Who will pay for the “bicycle cemetery”? Evolutionary game analysis of recycling abandoned shared bicycles under dynamic reward and punishment

Highlights

• Evolutionary game is used to study the dynamic behavior of bicycle recycling.

• The incentive strategy of "reward first, punishment second" is more effective.

• Obtain the optimal recycling standard.

• Excessive regulatory investment will make enterprises free-rider.

Abstract

China is the largest market for shared bicycles, and its “bicycle cemetery” phenomenon has attracted widespread attention. The end treatment of abandoned bicycles has become a key issue in the promotion of green travel and sustainable transportation. This paper introduces extended producer responsibility (EPR) and green tax, two WEEE recycling methods in the recycling system for abandoned bicycles in China, and builds a two-party game model based on local government supervision and corporate recycling strategies. The results show that setting a minimum recycling standard of 0.65 and implementing a strategy of rewards as the primary factor and punishment as a supplement helps enterprises to choose the more environmentally friendly EPR approach. The conversion rate and the level of enterprise effort positively affect the adoption of EPR by enterprises and can speed up the evolution of local governments to equilibrium strategies. What's more interesting is that controlling the government's regulatory investment to prevent enterprises from “free-riding” helps to promote the coordinated recycling of abandoned bicycles. This research is based on the current situation in China and offers a new perspective on recycling abandoned bicycles. The conclusions provide a reference for other countries seeking to formulate effective policies for the management of abandoned bicycles.

Introduction

With the rapid development of information technology, countries worldwide are actively advancing Internet strategies. The sharing economy based on the “Internet +” model is highly favored as a new type of shared profit model (Boysen, Briskorn & Schwerdfeger, 2019; Huurne, Ronteltap, Corten & Buskens, 2017). Among them, the bicycle-sharing system (BSS), a typical service of the sharing economy system in the field of transportation, has become a popular choice for urban residents faced with the “first and last mile” travel problem, owing to its green, low-carbon, and healthy characteristics (Xie & Wang, 2018). So far, about 500 bicycle-sharing services have appeared in 1175 cities in 63 countries around the world. Coupled with the new crown pneumonia epidemic that swept the world in 2020, this has prompted people to reduce their use of public transportation in favor of a safe and green personal vehicle, accelerating the development of bicycle-sharing services worldwide (D'Almeida et al., 2021).

China is one of the countries with the fastest and most extensive development of bicycle-sharing services. According to the White Paper on Bicycle-Sharing and Urban Development (Mobike, 2017), within 1 year of their launch in 2015, shared bicycles became the fourth most frequently used mode of travel in China, after cars, buses, and subways. Realized the humanized revival of China's identity as the “kingdom of bicycles”. However, although bicycle-sharing in China has enhanced government services and provided convenience to the public (Luo, Kou, Zhao & Cai, 2019; Zervas, Proserpio & Byers, 2017), it is also associated with a series of drawbacks including the so-called tragedy of the commons, waste of resources, and high rates of damage (Faghih-Imani, Hampshire, Marla & Eluru, 2017; Jie, Wei & Jiang, 2020). The sustainable development of shared bicycles is also facing severe challenges. According to the Report on the Development of China's Bicycle-Sharing Industry (2018), China's bicycle-sharing industry released a total of 23 million bicycles in 2017, covering 200 cities. Given the 3-year mandatory scrapping standard for bicycle-sharing issued by Beijing and Shanghai, this means that China will generate a large “scrap tide” of shared bicycles from 2020 onward, with at least 10 million bicycles waiting to be scrapped. According to estimates, the discarded shared bicycles will generate nearly 300,000 tons of scrap metal, equivalent to the weight of five aircraft carrier structural steels. However, if these bicycles can be effectively recycled, it can reduce bauxite mining by 110,000 tons, reduce vegetation damage by 170,000 m²s, and reduce carbon emissions by 580,000 tons. Therefore, it is urgent to establish a suitable and effective shared bicycle recycling system for full life-cycle management of shared bicycles.

The complex components contained in the body of shared bicycles, such as smart electronic locks, GPS locators, batteries, and circuit boards, are very similar to waste electronic and electrical equipment (WEEE). They all have the characteristics of difficult maintenance, low recycling price, and heavy pollution (Si, Shi, Wu & Wang, 2020). The extended production responsibility (EPR) system and Pigovian taxes have extensive and mature applications in WEEE recycling worldwide. In 1990, the organisation for Economic Co-operation and Development put forward its EPR environmental protection strategy, which aims to force producers to make recovery, recycling, and final disposal efforts to realize full life-cycle management of products (OECD, 2001). Based on the “polluter pays” principle, Pigovian taxs allow governments to control the negative externalities of environmental pollution through taxes or subsidies (Jaakkola, 2019). Therefore, this article argues that the implementation of EPR on bicycle-sharing companies or the levying of Pigovian tax (combined with China's national conditions, namely the green tax [GT]) can provide appropriate recycling treatment options for abandoned shared bicycles.

The recycling of abandoned shared bicycles is a major social problem facing China and other countries with shared bicycles schemes. Excessive competition and evasion of governance on the part of bicycle-sharing companies have caused a gradual failure in the market. One of the key methods of correcting market failures is to intervene with “visible hands,” that is, to strengthen government supervision. As consumers only have the right to use bicycles and do not own them, the recycling process for shared bicycles mainly involves local governments and bicycle-sharing companies. To clarify how the dynamic decision-making process of local governments and enterprises in the recycling chain affects the construction of the recycling mechanism for discarded bicycles, this article uses evolutionary game theory to simulate the strategic choice and Nash equilibrium of the two major actors under the background of dynamic learning and bounded rationality (Mezzetti, 1998).

The primary purposes of this study are to analyze the dynamic evolution of local governments and bicycle-sharing enterprises in the process of recycling and governance of abandoned shared bicycles; to explore the evolution path of the game subject under a dynamic reward and punishment system; and to identify driving factors that encourage both parties to participate actively in the game. This study contributes four main innovations. (1) It introduces EPR and GT, two WEEE recycling methods, into the recycling system for abandoned bicycles, builds a two-party game model, and analyzes in depth the relationship between government supervision and corporate recycling strategies. (2) It assigns values to various parameter variables based on actual data from the shared bicycle market, with reference to relevant literature materials that realistically and scientifically simulates the evolution path of the game subject. (3) It constructs a dynamic reward and punishment function based on the minimum recycling standard, and explores the influence of different rewards and punishments on the behavioral choices of bicycle-sharing enterprises. (4) It introduces relevant parameters into the model, such as conversion rate and corporate recycling effort level, to quantify their impact on the behavioral choices of game players and to provide a powerful reference for governments and enterprises seeking to formulate relevant policies and regulations.

The rest of this article is arranged as follows. Section 2 reviews the relevant literature, and Section 3 constructs the model. Section 4 sets out the empirical analysis and simulation, and Section 5 explains the sensitivity analysis of the target parameters. Section 6 contains the discussion of the findings, and Section 7 presents the conclusions and policy recommendations.