the determinants and pathways through which green building development influences urban greening. Drawing upon an extensive review of the existing literature, it conceptualizes 4 primary factors as potential variables and 20 secondary factors as observed variables within a causal framework describing factors that impact green building progression. From stakeholders involved in green building initiatives, 403 valid questionnaires were collected, and structural equation model was employed to assess hypotheses utilizing SPSS and AMOS software. The findings indicate that policies encouraging green building, the long-term economic viability of such constructions, the prospective market for green buildings, and their associated advanced technologies play a crucial role in promoting urban greening. Path coefficient analysis confirms a ranked influence, with policy (β ≈ 0.56) demonstrating the strongest effect, followed by market (β ≈ 0.55), technology (β ≈ 0.49), and economic factors (β ≈ 0.38). These results highlight the need for targeted policy instruments, strong fiscal incentives, and technology-driven strategies to drive green development and ecological enhancement in urban areas.

Importantly, these findings contribute directly to several components of the United Nations Sustainable Development Goals (SDGs). Specifically, the promotion of green buildings supports SDG 11 (Sustainable Cities and Communities) by enhancing urban livability and environmental quality, SDG 13 (Climate Action) through the reduction of urban carbon footprints and mitigation of the heat island effect, SDG 7 (Affordable and Clean Energy) via integration of renewable energy in building design, and SDG 9 (Industry, Innovation and Infrastructure) by advancing green construction technologies and industrial collaboration. By framing the pathways of urban greening within the SDG framework, this study underscores the broader global relevance and long-term value of green building initiatives.

However, the study is limited as the current survey scope is concentrated in provinces in China where green building development is relatively advanced and well-established. To improve the universality of the results and support SDG 10 (Reduced Inequalities), future research will aim to include provinces where green building development is less advanced, thereby enriching the findings and offering more inclusive policy insights across diverse socioeconomic contexts.