CleanPowerSF Sfpuc Commission
April 26, 2022

OUR MISSION: To provide our customers with high-quality, efficient and reliable water, power and sewer services in a manner that values environmental and community interests and sustains the resources entrusted to our care.

525 Golden Gate Avenue, 13th Floor
San Francisco, CA 94102
T 415.554.3155
F 415.554.3161
TTY 415.554.3488

San Francisco Public Utilities Commission
Correspondence Log
April 1 through April 19, 2022

Date Received From Subject
April 1, 2022 Masood Ordikhani State Legislation on Water Rate Decoupling and Water Fees
April 12, 2022 Dave Warner Full comments for the record April 13, 2022 Eileen Boken California could shrink water use in cities by 30% or more, study finds
April 14, 2022 Steve Ritchie Department of Finance and Plan Bay Area Projections
April 15, 2022 Steve Ritchie Waiver of Minimum Purchase Requirements During Drought (Water
Supply Agreement Section 3.07
Steve Ritchie Minimum Purchase Waiver for Fiscal
Year 2022-2023
April 18, 2022 Eileen Boken California gives rivers more room to flow to stem flood risk
April 19, 2022 Nicole Sandkulla Comments on the Revised Water System Improvement Program

Weekly Media Clips
• April 1, 2022 to April 8, 2022

OUR MISSION: To provide our customers with high-quality, efficient and reliable water, power and sewer services in a manner that values environmental and community interests and sustains the resources entrusted to our care.

525 Golden Gate Avenue, 13th Floor
San Francisco, CA 94102
T 415.554.3155
F 415.554.3161
TTY 415.554.3488

April 1, 2022

TO: Commissioner Anson Moran, President
Commissioner Newsha Ajami, Vice President
Commissioner Sophie Maxwell
Commissioner Tim Paulson

THROUGH: Dennis J. Herrera, General Manager

FROM: Masood Ordikhani, Assistant General Manager, External Affairs Megan Scott, Acting Director of Policy and Government Affairs

RE: State Legislation on Water Rate Decoupling and Water Fees

During the March 8th SFPUC Commission meeting, Vice President Ajami asked staff to look into the status of state legislation regarding the decoupling of water rates. Policy and Government Affairs (PGA) staff identified one bill on this topic (SB 1469) which applies only to investor-owned water utilities.

PGA also worked with our state lobbyists to review a large volume of bills more broadly related to water rates and fees. Currently, the bill most relevant to SFPUC is AB 2536 which defines standards and procedures for local agencies that conduct studies to impose connection fees and capacity charges on new development. This memo summarizes both bills.

SB 1469 (Bradford) – Water Rate Decoupling for Water Corporations As California faces longer and more severe droughts, water conservation efforts are critical to ensuring a reliable water supply. However, water suppliers face challenges when implementing water conservation programs. These programs lead to reduced demand and this results in reduced revenue.

Municipal water systems can recover some of these savings by utilizing rate stabilization funds, though these funds can be quickly depleted. Investor-owned water corporations do not have rate stabilization funds.

Decoupling is a mechanism that breaks the link between revenue and actual sales, allowing the utility to recover revenue lost due to reduced demand to cover the fixed cost of providing service. Since 2008, the California Public Utilities Commission (CPUC) has made use of the Water Revenue Adjustment

2 Mechanism (WRAM) to decouple revenue and sales among water corporations. In August 2020, the CPUC repealed the WRAM due to affordability concerns in favor of an alternate model. This has raised concerns amongst water conservation and efficiency advocates.

SB 1469 seeks to address this issue by requiring the CPUC to allow water corporations to adjust their rates to cover costs when water conservation programs contribute to reduced revenue, while also ensuring consumers are not overcharged. This provision already applies to electrical companies under current law.

While SB 1469 does not apply to the SFPUC, PGA will track this bill as it moves through the state legislative process.

AB 2536 (Grayson) – Water/Sewer Connection Fees & Capacity Charges AB 2536 is the most relevant bill to SFPUC this legislative session on the broader topic of water rates and fees. This bill would require local agencies that impose connection fees or capacity charges and conduct studies to support the reasonable cost of providing the service to follow certain standards and procedures. These include:
 Before adopting a fee or capacity charge, adopt a study to support the estimate of the reasonable cost of providing service.
 Including information in the study that supports the local agency actions, pursuant to current state law (Government Code, Section 66001(a)).
 Identifying the existing and new levels of service for each public facility, and an explanation of why the new level of service is appropriate.  If a study supports the increase of an existing fee or capacity charge, the assumptions of the study supporting the fee or charge and evaluate the amount of fees or charges collected under the fee or charge must be reviewed.
 All studies must be adopted at a public hearing with at least 30 days’ notice.
 Studies must be updated at least every eight years.

SFPUC already conducts these studies in accordance with these provisions so, as currently written, the bill would not change our practices. PGA will closely track AB 2536 during the state legislative process to flag any amendments for staff review.

We hope that this is responsive to the Commission’s request and are happy to provide any additional information or answer any questions that you may have.

We will keep the Commission updated on developments with these bills and any related items that arise during the state legislative session.

From: Dave Warner
To: Hood, Donna
Subject: Full comments for the record
Date: Tuesday, April 12, 2022 1:49:45 PM
Attachments: Dave Warner SFPUC Public comment 2022-04-12 50% rationing.docx CAUTION: This email originated from outside of the organization. Do not click links or open attachments unless you recognize the sender and know the content is safe.

Hi Donna,
Thanks for managing our ever more complex SFPUC meetings! Attached is a copy of my comments from item 5 public comment.

Best regards,
Dave
I’m Dave Warner from Palo Alto. Thank you for your service.

In your 2020 urban water management plan, there are references to the potential for 49% overall rationing if the Bay Delta Plan is implemented. While technically the math works, in reality such rationing is highly unlikely to ever occur and is an overstatement of risk. Please consider not using such a figure in public settings.

I’d put this figure in a similar vein to the socioeconomic studies, which you have graciously stopped quoting. Those reports had the unfortunate result of coming out as the 2013-2016 drought was developing and projected a downturn when just the opposite happened, the economy improved. Like the roughly 50% rationing figure, the math could be understood but something entirely different occurred.

The 50% rationing figure is based on higher demand than today which has been called an “outside envelope” of demand projections, and they are based on the design drought model, which may have been prudent when it was first developed, but is now looking overly
conservative. The Bay Delta Plan would also have to be implemented as is, and the Water Board would have to not relax flow requirements in a time of critical need. A lot of events have to align for the 50% rationing figure to ever occur.

We’re in an odd set of circumstances today. Despite our drought, we have four and a half years supply of water in storage. Contrast us to Valley Water, who doesn’t have Anderson reservoir available and is facing a shortfall from the State Water Project. It doesn’t seem right for us to be mentioning 50% rationing when we are nowhere close to Valley Water’s situation.

I think the 50% reference was last used on March 22nd when BAWSCA CEO Sandkulla was imploring the SFPUC to get back to the negotiating table. While the 50% rationing reference was disappointing, her push to negotiate seemed prescient given the recent announcement of $2.6 billion dollars associated with a proposed solution for other rivers. MID and TID could upgrade their distribution systems and improve their water efficiency and perhaps we get funding for alternative water supplies which would avoid the need for us to increase water rates. Wouldn't that be a win-win.

Please consider avoiding references to 50% rationing.

I will be submitting full comments for the record.

From: Ritchie, Steve
To: Hood, Donna
Cc: Herrera, Dennis J
Subject: Department of Finance and Plan Bay Area projections Date: Thursday, April 14, 2022 8:54:52 AM
Attachments: DOF Projections_Methodology.pdf
PBA50_Attachment-A_Regional-Growth-Forecast-Methodologyv2.pdf The message below is what I sent to Commissioner Moran along with the two attachments. Please distribute to the other Commissioners

Thanks,
Steve.
****************************************
Attached are 2 documents.

They are the methodology reports for the Department of Finance population projections and the Plan Bay Area projections. The Plan Bay Area (ABAG) projections are developed based on more complexity than the DOF projections.

For San Francisco retail service area, we rely on projections provided by the SF Planning Department which start with the ABAG projections and then add in some local factors such as the Mayor’s goal of adding 5,000 new housing units each year.

Ultimately we use the SF Planning projections for
housing and jobs. For the residential piece, we don’t use population directly. We use projections of water use per housing unit by housing unit type.

Let me know if you have any questions,
Steve.

Steven R. Ritchie
Assistant General Manager, Water
San Francisco Public Utilities Commission
Phone: (415) 934-5736
E-mail: [email protected]
www.sfwater.org
Please conserve water today!

Population Projections Methodology (2019 Baseline)
California State Department of Finance
Demographic Research Unit
915 L Street
Sacramento, CA 95814
Phone: 916-323-4086; E-mail: [email protected]
September 2020
Introduction
The California Department of Finance (DOF), Demographic Research Unit is responsible by statute for maintaining postcensal population projections which are calculated using the demographic balancing equation:

Current Population = Previous Population + (Births - Deaths) + Net Migration This method calculates the population in the target year by starting with the population from the previous year, adding natural increase (births minus deaths) and net migration that occurred between the two years. The births, deaths, and migration anticipated during this time are called the components of change. A cohort-component method traces people born in a given year throughout their lives. As each year passes, cohorts change due to the mortality and migration assumptions. Applying fertility assumptions to women of childbearing age forms new cohorts at age zero. Events are simulated for the population in households in the following order: births, deaths, in-migration, out-migration. Then all individuals, except new births and special populations, are aged forward one year to simulate the process of aging. The special populations, such as group quarters (GQ) population, are adjusted to agree with DOF’s estimated change in each county-level GQ population and that population is added to the household population to complete the starting total population for the next year’s projection.

The 2019 baseline projections incorporate the latest historical population, birth, death, and migration data available as of December 2019 for information through July 1, 2019. Historical trends from 1990 through 2019 for births, deaths, and migration are examined. County populations by age, sex, and race/ethnicity are projected to 2060. The county projections are summed to obtain state totals. Please note, these projections were published before the Coronavirus Disease 2019 ( COVID-19) pandemic and do not reflect any possible effects on future economic and demographic trends.

Summary of results
The total population for California will slowly grow from 40.1 million people in 2020 to 45.3 million residents in 2060, a change of 13 percent over 40 years. California’s annual growth rate is expected to decline, but remain positive, throughout the projection period.

Births are expected to continue declining, leveling off after 2050. The increases in births to older women will not be enough to offset the downward trends in younger women. The total number of deaths grows during the projections period driven by the aging of the youngest baby boomers to 95 years old by 2060.

Although net migration was negative in 2018 and 2019, it is projected to return to a positive figure and grow to approximately 100,000 migrants during 2024-2025. Starting in 2025, the gross migration ratios by age are held constant throughout the projection period causing net migration to grow from almost 100,000 people in 2025 to 108,000 migrants in 2060.

Due to these trends, the natural increase of population (births minus deaths) is forecast to become negative in 2043. From that point onward, net population growth will be solely due to migration. The continued aging of the baby boom and the lack of replacement births will cause the median age to rise from 37.4 years in 2020 to 44.3 years in 2060.
1 Assumptions and Limitations
Projection models heavily rely on trends and relationships observed in the past. Users are cautioned that projections may not accurately project the future population of the state or the counties when future events do not follow past trends. These projections should be used with an awareness of events that have taken place since their publication.

The following general assumptions guided decisions while developing these projections. Specific assumptions for each component of change are discussed later. • These projections were published before the COVID-19 pandemic and do not reflect any possible effects on future economic and demographic trends.
• These projections assume economic stability throughout the forecast period. • Changes in immigration, education, housing, or transportation policy could have significant effects and are not considered here.
• People have the right to migrate where they choose within the United States. • The international immigration rules are stable, as are the global economic conditions that drive migration.
• Resource constraints such as water, housing, and transportation capacity grow at a sufficient pace to accommodate projected population growth.
• There are no unforeseen changes in productivity and technology (particularly reproductive technology and healthcare).
• No major natural catastrophes or wars will befall the state or the nation.

Data and Methods

1. Base Population
The basis of the DOF 2019 population projection series is the April 1, 2010, decennial census population count of 37,253,956 by age, sex, and race/ethnicity for California. The race/ethnicity groups presented in this report are the Non-Hispanic Races: White; Black; American Indian or Alaska Native; Asian; Native Hawaiian or Pacific Islander; Multiracial; and Total Hispanic ethnicity of any race.

2. Fertility
Trends: Births are expected to continue to decline throughout the projections period, leveling off after 2050. Births begin at 459,000 in 2019-2020 and decline to 424,000 in 2059-2060. The total fertility rate for California has been below the replacement level of 2.1 births per woman since 2009 and it will drop to

1.5 children per woman in 2040. This level of fertility puts California in the company of European countries like Spain and Italy which have had a similarly low total fertility rate (TFR) since the 1970s.

Data Source: Records of births, including selected characteristics of children and parents, for 1990 to 2019 are obtained under an agreement with the California Department of Public Health. Data were evaluated for quality. Missing values in birth records were filled-in using hotdeck imputation where the missing value was pulled from a randomly selected similar record Methods: Using historical birth data since 1990, fertility trends are calculated for females by age and race/ethnicity cohort in each county. In projecting future births, there are two key considerations for each year of the projections period: (1) the age and race/ethnic structure of the female population and (2) the fertility rate that will be applied to each cohort a given year. The number of births after July 1, 2019, is determined by applying the projected fertility rate to the projected population for females by age and race/ethnicity. Fertility rates are calculated by the number of births during the year divided by the midyear population of women in each age cohort. The fertility model uses the race/ethnicity of mothers to assign the race/ethnicity to their children. Children are assigned a slightly higher probability of male sex (105 males per 100 females). Births generate a new population cohort at age zero which is added to the projections dataset.
2 Specific Assumptions and Decisions: It is assumed there will be a continuation of declining fertility for cohorts under 30, stable fertility for those 30-34 years of age, and continued modest increases in fertility for women 35-44 years old. The fertility increases in the oldest cohorts are not enough to offset the downward trends in the younger cohorts.

During the projections review process, decisions were made to override the median projected rate in favor of higher or lower rates to achieve a county-level target for the total fertility rate. We found that some counties had very recent changes in fertility, so used a decaying linear or exponential weight as a function of N years back from the most current year to give more or less weight to the latest data.

Modestly higher TFR for ages above 30 were allowed for these counties: Marin, Plumas, San Francisco, and Santa Clara. We also used exponential weights for prime childbearing ages under 30 in Butte, Marin, Mono, Modoc, Siskiyou, and Trinity Counties to reflect the continuation of recent rapid declines in fertility.

3. Mortality
Trends: Deaths are expected to rise during the projections period from 276,000 in 2019 to 489,000 in 2060. The rising deaths are a result of the aging of the baby boomers who will all be over 95 years old by the end of the forecast period.

Data Source: Records of deaths, including selected characteristics of the deceased, are obtained under an agreement with the California Department of Public Health. Data were evaluated for quality. Missing values in death records were filled-in using hot deck imputation where the missing value was pulled from a randomly selected similar record. Age values were verified against birthdate. If age is missing or very high, the age based on the birthdate was used.

Methods: Using historical mortality since 1990, mortality trends are calculated for each sex, age, and race/ethnicity cohort in each county. In projecting deaths, there are two key considerations for each year of the projections period: (1) the sex, age, and race/ethnic structure of the population, and (2) the mortality rate that will be applied to each cohort in any given year. Mortality rates are calculated when the number of deaths in a given cohort during the year is divided by the midyear population in that cohort.

Specific Assumptions and Decisions: While gains in life expectancy are expected during the projection period, improvements in mortality have slowed somewhat in recent years and this is reflected in the projections series.

During the projections review process, decisions are made that may override the median projected rate in favor of a higher or lower rate to achieve a county-level target for life expectancy at birth.

To reflect the slowdown of improvements in life expectancy, higher than median mortality rates for older ages were used in all counties except Glenn, Nevada, San Joaquin, San Luis Obispo, Santa Barbara, Siskiyou, and Tuolumne. On the other hand, lower mortality rates for younger ages were applied in most counties, except for Fresno and Merced (only newborns), and Solano and Tuolumne. This was done to reflect recent mortality improvements in younger ages.

4. Migration
Trends: Migration is the most critical component of California’s population change and the hardest to project due to inherent variability and the absence of full administrative data. Although net migration for California has been negative for 2018 (-39,476) and 2019 (-13,074), migration is forecast to return to the decade’s average by 2025. This results in net migration of approximately 100,000 migrants during 2024- 2025. Net migration is thus expected to return to a crude rate of net migration of about 2.5 persons per 1,000 population by 2025 and remain stable throughout the forecast period. Net migration will be large enough to counterbalance the negative natural increase resulting in small, annual population growth for the state.

Data Sources: Historical migration trends are based on the DOF estimated county net domestic and net foreign migration from the July 1 components of change in the Reports E-2 and E-6 State and County Population estimates series published in December 2019. Each net flow is then disaggregated into four different flows - domestic in and out and foreign in and out - using administrative data. Administrative 3 records include driver’s license address changes, IRS tax return data, Medicare and Medi-Cal enrollment, federal immigration reports, elementary school enrollments, and group quarters population. All data used to develop these flows are in summary tables and do not reveal the identity of any individual. Gross migrants from California to outside of the United States are calculated as the residual between estimated foreign in-migration and net foreign migration.

Methods: The DOF estimates method does not generate the age, sex, or race/ethnicity detail of net migrants. These are generated by first setting a base age distribution using the American Community Survey (ACS) Tables B07001 and B07401 for each year and then adding or subtracting records for the characteristics of individuals who move within, into, or out of California during the postcensal estimates from 2010 to 2019 for each flow.

To assign sex and race/ethnicity characteristics for gross in-flows for both domestic in-migration to California from another U.S. state and foreign immigration, observations are randomly selected from the ACS Public Use Microdata Sample (PUMS) data for each year from each migration flow/age cohort and are added to the projections dataset. For example, if there are 500 expected in-migrants in the 35-39 age group to a given county from other US states, the model would pull the equivalent of 500 randomly selected persons from the records of ACS respondents who were between age 35 and 39, inclusively, and reported moving into the county from another state. A record for each selected individual’s sex, race/ethnicity, and detailed age would be added to the projections dataset.

For county-to-county migration within California, the same procedure is used to add the individual to the projections dataset for the receiving county while a similar record is removed from the donating county.

For gross out-flows, observations are dropped from the projections dataset. The traits of domestic out- migrants are determined similarly to domestic in-migrants using the ACS PUMS. Migrants are randomly identified from the population of migrants from California to another US state by age, sex, and race/ethnicity.

In the case of foreign out-migration, the age structure is determined using the ACS PUMS rather than the ACS summary tables. Emigrants are generated using estimated gross emigration rates by sex, place of birth, and length of time in the US. The latest data for the 2016 age profile comes from the 2014-2018 ACS (5-year file). The age profiles (the number of foreign out-migrants by age divided by the county population in that age group) are carried forward to the last postcensal year from the latest available ACS data. Out-migrants are selected from the ACS based only on age.

Once the projections dataset for the postcensal (2010-2019) years has been populated for migration, the domestic in- and out- and foreign in-migration rates are calculated for each age, sex, and race/ethnicity cohort in each county. Foreign out-migrants are identified based on age-specific gross migration rates.

These rates are then used during the projection period to determine the annual number of migrants in each of the four flows that yield the expected total net-migration for each projection year.

Specific Assumptions and Decisions: Between the last postcensal year 2019 and 2024, the total number of migrants per age group per county are converged towards the average number of such migrants for the period 2010-2018. Starting in 2025, the gross migration flows by age are fixed to the age- specific ratios from the 2024-2025 period and held constant throughout the projection period causing net migration to vary between 100,000 and 110,000 during the projection period.

Several county-level adjustments were made for special cases or to reflect reviewer feedback. The following counties were modified:

In Butte County, an adjustment was made to handle the migration consequences of the Camp Fire in late 2018, which displaced a large number of residents from Butte County into neighboring counties. Rather than converge from 2019 levels, we set the model to continue from 2018 and incorporated some catch-up growth during 2020-2025 to implement our expectation of higher than average housing unit growth and in-migration during the recovery.

In Glenn and Contra Costa Counties, net migration was trended to 2025 without 2019. For Calaveras, Contra Costa, Inyo, San Diego, San Francisco, San Luis Obispo, and Solano Counties, it was felt that 4 both domestic in- and out-migration was too high and was adjusted downward by 2.5 percent. Similarly, only domestic out-migration was decreased for Kern and Los Angeles Counties (2.5 percent), and Kings, Merced, and Mono Counties (7.5 percent). On the other hand, we increased domestic in-migration by 5 percent for Inyo, Kings, Lassen, Merced, San Benito, and Yuba Counties.

5. Special Populations
Special treatment is required for the population living in group quarters (GQ), including prisons, dormitories, military barracks, residential hospitals or nursing homes, monasteries, and other group accommodations. There were 819,816 persons enumerated in GQ in the 2010 Census. These populations are not subject to the same mortality, fertility, or migration hazards as those living in households. For each year during 2010-2019, the size of the GQ population is controlled to the DOF- estimated total GQ population, and the 2010 Census SF2 file is used to establish the age, race/ethnicity, and sex distribution. After 2019, the total GQ population is held constant.

University students living in households rather than dorms exhibit similar population dynamics to the dorm population rather than the household population. These populations maintain a stable age structure, as outgoing students are generally replaced by incoming students. They are treated like special population records for the population projection; e.g., to simulate the dynamic of replenishment through graduation and new enrollment, they are not aged forward.

External Review
The preliminary projections are offered for review to the four large regional planning agencies in California. Their feedback is evaluated by DOF and adjustments are made for specific counties before final production with the latest available data. The preliminary projections were produced before the release of the July 2019 estimates which had a significant downward revision in the migration profile.

An internal review was conducted within the Department of Finance to ensure consistency with economic and other forecast assumptions.

Published Data
Summarized data are published as P-1 series (statewide) and P-2 series (county) projections. The complete public use dataset (P-3) contains counts of the population for each California county for July 1 of every year from 2010 through 2060, by individual year of age (0-100+), sex, and seven race/ethnicity groups. See the DOF website at dof.ca.gov/Forecasting/Demographics/Projections/ Authority
The population projections were prepared under the mandate of the California Government Code (Cal.

Gov't Code § 13073, 13073.5). It is state policy that all state plans make use of the “. . . population projections and demographic data that is provided by the State's Demographic Research Unit” (Cal. State Admin. Manual § 1100).

Acknowledgments
Research design, data collection, analysis and interpretation, technical report and dataset by Ethan Sharygin and Andrés Gallardo. Reviewers: Walter Schwarm and Eddie Hunsinger.

Suggested citation
Population Projections: State of California, Department of Finance. Demographic Research Unit. State And County Population Projections 2010-2060 [computer file]. Sacramento, California. January 2020.

Methodology report: State of California, Department of Finance. Demographic Research Unit. Population Projections Methodology (2019 Baseline). Sacramento, California. September 2020. 5 J:\COMMITTE\ABAG Executive Board\Agendas\2019\EB 20190919\EB 20190919 Item 07C PBA50 Attachment A Regional Growth Forecast Methodology v2.docx

M E T R O P O L I T A N T R A N S P O R T A T I O N C O M M I S S I O N

A S S O C I A T I O N O F B A Y A R E A G O V E R N M E N T S

M E M O R A N D U M

TO: ABAG Executive Board DATE: September 19, 2019
FR: Cynthia Kroll, Chief Economist and Assistant Director RE: Plan Bay Area 2050: Regional Growth Forecast Methodology

Summary

The Regional Growth Forecast is an important element of the Plan Bay Area 2050 long-range planning process. While the future is always uncertain, the forecast identifies how much the Bay Area might grow between today and 2050, and for characteristics of that growth. These include total employment and employment by major industrial sectors, total population and population by age and ethnic characteristics, and the number, size, demographic characteristics and income of households. This information in turn informs where growth (employment and households) may go and the nature and amount of travel demand associated with it, as well as expectations for housing production. The Regional Growth Forecast is a key analytical underpinning of much of the policy work associated with the regional planning process.

This document describes the forecast methodology at the regional level, explains its relation to other forecasting and modeling work for Plan Bay Area 2050. A draft version of this methodology was circulated for public comment over in July and August; this version incorporates clarifications and minor technical revisions, including a few modifications in response to comments, and updates the schedule.

Opportunities for Input on the Methodology

Staff has sought public and stakeholder input on the Regional Growth Forecast methodology through public meetings in June and July, including:
- Regional Advisory Working Group (June 2019)
- MTC Policy Advisory Council (June 2019)
- MTC Planning Committee & ABAG Administrative Committee (July 2019) - ABAG Executive Board (July 2019)

To allow for additional public comment before the public hearing and adoption by the ABAG Executive Board in September, ABAG/MTC opened a public comment period on this document between July 19, 2019 and August 19, 2019. Comments will be addressed by staff and results reported at the September 19, 2019 public hearing on the Regional Growth Forecast Methodology at the ABAG Executive Board meeting, consistent with the BIA Bay Area settlement agreement.

Further public input will be sought through fall 2019 and spring 2020 public outreach on the Blueprint for Plan Bay Area 2050. As the final Regional Growth Forecast will not be adopted in September – just the methodology needs to be approved at this juncture – there will be additional time and further opportunities for review of the Regional Growth Forecast in the months ahead.

Attachment A
Plan Bay Area 2050: Regional Growth Forecast Methodology Proposed Methodology

Tools and Expertise Underlying the Regional Growth Forecast Methodology

The Plan Bay Area 2050 Regional Growth Forecast is produced by ABAG/MTC Planning staff with consultant and technical advisory committee input. The Regional Growth Forecast makes use of multipurpose tools that can be used to describe future possibilities and to test the effects of different assumptions and strategies on future projections.

Expertise

The Regional Growth Forecast is being developed in consultation with the Center for Continuing Study of the California Economy, with input and review by a technical advisory committee of experts as well as from ABAG and MTC advisory committees.

The technical advisory committee (list and affiliation at the end of this memo) includes:
 6 Bay Area economists
 3 California Department of Finance experts (chief economist, senior economist and demographer)
 3 megaregion representatives (Sacramento Area Council of Governments, San Joaquin Council of Governments, University of the Pacific)
 3 experienced REMI users (from the Atlanta Regional Commission, a Michigan think tank, and a Colorado nonprofit)

Staff met with the technical advisory committee in October 2018 and in May 2019. Staff will seek further input from the TAC in the fall as the preliminary forecast is developed, through individual consultations and/or an additional meeting.

ABAG/MTC staff also spoke with California Department of Finance (DOF) and Housing and Community Development (HCD) staff in July 2019 to discuss methods for estimating households from population and housing unit estimates. Staff will continue to speak with DOF and HCD staff working on developing accurate counts of housing units and occupancy, as well as with other experts.

Tools

Central to the Regional Growth Forecast development is the REMI (Regional Economic Modeling Inc.) model for the San Francisco Bay Area [version 2.2]. The REMI model integrates into one package a dynamic accounting of the core components of the economy – industry structure and competitiveness relative to other regions; propensity to export; and population and labor market structure. The population is explicitly connected to industry growth and demand for labor, with migration increasing in times of strong employment growth. 1 The model specifically characterizes
the local economy in the context of the national economy, recognizing the relationships to the state, nation, and surrounding metropolitan planning areas. Downstream, separate staff modules are used to compute households, income distribution, and in-commute levels. The Regional Growth Forecast then serves as an input into the small-scale distribution of land uses (including employment, population and households) using UrbanSim 2.0, a land use model that simulates the

1 REMI is an integrated set of input-output, computable general equilibrium, econometric, and economic geography methodologies that describe the key relationships in the economy. See Regional Economic Modeling Inc., REMI PI+ v. 2.2, REMI Transight v.4.2, REMI Tax-PI v.2.2, Metro PI v. 2.2 Model Equations.

Plan Bay Area 2050: Regional Growth Forecast Methodology Proposed Methodology

urban development process and the location choices of employers and households.2 The local allocation in turn informs the modeling of travel patterns and investments using Travel Model 1.5.

The relationship among these models is described further below, followed by brief discussions of major elements of the models. Detailed descriptions of the versions of these tools used for Plan Bay Area 2040 can be found in http://2040.planbayarea.org/reports (under the Land Use and Transportation sections).

Adjustments to the Overall Forecast Methodology from Plan Bay Area 2040

This will be the first Plan Bay Area done with a consolidated regional planning team for ABAG and MTC, the two regional agencies responsible for crafting the long-range plan. While the overall suite of tools is similar to the Plan Bay Area 2040 approach, staff will make use of the model output and analytic results in a more iterative fashion between models to better capture feedback mechanisms in the economy. This will ideally create stronger bridges among the different technical elements of the forecast for Plan Bay Area 2050, including the Regional Growth Forecast, the small area distribution of the forecast using UrbanSim 2.0, and forecasts of travel patterns and transportation impacts using Travel Model 1.5.

For decades, the general approach to forecasting proceeded in a linear fashion consisting of the steps outlined in Figure 1, although the specific tools used at each step changed over time. The Regional Growth Forecast of employment, population and households fed directly into the small area analysis, which then provided data used by the travel model.

Figure 1: Historic Approach to Regional, Spatial and Transportation Forecasts

With a changeover of tools for the Plan Bay Area 2040 forecast, the land use and travel modelers added additional feedback loops between the small area analysis (developed using UrbanSim 1.0) and Travel Model 1.0, as shown in Figure 2. We have long known that land use impacts transportation demand, but it has also been recognized that transportation, through accessibility, in turn impacts land use patterns. The model system was modified to include this two-way connection, so that the location of growth can be influenced by improved accessibility following planned transportation investments. At the same time, growth and location affect congestion and multimodal accessibility, shifting transportation investment decisions. This coupling of land use and transportation was reflected in the modeling approach for the first time in Plan Bay Area 2040 but did not include a feedback loop to the Regional Growth Forecast.

2 http://www.urbansim.com/urbansim
Regional
Models
Small
Area
Analysis
Travel
Model
Regional Population,
Jobs & Households
Spatial Distribution of
Households & Jobs
Plan Bay Area 2050: Regional Growth Forecast Methodology Proposed Methodology

Figure 2: Approach Used in Plan Bay Area 2040

Land use and transportation are not the only connected systems, however. Local land markets may have regional implications. For example, economists have pointed to constrained housing markets as in turn reducing the overall size of the economy. When preparing a Regional Growth Forecast for Plan Bay Area 2050, we intend to consider how model results from UrbanSim 2.0 and Travel Model 1.5 could be factored into the modified REMI model, altering the Regional Growth Forecast. For example, where and how much housing is built could change the cost of housing, as well as the cost and demand for labor. Similarly, a change in housing prices and location overall could further change the number and types of jobs that can be generated in the region as well as the labor force that can live in the region (see Figure 3).

Figure 3: Proposed Iterative Approach for Plan Bay Area 2050

The first aim of this integration is to seek a fuller representation of these types of effects. The second aim is, by having a better accounting of housing markets across the model systems, to better capture effects of policy interventions (i.e., strategies) addressing housing and labor markets. If we are successful in incorporating housing changes into the regional employment and population analysis, we may also be better positioned to then analyze the effects of other strategies, such as economic development strategies like workforce training programs and Priority Production Areas, which could affect the ability of middle-wage jobs to remain in the region.

Modified
REMI,
other
modules
Urban
Sim

1.0 Travel
Model

1.0 Regional Population,
Jobs & Households
Spatial Distribution of
Households & Jobs
Accessibility
Modified
REMI,
other
modules
Urban
Sim

2.0 Regional Population,
Jobs & Households
Spatial Distribution
of
Households & Jobs
Travel
Model

1.5 Accessibility
Housing Supply and
Prices; Job Location and
Type
Regional Strategies
Transportation,
Housing, Economic,
and Environmental
Policies
Plan Bay Area 2050: Regional Growth Forecast Methodology Proposed Methodology

The remainder of the memo focuses on the first of the three elements of the Regional Growth Forecast: the projection of jobs, population, and households at the regional level.

What Does the Regional Forecast Do?

The Regional Growth Forecast projects total employment, population, households, income distribution and in-commute change for the region as a whole between the Plan baseline year of 2015 and the Plan horizon year of 2050. As part of the iterative process, we will begin with a baseline employment and population forecast that will be consistent with likely national economic and demographic trends, layering in new strategies as the Blueprint for Plan Bay Area 2050 is developed. Table 1 summarizes the approach this cycle and how this was done in the last cycle.

The Regional Growth Forecast begins with the structure of the REMI model, which describes employment, population, gross regional product, and total personal income for the historical period back to 2000 and for a forecast period through 2060 (our forecast goes only to 2050). The model includes a built-in forecast that reflects one of several possible sets of assumptions about the factors underlying growth at the national level and a set of interrelated regional forecasts for 22 custom-designed “regions” for our Bay Area version of the model. The regions include each of the nine Bay Area counties, metropolitan areas bordering our region and an aggregation of small non-MPO counties at the north of the region, each of the southern California counties in SCAG, the rest of California, and the rest of the US. Our focus when developing the Regional Growth Forecast described here is on the nine Bay Area counties as aggregated into one region. REMI is designed to be adjusted to be customized by the user to better reflect expectations about national trends, as well as their detailed knowledge about the relevant region.

As described in Table 1, we propose to use the REMI model with multiple adjustments, after consultation with CCSCE and the technical advisory committee, to describe the employment and population forecasts. We then separately forecast households, income distribution, and any change in the level of in-commuting. The types of assumptions underlying the adjustments to the REMI model and the other elements of the Regional Growth Forecast are summarized below, by element of the forecast.

Plan Bay Area 2050: Regional Growth Forecast Methodology Proposed Methodology

Table 1: Summary of Approach to Regional Growth Forecast Forecast
Element
Plan Bay Area 2040 Plan Bay Area 2050 (Draft)
Employment Adjustments to REMI, with input
from CCSCE and TAC
Update – different base compared to
Plan Bay Area 2040, CCSCE and TAC
recommended adjustments to REMI
Population Minor REMI adjustments only Update – REMI adjusted to some DOF fertility assumptions and calibrated to
match labor force requirements.

Households Average headship rates for the
most recent 5 years, some
decrease over time for seniors and
multigenerational households
Update – Goal in this cycle is to provide a
more detailed accounting of households
by size, number of workers, and income
level categories. Headship, or household
formation rates in consultation with TAC
and CCSCE, are applied to population age
and race estimates. ACS workforce
characteristics will be added to
households. Distribution of income
among households will be based on
historic patterns and regional economic
forecast trends.

Income
distribution
Econometric equations for each of
four categories based on national
cross-sectional data by income
category. Reconciliation of
numbers to total household
control.

In-commute
change
Took the larger of two alternative
estimates drawn from REMI data
on residence workforce, labor
force and jobs
No change in method, but further
informed by iterations with other models
and by multiregional results of REMI
model.

Employment

Baseline employment for the Bay Area is driven by national trends in population growth and employment, by the Bay Area employment mix by sector and by the competitiveness of Bay Area sectors relative to the equivalent sectors in the US. REMI accounts for the Bay Area’s strong competitiveness in many industries relative to other regions, leading to a representation of a generally favorable jobs outlook across a range of sectors, which in turn grows the labor force through migration. At the same time, REMI represents the relatively high cost of housing and labor as well, which all other things equal serves to temper the growth outlook.

In the previous Regional Growth Forecast, there was a great deal of uncertainty about how the region would fare both in the near future and over the decades of the plan’s forecast. The forecast for Plan Bay Area 2040 is quite low compared to recent experience, when Bay Area knowledge sectors rapidly expanded employment for almost a decade. Should we adjust the forecast upward to account for this continued strength, or consider the possibility that going forward a national recession or a reversal of fortunes of our leading sectors could lead to an extended period of stagnant growth or job loss? In the last three decades, formation of new industries has led to strong surges of growth in the region following downturns. Will we continue to have this generative capacity over the next 30 years?

Plan Bay Area 2050: Regional Growth Forecast Methodology Proposed Methodology

These uncertainties were initially addressed as part of Horizon, the predecessor planning process to Plan Bay Area 2050. As part of the Futures Planning effort, we modified the built-in REMI forecast based on widely varying assumptions about external forces beyond our control—national policy, international events, and the possibility of severe natural hazards. We modeled the range of possible Futures for the region should these events occur in the policy framework encompassed in the last Plan Bay Area (Plan Bay Area 2040). These forecasts gave several possible trajectories of growth, as shown in Figure 4. In one future, Rising Tides, Falling Fortunes, with high sea level rise and low government spending, there is a long period of stagnation followed by modest job growth, leaving little net change overall. At the other extreme, Back to the Future, with few land use constraints on growth and generous public spending leads to growth that far exceeds our projections from Plan Bay Area 2040. The third future, Clean and Green, is closer to our previous projected level of growth, but with a very different occupation mix, high levels of taxation through a carbon tax, but also selected high levels of public investment.

Source: ABAG/MTC Analysis, 2019

As we proceed into analysis for Plan Bay Area 2050, we will craft a base employment forecast with a less divergent set of assumptions at the national level, assuming policies similar to those today and those encompassed in Plan Bay Area 2040. We will also examine how additional strategies proposed for Plan Bay Area 2050 could affect employment. Strategies to be tested iteratively before reaching a final employment figure could include, for example:

 Improved access to housing in the region: this can change the cost of labor, affecting rates of growth of middle and lower wage sectors.
 Workforce training: this could have complex effects, improving productivity, allowing higher output without necessarily more jobs, although a more skilled workforce could also attract additional employers.
 Priority Production Area protections: this could slow further declines in industrial sectors and associated middle-wage jobs, especially in production, distribution, and repair sectors.

0 1
2 3
4 5
6 7
8 2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
Millions of Jobs in Bay Area
Figure 4: Plan Bay Area 2040 Forecast Compared to Three Horizon Futures Plan Bay Area 2040 Rising Tides, Falling Fortunes
Clean and Green Back to the Future
Plan Bay Area 2050: Regional Growth Forecast Methodology Proposed Methodology

Population

REMI, like most population projection models, predicts future population growth based on a detailed accounting of the population in terms of age, gender and ethnicity, with schedules of fertility and mortality determining natural increase, while migration is determined through the interaction with the economic portion representing labor market demand of the model.

Retirement migration is also represented. While California Department of Finance (DOF) similarly uses a cohort-component model, the differences are in how some of the population is categorized, as well as assumptions for future mortality, fertility and migration rates. At this stage we note that apart from population totals, there are age and ethnic differences between the REMI forecast and the DOF 2017 forecast that we are assessing. This will help us create a population forecast that is both consistent with expected growth levels and reflective of our understanding of the composition of the California population.

Through the iterative process, it may be possible to capture benefits from strategies to increase housing production and lower housing prices. These strategies may include market mechanisms or subsidies, helping to retain lower- and middle-wage earners while encouraging economic in- migration to increase working age population. Separately, demand-side rental subsidies would also help to retain lower- and middle-income households.

Plan Bay Area 2050: Regional Growth Forecast Methodology Proposed Methodology

Households

The vast majority (currently 98 percent) of the population lives in households, with a small remainder living in group quarters. ABAG/MTC translates a given population age structure into households using headship rates. Headship rate is defined as the share of adults in a particular age group (e.g., 25 to 29 years old) who are heads of households. The rate underlies the average household size and thereby how much housing will be needed to house the population. The share can be applied to population projections by age and race/ethnicity to estimate the number of households by these demographic characteristics. A higher headship rate would imply lower average household sizes.

The household estimate for Plan Bay Area 2040 was built using headship rates for the 2006 to 2014 period, with additional marginal adjustments. The Plan Bay Area 2050 analysis will be developed in consultation with DOF staff. Two key questions:
 Are rates relatively constant over time, or do they move with other factors, such as unemployment or the cost of housing?
 What determines the differences in rates of household formation among different ethnic groups, and how does this propensity change over time for new immigrants?

Staff will explore different headship rates that come from varying assumptions about these factors, such as using the most recent headship rates (2012-2017), gradually converging rates to the previous 2005-2009 rates, averaging over a full economic cycle (2010-2017), or an approach that may be used by DOF, averaging rates from 2000 to 2010.

In making these tests, we want to explore potential challenges, such as:
i) The current headship rate may be artificially compressed due first to the Great Recession and then to the high cost of housing. We will seek a way to capture a wider mix of economic experiences in the rate used.
ii) Hispanic and Asian/Other headship rates may converge toward the average headship of the two other ethnic categories, as the native-born share of households in those groups increases and the household characteristics of immigrants move towards those in the U.S.

Figure 6 illustrates the variability of headship rates by age category, ethnic group and over time, with both Asian and Hispanic ethnic categories have lower headship rates (higher household sizes) than their counterpart white or black households (with generally much lower shares of immigrant households).

High housing costs may affect not only labor markets and money available for other goods, but how families form households and consume housing. Housing strategies may affect overall household formation leading to lower or higher household sizes, changing costs, and changing locations of new households. Through the iterative process, it may be possible to reflect the benefits of housing strategies that allow new households to form (increasing headship rates among young adults, for example) as well as the type of new units (which may target young adults or seniors with smaller household sizes).

Plan Bay Area 2050: Regional Growth Forecast Methodology Proposed Methodology

Figure 6: Comparative Headship Rates by Ethnicity, Age Group and Time Period

Income Distribution by Household

The household income distribution is generally determined both by overall wages and other sources of incomes, and separately by how households tend to form, including how persons in different parts of the income spectrum pair up, or not. Figure 7 offers information on how many households have, respectively, 0, 1, 2 and 3 or more workers in them, and for each of these household types, the share in different income groups. Counts are shown for 1980, 1990, 2000, 2010, and 2016. There are about the same number of households with one or two workers in them, but two-worker households are much more likely to be in the highest income quartile. Conversely, households with zero workers, typical for seniors, are frequently lower income (though some of these may be relatively wealthy).

The method for this calculation will seek to link age of head of household and number of working household members with income levels. Other factors that may also influence overall household income categories will include the overall change in the economy between high and low wage sectors, the relationship of output to employment (e.g., is value added rising, dropping or remaining constant in the growing sectors), and any changes between the proportions of wage income compared to shares from other income sources.

Plan Bay Area 2050: Regional Growth Forecast Methodology Proposed Methodology

Figure 7: Change in Workers per Household, by income quartile, 1980-2016 (Source: IPUMS)

Through the iterative process, it may be possible to reflect strategies that affect the income mix of the region, ranging from incentives for middle-wage jobs in housing-rich areas to affordable housing programs to transit subsidies. To the extent that these strategies are modeled in terms of employment or population impacts, they may in turn be translated into household and income level implications. Alternatively, if the specific impacts cannot be reliably modeled, the qualitative implications will be discussed.

In-Commute

The in-commute analysis was conducted in Plan Bay Area 2040 as diagrammed in Figure 7. We propose doing a more nuanced in-commute analysis compared to the approach used for Plan Bay Area 2040. Rather than simply estimating the overflow, ABAG/MTC will examine how the distribution of employment location may change for some sectors between the Bay Area and its neighboring MPOs in the REMI model, possibly reducing the need for in-commuting. In the iterative process, we will look at how housing availability may change based on policies affecting the amount of housing built and the cost-mix of housing between market rate and subsidized housing.

Through this iterative process, ABAG/MTC can test to what extent a larger housing stock may decrease in-commuting versus increasing employment growth.

Apart from these efforts, if adjustments are needed to reduce the in-commute, we will follow the method used in Plan Bay Area 2040.

Plan Bay Area 2050: Regional Growth Forecast Methodology Proposed Methodology

Figure 8: Schematic for Adding In-Commute Housing to the Regional Housing Total, PBA 2040

In reality, commute flows occur across MPOs for many reasons, going in two directions, a function of the size and pull of job centers, the resident labor force in the subregions, as well as the difference in housing costs and the relative ease of transportation. For a large region such as the Bay Area, it is expected that the concentration and diversity of specialized functions will attract workers from beyond the region’s labor force. Further, a strong job node on the edge of the region, such as parts of the Tri-Valley, is much closer to the resident labor force of Tracy and Stockton than it is too many jurisdictions in the Bay Area counties. This draw will continue, even with more housing added west of the Altamont Pass. This is evident in Figure 9, where most commute flows into and outside the region are very small, but the largest inflows are to Santa Clara County from its southern neighbors and to Alameda County from the Central Valley, with significant impacts on those travel corridors. Using the multiregional REMI model – an enhancement since Plan Bay Area 2040 – will allow us to examine further how Bay Area strategies may affect the net in- or out- commute flows the different regions experience as well as possible changes in job mix that occur because of the different strategies.

A number of strategies may affect in-commuting:
 Construction of housing for low- and middle-income workers could reduce the numbers commuting in from outside the region.
 Improved rail networks and bus rapid transit could reduce this number of current in- commuters by auto, although the net effects on total in-commuting would be more complex.
 Higher tolls on freeways and subsidies for transit ridership would reduce the number of in- commuters in private vehicles, but not necessarily in-commuting overall.

Plan Bay Area 2050: Regional Growth Forecast Methodology Proposed Methodology

Figure 9: Commute Flows to and from the Bay Area, 2015

Note: Subregion definitions by county—West Bay-Marin, San Francisco, San Mateo; South Bay-Santa Clara; East Bay-Alameda, Contra Costa; North Bay-Napa, Solano, Sonoma

Beyond the Regional Growth Forecast: Crafting the Growth Pattern

The Regional Growth Forecast focuses on the nine-county total level of growth for jobs, population, and households, acting as a key input into the modeling process. To develop the Plan’s growth pattern on a localized level, MTC/ABAG will use Bay Area UrbanSim 2.0, a spatially explicit economic model that forecasts future business and household locations. MTC/ABAG used a version of the Bay Area UrbanSim 1.0 model to inform the environmental assessment for the region’s first RTP/SCS (Plan Bay Area) and both the Plan process and the environmental assessment for the region’s second RTP/SCS (Plan Bay Area 2040). An updated version of Bay Area UrbanSim (Version 1.5) is also currently being used for the Horizon long-range planning process.

Bay Area UrbanSim 2.0 forecasts future land use change (e.g., development or redevelopment) starting from an integrated (across different source data) base year database containing information on the buildings, households, businesses and land use policies within the region. Running in five-year steps, the model predicts that some households will relocate and a number of new households will be formed or enter the region (as determined by the adopted regional growth forecasts). The model system micro-simulates the behavior of both these types of currently unplaced households and assigns each of them to a currently empty housing unit. A similar process is undertaken for businesses and jobs. The various submodels are “trained” on existing data in order to represent how households or businesses “respond” to different features of locations considered; from accessibility to jobs and open space to the relative cost of real estate. During the simulation, Bay Area UrbanSim 2.0 micro-simulates the choices real estate developers make on how much of, what, and where to build. This adds additional housing units and commercial space in profitable locations (i.e., land use policies at the site allow the construction of a building that is profitable under forecast demand).

Plan Bay Area 2050: Regional Growth Forecast Methodology Proposed Methodology

In this way, the preferences of households, businesses and real estate developers are combined with the existing landscape of building and policies to generate a forecast of the overall land use pattern in future years. As the model is explicit in how the urban environment changes, the model system is ideally suited to a range of systematic what-if tests: Given behavioral information on how households and businesses tend to locate based on observed data, and given land use policy assumptions, what might happen to overall patterns over time as regulations change, constraints are variably eased and increased in different parts of the region? For example, the land use policies in place in the base year can be changed (e.g., allowable zoned residential density could be increased) and Bay Area UrbanSim

2.0 responds by forecasting a different land use pattern consistent with the constraints or opportunities resulting from the change. After each five-year step, the model produces a zonal output file for the transportation model that contains household counts by type and employee counts by sector. This provides the travel model with information on land use intensity in different locations and the spatial distribution of potential origins and destinations within the region. Documentation for Bay Area UrbanSim 2.0 is available online

3. To build the forecasted land use development pattern, Bay Area UrbanSim 2.0 will be used to iteratively build the Blueprint in a manner that is vetted and assessed for policy realism by regional planners and using feedback from local jurisdictions. Through this iterative process including both human planners and computer simulation tools, we aim for a forecasted development pattern that provides a balance of community and regional goals. This process will also include robust public engagement with “pop-up” and traditional workshops, among other means; it is important to understand local and regional preferences to tackle jobs-housing imbalances, for example. This feedback, as well as the draft and final growth patterns, will be presented to the MTC Commission and the ABAG Executive Board for their consideration.

Comments Received during Public Comment Period

Comments received on the Regional Growth Forecast Methodology fell into four categories: (1) concern about using jobs as a key factor in the forecast, (2) concern about the concentration of growth in PDAs, (3) concern about regional strategies to address the “housing crisis”, and (4) the need for opportunities for input on the forecast and Plan. Staff responses are provided in Attachment B.

Next Steps

Final approval of the proposed Regional Growth Forecast methodology will be requested at the September ABAG Executive Board meeting. Staff will work to develop a Draft Regional Growth Forecast by fall 2019, followed by further iterative testing of strategies through REMI, UrbanSim

2.0, and Travel Model 1.5 in winter 2020. The final Regional Growth Forecast is slated for adoption in spring 2020. Table 2 shows the timing for the Regional Growth Forecast and its place in the development of the Blueprint.

3 Bay Area UrbanSim documentation is available at: http://bayareametro.github.io/bayarea_urbansim/ Plan Bay Area 2050: Regional Growth Forecast Methodology Proposed Methodology

Table 2: Regional Growth Forecast Schedule for Plan Bay Area 2050 (subject to change)

Year 2019 2020
Month May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr
Public Input
Methodology ◊ ◊ ◊ ◊
Public Workshops ◊ ◊
Methodology
Presentations and Review ◊ ◊
Modifications as Needed
Public Comment Period
Public Hearing/Approval ◊
Preliminary Baseline
Employment
Population
Households
Income Distribution
Iterations
Testing
Strategy Integration
Forecast
Draft Forecast ◊
Comments and Revisions
Final Forecast
Final Forecast Approval ◊

J:\COMMITTE\ABAG Executive Board\Agendas\2019\EB 20190919\EB 20190919 Item 07C PBA50 Attachment A Regional Growth Forecast Methodology v2.docx
Technical Advisory Committee for the Regional Growth Forecast

Organization Title Name
City of San Francisco Chief Economist Ted Egan
Center for Business and Policy
Research, University of the Pacific Director Jeffrey Michael Trulia Chief Economist Issi Romen
SPUR Research Manager Sarah Jo Szambelan
Bay Area Council Economic Institute Acting Executive Director Jeff Bellisario San Joaquin Council of Governments Senior Regional Planner Kim Anderson California Department of Finance Chief Economist Irena Asmundson Atlanta Regional Commission Principal Planner Colby Lancelin Sonoma State University Dean Robert Eyler
Common Sense Policy Roundtable Director, Policy and Research Chris Brown Economic Growth Institute, University
of Michigan Senior Research Area Specialist Don Grimes
Sacramento Council of Governments Senior Regional Planner Garett Ballard-Rosa California Department of Finance Researcher Ethan Sharygin Indeed.com Chief Economist Jed Kolko
MTC/ABAG Staff Advisors
Deputy Director Matt Maloney
Assistant Director Dave Vautin
Principal Planner Mike Reilly

Consultant: Stephen Levy, President, Center for Continuing Study of the California Economy

Services of the San Francisco Public Utilities Commission

OUR MISSION: To provide our customers with high-quality, efficient and reliable water, power and sewer services in a manner that values environmental and community interests and sustains the resources entrusted to our care.

525 Golden Gate Avenue, 13th Floor
San Francisco, CA 94102
T 415.554.3155
F 415.554.3161
TTY 415.554.3488

April 15, 2022

Tony Ndah
Public Works Director
City of Milpitas
455 E. Calaveras Blvd.

Milpitas, CA 95034-5479

Mansour Nasser
Water & Sewer Division Manager
City of Sunnyvale
P.O. Box 3707
Sunnyvale, CA 94088-3707

Lisa Au
Assistant Director of Public Works
City of Mountain View
500 Castro Street
P.O. Box 7540
Mountain View, CA 94039-7540

Leonard Ash
Water Resource Planning Engineer
Alameda County Water District
P.O. Box 5110
Fremont, CA 94537

Re: Waiver of Minimum Purchase Requirements During Drought (Water Supply Agreement Section 3.07)

Dear Lisa, Mansour, Tony, and Leonard:

In concert with the San Francisco Public Utilities Commission’s April 15, 2022 Final Water Supply Availability Estimate, and continued drought conditions, we are extending the waiver of the Water Supply Agreement (WSA) Minimum Purchase Requirements for the Cities of Milpitas, Mountain View, and Sunnyvale, and Alameda County Water District for Fiscal Year 2022-2023.

This action aligns with the continued Statewide drought emergency, the SFPUC’s declaration of a water shortage emergency on November 23, 2021, and Governor Newsom’s March 28, 2022 Executive Order calling for continued drought actions, including implementation of Stage 2 of water shortage contingency plans by each urban water supplier.

Section 3.07C of the WSA states that Minimum Purchase Requirements “may be waived during a state of emergency declared by the Governor of California that impacts water supply use or deliveries from the Regional Water System.” The Governor’s July 8, 2021 Executive Order extended an emergency declaration to all of the Wholesale Customers and called for a 15 percent water use reduction that applies to all Californians including the minimum purchase

customers. Those customers may not be able to purchase their full take or pay amounts without violating the call for reductions.

Of course, our expectation is that the minimum purchase customers will abide by the spirit of the purpose of the Minimum Purchase Requirement by not purchasing extra supplies from other sources instead of purchasing water from the Regional Water System.

Please let me know if you have any questions.

Sincerely,

Steven R. Ritchie
Assistant General Manager for Water

cc: SFPUC Commissioners
Dennis J. Herrera, General Manager, SFPUC
Nicole Sandkulla, CEO /General Manager, BAWSCA

Services of the San Francisco Public Utilities Commission

OUR MISSION: To provide our customers with high-quality, efficient and reliable water, power and sewer services in a manner that values environmental and community interests and sustains the resources entrusted to our care.

525 Golden Gate Avenue, 13th Floor
San Francisco, CA 94102
T 415.554.3155
F 415.554.3161
TTY 415.554.3488

TO: SFPUC Wholesale Customers

FROM: Steven R. Ritchie, Assistant General Manager, Water

DATE: April 15, 2022

RE: Minimum Purchase Waiver for Fiscal Year 2022-2023

Today, April 15, 2022, the SFPUC sent out our final Water Supply Availability Estimate for the 2022-23 Water Year. As stated in that memo, our goal remains a 10% systemwide reduction below Fiscal Year 2019-20 levels. Dry conditions and Governor Newsom’s March 28
th, 2022 Executive Order continue the need
for conservation systemwide, and dependent on final emergency regulations pending from the State Water Resources Control Board in response to the Executive Order, additional conservation measures may be necessary.

Given continued drought conditions and expected additional requirements from the State, we are hereby informing the Wholesale Customers of our continued waiver of the minimum purchase requirements for the Cities of Milpitas, Mountain View, and Sunnyvale, and Alameda County Water District consistent with Section 3.07 of the Water Supply Agreement for Fiscal Year 2022-2023.

This waiver has been confirmed to the individual agencies by separate letter.

We continue to appreciate all of our customers’ commitments to water conservation. Our combined conservation efforts in the coming months will help ensure our ability to carryover water in our reservoirs to next year and reduce the need for further cuts in water allocations. Our efforts this year will assist in maintaining supplies should the coming year also be dry.

cc: SFPUC Commissioners
Dennis J. Herrera, General Manager, SFPUC
Nicole Sandkulla, CEO/General Manager, BAWSCA

155 Bovet Road, Suite 650, ⚫ San Mateo, CA 94402 ⚫ ph 650 349 3000 ⚫ fx 650 349 8395 ⚫ www.bawsca.org

April 19, 2022
Via E-Mail

The Honorable Anson Moran, President
San Francisco Public Utilities Commission
525 Golden Gate Avenue, 13th Floor
San Francisco, CA 94102

SUBJECT: Comments on the Proposed March 2022 Revised Water System Improvement Program (WSIP)

Dear President Moran:

On March 25, 2022, the San Francisco Public Utilities Commission (SFPUC) notified the Bay Area Water Supply and Conservation Agency (BAWSCA) that it would be considering proposed changes to the Water System Improvement Program (WSIP) in accordance with the Wholesale Regional Water System Security and Reliability Act (AB 1823, Water Code Section 73500 et seq.) BAWSCA performed a thorough review of the Notice of Public Hearing and associated documents and met with SFPUC staff.

Overall, BAWSCA has three key concerns with the proposed changes:
• The proposed changes in the overall WSIP schedule are significant and will result in a need to extend existing State oversight. As currently in place, the WSIP was to be completed by March 5, 2023. This change proposes a four-year extension for completion to February 1, 2027. While BAWSCA understands the need for the schedule extension, BAWSCA desires to see the work completed without the need for future extensions. The existing State oversight of SFPUC’s implementation of the WSIP, currently set to expire on January 1, 2026, will also need to be extended to ensure critical oversight through program completion.
• There is no proposed change to the overall WSIP budget. This implies that the existing WSIP Director’s Reserve has the required funds to cover any increases in construction costs for the remaining work. Phase 2 of the Regional Groundwater Storage and Recovery Project (RSGS) remains in the design stage and is yet to start construction. Given the experience with Phase 1 of the project, there are likely to be several challenges associated with completion of Phase 2. Given various financial pressures, such as increases in material costs and inflation, and based on the funds that remain in reserve, BAWSCA is of the opinion that additional funding will be needed to complete the project, which in turn will require another WSIP change in the coming years.
• The NOC fails to discuss the distinct possibility that neither the Alameda Creek Recapture Project (ACRP) or the RGSRP, once implemented, will meet the individual project water yield and achieve the respective WSIP Level of Service (LOS) goals as planned for by the SFPUC. A failure of these projects to achieve their President Moran, SFPUC
April 19, 2022

individual water yield will result in the need for the SFPUC to identify and implement other projects and programs to make up for those reductions. Those other projects or programs that would need to be implemented to make up the LOS shortfall would be considered as substitute WSIP projects. Since 2018, when a previous WSIP change identified that both the ACRP and the RGSR Project required scope changes, BAWSCA has asked the SFPUC to provide updated project yield estimates and detail further how that yield shortfall would be addressed through WSIP. Information supplied by the SFPUC at that time noted that the ACRP will have lower yield than originally proposed.

To date, no such formal yield estimate for the RGSR Project has been prepared, nor has any proposal for addressing the yield shortfall under WSIP been provided.

Scope: What impact does the proposed revisions have on the individual project scopes as compared to the currently adopted scopes? Do the projects, as proposed, continue to meet the WSIP Level of Service (LOS) goals?

Scope Finding 1: The are scope refinements proposed for the following projects:

Regional Groundwater Storage and Recovery, Watershed and Environmental Improvement Program, and WSIP Closeout – Sunol Valley Region.

Scope Finding 2: Regarding the RGSR Project scope, two out of three of the proposed test wells, Ludeman North and Centennial Trail, were installed. The third well, Ludeman South, was not built due to siting and constructability issues. The SFPUC decided, based on information collected during testing, that the wells would not be converted to production wells at this time.

Scope Finding 3: A number of modifications to well treatment and water quality monitoring systems were made during Phase 1 of the RGSR Project construction.

Scope Finding 4: The approved scope for the Watershed and Environmental Improvement Program (WEIP) is unchanged as compared with that approved in April 2018. However, scope refinements would move available funding remaining from the Southern Skyline Boulevard Ridge Trail Extension project to fund land acquisition within in the Alameda Creek watershed.

Scope Recommendations: BAWSCA recommends that the Commission direct staff to:

1. Provide a written report to the Commission in January 2023 that includes the following content:
a. An updated yield estimate for the RGSR, informed by the continued pump and project operational testing that has taken place since 2018, and in light of the fact that additional wells, as installed during the above-noted well testing efforts, would not be made permanent.
b. Updated schedule, cost estimates, and funding considerations for how Phase 2 of the RGSR will be implemented.

President Moran, SFPUC
April 19, 2022

c. A discussion of what projects the SFPUC proposes to include in WSIP to address any estimated LOS goal shortfall.

Schedule: Have the completion dates for individual WSIP projects been extended and if so, why? Is there an increased risk to public health and safety for any schedule extension?

Schedule Finding 1. The proposed schedule change includes extensions for four (4) active projects, three (3) support projects, and the Program Management Project.

Schedule Finding 2: RGSR has a proposed schedule extension of 61 months. This project will be the last to be completed under the WSIP, and serves as the primary driver in the overall WSIP completion date. This time extension is due to continued delays in permitting, project design needs, and assorted contracting and construction delays.

Schedule Finding 3: ACRP has a proposed schedule extension of 15 months. This time extension is required to accommodate construction delays related to securing a Department of Water Resources encroachment permit; design changes; coordination with Hanson Quarry related to ongoing erosion around the project site; and additional time for facility startup and testing, operator training, and projected project close-out work.

Schedule Finding 4: The Bioregional Habitat Improvement Project has a proposed schedule extension of 36 months. This time extension is due to the delay in securing permitting for the Trousdale Oaks Tree Removal sub-project.

Schedule Finding 5: The Long-Term Mitigation Endowment has a proposed schedule extension of 36 months. The time extension is required to align with the extension of the Bioregional Habitat Improvement project.

Schedule Finding 6: The Program Management Project has been extended by 61 months. This project is not counted as one of the 52 WSIP Regional Projects, It includes program management activities such as reporting and controls. This extension is necessary due to the proposed overall extension in WSIP.

Schedule Finding 7: The schedules for the Calaveras Dam Replacement Project, WSIP Closeout Sunol Valley, and the Watershed Environmental Improvement Program have all been extended. Yet, all of the three projects are projected to be completed by on or before the end of June 2022.

Schedule Recommendations: BAWSCA recommends that the Commission direct staff to:

2. Include in a proposed report to the Commission in January of 2023, and as detailed in BAWSCA Recommendation 1b, an updated schedule for RGSR if required due to possible scope changes as necessary for the construction of Phase 2 of the RGSR Project.

Budget: What is the impact of the proposed revisions on the individual project budgets and overall WSIP budget as compared to the currently adopted budget? President Moran, SFPUC
April 19, 2022

Budget Finding 1: There are no proposed changes in the overall WSIP budget.

Budget Finding 2: The SFPUC proposes a number of budget revisions for those projects that remain in the WSIP. Those revisions include a mix of cost increases, cost reductions, and use of reserve funds.

Budget Finding 3: Two WSIP projects are predicted to be completed with the budget remaining (the Calaveras Replacement Dam Project, having $29M in savings, and the Bioregional Habitat Restoration, having $1.2M in savings).

Budget Finding 4: Three WSIP projects are predicted to require additional funds (RGRS, requiring an additional $19.6M; ACRP, requiring an additional $10M, and Program Management, requiring an additional $4.6M).

Budget Finding 5: An additional $4M, as required to balance the budget and available funds, will be taken from the WSIP Director’s Reserve.

Budget Recommendations: BAWSCA recommends that the Commission direct staff to:

3. Include in a proposed report to the Commission in January of 2023, and as detailed in BAWSCA Recommendation 1b, an updated budget for the RGSR Project if required due to possible scope changes as necessary for the construction of Phase 2 of the RGSR Project

BAWSCA urges the Commission to consider BAWSCA’s three recommendations particularly in light of the continued work effort needed to complete the RGSR Project. Meeting the adopted LOS Goals are an integral part of the overall WSIP and for ensuring the protection of public health and safety through the delivery of a reliable water supply from the San Francisco Regional Water System (System). BAWSCA will continue to press the SFPUC and the Commission to address these issues as appropriate given its authority under State law to represent the water users outside of San Francisco that rely on the System.

The efforts of Ms. Miller and her staff in meeting with BAWSCA representatives to discuss the proposed changes and providing supporting documentation for the recommendations under consideration are much appreciated. BAWSCA continues to support the SFPUC’s efforts to implement the WSIP on time, on budget, and within scope for the protection of the 2.6 million residents and associated businesses that rely on the System for a reliable supply of high-quality water.

Sincerely,

Nicole Sandkulla
Chief Executive Officer/General Manager

President Moran, SFPUC
April 19, 2022

cc: Dennis Herrera., SFPUC General Manager
Alan Johanson, SFPUC Acting AGM – Infrastructure
Steve Ritchie, SFPUC AGM – Water Enterprise
Charles Perl, SFPUC Acting AGM - Finance
Katie Miller, SFPUC Program Director, WSIP
Allison Schutte, Hanson Bridgett
BAWSCA Board of Directors
BAWSCA Water Management Representatives
Alison Kastama, BAWSCA Liaison, SFPUC
Wesley Opp, Chief Consultant, Joint Legislative Audit Committee Richard McCarthy, Executive Director, Alfred E. Alquist Seismic Safety Commission Fred Turner, Structural Engineer, Alfred E. Alquist Seismic Safety Commission Vlad Rakhamimov, Associate Engineer, No. Coastal Sect., SWRCB Div. of Drinking Water Marco Pacheco, San Francisco District Engineer, SWRCB Div. of Drinking Water Darrin Polhemus, Deputy Director, SWRCB, Div. of Drinking Water Daniel Newton, Assistant Deputy Director, SWRCB, No. Ca. Drinking Water Field Ops

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San Francisco Public Utilities Commission
Earned Media Clips
April 1 – April 8

This is a weekly roundup of local, regional and national media stories either related to the SFPUC or that involved proactive or reactive efforts by the Communications Division.

Date Outlet Story
4/6/2022 KQED Punishing Bay Area Drought Prompts Calls
for Major Water Rethink | KQED
4/7/2022 SF Chronicle

California drought: Disappointing rain and
snow mean tighter water rules ahead
(sfchronicle.com) – PDF Attached
4/8/2022 I3 Investor U.S. San Francisco mayor announces new programs to reduce carbon emissions |
I3investor
4/8/2022 Palo Alto Weekly The Muwekma Ohlone have been here for more than 2,000 years. The government
says they're not a tribe. | News | Palo Alto
Online |