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Article

How Do Environmental Regulations, Technological Innovation, and Transformation Intentions Enhance the Green Development Level of Real Estate Enterprises? A Study on Synergistic Effects from a Configurational Perspective

by
Zhao Yang
1,
Hong Fang
2,
Xiaojuan Deng
3,* and
Xiaoyan Chen
1
1
School of Urban Economics and Management, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
2
School of Economics and Management, Beihang University, Beijing 100191, China
3
Department of Business Administration, Gingko College of Hospitality Management, Chengdu 611743, China
*
Author to whom correspondence should be addressed.
Buildings 2026, 16(1), 119; https://doi.org/10.3390/buildings16010119 (registering DOI)
Submission received: 20 November 2025 / Revised: 18 December 2025 / Accepted: 25 December 2025 / Published: 26 December 2025
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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Abstract

While driving rapid economic growth, China’s real estate industry has also caused severe environmental issues. The green development and transformation of this sector have become crucial for achieving the “dual carbon” goals. Accurately evaluating the green development efficiency of real estate enterprises and analyzing pathways for improvement are therefore essential. The green development efficiency of real estate enterprises was calculated in this study. Building upon this foundation, the allocative effects of environmental regulations, technological innovation, and transformation willingness on efficiency improvement were explored. The findings reveal: (1) The average green efficiency of the sample enterprises is 0.758, showing an overall increasing trend, but with significant inter-firm differences; (2) Three pathways for green transformation exist: co-driven by environmental investment and transition intentions, co-driven by R&D innovation and environmental penalties, and driven solely by environmental regulations. (3) The government can effectively enhance corporate green development efficiency by establishing appropriate environmental regulation intensity. Enterprises, in turn, need to increase innovation investment and transformation intentions while establishing environmental management systems.

1. Introduction

The rapid expansion of the real estate sector once fueled China’s economic boom and created employment opportunities for millions, but it has triggered severe environmental issues, with the industry’s share of carbon emissions continuing to rise [1]. By 2022, the real estate industry accounted for 50.9% of China’s total carbon emissions [2]. Public demand for housing has shifted from merely “having a place to live” to seeking “quality housing.” The “Proposal of the Central Committee of the Communist Party of China on Formulating the 15th Five-Year Plan for National Economic and Social Development” advocates promoting high-quality development in real estate, building safe, comfortable, green, and smart “quality homes.” Real estate enterprises are required to transition from pursuing speed and scale to prioritizing quality, service, and efficiency; enhancing competitiveness; and moving toward green and sustainable development.
The path toward green development and transformation in the real estate industry faces numerous challenges [3]. Extensive empirical research has demonstrated that innovation capacity serves as a core mediating variable for achieving green performance, directly determining whether environmental compliance costs can be effectively transformed into competitive market advantages [4]. Technological innovation in real estate enterprises constitutes a systematic endeavor encompassing smart construction and green building [5,6]. At its core, digital design centered on BIM (Building Information Modeling) spans the entire project lifecycle. Through 3D collaboration and virtual construction, it significantly reduces design conflicts and material waste. Simultaneously, technologies such as prefabricated construction, robotic construction, and low-carbon new materials collectively drive the industrialization of construction. These technologies play a fundamental role in green transformation: BIM’s precise control and prefabricated processes reduce resource consumption and construction carbon emissions at the source, while intelligent operation and maintenance systems continuously optimize building energy consumption through algorithms, achieving deep energy savings during the operational phase. Currently, real estate enterprises show generally low willingness to adopt these technologies for green transformation at scale. The core obstacle lies in the industry undergoing profound restructuring, with most companies facing severe profit declines and cash flow pressures [7]. Survival has become the primary concern, while investments in these new technologies often feature long payback periods and slow-to-materialize benefits [8,9]. They struggle to translate into immediate financial gains in the short term and may even erode existing profits [10]. This temporal mismatch between costs and benefits significantly weakens companies’ intrinsic motivation, leading to low willingness for proactive transformation [11]. Relevant research indicates that transformation willingness significantly influences the success of corporate transformation.
Internal oversight mechanisms within real estate firms—such as establishing green building committees and setting ESG performance metrics—serve as foundational governance frameworks for internalizing social responsibility and systematically advancing green technology adoption (e.g., BIM, prefabrication) [12]. By clarifying responsibilities and processes, these mechanisms directly influence environmental performance across project design, construction, and operations. However, internal oversight alone cannot fully drive transformation, primarily because its effectiveness is constrained by corporate economic rationality. The long-term environmental benefits of green innovation often clash with short-term cost pressures (such as incremental costs and investment payback periods), particularly during industry downturns when survival demands frequently override internal oversight objectives. Therefore, external environmental regulations—such as mandatory green building standards, carbon emissions trading, and green credit subsidies—must be leveraged synergistically [13]. By establishing predictable market rules, economic incentives, and penalty mechanisms, these measures compensate for internal motivation gaps, internalize the externalities of green development, and build a mandatory transformation mechanism that integrates internal and external drivers [14,15]. In summary, transformation intentions, technological innovation capacity, and environmental regulation intensity collectively form the three core dimensions influencing real estate enterprises’ green development. The complex interplay among these dimensions ultimately determines the industry’s transformation outcomes.
Currently, scholars have explored the green development of the real estate industry in areas such as management approaches [16], marketing and development strategies [17], analysis of influencing factors [18], and discussion of transformation pathways [19]. By reviewing these studies, the following shortcomings are identified: (1) In analyzing influencing factors, the impact of transformation intentions on outcomes is often overlooked, with most selected factors being economic and social in nature. (2) While some research examines environmental regulations as factors affecting the green transformation of the construction industry, few studies focus specifically on how environmental regulations influence the green development and transformation of real estate enterprises. (3) Previous studies on green transition pathways for the real estate sector predominantly either directly propose recommendations based on objective data or adopt single-factor perspectives, without thoroughly analyzing the configurational relationships among various factors.
Therefore, relevant data from 15 real estate companies featured in the 2018–2023 “China Green Low-Carbon Real Estate Index Report Top 30” published by Standard Ranking Agency are selected for analysis. These enterprises are benchmarks for green development within China’s real estate sector, and analyzing their implementation pathways can provide valuable insights for other companies pursuing green development. This study employs the Green Credit Index from the report as the green output metric for these enterprises. This index incorporates multiple indicators, including environmental disclosure, pollutant emissions, green building area, and water consumption, enabling a comprehensive reflection of each company’s green development status. The Slack-Based Data Envelopment Analysis (SBM-DEA) model is applied to assess corporate green development efficiency. Building upon this foundation, the fuzzy set qualitative comparative analysis (fsQCA) method is applied to analyze the configurational effects of environmental regulations, transformation intentions, and technological innovation on the green development efficiency of real estate enterprises. The aim is to provide relevant recommendations and benchmarks for the green development of other real estate enterprises.

2. Literature Review

2.1. Environmental Regulation and Green Development

The relationship between environmental regulation and green development is a core issue in environmental economics and sustainability research. Its theoretical foundation is rooted in market failure theory, which posits that the public good attributes of environmental resources lead to negative externalities from corporate production activities that cannot be spontaneously corrected by market mechanisms [20]. Therefore, governments must implement regulatory measures to internalize environmental costs, thereby incentivizing corporate green transformation [21].
Early “cost-compliance theory” posited that environmental regulations increase firms’ compliance costs, diverting resources from productive investments and suppressing short-term economic performance—the so-called “green paradox” [22]. Gray et al. analyzed the mechanism by which pollution control costs in U.S. manufacturing plants affect productivity, finding that stringent environmental regulations reduce firm productivity in the short term [23]. Rubashkina et al. studied European manufacturing and concluded that environmental regulations may constrain production inputs, thereby suppressing firm productivity [24]. However, with theoretical refinement and empirical accumulation, the Porter Hypothesis has emerged as the dominant explanation. It posits that well-designed environmental regulations can stimulate technological innovation and management optimization through an “innovation compensation” effect [25]. The resulting efficiency gains and new product revenues sufficiently offset compliance costs, ultimately achieving a win-win outcome for both economic and environmental performance—a process termed the “forcing mechanism” [26]. Debnath’s analysis of Toyota hybrid vehicles and Nissan electric vehicles reveals that environmental regulations can enhance corporate innovation capabilities [27]. Albrizio et al.’s study, based on panel data from OECD and EU countries, finds that after controlling for other factors, stricter environmental policies are significantly positively correlated with a country’s green technology innovation index, share of renewable energy, and environmentally adjusted productivity indicators [28].
Existing literature broadly confirms that environmental regulations drive green development primarily through two pathways: First, the barrier effect—by raising environmental entry standards [29], phasing out outdated production capacity [30], and optimizing resource allocation [31], regulations structurally propel industries toward green and low-carbon upgrades. Second is the aforementioned forced mechanism [32], where regulatory pressure compels enterprises to increase R&D investment in energy-saving and emission-reduction technologies, clean production processes, and end-of-pipe treatment technologies [33]. This enhances resource productivity at the micro level and drives green growth in total factor productivity at the macro level [34]. Empirical studies across manufacturing [35], power [36], agriculture [37], and other sectors largely confirm the positive impact of environmental regulations on core indicators such as green total factor productivity and carbon productivity. However, these effects are modulated by regulatory type (command-and-control, market-based incentives, or voluntary), regional development levels, and firm heterogeneity [38].

2.2. Technological Innovation and Green Development

Against the backdrop of deepening global sustainable development agendas, technological innovation has been widely recognized as the core engine and fundamental driver propelling green development [39,40]. Existing literature has largely transcended the traditional perception of technology as an exogenous variable, instead delving into how technological innovation functions as an endogenous force [41]. It systematically reshapes corporate production models and industry growth trajectories, thereby achieving synergistic gains in both economic performance and environmental benefits [42].
The catalytic role of technological innovation manifests primarily through two pathways. On the direct pathway, technological innovation directly breaks through resource and environmental constraints through concrete means such as process innovation, material substitution, and energy efficiency improvements [43]. For instance, clean production technologies reduce pollution at the source, recycling technologies transform waste into valuable resources, and renewable energy technologies directly replace fossil fuels [44]. These innovations significantly lower resource consumption and pollution intensity at the micro level, enhancing green total factor productivity. Moreover, such green technological innovations create cost advantages for enterprises and enable them to capture emerging markets by developing green products. This builds unique green competitive advantages, transforming environmental constraints into market opportunities [45]. Flammer found that robust green innovation and transparent environmental performance have become integral components of brand equity, capable of attracting and retaining consumers and talent who share aligned values [46]. Fatica contends that with the rise of ESG investing, companies demonstrating excellence in green innovation gain easier access to “green financing” (such as green bonds and sustainability-linked loans) at lower funding costs [47].
On the indirect pathway, extensive empirical research confirms that technological innovation serves as a critical mediating variable [48]. Particularly in the relationship between environmental regulations and green development, regulatory policies do not exert direct effects but instead “force” or “induce” enterprises to increase R&D investment. The resulting innovations then achieve green transformation objectives [49]. This “innovation compensation” effect, as described by the “Porter Hypothesis,” has been validated across multiple sectors, including coal [50], construction [51], and tourism [52]. Stringent environmental policies ultimately promote green upgrading at both the enterprise and industry levels through the intermediary channel of technological innovation.
Technological innovation and green development form a dynamically reinforcing virtuous cycle. Policy guidance provides the initial impetus for green technological innovation, while the direct application of innovative outcomes reduces the costs of green transformation and enhances resource efficiency. Meanwhile, the growing market demand for green products and services further feeds back and incentivizes sustained innovation activities [53].

2.3. Transformation Intentions and Green Development

In studies examining the drivers of corporate green transformation, the subjective initiative of decision-makers has garnered increasing scholarly attention [54,55]. The Theory of Planned Behavior provides a robust theoretical foundation for this research, positing that behavioral intention serves as the most direct and core predictor of actual action. When applied at the corporate level, senior leaders’ commitment to green development is regarded as a key antecedent variable for initiating and sustaining the green transformation process [56]. Huang et al. argue that the values and strategic vision of senior executives are key drivers for companies to go beyond compliance and pursue forward-looking green investments [57].
Existing literature indicates that a strong transformation intent first directly influences the strategic and institutional dimensions of an enterprise [58]. Green-oriented senior managers lead the formulation of explicit green development strategies, systematically integrating sustainability principles into corporate vision and policies. This manifests in establishing dedicated sustainability committees and incorporating environmental performance into executive evaluation systems. Such top-level design ensures the strategic prominence of green development, providing legitimacy and resource support for the enterprise’s overall green initiatives [59]. Robertson and Barling’s research reveals that leaders with strong transformation aspirations effectively drive corporate green transformation by translating personal commitment into collective action. They achieve this through shaping a shared vision, igniting employees’ passion for environmental protection, and establishing a green organizational culture [60].
Second, this commitment drives critical resource reallocation, particularly increased investment in green technologies and innovation. For traditional high-energy-consuming sectors like real estate and manufacturing, this means prioritizing R&D in energy-efficient materials, green construction techniques, and digital tools such as Building Information Modeling (BIM) [61]. These innovations directly translate into lower-energy buildings and reduced construction waste, thereby enhancing green performance from the production side [62]. Eccles et al. argue that leaders with a strong commitment to transformation will reallocate financial and human resources. Empirical evidence indicates that companies led by such leaders typically allocate a higher proportion of capital expenditures toward clean technology R&D and energy efficiency improvements [63].
Finally, leadership commitment permeates internal management and cultural development. By integrating concepts like green production safety and circular economy into employee training systems, supplemented by incentive mechanisms, enterprises can progressively build organizational cultures and behavioral norms that support sustainability [64]. This internal capacity-building enhances employees’ environmental awareness and execution capabilities, ensuring effective operational implementation of green strategies and ultimately achieving comprehensive improvements in corporate green production efficiency [65].
Based on market failure theory, firm competition theory, and the theory of planned behavior, fsQCA is employed to investigate the configurational effects of environmental regulations, technological innovation, and transformation intentions on the green development process of real estate enterprises. The resulting research model is illustrated in Figure 1.

3. Research Methods and Data Sources

3.1. Selection of Research Methods

3.1.1. SBM-DEA Model

The Slack-Based Data Envelopment Analysis (SBM-DEA) model, proposed by Tone in 2001, incorporates slack variables into the objective function to rationally address the slackness issues in input and output variables within the original DEA model [66].
β 0 * = min 1 − 1 m ∑ i = 1 m s i 0 − x i 0 1 + 1 q ∑ r = 1 q s r 0 + y r 0 ST . x 0 = X λ + s 0 − y 0 = Y λ − s 0 + s 0 − ≥ 0 , s 0 + ≥ 0 , λ ≥ 0
where β 0 * denotes the efficiency score, ranging from 0 to 1, with higher values indicating greater efficiency. A value of β 0 * = 1 signifies absolute efficiency. s represents the slack variable, and λ denotes the weight vector.

3.1.2. Fuzzy Set Qualitative Comparative Analysis (fsQCA)

Fuzzy Set Qualitative Comparative Analysis (fsQCA) integrates fuzzy sets with Qualitative Comparative Analysis (QCA), addressing QCA’s limitation in handling only dichotomous variables while accommodating interval variables encountered in statistical analysis. Variables can take values between 0 and 1, with higher membership degrees yielding higher scores. Compared to QCA, fsQCA offers broader applicability and is more suitable for this study.

3.2. Data Sources

3.2.1. Green Development Efficiency Indicators

Based on relevant research and data collected by the author [67,68,69,70], the following indicators are selected for measurement:
(1) Input Indicators: Total Assets, Total Number of Employees, Total Energy Consumption.
(2) Output Indicators: Total Profit, Completed Construction Area.
(3) Green Indicators: Green Credit Index.
All enterprises selected for this study are listed in the “China Green Low-Carbon Real Estate Index Report Top 30” from 2018 to 2023. These enterprises are recognized as benchmarks for green development within China’s real estate industry. As research samples, they provide effective development templates for other real estate enterprises. Data sources include the annual reports and sustainability reports of each enterprise.

3.2.2. Selection of Configuration Indicators

(1) Environmental Regulation. Drawing on prior research, the number of environmental administrative penalty cases in the province where the enterprise is located and the proportion of environmental pollution control investment relative to GDP are adopted as indicators. However, due to significant variations in the number of enterprises across provinces and municipalities, the number of environmental administrative penalty cases must be adjusted by dividing it by the total number of local enterprises. Data sources include the China Environmental Statistics Yearbook, China Statistical Yearbook, and official websites.
The higher the number of environmental administrative penalty cases indicates a more resolute stance by the government in addressing environmental violations, which helps deter potential offenders and compels enterprises to reform. The proportion of environmental pollution control investment relative to GDP reflects a region’s level of commitment and investment in environmental protection. Government investment in public pollution treatment facilities reduces enterprises’ costs of building their own environmental facilities, benefiting their green development. This indicator also reflects the local government’s emphasis on environmental protection. A high level of emphasis leads to supportive policies that facilitate enterprises’ transition to green development.
(2) Technological Innovation. Drawing on relevant research, the cumulative number of patent authorizations obtained by a company is selected as the core indicator for measuring its innovation output. This metric objectively reflects the substantive achievements attained by enterprises in their technological R&D activities, serving as a key proxy variable for their innovation activity and technological innovation capabilities. It is generally recognized that enterprises with richer patent portfolios possess stronger knowledge accumulation and technological capabilities. Consequently, they demonstrate greater potential to address environmental regulations, enhance resource efficiency, and drive green transformation through process innovation and product upgrades. All data in this study are sourced from enterprises’ publicly released annual reports and sustainability reports.
(3) Transformation Attitude of Senior Management. The stance of senior management is crucial for a company’s commitment to green development. Building upon Yu-Shan Chen (2010) [71] and adapting to practical circumstances, the author employs a five-point Likert scale to assess factors such as whether environmentally friendly new processes and materials are frequently adopted in production, whether consistent investments are made in green technology and environmental protection, and whether green concepts are integrated into management with a robust environmental management system established [71]. Higher scores indicate greater frequency or higher completion levels of these activities. Scoring references are drawn from each company’s published sustainable development annual reports.

3.3. Calibration

Calibration is a process that assigns membership degrees to each enterprise data point through corresponding calculations, with values ranging from 0 to 1. Referencing relevant research, the upper quartile, median, and lower quartile are set as reference anchor points in the membership degree assignment process [72]. Data points are then assigned membership degrees based on these defined anchor points. The anchor values for each variable are shown in Table 1.

4. Result Analysis

4.1. Green Efficiency Evaluation

As shown in Table 2, China Shipping Development demonstrates the highest green production efficiency, with an average value of 0.962. Its green efficiency score remained at 1 throughout the three-year period from 2021 to 2023, indicating the company’s consistent commitment to green development principles. As stated in the company’s sustainability report, it achieved over 100 million square meters of certified green building area in 2023 and ranked first in the 2023 Real Estate Green Index. These indicators demonstrate that the company is at the forefront of green development and has become a benchmark in this field. The average green efficiency of China Energy Construction Group follows closely behind at 0.806, with most years exceeding 0.8, demonstrating the company’s effective implementation of green production practices. According to the company’s sustainability report, the coverage rate of environmental training rose from 61% in 2019 to 100% in 2021. Green principles are integrated into construction management, with strengthened requirements for dust control, construction wastewater recycling, and waste disposal, promoting standardized and normalized green construction practices. In 2023, the company received 61 provincial-level or higher awards for energy conservation and environmental protection, with an average annual environmental investment reaching 558 million yuan. The company places significant emphasis on environmental protection within its production activities, comprehensively enhancing the scale and quality of its green industries. The enterprise with the lowest green average score is Hopson Development, at just 0.659. Although its average score is relatively low, its inclusion in the green real estate enterprise rankings demonstrates the company’s strong capacity for green development. However, compared to other listed enterprises, it still has room for improvement and needs to continue enhancing its performance.

4.2. Necessary Condition Analysis

If an event consistently occurs in the presence of a specific condition, that condition is deemed a necessary condition for the observed event. As shown in Table 3, the consistency coefficients for all indicators are below 0.9, indicating that no necessary conditions exist. This also highlights the high complexity of green transformation in real estate enterprises, necessitating further configuration analysis.

4.3. Configuration Analysis

The consistency threshold and case threshold settings are two core parameters determining the robustness, validity, and interpretability of analytical results. The consistency value measures whether a combination of judgment conditions constitutes a “sufficient condition” for the outcome. Higher consistency indicates a greater likelihood that this condition combination leads to the result. The case threshold is typically set to at least 1 (meaning at least one case must broadly match the combination), but for small to medium sample sizes (e.g., 10–50 cases), it is often set to 1 or 2. This study references relevant research by setting the consistency threshold and case threshold at 0.7 and 1, respectively [73]. Based on these parameters, the configuration results for real estate enterprises achieving high green development efficiency were calculated. As shown in Table 4, the overall consistency reached 0.862 and the overall coverage was 0.782, indicating appropriate data selection and high explanatory power.
The results reveal four configurations for promoting green development in real estate enterprises, which we categorize into three types.
(1) Co-dominant Environmental Investment and Transformation Intentions Model
In Model 1, the province where the enterprise is located invests a relatively high proportion of funds in environmental construction, and local enterprises also demonstrate strong transformation intentions. Four enterprises—China Energy Construction Group, China Jinmao, Kaisa Group, and Shimao Group—fall into this category. Substantial environmental investment enhances local environmental systems and infrastructure, creating favorable external conditions for corporate green development. Strong transformation intent serves as the core internal driver, compelling enterprises to integrate green development principles into production processes. China Energy Construction Group exemplifies this approach by accelerating innovation-driven and green-low-carbon transformations centered on innovation, sustainability, and integration. Whether measured by patent holdings or R&D investment, China Energy Construction ranks among the top tier of real estate enterprises. Its robust R&D and innovation capabilities ensure a commanding lead in green development. Furthermore, the company’s Beijing headquarters benefits from the capital city’s stringent environmental regulations and heightened government scrutiny of environmental cases, providing a powerful external impetus for green development among real estate enterprises. Previous research indicates that the quality of government investment in infrastructure development and the willingness to take action can drive green economic growth in Saudi Arabia. This aligns with the findings of this paper, suggesting that this model can also serve as a reference for green development in other countries [74].
(2) R&D Innovation and Environmental Penalties Co-dominant Model
In Model 2, the province where the enterprise is located imposes relatively stringent penalties for environmental administrative violations, while the enterprise itself possesses strong technological innovation capabilities. The severity of penalties compels enterprises to undertake green reforms, phase out outdated production tools, develop clean production technologies, and achieve green transformation. Concurrently, the enterprise’s robust innovation capacity enables it to develop new energy-saving and environmentally friendly materials and technologies. China Vanke, China Shipping Development, China Resources Land, and Greentown China fall into this category. Taking China Vanke as an example, its board of directors stated that against the backdrop of the carbon-neutral economy era, Vanke positions green and low-carbon transformation as the core direction of its strategic development, firmly integrating environmental responsibility with business growth. Vanke actively adopts cutting-edge low-carbon technologies in production, including near-zero energy buildings, photovoltaic power generation with integrated storage and flexible grid integration. It continuously optimizes management across multiple domains—green buildings, green construction, and green operations—collaborating with stakeholders to advance green development. Located in Guangdong Province, Vanke benefits from the provincial government’s substantial investments in energy conservation and environmental protection. These investments provide a robust material and infrastructure foundation for green development among enterprises within the province. Driven by both factors, the company has achieved green, high-quality development. Tansuchat found that when strong technological innovation is combined with relatively stringent environmental regulation, it can effectively enhance green development efficiency and drive green transformation. This indicates that the model is not only applicable to China but also holds certain reference value for green development in other countries [75].
(3) Environmentally Regulated Dominant Model
Model 3 refers to enterprises operating in provinces with stringent environmental penalties and relatively high environmental investment ratios. Longfor Group and Sino-Ocean Group fall into this category. These enterprises are based in Chongqing and Shanghai, respectively, two provinces sharing common environmental regulatory characteristics. Local governments in both regions drive green development in real estate companies through a dual approach: enacting stringent environmental laws and regulations while increasing environmental protection investments. Although both companies exhibit some shortcomings in transformation intentions and innovation capabilities, their green development efficiency has steadily improved year by year under the strong impetus of provincial environmental regulations, making them benchmarks for green development.
Figure 2 illustrates the mechanism of action for the three models. External factors and internal factors jointly drive green development. The red, blue, and yellow pathways represent the respective action paths for the three models.

5. Conclusions and Recommendations

5.1. Research Findings

The SBM-DEA model is employed to measure the green development efficiency of 15 real estate enterprises featured in the China Green Low-Carbon Real Estate Index Report Top 30. Building upon this, the fsQCA model is utilized to investigate the configurational effects of environmental regulations, technological innovation, and innovation intentions on green development efficiency. The conclusions are as follows:
(1) Empirical data from 2018 to 2023 indicates that the average green efficiency value of the sampled enterprises was 0.758. This figure demonstrates that, during the observation period, the selected enterprises collectively established preliminary pathways for green development. Their comprehensive performance in resource consumption, environmental management, and ecological benefits reached a high benchmark, positioning them as pioneers and benchmarks within the industry for implementing sustainable development principles. Further observation of their dynamic evolution reveals that the green development efficiency of most enterprises exhibits a clear upward trend. This sustained improvement trajectory clearly reveals that the sample enterprises are not merely maintaining static environmental standards. Instead, through proactive strategic initiatives—including technological innovation, management optimization, and structural reforms—they are continuously driving a deep transformation of their operational models and product forms toward greater environmental sustainability. This endogenous drive for improvement ensures that corporate green performance transcends mere compliance, forming a virtuous cycle of self-reinforcing and self-improving development.
(2) From a configuration analysis perspective, the combined effects of environmental regulations, technological innovation, and innovation intent on the green development of real estate enterprises can be summarized into four equivalent pathways, which can be further consolidated into three major types. Research reveals that enterprises’ green transformation does not rely on a single linear path but results from the complex interaction of multiple conditions. The vast majority of successful configurations indicate that external drivers (such as stringent and precise environmental regulations) and internal empowerment (such as solid technological reserves and proactive internal intentions) must synergize and resonate to effectively propel enterprises onto a path of green and sustainable development. This dual-driver model underscores that policy design must transcend isolated perspectives. It should focus on establishing incentive-compatible mechanisms that stimulate internal corporate motivation while aligning with external regulations, thereby systematically enhancing the green transformation efficiency of the entire industry.
(3) The synergistic drivers of corporate green development, jointly guided by environmental investment and transformation willingness, stem from effective coordination between senior management’s firm commitment to green strategies and the efficient policy environment established by external governments. The government has not only established sound environmental regulatory policies but also driven the digital transformation of the construction industry through national-level digital development strategies and dedicated research funds. The application of technologies such as Building Information Modeling (BIM), the Internet of Things, big data, and artificial intelligence has reduced material waste and energy consumption during construction while significantly enhancing resource utilization efficiency in the operation and maintenance phase. This digital revolution in construction has injected robust technological momentum into the green development of the real estate sector. Strong leadership ensures environmental budgets receive priority allocation and effective implementation, while targeted government support (subsidies, tax incentives, and technical guidance) significantly reduces the institutional costs and uncertainties of transformation. This resonance between endogenous motivation and exogenous support collectively propels enterprises to proactively pursue green transformation. A dual-driver model driven by R&D innovation and avoidance of environmental penalties. This pathway highlights the coercive mechanisms of regulatory power. Local governments substantially increase pollution costs for enterprises through stringent environmental oversight and deterrent economic penalties. To avoid penalties and maintain operational legitimacy, enterprises proactively redirect resources toward innovation activities like pollution control technologies and green process R&D, rather than reacting passively. This strategy of responding to regulatory pressure through innovation not only ensures compliance but also indirectly enhances environmental performance. Enterprises dominated by environmental regulation primarily achieve transformation under the impetus of external coercive forces. Local governments have established systematic and rigorous environmental regulatory frameworks and normalized enforcement mechanisms. By delineating clear compliance boundaries and stable institutional expectations, they chart a mandatory green development path for enterprises. Under this pathway, even enterprises with initially weak intrinsic motivation are compelled by powerful external regulatory pressure to passively or incrementally adjust their behavioral patterns, ultimately being guided onto the track of green development.

5.2. Recommendations

For governments, scientifically and flexibly setting the intensity of environmental regulation is a crucial lever for driving real estate enterprises toward green and sustainable development. Research indicates that under the combined effects of higher environmental investment and stricter regulatory policies, even enterprises lacking intrinsic motivation for transformation can enhance their green performance through external pressure and support. The key lies in aligning regulatory measures with companies’ specific conditions to create synergistic effects that stimulate green innovation. Therefore, governments should avoid a one-size-fits-all approach and instead adopt differentiated environmental policies. By considering regional development stages, industry characteristics, and companies’ actual capabilities, they can establish appropriate regulatory intensity and incentive mechanisms, thereby fostering mutual benefits for both economic growth and environmental protection.
For enterprises, increasing R&D investment and strengthening commitment to green transformation represent two primary internal drivers of green development. To secure long-term competitiveness and align with policy direction, companies must prioritize green development, continuously investing in environmental technology R&D and innovation. This requires enterprises not only to strategically prioritize green principles but also to embed them into daily operations by establishing robust environmental management systems with clear environmental targets and evaluation criteria. By actively adopting green processes, energy-efficient equipment, and eco-friendly materials, enterprises can reduce pollution at the source, enhance resource efficiency, and transform environmental responsibility into new drivers of development, ultimately achieving dual improvements in both environmental quality and economic performance.

5.3. Limitations and Future Directions

This paper examines the configurational effects of environmental regulations, technological innovation, and transformation willingness on the green development of China’s real estate industry. It not only offers relevant recommendations for Chinese enterprise development and government policy formulation but also provides reference points for green development in other countries. However, the study has limitations. Grounded in research specific to Chinese society, its conclusions may not fully apply to other nations due to significant differences in institutional frameworks and real estate development levels. Further research is needed to determine the applicability of these findings.
Future research should broaden the scope of external factor analysis. For instance, it should examine whether trends in construction industrialization and digitalization can effectively drive transformation within the real estate sector. The study should also explore the configurational dynamics between these factors and other relevant elements in shaping the green development of real estate enterprises. Additionally, comparative case studies between China and other nations should be conducted to formulate tailored green development recommendations for different countries.

Author Contributions

Conceptualization, Z.Y. and X.D.; methodology, H.F.; software, Z.Y.; validation, Z.Y., H.F. and X.D.; formal analysis, H.F.; investigation, Z.Y.; resources, Z.Y.; data curation, X.D.; writing—original draft preparation, Z.Y. and X.D.; writing—review and editing, Z.Y. and X.D.; visualization, H.F.; supervision, H.F. and X.C.; project administration, Z.Y.; funding acquisition, Z.Y., H.F. and X.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant number 72401020 (X.C.); the Major Project for Tackling Key Issues in Humanities and Social Sciences by the Ministry of Education, grant number 17JZD023 (H.F.); and the Categorized Development Quota Project: Practice-Based Course Construction Project (School of Urban Economic Management, Beijing University of Civil Engineering and Architecture) (2025), grant number 05080825008 (Z.Y.).

Data Availability Statement

The data are available on reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Buildings 16 00119 g001
Figure 1. Configuration Effect Model of Green Development in Real Estate Enterprises.
Figure 1. Configuration Effect Model of Green Development in Real Estate Enterprises.
Buildings 16 00119 g001
Buildings 16 00119 g002
Figure 2. Mechanism of Action Diagram.
Figure 2. Mechanism of Action Diagram.
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Table 1. Anchor Values of Each Variable.
Table 1. Anchor Values of Each Variable.
VariableFull MembershipCrossover PointFull Non-Membership
Green Efficiency0.780.750.71
Penalty Case26.6624.6317.62
Environmental investment1.93%1.33%1.12%
technological innovation323.70177.2072.5
transformation intentions4.1543.85
Table 2. Enterprise Green Development Efficiency.
Table 2. Enterprise Green Development Efficiency.
Enterprise201820192020202120222023Average
Greentown China0.7310.739 0.743 0.783 0.836 0.895 0.788
China Merchants Shekou Holdings0.628 0.638 0.649 0.684 0.759 0.799 0.693
China Vanke0.583 0.746 0.748 0.773 0.776 0.804 0.738
China Shipping Development 0.795 0.983 0.995 1.000 1.000 1.000 0.962
China Resources Land0.635 0.707 0.711 0.723 0.745 0.837 0.726
China Jinmao0.615 0.632 0.668 0.706 1.000 1.000 0.770
Poly Development 0.619 0.623 0.679 0.750 0.780 0.862 0.719
Yuexiu Property0.567 0.647 0.663 0.783 0.790 0.805 0.709
Longfor Group0.741 0.771 0.775 0.776 0.784 0.786 0.772
Sino-Ocean Group0.516 0.680 0.751 0.780 0.781 1.000 0.751
Hopson Development 0.599 0.646 0.670 0.673 0.682 0.684 0.659
China Energy Construction Group0.640 0.721 0.803 0.826 0.847 1.000 0.806
Kaisa Group 0.602 0.609 0.625 0.762 0.806 1.000 0.734
R&F Properties0.549 0.623 0.649 0.685 0.734 1.000 0.707
ShiMao Group0.633 0.661 0.852 0.853 1.000 1.000 0.833
Average0.630 0.695 0.732 0.770 0.821 0.898 0.758
Table 3. Necessity Analysis Results.
Table 3. Necessity Analysis Results.
VariableGreen Efficiency
ConsistencyCoverage
Penalty Case0.6800.828
Environmental investment0.6630.866
technological innovation0.7700.803
transformation intentions0.5180.647
Table 4. Configuration Analysis Results.
Table 4. Configuration Analysis Results.
Conditional VariableConfiguration 1Configuration 2Configuration 3Configuration 4
technological innovation⬤⬤⊙
transformation intentions⬤ ⬤
Penalty Case ⬤⬤⊙
Environmental investment⬤ ⬤⬤
Consistency0.8890.8530.9130.862
Original Coverage0.4700.3750.3580.399
Unique Coverage0.1790.1430.0610.084
Overall Consistency0.862
Overall Coverage0.782
Note: ⬤ indicates the core condition exists, ⊙ indicates the peripheral condition does not exist, and blank indicates whether the condition variable appears or not does not affect the result.
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Yang, Z.; Fang, H.; Deng, X.; Chen, X. How Do Environmental Regulations, Technological Innovation, and Transformation Intentions Enhance the Green Development Level of Real Estate Enterprises? A Study on Synergistic Effects from a Configurational Perspective. Buildings 2026, 16, 119. https://doi.org/10.3390/buildings16010119

AMA Style

Yang Z, Fang H, Deng X, Chen X. How Do Environmental Regulations, Technological Innovation, and Transformation Intentions Enhance the Green Development Level of Real Estate Enterprises? A Study on Synergistic Effects from a Configurational Perspective. Buildings. 2026; 16(1):119. https://doi.org/10.3390/buildings16010119

Chicago/Turabian Style

Yang, Zhao, Hong Fang, Xiaojuan Deng, and Xiaoyan Chen. 2026. "How Do Environmental Regulations, Technological Innovation, and Transformation Intentions Enhance the Green Development Level of Real Estate Enterprises? A Study on Synergistic Effects from a Configurational Perspective" Buildings 16, no. 1: 119. https://doi.org/10.3390/buildings16010119

APA Style

Yang, Z., Fang, H., Deng, X., & Chen, X. (2026). How Do Environmental Regulations, Technological Innovation, and Transformation Intentions Enhance the Green Development Level of Real Estate Enterprises? A Study on Synergistic Effects from a Configurational Perspective. Buildings, 16(1), 119. https://doi.org/10.3390/buildings16010119

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