Trade wars cause climate change --- only the WTO solves both by balancing

Abstract The recent “US–China trade war” has aroused concern over trade-related environmental impacts. This study built a multiregional computable general equilibrium model to simulate environmental impacts of the “US–China trade war” under different scenarios of tariff and nontariff barriers and the battlefield spreading ranges. The present study found that although the trade war will cause a global economic downturn, which will seemingly reduce environmental pressure globally, global carbon emissions are expected to increase rather than decline. On the one hand, the CO2 emission increase caused by land-use changes in Brazil and Argentina will far exceed the emission reduction because of decreased global production. On the other hand, some countries/economies especially those developing countries such as Vietnam, Russia, and India will face emission increases driven by scale effects. Countries such as Korea, the UK, and France will enjoy a reduction in emissions driven by structural effects. China and the US will face a reduction in production and CO2 emissions, but their CO2 emission intensities will rise. The results remind us that as global production and supply chains are formed, it is important to closely monitor trade-related environmental impacts. Efforts should be made to balance the interests of trade and the environment. This publication is licensed under the terms of your institutional subscription. Request reuse permissions. 1. Introduction ARTICLE SECTIONS The US and China are the world’s two largest economies, and the trade volume between the two countries accounts for 23% of world trade. Especially after China’s accession to the WTO in 2001, bilateral trade between China and the US has developed more rapidly. The value of their bilateral trade in goods rose from $80.6 billion in 2001 to $584.8 billion in 2017. (1) The rapid and stable growth of US–China trade is related to the considerable differences in their production factors and the strong complementarity in their industrial competitiveness. (2) However, trade friction has always existed, especially in recent years, the trade friction between the two countries escalated into a “trade war” (see S1, Supporting Information for a timeline of the US–China trade war). The battlefield of the trade war has continued to expand to Mexico, the European Union, and even the world. The trade war has shocked the trade and economic paradigm of the two countries and even the world and increased the uncertainty of global economic development. (3−16) Although many believe that the US–China trade war will depress global economic production and consumption, reduce related emissions, and benefit the global environment at large, (17−19) the prospective outcome could be complicated and uncertain. The policy shock imposed on trade could lead to changes in production volume and structure and then change the energy and resource use of one country and the world and result in changes in the global CO2 and pollutant emissions and environmental quality. (20−35) The most direct and obvious impact in the short term is Brazil’s environment. The increased soybean production in Brazil caused by Chinese increased soybean import demand may exacerbate deforestation in Brazil, destroy biomes, and release large amounts of carbon. (7) Lin, et al., (17) Liu, et al. (18) and Du et al. (19) used different computable general equilibrium (CGE) models and set tariff reduction scenarios to explore the environmental impact of the US–China trade war and found the trade restrictions brought by the trade war will reduce global carbon and pollutant emissions due to the economic losses. However, it may be not conducive to the development of clean energy in less-developed regions. Considering the complexity of global environmental impacts of international trade, it is of great interest to explore the impacts of the US–China trade war on the changes in global production (supply chain) and then on the total emissions and their spatial distributions. However, the current limited studies on the environmental impacts of the US–China trade war only focused on the impacts of either fossil fuel energy use (and sometimes industrial process) emission or deforestation causing emissions separately, without a combination of the two aspects. Besides, in the policy shock scenario settings of most of the previous trade-economy or trade-environment impact research literatures, only tariff barriers were taken into consideration but nontariff barriers (NTBs) were neglected. Currently, the global tariff level has been generally low, and the NTBs have stood out as important impeding factors on the international trade. (36−39) In addition, during the trade war, China and the US had interactions with other economies, for example, the US signed trade agreements with Mexico, Canada, and Japan but intensified trade conflicts with the EU, and so forth. These issues are also among the influencing factors to the economic-environmental outcome of the trade war. Thus, this study comprehensively considers the potential emissions from fossil fuel combustion, industrial processes, and land use changes and adds NTBs and the multilateral trade practices to the trade war scenario settings, which makes this research more comprehensive and different from the existing studies. Based on a chain reaction assessment framework of “trade policy/measures → trade and economic impacts → environmental impacts” to carry out analysis, (40) this study built a multiregional CGE model and set up different trade war scenarios to simulate and explore the impacts caused by the US–China trade war on the global environment. 2. Methods and Data 2.1. Model Framework 2.1.1. GTAP Model The main body of the chain reaction method in this study was a classic multiregional and multisector comparative static CGE model, GTAP, which was developed by the Global Trade Analysis Project at the Purdue University, (41) whose database has been updated to version 10. (42) Like other classical CGE models, under the assumption of perfect competition and constant returns to scale, the GTAP model uses utility- and profit-maximizing behavior of consumers and producers as the optimal conditions. (43) For the producer side, firms combine the intermediate inputs and the primary factors to produce output. The production structure is based on a three-level nested constant elasticity of the substitution function. The top level nest is a composite of intermediate demand and value added. The second level nests decompose the intermediate demand into demands for various individual intermediate goods and value added into demands for various individual factors, respectively. The third nest decomposes the demand for individual intermediate goods into domestic and imported composites. This study adopted the standard assumption in GTAP where there is no substitution between the intermediate goods and input factors (represented by value added) and between different intermediate goods. In this model, household and government consumers maximize utility subject to budget constraints. The utility function for private spending is based on the constant differences of elasticities implicitly additive expenditure (CDE) function. The utility function for government expenditure is based on a Cobb–Douglas structure, where the spending shares are fixed. Household and governmental savings are subsequently translated into investment. Households, governments, and firms choose to buy a combination of domestic goods and imported goods following Armington assumption, with full consideration of prices and associated transport costs, export taxes, and tariffs. Countries/economies and regions are linked together through multilateral trade. Households bring together all factor incomes, and the government brings all fiscal revenues from all tax incomes, which are distributed over the three forms of final demand: private household expenditures, government expenditures, and household and government savings, so as to maximize the constructed regional utility. This study employed the full GTAP 10 database, which describes the world economy for 2014 and distinguishes 65 sectors in each of the 141 countries/economies. In the present study, the original database of GTAP 10 was aggregated into 23 regions, each with 24 economic sectors, with an emphasis on the main trade partners of China and the US (see Tables S1 and S2, Supporting Information). 2.1.2. Policy Shocks Although the current GTAP-E model has the energy and CO2 emission data, it lacks some key environmental and nature resource information, such as the local pollutant information and carbon emissions from the LULUCF (land use, land use change, and forestry). This study establishes a soft link between trade and economic behavior and the environmental impacts with reference to Song et al. (32) Policy shocks of tariff and NTBs were introduced into the GTAP model to obtain the economic impact αr,i,j, which is the change rate of economic variable j (such as output, import and export volume, etc.) in sector i of region r. The absolute impact can be calculated by Zr,i,j = αr,i,j × Vr,i,j, where Zr,i,j is r country/economy’s value or quantity change in economic variable j in sector i and Vr,i,j is r country/economy’s status quo value or quantity of economic variable j in sector i. Finally, the change in the emissions of pollutant k in sector i of country/economy r caused by the policy shock was calculated by Pr,i,k = er,i,k × Zr,i,output, where er,i,k is the emission coefficient of pollutant k in sector i of r country/economy and Zr,i,output is country/economy r’ s output variation in sector i. 2.2. Trade Measure Scenarios The baseline scenario is an equilibrium without policy shocks, which was the original balance in GTAP. Three trade policy shock scenarios with tariffs and NTBs were set that depended on the depth and scope of the US–China trade war (see S3, Supporting Information): Scenario 1: The trade war involves the US and China only. Policy shocks for this scenario were set based on tariffs mutually imposed after September 24, 2018. (44,45) In this scenario, the US imposes an extra 25% tariffs on $50 billion and extra 10% tariffs on $200 billion of goods imports from China, and China imposes extra 25% retaliatory tariffs on $50 billion and extra 5 and 10% retaliatory tariffs on $60 billion of products imported from the US. Both sides impose a certain degree of NTBs on each other. Scenario 2: Policy shocks for this scenario were based on the situation after May 13, 2019, before the 13th round of China and US trade negotiations. (45,46) This scenario also involves new trade agreements between the US and other countries. The US imposes an extra 25% tariffs on $250 billion of imports from China, and China imposes extra 25% tariffs on $50 billion of imports from the US and another four tariff rates, that is, 5, 10, 15, and 25% on $60 billion of imports from the US. At the same time, the new trade agreement among the US, Canada, and Mexico, that is, the USMCA, and the trade agreement between the US and Japan were taken into consideration. Scenario 3: The trade war becomes more intense and expands to other economies. It is assumed that the US and China impose extra 25% tariffs and more NTBs on all traded goods from each other. In addition, this scenario also assumes that the US imposes extra 5% tariffs on all imports from Mexico, extra 10% tariffs on EU aircrafts, and extra 25% tariffs on EU agricultural products. (47) 2.3. Effect Decomposition Decomposition analysis is an important tool for understanding and quantifying the driving forces behind changes in related indicators. (48) Index decomposition analysis (IDA) is widely used in driving force decompositions for carbon emission changes. (49−53) The logarithmic mean Divisia index (LMDI) (54) is a common arithmetic method for solving IDA problems. In this study, we use the IDA-LMDI method to decompose the driving forces of trade war-induced changes in CO2 emissions from fossil fuel combustion and production processes in specific countries/economies. For the total CO2 emissions Q, the IDA equation in the period dimension can be written as follows (1) where Xt is the total output in period t, which denotes the scale effect; Sit is sector i’s output share in the total output in period t, which represents the structural effect; and Eit is sector i’s CO2 emission intensity per unit output in period t, which represents the technical effect. This study adopted the standard assumption in GTAP where there is no scope for substitution between intermediate inputs and does not allow energy substitution within a sector, meaning that CO2 emissions per unit of the sectoral output in a specific country/economy in the short term are constant or that the short-term technical effect is zero. The change in the total CO2 emissions in period t relative to period 0 can be expressed as follows (2) According to Ang, (54) the LMDI decomposition results for each factor are as follows (3) (4) (5) where ΔQscl is CO2 emission change from the scale effect; ΔQstr is CO2 emission change from the structural effect; and ΔQtech is CO2 emission change from the technical effect. 2.4. Calculation of CO2 Emissions from Land Use Change Except for the CO2 emissions from fossil fuel combustion and industrial processes, the change in land use was also a major source of carbon emissions. This was especially true for countries such as Brazil, where approximately 58% of the Brazilian territory is covered by natural and planted forests, (55) making it an important carbon sink. The greatest carbon emissions caused by land use changes are attributed to the conversion between native vegetation and cropland. (56−58) So we focused on land use changes caused by agriculture production. When focusing on land use changes brought by the US–China trade war, the most substantially affected agricultural product is soybean, which accounts for around 70% of China’s imports of the US agricultural products in 2017. Brazil and Argentina are the second and third largest soybean exporters to China following the US and are the major soybean export beneficiaries from the US–China trade war. Forests in Amazon and Cerrado area in Brazil and Chaco forest in Argentina have suffered a lot from deforestation because of soybean expansion. (59−61) These forests have extremely high carbon content and rich biodiversity, playing a significant role in global carbon balance. (62−65) Studies have shown that changes in the structure of soybean trade caused by the trade war can affect soybean plantations in Brazil (3,7) and Argentina, (8,66,67) if China withdrew soybean orders from the US and turned to Brazil and Argentina to fill in the supply gap, and it is very likely to increase deforestation and carbon emissions. Though there might be the possibility for Brazil and Argentina to increase soybean exports in other ways such as importing soybeans from the US for re-export to China instead of increasing soybean production in Brazil and Argentina, however this could not be the case due to the fact that, first, the tariffs are supposed to be imposed on the US-produced product according to the principle of the origin of goods, (44,46,68) which means that the re-exported US-produced soybean must be also subject to tariff. Second, when checking the US and Brazil custom statistics, it was found that before and after the implementation of China’s retaliation trade measures on the US soybean in June 2018, the US soybean exports to Brazil has always been zero. (69,70) The Brazil custom statistics also proved that there is no change in soybean imports from other countries too. (68) Comparing soybean trade data of Argentina in 2019 and 2018, it was found that soybean export increased and import decreased, indicating that the increase of net export was due to the domestic production expansion. (1) Therefore, in the present study, the increase in carbon emissions from Brazil and Argentina land use changes triggered by the US–China trade war must be taken into consideration (see S4, Supporting Information for the detailed calculation process). Africa and Southeast Asia also potentially have problem of land use change emissions, which will potentially increase the agricultural output due to the trade war. However, in Africa, the output value of the agricultural sector will only increase slightly by 0.09–0.15% according to the present study. In Southeast Asia, the agricultural output value will only increase slightly by 0.08% in scenario 1. Therefore, we do not discuss the land use change emissions in these regions. 2.5. Data Sources We focused on the impacts of the trade war on global CO2 emissions and were also concerned about the ancillary impacts on the local air pollutants of sulfur dioxide (SO2), nitrogen oxides (NOx), carbon monoxide (CO), fine particulate matter (PM2.5), carbonaceous speciation (BC), and non-methane volatile organic compound (NMVOC) emissions. The emissions will be considered at the global, national, and sectoral levels according to data availability. The national and sectoral CO2 emission data in 2014 were drawn from the version v5.0 of the Emissions Database for Global Atmospheric Research (EDGARv5.0) (71) and the GTAP database. (42) The industrial air pollutant emission data in 2014 were also drawn from EDGARv5.0 global air pollutant emission data (72) (for the detailed data sources, please see S5, Supporting Information).