Introduction
Implementation of California's climate policy (Executive Order S-3-05) to
reduce GHG emissions 80 % below 1990 levels by the year 2050 will require
widespread adoption of low-carbon energy supply and demand technologies
across the state's entire economy. These changes will not only reduce
California's contribution to climate change, they will also alter the
chemical composition, spatial pattern, and attributable adverse health
effects of the state's serious air pollution problem. Reducing long-term
exposure to fine airborne particulate matter (PM2.5) and ozone
(O3) will improve public health through a reduction in premature
mortality (Krewski et al., 2009; Lepeule et al., 2012).
California's near-term measures to mitigate greenhouse gas (GHG) emissions
are required by the Global Warming Solutions Act of 2006 (Assembly Bill (AB)
32). Since the adoption of AB 32, a wave of incentives, mandates, carbon
markets, fees, and standards have been implemented to curb the rate of the
state's GHG emissions. Regulations include the Renewables Portfolio Standard
for the electricity generation sector, the Low Carbon Fuel Standard aimed at
reducing carbon intensity of transport fuels, the Pavley Clean Car Standards
for fuel economy and CO2 emissions, and the Cap-and-Trade Program.
Zapata et al. (2012) analyzed
the air quality co-benefits of AB 32 and found that the GHG mitigation
measures had the co-benefit of reducing PM2.5 concentrations in
California by ∼ 6 % in the year 2030 with a corresponding decrease in
mortality due to air pollution. Additional measures will be needed to meet
the targets included in California's Executive Order S-3-05 that calls for
GHG emissions to decrease 80 % below 1990 GHG levels by the year 2050.
Numerous previous studies have examined the relationship between climate
policies and air quality using methods tailored to match the region of
interest (Table S1 in the Supplement). For example, Jacobson et al. (2014, 2015) examined how a scenario of 100 %
wind, water, and solar would alter all economic sectors, leading to changes
in air quality and health impacts for California and the United States in
2050. This bounding analysis is extremely valuable since it quantifies the
maximum possible air quality benefits associated with climate policies, but a
recent analysis suggests that scenarios incorporating a broader range of
technologies may be more realistic (Clack et al., 2017). The
debate on this point is ongoing (Jacobson et al., 2017). For studies that consider a broad range of technologies, multiple
approaches have been used to select between the diverse technologies
available in these future scenarios, but the majority of these studies rely
on the expert opinions of the authors rather than an objective analysis. For
example, Shindell et al. (2012) created a
future scenario by selecting measures that were “assumed to improve air
quality” and mitigate both long-lived GHGs and short-lived criteria
pollutants after ranking them by climate impact. The extensive study by
van Aardenne et al. (2010) explored six scenarios with
wider levels of air and/or climate policy, as well as the option of biofuel
consumption; however, technology adoption is again largely dependent on
author-specified assumptions on shares of existing technologies. Since the
technology choices in each scenario strongly affect the air quality
outcomes, the author assumptions in these previous studies have a strong
influence on the calculated health benefits stemming from reduced air
pollution concentrations. As a secondary limitation, many previous studies
have been carried out for regions much larger than California which requires
the use of coarse grid cells that do not completely resolve important
spatial patterns of pollutants within the state's complex topography (West et al., 2013; Garcia-Menendez et al., 2015).
Here we build on the previous work on climate policy–air quality
interactions by conducting an optimized emissions analysis at high spatial
resolution for California. The state of California has a very large and
diverse economy and so it is difficult to design optimal GHG mitigation
strategies using expert opinions alone. Energy–economic optimization
models are needed to find least-cost scenarios that achieve GHG objectives
within the resource constraints of the state. California also has
significant existing environmental regulations and so detailed analysis is
required to account for the impact of technology, fuel, and behavioral
changes implied by broad GHG policies on the landscape of preexisting
rules. All of this analysis must be carried out at high spatial resolution
to properly calculate air pollution exposure in major cities that often
experience a sharp gradient of pollutant concentrations across their
boundaries.
Zapata et al. (2017) used the CA-REMARQUE
(California Regional Multisector Air Quality Emissions) model to predict
criteria pollutant emissions associated with two economically optimized
scenarios for California in the year 2050: (i) a business-as-usual (BAU)
scenario that includes all existing environmental laws in California
including AB 32 and (ii) a greenhouse gas mitigation (GHG-Step) scenario
including additional least-cost policy and technology adoption needed to
achieve the 80 % GHG reduction objective of Executive Order S-3-05 using a
CO2 constrained step function. The results indicated that adoption of
the measures in the GHG-Step scenario could cause decreases or increases in
criteria pollutant emissions in different economic sectors and locations due to
the trade-offs involved in the statewide cost minimization approach. As a
further complication, switching to alternative lower-carbon-intensive fuels
in the GHG-Step scenario altered the composition of reactive organic gas emissions and the size and composition of particulate matter
emissions. These findings reinforce the need for sophisticated analysis
methods in complex regions like California.
The overall goal of the present study is to quantify air pollution and
health implications associated with the BAU and GHG-Step scenarios described
by Zapata et al. (2017) acting across the
entire California energy economy
in the year 2050. The air pollution
concentrations associated with the BAU and GHG-Step scenarios are calculated
at 4 km resolution using a regional chemical transport model and the avoided
mortality is estimated using established relationships from air pollution
epidemiology. Economic benefits are then calculated with the value of a
statistical life (VSL). Finally, the total public health benefits from
avoided air pollution are compared to the total incremental cost for
adoption of low-carbon energy in California to better understand the net
costs for the GHG mitigation program.
Methodology
Air quality and health impacts associated with energy scenarios in the year
2050 were determined by combining estimated changes to criteria pollutant
emissions inventories with downscaled meteorology as inputs to a regional
air quality model to predict air quality with 4 km resolution over
California. Epidemiology risk exposure functions and mortality data were
then used to estimate premature deaths. Figure 1 summarizes the calculations
with additional details provided below.
Process diagram of sequence of stages for modeling and
analysis.
Criteria pollutant emissions
Criteria pollutant emissions were predicted with the California Regional
Multisector Air Quality Emissions (CA-REMARQUE) model (Zapata et al., 2017)
for the BAU and GHG-Step scenarios. Both scenarios were constructed using
CA-TIMES, a technology-rich, bottom-up, energy economics model that
determines the least-cost mix of technology–fuel options for all sectors of
the statewide economy. CA-REMARQUE translated these behavior, technology, and
fuel changes into spatially and temporally resolved criteria pollutant
emissions inventories. CA-REMARQUE predicted that adoption of the GHG-Step
policies in place of the BAU policies would cause decreases in emissions of
primary PM0.1 (-36 %), PM2.5 (-3.6 %), oxides of nitrogen
(NOx, -13.7 %), and ammonia (NH3, -27.5 %) but cause
increases in emissions of carbon monoxide (CO, +37 %) and oxides of
sulfur (SOx, +14 %). Some components of primary PM2.5 emissions
responded more strongly to different technology changes yielding nonuniform
reductions of PM2.5 elemental carbon (elemental carbon, -10.6 %),
PM2.5 organic carbon (organic carbon, -13.3 %), and PM2.5
copper (Cu, -63 %). The spatial allocation of emission rates was
determined by either using existing 4 km spatial patterns of emissions
sources or finding new optimal locations for new emissions sources such as
biorefineries that were placed near high-biomass-feedstock regions. The
future BAU and GHG-Step scenarios considered in the present study do not
include nuclear or coal-fired (with or without carbon capture and
sequestration) electricity generation in California. Electricity generation
in the 2050 GHG-Step scenario is dominated by wind (34 %), solar (34 %),
and natural gas (18 %) with smaller contributions from tidal, geothermal,
and hydro. A comprehensive analysis of all emissions changes including
spatial plots is provided by Zapata et al. (2017).
Meteorology fields
Meteorology simulations using the Weather Research and Forecasting (WRF)
model v3.2.1 (University Corporation of Atmospheric Research
2010) conducted previously (Zhang et al., 2014) for the years 2048–2054
were used as meteorological inputs in this study. Hourly-averaged fields
describing spatial and temporal wind speed and direction, humidity,
temperature, planetary boundary layer height, downward shortwave
radiation, air density, and precipitation were formatted for use with the
regional chemical transport model. The 2054 calendar year was the median
year over the period 2048–2054 for domain-average PM2.5 concentrations
within the South Coast Air Basin that contains the majority of the
population in California. The focus of the current study is to evaluate how
the emissions changes lead to different air quality outcomes. Both emissions
scenarios are evaluated using the same meteorology, which minimizes the
variability introduced by the climate signal.
Regional chemical transport model configuration and simulation
Air quality was simulated using the UCD-CIT (University of California, Davis
– California Institute of Technology) 3-D regional chemical transport model
(Kleeman and Cass, 2001; Ying et al., 2007; Hu et al., 2015). The SAPRC11
(Carter and Heo, 2012; Carter et al., 2012) chemical mechanism was used to
represent gas-phase chemical reactions. Gas-to-particle conversion was
simulated as a dynamic process based on the concentration of semi-volatile
gas-phase compounds at the particle surface in equilibrium with the condensed
material inside each particle. Thermodynamic equilibrium within each particle
for inorganic species was calculated using the ISORROPIA model (Nenes et al.,
1998). Thermodynamic equilibrium within each particle for organic species was
calculated using a two-product model (Carlton et al., 2010). Particulate
matter emissions profiles include 18 organic, inorganic, and metal
particulate species distributed across 15 size bins.
Air quality simulations were conducted over three horizontal domains, a
coarse 24 km parent domain, and two 4 km resolution child domains. The
coarse domain covered all of California and the adjacent Pacific Ocean to
provide boundary inputs to the higher-resolution child domains over
populated regions in northern and southern California. A total of 16 telescoping
vertical layers were used up to a total height of 5 km above ground.
Simulations were conducted for the first 28–29 days of each month for the
2054 calendar year. The first 3 days of every month were excluded to
minimize the effects of initial conditions which are not known exactly,
leaving 301 simulation days to be used in the statistical analysis.
Population projections
A 2050 California population projection at 4 km spatial resolution was used
for both population-weighted concentration estimates and mortality
estimates. This population projection is based on the highly resolved
block-group 2010 census population data in shapefile format (US Census Bureau) which was intersected with the regular air
quality grid. The 4 km resolution population field was then scaled according
to
the projected populations for each county in 2050 (California
Department of Finance. Demographic Research Unit 2014) relative to 2010
(Table S2). This procedure was conducted separately for population age
> 35 and for all ages (see Fig. S1) to be used for the
population-weighted code (all ages) and the mortality estimates
(> 35 years). The combined southern and northern 4 km resolution
modeling domains encompassed 92 % of California's projected 2050
population (summarized in Table S3).
Population acts as a spatial surrogate for distributing emissions and as a
receptor for calculating the public health effects of air pollution.
Consistent population fields were used for both of these tasks in the
current study. Population growth rates by county are summarized by Zapata et al. (2017).
Statistical and exceedance analysis
Several statistical analyses were conducted across space, seasons, and
scenarios. Annual-average concentration plots were estimated by taking the
average of 301 daily concentration fields. A two-tailed paired t test was
used to identify significant differences between BAU and GHG-Step
concentrations. Annual or seasonal concentration field plots were condensed
to a statewide, air basin, or county population-weighted concentration
estimate by summing the concentration multiplied by the population in each
cell and then dividing the resulting sum by the entire population for the
region of interest.
Daily maximum 8 h average O3 concentrations were calculated for each
model grid cell. Subsequent seasonal or annual averages used the daily
maximum 8 h average concentrations for a given state, basin, or county. To
determine whether a county was in compliance with the 70 ppb O3
National Ambient Air Quality Standards (NAAQS), the fourth highest
population-weighted maximum 8 h O3 concentration was calculated. The
number of days exceeding this standard was also tabulated.
Mortality and cost estimates
Premature mortality estimates from long-term exposure to PM2.5 and
O3 were calculated using annual-average 4 km resolution concentration
fields for the BAU and GHG-Step scenarios. The attributable fraction (AF) is
the portion of deaths or incidences that can be associated with the cause of
interest, in this case the fraction of deaths due to annual PM2.5 and
O3 exposure. The AF quantifies the change in the relative risk.
AFi=RRi-1RRi=eβ(xi-xi,bkg)-1eβ(xi-xi,bkg)
The log-linear incidence rate function is assumed when calculating the risk
ratio (RR) as shown in Eq. (1). The beta coefficient (β) is derived
from taking the natural log of the RR found in epidemiology literature.
PM2.5 RR for all-cause mortality associated with a 10 µm m-3 increase in long-term PM2.5 exposure is estimated at 1.062
based on a worldwide meta-analysis (Hoek et al., 2013) or 1.036 based on the American Cancer Society follow-up
(Krewski 2009). An O3 RR of 1.04 for respiratory mortality from
long-term O3 exposure is based on Jerrett et al. (2009). The change in concentration is based on taking the annual-average
concentration for a given grid cell (xi) and subtracting it from the
background concentration (xi,bkg). Background concentrations on the
west coast of North America are often measured at mountain sites that sample
the free troposphere. Herner et al. (2005)
measured PM1.8 concentrations of 4 µg m-3 at Sequoia National
Park (elevation 535 m) during periods when this site was in the free
troposphere. McKendry (2006) surveyed published literature and
reviewed monitoring data in British Columbia on the west coast of North
America and estimated that background PM2.5 concentrations are 2 µm m-3 with little evidence of change over time. A background PM2.5
concentration of 3 µm m-3 and O3 concentration of 35 ppb
was assumed in the current study. The beta coefficient, change in cell
concentration, is then used to calculate the risk ratio (RRi) and
subsequently the AF.
(a) Population-weighted 8 h average ozone concentration by
region. (b) Population-weighted PM2.5 mass
concentration by region. Averages are shown for the winter, summer, and
annual time periods in the year 2054. SJV, SD, SF, and SC represent the San
Joaquin Valley, San Diego, San Francisco, and South Coast, respectively.
A P value < 0.0001 was found for each difference between
concentrations calculated with the BAU emissions (white bars) versus the
GHG-Step emissions (gray bars).
(a) Annual average of daily 8 h average ozone
concentration (ppb) under the BAU scenario, (b) change in 8 h average ozone
concentrations (ppb) under the GHG-Step scenario, and p-value significance
level of the difference between concentrations predicted using the BAU and
GHG-Step scenarios. All simulations for the year 2054. Both 24 km resolution
results and the finer 4 km resolution results are shown, with the finer,
smaller southern California or central-northern California domains
overlaid upon the coarse California domain results.
Es=∑iAFiBcPi
The mortality (Es) for each scenario for a given region was calculated
using Eq. (2) by taking the product of the population and mortality rate to
get the deaths, followed by multiplying the fraction that is attributable to
pollution (see Eq. 1). Population (Pi) projections for ages 35 and
older were used in this calculation due to high uncertainty for younger age
groups. Averaged 2009–2013 California all-cause (all ICD 10 codes) and
respiratory (ICD 10 codes J0–J98) mortality rates (Bc), calculated in
deaths per 100 000, were determined for each California county for ages 35
and older from the CDC WONDER database (United
States Department of Health and Human Services (US DHHS) et al., 2014).
Costs associated with premature death from long-term air pollution exposure
were estimated using the VSL method,
assuming that a death equates to USD 7.6 million, based on the distribution of
26 economic reports (Viscusi and Aldy 2003) and the suggested value
by the EPA (Industrial Economics, 2011; Bart Ostro, personal communication, 2015; RTI International
2015). This value can be adjusted to a future year with an average discount
rate “i” by multiplying with the value (1+i)futureyear-baseyear,
where the base year is 2006. VSL is estimated based on a willingness to pay
for small reductions in mortality risk through the selection of different
job types. “Willingness to pay” estimates are thought to incorporate
“cost of illness” including morbidity but they do not capture non-health
damage.
The fourth highest maximum daily 8 h average ozone concentration and
number of days exceeding the standard during June–August months. Counties
with fourth highest 8 h ozone concentrations ≥ 70 ppb are shown in
bold. See Table S4 for 2010
O3 designation values and areas.
Basin
County or statewide
Fourth highest 8 h O3 conc.
No. of days exceeding 8 h std.
(ppb)
of 70 ppb
2010
2050
2050
2010
2050
2050
BAU
GHG-Step
BAU
GHG-Step
North Central Coast (NCC)
Monterey
75
72
64
12
3
0
San Benito
97
75
65
44
31
0
Santa Cruz
81
72
67
17
15
0
South Coast (SC)
Los Angeles
95
69
70
45
0
3
Orange
92
63
70
43
0
4
Riverside
123
80
79
62
47
43
San Bernardino
121
80
82
63
45
49
South Central Coast (SCC)
Ventura
83
66
63
46
0
0
San Diego (SD)
San Diego
93
68
67
48
1
2
San Francisco (SF)
Alameda
65
65
65
1
0
0
Contra Costa
73
67
64
14
0
0
Marin
70
65
64
2
0
0
Napa
78
72
63
20
4
0
San Francisco
52
53
63
0
0
0
San Mateo
45
56
61
0
0
0
Santa Clara
69
68
67
3
2
1
Solano
82
71
64
36
10
0
Sonoma
74
66
58
7
0
0
San Joaquin Valley (SJV)
Fresno
98
70
63
50
3
0
Kern
111
68
60
66
1
0
Kings
103
68
61
57
2
0
Merced
98
71
63
59
5
0
San Joaquin
95
72
65
55
13
0
Stanislaus
100
71
65
63
7
0
Tulare
112
71
62
70
6
0
Sacramento Valley (SV)
Sacramento
100
75
64
59
22
0
California (CA)
Statewide
87
66
66
42
0
0
Results and discussion
Ozone (O3) concentration
Annual average and seasonal ozone changes
Figure 2a shows the population-weighted daily
maximum 8 h ozone concentrations for the 2054 meteorological year under the
BAU and GHG-Step emissions scenarios. Box and whisker plots are shown for
winter, summer, and annual time periods to consider both cyclical and yearly
effects. Figure 3a illustrates the spatial distribution of ozone
concentrations in the BAU scenario while
Fig. 3b illustrates the changes induced by the GHG-Step scenario.
The annual-average BAU 8 h ozone concentration reaches a maximum of 61 ppb
in Southern California downwind (east) of Los Angeles and San Bernardino. In
the northern-central California domain, the annual-average BAU 8 h ozone
concentration has a maximum value of 57 ppb along the Northern Central Coast
Air Basin, around Santa Clara and San Benito County.
Figure 3b illustrates that regional 8 h average ozone concentrations
(annually averaged) in the San Joaquin Valley (SJV) air basin (containing
the cities of Bakersfield and Fresno) decrease by 2–3 ppb under the
GHG-Step scenario. GHG mitigation strategies did not reduce ozone
concentrations in major population centers including the San Francisco (SF)
air basin and the South Coast (SC) Air Basin (containing the city of Los
Angeles). To the contrary, ozone concentrations increased in these dense
urban regions because BAU conditions have excess NOx concentrations
that titrate ozone. The extent of NOx emission reductions under the
GHG-Step scenario is insufficient to shift the chemical regime to one in
which
decreases in NOx lead to O3 reductions, instead favoring more
ozone formation (Seinfeld and Pandis, 2006).
Figure 2a illustrates that population-weighted annual-average 8 h ozone
concentrations in the rural SJV decreased by -4.3 % (52 to 50 ppb)
in the GHG-Step scenario with the greatest reductions occurring in the summer
months (-9.4 %). In contrast, population-weighted annual-average 8 h
ozone concentrations increased in urbanized regions (SC +5.1 %, San Diego
(SD)
+2.8 %, SF +6.5 %) consistent with the regional trends illustrated in
Fig. 3b. Population-weighted ozone concentrations under the GHG-Step scenario
increased in SC, SD, and SF during winter (+7.0, +9.3, and
+17 %, respectively) but had mixed trends during summer: ozone
concentrations in SC and SF (highest population density) increased by
+3.2 and +6.1 %, respectively, under the GHG-Step scenario but
concentrations in SD (slightly lower population density) decreased -2.2 %
during the summer season.
Overall, a statewide increase of +3.9 % in population-weighted
annual-average 8 h ozone concentrations occurred under the GHG-Step scenario
because increased ozone concentrations in heavily populated SF, SC, and SD
overwhelmed decreased ozone concentrations in the SJV. The regulatory and
health implications of this finding will be discussed in subsequent
sections.
High ozone events and number of exceedance days
Most benchmarks for ozone concentrations decrease strongly across California
in the 2050 BAU scenario relative to current 2010 levels. Simulations
carried out using identical 2010 summer meteorological fields but different
emissions inputs (2010 vs. 2050) demonstrate that emission changes – rather
than weather inputs – were the primary cause of these decreasing O3
concentrations. Table 1 summarizes the fourth highest maximum 8 h average ozone
concentration and the number of days exceeding the 70 ppb 8 h average ozone
standard for different California counties. The fourth highest 8 h average
O3 concentration of each year, averaged over 3 years, is used to
determine if a given area is in compliance with the NAAQS. Many California
air districts violate the 8 h O3 NAAQS, with classifications ranging
from moderate, serious, severe, to extreme levels of O3 (Table S4). The
county median of the fourth highest 8 h simulated ozone concentration in 2010
is 92.2 ppb (interquartile range (IQR): 74.0–99.1 ppb) with 23 out of 26 counties analyzed
reaching levels ≥ 70 ppb. The county median of the fourth highest 8 h
average ozone concentration in the 2050 BAU scenario decreases to 69.2 ppb
(IQR: 66.2–71.9 ppb) with a further decrease to 64.2 ppb (IQR: 62.8–66.4 ppb) in the GHG-Step scenario.
Almost half (10 of 23) of the counties exceeding the O3 NAAQS in 2010
would achieve attainment of the standards in the 2050 BAU scenario and
nearly all (19 out of 23) counties would achieve attainment under the 2050
GHG-Step scenario. Only the SC counties of Los Angeles, Orange, Riverside,
and
San Bernardino are predicted to remain out of attainment of the ozone
NAAQS in the 2050 GHG-Step scenario.
As noted above, some regions experience ozone disbenefits under the
GHG-Step scenario, which has implications for compliance with the ozone
NAAQS. Table 1 illustrates that increases in the fourth highest 8 h ozone
concentrations under the GHG-Step scenario may prevent Orange and Los
Angeles counties from complying with the 70 ppb standard. The fourth
highest 8 h ozone concentrations in San Bernardino County would not comply
with the O3 NAAQS under either emissions scenario, with concentrations
increasing from 80 ppb in the BAU scenario to 82 ppb in the GHG-Step
scenario. Both San Francisco and San Mateo counties were predicted to
experience higher ozone concentrations in the GHG-Step scenario but would
remain in compliance, with maximum concentrations of 63 and 61 ppb,
respectively.
Number of days in the months of June–August 2054 in which
the county population-weighted daily maximum 8 h average ozone concentration
exceeds the 8 h ozone NAAQS of 70 ppb for each current and future year
scenario.
Figure 4 illustrates the number of days exceeding
the 8 h ozone standard of 70 ppb in California under 2010 conditions, the
2050 BAU scenario, and the 2050 GHG-Step scenario. Most counties in central
California have ∼ 60 ozone exceedance days in 2010,
∼ 5–10 ozone exceedance days in the 2050 BAU scenario, and
zero ozone exceedance days in the 2050 GHG-Step scenario. North Central
Coast (NCC) basin ozone reductions in Monterey, San Benito, and Santa Cruz
counties also enabled those counties to comply with the O3 standards in
the GHG-Step scenario. The relatively small increase in ozone exceedance
days in southern California counties like Los Angeles, Orange, San
Bernardino, and San Diego will require extra mitigation strategies to
achieve compliance with the ozone NAAQS.
PM2.5 mass concentration
PM2.5 concentrations can be analyzed on timescales ranging from
seconds to years, but annual-average PM2.5 concentrations are most
commonly used to calculate mortality and health damages.
Figure 5 illustrates annual-average PM2.5
concentrations in northern-central California and southern California in
2054 under the BAU scenario (Fig. 5a) and the
differences induced by the GHG-Step scenario (Fig. 5b). Both results use identical 2054 meteorology, ensuring that the
concentration differences reflect changes between each scenario's emissions
inventory. The highest BAU annual-average PM2.5 concentration in
southern California is ∼ 18 µg m-3 in the city of
San Bernardino located east of Los Angeles, with the next highest PM2.5
hot spots occurring at San Diego and near the busy Port of Los Angeles and Long
Beach. In Northern California, the annual-average PM2.5 peaks at 25.3 µg m-3 between the cities of Oakland and SF.
Maximum PM2.5 reductions in the GHG-Step scenario
(Fig. 5b) occur between Oakland and SF
(-6 µg m-3), in SD county (-5.3 µg m-3), and in San Bernardino county (-3.5 µg m-3).
Overall, the reductions are significant (p value ≤ 0.1) over the
majority of northern and southern California; the only non-significant
PM2.5 changes are two locations inland in northern Los Angeles around
Lancaster and in midwestern San Bernardino, where BAU concentrations were
low. Significant PM2.5 increases of +0.5 µg m-3 do occur
in ocean shipping routes because more fossil fuel is used for marine vessels
in the GHG-Step scenario than in the BAU scenario. The GHG-Step scenario
requires increased biofuel use as part of the overall strategy to reduce
GHG emissions. This increased biofuel production is associated with higher
biofuel costs since the least expensive biofuel feedstocks are used first
followed by progressively more expensive feedstocks. As biofuel utilization
increases, the demand and cost for conventional fossil fuels decreases. The
decreased cost for fossil fuels in the GHG-Step scenario makes these fuels
attractive for use by marine sources.
(a) Annual-average PM2.5 mass
concentration (µg m-3) under the BAU scenario,
(b) change in PM2.5 mass concentrations (µg m-3) under the GHG-Step scenario, and p-value
significance level of the difference between concentrations predicted using
the BAU and GHG-Step scenarios. All simulations for the year 2054. Both 24 km resolution results and the finer 4 km resolution results are shown, with
the finer, smaller southern California or central-northern California
domains
overlaid upon the coarse California domain results.
Population-weighted PM2.5 concentrations (Fig. 2b) decrease for all
regions in all seasons under the 2050 GHG-Step scenario relative to the BAU
scenario. Variability in PM2.5 concentrations is highest during the
winter, with periods of intense stagnation intermixed with periods of
vigorous atmospheric mixing. PM2.5 concentrations are less variable in
the summer months as demonstrated by the smaller IQR
in Fig. 2b. The annual population-weighted
PM2.5 concentration drops from 6.0 to 4.8 µg m-3 (-20 %)
in the SJV, 8.3 to 6.2 µg m-3 (-25 %) in
SD, 9.5 to 7.8 µg m-3 (-18 %) in SF, and 7.6 to 6.5 µg m-3 (-14 %) for the SC air basin. Additional detail of the PM2.5 species that decreases
the most (e.g., nitrate) and the changes in the particulate size distribution
are further described in the Supplement and summarized in Table S5.
Certain PM2.5 spatial patterns illustrated in Fig. 5 were difficult
to anticipate based exclusively on statewide emissions totals. For example,
the PM2.5 co-benefits from widespread adoption of new vehicle
technology contribute significantly to statewide emissions reductions, but
these changes were distributed over a larger area than the benefits
associated with the decarbonization of freight modes (e.g., rail, aviation,
and marine). Most on-road vehicles in California already have relatively low
emissions rates for criteria pollutants. Further vehicular emissions savings
result from small reductions that are distributed over the large number of
vehicles across the entire state. This spreads the air quality improvements
associated with vehicles over a large area. In contrast, freight modes use
fuel with higher sulfur content burned in engines with less aftertreatment
control (e.g., particulate filter) leading to higher particulate matter emission rates per
energy consumed (e.g., mg J-1). These sources are localized to goods
movement corridors (shipping lanes, rail lines, etc.) that intersect at
transport distribution hubs near ports. This leads to localized reductions
in particulate matter concentrations associated with freight modes compared
to more diffuse reductions associated with on-road sources. These trends
were not obvious from statewide emissions tables but are clearly
illustrated by the results from regional air quality modeling.
Associated PM2.5 and O3 mortality, mortality rate, and costs
Figures 6 and 7 illustrate the deaths, death rate, and cost associated with
premature deaths from long-term annual exposure to both PM2.5 and ozone
(O3).
(a) PM2.5 and O3 long-term exposure deaths and
(b) mortality rate by county, year, and emission scenario, based
on combined Krewski et al. (2009) all-cause deaths associated with PM2.5
risk ratio (RR) and Jerrett et al. (2009) respiratory deaths associated with
ozone RR.
(a) Deaths and cost and (b) death rate for the
high-resolution modeling domains covering 93 % of California's
population. PM2.5 damages are estimated using methods
derived by Krewski et al. (2009) (blue bars) and Hoek et al. (2013) (gray bars).
Ozone damages are estimated using the methods derived by Jerrett et
al. (2009)
(orange bars). Only bars with the same color should be compared between
2010, 2050 BAU, and 2050 GHG-Step. The “2050 Diff” category shows the
difference between the 2050 GHG-Step and BAU scenarios.
Mortality
County and statewide PM2.5- and O3-associated deaths are displayed
in Figs. 6a and 7a. The calculations summarized in Fig. 7a
predict that 6400–10 600 people would die annually in the California 2050
BAU scenario due to exposure to PM2.5 and O3. The
medium estimate for mortality falls between these low and high estimates.
The range includes population growth through 2050. In the California
GHG-Step scenario, total PM2.5 and O3 mortality would decrease to
4800–7900 deaths annually (24–26 % reduction) due to reductions in
pollutant concentrations. More than 95 % of the premature mortality is
associated with PM2.5 while only 2.0–4.4 % is attributed to
O3. As a result, the O3 increases associated with the GHG-Step
scenario have a minor effect on mortality relative to PM2.5. Spatial
trends for PM2.5 and O3 mortality are similar, with the highest
rates occurring in highly populated regions (see Figs. 3a and 5a). Likewise, most
of the avoided mortality in the GHG-Step scenario also occurs in the regions
with the highest populations.
Mortality rate
Air pollution mortality rates (deaths per 100 000 people) plotted in
Figs. 6b and 7b
help to compare health effects across urban and rural areas (both of which
can experience high pollution events in California). The 2050 statewide air
pollution mortality rate drops by 54–56 % in the 2050 GHG-Step
scenario vs. the 2010 scenario and 24–26 % in the GHG-Step scenario
vs. the BAU scenario. Reductions in the air pollution mortality rate were
predicted in all counties under the GHG-Step scenario vs. the BAU scenario
(Fig. 6b). In the 2050 BAU scenario, SF, San Mateo, Alameda, Contra Costa, Sacramento, SD, and San
Bernardino counties are predicted to have air pollution mortality rates
higher than the statewide average of 19.3–32.2 deaths per 100 000 people (see
Fig. 6b). Under the GHG-Step scenario, SF, San Mateo, and Alameda counties continue to have the highest
death rates associated with PM2.5 and O3. Mortality rates in SF
are more than double the statewide average due to the proximity of major
construction projects and growing populations. Overall, Sacramento, Solano,
Contra Costa, and SF counties are predicted to have the greatest
reduction in PM2.5 and O3 mortality rates due to the adoption of
GHG mitigation strategies. These patterns reflect a reduction in the
emissions of criteria pollutants from construction projects but an increase
in emissions from locations that produce new energy sources such as
biofuels.
O3 mortality is expected to increase from 260 deaths yr-1 in the
BAU scenario to 490 deaths yr-1 in the GHG-Step scenario due to the
increase in O3 in key populated areas (mainly greater Los Angeles). The
largest number of O3-associated deaths (∼ 25 %) are
estimated to occur in southern California due to the combination of high
population and excess NOx in the BAU scenario leading to increased
O3 concentrations when NOx emissions decrease in the GHG-Step
scenario. The portion of air pollution deaths due to O3 would increase
from 2.4 to 4 % in the BAU scenario to 6.2–10.1 % in the GHG-Step
scenario, but overall mortality still decreases due to the overwhelming
effect of PM2.5 reductions.
Benefits
Using a VSL equal to USD 7.6 million per avoided
death (Industrial Economics, 2011; Bart Ostro, personal communication, 2015), total costs for
premature deaths in California equal ∼ USD 47.0–78.5 billion yr-1 in the 2050 BAU emissions scenario, with a savings of
USD 11.4–20.4 billion yr-1 in the GHG-Step emissions scenario (right axis
Fig. 7a). Los Angeles County has the highest
premature mortality associated with air pollution (25 % of California) and
thus the highest air pollution mortality cost under all emissions scenarios.
Air pollution damages in Los Angeles County are valued at
USD 15.2–25.5 billion yr-1 in 2010, which decreases to
USD 12.1–19.6 billion yr-1 in 2050 BAU. Adoption of the GHG mitigation
strategies in California reduces air pollution damages in Los Angeles County
by USD 1.9–3.6 billion yr-1 (17–18 % reduction). Other major
counties also experience reduced air pollution costs under the GHG-Step
scenario relative to BAU, including SD (USD 1.7–2.9 billion yr-1
reduction; 15 %–16 %) and Sacramento (USD 0.70–1.3 billion yr-1
reduction; 6.4 %). However, the largest cost savings per capita are
predicted to occur in and around counties near SF based on the
higher mortality rate reductions.
Implications
The costs for reducing California GHG emissions 80 % below 1990 levels by
the year 2050 depend strongly on numerous assumptions about external factors
such as the global price of oil. Only a few California energy models are
available that attempt to calculate costs across the entire economy (Morrison et al., 2014, 2015). Analyses
produced by the E3 PATHWAYS model (Williams et al., 2012;
Energy+Environmental Economics (E3), 2015) suggest that meeting an
intermediate target (40 % reduction in GHG emissions by the year 2030)
using a non-optimized energy portfolio scenario would reduce personal income
by USD 4.95 billion yr-1 (-0.15 %) and lower overall state gross domestic product by
USD 16.1 billion yr-1 (-0.45 %). An analysis produced by the CA-TIMES model
(Yang et al., 2014, 2015) indicates that the
optimized GHG-Step scenario is less expensive than the BAU
scenarios.
The air pollution analysis carried out in the current study predicts that
the GHG-Step scenario will provide public health benefits equivalent to
USD 11.4–20.4 billion yr-1 relative to the BAU scenario in 2050. The
public health benefits described here have relatively tight uncertainty
ranges with median values that are comparable to the more pessimistic of
these two cost estimates for the adoption of low-carbon energy.
Figure 8 illustrates the public health savings
associated with the GHG-Step scenario alongside the “fair-share” benefits
of federal programs (United States Office of Management and Budget,
2016) that affect California. Fair-share benefits are calculated using the
fraction of US residents living in California multiplied by the total US
benefits. The GHG-Step scenario yields benefits that are larger than those
from of any program under the Federal Department of Agriculture, Energy,
Health & Human Services, Labor, and Transportation. Only the National
Ambient Air Quality Standards (NAAQS) under the US EPA have greater public
health savings associated with reduced concentrations of air pollution. As
shown throughout Sect. 3, strategies to reduce GHG emissions have benefits
that overlap with NAAQS objectives and produce air quality improvements
that would otherwise be challenging or impossible to achieve under the BAU
scenario.
Taken together, the immediate and long-term savings associated with the
GHG-Step scenario make a compelling case for the shift to a low-carbon
energy system in California.
Annual “fair-share” benefits of federal programs that
affect California in 2016. The fair-share fraction of US total is proportional
to the fraction of US population living in California. “Low Carbon Energy”
represents the difference between the 2050 GHG-Step–BAU scenarios calculated
in the present study.
Conclusions
Measures to reduce GHG emissions to 80 % below 1990 levels in California
under the GHG-Step scenario altered emissions of criteria pollutants (or
their precursors) that generally brought nearly all regions of California
into compliance with the O3 NAAQS. A few of the dense urban areas
experienced minor ozone disbenefits due to the effects of reduced NOx
concentrations leading to slightly higher ozone concentrations. Additional
O3 abatement strategies may be required to offset these minor effects,
but the overall improvements in O3 concentrations across the rest of
the state appear to largely solve California's O3 non-attainment
problem. The nonlinear nature of the O3 response to emissions changes
emphasizes the need for the research community to include realistic chemical
reaction models as a function of location in mitigation exercises.
The GHG-Step scenario reduced PM2.5 concentrations across all regions
of California through decreases in primary emissions and secondary formation
pathways. PM2.5 concentrations increased over ocean shipping lanes in
the GHG-Step scenario but this has a negligible health impact. The inland
PM2.5 reductions drive the majority of the mortality reductions
associated with the climate-friendly scenario. Total air pollution deaths in
California decreased from 6400 to 10 600 per year in the 2050 BAU scenario to
4800–7900 per year in the GHG-Step scenario. These avoided deaths have a
value of USD 12.2–20.5 billion yr-1 using a value of a statistical life
equal to USD 7.6 million yr-1. The avoided mortality benefits of low-carbon energy
adoption in California exceed the present-day fair-share benefits of the
combined programs under the Federal Department of Agriculture, Energy,
Health & Human Services, Labor, and Transportation. Only the National
Ambient Air Quality Standards (NAAQS) under the US EPA have greater public
health benefits than adoption of low-carbon energy in California. These GHG
measures and air quality programs complement and enhance one another, since
adoption of low-carbon energy helps achieve compliance with the NAAQS that
would otherwise be challenging or impossible to achieve under the BAU
scenario. The public health benefits described here are comparable in value
to published worst-case cost estimates for the adoption of low-carbon
energy in California. Combined with other potential long-term benefits,
these immediate health benefits strengthen the argument for the adoption of
scenarios that reduce GHG emissions in California.