Urban decarbonization efforts increasingly recognize that achieving climate goals requires addressing uneven energy costs and efficiency conditions across communities. Yet fine-scale empirical evidence—especially for small and mid-sized cities—remains limited. This study develops a scalable analytical framework to evaluate neighborhood-level energy burden and residential energy efficiency, integrating principal component analysis, clustering, spatial hotspot detection, and spatial regression. Applied to Ithaca, New York, a city pursuing carbon neutrality by 2030, the framework identifies five neighborhood types and reveals pronounced spatial clustering shaped by income disparities, rental concentration, and building age. A key finding is that higher energy efficiency does not always correspond to lower energy burden; in several tracts, increased electrification without efficiency upgrades heightens cost stress. This decoupling underscores the need to consider both affordability and technical efficiency quality in local decarbonization planning. The results provide tract-level insights that can inform more equitable and place-sensitive energy transition strategies.