Research tools

The Electricity Marginal Factors Estimate Opens in new window estimates the marginal emissions and damage factors for the U.S. electricity sector.

To date, methods used to disambiguate inventors in the United States Patent and Trademark Office (USPTO) database have been rule- and threshold-based (requiring and leveraging expert knowledge) or semi-supervised algorithms trained on statistically generated artificial labels. Using a large, hand-disambiguated set of 98,762 labeled USPTO inventor records from the field of optoelectronics consisting of four sub-samples of inventors with varying characteristics (Akinsanmi et al., 2014) and a second large, hand-disambiguated set of 53,378 labeled inventor records corresponding to a subset of academics in the life sciences (Azoulay et al., 2012), we provide the first supervised learning approach for USPTO inventor disambiguation.

Using these two sets of inventor records, we also provide extensive evaluations of both our algorithm and three examples of prior approaches to USPTO disambiguation, arguably representative of the range of approaches used to date. We show that the three past disambiguation algorithms we evaluate demonstrate biases depending on the feature distribution of the target disambiguation population. Both the rule- and threshold-based methods and the semi-supervised approach perform poorly (10–22% false negative error rates) on a random sample of optoelectronics inventors – arguably the closest of our sub-samples to what might be expected of the majority of inventors in the USPTO (based on disambiguation-relevant metrics).

The supervised learning approach, using random forests and trained on our labeled optoelectronics dataset, consistently maintains error rates below 3% across all of our available samples. We make public both our labeled optoelectronics inventor records and our code to build supervised learning models and disambiguate inventors. Our code also allows users to implement supervised learning approaches with their own representative labeled training data.

In June 2014, the U.S. Environmental Protection Agency (EPA) proposed a Clean Power Plan under Section 111(d) of the Clean Air Act for the state-level regulation of carbon dioxide (CO2) emitted from existing electric generating plants. The proposal, which will be finalized this mid-summer, sets state-specific goals for CO2 emissions but provides each state with flexibility to choose how to meet its goal.

ISOMAP allows users to evaluate a range of plant-specific technical options that will reduce CO2 emissions from each major U.S. coal-fired power plant. As the EPA modifies its ruling, the CMU research team will update the tool to meet the new requirements.

PHORUM is a lightweight, transparent simulation tool for academia, industry, and government that allows researchers to investigate how changes to PJM will affect generators, electricity prices, emissions, and human health.

PowerOptInvest is an investment decision model that determines the least cost investment and operating strategies for electric power generation facilities. The Utility Investment Data Tool is software that is used to enter data into PowerOptInvest.

This inventory provides global fossil fuel fugitive methane (CH4) and ethane (C2H6) emissions estimates, including uncertainties, using best available knowledge of emissions factors and fuel production data at the country and regional level. The inventory can be used as Bayesian a priori estimates in top-down modeling to further reduce emissions uncertainty constrained by global atmospheric CH4 and C2H6 measurements.

The downloadable Excel spreadsheet Opens in new window provides the marginal emissions factors estimated as part of the work published in Siler-Evans. K., Azevedo, I.L., Morgan, M.G., (2012). Marginal emissions factors for the US electricity system Opens in new window. Environmental Science & Technology, 46 (9), pp. 4742–4748. Marginal emissions factors (MEFs) from 2006 through 2011 were estimated by Kyle Siler-Evans using data from the Environmental Protection Agency’s Continuous Emissions Monitoring System (CEMS).

The data repository includes full results for the health, environmental, and climate benefits of wind and solar generation, as published in Siler-Evans. K., Azevedo, I.L., Morgan, M.G., Apt, J. (2013). Regional variations in the health, environmental, and climate benefits of wind and solar generation Opens in new window. Proceedings of the National Academy of Sciences.

This tool Opens in new window consists of two distinct sets of materials, a combination of which allows the user to become more informed on available Low-Carbon Technologies and explore the outcomes of energy generation policy decisions on environmental conditions.

The initial segment of materials presented is designed to convey basic information on available generation technologies and their relative impacts. The second segment is an Excel-based tool that allows users to build their own power plant combination to supply additional capacity needed in Pennsylvania over the next 25 years, assuming a congressionally mandated carbon constraint.

This model Opens in new window allows for the exploration of international policies related to nuclear power and the economic and environmental consequences that could result from a reevaluation of those policies. Some of the consequences considered include the reliability of the electricity supply, electricity costs, air pollution levels, carbon emission levels, and the impact of fossil fuel reliance.

This water quality reuse classification tool Opens in new window is designed to facilitate decision-making regarding human water uses. The tool's focus is to assist utilities, industries, and governments in determining whether a process effluent is of acceptable quality for the influent of another process. By ranking water quality required for various types of reuse in a tabular format, utility managers, industrial operators, and government officials can propose application effluents as potential influents for other activities and evaluate the feasibility of specific couplings.

IECM is a computer modeling program that performs systematic cost and performance analyses of emission control equipment at coal-fired power plants. It is intended for use by engineers, policymakers, and researchers for preliminary design and analysis of electricity generation options. The IECM allows the user to configure the plant to be modeled from a variety of pollutant control technologies.

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The Estimating Air Pollution Social Impact Using Regression (EASIUR) model Opens in new window is an easy-to-use tool for estimating the social cost of air quality in the United States.

The Air Pollution Emission Experiments and Policy Analysis (APEEP) Opens in new window model is an integrated assessment model that links air pollution emissions to exposures, physical effects, and monetary damages in the contiguous United States.