GMO approvals under pressure: How climate shocks shape policy across nations

PLOS One | https://doi.org/10.1371/journal.pone.0332298 October 22, 2025 16 / 20 In contrast, no significant effect is found in countries with a comparative advantage. This suggests that countries lacking specialization in GMO technology may adopt a more protective stance following climate shocks, potentially delaying approvals and contributing to international GMO disputes. Prior studies have similarly noted that restrictive GMO policies can serve as non-tariff barriers in countries lacking specialization in GMOs [33]. 7. Robustness checks: Droughts and drought-tolerance GMO approval As a robustness check, this paper narrows its focus to drought-specific events to better isolate their impact on GMO approvals—particularly for drought-tolerant traits. Table 9 presents local projection estimates of the cumulative effects of drought shocks on approvals of drought-tolerant GMOs. Fig 6 visualizes the relationship between the logarithm of GDP per capita and the number of approvals seven years after the shock, holding the number of drought events constant. The results reveal a similar pattern to previous findings: in lower-income countries, approvals of drought-tolerant GMOs remain negative even seven years after a drought. This suggests that despite the severe agricultural disruptions caused Table 9. Impact of total droughts on drought-related GMO approvals. Horizon 1 Horizon 2 Horizon 3 Horizon 4 Horizon 5 Horizon 6 Horizon 7 Total Shock 0.011 0.019 −0.034 0.006 −0.007 0.003 0.053 (0.034) (0.020) (0.041) (0.040) (0.047) (0.028) (0.045)

Log (GDP per capita) 0.007 0.025*** 0.018** 0.010* 0.014** 0.037*** 0.012 (0.004) (0.004) (0.006) (0.005) (0.005) (0.007) (0.008) Total Shock x Log (GDP per capita) −0.002 −0.003 0.003 −0.001 0.001 −0.002 −0.007 (0.005) (0.003) (0.005) (0.006) (0.006) (0.004) (0.006) Lag_Approval 0.025*** −0.002 0.015** 0.022*** 0.021*** 0.000 0.021** (0.003) (0.002) (0.005) (0.005) (0.004) (0.003) (0.007) Agri Share 0.000 0.002* 0.001 0.000 −0.001 0.001 −0.001 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Arable Land −0.067 −0.084 −0.063 −0.033 0.022 −0.008 −0.011 (0.038) (0.048) (0.051) (0.044) (0.054) (0.050) (0.075) UPOV −0.008* 0.004 −0.002 0.001 0.002 0.005 0.014* (0.003) (0.004) (0.004) (0.004) (0.005) (0.005) (0.007) Infrastructure −0.061 0.030 −0.014 0.055 0.286 0.326 0.123 (0.116) (0.145) (0.163) (0.126) (0.175) (0.220) (0.229) Observations 4308 4052 3796 3540 3284 3028 2772 R-squared 0.111 0.046 0.069 0.092 0.082 0.063 0.086 Note. The dependent variable is the number of GMO events approved, so coefficients are interpreted in unit terms. Horizons refer to the response years. Standard errors are in parentheses. Statistical significance is denoted by *** (1%), ** (5%), and * (10%) levels. Country fixed effect and commodity fixed effect are included. https://doi.org/10.1371/journal.pone.0332298.t009