4.2. Regional CEECI Calculations
The MAXDEA software was used to set up non-desired outputs as carbon emissions by setting up super-efficiency, non-direction, and variable return to scale. MaxDEA is a professional data envelope analysis software. There is no limit to the number of data cells, as it can run very large DEA models and can quickly and accurately apply the built-in super-SBM model to calculate CEECI. The CEECI for the 30 provinces, municipalities, and autonomous regions for 2018 and 2019 was calculated year-by-year, with specific data for each section shown in
Table 4. As can be seen from
Table 4, the overall average CEECI of the participating areas is 0.66, with a low intermediate CEECI and more room for development. By region, the CEECI is higher in the East and Central areas, with an average CEECI above 1.07. In comparison, the CEECI is lower in the Northeast and West regions, with an average CEECI below 0.47. In recent years, the east and central areas had higher economic, market, and technological advantages, with CEECI being significantly higher than the national average. However, the vast majority of provinces in the west and northeast regions are unable to provide good support for the development of the construction industry due to their insufficient level of economic growth and scientific and technological innovation, and the low level of market openness and business management in the construction industry, as well as rather traditional energy consumption, resulting in the overall construction industry in the west and northeast regions being in a rough development stage and showing a relatively sluggish development situation.
The intra-regional comparison reveals that there are also significant differences in provincial CEECI levels within each region. For example, the mean value of Beijing in the eastern area (1.42) is significantly higher than the mean values of Hebei and Tianjin (0.22, 0.37), the mean value of Hubei in the central area (0.8) is considerably higher than the mean value of Shanxi (0.33), the mean value of Guangxi in the western area (1.14) is quiet higher than the mean value of Qinghai (0.33), while the mean values of the three provinces in the northeast region were not considerably different. It can be seen that areas with similar economic development within an area can have vastly different CEECI, thus showing that a single factor, such as the level of economic growth, is not the only condition that determines CEECI. Most of the provinces with lower efficiency have a lower level of economic growth and a lower capacity for technological innovation. Still, there are exceptions, such as Hubei, which relies on the advantages of a clean construction energy consumption structure to form a green production model and combine a higher level of business management and market openness, which can promote technological integration and innovation in the regional construction industry. On the other hand, an open market environment is conducive to injecting high-quality capital and technology, thus accelerating the enhancement of CEECI and ultimately driving high-quality development in the construction industry. Conversely, the provinces with high efficiency do not have good economic, innovation, and other environmental conditions, and there are also provinces with a better regional development base but low efficiency. For example, Shandong province has a high level of economic development and scientific and technological innovation, and a high level of business management in the construction industry, but its CEECI is not high. In terms of condition variables, the construction industry in Shandong province has a more traditional energy consumption structure and a higher degree of market openness, which on the one hand ignores green production, and on the other hand the higher degree of market may have caused vicious competition among construction enterprises, and Tianjin has a high level of economic development and scientific and technological innovation, but the construction industry has a low level of business management, which leads to waste of resources in the production process and a high degree of market openness, resulting in vicious competition among regional construction enterprises. The two regions are more economically developed, but the quality development of the local construction industry has been hindered by the unreasonable allocation of resources.
On the whole, regional economic development will bring a vast market to the construction industry in each province. Scientific and technological innovation can promote technological innovation and technological upgrading in the construction industry in each area, prompting quality change, efficiency change, and power change in the construction industry, enhancing the competitiveness of construction enterprises. The opening up of the construction market will be able to bring good technology and talents to the construction industry in each province, but if the regional construction industry cannot make reasonable use of its conditions, make up for its shortcomings, and form a positive interaction between resource conditions, it will result in redundant resources and fierce competition, which is counterproductive. Therefore, regions must fully understand their needs, optimize the allocation of resources, and find a suitable path for their development, which is the key to the efficient development of the regional construction industry. To this end, the fsQCA method was used to explore how multiple factors jointly influence CEECI and to explore differentiated driving paths for efficient regional CEECI development, which is of high research and practical value in promoting high-quality products in the construction industry.
4.5. Efficient CEECI Pathway Analysis
This section explores the multiple concurrent causalities of CEECI using fsQCA 3.0 software. The consistency threshold for configuration analysis was set at 0.8, and the frequency threshold was set at 1 about the study by Fiss [
51]. The data were then normalized and analyzed to obtain complex, parsimonious, and intermediate solutions. The intermediate solution was considered to reflect the results of the study best, and the condition variables that appeared in both the medium and parsimonious solutions were core conditions. In contrast, those that appeared only in the middle solution were marginal conditions. As can be seen from
Table 7, four conditional configurations for six histories led to the emergence of efficient CEECI expectation results in both 2018 and 2019. The consistency of individual designs for all histories was more significant than the minimum criterion of 0.75, indicating that the various histories can be considered as sufficient combinations of conditions to achieve efficient CEECI and that the results of the histories analysis are valid. The consistency of the broad configurations in 2018 and 2019 were 0.9167 and 0.9233, with coverage of 0.6200 and 0.5510, respectively, and with high overall coverage explaining approximately 62% and 55% of the reasons for increased efficiency, respectively.
The highly efficient CEECI groupings for 2018 and 2019 show a very high degree of consistency, demonstrating the robustness of the research results. Based on the conditional variables and the differences between the 2018 and 2019 groupings, the efficient CEECI drive paths can be summarized as “low energy management”, “scale management”, and “scale market opening”, and the specific analysis is as follows.
Low energy consumption management type. 2018–2019 Histogram 1, “management level x energy consumption structure” is the core condition, and the level of economic development, market openness, and technological innovation are the missing marginal conditions. This shows that good management and a clean energy consumption structure are the core of efficient CEECI in the region, compensating for the lack of other states for efficient CEECI. This path shows that a higher level of business management positively contributes to the development of the construction industry along the lines of standardization, modernization and scaling, and significantly improves the regional CEECI by raising the level of business management of enterprises. At the same time, combining a higher level of business management and a cleaner energy consumption structure can better play a role in carbon emission reduction.
Scale management type. Grouping 2 for 2018–2019, with “level of economic development x level of management” as a core condition, energy consumption structure and level of technological innovation as marginal conditions, and degree of market openness as a marginal missing condition, the cases that fit this grouping include Beijing, Shanghai, and Guangdong. This path shows that sound business management skills can efficiently translate into a giant construction market due to higher levels of economic development, thus contributing to the efficient development of the construction industry and enhancing CEECI. It is also necessary to strengthen investment in science and technology innovation and optimize the energy consumption structure. The path suggests that on the basis of good primary conditions in regions with a high economic environment and business management level, the construction industry development model should be fundamentally changed by improving scientific and technological innovation capabilities, promoting quality, efficiency and dynamic changes in the construction industry, promoting innovation spillover and innovation absorption by avoiding vicious competition among construction enterprises in the area, making the region’s construction enterprises as a whole take a high technology development route, preventing an imbalance between innovation inputs, and this will enable the construction enterprises in the region to follow a high technology development route as a whole, avoiding an imbalance between innovation input and output, thus obtaining more innovation benefits and stimulating construction enterprises to innovate continuously, ultimately promoting the high-quality development of the construction industry in the region.
Open market type at scale. For 2018–2019 Cluster 3, “level of economic development x degree of market openness” is the core condition, and energy consumption structure and level of technological innovation are the marginal conditions. The level of management is the marginal missing condition, and cases that fit this cluster include Zhejiang Province, Jiangsu Province, and Fujian Province. This path shows that in areas where the regional economy is more developed and has a larger construction market, but where construction enterprises themselves have insufficient management capacity, opening up the construction market can increase competition among construction enterprises, allowing enterprises with advanced management models, highly skilled personnel, and other advantages to develop and play an moral role, which in turn generates technological spillover to construction enterprises in the region through the human capital flow effect, the good effect, the competitive effect, and the correlation effect. Following that, by matching the higher regional science and technology innovation capacity, conversely, it can provide a constant supply of innovative talents and advanced technologies for the region’s enterprises, fundamentally changing the construction industry’s development model, promoting quality, efficiency, and dynamic changes in the construction industry, and ultimately contributing to the enhancement of CEECI in the region.