Study on the path of high-quality development of the construction industry and its applicability.

Applicability studies Enhancement paths Five development concepts NCA fsQCA

Journal

Scientific reports

ISSN: 2045-2322

Titre abrégé: Sci Rep

Pays: England

ID NLM: 101563288

Informations de publication

Date de publication:
26 Jun 2024

Historique:

received: 01 01 2024

accepted: 13 06 2024

medline: 27 6 2024

pubmed: 27 6 2024

entrez: 26 6 2024

Statut: epublish

Résumé

Exploring the influencing factors and enhancement paths of high-quality development of the construction industry is crucial for promoting sustainable development of the construction industry. Based on the concepts of "five development", this paper takes the construction industry data of 29 provinces (autonomous regions and municipalities) in China as a sample, utilizes comprehensively the combination method of NCA and fsQCA to build a high-quality development driving model of the construction industry, and explores the coupling effect of factors, like the level of scientific and technological innovation, structural degree, precast building model, external market vitality, resources, and environment, on the development of the industry, revealing the path of high-quality development of the construction industry and analyze its applicability. These findings demonstrate that: (1) The level of scientific and technological innovation, the degree of structure, and the vitality of the external market are the core conditions, and a single factor does not constitute the necessary conditions for the high-quality development of the construction industry; (2) There are three paths for the high-quality development of the construction industry, among which the number of representative cases of linkage development led by openness innovation coordination is the largest, and has strong applicability; (3) There are two non-high-quality development paths in the construction industry, and there is a non-simple opposition relationship with the three high-quality development paths in the construction industry.

Identifiants

DOI: 10.1038/s41598-024-64786-y PMID: 38926442

pubmed: 38926442

doi: 10.1038/s41598-024-64786-y

pii: 10.1038/s41598-024-64786-y

doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

Pagination

14727

Subventions

Organisme : The National Natural Science Foundation of China

ID : 71173067

Informations de copyright

Références

Zhao, M. China’s development plan for the utilization of foreign capita l during the 14th five-year period: Prospects and analysis. China Wto Rev. 8, 331–360. https://doi.org/10.14330/cwr.2022.8.2.05 (2022).

doi: 10.14330/cwr.2022.8.2.05

Xue, S., Na, J., Wang, L., Wang, S. & Xu, X. The outlook of green building development in China during the “fourteenth five-year plan” period. Int. J. Environ. Res. Public Health 20, 5122. https://doi.org/10.3390/ijerph20065122 (2023).

doi: 10.3390/ijerph20065122 pubmed: 36982033 pmcid: 10049407

Feng, X., Jin, R. Q., Chiu, Y. H. & Zhang, L. A. The government-production nexus of energy efficiency in China’s construction industry: Regional difference and factor analysis. Environ. Sci. Pollut. Res. 30, 106227–106241. https://doi.org/10.1007/s11356-023-29470-0 (2023).

doi: 10.1007/s11356-023-29470-0

Wang, X. Y., Zhang, M. Y., Jie, S. Y., Zhang, M. & Zhang, Z. Exploring the coordinated evolution mechanism of regional sustainable development and tourism in China’s “Beautiful China” initiative. Land 12, 1003. https://doi.org/10.3390/land12051003 (2023).

doi: 10.3390/land12051003

Zhong, J. & Li, T. Impact of financial development and its spatial spillover effect on green total factor productivity: evidence from 30 Provinces in China. Math. Probl. Eng. 1–11, 2020. https://doi.org/10.1155/2020/5741387 (2020).

doi: 10.1155/2020/5741387

Ouyang, T., Liu, F. & Huang, B. Dynamic econometric analysis on influencing factors of production efficiency in construction industry of Guangxi province in China. Sci Rep 12, 17509. https://doi.org/10.1038/s41598-022-22374-y (2022).

doi: 10.1038/s41598-022-22374-y pubmed: 36266419 pmcid: 9585032

Hua, X., Lv, H. & Jin, X. Research on high-quality development efficiency and total factor productivity of regional economies in China. Sustainability 13, 8287. https://doi.org/10.3390/su13158287 (2021).

doi: 10.3390/su13158287

Atmaca, A. & Atmaca, N. Carbon footprint assessment of residential buildings, a review and a case study in Turkey. J. Clean. Prod. 340, 130691. https://doi.org/10.1016/j.jclepro.2022.130691 (2022).

doi: 10.1016/j.jclepro.2022.130691

Sun, Y., Xie, H. & Niu, X. Characteristics of cyclical fluctuations in the development of the Chinese construction industry. Sustainability 11, 4523. https://doi.org/10.3390/su11174523 (2019).

doi: 10.3390/su11174523

Yang, Z., Guan, G., Fang, H. & Xue, X. Average propagation length analysis for the change trend of China’s construction industry chain. J. Asian Archit. Build. Eng. 21, 1078–1092. https://doi.org/10.1080/13467581.2021.1928507 (2021).

doi: 10.1080/13467581.2021.1928507

Li, X. et al. Predicting the factors influencing construction enterprises’ adoption of green development behaviors using artificial neural network. Humanit. Soc. Sci. Commun. https://doi.org/10.1057/s41599-022-01253-x (2022).

doi: 10.1057/s41599-022-01253-x pubmed: 36276916 pmcid: 9575629

Chi, M. Q., Muhammad, S., Khan, Z., Ali, S. & Li, R. Y. M. Is centralization killing innovation? The success story of technological innovation in fiscally decentralized countries. Technol. Forecast. Soc. Change 168, 120731. https://doi.org/10.1016/j.techfore.2021.120731 (2021).

doi: 10.1016/j.techfore.2021.120731

Shao, X. F., Zhong, Y. F., Liu, W. & Li, R. Y. M. Modeling the effect of green technology innovation and renewable energy on carbon neutrality in N-11 countries? Evidence from advance panel estimations. J. Environ. Manag. 296, 113189. https://doi.org/10.1016/j.jenvman.2021.113189 (2021).

doi: 10.1016/j.jenvman.2021.113189

Wang, Q. & Qi, Y. Dynamic correlation analysis of construction industry development level and science and technology innovation. IOP Conf. Ser. Earth Environ. Sci. 371, 022008. https://doi.org/10.1088/1755-1315/371/2/022008 (2019).

doi: 10.1088/1755-1315/371/2/022008

Wang, W., Tian, Z., Xi, W., Tan, Y. R. & Deng, Y. The influencing factors of China’s green building development: An analysis using RBF-WINGS method. Build. Environ. 188, 107425. https://doi.org/10.1016/j.buildenv.2020.107425 (2021).

doi: 10.1016/j.buildenv.2020.107425

Li, H. et al. Systematic identification of the influencing factors for the digital transformation of the construction industry based on LDA-DEMATEL-ANP. Buildings 12, 1409. https://doi.org/10.3390/buildings12091409 (2022).

doi: 10.3390/buildings12091409

Chen, H.-P. & Ying, K.-C. Artificial intelligence in the construction industry: Main development trajectories and future outlook. Appl. Sci. 12, 5832. https://doi.org/10.3390/app12125832 (2022).

doi: 10.3390/app12125832

Yousif, O. S. et al. Review of big data integration in construction industry digitalization. Front. Built Environ. https://doi.org/10.3389/fbuil.2021.770496 (2021).

doi: 10.3389/fbuil.2021.770496

Wang, Y., Li, Z. & Shi, F. Factors influencing mechanism of construction development transformation in China based on SEM. Discret. Dyn. Nat. Soc. 1–10, 2015. https://doi.org/10.1155/2015/219865 (2015).

doi: 10.1155/2015/219865

Kamaruzzaman, S. N., Lou, E. C. W., Wong, P. F., Wood, R. & Che-Ani, A. I. Developing weighting system for refurbishment building assessment scheme in Malaysia through analytic hierarchy process (AHP) approach. Energy Policy 112, 280–290. https://doi.org/10.1016/j.enpol.2017.10.023 (2018).

doi: 10.1016/j.enpol.2017.10.023

Li, M., Xu, K. & Huang, S. Evaluation of green and sustainable building project based on extension matter-element theory in smart city application. Comput. Intell. https://doi.org/10.1111/coin.12286 (2020).

doi: 10.1111/coin.12286

Karji, A., Namian, M. & Tafazzoli, M. Identifying the key barriers to promote sustainable construction in the United States: A principal component analysis. Sustainability 12, 5088. https://doi.org/10.3390/su12125088 (2020).

doi: 10.3390/su12125088

Shamseldin, A. K. M. Including the building environmental efficiency in the environmental building rating systems. Ain Shams Eng. J. 9, 455–468. https://doi.org/10.1016/j.asej.2016.02.006 (2018).

doi: 10.1016/j.asej.2016.02.006

Lu, W. et al. Design for manufacture and assembly (DfMA) in construction: The old and the new. Archit. Eng. Des. Manag. 17, 77–91. https://doi.org/10.1080/17452007.2020.1768505 (2020).

doi: 10.1080/17452007.2020.1768505

Lekan, A., Clinton, A., Fayomi, O. S. I. & James, O. Lean thinking and industrial 4.0 approach to achieving construction 4.0 for industrialization and technological development. Buildings 10, 221. https://doi.org/10.3390/buildings10120221 (2020).

doi: 10.3390/buildings10120221

Yashiro, T. Conceptual framework of the evolution and transformation of the idea of the industrialization of building in Japan. Constr. Manag. Econ. 32, 16–39. https://doi.org/10.1080/01446193.2013.864779 (2014).

doi: 10.1080/01446193.2013.864779

Begum, R. A., Satari, S. K. & Pereira, J. J. Waste generation and recycling: Comparison of conventional and industrialized building systems. Am. J. Environ. Sci. 6, 383–388. https://doi.org/10.3844/ajessp.2010.383.388 (2010).

doi: 10.3844/ajessp.2010.383.388

Ji, F., Shi, J., Zhu, T. & Hu, X. Risk assessment in the industry chain of industrialized construction: A Chinese case study. Buildings 12, 1688. https://doi.org/10.3390/buildings12101688 (2022).

doi: 10.3390/buildings12101688

Chen, Y., Huang, D., Liu, Z., Osmani, M. & Demian, P. Construction 4.0, industry 4.0, and building information modeling (BIM) for sustainable building development within the smart city. Sustainability 14, 10028. https://doi.org/10.3390/su141610028 (2022).

doi: 10.3390/su141610028

Jahanger, A. Influence of FDI characteristics on high-quality development of China’s economy. Environ. Sci. Pollut. Res. Int. 28, 18977–18988. https://doi.org/10.1007/s11356-020-09187-0 (2021).

doi: 10.1007/s11356-020-09187-0 pubmed: 32418088

Liu, Y., Liu, M., Wang, G., Zhao, L. & An, P. Effect of environmental regulation on high-quality economic development in China-an empirical analysis based on dynamic spatial durbin model. Environ. Sci. Pollut. Res. Int. 28, 54661–54678. https://doi.org/10.1007/s11356-021-13780-2 (2021).

doi: 10.1007/s11356-021-13780-2 pubmed: 34018107

Shi, X.-H., Chen, X., Han, L. & Zhou, Z.-J. The mechanism and test of the impact of environmental regulation and technological innovation on high quality development. J. Comb. Optim. https://doi.org/10.1007/s10878-022-00984-6 (2023).

doi: 10.1007/s10878-022-00984-6

Wang, Y. & Wu, X. Research on high-quality development evaluation, space-time characteristics and driving factors of China’s construction industry under carbon emission constraints. Sustainability 14, 10729. https://doi.org/10.3390/su141710729 (2022).

doi: 10.3390/su141710729

Bei, J. Study on the “high-quality development” economics. China Political Econ. 1, 163–180. https://doi.org/10.1108/cpe-10-2018-016 (2018).

doi: 10.1108/cpe-10-2018-016

Franz, T. Power balances, transnational elites, and local economic governance: The political economy of development in Medellín. Local Econ. J. Local Econ. Policy Unit 33, 85–109. https://doi.org/10.1177/0269094218755560 (2018).

doi: 10.1177/0269094218755560

Floridi, L. & Cowls, J. A unified framework of five principles for AI in society. Harvard Data Sci. Rev. https://doi.org/10.1162/99608f92.8cd550d1 (2019).

doi: 10.1162/99608f92.8cd550d1

Li, B. & Wang, H. Comprehensive evaluation of urban high-quality development: A case study of Liaoning Province. Environ. Dev. Sustain. 25, 1809–1831. https://doi.org/10.1007/s10668-022-02129-5 (2022).

doi: 10.1007/s10668-022-02129-5

Feng, M. & Guo, H. X. Research on the evaluation of high-quality economic development based on factor analysis. J. Sci. Ind. Res. 78, 827–830 (2019).

Yulenkova, I. B. Factors in innovative development of a region. Regionol. Regionol. Russian J. Reg. Stud. 27, 661–677. https://doi.org/10.15507/2413-1407.109.027.201904.661-677 (2019).

doi: 10.15507/2413-1407.109.027.201904.661-677

Yang, Y. Q., Lu, X. Y., Chen, J. & Li, N. Factor mobility, transportation network and green economic growth of the urban agglomeration. Sci. Rep. https://doi.org/10.1038/s41598-022-24624-5 (2022).

doi: 10.1038/s41598-022-24624-5 pubmed: 36585477 pmcid: 9803652

Zhao, Y., Shuai, J., Shi, Y., Lu, Y. & Zhang, Z. Exploring the co-opetition mechanism of renewable energy trade between China and the “Belt and Road” countries: A dynamic game approach. Renew. Energy 191, 998–1008. https://doi.org/10.1016/j.renene.2022.04.022 (2022).

doi: 10.1016/j.renene.2022.04.022

Luo, L., Wu, X., Hong, J. K. & Wu, G. D. Fuzzy cognitive map-enabled approach for investigating the relationship between influencing factors and prefabricated building cost considering dynamic interactions. J. Constr. Eng. Manag. https://doi.org/10.1061/(asce)co.1943-7862.0002336 (2022).

doi: 10.1061/(asce)co.1943-7862.0002336

Xie, L., Chen, Y. & Chang, R. Scheduling optimization of prefabricated construction projects by genetic algorithm. Appl. Sci. 11, 5531. https://doi.org/10.3390/app11125531 (2021).

doi: 10.3390/app11125531

Sun, S., Chen, Y., Wang, A. & Liu, X. An evaluation model of carbon emission reduction effect of prefabricated buildings based on cloud model from the perspective of construction supply chain. Buildings 12, 1534. https://doi.org/10.3390/buildings12101534 (2022).

doi: 10.3390/buildings12101534

Ma, D. B., Zhang, J., Wang, Z. Y. & Sun, D. Q. Spatio-temporal evolution and influencing factors of open economy development in the Yangtze River delta area. Land 11, 1813. https://doi.org/10.3390/land11101813 (2022).

doi: 10.3390/land11101813

Wang, Y., Li, S. & Qi, C. Market concentration, market power, and firm growth of construction companies. Adv. Civ. Eng. 1–9, 2021. https://doi.org/10.1155/2021/9990846 (2021).

doi: 10.1155/2021/9990846

Wang, X., Chan, C.K.-C. & Yang, L. Do workers benefit from economic upgrading in the Pearl River Delta, China?. Humanit. Soc. Sci. Commun. https://doi.org/10.1057/s41599-022-01326-x (2022).

doi: 10.1057/s41599-022-01326-x pubmed: 36530544 pmcid: 9643889

Jin, H. M., Li, H., Zhao, T. B. & Pang, Y. Role of the sharing economy in the achievement of energy efficiency and sustainable economic development: Evidence from China. J. Innov. Knowl. 8, 100296. https://doi.org/10.1016/j.jik.2022.100296 (2023).

doi: 10.1016/j.jik.2022.100296

Zhumadillayeva, A. et al. Models for oil refinery waste management using determined and fuzzy conditions. Information 11, 299. https://doi.org/10.3390/info11060299 (2020).

doi: 10.3390/info11060299

Douglas, E. J., Shepherd, D. A. & Prentice, C. Using fuzzy-set qualitative comparative analysis for a finer-grained understanding of entrepreneurship. J. Bus. Ventur. 35, 105970. https://doi.org/10.1016/j.jbusvent.2019.105970 (2020).

doi: 10.1016/j.jbusvent.2019.105970

Dul, J. Necessary condition analysis (NCA): Logic and methodology of “necessary but not sufficient” causality. SSRN Electron. J. https://doi.org/10.2139/ssrn.2588480 (2015).

doi: 10.2139/ssrn.2588480

Vis, B. & Dul, J. Analyzing relationships of necessity not just in kind but also in degree: Complementing fsQCA with NCA. Sociol. Methods Res. 47, 872–899. https://doi.org/10.1177/0049124115626179 (2018).

doi: 10.1177/0049124115626179 pubmed: 30443090

Ding, H. What kinds of countries have better innovation performance?–A country-level fsQCA and NCA study. J. Innov. Knowl. 7, 100215. https://doi.org/10.1016/j.jik.2022.100215 (2022).

doi: 10.1016/j.jik.2022.100215

Dul, J., van der Laan, E. & Kuik, R. A statistical significance test for necessary condition analysis. Organ. Res. Methods 23, 385–395. https://doi.org/10.1177/1094428118795272 (2018).

doi: 10.1177/1094428118795272

Li, R. Y. M. et al. Modularity clustering of economic development and ESG attributes in prefabricated building research. Front. Environ. Sci. https://doi.org/10.3389/fenvs.2022.977887 (2022).

doi: 10.3389/fenvs.2022.977887

Pappas, I. O. & Woodside, A. G. Fuzzy-set qualitative comparative analysis (fsQCA): Guidelines for research practice in information systems and marketing. Int. J. Inf. Manag. 58, 102310. https://doi.org/10.1016/j.ijinfomgt.2021.102310 (2021).

doi: 10.1016/j.ijinfomgt.2021.102310

Du, Y. & Kim, P. H. One size does not fit all: Strategy configurations, complex environments, and new venture performance in emerging economies. J. Bus. Res. 124, 272–285. https://doi.org/10.1016/j.jbusres.2020.11.059 (2021).

doi: 10.1016/j.jbusres.2020.11.059

Li, J., Ji, J., Zuo, J. & Tan, Y. Is policy the necessary or sufficient driving force of construction and demolition waste recycling industry development? Experience from China. Int. J. Environ. Res. Public Health 20, 4936. https://doi.org/10.3390/ijerph20064936 (2023).

doi: 10.3390/ijerph20064936 pubmed: 36981845 pmcid: 10049083

van der Valk, W., Sumo, R., Dul, J. & Schroeder, R. G. When are contracts and trust necessary for innovation in buyer-supplier relationships? A necessary condition analysis. J. Purch. Supply Manag. 22, 266–277. https://doi.org/10.1016/j.pursup.2016.06.005 (2016).

doi: 10.1016/j.pursup.2016.06.005

Cruz-Ros, S., Garzón, D. & Mas-Tur, A. Entrepreneurial competencies and motivations to enhance marketing innovation in Europe. Psychol. Mark. 34, 1031–1038. https://doi.org/10.1002/mar.21042 (2017).

doi: 10.1002/mar.21042

Jin, B. & Li, W. External factors impacting residents’ participation in waste sorting using NCA and fsQCA methods on pilot cities in China. Int. J. Environ. Res. Public Health 20, 4080. https://doi.org/10.3390/ijerph20054080 (2023).

doi: 10.3390/ijerph20054080 pubmed: 36901091 pmcid: 10001695

Fiss, P. C. Building better causal theories: A fuzzy set approach to typologies in organization research. Acad. Manag. J. 54, 393–420. https://doi.org/10.5465/amj.2011.60263120 (2011).

doi: 10.5465/amj.2011.60263120

Zheng, W., Qiu, H. L. & Morrison, A. M. Applying a combination of SEM and fsQCA to predict tourist resource-saving behavioral intentions in rural tourism: An extension of the theory of planned behavior. Int. J. Environ. Res. Public Health 20, 1349. https://doi.org/10.3390/ijerph20021349 (2023).

doi: 10.3390/ijerph20021349 pubmed: 36674103 pmcid: 9859214