School of Business, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
China Institute of Manufacturing Development, Nanjing University of Information Science &Technology, Nanjing, 210044, China.
Environ Sci Pollut Res Int. 2021 Oct;28(37):51120-51136. doi: 10.1007/s11356-021-14298-3. Epub 2021 May 11.
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.
传统的数据包络分析（DEA）模型通常忽略了外部环境因素和随机干扰的影响，这容易导致效率估计的偏差。为了解决这个问题，采用三阶段 DEA 模型从非管理因素的角度更好地反映了中国建筑业的碳排放量效率（CEECI）（2006-2017）。通过 Tobit 模型分析了 CEECI 的内部影响因素，为制定政策提供了更准确的依据。结果表明，CEECI 受 GDP、工业化水平、开放程度、技术创新和能源结构的显著影响。在排除环境因素和随机干扰后，CEECI 的平均水平提高了 16%。由此产生的计算结果在三个方面值得注意。首先，CEECI 存在显著的区域差异。其次，CEECI 的多极化现象和区域差异也随着时间的推移逐渐减少。CEECI 可以分解为纯碳排放效率（PCEE）和规模效率（SE），主要是由 SE 造成的。排除外部环境因素和随机干扰对 CEECI 会有特定的影响。将 30 个省份分为四类，分析了各省 CEECI 差异的原因和解决方案。最后，许多因素对 CEECI、PCEE 和 SE 都有抑制作用，包括能源结构优化、劳动力数量、建筑设备总功率和建筑业施工强度。然而，建筑业的发展水平确实有显著的积极影响。
