See how policy
moves the whole system.
Explore household choices, delivery constraints and the consequences that return to shape the next decision.
Completed gas exits
The illustrative reference shares your Context assumptions and resets Policy and Delivery controls to the reference values in Model & evidence.
Pointwise P10–P90 across 40 equally weighted, independent illustrative assumption draws. This is not a confidence interval or a probability forecast.
Policy timetable
Each change applies from its start year until a later change to the same setting. The sliders set the value before the first change. Status labels separate current policy, regulatory decisions, proposals and assumptions; a proposal is not enacted policy.
Adoption by household
A hollow dot marks a lower-income group that gets no priority support under these settings. Home Energy Support is limited to owner-occupiers.
Synthetic segments. Rental decisions include owner approval; owner repayments are excluded from tenant costs unless rental pass-through is selected.
A cold-day demand profile
Synthetic 24-hour winter profile, not a power-flow or reliability study. EV stock is an external scenario.
Compare the outcomes
A decision today changes
tomorrow’s choices.
Gas exits → fixed costs
Fewer connected homes can increase costs for those remaining. Retirement response and the assumed charge ceiling determine the unrecovered gap.
Demand → installation queues
More requests can exceed delivery capacity. Waiting reduces new requests; workforce growth takes time.
Electrification → network costs
Winter demand can trigger upgrades. A two-year delivery lag and subsequent tariff recovery feed back into household economics.
Where does one policy depend on another?
Every cell reruns the model with the selected parameter pair. Other controls stay fixed. Select a cell to apply that combination.
A darker cell means a higher value of the chosen metric, which is not necessarily a better outcome. Each cell is a scenario, not an estimated causal effect.
What moves the result most?
Change one parameter at a time by ±10% of its allowed range, holding the rest fixed. Bars show change from the current scenario.
Local sensitivity
Settings that the policy timetable changes vary only before their first scheduled change.
Ranking depends on the chosen ranges and the evaluation year. These are local scenario responses; they do not identify causal effects or capture all interactions.
Every result has an assumption behind it.
Version 1.0 is a working exploratory model. Current policy facts anchor the questions; synthetic parameters drive the simulation. It has not been fitted to ACT households or validated as a forecast.
Existing homes, complete gas exits
A fixed cohort of 10,000 initially gas-connected homes, represented by six weighted segments. A completed package replaces space heating, hot water and cooking. No population growth, gas reconnections or second appliance replacement within the horizon.
Annual choices; cold-day electricity
Annual decisions from 2027 to 2045, with a common 2026 starting state. Financial values are real 2026 AUD, with zero inflation assumed. Present values use 4%; loan concession costs use a 5% funding benchmark. Household outlay includes energy and active repayments, excluding initial cash contributions.
Equations and feedback rules
1. Household requests
requests = (gas homes − queued homes) × 0.12 × sigmoid(V) × accessV = −0.7 + financial response × (0.0015 × perceived annual saving − 0.0007 × annual repayment − upfront cash / capital scale) + 1.4 × advice + 0.5 × cohort trust + 0.9 × adoption share − 0.65 × waiting years (capped at 5). All behavioural coefficients are illustrative. Owner capital scale = max(A$4,000, 15% of income); rental-owner scale = A$18,000. Landlords value 15% of tenant savings plus 60% × the pass-through setting. These are explicit hypotheses to replace with empirical estimates.
2. Orders, installations and gas exits
queue(t+1) = queue(t) + requests(t) − installations(t)Capacity is shared in proportion to queued requests. If grants, assistance and loan concessions exceed the annual budget, all allocations are scaled proportionately. The queue is retained without cancellation. Installed homes exit gas completely. Capacity grows after the chosen training lead time.
3. Financing and programme cost
repayment = principal × r / [1 − (1+r)⁻¹⁰]At 0% interest, repayment = principal / 10. Expected principal uses each cohort’s borrowing share and a A$20,000 ceiling, after grants. Concession cost = max(0, principal − PV of repayments at 5%). The support budget covers grants + concession cost + advice delivery. Gross loan principal is shown separately; it is not counted twice. Workforce expansion, charging-programme delivery, gas decommissioning and enforcement costs are not estimated. Public support is therefore not total fiscal or social cost.
4. Remaining gas costs
fixed revenue need = A$350 × 10,000 × [1 − retirement response × adoption share]Divide by remaining connections, with an illustrative A$2,500 per-connection annual ceiling. Any unrecovered amount is reported without assuming a bailout or allocating it to households. This cohort-level approximation is not a regulated Evoenergy tariff model. Actual network boundaries extend beyond the ACT.
5. Energy, networks and emissions
net load = other demand + electric heat + hot water/cooking + EV chargingThermal heating = 9,000 kWh/year × cohort factor; gas heating efficiency = 0.8. Hot-water thermal demand = 2,500 kWh/year (gas efficiency 0.7; heat-pump COP 3). Cooking = 400 kWh useful energy (gas efficiency 0.45; electric 0.85). Heating demand uplift changes comfort. Cold-day heating uses 65% of seasonal COP. Network additions cost A$800,000/MW, take two years, and recover costs at 5% over 30 years, across modelled electricity consumption. Tariff feedback into decisions has a one-year lag. No photovoltaic or battery dispatch is modelled.
Gas combustion uses 0.05153 kg CO₂e/MJ as a documented generic factor. Home electricity uses an illustrative physical grid pathway from 0.55 kg CO₂e/kWh in 2026 to the selected 2045 value. This is not ACT renewable procurement accounting or a marginal-emissions estimate. Transport, upstream fuel, embodied and refrigerant emissions are excluded. EV charging affects demand and spending but transport emissions and vehicle acquisition costs remain outside scope.
Synthetic population and fixed assumptions
Every cohort share, income, trust, access and borrowing fraction is synthetic. Other electricity is 3,000 kWh/year/home plus A$500/year fixed electricity charges. EVs use 2,200 kWh/year and start at an illustrative 10% ownership. Managed charging redistributes energy across overnight hours without reducing energy consumption. These figures are editable in the source model and are not estimates from a population survey.
Parameter register
Policy evidence, checked 24 October 2026
Calibration and validation needed before advisory use
- Replace synthetic population weights with verified ACT dwelling, tenure, income and appliance-stock data.
- Estimate choice and replacement timing parameters using representative household panels, choice experiments and verified installations. Separate landlord decisions from tenant preferences.
- Fit energy demand and cold-weather performance to consented billing, interval-meter, weather and appliance data.
- Replace assumed network headroom, costs and retirement rules with Evoenergy and regulatory evidence, including the relevant NSW network customers.
- Estimate intervention effects through trials or credible quasi-experimental comparisons; account for selection, changing prices and other policies.
- Validate on held-out years and household groups. Test annual uptake, actual savings, peak load and distributional outcomes. Report uncertainty, model version and limitations in every briefing.
Outputs are scenario implications, not proven policy effects. No optimiser selects a socially “best” policy. Outcomes should be compared against explicit public objectives and constraints.