Forecasting the Energy Embodied in Construction Services Based on a Combination of Static and Dynamic Hybrid Input-Output Models

The energy embodied in construction services (EECS) to increase industrial production capacity, contributes to total primary energy consumption (TPEC) in developing countries like China. Forecasting EECS is important for creating energy policies, but has not received enough attention. There are some defects in the main two methods of EECS forecasting: the static hybrid input-output (HI/O) model and the dynamic HI/O model. The former cannot identify the quantity of construction services, whereas the latter is unstable for EECS forecasting. To tackle these problems, we propose a new model, which is a combination of the static and dynamic hybrid input-output model (CSDHI/O model), for EECS forecasting. Taking China as a case study, we forecast the EECS and TPEC of China until 2020 and analyze the sensitivities of four influencing factors. The results show that the EECS of China will reach 1.79 billion tons of coal equivalent in 2020. The improvement of fabrication level is identified as the most important factor for conserving both TPEC and EECS. A sudden drop in gross domestic product (GDP) growth rate and decreasing the investment in the service industry can also restrict EECS growth.