Regional Analysis

This thesis explores the evolution of the electric power sector in New England under the expansion of transmission capacity and under policy with increasing Clean Energy Standards (CES). I use EleMod, a Capacity Expansion Planning model, to compare (1) the reference case of current transmission assets, (2) increasing transmission interface capacities within New England, (3) increasing interconnection capacities with Canada, and (4) both capacity expansions. Transmission expansion allows electricity trade between states and enables them to take advantage of localized, intermittent resources like wind power. Increasing the interconnection capacity with Canada allows the system to optimally allocate the available hydropower energy for imports in the hours of highest net demand. Both transmission expansions together make even stronger use of their contributions.

For the capacity expansion model, I choose a set of generation technologies available in New England, and supply cost and operational data from public domain sources. My contributions to EleMod include: (1) the representation of transmission interfaces for New England; (2) the addition of an CES policy standard forcing generation shares from a subset of CES-eligible resources; (3) the modeling of an external hydro reservoir resource in Canada that can be used to supply the load in New England based on cross-border interconnection constraints and the total available energy per year; and (4) the detailed state-level representation of the New England power sector with generation technologies, installed capacities, transmission interface capacities, and CES targets.

Policy scenarios increase CES from an average of 25% in 2018 in the base scenario to 95% in 2050 in the decarbonization scenario. Through all policy scenarios, combined-cycle gas plants (GasCC) with carbon capture and storage (CCS) technology dominate the capacity expansions. Increases in transmission capacity lead to higher shares of wind in generation, especially when both transmission and interconnection are expanded. Natural gas, in the form of GasCC with and without CCS, takes shares of the generation mix of up to 85% by 2050. Thus, I also assess the role of pipeline capacities into New England. Because other natural gas uses like residential heating demand have priority over generators, gas-fired power plants cannot expect to meet all their demand during critical periods of shortage in the winter. However, this issue is part of a larger integrated resource planning process.

Both transmission and interconnection expansion reduce total system costs by an annual 3.95% and 4.29%, respectively. Because transmission costs are not included in the model, I separately assess the costs and benefits of both transmission expansion scenarios. Transmission expansions from Maine to Massachusetts of 2,000 MW and interconnection expansions to Canada of 3,000 MW and 4,500 MW from Maine and Vermont, respectively, allow for optimal allocation of flows across lines in over 90% of the hours. For interconnection, the calculation estimates costs to be about 1% higher than the benefits, and for transmission within the region the benefits exceed the costs by about 40%.

China's rapid economic growth in the twenty-first century has driven, and been driven by, concomitant motorization and growth of passenger and freight mobility, leading to greater energy demand and environmental impacts. In this dissertation I develop methods to characterize the evolution of passenger transport demand in a rapidly-developing country, in order to support projection and policy assessment.

In Essay #1, I study the role that vehicle tailpipe and fuel quality standards (“emissions standards”) can play vis-a-vis economy-wide carbon pricing in reducing emissions of pollutants that lead to poor air quality. I extend a global, computable general equilibrium (CGE) model resolving 30 Chinese provinces by separating freight and passenger transport subsectors, road and non-road modes, and household-owned vehicles; and then linking energy demand in these subsectors to a province-level inventory of primary pollutant emissions and future policy targets. While climate policy yields an air quality co-benefit by inducing shifts away from dirtier fuels, this effect is weak within the transport sector. Current emissions standards can drastically reduce transportation emissions, but their overall impact is limited by transport's share in total emissions, which varies across provinces. I conclude that the two categories of measures examined are complementary, and the effectiveness of emissions standards relies on enforcement in removing older, higher-polluting vehicles from the roads.

In Essay #2, I characterize Chinese households' demand for transport by estimating the recently-developed, Exact affine Stone index (EASI) demand system on publicly-available data from non-governmental, social surveys. Flexible, EASI demands are particularly useful in China's rapidly-changing economy and transport system, because they capture ways that income elasticities of demand, and household transport budgets, vary with incomes; with population and road network densities; and with the supply of alternative transport modes. I find transport demand to be highly elastic (ϵx = 1.46) at low incomes, and that income-elasticity of demand declines but remains greater than unity as incomes rise, so that the share of transport in households' spending rises monotonically from 1.6% to 7.5%; a wider, yet lower range than in some previous estimates. While no strong effects of city-level factors are identified, these and other non-income effects account for a larger portion of budget share changes than rising incomes.

Finally, in Essay #3, I evaluate the predictive performance of the EASI demand system, by testing the sensitivity of model fit to the data available for estimation, in comparison with the less flexible, but widely used, Almost Ideal demand system (AIDS). In rapidly-evolving countries such as China, survey data without nationwide coverage can be used to characterize transport systems, but the omission of cities and provinces could bias results. To examine this possibility, I estimate demand systems on data subsets and test their predictions against observations for the withheld fraction. I find that simple EASI specifications slightly outperform AIDS under cross-validation; these offer a ready replacement in standalone and CGE applications. However, a trade-off exists between accuracy and the inclusion of policy-relevant covariates when data omit areas with high values of these variables. Also, while province-level fixed-effects control for unobserved heterogeneity across units that may bias parameter estimates, they increase prediction error in out-of-sample applications—revealing that the influence of local conditions on household transport expenditure varies significantly across China's provinces. The results motivate targeted transport data collection that better spans variation on city types and attributes; and the validation technique aids transport modelers in designing and validating demand specifications for projection and assessment.

ABSTRACT: Chloroform contributes to the depletion of the stratospheric ozone layer. However, due to its short lifetime and predominantly natural sources, it is not included in the Montreal Protocol that regulates the production and uses of ozone-depleting substances. Atmospheric chloroform mole fractions were relatively stable or slowly decreased during 1990–2010. Here we show that global chloroform mole fractions increased after 2010, based on in situ chloroform measurements at seven stations around the world. We estimate that the global chloroform emissions grew at the rate of 3.5% yr−1 between 2010 and 2015 based on atmospheric model simulations. We used two regional inverse modelling approaches, combined with observations from East Asia, to show that emissions from eastern China grew by 49 (41–59) Gg between 2010 and 2015, a change that could explain the entire increase in global emissions. We suggest that if chloroform emissions continuously grow at the current rate, the recovery of the stratospheric ozone layer above Antarctica could be delayed by several years.

Jennifer Chu | MIT News Office 
December 20, 2018

Earlier this year, the United Nations announced some much-needed, positive news about the environment: The ozone layer, which shields the Earth from the sun’s harmful ultraviolet radiation, and which was severely depleted by decades of human-derived, ozone-destroying chemicals, is on the road to recovery.

Atmospheric nitrous oxide (N2O) significantly impacts Earth’s climate due to its dual role as an inert potent greenhouse gas in the troposphere and as a reactive source of ozone-destroying nitrogen oxides in the stratosphere. Global atmospheric concentrations of N2O, produced by natural and anthropogenic processes, continue to rise due to increases in emissions linked to human activity. The understanding of the impact of this gas is incomplete as there remain significant uncertainties in its global budget. The experiment described in this thesis, in which a global chemical transport model (MOZART-4), a fine-scale regional Lagrangian model (NAME), and new high-frequency atmospheric observations are combined, shows that uncertainty in N2O emissions estimates can be reduced in areas with continuous monitoring of N2O mole fraction and site-specific isotopic ratios.

Due to unique heavy-atom (15N and 18O) isotopic substitutions made by different N2O sources, the measurement of N2O isotopic ratios in ambient air can help identify the distribution and magnitude of distinct sources. The new Stheno-TILDAS continuous wave laser spectroscopy instrument developed at MIT, recently installed at the Mace Head Atmospheric Research Station in western Ireland, can produce high-frequency timelines of atmospheric N2O isotopic ratios that can be compared to contemporaneous trends in correlative trace gas mole fractions and NAME-based statistical distributions of the origin of air sampled at the station. This combination leads to apportionment of the relative contribution from five major N2O sectors in the European region (agriculture, oceans, natural soils, industry, and biomass burning) plus well-mixed air transported from long distances to the atmospheric N2O measured at Mace Head.

Bayesian inverse modeling methods that compare N2O mole fraction and isotopic ratio observations at Mace Head and at Dübendorf, Switzerland to simulated conditions produced using NAME and MOZART-4 lead to an optimized set of source-specific N2O emissions estimates in the NAME Europe domain. Notably, this inverse modeling experiment leads to a significant decrease in uncertainty in summertime emissions for the four largest sectors in Europe, and shows that industrial and agricultural N2O emissions in Europe are underestimated in inventories such as EDGAR v4.3.2. This experiment sets up future work that will be able to help constrain global estimates of N2O emissions once additional isotopic observations are made in other global locations and integrated into the NAME-MOZART inverse modeling framework described in this thesis.

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