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

    【Schema】
    【DB_ID】 donor
【Schema】
# Table: donations
[
(donationid:TEXT, Primary Key, Examples: [000000a91a14ed37bef82e125c102e77]),
(projectid:TEXT, Examples: [ffffac55ee02a49d1abc87ba6fc61135]),
(donor_acctid:TEXT, Examples: [22cbc920c9b5fa08dfb331422f5926b5]),
(donor_city:TEXT, Examples: [Peachtree City, Rockville, Salem]),
(donor_state:TEXT, Examples: [GA, MD, IN]),
(donor_zip:TEXT, Examples: [30269, 20853, 47167]),
(is_teacher_acct:TEXT, Examples: [f, t]),
(donation_timestamp:DATETIME, Examples: [2011-08-25 14:27:34.807000]),
(donation_to_project:REAL, Examples: [42.5, 26.83, 55.35]),
(donation_optional_support:REAL, Examples: [7.5, 4.73, 0.0]),
(donation_total:REAL, Examples: [50.0, 31.56, 55.35]),
(dollar_amount:TEXT, Examples: [10_to_100, under_10, 100_and_up]),
(donation_included_optional_support:TEXT, Examples: [t, f]),
(payment_method:TEXT, Examples: [no_cash_received, creditcard, paypal]),
(payment_included_acct_credit:TEXT, Examples: [f, t]),
(payment_included_campaign_gift_card:TEXT, Examples: [t, f]),
(payment_included_web_purchased_gift_card:TEXT, Examples: [f, t]),
(payment_was_promo_matched:TEXT, Examples: [f, t]),
(via_giving_page:TEXT, Examples: [f, t]),
(for_honoree:TEXT, Examples: [f, t]),
(donation_message:TEXT)
]
# Table: projects
[
(projectid:TEXT, Primary Key, Examples: [00001ccc0e81598c4bd86bacb94d7acb]),
(teacher_acctid:TEXT, Examples: [42d43fa6f37314365d08692e08680973]),
(schoolid:TEXT, Examples: [c0e6ce89b244764085691a1b8e28cb81]),
(school_ncesid:TEXT, Examples: [063627006187, 483702008193, 410327000109]),
(school_latitude:REAL, Examples: [36.57634, 32.911179, 45.166039]),
(school_longitude:REAL, Examples: [-119.608713, -96.72364, -122.414576]),
(school_city:TEXT, Examples: [Selma, Dallas, Colton]),
(school_state:TEXT, Examples: [CA, TX, OR]),
(school_zip:INTEGER, Examples: [93662, 75243, 97017]),
(school_metro:TEXT, Examples: [urban, rural, suburban]),
(school_district:TEXT, Examples: [Selma Unified Sch District]),
(school_county:TEXT, Examples: [Fresno, Dallas, Clackamas]),
(school_charter:TEXT, Examples: [f, t]),
(school_magnet:TEXT, Examples: [f, t]),
(school_year_round:TEXT, Examples: [f, t]),
(school_nlns:TEXT, Examples: [f, t]),
(school_kipp:TEXT, Examples: [f, t]),
(school_charter_ready_promise:TEXT, Examples: [f, t]),
(teacher_prefix:TEXT, Examples: [Mrs., Mr., Ms.]),
(teacher_teach_for_america:TEXT, Examples: [f, t]),
(teacher_ny_teaching_fellow:TEXT, Examples: [f, t]),
(primary_focus_subject:TEXT, Examples: [Literature & Writing, Literacy, Social Sciences]),
(primary_focus_area:TEXT, Examples: [Literacy & Language, History & Civics, Math & Science]),
(secondary_focus_subject:TEXT, Examples: [College & Career Prep]),
(secondary_focus_area:TEXT, Examples: [Applied Learning, Literacy & Language, Math & Science]),
(resource_type:TEXT, Examples: [Books, Technology, Other]),
(poverty_level:TEXT, Examples: [highest poverty, high poverty, moderate poverty]),
(grade_level:TEXT, Examples: [Grades 6-8, Grades PreK-2, Grades 3-5]),
(fulfillment_labor_materials:REAL, Examples: [30.0, 35.0, 9.0]),
(total_price_excluding_optional_support:REAL, Examples: [555.81, 296.47, 430.89]),
(total_price_including_optional_support:REAL, Examples: [653.89, 348.79, 506.93]),
(students_reached:INTEGER, Examples: [32, 22, 17]),
(eligible_double_your_impact_match:TEXT, Examples: [f, t]),
(eligible_almost_home_match:TEXT, Examples: [f, t]),
(date_posted:DATE, Examples: [2014-05-12])
]
# Table: essays
[
(projectid:TEXT, Examples: [ffffc4f85b60efc5b52347df489d0238]),
(teacher_acctid:TEXT, Examples: [c24011b20fc161ed02248e85beb59a90]),
(title:TEXT, Examples: [iMath]),
(short_description:TEXT),
(need_statement:TEXT),
(essay:TEXT)
]
# Table: resources
[
(resourceid:TEXT, Primary Key, Examples: [0000037fecc4461faf0e49328ae66661]),
(projectid:TEXT, Examples: [ffffc4f85b60efc5b52347df489d0238]),
(vendorid:INTEGER, Examples: [430, 82, 767]),
(vendor_name:TEXT, Examples: [Woodwind and Brasswind]),
(project_resource_type:TEXT, Examples: [Technology, Supplies, Other]),
(item_name:TEXT),
(item_number:TEXT, Examples: [249995.001, BB11216668, VX748]),
(item_unit_price:REAL, Examples: [149.0, 148.0, 69.95]),
(item_quantity:INTEGER, Examples: [4, 1, 2])
]
【Foreign keys】
donations.projectid=projects.projectid
resources.projectid=projects.projectid

    【Question】
    What percentage of projects in the City of Santa Barbara are in suburban metro? City of Santa Barbara refers to school_city = 'Santa Barbara'; percentage refers to DIVIDE(school_metro = 'suburban'; school_metro)*100

    【Evidence】
    City of Santa Barbara refers to school_city = 'Santa Barbara'; percentage refers to DIVIDE(school_metro = 'suburban'; school_metro)*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 school_metro = 'suburban' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(projectid) FROM projects WHERE school_city = 'Santa Barbara'