Each region has a population P and a fixed energy-infrastructure capacity. Effective energy access E is that capacity shared across today's population — so a region gets more crowded, its own per-capita access falls (a real congestion effect). Every simulated quarter, a gravity-model migration equation — the standard distance-decay, attractiveness-differential form used in migration studies since Zipf's gravity law — computes how many people move from each region toward every other region with higher access:
E_i = clamp(capacity_i / pop_i, 0, 1)
w_ij = max(0, E_j − E_i) / dist_ij^γ (attractiveness ÷ distance²ish)
O_i = pop_i · sensitivity · (1 − e^(−ΣW_i·k)) (total emigrants from i)
M_ij = O_i · w_ij / ΣW_i (split across destinations by pull)
- Migration sensitivity — how readily people act on an energy-access gap; higher values empty low-access regions faster.
- Distance-decay γ — how steeply pull weakens with distance; low γ lets people leapfrog past nearby options straight to the best one, high γ keeps moves local.
- Invest (click a region) — raises that region's capacity directly, the real-world lever of energy infrastructure spending, which lowers its outbound pull.
- Climate shock — knocks out a chunk of one region's capacity instantly (a storm, flood or grid failure), the trigger behind real energy-driven climate migration — watch the flow lines redirect immediately.
This is the same mechanism the article describes: energy access shapes where people can afford to live, climate shocks to that infrastructure displace them, and new arrivals then strain the energy systems of the regions that receive them.