This article focuses on carbon emission quota allocation regulations and their impact on investments in low-carbon technology within the electric power industry, as well as other enterprises’ decisions and profits under peak-valley price policy and cap-and-trade mechanism. An electricity supply-chain model is constructed in a Stackelberg game, which comprises an electricity-generating enterprise (leader) and an electricity retailer (follower). The backward-induction method is used to obtain the investment in low-carbon technology and electricity price under three scenarios of carbon emission quota allocation regulations: no regulation, grandfathering allocation, and benchmarking allocation. The main results are: first, benchmarking allocation always results in higher investment in low-carbon technology, but generates greater total carbon emissions than grandfathering allocation under most conditions; and second, under benchmarking allocation, as the unit carbon emission quota increases, total carbon emissions first increase and then decrease. Therefore, benchmarking allocation is the better regulation for the low-carbon technology investment. Moreover, benchmarking allocation is also the better regulation for total carbon emissions with a higher unit carbon quota or a higher carbon price.

This paper explores the incentives of investment in renewable energy of two utility firms who compete or cooperate under either a cap-and-trade grandfathering mechanism (GM) or benchmarking mechanism (BM). We find that utility firms will invest in renewable energy more under BM than under GM, in both competitive and cooperative markets, and they will invest more in a competitive market than in a cooperative market, under either GM or BM. Furthermore, utility firms will produce more electricity and generate more total carbon emissions under BM than under GM. The profits of two firms, however, are higher in cooperative market than in competitive market. The government will benefit from implementing a BM to encourage utility firms to invest in renewable energy in a competing market.

Purpose: The purpose of this paper is to study the effects of relational mechanisms and market contracts on cross-enterprise knowledge trading in supply chain and to examine the role of market contracts. Relational mechanism is categorized into indirect and direct relational mechanism in this paper. Cross-enterprise knowledge trading is categorized into explicit and tacit knowledge trading. The indirect relational mechanism is mainly expressed by knowledge brokers, while the direct relational mechanism consists of shared goals and trust. Design/methodology/approach: Multiple regression analysis was performed on questionnaire data from 256 Chinese manufacturing enterprises in supply chain in order to assess the relationships between relational mechanisms, market contracts and cross-enterprise knowledge trading. Findings: The results show that knowledge brokers and market contracts have significant and positive effects on explicit knowledge trading, but the effects on tacit knowledge trading are not significant. Shared goals and trust have significant and positive effects not only on explicit knowledge trading but also on tacit knowledge trading, while trust has a stronger positive effect on tacit knowledge trading than explicit knowledge trading. Finally, the moderating effects of market contracts are proven in the relationships between relational mechanisms and knowledge trading, excluding the relationship between knowledge brokers and tacit knowledge trading. Originality/value: Previous studies about the cross-enterprise knowledge trading in supply chain focused on theoretical research which did not match with reality, especially in China, where the relational mechanism in trading activities is strong. Based on relational exchange theory and transaction cost theory, a conceptual model for the effects of relational mechanisms and market contracts on cross-enterprise knowledge trading in supply chain is proposed in this paper, and then empirically tested using the data collected from 256 Chinese manufacturing enterprises in supply chain with multiple regression models. The findings provide a theoretical basis for knowledge trading participants selecting an appropriate governance mechanism to promote knowledge trading, and these also guide the knowledge trading among members of supply chain in practice.

This article constructs a two-stage supply chain consisting of a manufacturer (producing both fuel vehicles (FV) and new energy vehicles) and a retailer (selling both fuel vehicles and new energy vehicles) based on dual credit policy, considers three different power structure models, including the vertical Nash game model, the manufacturer Stackelberg game model, the retailer Stackelberg game model, and explores the operational strategy issues of new energy vehicle (NEV) enterprises under the dual credit policy. By comparing the optimal equilibrium solutions under different channel power structures, our findings indicate that (1) When the demand function is linear, the vertical Nash game model can achieve the highest system profit of the automotive supply chain, and in the manufacturer Stackelberg game model, the profit of the automaker is higher than that of the retailer, while in the retailer Stackelberg game model, the profit of the automaker is lower than that of the retailer. (2) The demand and pricing for FV and NEV in different models are determined by the range of FV and NEV production costs, credit trading prices, and the proportion of NEV credits. (3) Both the increase in the credit trading prices and the proportion of NEV credits will promote increased profits for the manufacturer and retailer. (4) The increase of the price sensitivity coefficient will reduce the demand as well as the profit of auto supply chain members.

This paper investigates and develops an electricity supply chain with an electricity generator and electricity retailer. The electricity generator invests in renewable energy and sells the electricity to the electricity retailer; the retailer then sells the electricity to consumers according to electricity service quality. Three models are considered: the vertical Nash game, the electricity-generator Stackelberg model, and the electricity-retailer Stackelberg model. Finally, the optimal solutions under different power-structure models are compared. Certain conclusions can be drawn from the findings: (1) the highest electricity service quality and renewable energy investment are obtained in the vertical Nash game model, the electricity generator obtains more profits in the electricity-generator Stackelberg model, and the electricity retailer obtains more profits in the electricity-retailer Stackelberg model. (2) The investment in renewable energy in the electricity-generator Stackelberg model is less than that in the electricity-retailer Stackelberg model, and the investment in electricity service quality in the electricity-retailer Stackelberg model is less than that in the electricity-generator Stackelberg model (under certain conditions). (3) Both increasing the electricity service quality sensitivity and renewable energy sensitivity could increase the optimal electricity price, electricity demand, electricity service quality, renewable energy investment, and supply-chain enterprises' profit.