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

PLOS One | https://doi.org/10.1371/journal.pone.0332298 October 22, 2025 3 / 20 This paper contributes to the literature on GMO policy and climate change by offering a novel framework for under- standing how climate shocks influence GMO approval processes through their effects on comparative advantage. The findings provide important insights for policymakers, emphasizing that restrictive GMO regulations may amplify the nega- tive consequences of climate shocks—worsening food insecurity and hindering the adoption of critical agricultural innova- tions needed for climate resilience. The remainder of the paper is organized as follows. Section 2 develops the conceptual framework and theoretical model. Section 3 and 4 describe the data and methodology. Section 5 presents the empirical findings on the effects of climate shocks on GMO approvals. Section 6 explores empirically the mechanism by examining the role of comparative advantage in GMO technology, and Section 7 is robustness check. Finally, Section 8 is the conclusion. 2. Model 2.1. Consumption Consider a two-country two-sector model. Following prior works [15–17], this model assumes a constant elasticity of sub- stitution (CES) demand function with an elasticity of substitution τ ∈ (0, +∞) over GMO and non-GMO sectors. Consum- ers in country j maximize Uj = C0 j + (Cs j )τ (C–s j )1–τ (1) where C s j aggregates GMO commodity demand and C–s j denotes non-GMO commodity demand. The model assumes consumption relating to GMO (l = s) and non-GMO (l = –s) sectors is substitutable. The consumption quantity of sector l

(i.e., l ∈ s, –s) is subject to a budget constraint s.t. ∑ l pl j C l j = Ij (2) where I j is the nominal consumption income of country j . This model assumes p l j denotes the factory-gate price of sector l in country j ’s market. The utility maximization problem solves that Cs j = τ Ij ps j ; C–s j = (1 – τ ) Ij p–s j (3) 2.2. Production and climate shocks On the supply side, this model assumes producers are price setter under monopolistic competition. A producer maximizes its profit by allocating capital to agricultural technology adoption (e.g., GM innovation and research) and hiring labor. A producer’s profit in sector l in country i is shown as maxK l i,Ll i πl i = pl i nl i Al i Kl i αLl i 1–α – r i Kl i – w i L l i (4) where α is the elasticity of production with respect to capital, Al i denotes total factor productivity (TFP) in sector l , and n l i(ωi ) denotes the quantitative exercise of productivity affected by climate shocks ωi (∂nl i(ωi) ∂ωi < 0). Al i nl idenotes the productivity affected by climate shocks in sector l . r i and w i are the unit cost of technology innovation and hiring labor, respectively. This model assumes that country i ’s endowment of capital can move between GMO and non-GMO sectors such that the market clearing condition for labor is Ks i + K–s i = 1 and relative capital between GMO and non-GMO sectors is θi = Ks i /K–s i ; accordingly, Ks i = θi 1+θi . The model also assumes that country i ’s labor in both sectors is L s i = L–s i = 1. The reason this