The electric power sector, which accounts for approximately 40% of U.S. carbon dioxide emissions, will be a critical component of any policy the U.S. government pursues to confront climate change. In the context of uncertainty in future policy limiting emissions and future technology costs, society faces the following question: What should the electricity mix we build in the next decade look like? We can continue to focus on conventional generation or invest in low-carbon technologies. There is no obvious answer without explicitly considering the risks created by uncertainty.

This research investigates socially optimal near-term electricity investment decisions under uncertainty in future policy and technology costs. It employs a novel framework that models decision-making under uncertainty with learning in an economy-wide setting that can measure social welfare impacts. Specifically, a computable general equilibrium (CGE) model is formulated as a two-stage stochastic dynamic program focused on decisions in the electric power sector.

The new model is applied to investigate a number of factors affecting optimal near-term electricity investments: (1) policy uncertainty, (2) expansion rate limits on low-carbon generation, (3) low-carbon technology cost uncertainty, (4) technological learning (i.e., near-term investment lowers the expected future technology cost), and (5) the inclusion of a safety valve in future policy which allows the emissions cap to be exceeded, but at a cost.

In modeling decision-making under uncertainty, an optimal electricity investment hedging strategy is identified. Given the experimental design, the optimal hedging strategy reduces the expected policy costs by over 50% compared to a strategy derived using the expected value for the uncertain parameter; and by 12-400% compared to strategies developed under a perfect foresight or myopic framework.

This research also shows that uncertainty has a cost, beyond the cost of meeting a policy. In the experimental design used here, uncertainty in the future policy increases the expected cost of policy by over 45%. If political consensus can be reached and the climate science uncertainties resolved, setting clear, long-term policies can minimize expected policy costs.

In addition, this work contributes to the learning-by-doing literature by presenting a stochastic formulation of technological learning in which near-term investments in a technology affect the probability distribution of the future cost of that technology. Results using this formulation demonstrate that learning rates lower than those found in the literature can lead to significant additional near-term investment in low-carbon technology in order to lower the expected future cost of the technology in case a stringent policy is adopted.

Ultimately, this dissertation demonstrates that near-term investments in low-carbon technologies should be greater than what would be justified to meet near-term goals alone. Near-term low-carbon investments can lower the expected cost of future policy by developing a less carbon-intensive electricity mix, spreading the burden of emissions reductions over time, helping to overcome technology expansion rate constraints, and reducing the expected future cost of low-carbon technologies—all of which provide future flexibility in meeting a policy. The additional near-term cost of low-carbon investments is justified by the future flexibility that such investments create. The value of this flexibility is only explicitly considered in the context of decision-making under uncertainty.

To control rising energy use and CO₂ emissions, China׳s leadership has enacted energy and CO₂ intensity targets as part of the Twelfth Five-Year Plan (the Twelfth FYP, 2011–2015). Both to support achievement of these targets and to lay the foundation for a future national market-based climate policy, at the end of 2011, China׳s government selected seven areas to establish pilot emissions trading systems (ETS). In this paper, we provide a comprehensive overview of current status of China׳s seven ETS pilots. Pilots differ in the extent of sectoral coverage, the size threshold for qualifying installations, and other design features that reflect diverse settings and priorities. By comparing the development of the ETS pilots, we identify issues that have emerged in the design process, and outline important next steps for the development of a national ETS.

© 2014 Elsevier Ltd

Decoupling fossil energy demand from economic growth is crucial for China's sustainable development, especially for addressing severe local air pollution and global climate change. An absolute cap on coal or fossil fuel consumption has been proposed as a means to support the country's energy and climate policy objectives. We evaluate potential energy cap designs that differ in terms of target fuel, point of control, and national versus regional allowance trading using a global numerical general equilibrium model that separately represents 30 provinces in China. First, we simulate a coal cap and find that relative to a cap on all fossil fuels, it is significantly more costly and results in high localized welfare losses. Second, we compare fossil energy cap designs and find that a national cap on downstream fossil energy use with allowance trading across provinces is the most cost effective. Third, we find that a national fossil energy cap with trading is nearly as cost effective as a national CO₂ emissions trading system that penalizes energy use based on carbon content. As a fossil energy cap builds on existing institutions in China, it offers a viable intermediate step toward a full-fledged CO₂ emissions trading system.

Black carbon (BC) is an important aerosol constituent in the atmosphere and climate forcer. A good understanding of the radiative forcing of BC and associated climate feedback and response is critical to minimize the uncertainty in predicting current and future climate influenced by anthropogenic aerosols. One reason for this uncertainty is that current emission inventories of BC are mostly obtained from the so-called bottom-up approach, an approach that derives emissions based on categorized emitting sources and emission factors used to convert burning mass to emissions. In this work, we provide a first global-scale top-down estimation of global BC emissions, as well as an estimated error range, by using a Kalman Filter. This method uses data of both column aerosol absorption optical depth and surface concentrations from global and regional networks to constrain our fully coupled climate-aerosol-urban model and thus to derive an optimized estimate of BC emissions as 17.8 ± 5.6 Tg/yr, a factor of more than 2 higher than commonly used global BC emissions data sets. We further perform 22 additional optimization simulations that incorporate the known uncertain ranges of various important physical, model, and measurement parameters and still yield an optimized value within the above given range, from a low of 14.6 Tg/yr to a high of 22.2 Tg/yr. Furthermore, we show that the emissions difference between our optimized and a priori estimation is not uniform, with East Asia, Southeast Asia, and Eastern Europe underestimated, while North America is overestimated in the a priori inventory.

© 2014 the authors