Zero-sum gains data envelopment analysis (ZSG-DEA) is a commonly used optimization tool for allocation and efficiency assessment, particularly in carbon quota allocation. Despite expansions, existing ZSG-DEA models is still insufficient in allocating bounded negative data. However, the allocation involved in negative data is critical, as the heterogeneity among decision-making units can be considered under tiny total quotas, while most countries have set net-zero emission goals recently. This paper proposes an enhanced ZSG-DEA to allocate and evaluate both positive and bounded negative allowance simultaneously, taking the provincial carbon quota allocation in China in 2060 as a case study when the total carbon equivalents allowance is zero. The results reveal the model’s capacity to assign positive, zero, or negative quotas in China. Four provinces are projected to have negative quotas, while nine provinces are expected to receive zero quotas in 2060, resulting in a savings of 431.4 billion yuan due to the consistent quotas. This research provides a crucial tool for policymakers to address the challenges associated with low or zero national carbon emission equivalents targets and to progress toward climate goals.