Environmental Impacts

Environmental Impact sub-model consists of one module, which calculates primary GHG emissions (CO2), air pollution (SO2, NOx, and PM), and water usage (withdrawal and consumption) under different scenarios. This module follows Equations (7) and (8) to capture existing and potential future environmental impacts of different scenarios.

G"##$% '"()* = ,-ℎ /)%)*"(0$% × 2$%3)*40$% 5"2($*(/"##$%/,-ℎ) (7)

9$% :;: )<0440$% & "0* >$##?(0$%

= ,-ℎ /)%)*"(0$% × 2$%3)*40$% 5"2($*(($%/,-ℎ)

(8)

where MWh is megawatt-hour electricity generation and different energy sources have different conversion factors. These factors are calculated from actual data when exist (historical EIA, EPA data, and utility companies integrated resource planning), or else from the energy literature. Table 2-5 summarizes the conversion factors utilized in the environmental impact estimation and their corresponding sources. Note that all of the conversion factors for coal-fired power plants are higher than those of NG.

Table 2-5: Conversion factors used in estimating water usage, GHG emission, and air pollution.

NG (peaker) NG (baseload) Coal Source

Mercury (lbs/GWh) 0 0 0.0172 PNM (2014, p. 37); EIA; EPA

PM (lbs/MWh) 0.0975 0.0628 0.094 PNM (2014, p. 37); EIA; EPA CO2 (lbs/MWh) 1,569.27 961.84 2,150.7 PNM (2014, p. 37); EIA; EPA NOx (lbs/MWh) 2.8879 0.1293 6.77 PNM (2014, p. 37); EIA; EPA SO2 (lbs/MWh) 0.008 0.005 1.691 PNM (2014, p. 37); EIA; EPA Water Withdrawal

(gallon/MWh)

250 250 10,180

Tidwell et al. (2009, p. 17); EIA;

EW3 (UCS, 2012) Water Consumption

(gallon/MWh)

160 160 630

Tidwell et al. (2009, p. 17); EIA;

EW3 (UCS, 2012)

Once potential environmental impacts are estimated, we calculate the potential GHG and air pollution reduction relative to the reference case scenario. From these values, we quantify economic benefits/costs based on GHG and air pollution’s social cost. In so doing, we utilize USD/ton multipliers used in the U.S. regulatory agencies such as Environmental Protection Agency (EPA, 2016a, 2016b) and academic literature (Sovacool, 2009; McCubbin & Sovacool, 2013; Wiser et al., 2015; Heo et al., 2016; Heo et al., 2016b; Millstein et al., 2017) and multiply them by the estimated ton emissions (CO2, SO2, NOx, and PM) to calculate dollar values.

To estimate social benefit of air pollution and GHG emission, we use multipliers from the Estimating Air pollution Social Impact Using Regression (EASIUR) model,

developed by Heo et al. (2016a, 2016b), and EPA (2016b) respectively. The EASIUR⁴⁸ predicts marginal benefits of “primary” and “secondary” PM2.5, where secondary PM2.5 includes SO2 and NOx. Similarly, the EPA model predicts social benefits of avoiding CO2 emissions. As acknowledged by Wiser et al. (2015), these models are common practice and are based on the state-of-the-art air-quality models, which best serves our purpose.

The EASIUR model estimates marginal social cost of “primary” and “secondary”

PM2.5 in USD per ton. As avoiding air pollution (SO2, NOx, and PM) reduces corresponding risk of premature mortality, the derived EASIUR multipliers can be viewed as marginal social benefit as well. We use three sets of marginal social benefit estimates for NOx, SO2 and PM2.5 at ground-level and by county. Although EPA takes a similar approach in estimating social benefit of CO2, it is rather less finely determined spatial resolution. EPA values (USD/ton) are developed for the entire U.S. We follow Wiser et al. (2016) but only use the central set of estimates, which are calculated based on a 3-percent discount rate.⁴⁹ The social benefit of reducing carbon is intended to capture (but is not limited to) changes in net agricultural productivity, human health, avoiding property damages from increased flood risk, and the value of ecosystem services due to climate change (EPA, 2016b).

EPA (2016a) estimates premature mortality, morbidity, and non-fatal heart attack incidence per ton of NOx and SO2 for three US regions: East, West, and California. We

48 The EASIUR model and multipliers can be find at: https://barney.ce.cmu.edu/~jinhyok/easiur/ (accessed 1/8/19)

49 See Table A1 of EPA (2016, p. 25) report for a description of the multipliers. Note that those values are 2007 USD/metric ton and ours are converted to 2010 USD/ton. We use US$45 per tCO2 in 2017, US$57 per tCO2 in 2030, and US$79 per tCO2 in 2050. These multipliers are national estimates and are not specific to New Mexico.

use EPA’s West incidence-per-ton estimates to assess human premature mortality and morbidity reduction relative to baseline scenario.⁵⁰ Lastly, we utilize estimated

multipliers by Sovacool (2009, p. 2246), McCubbin & Sovacool (2013, p.437), and Walston et al. (2016, p. 411)⁵¹ to estimate avian mortality reduction caused by coal, NG, wind turbines, and PV panels.

2.5. Data

Data were obtained from numerous sources including: EIA (various survey forms, AEO2018, and Layer Information for Interactive State Maps shapefiles), Emissions and Generation Resource Integrated Database (eGRID) of EPA, NREL (JEDI, Annual Technology Baseline, Wind Data, and Solar Data), NM Public Regulation Commission, United States Geological Survey, Bureau of Economic Analysis, United States Census Bureau, Western Electricity Coordinating Council, and the energy literature. Except for RPV data, we obtained generation data from EIA-923 and EIA-861. The data includes historical nameplate capacity of the existing power plants, generation, power plants’

locations (county and latitude/longitude), operating and planned retirement year times, and capacity factors. The data for existing RPV capacity were obtained from NM Public Regulation Commission. Further, we purchased the IMPLAN 2016 data to calculate jobs and output multipliers for each energy source. Lastly, economic benefit/cost of air

50 See tables: Table 4A-3 to Table 4A-7 of EPA (2016, pages 242 to 245).

51 See tables 3, 4, and 1 respectively. Following McCubbin & Sovacool (2013), we assume NG kills half as many birds as coal-fired power plants. Coal, NG, wind and PV avian mortality multipliers are: 5.18, 5.18/2, 0.4, and 0.23 birds per gigawatt-hour electricity generation respectively.

pollution and GHG reduction multipliers came from the energy literature. Table 2-6 summarizes the key data sources.

Table 2-6: Sources of data for key variables.

Data for Source

Electricity demand EIA

Population United States Census Bureau

Gross state product Bureau of Economic Analysis

Generation data EIA-860, EIA-861, EIA-923

Existing RPV capacity New Mexico Public Regulation Commission

RPV potential Solar for all – NREL

Wind Potential Wind Data – NREL

PV Potential Solar Data – NREL

NG Potential Layer Information for Interactive State Maps – EIA Levelized Cost of Energy Cole et al., (2018) – NREL

Job multiplier IMPLAN and JEDI (NREL)

Output multiplier IMPLAN

GHG social benefit multipliers EPA (2016b)

Air pollution social benefit multipliers Heo et al. (2016a, 2016b)

Human mortality and morbidity multipliers EPA (2016a), Krewski et al. (2009), Lepeule et al.

(2012), and Woodruff et al., (1997)

Avian mortality multipliers Sovacool (2009); McCubbin & Sovacool (2013);

Walston et al. (2016); Dissanayake and Ando (2014)

2.6. Results

In this section, we present our results. We first review electricity generation under the four modeled scenarios. Next, we discuss state-level and then county-level economic and environmental impacts. Economic impact results are presented for FTE employment and gross economic output, wherein we distinguish between the construction and

operation periods respectively. The construction period represents a short-term infusion of investment and economic activity. The operations period represents a more modest, but longer-term infusion of dollars into the local and state-wide economy. Environmental impacts, on the other hand, are reported in terms of GHG emissions, air pollution, water usage, and human and avian mortality associated with each of our four modeled

scenarios. These impacts are experienced once the plants are in the O&M phase. Thus, environmental impact results are reported for operations period solely and on a state- and level basis. In what follows, results are presented in this order: state- and county-level job and output impact, state- and county-county-level water usage impact, state- and county-level GHG-reduction benefits, state- and county-level air pollution impact, lastly state-level human and avian mortality associated with each of our four modeled

scenarios.

2.6.1. Generation

Figure 2-10 shows total electricity generation under four modeled scenarios and Figure 2-11 presents the generation mix through 2050 in the four modeled scenarios.

Based on the reference case scenario, as with the other three scenarios, RE and FF generations encompassed 17% and 83% of total generation in 2017. In 2030, generation

shares are 15% and 85% for RE and FF respectively. Relative to the reference case scenario, RE generations will comprise 2% and 9% higher in the generation mix under Scenario 1 (17%) and Scenario 2 (24%) respectively, and 5% lower under Scenario 3 (12%). All scenarios estimate a dip in electricity generation from 2036 until the end of 2037. This is due to the decommissioning of the existing coal-fired power plants in that period. As presented in Figure 2-11, scenarios estimate the amount and type of energy source to replace the foregone coal generation. By 2050, RE generation increases to 52%, while FF generation drops to 48% under the reference case scenario. Scenario 1 (55%) and Scenario 2 (59%) result in 7% and 11% higher and Scenario 3 (4%) to 48% lower RE generation compared with the reference case scenario. Recall that RPS requires utility companies generate a portion of their in-state sales from RE. Thus, it is possible to have FF generation even under the 100% RPS scenario (Scenario 2). Figure 2-12 presents RE generation versus required generation to meet RPS constraints by the four modeled scenarios. Take away here is that different energy scenarios will lead to different energy mix, thus different environmental and economic impacts.

Figure 2-10: Total annual electricity generation under four modeled scenarios.

Figure 2-11: Annual electricity generation by all six energy sources under four modeled scenarios.

Thousands GWh ^{Scenario 1}_{Scenario 2}

Scenario 3

Reference Case Scenario

Current (2016) Future Projections

0 10 20 30 40

2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049 Thousands GWh ^{Coal NG-BL}_{NG_P wind}

PV RPV

2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049

Scenario 2

2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049

Thousands GWh

2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049

Scenario 1

Reference Case Scenario 3

Figure 2-12: Renewable generation versus required generation to meet RPS constraints by the four modeled scenarios.