From: Energy efficiency in China: optimization and comparison between hydropower and thermal power
Aspect | Main direction | Literature | Research methods | Main findings |
---|---|---|---|---|
Influencing factors of energy consumption and efficiency | National or regional energy consumption and related CO2 | Logarithmic mean Divisia index (LMDI) | The intensity effect, technological change effect, and energy savings play important roles in reducing energy consumption | |
Energy efficiency of a certain industry | Partial least squares (PLS) and generalized autoregressive conditional heteroscedasticity (GARCH) | Renewable energy should be encouraged to improve power generation efficiency | ||
Evaluation of energy efficiency and related eco-environmental efficiency | Eco-environmental efficiency | Mei et al. [19], Perez et al. [32], Beltrán-Esteve et al. [33], Xue et al. [34], Wang et al. [35] | A meta-Frontier slack-based measure, DEA and Malmquist indices, life cycle analysis (LCA), a meta-Frontier directional distance function (MFDDF) approach, and an undesirable-super slack-based measure (US-SBM) | Technological innovation and renewable energy use can improve eco-efficiency, while excessive emissions cause low efficiency |
Company-/microlevel energy efficiency | Shrivastava et al. [13], Moon et al. [14], Xie et al. [23], Wang et al. [24], Wu et al. [25], Eguchi et al. [20] | Charnes–Cooper–Rhodes (CCR) and Banker–Charnes–Cooper (BCC) models, a two-stage network DEA model, the weighted Russell directional distance method (WRDDM), a decomposition framework, a meta-Frontier epsilon-based measure (MEBM), econometric models, and a meta-Frontier DEA decomposition approach | Excessive energy consumption makes power plants inefficient. Longer equipment utilization hours and technological catch-up can enhance power plant efficiency | |
Regional-/macrolevel energy efficiency | Ghosh and Kathuria [36], Choi et al. [15], Bi et al. [16], Song et al. [17], Chang et al. [11], Yang and Wei [12] | Stochastic Frontier analysis (SFA), a slack-based DEA, a slack-based endogenous directional distance function (SBEDDF) model, the total-factor energy productivity index, and game cross-efficiency DEA | The energy efficiency of China’s power generation industry is low. Labor redundancy and emission pollution affect energy efficiency | |
Renewable energy efficiency evaluation and the relationship with traditional energy | Hydropower efficiency | DEA, the virtual Frontier dynamic range adjusted model (VDRAM), a case study, and a set of evaluation indices | Hydropower efficiency analysis can attract public attention to renewable energy | |
Relationship between renewable energy and traditional energy | The Granger causality test, the autoregressive distributed lag (ARDL) model, generalized impulse response, and the vector autoregressive (VAR) model | Hydropower and thermal power jointly ensure a stable power supply in China. Thermal power is dominant, while hydropower is passive |