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

PLOS One | https://doi.org/10.1371/journal.pone.0332298 October 22, 2025 11 / 20 advantages are generally more restrictive. This section builds on that framework by empirically examining how compara- tive advantage moderates the relationship between climate shocks and GMO approval decisions. Comparative advantage in GMO technology is measured in two ways. First, the one-year lag of logarithm of a country’s GDP per capita is used as a proxy for its capacity to invest in and adopt GMO innovations. Countries with higher income levels are presumed to have greater technological readiness and institutional support for GMO adoption. To capture this dynamic, the model includes an interaction term between GDP per capita and climate shocks. Table 6 reports the esti- mated effects, while Fig 4 visualizes the relationship seven years after a shock. The results indicate that GMO approvals increase with GDP per capita. However, at lower income levels, approvals may remain negative following a shock and only turn positive once GDP exceeds a certain threshold. For example, in countries experiencing ten climate shocks in a given year, approvals tend to rise only if GDP per capita is above exp(6.2), or approximately $493. This suggests that limited economic resources and weaker technological capacity may prevent low-income countries from advancing GMO approvals in response to extreme climate events. To further isolate the policy response toward GMO varieties already approved elsewhere, the analysis is repeated

using only pre-approved events—those that can typically be fast-tracked due to established safety records. Table 7 and Fig 5 present the results. In countries with GDP per capita above exp(6.5), or about $665, climate shocks are associated Table 4. Local projections of the cumulative effects on GMO approvals relative to separate climate shocks (Up to 7 Years Post-Shock). Horizon 1 Horizon 2 Horizon 3 Horizon 4 Horizon 5 Horizon 6 Horizon 7 Cold wave 0.171* −0.269*** 0.304** 0.031 0.188* −0.217** 0.290* (0.075) (0.073) (0.098) (0.079) (0.076) (0.072) (0.112) Observation 4308 4052 3796 3540 3284 3028 2772 R-squared 0.235 0.331 0.273 0.266 0.296 0.286 0.292 Drought 0.025 −0.170* −0.029 −0.014 0.037 −0.266*** −0.074 (0.088) (0.067) (0.076) (0.070) (0.069) (0.060) (0.167) 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 −0.006 −0.006 −0.026 −0.020 0.003 0.000 0.009 (0.017) (0.015) (0.022) (0.018) (0.019) (0.020) (0.022) Observation 4308 4052 3796 3540 3284 3028 2772 R-squared 0.234 0.328 0.271 0.266 0.295 0.285 0.29 Heat wave −0.113 0.002 −0.161 0.095 −0.024 0.142 0.023 (0.115) (0.082) (0.123) (0.117) (0.083) (0.129) (0.138) Observation 4308 4052 3796 3540 3284 3028 2772 R-squared 0.235 0.328 0.271 0.266 0.295 0.285 0.29 Severe winter condition −0.389*** 0.280** −0.147** −0.110 0.334*** 0.175 −0.264*** (0.107) (0.092) (0.047) (0.077) (0.096) (0.155) (0.061)

Observation 4308 4052 3796 3540 3284 3028 2772 R-squared 0.236 0.329 0.271 0.266 0.296 0.285 0.29 Storm −0.003 0.002 0.017 −0.092** −0.027 −0.016 −0.032 (0.025) (0.028) (0.021) (0.031) (0.036) (0.034) (0.042) Observation 4308 4052 3796 3540 3284 3028 2772 R-squared 0.234 0.328 0.27 0.269 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.t004