How can climate adaptation policy enhance urban climate resilience? Empirical evidence from China

PLOS One | https://doi.org/10.1371/journal.pone.0335736 November 6, 2025 10 / 21 3.3.2. Control variables. We selected three control variables for analysis: the level of urbanization (urb), the number of days with extreme heat in a year (heatdays), and the number of days with a rainstorm in a year (rainstorms). The urb data were from the China City Statistical Yearbook. The raw data for heatdays and rainstorms were sourced from the National Centers for Environmental Information (NCEI) database, which is part of the National Oceanic and Atmospheric Administration (NOAA) in the United States. We counted the number of days with urban daily temperatures above 35°C and the number of days with rainfall exceeding 50 mm. 3.3.3. Mediating and moderating variables. In this study, the mediating mechanism variable (GB) data were from the CSMAR database. The moderating mechanism variables in this study are education (Edu), public environmental concern (Pec), and mineral resource dependence (Mrd ). Pec was expressed using the urban residents' Baidu search index for “Environmental Pollution,” sourced from the Baidu Index website. Eduwas measured by the proportion of students enrolled in universities in the city to the number of household residents. Mrd was calculated using the share of urban employment accounted for by the number of people employed in the mining industry. The raw data for measuring Edu and Mrd were obtained from the China City Statistical Yearbook. All the data were standardized.

The data used in the analysis are attached as Supporting Information, as detailed in the S1 File. The descriptive statis- tics of the primary variable data are presented in Table 2. 4. Results 4.1. CACCP policy effect We employed the Synthetic DID method to evaluate the effect of the CACCP policy on urban climate resilience in the Yellow River Basin. Fig 2 depicts the results of the Synthetic DID method for assigning weights to individuals in the control group. Fig 3 depicts the temporal evolution of the policy’s impact. As illustrated in Fig 3, before the implementation of the CACCP policy (i.e., on the left side of the red vertical line), the Resilience of the experimental group was significantly lower than that of the control group. However, after the implementation of the policy (i.e., the right side of the red vertical line), the Resilience of the experimental group exhibited a notable acceleration. By 2019, it had surpassed that of the control group, indicating that the urban climate resilience of the pilot cities in the Yellow River Basin had benefited consid- erably from implementing the CACCP policy. As illustrated in Table 3, the mean treatment effect of the CACCP policy on the urban climate resilience in the Yellow River Basin is 0.0118, a statistically significant figure at the 5% level. This study confirms the validity of research hypothesis H1, further enhancing existing research conclusions on the effectiveness of climate adaptation policies [66,67]. Considering that the empirical data in this study were standardized to be between

0 and 1, the marginal contribution or average treatment effect value of the CACCP policy is only 0.0118. Therefore, the CACCP policy still requires further optimization to enhance its effectiveness. Table 2. Data descriptive statistics. Variables Name N Mean S.D. Min Max Outcome variable Resilience 741 0.0642 0.0587 0.0124 0.3345 Policy variable treated 741 0.0729 0.2601 0.0000 1.0000 Control variables heatdays 741 0.0841 0.1283 0.0000 1.0000 rainstorms 741 0.0735 0.1502 0.0000 1.0000 urb 741 0.5461 0.1556 0.1848 0.9595 Mediating variable GB 741 0.2221 0.1869 0.0000 1.0000 Moderating variables Edu 741 0.1436 0.2005 0.0000 1.0000 Pec 741 0.1747 0.1904 0.0000 1.0000 Mrd 741 0.1044 0.1365 0.0000 1.0000 https://doi.org/10.1371/journal.pone.0335736.t002