Effects of Urban Growth Patterns on Production–Living–Ecological Functions: Performance Assessment and Comparative Analysis Based on Urban Scaling Laws

Optimising urban growth patterns (UGPs) and enhancing production-living-ecological (PLE) functions are crucial for achieving sustainable urbanisation. However, systematic comparisons remain limited regarding how PLE functions vary with population size under different urban growth patterns and how their relative performance differs. This gap constrains a comprehensive understanding of urban expansion quality and hinders the place-based optimisation of spatial governance strategies. This study analyses 348,817 newly added built-up patches in mainland China, identifies expansion patterns using the landscape expansion index (LEI), and evaluates their functional performance with urban scaling law and the Scale-Adjusted Metropolitan Indicator (SAMI). The findings can be summarized as follows. (1) Edge expansion dominated China’s urban growth over the past decade (64.88%), while infill accounted for the smallest share (14.17%). (2) Infill expansion achieves the highest efficiencies in production (β = 0.662) and living (β = 0.032) functions but suppresses ecological function (β = −0.003), making it more suitable for areas under strong economic development pressure. Outlying expansion shows the opposite pattern, with a slight positive scaling effect on ecological function (β = 0.009) but the lowest efficiencies in production (β = 0.485) and living (β = 0.019) functions, thus fitting regions with strong environmental constraints. Edge expansion lies between the two, with a neutral ecological effect (β = 0.000), and is suited to areas with moderate development needs that also require ecological stability. (3) The SAMI further confirms that infill expansion performed significantly better than expected in production functions, whereas outlying expansion and edge expansion showed clear advantages in ecological functions. These findings reveal differences across urban growth patterns in the scaling and relative performance of PLE functions, providing empirical evidence for selecting growth patterns according to regional functional needs.