Optimizing Nighttime Warming for Solar Greenhouse Cucumber: An Integrated Bio-Economic Framework Combining Non-Linear Cost–Volume–Profit and Data Envelopment Analysis

High energy consumption in winter greenhouses poses a challenge to agricultural sustainability in Northern China, where heating costs typically account for 40–60% of total operating expenses. This study integrated a non-linear cost–volume–profit (CVP) analysis and data envelopment analysis (DEA) to balance cucumber yields with escalating energy costs. A single-season, single-factor experiment was conducted using insulated greenhouse compartments to evaluate four night temperature gradients (10 °C, 13 °C, 16 °C, and 19 °C). Results showed that although the 19 °C treatment (T3) achieved the highest marketable yield, it was associated with lower economic return because heating costs increased disproportionately. Among the four tested nighttime temperatures, the 16 °C treatment (T2) showed the most favorable observed combination of yield, net profit, and DEA-based efficiency indicators under the present experimental conditions. However, because the experiment was conducted in a single season within a compartment-based greenhouse system and the CVP relationship was fitted using treatment-level means, this result should be interpreted as a preliminary and condition-specific finding rather than as definitive evidence of a universal optimum temperature. Accordingly, the integrated bio-economic framework presented here is best viewed as an analytical prototype that merits further validation across multiple seasons, cultivars, and greenhouse systems.