The drivers of energy intensity in China: A spatial panel data approach

- We use a spatial Durbin error model to estimate an energy intensity-income relation.
- We find an inverted U-shaped curve between energy intensity and GPP.
- Secondary sector in the own and in neighboring provinces drive energy intensity up.
- Spatial spillover effects of foreign direct investment reduce energy intensity.
- Capital labor ratio in the own and in neighboring provinces reduces energy intensity.

We use a panel of 29 Chinese provinces for the period 2003-2011 to estimate the drivers of energy intensity by means of a spatial Durbin error model. We find an inverted U-shaped relationship between energy intensity and income (energy intensity Kuznets curve). Ten provinces, notably the developed east coast provinces, have already passed the turning point of 29,673 RMB. The number of years for the other 19 provinces to reach the turning point ranges between 8.3 (Jilin) and 21.8 (Yunnan). The share of the secondary sector in the own province and in neighboring provinces causes an increase in energy intensity, the capital-labor ratio a decrease. Foreign direct investment (FDI) has a significant negative spatial spillover impact on energy intensity. To improve the sustainability of its energy resources and its environmental conditions, China needs to continue reducing its energy intensity by further developing modern industrial systems to counterbalance the negative effects of its economic growth and energy consumption. An adequate policy handle is investment in research and development and stimulation of their introduction into production processes. For that purpose, market mechanisms can be readily applied, particularly energy prices that adequately reflect energy scarcity and external effects. FDI is also an effective tool to transfer advanced technology to China.