pp. 6476·29. juni 2026.· Issue No. 1
The role of jovian planets in the dynamical stability ofhabitable zones around red dwarfs: water delivery, orbitalstability, and the sign-changing leverage of giant companions DOI: 10.65932/CAR-2026-1-4Creative Commons BY 4.0 CC BY 4.0
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The role of jovian planets in the dynamical stability ofhabitable zones around red dwarfs: water delivery, orbitalstability, and the sign-changing leverage of giant companions
For Sun-like stars, a comfortable heuristic holds that an outer giant planet is good forthe habitability of inner terrestrial worlds — it helps deliver water and, in the popular imagination,shields the inner system from bombardment. This article argues that the heuristic does not transfer to red dwarfs and that, for the compact resonant systems typical of low-mass stars, an outergiant is more often a liability than an asset. I develop the Giant-Planet Habitability Leverageframework, which decomposes a giant's net effect on an inner terrestrial planet's long-term habitability into three channels — water and volatile delivery, dynamical stabilization versus excitation, and impact or clearing modulation — and combines them into a single signed index (GHLI)that can be positive (the giant helps) or negative (the giant harms). Applying the framework heuristically to three archetypes — a Sun-like system with a Jupiter, a hypothetical M-dwarf compactchain with an imposed cold giant, and a giant-free compact chain of the TRAPPIST-1 type — Ifind that the leverage changes sign with stellar mass: mildly positive to neutral for the solar case(GHLI ≈ +0.3) but distinctly negative when a cold giant is added to a tightly packed M-dwarfchain (GHLI ≈ −0.5). The mechanism is that compact M-dwarf systems obtain their water endogenously, through pebble drift across the ice line during formation, and derive their stabilityfrom resonant locking, so an outer giant supplies little that the system lacks while threatening thefragile resonant architecture on which its stability depends. The upshot is that the observed scarcity of giant planets around red dwarfs, rather than being an obstacle to habitability, is plausiblyone of its enabling conditions. The framework yields concrete, falsifiable predictions and identifies where current occurrence-rate and dynamical constraints most need tightening.

For Sun-like stars, a comfortable heuristic holds that an outer giant planet is good forthe habitability of inner terrestrial worlds — it helps deliver water and, in the popular imagination,shields the inner system from bombardment. This article argues that the heuristic does not transfer to red dwarfs and that, for the compact resonant systems typical of low-mass stars, an outergiant is more often a liability than an asset. I develop the Giant-Planet Habitability Leverageframework, which decomposes a giant's net effect on an inner terrestrial planet's long-term habitability into three channels — water and volatile delivery, dynamical stabilization versus excitation, and impact or clearing modulation — and combines them into a single signed index (GHLI)that can be positive (the giant helps) or negative (the giant harms). Applying the framework heuristically to three archetypes — a Sun-like system with a Jupiter, a hypothetical M-dwarf compactchain with an imposed cold giant, and a giant-free compact chain of the TRAPPIST-1 type — Ifind that the leverage changes sign with stellar mass: mildly positive to neutral for the solar case(GHLI ≈ +0.3) but distinctly negative when a cold giant is added to a tightly packed M-dwarfchain (GHLI ≈ −0.5). The mechanism is that compact M-dwarf systems obtain their water endogenously, through pebble drift across the ice line during formation, and derive their stabilityfrom resonant locking, so an outer giant supplies little that the system lacks while threatening thefragile resonant architecture on which its stability depends. The upshot is that the observed scarcity of giant planets around red dwarfs, rather than being an obstacle to habitability, is plausiblyone of its enabling conditions. The framework yields concrete, falsifiable predictions and identifies where current occurrence-rate and dynamical constraints most need tightening.