4.2.1. Analysis of Influencing Factors

In this paper, we use the factor detector to obtain and rank the q-values for the three time points of 2011, 2015, and 2019. Every component chosen in the preceding research that had explanatory power passed the 1% significance level test. Table 5 displays the specific outcomes.

The impact of resource endowment, social and economic factors, and environmental awareness factors on the efficiency of green technology innovation in the construction industry can be seen as having substantial disparities from the perspective of factor classification. Overall, socioeconomic and environmental awareness factors have a greater effect on efficiency values than resource endowment in the construction industry, which has a relatively lesser effect on them. The *q*-values of all factors, taken in the context of the 10 driving factors, range from 0.14 to 0.84, demonstrating that there are clear distinctions between the effects of various driving factors on the effectiveness of green technology innovation in the construction sector. According to rankings and *q*-values for 2011, 2015, and 2019, environmental regulation, economic development level, public environmental concern, urbanization level, and foreign direct investment have a significant impact on the efficiency of green technology innovation in the construction industry. The total *q*-value is greater than 0.5, and these influencing factors are the main driving factors. Among the

remaining influencing factors, the explanatory power of industry scale decreased slightly and then increased. The efficiency of green technology innovation moved up to the second spot in the construction sector in 2019, and its *q*-value was 0.75, indicating a persistent increasing trend. Therefore, the scale of the industry is chosen as a potential variable affecting the efficiency of green technology innovation in the construction sector. In addition, the influence of education level and technical equipment rate on green technology innovation efficiency in the construction industry is relatively weak.


**Table 5.** Factor detection results of space-time differentiation of green technology innovation efficiency in the Chengdu–Chongqing urban agglomeration.

From the time dimension, the driving factors fluctuated to different degrees during 2011–2019. The explanatory power revealed a general rising trend that the influence of leading factors and potential variables on the spatial difference of green technology innovation efficiency in the construction industry gradually increased. Among them, there has been a noticeable increase in the degree of economic development, foreign direct investment, and environmental regulation. The industrial scale, urbanization level, and public environmental attention show V-shaped fluctuations, which first decline and then rise. The construction industry generally exhibits an increase in the explanatory power of green technology innovation efficiency. The education level and the rate of technical equipment both increased first and then decreased. The explanatory power of education level on the efficiency of green technology innovation in the construction industry is unstable and fluctuates greatly. However, in terms of explanatory power, the rate of technical equipment revealed a declining tendency.

4.2.2. Analysis on the Interaction of Spatial-Temporal Differentiation of Green Technological Innovation Efficiency

To further investigate the variation of explanatory power on green technology innovation efficiency in the construction industry when different driving factors interact, the dominant factors and potential factors after factor detection were selected to analyze their interaction mechanisms affecting the spatial divergence of green technology innovation efficiency in the construction industry. Due to the interaction between the two driving factors, it is not a simple linear addition [73]. Therefore, the *q-*value of the interaction influence of two drivers on green technology innovation in the construction industry is studied using the interaction detection of geographic detectors.

Table 6 displays the findings from the interactive detection investigation using the data from 2011 through 2019: There is a close relationship between the selected leading factors and the potential factors, and the *q-*value of each influencing factor shows different degrees of improvement after complex interaction. There are two modes of combination: double factor enhancement and nonlinear enhancement, and the interaction of all influencing variables has a greater impact on the geographic and temporal diversity of green technology innovation efficiency in the construction sector than any single factor. The interaction between industrial scale and urbanization level, foreign direct investment, and

environmental regulation degree is explained by more than 60%, showing a stable growth trend over time. The level of economic development interacts with foreign direct investment, the degree of environmental regulation, and the public's environmental concern, respectively, and the explanatory force is above 50%. As for the interaction between foreign direct investment and public environmental concern, the explanation strength is more than 80%, and the impact degree on the spatial and temporal differentiation of green technology innovation efficiency in the construction industry shows a continuous increasing trend from 2011 to 2019. The interaction effect of industrial scale and public environmental concern with urbanization level and environmental regulation degree is obvious, and the explanatory power is relatively strong. The explanatory power of the interaction between environmental regulation and public environmental attention is significantly improved compared with the single factor, and its explanatory power is relatively stable and above 90%, which is larger than the pairwise interaction result between other types of factors. From 2011 to 2019, each influencing factor experienced a transition between double-factor enhancement and nonlinear enhancement. The last mode of action stabilized at twofold factor enhancement in order to strengthen the justification of interaction factors on the efficiency of green technology innovation in the construction sector.

**Table 6.** Interactive detection results of spatial differentiation of green technology innovation efficiency of construction industry.


DE: Double enhancement; NE: nonlinear enhancement.

#### **5. Discussion and Conclusions**

*5.1. Research Conclusions*

The Chengdu–Chongqing urban agglomeration is a crucial growth pole for developing high-quality economic development in western China. This research assessed the green technology innovation efficiency of the construction sector in each city using data over the period of 16 cities in the Chengdu–Chongqing urban agglomeration from 2011 to 2019. Using a gravity model and a geographic detector, the geographical and temporal development characteristics of green technology innovation efficiency in the construction sector were explored. At the same time, the pertinent driving factors were identified, and the extent to which each driving element affects the efficiency of green technology innovation in the construction sector was explored for both single-factor and double-factor analyses. The ensuing conclusions were reached: (1) Within the Chengdu–Chongqing urban agglomeration, there are considerable regional variations in the efficiency of green technology innovation in the construction industry, and the overall trend is upward. (2) The research area exhibits spatially heterogeneous characteristics in terms of the efficiency of green technology innovation in the construction industry. Additionally, it demonstrates the

tendency whereby the area with high efficiency levels gradually spreads to the surrounding areas with lower efficiency levels, and the area with low efficiency levels gradually decreases in scope. (3) The Chengdu–Chongqing urban agglomeration's geographical spillover impact is undoubtedly constrained by distance. Additionally, the western region's spatial spillover impact is superior to that of Chongqing's eastern region. The western portion of the Chengdu–Chongqing urban agglomeration has a better spatial spillover impact than the eastern portion, which is represented by Chongqing. Moreover, the spatial spillover effect is significantly limited by distance. (4) Environmental regulation, the level of economic development, public environmental concern, the level of urbanization, and foreign direct investment, as the dominant factors of green technology innovation efficiency in the construction industry, and the industry's scale as a potential factor, all have significant effects on the efficiency of green technology innovation in the construction industry. (5) In comparison to the single component, the interaction between the leading factor and the potential factor has a greater influence on the regional and temporal differentiation of green technology innovation efficiency in the construction sector.

#### *5.2. Theoretical Contribution*

This paper's theoretical contribution, as compared to previous studies, focuses primarily on three areas:

Firstly, prior to measuring the efficiency of green technology innovation in the research area's construction industry, the undesirable output is fully taken into account. It is discovered that the research area's overall innovation efficiency in green technology is notably different and exhibits an upward trend. This confirms the opinion of Qian et al. (2022) [74] that there is an imbalance in green technology innovation in inland areas and that there are obvious differences between regions. Additionally, it was discovered that places distant from the central cities were more likely to have severe solidification and ultra-low efficiency, which was in line with the findings of Xu et al. (2020) [75]. Based on these findings, this study investigates and analyzes the characteristics of the green technology innovation efficiency of the construction sector in the study area over time and space, as well as further examining the variations between cities and the degree of spatial connectivity.

Secondly, green technology innovation in the construction sector has had an optimistic spillover effect in the study area, gradually transferring from the high-efficiency-level to the neighboring low-efficiency-level areas, and the low-efficiency-level area's scope gradually exhibiting a trend of narrowing. The findings of Hu et al. (2022) [76], Wang et al. (2022) [77], and Zhao et al. (2021) [78] are in agreement with this finding. They believe that highefficiency areas have radiation effects on low-efficiency areas and narrow the gap between cities. In order to intuitively reveal the spillover effect between different spatial units, this paper introduces the gravity model and utilizes the spatial spillover network structure diagram. As a result, the research findings on the efficiency of green technology innovation in the construction industry are further enhanced.

Thirdly, this research analyzes the factors that affect the efficiency of green technology innovation in the construction sector. The results are consistent with those of Zhao et al. (2022) [72], Li et al. (2022) [45], and Stucki et al. (2018) [79] and indicate that environmental regulation and economic development levels have a significant impact on green technology innovation efficiency in the sector. According to Porter's theory, environmental regulation, to some extent, has a favorable effect on the development of green technology [80]. High economic development locations typically have enough funding for green technology innovation activities, which can significantly encourage the improvement of green technology innovation efficiency. Existing studies consider the influencing factors to be thin and do not include multiple influencing factors in the same space for interaction impact analysis. In order to make up for these deficiencies, based on the characteristics of geographic detectors, factors of multicollinearity can be included in the same framework system for discussion. This paper expands the influencing factor system of green technology innovation efficiency in the construction industry and enriches the research findings

by taking into account and examining the driving role of related influencing factors from the three aspects of the construction industry's resource endowment, social economy, and environmental awareness.

#### *5.3. Management Inspiration*

The Chengdu–Chongqing City cluster is situated at the intersection of the "Belt and Road" and the Yangtze River Economic Belt, which has considerable regional advantages and serves as an essential platform for the development of the western province. With the continuous promotion of the "double carbon" policy, the construction industry is in urgent need of green and low-carbon transformation. Therefore, the following suggestions are put forward:

Firstly, develop differentiated environmental regulation policies to enhance the institutional environment for the development of green technology innovation. The Chengdu– Chongqing region's construction industry's use of green technology innovation is best explained by environmental regulation, which has the strongest overall impact. Increase government involvement, bolster the administration's commitment to environmental protection, develop local conditions-specific environmental regulation laws, enhance the relevance and efficiency of environmental regulation, and facilitate the balanced development of green technology innovation efficiency.

Secondly, focus on bringing in top-notch foreign funding and promoting the advancement of green technology innovation in the construction industry. The spillover impact of technology, funding, resources, and knowledge delivered by foreign direct investment is fully utilized through the infusion of high-quality foreign investment by the government. This is a significant technique to increase the efficiency of green technology innovation in the research domain and is conducive to accelerating the transition of green technology innovation accomplishments in the construction sector.

Thirdly, encourage the public's excitement about environmental issues and fully utilize the public's oversight role. Develop policies to support and encourage public participation in environmental governance while continuously improving and standardizing the format of letters and media reports. This supports modernizing and scientifically validating environmental governance, ensures the timely and efficient implementation of public supervision and management, and is a crucial building block for attaining green, sustainable, and healthy development in the construction industry.

Finally, strengthen coordinated development among regions to narrow the imbalance. An efficient method of coordinating and promoting the growth of green technology innovation in the construction sector in each prefecture-level city in the Chengdu–Chongqing region is to improve the level of technical openness among cities. Give large cities such as Chengdu and Chongqing their due as "leading goose", radiate these cities' advantages in cutting-edge technology and resources to neighboring cities with low rates of green technology innovation, and encourage the integration and sustainable growth of the construction sector in this area.

### *5.4. Limitations and Deficiencies*

In this study, the efficiency of green technology innovation in the construction sector is evaluated. The gravity model and geographic detector are used to investigate the characteristics of the spatial and temporal evolution of efficiency and its affecting elements. It expands and enriches the research theory of green technology innovation in the construction industry and helps to promote the green and low-carbon transformation of the construction industry. This study may have several shortcomings, which should be addressed and resolved in further studies. First of all, only the Chengdu–Chongqing urban agglomeration in China is used as the research region for this work, which focuses on the efficiency and impact variables of green technology innovation in the construction industry of 16 of those cities. However, there might be regional differences in the construction industry's use of green technology innovation in various metropolitan agglomerations. In

the future, a comparison study of typical regions such as the Beijing-Tianjin-Hebei urban agglomeration and the Yangtze River Delta urban agglomeration will be necessary. Explore in further detail the regulations for green technology innovation in the construction industry in various urban agglomerations. Secondly, the research object is not sufficiently detailed. The construction industry of each city in the region is the research object of this paper, and the research scope is broad. It can be refined further in future research, and the city can be refined further for each construction enterprise or county for more in-depth research. Last but not least, this essay primarily focuses on the effects of resource abundance, social economics, and environmental consciousness in light of the influencing variables of green technology innovation and efficiency in the construction industry. It might also be impacted by factors such as the energy consumption structure, ancillary industries, and management levels, among others, which will require more investigation and in-depth debate in the future.

**Author Contributions:** Conceptualization, B.W.; methodology, H.C.; software, H.C.; formal analysis, Y.A.; investigation, F.L.; resources, B.W.; data curation, F.L. and B.W.; writing—original draft preparation, H.C.; writing—review and editing, F.L. and B.W.; supervision, Y.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by National Social Science Foundation of China (22BJY142).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Abbreviations**

SBM: slacks-based measure. SFA: stochastic frontier analysis. DEA: data envelopment analysis. BCC: Banker-Charnes-Cooper. CCR: Charnes-Cooper-Rhodes. OECD: Organization for Economic Co-operation and Development. DMU: decision-making units. R&D: research and development. GDP: gross domestic product.
