Skip to main content

Table 1 Main research directions of energy and eco-environmental efficiency

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 Lima et al. [27], Liu et al. [28], Wang and Zhou [29] 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 Meng et al. [30], Li et al. [31] 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 Barros [37], Barros et al. [38], Chang et al. [26] 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 Wang et al. [39], Zhou et al. [40] 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