You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 simpson_episodes
【Schema】
# Table: Credit
[
(episode_id:TEXT, Examples: [S20-E10, S20-E13, S20-E14]),
(category:TEXT, Examples: [Casting Department, Animation Department, Sound Department]),
(person:TEXT, Examples: [Bonita Pietila, Adam Kuhlman, Carlton Batten]),
(role:TEXT, Examples: [casting, additional timer, adr engineer]),
(credited:TEXT, Examples: [true, false])
]
# Table: Award
[
(award_id:INTEGER, Primary Key, Examples: [325, 326, 327]),
(organization:TEXT, Examples: [Primetime Emmy Awards]),
(year:INTEGER, Examples: [2009, 2010, 2017]),
(award_category:TEXT, Examples: [Primetime Emmy, Annie, Image Award]),
(award:TEXT),
(person:TEXT, Examples: [Dan Castellaneta, Hank Azaria, Harry Shearer]),
(role:TEXT, Examples: [executive producer]),
(episode_id:TEXT, Examples: [S20-E18, S20-E16, S20-E8]),
(season:TEXT),
(song:TEXT),
(result:TEXT, Examples: [Winner, Nominee])
]
# Table: Person
[
(name:TEXT, Primary Key, Examples: [Adam Greeley, Adam Klein, Adam Kuhlman]),
(birthdate:TEXT, Examples: [1963-05-04, 1975-03-30, 1969-12-04]),
(birth_name:TEXT, Examples: [Marc Edward Wilmore, Valentina Lantigua, Joseph Stewart Burns]),
(birth_place:TEXT, Examples: [USA, Brooklyn, Chicago]),
(birth_region:TEXT, Examples: [California, New York, Illinois]),
(birth_country:TEXT, Examples: [USA, Canada, Ireland]),
(height_meters:REAL, Examples: [1.85, 1.75, 2.03]),
(nickname:TEXT, Examples: [Jim, Doofus, Hoops])
]
# Table: Vote
[
(episode_id:TEXT, Examples: [S20-E1, S20-E10, S20-E11]),
(stars:INTEGER, Examples: [2, 3, 4]),
(votes:INTEGER, Examples: [16, 20, 36]),
(percent:REAL, Examples: [1.3, 1.7, 3.0])
]
# Table: Character_Award
[
(award_id:INTEGER, Examples: [325, 326, 327]),
(character:TEXT, Examples: [Homer Simpson, Moe Szyslak, Kent Brockman])
]
# Table: Episode
[
(episode_id:TEXT, Primary Key, Examples: [S20-E1, S20-E10, S20-E11]),
(season:INTEGER, Examples: [20]),
(episode:INTEGER, Examples: [1, 2, 3]),
(number_in_series:INTEGER, Examples: [421, 422, 423]),
(title:TEXT, Examples: [Sex, Pies and Idiot Scrapes]),
(summary:TEXT),
(air_date:TEXT, Examples: [2008-09-28, 2008-10-05, 2008-10-19]),
(episode_image:TEXT),
(rating:REAL, Examples: [7.2, 7.0, 7.1]),
(votes:INTEGER, Examples: [1192, 1055, 1015])
]
# Table: Keyword
[
(episode_id:TEXT, Primary Key, Examples: [S20-E1, S20-E10, S20-E11]),
(keyword:TEXT, Primary Key, Examples: [1930s to 2020s, erotic bakery, cake])
]
【Foreign keys】
Credit.episode_id=Episode.episode_id
Credit.person=Person.name
Award.person=Person.name
Award.episode_id=Episode.episode_id
Vote.episode_id=Episode.episode_id
Character_Award.award_id=Award.award_id
Keyword.episode_id=Episode.episode_id

    【Question】
    Calculate the percentage of people who were born after 1970 and from California. born after 1970 refers to birthdate > 1970; from California refers to birth_region = 'California'; percentage = divide(count(birthdate > 1970 and birth_region = 'California'), total(birthdate)) * 100%

    【Evidence】
    born after 1970 refers to birthdate > 1970; from California refers to birth_region = 'California'; percentage = divide(count(birthdate > 1970 and birth_region = 'California'), total(birthdate)) * 100%

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT CAST(SUM(CASE WHEN birth_region = 'California' AND SUBSTR(birthdate, 1, 4) > '1970' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(birthdate) FROM Person;