Reverse logistics is an important way to realize sustainable production and consumption. With the emergence of professional third-party reverse logistics service providers, the outsourcing model has become the main mode of reverse logistics. Whether the distribution of cooperative profit among multiple participants is fair or not determines the quality of the implementation of the outsourcing mode. The traditional Shapley value model is often used to distribute cooperative profit. Since its distribution basis is the marginal profit contribution of each member enterprise to different alliances, it is necessary to estimate the profit of each alliance. However, it is difficult to ensure the accuracy of this estimation, which makes the distribution lack of objectivity. Once the actual profit share deviates from the expectation of member enterprise, the sustainability of the reverse logistics alliance will be affected. This study considers the marginal efficiency contribution of each member enterprise to the alliance and applies it to replace the marginal profit contribution. As the input and output data of reverse logistics cannot be accurately separated from those of the whole enterprise, they are often uncertain. In this paper, we assume that each member enterprise’s input and output data are fuzzy numbers and construct an efficiency measurement model based on fuzzy DEA. Then, we define the characteristic function of alliance and propose a modified Shapley value model to fairly distribute cooperative profit. Finally, an example comprising of two manufacturing enterprises, one sales enterprise, and one third-party reverse logistics service provider is put forward to verify the model’s feasibility and effectiveness. This paper provides a reference for the profit distribution of the reverse logistics.

Air pollutants and CO2 emissions have a common important source, namely energy consumption. Considering fairness and efficiency, the provincial coordinated allocation of energy consumption, air pollutant emission, and carbon emission (EAC) quotas is of great significance to promote provincial development and achieve national energy conservation and emission reduction targets. A weighted environment zero-sum-gains data envelopment analysis (ZSG-DEA) model is constructed to optimize the efficiency of the initial provincial quotas under the fairness principle, so as to realize the fairness and efficiency of allocation. The empirical analysis in 2020 shows that the optimal allocation scheme proposed in this study is better than the national planning scheme in terms of fairness and efficiency, and the optimal scheme based on the initial allocation of priority order of “capacity to pay egalitarianism > historical egalitarianism > population egalitarianism” is the fairest. The optimal allocation scheme in 2025 can achieve absolute fairness. In this scheme, the pressures of energy conservation and emission reduction undertaken by different provinces vary greatly. The implementation of regional coordinated development strategies can narrow this gap and improve the enforceability of this scheme. Combined with the analysis of energy conservation and emission reduction in seven categories and three major national strategic regions, we put forward corresponding measures to provide decision support for China’s energy conservation and emission reduction.

Energy consumption is an important source of the emissions of CO2 and air pollutants such as SO2 and NOX. Reducing energy consumption can realize the simultaneous reduction of air pollutants and CO2 emissions to a certain extent. This study examines the collaborative allocation of energy consumption and the emissions of SO2, NOX and CO2 in China. In contrast to previous studies, this paper proposes an improved centralized DEA model that takes into account the correlation between energy consumption and air environmental emissions, the economic development demand and the energy resource endowment of different provinces. The initial allocation scheme is obtained based on the principle of equity. Then, the initial allocation results are brought into the improved centralized DEA model to maximize the expected output. The empirical analysis of projected data for 2025 shows that the looser the restrictions of energy consumption, the greater the optimal economic output. When the energy consumption of each province is allowed to fluctuate within the range of 85% to 115% of the initial quota, the total GDP is the largest and 20.62% higher than the initial GDP. The optimal allocation scheme is more equitable than the initial scheme and realizes absolute interpersonal equity and economic equity. Eighteen provinces bear the pressures of energy saving, emission reduction or GDP growth, with average pressure indexes of 11.46%, 16.85% and 40.62%, respectively. The pressures on the major regions involved in the “Belt and Road”, Beijing-Tianjin-Hebei region and Yangtze River Economic Belt national strategies will thus be reduced significantly; the maximum pressures on energy saving, emission reduction and GDP growth are 10.03%, 12.17% and 29.84%, respectively. China can take a series of measures to promote regional coordinated development and improve the realization of optimal allocation schemes, including establishing unified resource asset trading platforms, improving the methods of regional cooperation, building effective transportation and logistics transport networks to weaken the barriers among regions and implementing differentiated regional policies and regional interest coordination mechanisms.