Regional Analysis

Summary: South Korea’s Nationally Determined Contribution (NDC) to the Paris Agreement included a pledge to reduce its greenhouse gas (GHG) emissions by 37 percent in 2030 from levels projected for that year under business-as-usual policies. Using an economy-wide model, researchers at the MIT Joint Program on the Science and Policy of Global Change projected that under South Korea’s Emissions Trading System (KETS) with fuel economy standards, the country would require a 2030 carbon price of $89 per metric ton of carbon dioxide-equivalent to meet its NDC target. According to their analysis, if economic benefits from avoided climate damages are excluded from consideration, the combined policies would reduce 2030 GDP by $20.6 billion (1.0 percent) and consumer welfare by 7.9 billion (0.7 percent). Adding the fuel economy standard to KETS increases the cost of meeting South Korea’s NDC: it reduces GDP and consumer welfare by, respectively, $4.2 billion and $1.1 billion relative to reducing emissions with just an ETS. The researchers also found that production declines under the combined policies are largest for three sets of sectors: fossil-based energy; chemical, rubber and plastic products; and iron and steel.

A substantial nationwide tax on carbon dioxide (CO2) could accelerate a transition to a low-carbon economy in the United States consistent with efforts to reverse global climate change. Such a tax would also raise significant revenue, ranging from $142 billion to $579 billion in 2050 under various carbon tax rates explored in this paper. A critical issue is what to do with the revenue. Proposals range from using it to reduce disparities among households, to reducing personal income taxes, corporate income taxes or other taxes. A better understanding of the implications of how the tax revenues are recycled could enable decision-makers to choose among these options.

Toward that end, researchers from the MIT Joint Program on the Science and Policy of Global Change, HEC Montreal and the National Renewable Energy Laboratory (NREL) completed a comprehensive analysis of the potential economic impacts of a national tax on U.S. CO2 emissions, with an emphasis on the household-level effects of different CO2 tax revenue distribution methods. They performed the analysis by linking a computational general equilibrium model of the U.S. economy (MIT’s U.S. Regional Energy Policy (USREP) model) with a detailed model (NREL’s Regional Energy Deployment System (ReEDS)] of the electricity sector, the largest source of U.S. CO2 emissions.

Evaluating the economic impacts on households of different income levels resulting from a wide range of per-ton CO2 taxes (initial amounts and rates of increase) and revenue distribution methods (e.g. recycling revenue through capital income tax rebates, labor income tax rebates, or lump-sum transfers to households), the researchers found a clear trade-off between emissions-reduction efficiency and economic equity among those methods. They determined that a hybrid approach that combines capital income tax rebates with lump-sum transfers to low-income households provided an effective way for reducing disparities among households of different income levels, while reaping some of the benefits of reduced taxes on capital income.

Abstract: The Energy Modeling Forum (EMF) 32 study on carbon tax scenarios analyzed a set of illustrative policies in the United States that place an economy-wide tax on fossil-fuel-related carbon dioxide (CO2) emissions, a carbon tax for short. Eleven modeling teams ran these stylized scenarios, which vary by the initial carbon tax rate, the rate at which the tax escalates over time, and the use of the revenues. Modelers reported their results for the effects of the policies, relative to a reference scenario that does not include a carbon tax, on emissions, economic activity, and outcomes within the U.S. energy system. This paper explains the scenario design, presents an overview of the results, and compares results from the participating models. In particular, we compare various outcomes across the models, such as emissions, revenue, gross domestic product, sectoral impacts, and welfare.

From the Conclusion: In short, the results here are consistent with much of the existing modeling literature on carbon pricing in the United States. Across all models, we find that the core carbon price scenarios lead to significant reductions in CO2 emissions, with the vast majority of the reductions occurring in the electricity sector and disproportionately through reductions in coal. Emissions reductions are largely independent of the uses of the revenues modeled here. Expected economic costs (not accounting for any of the benefits of GHG and conventional pollutant mitigation), in terms of either GDP or welfare, are modest, but they vary across models and policies. Using revenues to reduce preexisting capital or, to a lesser extent labor taxes, reduces welfare losses in most models relative to providing household rebates, but the magnitudes of the cost savings vary.

As the world's largest consumer of total primary energy and energy from coal, and the largest emitter of carbon dioxide (CO2), China is now taking an active role in controlling CO2 emissions. Given current coal use in China, and the urgent need to cut emissions, ‘clean coal’ technologies are regarded as a promising solution for China to meet its carbon reduction targets while still obtaining a considerable share of energy from coal. Using an economy-wide model, this paper evaluates the impact of two existing advanced coal technologies – coal upgrading and ultra-supercritical (USC) coal power generation – on economic, energy and emissions outcomes when a carbon price is used to meet China's CO2 intensity target out to 2035. Additional deployment of USC coal power generation lowers the carbon price required to meet the CO2 intensity target by more than 40% in the near term and by 25% in the longer term. It also increases total coal power generation and coal use. Increasing the share of coal that is upgraded leads to only a small decrease in the carbon price. As China's CO2 intensity is set exogenously, additional deployment of the two technologies has a small impact on total CO2 emissions.

Amid much uncertainty about the future of the global climate and efforts aimed at preventing its most damaging impacts, graduate students affiliated with the MIT Joint Program on the Science and Policy of Global Change are hard at work exploring some of the challenges and possible solutions that lie ahead. They are also sharing their knowledge with the MIT community.

Short-term variability and long-term change in climate pose a challenge to water planners. Some climate uncertainties can be reduced over time as new information is collected, while others are irreducible. This presentation shows how flexible water-supply infrastructure planning can help mitigate climate risk at lower cost, especially for uncertainties with high learning potential

MIT Joint Program-affiliated researcher and Institute for Data, Systems, and Society (IDSS) PhD student Sarah Fletcher has won a 2017 American Geophysical Union (AGU) Outstanding Student Paper Award for her paper, “Urban water supply infrastructure planning under predictive groundwater uncertainty: Bayesian updating and flexible design.” Granted to the top five percent of student participants, the award inc

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