Optimizing production of new energy vehicles with across-chain cooperation under China’s dual credit policy

doi:10.1016/j.energy.2019.116832

Energy

Volume 194, 1 March 2020, 116832

https://doi.org/10.1016/j.energy.2019.116832 Get rights and content

Highlights

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Developing production models of NEVs/CVs under dual credit policy.
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Investigating across-chain credit trading cooperation using MILP approach.
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Comparing the impact of both subsidy and dual credit policy on the CV supply chain and NEV supply chain system.

Abstract

New energy vehicles (NEVs) are becoming more and more prevalent for economic and environmental reasons. This paper investigates the issue of the impacts of subsidy policy and dual credit policy on NEVs and conventional vehicles (CVs) production decision from an across-chain perspective, in a co-opetitive context, where exists a CV supply chain and a NEV supply chain with two important schemes involved, i.e., government subsidies and dual credit. While previous literature has discussed government subsidies excessively, they seldom study the role of dual credit policy in promoting NEVs. To examine the differences between two schemes, a mixed integer linear programing (MILP) is utilized to develop a stylized production model for a CV supply chain and NEV supply chain system that incorporating subsidies and dual credit trading simultaneously. Using a Lagrange heuristic algorithm to provide an optimal solution regarding NEV and CV production decision as well as dual-credit trading. Simulations are performed on realistic profiles that show, (i) implementing the dual credit policy increases the profit of NEV supply chain, whereas the profit of CV supply chain and of whole supply chain system decline simultaneously, and the schedule of CVs/NEVs without across-chain cooperation is arranged more evenly than that with across-chain cooperation during the transit period to NEVs. Meanwhile, (ii) under dual credit policy, gradually-decreasing subsidies can partially offset the negative impacts of dual credit policy on the NEV supply chain, the subsidies can only serve as a temporary supplement to profits. In addition, (iii) there exists an optimal NEV credit price $p^{*}$ to maximize the overall profit of the whole system, and a corresponding threshold value of $p^{*}$ for two categories of cars, when above the threshold, the per-CV profit outperforms the per-NEV one and vice versa.

Introduction

Greenhouse gases are one of the main critical factors effecting environment and responsible for climate change. Fossil fuel-based vehicles in automobile industry are considered as a major source to greenhouse gas emissions. To tackle such dreadful problem, the development of new energy vehicles (NEVs) has become an accessible way to reduction of reliance on fossil fuels. Meanwhile, widespread usage of NEVs can alleviate global warming and shortage of non-renewable energy, and in turn ultimately benefit the sustainability of development of automobile industry.

However, the NEV industry is still in an infancy and incubation period in recent years. As an example, China, as a major country of production and sales of NEVs, is experiencing a transit period, a report from China National Bureau of Statistics revealed that the production volume of China’s NEVs reached to 0.794 million and sales to 0.777 million in 2017, an increase of 53.8% and 53.3% year-on-year, only accounting for 2.74% and 2.69% of the aggregate production and sales of vehicles, respectively. At the initial stage of development, promoting NEVs will inevitably encounter various obstacles, some of which are unique to China, such as imperfect government policies, local protectionism, while the others shared with some countries include high costs of battery [1], prohibitive initial production [2], short cruise distance [2], insufficient charging infrastructure [3,4], unsafe and unreliable battery [5], technical defects [6,7], pre-owned NEV and battery waste disposal [8]. Although these defects need further fixing, it is no doubt that the development of NEVs is an irreversible future trend for automobile industry.

Many countries have formulated various policies to incentive manufacturing and using NEVs, and restricting the production and sales of CVs. Among them, two typical schemes are the Cap-and-Trade policy issued by EU (European Union) and the California’s Zero Emission Vehicle (ZEV) mandate by the United States [9]. Different from the EU and US counterparts, Chinese governments have adopted subsidy policies to promote the penetration of NEVs since 2009 [10]. Although the subsidy mechanism has made considerable progress in ushering NEVs into markets in China, it also has caused some obvious shortcomings. Firstly, a large amount of subsidies for NEVs have imposed a heavy fiscal burden on Chinese government [11]. Secondly, imperfect policies have driven a few enterprises to cheat on NEV production numbers to gain central and local government subsidies illegally. Thirdly, current policies poorly facilitate car makers to focus on improving the technological level of NEVs as well as CVs. Therefore, Chinese governments have decided to revise the previous subsidy policies to amend the above drawbacks.

The newly issued policy called as the New-Energy Vehicle Credit Program and Corporate Average Fuel Consumption Regulation (hereinafter referred as dual credit policy) was regulated by Chinese government in September 2017. This policy applies only to passenger cars and has formally taken effect from April 1, 2018. Meanwhile, the previous subsidy policy will have been gradually withdrawn until 2020. Hence, dual credit policy is playing a crucial role in the development of China’s automobile industry for improving fuel saving and developing NEVs. Apart from that the dual credit policy has a great impact on car makers’ strategic choice, it also influences their operational decision-making, which manifests two spheres: (i) how to make production decision regarding NEVs and CVs; (ii) how to trade NEV credits under across-chain cooperation.

In line with dual credit policy, a CV maker is required to meet two conditions before being allowed to manufacture, namely, one is that each CV unit needs a specific amount of NEV credits, the other is that the actual corporate average fuel consumption (CAFC) for production-planning CVs must be lower than the national standards, otherwise, additional NEV credits are needed. As a result, with an aim to produce CVs in a planning period, a CV manufacturer must possess enough NEV credits through two channels, i.e., making NEVs, and purchasing NEV credits.

In reality, to handle the ongoing dual credit policy there exists four alternatives for car makers to obtain NEV credits: independent production, joint production, outsourcing, and purchasing NEV credits. (i) In the case of independent production, some traditional CV makers, like BYD, BAIC BJEV, have run NEV business at an early stage, their own annual accumulated credits from self-producing NEVs guarantee themselves to manufacture CVs. (ii) For the case of joint production, those CV makers without the capabilities of producing NEVs must cooperate with outside partners through jointly establishing a NEV firm, thus acquire sufficient credits to produce CVs. For instance, the cooperation between BMW and Great Wall Motor, Volkswagen and JAC, Ford and Zotye Auto belong to this category. (iii) In the case of outsourcing, some Internet-based NEV makers have newly emerged in the market, they are also referred to the new forces of making NEVs (like Xiaopeng, Weilai, Qidian), for the lack of the license of making NEVs at present, adoption of outsourcing strategy is only choice for them to manufacture NEVs. (iiii) Those CV makers unwilling to make NEVs must purchase credits from other NEV makers with credit surplus, thus enable them to continuously run their CV business, for instance, SAIC Motor, Beijing-Hyundai Auto prefer to this group. In brief, no matter which alternative is chosen, getting sufficient credits from inside or outside is key issue for car makers to run their production business under dual credit policy.

Therefore, this paper focuses on the issue of production under across-chain cooperation between a NEV maker and a CV maker in the presence of dual credit policy, and handles the following questions: (1) to optimize production schedule between a CV supply chain and a NEV supply chain under dual credit policy; (2) to compare the impacts of the two policies on supply chain’s performance without and with across-chain cooperation; (3) to examine optimal trading credit price and credit conversion rate under dual credit policy.

To address these questions, we develop two stylized models that capture the elements of the NEV and CV production environment, where there is a supply chain system with two supply chains, one is to manufacture CVs, the other to NEVs, with a goal of maximizing the total profit of two supply chains. We first analyze production schedule of the whole supply chain system under subsidy policy without across-chain cooperation, then we extend to the model by considering dual credit policy with across-chain cooperation. Finally, we utilize Lagrange heuristic algorithm to compute out the optimal production solution, and examine the impacts of dual credit policy on the overall profit and each individual chain profit, with and without across-chain cooperation, respectively. Using these results, we determine how to arrange production schedule as well as examine the optimal trading credit price.

The main contributions in this paper lie in three facets: (1) unlike previous literature only focusing one single supply chain, we explore the optimal production of NEVs and CVs in the context of two supply chains (i.e. a CV supply chain and a NEV supply chain) under subsidy policy; (2) we introduce dual credit policy to investigate production decision making under across-chain cooperation between a CV supply chain and a NEV supply chain by utilizing MILP approach; (3) we examine the differences of two supply chains in performance and production schedule under two different policies, which is seldom addressed before.

The remainder of this paper is configured as follows. In the next section the relevant literature is briefly reviewed. Section 3 simply describes the related parameters, scenario assumptions. and proposes the model under subsidy policy and dual credit policy, respectively. Section 4 introduces the input data. The optimization results and analysis are discussed in Section 5, which is followed by the conclusions in Section 6. The Lagrange heuristic algorithm and the corresponding pseudocode are shown in Appendix.

Section snippets

Literature review

An army of extant literature concerning incentive policies for reducing gasoline consumption and promoting the usage of NEVs have been published by scholars. Among these literature, Jun et al. argue that the Korea’s CAFE policy has a direct and signiﬁcant impact on improving fuel economy and increasing the market share of fuel-efﬁcient vehicles, thus boosting the development of technologies for enhancing fuel economy [12]. Meanwhile, Greene et al. investigate the role of the California’s ZEV

Sets and parameters

Sets.

$g \in G {g = N, C}$ G is the set of vehicle category, where $g = N$ denotes new energy vehicles and $g = C$ denotes conventional vehicles
$i \in I$ ${i = 1,2}$ I is the set of NEVs, where i = 1 denotes battery electric vehicles; i = 2 denotes plug-in hybrid electric vehicles
$j \in J {j = 1,2}$ J is the set of CVs, where j = 1 denotes low-fuel consumption vehicles; j = 2 denotes high-fuel consumption vehicles
$t \in T$ ${t = 1,2 \dots}$ T is the set of production period

Related variables.

$p^{*}$ unit new energy vehicle credit price
$P_{i t}^{N}, P_{j t}^{C}$ retail

Data input

The input data of the proposed model include retail prices, production setup costs, production costs and inventory holding costs with respect to NEVs and CVs, as well as fuel efficiency. These data shown in Table 1 are from both of companies, namely, Jiangling Electric Vehicle Limited (short for JMEV) and Jiangling Motors Corporation Group (short for JMC). For JMEV, as a major NEV maker in Jiangxi province of China, its annual production and sales of NEVs reached over 0.038 million in 2017, its

Optimal results

The model without across-chain cooperation under government subsidy policy and with across-chain cooperation under dual credit policy are a MILP problem. We use the Lagrange heuristic algorithm (see Appendix) to compute the optimal solutions of the two models through the MATLAB software [32,33,35], and the results of the two models are shown in Table 5.

From the perspective of profit, as shown in Table 5, the total profits of two supply chains under dual credit policy are lower than that under

Conclusions

This paper proposes two MILP models to help decision makers to determine the optimal production of NEVs without across-chain cooperation under subsidy policy and with across-chain cooperation under dual credit policy. Then the numerical experiment and sensitivity analysis are conducted, to examine how the government subsidy, credit conversion rate, NEV credit price and the proportion of constructing charging station have impacts on the whole system’s performance. The specific findings are as

Acknowledgements

The authors would like to thank the editor and the reviewers’ valuable comments for revising and improving our paper. The paper is supported by the National Natural Science Foundation of China (71964023, 71472143, 71872076); Social Science Key Program of Jiangxi and Hubei Province (17GL01, GL17121, 20181ACB29003, 19D048).

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Optimizing production of new energy vehicles with across-chain cooperation under China’s dual credit policy

Highlights

Abstract

Introduction

Section snippets

Literature review

Sets and parameters

Data input

Optimal results

Conclusions

Acknowledgements

Renew Sustain Energy Rev

Energy Policy

Renew Sustain Energy Rev

Renew Sustain Energy Rev

Renew Sustain Energy Rev

Renew Sustain Energy Rev

Renew Sustain Energy Rev

Renew Sustain Energy Rev

J Clean Prod

Energy

Energy Policy

Energy Strateg Rev

Transp Res A Policy Pract

Energy Policy

Energy Policy

Comput Ind Eng

Eur J Oper Res