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April 12, 2026Sustainability1 citationsOpen Access

Economic–Environmental Synergy in Construction: An Integrated CCD-PDA-GCA Framework for 30 Developed Economies

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JSJiachen SunAOAtasya OsmadiFLF. Liu

Key Points

  • The aim is to evaluate the interplay between economic growth and environmental systems in the construction industry and determine key drivers for sustainability.
  • Established an analytical framework utilizing coupling coordination degree (CCD) evaluation.
  • Applied Environmental Kuznets Curve (EKC) for decoupling analysis.
  • Utilized panel data analysis (PDA) to identify key drivers affecting economic-environment nexus.
  • Conducted Granger causality analysis (GCA) to examine predictive relationships.
  • The coupling coordination degree (CCD) between economy and environment in construction industry is high (0.70-0.90).
  • A 1% increase in per capita GDP leads to a 0.035 increase in coupling environmental-economy interaction.
  • A 1% rise in science and technology investment contributes to a 0.045 increase in interaction.
  • Each unit rise in building energy use decreases interaction by 0.008.
  • Bidirectional correlation exists between GDP and coupling, while a one-way correlation is noted from coupling to ST investment.

Abstract

As a primary energy consumer and carbon emitter, the construction industry (CI) faces a growing conflict between traditional energy-intensive growth models and global sustainable development goals. To promote the sustainable development of the CI, this study establishes a sequential analytical framework following the logic of “coupling evaluation–driving force identification–causal inference” across 30 developed economies (DE) from 2000 to 2022. Initially, the coupling coordination degree (CCD) between the economic and environmental systems of the CI was evaluated, utilizing the Environmental Kuznets Curve (EKC) to characterize the transition from relative to absolute decoupling. The results show that the economy and the environment in the construction industry (CEECI) for DE is generally high (0.70–0.90). Subsequently, based on Green Innovation Growth (GIG) theory, Panel Data Analysis (PDA) is employed to identify the key drivers of the coupling between the economy and CEECI. The results show that for every 1% increase in per capita GDP, CEECI increases by approximately 0.035; for every 1% increase in science and technology investment (ST Inv), CEECI increases by 0.045; and for every 1 unit increase in building energy use (BEU), CEECI decreases by 0.008. Furthermore, Granger causality analysis (GCA) was used to examine the bidirectional predictive relationship. Furthermore, there is a two-way correlation between GDP and CEECI, and a one-way correlation between CEECI and ST Inv. Overall, our results show that further decoupling requires innovation, not just economic growth; therefore, the CI should optimize its industrial structure, prioritize technological innovation, strengthen lifecycle energy management, and promote coordinated global CI improvement.

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Cite This Study

Sun et al. (2026) studied this question.

synapsesocial.com/papers/69db380f4fe01fead37c6402https://doi.org/10.3390/su18083765
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