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

PLOS One | https://doi.org/10.1371/journal.pone.0332298 October 22, 2025 10 / 20 is positively correlated with approvals, reinforcing the role of international institutional alignment in facilitating regulatory adoption. Table 4 breaks down effects by the type of climate shock—cold waves, severe winters, heatwaves, droughts, floods, and storms—while controlling for country, commodity, and other variables. Cold waves exhibit the most sustained impact: one additional cold wave leads to a 0.290-unit increase in GMO approvals by year seven. Conversely, droughts exhibit a delayed but negative effect, significantly reducing approvals in years two and six. Severe winter events (including snow, ice, frost, and freeze) are linked to a cumulative 0.264-unit reduction in approvals over the seven-year window. To focus specifically on agriculturally relevant shocks, Table 5 restricts the analysis to climate events occurring in key agricultural areas. These regions are identified by spatially matching disaster locations with the SPAM production map [37]. EM-DAT provides data on disasters at the state level. As the SPAM data is at the commodity level, this paper aligns EM-DAT with SPAM data, specifically identifying countries’ main agricultural regions (i.e., states) by ascertaining each commodity—namely, soybeans, maize, cotton, and rape or colza—exhibits an agricultural yield greater than zero. Con- sistent with earlier results, cold waves again show the most persistent positive effect on GMO approvals, while droughts

notably suppress approvals six years after the shock. These findings emphasize that not all climate shocks lead to adap- tive regulatory responses and underscore the importance of spatial and sectoral context in shaping GMO policy outcomes. 6. Mechanism: Comparative advantage of GMOs In the theoretical model, this paper shows that countries with a strong comparative advantage in developing GMO technol- ogies tend to adopt less restrictive regulatory stances toward GMO crops. In contrast, countries with weaker comparative Table 3. Local projections of the cumulative effects on GMO approvals relative to total climate shocks (Up to 7 Years Post-Shock). Horizon 1 Horizon 2 Horizon 3 Horizon 4 Horizon 5 Horizon 6 Horizon 7 Total Shock −0.009 −0.019 −0.001 −0.035* 0.008 −0.015 0.000 (0.012) (0.013) (0.014) (0.014) (0.017) (0.014) (0.017) Lag_Approval 0.138*** 0.351*** 0.143*** 0.020 0.234*** 0.062* −0.041 (0.021) (0.031) (0.027) (0.018) (0.038) (0.031) (0.032) Observations 4624 4352 4080 3808 3536 3264 2992 R-squared 0.232 0.33 0.265 0.262 0.295 0.285 0.288 Total Shock −0.004 −0.015 0.002 −0.031* 0.011 −0.014 0.003 (0.012) (0.012) (0.015) (0.014) (0.016) (0.015) (0.018) Lag_Approval 0.129*** 0.334*** 0.134*** 0.008 0.220*** 0.052 −0.049 (0.022) (0.031) (0.028) (0.019) (0.038) (0.031) (0.034) Agri Share −0.031 −0.034 −0.05 −0.054 −0.043 −0.037* −0.050* (0.018) (0.018) (0.026) (0.028) (0.022) (0.018) (0.022) Arable Land 0.922 0.169 0.386 0.319 1.076 (0.057) 0.490

(0.995) (0.587) (0.675) (0.766) (1.008) (1.120) (0.923) UPOV 0.526** 0.574** 0.605*** 0.640*** 0.346* 0.217 0.381*** (0.175) (0.181) (0.162) (0.158) (0.136) (0.113) (0.109) Infrastructure 2.366 3.160 4.990 4.043 4.398* 4.600 4.193 (2.040) (1.878) (2.644) (3.104) (1.983) (2.322) (2.216) Observations 4308 4052 3796 3540 3284 3028 2772 R-squared 0.234 0.329 0.27 0.267 0.295 0.285 0.29 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.t003