Abstract: The shale gas boom in the US is widely seen as responsible for reducing US CO2 emissions through substitution of gas for coal in power generation. The story is more complex because increased gas use in other sectors may not be displacing carbon-intensive fuels, but rather reducing incentives to adopt more efficient processes and less carbon-intensive products. In this paper we consider the emissions implications for the U.S. under a counterfactual modeling of the 2011 US economy without the shale gas boom. We apply a general equilibrium model of the 2011 US economy, estimating the supply responses of coal-fired and gas-fired generations based on U.S. state-level data. We find that under the counterfactual, the higher gas price has a dampening effect on economic activities and consequently lowers non-power sectors’ emissions. As many have observed, absent a full economy-wide model, power sector emissions increase because of gas-to-coal switch as a result of higher gas prices. However, we find across a wide range of model settings that if gas prices would have remained at 2007 levels in 2011, economy-wide emissions would have been lower. Only a model setting that allowed very little reduction in electricity demand, reflecting a short-run demand response, generated an increase in economy-wide emissions. In other words, the shale gas boom likely led to higher emissions except possibly in the very short run, and in all cases in the long run if the low gas prices persist.

Summary: This study examines how climate change impacts and global mitigation policies relate to the economic interests of developing countries to 2050. Focusing on Malawi, Mozambique and Zambia, the co-authors apply a biophysical and economic modeling approach that incorporates climate uncertainty and allows for rigorous comparison of climate, biophysical and economic outcomes across a wide range of global mitigation policy scenarios.

The researchers find that effective global mitigation policies generate two key benefits for these nations: (1) more favorable and less variable economic outcomes due to mitigation of climate impacts, and (2) reduced global fossil fuel production prices, relative to an unconstrained emissions scenario, leading to significantly reduced fuel import costs. Combined, these economic benefits exceed projected mitigation costs for each country. These results show that for most energy-importing developing countries, global mitigation policies are advantageous even in the relatively near term, with much larger benefits accruing after 2050.

Abstract: Water supply infrastructure planning faces many uncertainties. Uncertainty in short-term in rainfall and runoff, groundwater storage, and long-term climate change impacts water supply forecasts. Population and economic growth drive urban water demand growth at rapid but uncertain rates. Overbuilding infrastructure can lead to expensive stranded assets and unnecessary environmental impacts, while under building can cause reliability outages with impacts on the economy, ecosystems, and human health. This dissertation assesses the potential for Bayesian learning about uncertainty to enable flexible, adaptive approaches in which infrastructure can be changed over time to reduce cost risk while achieving reliability targets. It develops a novel planning framework that: 1) classifies uncertainties and applies appropriate, differentiated uncertainty analysis tools, 2) applies Bayesian inference to physical models of hydrology and climate to develop dynamic uncertainty estimates, and 3) uses stochastic dynamic programming and engineering options analysis to assess the value of flexibility in mitigating cost and reliability risk. This framework is applied to three applications. Chapter 3 evaluates the potential for modular desalination design to manage multiple, diverse uncertainties — streamflow, demand growth, and the cost of water shortages — in Melbourne, Australia. Chapter 4 addresses uncertainty in groundwater resources in desalination planning in Riyadh, Saudi Arabia, and Chapter 5 addresses model uncertainty in climate change projections in a dam design problem in Mombasa, Kenya. Across all three applications, we find value in flexible infrastructure planning with a 9–28% reduction in expected cost. However, the performance of flexible approaches compared to traditional robust approaches varies considerably and is influenced by technology choice, economies of scale, discounting, the presence of irreducible stochastic variability, and the value society places on water reliability.

The latest round of United Nations climate talks in Poland in December 2019 sought to get the world on track to meet the Paris Agreement’s long-term goal of keeping global warming well below two degrees Celsius (2ºC). Toward that end, negotiators from the Agreement’s nearly 200 signatory nations were asked to report on their country or region’s progress toward fulfilling its Paris pledge, or Nationally Determined Contributions (NDC). But just how accurate were those progress reports? That depends on the integrity of the underlying greenhouse gas emissions data that each country used to assess its performance toward meeting the emissions reduction targets spelled out in its NDC. The measurement, reporting and verification (MRV) of a country’s overall emissions and emissions reductions involves culling and validating emissions data from multiple sources, including firms—industrial, nonprofit and government entities—in different economic sectors. Building reliable firm-based systems for emissions MRV is no easy task, especially in developing countries where misreporting of environmental data can be significant, but a new MIT-led study in Nature Climate Change identifies challenges and opportunities to achieve that goal.

Co-authored by researchers at MIT, Tsinghua University and Wuhan University, the study focuses on China, the world’s largest carbon dioxide (CO₂) emitter. China’s climate-change mitigation strategy centers on a national emissions trading system (ETS) whose success depends upon accurate emissions reporting at the firm level. Using data obtained from two of China’s pilot regional ETS programs, one in Beijing, a highly developed major city, the other in Hubei, a less developed province, the researchers compared firms’ self-reported CO₂ emissions numbers with those verified by independent third parties. The average discrepancy in these numbers decreased significantly in Beijing, going from 17 percent in 2012 to 4 percent in 2014 and 2015 for approximately 400 firms. In Hubei, which launched its system one year later, the number of discrepancies started lower and showed a statistically-insignificant decrease (from 6% in 2014 to 5% in 2015).

The study emphasized that building effective MRV systems at firms in China and other developing countries takes time, resources and attention to detail. Among its recommendations to increase reporting accuracy and prevent manipulation or collusion is to provide external funding from governments or multilateral entities, at least in early years, to pay the independent verifiers. If firms pay for verification, government back-checks are essential to ensure reporting integrity. The study also maintains that strong law enforcement will be necessary to punish any detected incidents of collusion between verifiers and firms.