Analysis of the Coal Sector under Carbon Constraints

Abstract in HTML and technical report in PDF available on the MIT Joint Program on the Science and Policy of Global Change website (http://mit.edu/globalchange/www/).Application of the MIT Emissions Prediction and Policy Analysis (EPPA) model to assessment of the future of coal under climate policy revealed the need for an improved representation of load dispatch in the representation of the electric sector. A new dispatching algorithm is described and the revised model is applied to an analysis of the future of coal use to 2050 and 2100 under alternative assumptions about CO2 prices, nuclear expansion and prices of natural gas. Particular attention is devoted to the potential role of coal-electric generation with CO2 capture and storage. An appendix provides a comparison of a subset of these results with and without the more detailed model of electric dispatch.Development of the analysis model used in this research was supported by the U.S. Department of Energy, Office of Biological and Environmental Research [BER] (DE-FG02-94ER61937), by the U.S. Environmental Protection Agency (XA-83042801-0), the Electric Power Institute, and by a consortium of industry and foundation sponsors

Land-Based Learning: A Learning Paradigm for Building Community and Sustainable Farms

Mitigating complex problems is increasingly essential to sustaining life on Earth. Empowering current and future generations to address these problems requires rethinking traditional education approaches. This article serves as a primer for land-based learning—defined as a pedagogical approach in which learners collaborate with community members to implement place-based interventions within agricultural systems to increase the sustainability of their community. As an introduction to land-based learning, the article (a) describes critical checkpoints within land-based learning, (b) illuminates the role of Extension educators in facilitating land-based learning, and (c) introduces a case study of land-based learning in Michigan\u27s Upper Peninsula

Technology and Technical Change in the MIT EPPA Model

Abstract in HTML and technical report in PDF available on the Massachusetts Institute of Technology Joint Program on the Science and Policy of Global Change website (http://mit.edu/globalchange/www/).Potential technology change has a strong influence on projections of greenhouse gas emissions and costs of control, and computable general equilibrium (CGE) models are a common device for studying these phenomena. Using the MIT Emissions Prediction and Policy Analysis (EPPA) model as an example, two ways of representing technology in these models are discussed: the sector-level description of production possibilities founded on social accounting matrices and elasticity estimates, and sub-models of specific supply or end-use devices based on engineering-process data. A distinction is made between exogenous and endogenous technical change, and it is shown how, because of model structure and the origin of key parameters, such models naturally include shifts in production process that reflect some degree of endogenous technical change. As a result, the introduction of explicit endogenous relations should be approached with caution, to avoid double counting.The CGE model underlying this analysis was supported by the US Department of Energy, Office of Biological and Environmental Research [BER] (DE-FG02-94ER61937), the US Environmental Protection Agency (X-827703-01-0), the Electric Power Research Institute, and by a consortium of industry and foundation sponsor

Factors Affecting the Rate of Penetration of Large-Scale Electricity Technologies: The Case of Carbon Sequestration

This project falls under the Technology Innovation and Diffusion topic of the Integrated Assessment of Climate Change Research Program. The objective was to better understand the critical variables that affect the rate of penetration of large-scale electricity technologies in order to improve their representation in integrated assessment models. We conducted this research in six integrated tasks. In our first two tasks, we identified potential factors that affect penetration rates through discussions with modeling groups and through case studies of historical precedent. In the next three tasks, we investigated in detail three potential sets of critical factors: industrial conditions, resource conditions, and regulatory/environmental considerations. Research to assess the significance and relative importance of these factors involved the development of a microeconomic, system dynamics model of the US electric power sector. Finally, we implemented the penetration rate models in an integrated assessment model. While the focus of this effort is on carbon capture and sequestration technologies, much of the work will be applicable to other large-scale energy conversion technologies

Policy Insights From the EMF 32 Study on U.S. Carbon Tax Scenarios

The Stanford Energy Modeling Forum exercise 32 (EMF 32) used 11 different models to assess emissions, energy, and economic outcomes from a plausible range of economy-wide carbon price policies to reduce carbon dioxide (CO2) emissions in the United States. Here we discuss the most policy-relevant results of the study, mindful of the strengths and weaknesses of current models. Across all models, carbon prices lead to significant reduc- tions in CO2 emissions and conventional pollutants, with the vast majority of the reductions occurring in the electricity sector. Importantly, emissions reductions do not significantly depend on the rebate or tax cut used to return revenues to the economy. Expected economic costs, as modeled by either GDP or welfare, are modest, but vary across models. These costs are offset by benefits from avoided climate damages and health benefits from reductions in conventional air pollution. Using revenues to reduce preexisting capital or labor taxes reduces costs in most models relative to lump-sum rebates, but the size of the cost reductions varies significantly. Devoting at least some revenue to household rebates can significantly reduce adverse impacts on low income households. Carbon prices at $25/ton or even lower levels cause significant shifts away from coal as an energy source with responses of other energy sources highly dependent upon technology cost assumptions. Beyond 2030, we conclude that model uncertainties are too large to make quantitative results useful for near-term policy design. We close by describing recommendations for policymakers on interacting with model results in the future

Editorial: small scale spatial and temporal patterns in particles, plankton, and other organisms

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Nayak, A. R., Jiang, H., Byron, M. L., Sullivan, J. M., McFarland, M. N., & Murphy, D. W. Editorial: small scale spatial and temporal patterns in particles, plankton, and other organisms. Frontiers in Marine Science, 8, (2021): 669530, https://doi.org/10.3389./fmars.2021.669530Scientists have long known that small-scale interactions of aquatic particles, plankton, and other organisms with their immediate environment play an important role in diverse research areas, including marine ecology, ocean optics, and climate change (Guasto et al., 2012; Prairie et al., 2012). Typically, the distribution of particles and other organisms in the water column tends to be quite “patchy,” i.e., non-homogeneous, both spatially and temporally (Durham and Stocker, 2012). Patchiness can manifest itself through well-known phenomena such as harmful algal blooms (HABs), phytoplankton and zooplankton “thin layers,” deep scattering layers, and schooling of marine organisms such as krill and fish. This non-homogeneous distribution can significantly influence predator-prey encounters and outcomes, export fluxes, marine ecosystem health, and biological productivity (Sullivan et al., 2010; Durham et al., 2013). Thus, there is a continuing need to study and characterize the small-scale biological-physical interactions between particles/organisms and their local environment, as well as the scaled-up effects of these small-scale interactions on larger-scale dynamics. These studies are also directly linked to broader research topics listed as part of the future “grand challenges” in marine ecosystem ecology, as outlined in Borja et al. (2020).AN was supported through a National Academy of Sciences, Engineering, and Medicine (NASEM) Gulf Research Program (GRP) Early Career Research Fellowship and a faculty start-up grant at Florida Atlantic University. HJ was supported by US National Science Foundation awards (OCE-1559062 and IOS-1353937). MB was supported by a faculty start-up grant at Penn State University. AN, JS, and MM were supported by US National Science Foundation awards (OCE-1634053 and OCE-1657332). DM was supported by the US National Science Foundation (CBET-1846925)

The MIT Emissions Prediction and Policy Analysis (EPPA) Model: Version 4

Abstract in HTML and technical report in PDF available on the Massachusetts Institute of Technology Joint Program on the Science and Policy of Global Change website (http://mit.edu/globalchange/www/).The Emissions Prediction and Policy Analysis (EPPA) model is the part of the MIT Integrated Global Systems Model (IGSM) that represents the human systems. EPPA is a recursive-dynamic multi-regional general equilibrium model of the world economy, which is built on the GTAP dataset and additional data for the greenhouse gas and urban gas emissions. It is designed to develop projections of economic growth and anthropogenic emissions of greenhouse related gases and aerosols. The main purpose of this report is to provide documentation of a new version of EPPA, EPPA version 4. In comparison with EPPA3, it includes greater regional and sectoral detail, a wider range of advanced energy supply technologies, improved capability to represent a variety of different and more realistic climate policies, and enhanced treatment of physical stocks and flows of energy, emissions, and land use to facilitate linkage with the earth system components of the IGSM. Reconsideration of important parameters and assumptions led to some revisions in reference projections of GDP and greenhouse gas emissions. In EPPA4 the global economy grows by 12.5 times from 2000 to 2100 (2.5% per year) compared with an increase of 10.7 times (2.4% per year) in EPPA3. This is one of the important revisions that led to an increase in CO2 emissions to 25.7 GtC in 2100, up from 23 GtC in 2100 projected by EPPA3. There is considerable uncertainty in such projections because of uncertainty in various driving forces. To illustrate this uncertainty we consider scenarios where the global GDP grows 0.5% faster (slower) than the reference rate, and these scenarios result in CO2 emissions in 2100 of 34 (17) GtC. A sample greenhouse gas policy scenario that puts the world economy on a path toward stabilization of atmospheric CO2 at 550 ppmv is also simulated to illustrate the response of EPPA4 to a policy constraint.This research was supported by the U.S Department of Energy, U.S. Environmental Protection Agency, U.S. National Science Foundation, U.S. National Aeronautics and Space Administration, U.S. National Oceanographic and Atmospheric Administration; and the Industry and Foundation Sponsors of the MIT Joint Program on the Science and Policy of Global Change: Alstom Power (France), American Electric Power (USA), BP p.l.c. (UK/USA), Chevron Corporation (USA), CONCAWE (Belgium), DaimlerChrysler AG (Germany), Duke Energy (USA), J-Power (Japan), Electric Power Research Institute (USA), Electricité de France, ExxonMobil Corporation (USA), Ford Motor Company (USA), General Motors (USA), Murphy Oil Corporation (USA), Oglethorpe Power Corporation (USA), RWE Power (Germany), Shell Petroleum (Netherlands/UK), Southern Company (USA), Statoil ASA (Norway), Tennessee Valley Authority (USA), Tokyo Electric Power Company (Japan), Total (France), G. Unger Vetlesen Foundation (USA)

Augmenting Adaptive Approach to Control of Flexible Systems

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77087/1/AIAA-2441-416.pd

Register shifting of an insulin peptide–MHC complex allows diabetogenic T cells to escape thymic deletion

A single amino acid shift in TCR recognition of self peptide–MHC determines whether potentially diabetogenic CD4 T cells will be purged in the thymus or have the opportunity to undergo activation in the islets of Langerhans of mice

Bio-optical Properties of Cyanobacteria Blooms in Western Lake Erie

There is a growing use of remote sensing observations for detecting and quantifying freshwater cyanobacteria populations, yet the inherent optical properties of these communities in natural settings, fundamental to bio-optical algorithms, are not well known. Toward bridging this knowledge gap, we measured a full complement of optical properties in western Lake Erie during cyanobacteria blooms in the summers of 2013 and 2014. Our measurements focus attention on the optical uniqueness of cyanobacteria blooms, which have consequences for remote sensing and bio-optical modeling. We found the cyanobacteria blooms in the western basin during our field work were dominated by Microcystis, while the waters in the adjacent central basin were dominated by Planktothrix. Chlorophyll concentrations ranged from 1 to over 135 μg/L across the study area with the highest concentrations associated with Microcystis in the western basin. We observed large, amorphous colonial Microcystis structures in the bloom area characterized by high phytoplankton absorption and high scattering coefficients with a mean particle backscatter ratio at 443 nm \u3e 0.03, which is higher than other plankton types and more comparable to suspended inorganic sediments. While our samples contained mixtures of both, our analysis suggests high contributions to the measured scatter and backscatter coefficients from cyanobacteria. Our measurements provide new insights into the optical properties of cyanobacteria blooms, and indicate that current semi-analytic models are likely to have problems resolving a closed solution in these types of waters as many of our observations are beyond the range of existing model components. We believe that different algorithm or model approaches are needed for these conditions, specifically for phytoplankton absorption and particle backscatter components. From a remote sensing perspective, this presents a challenge not only in terms of a need for new algorithms, but also for determining when to apply the best algorithm for a given situation. These results are new in the sense that they represent a complete description of the optical properties of freshwater cyanobacteria blooms, and are likely to be representative of bloom conditions for other systems containing Microcystis cells and colonies