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 8 / 23 PLOS One Trade agreement, climate change and geography Equ. (1) explains that beta (β) and delta (δ) are explanatory and control variables respectively, EXPijt indicates the exports between the origin country to the destination country, CO it 2 is the carbon dioxide emissions of a country i in year t , PhyDISTit is the physical distance of a country i in year t , AOit is access to Indian or Pacific Ocean of country i in year, CBit is the common border of country i with country j in year t , GDPijt demonstrates the GDP of country i and j in year t , and Popijt is the population of the exporting countries. Distance is measured in kilometers from the capital to the capital of the trading country. Mills ratio is the coefficients of the Heckman model (1979). In addition, we extend the empirical model by adding the trade agreement and carbon dioxide emissions in the Equ. (2) to analyze the effect of simultaneous impact of trade agree- ment and carbon dioxide emissions (CO2) on bilateral exports. Thus, the empirical model is following: ln EXP ln CO ln PhyDIST ln AOij ln C ijt it ij ( ) = + ( )+ ( )+ ( )+ β β β β β 0 1 2 3 4 2 BB ln GDP PTA GDP GDP CO W ij it ijt it it ( ) + ( )+ ( )+ ( )+ ( )+ β β β β β 5 6 7 2 8 9 2 * TTOX ijt it ( )+ ε (2) Equ. (6) shows that export (s) is a function of carbon emissions (CO2), trade agreement (TDA), access to Ocean (AO), common border (CB), gross domestic product (GDP), non-

square of economic GDP, and interaction term of economic GDP and CO2 (GDP*CO2). More specifically, PTAijt denotes preferential trade agreement between country ‘i’ and country ‘j’ over time, whereas, the GDP*CO2 represents the joint effect of economic GDP and carbon emissions on bilateral exports. In addition, the GDP2 it indicates the non-linear effect of eco- nomic GDP on bilateral exports. WTOXijt indicates the four-dimensional (dummy) variable with 0 or 1 for each WTOX provision. Data The panel dataset covers the 46 Asian countries that collect information period from 2000 to 2020 on suitable variables through several international organizations (e.g., the World Bank, CEPII, and the United Nations Conference on Trade and Development (UNCTAD). The bilateral export data is collected by UNCTAD. The data on natural geography variables from World Development Indicators (WDI) and the man-made geography variables from CEPII are collected. The informa- tion on a country’s characteristics (common border, landlocked, distance, GDP, and population) is collected by the WDI. In addition, the trade agreement (PTA)’s data are collected from WTO website, and complemented with information from Baier et al. (2008)3. PTA dummy variable refers to one if exporter and importer listed were in any one of PTA’s listed either the WTO website. Besides, the data on CO2 emissions are collected from the World Input-Output Database (WIOD4) website. Moreover, the further detail is explained in the Table 1. Estimation method

Our concern is to estimate the role of geographic factors, climate change and trade agree- ments in determining export activities at the bilateral level. The literature has demonstrated that natural geographic factors have a direct and indirect impact on international trade and infrastructure, as well as production and consumption [10,70,71]. Similarly, in man-made geography, transportation costs create economic activities in terms of adopting different strategies, such as substituting export strategies and increasing returns to scale, especially in the manufacturing sector. We employ the structural gravity model [72–74] to investigate the influence of geographic factors, climate change and trade agreements on bilateral exports for Asian countries. In the