A California Greenhouse Gas Inventory Spreadsheet (GHGIS) model was developed to explore the impact of combinations of state policies on state greenhouse gas (GHG) and regional criteria pollutant emissions. The model included representations of all GHG- emitting sectors of the California economy (including those outside the energy sector, such as high global warming potential gases, waste treatment, agriculture and forestry) in varying degrees of detail, and was carefully calibrated using available data and projections from multiple state agencies and other sources. Starting from basic drivers such as population, numbers of households, gross state product, numbers of vehicles, etc., the model calculated energy demands by type (various types of liquid and gaseous hydrocarbon fuels, electricity and hydrogen), and finally calculated emissions of GHGs and three criteria pollutants: reactive organic gases (ROG), nitrogen oxides (NOx), and fine (2.5 ?m) particulate matter (PM2.5). Calculations were generally statewide, but in some sectors, criteria pollutants were also calculated for two regional air basins: the South Coast Air Basin (SCAB) and the San Joaquin Valley (SJV). Three scenarios were developed that attempt to model: (1) all committed policies, (2) additional, uncommitted policy targets and (3) potential technology and market futures. Each scenario received extensive input from state energy planning agencies, in particular the California Air Resources Board. Results indicate that all three scenarios are able to meet the 2020 statewide GHG targets, and by 2030, statewide GHG emissions range from between 208 and 396 MtCO2/yr. However, none of the scenarios are able to meet the 2050 GHG target of 85 MtCO2/yr, with emissions ranging from 188 to 444 MtCO2/yr, so additional policies will need to be developed for California to meet this stringent future target. A full sensitivity study of major scenario assumptions was also performed. In terms of criteria pollutants, targets were less well-defined, but while all three scenarios were able to make significant reductions in ROG, NOx and PM2.5 both statewide and in the two regional air basins, they may nonetheless fall short of what will be required by future federal standards. Specifically, in Scenario 1, regional NOx emissions are approximately three times the estimated targets for both 2023 and 2032, and in Scenarios 2 and 3, NOx emissions are approximately twice the estimated targets. Further work is required in this area, including detailed regional air quality modeling, in order to determine likely pathways for attaining these stringent targets.

This paper examines policy and technology scenarios in California, emphasizing greenhouse gas (GHG) emissions in 2020 and 2030. Using CALGAPS, a new, validated model simulating GHG and criteria pollutant emissions in California from 2010 to 2050, four scenarios were developed: Committed Policies (S1), Uncommitted Policies (S2), Potential Policy and Technology Futures (S3), and Counterfactual (S0), which omits all GHG policies. Forty-nine individual policies were represented. For S1-S3, GHG emissions fall below the AB 32 policy 2020 target [427 million metric tons CO2 equivalent (MtCO2e) yr 1], indicating that committed policies may be sufficient to meet mandated reductions. In 2030, emissions span 211 to 428 MtCO2e yr 1, suggesting that policy choices made today can strongly affect outcomes over the next two decades. Long-term (2050) emissions were all well above the target set by Executive Order S-3-05 (85 MtCO2e yr 1); additional policies or technology development (beyond the study scope) are likely needed to achieve this objective. Cumulative emissions suggest a different outcome, however: due to early emissions reductions, S3 achieves lower cumulative emissions in 2050 than a pathway that linearly reduces emissions between 2020 and 2050 policy targets. Sensitivity analysis provided quantification of individual policy GHG emissions reduction benefits.

Ridesourcing fleets present an opportunity for rapid uptake of battery electric vehicles (BEVs) but adoption has largely been limited to small pilot projects. Lack of charging infrastructure presents a major barrier to scaling up, but little public information exists on the infrastructure needed to support ridesourcing electrification. With data on ridesourcing trips for New York City and San Francisco, and using agent-based simulations of BEV fleets, we show that given a sparse network of three to four 50 kW chargers per square mile, BEVs can provide the same level of service as internal combustion engine vehicles (ICEVs) at lower cost. This suggests that the cost of charging infrastructure is not a significant barrier to ridesourcing electrification. With coordinated use of charging infrastructure across vehicles, we also find that fleet performance becomes robust to variation in battery range and placement of chargers. Our analysis suggests that mandates on ridesourcing such as the California Clean Miles Standard could achieve electrification without significantly increasing the cost of ridesourcing services.

Shared automated electric vehicles (SAEVs) hold great promise for improving transportation access in urban centers while drastically reducing transportation-related energy consumption and air pollution. Using taxi-trip data from New York City, we develop an agent-based model to predict the battery range and charging infrastructure requirements of a fleet of SAEVs operating on Manhattan Island. We also develop a model to estimate the cost and environmental impact of providing service and perform extensive sensitivity analysis to test the robustness of our predictions. We estimate that costs will be lowest with a battery range of 50-90 mi, with either 66 chargers per square mile, rated at 11 kW or 44 chargers per square mile, rated at 22 kW. We estimate that the cost of service provided by such an SAEV fleet will be $0.29-$0.61 per revenue mile, an order of magnitude lower than the cost of service of present-day Manhattan taxis and $0.05-$0.08/mi lower than that of an automated fleet composed of any currently available hybrid or internal combustion engine vehicle (ICEV). We estimate that such an SAEV fleet drawing power from the current NYC power grid would reduce GHG emissions by 73% and energy consumption by 58% compared to an automated fleet of ICEVs.

Harnessing solar energy to drive photoelectrochemical reactions is widely studied for sustainable fuel production and versatile energy storage over different timescales. However, the majority of solar photoelectrochemical cells cannot drive the overall photosynthesis reactions without the assistance of an external power source. A device for simultaneous and direct production of renewable fuels and electrical power from sunlight is now proposed. This hybrid photoelectrochemical and photovoltaic device allows tunable control over the branching ratio between two high-value products of solar energy conversion, requires relatively simple modification to existing photovoltaic technologies, and circumvents the photocurrent mismatches that lead to significant loss in tandem photoelectrochemical systems comprising chemically stable photoelectrodes. Our proof-of-concept device is based on a transition metal oxide photoanode monolithically integrated onto silicon that possesses both front- and backside photovoltaic junctions. This integrated assembly drives spontaneous overall water splitting with no external power source, while also producing electricity near the maximum power point of the backside photovoltaic junction. The concept that photogenerated charge carriers can be controllably directed to produce electricity and chemical fuel provides an opportunity to significantly increase the energy return on energy invested in solar fuels systems and can be adapted to a variety of architectures assembled from different materials.