Trace urban heat, water and materials
Compare mechanisms and controlled methods alongside a repair ledger.
Learning outcome: Identify confounded temperature comparisons and conserve material without assuming equal service.
Starting recap
Surface temperature, shaded air temperature and personal exposure are different quantities.
All lessons are open directly. Your saved experiments remain when you leave and return.
Try
Repair of a 20 kg stock retains 18, discards 2 and adds 2; replacement adds and discards 20.
Open the experimentPredict
Does a lower material input establish equal lifetime performance?
Experiment
Critique roof method cards, map water pathways and close repair/replacement accounts on paper.
Compare occupancy, empty travel and served requests
Original travel-service scenario · separate historical occupancy evidence · Original scenario with 120 one-way passenger trips of 10 km each. Capacity excludes drivers. Occupancy, trip limits and operational CO2e factors are stipulated, not observed technology averages. Empty returns are explicit only in their selected packet; time and fleet feasibility are not calculated. Report delivered and unmet service separately; no lifecycle, exposure or universal best-mode inference.
Reset keeps your prediction, captures and practice history.
Keep the travel request fixed
Scenario bus; Consolidated occupancy. There are 120 one-way passenger-trip requests of 10 km each, totaling 1200 requested passenger-km. These need not be 120 unique people over a longer period. Predict whether the selected plan serves every request before comparing its distance or impact indicators.
The vehicle has 40 passenger seats, excluding its driver. The stipulated target load is 30 passengers per loaded trip and the available loaded-trip cap is 24. We dispatch 4 loaded trips, serving 120 passenger trips and leaving 0 unmet. The window has no specified duration; trip limits are supplied constraints, not a scheduling calculation.
Loaded travel is 40 vehicle-km; empty travel is 0; total is 40. Adding an empty return adds vehicle distance without adding passenger distance. The selected extra travel does not reduce the trip cap because this model has no time budget or fleet-feasibility calculation.
Factor assumption beside the result: 1.2 kg CO₂e per vehicle-km × 40 vehicle-km = 48 kg scenario operational CO₂e. This is an original hypothetical factor, identical for loaded and empty travel, not measured exhaust or an empirical modal average. Construction, fuel/electricity supply, maintenance and end-of-life are omitted. It is not total lifecycle impact.
| Requested / served / unmet passenger trips | 120 / 120 / 0 |
|---|---|
| Requested / served / unmet passenger-km | 1200 / 1200 / 0 |
| Request / passenger-km balance residuals | 0 trips / 0 passenger-km |
| Passenger seats / target load | 40 seats / 30 passengers |
| Loaded trips / available cap | 4 / 24 |
| Loaded / empty / total vehicle-km | 40 / 0 / 40 |
| Offered loaded-seat-km | 1600 |
| Realized mean loaded occupancy | 30 passengers per loaded vehicle |
| Loaded-seat utilization | 75% |
| Average load across all vehicle distance | 30 passenger-km per vehicle-km |
| Scenario operational intensity | 0.04 kg CO₂e per served passenger-km (rounded where needed) |
Inspect every dispatched passenger load
Ordered loads, one number per loaded trip: 30, 30, 30, 30. Their sum is 120. Target occupancy is a supplied maximum for loading each dispatched trip; the last vehicle would be partially loaded if fewer requests remained.
Check the denominator before ranking
Loaded-seat utilization is served passenger-km divided by offered loaded-seat-km. Average load over all distance includes empty vehicle-km in its denominator. They answer different questions. Historical revenue vehicle distance is different again: revenue service may run empty. Do not silently swap these denominators.
Operational intensity divides by 1200 served passenger-km. Unserved requests are not delivered service and do not earn denominator credit. No impact is invented for the unserved trips; another means of serving them would need its own account.
Unavailable: lifecycle impact, travel time and exposure effects are not calculated. Changing vehicle type does not itself predict consolidation, and this model does not establish affordability, accessibility, safety or a universally best mode.
Check the ranking reversal and a partial-load transfer
With dispersed occupancy, all 120 trips are served and scenario operational totals are small vehicle 120, minibus 160 and bus 288 kg CO₂e. With consolidated occupancy, all 120 are still served but totals are bus 48, minibus 60 and small vehicle 120. The ranking reverses under the supplied assumptions. These are controlled calculations, not a real technology ranking.
Disrupted small-vehicle availability serves only 600 passenger-km and produces 60 kg scenario CO₂e. Dividing by requested 1200 would incorrectly suggest 0.05 kg per passenger-km; the actual served denominator gives 0.1. The lower scenario operational total here accompanies lost service.
Separate paper transfer: 125 requests with target 30, capacity 40 and an adequate trip cap require five loads [30,30,30,30,5]. Actual mean load is 25 and seat utilization 62.5%. At 10 km each, served distance is 1250 passenger-km. This changed request is not a selectable lab recipe.
Served 120 of 120 passenger trips (1200 passenger-km); unmet 0 trips (0 passenger-km).
40 loaded + 0 empty = 40 vehicle-km. At the hypothetical 1.2 kg CO2e/vehicle-km factor: 48 kg scenario operational CO2e. Intensity 0.04 kg per served passenger-km; omitted lifecycle effects are not zero.
Captures preserve recipes and replay their calculated results. They are synthetic model evidence, not physical measurements.
Understand
Apply
Public practice checks a specific claim. It does not establish independent mastery.
Name missing performance, site-condition and lifecycle evidence before ranking interventions.
Continue
Completion requires a saved prediction, at least one capture and a reflection. Presence checks do not grade your reasoning.
Skipping retains experiments and awards no mastery.
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