The traditional data envelopment analysis (DEA) model usually ignores the influence of external environmental factors and random interference. This can easily lead to deviations in efficiency estimates. In order to solve this problem, a three-stage DEA model was used to better reflect the carbon emission efficiency of Chinese construction industry (CEECI) (2006-2017) from the perspective of non-management factors. The internal influencing factors of CEECI are analyzed by the Tobit model, which provides a more accurate basis for formulating policies. It is found that the CEECI is significantly affected by the GDP, the level of industrialization, the degree of opening-up, technological innovation, and energy structure. After excluding environmental factors and random interference, the average CEECI increased by 16%. The resulting calculations are noteworthy in three aspects. First, there are significant regional differences in the CEECI. Both the multi-polarization phenomenon of CEECI and regional differences also reduced gradually over time. Second, the CEECI can be decomposed into pure carbon emission efficiency (PCEE) and scale efficiency (SE), which is mainly caused by SE. Excluding external environmental factors and random interference will have a specific impact on the CEECI. All the 30 provinces are divided into four categories to analyze the reasons and solutions of the differences in the CEECI in provinces. Third, many factors had inhibitory effects on the CEECI, PCEE, and SE; these included energy structure optimization, labor force number, total power of construct ion equipment, and construction intensity in the construction industry. Nevertheless, the development level of the construction industry did have a significant positive effect.
Keywords: Carbon emission efficiency; China; Construction industry; External environmental factors; Internal influencing factors; Three-stage DEA.
© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.