Modeling Regional Production Capacity Loss Rates Considering Response Bias: Insights from a Questionnaire Survey on Zhengzhou Flood

Abstract. Flood disasters in specific regions not only cause physical damage but also disrupt the production and operations of enterprises, making economic system more vulnerable. Assessing production capacity loss rate (PCLR) in enterprises is crucial for quickly evaluating disaster losses. However, PCLR in 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 state, and finally integrated the probabilistic information with the expectation of loss rate under each state to construct PCLR curves. 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, wholesale and retail trade sector suffers the greatest losses; however, when the inundation depth exceeds 80 cm, we should pay more attention to manufacturing sector. Monte Carlo simulation (MCS) established the prediction intervals of PCLR curves, offering an alternative for PCLR. This study effectively accounts for response bias, providing input conditions for assessing ripple losses.