The impact of transformation policies on the resilience of mining resource-based cities
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摘要: 资源型城市由于产业结构单一、生态环境恶化、社会风险突出等问题，韧性普遍不足。在全面推进高质量发展的战略背景下，客观评价转型政策对资源型城市韧性的影响，对降低城市风险、补齐政策短板意义重大。基于2006—2020年278个地级市的面板数据，采用PSM-DID方法构建双向面板固定效应模型，考察《全国资源型城市可持续发展规划(2013—2020年)》在推进资源型城市转型中对城市韧性的影响，并进行实证分析。研究发现：《规划》对改善城市韧性具有显著促进作用，但存在政策滞后性；机制分析表明，《规划》主要通过优化产业结构和改善住房条件促进城市韧性水平提高；异质性分析表明，不同类型资源型城市的政策效果存在明显差异，其中衰退型资源型城市韧性的改善效果最为显著。Abstract: Problems like a lack of diverse industries, environmental degradation and major social risks can lead to low resilience in resource-based cities.As part of a broader strategy to achieve high quality development, it therefore remains imperative to objectively assess the effects of transformation policies on the resilience of these cities.This will help to minimize urban risks and fill gaps in policy making.Based on the panel data of 278 prefecture-level cities from 2006 to 2020, this study proposed a two-way panel fixed-effects model using the PSM-DID method to examine the impact of the National Plan for Sustainable Development of Resource-based Cities(2013-2020) on urban resilience in promoting the transformation of resource-based cities.Through the present empirical analysis, we found that: the Plan does have a significant role to play in boosting urban resilience, but there is a policy lag; the mechanism analysis shows that the Plan raises urban resilience level mainly through optimizing industrial structure and improving housing conditions; the heterogeneity analysis shows that there are significant differences in the policy effects on different types of resource-based cities, among which declining resource-based cities exhibit the most significant improvement in their urban resilience.
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Key words:
- mining resource-based cities /
- transformation policies /
- urban resilience /
- policy evaluation /
- PSM-DID
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图 1 城市韧性指标集合及测算示意图
Figure 1. The set of urban resilience indicators and their measurement
图 2 城市样本倾向得分匹配前后的密度函数
Figure 2. Density function plots before and after matching of propensity scores for urban samples
表 1 PSM匹配结果
Table 1. PSM matching results
样本 未匹配样本 匹配样本 总计 控制组 186 2 364 2 550 实验组 0 1 620 1 620 总计 186 3 984 4 170 
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表 2 倾向得分匹配前后样本平衡性检验结果
Table 2. Results of sample balance test before and after propensity score matching
匹配方法 伪R2 LR统计量 标准化偏差/% 匹配前 0.097 542.44 29.9 马氏距离匹配 0.001 6.72 1.4 最近相邻匹配 0.002 7.88 2.8 卡尺(半径)匹配 0.002 10.48 3.8 卡尺内最近相邻匹配 0.002 10.48 3.8 核匹配 0.002 6.77 3.1
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表 3 基准回归估计结果
Table 3. Baseline regression estimation results
变量 M1 M2 M3 M4 效应系数 0.009** 0.022*** 0.023*** 0.025*** (0.003) (0.002) (0.003) (0.004) 控制变量 否 是 是 是 C 否 否 是 是 Y 否 否 否 是 R2 0.50 0.55 0.55 0.56 N 3 984 3 984 3 984 3 984 注：括号内为标准误差；*** P < 0.01；** P < 0.05。
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表 4 改变政策冲击时间的检验结果
Table 4. Test results for changing the timing of policy shocks
变量 M5(2010) M6(2011) M7(2012) 效应系数 0.003 0.003 0.003* (0.002) (0.002) (0.002) 控制变量 是 是 是 C 是 是 是 Y 是 是 是 R2 0.61 0.61 0.61 N 3 984 3 984 3 984 注：*P＜0.1。
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表 5 影响机制检验结果
Table 5. Results of impact mechanism test
变量 M8 M9 M10 con R sta R inv R 效应系数 0.034** 0.024*** -0.031** 0.023*** 1.477*** 0.025*** (0.032) (0.004) (0.033) (0.004) (0.195) (0.003) con — 0.015*** — — — — — (0.007) — — — — sta — — — 0.004** — — — — — (0.007) — — inv — — — — — 0.071*** — — — — — (0.007) 控制变量 是 是 是 是 是 是 C 是 是 是 是 是 是 Y 是 是 是 是 是 是 R2 0.643 0.603 0.717 0.586 0.755 0.646 N 3 984 3 984 3 984 3 984 3 984 3 984
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表 6 不同类型城市的异质性检验结果
Table 6. Heterogeneity test results for different types of cities
变量 M11
(成长型)M12
(成熟型)M13
(衰退型)M14
(再生型)效应系数 0.004** 0.012** 0.041*** 0.004 (0.004) (0.002) (0.002) (0.003) 控制变量 是 是 是 是 C 是 是 是 是 Y 是 是 是 是 R2 0.522 0.563 0.644 0.115 N 2 106 3 201 2 256 2 680
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