Impact of on enterprises decision and profits under PF.

(a)Impact of on the carbon emission reduction level. (b)Impact of on profit difference.</p

Comparison of the results under different models with different preferential margin of interest rate.

(a) Comparison of the carbon emission reduction level. (b) Comparison of the retailer’s profit. (c) Comparison of the supplier’s profit.</p

Feasible region of CS.

(a) Impact of the interest rate on the feasible region of CS. (b) Impact of the sensitivity of market demand to carbon emission reduction level on the feasible region of CS.</p

S1 File -

There are significant differences in energy footprints among individual households. This study uses an environmentally extended input-output approach to estimate the per capita household energy footprint (PCHEF) of 10 different income groups in China’s 30 provinces and analyzes the heterogeneity of household consumption categories, and finally measures the energy equality of households in each province by measuring the energy footprint Gini coefficient (EF-Gini). It is found that the energy footprint of the top 10% income households accounted for about 22% of the national energy footprint in 2017, while the energy footprint of the bottom 40% income households accounted for only 24%. With the growth of China’s economy, energy footprint inequality has declined spatially and temporally. Firstly, wealthier coastal regions have experienced greater convergence in their energy footprint than poorer inland regions. Secondly, China’s household EF-Gini has declined from 0.38 in 2012 to 0.36 in 2017. This study shows that China’s economic growth has not only raised household income levels, but also reduced energy footprint inequality.</div

S1 Appendix -

Capital constraints hinder enterprises’ carbon reduction efforts and affect the sustainability of the supply chain. To alleviate this limitation, the core enterprise considers offering two financial-based carbon reduction incentive mechanisms: cost-sharing mechanism (CS) and preferential financing mechanism (PF). In a supply chain with the dual sensitivity of market demand to price and carbon reduction, we model each incentive mechanism, discussing their impact, value, and selection strategies. The results show that neither party under CS pursues an excessively high share ratio. Only a below-threshold sharing ratio can promote the supplier’s carbon reduction behavior and improve efficiency for both parties. Conversely, PF has a stable incentive effect on the supplier’s carbon reduction behavior and can effectively increase the retailer’s profits. However, a reasonable carbon reduction standard is needed to attract the supplier. In addition, as market demand becomes more sensitive to carbon reduction, the feasible range of CS narrows and that of PF expands. We compare players’ preferences of PF and CS and find a Pareto region in which all players prefer PF to CS. Finally, we test the robustness of our findings by an extending model. Our study provides guidance for supply chain decisions facing dual pressures of financial constraints and carbon reduction.</div

Symbols and corresponding notes.

Capital constraints hinder enterprises’ carbon reduction efforts and affect the sustainability of the supply chain. To alleviate this limitation, the core enterprise considers offering two financial-based carbon reduction incentive mechanisms: cost-sharing mechanism (CS) and preferential financing mechanism (PF). In a supply chain with the dual sensitivity of market demand to price and carbon reduction, we model each incentive mechanism, discussing their impact, value, and selection strategies. The results show that neither party under CS pursues an excessively high share ratio. Only a below-threshold sharing ratio can promote the supplier’s carbon reduction behavior and improve efficiency for both parties. Conversely, PF has a stable incentive effect on the supplier’s carbon reduction behavior and can effectively increase the retailer’s profits. However, a reasonable carbon reduction standard is needed to attract the supplier. In addition, as market demand becomes more sensitive to carbon reduction, the feasible range of CS narrows and that of PF expands. We compare players’ preferences of PF and CS and find a Pareto region in which all players prefer PF to CS. Finally, we test the robustness of our findings by an extending model. Our study provides guidance for supply chain decisions facing dual pressures of financial constraints and carbon reduction.</div

Data description of numerical examples.

Capital constraints hinder enterprises’ carbon reduction efforts and affect the sustainability of the supply chain. To alleviate this limitation, the core enterprise considers offering two financial-based carbon reduction incentive mechanisms: cost-sharing mechanism (CS) and preferential financing mechanism (PF). In a supply chain with the dual sensitivity of market demand to price and carbon reduction, we model each incentive mechanism, discussing their impact, value, and selection strategies. The results show that neither party under CS pursues an excessively high share ratio. Only a below-threshold sharing ratio can promote the supplier’s carbon reduction behavior and improve efficiency for both parties. Conversely, PF has a stable incentive effect on the supplier’s carbon reduction behavior and can effectively increase the retailer’s profits. However, a reasonable carbon reduction standard is needed to attract the supplier. In addition, as market demand becomes more sensitive to carbon reduction, the feasible range of CS narrows and that of PF expands. We compare players’ preferences of PF and CS and find a Pareto region in which all players prefer PF to CS. Finally, we test the robustness of our findings by an extending model. Our study provides guidance for supply chain decisions facing dual pressures of financial constraints and carbon reduction.</div

EF-Gini and income Gini coefficient for overall, rural, and urban China in 2012 and 2017.

The EF-Gini and income Gini coefficient are divided into eight consumption expenditure categories.</p

The PCHEF of different income groups and EF-Gini of each province.

All provinces are ranked based on GDP per capita, from left to right, from the poorest province with the lowest GDP per capita (Guizhou in 2012, Gansu in 2017) to the highest (Tianjin in 2012, Beijing in 2017).</p

The feasible region of PF.

(a) Impact of the preferential margin of interest rate on the feasible region of PF. (b) Impact of the sensitivity of market demand to carbon emission reduction level on the feasible region of PF.</p