航空公司运营效率评价研究

Research on the Evaluation of Airline Operational Efficiency

摘要: 在全球航空运输市场开放深化的背景下，提升运营效率成为航空公司增强国际竞争力的核心路径。本文以2020~2022年国内外9家航空公司(含国有、民营及国际航司)为样本，构建融合因子分析法与数据包络分析(DEA)的评价模型。研究表明：(1) 效率动态特征方面，疫情冲击下行业效率呈“V型”波动，综合技术效率均值从2020年0.524降至2021年0.4，2022年回升至0.735。国有航司(如南方航空)初期凭借规模优势维持效率，但后期灵活性不足；民营航司(如春秋航空)通过低成本模式实现2022年DEA有效(TE = 1)。(2) 效率分解差异方面，纯技术效率普遍趋近1，反映技术管理水平较高；规模效率成为关键制约(2021年均值仅0.441)。东方航空等大型航司因规模报酬递减导致效率损失，吉祥航空等中小航司则因规模不足需优化资源配置。(3) 影响因素方面，Tobit回归显示，民营及外资航司技术效率领先(春秋航空0.44~0.48 vs东航0.42~0.48)，印证管理技术与资源配置优势；市场份额与效率呈正相关，但企业规模存在最优区间(如阿联酋航空规模效率0.42~0.45的波动)。基于此，提出优化路径：科学评估规模报酬阶段，避免盲目扩张；加强技术创新与资源协同；提升市场份额质量以强化产品市场联动。

Abstract: Against the backdrop of deepening liberalization in the global air transport market, enhancing operational efficiency has become a core pathway for airlines to strengthen their international competitiveness. This study employs a sample of nine domestic and international airlines (including state-owned, private, and international carriers) from 2020 to 2022 to construct an evaluation model integrating Factor Analysis and Data Envelopment Analysis (DEA). The research reveals: (1) Dynamic Efficiency Characteristics: Industry efficiency exhibited a “V-shaped” fluctuation under the impact of the pandemic. The average comprehensive technical efficiency (TE) dropped from 0.524 in 2020 to 0.400 in 2021, before rebounding to 0.735 in 2022. State-owned airlines (e.g., China Southern Airlines) initially maintained efficiency through scale advantages but later suffered from insufficient flexibility; private airlines (e.g., Spring Airlines) achieved DEA efficiency (TE = 1.0) in 2022 via their low-cost model. (2) Efficiency Decomposition Differences: Pure technical efficiency (PTE) generally approached 1.0, reflecting high levels of technological and managerial proficiency. Scale efficiency (SE) emerged as the key constraint (average SE was only 0.441 in 2021). Large carriers like China Eastern Airlines experienced efficiency losses due to decreasing returns to scale, while medium and small airlines like Juneyao Airlines required optimized resource allocation due to insufficient scale. (3) Influencing Factors: Tobit regression analysis indicates that private and foreign airlines led in technical efficiency (Spring Airlines: 0.44~0.48 vs. China Eastern: 0.42~0.48), confirming their advantages in management techniques and resource allocation. Market share showed a positive correlation with efficiency, but firm size demonstrated an optimal range (e.g., Emirates’ SE fluctuated between 0.42~0.45). Based on these findings, this paper proposes optimization pathways: scientifically assessing the stage of returns to scale to avoid blind expansion; strengthening technological innovation and resource synergy; and enhancing the quality of market share to reinforce the linkage between product offerings and market dynamics.