Research on the supervision mechanism of new energy time-sharing rental vehicles in the background of carbon peak

Abstract

At present, new energy time-sharing rental vehicles have become a new generation of travel modes, playing a positive role in environmental protection and being regarded as one of the most promising travel modes in the future. However, due to the rapid pace of market expansion, the problems in their development have come one after another, such as the leakage of personal information, difficulty refunding the deposit and other phenomena frequently occurring, causing problems for consumer user experience. The reasons are as follows: government policies and regulations need to be perfected; the level of enterprise operation needs to be observed, financing is difficult, and the level of consumer participation in supervision is low. Based on the traits of bounded rationality in the internet plus the periodic rental market, this paper introduces the government into the market subject and constructs a three-party dynamic evolution game model between the government, enterprises, and consumers to reveal the control strategy. This paper conducts a concrete analysis to verify the model using a case study by revealing the game process between the regulation strategy and behavior decisions of enterprises and consumers and provides a theoretical basis and reference for policy-making and decision-making. The results showed that when the key parameters are in different numerical ranges, the system has four evolutionary stability results. By appropriately increasing the number of subsidies and penalties, increasing the proportion of the compensation coefficient to consumers, and urging enterprises to reduce operating costs, government participation is conducive to the healthy development of the new energy time-sharing automobile industry.

Keywords

New energy time-sharing leasing evolutionary game supervision

Introduction

In September 2020, at the 75th Session of the Nations General Assembly, President Xi Jinping proposed China's carbon peak by 2030 and carbon neutral by 2060. At present, China is embarking on a new journey to build a modern socialist country in an all-round way. Achieving carbon peak and carbon neutral is crucial to accelerating ecological progress and promoting high-quality development. As the world's largest developing country and carbon emitter, China needs to achieve rapid emission reduction while promoting development, which is an arduous task. New energy vehicles are an important way to achieve carbon peak and carbon neutrality. With the development of the new energy vehicle industry and the sharing economy, the time-sharing rental vehicles emerged and developed rapidly, and became an integral part of urban public transportation.

With the booming development of the internet and the popularization of mobile terminals, the sharing economy, based on platforms created by third parties, has spawned a large number of new economic models by making full use of idle resources, integrating offline resources such as cars, bicycles and labor, and connecting supply and demand. Time-sharing rentals in the sharing economy are a new public transportation mode emerging in China in recent years. The concept of time-sharing rental first appeared in Zurich Co-op in Switzerland in 1948 and is now a car rental model based on the internet and the internet of vehicle technology Shaheen and Cohen,¹ Dimatulac et al.² which changed the traditional concept of car ownership Schaefers³ and played a positive role in reducing the total number of traditional vehicles, environmental impact and road congestion Firnkorn,⁴ Jorg Firnkorn⁵ and Becker.⁶ This is regarded as one of the most promising modes of travel in the future. Time-sharing rental maximizes vehicle utilization benefits through the rational deployment of limited vehicles to serve different customers with the right of use at different times Baptista et al.⁷ Time-sharing rental enables owners and users to achieve a win–win situation, meet the needs of market development and create new space for their own development Bardhi et al.,⁸ Carrera et al.⁹ and Lu et al.¹⁰

With the proposal of “carbon peak carbon neutrality”, the use of new energy has received increasing attention and has been gradually applied to various production fields. New energy vehicles are one of the typical representatives. New energy vehicles are new technology vehicles driven by primary energy, such as solar energy, wind energy, biomass energy, geothermal energy, ocean energy and secondary energy, such as hydrogen energy and fuel cells Zhou et al.,¹¹ Niu et al.¹² New energy vehicles abandon the defects of previous cars that can only be powered with fossil fuels. Environmental protection, energy savings and other aspects have made great contributions in recent years in the international car market Li et al.,¹³ Zuo et al.¹⁴ and Zhang et al.¹⁵ The Chinese government has issued a number of policies to promote the development and use of new energy vehicles to solve the problems of excessive energy consumption and environmental pollution. Representative new energy vehicle companies include Tesla of the United States and BYD of China, and many traditional auto companies, such as BMW and Audi, have also launched their own new energy vehicles. This shows that as an emerging market, new energy vehicles have been on the rise. Although China's new energy vehicle industry has made remarkable achievements, new energy vehicles still have high prices, short range, insufficient driving experience, weak charging infrastructure and other problems compared with traditional vehicles. As a result, the promotion and market operation of new energy vehicles in China still face some bottlenecks Liu et al.¹⁶

A new energy time-sharing leased vehicle is a shared product formed by the combination of new energy vehicles and a time-sharing rental mode. On the one hand, it accelerates its development with the sharing economy, and on the other hand, with the state vigorously supporting the new energy vehicle industry, it sets off an upsurge of time-sharing rental of new energy vehicles. In recent years, research on the time-sharing rental of new energy vehicles has focused on pricing strategies, vehicle scheduling and government policy formulation. Some studies have found that the biggest problem of time-sharing rental is the insufficient number of rental points and coverage Xu.¹⁷ Given the location of the rental point, some scholars have targeted the profits of the operating organization, taking into account depreciation of the vehicle and other factors, and proposed a linear programming model based on the number of selected time-based rental locations and parking spaces Correia et al.,¹⁸ Zhou et al.¹⁹ For time-sharing lease scheduling, a time-sharing lease dynamic scheduling model is established with the lowest operating cost as the objective function, and rental pricing is discussed based on management concepts Kaplan et al.,²⁰ Lahkar.²¹ Some scholars apply the relevant theories of income management to the allocation and scheduling of time-sharing rental vehicles, while others use decision support tools to evaluate the efficiency of the car rental system, aiming to maximize the user's demand for the minimum number of vehicles, which is also a scheduling problem Chen et al.,²² El Fassi et al.²³ and Nöldeke et al.²⁴ Some scholars have also conducted relevant studies on the optimization of time-sharing leases. Based on the goal of maximizing profits for operators, a rental vehicle allocation model was established. By employing the model, the maximum benefit of rental peak period operators and the corresponding optimal user reservation allocation plan are obtained.²⁵

In recent years, research on the regulation of new energy vehicles has achieved significant results. According to the research results, the current supervision of new energy vehicles is mainly focused on the formulation of government policies and the layout of charging stations, parking spots and other facilities. Yuan et al.²⁶ and Liu et al.²⁷ proposed that policy guidance and planning play a crucial role in the development of the new energy vehicle industry by analyzing existing technologies and policies. Zhong²⁸ and Huang et al.²⁹ used different methods to analyze the government's subsidy strategy for new energy vehicles. In addition, Zhong et al.,²⁸ Fan et al.³⁰ and Zhang et al.¹⁵ analyzed the adverse selection problem in the process of subsidies by using a signal game and proposed suggestions to improve the efficiency of subsidy policies. Huang et al.³¹ constructed an evolutionary game model of new energy and studied the competitive strategy of time-sharing car rentals in the market. Chen et al.³² used an evolutionary game model to study the dynamics of “cheating and subsidies-supervision” between new energy vehicle enterprises and the government under the condition of bounded rationality.

Based on the above studies, the policy suggestions proposed in our conclusion all start from the level of government supervision and only consider the problem of regulatory failure when the government is an independent regulatory subject, without considering the introduction of a third party into the regulatory system for research.

At present, some scholars have studied the regulatory failure of the government as a single subject and proposed that the information asymmetry between the government and enterprises is the main reason. Stiglitz et al.³³ believed that information asymmetry was an important reason for the decline in the effect and increase in the cost of government regulation. Tirole et al.³⁴ and other scholars also put forward that in government regulation under the condition of information asymmetry, efficiency and information rent are contradictory, and government regulation needs to pay the price of efficiency. Zhou et al.³⁵ studied the information asymmetry among consumers, enterprises and government. In fact, consumers’ participation in supervision as a third party is an effective method to reduce the degree of information asymmetry between the government and enterprises. Some scholars have brought consumers into the regulatory system as a third party and achieved good results. Qin et al.³⁶ and Chen³⁷ adopted the perfect information dynamic game method to build a tripartite game model based on the government, enterprises and consumers to study the market promotion and supervision of electric vehicles, emphasizing the importance of tripartite participation of the government, enterprises and consumers in the supervision process of new energy vehicles. Wang et al.³⁸ proposed that the strategic choice of new energy vehicle manufacturers would be influenced by government subsidies, penalties and consumers’ environmental awareness.

From the preceding discussion, we propose that existing studies lack analysis on the regulatory system of new energy vehicles in the time-sharing rental field. In fact, the government, enterprises and consumers form a multidynamic game relationship under asymmetric information in the regulation of the new energy time-sharing rental automobile industry. However, the current research focuses on static games or perfect information. Dynamic evolutionary game strategy research and path analysis on the regulatory system of the new energy time-sharing rental vehicle industry in the absence of incomplete information. In view of the special situation of the new energy time-sharing rental vehicle industry, it is difficult to break the “production and marketing dilemma” only through the game between producers and consumers. Only by involving the government as a third party in the game process can demand growth be stimulated and sustainable development of the industry be promoted.

Therefore, this article will focus on new energy timeshare rental car tripartite regulatory analysis, build the information asymmetry between and among the dynamic evolutionary game model, explore the game relationship between the three groups, try to determine the optimal strategy choice set three types of groups in different periods, and allow consumers to participate in the game model to design the optimal contract mechanism to achieve the new energy time-sharing rental vehicle industry supervision of the optimal state and give the corresponding policy suggestions.

Supervision status of the new energy time-sharing rental vehicle industry

The development of new energy vehicles has become the general trend, and the global automotive industry transformation and upgrading of the consensus, including China and some other countries, have put forward the full electrification of vehicles, banning the sale of traditional fuel vehicles and other strategic goals. In 2015, the number of new energy vehicles in China was 420,000, and in 2020, the number of new energy vehicles was 4.92 million, with a compound growth rate of approximately 63.6% in the past five years. The penetration rate of new energy vehicles was approximately 1.8%, indicating broad room for growth. Production and sales ranked first in the world for five consecutive years, and the cumulative promotion exceeded 5 million, accounting for more than 50% of the world (Figure 1).

Figure 1.

2015-2020 New energy vehicle holdings and annual growth rate.

New energy time-sharing rental car companies have been entering the market in China since 2015 Li.³⁹ At present, there are more than 100 new energy vehicle time-sharing leasing enterprises in China, with more than 200,000 vehicles Feng.⁴⁰ Time-sharing leasing of new energy vehicles can improve vehicle efficiency, promote the conversion of new and old energy sources, and alleviate environmental problems. Therefore, the development of shared cars will be strongly supported by society and the government. However, the problems existing in the operation of new energy time-sharing rental vehicles are complex and frequent, which have caused adverse effects on society. Therefore, the regulation of new energy time-sharing rental vehicles is imperative. However, since the new energy time-sharing rental vehicle model emerged and developed in China in a short time and is still in the exploratory stage, all time-sharing rental vehicle platforms are still making active attempts and adjustments in operation mode, revenue and expenditure mode, user experience and other aspects. At present, China's supervision of the new energy time-sharing rental vehicle industry is still in its early stage, systematic, perfect and special laws and regulations have not been issued, and the supervision system is not perfect.

Government laws and regulations often have a lag. For example, when new energy vehicles were just emerging, the government, in the attitude of supporting enterprises, gave strong support to the production, technological innovation and sales tax of new energy vehicles, but they were taken advantage of by criminals, and there were “subsidy fraud” incidents. Therefore, the government adjusted the subsidy amount and subsidy policy for new energy vehicles in subsequent policy documents, which is the so-called “retrograde” policy Ma et al.⁴¹ Faced with problems such as ineffective operation, lax review, difficulty in defining rights and responsibilities, and difficulty in improving the credit system, further studies are needed on what targeted policies should be issued by government departments and how to manage them to deal with the emerging shared travel mode of “new energy” plus “time-sharing leasing”. Therefore, in the process of industry supervision, the government, as the subject of supervision, needs to cooperate with consumers for good information exchange and should try to reduce the information asymmetry between enterprises to fulfill its responsibilities and play the role of the subject of supervision.

At present, new energy time-sharing leasing vehicle enterprises can be divided into two categories: active management and passive management. However, in the passive management of enterprises, there are also some due to the lag of policies and regulations, and enterprises have to operate passively at high costs. Therefore, it is a very important factor to know enterprises’ effort level in operation. The operation of new energy time-sharing leasing vehicle enterprises includes the production or purchase of vehicles, platform operation, vehicle maintenance and inspection, qualification examination and after-sales service. It is difficult to observe the effort level in most links. The more transparent the information is, the higher the probability that the negative operation and illegal operation of the enterprise will be discovered, and the industry will develop increasingly in a healthy direction. To observe the level of enterprise operation, it is difficult to rely on the supervision by government departments alone, and consumers must be encouraged to participate in the supervision.

However, in the use of new energy time-sharing rental vehicles, unethical or illegal operation of enterprises has the greatest impact on consumers. However, in the case where there is no great personal injury or property loss, most consumers will choose to remain silent and not complain about the enterprise. The reason for this situation lies in the high cost of consumer participation in oversight, mainly reflected in time cost, energy cost and so on. However, enterprises will not necessarily be found to be operating illegally after examination, even after consumers pay a high cost Consumers may obtain less than expected benefits, which will greatly reduce enthusiasm for oversight. To change this situation, two approaches are needed. The first is to improve the probability of illegal enterprises being investigated and punished. The decisive factor of this change lies in the strategic choice of government departments. The second is to establish an incentive mechanism for consumers. That is, the compensation amount after consumers participate in the oversight should be clear. Only when these two issues are optimized can the current low level of consumer participation in oversight be improved.

Game theory model

Hypothesis

Evolutionary game theory originated from Darwin's theory of evolution.⁴² The basic idea is that the decision-makers of bounded rationality constantly adjust the strategy in the process of repeated games and finally form a stable equilibrium point in the evolution process.⁴³ Evolutionary game theory is used widely in the field of regulation by means of replication dynamics.⁴⁴

Under the supervision of many parties, the study of evolutionary games has become increasingly mature. Some scholars have successfully applied the three-party evolutionary game model to the tourism market,^{45, 46} food safety,⁴⁷ environmental pollution⁴⁸ and medical treatment⁴⁹ and developed market evolution conclusions and proposed policy regulatory recommendations.

This paper assumes that the main players involved in the new energy time-sharing rental car supervision process are the government, enterprises, and consumers. Brief definitions and strategic choices of each of these players are given as follows:

Government: The new energy time-leasing departments of automobile industry supervision and subsidies, including the Ministry of Communications, the Ministry of Communications, the Ministry of Finance, the Ministry of Science and Technology, and the National Development and Reform Commission, play joint roles in supervision and coordinated promotion of governance. As the government is the policy-maker and the dominant player in the game, the choice of the game strategy is regulation and nonregulation; that is, the two strategies are adopted to supervise the behavior of enterprises to achieve the optimal situation of industry governance and let the industry develop freely to achieve a stable state.

Enterprise: A shared automobile enterprise that adopts the characteristics of new energy for automobile power, a time-lease business mode for renting while running, and parking mode for vehicles as “stop and go”. Due to either cost constraints or greater benefits, enterprises involved in the production of new energy vehicles will have to deduct the subsidies paid by the government and cut corners. In addition, in the operation process of time-sharing car rentals, consumers are not strictly audited, the service is not good, the number of parking and charging points is too small or the planning is unreasonable. Under the supervision of the government and consumers, enterprises plan to adopt the two strategies of “endeavor” and “don't endeavor”.

Consumers: As direct users of this emerging shared economic phenomenon, there will be negative comments on the new energy time-sharing car rental industry in the process of using the new energy timesharing car rental service due to enterprises’ bad operation and other behaviors. Weighing the gains and losses of participation in supervision costs and service improvement and compensation gains determines the set of strategies of consumers in the game system, which are to either participate in supervision-, report- and feedback-related bad behaviors or maintain a wait-and-see attitude.

From the above definition of concepts and model assumptions, a three-party game model of government–enterprise–consumer in the new energy-time leasing period and supervision process is established, as shown in Figure 2.

Figure 2.

Three-party game theory tree.

The following assumptions are made about the model:

Hypothesis 1: In a three-way game, the users, as the supervisor and beneficiary, will not intentionally damage the vehicle under the constraints of ethics and public opinion. At the same time, the users will use the supervisory power correctly and will not cheat to obtain compensation.

Hypothesis 2: In the game between government, enterprises, and consumers, the probability of government choosing supervision is x, the probability that enterprises choose to work hard is y, and the probability that consumers choose to participate in supervision is z, where $x, y, z \in [0, 1]$ are functions of time t.

Hypothesis 3: In order to encourage enterprises to increase the use of new energy vehicles and contribute to reducing carbon emissions and protecting the environment, the government will provide subsidies of S in total to enterprises. If the operating conditions of the enterprise need to meet the government's audit standards, the subsidy will be normally issued every year; If not, there will be a probability of $p_{1}$ being discovered in the absence of consumer participation and government supervision, and subsidies for enterprises will be cancelled.

Hypothesis 4: The cost of the government's choice of regulatory strategy is $C_{1}$ . After discovering that the company does not work hard, the government will charge the enterprise a fine of F. The social benefit obtained after successful supervision is R.

Hypothesis 5: Social benefits R of government regulation include market order correction, reduction of environmental pollution, road traffic improvement, safety hazard elimination and reduction of adverse public opinion’. If the government chooses not to supervise and the company chooses not to work hard, the amount of damage caused to the government is R.

Hypothesis 6: The cost of the business operation phase is divided into fixed cost $C_{2}$ and effort cost $C^{'}$ . The fixed cost is the basic cost when the enterprise maintains normal operation, and the effort cost is paid by an enterprise to achieve audit requirements and market demands.

Hypothesis 7: When a consumer experiences a substandard product, the loss suffered is $L_{1}$ . After the consumer chooses to participate in the supervision and feedback to the government department, the probability that the government supervises the enterprise's ineffective business behaviors $p_{2} (p_{2} > p_{1})$ and the cost of consumer participation supervision are $C_{3}$ . After the enterprise's inactivity is detected, the consumers are compensated with a value of $k C^{'}$ , k is the compensation coefficient of the enterprise to consumers, and the reputation loss suffered by the enterprise is $L_{2}$ .

Hypothesis 8: When consumers choose to participate in the supervision and enterprises do not work hard to operate, if the government's strategy is to choose nonsupervision, then consumers’ willingness is not responded, leading to a decline in the credibility of the government, and the loss value is $L_{3}$ .

Hypothesis 9: When the company chooses not to work hard, the income obtained is $R_{1}$ . When the company chooses to work hard, the income obtained is $R_{2} (R_{2} > R_{1})$ .

Function

According to the above assumptions, under the condition that all three parties are rationally bounded and the information is asymmetric, the payment matrix is listed in Table 1 and Table 2.

Table 1.

The payment matrix of enterprises and consumers when the government regulates.

		Enterprises
		Work hard	Not working hard
consumer	Involved	$R - S - C_{1},$ $S + R_{2} - C_{2} - C^{'},$ $- C_{3}$	$(F + R) p_{2} - (1 - p_{2}) (S + L_{3} + R) - C_{1},$ $R_{1} - C_{2} + (1 - p_{2}) S - p_{2} (L_{2} + k C^{'} + F),$ $p_{2} k C^{^{'}} - C_{3} - L_{1}$
consumer	Not involved	$R - S - C_{1},$ $S + R_{2} - C_{2} - C^{'},$ $0$	$(F + R) p_{1} - (1 - p_{1}) (S + R) - C_{1},$ $R_{1} - C_{2} + (1 - p_{1}) S - p_{1} (L_{2} + k C^{'} + F),$ $- L_{1}$

Table 2.

The payment matrix of enterprises and consumers when the government does not regulate.

		enterprises
		Work hard	Not working hard
consumer	Involved	$R - S,$ $S + R_{2} - C_{2} - C^{'},$ $- C_{3}$	$- R - S - L_{3},$ $S + R_{1} - C_{2} - L_{2},$ $- C_{3} - L_{1}$
consumer	Not involved	$R - S,$ $S + R_{2} - C_{2} - C^{'},$ $0$	$- R - S,$ $R_{1} + S - C_{2},$ $- L_{1}$

Note: In Tables 1 and 2, the first function item in each table represents government revenue, the second function item represents enterprise revenue, and the third function item represents consumer revenue.

Analysis

Before the game analysis, we first analyze the expected returns of the government, enterprises, and consumers.

The expected return of the government's choice of a regulatory strategy is:

E_{11} = y (R - S) + z (1 - y) [(F + R) p_{2} - (1 - p_{2}) (S + L_{3} + R)] + (1 - y) (1 - z) [(F + R) p_{1} - (1 - p_{1}) (S + R)] - C_{1}

The expected return of the government's choice of a nonregulatory strategy is:

E_{12} = y (R - S) + (1 - y) (- R - S) + z (1 - y) (- L_{3})

The average expected return of the government is:

\bar{E_{1}} = x E_{11} + (1 - x) E_{12}

The expected return of the enterprise's choice of a hard work strategy is:

E_{21} = (S + R_{2} - C_{2} - C^{'})

The expected return of the company's choice of not working hard is:

E_{22} = R_{1} - C_{2} + (1 - x) S - z (1 - x) L_{2} + x z [(1 - p_{2}) S - p_{2} (L_{2} + k C^{'} + F)] + x (1 - z) [(1 - p_{1}) S - p_{1} (L_{2} + k C^{'} + F)]

The average expected return of the company is:

\bar{E_{2}} = y E_{21} + (1 - y) E_{22}

The expected return of consumers choosing to participate in the monitoring strategy

is:

E_{31} = x (1 - y) p_{2} k C^{'} - (1 - y) L_{1} - C_{3}

The expected return of consumers choosing not to participate in supervision is:

E_{32} = (1 - y) (- L_{1})

The average expected return of consumers is:

\bar{E_{3}} = z E_{31} + (1 - z) E_{32}

Government regulatory stability strategy

The government's choice of supervision of the replication dynamic equation is:

\frac{d x}{d t} = F (x) = x (1 - x) (E_{11} - E_{12})

= x (1 - x) * [z (1 - y) [(F + R) p_{2} - (1 - p_{2}) (S + L_{3} + R) + L_{3}]

+ (1 - y) (R + S) + (1 - y) (1 - z) [(F + R) p_{1} - (1 - p_{1}) (S + R)] - C_{1}]

（1）The effect of y on value x is:

X_{y} = 1 - \frac{C_{1}}{\begin{matrix} z [(F + R) p_{2} - (1 - p_{2}) (S + L_{3} + R) + L_{3}] \\ + (1 - z) [(F + R) p_{1} - (1 - p_{1}) (S + R)] + R + S \end{matrix}},

y = X_{y}

, then

F (x) \equiv 0

, it is always in a stable state; if

y \neq X_{y}

, let

F (x) = 0

, then

x = 0

x = 1

; if

y > X_{y}

, when

x = 0

, it is in a stable state; if

y < X_{y}

, when

x = 1

, it is in a stable state. Therefore, the conclusion is as follows:

Conclusion 1:

The probability that the government chooses a regulatory strategy will decrease as the probability of the firm choosing to work harder increases.

Proof:

$\frac{\partial F (x)}{\partial x} = (1 - 2 x) [z (1 - y) [(F + R) p_{2} - (1 - p_{2}) (S + L_{3} + R) + L_{3}] + (1 - y) (R + S) + (1 - y) (1 - z) [(F + R) p_{1} - (1 - p_{1}) (S + R)] - C_{1}]$

If $y > X_{y}$ and $x = 0$ , $F^{'} (x) < 0$ , when $x = 0$ , it is in a stable state.

Conclusion 1 is supported.

（2）The effect of z on x value

$X_{z} = - \frac{[(F + R) p_{1} - (1 - p_{1}) (S + R)] + (1 - y) (R + S) - C_{1}}{[(F + R) (p_{2} - p_{1}) + (p_{1} - p_{2}) (S + R) + p_{2} L_{3}] (1 - y)}$ , if $z = X_{z}$ , then $F (x) \equiv 0$ , it is always in a stable state; if $z \neq X_{z}$ , let $F (x) = 0$ , then $x = 0$ or $x =$ 1; if $z > X_{z}$ , when $x = 1$ , it is in a stable state; if $z < X_{z}$ , when $x = 0$ , it is in a stable state. Therefore, the conclusion is as follows:

Conclusion 2:

The probability that the government chooses a regulatory strategy increases as the probability of consumers choosing to participate in supervision increases.

Proof:

$\frac{\partial F (x)}{\partial x} = (1 - 2 x) [z (1 - y) [(F + R) p_{2} - (1 - p_{2}) (S + L_{3} + R) + L_{3}] + (1 - y) (R + S) + (1 - y) (1 - z) [(F + R) p_{1} - (1 - p_{1}) (S + L_{3} + R)] - C_{1}]$

If $z > X_{z}$ and $x = 1$ , $F^{'} (x) < 0$ , when $x = 1$ , it is in a stable state.

Conclusion 2 is supported.

Enterprises strive to operate stable strategies

The replication dynamic equation that the enterprise chooses to work hard is:

\frac{d y}{d t} = F (y) = y (1 - y) (E_{21} - E_{22}) = y (1 - y) [R_{2} - R_{1} - C^{'} + x S

+ z (1 - x) L_{2} - x z [(1 - p_{2}) S - p_{2} (L_{2} + k C^{'} + F)] - x (1 - z) [(1 - p_{1}) S - p_{1} (L_{2} + k C^{'} + F)]

（1）The effect of x on y value

$Y_{x} = - \frac{R_{2} - R_{1} - C^{'} + z L_{2}}{S - z L_{2} - z [(1 - p_{2}) S - p_{2} (L_{2} + k C^{'} + F)] - (1 - z) [(1 - p_{1}) S - p_{1} (L_{2} + k C^{'} + F)]}$ , if $x = Y_{x}$ , then $F (x) \equiv 0$ , it is always in a stable state; if $x \neq Y_{x}$ , let $F (y) = 0$ , then $y = 0$ or $y = 1$ ; if $x > Y_{x}$ , when $y = 1$ , it is in a stable state; if $x < Y_{x}$ , when $y = 0$ , it is in a stable state. Therefore, the conclusion is as follows:

Conclusion 3:

The probability that an enterprise chooses to work hard increases as the probability of the government choosing to monitor increases.

Proof:

$\frac{\partial F (y)}{\partial y} = (1 - 2 y) [R_{2} - R_{1} - C^{'} + x S + z (1 - x) L_{2} - x z [(1 - p_{2}) S - p_{2} (L_{2} + k C^{'} + F)] - x (1 - z) [(1 - p_{1}) S - p_{1} (L_{2} + k C^{'} + F)]$

If $x > Y_{x}$ and $y = 1$ , $F^{'} (y) < 0$ , when $y = 1$ , it is in a stable state.

Conclusion 3 is supported.

The effect of z on y value

Y_{z} = - \frac{R_{2} - R_{1} - C^{'} + x S - x [(1 - p_{1}) S - p_{1} (L_{2} + k C^{'} + F)]}{(1 - x) L_{2} + x [(p_{2} - p_{1}) S + (p_{2} - p_{1}) (L_{2} + k C^{'} + F)]}

, if

z = Y_{z}

, then

F (x) \equiv 0

, it is always in a stable state; if

z \neq Y_{z}

, let

F (y) = 0

, then

y = 0

y = 1

; if

z > Y_{z}

, when

y = 1

, it is in a stable state; if

z < Y_{z}

, when

y = 0

, it is in a stable state. Therefore, the conclusion is as follows :

Conclusion 4:

The probability that an enterprise chooses to work hard increases as the probability of consumers choosing to participate in supervision increases.

Proof:

If $z > Y_{z}$ , and $y = 1$ , $F^{'} (y) < 0$ , when $y = 1$ , it is in a stable state.

Conclusion 4 is supported.

Consumer participation in monitoring and stabilizing strategies

The replication dynamic equation that consumers choose to participate in supervision is

\frac{d z}{d t} = F (z) = z (1 - z) (E_{31} - E_{32}) = z (1 - z) * [x (1 - y) p_{2} k C^{'} - C_{3}]

The effect of x on z value

Z_{x} = \frac{C_{3}}{(1 - y) p_{2} k C^{'}}

, if

x = Z_{x}

, then

F (z) \equiv 0

, it is always in a stable state; if

x \neq Z_{x}

, let

F (z) = 0

, then

z = 0

z = 1

; if

x > Z_{x}

, when

z = 1

, it is in a stable state; if

x < Z_{x}

, when

y = 0

, it is in a stable state. Therefore, the conclusion is as follows:

Conclusion 5:

The probability that a consumer chooses to participate in a surveillance strategy increases as the probability of government selection for regulation increases.

Proof:

$\frac{\partial F (z)}{\partial z} = (1 - 2 z) [x (1 - y) p_{2} k C^{'} - C_{3}]$

If $x > Z_{x}$ and $z = 1$ , $F^{'} (z) < 0$ , when $z = 1$ , it is in a stable state.

Conclusion 5 is supported.

(1) The effect of y on z value

$Z_{y} = \frac{x p_{2} k C^{'} - C_{3}}{p_{2} k C^{'} + (x - 1) (C_{3} + L_{1})}$ , if $y = Z_{y}$ , then $F (z) \equiv 0$ , it is always in a stable state; if $y \neq Z_{y}$ , let $F (z) = 0$ , then $z = 0$ or $z = 1$ ; if $y > Z_{y}$ , when $z = 0$ , it is in a stable state; if $y < Z_{y}$ , when $y = 1$ , it is in a stable state. Therefore, the conclusion is as follows :

Conclusion 6:

The probability that a consumer chooses to participate in a supervisory strategy decreases as the probability of the firm choosing to work hard increases.

Proof:

$\frac{\partial F (z)}{\partial z} = (1 - 2 z) [x (1 - y) p_{2} k C^{'} - C_{3}]$

If $y > Z_{y}$ and $z = 0, F^{'} (z) < 0$ , when $z = 0$ , it is in a stable state.

Conclusion 6 is supported.

Evolutionary game path analysis

In the game analysis above, the probabilities that governments, enterprises, and consumers adopt strategies $x, y,$ and z are parameters related to time t. The replication dynamic equations’ solution domain is $[0, 1] * [0, 1] * [0, 1]$ . Let $F (x) = 0, F (y) = 0,$ and $F (z) = 0$ , then 14 equilibrium points are obtained during the game; these are $N_{1} (0, 0, 0)$ 、 $N_{2} (1, 0, 0)$ 、 $N_{3} (1, 1, 0)$ 、 $N_{4} (1, 0, 1)$ 、 $N_{5} (0, 1, 0)$ , $N_{6} (0, 1, 1)$ 、、、 $N_{11} (Z_{x}, 0, X_{z})$ 、 $N_{12} (Z_{x}, 1, X_{z})$ . The first eight equilibrium points constitute the boundary Q of the three-party evolutionary game solution domain. $Q = {(x, y, z) | 0 \leq x \leq 1, 0 \leq y \leq 1, 0 \leq z \leq 1}$ . The latter six equilibrium points exist within the three-dimensional spatial solution domain, satisfying the situation where the change rate of government, enterprise, and consumer policy choices is zero.

From the analysis of conclusions 1–6, we can see that the strategies of the government, enterprises, and consumers in the game process will change with the changes of the other two parties over time, and the result is also the initial game. The size of each parameter set has a close relationship. The partial dynamic derivatives of x, y, and z are obtained for the replication dynamic equations of the government, enterprises, and consumers, respectively, and the Jacobian matrix J (Friedman, 1991) is obtained.

J = [\begin{matrix} \frac{\partial F (x)}{\partial x} & \frac{\partial F (x)}{\partial y} & \frac{\partial F (x)}{\partial z} \\ \frac{\partial F (y)}{\partial x} & \frac{\partial F (y)}{\partial y} & \frac{\partial F (y)}{\partial z} \\ \frac{\partial F (z)}{\partial x} & \frac{\partial F (z)}{\partial y} & \frac{\partial F (z)}{\partial z} \end{matrix}]

The Jacobian matrix is solved to obtain the eigenvalues corresponding to the respective equalization points, as shown in Table 3.

Table 3.

Characteristic values corresponding to each equilibrium point.

Equilibrium point	Eigenvalues
$N_{1} (0, 0, 0)$	$p_{1} (2 R + S + F) - C_{1}$	$R_{2} - R_{1} - C^{'}$	$- C_{3}$
$N_{2} (1, 0, 0)$	$C_{1} - p_{1} (2 R + S + F)$	$R_{2} - R_{1} - C^{'} + p_{1} S$ $+ p_{1} (L_{2} + k C^{'} + F)$	$p_{2} k C^{'} - C_{3}$
$N_{3} (1, 1, 0)$	$C_{1}$	$- R_{2} + R_{1} + C^{'} - p_{1} S$ $- p_{1} (L_{2} + k C^{'} + F)$	$- C_{3}$
$N_{4} (1, 0, 1)$	$- [2 (1 - p_{2}) R + (2 - p_{2}) (S + L_{3}) + F p_{2}]$	$R_{2} - R_{1} - (1 + p_{2} k) C^{'} - p_{2} (L_{2} + F)$	$- (p_{2} k C^{'} - C_{3})$
$N_{5} (0, 1, 0)$	$- C_{1}$	$C^{'} - R_{2} + R_{1}$	$- C_{3}$
$N_{6} (0, 1, 1)$	$- C_{1}$	$- (R_{2} - R_{1} - C^{'} + L_{2})$	$C_{3}$
$N_{7} (0, 0, 1)$	$[2 (1 - p_{2}) R + (2 - p_{2}) (S + L_{3}) + F p_{2}]$	$(R_{2} - R_{1} - C^{'} + L_{2})$	$C_{3}$
$N_{8} (1, 1, 1)$	$C_{1}$	$- [R_{2} - R_{1} - (1 + p_{2} k) C^{'} - p_{2} (L_{2} + F)]$	$C_{3}$
$N_{9} \sim N_{14}$	There are eigenvalues with different symbols, no longer detailed

In the game system, each equilibrium point corresponds to an equilibrium selection. For the eigenvalues of the Jacobian matrix, J, there are negative real parts, and the corresponding equilibrium point is a stable point, whereas the equilibrium point corresponding to the real part is an unstable point (Friedman, 1998; Di, 2014). In other words, when all eigenvalues are negative, the corresponding equilibrium point is the evolutionary stability strategy of the system. When all eigenvalues are positive, the corresponding equilibrium point is the unstable point. When the eigenvalue is one minus two plus or one plus two minus, the corresponding equilibrium point is the saddle point.

The three eigenvalue symbols for each of the points $N_{9} \sim N_{14}$ are different. It is impossible to have three eigenvalues that are either positive or negative at the same time. Therefore, the six equalization points must be saddle points. Note that no matter how the parameter values of the system change, these 6 points will not become the equilibrium point of the system. Then, we analyze the eigenvalues of $N_{1} \sim N_{8}$ and draw the following conclusions:

Conclusion 7:

(Regulated, hard-working, not involved in supervision), (not regulated, hard-working/not working hard, participating in supervision), (regulation, hard work, participation in supervision). These four strategic options do not exist

Proof:

In , there are eigenvalues that are not significantly negative. Taking $N_{3} (1, 1, 0)$ as an example, one of the eigenvalues is $C_{1}$ . Since the cost parameters are positive in the model hypothesis, $N_{3} (1, 1, 0)$ must not be the equilibrium point. By analogy, the other three boundary points are not stable points of system evolution.

Key parameter analysis

The following four boundary equilibrium points of and $N_{5}$ are analyzed, and real-world situation analysis is used to analyze the key parameters affecting government, enterprise, and consumer strategy choices:

(1) Key parameters affecting government strategy selection

Among the four boundary equilibrium points, the values of $\frac{\partial F (x)}{\partial x}$ in are significantly less than 0, so they are not analyzed in this section.

In the $N_{1}$ and $N_{2}$ states, the government's strategic choices are not regulation and supervision. If the condition of $\frac{\partial F (x)}{\partial x} < 0$ is satisfied, the two evolution states tend to be stable. The condition is $p_{1} (2 R + S + F) - C_{1} < 0$ and $p_{1} (2 R + S + F) - C_{1} > 0$ . The analysis of the formula shows that the subsidy amount S and the penalty strength F are important parameters that influence the choice of government strategy. In the case of a small amount of subsidies and punishment, the government expects that the expected return of supervision is less than zero. Since the government does not participate in supervision, the probability of successful government supervision is low, and the income obtained is not enough to offset the cost of supervision $C_{1}$ . Therefore, changing the government's strategy in the game system to the ideal state requires increasing the number of subsidies and penalties until the expected return is greater than zero.

(2) Key parameters affecting consumer strategy choices

In the state of $N_{2}$ and $N_{4}$ in the supervision process of the new energy time-sharing rental car industry, the participation of consumers in the supervision depends on the positive and negative values of the conditional formula $p_{2} k C^{'} - C_{3}$ . In the $N_{2}$ and $N_{4}$ states, since the enterprise's effort cost has a high value, the key parameter that determines the establishment of $p_{2} k C^{'} - C_{3} > 0$ is the compensation coefficient. The larger the compensation coefficient is, the larger the value on the left side of the inequality. Therefore, it can be seen from the formula that, in the case of a small compensation coefficient, the realistic situation corresponding to state $N_{2}$ is that consumers would rather bear the loss of $L_{1}$ than pay labor, time and other supervision costs to participate in the supervision of the industry. To change the status of $N_{2}$ to $N_{4}$ , the government needs to provide complaint channels to reduce the cost of consumer participation in supervision, increase the amount of compensation paid to consumers when penalizing problems, and increase consumers’ willingness to participate in supervision.

(3) Key parameters affecting corporate strategy selection

The most ideal state for the supervision of new energy time-sharing car companies is $N_{5}$ (not regulated, hard-working, not involved in supervision). In this state, the proportion of enterprises’ efforts in business strategy selection is relatively high, and the industry has embarked on the road of self-discipline. Comparing state $N_{1}$ with state $N_{5}$ , it is found that the key condition affecting enterprise policy selection is $R_{2} - R_{1} - C^{'}$ . When the cost of efforts to select a company to operate is greater than is the difference between $R_{2}$ and $R_{1}$ , the strategic choice of the company tends to be not to work hard. For the enterprise itself, the most important thing is to promote technology upgrades and increase investment in research and development funds in the enterprise's technical department, thereby reducing the cost of efforts to achieve the optimal state of enterprise development.

Simulation analysis

This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, and the experimental conclusions that can be drawn.

Based on the above analysis, the evolutionary stability of the system depends on the initial conditions and changes of the relevant parameters. To more intuitively reflect the subject's behavioral evolutionary path and the influence of parameter values on the evolutionary stability results, this section uses numerical simulation analysis for the above evolutionary game model using $p y t h o n 3.6$ software. The key parameters involved are the amount of subsidy, $S$ , penalty strength, $F$ , effort cost, $C^{'}$ , and compensation coefficient, $k$ , and the initial values of each parameter are: R = 5, R1 = 17, R2 = 20, p1 = 0.3, p2 = 0.6, L1 = 2, L2= 5, L3 = 5, k = 0.4, S = 1, F = 1, C1 = 5, C2 = 3, C3 = 5, and C′ = 10.

The initial proportion of government, enterprise, and consumer behavior decisions is 0.8.

The impact of subsidies and punishment on system evolution

In the case where the subsidy S and the penalty force F are set to $(1, 1), (1, 5), (5, 1), (5, 5)$ , the system state evolution simulation diagram is shown in Figures 3–6.

Figure 3.

Simulation of system state evolution at S=1, F=1.

Figure 4.

Simulation of system state evolution at S=1, F=5.

Figure 5.

Simulation of system state evolution at S=5, F=1.

Figure 6.

Simulation of system state evolution at S=5, F=5.

With the subsidy and punishment being small, over time, the government, enterprises, and consumers’ strategy choices show a trend of evolution to state $N_{1}$ , and the value of subsidy and punishment is increased in a certain range. Excessive subsidies and penalties also slow down the speed at which policy choices evolve toward the state $N_{1}$ . After the subsidy S and the penalty force F are raised to meet the condition of $p_{1} (2 R + S + F) - C_{1} > 0$ , the policy choices of the government, enterprises, and consumers evolve toward the state $N_{2}$ as time progresses. Under the condition that the subsidy and punishment strength values meet the conditions, the rest of the parameter changes are not considered. Only the government's strategic choices change, which proves that only the change of subsidy and punishment cannot promote the evolution of the entire regulatory game system to the ideal state.

Therefore, to make the system evolve to an ideal state, government departments need to increase the support for new energy-time leasing auto companies and strictly check the actual operation of the subsidized enterprises and the quality of any substandard, fraudulent subsidies. Enterprises with such issues should be charged high fines and, in severe cases, penalties imposed such as shutting them down until the issues are resolved.

The influence of the compensation coefficient on system evolution

In the simulation analysis of the compensation coefficient, to avoid the influence of the irrelevant variable subsidy S and the penalty force F, the amount of the subsidy S and the penalty force F are set to 5. The simulation diagram of the system state evolution under the condition that the compensation coefficients are set to 0.4 and 0.8 is shown in Figure 7 and Figure 8.

Figure 7.

Simulation of system state evolution at k=0.4.

Figure 8.

Simulation of system state evolution at k=0.8.

In the case where the compensation coefficient is set high, the consumer's strategy choice evolves to participate in the supervision after a certain time node, meaning that the value of the compensation coefficient plays a decisive role in the consumer's strategy choice if the remaining parameters are not changed.

Therefore, as consumers participate in the supervision of the positive promotion of new energy-time leasing vehicle supervision, the government should require enterprises to compensate consumers to a higher degree when they punish a problem enterprise and improve consumer participation in industry regulation.

The impact of effort cost on system evolution

Combined with the previous analysis, if the whole system evolves to the state $N_{5}$ , the fundamental condition is $R_{2} - R_{1} - C^{'} > 0$ . The effort cost $C^{'}$ is set to 10 and 2, respectively, and the obtained system state evolution simulation diagram is shown in Figure 9 and Figure 10.

Figure 9.

Simulation of system state evolution at C′=10.

Figure 10.

Simulation of system state evolution at C′=2.

Under the condition that enterprises are struggling to operate at low cost, the spontaneous choice of enterprises will tend to be work hard, improve the vitality of the industry, and expand the influence of the industry. The changes brought by this series of actions will drive the choices made by the government and consumers. Without supervision and nonparticipation in supervision, companies will also choose to work hard. Currently, the new energy time-sharing car rental industry is on the right track, and the necessity for supervision by the government and consumers has declined. To save manpower and material resources, status $N_{5}$ has become a dominant strategy in the regulatory game system. This situation is new. Energy time-sharing leasing is the ideal state of automotive industry governance.

Therefore, the government should urge enterprises to use subsidies to support and streamline institutions; improve R&D technology and optimize operational planning; reduce the construction costs of facilities, such as parking spots and charging points; reduce operating costs; and achieve the ideal state of new energy-time leasing vehicle governance.

Conclusion and suggestion

This paper analyzes the regulatory issues in the new energy time-sharing rental automobile industry, establishes a tripartite evolutionary game model for the government, enterprises and consumers, and obtains four key parameters affecting the game process: subsidy, penalty intensity, effort cost and compensation coefficient.

From the analysis of Figure 3 to Figure 6, the subsidy range and punishment intensity have a positive effect on the evolution of the system to the ideal state. An appropriate increase in the scope of subsidies and penalties is conducive to the supervision of the new energy time-sharing car industry, which can promote the development of the industry, achieve the purpose of saving resources, protecting the environment and reducing carbon emissions. In the early stage of the development of the new energy time-sharing leasing industry, the government should appropriately increase the amount of subsidies for supportive attitudes and increase punishment for enterprises with bad business practices. This will accelerate the development of the industry toward a positive situation. Because the industry belongs to the form of heavy assets, when the subsidy is too low, the development of the enterprise will die due to capital factors. When the punishment intensity is too low, the enterprise that does not work hard is in a state of low risk and high return, and the enterprise will be inclined to not work hard because of the lack of incentive. Therefore, the threshold for enterprises to obtain subsidies should be raised, and strict supervision requires enterprises to produce or purchase automobiles of a certain scale and a certain quality standard to obtain subsidies. The form of subsidies has also been adjusted, from cars and charging stations to electricity prices to coordinate subsidies. At the same time, the government has established industry access rules, safety protection standards, commercial competition standards. For enterprises accused of malicious competition, subsidy fraud. lax review, inferior quality of the automobile and other phenomenon, they will be subject to a high fine, and serious cases will be shut down as punishment until the issues are resolved.

From the analysis of Figure 7 and Figure 8, the consumer compensation coefficient has a positive effect on the evolution of the system to the ideal state. Increasing the consumer compensation coefficient appropriately is conducive to the supervision of the new energy time-sharing car industry. Since consumers are the direct experiencers of new energy time-sharing rental cars, they are the first to know whether the business is working hard or not. If consumers are involved in supervision, this can reduce the inevitable lag in government regulation. To enhance consumers’ willingness to participate in supervision, on the one hand, the government information platform should be optimized to provide consumers with more reporting channels. On the other hand, when penalizing problem enterprises, the income from illegal enterprises should be constrained to a certain extent in the consumption process. Consumers who have a bad experience should be encouraged to participate more in the supervision of new energy time-sharing rental cars, thereby reducing government supervision costs and urging enterprises to work harder to improve the user experience.

From the analysis of Figure 9 and Figure 10, the effort costs have a negative effect on the evolution of the system to the ideal state. After subsidies, penalties, and compensation coefficient are at the desired level, the government should further urge enterprises to improve R&D technology and operating costs. In terms of charging points, the layout of charging station points should be optimized, construction locations should be coordinated, and the approval time for enterprises should be reduced. Enterprises should be required to use part of subsidies to support technology research and development and new product production. After certain new energy vehicles are put into the time-sharing leasing market, incentive policies such as tax reduction are adopted to accelerate the benign development of the new energy time-sharing leasing vehicle industry.

It is necessary to promote the new energy time-sharing rental vehicle industry to adopt a more flexible regulatory system. It is certain that after consumers participate in supervision, the total social income will increase when enterprises operate normally. This also shows that supervision through user participation is an efficacious measure to improve the business level of enterprises. However, to solve the fundamental problems of the industry, the government needs to make efforts to empower the consumer compensation coefficient and attract consumers to participate in supervision to change the current government-led supervision mode in China. Changes in the regulatory model can reduce the social problems caused by supporting the development of new energy time-sharing rental vehicle enterprises, enhance user experience to attract more people to accept this new mode of travel, so to reduce environmental stress, achieve low carbon emission reduction targets.

Supplemental Material

sj-docx-1-sci-10.1177_00368504221075480 - Supplemental material for Research on the supervision mechanism of new energy time-sharing rental vehicles in the background of carbon peak

Supplemental material, sj-docx-1-sci-10.1177_00368504221075480 for Research on the supervision mechanism of new energy time-sharing rental vehicles in the background of carbon peak by Wei Zhang, Hongli Liu, Yejian Chen and Xiaoyu Wan in Science Progress

Supplemental Material

sj-docx-2-sci-10.1177_00368504221075480 - Supplemental material for Research on the supervision mechanism of new energy time-sharing rental vehicles in the background of carbon peak

Supplemental material, sj-docx-2-sci-10.1177_00368504221075480 for Research on the supervision mechanism of new energy time-sharing rental vehicles in the background of carbon peak by Wei Zhang, Hongli Liu, Yejian Chen and Xiaoyu Wan in Science Progress

Footnotes

Acknowledgements

The authors gratefully thank the project of humanities and social sciences Ministry of Education in China(18YJC790224), the Science and technology research project of Chongqing Education Commission (KJQN202100636), the planned project of Chongqing social science(2016BS057).

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Hongli Liu

Supplemental material

Supplemental material for this article is available online.

Author biographies

Wei Zhang is an Associate Professor of Management Science and Engineering at Chongqing University of Posts and Telecommunications. His main research interests are digital economy and energy economy.

Hongli Liu is pursuing a master's degree at Chongqing University of Posts and Telecommunications. His main research interests are information economy and low-carbon supply chain.

Yejian Chen, graduated fm Chongqing University of Posts and Telecommunications. During his postgraduate studies, his research area is mainly information economics.

Xiaoyu Wan, is currently a professor in Management Science and Engineering at Chongqing University of Posts and Telecommunications. His main research interests are consumer behaviour and sharing economy.

References

Shaheen

Cohen

. Growth in worldwide Car sharing: an international comparison[J]. Transportation Research Record Journal of the Transportation Research Board 2007; 1992: 81–89.

Dimatulac

Maoh

Khan

, et al. Modeling the demand for electric mobility in the Canadian rental vehicle market[J]. Transp Res D, Transp Environ 2018; 65: 138–150.

Schaefers

. Exploring carsharing usage motives: a hierarchical means-end chain analysis[J]. Transportation Research Part A 2013; 47: 69–77.

Firnkorn

Müller

. What will be the environmental effects of new free- car-sharing systems? The case of car2go in Ulm[J]. Ecol Econ 2011; 70: 1519–1528.

Firnkorn

Müller

. Free-floating electric car sharing-fleets in smart cities: the dawning of a post-private car era in urban environments? [J]. Environ Sci Policy 2015; 45: 30–40.

Becker

Ciari

Kay

. Measuring the car ownership impact of free-floating car-sharing –A case study in basel, Switzerland[J]. Transp Res D, Transp Environ 2018; 65: 51–62.

Baptista

Melo

Rolim

. Car sharing systems as a sustainable transport policy: a case study from Lisbon, Portugal [M]. sustainable urban transport. Bingley, West Yorkshire: Emerald Group Publishing Limited, 2015.

Bardhi

Eckhardt G

. Access-based consumption: the case of car sharing[J]. Journal of consumer research 2012; 39: 881–898.

Carrera

. Evolutionary dynamics of poverty traps [J]. Journal of Evolutionary Economics 2019; 29: 611–630.

10.

Qiang

Yang

. Symbol regression algorithm based on monte carlo tree search [J]. Computer Engineering and Design 2020; 41: 2158–2164. (in Chinese).

11.

Yahong

Yulu

Shiyi

, et al. Government support and development of new industries: a case study of new energy [J]. Economic research journal 2015; 50: 147–161. (in Chinese).

12.

Niu

. Study on the time-sharing lease mode of new-energy cars in China; proceedings of the Proceedings Of the 2016. In: 5th International Conference on Sustainable Energy And Environment Engineering, F, 2016 [C]. Atlantis Press.

13.

Long

Chen

. Consumers’ evaluation of national new energy vehicle policy in China: an analysis based on a four-paradigm model [J]. Energy Policy 2016; 99: 33–41.

14.

Zuo

Wang

. Research on the optimization of new energy vehicle industry research and development subsidy about generic technology based on the three-way decisions [J]. J Cleaner Prod 2019; 212: 46–55.

15.

Zhang

Cai

. Subsidy strategy on new-energy vehicle based on incomplete information: a case in China[J]. Physica A 2020; 541: 123370.

16.

Liu

Zhang

, et al. Research on the innovation of time-sharing leasing business model of electric vehicles in China based on city level. Scientific Decision making 2017; 11: 29–53. (in Chinese).

17.

Chenxi

. Electric vehicle charging equipment interoperability, new national standard to cure charging stubborn problem [J]. China Strategic Emerging Industries 2016: 46–48. (in Chinese (in Chinese).

18.

De Almeida Correia

Antunes

. Optimization approach to depot location and trip selection in one-way car-sharing systems[J]. Transportation Research Part E: Logistics and Transportation Review 2012; 48: 233–247.

19.

Zhou

Cao

. Energy flexibility investigation of advanced grid-responsive energy control strategies with the static battery and electric vehicles: a case study of a high-rise office building in Hong Kong[J]. Energy Convers Manage 2019; 199: 111888.

20.

Kaplan

Haenlein

. Users of the world, unite! The challenges and opportunities of social media [J]. Bus Horiz 2010; 53: 59–68.

21.

Lahkar

. Elimination of non-individualistic preferences in large population aggregative gaes [J]. Journal of Mathe-matical Economics 2019; 84: 150–165.

22.

Chen

. Car rental pricing and vehicle configuration based on revenue management [J]. Systems Engineering Theory and Method Application 2005; 6,14: 264–267.

23.

El Fassi

Awasthi

Viviani

. Evaluation of carsharing network’s growth strategies through discrete event simula-tion[J]. Expert Syst Appl 2012; 39: 6692–6705.

24.

Nöldeke

Peña

. Group size and collective action in a binary contribution game[J]. Journal of Mathematical Eco-nomics 2020; 88: 42–51.

25.

Wang

Sun

. User-assigned allocation model for vehicle time-sharing model during peak hours[J]. Journal of Beijing Jiaotong University: Natural Science Edition 2017; 41: 76–81. (in chinese).

26.

Yuan

Liu

Zuo

. The development of new energy vehicles for a sustainable future: a review [J]. Renewable Sustainable Energy Rev 2015; 42: 298–305.

27.

Liu

Feng

. Development of saving energy and New energy vehicles [J]. Bus Technology & Research 2010; 1: 4–7.

28.

Zhong

. Research on New energy vehicle subsidy strategy based on game theory[J]. CMS 2015; 23: 817–822.

29.

Jian

Mingming

Liping

, et al. Promoting electric automobiles: supply chain analysis under a government’s sub-sidy incentive scheme [J]. IIE Trans 2013; 45: 826–844.

30.

Ruguo

Xiaodan

. Research on local government subsidy strategy for new energy vehicles in the post-subsidy era [J]. China Population, Resources and Environment 2017; 27: 30–38. (in Chinese).

31.

Yixiang

Yongjian

. Research on evolutionary game model of New energy vehicle time-sharing leasing market competition [J]. Chinese Management Science 2018; 26: 79–85. (in Chinese).

32.

Hongzhuan

Huijuan

. Analysis on the regulatory evolution stability of fiscal subsidy policy for new energy vehicles [J]. Industrial technology economics 2019; 38: 114–119. (in Chinese).

33.

Stiglitz

Weiss

. Credit rationing in markets with imperfect information[J]. American Economic Review 1981; 71: 393–410.

34.

Tirole

. A theory of incentives in procurement and regulation[M]. Cambridge: MIT Press, 1993.

35.

Deyi

Haijuan

. Information asymmetry and government supervision mechanism in food quality and safety management [J]. Chinese Rural Economy 2002; 6: 29–35. (in Chinese).

36.

Zixing

. Analysis of electric vehicle market promotion based on tripartite game between government, enterprises and consumers [J]. Ind Eng 2015; 4: 1–8 + 42. (in Chinese).

37.

Chen K

. Game analysis of the government, enterprises and consumers in the New energy vehicle industry [J]. Economic Research Guide 2015; 12: 72–75. (in Chinese).

38.

Wang

Fan

Zhao

, et al. The impact of government subsidies or penalties for new-energy vehicles a static and evolutionary game model analysis[J]. Journal of Transport Economics and Policy (JTEP) 2015; 49: 98–114.

39.

Lumiao

. Development status, problems and suggestions of time-sharing leasing of New energy vehicles in China [J]. Auto Horizon 2019; 8: 46–48. (in chinese).

40.

Liang

. Shared Car outlook and future [J]. Science & Technology Economic Guide 2019; 21: 51.

41.

Liang

Weijun

Shue

. Government subsidies, access restrictions and the development of new energy vehicle industry [J]. Shanghai Economic Research 2017; 04: 17–25. (in Chinese).

42.

Cai

Kock

. An evolutionary game theoretic perspective on e-collaboration: the collaboration effort and media relativeness[J]. Eur J Oper Res 2009; 194: 821–833.

43.

Zhijie

Yanping

Dongchu

, et al. Local government regulation and the evolution of rare earth upstream enterprises game analysis [J]. Res Manage 2014; 35: 145–152. (in Chinese).

44.

Liang

Yuyan

. Research on collaboration mechanism in public service outsourcing: an evolutionary game analysis [J]. Manage Rev 2017; 29: 219–230. (in Chinese).

45.

Hui

Shuling

Yamei

CUI

. Research on tourism market regulation based on evolutionary game [J]. J Syst Eng 2016; 31: 618–624. (in Chinese).

46.

Kai

Zhi

. Tripartite game analysis on the evolution of tourism public crisis with government participation [J]. Tourism Tribune 2018; 33: 46–58. (in Chinese).

47.

Guoxing

Wanxia

Xin

. Evolutionary game model of food safety supervision based on third-party supervision [J]. J Syst Eng 2015; 30: 153–164. (in Chinese).

48.

Cao

Zhang L

. The evolutionary game analysis on green technological innovation of enterprises under the environmental regulations——based on the view of stakeholder[J]. Syst Eng 2017; 35: 103–108.

49.

Xiang

Lulin

Xiaoyang

. Advance of telemedicine service based on evolutionary game [J]. Syst Eng 2017; 2: 95–102.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.16 MB

0.02 MB