Carbon +
Water
Intelligence Studio

class HotspotDetector: def detect_emission_hotspots(self, facility_data): # Rule 1: Pareto - sources driving 80% of emissions sorted_e = sorted(facility_data, key=lambda x: x["co2e"], reverse=True) total = sum(e["co2e"] for e in facility_data) cum, hotspots = 0, [] for e in sorted_e: cum += e["co2e"] e["share_pct"] = e["co2e"] / total * 100 if cum / total <= 0.80: hotspots.append(e) # Rule 2: Z-score intensity anomaly vs peer group z = stats.zscore([e["intensity"] for e in facility_data]) for i, e in enumerate(facility_data): if z[i] > 1.5 and e not in hotspots: e["anomaly_flag"] = "HIGH_INTENSITY" hotspots.append(e) # Rule 3: Trend reversal detection for e in facility_data: series = e.get("monthly_series", []) if len(series) >= 6: slope, _, r, _, _ = stats.linregress(range(len(series)), series) if slope > 0 and r**2 > 0.5: e["trend_flag"] = "INCREASING_TREND" return hotspots

-- Tenant CREATE TABLE tenants ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), name VARCHAR(200) NOT NULL, slug VARCHAR(80) UNIQUE NOT NULL, plan VARCHAR(20) DEFAULT 'mvp', base_year INTEGER, target_year INTEGER ); -- Facility CREATE TABLE facilities ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), tenant_id UUID REFERENCES tenants(id) ON DELETE CASCADE, name VARCHAR(200) NOT NULL, country VARCHAR(3), sector VARCHAR(80), annual_output NUMERIC, output_unit VARCHAR(30), lat NUMERIC, lon NUMERIC ); -- Activity Entry CREATE TABLE activity_entries ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), tenant_id UUID REFERENCES tenants(id), facility_id UUID REFERENCES facilities(id), scope VARCHAR(20), category VARCHAR(80), period_start DATE, period_end DATE, activity_value NUMERIC NOT NULL, activity_unit VARCHAR(30), ef_id UUID REFERENCES emission_factors(id), ghg_result_co2e NUMERIC, status VARCHAR(20) DEFAULT 'draft', audit_hash VARCHAR(64) ); -- Water Entry CREATE TABLE water_entries ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), tenant_id UUID REFERENCES tenants(id), facility_id UUID REFERENCES facilities(id), entry_type VARCHAR(30), source_type VARCHAR(40), volume_m3 NUMERIC NOT NULL, period_start DATE, period_end DATE ); -- AI Recommendations CREATE TABLE recommendations ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), tenant_id UUID REFERENCES tenants(id), facility_id UUID REFERENCES facilities(id), action_class VARCHAR(60), title VARCHAR(200), reduction_co2e_t NUMERIC, reduction_water_m3 NUMERIC, annual_saving_usd NUMERIC, capex_usd NUMERIC, payback_years NUMERIC, complexity_score NUMERIC, priority_rank INTEGER, priority_score NUMERIC, status VARCHAR(20) DEFAULT 'open' );