The Impact of Smart Construction Pilot City Policies on the Development of New Quality Productive Forces in the Construction Industry

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Research on the Impact of the Smart Construction Pilot City Policy on the Development of New Quality Productive Forces in the Construction Industry

By Liu Meixia, Zhang Shibin, Yu Dehu, Yu Xianhui, Liu Hong'e, Shao Di, Zheng Haichao and Lu Zhongde

Summary

Abstract

Based on panel data for 24 smart construction pilot cities and 24 non-pilot cities in China from 2019—2023, this study uses a difference-in-differences model to verify the promoting effect of the smart construction pilot city policy on improving total factor productivity in the construction industry, and further analyzes its impact on the development of new quality productive forces in the construction industry. The results show that, compared with non-pilot cities, total factor productivity in the construction industry of smart construction pilot cities increased by 5.9%, reflecting that the smart construction pilot city policy has significantly promoted total factor productivity in the construction industry and driven the development of new quality productive forces in the sector. The policy improves total factor productivity in the construction industry mainly by reducing energy consumption in the construction industry and increasing the strength of government support. The heterogeneity test shows that the smart construction pilot city policy exhibits significant heterogeneity across city administrative ranks, that is, it has a more pronounced promoting effect on cities with higher administrative ranks.

0 Introduction

General Secretary Xi Jinping has called for accelerating the formation of new quality productive forces, pointing out that new quality productive forces are marked at the core by a substantial increase in total factor productivity, are characterized by innovation, hinge on quality, and are in essence advanced productive forces. Total factor productivity (TFP) refers to the residual of total output that cannot be explained by growth in the inputs of production factors such as capital and labor, and it is an important indicator of high-quality economic development [1]. At present, many scholars study the formation and development of new quality productive forces in various industries from the perspective of total factor productivity. Zhang Haipeng et al. [2] hold that new agricultural technologies, new models and new business forms are important means of raising total factor productivity in agriculture and of forming new quality productive forces in agriculture; Ling Chen et al. [3] argue that the improvement of total factor productivity in manufacturing takes the digital transformation of enterprises as its means, promoting the formation of new quality productive forces in manufacturing. In July 2020, 13 departments, including the Ministry of Housing and Urban-Rural Development (MOHURD), jointly issued the “Guiding Opinions on Promoting the Coordinated Development of Smart Construction and Building Industrialization”, explicitly proposing the development of smart construction. Academician Ding Lieyun [4] points out that smart construction is an innovative model of engineering construction formed by the integration of new information technologies and engineering construction; through standardized modeling, networked interaction, visual cognition, high-performance computing and intelligent decision support, it achieves integrated and highly efficient collaboration across project initiation and planning, planning and design, construction and production, and O&M services under the drive of a digital chain. Smart construction is a typical representative of new quality productive forces in the construction industry, and it is crucial for improving total factor productivity in the construction industry and for driving the sector toward digital and intelligent development [5].

As one of the important means by which China promotes the development of smart construction, the smart construction pilot city policy is of great significance to evaluate. In October 2022, in order to vigorously develop smart construction, MOHURD published the list of 24 smart construction pilot cities and rolled out the smart construction pilot city policy. The pilot program began on the date of publication and runs for three years. Each pilot city is required to strictly implement its pilot implementation plan and to adhere to the working principle of “overall planning and adaptation to local conditions”. Four mandatory tasks were set—improving the policy system, cultivating the smart construction industry, building pilot demonstration projects and innovating management mechanisms—while four further tasks were offered for localities to choose from according to their actual conditions: building smart factories for components and parts, promoting technology research and development and the transformation of achievements, improving the standards system, and cultivating professional talent. Pilot cities may also propose new task directions in line with the pilot objectives. Since the policy was implemented, as of December 2023, MOHURD had approved seven smart construction pilot projects of six enterprises with a total investment of more than 9 billion yuan, and had summarized and promoted 130 replicable practices. The 24 smart construction pilot cities have carried out work around the main tasks of improving the policy system and cultivating the smart construction industry, issuing a series of supporting policies on land, planning, finance and science and technology; supporting relevant organizations in building 39 smart construction science and technology innovation platforms; including 506 backbone smart construction enterprises; publishing 758 smart construction pilot demonstration projects; and promulgating and implementing 47 smart construction-related standards, consumption quota standards and guidelines, thereby promoting the development of smart construction. Scholars have already begun to study the relationship between smart construction and new quality productive forces in the construction industry. Niu Weirui et al. [6] point out that smart construction plays an important role at every stage of the evolution of new quality productive forces and is a powerful lever and technical support for building new quality productive forces in the construction industry; Yu Jing and Zhang Chunwei et al. [7-8] propose that research on and application of smart construction technologies is an important support for driving the transformation and upgrading of the traditional construction industry, and that smart construction science and technology innovation empowers new quality productive forces in the construction industry.

Existing research on smart construction and new quality productive forces is relatively scarce, and it is mostly qualitative with little quantitative study. This paper, based on panel data for 24 smart construction pilot cities and 24 non-pilot cities in China from 2019—2023, uses a difference-in-differences model to verify the promoting effect of the smart construction pilot city policy on improving total factor productivity in the construction industry, explores the influence of city administrative rank on the implementation effect of the smart construction pilot city policy, analyzes the role of the policy in reducing energy consumption and in increasing the strength of government support, and puts forward targeted recommendations for each.

1 Research Hypotheses

Total factor productivity refers to the part of economic growth that cannot be explained after deducting the productivity contributions of factors such as capital input and labor; it shows the contribution to economic growth of factors other than input factors such as physical capital and labor [9], and a significant increase in it is the core marker of new quality productive forces. This paper sets total factor productivity in the construction industry as the dependent variable [10]. Based on the above analysis, Hypothesis 1 is proposed: the smart construction pilot city policy can improve total factor productivity in the construction industry, thereby promoting the formation and development of new quality productive forces in the construction industry.

At the same time, drawing on the research of Wang Guangming, An Min et al. [11-12], this paper studies the impact of the smart construction pilot city policy on total factor productivity in the construction industry from two aspects: reducing energy consumption in the construction industry and increasing the strength of government support. On the one hand, energy consumption in the construction industry is a key indicator measuring the ratio of the gross output value of the construction industry to its total energy consumption [13]. Smart construction pilot cities can significantly reduce energy consumption through green technology research and development and the transformation of scientific and technological achievements, thereby improving total factor productivity in the construction industry and promoting the development of new quality productive forces in the sector. This paper measures energy consumption in the construction industry by energy consumption per unit of floor area, calculated by converting the energy consumed by buildings into a total amount of standard coal and then dividing it by the floor area, with the unit of 10,000 t/m2 [12]. On the other hand, smart construction pilot cities can improve total factor productivity in the construction industry through supporting policies such as fiscal subsidies and tax preferences [14], and thereby promote the development of new quality productive forces in the sector. This paper measures the strength of government support for promoting smart construction in each city by the word frequency of “smart construction” in municipal government work reports from 2019—2023 [15]. Based on the above analysis, Hypothesis 2 is proposed: the smart construction pilot city policy improves total factor productivity in the construction industry by reducing energy consumption in the construction industry and by increasing the strength of government support, thereby promoting the formation and development of new quality productive forces in the construction industry.

2 Model Construction

A difference-in-differences model is constructed to verify the impact of the smart construction pilot city policy on total factor productivity in the construction industry. Here, the dependent variable is “total factor productivity in the construction industry”, the core explanatory variable is the “smart construction pilot city policy”, and the control variables include “population density”, “urbanization level” and so on.

2.1 Research Subjects

This paper sets a treatment group and a control group in order to study the differences in changes in total factor productivity in the construction industry between the two groups of cities from 2019—2023. The 24 smart construction pilot cities are taken as the treatment group, of which 16 cities such as Beijing and Tianjin belong to the eastern economic belt, four cities such as Changsha and Wuhan belong to the central economic belt, and four cities such as Chongqing and Urumqi belong to the western economic belt. Considering the similarity between the control group and the treatment group in terms of regional distribution, city size, economic development level and construction industry development level, as well as the comparability and availability of data, the control group selected consists of 16 eastern economic belt cities such as Jinan and Shijiazhuang, four central economic belt cities such as Yueyang and Jingzhou, and four western economic belt cities such as Zigong and Turpan. The panel data of these 48 cities from 2019—2023 are used as the research objects, including the total assets of the construction industry in each city, the number of employees and of enterprises in the construction industry, the gross output value and completed floor area of the construction industry, population density, and the gross regional product of each city. The data come from the national statistical yearbook, municipal statistical yearbooks, and communiqués on national economic and social development.

(1) Dependent variable. New quality productive forces are marked at the core by a substantial increase in total factor productivity; therefore, an improvement in total factor productivity in the construction industry is an important manifestation of the formation and development of new quality productive forces. Referring to existing research, this paper uses the DEA-Malmquist (DEA) index to measure total factor productivity in the construction industry [16-17], taking the total assets of urban construction enterprises, the number of employees and the number of enterprises as input indicators, and the completed floor area and the gross output value of the construction industry as output indicators. The data come from the “number of enterprise units, year-end employees, gross output value of the construction industry and completed floor area” in the basic information of construction enterprises, and from the “total assets” in the main financial indicators of construction enterprises, in municipal statistical yearbooks.

(2) Core explanatory variable. This paper takes the “smart construction pilot city policy” as the policy shock variable. If a city was designated as a smart construction pilot city in 2022, the impact coefficient for total factor productivity in the construction industry, Treat, is set to 1 in that year and in subsequent years, and to 0 otherwise.

(3) Control variables. The control variables are selected in order to eliminate or reduce the influence of other potential factors on the dependent variable, so that the impact of the smart construction pilot city policy on total factor productivity in the construction industry can be estimated more accurately. Referring to existing research [18-20], this paper selects the following control variables: the population density, urbanization level, construction scale, per capita gross domestic product (GDP) and industrial output value of each city. The data come from the national statistical yearbook, municipal statistical yearbooks, and communiqués on national economic and social development.

Population density is the number of people per unit of land area; its growth directly drives up urban demand for housing, commerce and infrastructure. This indicator is assessed by the ratio of the permanent resident population of each city to its administered land area, with the unit of persons/km2, and the data come from the “permanent resident population density” under population and employment in municipal statistical yearbooks. An increase in the urbanization level means that population and economic activities cluster in cities and towns, and the volume of urban construction work increases accordingly; this indicator is assessed by the ratio of the urban population to the total population, calculated from the “urban population” and “total population” figures in municipal statistical yearbooks. Construction scale concerns the quantities of building materials, equipment and labor required for the development of the construction industry; this indicator is assessed by the ratio of the gross output value of the construction industry to regional GDP, calculated from the “gross output value of the construction industry” in the basic information of construction enterprises and the “gross regional product” in the national economic accounts of municipal statistical yearbooks. Per capita GDP represents the living standards and consumption capacity of residents, as well as the demand for improving the living environment and for urban infrastructure construction, with the unit of yuan, and the data come from the “per capita gross regional product” in the national economic accounts of municipal statistical yearbooks. Growth in industrial output value drives the development of related industries such as building materials and equipment, and there is a close industrial chain relationship with the construction industry [21]; this indicator is assessed by the ratio of the gross industrial output value of each city to regional GDP, calculated from the “gross industrial output value of enterprises above designated size” and the “gross regional product” in the national economic accounts of municipal statistical yearbooks.

2.2 Model Specification

The difference-in-differences model is a statistical analysis method based on natural experiments; the quasi-natural experiment in this paper refers to the “launch of the smart construction pilot city policy”. Before and after the experiment, the sample is divided into two groups: the treatment group affected by the policy at the quasi-natural experiment time point t and the control group not affected by the policy. The effect of the policy is evaluated by comparing the changes in the treatment group and the control group before and after the quasi-natural experiment [22]. Referring to the research of Wei Dongming and Masayuki et al. [15,23], this paper applies a difference-in-differences model to analyze the impact of the smart construction pilot city policy on total factor productivity in the construction industry. The specific specification is as follows:

where: β 0 denotes the constant term; β 1 denotes the impact coefficient of the smart construction pilot city policy effect; Yit denotes the total factor productivity of the construction industry of city i in year t; Treatit denotes the policy dummy variable for “smart construction pilot city”; Xit denotes the control variables; λi and ηt denote the individual and year fixed effects respectively (excluding individual differences among cities and differences across years); εit denotes the random error term (randomly distributed influencing factors not covered by the explanatory variables of the model).

To verify the theoretical mechanism, an impact mechanism model is constructed for testing, referring to the research of Che Maoran and Mao Qilin et al. [24-25]. The specific specification is as follows:

where: IMit denotes the mechanism variable; in this paper the mechanism variables are the strength of government support and energy consumption in the construction industry; γ 1 denotes the estimated coefficient of the interaction term between the smart construction pilot city policy shock and the mechanism variable ( Treat × IM).

γ 1: the test procedure is as follows. First, based on equation (1), verify the promoting effect of the smart construction pilot city policy on improving total factor productivity in the construction industry. Second, run the regression of equation (2); if, for the interaction term between the smart construction pilot policy shock and the mechanism variable ( Treat × IM), the estimated coefficient γ 1 is significant, this indicates that the mechanism variable is a channel through which the smart construction pilot city policy promotes the improvement of total factor productivity in the construction industry, and the sign of γ 1 shows whether the improvement in total factor productivity in the construction industry is driven by raising or by lowering this mechanism variable.

3 Empirical Analysis

The empirical analysis uses the constructed difference-in-differences model to analyze the panel data of the 24 smart construction pilot cities and the 24 non-pilot cities, in order to verify the validity of the hypotheses in Section 1. It comprises the baseline regression, robustness tests, the heterogeneity test and the impact mechanism test. The baseline regression is intended to verify that the smart construction pilot city policy can promote the improvement of total factor productivity in the construction industry; the robustness tests are intended to verify the reliability of the model in this paper; the heterogeneity test is intended to verify that the smart construction pilot city policy exhibits significant heterogeneity across city administrative ranks; and the impact mechanism test is intended to verify that the smart construction pilot city policy can improve total factor productivity in the construction industry by increasing the strength of government support and by reducing energy consumption in the construction industry.

3.1 Baseline Regression

The baseline regression results are shown in Table 1. According to the regression results of model (1), when no control variables are introduced, the impact coefficient of the smart construction pilot city policy on Treat is 0.061 and passes the 10% significance test, indicating that, compared with non-pilot cities, total factor productivity in the construction industry of smart construction pilot cities increased by 6.1%, reflecting the development of new quality productive forces in the construction industry of smart construction pilot cities. According to the regression results of model (2), after the control variables are introduced, the impact coefficient of the smart construction pilot city policy on Treat is 0.059 and passes the 10% significance test, indicating that, after the effects of control variables such as population density, urbanization level and construction scale are eliminated, total factor productivity in the construction industry of smart construction pilot cities still increased by 5.9% compared with non-pilot cities. This once again shows that the smart construction pilot city policy can significantly improve total factor productivity in the construction industry and thereby promote the development of new quality productive forces in the construction industry. Therefore, Hypothesis 1 holds.

Table 1 Baseline Regression

Note: \*\*\*, \*\* and \* denote the 1%, 5% and 10% significance levels respectively; standard deviations are shown in parentheses. The same applies below.

3.2 Robustness Tests

To verify the validity of the model, this paper uses Stata17 to analyze the panel data of the 24 smart construction pilot cities and the 24 non-pilot cities, including a parallel trend test, a placebo test and a test with a replaced dependent variable, so as to reduce existing errors and verify the reliability of the model.

3.2.1 Parallel Trend Test

The treatment group and the control group must share a common development trend in total factor productivity in the construction industry so that the two are comparable. That is, in the absence of the smart construction pilot city policy, total factor productivity in the construction industry of pilot cities and non-pilot cities is assumed to follow a common trend of change; therefore, a parallel trend test is required. Drawing on the research of Shi Shaobin et al. [26], this paper introduces the interaction terms between the year dummy variables for each year before the policy was implemented and the group dummy variable for the test, using the regression coefficients of the interaction terms to indicate the degree of difference between the two groups, with the difference increasing as the regression coefficient increases. The test results are shown in Figure 1, in which the square points represent the regression coefficients at each time point. The regression coefficients for all time points before the policy shock year of 2022 are insignificant and fall within the 95% confidence interval, indicating that there was no significant difference in total factor productivity in the construction industry between pilot cities and non-pilot cities before the pilot policy was implemented, which satisfies the parallel trend assumption.

Figure 1 Parallel Trend Test

3.2.2 Placebo Test

Omitting other hard-to-observe factors may introduce bias into the estimation results, so a placebo test is necessary; if the test is passed, it shows that the regression result of the smart construction pilot city policy on total factor productivity in the construction industry is not affected by other hard-to-observe factors. Drawing on the research of Zhao Peng et al. [27], this paper conducts 500 repeated regression simulations of equation (1). The test results are shown in Figure 2: the P values of the regression estimates basically follow a normal distribution and most of them are greater than 0.1, so the placebo test is passed, indicating that the baseline regression in this paper is stable.

Figure 2 Placebo Test

3.2.3 Test with a Replaced Dependent Variable

Replacing the dependent variable tests whether the model relies excessively on a particular dependent variable and further examines the validity of the empirical results. The share of the value added of the construction industry in GDP is also an important indicator for measuring the development of the construction industry. Therefore, drawing on the research of Li Zhan and Chen Zhihui et al. [28-29], this paper uses it as an alternative dependent variable in place of total factor productivity in the construction industry, with the data calculated as the ratio of the “value added of the gross output value of the construction industry” in the basic information of construction enterprises to the “gross regional product” in the national economic accounts of municipal statistical yearbooks. The regression results are shown in Table 2: the estimated coefficient of the smart construction pilot city policy is 0.380 and passes the 1% significance test, indicating that the positive effect of the smart construction pilot city policy on improving total factor productivity in the construction industry still exists, further verifying the stability of the results in Section 1 of this paper.

Table 2 Test with a Replaced Dependent Variable

3.3 Heterogeneity Test

Cities of different ranks in China clearly differ in their administrative authority, and the policies and measures adopted by different pilot cities are not identical either. Therefore, in order to gain a deeper understanding of the impact of the smart construction pilot city policy on total factor productivity in the construction industry of different cities, this paper draws on the research of Chen Xinxin [30] and divides the research sample into two categories based on city administrative rank: the first is municipalities directly under the central government and provincial capital cities, and the second is ordinary cities, so as to analyze the differences in the effect of the smart construction pilot city policy among different cities. The test results are shown in Table 3.

Table 3 Heterogeneity Test

The results of model (3) show that after the implementation of the pilot city policy, total factor productivity in the construction industry of the municipalities directly under the central government and provincial capital cities among the pilot cities increased by 14.4% compared with their counterparts among the non-pilot cities. The results of model (4) show that after the implementation of the pilot city policy, total factor productivity in the construction industry of the ordinary cities among the pilot cities increased by 1.9% compared with the ordinary cities among the non-pilot cities. This reflects that the improvement in total factor productivity in the construction industry is more pronounced for municipalities directly under the central government and provincial capital cities than for ordinary cities. The reason may be that municipalities directly under the central government and provincial capital cities enjoy advantages such as larger city size, a higher level of industrial agglomeration and a greater concentration of talent, and therefore gain relatively more dividends from the smart construction pilot city policy.

3.4 Impact Mechanism Test

The impact mechanism test answers the question of which key variables the smart construction pilot city policy influences in order to improve total factor productivity in the construction industry. The test results are shown in Table 4. On the one hand, a mechanism analysis is conducted for the strength of government support. According to the test results of model (5), the estimated coefficient for the smart construction pilot city policy and the strength of government support ( Treat × IM) is 3.422 and passes the 1% significance test, indicating that the strength of government support is a channel through which the smart construction pilot city policy improves total factor productivity in the construction industry; the positive interaction coefficient shows that the smart construction pilot city policy improves total factor productivity in the construction industry by increasing the strength of government support. On the other hand, a mechanism analysis is conducted for energy consumption in the construction industry. According to the test results of model (6), the estimated coefficient for the smart construction pilot city policy and energy consumption in the construction industry ( Treat × IM) is -0.068 and passes the 5% significance test, indicating that energy consumption is a channel through which the smart construction pilot city policy improves total factor productivity in the construction industry; the negative estimated coefficient shows that the smart construction pilot city policy improves total factor productivity in the construction industry by reducing energy consumption.

Table 4 Impact Mechanism Test

In summary, both mechanisms—increasing the strength of government support and reducing energy consumption in the construction industry—have been verified, and Hypothesis 2 holds.

4 Conclusions

Based on panel data for 24 smart construction pilot cities and 24 non-pilot cities in China from 2019—2023, this paper uses a difference-in-differences model to verify the promoting effect of the smart construction pilot city policy on improving total factor productivity in the construction industry, and further analyzes its impact on the development of new quality productive forces in the construction industry. The main conclusions are as follows:

(1) The smart construction pilot city policy has significantly promoted total factor productivity in the construction industry and driven the formation and development of new quality productive forces in the construction industry. Around improving the policy system, cultivating the smart construction industry, building pilot demonstration projects and innovating management mechanisms, the 24 smart construction pilot cities have each formulated their own “Smart Construction Pilot City Implementation Plan” in light of their actual development conditions, promoting the improvement of total factor productivity in the construction industry and advancing the development of smart construction. According to statistics, as of December 2023 the pilot cities had actively cultivated the industry, and 506 enterprises had been included in the list of backbone smart construction enterprises for cultivation.

(2) The smart construction pilot city policy improves total factor productivity in the construction industry through two aspects: reducing energy consumption in the construction industry and increasing the strength of government support. In terms of energy consumption, enterprises can optimize design and construction processes through technology research and development and the transformation of achievements—for example, by introducing smart construction equipment such as construction robots, integrating technologies such as multi-information sensing, fault diagnosis and high-precision positioning and navigation, and providing networked interaction and intelligent decision support for engineering projects—thereby effectively improving construction efficiency and quality, reducing energy consumption and saving resources. In terms of government support, as of June 2023 all pilot cities had formulated implementation plans suited to their own conditions and had successively issued a series of supporting policies. For example, Hefei provides financial rewards to construction enterprises that invest in smart construction-related software research and development, equipment procurement and information technology services; Guangzhou has legislated to make clear that BIM can be used in the declaration and approval of engineering construction projects, and has issued local standards and specifications around industrialized and digital construction processes.

(3) The smart construction pilot city policy exhibits significant heterogeneity across city administrative ranks, that is, its effect in improving total factor productivity in the construction industry is more pronounced for cities with higher administrative ranks. Cities with higher administrative ranks usually have a better foundation for developing smart construction, including a stronger industrial base, higher construction standards and capabilities, and an ample supply of project owners and construction projects, and can therefore give better play to the role of the smart construction pilot city policy. For example, Wuhan leverages the agglomeration advantages of large enterprises and research institutes, has developed and applied a batch of landmark smart construction technology products such as building machines, bridge erection machines and tower construction machines, and has deployed the construction of a BIM-based full-process approval and management system for engineering projects in an effort to explore new models of engineering construction.

5 Policy Recommendations

(1) Implement promotion strategies suited to local conditions. Summarize the replicable experience of the smart construction pilot cities and gradually extend the program to more cities that meet the conditions. At the same time, taking into account the influence of city administrative rank, the promotion of the smart construction pilot city policy should be adapted to local conditions, with suitable promotion strategies formulated according to factors such as the administrative rank, economic development level, and technological and industrial foundation of different cities. For cities with higher administrative ranks, the government can continue to issue incentive policies on land, planning, fiscal affairs, finance and scientific and technological innovation, giving play to the demonstration and leading role of cities with higher administrative ranks and promoting the efficient development of smart construction. For cities with lower administrative ranks, the government should consolidate the foundation for smart construction development, support their continued progress in economic construction, formulate clear plans, goals and tasks for smart construction development, and establish a sustainable smart construction development model, so as to advance the development of smart construction.

(2) Optimize the energy structure of the construction industry. Smart construction pilot cities have effectively improved total factor productivity in the construction industry by reducing its energy consumption. It is therefore recommended that the government vigorously issue relevant supporting policies, encourage construction enterprises to use clean energy and green energy-saving materials, and step up the promotion of advanced energy-saving technologies and equipment. At the same time, the application of renewable energy in the building sector should be deepened, the level of building electrification raised, building photovoltaics supported, and buildings encouraged to reduce fossil-energy heating, so as to create green and low-carbon buildings.

(3) Increase the strength of government support. Drawing on the innovative practices of different pilot cities, effective supporting measures should be formulated. First, an overall plan should be developed: each city should learn from the implementation plans and measures of the pilot cities and formulate key tasks and implementation paths suited to its own smart construction development. Second, local standards and specifications should be issued, and the regulatory and institutional system of the construction industry adapted to smart construction should be improved more quickly. Third, full play should be given to cross-departmental coordination mechanisms, guiding policy resources in scientific research, finance and talent toward the field of smart construction, strengthening policy synergy and jointly promoting the development of smart construction.

(4) Accelerate the formation of new relations of production adapted to new quality productive forces. Reform of the economic system, the science and technology system and other systems should be deepened, with efforts focused on removing the bottlenecks and obstacles that constrain the development of smart construction, establishing a high-standard market system, and innovating the way production factors for smart construction are allocated. Through pilot demonstrations, the smooth flow of all kinds of advanced and high-quality production factors toward the development of new quality productive forces should be accelerated. At the same time, the virtuous cycle among education, science and technology, and talent should be facilitated, and the working mechanisms for talent cultivation, recruitment, deployment and rational mobility should be improved.

References

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