The effect of energy end-use efficiency improvement on China’s energy use and CO2 emissions: a CGE model-based analysis

Abstract

With its rapid economic growth, China is now confronted with soaring pressure from both its energy supply and the environment. To deal with this conflict, energy end-use efficiency improvement is now promoted by the government as an emphasis for future energy saving. This study explores the general equilibrium effect of energy end-use efficiency improvement on China's economy, energy use, and CO 2 emissions. This paper develops a static, multisector computable general equilibrium model (CGE) for China, with specific detail in energy use and with the embodiment of energy efficiency. In order to explore the ability of subsidizing non-fossil-generated electricity on moderating potential rebound effects, in this model, the electricity sector was deconstructed into five specific generation activities using bottomup data from the Chinese electricity industry. The model is calibrated into a 16-sector Chinese Social Accounting Matrix for the year 2002. In the analysis, seven scenarios were established: business as usual, solely efficiency improvement, and five policy scenarios (taxing carbon, subsidized hydropower, subsidized nuclear power, combination of taxing carbon and subsidized hydropower, combination of taxing carbon and subsidized nuclear power). Results show that a sectoral-uniform improvement of energy enduse efficiency will increase rather than decrease the total energy consumption and CO 2 emissions. The sensitivity analysis of sectoral efficiency improvement shows that efficiency improvements happened in different sectors may have obvious different extents of rebound. The three sectors, whose efficient improvements do not drive-up total national energy use and CO 2 emissions, include Iron and Steel, Building Materials, and Construction. Thus, the improvement of energy end-use efficiency should be sectoral specific. When differentiating the sectoral energy-saving goal, not only the saving potential of each sector but also its potential to ease the total rebound should be taken into account. Moreover, since the potential efficiency improvement for a sector over a certain period will be limited, technology measures should work along with a specific policy to neutralize the rebound effect. Results of policy analysis show that one relatively enhanced way is to combine carbon taxing with subsidized hydropower.

The technology gap of Chinese regions' energy efficiency and spatial convergence—Based on the hybrid meta-frontier data envelopment analysis

“…Shi and Shen (2008) input the stock of knowledge as inputs in the production function and use the super efficiency DEA method to calculate the energy efficiency China's provinces. Liang et al (2009) develop a static, multisector computable general equilibrium model (CGE) for China, with specific detail in energy use and with the embodiment of energy efficiency. Based on provincial panel data and the meta-frontier, Wang et al (2012) construct the non-parametric frontier by using data envelopment analysis and compare the regional differences of China's energy efficiency during 2000-2008.…”

How China’s current energy pricing mechanisms will impact its marginal carbon abatement costs?

“…CEEPA is a multi-sector CGE model developed to analyze China energy and environmental policies by the Centre for Energy and Environmental Policy Research, Beijing Institute of Technology. It has been successfully applied to assess the effects of different energy saving and emission reduction policies, such as C taxes (Liang et al 2007), improvements in end-use energy efficiency (Liang et al 2009), electricity produced by renewable energy (Wei et al 2008), and the distribution impacts of C taxes in China (Liang and Wei 2012;Liang et al 2013;Wang and Liang 2014).…”

“…Labor market is not cleared in the current version of CEEPA. The main equations of basic CEEPA have been described in detail elsewhere (Liang et al 2007(Liang et al , 2009(Liang et al , 2013Wang and Liang 2014). To analyze MACs for China, we extend and improve the basic CEEPA model.…”

Determinants of Electricity Demand in Nonmetallic Mineral Products Industry: Evidence from a Comparative Study of Japan and China

“…Therefore, considering that R&D expenditure consists of the investment in physical capital (purchase of equipment) as well as the investment in human capital (payment for researchers in R&D laboratories), the indictor of R&D expenditure used in this paper is also acceptable and meaningful. Additionally, several studies have also used the indicator of R&D expenditure to represent technology such as Liang et al [69], Lin et al [55], Lin and Wang [70], etc. Based on the existing literature, the indicator of R&D investment out of total sales, which reflects the level of R&D investment as well as the capacity of R&D of profit-seeking enterprises, is used in this paper.…”