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Emissions inspection and maintenance (I/M) programs for light-duty motor vehicles manage ambient air quality by enforcing emissions standards and requiring non-compliant vehicles to be repaired or retired. I/M programs in the United States typically identify over-emitters through on-board diagnostics (OBD) systems and vehicles' proprietary firmware (i.e., indirect tests), rather than through physical measurements of exhaust gases (i.e., tailpipe tests). Analyzing data from Colorado's I/M program, this study finds the OBD test to have an accuracy of 87%, but a false pass rate of 50%, when predicting the result of a corresponding tailpipe test. As an alternative, transparent data-driven models--using logistic regression and gradient boosting machines--to consistently identify over-emitting vehicles are proposed. These models were up to 24% more accurate, or 85% more sensitive than the current OBD test in a stratified data sample. A key benefit of transparent statistical models--jurisdictions' ability to tune the test methods to best suit program needs--is also assessed. Finally, this study shows how these results support a vision for cloud-based, selective I/M programs where statistical models are applied to OBD data--collected over-the-air from vehicles--to identify and require additional inspection for only the most probable over-emitters.

Hydropower may be a low-carbon option to increase power generation in developing countries, but these countries are some of the most vulnerable to climate change. Climate change can affect hydropower generation through changes in the timing and magnitude of precipitation, rising temperatures, and glacier mass changes. Evaluating climate impacts on hydropower generally requires detailed local input data and hydrological models, which may not be available in many developing nations. Nevertheless, the need to understand the impacts is essential for the developing world. Here we present a modeling framework that relies on remotely sensed and global gridded datasets forced by an ensemble of 21 general circulation models (GCMs) under two representative concentration pathways (RCPs) to evaluate climate-induced impacts on hydropower through the 21st century. We include 134 hydropower plants (>100 MW), representing 42% of hydropower installed capacity in South America, across five regions of Brazil, Colombia, and Peru. Our results suggest the median monthly usable capacity would increase for Colombia (+2.6% to +8.4% for RCP 4.5 and 8.5, respectively) and Peru (+6.7% to +9.3% for RCP 4.5 and 8.5, respectively) by 2100 relative to the late 20th century. For Brazil, we observe a mix of reductions and increases in usable capacity. While our results suggest potential reductions for the dry season usable capacity in the Parana, Paraguay, and Southeast Atlantic regions of Brazil, we also observe slight increases in usable capacity during the rainy months for all its regions. These results can help inform future planning decisions and potential interconnections between the three countries. Additionally, the proposed framework can contribute to an increased capability to evaluate climate-induced risks to power systems in developing countries, where data and computation resources can be limited.

Past studies have shown that public support for battery electric (BEV) vans is justified by their emission external cost savings compared to their diesel equivalent. This study builds on this previous work and focuses on temperature effects on costs and energy use of large BEV and diesel vans. The authors find that temperature effect has relatively small private costs, when compared to the overall operational costs. However, it could reduce large BEV vans' average range by up to 20% in cold cities. Pre-heating the vans could mitigate these negative effects by 5-10%, 90-95% and 100% for 23.4, 46.8 and 70.2 kWh large BEV vans, respectively. Hence, policy makers should adopt different strategies according to city temperature profiles, prioritiz-ing subsidies to charging stations in cold cities.

Transportation network companies (TNC) provide mobility services that are influencing travel behavior in unknown ways due to limited TNC trip-level data. How they interact with other modes of transportation can have direct societal impacts, prompting appropriate policy intervention. This paper outlines a method to inform such policies through a data-driven approach that specifically analyzes the interaction between TNCs and bus services in Pittsburgh, PA. Uber surge multiplier data is used over a 6-month time period to approximate TNC usage (i.e., demand over supply ratio) for ten predefined points of interest throughout the city. Bus boarding data near each point of interest is used to relate TNC usage. Data from multiple sources (weather, traffic speed data, bus levels of service) are used to control for conditions that influence bus ridership. We find significant changes in bus boardings during periods of unusually high TNC usage at four locations during the evening hours. The remaining six locations observe no significant change in bus boardings. We find that the presence of a dedicated bus way transit station or a nearby university (or dense commercial zones in general) both influence ad-hoc substitutional behavior between TNCs and public transit. We also find that this behavior varies by location and time of day. This finding is significant and important for targeted policies that improve transportation network efficiency.

National Renewable Energy Laboratory's Materials Flows through Industry tool was developed to analyze energy consumption across the supply chains of U.S. manufacturing industries. Due to the limitation of the data sources, the Tool may have incomplete data in some industries’ supply chains. To overcome this issue, this study provides a method to identify data gaps in the MFI tool with the information in input–output life cycle assessment models. Results show that around 5% processes in the MFI tool have potential data gaps in the supply chain. LCA practitioners can use this information to optimize activities to improve LCA models.

Author(s): Bergerson, Joule A; Brandt, Adam; Cresko, Joe; Carbajales‐Dale, Michael; MacLean, Heather L; Matthews, H Scott; McCoy, Sean; McManus, Marcelle; Miller, Shelie A; Morrow, William R; Posen, I Daniel; Seager, Thomas; Skone, Timothy; Sleep, Sylvia

Uncrewed aerial vehicles (UAVs) for last-mile deliveries will affect the energy productivity of delivery and require new methods to understand energy consumption and greenhouse gas (GHG) emissions. We combine empirical testing of 188 quadcopter flights across a range of speeds with a first-principles analysis to develop a usable energy model and a machine-learning algorithm to assess energy across takeoff, cruise, and landing. Our model shows that an electric quadcopter drone with a very small package (0.5 kg) would consume approximately 0.08 MJ/km and result in 70 g of CO

We autonomously directed a small quadcopter package delivery Uncrewed Aerial Vehicle (UAV) or “drone” to take off, fly a specified route, and land for a total of 209 flights while varying a set of operational parameters. The vehicle was equipped with onboard sensors, including GPS, IMU, voltage and current sensors, and an ultrasonic anemometer, to collect high-resolution data on the inertial states, wind speed, and power consumption. Operational parameters, such as commanded ground speed, payload, and cruise altitude, were varied for each flight. This large data set has a total flight time of 10 hours and 45 minutes and was collected from April to October of 2019 covering a total distance of approximately 65 kilometers. The data collected were validated by comparing flights with similar operational parameters. We believe these data will be of great interest to the research and industrial communities, who can use the data to improve UAV designs, safety, and energy efficiency, as well as advance the physical understanding of in-flight operations for package delivery drones., Measurement(s) atmospheric wind speed • atmospheric wind direction • battery current • battery voltage • position Technology Type(s) anemometer • Current / Voltage measurement sensor • GPS navigation system • inertial measurement unit Factor Type(s) programmed speed during cruise • programmed altitude during cruise • payload mass Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13814225

Complete streets facilitate multimodal travel by improving both transportation access and safety by emphasizing the user, not the automobile. This case study evaluates the impacts of a comp...

Despite a long-term focus on passenger vehicle safety, there are still 38,000 vehicle-related fatalities annually. Some are the result of failure to maintain safety components of vehicles, such as brakes, tires, or headlights. Following NHTSA guidelines, 18 states have implemented periodic safety inspection programs in which certified inspectors assess components, and owners are required to repair or replace deficient components. In the case of tires, when the tread depth falls to 2/32 in., its stopping distance becomes excessive. Thus, this tread depth level was built into the safety inspection thresholds for tires. One challenge is that, in an annual vehicle inspection, if a tire passes at a level of 3/32 in., it may fall below the safe (2/32 in.) threshold soon after inspection. In an era of higher vehicle miles traveled (VMT) and reduced attention to maintenance, perhaps the thresholds set for safety inspections should be higher than the “safe level” to provide a buffer. Using 6 million safety inspection records from Pennsylvania from 2006 to 2016 we calculated tread depth deterioration and annual VMT at the vehicle level. We estimated the “percent of vehicles at risk of having unsafe tires before the next inspection” (using the 2/32 in. threshold) to be about 30%. We also estimated how the percent of at-risk vehicles decreases as the inspection thresholds are raised, and found an attractive threshold at about 5/32 in. for which the percent of at-risk vehicles would be very low. Such changes could further reduce fatal and non-fatal accidents.

The widespread adoption of smartphones followed by an emergence of transportation network companies (TNC) have influenced the way individuals travel. The authors use the 2017 National Household Travel Survey to explore socioeconomic, frequency of use, and spatial characteristics associated with TNC users. The results indicate that TNC riders tend to be younger, earn higher incomes, have higher levels of education, and are more likely to reside in urban areas compared to the aggregate United States population. Of the TNC users, 60% hailed a ride three times or less in the previous month, indicating that TNC services are primarily used for special occasions. TNC users use public transit at higher rates and own fewer vehicles compared to the aggregate United States population. In fact, the TNC user population reported similar frequencies of use for both TNC services and public transit during the previous month. Approximately 40% of TNC users reside in regions with population densities greater than 10, 000 persons per square mile compared to only 15% for non-TNC users. Lastly, reported use of public transit for TNC users living in large cities (> 1 million) with access to heavy rail was almost three times greater when compared to similar sized cities without heavy rail. The average monthly frequency of TNC use was also elevated when heavy rail was present.

Uncertainty assessment is crucial to the reliability of decisions made based on results of life cycle assessment (LCA) models. However, the most popular uncertainty analysis method in LCA models defines uncertainty as quantified empirical judgment of the sources of the inventory data, which lacks the deviations due to the measurements of the data. We create a modified range method that leverages an array of publicly available data to represent the uncertainty of the actual values used in input-output models as ranges. Using these ranges, we propagate and visualize results from the uncertainties. We demonstrate the utility of the method using the Economic Input-Output Life Cycle Assessment (EIO-LCA) model and focus on the uncertainty of estimated energy consumption. Results show that for energy consumption values in the model, average uncertainty ranges are within ±40% with some outliers. Our method screens based on the magnitude of impacts and the relative uncertainty. Improved uncertainty assessment supports various types of decisions, such as product comparisons, hotspot analysis, and overall energy analyses. We used three case studies to demonstrate the implementations of our method. This method can be extended to additional types of flows, beyond energy, and to process-matrix-based LCA models.

Poor air quality in urban areas and environmental concerns attributable to road transportation are growing and significant problems for governments. Many different options have been proposed to lower emissions, and a critical one is the use of battery electric vehicles (BEVs). Since city-logistics accounts for about 25% of urban mobility emissions, we focus on battery electric and diesel delivery vans. The aim of this paper is to present a holistic view of the problem, comparing the environmental, social and economic impact of BEV and diesel delivery vans, providing useful insights to policy makers and fleet owners willing to replace or select delivery vans to include in their city-logistics fleets. In cities where new BEV vans replace old diesel vans and the electricity mix is relatively clean, CO2 emissions and air pollutants decrease by 93–98% and 85–99%, respectively. If BEVs use electricity coming from coal energy and are compared to new diesel vans, reductions of CO2 emissions and air pollutants are in the order of 12–13% and 0–92%, respectively. Longer battery life and greater annual mileage improve these results and decrease cost differences. Results also reveal that annual emissions benefits of replacing older diesel vans with BEVs are on the same order of magnitude of equivalent annual cost differences. From a strictly business perspective, BEV vans are already economically attractive in some cities with existing incentives; however, for other cities incentives equal to the value of their emissions reduction benefits are needed, but might not be sufficient to justify BEV acquisition.

Commercial cargo bicycles have the potential to reduce greenhouse gas emissions, air pollution, traffic congestion, and delivery times. However, urban physical barriers and small cargo capacities could limit their uptake in city logistics. This analysis explores the effects of payload, air density, wind, and topography on commercial cargo bicycle energy-use in cities with a focus on six European capitals (Berlin, Paris, Rome, Lisbon, Oslo, and London), and is supported by an empirical model using GPS traces and hourly weather data. The study finds that city hilliness could deplete up to 13-34% of the riders’ energy-use, while wind speed can negatively affect the bikes’ energy-use by up to 12-23%. Air-density effects are small; however, in cold cities like Oslo, they could reduce riders’ energy-use by up to 8% in wintertime. These estimates can enable future studies to assess the operational feasibility of commercial cargo bicycles and help policy makers to promote their use by subsidizing the purchase of batteries to mitigate energy-use differences across cities due to topographic or weather effects. Results reveal that, across the cities in this study, this “ideal battery size” varies from 0.1-0.8 kWh in Berlin to 1.6-2.1 kWh in Lisbon.

Blockchain and distributed ledger technologies (DLTs) have the capacity to improve how many companies and organizations conduct transactions or store information securely, among many other potential benefits. However, their development and implementation does not occur in a contextual vacuum and instead must adapt to the needs and requirements of their given user. As such, we argue that the governance of DLT and blockchain must be applied against two core questions: who should have access to information within a given DLT/blockchain, and should management of that system be open or restricted/permissioned? While the technology is still emerging, its application to and success within various organizations will be largely dependent upon these key governance concerns.

Life cycle impact assessment (LCIA) transforms inventories of environmental flows to environmental impacts in life cycle assessment (LCA) studies. Many impact assessment methods have been developed for impact categories such as global warming, acidification, and ecotoxicity. These impact assessment methods provide different characterization factor values and impact units for the same impact category. LCA studies often report results for one or at most a few impact assessment methods and overlook the uncertainty caused by selecting and using different methods. In this study, we systematically evaluated uncertainties that result from considering different LCIA methods; cases from the US LCI and the ecoinvent database were used to demonstrate the uncertainties. Results showed that the discrepancies of the total impact results were caused by differences in 1) the total emission values included in the inventory; 2) the coverages of substances in methods, and 3) the differences in the characterization factor values of the substances. Case studies showed large uncertainties for all impact categories except for global warming. For most of the categories studied, the maximum values were 10,000 times larger than the minimum values. These results should be taken into consideration for impact assessments that involve categories with large uncertainties. The information provided by this paper can help researchers and software developers to understand the uncertainty caused by choosing different LCIA methods and the importance of communicating such information to LCA practitioners. The results can also help decision-makers to build guidance that is related to build and choose LCIA methods for various topics.

Living in different areas is associated with different impacts; the movement of people to and from those areas will affect energy use and emissions over the U.S. The emissions implications of state-to-state migration on household energy and GHG emissions are explored. Three million households move across state lines annually, and generally move from the North East to the South and West. Migrating households often move to states with different climates (thus different heating and cooling and needs), different fuel mixes, and different regional electricity grids, which leads them to experience changes in household emissions as a result of their move. Under current migration trends, the emissions increases of households moving from the Northeast to the South and Southwest are balanced by the emissions decreases of households moving to California and the Pacific Northwest. The net sum of emissions changes for migrating households is slightly positive but near zero; however, that net zero sum represents the balance of many emission changes. Planning for continued low carbon growth in low carbon regions or cities experiencing high growth rates driven by migration is essential in order to offset the moderate emissions increases experienced by households moving to high carbon regions.

United States federal regulations require increasing renewable fuel blending in the transportation sector, a majority of which is corn ethanol. Nationally, ethanol is blended with gasoline ...

Modern technology, together with an advanced economy, can provide a good or service in myriad ways, giving us choices on what to produce and how to produce it. To make those choices more intelligently, society needs to know not only the market price of each alternative, but the associated health and environmental consequences. A fair comparison requires evaluating the consequences across the whole “life cycle”—from the extraction of raw materials and processing to manufacture/construction, use, and end-of-life—of each alternative. Focusing on only one stage (e.g., manufacture) of the life cycle is often misleading. Unfortunately, analysts and researchers still have only rudimentary tools to quantify the materials and energy inputs and the resulting damage to health and the environment. Life cycle assessment (LCA) provides an overall framework for identifying and evaluating these implications. Since the 1960s, considerable progress has been made in developing methods for LCA, especially in characterizing, qualitatively and quantitatively, environmental discharges. However, few of these analyses have attempted to assess the quantitative impact on the environment and health of material inputs and environmental discharges. Risk analysis and LCA are connected closely. While risk analysis has characterized and quantified the health risks of exposure to a toxicant, the policy implications have not been clear. Inferring that an occupational or public health exposure carries a nontrivial risk is only the first step in formulating a policy response. A broader framework, including LCA, is needed to see which response is likely to lower the risk without creating high risks elsewhere. Even more important, LCA has floundered at the stage of translating an inventory of environmental discharges into estimates of impact on health and the environment. Without the impact analysis, policymakers must revert to some simple rule, such as that all discharges, regardless of which chemical, which medium, and where they are discharged, are equally toxic. Thus, risk analysts should seek LCA guidance in translating a risk analysis into policy conclusions or even advice to those at risk. LCA needs the help of RA to go beyond simplistic assumptions about the implications of a discharge inventory. We demonstrate the need and rationale for LCA, present a brief history of LCA, present examples of the application of this tool, note the limitations of LCA models, and present several methods for incorporating risk assessment into LCA. However, we warn the reader not to expect too much. A comprehensive comparison of the health and environmental implications of alternatives is beyond the state of the art. LCA is currently not able to provide risk analysts with detailed information on the chemical form and location of the environmental discharges that would allow detailed estimation of the risks to individuals due to toxicants. For example, a challenge for risk analysts is to estimate health and other risks where the location and chemical speciation are not characterized precisely. Providing valuable information to decisionmakers requires advances in both LCA and risk analysis. These two disciplines should be closely linked, since each has much to contribute to the other.

Rail, truck, commercial bus, and aircraft have federally mandated safety inspection programs in the United States, while inspections of personal vehicles, which make up the majority of passenger miles, are optionally imposed at the state level. In recent years, some states have chosen to eliminate the vehicle safety inspection program because of budget constraints and concerns about program effectiveness. Currently, 26 states have a schedule for conducting safety inspections, but Pennsylvania is one of thirteen states that currently require all personal light duty vehicles to be inspected every year. The remaining states have completely eliminated safety inspection programs. However, as automobiles become safer, Pennsylvania legislators are now pushing to phase out the inspection program to reduce the costs of owning a vehicle. This study combines Pennsylvania vehicle registration data with two large samples of results from state safety inspections. The authors find that the state safety inspection fail rate for light-duty vehicles is 12–18%, well above the often-cited rate of 2%. Vehicles that are older than three years old or have more than about 30,000 miles can have much higher rates. When analyzing new vehicles, less than or equal to one year old, it is found that even these vehicles have a failure rate greater than zero. Furthermore, while the vehicle fleet appears to be getting safer over the past few years by improvements in technology or other external circumstances, the inspection failure rate does not appear to be trending toward zero in the near future. The authors also show that accurate inspection data is limited and often incorrectly analyzed. Lastly, the importance of vehicle maintenance over a vehicle’s lifetime is proven to be evident, since regular usage causes vehicles to deteriorate. The authors conclude that vehicle safety inspections should continue to be implemented in order to keep driving conditions safe.

Marcellus Shale development is occurring rapidly and relatively unconstrained across Pennsylvania (PA). Through 2013, over 7400 unconventional wells had been drilled in the Commonwealth. Well pads, access roads, and gathering lines fragment forestland resulting in irreversible alterations to the forest ecosystem. Changes in forest quantity, composition, and structural pattern can result in increased predation, brood parasitism, altered light, wind, and noise intensity, and spread of invasive species. These fragmentation effects pose a risk to PA's rich biodiversity. This study projects the structure of future alternative pathways for Marcellus shale development and quantifies the potential ecological impact of future drilling using a core forest region of Bradford County, PA. Modeling presented here suggests that future development could cause the level of fragmentation in the study area to more than double throughout the lifetime of gas development. Specifically, gathering lines are responsible for approximately 94% of the incremental fragmentation in the core forest study region. However, by requiring gathering lines to follow pre-existing road routes in forested regions, shale resources can be exploited to their full potential, while essentially preventing any further fragmentation from occurring across the core forested landscape of Bradford County. In the study region, assuming an estimated ultimate recovery (EUR) of 1–3 billion cubic feet (Bcf) per well, this policy could be implemented for a minimal incremental economic investment of approximately $0.005–$0.02 per Mcf of natural gas produced over the modeled traditional gathering line development.

The paper examines the potential effects of failure of heavily used, outdated locks and dams on the Monongahela River in southwestern Pennsylvania. Catastrophic failure would result in lengthy outage of barge traffic. The displaced volume of coal shipments from mines to power plants is estimated using Energy Information Administration survey data. The resilience of the impacted facilities, the viability of their shipping alternatives, and their ability to re-organize into new markets is assessed. Lost revenues are estimated for facilities that close due to an inability to adapt, as well as the replacement cost of towboats and barges trapped by a catastrophic and sudden failure. The aggregate costs to these facilities as a result of a year-long closure are estimated at $0.56-1.7 billion.

This study reviewed 147 life cycle studies, with 28 found suitable for harmonizing food waste management methods’ climate and energy impacts. A total of 80 scientific soil productivity studies were assessed to rank management method soil benefits. Harmonized climate impacts per kilogram of food waste range from −0.20 kg of carbon dioxide equivalents (CO2e) for anaerobic digestion (AD) to 0.38 kg of CO2e for landfill gas-to-energy (LFGTE). Aerobic composting (AC) emits −0.10 kg of CO2e. In-sink grinding (ISG) via a food-waste disposer and flushing for management with other sewage at a wastewater treatment plant emits 0.10 kg of CO2e. Harmonization reduced climate emissions versus nonharmonized averages. Harmonized energy impacts range from −0.32 MJ for ISG to 1.14 MJ for AC. AD at 0.27 MJ and LFGTE at 0.40 MJ fall in between. Rankings based on soil studies show AC first for carbon storage and water conservation, with AD second. AD first for fertilizer replacement, with AC second, and AC and AD tied for fir...

The Power Sector Carbon Index provides an estimate of the carbon dioxide intensity of the U.S. power sector using publicly available data sources. The most current information and all index charts are available at www.emissionsindex.org. The analysis code and data are available for download at https://github.com/EmissionsIndex/Emissions-Index. The Power Sector Carbon Index was created by researchers in Carnegie Mellon University’s Scott Institute for Energy Innovation.

Natural gas (NG)-related fugitive methane (CH4) emissions estimates from life cycle assessments (LCA) and local field measurements are highly uncertain. Globally distributed long-term atmospheric measurements and top-down modeling can help understand whether LCA and field studies are representative of the global industry average. Attributing sources, such as the NG industry, to global total top-down emissions estimates requires detailed and transparent global a priori bottom-up emissions inventories. Establishing an a priori bottom-up inventory as a tool for top-down modeling is the focus of this work, which extends existing fossil fuel (FF) inventories over the past three decades: (i) It includes ethane (C2H6) emissions, which is a convenient FF tracer gas given available global C2H6 observations. (ii) Fuel specific CH4 and C2H6 emissions uncertainties are quantified. (iii) NG CH4 and C2H6 emissions are estimated for different fugitive emissions rate (FER; % of dry production) scenarios as a basis for qu...