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

PLOS One | https://doi.org/10.1371/journal.pone.0332298 October 22, 2025 15 / 20 than export specialization. In our context, however, GMO adoption is more closely tied to a country’s export capacity and engagement with global biotechnology markets. Because the RC index reflects import behavior and trade balances, it risks conflating comparative advantage with trade protection or domestic consumption patterns, which are not the focus of our export-oriented framework. Second, the RSCA index is a normalized transformation of the RCA index intended to improve cross-country comparability by addressing scaling and symmetry. While useful in studies relying on continuous measures, our empirical strategy relies on a binary classification—distinguishing countries with a positive versus negative comparative advantage based on the sign of lagged RCA values. Since RSCA preserves the same directional classifica- tion, it does not offer additional analytical value for our purposes. Controlling for country and commodity fixed effects, Table 8 reports how climate shocks affect GMO approvals in both groups. The results show that the negative effects of shocks are more persistent in countries with a comparative disad- vantage. Specifically, four years after a shock, GMO approvals decline by 0.037 events on average in these countries. Table 8. Impact of total climate shocks on GMO approvals in subsamples in terms of comparative advantages. Horizon 1 Horizon 2 Horizon 3 Horizon 4 Horizon 5 Horizon 6 Horizon 7 Comparative advantaged countries

Total Shock 0.005 −0.005 −0.046 0.000 0.019 −0.01 −0.024 (0.021) (0.034) (0.033) (0.027) (0.030) (0.048) (0.036) Lag_Approval 0.086 0.248** −0.015 −0.101* 0.123 −0.052 −0.046 (0.086) (0.078) (0.057) (0.041) (0.120) (0.068) (0.079) Agri Share −0.109 −0.090 −0.175* −0.094 −0.007 −0.065 −0.044 (0.056) (0.045) (0.065) (0.072) (0.059) (0.058) (0.072) Arable Land 1.091 1.082 0.843 1.334 3.458 1.228 1.891 (0.951) (1.180) (1.275) (2.091) (2.588) (3.024) (2.002) UPOV −0.600 −0.409 −0.379 −0.077 −0.312 −0.488 0.375 (0.433) (0.546) (0.459) (0.370) (0.502) (0.370) (0.318) Infrastructure 8.007 7.228 13.141 12.851 2.599 4.798 3.452 (5.382) (6.339) (8.378) (9.717) (5.034) (6.758) (6.784) Observations 734 690 646 600 551 504 458 R-squared 0.253 0.305 0.303 0.302 0.290 0.305 0.332 Comparative disadvantaged countries Total Shock −0.004 −0.017 0.011 −0.037* 0.010 −0.015 0.010 (0.013) (0.013) (0.015) (0.016) (0.018) (0.015) (0.020) Lag_Approval 0.121*** 0.342*** 0.152*** 0.013 0.230*** 0.057 −0.068 (0.019) (0.035) (0.029) (0.021) (0.034) (0.033) (0.037) Agri Share −0.027* −0.032* −0.042* −0.054* −0.050* −0.036* −0.047* (0.013) (0.014) (0.018) (0.024) (0.023) (0.017) (0.023) Arable Land 0.725 −0.296 −0.291 −0.338 −0.120 −0.933 −0.324 (1.472) (0.998) (1.129) (1.056) (0.783) (0.801) (0.742) UPOV 0.660*** 0.685*** 0.703*** 0.727*** 0.403** 0.331* 0.366* (0.147) (0.136) (0.129) (0.145) (0.133) (0.134) (0.152)

Infrastructure 1.524 2.368 3.209 2.250 4.390* 4.399 4.066 (2.081) (1.958) (2.203) (2.373) (2.176) (2.466) (2.223) Observations 3574 3362 3150 2940 2733 2524 2314 R-squared 0.248 0.349 0.287 0.284 0.314 0.303 0.298 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.t008