Climate change, geography and trade agreements: A perspective of Asian bilateral trade

PLOS One | https://doi.org/10.1371/journal.pone.0320363 April 25, 2025 13 / 23 PLOS One Trade agreement, climate change and geography The estimates of the structure gravity model and multilateral resistance terms for geo- graphical factors are reported in Table 5. We perform the structure gravity model and the two-stage sample selection [37] to examine the influence of trade barriers and the correlation of unobserved variables among the explanatory and error terms, respectively. The gravity variable, distance, is found with a negative and significant magnitude that reduces the export level in both dimensions (domestic and partners), but the partner countries’ distance is greatly affected compared with that of the domestic country. The main geographic factors have a significant influence on the bilateral export level and its probability. During bilateral exports, access to an ocean is found to have a greater impact on exports in the importing country’s geography than the exporting country’s geography. Access to an ocean and common border geographical factors also positively influence the bilateral exports between trading partner countries. The country characteristic variables, such as population, decline in the export level and probability in exporter countries but increase in Table 5. A Structural Gravity with Trade Agreement and CO2 Emissions. Exporting Countries Model Importing Counties Model Variable Linear Non-linear Interaction Linear Non-linear Interaction CO2 -0.250 0.176** 0.117*** 0.132 0.152*** 0.169**

(0.045) (0.051) (0.054) (0.110) (0.041) (0.021) Physical-distance -0.125*** -0.130*** -0.228*** -0.186*** -0.179*** 0.140*** (0.001) (0.020) (0.009) (0.003) (0.001) (0.007) Access to Ocean 0.151*** 0.321** 0.622 * 1.006*** 0.153*** 0.108*** (0.001) (0.021) (0.086) (0.002) (0.002) (0.030) Common border 0.627*** 0.017 * 0.142*** 0.197*** 0.104*** 0.054*** (0.011) (0.014) (0.030) (0.080) (0.032) (0.031) LnGDP 0.107*** 0.132** 0.121*** 0.372*** 0.032 * 0.043*** (0.001) (0.032) (0.032) (0.102) (0.021) (0.032) PTA 0.114*** 0.204*** 0.109*** 0.140*** 0.128*** 0.157*** (0.001) (0.023) (0.011) (0.015) (0.036) (0.011) CPM 0.242** – – 0.312*** – – (0.002) – – (0.001) – – CPP 0.111*** – – 0.091*** – – (0.020) – – (0.031) – LMB – 0.190** – – 0.173*** – – (0.004) – – (0.023) – EVS – – 0.201*** – – 0.131*** – (0.010) – – (0.004) GDP2 – 0.102*** – – 0.104*** – – (0.030) – – (0.020) – GDP*CO2 – – -0.035*** – – -0.013*** – – (1.070) – – (0.051) Constant 1.971*** 0.564** 0.126** 0.486 * 0.214 0.127*** (0.047) (0.070) (0.100) (0.028) (0.041) (0.308) R-squared 0.145 0.104 – 0.449 – – Number of exporters – 46 46 – 46 46 Note: the term ***, ** and * denote that significance level at 1%, 5% and 10% respectively. Ln indicates logarithm for variables. https://doi.org/10.1371/journal.pone.0320363.t005