Climate effects on the diversification strategy of export firms: Evidence from China

expression: po ¼ s s 1 � MC ð7Þ This leads to the optimal profit: P∗ o ¼ P � φo � ðs 1Þ Apkbs � �s 1 � Y s fe ð8Þ Where A ¼ a b �b þ b a �a . When a firm adds products, it increases its sunk costs and reduces its profits [34]. There- fore, if a firm produces n products, it is expected that product n* should satisfy P∗ o ¼ 0. If this condition is not met, the firm would continue to produce new products. Combining Eq (4) with the zero-profit condition, we obtain the following equation: Z1 n∗ φ ¼ 1 φðTÞ � Apk b ðs 1ÞP � s Y � � s s 1 fe 1 s 1 ð9Þ Extreme temperatures and export product diversification According to Eq (9), the derivative of the number of exported product categories (n*) with respect to temperature gives the following equation: @n∗ @T ¼ φ0ðTÞ φðTÞ � lnZφ ð10Þ As φ(T) and lnηφ are both positive, the sign of n*0(T) depends on the φ0(T). Thus, for T<T*, indicating temperatures below the optimum, the number of exported product catego- ries n* increases with increasing temperatures, promoting diversification. Conversely, for T>T*, temperatures above the optimum lead to n*0(T)<0. In this scenario, increasing temper- atures lead to a decrease in the number of exported product categories, reducing diversifica- tion. In summary, export diversification has an inverted U-shaped relationship with temperature. Extreme temperatures have an unfavorable effect on the implementation of diversification strategies by firms. Empirical methodology and data description Empirical methodology

Following Deschênes and Greenstone (2007) [35], this study uses a non-parametric model to analyze the impact of daily extreme temperature increases on firms’ export diversification strategies. The use of this model has several advantages: Firstly, the non-parametric model’s flexibility in specification enables the examination of the non-linear relationship between tem- perature and export diversification without necessitating the pre-specification of a functional form. Secondly, the model includes a series of temperature bin variables with interval lengths of 5˚C. These variables cover all temperature bins, ranging from the number of days with tem- peratures below -7˚C, the number of days between -7˚C and -2˚C, and so forth, up to the num- ber of days with temperatures above 28˚C. This illustrates the distribution of daily temperatures over a year. For instance, if the temperature on a particular day is 0˚C, that day will be counted in the -2˚C to 3˚C temperature bin. This methodology facilitates the direct observation of the impacts of extreme temperatures, both high and low. In contrast, when PLOS ONE Climate effects on the diversification strategy of export firms PLOS ONE | https://doi.org/10.1371/journal.pone.0308391 August 16, 2024 5 / 28