Drone flight data reveal energy and greenhouse gas emissions savings for very small package delivery.

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 CO2e per package in the United States. We compare drone delivery with other vehicles and show that energy per package delivered by drones (0.33 MJ/package) can be up to 94% lower than conventional transportation modes, with only electric cargo bicycles providing lower GHGs/package. Our open model and coefficients can assist stakeholders in understanding and improving the sustainability of small package delivery.

In-flight positional and energy use data set of a DJI Matrice 100 quadcopter for small package delivery.

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.

Life-Cycle Assessment Harmonization and Soil Science Ranking Results on Food-Waste Management Methods.

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 first for plant yield increase. ISG ranks third and LFGTE fourth on all four soil-quality and productivity indicators. Suggestions for further research include developing soil benefits measurement methods and resolving inconsistencies in the results between life-cycle assessments and soil science studies.

Energy and Emissions from U.S. Population Shifts and Implications for Regional GHG Mitigation Planning.

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.

Life cycle greenhouse gas emissions from U.S. liquefied natural gas exports: implications for end uses.

This study analyzes how incremental U.S. liquefied natural gas (LNG) exports affect global greenhouse gas (GHG) emissions. We find that exported U.S. LNG has mean precombustion emissions of 37 g CO2-equiv/MJ when regasified in Europe and Asia. Shipping emissions of LNG exported from U.S. ports to Asian and European markets account for only 3.5-5.5% of precombustion life cycle emissions, hence shipping distance is not a major driver of GHGs. A scenario-based analysis addressing how potential end uses (electricity and industrial heating) and displacement of existing fuels (coal and Russian natural gas) affect GHG emissions shows the mean emissions for electricity generation using U.S. exported LNG were 655 g CO2-equiv/kWh (with a 90% confidence interval of 562-770), an 11% increase over U.S. natural gas electricity generation. Mean emissions from industrial heating were 104 g CO2-equiv/MJ (90% CI: 87-123). By displacing coal, LNG saves 550 g CO2-equiv per kWh of electricity and 20 g per MJ of heat. LNG saves GHGs under upstream fugitive emissions rates up to 9% and 5% for electricity and heating, respectively. GHG reductions were found if Russian pipeline natural gas was displaced for electricity and heating use regardless of GWP, as long as U.S. fugitive emission rates remain below the estimated 5-7% rate of Russian gas. However, from a country specific carbon accounting perspective, there is an imbalance in accrued social costs and benefits. Assuming a mean social cost of carbon of $49/metric ton, mean global savings from U.S. LNG displacement of coal for electricity generation are $1.50 per thousand cubic feet (Mcf) of gaseous natural gas exported as LNG ($.028/kWh). Conversely, the U.S. carbon cost of exporting the LNG is $1.80/Mcf ($.013/kWh), or $0.50-$5.50/Mcf across the range of potential discount rates. This spatial shift in embodied carbon emissions is important to consider in national interest estimates for LNG exports.

Changing the renewable fuel standard to a renewable material standard: bioethylene case study.

The narrow scope of the U.S. renewable fuel standard (RFS2) is a missed opportunity to spur a wider range of biomass use. This is especially relevant as RFS2 targets are being missed due to demand-side limitations for ethanol consumption. This paper examines the greenhouse gas (GHG) implications of a more flexible policy based on RFS2, which includes credits for chemical use of bioethanol (to produce bioethylene). A Monte Carlo simulation is employed to estimate the life-cycle GHG emissions of conventional low-density polyethylene (LDPE), made from natural gas derived ethane (mean: 1.8 kg CO2e/kg LDPE). The life-cycle GHG emissions from bioethanol and bio-LDPE are examined for three biomass feedstocks: U.S. corn (mean: 97g CO2e/MJ and 2.6 kg CO2e/kg LDPE), U.S. switchgrass (mean: -18g CO2e/MJ and -2.9 kg CO2e/kg LDPE), and Brazilian sugar cane (mean: 33g CO2e/MJ and -1.3 kg CO2e/kg LDPE); bioproduct and fossil-product emissions are compared. Results suggest that neither corn product (bioethanol or bio-LDPE) can meet regulatory GHG targets, while switchgrass and sugar cane ethanol and bio-LDPE likely do. For U.S. production, bioethanol achieves slightly greater GHG reductions than bio-LDPE. For imported Brazilian products, bio-LDPE achieves greater GHG reductions than bioethanol. An expanded policy that includes bio-LDPE provides added flexibility without compromising GHG targets.

Natural gas fugitive emissions rates constrained by global atmospheric methane and ethane.

The amount of methane emissions released by the natural gas (NG) industry is a critical and uncertain value for various industry and policy decisions, such as for determining the climate implications of using NG over coal. Previous studies have estimated fugitive emissions rates (FER)--the fraction of produced NG (mainly methane and ethane) escaped to the atmosphere--between 1 and 9%. Most of these studies rely on few and outdated measurements, and some may represent only temporal/regional NG industry snapshots. This study estimates NG industry representative FER using global atmospheric methane and ethane measurements over three decades, and literature ranges of (i) tracer gas atmospheric lifetimes, (ii) non-NG source estimates, and (iii) fossil fuel fugitive gas hydrocarbon compositions. The modeling suggests an upper bound global average FER of 5% during 2006-2011, and a most likely FER of 2-4% since 2000, trending downward. These results do not account for highly uncertain natural hydrocarbon seepage, which could lower the FER. Further emissions reductions by the NG industry may be needed to ensure climate benefits over coal during the next few decades.

Impacts of variability in cellulosic biomass yields on energy security.

The practice of modeling biomass yields on the basis of deterministic point values aggregated over space and time obscures important risks associated with large-scale biofuel use, particularly risks related to drought-induced yield reductions that may become increasingly frequent under a changing climate. Using switchgrass as a case study, this work quantifies the variability in expected yields over time and space through switchgrass growth modeling under historical and simulated future weather. The predicted switchgrass yields across the United States range from about 12 to 19 Mg/ha, and the 80% confidence intervals range from 20 to 60% of the mean. Average yields are predicted to decrease with increased temperatures and weather variability induced by climate change. Feedstock yield variability needs to be a central part of modeling to ensure that policy makers acknowledge risks to energy supplies and develop strategies or contingency plans that mitigate those risks.

Implications of near-term coal power plant retirement for SO2 and NOX and life cycle GHG emissions.

Regulations monitoring SO(2), NO(X), mercury, and other metal emissions in the U.S. will likely result in coal plant retirement in the near-term. Life cycle assessment studies have previously estimated the environmental benefits of displacing coal with natural gas for electricity generation, by comparing systems that consist of individual natural gas and coal power plants. However, such system comparisons may not be appropriate to analyze impacts of coal plant retirement in existing power fleets. To meet this limitation, simplified economic dispatch models for PJM, MISO, and ERCOT regions are developed in this study to examine changes in regional power plant dispatch that occur when coal power plants are retired. These models estimate the order in which existing power plants are dispatched to meet electricity demand based on short-run marginal costs, with cheaper plants being dispatched first. Five scenarios of coal plant retirement are considered: retiring top CO(2) emitters, top NO(X) emitters, top SO(2) emitters, small and inefficient plants, and old and inefficient plants. Changes in fuel use, life cycle greenhouse gas emissions (including uncertainty), and SO(2) and NO(X) emissions are estimated. Life cycle GHG emissions were found to decrease by less than 4% in almost all scenarios modeled. In addition, changes in marginal damage costs due to SO(2), and NO(X) emissions are estimated using the county level marginal damage costs reported in the Air Pollution Emissions Experiments and Policy (APEEP) model, which are a proxy for measuring regional impacts of SO(2) and NO(X) emissions. Results suggest that location specific parameters should be considered within environmental policy frameworks targeting coal plant retirement, to account for regional variability in the benefits of reducing the impact of SO(2) and NO(X) emissions.

Historical carbon footprinting and implications for sustainability planning: a case study of the Pittsburgh region.

This study estimates fossil-based CO(2) emissions and energy use from 1900-2000 for Allegheny County, PA. Total energy use and emissions increased from 1900 to 1970, reflecting the significant industrial, economic, and population growth that occurred in Allegheny County. From 1970 to 2000, Allegheny County experienced a 30% decrease in total emissions and energy use from peak values, primarily because of a decline in industrial activity (40% decrease in value added) and the loss of a quarter of its population. Despite these dramatic economic and demographic transitions, per capita emissions remained stable from 1970 to 2000, buoyed by relatively stable or slightly increasing emissions in the commercial and transportation sectors. Allegheny County's history suggests the scale of change needed to achieve local emissions reductions may be significant; given years of major technological, economic, and demographic changes, per capita emissions in 1940 were nearly the same in 2000. Most local governments are planning emissions reductions rates that exceed 1% per year, which deviate significantly from historical trends. Our results suggest additional resources and improved planning paradigms are likely necessary to achieve significant emissions reductions, especially for areas where emissions are still increasing.

Potentials for sustainable transportation in cities to alleviate climate change impacts.

Reducing greenhouse gas emissions (GHG) is an important social goal to mitigate climate change. A common mitigation paradigm is to consider strategy "wedges" that can be applied to different activities to achieve desired GHG reductions. In this policy analysis piece, we consider a wide range of possible strategies to reduce light-duty vehicle GHG emissions, including fuel and vehicle options, low carbon and renewable power, travel demand management and land use changes. We conclude that no one strategy will be sufficient to meet GHG emissions reduction goals to avoid climate change. However, many of these changes have positive combinatorial effects, so the best strategy is to pursue combinations of transportation GHG reduction strategies to meet reduction goals. Agencies need to broaden their agendas to incorporate such combination in their planning.

Relevance of emissions timing in biofuel greenhouse gases and climate impacts.

Employing life cycle greenhouse gas (GHG) emissions as a key performance metric in energy and environmental policy may underestimate actual climate change impacts. Emissions released early in the life cycle cause greater cumulative radiative forcing (CRF) over the next decades than later emissions. Some indicate that ignoring emissions timing in traditional biofuel GHG accounting overestimates the effectiveness of policies supporting corn ethanol by 10-90% due to early land use change (LUC) induced GHGs. We use an IPCC climate model to (1) estimate absolute CRF from U.S. corn ethanol and (2) quantify an emissions timing factor (ETF), which is masked in the traditional GHG accounting. In contrast to earlier analyses, ETF is only 2% (5%) over 100 (50) years of impacts. Emissions uncertainty itself (LUC, fuel production period) is 1-2 orders of magnitude higher, which dwarfs the timing effect. From a GHG accounting perspective, emissions timing adds little to our understanding of the climate impacts of biofuels. However, policy makers should recognize that ETF could significantly decrease corn ethanol's probability of meeting the 20% GHG reduction target in the 2007 Energy Independence and Security Act. The added uncertainty of potentially employing more complex emissions metrics is yet to be quantified.

Uncertainty in life cycle greenhouse gas emissions from United States natural gas end-uses and its effects on policy.

Increasing concerns about greenhouse gas (GHG) emissions in the United States have spurred interest in alternate low carbon fuel sources, such as natural gas. Life cycle assessment (LCA) methods can be used to estimate potential emissions reductions through the use of such fuels. Some recent policies have used the results of LCAs to encourage the use of low carbon fuels to meet future energy demands in the U.S., without, however, acknowledging and addressing the uncertainty and variability prevalent in LCA. Natural gas is a particularly interesting fuel since it can be used to meet various energy demands, for example, as a transportation fuel or in power generation. Estimating the magnitudes and likelihoods of achieving emissions reductions from competing end-uses of natural gas using LCA offers one way to examine optimal strategies of natural gas resource allocation, given that its availability is likely to be limited in the future. In this study, the uncertainty in life cycle GHG emissions of natural gas (domestic and imported) consumed in the U.S. was estimated using probabilistic modeling methods. Monte Carlo simulations are performed to obtain sample distributions representing life cycle GHG emissions from the use of 1 MJ of domestic natural gas and imported LNG. Life cycle GHG emissions per energy unit of average natural gas consumed in the U.S were found to range between -8 and 9% of the mean value of 66 g CO(2)e/MJ. The probabilities of achieving emissions reductions by using natural gas for transportation and power generation, as a substitute for incumbent fuels such as gasoline, diesel, and coal were estimated. The use of natural gas for power generation instead of coal was found to have the highest and most likely emissions reductions (almost a 100% probability of achieving reductions of 60 g CO(2)e/MJ of natural gas used), while there is a 10-35% probability of the emissions from natural gas being higher than the incumbent if it were used as a transportation fuel. This likelihood of an increase in GHG emissions is indicative of the potential failure of a climate policy targeting reductions in GHG emissions.

Inventory development and input-output model of U.S. land use: relating land in production to consumption.

As populations and demands for land-intensive products, e.g., cattle and biofuels, increase the need to understand the relationship between land use and consumption grows. This paper develops a production-based inventory of land use (i.e., the land used to produce goods) in the U.S. With this inventory an input-output analysis is used to create a consumption-based inventory of land use. This allows for exploration of links between land used in production to the consumption of particular goods. For example, it is possible to estimate the amount of cropland embodied in processed foods or healthcare services. As would be expected, agricultural and forestry industries are the largest users of land in the production-based inventory. Similarly, we find that processed foods and forest products are the largest users of land in the consumption-based inventory. Somewhat less expectedly this work finds that the majority of manufacturing and service industries, not typically associated with land use, require substantial amounts of land to produce output due to the purchase of food and other agricultural and wood-based products in the supply chain. The quantitative land use results of this analysis could be integrated with qualitative metrics such as weighting schemes designed to reflect environmental impact or life cycle impact assessment methods.

Policy implications of uncertainty in modeled life-cycle greenhouse gas emissions of biofuels.

Biofuels have received legislative support recently in California's Low-Carbon Fuel Standard and the Federal Energy Independence and Security Act. Both present new fuel types, but neither provides methodological guidelines for dealing with the inherent uncertainty in evaluating their potential life-cycle greenhouse gas emissions. Emissions reductions are based on point estimates only. This work demonstrates the use of Monte Carlo simulation to estimate life-cycle emissions distributions from ethanol and butanol from corn or switchgrass. Life-cycle emissions distributions for each feedstock and fuel pairing modeled span an order of magnitude or more. Using a streamlined life-cycle assessment, corn ethanol emissions range from 50 to 250 g CO(2)e/MJ, for example, and each feedstock-fuel pathway studied shows some probability of greater emissions than a distribution for gasoline. Potential GHG emissions reductions from displacing fossil fuels with biofuels are difficult to forecast given this high degree of uncertainty in life-cycle emissions. This uncertainty is driven by the importance and uncertainty of indirect land use change emissions. Incorporating uncertainty in the decision making process can illuminate the risks of policy failure (e.g., increased emissions), and a calculated risk of failure due to uncertainty can be used to inform more appropriate reduction targets in future biofuel policies.

Uncertainty analysis of life cycle greenhouse gas emissions from petroleum-based fuels and impacts on low carbon fuel policies.

The climate change impacts of U.S. petroleum-based fuels consumption have contributed to the development of legislation supporting the introduction of low carbon alternatives, such as biofuels. However, the potential greenhouse gas (GHG) emissions reductions estimated for these policies using life cycle assessment methods are predominantly based on deterministic approaches that do not account for any uncertainty in outcomes. This may lead to unreliable and expensive decision making. In this study, the uncertainty in life cycle GHG emissions associated with petroleum-based fuels consumed in the U.S. is determined using a process-based framework and statistical modeling methods. Probability distributions fitted to available data were used to represent uncertain parameters in the life cycle model. Where data were not readily available, a partial least-squares (PLS) regression model based on existing data was developed. This was used in conjunction with probability mixture models to select appropriate distributions for specific life cycle stages. Finally, a Monte Carlo simulation was performed to generate sample output distributions. As an example of results from using these methods, the uncertainty range in life cycle GHG emissions from gasoline was shown to be 13%-higher than the typical 10% minimum emissions reductions targets specified by low carbon fuel policies.

Primary and embedded steel imports to the U.S.: implications for the design of border tax adjustments.

Carbon Border Tax Adjustments (BTAs) are a politically popular strategy for avoiding competitive disadvantage problems when a country implements a unilateral climate change policy. A BTA taxes carbon embodied in imported goods in order to protect domestic industry and motivate other countries to implement climate change policy. To estimate the effectiveness of a BTA, is it is necessary to know which products are covered, where they were originally produced and ultimately exported from, and how the covered amount compares to total production in foreign countries. Using a scrap-adjusted, mixed-unit input-output model in conjunction with a multiregional input-output model, this analysis evaluates the effectiveness of BTAs for the case study of U.S. steel imports. Most imported steel by mass is embedded in finished products (60%), and 30% of that steel is produced in a different country than the one from which the final good is exported. Given the magnitudes involved and complexities of global supply chains, a BTA that protects domestic industry will be a challenge to implement. We propose a logistically feasible BTA structure that minimizes the information burden while still accounting for these complexities. However, the amount of steel imported to the U.S. is negligible (5%) compared to foreign production in BTA-eligible countries and is unlikely to motivate affected countries to impose an emissions reduction policy.

Economic sources and spatial distribution of airborne chromium risks in the U.S.

We present a model that integrates the economic input-output approach of life cycle assessment with environmental fate, exposure, and risk assessment to estimate the spatial distribution of air toxic health risks due to sector-specific economic activity in the U.S. The model is used to relate the economic activity and exposure potential (population density and meteorology) associated with point source emissions of the heavy metal and carcinogen, hexavalent chromium, or Cr(VI), on a county basis. Total direct annual airborne emissions of Cr(VI) in the U.S. were 44 tonnes in 2002, with 97% from facilities in four major sectors: power generation, wood, plastics, and chemicals, metals, and scientific services. These include 6 tonnes of Cr(VI) emitted in the supply chains of these sectors. A highly variable national distribution of lifetime cancer risk is predicted, with a population-weighted mean of 2.7 x 10(-7), but with hot-spot counties with lifetime risks as high as 6 x 10(-6). Furthermore, high exposures and risks tend to occur in more highly populated counties. In particular, the population of Los Angeles County is exposed to the highest level of risk in the country and almost three-quarters of the total predicted cancer incidence due to inhalation of airborne Cr(VI) emissions. This finding can be attributed largely to the use of Cr(VI) as a corrosion inhibitor by the scientific services sector facilities in the county, the use of shorter facility stacks, and their sitting within a highly populated area. These results indicate that linking economic activity, emission estimates, and fate and transport models for air toxics can inform both life cycle impact and comparative health risk assessments, allowing us to better target emission reductions to minimize hot-spots of risk.