CADENCE: A Computational Model for Deployment Realism in Net-Zero Energy Transitions

Abstract

To reach net-zero by 2050, clean energy deployment must accelerate at an unprecedented pace and scale. Yet project development pipelines are growing faster than they are being completed, with abandonment rates exceeding 80%. Current net-zero transition models, like Princeton’s Net-Zero Australia (NZAu) framework, fail to incorporate deployment realism into clean energy trajectories. This thesis presents CADENCE (Capital-Aware Deployment of Energy Networks with Coordinated Estimation), which extends the NZAu framework to incorporate real-world frictions. CADENCE is a computational model constructed with a Directed Acyclic Graph (DAG) and Critical Path Method (CPM) to temporally downscale NZAu clean energy trajectories into discrete projects exposed to cross-sectoral interdependencies, capital discipline, stochastic duration variability, and project abandonment. Across three scenarios simulating real-world coordination regimes – fragmented deployment, an IRA incentive rush, and China’s coordinated planning – CADENCE reveals that real-world deployment frictions cause capacity shortfall of NZAu targets by 11-30%. The results indicate that infrastructure sequencing and coordination are key to decarbonization, even more so than increasing capacity volume in development pipelines.