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

PLOS One | https://doi.org/10.1371/journal.pone.0332298 October 22, 2025 12 / 20 with an increase in approvals of pre-cleared GMO events. In contrast, at lower GDP levels, shocks reduce the likelihood of approving even these events. This finding supports the view that capacity constraints—not just regulatory attitudes— shape how countries respond to environmental stress. In low-income settings, even well-established GMO technologies may face additional barriers after a climate shock, while wealthier nations tend to view such shocks as motivation to accel- erate innovation. Second, this paper uses the standard Balassa-based Revealed Comparative Advantage (RCA) index to measure a country’s relative specialization in GMO-related products. RCA is calculated as RCAijt = log Eij Eit – log E–i,j E–i,t . Eij denotes the exports of country i in industry j . Eit is country i ’s total exports. E–i,j indicates all other countries’ exports in industry j , and E–i,t denotes the other countries’ total exports. The RCA compares a country’s share of exports in a given product to its overall export profile. An RCA value greater than zero indicates comparative advantage, while a value less than zero signals disadvantage. The sample is split into two groups—advantaged and disadvantaged—based on the country’s lagged RCA score, ensuring that current shocks do not affect group classification. Using zero as a reference point, the two subsamples are relatively large and comparable in size.

We carefully considered alternative measures of comparative advantage, including the Revealed Competitive Advan- tage (RC) index [38] and the Revealed Symmetric Comparative Advantage (RSCA) index [38,39] but ultimately chose not to use them. First, the RC index incorporates net trade and import values, capturing overall trade performance rather Table 5. Local projections of the cumulative change in GMO approvals relative to separate climate shocks occurring in main cropping regions (Up to 7 Years Post-Shock). Horizon 1 Horizon 2 Horizon 3 Horizon 4 Horizon 5 Horizon 6 Horizon 7 Cold wave 0.268* −0.220** 0.263* 0.191 0.158 −0.174* 0.215 (0.111) (0.080) (0.114) (0.122) (0.096) (0.070) (0.148) Observation 4308 4052 3796 3540 3284 3028 2772 R-squared 0.236 0.33 0.272 0.267 0.296 0.286 0.29 Drought 0.016 −0.218** −0.050 −0.033 −0.016 −0.262*** −0.095 (0.110) (0.081) (0.083) (0.085) (0.088) (0.073) (0.196) Observation 4308 4052 3796 3540 3284 3028 2772 R-squared 0.234 0.329 0.27 0.266 0.295 0.286 0.29 Flood −0.034 0.012 −0.035 −0.069 0.027 −0.016 −0.012 (0.024) (0.022) (0.027) (0.045) (0.035) (0.033) (0.040) Observation 4308 4052 3796 3540 3284 3028 2772 R-squared 0.235 0.328 0.271 0.267 0.295 0.285 0.29 Heat wave −0.003 0.091 −0.075 0.261 −0.123 0.140 0.256 (0.109) (0.120) (0.102) (0.172) (0.107) (0.109) (0.215) Observation 4308 4052 3796 3540 3284 3028 2772 R-squared 0.234 0.329 0.27 0.267 0.295 0.285 0.29

Severe winter condition −0.018 −0.008 0.003 −0.053 −0.010 −0.057 −0.026 (0.031) (0.019) (0.031) (0.042) (0.038) (0.047) (0.046) Observation 4308 4052 3796 3540 3284 3028 2772 R-squared 0.234 0.328 0.27 0.267 0.295 0.286 0.29 Storm −0.450*** 0.483** −0.324*** −0.039 0.518** 0.003 −0.305*** (0.108) (0.152) (0.067) (0.095) (0.157) (0.226) (0.081) Observation 4308 4052 3796 3540 3284 3028 2772 R-squared 0.236 0.33 0.271 0.266 0.297 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.t005