###### Abstract
It has generated large proportions of emission reductions for some regions, provinces, or municipalities, making it difficult to achieve the targets and causing pain to the economies. Therefore, it is worthwhile and necessary to study an alternative allocation method that considers differences in regional industrial structures. As regions (provinces or municipalities) are different in economic and social development, emission reduction potential varies. Taking into account differences in carbon quota allocation schemes undoubtedly reduces the bias of targets and enhances feasibility. The differences among the three major industries (i.e., agriculture, manufacturing and mining, and service industries, hereinafter referred to as primary industry, secondary industry, and tertiary industry) in carbon quota allocation have not yet attracted attention in academia. In the literature, scholars propose that policymakers need to consider the criteria of efficiency and equality to better account for the unbalanced development among provinces. Many papers report on the allocation of provincial-level carbon quotas based on these two criteria. They show empirically that the heterogeneity arising from different sectors, regions, or industries has a significant impact on the allocation of carbon quotas [7; 8; 9; 10]. However, they have not provided a quantitative decision tool to incorporate the impact of the uneven development of provinces and the heterogeneity among the three major industries on carbon quota allocation. Hence, it is necessary to design a suitable mechanism to improve the provincial-level carbon quota allocation in China. In the literature, scholars tend to analyze a production process that simultaneously generates desirable and undesirable products as byproducts (e.g., pollutants) based on the joint-production theory of production economics [11; 12]. In addition, as a non-parametric evaluation technique, they often apply the Data Envelopment Analysis (DEA) technique to model joint-production processes with both desirable and undesirable outputs. Some researchers have proposed DEA-based models, taking into account efficiency and equality, and used them in provincial carbon quota allocation in China [13; 14]. Regarding the equality criterion, the Gini coefficient is a standard tool widely used to analyze income inequality. Many studies propose to measure the equality of carbon emissions among countries by the Gini coefficient [15; 16]. Based on the current studies, in this paper, to model the differences in regional industrial structures, we propose a parallel DEA model to allocate quotas and balance efficiency and equality considerations. Regarding the equality criterion, we draw on existing studies and use the Gini coefficient as an indicator of equality for carbon quota allocation. Regarding the efficiency criterion, this paper aims to maximize the overall desirable output potentials. The parallel DEA-based allocation model can generate more efficient results since it is capable of utilizing the information on the internal structural features of decision-making units (DMUs) [17; 18]. Finally, note that even though the three major industries belong to different fields and have their industry characteristics, they utilize similar input factors. Thus, the three major industries are modeled as three parallel sub-decision-making units (SDMUs). In general, the paper aims to investigate and recommend carbon quota schemes for the three major industries at the provinciallevel in China from the perspective of efficiency and equity. The contributions of this paper are twofold: first, distinct from the previous research, this paper takes into account the differences in the structure and development of the three major industries in each province and provides a more scientific and reasonable carbon quota allocation scheme among provincial industries under the criterion of balancing efficiency and equality.