Energy efficiency in the Chinese provinces: a fixed effects stochastic frontier spatial Durbin error panel analysis

Energy efficiency improvement has been a key objective of China’s long-term energy policy. In this paper, we derive single-factor technical energy efficiency (abbreviated as energy efficiency) in China from multi-factor efficiency estimated by means of a translog production function and a stochastic frontier model on the basis of panel data on 29 Chinese provinces over the period 2003–2011. We find that average energy efficiency has been increasing over the research period and that the provinces with the highest energy efficiency are at the east coast and the ones with the lowest in the west, with an intermediate corridor in between. In the analysis of the determinants of energy efficiency by means of a spatial Durbin error model both factors in the own province and in first-order neighboring provinces are considered. Per capita income in the own province has a positive effect. Furthermore, foreign direct investment and population density in the own province and in neighboring provinces have positive effects, whereas the share of state-owned enterprises in Gross Provincial Product in the own province and in neighboring provinces has negative effects. From the analysis it follows that inflow of foreign direct investment and reform of state-owned enterprises are important policy handles.

Impacts of FDI Renewable Energy Technology Spillover on China's Energy Industry Performance

Environmental friendly renewable energy plays an indispensable role in energy industry development. Foreign direct investment (FDI) in advanced renewable energy technology spillover is promising to improve technological capability and promote China’s energy industry performance growth. In this paper, the impacts of FDI renewable energy technology spillover on China’s energy industry performance are analyzed based on theoretical and empirical studies. Firstly, three hypotheses are proposed to illustrate the relationships between FDI renewable energy technology spillover and three energy industry performances including economic, environmental, and innovative performances. To verify the hypotheses, techniques including factor analysis and data envelopment analysis (DEA) are employed to quantify the FDI renewable energy technology spillover and the energy industry performance of China, respectively. Furthermore, a panel data regression model is proposed to measure the impacts of FDI renewable energy technology spillover on China’s energy industry performance. Finally, energy industries of 30 different provinces in China based on the yearbook data from 2005 to 2011 are comparatively analyzed for evaluating the impacts through the empirical research. The results demonstrate that FDI renewable energy technology spillover has positive impacts on China’s energy industry performance. It can also be found that the technology spillover effects are more obvious in economic and technological developed regions. Finally, four suggestions are provided to enhance energy industry performance and promote renewable energy technology spillover in China.

Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.