Region selection for carbon

SAMPLE REGION INTENSITIES (gCO2/kWh, approximate):

VERY CLEAN (mostly renewable):
  eu-north-1 (Sweden: hydro):              ~50
  ca-central-1 (Quebec: hydro):             ~50
  europe-north1 (Finland, low-carbon):       ~30

MEDIUM:
  us-west-2 (Oregon: mixed hydro/renew):   ~120
  us-west-1 (California: solar-heavy):      ~250
  eu-west-1 (Ireland: wind):                ~180

HIGH CARBON:
  us-east-1 (Virginia: gas + some coal):    ~350
  us-east-2 (Ohio: coal-heavy):              ~400
  ap-southeast-2 (Sydney: coal-heavy):       ~700

EXTREMELY HIGH:
  ap-southeast-1 (Singapore: coal/gas):     ~500-600
  ap-east-1 (Hong Kong):                      ~550

LOW-CARBON CHARACTERISTICS:
  Hydroelectric grids                    Norway, Sweden, Quebec
                                        baseline ~30-50 gCO2/kWh

  Solar-heavy + low coal                 California, parts of Asia
                                        baseline ~150-250 gCO2/kWh

  Wind-heavy                            Texas, Northern Europe
                                        baseline ~100-200 gCO2/kWh

  Nuclear-heavy                         France, parts of E. Europe
                                        baseline ~50-100 gCO2/kWh

HIGH-CARBON CHARACTERISTICS:
  Coal-heavy grids                       Parts of US, Australia,
                                        Southeast Asia
                                        baseline 400-700 gCO2/kWh

  Gas-heavy grids                         Some US East regions
                                        baseline 300-450 gCO2/kWh

TRADE-OFF                                  IMPACT
──────────────────────────────────────────────────────────────────
LATENCY                                    Distance from users
                                          increases latency
                                          (acceptable for batch;
                                          not for real-time)

PRICING                                     Cleaner regions sometimes
                                          slightly pricier
                                          (variance: ±5-15%)

SERVICE AVAILABILITY                       Newer / smaller regions
                                          have fewer services
                                          (Lambda, Bedrock, etc.
                                          may not be everywhere)

DATA RESIDENCY                             Compliance requirements
                                          may mandate region
                                          (GDPR, HIPAA, etc.)

DATA TRANSFER                              One-time migration cost
                                          + ongoing inter-region
                                          transfer if hybrid

COMPLIANCE                                 SOC 2, ISO require
                                          documented data handling

WORKLOAD TYPE                  PRIORITY OF FACTORS
──────────────────────────────────────────────────────────────────
Real-time customer-facing       Latency >> Carbon >> Cost
                                Region near users wins

Batch processing                Cost ≈ Carbon (latency low priority)
                                Clean-grid region usually wins

Backup / archive                Cost ≈ Carbon
                                Cheapest clean region wins

ML training                     Cost > Carbon (some flex)
                                Mixed; depends on org's ESG goals

Development                     Cost > Carbon
                                Closer to engineers usually wins

Internal tools                   Cost > Carbon
                                Closer to office wins for latency

BEFORE MIGRATING regions for carbon reduction:

  □ Latency check: target region's latency still acceptable for
    customer experience?

  □ Cost check: comparable cost in target region?
    (allow ±10% acceptable; >15% may not justify)

  □ Service availability: all needed services present in target
    region? Check each cloud service in your stack.

  □ Data transfer one-time cost: how much to move existing data?

  □ Data transfer ongoing cost: will hybrid setup create new
    inter-region transfer charges?

  □ Compliance: data residency rules permit target region?

  □ Team familiarity: any operational concerns with new region?

  IF ALL GREEN: proceed with migration plan
  IF ANY YELLOW: investigate; may need different approach
  IF ANY RED: this workload not a migration candidate

PATTERN: primary + secondary in different regions
  Primary:    closer to users (cost + latency priority)
  Secondary:  cleaner grid (carbon priority)

WHEN PRIMARY HEALTHY: traffic stays primary
WHEN PRIMARY DEGRADED: traffic shifts to secondary
                       (now running at lower carbon during
                       a less-than-optimal period)

The secondary serves both DR and a carbon backup function.

TYPICAL ML training workload comparison:

  In us-east-1 (350 gCO2/kWh):
    100 GPU-hours × 1.5 kW × 0.35 kg/kWh = 52.5 kg CO2 per run

  In eu-north-1 (50 gCO2/kWh):
    Same 100 GPU-hours × 1.5 kW × 0.05 kg/kWh = 7.5 kg CO2 per run

  REDUCTION: 86% for the same compute

  Annual (if run daily):
    us-east-1: 52.5 kg × 365 = 19.2 tons CO2/year
    eu-north-1: 7.5 kg × 365 = 2.7 tons CO2/year
    Annual savings: 16.4 tons (86%)

MIGRATION PLAN typical steps:

  WEEK 1-2: Assessment
    Calculate carbon impact of current and target regions
    Verify latency / cost / compliance per checklist
    Estimate migration effort

  WEEK 3-4: Pilot
    Migrate one non-critical workload first
    Measure: latency, cost, carbon
    Validate against expectations

  WEEK 5-8: Production migration
    Move main workload in waves
    Maintain rollback capability

  WEEK 9-10: Verification + decommission
    Confirm target region performance matches
    Decommission source-region resources
    Document the migration in audit log + carbon report

EFFORT: 2-4 weeks per workload for clean migrations
ANNUAL CARBON IMPACT: 70-90% reduction typical

MISTAKE                                   FIX
──────────────────────────────────────────────────────────────────
Don't check latency for the new region    Test from user-representative
                                          locations before committing

Forget inter-region data transfer costs   Include in TCO; can be
                                          significant for data-heavy
                                          workloads

Migrate first; check compliance later     Compliance check is the
                                          first gate; some workloads
                                          can't move at all

Migrate everything to "save the world"     Some workloads have hard
                                          constraints; respect them

Underestimate operational change           New region means new ops;
                                          team familiarity matters

TEAM:    ML platform team
WORKLOAD: nightly ML training cluster
COST:    $40K/month
COMPUTE: 100 GPU-hours/day average

CURRENT STATE:
  Region: us-east-1
  Annual carbon: 18.4 tons CO2/year (350 gCO2/kWh)

CANDIDATE REGIONS:
  Option A: eu-north-1 (50 gCO2/kWh):
    Annual carbon: 2.6 tons CO2/year (86% reduction)
    Latency to ML pipeline: +50ms (acceptable; async batch)
    Cost: comparable ±2%
    Services available: all needed (verified)
    Compliance: no concerns (training data not subject to residency)
    Data transfer cost: ~$3K one-time migration

  Option B: us-west-2 (120 gCO2/kWh):
    Annual carbon: 6.3 tons CO2/year (66% reduction)
    Latency: +30ms
    Cost: comparable
    Services: all available
    Data transfer: ~$1K

DECISION ANALYSIS:
  Option A delivers 86% reduction; effort is higher (overseas)
  Option B delivers 66% reduction; effort lower
  Annual carbon saved:
    A: 15.8 tons
    B: 12.1 tons

DECISION: Option A (eu-north-1)
  Rationale: largest carbon reduction; cost neutral

EXECUTION:
  Week 1-2: detailed migration plan, pilot batch ML run
  Week 3-4: full production migration in 3 waves
  Week 5: decommission us-east-1 resources
  Total effort: 1 engineer × 5 weeks = ~$25K loaded

ROI on the migration effort:
  Carbon: 15.8 tons/year saved (significant for ESG report)
  Cost: roughly neutral (small data transfer cost amortized)
  Verdict: clear win for sustainability narrative

DOCUMENTED in carbon report; presented to leadership as a
positive ESG signal.

WORKLOAD candidate:    __________
  Class: batch / async / customer-facing / latency-sensitive

CURRENT region:    __________
  Carbon intensity: __________ gCO2/kWh (look up)

CANDIDATE cleaner region:    __________
  Carbon intensity: __________ gCO2/kWh

CARBON REDUCTION estimate:
  Current annual: __________ tons CO2
  Target annual: __________ tons CO2
  Reduction: __________ tons (____ %)

CHECKLIST:
  □ Latency acceptable for this workload?
  □ Cost variance within 10%?
  □ All services available in target?
  □ Compliance / residency permits target?
  □ Team familiar with operating in target region?

EFFORT estimate:    __________ weeks

DECISION:
  □ Migrate (carbon impact justifies)
  □ Defer (constraints unresolved)
  □ Not migratable (compliance / latency)

If migrate: schedule pilot for: __________

BEFORE  A ZopNight account with one cloud connected. Resources spread across more than one region.
ASK     "List our resources by region and what they cost."
CHECK   region concentration. Carbon reporting and cost reporting share a denominator: where the estate actually runs.