Apart from promoting social-economic development and increasing social employment, the real estate industry in China has also brought up problems such as high energy consumption and high emissions. Scholars now focus more on energy conservation, emission reduction and sustainable development of real estate companies in their current research. The data used by this paper are three-year panel data from 2015 to 2018, with observations from 15 representative real estate companies. CO₂ and green credit index are introduced as the undesirable output and the green output of real estate companies respectively. First, with the DEA model and the Malmquist index model, this paper evaluates the green productivity of real estate companies statically and dynamically. The Tobit model is then employed by the author to analyze factors that may affect green productivity. Our results indicate that (1) the green productivities of 15 Chinese real estate companies have improved by various degrees. The average green productivity rises from 0.701 in 2015 to 0.849 in 2018, indicating that the energy utilization rate of enterprises has gradually increased. From the calculation and decomposition of the Malmquist total factor productivity index, we know that technological progress is vital in improving the green productivity of real estate companies. (2) As for the influencing factors, the green productivity is positively related to factors such as policy compliance indicator P, environmental responsibility commitment indicator R, indicator of green innovation capability I, and indicator of green development information disclosure M. The asset-liability ratio on the contrary has a negative impact on green productivity. It’s worth to point out that the green innovation index and green productivity is significantly correlated and the correlation coefficient can be up to 0.636, which implies that the key to improving green productivity is to increase research and development investment.

In this paper, a new three-stage virtual frontier SBM-DEA (Slacks-Based Measure of Efficiency in Data Envelopment Analysis) model is proposed to evaluate the sustainable efficiency of the construction industry. The relevant construction industry data of 30 Chinese PARs (provincial administrative regions) from 2011 to 2016 are brought into the model to calculate the sustainable efficiency of each administrative region and verify the rationality of the model. In order to solve the problem that the efficiency values of multiple effective decision units cannot be differentiated in traditional DEA model evaluation, the reference decision unit set and the evaluation decision unit set are respectively different sets in the virtual frontier SBM-DEA model. In the three-stage virtual frontier SBM-DEA, the reference decision unit set and evaluation decision unit set are two different sets that can distinguish among the effective decision units of DEA. The analysis results show that the average sustainable efficiency values of the construction industry in eastern, middle and western China exhibit a decreasing trend, at 0.643, 0.586 and 0.4830, respectively. Moreover, the technical equipment rate and external environmental factors play an important role in the sustainable efficiency of the construction industry and the CO2 emission reduction potential of most PARs shows a downward trend. GRAPHICAL ABSTRACT