Land Resources for Wind Energy Development Requires Regionalized Characterizations.

Estimates of the land area occupied by wind energy differ by orders of magnitude due to data scarcity and inconsistent methodology. We developed a method that combines machine learning-based imagery analysis and geographic information systems and examined the land area of 318 wind farms (15,871 turbines) in the U.S. portion of the Western Interconnection. We found that prior land use and human modification in the project area are critical for land-use efficiency and land transformation of wind projects. Projects developed in areas with little human modification have a land-use efficiency of 63.8 ± 8.9 W/m2 (mean ±95% confidence interval) and a land transformation of 0.24 ± 0.07 m2/MWh, while values for projects in areas with high human modification are 447 ± 49.4 W/m2 and 0.05 ± 0.01 m2/MWh, respectively. We show that land resources for wind can be quantified consistently with our replicable method, a method that obviates >99% of the workload using machine learning. To quantify the peripheral impact of a turbine, buffered geometry can be used as a proxy for measuring land resources and metrics when a large enough impact radius is assumed (e.g., >4 times the rotor diameter). Our analysis provides a necessary first step toward regionalized impact assessment and improved comparisons of energy alternatives.

Gaussian Process Regression as a Replicable, Streamlined Approach to Inventory and Uncertainty Analysis in Life Cycle Assessment.

Life cycle assessment plays a critical role in quantifying environmental impacts, but its credibility remains challenged when data and uncertainty analysis are lacking. In this study, we propose a data compilation framework to address these two issues. The framework first quantifies the correlations of production activities among existing data in temporal, geographical, and taxonomic dimensions. The framework then introduces covariance functions to convert these correlations to a similarity matrix, and the Gaussian process regression model is adopted to predict new data based on these covariance functions. The associated uncertainty is automatically characterized using the posterior distribution of predictions. The framework is demonstrated on the nitrogen fertilizer application rate for food production─an activity recognized for its environmental burden─with results capable of reflecting temporal and geographical variations. By introducing the concept of phylogenetic distance as a correlation of taxonomy, the framework provides a quantitative basis for predictions in a proxy data usage scenario. The framework can be used in developing temporally and regionally representative life cycle inventories and databases and can facilitate consistent uncertainty quantification in future life cycle assessment methodologies.

Implications of Generation Efficiencies and Supply Chain Leaks for the Life Cycle Greenhouse Gas Emissions of Natural Gas-Fired Electricity in the United States.

Uncertainties in supply chain emissions raise questions about the benefits of natural gas as a bridge fuel, but recent efficiency improvements in gas-fired electricity generation remain overlooked. Our comprehensive analysis of supply chain infrastructure and electricity generation across the United States informs spatially and temporally resolved estimates of life cycle greenhouse gas emissions. Results show decreasing life cycle emissions over each year examined: 629, 574, and 525 kg CO2 eq MWh-1 in 2005, 2010, and 2015, respectively. Electricity generation contributed 86% of emissions or greater for each year. Despite concerns over uncertain methane leaks, efficiency improvements make it much more likely that natural gas electricity has an unambiguous greenhouse gas benefit relative to coal. Methane leaks would have to be 4.4 times the Environmental Protection Agency (EPA) value in 2015 to reverse these benefits over 20-year time horizons. With retiring coal plants and scrutinized supply chain emissions, our results show that natural gas can provide a lower emissions option to coal in an increasingly decarbonized power sector.

Grid-Scale Life Cycle Greenhouse Gas Implications of Renewable, Storage, and Carbon Pricing Options.

Models that characterize life cycle greenhouse gases from electricity generation are limited in their capability to estimate emissions changes at scales that capture the grid-scale benefits of technologies and policies that enhance renewable systems integration. National assumptions about generation mixes are often applied at annual time steps, neglecting spatiotemporal resolutions that provide insights on impacts from time-variable emissions. Our grid-scale model incorporates details of transmission and generation planning that allows a geographically and temporally textured and more realistic assessment of the life cycle greenhouse gas emissions outcomes, using a case study of the Western Interconnection of North America. Results from a co-optimized model of generation, transmission, and operations-the Johns Hopkins Stochastic Multistage Integrated Network Expansion Model-provide a detailed characterization of twenty-one scenarios with different configurations of storage additions, new renewable capacity, and carbon prices. Life cycle results suggest that optimization models that focus on generation alone may underestimate emissions by 18-29% because only emissions from power generation are quantified (i.e., supply chain emissions are omitted) but also that carbon pricing is the predominant driver of reducing emissions in the scenarios we examine. Life cycle assessment of electricity generation should move beyond individual technologies toward capturing the influence of policies at the system level to better understand technology-policy dynamics for the grid.

Potential Uses of Coal Methane in China and Associated Benefits for Air Quality, Health, and Climate.

China is the world's largest producer and consumer of coal, but the country has recently set ambitious targets for cleaner energy sources. These include goals to capture and utilize methane from coal seams as a source of unconventional natural gas. We investigate the impacts of using coal methane to displace coal power plants and residential coal combustion across northern China. We compare the greenhouse gas emissions, air quality, and public health impacts of several scenarios for coal methane utilization. We find that China's existing goals would decrease the country's total carbon emissions by ∼2.3% (284 million tons CO2eq). Furthermore, these reductions are dominated by mitigated methane emissions and therefore confer a much larger climate benefit than would be expected from other forms of natural gas. Our results also indicate that the air quality and health impacts strongly depend on how the methane is utilized. Using the methane to displace coal-fired electricity would reduce annual mean ambient PM2.5 concentrations by up to >2.5 µg/m3 and prevent up to 9290 premature mortalities annually (95% confidence interval: 7862-9992). By contrast, utilizing coal methane in home heating yields smaller changes to ambient air quality (∼0.6 µg/m3), but improvements to indoor air quality could produce comparable reductions in premature mortality.

Country-Level Life Cycle Assessment of Greenhouse Gas Emissions from Liquefied Natural Gas Trade for Electricity Generation.

In the determination of the net impact of liquefied natural gas (LNG) on greenhouse gas emissions, life cycle assessments (LCA) of electricity generation have yet to combine the effects of transport distances between exporting and importing countries, country-level infrastructure in importing countries, and the fuel sources displaced in importing countries. To address this, we conduct a LCA of electricity generated from LNG export from British Columbia, Canada with a three-step approach: (1) a review of viable electricity generation markets for LNG, (2) the development of results for greenhouse gas emissions that account for transport to importing nations as well as the infrastructure required for power generation and delivery, and (3) emissions displacement scenarios to test assumptions about what electricity is being displaced in the importing nation. Results show that while the ultimate magnitude of the greenhouse gas emissions associated with natural gas production systems is still unknown, life cycle greenhouse gas emissions depend on country-level infrastructure (specifically, the efficiency of the generation fleet, transmission and distribution losses and LNG ocean transport distances) as well as the assumptions on what is displaced in the domestic electricity generation mix. Exogenous events such as the Fukushima nuclear disaster have unanticipated effects on the emissions displacement results. We highlight national regulations, environmental policies, and multilateral agreements that could play a role in mitigating emissions.

Regional water implications of reducing oil imports with liquid transportation fuel alternatives in the United States.

The Renewable Fuel Standard (RFS) is among the cornerstone policies created to increase U.S. energy independence by using biofuels. Although greenhouse gas emissions have played a role in shaping the RFS, water implications are less understood. We demonstrate a spatial, life cycle approach to estimate water consumption of transportation fuel scenarios, including a comparison to current water withdrawals and drought incidence by state. The water consumption and land footprint of six scenarios are compared to the RFS, including shale oil, coal-to-liquids, shale gas-to-liquids, corn ethanol, and cellulosic ethanol from switchgrass. The corn scenario is the most water and land intense option and is weighted toward drought-prone states. Fossil options and cellulosic ethanol require significantly less water and are weighted toward less drought-prone states. Coal-to-liquids is an exception, where water consumption is partially weighted toward drought-prone states. Results suggest that there may be considerable water and land impacts associated with meeting energy security goals through using only biofuels. Ultimately, water and land requirements may constrain energy security goals without careful planning, indicating that there is a need to better balance trade-offs. Our approach provides policymakers with a method to integrate federal policies with regional planning over various temporal and spatial scales.

Land and water impacts of oil sands production in Alberta.

Expansion of oil sands development results not only in the release of greenhouse gas emissions, but also impacts land and water resources. Though less discussed internationally due to to their inherently local nature, land and water impacts can be severe. Research in key areas is needed to manage oil sands operations effectively; including improved monitoring of ground and surface water quality. The resulting information gap means that such impacts are not well understood. Improved analyses of oil sands products are required that compare land and water use with other transportation fuel pathways and use a regional perspective so local effects can be considered and mitigated.

Land use greenhouse gas emissions from conventional oil production and oil sands.

Debates surrounding the greenhouse gas (GHG) emissions from land use of biofuels production have created a need to quantify the relative land use GHG intensity of fossil fuels. When contrasting land use GHG intensity of fossil fuel and biofuel production, it is the energy yield that greatly distinguishes the two. Although emissions released from land disturbed by fossil fuels can be comparable or higher than biofuels, the energy yield of oil production is typically 2-3 orders of magnitude higher, (0.33-2.6, 0.61-1.2, and 2.2 5.1 PJ/ha) for conventional oil production, oil sands surface mining, and in situ production, respectively). We found that land use contributes small portions of GHGs to life cycle emissions of California crude and in situ oil sands production ( <0.4% or < 0.4 gCO2e/MJ crude refinery feedstock) and small to modest portions for Alberta conventional oil (0.1-4% or 0.1-3.4 gCO2e/MJ) and surface mining of oil sands (0.9-11% or 0.8-10.2 gCO2e/MJ).Our estimates are based on assumptions aggregated over large spatial and temporal scales and assuming 100% reclamation. Values on finer spatial and temporal scales that are relevant to policy targets need to account for site-specific information, the baseline natural and anthropogenic disturbance.