Research Article
Technical and Energy Efficiency of Urban Logistics in China: Empirical Analysis of 216 Prefecture-Level Cities
Table 5
Estimation results of the translog SFA model with impact factors.
| | Variables | Model 6 (no time lag) | Model 7 (1-year lag) | Model 8 (2-year lag) | Model 9 (3-year lag) |
| | Estimation of stochastic frontier function | | lnK | −0.958 (−4.095) | −991 (−3.961) | −1.003 (−3.916) | −1.041 (−3.874) | | lnL | 0.026 (1.533) | 0.178 (0.954) | 0.129 (−0.658) | 0.156 (0.764) | | (lnK)2 | 0.164 (4.309) | 0.17 (4.173) | 0.173 (4.141) | 0.181 (4.108) | | (lnL)2 | −0.014 (−4) | −0.035 (−0.956) | −0.05 (−1.254) | −0.063 (−1.567) | | lnKlnL | −0.017 (−0.287) | 0.013 (0.215) | 0.03 (0.641) | 0.026 (0.391) |
| | Estimation of factors influencing efficiency based on technical inefficiency function | | DIGIT | −0.44 (−7.518) | −0.486 (−6.479) | −496 (−5.649) | −0.443 (−5.088) | | ENV | 0.167 (3.46) | 0.184 (2.816) | 0.17 (2.4) | 0.144 (1.865) | | GOV | 0.697 (2.412) | 0.772 (2.671) | 0.801 (2.492) | 0.753 (1.841) | | EDU | −0.075 (−1.421) | −0.141 (−1.856) | −0.186 (−2.066) | −0.207 (−1.966) | | DEN | −0.435 (−9.15) | −0.463 (−8.152) | −0.5 (−7.187) | −0.561 (−6.52) | | σ2 | 0.461 (15.013) | 0.496 (13.072) | 0.5198 (9.421) | 0.532 (9.222) | | Γ | 0.663 (13.274) | 0.658 (13.265) | 0.654 (12.318) | 0.641 (11.195) |
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Notes: , , and denote statistical significance at 10%, 5%, and 1% levels, respectively. |
