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The Future of Solar Energy: An Interdisciplinary MIT Study

Solar electricity generation is one of very few low-carbon energy technologies with the potential to grow to very large scale. As a consequence, massive expansion of global solar generating capacity to multi-terawatt scale is very likely an essential component of a workable strategy to mitigate climate change risk. Recent years have seen rapid growth in installed solar generating capacity, great improvements in technology, price, and performance, and the development of creative business models that have spurred investment in residential solar systems. Nonetheless, further advances are needed to enable a dramatic increase in the solar contribution at socially acceptable costs. Achieving this role for solar energy will ultimately require that solar technologies become cost-competitive with fossil generation, appropriately penalized for carbon dioxide (CO2) emissions, with — most likely — substantially reduced subsidies.

This study examines the current state of U.S. solar electricity generation, the several technological approaches that have been and could be followed to convert sunlight to electricity, and the market and policy environments the solar industry has faced. Our objective is to assess solar energy’s current and potential competitive position and to identify changes in U.S. government policies that could more efficiently and effectively support the industry’s robust, long-term growth.

We focus in particular on three preeminent challenges for solar generation: reducing the cost of installed solar capacity, ensuring the availability of technologies that can support expansion to very large scale at low cost, and easing the integration of solar generation into existing electric systems. Progress on these fronts will contribute to greenhouse-gas reduction efforts, not only in the United States but also in other nations with developed electric systems. It will also help bring light and power to the more than one billion people worldwide who now live without access to electricity.

The MIT EPPA6 Model: Economic Growth, Energy Use, and Food Consumption

The MIT Economic Projection and Policy Analysis (EPPA) model has been broadly applied on energy and climate policy analyses. In this paper, we provide an updated version of the model based on the most recent global economic database with the base year data of 2007. Also new in this version of the model are non-homothetic preferences, a revised capital vintaging structure, separate accounting of residences, and an improved model structure that smooths its functioning and makes future extensions easier. The study finds that, as the economies grow, the empirically observed income elasticities of demand are better represented by our setting than by a pure Stone-Geary approach, and simulation results are more sensitive to GDP growth than energy and non-energy substitution elasticities and autonomous energy efficiency improvement.

Revised October 2015.

The MIT EPPA6 Model: Economic Growth, Energy Use, Emissions, and Food Consumption

The MIT Emissions Prediction and Policy Analysis (EPPA) model has been broadly applied on energy and climate policy analyses. In this paper, we present our newest model: EPPA6-L. Besides adopting the GTAP8 database as the core economic data, EPPA6-L incorporates the latest energy, emissions, and cost estimates from existing studies, and enhances the model structure and implementation to facilitate future extension. With these improvements, the projected business-as-usual CO2 emissions in 2100 are lowered by 6.3% compared to the EPPA5 number. We also present how projections for the consumption of crops, livestock, and food products are improved with non-homothetic preference, and how various assumptions for business-as-usual GDP growth, elasticity of substitution between energy and non-energy input, and autonomous energy efficiency improvement may change CO2 emissions and prices.

21st Century Changes in Precipitation Extremes Over the United States: Can Climate Analogues Help or Hinder?

Global warming is expected to alter the frequency and/or magnitude of extreme precipitation events. Such changes could have substantial ecological, economic, and sociological consequences. However, climate models in general do not correctly reproduce the frequency and intensity distribution of precipitation, especially at the regional scale. In this study, gridded data from a dense network of surface precipitation gauges and a global atmospheric analysis at a coarser scale are combined to develop a diagnostic framework for the large-scale meteorological conditions (i.e. flow features, moisture supply) that dominate during extreme precipitation. Such diagnostic framework is first evaluated with the late 20th century simulations from an ensemble of climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5), and is found to produce more consistent (and less uncertain) total and interannaul number of extreme days with the observations than the model-based precipitation over the south-central United States and the Western United States examined in this study. The framework is then applied to the CMIP5 multi-model projections for two radiative forcing scenarios (Representative Concentration Pathways 4.5 and 8.5) to assess the potential future changes in the probability of precipitation extremes over the same study regions. We further analyze the accompanying circulation features and their changes that may be responsible for shifts in extreme precipitation in response to changed climate. The results from this study may guide hazardous weather watches and help society develop adaptive strategies for preventing catastrophic losses.

21st Century Changes in U.S. Heavy Precipitation Frequency Based on Resolved Atmospheric Patterns

Gridded precipitation-gauge observations and global atmospheric reanalysis are combined to develop an analogue method for detecting the occurrence of heavy precipitation events based on the prevailing large-scale atmospheric conditions. Combinations of different atmospheric variables for circulation features (geopotential height and wind vector) and moisture plumes (surface specific humidity, column precipitable water, and precipitable water up to 500hPa) are examined to construct the analogue schemes for the winter (DJF) of the Pacific Coast California (PCCA) and the summer (JJA) of the Midwestern United States (MWST). The detection diagnostics of various analogue schemes are calibrated with 27-yr (1979–2005) and then validated with 9-yr (2006–2014) NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA). All of the analogue schemes are found to significantly improve upon MERRA precipitation in characterizing the number and interannual variations of observed heavy precipitation events in the MWST which is one of weakest regions for MERRA summer precipitation. When evaluated with the late 20th century simulations from an ensemble of climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5), all analogue schemes produce model medians of heavy precipitation frequency that are more consistent with observations and have smaller inter-model discrepancies when compared with the model-based precipitation. Further, the performances of analogue schemes with vector winds are comparable to those of geopotential height, and no analogue scheme with one of three water vapor content variables is clearly superior to another. Under two radiative forcing scenarios (Representative Concentration Pathways 4.5 and 8.5), the CMIP5-based analogue schemes produce a trend in the occurrence of heavy events through the 21st century consistent with the model-based precipitation, but with smaller inter-model disparity. The strongest reduction in the disparity of the results is seen for the RCP8.5 scenario. The median trends in DJF heavy precipitation frequency for PCCA are positive, but for JJA heavy event frequency over the MWST region, the median trends are slightly negative. Overall, the presented analyses highlight the potential of the analogue as a powerful diagnostic tool for model deficiencies and its complementarity to an evaluation that considers modeled precipitation alone to assess heavy precipitation frequency. The consistency found here between projections from analogues and model precipitation increases confidence in projected heavy precipitation frequency changes in a warming climate.

A Drought Indicator based on Ecosystem Responses to Water Availability: The Normalized Ecosystem Drought Index

Drought is one of the most destructive natural disasters causing serious damages to human society, and studies have projected more severe and widespread droughts in the coming decades associated with the warming climate. Although several drought indices have been developed for drought monitoring, most of them were based on large scale environmental conditions rather than ecosystem transitional patterns to drought. Here, we propose using the ecosystem function oriented Normalized Ecosystem Drought Index (NEDI) to quantify drought severity, loosely related to Sprengel’s and Liebig’s Law of the Minimum for plant nutrition. Extensive eddy covariance measurements from 60 AmeriFlux sites across 8 IGBP vegetation types were used to validate the use of NEDI. The results show that NEDI can reasonably capture ecosystem transitional responses to limited water availability, suggesting that drought conditions detected by NEDI are ecosystem function oriented. The wildly used Palmer Drought Severity Index (PDSI), on the other hand, does not have a clear relationship with ecosystem responses to drought conditions because ecosystem adaptation ability is not considered in PDSI calculation.

Agriculture, forestry, and other land-use emissions in Latin America

Nearly 40% of greenhouse gas (GHG) emissions in Latin America were from agriculture, forestry, and other land use (AFOLU) in 2008, more than double the global fraction of AFOLU emissions. In this article, we investigate the future trajectory of AFOLU GHG emissions in Latin America, with and without efforts to mitigate, using a multi-model comparison approach. We find significant uncertainty in future emissions with and without climate policy. This uncertainty is due to differences in a variety of assumptions including (1) the role of bioenergy, (2) where and how bioenergy is produced, (3) the availability of afforestation options in climate mitigation policy, and (4) N2O and CH4 emission intensity. With climate policy, these differences in assumptions can lead to significant variance in mitigation potential, with three models indicating reductions in AFOLU GHG emissions and one model indicating modest increases in AFOLU GHG emissions.

Air Quality Co-Benefits of Sub-National Carbon Policies

To mitigate climate change, governments ranging from city to multi-national have adopted greenhouse gas (GHG) emissions reduction targets. While the location of GHG reductions does not affect their climate benefits, it can impact human health benefits associated with co-emitted pollutants. Here, an advanced modeling framework is used to explore how subnational level GHG targets influence air pollutant co-benefits from ground level ozone and fine particulate matter. Two carbon policy scenarios are analyzed, each reducing the same total amount of GHG emissions in the Northeast US: an economy-wide Cap and Trade (CAT) program reducing emissions from all sectors of the economy, and a Clean Energy Standard (CES) reducing emissions from the electricity sector only. Results suggest that a regional CES policy will cost about 10 times more than a CAT policy. Despite having the same regional targets in the Northeast, carbon leakage to non-capped regions varies between policies. Consequently, a regional CAT policy will result in national carbon reductions that are over six times greater than the carbon reduced by the CES in 2030. Monetized regional human health benefits of the CAT and CES policies are 844% and 185% of the costs of each policy, respectively. Benefits for both policies are thus estimated to exceed their costs in the Northeast US. The estimated value of human health co-benefits associated with air pollution reductions for the CES scenario is two times that of the CAT scenario.

Air Quality Impacts and Benefits under U.S. Policy for Air Pollution, Climate Change, and Clean Energy

Policies that reduce greenhouse gas emissions can also reduce outdoor levels of air pollutants that harm human health by targeting the same emissions sources. However, the design and scale of these policies can affect the distribution and size of air quality impacts, i.e. who gains from pollution reductions and by how much. Traditional air quality impact analysis seeks to address these questions by estimating pollution changes with regional chemical transport models, then applying economic valuations directly to estimates of reduced health risks. In this dissertation, I incorporate and build on this approach by representing the effect of pollution reductions across regions and income groups within a model of the energy system and economy. This new modeling framework represents how climate change and clean energy policy affect pollutant emissions throughout the economy, and how these emissions then affect human health and economic welfare. This methodology allows this thesis to explore the effect of policy design on the distribution of air quality impacts across regions and income groups in three studies. The first study compares air pollutant emissions under state-level carbon emission limits with regional or national implementation, as proposed in the U.S. EPA Clean Power Plan. It finds that the flexible regional and national implementations lower the costs of compliance more than they adversely affect pollutant emissions. The second study compares the costs and air quality co-benefits of two types of national carbon policy: an energy sector policy, and an economy-wide cap-and-trade program. It finds that air quality impacts can completely offset the costs of a cost-effective carbon policy, primarily through gains in the eastern United States. The final study extends the modeling framework to be able to examine the impacts of ozone policy with household income. It finds that inequality in exposure makes ozone reductions relatively more valuable for low income households. As a whole, this work contributes to literature connecting actions to impacts, and identifies an ongoing need to improve our understanding of the connection between economic activity, policy actions, and pollutant emissions.

An analysis of China's climate policy using the China-in-Global Energy Model

The primary approach to address climate change in China has been the use of CO2 intensity targets coupled with targets for low carbon energy deployment. We evaluate the impact of extending similar targets through 2050 on China's energy use profile and CO2 emissions trajectory using the China-in-Global Energy Model (C-GEM). The C-GEM is a global computable equilibrium model that includes energy and economic data provided by China's statistical agencies, calibration of savings, labor productivity, and capital productivity dynamics specific to China's stage of development, and regional aggregation that resolves China's major trading partners. We analyze the combined impact of extending CO2 intensity targets, implemented via a cap-and-trade program, and low carbon energy policies (directives for nuclear power expansion and feed-in tariffs for wind, solar, and biomass energy) through 2050. Although with the policy, simulated CO2 emissions are around 43% lower in 2050 relative to a reference (No Policy) counterfactual, China's CO2 emissions still increase by over 60% between 2010 and 2050. Curbing the rise in China's CO2 emissions will require fully implementing a CO2 price, which will need to rise to levels higher than $25/ton in order to achieve China's stated goal of peaking CO2 emissions by 2030.

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