Eco-Efficiency Measurement and Regional Optimization Strategy of Green Buildings in China Based on Three-Stage Super-Efficiency SBM-DEA Model

With the increasing attention of society to sustainable development, green building as an important sustainable building form has attracted much attention. However, the comprehensive assessment of eco-efficiency of green buildings faces many challenges, including the insufficient comprehensive analysis of all stages of the building life cycle and the oversimplification of multidimensional input-output relationships. In addition, the existing methods have subjectivity and uncertainty in data processing and weight allocation, which reduces the reliability of evaluation. To overcome these difficulties, a measurement method based on the three-stage super-efficient data Enveloping analysis (SBM-DEA) model is introduced in this study. By constructing a three-stage super-efficiency SBM-DEA model, the eco-efficiency measurement model of green buildings is established, taking building resources and energy as input and economic and environmental value as output. The results show that after removing the interference of external environment variables and random errors, the measurement results of stage 3 are more reasonable. From 2011 to 2018, the eco-efficiency of green buildings in China showed obvious regional differences, showing a decreasing trend of "the highest in the east (0.884), followed by the central (0.704) and the lowest in the west (0.578)". The innovation of this study lies in the full consideration of timing and dynamics, which provides new theoretical and practical ideas for promoting sustainable development in the field of green building, and is expected to improve the assessment accuracy and reliability in the field of green building.

@article{Qin2025,
  title     = {Eco-Efficiency Measurement and Regional Optimization Strategy of Green Buildings in China Based on Three-Stage Super-Efficiency SBM-DEA Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Xianhong Qin and Yaou Lv and Yunfang Wang and Jian Pi and Ze Xu},
  doi       = {10.14569/IJACSA.2025.0160246},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160246}
}