The impact of Uber and Lyft on vehicle ownership, fuel economy, and transit across U.S. cities.

We estimate the effects of transportation network companies (TNCs) Uber and Lyft on vehicle ownership, fleet average fuel economy, and transit use in U.S. urban areas using a set of difference-in-difference propensity score-weighted regression models that exploit staggered market entry across the U.S. from 2011 to 2017. We find evidence that TNC entry into urban areas causes an average 0.7% increase in vehicle registrations with significant heterogeneity in these effects across urban areas: TNC entry produces larger vehicle ownership increases in urban areas with higher initial ownership (car-dependent cities) and in urban areas with lower population growth (where TNC-induced vehicle adoption outpaces population growth). We also find no statistically significant average effect of TNC entry on fuel economy or transit use but find evidence of heterogeneity in these effects across urban areas, including larger transit ridership reductions after TNC entry in areas with higher income and more childless households.

Effects of Air Emission Externalities on Optimal Ridesourcing Fleet Electrification and Operations.

Ridesourcing services from transportation network companies, like Uber and Lyft, serve the fastest growing share of U.S. passenger travel demand.1 Ridesourcing vehicles' high use intensity is economically attractive for electric vehicles, which typically have lower operating costs and higher capital costs than conventional vehicles. We optimize fleet composition (mix of conventional vehicles (CVs), hybrid electric vehicles (HEVs), and battery electric vehicles (BEVs)) and operations to satisfy demand at minimum cost and compare findings across a wide range of present-day and future scenarios for three cities. In nearly all cases, the optimal fleet includes a mix of technologies, HEVs and BEVs make up the majority of distance traveled, and CVs are used primarily for periods of peak demand (if at all). When life cycle air pollution and greenhouse gas emission externalities are internalized via a Pigovian tax, fleet electrification increases and externalities decrease, suggesting a role for policy. Externality reductions vary from 10% in New York (where externality costs for both gasoline and electricity consumption are relatively high and a Pigovian tax induces a partial shift to BEVs), to 22% in Los Angeles (where high gasoline and low electric grid externalities lead a Pigovian tax to induce a near-complete shift to BEVs).

Global food system emissions could preclude achieving the 1.5° and 2°C climate change targets.

The Paris Agreement's goal of limiting the increase in global temperature to 1.5° or 2°C above preindustrial levels requires rapid reductions in greenhouse gas emissions. Although reducing emissions from fossil fuels is essential for meeting this goal, other sources of emissions may also preclude its attainment. We show that even if fossil fuel emissions were immediately halted, current trends in global food systems would prevent the achievement of the 1.5°C target and, by the end of the century, threaten the achievement of the 2°C target. Meeting the 1.5°C target requires rapid and ambitious changes to food systems as well as to all nonfood sectors. The 2°C target could be achieved with less-ambitious changes to food systems, but only if fossil fuel and other nonfood emissions are eliminated soon.

Climate and Health Benefits of Rapid Coal-to-Gas Fuel Switching in the U.S. Power Sector Offset Methane Leakage and Production Cost Increases.

In this paper, we ask whether rapidly displacing coal electricity generation with underutilized, existing natural gas capacity has net societal benefits or net costs when considering climate change, economics, and air pollution. We use a power plant dispatch model to quantify the effects of dispatching because of a tax on carbon or because of a tax on carbon, methane leakage, and air pollution. We explicitly model exhaust stack CO2 emissions, production costs, health damages caused by criteria air pollutants, and methane leakage from the natural gas infrastructure. We show that (1) the optimal coal-to-gas redispatch displaces 62-77% of coal energy, leaving some coal online, (2) the health benefits of redispatch are larger in magnitude than the climate benefits, (3) reducing methane leakage rates from 2.3 to 2.0% increases the net climate benefits of redispatch by $1.1B-$1.4B, (4) although internalizing methane leakage, climate damages, and health damages in the power plant dispatch maximizes the net benefits of redispatch, 75-87% of these benefits can be achieved using a carbon tax mechanism alone, and (5) when choosing an optimal carbon tax, focusing on climate at the exclusion of health-and vice-versa-provides less net benefit than looking at both issues jointly.

Optimizing Emissions Reductions from the U.S. Power Sector for Climate and Health Benefits.

Improved air quality and human health are often discussed as "co-benefits" of mitigating climate change, yet they are rarely considered when designing or implementing climate policies. We analyze the implications of integrating health and climate when determining the best locations for replacing power plants with new wind, solar, or natural gas to meet a CO2 reduction target in the United States. We employ a capacity expansion model with integrated assessment of climate and health damages, comparing portfolios optimized for benefits to climate alone or both health and climate. The model estimates county-level health damages and accounts for uncertainty by using a range of air quality models (AP3, EASIUR, and InMAP) and concentration-response functions (American Cancer Society and Harvard Six Cities). We find that reducing CO2 by 30% yields $21-68 billion in annual health benefits, with an additional $9-36 billion possible when co-optimizing for climate and health benefits. Additional benefits accrue from prioritizing emissions reductions in counties with high population exposure. Total health benefits equal or exceed climate benefits across a wide range of modeling assumptions. Our results demonstrate the value of considering health in climate policy design and the need for interstate cooperation to achieve additional health benefits equitably.

Fine Particulate Air Pollution from Electricity Generation in the US: Health Impacts by Race, Income, and Geography.

Electricity generation is a large contributor to fine particulate matter (PM2.5) air pollution. However, the demographic distribution of the resulting exposure is largely unknown. We estimate exposures to and health impacts of PM2.5 from electricity generation in the US, for each of the seven Regional Transmission Organizations (RTOs), for each US state, by income and by race. We find that average exposures are the highest for blacks, followed by non-Latino whites. Exposures for remaining groups (e.g., Asians, Native Americans, Latinos) are somewhat lower. Disparities by race/ethnicity are observed for each income category, indicating that the racial/ethnic differences hold even after accounting for differences in income. Levels of disparity differ by state and RTO. Exposures are higher for lower-income than for higher-income, but disparities are larger by race than by income. Geographically, we observe large differences between where electricity is generated and where people experience the resulting PM2.5 health consequences; some states are net exporters of health impacts, other are net importers. For 36 US states, most of the health impacts are attributable to emissions in other states. Most of the total impacts are attributable to coal rather than other fuels.

Fine particulate matter damages and value added in the US economy.

Emissions of most pollutants that result in fine particulate matter (PM2.5) formation have been decreasing in the United States. However, this trend has not been uniform across all sectors or regions of the economy. We use integrated assessment models (IAMs) to compute marginal damages for PM2.5-related emissions for each county in the contiguous United States and match location-specific emissions with these marginal damages to compute economy-wide gross external damage (GED) due to premature mortality. We note 4 key findings: First, economy-wide, GED has decreased by more than 20% from 2008 to 2014. Second, while much of the air pollution policies have focused to date on the electricity sector, damages from farms are now larger than those from utilities. Indeed, farms have become the largest contributor to air pollution damages from PM2.5-related emissions. Third, 4 sectors, comprising less than 20% of the national gross domestic product (GDP), are responsible for ∼75% of GED attributable to economic activities. Fourth, uncertainty in GED estimates tends to be high for sectors with predominantly ground-level emissions because these emissions are usually estimated and not measured. These findings suggest that policymakers should target further emissions reductions from such sectors, particularly in transportation and agriculture.

Reduced-Order Dispatch Model for Simulating Marginal Emissions Factors for the United States Power Sector.

This study develops a reduced-order power plant dispatch model and uses it to simulate marginal emissions factors (MEFs) for the 2014-2017 United States (U.S.) electric grid at the North American Electric Reliability Corporation (NERC) regional level. MEFs help quantify the health, environmental, and climate change impacts caused by changes in marginal net electricity consumption, which could result, for example, from new technologies or policies. This study develops the model, validates it against historical data, and compares its simulated MEFs against historically derived regression-based MEFs. Our method accurately reproduces CO2, SO2, and NOx emissions for multiple U.S. NERC regions and years and enables us to analyze future scenarios that are absent from the historical data. Though historically derived regression-based MEFs are generally more accurate, our simulated MEFs provide a more nuanced picture of how clusters of low- or high-emitting power plants of similar production cost create large swings in MEFs throughout the day. Policymakers could use these dynamic MEFs to target demand-reduction strategies at high-emissions portions of the power plant merit order.

Trace Element Mass Flow Rates from U.S. Coal Fired Power Plants.

Trace elements (TEs) exit coal-fired power plants (CFPPs) via solid, liquid, and gaseous waste streams. Estimating the TE concentrations of these waste streams is essential to selecting pollution controls and estimating emission reduction benefits. This work introduces a generalizable mass balance model for estimating TE mass flow rates in CFPP waste streams and evaluates model accuracy for the U.S. coal fleet given current data constraints. We stochastically estimate, using a bootstrapping approach, the 2015 plant-level mass flow rates of Hg, Se, As, and Cl to solid, liquid, and gas phase waste streams by combining publicly available data for combusted coal TE concentrations with estimates of TE partitioning within installed air pollution control processes. When compared with measured and reported data on TE mass flow rates, this model generally overestimates masses by 30-50%, with larger errors for Hg. The partitioning estimates are consistent for Se, As, and Cl removal from flue gas, but tend to underestimate Hg removal. While our model is suitable for first-order estimates of TE mass flows, future work to improve model performance should focus on collecting and using new data on TE concentrations in the coal blend, where data quality is the weakest.

Expert assessments of the cost and expected future performance of proton exchange membrane fuel cells for vehicles.

Despite decades of development, proton exchange membrane fuel cells (PEMFCs) still lack wide market acceptance in vehicles. To understand the expected trajectories of PEMFC attributes that influence adoption, we conducted an expert elicitation assessment of the current and expected future cost and performance of automotive PEMFCs. We elicited 39 experts' assessments of PEMFC system cost, stack durability, and stack power density under a hypothetical, large-scale production scenario. Experts assessed the median 2017 automotive cost to be $75/kW, stack durability to be 4,000 hours, and stack power density to be 2.5 kW/L. However, experts ranged widely in their assessments. Experts' 2017 best cost assessments ranged from $40 to $500/kW, durability assessments ranged from 1,200 to 12,000 hours, and power density assessments ranged from 0.5 to 4 kW/L. Most respondents expected the 2020 cost to fall short of the 2020 target of the US Department of Energy (DOE). However, most respondents anticipated that the DOE's ultimate target of $30/kW would be met by 2050 and a power density of 3 kW/L would be achieved by 2035. Fifteen experts thought that the DOE's ultimate durability target of 8,000 hours would be met by 2050. In general, experts identified high Pt group metal loading as the most significant barrier to reducing cost. Recommended research and development (R&D) funding was allocated to "catalysts and electrodes," followed in decreasing amount by "fuel cell performance and durability," "membranes and electrolytes," and "testing and technical assessment." Our results could be used to inform public and private R&D decisions and technology roadmaps.

Net-zero emissions energy systems.

Some energy services and industrial processes-such as long-distance freight transport, air travel, highly reliable electricity, and steel and cement manufacturing-are particularly difficult to provide without adding carbon dioxide (CO2) to the atmosphere. Rapidly growing demand for these services, combined with long lead times for technology development and long lifetimes of energy infrastructure, make decarbonization of these services both essential and urgent. We examine barriers and opportunities associated with these difficult-to-decarbonize services and processes, including possible technological solutions and research and development priorities. A range of existing technologies could meet future demands for these services and processes without net addition of CO2 to the atmosphere, but their use may depend on a combination of cost reductions via research and innovation, as well as coordinated deployment and integration of operations across currently discrete energy industries.

Marginal Emissions Factors for Electricity Generation in the Midcontinent ISO.

Environmental consequences of electricity generation are often determined using average emission factors. However, as different interventions are incrementally pursued in electricity systems, the resulting marginal change in emissions may differ from what one would predict based on system-average conditions. Here, we estimate average emission factors and marginal emission factors for CO2, SO2, and NOx from fossil and nonfossil generators in the Midcontinent Independent System Operator (MISO) region during years 2007-2016. We analyze multiple spatial scales (all MISO; each of the 11 MISO states; each utility; each generator) and use MISO data to characterize differences between the two emission factors (average; marginal). We also explore temporal trends in emissions factors by hour, day, month, and year, as well as the differences that arise from including only fossil generators versus total generation. We find, for example, that marginal emission factors are generally higher during late-night and early morning compared to afternoons. Overall, in MISO, average emission factors are generally higher than marginal estimates (typical difference: ∼20%). This means that the true environmental benefit of an energy efficiency program may be ∼20% smaller than anticipated if one were to use average emissions factors. Our analysis can usefully be extended to other regions to support effective near-term technical, policy and investment decisions based on marginal rather than only average emission factors.

Estimating the effect of multiple environmental stressors on coral bleaching and mortality.

Coral cover has been declining in recent decades due to increased temperatures and environmental stressors. However, the extent to which different stressors contribute both individually and in concert to bleaching and mortality is still very uncertain. We develop and use a novel regression approach, using non-linear parametric models that control for unobserved time invariant effects to estimate the effects on coral bleaching and mortality due to temperature, solar radiation, depth, hurricanes and anthropogenic stressors using historical data from a large bleaching event in 2005 across the Caribbean. Two separate models are created, one to predict coral bleaching, and the other to predict near-term mortality. A large ensemble of supporting data is assembled to control for omitted variable bias and improve fit, and a significant improvement in fit is observed from univariate linear regression based on temperature alone. The results suggest that climate stressors (temperature and radiation) far outweighed direct anthropogenic stressors (using distance from shore and nearby human population density as a proxy for such stressors) in driving coral health outcomes during the 2005 event. Indeed, temperature was found to play a role ~4 times greater in both the bleaching and mortality response than population density across their observed ranges. The empirical models tested in this study have large advantages over ordinary-least squares-they offer unbiased estimates for censored data, correct for spatial correlation, and are capable of handling more complex relationships between dependent and independent variables. The models offer a framework for preparing for future warming events and climate change; guiding monitoring and attribution of other bleaching and mortality events regionally and around the globe; and informing adaptive management and conservation efforts.

Spatially resolved air-water emissions tradeoffs improve regulatory impact analyses for electricity generation.

Coal-fired power plants (CFPPs) generate air, water, and solids emissions that impose substantial human health, environmental, and climate change (HEC) damages. This work demonstrates the importance of accounting for cross-media emissions tradeoffs, plant and regional emissions factors, and spatially variation in the marginal damages of air emissions when performing regulatory impact analyses for electric power generation. As a case study, we assess the benefits and costs of treating wet flue gas desulfurization (FGD) wastewater at US CFPPs using the two best available treatment technology options specified in the 2015 Effluent Limitation Guidelines (ELGs). We perform a life-cycle inventory of electricity and chemical inputs to FGD wastewater treatment processes and quantify the marginal HEC damages of associated air emissions. We combine these spatially resolved damage estimates with Environmental Protection Agency estimates of water quality benefits, fuel-switching benefits, and regulatory compliance costs. We estimate that the ELGs will impose average net costs of $3.01 per cubic meter for chemical precipitation and biological wastewater treatment and $11.26 per cubic meter for zero-liquid discharge wastewater treatment (expected cost-benefit ratios of 1.8 and 1.7, respectively), with damages concentrated in regions containing a high fraction of coal generation or a large chemical manufacturing industry. Findings of net cost for FGD wastewater treatment are robust to uncertainty in auxiliary power source, location of chemical manufacturing, and binding air emissions limits in noncompliant regions, among other variables. Future regulatory design will minimize compliance costs and HEC tradeoffs by regulating air, water, and solids emissions simultaneously and performing regulatory assessments that account for spatial variation in emissions impacts.

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.

Evaluating the benefits of commercial building energy codes and improving federal incentives for code adoption.

The federal government has the goal of decreasing commercial building energy consumption and pollutant emissions by incentivizing the adoption of commercial building energy codes. Quantitative estimates of code benefits at the state level that can inform the size and allocation of these incentives are not available. We estimate the state-level climate, environmental, and health benefits (i.e., social benefits) and reductions in energy bills (private benefits) of a more stringent code (ASHRAE 90.1-2010) relative to a baseline code (ASHRAE 90.1-2007). We find that reductions in site energy use intensity range from 93 MJ/m(2) of new construction per year (California) to 270 MJ/m(2) of new construction per year (North Dakota). Total annual benefits from more stringent codes total $506 million for all states, where $372 million are from reductions in energy bills, and $134 million are from social benefits. These total benefits range from $0.6 million in Wyoming to $49 million in Texas. Private benefits range from $0.38 per square meter in Washington State to $1.06 per square meter in New Hampshire. Social benefits range from $0.2 per square meter annually in California to $2.5 per square meter in Ohio. Reductions in human/environmental damages and future climate damages account for nearly equal shares of social benefits.