汶川Ms 8.0地震极重灾区的经济韧性测度及恢复效率

doi:10.11821/dlxb201910009

摘要/Abstract

摘要：

揭示灾后经济发展状态和恢复过程,按照灾区经济韧性科学制定灾后经济重建的调控政策与恢复手段,对增强灾害防范与系统应对能力具有重要意义。以汶川地震极重灾区为案例,基于长时序社会经济面板数据与ARIMA模型,测算灾区经济韧性指数,并运用改进的规模报酬可变DEA模型、Malmquist生产率指数分析灾后年际经济恢复效率及影响效应。结果显示：① 汶川地震极重灾区经济韧性指数为0.877,地震导致灾区短期经济衰退,但2年内恢复至震前水平,工业经济韧性低于农业和服务业。② 受灾当年灾区经济恢复综合效率为0.603,其中,平原丘陵区综合效率、纯技术效率、规模效率均显著优于高原山地区。③ 灾后全要素生产率的年际波动强烈,经济恢复效率显著下降造成短期经济衰退,灾后2年TFP指数下降33.7%和15.2%后恢复至稳态。④ 灾后恢复效率显著下降主因为技术变动因素,而生产体系更新程度决定了灾后经济韧性。随着灾后经济恢复的规模收益下降,灾区经济恢复主要依赖纯技术效率,提升纯技术效率是保持灾后经济长期增长的动力。可见,针对重建自然条件和经济发展阶段的差异性,灾区当地需积极进行经济结构调整和再适应,同时,通过生产体系更新提升技术进步水平,以发挥灾后大规模资金、设施、人力等要素投入的规模效应,从而增强应对灾害冲击的经济韧性与恢复效率。

关键词: 经济韧性, 恢复效率, 灾后重建地区, 汶川地震, Malmquist生产率指数

Abstract:

It is of great significance to enhance disaster prevention and response capacity to reveal the post-disaster economic development and recovery process, and to formulate the control policies and recovery methods for post-disaster economic reconstruction according to the economic resilience. Based on the long-term socio-economic data and ARIMA model, this paper calculated the economic resilience index of severely affected area of Wenchuan earthquake, and adopted the improved Variable Return to Scale (VRS) DEA model and Malmquist productivity index to analyze the efficiency and effect of annual post-disaster recovery. The results show that: (1) The economic resilience index of earthquake severely affected area is 0.877. The earthquake caused a short-term economic recession in the affected areas, but the economy returned to its pre-quake state within two years. In addition, the industrial economy is less resilient than agriculture and service industries. (2) The comprehensive economic recovery efficiency of the disaster-stricken area in the year after the disaster is 0.603. The comprehensive efficiency, pure technical efficiency and scale efficiency of the plain hilly area are significantly higher than those of the plateau mountain area. (3) The annual fluctuation of total factor productivity after the disaster was strong, and the economic recovery efficiency declined significantly, resulting in a short-term economic recession. The TFP index returned to steady state after a decline of 33.7% and 15.2% in the two years after the disaster. (4) The significant decline in the post-disaster recovery efficiency is mainly caused by technological changes, and the renewal of production system is the leading factor in determining the economic resilience after the disaster. With the decline in the scale of economic recovery, the long-term economic recovery in the study areas mainly depends on pure technical efficiency, and the improvement of pure technical efficiency is the driving force to maintain the long-term growth of post-disaster economy. Therefore, in view of the differences between the reconstruction of natural conditions and the stage of economic development, the disaster-stricken areas need to change and readjust their economic structures actively. Meanwhile, we should pay attention to updating production system to enhance the level of technological progress, and give full play to the scale effect of large-scale capital, facilities, manpower and other factors investment, so as to enhance the response to the disaster impact of economic resilience and recovery efficiency.

Key words: economic resilience, recovery efficiency, post-disaster reconstruction area, Wenchuan earthquake, Malmquist productivity index

周侃, 刘宝印, 樊杰. 汶川Ms 8.0地震极重灾区的经济韧性测度及恢复效率[J]. 地理学报, 2019, 74(10): 2078-2091.

ZHOU Kan, LIU Baoyin, FAN Jie. Economic resilience and recovery efficiency in the severely affected area of Ms 8.0 Wenchuan earthquake[J]. Acta Geographica Sinica, 2019, 74(10): 2078-2091.

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地区	综合效率	纯技术效率	规模效率	规模收益
汶川县	0.505	1.000	0.505	递增
北川羌族自治县	0.293	1.000	0.293	递增
绵竹市	1.000	1.000	1.000	不变
什邡市	1.000	1.000	1.000	不变
青川县	0.321	1.000	0.321	递增
茂县	0.269	1.000	0.269	递增
都江堰市	0.912	0.939	0.971	递减
平武县	0.299	0.580	0.515	递增
彭州市	0.637	1.000	0.637	递减
安县	0.798	0.981	0.813	递增
平均值	0.603	0.950	0.632	—

年份	综合效率	技术变动率	纯技术效率	规模效率	全要素增长率
2007年	0.924	1.131	0.974	0.948	1.044
2008年	1.027	0.646	1.053	0.975	0.663
2009年	0.957	0.886	0.854	1.121	0.848
2010年	1.011	1.030	0.988	1.023	1.041
2011年	0.930	1.092	1.012	0.919	1.015
2012年	0.993	1.039	1.060	0.937	1.032
2013年	1.019	1.011	1.069	0.953	1.030
2014年	1.037	0.999	1.029	1.007	1.036
2015年	1.037	0.977	0.991	1.047	1.014