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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.

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.

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

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

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.

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.

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...

Until recently, the main environmental concerns associated with information and communication technologies (ICTs) have been their use-phase electricity consumption and the chemicals associated with their manufacture, and the environmental effects of these technologies on other parts of the economy have largely been ignored. With the advent of mobile computing, communication, and sensing devices, these indirect effects have the potential to be much more important than the impacts from the use and manufacturing phases of this equipment. This article summarizes the trends that have propelled modern technological societies into the ultralow-power design space and explores the implications of these trends for the direct and indirect environmental impacts associated with these new technologies. It reviews the literature on environmental effects of information technology (also with an emphasis on low-power systems) and suggests areas for further research.

Residential electricity users need more detail than monthly bills to reduce consumption. With the emergence of technologies that provide detailed usage estimates for energy consumption, two questions arise. First, how many different energy-consuming appliances contribute to household electricity load, and secondly which appliances? Using national average penetration rates, the Residential Energy Consumption Survey (RECS), estimates that 42 unique appliances account for 93% of electricity consumption, while 12 appliances account for 80% of average household electric load. A typical scenario is developed from national and regional penetration rates and find that eight appliances are responsible for 80% of a household's electric load in the United States. Four household scenarios are developed: a house that uses electric appliances, gas appliances, the average household, and typical household. It is concluded that RECS cannot be used as a representative household as it overestimates the number of appliances that contribute to a household electric load. The number of significant appliances is affected by appliance ownership and use, which is more variable between homes than between census divisions. These results can be used to design and maximize the value of residential energy information and management systems.

With urban space at a premium, roofs are being targeted as an opportunity to deploy sustainable energy technologies for buildings. This research evaluates the combination of green roofs and solar photovoltaics specifically through their temperature and electricity production relationship. Measurements over a one year study period from July 1, 2011 to June 30, 2012 from a large field project in Pittsburgh, Pennsylvania were used to determine the differences in power output from green and black roofs as well as to derive two regression functions for back-surface panel temperature and photovoltaic (PV) output. These estimation functions were applied to three different cities (San Diego, CA; Huntsville, AL; and Phoenix, AZ) chosen to represent a wide range of irradiance and temperature values. Based on the specific test-bed configuration, the green or black roof choice under the PV panels had little impact on the PV performance. The difference in magnitude of power generation for green roof–PV compared with black roof–PV assemblies was small (0.5%) corresponding to an annual loss of $9/60 panels in Pittsburgh and a benefit of approximately $8/60 panels per year in Phoenix. Results also suggest that sites consistently above 25 °C (77 °F) will most likely see a small, positive impact from a green roof–PV combination. Building managers and designers should consider this temperature and power output interaction a minor economic factor in roof decisions.

This paper presents a nonintrusive electricity load-monitoring approach that provides feedback on the energy consumption and operational schedule of electrical appliances in a residential building. This approach utilizes simple algorithms for detecting and classifying electrical events on the basis of voltage and current measurements obtained at the main circuit panel of the home. To address the necessary training and calibration, this approach is designed around the end-user and relies on user input to continuously improve its performance. The algorithms and the user interaction processes are described in detail. Three data sets were collected with a prototype system (from a power strip in a laboratory, a house, and an apartment unit) to test the performance of the algorithms. The event detector achieved true positive and false positive rates of 94 and 0.26%, respectively. When combined with the classification task, the overall accuracy (correctly detected and classified events) was 82%. The advantages a...

Costs and benefits of building energy efficiency are estimated as a means of reducing greenhouse gas emissions in Pittsburgh, PA and Austin, TX. The analysis includes electricity and natural gas consumption, covering 75% of building energy consumption in Pittsburgh and 85% in Austin. Two policy objectives were evaluated: maximize GHG reductions given initial budget constraints or maximize social savings given target GHG reductions. This approach evaluates the trade-offs between three primary and often conflicting program design parameters: initial capital constraints, social savings, and GHG reductions. Results suggest uncertainty in local stocks, demands, and efficiency significantly impacts anticipated outcomes. Annual GHG reductions of 1 ton CO 2 eq/capita/yr in Pittsburgh could cost near nothing or over $20 per capita annually. Capital-constrained policies generate slightly less social savings (a present value of a few hundred dollars per capita) than policies that maximize social savings. However, sectors and end uses targeted for intervention vary depending on policy objectives and constraints. Optimal efficiency investment strategies for some end uses vary significantly (in excess of 100%) between Pittsburgh and Austin, suggesting that resources and guidance conducted at the national scale may mislead state and local decision-makers. Results are used to provide recommendations for efficiency program administrators.

Since 2004, wildfires have been responsible for the destruction of 3.5 million hectares of forestland per year. Fuel reduction activities such as prescribed fires, cutting and burning in situ, and biomass removal (thinning) have been shown to reduce wildfire severity, but with mounting costs of fighting wildfires and a tightening budget, public agencies such as the USDA Forest Service may find it difficult to continue funding these preventative treatments. By using thinned biomass as a cellulosic ethanol feedstock, these agencies may be able to generate funds for these treatments. Here, we estimate costs of producing cellulosic ethanol from forest thinnings based on forest thinning supply curves. Nationally, 27 to 34 million Mg of biomass could be removed from overcrowded forests per year at collection costs of $55 to $110 per dry Mg. Given a mature cellulosic ethanol industry, ethanol produced from these thinnings could generate revenue at gasoline prices of $0.5 to $0.8 per liter. By using thinned biomass as an ethanol feedstock, it may be possible to generate significant funds for socially beneficial thinning treatments.

Cellulosic ethanol has been identified as a promising alternative to fossil fuels to provide energy for the transportation sector. One of the obstacles cellulosic ethanol must overcome in order to contribute to transportation energy demand is the infrastructure required to produce and distribute the fuel. Given a nascent cellulosic ethanol industry, locating cellulosic ethanol refineries and creating the accompanying infrastructure is essentially a greenfield problem that may benefit greatly from quantitative analysis. This study models cellulosic ethanol infrastructure investment using a mixed integer program (MIP) that locates ethanol refineries and connects these refineries to the biomass supplies and ethanol demands in a way that minimizes the total cost. For the single- and multi-state regions examined in this study, larger facilities can decrease ethanol costs by $0.20-0.30 per gallon, and placing these facilities in locations that minimize feedstock and product transportation costs can decrease ethanol costs by up to $0.25 per gallon compared to uninformed placement that could result from influences such as local subsidies to encourage economic development. To best benefit society, policies should allow for incentives that encourage these low-cost production scenarios and avoid politically motivated siting of plants.

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.

Summary Nonintrusive load monitoring (NILM) is a technique for deducing the power consumption and operational schedule of individual loads in a building from measurements of the overall voltage and current feeding it, using information and communication technologies. In this article, we review the potential of this technology to enhance residential electricity audits. First, we review the currently commercially available whole-house and plug-level technology for residential electricity monitoring in the context of supporting audits. We then contrast this with NILM and show the advantages and disadvantages of the approach by discussing results from a prototype system installed in an apartment unit. Recommendations for improving the technology to allow detailed, continuous appliance-level auditing of residential buildings are provided, along with ideas for possible future work in the field.

“Because of its emphasis on the use of system perspectives, industrial ecology is well suited to analysis of both the benefits and impacts of ICT. . . . this collection of articles brings these topics into sharper focus, highlights future directions for ICT applications and their potential positive implications, and reinforces the importance of future research work. . .” When the first commercialized transistor ushered in the solid-state era nearly 60 years ago, few could have imagined the many ways that information and communication technology (ICT) would permeate and transform our lives in the 21st century. Today, ICT is a critical component of nearly every sector of the global economy, and has led to considerable transformations in the way that humans interact with each other and with the world around them. Information and communication technology can be broadly defined as systems whose fundamental functions are anchored in the generation, processing, storage, communication, and/or presentation of digital information. Many ICT systems employ most or all of these elements, and the vast majority of these systems are based entirely on solid-state technologies. Arguably the most prominent example of a transformative ICT system is the Internet, which serves as the global

The field of engineering is changing rapidly as the growing global population puts added demands on the earth's resources: engineering decisions must now account for limitations in materials and energy as well as the need to reduce discharges of wastes. This means educators must revise courses and curricula so engineering graduates are prepared for the new challenges as practicing engineers. The Center for Sustainable Engineering has been established to help faculty members accommodate such changes through workshops and new educational materials, including a free access website with peer-reviewed materials.

This scoping study takes a broad look at how information technology-enabled monitoring and control systems could assist in mitigating energy use in residences by more efficiently allocating the delivery of services by time and location. A great deal of energy is wasted in delivering services inefficiently to residents such as heating or cooling unoccupied spaces, overheating/undercooling for whole-house comfort, leakage current, and inefficient appliances. We construct a framework to estimate different categories of inefficient energy services and the result of our initial estimate is that over 39% of residential primary energy is wasted. We next discuss how monitoring and control technologies could manage home energy use to reduce waste. Technologies considered here include programmable thermostats, smart meters and outlets, zone heating, automated sensors, and wireless communications infrastructures. The level of energy services delivered is assumed to remain unchanged, with all energy savings being realized through better management. A final discussion on barriers to adoption of these systems speculates that a lack of consumer awareness of the technologies, high costs due to lack of economies of scale, and difficult user interfaces are currently the major hurdles toward adoption.

On March 10, 2009, the US Environmental Protection Agency (EPA) proposed a new rule, Mandatory Reporting of Greenhouse Gases. When final, the rule would compel most large sources of greenhouse gases (GHGs) to report their emissions to EPA as well as fossil fuel suppliers and vehicle engine manufacturers to report their fuel sales and engine emissions rates, respectively. We suggest a number of improvements to the rule that would enhance compatibility with expected future climate legislation and enable a broader range of policies and analysis: (1) lower the threshold for reporting to a level more consistent with expected future legislation, (2) require reporting of electricity use along with direct emissions, (3) require reporting of emissions per unit output for a small number of selected sectors, (4) include a system of identifying corporate ownership of reporting facilities, and (5) identify a path toward coverage for sectors that were left out of the proposal due to underdeveloped reporting protocols.

The combination of current and planned 2007 U.S. ethanol production capacity is 50 billion L/yr, one-third of the Energy Independence and Security Act of 2007 (EISA) target of 136 billion L of biofuels by 2022. In this study, we evaluate transportation impacts and infrastructure requirements for the use of E85 (85% ethanol, 15% gasoline) in light-duty vehicles using a combination of corn and cellulosic ethanol. Ethanol distribution is modeled using a linear optimization model. Estimated average delivered ethanol costs, in 2005 dollars, range from $0.29 to $0.62 per liter ($1.3-2.8 per gallon), depending on transportation distance and mode. Emissions from ethanol transport estimated in this work are up to 2 times those in previous ethanol LCA studies and thus lead to larger total life cycle effects. Long-distance transport of ethanol to the end user can negate ethanol's potential economic and environmental benefits relative to gasoline. To reduce costs, we recommend regional concentration of E85 blends for future ethanol production and use.

The growing interest in energy-efficient buildings is driving changes in investment, design, and occupant behavior. To better focus cost and resource conservation efforts, electricity consumption feedback can be used to provide motivation, guidance, and verification. Disaggregating by end-use helps both consumers and producers to identify targets for conservation. While hardware-based sub-metering is costly and labor-intensive, non-intrusive load monitoring (NILM) is capable of gathering detailed energy-use data with minimal equipment cost and installation time. However, variations in measurements between metering devices complicate the process of compiling the necessary appliance profiles. Future work involves the development of NILM algorithms using sensor fusion and detailed appliance-level data gathered from a highly-sensed house currently being constructed near Pittsburgh, Pennsylvania.

Because of increasing concern about global climate change and carbon emissions as a causal factor, many companies and organizations are pursuing "carbon footprint" projects to estimate their own contributions to global climate change. Protocol definitions from carbon registries help organizations analyze their footprints. The scope of these protocols varies but generally suggests estimating only direct emissions and emissions from purchased energy, with less focus on supply chain emissions. In contrast approaches based on comprehensive environmental life-cycle assessment methods are available to track total emissions across the entire supply chain, and experience suggests that following narrowly defined estimation protocols will generally lead to large underestimates of carbon emissions for providing products and services. Direct emissions from an industry are, on average, only 14% of the total supply chain carbon emissions (often called Tier 1 emissions), and direct emissions plus industry energy inputs are, on average, only 26% of the total supply chain emissions (often called Tier 1 and 2 emissions). Without a full knowledge of their footprints, firms will be unable to pursue the most cost-effective carbon mitigation strategies. We suggest that firms use the screening-level analysis described here to set the bounds of their footprinting strategy to ensure that they do not ignore large sources of environmental effects across their supply chains. Such information can help firms pursue carbon and environmental emission mitigation projects not only within their own plants but also across their supply chain.

Analysis of household consumption and its environmental impact remains one of the most important topics in sustainability research. Nevertheless, much past and recent work has focused on domestic national averages, neglecting both the growing importance of international trade on household carbon footprint and the variation between households of different income levels and demographics. Using consumer expenditure surveys and multi-country life cycle assessment techniques, this paper analyzes the global and distributional aspects of American household carbon footprint. We find that due to recently increased international trade, 30% of total US household CO 2 impact in 2004 occurred outside the US. Further, households vary considerably in their CO 2 responsibilities: at least a factor of ten difference exists between low and high-impact households, with total household income and expenditure being the best predictors of both domestic and international portions of the total CO 2 impact. The global location of emissions, which cannot be calculated using standard input–output analysis, and the variation of household impacts with income, have important ramifications for polices designed to lower consumer impacts on climate change, such as carbon taxes. The effectiveness and fairness of such policies hinges on a proper understanding of how income distributions, rebound effects, and international trade affect them.

Despite significant recent public concern and media attention to the environmental impacts of food, few studies in the United States have systematically compared the life-cycle greenhouse gas (GHG) emissions associated with food production against long-distance distribution, aka "food-miles." We find that although food is transported long distances in general (1640 km delivery and 6760 km life-cycle supply chain on average) the GHG emissions associated with food are dominated by the production phase, contributing 83% of the average U.S. household's 8.1 t CO2e/yr footprint for food consumption. Transportation as a whole represents only 11% of life-cycle GHG emissions, and final delivery from producer to retail contributes only 4%. Different food groups exhibit a large range in GHG-intensity; on average, red meat is around 150% more GHG-intensive than chicken or fish. Thus, we suggest that dietary shift can be a more effective means of lowering an average household's food-related climate footprint than "buying local." Shifting less than one day per week's worth of calories from red meat and dairy products to chicken, fish, eggs, or a vegetable-based diet achieves more GHG reduction than buying all locally sourced food.

We update a previously presented Linear Programming (LP) methodology for estimating state level costs for reducing CO2 emissions from existing coal-fired power plants by cofiring switchgrass, a biomass energy crop, and coal. This paper presents national level results of applying the methodology to the entire portion of the United States in which switchgrass could be grown without irrigation. We present incremental switchgrass and coal cofiring carbon cost of mitigation curves along with a presentation of regionally specific cofiring economics and policy issues. The results show that cofiring 189 million dry short tons of switchgrass with coal in the existing U.S. coal-fired electricity generation fleet can mitigate approximately 256 million short tons of carbon-dioxide (CO2) per year, representing a 9% reduction of 2005 electricity sector CO2 emissions. Total marginal costs, including capital, labor, feedstock, and transportation, range from $20 to $86/ton CO2 mitigated,with average costs ranging from $20 to $45/ton. If some existing power plants upgrade to boilers designed for combusting switchgrass, an additional 54 million tons of switchgrass can be cofired. In this case, total marginal costs range from $26 to $100/ton CO2 mitigated, with average costs ranging from $20 to $60/ton. Costs for states east of the Mississippi River are largely unaffected by boiler replacement; Atlantic seaboard states represent the lowest cofiring cost of carbon mitigation. The central plains states west of the Mississippi River are most affected by the boiler replacement option and, in general, go from one of the lowest cofiring cost of carbon mitigation regions to the highest. We explain the variation in transportation expenses and highlight regional cost of mitigation variations as transportation overwhelms other cofiring costs.

Summary Recent changes to the management of inventory and warehousing methods have created significant changes in business processes. These changes have produced economic savings to firms from reduced handling of supplies. The system-wide impacts of this shift in methods on overall cost and the environment are still unclear, however. Reductions in inventories can provide significant environmental savings. In this article, we analyze the changes in inventory control methods and assess the environmental and cost tradeoffs between increased trucking and more efficient centralized warehouses. We consider the case of consolidating the spare-parts inventory at U.S. Department of Defense warehouses and discuss similarities to other existing businesses. The case suggests large economic and environmental benefits due to reductions in warehousing expenses, despite higher transportation costs.

The construction industry is responsible for environmental impacts that include air emissions, waste generation, and the use of land, water, and energy. However, most prior construction research attention has focused on these impacts only for material selection and building operation. The focus here is the environmental and energy implications of the construction process, specifically on-site energy consumption. Existing estimates of energy use and environmental emissions for the industry are tied to large construction vehicles, but other equipment consumes energy in various forms. This research creates a broader boundary for considering the energy use and environmental impacts of engines and vehicles used for construction activities by estimating these effects and comparing them to other national-level impacts. Results indicate that fuel use estimates for construction equipment are almost double the levels suggested by government reports; accordingly, air emission impacts are 30% larger for particulate matter and almost double levels of oxides of nitrogen and volatile organic compounds. Quantitative and qualitative analysis regarding how U.S. diesel engine and fuel regulations could significantly reduce air emissions from construction sites is also provided. If fully implemented, these regulations could initiate the manufacture and use of portable generators that make on-site electricity generation comparable to the electricity grid in terms of air emissions.

This paper presents a linear programming (LP) methodology for estimating the cost of reducing a state's coal-fired power plant carbon dioxide emissions by cofiring switchgrass and coal. LP modeling allows interplay between regionally specific switchgrass production forecasts, coal plant locations, and individual coal plant historic performance data to determine an allocation of switchgrass minimizing cost or maximizing carbon reduction. The LP methodology is applied to two states, Pennsylvania (PA) and Iowa (IA), and results are presented with a discussion of modeling assumptions, techniques, and carbon mitigation policy implications. The LP methodology estimates that, in PA, 4.9 million tons of CO2/year could be mitigated at an average cost of less than $34/ton of CO2 and that, in IA, 7 million tons of CO2/year could be mitigated at an average Cost of Mitigation of $27/ton of CO2. Because the factors determining the cofiring costs vary so much between the two states, results suggest that cofiring costs will also vary considerably between different U.S. regions. A national level analysis could suggest a lowest-cost cofiring region. This paper presents techniques and assumptions that can simplify biomass energy policy analysis with little effect on analysis conclusions.

Significant recent attention has been given to quantifying the environmental impacts of international trade. However, the United States, despite being the world's largest emitter of greenhouse gases and having large recent growth in international trade, has seen little analysis. This work uses a multi-country input-output model of the U.S. and its seven largest trading partners (Canada, China, Mexico, Japan, Germany, the UK, and Korea) to analyze the environmental effects of changes to U.S. trade structure and volume from 1997 to 2004. It is shown that increased import volume and shifting trade patterns during this time period led to a large increase in the U.S.' embodied emissions in trade (EET) for CO2, SO2, and NO(x). Methodological uncertainties, especially related to uncertainties of international currency conversion, lead to large differences in estimation of the total EET, but we estimate that the overall embodied CO2 in U.S. imports has grown from between 0.5 and 0.8 Gt of CO2 in 1997 to between 0.8 and 1.8 Gt of CO2 in 2004, representing between 9-14% and 13-30% of U.S. (2-4% to 3-7% of global) CO2 emissions in 1997 and 2004, respectively.

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.

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.

This article details the planning/implementation, and assessment of a worldwide electronics packaging take-back system by a key supply chain partner. The take-back system was a financial and environmental tradeoff between reducing cardboard packaging against increased use of ground and water transportation. Through Internetenabled life cycle analysis, the new system was shown to significantly reduce waste as well as energy and environmental impacts. If such methods were introduced more formally in the electronics industry, $150 million as well as billions of pounds of waste could be saved per year.

The advent of the Internet and e-commerce has brought a new way of marketing and selling many products, including books. The system-wide impacts of this shift in retail methods on cost, energy, and the environment are still unclear. While reductions in inventories and returns provide significant savings, some of the major concerns of e-commerce business models are the energy and packaging materials used by the logistics networks for product fulfillment and delivery. In this paper, we analyze the different logistics networks and assess the energy and cost impacts of different delivery systems in the United States and Japan. In Japan, the results suggest a crossover in energy use according to population density. In the United States, the crossover also exists, based on delivery method and distance to local bookstores. In neither case, however, are the potential spillover energy benefits from e-commerce-based methods considered. The results show notable differences for the two distribution methods, and suggest areas where further energy improvements may be possible.

Cities have become key players in climate change mitigation policy. To develop their climate policies, cities need good assessments of their current and future emissions. We use publically available national datasets to develop an integrated approach for estimating GHG emissions at the metropolitan level over time, between multiple locations, and across sectors. We estimate consistent production-based GHG emissions for the 100 most populated metropolitan areas in the United States in 2014. We find that total 2014 metropolitan CO2 emissions range from 4.1 million metric tons in Lancaster, Pennsylvania to nearly 170 million metric tons in the Houston, Texas; with an overall average of 27 million metric tons. The top 20 absolute emitters and top 20 per capita emitters only overlap for 9 locations. Per capita emissions also show a wide variation: from 5 metric tons per person in the Tucson, Arizona to 65 metric tons per person in the Baton Rouge, Louisiana; with an overall average of 14 metric tons per person. We also compute estimates for 2002 and 2011 and compare to our 2014 emission estimates. Across all locations analyzed, average total emissions increased by 3% and average per capita emissions decreased by 14%. Where possible, we also compare our emission estimates to those reported by the cities in their climate action plans and find an average absolute difference between our estimates and those reported by the cities of 5.6 metric tons CO2 per person, likely due to temporal and scope differences between the two estimates. Our integrated emission estimation approach complements bottom-up approaches typically employed by municipalities and helps practitioners divert their attention and resources away from continuous emission accounting toward more impactful emission mitigation efforts.

Analyzing the effectiveness of environmental programs and regulations requires comparing the costs and benefits of reducing different pollutants or further abatement. To make useful comparisons, the various benefits from reduced pollution must be translated into dollars. Unfortunately, estimating the dollar value of environmental damages is complicated, controversial, and generally uncertain. Often these estimates have been misused. This paper identifies the need for benefit estimates, shows how they are constructed, and demonstrates how they can be used to improve environmental policy analysis.