15 Beach since the treatment plants are located outside Imperial Beach boundaries, therefore, the GHG emissions are accounted for in this water category.
GHG emissions associated with water distribution were estimated by multiplying potable water used by Imperial Beach by the energy intensity for local water distribution and the SDG&E electricity emission factor. The portion of electricity and GHG emissions associated with water distribution occurs within the City boundary (within CalAm Southern Division service area) and have been subtracted from the electricity category, as they are accounted for in the water category.
In addition to providing potable water to Imperial Beach, Padre Dam MWD also produces and delivers recycled water to Imperial Beach. The recycled water is treated at the Padre Dam MWD’s Ray Stoyer Water Recycling Facility (Ray Stoyer WRF).42 The energy use from recycled water treatment and distribution is calculated based on the Padre Dam MWD local operation energy intensity given in Table
11. Because Ray Stoyer WRF is located outside Imperial Beach, the electricity use associated with recycled water treatment and distribution would not be included in the electricity category and are accounted for in the water category.
No recycled water was supplied to the City during the inventory years 2012 to 2014.
In 2012, 93 percent of the GHG emissions in the water category were from upstream supply and conveyance. The breakdown of emissions for the water category is given in Figure 6.
Figure 6 Emissions from the Water Category by Water System Segment (Imperial Beach, 2012) The total and per-capita potable water supplied, as well as the corresponding GHG emissions from the water category for the years 2012-2014 are given in Table 12.
42 Padre Dam Municipal Water District (October 2016). 2015 Urban Water Management Plan, Section 6.4 Wastewater and Recycled Water.
- 200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000
Emissions From Local Water Treatment
Emissions From Local Water Distribution
Emissions from Upstream and Conveyance
metric ton CO2e
Energy Policy Initiatives Center, 2017
16 Table 12 Water Supplied and GHG Emissions from the Water Category (Imperial Beach, 2012-2014) Year
Potable Water
Supplied
(acre-feet)
Per Capita Potable
Water Supplied
(gallons/person/day)
GHG Emissions
(MT CO2e)
2012 4,001 134 1,900
2013 3,692 122 1,800
2014 3,952 130 1,800
GHG emissions for each category are rounded to the nearest hundreds. Values are not rounded in the intermediary steps in the calculation.
Energy Policy Initiatives Center, 2018.
Emissions associated with water end-use, such as water heating and cooling, are included in the electricity and natural gas category, and not in this water category, as data are not available to separate out those values.
4.6 Wastewater
The emissions from wastewater generated by Imperial Beach were estimated by multiplying the total amount of wastewater generated in a given year with the emission factor of the wastewater treatment processes.
The City’s Public Works Department operates and maintains the wastewater collection system within the City. The wastewater is delivered to the City of San Diego Metropolitan Sewerage System and treated at its wastewater treatment plants (WWTPs).
The wastewater treatment emission factor (MT CO2e/million gallon) at Point Loma WWTP, one of the WWTPs in the Metropolitan Sewage System, is used to estimate the wastewater emissions. Point Loma WWTP reports the wastewater flow in its plant annual report43 and plant operation GHG emissions to CARB under the Mandatory GHG Reporting Regulation (MRR).44 The reported GHG emissions include three components: 1) direct CO2 from combustion of anaerobic digester gas; 2) CH4 and N2O emissions from digester gas combustion; and 3) operational fossil fuel emissions from complete combustion. The direct CO2 from combustion of anaerobic digester gas is considered biogenic, while the other two components of CO2 emissions are considered non-biogenic emissions.
The wastewater emission factor derived from Point Loma WWTP was applied to all annual wastewater flow from the City of Imperial Beach. The total wastewater flow, wastewater emission factors, as well as the corresponding GHG emissions are given in Table 13.45
43 City of San Diego, Public Utilities. Point Loma Wastewater Treatment Plant & Ocean Outfall – Annual Reports. 44 CARB. Mandatory GHG Reporting – Reported Emissions. http://www.arb.ca.gov/cc/reporting/ghg-rep/reported-data/ghgreports.htm 45 2010–2016 Wastewater (million gallons per day) flow from El Cajon to Metropolitan Sewerage System were provided by City of San Diego through a Public Records Request in July 2017 and converted to million gallons per year.
17 Table 13 Wastewater Generated and Treated at Centralized Treatment Plant (Imperial Beach, 2012–2014) Year
Total Wastewater
Generated
(Million Gallons/year)
Wastewater Emission
Factor
(MT CO2e/ Million Gallon)
GHG
Emissions
(MT CO2e)
2012 784 0.41 300
2013 791 0.38 300
2014 789 0.45 400
GHG emissions for each category are rounded to the nearest hundreds. Values are not rounded in the intermediary steps in the calculation.
CARB, 2017; City of San Diego, 2017; Energy Policy Initiatives Center, 2018. 5 BUSINESS-AS-USUAL GHG EMISSIONS PROJECTIONS
To inform the development of GHG reduction strategies within a jurisdiction’s Climate Action Plan (CAP), GHG emissions are projected from the latest data available, as well as projections for population, housing, and job growth. The latest year with available data may be different for different inventory categories. This is used to develop a business-as-usual (BAU) projection, which demonstrates emissions growth in the absence of any new policies and programs. Next, emissions reductions attributable to federal and State policies and programs are applied in the future, creating a legislatively-adjusted BAU.
Figure 7 provides an illustrative example of the difference between a BAU and a legislatively-adjusted BAU. Only the BAU projection is discussed in this document; GHG reductions from the policies and programs included in the legislatively-adjusted BAU are considered later in the climate action planning process.
Figure 7 Illustrative Example Only: BAU and Legislatively-adjusted BAU Emissions Projections Section 5.1 provides a summary of the BAU emissions projections for years 2020, 2030, and 2050, and Section 5.2 provides the projection methodologies used for each category.
5.1 Emissions Projections for 2020, 2030, and 2050
The total GHG emissions in 2020 are projected to be approximately 81,100 MT CO2e, 16% lower than the 2012 emissions level and 10% lower than the 2014 emissions level. The total GHG emissions in 2030
18 are projected to be approximately 82,200 MT CO2e, and the total GHG emissions in 2050 are projected to be approximately 84,400 MT CO2e. Figure 8 below shows a comparison of the emissions breakdown by category for the inventory years and projection years.
Figure 8 BAU GHG Emissions Projections (Imperial Beach, 2020, 2030, and 2050) As shown in Figure 8, the on-road transportation category contributes the most to the overall emissions in each projection year. Emissions from on-road transportation are expected to decline through 2030 and then rise again through 2050, but are not projected to be higher than the on-road transportation emissions in 2012 and 2014. One of the likely reasons for the decline of on-road transportation emissions is the decline of average vehicle emission rates, as newer, more efficient vehicles replace old vehicles in the region. The total and distribution of projected emissions by category are presented in Table 14.
-
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
100,000
2012 2014 2020 2030 2050
metric ton CO2e
On-Road Transportation Electricity Natural Gas Solid Waste Water Wastewater Inventory Year Business-as-usual
Projection Year
Energy Policy Initiatives Center, 2018
19 Table 14 Projected Total and Category-GHG Emissions in Imperial Beach (2020, 2030, and 2050) Emissions Category Projected GHG Emissions (MT CO2e)
2020 2030 2050
On-Road Transportation 46,900 45,300 45,600
Electricity 15,100 16,200 17,000
Natural Gas 14,200 15,400 16,200
Solid Waste 2,800 3,100 3,200
Water 1,700 1,900 2,000
Wastewater 400 400 400
Total 81,100 82,200 84,400
Sum may not add up to totals due to rounding. Projected GHG emissions for each category are rounded to the nearest hundreds. Values are not rounded in the intermediary steps in the calculation.
Energy Policy Initiatives Center, 2018.
5.2 Methods to Project GHG Emissions
The SANDAG Series 13 Regional Growth Forecast was used as the basis of population and job growth in Imperial Beach, as shown in Table 15.46 The methods used to project future emissions are provided below for each emissions category.
Table 15 SANDAG Population Projection and Job Growth Forecast (Imperial Beach, 2020, 2030, and 2050) Year Population Jobs
2020 27,506 4,311
2030 29,928 4,473
2050 31,691 4,613
SANDAG, 2013.
5.2.1 On-Road Transportation
Average weekday O-D VMT forecast for each trip type in 2020, 2030, and 2050 were provided by SANDAG based on its Series 13 activity-based model, as shown in Table 16 (See Appendix A for original data tables provided).47
46 Population and jobs data are from the SANDAG Series 13 Regional Growth Forecast (updated in October 2013). SANDAG Data Surfer, accessed October 24, 2017. Series 13 has a baseline calibrated year of 2012. Therefore, projections from the 2012 baseline may differ from more recent estimates by the state, such as the Department of Finance (DOF). 47 Series 13 2020, 2030, and 2050 VMT average projections were provided by SANDAG (January 30, 2017 and October 18, 2017).
20 Table 16 Projected O-D VMT and Trip Types (Imperial Beach, 2020, 2030, and 2050) Trip Type (Miles/Average Weekday) 2020 2030 2050
Internal-Internal 10,268 9,882 9,027
Internal-External/External-Internal 634,583 669,726 677,960 External-External (Information only,
excluded from VMT and GHG
calculations)*
9,638 10,366 9,322
*Miles from External-External trips are the portion within the City boundary, not the entire trips.
SANDAG, 2017.
To convert VMT of each type to total VMT, the method discussed in Section 4.1 was used. The VMT was multiplied by the adjusted average vehicle emission rate derived from EMFAC2014 for each projection year. Two adjustments were made to the EMFAC2014 emission rates for the projections: 1) the electric vehicle penetration rate in 2016 was kept constant for all projection years;48 and 2) for all new vehicles entering the fleet after 2016, the emission rates are equal to the emission rates of new model year 2016 vehicles with the same vehicle class and fuel type.49
The projected total VMT, average vehicle emission rates, and corresponding GHG emissions from the on-road transportation category are given in Table 17.
Table 17 Projected VMT, Average Vehicle Emission Rate and GHG Emissions from On-Road Transportation Category (Imperial Beach, 2020, 2030, and 2050)
Year
Projected Total VMT Average Vehicle
Emission Rate
(g CO2e/mile)
Projected GHG
Emissions
(MT CO2e) Average
Weekday Miles
Average
Annual Miles
2020 327,559 113,663,077 412 46,900
2030 344,745 119,626,556 379 45,300
2050 348,007 120,758,437 377 45,600
Projected GHG emissions for each category are rounded to the nearest hundreds. Values are not rounded in the intermediary steps in the calculation.
CARB, 2015. SANDAG, 2017. Energy Policy Initiatives Center, 2018.
As newer, more efficient vehicles replace older, less efficient vehicles in the region, the average vehicle emission rate decreases.
5.2.2 Electricity
Electricity use in the City was projected separately for residential and non-residential customer classes.
For the residential customer class, the per-capita electricity use (metered electricity sales) in 2016 (1,352
48 This uses a fixed 2016 electric vehicle penetration rate of about 2% of light duty vehicles instead of using the estimated impact of the state Zero Emission Vehicle (ZEV) program on BAU emissions. The 2016 electric vehicle penetration rate is based on EMFAC2014 Technical Documentation, Section 3.2.2.4.3. The ZEV program requires auto manufacturers to make and sell ZEVs that will increase VMTs driven by ZEVs.
49 This uses a fixed actual emission rate of the new 2016 models instead of the effect of adopted federal and state vehicle efficiency standards 2017–2025 for light-duty and heavy-duty vehicles.
21 kWh/person/year), the latest year with available SDG&E data, was calculated by dividing the total electricity sales in the residential class by the population in 2016. The per-capita electricity use is held constant and used to project total electricity use for a future year by multiplying by the SANDAG Series 13 population forecast for the future year. The projected total electricity use was multiplied by the Cityspecific electricity emission factor in 2016 (542 lbs CO2e/MWh), held constant, for a projected total GHG emission. The City-specific electricity emission factor in 2016 is significantly lower than that of 2012 and 2014 because SDG&E has since reached 43% renewable energy in its power mix.50 A similar method was used for the non-residential class. The total non-residential electricity use was projected based on job growth and the per-job electricity consumption in 2016 (4,757 kWh/job/year) for all future years. The total projected net energy for load (electricity sales + transmission and distribution losses) and corresponding GHG emissions from the electricity category are given in Table 18.51 Table 18 Projected Net Energy for Load and GHG Emissions from the Electricity Category (Imperial Beach, 2020, 2030, and 2050)
Year
Projected Net Energy for Load
(electricity sales + losses)
(MWh)
Projected GHG
Emissions
(MT CO2e)
2020 61,567 15,100
2030 65,854 16,200
2050 69,038 17,000
Projected GHG emissions for each category are rounded to the nearest hundreds. Values are not rounded in the intermediary steps in the calculation.
Energy Policy Initiatives Center, 2018.
5.2.3 Natural Gas
The projection method for the natural gas category is similar to that for the electricity category. The natural gas use in residential and non-residential classes are calculated separately. The per-capita residential natural gas consumption (77 therms/person/year) and the per-job natural gas consumption (118 therms/job/year) in 2016 were held constant with population and job growth for the projection.
The natural gas emission factor used in Section 4.3 was held constant for future years. The projected total natural gas use and corresponding GHG emissions from the natural gas category are given in Table
19. 50 2016 renewable content in SDG&E bundled power is based on SDG&E’s 2016 power source disclosure report submitted to the California Energy Commission (CEC). The 2016 report was provided by CEC staff to EPIC in July 2017. 51 The net energy for load of each future year is adjusted using the method described in Section 4.2. The net energy for load does not include self-serve renewable supply, such as electricity generation from behind-the-meter PV systems.
22 Table 19 Projected Natural Gas Use and GHG Emissions from Natural Gas Category (Imperial Beach, 2020, 2030, and 2050)
Year
Projected Total
Natural Gas Use
(Million Therms)
Projected GHG
Emissions
(MT CO2e)
2020 2.6 14,200
2030 2.8 15,400
2050 3.0 16,200
Projected GHG emissions for each category are rounded to the nearest hundreds. Values are not rounded in the intermediary steps in the calculation.
Energy Policy Initiatives Center, 2018.
5.2.4 Solid Waste
The BAU solid waste disposal by Imperial Beach was projected using the population growth and the percapita solid waste disposed in 2016 (1.5 kg/person/day), held constant for future years, to be consistent with other categories. The projected emissions from the disposal were calculated by multiplying the disposal amount with the emission factor for mixed solid waste, provided in Section 4.4. The projected total waste disposal and corresponding GHG emissions from the solid waste category are given in Table
20. Table 20 Projected Solid Waste Disposal and GHG Emissions from Solid Waste Category (Imperial Beach, 2020, 2030, and 2050)
Year
Projected Solid
Waste Disposal
(MT)
Projected GHG
Emissions
(MT CO2e)
2020 15,212 2,800
2030 16,551 3,100
2050 17,526 3,200
Projected GHG emissions for each category are rounded to the nearest hundreds. Values are not rounded in the intermediary steps in the calculation.
Energy Policy Initiatives Center, 2018.
5.2.5 Water
The total water use for all projection years was based on the fixed 2016 per-capita water consumption and population growth. It is assumed that the current percentage of water from each supply source (SDCWA treated, SDCWA untreated, and local surface water) remained unchanged for the BAU projection. It is also assumed that no recycled water sources or new potable water sources are developed under the BAU projection.
The per-capita potable water used in 2016 (114 gallons/person/day), significantly lower than in 2012 (134 gallons/person/day) and 2014 (130 gallons/person/day). The energy intensity for each element of the water cycle (Table 11) and the electricity emission factor were held constant for all projection years.
The projected total water supply and corresponding GHG emissions from the water category are given in Table 21.
23 Table 21 Projected Water and GHG Emissions from the Water Category (Imperial Beach, 2020, 2030, and 2050) Year
Projected Water
Supply
(Acre-Feet)
Projected GHG
Emissions
(MT CO2e)
2020 3,522 1,700
2030 3,832 1,900
2050 4,058 2,000
Projected GHG emissions for each category are rounded to the nearest hundreds. Values are not rounded in the intermediary steps in the calculation.
Energy Policy Initiatives Center, 2018.
5.2.6 Wastewater
The total wastewater generation for all projection years is based on the fixed 2016 per-capita wastewater generation and projected population growth. Similarly, the 2016 Point Loma WWTP wastewater emission factor was held constant for the projection years.
The projected total wastewater generation and the GHG emissions from the wastewater category are given Table 22.
Table 22 Projected Wastewater Generated and GHG Emissions from the Wastewater Category (Imperial Beach, 2020, 2030, and 2050)
Year
Projected Wastewater
Generated
(Million Gallons)
Projected GHG
Emissions
(MT CO2e)
2020 769 400
2030 837 400
2040 886 400
Projected GHG emissions for each category are rounded to the nearest hundreds. Values are not rounded in the intermediary steps in the calculation.
Energy Policy Initiatives Center, 2018.
Energy Policy Initiatives Center A-1
Appendix A. IMPERIAL BEACH VMT BY TRIP TYPE
Average weekday VMT data tables were provided by SANDAG (from SANDAG ABM Series 13, Release
13.3.0). Revenue Constrained refers to the transportation network scenario adopted in San Diego Forward: The 2015 Regional Plan.52 Emphasis (red squares and text) was added by EPIC.
Figure A-1 Estimated Imperial Beach 2012 VMT by Trip Type (miles/weekday) Figure A-2 Estimated Imperial Beach 2014 VMT by Trip Type (miles/weekday)
52 San Diego Forward: The 2015 Regional Plan was adopted by the SANDAG Board of Directors on October 9, 2015.
Energy Policy Initiatives Center A-2
Figure A-3 Projected Imperial Beach 2020 VMT by Trip Type (miles/weekday) Figure A-4 Projected Imperial Beach 2030 VMT by Trip Type (miles/weekday)
Energy Policy Initiatives Center A-3
Figure A-5 Projected Imperial Beach 2050 VMT by Trip Type (miles/weekday)
Energy Policy Initiatives Center B-1
Appendix B. SOURCE DATA FOR THE SOLID WASTE EMISSION FACTOR Waste Component
Waste
Distribution
(%)1
Landfill Gas Emissions
CH4 without LFG
Recovery
(MT CO2e/short ton)
Source2
Paper 16.8% - -
Corrugated
Containers/Cardboard 5.0% 2.36 Exhibit 3-27, WARM v14 Containers /Packaging Newspaper 0.8% 0.95 Exhibit 3-27, WARM v14
Containers /Packaging
Magazine 0.6% 1.08 Exhibit 3-27, WARM v14
Containers /Packaging
Mixed Paper (general) 10.4% 2.14 Exhibit 3-27, WARM v14 Containers /Packaging
Plastic 8.9% - -
Glass 1.7% - -
Metal 3.5% - -
Organics 38.9% - -
Food 15% 1.57 Exhibit 1-49, WARM V14
Organic Materials
Tree 5.3% 0.77 Exhibit 2-11 WARM V14
Organic Materials
Leaves and Grass 6.8% 0.59 Exhibit 2-11 WARM V14
Organic Materials
Trimmings 3.5% 0.59 Exhibit 2-11 WARM V14
Organic Materials
Mixed Organics 8.3% 0.53 Exhibit 2-11 WARM V14
Organic Materials
Electronics 0.6% - -
Construction &
Demolition 24.6% - -
Household Hazardous
Waste 0.2% - -
Special Waste 3.1% - -
Mixed Residue 1.6% 0.53
Mixed Waste Emission Factor 0.744
Source: 1) City of San Diego 2014. 2) EPA Waste Reduction Model (WARM) Version 14 (2016)
Resilient Imperial Beach
Climate Action Plan
Appendices
Appendix B
Methods for Estimating Greenhouse Gas Emissions Reductions in the Imperial Beach Climate Action Plan
APPENDIX B. METHODS FOR ESTIMATING GREENHOUSE GAS EMISSIONS
REDUCTIONS IN THE IMPERIAL BEACH CLIMATE ACTION PLAN
February 2019
Prepared for the City of Imperial Beach
Prepared by the Energy Policy Initiatives Center
About EPIC
The Energy Policy Initiatives Center (EPIC) is a non-profit research center of the USD School of Law that studies energy policy issues affecting California and the San Diego region. EPIC’s mission is to increase awareness and understanding of energy- and climate-related policy issues by conducting research and analysis to inform decision makers and educating law students.
For more information, please visit the EPIC website at www.sandiego.edu/epic.
Prepared in partnership with the San Diego Association of Governments (SANDAG) and the Energy Roadmap Program. This Program is primarily funded by California utility customers and administered by San Diego Gas & Electric Company under the auspices of the California Public Utilities Commission.
Table of Contents
1 Overview .......................................................................................................................... 1
1.1 Rounding of Values in Tables and Figures .............................................................................. 1 2 Emission Reduction Targets .............................................................................................. 1 3 Summary of Emissions Reduction Estimates ..................................................................... 3 4 Background and Common Assumptions ............................................................................ 5
4.1 Common Background Data .................................................................................................... 5
4.2 Common Assumptions and Methods for Calculating Electricity Emissions Reductions ............ 6
4.2.1 GHG Emission Factor for Electricity ....................................................................................... 6
4.2.1.1 Renewables Content in Supply from SDG&E and Other Electric Service Providers ......................... 7
4.2.1.2 Renewables Content in a Potential Local Renewables and Zero-Carbon Program .......................... 7
4.2.1.3 Renewables Supply from behind-the-meter PV Systems ................................................................. 7
4.2.1.4 Weighted Average GHG Emission Factor for Electricity ................................................................... 7
4.2.2 Allocation of GHG Emissions Reductions from Measures that Increase Renewables in Electricity to State Policy, Local Programs or Behind-the-meter PV Supply ......................................... 8
4.3 Common Assumptions and Methods for Calculating Natural Gas Emissions Reductions ......... 9
4.4 Common Assumptions and Methods for Calculating On-Road Transportation Emissions Reduction ....................................................................................................................................... 10
4.4.1 GHG Emission Factor for On-Road Transportation .............................................................. 10
4.4.2 GHG Emissions Reduction from Increasing Zero Emission Vehicles .................................... 11 5 Emissions Reductions from Federal and State Actions .................................................... 12
5.1 SE.1: California Renewables Portfolio Standard ................................................................... 12
5.2 SE.2: California Solar Programs, Policies, and 2019 Mandates .............................................. 14
5.2.1 Solar Policies and Programs ................................................................................................. 14
5.2.2 2019 Building Energy Efficiency Standards PV Mandates .................................................... 16
5.2.3 All Solar Policies, Programs, and Mandates ......................................................................... 17
5.3 SE.3: California Energy Efficiency Program ........................................................................... 18
5.4 ST.1: Federal and California Vehicle Efficiency Standards ..................................................... 19 6 EMISSIONS reductions from local Measures ................................................................... 20
6.1 Strategy: Increase Clean and Efficient Transportation .......................................................... 20
6.1.1 Measure T.1: Increase Citywide Electric Vehicle Charging Stations (EVCSs) ....................... 20
6.1.2 Measure T.2: Clean Municipal Fleet ..................................................................................... 22
6.2 Strategy: Reduce Vehicle Miles Traveled (VMT) ................................................................... 24
6.2.1 Measure T.3: Increase Mass Transit Ridership .................................................................... 24
6.2.2 Measure T.4: Improve Pedestrian and Bicycle Facilities ...................................................... 25
6.2.3 Measure T.5: Reduce Municipal Employee VMT ................................................................. 27
6.3 Strategy: Increase Renewable and Zero-Carbon Electricity ................................................... 27
6.3.1 Measure E.1: Increase Grid-Supply of Renewable and Zero-Carbon Electricity .................. 28
6.3.2 Measure E.2: Increase Commercial Behind-the-Meter PV .................................................. 28
6.4 Strategy: Achieve Zero Waste .............................................................................................. 30
6.4.1 Measure W.1: Divert Waste from Landfill ........................................................................... 30
6.5 Strategy: Sequester Carbon ................................................................................................. 31
6.5.1 Measure S.1: Increase Urban Tree Planting ......................................................................... 31 Tables
Table 1 Emissions Projection, Reduction Targets, and Emissions Reduction Needed ................................. 2 Table 2 Summary of GHG Emissions Reduction by Strategy in the Imperial Beach CAP .............................. 3 Table 3 Summary of GHG Emissions Reductions by Measure in Imperial Beach CAP .................................. 4 Table 4 Common Data Used for the Imperial Beach Climate Action Plan .................................................... 6 Table 5 2016 and Projected 2030 GHG Emission Factor for Electricity in Imperial Beach ........................... 8 Table 6 Emissions Reduction from All Measures Increasing Renewable and Zero-Carbon Supply in Imperial Beach .............................................................................................................................................. 9 Table 7 Allocation of Emissions Reductions from Increasing Renewable (or Zero-Carbon) Supply in Imperial Beach .............................................................................................................................................. 9 Table 8 Average Vehicle Emission Rates in the San Diego Region .............................................................. 11 Table 9 Emissions Reduction from Increasing Miles Driven by Zero Emission Vehicles ............................. 11 Table 10 Allocation of GHG Emissions Reduction from Increasing Zero Emission Vehicles ....................... 12 Table 11 Electricity Providers and Projected 2030 Emissions Reduction from SE.1 (California Renewables Portfolio Standard) ...................................................................................................................................... 14 Table 12 Behind-the-meter PV Capacity and Estimated Electricity Generation (Imperial Beach and SDG&E Planning Area) ............................................................................................................................................. 15 Table 13 Estimated PV Requirement for New Homes after 2020 in Imperial Beach ................................. 17 Table 14 New Homes with PV Systems after 2020 in Imperial Beach ........................................................ 17 Table 15 Key Assumptions and Results for California Solar Policies, Programs, and Mandates ................ 18 Table 16 Estimated Energy Saving from California Energy Efficiency Program .......................................... 19 Table 17 Key Assumptions and Results for California Energy Efficiency Programs .................................... 19 Table 18 Key Assumptions and Results for ST.1 Federal and California Vehicle Efficiency Standards ....... 20 Table 19 Electric Vehicle Charging Efficiency by Level 2 Charger Type ...................................................... 21 Table 20 Key Assumptions and Results for Measure T.1 Increase Citywide Electric Vehicle Charging Stations (EVCS) ............................................................................................................................................ 22 Table 21 Imperial Beach Municipal Fleet Vehicle Replacement Schedule ................................................. 23 Table 22 Key Assumptions and Results for Measure T.2 Clean Municipal Fleet ........................................ 24 Table 23 Projected Average Annual Ridership of Rapid 925 and VMT Avoided ......................................... 25 Table 24 Key Assumptions and Results for Measure T.3: Increase Mass Transit Ridership ....................... 25 Table 25 Planned New Bicycle Infrastructure in Imperial Beach to be Completed by 2030 ...................... 26 Table 26 Key Assumptions and Results for Measure T.4 Improve Pedestrian and Bicycle Facilities ......... 27 Table 27 Key Assumptions and Results for Measure T.5 Reduce Municipal Employee VMT ..................... 27 Table 28 Key Assumptions and Results for Measure E.1: Increase Grid-Supply Renewables .................... 28 Table 29 Key Assumptions for Measure E.2: Increase Commercial Behind-the-Meter PV ........................ 29 Table 30 Key Assumptions and Results for Measure E.2 Increase Commercial Behind-the-Meter PV ...... 30 Table 31 Key Assumptions and Results for Measure W.1: Divert Waste from Landfill .............................. 31 Table 32 Key Assumptions and Results for Measure S.1 Urban Tree Planting ........................................... 32 Figures
Figure 1 California Statewide GHG Inventory and Emission Reduction Targets ........................................... 2 Figure 2 Imperial Beach GHG Emissions Trend (2012–2050) ....................................................................... 5 Figure 3 SB 100 RPS and Zero-Carbon Targets ............................................................................................ 13 Figure 4 Behind-the-meter PV Trend in Imperial Beach (2012–2030) ....................................................... 16
1 1 OVERVIEW
This document provides a summary of the methods used to calculate the greenhouse gas (GHG) emissions reductions for the strategies and measures included in the City of Imperial Beach (referred to as “the City” or “Imperial Beach”)’s Climate Action Plan (CAP).
Section 2 provides emission reduction targets for Imperial Beach in 2020 and 2030. Section 3 provides a summary of emissions reduction estimates in each emission category from federal and California (State) actions and five local CAP strategies in 2030. Section 4 provides the common data sources and methods used throughout the document. The detailed methods used to estimate emissions reductions from each strategy and action are presented in Sections 5 and 6.
1.1 Rounding of Values in Tables and Figures
Rounding is used only for the final GHG values within the tables and figures throughout the document.
Values are not rounded in the intermediary steps in the calculation. Because of rounding, some totals may not equal the values summed in any table or figure. 2 EMISSION REDUCTION TARGETS
California has a statewide target to reach the 1990 GHG emissions level by 2020 or 431 million metric tons of CO2e (MMT CO2e), and to reach 40% below the 1990 level by 2030, or 260 MMT CO2e.1 According to the California Air Resources Board (CARB)’s statewide inventory, the 2012 statewide total GHG emissions level was at 450 MMT CO2e.2
At the State level, the emission reduction target for 2020 is equivalent to 4% below 2012 and for 2030 it is equivalent to 42% below 2012, as illustrated in Figure 1.
1 CARB: California’s 2017 Climate Change Scoping Plan (November 2017), accessed on April 26, 2018. 2 CARB: California Greenhouse Gas Inventory for 2000–2016 (June, 2018), accessed on December 13, 2018.
2 Figure 1 California Statewide GHG Inventory and Emission Reduction Targets The Imperial Beach CAP has a baseline year of 2012. To be consistent with the emissions reduction targets at the State level, the target emission levels for Imperial Beach are set at 4% below the 2012 emissions level by 2020 and 42% below the 2012 emissions level by 2030.
Table 1 shows the business-as-usual (BAU) emissions projection, reduction targets, and CO2e reductions needed in 2020 and 2030 to achieve the target emission levels.3
Table 1 Emissions Projection, Reduction Targets, and Emissions Reduction Needed Year
Business-as-usual
Projection
(MT CO2e)
Target
Emission Level
(% below
baseline)
Target Emission
Levels
(MT CO2e)
Emissions
Reduction Needed
to Meet Target
(MT CO2e)
2012 96,400 - - -
2020 81,100 -4% 92,700 -11,500
2030 82,200 -42% 55,900 26,300
Emissions values are rounded.
Energy Policy Initiatives Center 2018.
No local actions are needed for the City to reach its 2020 target. In 2030, a reduction of 26,300 MT CO2e is needed for the City to meet its 2030 target. This document focuses on the State and local actions needed to reach the 2030 target.
3 The method to project emissions at 2020 and 2030 is provided in Appendix A, City of Imperial Beach Greenhouse Gas Emissions Inventory and Projection (EPIC, 2018).
3 3 SUMMARY OF EMISSIONS REDUCTION ESTIMATES
This section summarizes the GHG emissions reductions from strategies and measures included in the Imperial Beach CAP. Table 2 below presents a summary of emissions reductions from the five local strategies in the City’s CAP, as well as the federal and State actions considered.
Table 2 Summary of GHG Emissions Reduction by Strategy in the Imperial Beach CAP Year
Emissions Reduction by CAP Strategy (MT CO2e) Reductions from Federal
and State
Actions
(MT CO2e)
Total
Emissions
Reduction
(MT CO2e) *
Increase
Renewable and
Zero-Carbon
Electricity
Clean and
Efficient
Transportation
Reduce
Vehicle
Miles
Traveled
Achieve
Zero
Waste
Sequester
Carbon Total
2030 1,264 799 1,041 3,318 31 6,454 19,992 26,300
*The total emissions reduction in 2030 is rounded.
Source: EPIC 2018.
Each CAP strategy includes several quantifiable measures and non-quantifiable supporting activities.
Table 3 presents a detailed summary of the emissions reduction from each local measure and each federal and State action.
4 Table 3 Summary of GHG Emissions Reductions by Measure in Imperial Beach CAP Emission
Category Federal and State Actions, and Local CAP Measures 2030
Emissions
Reductions
(MT CO2e)
Energy
Local CAP Measures
E.1 Increase grid-supply renewables 1,204
E.2 Increase commercial behind-the-meter photovoltaic (PV) systems 59 Federal and State Actions
SE.1 California Renewables Portfolio Standard 6,022
SE.2 California solar policies, programs and 2019 mandates 1,856 SE.3 California energy efficiency programs 3,033
On-road
Transportation
Local CAP Measures
T.1 Increase citywide electric vehicle charging stations 751 T.2 Clean municipal fleet 48
T.3 Increase mass transit ridership 687
T.4 Improve pedestrian and bicycle facilities 342
T.5 Reduce municipal employee vehicle miles traveled (VMT) 13 Federal and State Actions
ST.1 Federal and California vehicle efficiency standards 9,081 Waste
Local CAP Measures
W.1 Divert waste from landfill 3,318
S.1 Urban tree planting 31
All Federal and State Actions 19,992
All Local CAP Measures 6,454
All (Federal, State, and Local CAP) * 26,400
*The total emissions reduction in 2030 is rounded.
Energy Policy Initiatives Center 2018.
Figure 2 provides a visualization of the emissions trend for the CAP horizon year, as well as an estimated future trend through 2050. The Imperial Beach CAP does not include a GHG reduction goal for 2050.
Emissions beyond 2030 are included to show future trends and potential reduction needed for the City to be consistent with the State’s long-term goal to reduce statewide emissions 80% below 1990 levels.
5 Figure 2 Imperial Beach GHG Emissions Trend (2012–2050) In Figure 2, the BAU emissions projection is represented along the top of the graph. The green dots represent the target emissions levels in 2020 and 2030. In this case, the City meets both 2020 and 2030 targets with the federal and State actions, and the local measures identified in the CAP. The colored wedges represent the reduction estimates of each CAP strategy and of federal and State actions. The grey area beneath the colored wedges represents the remaining emissions after all the actions have taken place.
4 BACKGROUND AND COMMON ASSUMPTIONS
Unless stated otherwise, all activity data and GHG emissions reported in this document are annual values for the calendar year, and all emission factors reported in this document are annual average values for the calendar year.
A set of common assumptions and sources was used to calculate potential emissions reductions for many of the measures included in the CAP. The following section provides assumptions that were applied to measures related to electricity, natural gas, and on-road transportation. Measures related to other categories do not have common assumptions. The detailed methods and data for each measure are provided in Section 5 and Section 6.
4.1 Common Background Data
Table 4 presents a summary of common data used to estimate overall GHG emissions levels and the reduction estimates across several CAP measures.
6 Table 4 Common Data Used for the Imperial Beach Climate Action Plan Year 2012 2020 2030
Population4 26,750 27,506 29,928
Labor Force5 12,300 13,604 14,116
Vehicle Miles Traveled (VMT)
(annual miles)6 112,551,781 113,663,077 119,626,556
Electricity Gross Generation (GWh)7 64 66 73
VMT projections are based on the SANDAG Series 13 forecast. 2012 is the Series 13 Base Year.
For other categories, data in 2012 are historical data and data in 2020 and 2030 are the latest available forecasted data as of October 2018, based on various sources.
Energy Policy Initiatives Center 2018.
4.2 Common Assumptions and Methods for Calculating Electricity Emissions Reductions The following overall assumptions and methods are used in the calculation of emissions reductions related to electricity, including from federal and State actions and local measures. Details for the calculation of each measure are provided in Sections 5 and 6.
4.2.1 GHG Emission Factor for Electricity
The GHG emission factor for electricity for a city, expressed in pounds of CO2e per megawatt-hour (lbs CO2e/MWh) is specific to each city and depends on the types of supply to the city. Therefore, for purposes of estimating GHG reduction from measures, the GHG emission factor for electricity in the City is the weighted average emission factor of gross generation from three sources of supply: the utility (San Diego Gas & Electric [SDG&E] and other electric service providers), a potential local renewables and zero-carbon program, and behind-the-meter PV systems. This city-wide emission factor is used to estimate the effects of State and local measures that increase the grid-supply of renewable electricity, and to estimate the impact of adding behind-the-meter photovoltaic (PV) systems.
The citywide emission factor is calculated based on the percentage of renewable content in, and the percentage of, gross generation from each source of supply as described below. This method is applied to 2016, the emissions projection starting year, as well as to each year included in the CAP horizon.8 As
the percentage of renewable supply in the mix increases, the weighted average emission factor of electricity supply decreases.
4 The 2012 population is from SANDAG’s Demographic & Socio-Economic Estimates (March 9, 2017 version). The population in 2020 and 2035 are from SANDAG’s Series 13 Regional Growth Forecast (Updated in October 2013). SANDAG Data Surfer, accessed on October 24, 2017. Series 13 has a base year of 2012. Projections from 2012 may differ from more recent estimates by the State, such as from the Department of Finance (DOF). 5 The 2012 labor force is from the California Employment Development Department (EDD) Database, accessed on October 25, 2017. The 2020 and 2030 labor force are based on SANDAG Series 13 forecast for civilian jobs estimates in 2020 and 2030, and the ratio of the 2016 labor force (latest EDD data available as of October 2017) and civilian jobs. SANDAG’s Series 13 Regional Growth Forecast (Updated in October 2013). SANDAG Data Surfer, accessed October 24, 2017. 6 Based on SANDAG Series 13 Origin-Destination weekday VMT, provided to EPIC (January 30, 2017 and October 18, 2017).
Weekday VMT were converted to annual VMT using the methods described in Appendix A. Greenhouse Gas Emissions Inventory and Projection (EPIC, 2018) 7
Gross generation is the sum of the forecasted utility electricity sales, electricity generated from behind-the-meter PV systems, additional load from electric vehicles (EVs) and transmission and distribution losses. 8 The method to project emissions at 2020 and 2030 is provided in Appendix A. Greenhouse Gas Emissions Inventory and Projection (EPIC, 2018).
7 4.2.1.1 Renewables Content in Supply from SDG&E and Other Electric Service Providers SDG&E’s power mix includes electricity generated from SDG&E’s own power plants and electricity procured by SDG&E (both specified and unspecified sources), known as bundled power. As of 2016, SDG&E’s bundled power mix is 43% renewable.9
It is assumed that SDG&E and the electric service
providers of SDG&E’s Direct Access customers will be at 60% renewable power by 2030, as required by the Renewables Portfolio Standard (RPS) under Senate Bill (SB) 100.10 The RPS mandate is discussed in detail in Section 5.1.
4.2.1.2 Renewables Content in a Potential Local Renewables and Zero-Carbon Program CAP Measure E.1 includes the potential for the City to join a local program, such as a regional Community Choice Energy (CCE) program. It is assumed that such a program would increase the renewable and zero-carbon electricity to 75% in 2030, or 15% beyond the RPS mandate for that year.
The renewable content of the program would affect the citywide weighted average emission factor.
Because the RPS requires all of California’s electricity retail providers, including local programs, to meet the RPS requirement, a portion of the emissions reduction from the program is attributed to RPS compliance as a reduction from State actions. The remaining portion of reductions, beyond the 60% in 2030, is attributed to the City action under CAP Measure E.1.
4.2.1.3 Renewables Supply from behind-the-meter PV Systems Electricity generation from behind-the-meter PV systems in the City, including residential and nonresidential PV systems, is considered part of the overall electricity supply. Electricity generation from PV is considered 100% zero carbon (i.e., GHG-free). The State’s solar policies and programs, the 2019 California Building Energy Efficiency Standards (Title 24, Part 6), residential PV mandates, and CAP Measure E.2 all contribute to increase behind-the-meter PV systems in the City; they are discussed in Sections 5.2 and 6.3.2.
Considering behind-the-meter PV as a supply source that contributes to the citywide emission factor allows for calculating the effects of energy efficiency programs that may reduce behind-the-meter electricity use or from additional load from electric vehicle (EV) charging which may come from behindthe-meter electricity sources and not just from grid supply.
4.2.1.4 Weighted Average GHG Emission Factor for Electricity The weighted average GHG emission factor for electricity is based on the percentage of gross generation supplied by each of the previously referenced supplies, as well as the percentage of renewable or zerocarbon content in each supply.
Table 5 shows the contribution from each supply to gross generation and their renewable content, as well as the overall citywide weighted average emission factors for 2016 and 2030.
9 California Energy Commission: Power Content Label. 10 SB100 (de León) California Renewables Portfolio Standard Program: emissions of greenhouse gases (2017–2018). The interim RPS targets are 44% by 2024 and 52% by 2027 from eligible renewable energy resources.
8 Table 5 2016 and Projected 2030 GHG Emission Factor for Electricity in Imperial Beach Year
Local Renewables and
Zero-Carbon Program Utility Behind-the-meter PV Overall Citywide % of Gross
Generation
Supplied
ZeroCarbon
Content in
Supply
% of Gross
Generation
Supplied
Renewable
Content in
Supply
% of Gross
Generation
Supplied
Zero-Carbon
Content in
Supply
Citywide
Renewable
and ZeroCarbon
Supply
Electricity
Emission Factor
(lbs CO2e/MWh)
2016 - - 94% 43% 6% 100% 47% 493
2030 67% 75% 17% 60% 16% 100% 76% 217
2016 is the latest year with utility data available. The 2016 electricity emission factor is used for business-as-usual emissions projection in future years including 2030.
2030 data are projections under the CAP based on current status, estimated future impact of State policies and programs, and assumptions used for CAP local measures.
Energy Policy Initiatives Center 2018.
In 2016, SDG&E supplied 94% of the gross generation with 43% eligible renewable sources; behind-themeter PV systems supplied the remainder. SDG&E’s 2016 bundled emission factor was 525 lbs CO2e/MWh, resulting in a citywide emission factor of 493 lbs CO2e/MWh in 2016.11 In 2030, the projected electricity supply from behind-the-meter PV systems is expected to be 16% of gross generation.12 To comply with the 2030 RPS target, the renewable content in SDG&E’s supply will increase to 60%; this document assumes the utility’s supply is fixed at the RPS mandate level to avoid overestimating the emissions reduction from the utility’s renewable program. The local renewables and zero-carbon program is assumed to have 75% zero-carbon sources in 2030, or 15% beyond the 2030 RPS eligible renewables mandate. Based on these supply contributions, the citywide weighted electricity emission factor in 2030 is projected to be 217 lbs CO2e/MWh (76% renewable or zero-carbon).13 This weighted citywide electricity emission factor is used to calculate the GHG reductions from CAP measures that increase renewable supply or reduce electricity use.
4.2.2 Allocation of GHG Emissions Reductions from Measures that Increase Renewables in Electricity to State Policy, Local Programs or Behind-the-meter PV Supply The projected citywide electricity emission factor is used to estimate the GHG emissions reductions from any measure that increases the overall renewable and zero-carbon supply. The total reductions from State (mandated RPS) and local CAP measures (Measures E.1 and E.2) to increase renewable and zerocarbon supply is given in Table 6, and calculated using projected gross generation in 2030 and the difference in the 2030 citywide emission factor and BAU emission factor.
11 The SDG&E bundled emission factor is calculated by EPIC and reported in the SANDAG Regional Climate Planning Framework (ReCAP) Technical Appendix I, Table 6. (2018). The 2016 citywide emission factor is 525 lbs CO2e/MWh * 94%. 12 Detail to estimate PV capacity and electricity generation are described in Section 5.2 and Section 6.3. 13 Using SDG&E’s bundled emission factor 525 lbs CO2e/MWh (43% renewable), the projected 2030 utility emission factor is 368 lbs CO2e/MWh (60% renewable) and the projected 2030 local program emission factor is 230 lbs CO2e/MWh (75% renewable or zero-carbon). The 2030 citywide emission factor is 230 lbs CO2e/MWh *67% + 368 lbs CO2e/MWh * 17%.
9 Table 6 Emissions Reduction from All Measures Increasing Renewable and Zero-Carbon Supply in Imperial Beach Year
Gross
Generation
(GWh)
BAU Projections
Projections with State and local
Measures Increasing Renewable and
Zero-Carbon Supply
Emissions
Reduction from
Increased
Renewable and
Zero-Carbon
Supply
(MT CO2e)
BAU Electricity
Emission Factor
(lbs CO2e/MWh)
BAU Emissions
from Electricity
(MT CO2e)
Projected
Electricity
Emission Factor
(lbs CO2e/MWh)
Projected
Emissions from
Electricity
(MT CO2e)
2030 73 493 16,313 217 7,172 9,142
The projections with increasing renewable supply are the projections under the CAP, including future impact of State policies and programs, and local CAP actions.
Energy Policy Initiatives Center 2018.
The BAU emission factor for 2016 (Table 5) is kept constant through the year 2030 as opposed to using the emission factor for the 2012 baseline year, because the additional renewable content in SDG&E’s supply and behind-the-meter PV supply in 2016 are already included in the BAU emissions projection.14 The total emissions reduction from increasing renewable supply (Table 6) is allocated to each policy based on the renewable (or zero-carbon, if beyond the RPS mandate) contribution from each supply to the total citywide renewable content. This allocation and its impact on GHG reduction from each type of measure is shown in Table 7.
Table 7 Allocation of Emissions Reductions from Increasing Renewable (or Zero-Carbon) Supply in Imperial Beach
Year Electricity Supply Total
Local Renewables
and Zero-Carbon
Program
Utility Behind-themeter PV
2030
% of Gross Generation
Supplied by Renewables and
Zero-Carbon Sources
76% 50% 10% 16%
Emissions Reduction from
Increased Renewables and
Zero-Carbon Supply
(MT CO2e)
9,142 6,022 1,204 1,916
2030 data are the projections under the CAP, including future impact of State policies and programs, and assumptions used for local CAP measures.
Energy Policy Initiatives Center 2018.
4.3 Common Assumptions and Methods for Calculating Natural Gas Emissions Reductions For the CAP actions related to reducing natural gas (e.g., reduce natural gas use in buildings) the default emission factor of 0.0054 MT CO2e per therm is used for all years to estimate emissions reduction.
14 The method to project emissions at 2020 and 2030 is provided in Appendix A. Greenhouse Gas Emissions Inventory and Projection (EPIC, 2018).
10 4.4 Common Assumptions and Methods for Calculating On-Road Transportation Emissions Reductions
The following assumptions and methods are used to calculate emissions reductions for strategies related to on-road transportation, including federal actions, State actions, and local CAP measures.
4.4.1 GHG Emission Factor for On-Road Transportation
The GHG emission factor for on-road transportation, expressed in grams of CO2e per mile (g CO2e/mile), is used in several ways throughout the document. It is used to estimate the effect of State actions to increase the vehicle fuel standard, the impact of reduced VMT, and the effect of State and local actions to increase the miles driven by electric vehicles (EVs).
The default outputs of the CARB’s Mobile Source Emissions Inventory EMFAC2014 model is used to determine the average vehicle emission rates for the San Diego region.15 The average vehicle emission rates for the San Diego region were used as proxy for Imperial Beach. The EMFAC2014 model outputs include all key federal and State regulations related to tailpipe GHG emissions reductions that were adopted before the model release date in 2015. The regulations embedded in the outputs are:
For passenger cars and light-duty vehicles - Federal Corporate Average Fuel Economy (CAFE) standards and California Advanced Clean Car (ACC) Program16 For heavy-duty vehicles (heavy-duty trucks, tractors, and buses) - U.S. Environmental Protection Agency’s Phase-I GHG Regulation and CARB Tractor-Trailer GHG Regulation17 The Low Carbon Fuel Standard (LCFS), which requires a reduction of at least 10% in the carbon intensity of California’s transportation fuels by 2020, is not included in the EMFAC2014 model because most of the emissions benefits come from the production aspect of the fuel cycle rather than the combustion cycle. Therefore, the LCFS does not have a significant impact on tailpipe GHG emissions reduction.18 Using the EMFAC2014 default output, the average vehicle emission rates (g CO2/mile) are calculated based on the distribution of VMT for each vehicle class and its emission rate. The results are adjusted to convert from g CO2/mile to g CO2e/mile to account for total GHG emissions, including CO2, CH4, and N2O.19 The average vehicle emission rates (Table 8) are used to estimate the GHG emissions reduction impact of policies that increase vehicle efficiency and increase the number of zero emissions vehicles (ZEVs) on the road.20
15 CARB: Mobile Source Emissions Inventory. EMFAC2014 was the latest model available at the beginning of the Imperial Beach CAP development process (mid-2017). The latest model is EMFAC2017 released in March 2018. 16 The California Advanced Clean Cars (ACC) program includes additional standards for vehicle model years 2017–2025, and the Zero Emission Vehicle (ZEV) program requires manufacturers to produce increasing numbers of ZEVs and plug-in hybrid electric vehicles for 2017–2025 model year vehicles. CARB: EMFAC2014 Technical Documentation, Section 1.4 (v1.0.7 May 2017). 17 EPA’s Phase-I GHG regulation includes GHG emission standards for heavy-duty vehicle model years 2014–2018. CARB’s Tractor-Trailer GHG Regulation includes the aerodynamic and tire improvements requirements to reduce GHG emissions from heavy-duty trucks. CARB: EMFAC2014 Technical Documentation, Section 1.4. 18 CARB: EMFAC2014 Technical Documentation. Section 1.4. In the previous version of the Mobile Source Emissions Inventory model, EMFAC2011, the emissions effects of the LCSF were incorporated into the model output. 19 The calculation and adjustment method are described in Section 4.1 of Appendix A. City of Imperial Beach Greenhouse Gas Emissions Inventory and Projection (EPIC 2018). 20 EVs are ZEVs, however, ZEVs may include vehicles with other technologies such as fuel cell vehicles. EMFAC2014 only modeled the impact of EVs as ZEVs, therefore, in this document EVs and ZEVs are interchangeable.
11 Table 8 Average Vehicle Emission Rates in the San Diego Region Year
Average Vehicle Emission Rate - with
the impact of all adopted State and
federal policies
(g CO2e/mile)
2016 446
2030 297
Based on CARB EMFAC2014 Model. The model includes all key federal and State regulations related to tailpipe GHG emissions reductions that were adopted before the model release date in 2015.
CARB 2015. Energy Policy Initiatives Center 2018.
The projected 2030 average vehicle emission rate in Table 8 is also used to estimate the emissions reduction from measures that reduce VMT (Section 6.2). Because vehicle efficiency improves, and the population of ZEVs increases over time, the average vehicle emission rate decreases. Therefore, measures that reduce VMT offset decreasing amounts of GHG emissions throughout the CAP horizon.
4.4.2 GHG Emissions Reduction from Increasing Zero Emission Vehicles CAP Measure T.1 aims to increase EV charging stations at public and private spaces in the City (Section
6.1.1), and the State ZEV program requires manufacturers to produce increasing numbers of ZEVs and plug-in hybrid electric vehicles (PHEVs); both contribute to increasing the miles driven by ZEVs.
To avoid double-counting, the maximum emissions related to all measures in the CAP that increase miles driven by ZEVs are set at the amount expected from statewide programs and policies. The effect of the ZEV program in future years is estimated by comparing the emissions rate in the BAU projection with no additional policy impacts after 2016 (fixed 2016 ZEV penetration rate for the CAP horizon) and the emissions rate with the impact of the ZEV program (EMFAC2014’s default ZEV penetration rate), as shown in Table 9.21 The BAU projection is based on 2016, not baseline year 2012, to be consistent with the projection method in the electricity category. The additional 2016 model year vehicle fuel efficiency and ZEVs are already taken into consideration in the BAU emissions projection.
Table 9 Emissions Reduction from Increasing Miles Driven by Zero Emission Vehicles Year
Projected VMT
(annual million
miles)
BAU Projection - With No Policy Impact
after 2016
With Impact of Adopted ZEV
Program Total
Emissions
Reduction
from ZEVs
(MT CO2e)
BAU Average
Vehicle Emission
Rate
(g CO2e/mile)
BAU Emissions
from On-Road
Transportation
(MT CO2e)
Average
Vehicle
Emission Rate
(g CO2e/mile)
Emissions from
On-Road
Transportation
(MT CO2e)
2030 120 379 45,317 361 43,144 2,173
The 2030 VMT projection is based on the SANDAG Series 13 Growth Forecast. The projected emission rates are the projections under the CAP, including future impact of State policies and programs used in the CARB EMFAC2014 model.
Energy Policy Initiatives Center, 2018.
21 The method to project emissions at 2020 and 2030 is provided in Appendix A. Greenhouse Gas Emissions Inventory and Projection (EPIC, 2018).
12 A portion of the total emissions reduction from ZEVs (2,173 MT CO2e) is allocated to Measure T.1 (751 MT CO2e) in proportion to the measure’s contribution of EV miles. Table 10 provides the key assumptions and results of allocating GHG emissions reduction from increasing ZEVs to Measure T.1.
Table 10 Allocation of GHG Emissions Reduction from Increasing Zero Emission Vehicles Year
Projected
miles driven by
EVs as Percent
of Total VMT
Projected miles driven by EVs
(annual million miles)
Emissions Reduction from EVs
(MT CO2e)
With Impact of
Adopted ZEV
Program
Measure T. 1
With Impact of
Adopted ZEV
Program
Allocated to
Measure T. 1
2030 7.6% 9.1 3.1 2,173 751
CAP Measure T.1: Increasing EV charging stations at public and private locations.
Projected miles driven by EVs as percent of total VMT are based on the assumptions in CARB EMFAC2014 model for the San Diego Region.
The emissions reduction from EVs is the projection under the CAP, including future impact of State policies and programs used in CARB EMFAC2014 model and assumptions used for local CAP measures.
Energy Policy Initiatives Center 2018.
Based on the EMFAC2014 model assumptions, in 2030, 7.6% of all VMT in the San Diego region will be driven by EVs, proportional to 9.1 million miles in Imperial Beach. Measure T.1 would result in about 3.1 million EV miles in 2030. Therefore, 35% (the ratio of 3.1 million miles to 9.1 million miles) of the emissions reduction from the ZEV program is allocated to Measure T.1.22 5 EMISSIONS REDUCTIONS FROM FEDERAL AND STATE ACTIONS
Federal and State actions are expected to reduce emissions significantly over the CAP horizon. This section provides a summary of the methods used to estimate the emissions reduction associated with the following federal and State actions to increase renewable energy and building efficiency (SE.1, SE.2, and SE.3), and clean and efficient transportation (ST.1):
SE.1: California Renewables Portfolio Standard
SE.2: California Solar Programs, Policies, and 2019 Mandates SE.3: California Energy Efficiency Programs
ST.1: Federal and California Vehicle Efficiency Standards
5.1 SE.1: California Renewables Portfolio Standard
SB 100, the 100 Percent Clean Energy Act of 2018, approved by Governor Brown in September 2018, adopts a 60% RPS for all of California’s retail electricity providers by 2030, which increases the current RPS standard from 50% to 60%. The legislation also provides goals for intervening years before 2030 and establishes a State policy requiring that “zero-carbon” resources supply 100% of all retail electricity sales to end-user customers and all State agencies by December 31, 2045.23 The SB 100 renewable and zerocarbon targets are shown in Figure 3, below.
22 35% of 2,173 MT CO2e is 751 MT CO2e 23 SB 100 (de León) California Renewables Portfolio Standard Program: emissions of greenhouse gases (2017–2018). The interim RPS targets are 44% by 2024 and 52% by 2027 from eligible renewable energy resources.
13 Figure 3 SB 100 RPS and Zero-Carbon Targets
All retail electricity providers are required to meet the State’s RPS requirements, including SDG&E, electric service providers for SDG&E’s Direct Access customers, and any local renewables and zerocarbon programs. In this document, a conservative approach is taken that assumes all providers for utility customers, including electricity provided to DA customers, will meet the RPS requirements for 2030 and not go beyond. Under this assumption, all emissions reductions from a utility reaching 60% renewables is due to RPS requirements.
For the local renewables and zero-carbon program, the CAP target is to reach 75% carbon-free by 2030.
A portion of the emissions reduction from the local program will be attributed to RPS compliance, and the remaining reduction will be attributed to CAP Measure E.1, as described in Section 6.3.1. Table 11 shows results from the California RPS (SE.1) only.
14 Table 11 Electricity Providers and Projected 2030 Emissions Reduction from SE.1 California Renewables Portfolio Standard
Year
(a) RPS-Related
Emissions
Reduction from
the Utility *
(MT CO2e)
(b) RPS-Related
Emissions Reduction
from Local Renewables
and Zero-Carbon
Program
(MT CO2e)
All RPS-Related
Emissions
Reductions (sum
of a and b)
(MT CO2e)
2030 1,204 4,817 6,022
*Includes utility and electricity providers of utility’s Direct Access customers. 2030 data are projections under the CAP based on current status, future impact of State policies and programs, and local CAP actions.
Energy Policy Initiatives Center 2018.
5.2 SE.2: California Solar Programs, Policies, and 2019 Mandates
5.2.1 Solar Policies and Programs
California has several policies and programs to encourage customer-owned, behind-the-meter PV systems, including the California Solar Initiatives, New Solar Home Partnership, Net Energy Metering, and electricity rate structures designed for solar customers.
The latest California Energy Demand 2018–2030 Revised Forecast, developed by the California Energy Commission (CEC), has projections for behind-the-meter PV generation in the SDG&E planning area through 2030.24 The demand forecast provides three scenarios: high demand case, mid demand case and low demand case. The PV projection from 2018–2030 in the SDG&E planning area mid demand case forecast is used to project the PV generation in Imperial Beach.25 The California Distributed Generation (DG) Statistics database includes capacities of behind-the-meter PV systems interconnected in a jurisdiction in a given year for each of the three Investor Owned Utility (IOUs) planning areas, including SDG&E. The DG Statistics provide detailed information about the behind-the-meter PV systems installed in a jurisdiction from the start year of incentive programs through the current year. This provides a historical record used to determine the capacity in GHG inventory years and can also help determine trends in PV installation.
A comparison of the estimated capacity and electricity generation from PV systems in the City and in the SDG&E planning area are given in Table 12. 26
24 Kavalec et al. 2018. California Energy Demand 2018 — 2030 Revised Forecast. California Energy Commission, Electricity Assessments Division. Publication Number: CEC-200-2018-002-CMF, accessed July 11, 2018. SDG&E planning area is larger than San Diego region.
25 Kavalec et al. 2018. Mid Case Revised Demand Forecast (February 2018). California Energy Commission, Electricity Assessments Division. Publication Number: CEC-200-2018-002-CMF, accessed July 11, 2018. 26 Capacity of all interconnected PV system in Imperial Beach are from California Distributed Generation Statistics NEM Currently Interconnected Data Set (current as of May 31, 2017), download date: September 12, 2017.
15 Table 12 Behind-the-meter PV Capacity and Estimated Electricity Generation (Imperial Beach and SDG&E Planning Area)
Year
Imperial Beach* SDG&E Planning Area** Ratio of Electricity Generation from PV
(Imperial Beach to
SDG&E)
PV Capacity
(MW)
Estimated Electricity
Generation
(GWh)
Estimated Electricity
Generation
(GWh)
2012 0.29 0.51 238 0.2%
2013 0.91 1.59 335 0.5%
2014 1.13 1.97 496 0.4%
2015 1.34 2.36 744 0.3%
2016 2.24 3.92 1,129 0.3%
Average 0.3%
* Estimated electricity generation based on PV capacity and 20% capacity factor. **California Energy Demand 2018–2030 Revised Forecast mid demand case (February 2018 version) California Distributed Generation Statistics 2017; CEC 2018; Energy Policy Initiatives Center 2018.
For future years, the electricity generation and capacity of behind-the-meter PV systems in the City are estimated based on the PV generation in the SDG&E planning area mid case forecast, and the average ratio of PV generation in the City to that of SDG&E’s planning area from 2012–2016 (0.3%). The estimated PV capacity in Imperial Beach because of the California Solar Programs and Policies is projected to be 6.4 MW, with 11.3 GWh electricity generation. The trend of behind-the-meter PV in the City is shown in Figure 4.
16 Figure 4 Behind-the-meter PV Trend in Imperial Beach (2012–2030)
5.2.2 2019 Building Energy Efficiency Standards PV Mandates The new California 2019 Building Energy Efficiency Standards which go into effect on January 1, 2020 require all newly constructed single-family homes, low-rise multi-family homes, and detached accessory dwelling units (ADUs) to have PV systems installed, unless the building qualifies for an exception.27 In the past few years, on average, eight new single-family homes were built in the city in one year.28 To estimate the impacts of the PV mandates, it is assumed that, on average, 15 new single-family homes and ADUs will be built every year in the CAP horizon. For multi-family homes in the city, the projection in the SANDAG Series 13 Forecast is used and assumed to all be low-rise multi-family homes; the Series 13 Forecast assumes 900 new multi-family homes will be added between 2020 and 2030.29 For the PV system size requirement of each housing unit type, the minimum qualified size required by the 2019 Building Energy Efficiency Standards is calculated for a unit type based on its average size, as shown in Table 13.30
27 CEC: 2019 Building Energy Efficiency Standards – 2019 Residential Compliance Manual (December 2018). For the requirements on newly constructed single-family and low-rise multi-family homes, see Section 7.2 Prescriptive Requirements for Photovoltaic System. For the requirements on newly constructed and detached ADU, see Section 9.3.5 Accessory Dwelling Units (ADUs).
28 Housing data provided by City to EPIC, April 2018.
29 SANDAG Series 13 Regional Growth Forecast (October 2013). SANDAG Data Surfer, accessed on October 24, 2017 30 The site plan of the Blue Wave Hotel & Residences Project at Imperial Beach was provided by the City to EPIC (October 2018).
The average size of apartments in the project, including studio, one-bedroom and two-bedroom apartments, is approximately 800 sq. ft.
17 Table 13 Estimated PV Requirement for New Homes after 2020 in Imperial Beach Housing Unit Type
Average Unit
Size
(sq. ft.) *
PV Required for
the Unit Size
(kWdc)**
Single-family 1,700 2.1
Multi-family 800 1.6
Accessory Dwelling Units (ADUs) 800 1.6
* For single-family, the average home size in San Diego-Carlsbad-San Marcos Area is shown. For multi-family, the average size is based on the unit size of new multi-family development projects in Imperial Beach.
** Calculated based on unit size (sq. ft.) and 2019 Building Energy Efficiency Standards Residential Compliance Manual Equation 7-1 and Table 7-1. Imperial Beach is in Climate Zone 10. Minimum size required.
Energy Policy Initiatives Center 2018
For a conservative approach for all housing unit types, it is assumed that 10% of the new homes will not be subject to the PV mandates. The number of new homes with PV systems because of the PV mandate, as well as the estimated system capacity, are given in Table 14. The number of new homes with PV systems and capacity are those added between 2020 and 2030.
Table 14 New Homes with PV Systems after 2020 in Imperial Beach Year
New Single-family Homes and
ADUs after 2020
New Multi-family Homes after
2020 All New Homes after 2020
Number of New
Homes with PV
Systems
PV System
Capacity
(kW)
Number of New
Homes with PV
Systems
PV System
Capacity
(kW)
PV System
Capacity
(MW)
Estimated
Electricity
Generation
(MWh)
2030 149 297 836 1,488 1.8 3,128
PV system capacity is the capacity in 2030 from all systems added at new homes and ADUs after 2020, and does not include existing PV or PV added on other new non-residential projects.
Energy Policy Initiatives Center 2018
5.2.3 All Solar Policies, Programs, and Mandates
The California Energy Demand 2018–2030 Revised Forecast, discussed in Section 5.2.1, does not include the additional impact of 2019 PV mandates; therefore, the PV installation trend shown in Figure 4 does not include the additional 1.8 MW PV capacity from new homes after 2020.31 The total estimated PV capacity as a result of California solar policies, programs, and PV mandates in Imperial Beach is projected to be 8.2 MW (the sum of 6.4 MW and 1.8 MW).
CAP Measure E.2 aims to increase PV systems on new and redeveloped commercial projects, with a projected additional 0.3 MW PV capacity in the City by 2030 that will bring the total PV capacity in 2030 to 8.5 MW. Methods to estimate the PV capacity from Measure E.2 are described in Section 6.3.2.
31 The 2018–2030 Revised Forecast assumes some single-family homes will install PV system without the mandates. However, it does not model the impact of PV mandates on ADUs and low-rise multi-family homes. Personal communication with CEC staff, December 14, 2018.
18 The emissions reductions from all State actions and CAP measures that increase the behind-the-meter renewable supply is 1,916 MT CO2e, given in Section 4.2.2 (Allocation of GHG Emissions Reductions from Measures that Increase Renewables in Electricity to State Policy, Local Programs or Behind-the-meter PV Supply) and Table 7 (Allocation of Emissions Reductions from Increasing Renewable (or Zero-Carbon) Supply in Imperial Beach). The total reduction is allocated based on estimated capacity (MW) that would result from each action. As shown in Table 15, GHG emissions reductions are the projected reduction amounts for 2030 only.
Table 15 Key Assumptions and Results for California Solar Policies, Programs, and Mandates Year State or City Action Total
Measure E.2 Increase
Commercial Behindthe-meter PV
California Solar
Polices, Programs,
and Mandates*
2030
Projected Behind-the-meter PV
Capacity (MW) 8.5 0.3 8.2
Projected Emissions Reduction
(MT CO2e) 1,916 59 1,856
*Solar policies, programs and mandates include the impact of the PV mandates from 2019 Building Energy Efficiency Standard.
The projected capacity and emissions reduction are the projections under the CAP, based on current status, future impact of State policies and programs, and CAP assumptions.
Energy Policy Initiatives Center 2018
In 2030, 97% (8.2 MW out of 8.5 MW) of the projected citywide PV capacity will be due to State polices, programs, and mandates; therefore, 97% of the total emissions reduction from increasing behind-themeter PV (1,856 MT CO2e) is allocated to this State action.
5.3 SE.3: California Energy Efficiency Program
In September 2017, the California Public Utilities Commission (CPUC) adopted energy efficiency goals for ratepayer-funded energy efficiency programs to begin in 2018 (Decision 17-09-025). The adopted energy saving goals for SDG&E’s service territory are given in the Decision on an annual basis from 2018 to 2030.32 The sources of the energy savings include, but are not limited to, rebated technologies, building retrofits, behavior-based initiatives, and codes and standards.33 To evaluate the impact of the energy efficiency program on the City, the total energy savings in SDG&E’s service territory is allocated to the City using a ratio of the City’s natural gas and electricity demand to those of SDG&E’s entire service territory. In the past few years, the ratios of City demand to SDG&E’s service territory demand have been 0.3% for electricity and 0.6% for natural gas.34 SDG&E’s service territory electricity and natural gas savings were allocated accordingly to the City, as shown in Table
32 CPUC: Decision 17-09-025. Adopting Energy Efficiency Goals for 2018–2030, accessed December 12, 2018. SDG&E’s electricity service territory is larger than San Diego region. 33 Navigant Consulting: Energy Efficiency Potential and Goals Study for 2018 and Beyond (August 2017), accessed December 12, 2018. Rebated technologies are the energy efficiency technologies from the utility’s historic incentive programs, including equipment and retrofits.
34 SDG&E service territory demand is from California Energy Demand 2018–2030 Revised Forecast, SDG&E planning area load 2014-2016. 2016 is the latest year with historical data in the demand forecast. Electricity and natural gas demand in Imperial Beach were provided to EPIC by SDG&E for the GHG inventory. Appendix A. Greenhouse Gas Emissions Inventory and Projection (EPIC, 2018).
19 16.35
Table 16 Estimated Energy Saving from California Energy Efficiency Program Year
Electricity Savings
(GWh)
Natural Gas Savings
(MMTherms)
SDG&E Service
Territory
Imperial
Beach
SDG&E Service
Territory
Imperial
Beach
2030 3,328 9.8 60 0.4
SDG&E service territory savings are the cumulative savings after 2018 based on the 2018-2030 annual saving goals in CPUC Decision 17-09-025.
Energy Policy Initiatives Center 2018
Emissions reductions from electricity savings are calculated by multiplying the electricity savings by the GHG emission factor for electricity, discussed in Section 4.2.1 (GHG Emission Factor for Electricity) and Table 5 (2016 and Projected 2030 GHG Emission Factor for Electricity in Imperial Beach). As the renewable and zero-carbon content in electricity increases, the emissions reduction from the energy efficiency program decreases accordingly. Similarly, emissions reductions from natural gas savings were calculated using the natural gas savings amount and natural gas emission factor. Table 17 summarizes the key assumptions and results; the energy savings and GHG emissions reductions are the projected amounts in 2030 only.
Table 17 Key Assumptions and Results for California Energy Efficiency Programs Year
Electricity Savings Natural Gas Savings
Total
Emissions
Reduction
(MT CO2e)
Electricity
Savings
(GWh)
Emission Factor
(lbs CO2e/MWh)
GHG
Reduction
from
Electricity
Savings
(MT CO2e)
Natural Gas
Savings
(MMtherm)
Emission Factor
(MT CO2e/therm)
GHG Reduction
from Natural
Gas Savings
(MT CO2e)
2030 9.8 217 960 0.4 0.0054 2,073 3,033
The emissions reductions are the projections under the CAP, including future impact of State policies and programs, and CAP assumptions.
Energy Policy Initiatives Center 2018
5.4 ST.1: Federal and California Vehicle Efficiency Standards As discussed in Section 4.4 (Common Assumptions and Methods for Calculating On-Road Transportation Emissions Reduction), CARB’s EMFAC2014 model includes all key federal and State regulations related to tailpipe GHG emissions reductions for both light-duty and heavy-duty vehicles that were adopted before the model release date in 2015.
Table 18 shows a comparison of the average vehicle emission rate and emissions from on-road transportation under the BAU projection, as well as with the impact of policies that increase vehicle efficiency and ZEVs. As discussed in Section 4.4.2 (GHG Emissions Reduction from Increasing Zero Emission Vehicles), to avoid double-counting, the maximum emissions related to all measures in the CAP
35 CPUC: Decision 17-09-025. Adopting Energy Efficiency Goals for 2018–2030, accessed December 12, 2018. The 2018 and beyond goals are given on an annual basis for each year from 2018 to 2030, different from previous studies, in which the cumulative goals are given. The cumulative savings in 2030 from 2018 are the sum of the annual savings.
20 that increase miles driven by ZEVs are set at the amount expected from statewide programs and policies. The effect of CAP Measure T.1 (Increase Citywide EV Charging Stations) is subtracted from the emissions reductions from State policies. Table 18 summarizes the key assumptions and results. The GHG emissions reduction is the projected reduction amount in 2030 only.
Table 18 Key Assumptions and Results for ST.1 Federal and California Vehicle Efficiency Standards Year
Projected
VMT
(annual
million miles)
BAU Projection - With no Policy
Impact after 2016
With Impact of Adopted
Statewide Policies
Emissions Reduction
(MT CO2e)
Average
Vehicle
Emission Rate
(g CO2e/mile)
Emissions
from On-Road
Transportation
(MT CO2e)
Average
Vehicle
Emission Rate
(g CO2e/mile)
Emissions
from On-Road
Transportation
(MT CO2e)
With
Impact of
Adopted
Statewide
Policies
From CAP
Local
Measure
T.1
Remaining
ST.1
2030 120 379 45,317 297 35,485 9,832 751 9,081
The 2030 VMT projections are based on SANDAG’s Series 13 Growth Forecast. The emission rates and emission reductions are the projections under the CAP, including current status, future impact of State policies and programs used in CARB EMFAC2014 model, and CAP assumptions.
Energy Policy Initiatives Center 2018
6 EMISSIONS REDUCTIONS FROM LOCAL MEASURES
The following section describes the methods used to estimate the GHG reductions from CAP measures, which are organized into the following five main strategies:
Increase Clean and Efficient Transportation
Reduce Vehicle Miles Traveled
Increase Renewable and Zero-Carbon Electricity
Achieve Zero Waste
Sequester Carbon
Each CAP strategy is supported by several measures and activities.
6.1 Strategy: Increase Clean and Efficient Transportation Increased clean and efficient transportation can be achieved by increasing the use of alternativelyfueled vehicles citywide through Measures T.1 and T.2.
6.1.1 Measure T.1: Increase Citywide Electric Vehicle Charging Stations (EVCSs) The goal of Measure T.1 is to add additional 100 electric vehicle charging stations (EVCSs) at both public and private spaces within the city by 2030. The addition of EVCSs encourages and promotes more EVs.
To add EVCSs at public sites, the City will work with agencies in the region, including SANDAG, the Center for Sustainable Energy (CSE), SDG&E, and others, to install EVCSs at the Civic Center, libraries, schools, public beaches, and Port of San Diego parking lots.
To add EVCSs at private sites, the City will work through its development review process to ensure new commercial projects, redeveloped commercial projects, and multi-family buildings install EVCSs at 5% of parking spaces to facilitate the increasing demand of EV infrastructure at commercial properties and multi-family homes. The projected starting date of this action is 2023, which is the start of the next California Green Building Standards (CalGreen) code cycle.
21 For the EVCSs, it is assumed that Level 2 chargers will be installed. At the new and redeveloped commercial projects, an EVCS is assumed to be used for charging vehicles on average five hours a day.
The EV miles that result from installation of these additional EVCSs are estimated based on a Level 2 charging capacity, EV drive efficiency, and hours in use, as shown in Table 19.36 On average, it is assumed that 70,000 EV miles per year are charged at public EVCS.
Table 19 Electric Vehicle Charging Efficiency by Level 2 Charger Type Type of
Charging
(Level 2)
Capacity
(kW)*
Hours in
Use per
Day
EV Load
(kWh/day)
Vehicle Drive
Efficiency
(kWh/mile) **
EV Miles per
Day of Charge
EV Miles per
Year per
EVCS
Low 3.3 5 17 0.25 66 24,090
Medium 6.6 5 33 0.25 132 48,180
High 9.6 5 48 0.25 192 70,080
Highest 19.2 5 96 0.25 384 140,160
Average 70,000
*Based on Electric Vehicle Charging Station Installation Best Practice, CSE, 2016. **Based on CEC Plug-in Electric Vehicle Infrastructure Projections: 2017–2025 vehicle driven efficiency assumptions.
Energy Policy Initiatives Center 2018
At new multi-family homes, the EVCSs will be used to charge the residents’ personal EVs. Based on the EMFAC2014 model, in the San Diego region, approximately 35 miles per day are driven by EVs.37 In the past three years, the City has approved three new or redevelopment commercial projects with a total of 270 new parking spaces.38 Assuming the trend continues, 5% of all new parking spaces will have EVCSs installed after 2023. The SANDAG Series 13 projections estimates that 900 new multi-family units will be added from 2020 to 2030.39 Because the Imperial Beach Municipal Code requires 1.5 off-street parking spaces for each multi-family unit, 52 of new multi-family unit parking spaces will have EVCSs installed after 2023.40
The GHG emissions reduction from this measure is estimated based on the ratio of projected EV miles resulting from the installation of EVCSs under this measure to the total EV miles from EMFAC2014 model estimates, as discussed in Section 4.4.2 (GHG Emissions Reduction from Increasing Zero Emission Vehicles) and shown in Table 10 (Allocation of GHG Emissions Reduction from Increasing Zero Emission
36 The Level 2 charger capacity range comes from the Center for Sustainable Energy: Electric Vehicle Charging Station Installation Best Practice (June 2016). The vehicle drive efficiency assumption is based on Bedir etl al 2018. California Plug-In Electric Vehicle Infrastructure Projections: 2017–2025. California Energy Commission. Publication Number: CEC-600-2018-001. 37 CARB: Mobile Source Emissions Inventory. EMFAC2014 San Diego County 2020-2030 estimates. 38 The three new or redevelopment projects are Breakwater Hotel, Blue Wave Hotel & Residences and Imperial Beach Resort.
The site plans for the three projects were provided to EPIC by the City (October 2018). Parking spaces given in the site plans include both surface parking lots and parking structures. 39 SANDAG Series 13 Regional Growth Forecast (October 2013). SANDAG Data Surfer, accessed on October 24, 2017. Using linear interpolation, approximately 690 new multi-family units are projected to be added from 2023 to 2030. 40 Imperial Beach Municipal Code. Chapter 19.48, Off-street Parking, accessed on October 25, 2017. Mixed-use projects in the City are eligible for parking reduction, however, data are not available to estimate the impact of mixed-use projects on the total number of parking spaces.
22 Vehicles). The number of parking spaces with EVCS, projected EV miles, and GHG emissions reductions are shown in Table 20. The GHG emissions reduction is the projected reduction amount in 2030 only.
Table 20 Key Assumptions and Results for Measure T.1 Increase Citywide Electric Vehicle Charging Stations (EVCS)
Year
New and Redeveloped Commercial
Projects Multi-Family Units Total
% of
Parking
Space with
EVCS
Additional
EVCS after
2023
EV Miles
Resulting from
Charging at
EVCS
(annual
million miles)
% of
Parking
Spaces
with EVCS
Additional
EVCS after
2023
EV Miles
Resulting
from Charging
at EVCS
(annual
million miles)
EV Miles
Resulting from
Charging at
EVCSs
(annual
million miles)
Emissions
Reduction
(MT CO2e)
2030 0.05 36 2.5 0.05 52 0.6 3.1 751
The emissions reduction is the projection under the CAP, including current status, future impact of State policies and programs used in CARB EMFAC2014 model and CAP assumptions.
Energy Policy Initiatives Center, 2018
6.1.2 Measure T.2: Clean Municipal Fleet
The goal of Measure T.2 is to replace 24 fossil fuel vehicles in the municipal fleet with EVs by 2030. The City will work with the Center for Sustainable Energy’s (CSE’s) “EV Expert” to refine the fleet assessment and implement the fleet conversion plan.
CSE’s “EV Expert” analyzed the City’s current fleet inventory and determined that there are 24 total vehicles (18 unique models) that can be replaced with an electric vehicle. Based on information from City staff, Imperial Beach’s municipal fleet has relatively low mileage, with approximately 5,000 miles driven per year. The fuel consumption of the identified vehicles was estimated based on the estimated mileage.41 The vehicles, their fuel economy (miles per gallon [MPG]), and a replacement schedule are provided in Table 21.42
41 Fuel consumption or mileage information of each individual vehicle were not available at the time of the CAP development. 42 CSE EV Expert: City of Imperial Beach Fleet Assessment (2018). The fuel use was calculated by EPIC based on the vehicle MPG and average 5,000 miles driven a year by a typical city vehicle.
23 Table 21 Imperial Beach Municipal Fleet Vehicle Replacement Schedule Count of
Vehicle
Model
Year Make Model
Miles per
Gallon
(MPG)
Replace by Fuel Use
(gallons/year)
1 1995 Ford Ranger P/U 20 2019 250
1 1997 Ford Ranger 20 2019 250
2 2002 Dodge Stratus 20 2020 250
1 2002 Dodge Dakota 17 2021 294
1 2004 Dodge Dakota 4x4 15 2021 333
1 2004 Ford Ranger 21 2022 238
1 2004 Ford F150 16 2022 313
1 2005 Ford Ranger 21 2023 238
3 2006 Ford Ranger P/U 21 2024 238
1 2011 Ford Ranger 20 2025 250
1 2013 Ford F150 15 2025 333
1 2014 Ford Explorer 20 2026 250
1 2014 Toyota Tacoma 21 2026 238
1 2014 Ford Expedition 16 2027 313
3 2015 Ford F150 22 2028 227
2 2016 Chevrolet Colorado 22 2029 227
1 2018 Ford Fusion S Hybrid 42 2030 119
1 2018 Chevrolet Colorado 22 2030 227
Total 5,997
MPG based on U.S Department of Energy fuel economy database. Fuel use is calculated based on vehicle MPG and 5,000 miles driven a year.
CSE EV Expert 2018; Energy Policy Initiatives Center 2018 Based on the CSE fleet assessment, the vehicles will be replaced by PHEVs (Ford Fusion PHEV) or electric pickups (Workhorse electric pickup); therefore, all gasoline fuel used by these vehicles will be offset with the conversion. The gasoline fuel savings are approximately 34% of the current municipal fleet gasoline use.43 The GHG emissions reduction in 2030 is shown in Table 22.44
43 2017 municipal fleet gasoline use is 17,755 gallons, provided by City to EPIC (October 2018). 44 Gasoline carbon content based on estimates from U.S. Energy Information Administration. Frequently Asked Questions.
Accessed October 24, 2018.
24 Table 22 Key Assumptions and Results for Measure T.2 Clean Municipal Fleet Year
Gasoline
Reduction
(gallons)
Gasoline Carbon
Content*
(lbs CO2/gallon)
GHG Emission
Reduction
(MT CO2e)
2030 5,997 18 48
*Assume gasoline blend has 10% ethanol.
The emissions reduction is the projection under the CAP assumptions.
Energy Policy Initiatives Center 2018
6.2 Strategy: Reduce Vehicle Miles Traveled (VMT)
Another strategy to reduce emissions from on-road transportation is to reduce VMT. This can be achieved by measures such as increasing alternative modes of transportation (Measures T.3 and T.4) for commuters and avoiding single-occupant driving, or by reducing municipal employee work-related VMT (Measure T.5).
6.2.1 Measure T.3: Increase Mass Transit Ridership
The San Diego Metropolitan Transit System (MTS) is the local public transit provider serving central, southern, and eastern parts of San Diego County. MTS recently received a grant for their Blue Line Rail Corridor Transit Enhancements program to support the Mid-Coast Trolley Extension.45 The Enhancements program includes a new rapid bus service, Rapid 925, with service between Imperial Beach and the Otay Mesa International Border crossing, via the Iris Avenue Transit Center where the bus would connect with the Blue Line and other bus routes.46 Rapid 925 is anticipated to start service by 2021 with peak headways of up to every 7.5 minutes during weekday services. The route would also complement the existing local Route 933/934 on Coronado Avenue/Imperial Beach Boulevard within the City, which is one of the MTS bus routes with the highest ridership. The route will be serviced by electric buses with zero tailpipe emissions. The average annual ridership, which is assumed to be new riders, and estimated VMT avoided due to the New Rapid 925 service are given Table 23.47
45 Extended trolley line from downtown San Diego and University of California San Diego/University Town Center. Currently under construction.
46 The Blue Line Rail Corridor Transit Enhancements grant application was provided by MTS to EPIC, October 2018. 47 The Blue Line Rail Corridor Transit Enhancements grant application was provided by MTS to EPIC, October 2018. The annual ridership at the starting year 2021, ridership annual rate of increase (1.18%), adjustment factor for transit dependency use (0.5) and length of average Rapid Bus trip (6.44 miles) are given in the grant application.
25 Table 23 Projected Average Annual Ridership of Rapid 925 and VMT Avoided Year
Average
Weekday
Ridership
Average
Annual
Ridership*
Annual VMT
Reduction of Full
Rapid 925 Bus Trip**
(miles per year)
Annual Imperial
Beach VMT ***
(miles per year)
2021 5,097 1,294,554 4,168,464 2,084,232
2022 5,157 1,309,854 4,217,731 2,108,866
2023 5,218 1,325,336 4,267,582 2,133,791
2024 5,280 1,341,000 4,318,021 2,159,011
2025 5,342 1,356,850 4,369,056 2,184,528
2026 5,405 1,372,887 4,420,697 2,210,349
2027 5,469 1,389,114 4,472,946 2,236,473
2028 5,534 1,405,532 4,525,814 2,262,907
2029 5,599 1,422,145 4,579,306 2,289,653
2030 5,666 1,438,953 4,633,429 2,316,715
*Based on weekday ridership and 255 workdays per year.
**Based on the assumptions in the MTS Blue Line Rail Corridor Transit Enhancements grant application, the adjustment factor for transit dependency use is 0.5 and the length of an average trip is 6.44 miles for the Rapid Bus.
***50% of the full trip VMT reduction is allocated to Imperial Beach based on Origin-Destination VMT allocation methods and assuming that bus trips have at least one trip-end within Imperial Beach.
Rapid 925 will also run on weekends. For the purpose of estimating commuting VMT reduction, the weekend ridership data are used in this table.
San Diego Metropolitan Transit System 2017; Energy Policy Initiatives Center 2018.
The avoided Imperial Beach VMT in Table 23 is converted to GHG emissions reduction using the average vehicle emission factor, discussed in Section 4.4.1 (GHG Emission Factor for On-Road Transportation).
The GHG emissions reduction in 2030 is shown in Table 24.
Table 24 Key Assumptions and Results for Measure T.3: Increase Mass Transit Ridership Year VMT Avoided
(miles per year)
Average Vehicle
Emission Rate
(g CO2e/mile)
GHG Emissions
Reduction
(MT CO2e)
2030 2,316,715 297 687
The emissions reduction is the projection under the CAP, including future impact of State policies and programs used in CARB EMFAC2014 model and CAP assumptions.
Energy Policy Initiatives Center 2018
6.2.2 Measure T.4: Improve Pedestrian and Bicycle Facilities Imperial Beach has many planned projects to install new or improve existing bicycle facilities throughout the City and improve pedestrian facilities.
The city has existing bicycle infrastructure, such as a Class I bicycle path along the Bayshore Bikeway, and Class II separated bike lanes along Thirteenth Street, Palm Avenue, and other streets throughout the City. Class III bicycle routes, where bicycles share the street with motor traffic, may also have some impact on increasing bicycle commuting. However, studies of the impact of bicycle facilities on
26 increasing bicycle commuting focus on separated bicycle lanes (Class II and better). The goal of this measure is to install 11 miles of Class II or better bicycle lane throughout the City by 2030. The locations of the new bicycle infrastructure projects and length of the bicycle lanes are provided in Table 25.48 Table 25 Planned New Bicycle Infrastructure in Imperial Beach to be Completed by 2030 Street
New Bicycle
Infrastructure
Type
New Planned
Length One-Way
(linear feet)
Total New
Planned Bike
Lanes
(miles)
Thirteenth Street* Class II 6,864 2.6
Palm Avenue/SR 75** Class II 8,600 3.3
Imperial Beach Boulevard Class II 7,200 2.7
Class III 1,200 0.5
9th Street Class II 5,280 2.0
Seacoast Drive Class III 2,640 0.5
Class II total 11
2030 Imperial Beach Developed Area (square miles) 2.7
Projected Additional Bicycle Lanes (miles per square mile) 4 *Completed in 2014, after SANDAG Series 13 VMT model base year 2012. **Includes converting existing Class III bicycle facility to Class II.
Projected to be completed by 2030. Seacoast Drive bike lane is only one way, the rest are both ways.
City of Imperial Beach 2018, SANDAG 2013.
The increase in percentage of commuters using bicycles is assumed to be proportional to the increase in bicycle lane miles per square mile. The elasticity of adding each additional mile of Class II bicycle lanes per square mile is associated with a roughly 1% increase in commuters by bicycle.49 Imperial Beach’s developed area is approximately 2.7 square miles; therefore, 11 miles of Class II or better bicycle lanes is equal to four bicycle lane miles per square mile, as shown in Table 25. 50 Bicycle lanes are used for both recreational and commuting purposes; however, for this measure, only the impact on avoiding single-occupant vehicle (SOV) commute VMT is quantified. To calculate annual SOV commute VMT avoided, the increase in percentage of commuters by bicycle was multiplied by an eight-mile commute distance avoided per day, assuming bicycle commuters are traveling within the City and that there are 255 workdays per year. The avoided VMT is converted to GHG emissions reduction using the average vehicle emission factor discussed in Section 4.4.1 (GHG Emission Factor for On-Road Transportation). The GHG emissions reduction in 2030 is shown in Table 26.
48 Bicycle lane locations, infrastructure type, length, and projected completion year were provided by AECOM to EPIC (October 2018).
49 Dill and Carr (2013). Bicycle Commuting and Facilities in Major U.S. Cities: If you build them, commuters will use them – another look. 50 Developed are based on SANDAG’s Series 13 Regional Growth Forecast (Updated in October 2013). SANDAG Data Surfer, accessed October 24, 2017.
27 Table 26 Key Assumptions and Results for Measure T.4 Improve Pedestrian and Bicycle Facilities Year
Labor Force
(number of
commuters)
Additional Bike
Lanes Added
(bike lane miles
per square
mile)
% of Additional
Labor Force
Using Bicycle to
Commute
Additional
Labor Force
Using Bicycle to
Commute
(number of
commuters)
Commute
VMT Avoided
(miles per
year)
Average
Vehicle
Emission Rate
(g CO2e/mile)
GHG
Emissions
Reduction
(MT CO2e)
2030 14,116 4 4% 565 1,151,826 297 342
Average VMT avoided by commuting by bicycle is assumed to be eight miles per workday and 255 workday per year.
The emissions reduction is the projection under the CAP, including future impact of State policies and programs used in CARB EMFAC2014 model and CAP assumptions.
Energy Policy Initiatives Center 2018
The City also plans to improve pedestrian infrastructure as part of Measure T.4, though the GHG emissions reductions for these improvements are not quantified because majority of the improvements include sidewalk widening and improvements to landscape, lighting, and benches.
6.2.3 Measure T.5: Reduce Municipal Employee VMT
To reduce municipal employee’s work-related VMT, the City plans to obtain and maintain a fleet of 10 electric bicycles for City staff to utilize for short, intra-City trips, such as building inspection trips within the City. Assuming ten employees use the electric bicycles per day and avoid 18 miles of vehicle driving a day, the employee VMT avoided is shown in Table 27.51
The avoided VMT is converted to GHG emissions reduction using the average vehicle emission factor, discussed in Section 4.4.1 (GHG Emission Factor for On-Road Transportation). The GHG emissions reduction in 2030 is shown in Table 27.
Table 27 Key Assumptions and Results for Measure T.5 Reduce Municipal Employee VMT Year
VMT Avoided
(miles per
workday)
VMT Avoided
(miles per year)
Average
Vehicle
Emission Rate
(g CO2e/mile)
GHG
Emissions
Reduction
(MT CO2e)
2030 180 45,900 297 13
Assumes 255 workdays per year.
The emissions reduction is the projection under the CAP, including future impact of State policies and programs used in CARB EMFAC2014 model and CAP assumptions.
Energy Policy Initiatives Center 2018
6.3 Strategy: Increase Renewable and Zero-Carbon Electricity The goal of this strategy is to increase grid and behind-the-meter renewables through Measures E.1 and E.2.
51 Miles per day estimated based on City building inspector’s work-related trips. The building inspector does 12 inspections per day and covers 18 miles per day.
28 6.3.1 Measure E.1: Increase Grid-Supply of Renewable and Zero-Carbon Electricity As discussed in Section 5.1, SB 100 (100 Percent Clean Energy Act of 2018) adopts a 60% RPS for all of California’s retail electricity providers by 2030. To further increase grid-supply renewable and zerocarbon electricity beyond the RPS mandate, CAP Measure E.1 includes the potential to join a local renewables and zero-carbon program, such as a regional CCE program, that increases the renewable/zero-carbon electricity to 75% in 2030, 15% beyond the RPS mandate for that year. It is assumed that the local program would supply 80% of the electricity load (not including the behind-themeter PV generation) in 2030.52 As previously explained in Section 5.1 and Table 7 (Allocation of Emissions Reductions from Increasing Renewable Supply), because the local renewables and zero-carbon program is required to comply with the State’s RPS mandates, a portion of the total emissions reduction from this measure is attributed to the State RPS compliance. The remaining emissions reduction beyond RPS compliance is allocated to this local Measure, E.1. The allocation of GHG emissions reductions in 2030 from this measure to State and local policies is shown in Table 28.
Table 28 Key Assumptions and Results for Measure E.1: Increase Grid-Supply Renewables Year State or City Action
Total for Local
Renewables and
Zero-Carbon
Program
RPS-Related Local
Renewables and
Zero-Carbon
Program (SE.1)
Beyond-RPS Local
Renewables and
Zero-Carbon
Program (E.1)
2030
Projected
Renewables and Zero
Carbon (%)
75% 60% 15%
Emissions Reduction
(MT CO2e) 6,022* 4,817 1,204
*Calculated in Table 5.
The emissions reduction is the projection under the CAP, including future impact of State policies and programs and CAP assumptions.
Energy Policy Initiatives Center 2018
6.3.2 Measure E.2: Increase Commercial Behind-the-Meter PV To increase behind-the-meter PV at commercial projects, the City plans to require PV installation at new and redeveloped commercial projects within the city, including, but not limited to, retail, office, hotels, and mixed-used projects. The projected starting year of the requirement is 2020. The goal of Measure E.2 is to add 0.3 MW of PV at new and redeveloped commercial projects by 2030; this will bring the total PV capacity in 2030 to 8.5 MW. In addition, the City will work with local agencies to install PV at existing public facilities, such as the Civic Center, public libraries, schools, and public parking spaces.
52 A 20% opt-out rate is a conservative assumption. Customer opt-out rates of current CCE programs in California range from 1% to 23%. The City of San Diego’s CCA feasibility study used a 20% opt-out rate in the Base Case scenario and the CCA business plan used a 5% opt-out rate. DA customers are assumed to remain DA customers and not become part of a CCE. City of San Diego: Feasibility Study for a Community Choice Aggregate. Final Draft. July 2017. CCA Business Plan and Feasibility Study Comparison. November 2018.
29 In the past three years, the City has added three new or redeveloped commercial projects with approximately 50,000 sq. ft. of building footprint.53 Assuming the trend continues, approximately 17,000 sq. ft. of commercial roof area will be added within the city annually that will be subject to this new commercial PV requirement. However, not all roof area is suitable for PV installation. Based on the 2019 Building Energy Efficiency Standards for nonresidential buildings, at least 15% of the total roof area must be reserved for the solar zone, where solar panels can be installed at a future date.54 Assuming PV will be installed in the minimum required solar zone, using both the ratio of PV module to roof area and the PV module power density based on a National Renewable Energy Lab study on rooftop solar potential, the projected PV capacity due to Measure E.2 is given in Table 29.55 Table 29 Key Assumptions for Measure E.2: Increase Commercial Behind-the-Meter PV Year
New Commercial
Roof Area Added
after 2020
(Sq. ft.)
Minimum
Solar Zone
to Roof
Area Ratio
New Solar
Zone Added
after 2020
(Sq. ft.)
Ratio of PV
Module to
Flat Roof*
New PV
Module Area
after 2020
(sq. ft.)
PV Module
Power
Density
(W/sq. ft.)*
New Commercial
Behind-the-meter
PV Capacity after
2020
(MW)
2030 187,000 15% 28,050 70% 17,672 15 0.3
*Based on National Renewable Energy Lab study, Gagnon et al, 2018.
Assume 10% of new or redeveloped projects are exempt from this requirement. The projected capacity are the projections under the CAP assumptions.
Energy Policy Initiatives Center 2018
The emissions reduction from all State and City actions that increase the behind-the-meter renewable supply is 1,916 MT CO2e, given in Section 4.2.2 (Allocation of GHG Emissions Reductions from Measures that Increase Renewables in Electricity to State Policy, Local Programs or Behind-the-meter PV Supply), Table 7 Allocation of Emissions Reductions from Increasing Renewable (or Zero-Carbon) Supply in Imperial Beach), and shown in Table 30. The total reduction is allocated based on estimated capacity (MW) that would result from each action, as shown in Table 30. The GHG emissions reductions are projected reduction amounts in the year 2030 only.
53 The three new or redevelopment projects are Breakwater Hotel, Blue Wave Hotel & Residences, and Imperial Beach Resort.
The site plans of all three projects were provided by the City to EPIC (October 2018). The site plans include building footprint, gross floor area, gross building area, etc.
54 CEC: 2019 Building Energy Efficiency Standards – 2019 Non-Residential Compliance Manual (December 2018). Chapter 9:
Solar Ready.
55 The ratios of PV module to roof area and PV module power density are based on Estimating rooftop solar technical potential across the US using a combination of GIS-based methods, lidar data, and statistical modeling. Gagnon et al (2018) Environ. Res.
Lett. 13 024027.
30 Table 30 Key Assumptions and Results for Measure E.2 Increase Commercial Behind-the-Meter PV Year State or City Action Total
California Solar
Polices,
Programs and
Mandates*
Measure E.2
Increase Commercial
Behind-the-Meter
PV
2030
Projected Behind-themeter PV Capacity (MW) 8.5 8.2 0.3 Projected Emissions
Reduction (MT CO2e) 1,916 1,856 59
*Solar policies, programs, and mandates include the impact of the PV mandates from 2019 Building Energy Efficiency Standard.
The projected capacity and emissions reduction are the projections under the CAP, including future impact of State policies and programs and CAP assumptions.
Energy Policy Initiatives Center 2018
6.4 Strategy: Achieve Zero Waste
The goal of this strategy is to reduce emissions from landfill waste through the following measure.
6.4.1 Measure W.1: Divert Waste from Landfill
Through Measure W.1, the City plans to adopt a “zero waste by 2050” policy and work with EDCO, the local waste hauler serving the city, to achieve an 80% waste diversion rate within the city by 2030. The 80% waste diversion rate is equivalent to reducing waste disposed at landfills to 1.6 pounds per person per day (PPD) by 2030.
The city’s waste disposal rate was 2.8 PPD in baseline year 2012, and 3.3 PPD in 2016, which are equivalent to approximately 65% and 58% diversion rates, respectively.56 The City has not completed a waste characterization study recently; therefore, it is assumed that the waste composition for the CAP time horizon would not change.57 The emissions from landfills depend on how much waste is disposed as well as how much of the resulting methane emissions are captured. Landfills in San Diego region are in the process of upgrading landfill gas collection systems58 and it is assumed that 90% of landfill gas will be captured in 2030, higher than the default 75% used in the BAU emissions projection. The emissions reduction from increasing the diversion rate is calculated by taking the difference between the BAU emissions projection from the solid waste category and the projected emissions from the solid waste category using the target diversion rates and PPD. Table 31 summarizes the key assumptions and results. The GHG emissions reduction projected is the reduction amount in the year 2030 only.
56 Method to convert PPD to estimated diversion rate is based on Calrecycle. Per Capita Disposal and Goal Measurement.
Jurisdiction PPD from 2012–2015 were downloaded from CalReycle Jurisdiction Diversion Summary. 57 Recent State actions include organic waste recycling, which may reduce the mixed waste emission factor in future years. 58 The main landfill, City of San Diego’s Miramar Landfill, has added a landfill gas recovery improvement project to be completed late 2018.
31 Table 31 Key Assumptions and Results for Measure W.1: Divert Waste from Landfill Year Projections Diversion
Rate
Per Capita Waste
Disposal After
Diversion
(pounds/person/day)
Emissions from
Solid Waste
Disposed in
Landfill
(MT/year)
Capture
Rate of
Landfill
Gas
(%)
Net Emissions
from Waste
Disposal
(MT CO2e)
GHG
Emissions
Reduction
(MT CO2e)
2030 BAU Projection 58% 3.3 16,551 75% 4,105 3,318 Projection with W.1 80% 1.6 7,928 90% 787 Emissions from waste are calculated based on a mixed waste emission factor (0.8 MT CO2e/short ton), oxidation rate (10%) and the waste capture rate. The projected emissions reduction are the projections under the CAP assumptions.
Energy Policy Initiatives Center 2018
6.5 Strategy: Sequester Carbon
Carbon sequestration helps to offset CO2 from the emissions categories and can be achieved through the Measure S.1 described below.
6.5.1 Measure S.1: Increase Urban Tree Planting
The most recent urban tree canopy assessment in the San Diego region, based on high-resolution Light Detection and Ranging (LiDAR), shows that the city has approximately 6% existing urban tree canopy.59 To increase urban trees at private spaces, the City plans to require tree planting at all new and redeveloped multi-family and commercial projects starting in 2025. For new and redeveloped multifamily homes in the city, the requirement will be to plant one tree per unit. The projected new multifamily units from 2025 to 2030 in the SANDAG Series 13 Regional Growth Forecast is used to base assumptions of how many new trees would be planted in the city under this requirement.60 For new and redeveloped commercial projects, the requirement will be to plant one tree per three surface parking spaces.61 In the past three years, the City has added three new or redeveloped commercial projects, with a total of 130 new parking spaces.62 Assuming the trend continues, 45 new commercial surface parking spaces will be added every year, on average, when subject to the requirement.
To increase urban trees at public spaces, the City plans to revise its landscape ordinance to increase planting along public rights-of-way. The goal is to plant 300 new trees by 2030 in public rights-of-way and to develop a program to track tree planting, maintenance, and removal.
The carbon sequestration potential from the net new trees is based on the total number of new trees planted and the CO2 absorption rate per tree.63 Table 32 summarizes the key assumptions and results.
The GHG emissions reduction is the projected reduction amount in year 2030 only.
59 The assessment was done in 2014 for all urban areas in San Diego County using the method developed by University of Vermont and the USDA Forest Service.
60 SANDAG Series 13 Regional Growth Forecast (October 2013). SANDAG Data Surfer , accessed on October 24, 2017. 61 The current Imperial Beach municipal code requires a minimum 45 sq. ft. landscape for every three surface parking spaces. 62 The three new or redevelopment projects are Breakwater Hotel, Blue Wave Hotel & Residences, and Imperial Beach Resort.
The site plans of all three projects were provided City to EPIC (October 2018). Parking spaces given in the site plans include both surface parking lots and parking structures.
63 On average the CO2 sequestration rate is 0.035 MT CO2e per tree per year. The carbon sequestration rate depends on the tree species, climate zone, planting location, and tree age; an average is used here. The actual carbon sequestration rate will be
32 Table 32 Key Assumptions and Results for Measure S.1 Urban Tree Planting Year
Tree
Planting
in Public
Rightsof-way
Tree Planting at Private Spaces
Total
New
Trees
Carbon
Sequestration
(MT CO2)
Multi-family Commercial
New Units
after 2025
S.1
Requirement
New Trees
Planted at
Units after
2025
New Surface
Parking
Spaces after
2025
S.1
Requirement
New Trees
Planted at
Parking
Spaces
after 2025
2030 300 476 1 tree per
unit 476 270 1 tree per three spaces 90 866 31
Carbon sequestration rate is 0.035 MT CO2 per tree per year. The projected carbon sequestration are the projections under the CAP assumptions.
Energy Policy Initiatives Center 2018
evaluated once the parameters are decided in implementation of the measure. California Emissions Estimator Model (CALEEMOD). Appendix E Technical Source Documentation. (September 2016).
Resilient Imperial Beach
Climate Action Plan
Appendices
Appendix C
CAP Measure Cost Overview
Appendix C. City of Imperial Beach CAP Measure Cost Overview
August 2018
Prepared for the City of Imperial Beach
Prepared by the Energy Policy Initiatives Center
2 About EPIC
The Energy Policy Initiatives Center (EPIC) is a non-profit research center of the USD School of Law that studies energy policy issues affecting California and the San Diego region. EPIC’s mission is to increase awareness and understanding of energy- and climate-related policy issues by conducting research and analysis to inform decision makers and educating law students.
For more information, please visit the EPIC website at www.sandiego.edu/epic.
Prepared in partnership with the San Diego Association of Governments (SANDAG) and the Energy Roadmap Program. This Program is primarily funded by California utility customers and administered by San Diego Gas & Electric Company under the auspices of the California Public Utilities Commission.
3 CAP MEASURE COST OVERVIEW
This Appendix provides an overview of potential cost and benefit impacts associated with measures included in the Imperial Beach Climate Action Plan (CAP).
This overview is based on a review of similar CAP measures included in currently available CAP costeffectiveness analyses in the San Diego region1 and identifies potential impacts to participants in the measure, that is, those who experience costs and/or benefits for directly engaging in the measure’s activities.
The participants impacted by each measure are identified Table 1 along with the policy type being pursued in the CAP (columns four and five respectively). A policy type is defined as being either mandatory or a City initiative; a mandatory policy requires participants to comply with the CAP measure and a City initiative seeks to encourage participants to engage in CAP measure activities to achieve greenhouse gas (GHG) emission reductions.
Table 1 ranks and color-codes the City of Imperial Beach’s CAP measures according to their relative costeffectiveness for participants using other cost-effectiveness studies (column 2).1,2 A measure’s costeffectiveness is based solely on the associated direct costs and benefits, and the measure’s ability to reduce GHG emissions. Measures included in the CAP may seek to achieve additional indirect goals (e.g., increase the resiliency of the urban forest); however, the cost-effectiveness to achieve those additional goals are not considered here. Measures ranked green typically provide a net benefit (positive net present value and $/MT CO2e) to participants. Measures ranked red typically come at a net cost (negative net present value and $/MT CO2e) to participants. The intensity of the color indicates whether the measure is more cost-effective (green) or less cost-effective (red) at reducing one metric ton of GHGs, but does not indicate the magnitude in difference.
For those measures where the City of Imperial Beach is a participant and for all other City programs identified in the CAP, the City anticipates that no additional staffing is necessary to implement CAP measures. The City currently has programs in place that can be expanded to accommodate all CAP measures aside from measure E.1 (Increase Grid Supply Renewables). For measure E.1, the City intends to leverage existing programs, such as the South Bay Energy Action Collaborative (SoBEAC), or partner with other jurisdictions to pursue other program options.
In addition to the benefits and costs experienced by measure participants, there are positive externalities, or co-benefits, experienced by the broader community. Co-benefits identified in the CAP for specific measures are included in Table 1 to provide some extra context on a measure’s impact (column 3) although a full identification and interactions of side effects is well beyond even the most complex studies in the literature.
The CAP does identify additional co-benefits as a result of CAP implementation that are not included in the table such as improved/increased natural habitat and an increase in local jobs. The inclusion of co-benefits may often have a positive, indirect monetary impact on the community at large, and differ by location and participant.
The cost-effectiveness of Imperial Beach CAP measures may differ from those used to determine the ranking (i.e., other jurisdictions in the region) in Table 1. Possible factors that can impact Imperial Beach-specific results include, but are not limited to:
Type of measure adopted (e.g., mandatory measures will typically lead to more GHG reductions than voluntary measures);
Specific measure details (e.g., who is/isn’t covered by the measure); Jurisdiction specific rates, costs, and rebates/incentives
1 CAP cost-effectiveness analyses reviewed include the County of San Diego and City of La Mesa. 2 The proposed measure to increase renewables in the grid-supply requires a feasibility study, which is outside the scope of previously conduced CAP cost-effectiveness analyses.
4 Table 1. CAP Measure Cost Overview
CAP Measure Relative CostEffectiveness
Ranking
Co-Benefits Participants Policy Type
T.5 Reduce Municipal
Employee VMT
Positive $/MT CO2e
Improved air quality, improved public health, reduced traffic congestion, improved mobility • City of Imperial Beach City Initiative
T.2 Clean Municipal
Fleet
Improved air quality, energy savings • City of Imperial Beach City Initiative E.2 Increase Commercial
Solar PV
Improved air quality, energy savings • New and redeveloped multi-family and commercial properties •EV owners
City Initiative
T.4 Improve Pedestrian
and Bicycle Facilities
Improved air quality, improved public health, reduced traffic congestion, improved mobility, improved safety • City of Imperial Beach • Commuters (new cyclists and pedestrians) City Initiative
T.3 Increase Mass
Transit Ridership
Improved air quality, energy savings, reduced traffic congestion, improved mobility • San Diego Metropolitan Transit System (MTS) • Commuters (new mass transit riders) City Initiative
T.1 Increase Citywide EV
Charging Stations
Negative $/MT CO2e
Improved air quality, energy savings • Charger installers • EV drivers
City Initiative
W.1 Divert Waste from
Landfill
Reduced waste, improved water quality
• Waste hauler (some or all costs may pass through to waste bills) Mandatory
S.1 Tree Planting
Improved air quality, improved water quality, reduced water run-off, reduced urban heat island effect • New and redeveloped multi-family and commercial properties Mandatory
E.1 Increase Grid-Supply
Renewables NA
Improved air quality, energy savings • City of Imperial Beach • Electricity customers
City Initiative
Resilient Imperial Beach
Climate Action Plan
Appendices
Appendix D
Consolidated Measure Implementation Action Matrix
1 Appendix D. Consolidated Measure Implementation Matrix Consolidated Measure Implementation Matrix
Emissions
Category
Reduction
Strategies and
Measures
Action Description Responsibility Timeline
On-Road
Transportation
Strategy: Clean and Efficient Transportation
T.1 Increase
Citywide EVCSs
1 Encourage and incentivize
EVCSs at new and
redeveloped multifamily
and commercial
developments through the
development review
process
City Near-term
2 Identify a list of priority
public facility installation
sites on City sites and in
collaboration with relevant
partner agencies
City Near-term
3 Identify and pursue
funding and financing
resources to support EVCS
installation
City Near- and
Mid-term
T.2 Clean Municipal
Fleet
1 Work with CSE’s EV Expert
to develop a municipal
fleet assessment and
conversion plan
City Mediumterm
2 Utilize fleet assessment
and conversion plan to
decide when to replace
vehicles
City Mediumterm
3 Work with other agencies
and jurisdictions to
identify potential joint EV
procurement options
City Near-term
Strategy: Reduce VMT
T.3 Increase Mass
Transit Ridership
1 Coordinate with San Diego
MTS to identify transit
connectivity opportunities
that improve transit access
and mobility
City Near-term
2 Work with San Diego MTS
to increase awareness of
the Rapid 925 and existing
bus routes as a means to
increase ridership
City Near-term
SE.1
Collaborate with
SANDAG to ensure that
the RTP includes transit
investments that
improve transit service
and connectivity
City Near-term
2 Consolidated Measure Implementation Matrix
Emissions
Category
Reduction
Strategies and
Measures
Action Description Responsibility Timeline
T.4 Improve Bicycle
and Pedestrian
Facilities
1 Complete the suite of
planned bicycle and
pedestrian projects by
2030
City Mediumterm
2 Evaluate the feasibility of
Class IV bicycle lanes in all
project review procedures
City Near-term
3 Work to expand the
number of bike parking
facilities at commercial
establishments
throughout the City
City Mediumterm
4 Work with scooter and
bikeshare and other
emerging companies to
analyze data and better
understand active
transportation patterns
and needs throughout the
City
City Mediumterm
T.5
1 Purchase 10 electric
bicycles City Near-term
2 Track the number of trips
and VMT avoided through
usage of the bicycles
City Mediumterm
Energy
Strategy: Increase Renewable Electricity
E.1 Increase GridSupply of
Renewable and
Zero Carbon
Electricity
1 Explore the potential to
join a regional CCE
through a partnership with
other jurisdictions
City Near-term
2 Work with identified
partnership jurisdictions to
conduct a feasibility study
and other related research
and administrative efforts
necessary to establish a
CCE
City Mediumterm
3 Evaluate the need for
additional resources to
increase to 100% gridsupplied renewable
power by 2030
City Mediumterm
3 Consolidated Measure Implementation Matrix
Emissions
Category
Reduction
Strategies and
Measures
Action Description Responsibility Timeline
SE.1
Work with SDG&E to
publicize energy efficiency
rebates and subsidies to
increase the efficiency of
Imperial Beach’s existing
building stock
City Near-term
E.2 Increase
Commercial
Behind-the-Meter
PV
1 Establish requirements or
incentives through the
development review and
approval process to spur
installation of commercial
solar PV
City Near-term
2 Develop a directory of
solar PV funding sources,
rebates, and incentives,
and leverage existing
efforts and materials from
the CSE, California Solar
Initiative, SDG&E, and
other organizations.
City Near-term
3 Review/revise applicable
building, zoning, and other
codes/ordinances to
encourage the
development of solar
ready commercial
developments.
City Mediumterm
SE.1
Identify opportunities to
install solar PV on public
facilities such as municipal
buildings, schools,
libraries, and parking lots.
City Mediumterm
Waste
Strategy: Reduce Solid Waste
W.1 Divert Waste
from Landfill
1 Adopt a Zero Waste by
2050 policy City Near-term
2 Work with the City’s waste
service company and
stakeholders to develop a
public outreach campaign
to increase awareness of
existing waste
management services and
drive behavioral change
City Mediumterm
Carbon
Sequestration
Strategy: Carbon Sequestration
S.1 Tree Planting 1
Plant 300 trees within City
ROW by 2030 City Mediumterm
4 Consolidated Measure Implementation Matrix
Emissions
Category
Reduction
Strategies and
Measures
Action Description Responsibility Timeline
2 Make changes to the City
code to require tree
planting in new and
redeveloped residential
and commercial
developments
City Near-term
SE.1
Identify opportunities to
enhance or conserve
habitat that would
sequester carbon in
collaboration with relevant
state and federal agencies
City Mediumterm
SE.2
Identify and pursue
funding to develop an
Urban Forest Management
Plan
City Mediumterm
SE indicates that the action is a Supporting Effort
59%
18%
17%
3%
2%
<1%
ON-ROAD TRANSPORTATION
ELECTRICITY
NATURAL GAS
SOLID WASTE
WATER
WASTEWATER
81,300 MT CO2e
Total GHG emissions
estimated for 2016
*This GHG inventory is based on 2016 data.
GHG INVENTORY*
27,901
population in 2016
4.4 square miles
9,510
occupied housing
units in 2016*
Current CAP progress:
In Progress
Subregion:
South Bay
JURISDICTION
QUICK FACTS
* Occupied housing does not include
group quarters.
RECENT ACCOMPLISHMENTS
1 Completed sea-level rise study in 2016.
2 Installed energy e cient windows at City Hall in 2017. 3 Replaced existing lighting with light-emitting diode (LED) lighting and occupancy sensors in 2019.
4 Received Coastal Commission grant in 2017 to support Local Coastal Program Update and CAP development.
5 Received a grant to help plant 200 trees.
The ReCAP Snapshot is prepared for the City as a part of the SANDAG Regional Climate Action Planning Framework (ReCAP) to support, but not replace, cities’ monitoring greenhouse gas (GHG) emissions and/or climate action plan (CAP) implementation over time. Climate planning activities vary by jurisdiction and are dependent on a variety of factors, such as funding and sta capacity. As the first edition of Snapshots
(November 2019), this document will set a baseline for monitoring trends into the future. More information, including a FAQ document and Methods and Data Sources Summary, is available at www.sandag.org/climate.
SNAPSHOT
CITY OF IMPERIAL BEACH
CAP
Re
Att ach ment 3
City of Imperial Beach | ReCAP Snapshot – November 2019 Edition * Snapshot activity data is from the years 2016 through 2018, depending on the most recently available year for each activity. Activity data represents a single year.
43% renewables in
grid electricity supply
3.1 MW PV online
55,490 MWh
of electricity consumed
2.6 million therms
of natural gas consumed
2,176 MWh electricity saved
through SDG&E programs
1,362 MWh
of electricity consumed
5,200 therms
of natural gas consumed
1 energy e ciency projects
installed, which resulted in
first-year reductions of:
39,174 kWh
3 public electric
vehicle chargers
1,723
clean vehicles registered
(9% total registered vehicles)
7 total miles of bike lanes
2,808
passengers on and o
transit per weekday
1 local business participating
in SANDAG iCommute
program events
112 gallons
water used/person/day
75 gallons wastewater
produced/person/day
3.3 lbs waste disposed
in landfill/person/day
59% waste diverted
COMMUNITY-WIDE (DATA YEAR: 2016-2017) COMMUNITY-WIDE (DATA YEAR: 2016-2017) MUNICIPAL (DATA YEAR: 2018)
COMMUNITY-WIDE (DATA YEAR: 2016)
COMMUNITY-WIDE (DATA YEAR: 2016)
COMMUNITY-WIDE (DATA YEAR: 2016-2018)
TRANSPORTATION ENERGY EFFICIENCY
WATER + WASTEWATER
SOLID WASTE
RENEWABLE ENERGY
=
These select activity data represent best available data* for common GHG reduction activities included in local CAPs across the SANDAG region and may not align precisely to GHG reduction measures and/or the metrics identified within a jurisdiction’s CAP. Community-wide activities occur within a jurisdiction's boundaries; municipal activities occur at City-owned facilities. For more information on data sources, the Methods and Data Sources Summary is available at www.sandag.org/climate.
ReCAP ACTIVITY DATA FOR THE
CITY OF IMPERIAL BEACH
CAP
Re
The ReCAP Snapshot is prepared for the City as a part of the SANDAG Regional Climate Action Planning Framework (ReCAP) to support, but not replace, cities’ monitoring greenhouse gas (GHG) emissions and/or climate action plan (CAP) implementation over time. Climate planning activities vary by jurisdiction and are dependent on a variety of factors, such as funding and staff capacity. As the second edition of Snapshots (November 2020), this document builds upon the baseline set by the first edition Snapshots published in November 2019 to support monitoring trends into the future. More information, including a FAQ document and Methods and Data Sources Summary, is available at sandag.org/climate.
GHG INVENTORY*
*This GHG inventory is based on best available data, which includes 2016 VMT data for the on-road transportation sector and 2018 data for all other sectors. See below for additional detail.
The 2018 GHG emissions inventory was prepared using the best available data for each emissions category.
The best available data for vehicle miles traveled (VMT) at this time continues to be estimates for the year 2016, based on the SANDAG Series 14 forecast and ABM2 transportation model. This same VMT dataset was used to prepare the 2016 inventory included in the first edition Snapshots (published in November 2019). As a result, the VMT used in the 2018 GHG emissions inventory is the same as that in the 2016 GHG emissions inventory
Estimated changes in VMT since 2016 will be reflected in the forthcoming 2020 GHG emissions inventory.
For the next ReCAP Snapshots, VMT estimates will be based on the forecast and land use used in the 2021 Regional Plan.
GHG emissions inventories are one tool for use in monitoring CAP implementation. Together, a GHG emissions inventory and activity data reflect CAP implementation progress. Until updated VMT estimates are available, performance of VMT-related CAP measures can be monitored based on activity data.
Additional information about the SANDAG transportation model is included in the Snapshot FAQ document, and further detail about CAP monitoring and reporting can be found in the ReCAP Technical Appendix VI.
SNAPSHOT
ON-ROAD TRANSPORTATION
ELECTRICITY
NATURAL GAS
SOLID WASTE
WATER
WASTEWATER
OFF-ROAD TRANSPORTATION
JURISDICTION
QUICK FACTS
27,202
population in 2018
4.4 square miles
9,106
occupied housing
units in 2018*
Current CAP progress:
Adopted 2019
Subregion:
South Bay
CITY OF IMPERIAL BEACH
*Occupied housing does not
include group quarters.
Total GHG emissions
estimated for 2018
79,300 MT CO2e
58%
18%
18%
2%
3% <1%
Attachment 4
RECAP ACTIVITY DATA FOR THE
These select activity data represent data for the year 2018 for common GHG reduction activities included in local CAPs across the SANDAG region and may not align precisely to GHG reduction measures and/or the metrics identified within a jurisdiction’s CAP. Community-wide activities occur within a jurisdiction’s boundaries; municipal activities occur at City-owned facilities. For more information on data sources, the Methods and Data Sources Summary is available at sandag.org/climate.
CITY OF IMPERIAL BEACH
TRANSPORTATION ENERGY EFFICIENCY
WATER + WASTEWATER
RENEWABLE ENERGY
SOLID WASTE
CARBON SEQUESTRATION
3 public electric
vehicle chargers
1,794 clean
vehicles registered
(10% of total registered vehicles)
8 total miles of bicycle lanes
3,022 passengers on and
off transit per weekday
1 local business participating
in SANDAG iCommute
program events
54,675 MWh
of electricity consumed
2.6 million therms
of natural gas consumed
2,249 MWh electricity saved
through SDG&E programs
1,362 MWh
of electricity consumed
5,200 therms
of natural gas consumed
Energy efficiency projects have
resulted in first year reductions of:
39,174 kWh
COMMUNITY-WIDE COMMUNITY-WIDE
MUNICIPAL
121 gallons
water used/person/day
88 gallons wastewater
produced/person/day
COMMUNITY-WIDE
43% renewables in
grid electricity supply
3.1 MW PV online
COMMUNITY-WIDE
3.0 lbs waste disposed
in landfill/person/day
63% waste diverted
COMMUNITY-WIDE
15 trees planted
MUNICIPAL
City of Imperial Beach – 2018 ReCAP Snapshot
=
City of Imperial Beach Re
CAP SNAPSHOT
Image 1: Total Freeway Vehicle Miles Traveled in
San Diego Region (2019, 2020, 2021) The ReCAP Snapshot is prepared for the City as 1.4 a part of the SANDAG Regional Climate Action 1.2
Planning Framework (ReCAP) to support, but
not replace, cities’ monitoring of greenhouse gas
(GHG) emissions and/or climate action plan (CAP)
implementation over time. The 2020 Snapshots are
unique as the Covid-19 pandemic had impacts on
our day-to-day activities in 2020. More information,
including a FAQ document is available at
FAQ (sandag.org).
2019
2020 1.0
1 2 3 4 5 6 7 8 9 10 11 12
Billion Miles
2021
0.8 0.6
0.4 Region wide peak
period highway
travel was down
-16.7% in 2020
from 2019
0.2 -
Month
Source: PeMS, Caltrans. SANDAG 2020 State of the Commute Report 2020 GHG
Inventory*
*This GHG inventory is based on MT CO2e
best available data from 2020
for all listed sectors and excludes 20,000
on-road transportation
IMPERIAL BEACH
QUICK FACTS
28,055 population
17,200 in 2020
ELECTRICITY 14,500
2,300
1,400
300 NOT
AVAILABLE
Image 2
4 square miles NATURAL GAS
9,646 occupied
SOLID WASTE 10,000 housing units in 2020
WATER Current CAP progress:
Adopted 2019
WASTEWATER
Subregion:
ON-ROAD South Bay TRANSPORTATION 0-
The 2020 GHG emissions inventory was prepared using the best available data for each emissions category and does not include on-road transportation. The activity data for electricity, natural gas, solid waste, and water sectors are based on tracked and observed usage for the year and therefore, correctly refect the impacts of Covid-19. However, for on-road transportation, the vehicle miles traveled (VMT) data are estimates from the SANDAG activity based model (ABM). The most recent SANDAG activity based model, ABM2+, was last calibrated with observed data from 2016 and forecasts 2020 VMT. While transportation historically accounts for the largest share of GHG emissions in an inventory, the forecasted VMT does not take into account the impacts of the Covid-19 pandemic and cannot be updated to provide 2020 VMT data for each jurisdiction. Caltrans PeMS data provides observed regional VMT on freeways for 2020. Image 1 compares the observed VMT on freeways in the San Diego region for the years 2019, 2020 and 2021. The observed data is only available for freeway and it cannot be scaled down to individual jurisdictions which have different confguration of roads. While other sources of data and anecdotal experience show lower VMT in 2020, there is no data for how each jurisdiction’s VMT changed during 2020. The Caltrans PeMS data set shows similar travel trends in 2021 as in 2019 which suggests that lower VMT in 2020 is likely due to the impacts of the Covid-19 pandemic and may not sustain long term.
GHG emissions inventories are one tool to support, but not replace, monitoring of CAP implementation at the local level. Together, a GHG emissions inventory and activity data refect CAP implementation progress.
Attachment 5
7 trees planted
MUNICIPAL
2.7 lbs waste disposed
in landfll/person/day
66% waste diverted
COMMUNITY-WIDE
Carbon Sequestration
31% renewables in SDG&E
electricity supply
4.6 MW PV Capacity
COMMUNITY-WIDE
Solid Waste
54,472 MWh of electricity
consumed
2.7 million therms
of natural gas consumed
COMMUNITY-WIDE
Renewable Energy
COMMUNITY-WIDE
115 gallons
of water used/person/day
73 gallons of wastewater
produced/person/day
ReCAP Activity Data for the
City of Imperial Beach
These select activity data represent data for the year 2020 for common GHG reduction activities included in local CAPs across the SANDAG region and may not align precisely with GHG reduction measures and/ or metrics identifed within a jurisdiction’s CAP. Community-wide activities occur within a jurisdiction’s boundaries; municipal activities occur at City-owned facilities. For more information on data sources, the Methods and Data Sources Summary is available at sandag.org/climate.
Transportation Energy Use
=
COMMUNITY-WIDE
3 public electric
vehicle chargers
1,756 alternative fuel
vehicles (9% of total registered
vehicles)
164 zero emission vehicles
(1% of total registered vehicles)
9 total miles of bicycle
lanes
2,146 passengers on and off
transit per weekday
1 local business
participating in SANDAG
iCommute program events
MUNICIPAL
1 clean vehicle in feet
Water + Wastewater
City of Imperial Beach | 2020 ReCAP Snapshot - April 2023 Edition
P a g e | 1
November 15, 2023
ITEM TITLE: DISCUSSION ABOUT THE ALLOWANCE OF THE SALE AND CONSUMPTION
OF ALCOHOL DURING SPECIAL EVENTS AND FACILITY RENTALS AT CITY PARKS AND FACILITIES. (0920-95)
ORIGINATING DEPARTMENT:
Parks & Recreation
EXECUTIVE SUMMARY:
The City of Imperial Beach may allow the consumption and sale of alcoholic beverages within City of Imperial Beach’s designated parks during special events with a One-Day Special Event Use Permit as referenced in Imperial Beach Municipal Code (“IBMC”) Section 9.04.010. Staff has received requests recently to allow alcohol beverages at other City facilities. Currently, the City does not allow the consumption/sale of alcoholic beverages during a facility rental of City facilities.
Staff is looking for direction from the City Council on which locations including, City parks and facilities, that alcohol may be allowed pursuant to applicable policies and procedures.
RECOMMENDATION:
That the City Council provide direction to staff on which City parks and facilities will be designated as locations that may allow for alcoholic beverage use during special events and facility rentals.
OPTIONS:
Provide direction to City staff to bring back an ordinance to amend the Imperial Beach Municipal Code to specify the locations in designated City Parks and Facilities where alcohol use may be allowed during a special event or facility rental. Provide staff with further information and ask staff to return with an additional report. Give direction to the City Manager.
BACKGROUND/ANALYSIS:
Currently, pursuant to the IBMC, the City Manager may allow an event organizer or applicant the ability to submit a request to serve or sell alcohol at a designated park in Imperial Beach during a special event with specified additional requirements. The parks that currently allow alcohol with an approved special event permit are Pier Plaza, Dunes Park, and Veterans Park. Alcohol is also allowed in certain circumstances with an approved special event permit that includes a parking lot or street closure.
P a g e | 2
Recently with the upgrades to the Senior Community Center staff has received requests to provide facility rentals that include serving alcohol. Currently, the City does not allow alcohol during approved and permitted City facility rentals.
It is the intent of the City and staff to provide specified areas where alcoholic beverages can be served in designated parks during special events and during limited special occasions in city facilities including wedding receptions, anniversaries, birthday parties and banquets pursuant to additional and appropriate requirements. All special event permits issued that currently allow alcohol consumption in parks are required to have additional insurance, a fenced in area, security guards, and also have an approved ABC license.
City staff has drafted an alcoholic beverage special event/ facility application and would like direction from City Council on which City parks and public facilities to include as designated locations. Staff is recommending that alcohol beverages only be allowed at one city facility, the Senior Community Center, and that alcohol service be limited to only beer and wine during facility rentals. Facility rentals if approved for alcoholic beverage use would require a security deposit, additional insurance, security guards, a licensed bartender/caterer, and an approved temporary ABC license if service includes the sale of alcoholic beverages.
Attached is a chart of park locations that are currently designated for special events and list those currently approved for alcohol during special events. Staff has also updated the list to include community and recreation centers that are open for the public to rent spaces. Any event organizers or groups wishing to consume alcoholic beverages within public parks and facilities will be required to adhere to the Imperial Beach Municipal Code and any rules and policies set by the City.
ENVIRONMENTAL DETERMINATION:
Not a project as defined by CEQA.
FISCAL IMPACT: N/A
ATTACHMENTS:
ATT 1 – IB Parks and Facilities List
ATT 2 – Alcohol Beverage Application
Imperial Beach Park Rental Spaces
Location Approval
Authority
Alcohol May be Allowed with
Appropriate Permit
Amplification
Allowed Special Considerations
Beach/sand
areas
Majority of the
City Council No Yes, restrictions apply
Cannot block or restrict access
to beach
Dunes Park Majority of the
City Council
Restrictions apply including but not
limited to security guards,
additional insurance and ABC
permit required
Yes, restrictions
apply
Limited to 2 weekends per
month
Pier Plaza Majority of the
City Council
Restrictions apply including but not
limited to security guards,
additional insurance and ABC
permit required
Yes, restrictions
apply
Limited to 2 weekends per
month
Sports Park City Staff No Yes, restrictions
apply
Veterans Park City Staff
Restrictions apply including but not
limited to security guards,
additional insurance and ABC
permit required
Yes, restrictions
apply
IB Boulevard
Boardwalk
Majority of the
City Council No Yes, restrictions apply
Street &
Parking Lot
Closures
Majority of the
City Council
Restrictions apply including but not
limited to security guards,
additional insurance and ABC
permit required
Yes, restrictions
apply
Bollards required for street
closures on Seacoast
Other parks City Staff No Yes, restrictions
apply
Imperial Beach Facility Rental Spaces
Location Approval
Authority
Alcohol May be Allowed with
Appropriate Permit
Amplification
Allowed Special Considerations
Sports Park
Recreation
Center
City Staff NO Yes, restrictions
apply
Senior
Community
Center
City Staff TBD Yes, restrictions
apply
For alcohol use restrictions
would apply including but not
limited to security guards,
additional insurance and only
beer and wine allowed.
ATTACHMENT 1
This document may be considered a public record subject to release under the CA Public Records Act.
Imperial Beach Alcoholic Beverage Permit Application Page 1
SPECIAL EVENT AND FACILITY RENTAL ALCOHOLIC BEVERAGE APPLICATION The City of Imperial Beach may allow the consumption and sale of alcoholic beverages within designated City of Imperial Beach parks and facilities with a One-Day Use Permit in conjunction with a special event or facility rental permit only. The City may provide specified areas where alcoholic beverages may be served at special events in parks and limited occasions in City facilities including wedding receptions, anniversaries, birthday parties and banquets. Those event organizers or groups wishing to consume alcoholic beverages within public parks and facilities must submit an application and adhere to the following provisions established by the City.
The City reserves the right to reject any application, modify the policy provisions, or discontinue the policy at any time.
The consumption of alcoholic beverages in Imperial Beach City parks and public facilities may only be allowed by first obtaining written approval (bottom of this form) by City Manager or designee. This form will be supplied by the Parks, Recreation and Community Services Department staff at the time the special event or facility permit is filled out, alcohol at event box is checked on permit application, and an alcoholic beverage permit is requested by applicant.
1. Alcoholic beverage permits must be requested by applicant before permit approval, use of the facility or park, and prior to the function. This form must be completed by the applicant and returned to the office of the Parks, Recreation and Community Services Department 60 days before the event.
2. Food and refreshments, including alcoholic beverages, shall be permitted in permitted area(s) only. Alcoholic beverages are not allowed without a permit.
3. Uniformed security guards are required at the expense of the applicant; one (1) uniformed security personnel up to 100 - 150 people (depending on event type and location), additional guards required as number of persons increase.
4. Security Guards will act as security forces and not as identification or age checkers. Additional guards may be required.
5. No alcoholic beverages shall be served to any person under the age of twenty-one (21) years.
6. If minors are in possession of alcoholic beverages, or if any event participant(s) appear to be under the influence of alcohol or controlled substance(s) or in possession of illegal drugs, the Permit may be revoked by the City, and the event will be terminated immediately.
ATTACHMENT 2
This document may be considered a public record subject to release under the CA Public Records Act.
Imperial Beach Alcoholic Beverage Permit Application Page 2
7. For the health, safety or welfare of the public, alcoholic beverage service may be terminated at any time by City staff, security, or law enforcement.
8. Alcoholic beverages shall not be purchased or brought into the Park or Facility by anyone other than the person responsible for the activity or a licensed caterer.
9. Alcoholic beverages are not to be consumed outside of the area designated in the Permit or the permitted Park or Facility. Alcohol consumption is strictly limited to the event time and area defined on the approved Permit and must comply with the terms of the ABC Daily On-Sale General License.
10. The alcoholic beverage applicant shall remove all such beverages and empty containers from the premises immediately following the approved function.
11. The sale of alcoholic beverages in Imperial Beach parks and public facilities may only be provided by an approved, licensed caterer, or temporary permit given by Alcoholic Beverage Control (ABC).
12. The applicant shall provide evidence of commercial general liability insurance in a form acceptable to the Risk Manager (and additional coverage(s) as appropriate for the activities of the Park or Facility use), naming the City of Imperial Beach as an additional insured, and with a coverage amount to be determined by the Risk Manager according to the size and risk factors of the event.
13. Commercial general liability insurance (CGL) including liquor liability coverage with a minimum limit of $2 million per occurrence is required when an event includes alcohol. The liquor liability coverage must be the same as the CGL, i.e., a lower sub-limit will not be accepted.
14. A City Special Event Permit or Facility Permit will be required in addition to the Alcoholic Beverage Permit.
15. The City shall assume the right to make policy changes as deemed necessary.
16. The applicant will sign a defense and indemnification agreement provided by the City and agrees to defend and indemnify the City related to any issues with the event.
I, the undersigned, do hereby agree that I will abide by the policies governing the use of this facility and I will be responsible for any damages to the facility or equipment caused by the occupancy of our organization to the premises.
Applicant Signature: ___________________________________________________________________________ Applicant Full Name (Please Print): _______________________________________________________________
This document may be considered a public record subject to release under the CA Public Records Act.
Imperial Beach Alcoholic Beverage Permit Application Page 3 Date of Birth: ________________________ California Driver’s License: __________________________________ Present Address: _________________________________________________________ (City, State, Zip Code) Telephone Number: ___________________________________ Email: _____________________________________