How to Reach the New Green Deal Targets: Analysing the Necessary Burden Sharing within the EU Using a Multi-Model Approach

The Green Deal of the European Union defines extremely ambitious climate targets for 2030 (−55% emissions compared to 1990) and 2050 (−100%), which go far beyond the current goals that the EU member states have agreed on thus far. The question of which sectors contribute how much has already been discussed, but is far from decided, while the question of which countries shoulder how much of the tightened reduction targets has hardly been discussed. We want to contribute significantly to answering these policy questions by analysing the necessary burden sharing within the EU from both an energy system and an overall macroeconomic perspective. For this purpose, we use the energy system model TIMES PanEU and the computational general equilibrium model NEWAGE. Our results show that excessively strong targets for the Emission Trading System (ETS) in 2030 are not system-optimal for achieving the 55% overall target, reductions should be made in such a way that an emissions budget ratio of 39 (ETS sector) to 61 (Non-ETS sector) results. Economically weaker regions would have to reduce their CO2 emissions until 2030 by up to 33% on top of the currently decided targets in the Effort Sharing Regulation, which leads to higher energy system costs as well as losses in gross domestic product (GDP). Depending on the policy scenario applied, GDP losses in the range of −0.79% and −1.95% relative to baseline can be found for single EU regions. In the long-term, an equally strict mitigation regime for all countries in 2050 is not optimal from a system perspective; total system costs would be higher by 1.5%. Instead, some countries should generate negative net emissions to compensate for non-mitigable residual emissions from other countries.

Economic Impacts of the US–China Trade War on the Asian Economy: An Applied Analysis of IDE-GSM

In this article, we attempt to estimate the economic impacts of the US–China trade war that began in 2018. We used IDE-GSM, a computational general equilibrium simulation model, to estimate the economic impacts of a ‘full-confrontation’ scenario wherein both countries impose 25% additional tariffs on all goods imported from each other for 3 years 2019 onwards. In our calculation, the economic impact for the United States is −0.4% and −0.5% for China. Some Asian countries benefit from the trade war. As far as it remains bilateral, the trade war is only an issue for the concerned parties. We also ran the US–world trade war scenario, wherein the United States and all other countries impose a 25% additional tariff on all goods. The negative impact on the global economy is –0.8%, much more significant than the 0.1% impact from the US–China trade war. Thus, it is clear that the world cannot afford to engage in a multilateral trade war. JEL Codes: C68, F13

Impacts of Reverse Global Value Chain (GVC) Factors on Global Trade and Energy Market

Since the outbreak of COVID-19 and the American decoupling policy, the global value chains (GVCs) have been switched to regional GVCs, and, in the worst case, are subject to a potential alteration of reversing the GVCs, ultimately entailing a severe impact on international trade and the global energy market. This paper applies a quantitative approach using a computational general equilibrium (CGE) model to estimate the effects of the reverse GVC factors on the global economy, trade, and energy market. These reverse GVC factors will decrease the global GDP, and such effect will bring a greater influence on both China as well as the United States, which is pursuing decoupling. The increased trade costs due to these factors will reduce the GVC indices, mostly in ASEAN by 0.2~1.15%, followed by Korea, Japan and China. Surprisingly, the GVC index in the United States is expected to be strengthened due to the enhanced GVC with its allies such as Canada and Mexico. In China, the use of oil, gas and petroleum is expected to decrease by around 10%, and similar effects are expected in Korea and the EU. Among the world’s major energy producers, it is estimated that the US will reduce energy exports by 16–62% depending on the energy source, and the Middle East and Russia will significantly reduce their gas exports. The global energy market is shrinking, but in particular, the international gas market is expected to decrease by 27.3~38.6%.

Estimation of the Spatial Impact of the Pohang Earthquake on Regional Economies: Development of the SCGE Model

This study analyzes the spatial effects of the Pohang earthquake on regional economies by developing a spatial computational general equilibrium (SCGE) model. The model is composed of regional modules of production and consumption and is linked with a transport demand model at the city and county levels. The effects are measured using changes in the gross regional product (GRP) derived from increases in travel time costs from the collapse of road networks by an earthquake. Three scenarios are considered in this study in terms of external shocks to changes in production capacities (capital stocks) and travel time costs. Counter-factual experiments show that the earthquake in Pohang may have led to economic losses in other regions besides the area in which the earthquake occurred. Results showed a decline in gross domestic product by 0.58%, GRP by 3.69% for southern Daegu in zones directly influenced by the earthquake, and 0.64% for capital regions in zones indirectly influenced by the earthquake. These negative outcomes on the economies depended on much more direct damage to production facilities rather than indirect damage such as the collapse of road networks after an earthquake.

Modeling Trade Tensions

In this paper, we investigate the mechanisms through which import tariffs impact the macroeconomy in two large scale workhorse models used for quantitative policy analysis: a computational general equilibrium (CGE) model (Purdue University GTAP model) and a multi-country dynamic stochastic general equilibrium (DSGE) model (IMF GIMF model). The quantitative effects of an increase in tariffs reflect different mechanisms at work. Like other models in the trade literature, in GTAP higher tariffs generate a loss in terms of output arising from an inefficient reallocation of resources between sectors. In GIMF instead, as in other DSGE models, tariffs act as a disincentive to factor utilization. We show that the two models/channels can be broadly interpreted as capturing the impact of tariffs on different components of a country’s aggregate production function: aggregate productivity (GTAP) and factor supply/utilization (GIMF). We discuss ways to combine the estimates from these two models to provide a more complete assessment of the macro effects of tariffs.