Modeling regional production capacity loss rates considering response bias: insights from a questionnaire survey on the Zhengzhou flood

Abstract

Flood disasters in specific regions not only cause physical damage but also disrupt the production and operations of enterprises, making economic systems more vulnerable. Assessing the production capacity loss rate (PCLR) of enterprises is crucial for quickly evaluating disaster losses. However, PCLR of enterprises is difficult to measure through physical damage. On-site investigations offer a compromise method, but inconsistencies between respondents and investigators in understanding production capacity may result in response bias. Therefore, this study employed the vulnerability curve method for categorizing damage states to divide PCLR into different damage states and constructed exceedance probability curves to mitigate response bias. Then, this study utilized distribution function fitting to calculate the expectation-of-loss rate for each damage state and finally integrated the probabilistic information with the expectation for each state to estimate PCLR. The proposed methodology is realized by the questionnaire data from the “7.20” extreme flooding event in Zhengzhou, Henan. We found that when the inundation depth is less than 80 cm, the wholesale and retail trade sector suffers the highest loss rate; however, when the inundation depth exceeds 80 cm, we should pay more attention to the manufacturing sector. Monte Carlo simulation (MCS) established the prediction intervals of PCLR, offering an alternative for PCLR. This study effectively accounts for response bias, providing input conditions for assessing ripple losses.