Regional Analysis

The Future of Coal in China

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.

With a single executive order issued at the end of March, the Trump administration launched a robust effort to roll back Obama-era climate policies designed to reduce U.S. carbon dioxide (CO2) emissions. Chief among those policies is the Clean Power Plan, which targets coal and natural gas-fired electric power plants that account for about 40 percent of the nation’s CO2 emissions.

Clean power planning

Accounting for nearly one-third of the global land surface, forests help regulate the climate and protect watersheds while providing consumer products and outdoor experiences that enhance the quality of life. Climate change will inevitably influence forests’ ability to deliver these services, but past studies have provided a limited picture of what changes may come this century. Now researchers from the Corvallis Forestry Sciences Laboratory, MIT, Ohio State University and the U.S.

The future of forests under climate change
Hedging Strategies: Electricity Investment Decisions under Policy Uncertainty

Given uncertainty in long-term carbon reduction goals, how much non-carbon generation should be developed in the near-term? This research investigates the optimal balance between the risk of overinvesting in non-carbon sources that are ultimately not needed and the risk of underinvesting in non-carbon sources and subsequently needing to reduce carbon emissions dramatically. We employ a novel framework that incorporates a computable general equilibrium (CGE) model of the U.S. into a two-stage stochastic approximate dynamic program (ADP) focused on decisions in the electric power sector. We solve the model using an ADP algorithm that is computationally tractable while exploring the decisions and sampling the uncertain carbon limits from continuous distributions.

The results of the model demonstrate that an optimal hedge is in the direction of more non-carbon investment in the near-term, in the range of 20-30% of new generation. We also demonstrate that the optimal share of non-carbon generation is increasing in the variance of the uncertainty about the long-term carbon targets, and that with greater uncertainty in the future policy regime, a balanced portfolio of non-carbon, natural gas, and coal generation is desirable.

Twenty-First-Century Changes in U.S. Regional Heavy Precipitation Frequency Based on Resolved Atmospheric Patterns

Precipitation-gauge observations and atmospheric reanalysis are combined to develop an analogue method for detecting heavy precipitation events based on prevailing large-scale atmospheric conditions. Combinations of atmospheric variables for circulation (geopotential height and wind vector) and moisture (surface specific humidity, column and up to 500-hPa precipitable water) are examined to construct analogue schemes for the winter [December–February (DJF)] of the “Pacific Coast California” (PCCA) region and the summer [June–August (JJA)] of the Midwestern United States (MWST). The detection diagnostics of analogue schemes are calibrated with 1979–2005 and validated with 2006–14 NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA). All analogue schemes are found to significantly improve upon MERRA precipitation in characterizing the occurrence and interannual variations of observed heavy precipitation events in the MWST. When evaluated with the late twentieth-century climate model simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5), all analogue schemes produce model medians of heavy precipitation frequency that are more consistent with observations and have smaller intermodel discrepancies than model-based precipitation. Under the representative concentration pathways (RCP) 4.5 and 8.5 scenarios, the CMIP5-based analogue schemes produce trends in heavy precipitation occurrence through the twenty-first century that are consistent with model-based precipitation, but with smaller intermodel disparity. The median trends in heavy precipitation frequency are positive for DJF over PCCA but are slightly negative for JJA over MWST. Overall, the analyses highlight the potential of the analogue as a powerful diagnostic tool for model deficiencies and its complementarity to an evaluation of heavy precipitation frequency based on model precipitation alone.

Pages

Subscribe to Regional Analysis