Across models, reference scenarios have a wide range of emissions by 2050: over 800 million metric tonnes CO2-equivalent per year (MMT CO2e/year) in the CCST and PATHWAYS models to under 500 MMT CO2e/year in CA-TIMES.Footnote 2 Reference scenarios help capture the underlying assumptions of the models: for example, the models with the highest GHG trajectories (PATHWAYS and CCST) had 10–20 % higher income and population assumptions by 2050 than more recently developed models,Footnote 3
In scenarios that achieve deep reductions in GHGs by 2050, the GHG trajectories also vary widely. Annual emissions decline to 8–46 % below 1990 levels by 2030 and 59–84 % by 2050, or to 230–396 MMTCO2e per year and 68–175 MMTCO2e per year, respectively (Fig. 1a). Most deep reduction scenarios have lagged emission reductions, meaning they wait until later years to make the steepest declines in reductions. Rates of change in annual GHG emissions vary between −0.3 and −5.3 % per year from today to 2030 (average across scenarios of −1.7 %), and −0.8 to −19.9 % per year from 2020 to 2050 (average of −5.2 %).
For ease of viewing in Fig. 1a/b, we only show the highest and lowest deep reduction scenario from each model that projects GHG emissions. Also shown are the linear and constant-percent reductions between the 2020 GHG target of 431 MMT CO2e/yearFootnote 5 to the 2050 target of 86 MMT CO2e/year (black lines). A linear interpolation was used between time steps. LEAP-SWITCH explores scenarios that achieve greater than 80 % reduction by 2050, but these scenarios are not presented in this paper,Footnote 6
Figure 1a and b help demonstrate the importance of early emissions reductions using a side-by-side comparison of annual and cumulative emissions.Footnote 8 For example, CALGAPS (S3) only achieves a 59 % emission reduction in annual emissions by 2050 (Fig. 1a) but has the lowest cumulative emissions between 2010 and 2050 (Fig. 1b). Conversely, the PATHWAYS (Hi Renew) scenario achieves an 80 % reduction by 2050 but has the highest cumulative emissions in 2050 due to its lagged reduction schedule. Others have shown that the cumulative emissions of a scenario are a robust indictor for whether that scenario stays below a particular level of global warming (Meinshausen et al. 2009). Of course, Fig. 1a/b use 2050 as the end point – if these trajectories were extended to 2070 the PATHWAYS scenario may have cumulatively lower emissions than the CALGAPS scenario.
Between 2001 and 2013, electricity generation in California (including both in-state and net imports before transmission losses) increased from 267 TWh to 296 TWh and the corresponding renewable fraction of generated energy increased from 14 to 20 %,Footnote 9
Footnote 10 In the same years, the capacity of the grid powering California expanded from 60.8 GW to 88.5 GW (CEC 2014).
The future expansion of the electricity grid poses both spatial and temporal challenges to energy planners (Hart and Jacobson 2011; Williams et al. 2012; Nelson et al. 2012; Wei et al. 2013). The models examined here differ widely in their geographic scope and resolution. For example, the SWITCH model includes a multi-state region (the Western Electricity Coordinating Council) which allows for optimal solutions across state boundaries. Other models assume a certain fraction of out-of-state generation is always available or, like CA-TIMES, assume all power generation after a certain year is generated in-state. SWITCH also is the only model that determines the geographic location and capacity of future power plants and transmission lines. The time dimension also differs widely between models. The models that include time-of-day dispatch models to better understand renewable intermittency problems include CA-TIMES, PATHWAYS, SWITCH, and WWS.Footnote 11
In CCST, SWITCH,Footnote 12 and WWS, demand for electricity is driven exogenously. PATHWAYS estimates demand using a “bottom-up” approach in which the electricity requirements of each individual end-use is first estimated then summed. CALGAPS estimates demand in a similar way as PATHWAYS, but electricity requirements are determined at the sector level rather than by end use. In CA-TIMES, electricity demand is determined endogenously based on the need to meet the 2050 GHG goal.
Across BAU scenarios, the total power generation from in-state plus imported electricity generation increases by 20–31 % above the 2013 level by 2030 and 45–75 % by 2050. In all deep reduction scenarios, the electricity grid shifts towards renewable generation – particularly after 2030 – and most end-uses are electrified by 2050. Because some sectors cannot be electrified or are difficult to decarbonize (e.g., aviation, marine, heavy duty road freight, agricultural fertilizer, etc.), GHG emissions from the electricity grid will likely need to be reduced beyond 80 % (Williams et al. 2012; Nelson et al. 2014; Yang et al. 2015).
As shown in Fig. 2, across deep reduction scenarios the total power generation increases by −2 to +40 % by 2030 and 8 to 226 % by 2050, relative to 2013. For WWS, these increases are much larger: 334 and 465 %. The renewable fraction of total generation is 30–58 % by 2030 and 30–89 % by 2050, with the majority of new generation coming from wind and solar. For WWS, the renewable fractions are 85 and 100 %, respectively. These renewable ranges include small but exclude large hydroelectric generation and therefore are roughly consistent with California’s definition of “renewable electricity” in its Renewable Portfolio Standard (RPS). In general, the lower values in these ranges reflect scenarios with greater nuclear and/or CCS deployment. Across scenarios, the implied build-out rate of in-state plus imported renewable electricity (mostly solar and wind) ranges between 0.2 and 4.2 GW per year from 2013 until 2030, with an average of 0.83 GW per year. The renewable build-out rate increases to between 1.5 and 10.4 GW per year from 2030 until 2050, with an average of 3.9 GW per year. Faster rates of grid expansion are assumed in the WWS model: an average of 17 GW of nameplate renewable capacity added per year from 2013 to 2050 to reach 652 GW of total renewable capacity by 2050. For perspective, from 2001 to 2013 the renewable capacity used by the state (in-state and imported electricity) expanded by 0.67 GW per year while non-renewable capacity expanded by 1.6 GW per year (CEC 2014).
Passenger transportation sector
A standard practice for modeling the transportation sector in energy models is to make exogenous assumptions about future energy service demand (e.g., statewide vehicle-miles travelled (VMT)) and then allow the model to estimate future fuel mix, vehicle/technology mix, and emissions. The models in this study all follow this practice. The lower the future demand assumptions, the less the need for low-GHG emitting fuels.
For example, in deep reduction scenarios statewide VMT for light-duty vehiclesFootnote 13 is assumed to change from 293 billion miles per year in 2010 (CARB 2012) to 226–600 billion miles in 2050. Therefore, the amount of near-zero CO2e emission energy used across these models differs widely. Figure 3 shows the passenger light-duty vehicle (LDV) energy projections (stacked columns) and the total transportation sector energy (red triangles) for the model reporting detailed LDV-specific results. Across deep reduction scenarios, total LDV energy use ranges from 8.6 to 25.2 billion gallons of gasoline equivalent (BGGE) in 2030 (1.1–3.3 exajoules (EJ)) and 8.1–19.6 BGGE in 2050 (1.1–2.6 EJ).
With the exception of the PATHWAYS-mitigation scenario, the passenger LDV energy drops from 2010 to 2030, and again from 2030 to 2050 in deep reduction scenarios. This decline results mainly from (1) the underlying assumptions about lower energy service demand in future years and (2) the improved efficiency of LDV technology. Across deep reduction scenarios, petroleum consumption declines 15–72 % by 2030 and 39–100 % by 2050 as the light-duty-vehicle fleet moves primarily to battery electric, plug-in hybrid electric, and hydrogen fuel cell vehicles, although the composition and magnitude of change varies between scenarios. For example, in CA-TIMES the combination of battery electric and hydrogen fuel cell vehicles makes up between 50 and 96 % of the LDV fleet in 2050. In the ARB VISION model’s mitigation scenario, these same technologies comprise over 80 % of the LDV fleet in 2050. Regardless of the exact fleet composition, hydrogen and electricity with near-zero life-cycle GHGs (e.g., from wind, solar, biomass, NG with CCS) are needed to power virtually all of the LDV fleet by 2050.
Contribution from bioenergy
Bioenergy assumptions are important drivers in energy planning models (Wei et al. 2013; Rose et al. 2014). The more “low-carbon” bioenergy assumed to exist, the fewer mitigation strategies that are needed in other sectors and technologies. Across models reviewed here (except WWS), between 4 and 13 billion gallons of gasoline equivalent (BGGE) (0.4–1.7 exajoules) are used in 2050 – up from about 1.0 BGGE today.Footnote 14 Models utilize biomass supply curves from Parker et al. (2010) or De la Torre Ugarte and Ray (2000).
Most models make simple assumptions regarding the carbon content of bioenergy. For example, SWITCH assumes bioenergy has 30 % lower carbon intensity than petroleum-based fuels today and improves to 80 % lower by 2050. PATHWAYS only includes biomass feedstocks produced in the U.S. that have a “net-zero” carbon intensityFootnote 15 on a lifecycle basis including corn stover, wheat straw, forest residues, forest thinning, and switchgrass (Williams et al. 2012). CA-TIMES assumes a carbon intensity of 75–80 gCO2e/MJ for corn ethanol, 25–30 gCO2e/MJ for cellulosic ethanol, and 13–30 gCO2e/MJ for waste-based or Fischer-Tropsch diesel. CALGAPS estimates net life-cycle GHG emissions for biofuels that include offsets based on the assumed in-state portion of biofuels produced. ARB VISION assumes that the average carbon intensity of all biofuel declines from 67 to 41 gCO2e/MJ. It should be emphasized that all models here assume point estimates rather than distributions in carbon intensity. A number of studies suggest that these carbon intensities are highly uncertain (e.g., Plevin et al. 2010).
Setting aside this concerns and assuming that bioenergy with low lifecycle GHG emissions will in fact exist in the future, CA-TIMES suggests these fuels are best utilized in the transportation sector (rather than in other sectors). This is mainly because fewer mitigation options are available in the transportation sector compared to other sectors, in particular in aviation, marine, and heavy duty road transport. Across scenarios, bioenergy accounts for a maximum of about 40 % of transportation energy in 2050. Not all long-term energy modeling suggests large quantities of biofuels are needed in the transportation sector. The WWS model, presents a vision of 2050 without bioenergy, relying instead solely on batteries and hydrogen. Biofuel and bioelectricity production with CCS are modeled in sensitivity analyses in the CA-TIMES and SWITCH models but their results are not presented here.
Non-CO2 GHG and criteria emissions
Reducing non-CO2 GHGs and criteria emissions is a major policy focus in California; however most energy planning models spend relatively little effort on characterizing their future levels. The relative contribution of non-energy and High Global Warming Potential (HGWP) GHGs to overall emissions levels is likely to increase in the coming decades. Greenblatt (2015) and Wei et al. (2013) find that, absent further policy, these emissions alone could exceed the 2050 emission goal.
For criteria emissions, California policymakers are contemplating how to transform the energy system to simultaneously meet GHG targets and the near-term (2023) and midterm (2032) National Ambient Air Quality Standards (NAAQS) for ozone. In particular, meeting the 2032 legally-binding NAAQS deadline will be challenging given historical vehicle turnover rates and higher costs of clean technology. CARB (2014) reports that additional strategies, early action, and more rapid development and adoption of zero-emission technologies is needed. How to do this while also meeting GHG targets remains a lively policy debate. Table OR.2 of the OR-1 compares (1) the criteria pollutants estimated by each model and (2) the spatial resolution of these estimations. The table shows that only three of the nine models estimate NOx, ROG, and PM2.5 and none do so at a highly resolute level.Footnote 16