Strategies for Promoting Consumer Participation in the Collection of Retired Electric Vehicle Batteries

OriginalStrategies for promoting consumer participation in the collection of retired electric vehicle batteries Hwarang Lee a , Jongdae Kim b,* a Korea Energy Economics Institute, 405-11 Jongga-ro, Jung-gu, Ulsan, 44543, Republic of Korea b School of Business Administration, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju, 61186, Republic of Korea

OriginalKeywords:

OriginalElectric vehicle battery Battery collection Battery recycling Battery reuse Waste management Conjoint analysis A B S T R A C T As leading countries in the battery market implement regulations on electric vehicle battery recycling and reuse, value chains for these processes are being developed.

OriginalIn particular, the operation of recycling and reuse businesses begins with the collection of electric vehicle batteries, which is currently left to the free market. This study explored the design of electric vehicle battery collection programs, focusing on the growing number of retired electric vehicle batteries.

OriginalA choice experiment was conducted to analyze consumer preferences for six program attributes: return method, program operator, obligatory contract, information provision, battery usage period, and reward.

OriginalThis study found that consumers have a strong preference for programs that provide a pickup service and information regarding the programs and emissions reduction from recycling and reusing electric vehicle batteries.

OriginalNotably, respondents were willing to accept lower rewards for a pickup service (USD 0.571/kWh) and for information provision (USD 0.649/kWh). This study offers practical implications for companies seeking to develop profitable battery collection programs.

OriginalThe findings also provide valuable insights for designing such programs and enhancing consumer participation in the battery collection.

Original1. Introduction

OriginalIn recent years, the global adoption of electric vehicles (EVs) has surged, leading to a significant increase in the number of retired EV batteries (Tan et al., 2022; Lee and Kim, 2024).

OriginalThis trend has raised growing theoretical and practical interest in exploring sustainable

Originalmethods for recycling and reusing these batteries, particularly within on

Originalthe framework of the circular economy (Hu et al., 2024) and Environmental, Social, Governance management. Businesses in this field are expected to grow from two perspectives.

OriginalFirst, processing retired EV batteries offers both economic opportunities and environmental benefits by reducing waste and emissions. Second, as the demand for key minerals for EVs, such as lithium, nickel, cobalt, and manganese, continues to rise, recycling or reusing of these batteries offer a viable approach to alleviating resource shortages (Wang et al., 2020; Yu et al., 2021) and enhancing resource security.

OriginalTherefore, as one of the initial stages of waste recycling and reuse, as well as a preliminary step in waste preprocessing (Darko et al., 2023), the collection of retired EV batteries has become increasingly significant.

OriginalGiven this trend, theoretical interest in the collection of electronic waste (e-waste), including retired EV batteries, has also continued to develop. E-waste is defined as waste generated from discarded electronic products (Robinson, 2009).

OriginalThe improper disposal of e-waste, driven by a massive global increase in consumption, can release hazardous materials such as lead and cadmium, posing serious risks to both the environment and human health (Luo et al., 2011; Qu et al., 2019; Islam et al., 2021; Tutton et al., 2022; Cai et al., 2023).

OriginalAs one of the fastest-growing waste segments, many studies have been conducted with attention to designing effective e-waste collection programs (Chi et al., 2014; Qu et al., 2019; Arain et al., 2020; Cai et al., 2023; Wang et al., 2023a).

OriginalRegarding retired EV batteries, Yu et al. (2021) examined the environmental effects of recycling EV lithium-ion batteries, focusing on the life-cycle greenhouse gas emissions, water consumption, and remanufacturing cost associated with different recycling methods.

OriginalThe

Originalresults showed that remanufacturing batteries through direct physical

Originalrecycling has the greatest potentials for environmental impact. Wang et al. (2020) developed an analytical model to optimize the recycling network for lithium-ion batteries, demonstrating that the proposed model could effectively reduce both costs and carbon dioxide emissions.

OriginalThis study explored how to design retired EV battery collection * Corresponding author. E-mail addresses: idu8819@snu.ac.kr (H. Lee), kim915@jnu.ac.kr (J. Kim). Contents lists available at ScienceDirect Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman Received 19 August 2024; Received in revised form 27 February 2025; Accepted 4 March 2025 Journal of Environmental Management 380 (2025) 124853 Available online 13 March 2025 0301-4797/© 2025 Elsevier Ltd.

OriginalAll rights are reserved, including those for text and data mining, AI training, and similar technologies. programs as the first major step towards the industrialization of recycling and reusing retired EV batteries.

OriginalIn particular, we investigated how such programs should be designed in the South Korean market, which we highlighted as it is not exempt from the growing concern over processing of retired EV batteries.

OriginalFurthermore, South Korea recently abolished the regulatory requirement for EV owners to return retired batteries to local governments (Ministries of the South Korean Government, 2024), thereby paving the way for industrialization by transferring battery ownership to EV owners.

OriginalIn response to this regulatory shift, leading South Korean corporations, such as Hyundai and SK, are actively advocating for the establishment of a relevant regulatory framework that supports sustainable battery collection programs, following the example of global corporations such as Redwood Materials, Li-Cycle, and BYD.

OriginalThis study utilized a choice experiment to examine consumer preferences for retired EV battery collection programs. The experiment considered six key attributes: return method, program operator, obligatory contract, information provision, battery usage period, and reward.

OriginalThe theoretical background supporting the selection of these attributes (Table 1) outlined as follows: (1) Most previous studies focused on a pricing attribute, such as costs or rewards for waste collection.

OriginalSeveral studies showed that consumers tend to prefer the option with lower costs (Jin et al., 2006; Nakatani et al., 2008; Czajkowski et al., 2017, 2019; Ko et al., 2020), while other studies incorporated pricing as the compensation (Yuan and Yabe, 2015; Arain et al., 2020) and both subsidy and fee (Rai et al., 2019) for waste collection.

Original(2) Collection methods were one of the important attributes in previous studies. For example, consumers preferred door-to-door collection services over self-service options in the mobile phone waste collection (Qu et al., 2019).

OriginalThis is because a pickup service can affect the accessibility to disposal and the convenience level of waste collection (Chi et al., 2014; Arain et al., 2020). (3) Operators of collection programs can influence not only efficiency of recycling and reuse (Wang et al., 2023b) but also consumer preferences for the programs.

OriginalQu et al. (2019) categorized collectors into public, private, and self-employed entities, further distinguishing between qualified and non-qualified collectors. Their findings indicated that consumers prefer public and qualified private collectors to other types of collectors.

Original(4) Institutional signals are institutional or governmental decisions that guide people to understand socially normative actions (Huber et al., 2018). Previous research argued that institutional signals can have conflicting effects on consumer pro-environmental behavior.

OriginalWhile highlighting the importance of particular problems may enhance social acceptance, such signals can also discourage voluntary participation because the government is taking an obligatory active role (Dodds et al., 2008).

OriginalAs a notable example, Huber et al. (2018) showed that institutional signals, such as the obligatory requirement regarding carbon emissions, can positively influence consumer pro-environmental behavior when the costs are low.

OriginalThe current study anticipates that consumer preferences can be affected by the presence of an obligatory contract requiring them to return their retired EV batteries. (5) Consumers' knowledge about waste and collection

Originalmethods is known to influence their preferences for collection programs.

OriginalArain et al. (2020) demonstrated that a lack of consumer knowledge regarding e-waste disposal and recycling locations is significant in e-waste collection. (6) Collection frequency is another frequently examined factor in the literature (Karousakis and Birol, 2008; Yuan and Yabe, 2015; Czajkowski et al., 2017; Almazán-Casali et al., 2019; Ko et al., 2020).

OriginalMeanwhile, previous research has explored other attributes (Table 1), such as noise reduction (Jin et al., 2006), charging methods (Sakata, 2007; Massarutto et al., 2019), packaging characteristics (Klaiman et al., 2017), life expectancy loss (Nakatani et al., 2008), landfill capacity (Nakatani et al., 2008), air pollution (Pek and Jamal, 2011), time required for recycling (Klaiman et al., 2017), distance from a collection site (Pek and Jamal, 2011; Rai et al., 2019), compensation modes (Qu et al., 2019), and installation of a sign to highlight the recycling application (Ouvrard et al., 2020).

OriginalThis study is notable as one of the first to propose strategies for designing efficient retired EV battery collection programs. Given the limited existing research on this topic, the current study not only provides theoretical contributions but also establishes a meaningful foundation for future research.

OriginalThe results offer practical insights for companies seeking to develop business models centered on retired EV batteries, particularly by identifying the key factors that influence consumer preference structures.

OriginalThis study also enhances understanding of how program attributes affect consumer utility, providing guidance on cost-reduction strategies and ways to promote consumer participation.

OriginalBy addressing these factors, this study contributes to the development of more effective and sustainable collection programs that align with both economic and environmental objectives.

Original2. Methodology

Original2.1. Survey design

OriginalThis study conducted pilot and main surveys online in July and August 2023, respectively, with the support of a survey company. Prior to the pilot survey, the study ensured that the public could understand EV battery collection, recycling, and reuse and subsequently revised the questionnaire.

OriginalBefore the main survey, the study reviewed estimation

Originalresults from the pilot survey data and refined the questionnaire again. In

Originalthe main survey, the survey company reviewed respondents' answers and excluded inconsistent responses to mitigate bias and enhance the reliability of the survey. The questionnaire provided information on EV battery collection, recycling, and reuse, as well as a detailed explanation of the attributes and levels of battery collection programs (see Appendix A).

OriginalAdditionally, it provided information on the capacities and prices of new EV batteries to help respondents understand the rewards associated with battery collection programs, compare program attributes, and determine their choices.

OriginalThis study analyzed 800 samples obtained through Table 1 Key attributes in previous studies. Authors Country Samples Key attributes Jin et al. (2006) China 493 Cost, noise reduction Sakata (2007) Japan 500 Cost, charging method Karousakis and Birol (2008) U.K.

Original188 Cost, collecting frequency Nakatani et al.

Original(2008) Japan 434 Cost, loss of life expectancy, landfill capacity Pek and Jamal (2011) Malaysia 873 Cost, distance, air pollution Chi et al. (2014) China 430 Cost, convenience level Yuan and Yabe (2015) China 394 Reward, collecting frequency Czajkowski et al.

Original(2017) Poland 418 Cost, collecting frequency Klaiman et al. (2017) US 1500 Time required to recycle, packaging characteristic Almazán-Casali et al. (2019) Liberia 246 Cost, collecting frequency Czajkowski et al.

Original(2019) Poland 1853 Cost, collecting frequency Massarutto et al. (2019) Italy 1010 Cost, collecting frequency, charging method Qu et al. (2019) China 197 Cost, pickup service, program operator, compensation mode Rai et al. (2019) Nepal 420 Subsidy and price, collecting frequency, distance Arain et al. (2020) US 1560 Reward, distance, pickup service, consumers' knowledge Ko et al. (2020) South Korea

Original550 Cost, collecting frequency

OriginalOuvrard et al. (2020) France 896 Cost, installation of a sign to highlight the recycling application H. Lee and J. Kim Journal of Environmental Management 380 (2025) 124853 the survey company to explain consumer preferences for battery collection programs.

OriginalAccording to the sample and population statistics in Table 2, the sample reflects the characteristics of the population. The sample statistics were calculated based on the survey results, while the population statistics were derived from the data provided by Statistics Korea (2024).

OriginalThis study considered six attributes describing battery collection programs (Table 3), with more detailed explanations provided in Appendix A. Using these attributes, 768 sets of battery collection programs were generated, from which 16 orthogonal alternatives were selected.

OriginalThe questionnaire asked respondents to determine their preferred program between two alternatives in eight choice cards (see Appendix A). For example, respondents who chose the first battery collection program in Fig. 1 preferred to visit a designated location, choose a battery recycling company, return their batteries after four years of use, and receive KRW 1000/kWh (USD 0.741/kWh) without an obligatory contract and with access both types of information.

Original2.2. Mixed logit model

OriginalThis study estimated Equation (1) based on a mixed logit model (Train, 2009). Equation (1) indicated respondent n's utility (U jt n ) when this respondent preferred a j th alternative from a t th choice card.

OriginalThe utility consisted of an explainable part (V jt n ) with program attributes (X jt n ) and an error term (εjt n ). As shown in a normal distribution of Equation (2), coefficient vector βn's mean and deviation are b and σ, respectively.

OriginalUjt n = Vjt n + εjt n = βʹ nXjt n + εjt n (1) βn ∼ N ( b, σ2 ) (2) This study reformulated Equation (1) as Equation (3) by incorporating program attributes. The reward was coded as a continuous variable, while the other attributes were treated as dummy variables.

OriginalEquation (4) represented the marginal willingness to accept (MWTA) for the attributes. MWTA, which indicated the compensation required for excluding specific attributes from battery collection programs, was calculated based on the coefficients of the reward and other attributes.

OriginalFor example, the MWTA for a pickup service represented the compensation respondents received when they decided to visit a designated location instead of using a pickup service.

OriginalU = β11X11 + ∑ k β2kX2k + β31X31 + ∑ k β4kX4k + ∑ k β5kX5k + β61X61 + ε (3) U: Utility βik: Attribute coefficient X11: If a pickup service is provided, 1; otherwise, 0 X21: If a program operator is a governmental agency, 1; otherwise, 0 X22: If a program operator is an EV company, 1; otherwise, 0 X31: If a contract is obligatory, 1; otherwise, 0 X41: If program information is provided, 1; otherwise, 0 X42: If emissions reduction information is provided, 1; otherwise, 0 X43: If program and emissions reduction information is provided, 1; otherwise, 0 X51: If the period of use is between 4 and 8 years, 1; otherwise, 0 X52: If the period of use is between 8 and 12 years, 1; otherwise, 0 X53: If the period of use exceeds 12 years, 1; otherwise, 0 X61: Reward (KRW/kWh) MWTAik = − βik β61 (4)

Original3. Results and discussion

OriginalThis study estimated respondents' preferences for battery collection program attributes using a mixed logit model (Table 4). All attribute coefficients were statistically significant, excluding those related to program operators.

OriginalUnlike previous studies, which generally showed that consumers prefer governmental and public agencies as program operators due to their trust in such programs (Qu et al., 2019), respondents in this study did not exhibit a clear preference for a specific program operator.

OriginalHowever, as noted by Bikash and Ichihashi (2022),

OriginalTable 2 Sample and population statistics.

OriginalAge Gender Sample Population Age Gender Sample Population 20s Male 10% 10% 60s (60–65) Male 7% 7% ​ Female 9% 9% ​ Female 7% 7% 30s Male 10% 10% All ages Male 51% 51% ​ Female 9% 9% ​ Female 49% 49% 40s Male 12% 12% Total 100% 100% ​ Female 12% 11% ​ ​ ​ ​ 50s Male 13% 12% ​ ​ ​ ​ ​ Female 12% 12% ​ ​ ​ ​ Note: The total sample size is 800.

OriginalThe population statistics are based on Statistics Korea (2024).

OriginalTable 3 Attributes of battery collection programs.

OriginalAttribute Description Attribute levels Return method Consumers can either visit a designated location to return EV batteries or request a free pickup service to the program operator Visiting a designated location Pickup service Program operator Battery collection programs can be operated by a governmental agency, an EV company, or a battery recycling company Governmental agency EV company Battery recycling company Obligatory contract Consumers can sign obligatory contracts to return their EV batteries upon purchasing EVs Without obligatory contract With obligatory contract Information provision Program information refers to details on battery recycling, reuse, and the life cycle history of consumers' batteries Emissions reduction information refers to the potential emissions reductions achieved through battery recycling and reuse Without information

OriginalProgram information Emissions reduction information Program and emissions reduction information Battery usage period Consumers can choose the timing of their battery returns Period 〈 4 years 4 years ≤ Period 〈8 years 8 years ≤ Period 〈12 years Period ≥ 12 years Reward Consumers are rewarded for returning their EV batteries KRW 1000/kWh (USD 0.714/kWh) KRW 2000/kWh (USD 1.429/kWh) KRW 3000/kWh (USD 2.143/kWh) KRW 4000/kWh (USD 2.857/kWh) Note: USD 1 = KRW 1400 (KEB Hana Bank, 2024).

OriginalLee and J. Kim Journal of Environmental Management 380 (2025) 124853 consumers may not have a distinct preference for public and private agencies. This could present an opportunity for companies planning to enter the retired EV battery business, although profits of the programs are influenced by operators (Sun et al., 2022).

OriginalRespondents were more likely to choose battery collection programs that offer a pickup service. This result is consistent with previous studies, which have shown that door-to-door (Qu et al., 2019) and pickup (Arain et al., 2020) services play a crucial role in collecting and recycling e-waste.

OriginalThe results indicated that respondents are willing to accept lower rewards (KRW 799/kWh) if programs provide a pickup service. Program operators may consider implementing a pickup service to encourage consumer participation in battery collection programs; however, they need to evaluate additional profits from offering the service, as the service incurs extra costs.

OriginalIn evaluating trade-offs between service provision and profitability from increased participation, program operators can employ marginal willingness to accept (MWTA) for a pickup service.

OriginalThe coefficient for offering an obligatory contract was also significant and positive, indicating that respondents tend to prefer battery collection programs that include such contract.

OriginalAlthough respondents have the option to decide whether to return their EV batteries to a program operator at the end of EV use, they were still willing to sign an obligatory contract at the time of purchase.

OriginalThis suggests that the utility of receiving rewards is larger than the disutility of losing the flexibility to choose whether to return retired EV batteries. Moreover, this result aligns with the findings of Huber et al. (2018) in that consumers are willing to accept restrictions on their behavior and act in a pro-environmental manner when the costs are low.

OriginalTherefore, program operators can enhance battery collection rates and increase program profitability by introducing an obligatory contract at the point of EV purchase. The estimation results also indicated that respondents are sensitive to the information provided by program operators.

OriginalInformation on program and emissions reduction positively influenced respondents' choices of battery collection programs. Providing both types of information was more effective in encouraging consumer participation in battery collection programs, although its effect was smaller than the sum of the effects of each type of information individually.

OriginalThe MWTA for both types of information (KRW 908/kWh) was higher than the MWTA of a pickup service (KRW 799/kWh), suggesting that providing comprehensive information is more effective in promoting consumer participation than offering a pickup service.

OriginalAdditionally, as noted by Arain et al. (2020), consumers' knowledge of recycling, reuse, and programs can enhance their participation in such programs. Respondents were more likely to prefer returning their EV batteries after more than four years of use rather than after less than four years.

OriginalTheir preference for the battery usage period followed the order of after 8–12 years, 4–8 years, and more than 12 years. Given that predicting battery quantities can influence recycling capacity plans (Du et al., 2024), this information allows program operators to estimate the timing and volume of battery collection and develop effective recycling and reuse plans.

OriginalAccording to the estimation results, rewards had a positive effect on respondents' participation in battery collection programs. The coefficient for rewards was 0.0003, indicating an increase in utility for each additional KRW 1/kWh provided as a reward.

OriginalThis study investigated potential savings in rewards through the implementation of a pickup service and compared respondents' choice probabilities between programs with and without a pickup service (Table 5).

OriginalThis scenario analysis was designed based on previous studies (Ko et al., 2020). This study assumed identical attribute levels for the Fig. 1. Choice card.

OriginalTable 4 Estimation results of a mixed logit model.

OriginalAttribute Mean of β p-value Median MWTA Pickup service 0.5690*** <0.001 -KRW 799/kWh (-USD 0.571/kWh) Governmental agency − 0.0930 0.195 – EV company − 0.0571 0.355 – With obligatory contract 0.0948* 0.097 -KRW 134/kWh (-USD 0.096/kWh) Program information 0.3471*** <0.001 -KRW 603/kWh (-USD 0.431/kWh) Emissions reduction information 0.3576*** <0.001 -KRW 626/kWh (-USD 0.447/kWh) Program and emissions reduction information 0.5228*** <0.001 -KRW 908/kWh (-USD 0.649/kWh) 4 years ≤ Period 〈8 years 0.7624*** <0.001 -KRW 1331/kWh (-USD 0.951/kWh) 8 years ≤ Period 〈12 years 0.8410*** <0.001 -KRW 1455/kWh (-USD 1.039/kWh) Period ≥ 12 years 0.4180*** <0.001 -KRW 466/kWh (-USD

Original0.333/kWh) Reward 0.0003*** <0.001 – Number of observations: 12,800 Log-likelihood: 3965.6689 Note: *** Significant at the 1% level, * Significant at the 10% level. USD 1 = KRW 1400 (KEB Hana Bank, 2024).

OriginalLee and J. Kim Journal of Environmental Management 380 (2025) 124853 obligatory contract, information provision, and battery usage period in both programs, but excluded the effects of program operators due to their insignificant coefficients.

OriginalA fixed reward of KRW 4000/kWh was assumed for the program without a pickup service. By contrast, for the programs with a pickup service, the reward was assumed to decrease from KRW 4000/kWh to KRW 0/kWh to analyze potential savings in rewards through the implementation of a pickup service.

OriginalAdditionally, this study accounted for uncertainty in respondents' preferences for a pickup service and considered scenarios in which a pickup service was either more or less preferred compared to Table 4.

OriginalIn both scenarios, the estimated coefficient was assumed to represent the upper and lower bounds of the 95% confidence interval, respectively. When the reward in both programs was KRW 4000/kWh in the reference scenario, 63% of respondents preferred the program with a pickup service (Fig. 2).

OriginalHowever, the probability of choosing the program with a pickup service decreased as the reward was reduced. When the reward for the program with a pickup service was KRW 2000/kWh, its choice probability was identical to that of the program offering KRW 4000/kWh without a pickup service. This suggests that, in this scenario analysis, operators can save KRW 2000/kWh per consumer by providing a pickup service despite incurring additional costs to offer a pickup service.

OriginalThis study assumed scenarios in which a pickup service was either more or less preferred, as the preference for its convenience may change over time (Fig. 2). When a pickup service was preferred more than in the reference scenario, operators could offer lower rewards in the program with a pickup service while maintaining the same consumer preference as the KRW 4000/kWh program without a pickup service.

OriginalIn other words, in the more preferred scenario, operators could reduce the reward by KRW 300/kWh compared to the reference scenario. Conversely, when consumers preferred the convenience of a pickup service less, operators need to offer KRW 500/kWh more than in the reference scenario.

OriginalOperators may consider implementing a pickup service to encourage consumer participation in battery collection programs or, alternatively, offer higher rewards without a pickup service.

OriginalTherefore, the scenario

Originalresults provide insights into the trade-offs between offering a pickup

Originalservice and increasing rewards. According to the results, the value of a pickup service was approximately KRW 2000/kWh per consumer. Based on these results, operators can determine whether to incur additional costs to provide a pickup service or to offer higher rewards without one.

OriginalFurthermore, since the trade-offs were found to depend on the value consumers place on a pickup service, operators need to understand consumer preferences for convenience to minimize rewards while maximizing program profitability.

OriginalThis study also analyzed potential savings in rewards through Table 5 Scenarios for analyzing consumers' sensitivity to pickup service. Scenario Pickup service availability With a pickup service Visiting a designated location Preference for a pickup service More preferred Reference Less preferred More preferred Reference Less preferred Mean of β11 (Mean of the coefficient for a pickup service) 0.6786 0.5690 0.4593 0.6786 0.5690 0.4593 Return method Pickup service Visiting a designated location Program operator Coefficients were insignificant Coefficients were insignificant Obligatory contract With obligatory contract With obligatory contract Information provision Program and emissions reduction information Program and emissions reduction information Battery usage period 8 years ≤ Period < 12 years 8 years ≤ Period < 12 years Reward KRW 4000/kWh KRW 4000/kWh KRW 3500/kWh KRW 3000/kWh KRW 2500/kWh KRW 2000/kWh KRW 1500/kWh KRW 1000/kWh KRW 500/kWh KRW 0/kWh Note: The 95% confidence interval for the pickup service coefficient is [0.4593,0.6786].

OriginalThe standard error of the pickup service coefficient is 0.0559. Fig. 2. Choice probabilities for the programs with and without a pickup service. Lee and J. Kim Journal of Environmental Management 380 (2025) 124853 information provision (Table 6).

OriginalIn scenarios with and without information, all attribute levels were assumed to be identical except for the information attribute. The reward in the program without information was fixed at KRW 4000/kWh, while the rewards in programs with information were assumed to decrease from KRW 4000/kWh to KRW 0/ kWh.

OriginalMoreover, this study considered uncertain scenarios in which information was either more or less preferred and assumed that the estimated coefficient represented the upper and lower bounds of the 95% confidence interval, respectively.

OriginalSince operators can reduce rewards by providing information, consumers have identical preferences for programs with and without information at a reward level of KRW 2300/kWh (Fig. 3).

OriginalIn other words, operators can save KRW 1700/kWh per consumer, despite the need to collect and provide information. Additionally, given the uncertainty in consumer preferences for information, the reward level at which consumers equally prefer programs with and without information may vary.

OriginalWhile operators save additional KRW 500/kWh in the more preferred scenario, they need to offer KRW 500/kWh more in the less preferred scenario. Information may be more attractive to program operators because it is less costly than a pickup service.

OriginalIn other words, providing the information that consumers need can be an efficient strategy to encourage consumer participation with minimal expenditure. However, as shown in Fig. 3, since the reward levels required by consumers are sensitive to information provision, operators need to understand the structure of consumer preferences for information and the specific types of information they require.

OriginalAlthough this study considered program and emissions reduction information, consumers may need other types of information to participate in the programs, such as recycling and reuse safety and corporate social responsibility.

OriginalIn particular, given that consumers are increasingly sensitive to pro-environmental behavior, providing environmental information beyond just emissions reduction, such as resource conservation, can help encourage their participation.

Original4. Conclusion

OriginalThis study examined consumer preferences for EV battery collection programs and the factors influencing their participation. The programs were described using six attributes: return method, program operator, obligatory contract, information provision, battery usage period, and reward.

OriginalAccording to the results, respondents preferred to return EV batteries after 8–12 years of use to programs that offered a pickup service, information, and higher rewards.

OriginalRespondents wanted to receive information on both program and emissions reduction, but showed no

OriginalTable 6 Scenarios for analyzing consumers' sensitivity to information provision.

OriginalScenario Information availability With program and emissions reduction information Without program and emissions reduction information Preference for information provision More preferred Reference Less preferred More preferred Reference Less preferred Mean of β43 (Mean of the coefficient for both information) 0.6860 0.5228 0.3597 0.6860 0.5228 0.3597 Return method Pickup service Pickup service Program operator Coefficients were insignificant Coefficients were insignificant Obligatory contract With obligatory contract With obligatory contract Information provision Program and emissions reduction information Without information Battery usage period 8 years ≤ Period < 12 years 8 years ≤ Period < 12 years Reward KRW 4000/kWh KRW 4000/kWh KRW 3500/kWh KRW 3000/kWh KRW 2500/kWh KRW 2000/kWh KRW 1500/kWh KRW 1000/kWh KRW 500/kWh KRW 0/kWh Note: The 95% confidence interval for the both-information coefficient is [0.3597,0.6860].

OriginalThe standard error for the both-information coefficient is 0.0832. Fig. 3. Choice probabilities for the programs with and without information. Lee and J. Kim Journal of Environmental Management 380 (2025) 124853 preference for program operators.

OriginalAlthough returning retired EV batteries to local governments was mandatory in South Korea, consumers can now handle these batteries independently following the abolition of the regulation (Ministries of the South Korean Government, 2024).

OriginalThis implies that the processes of battery collection, recycling, and reuse are independently determined by the battery market. Therefore, designing strategies to promote EV battery collection is essential, as collection must occur before profits can be generated from battery recycling and reuse.

OriginalMoreover, the efficient collection of retired EV batteries will become increasingly important with the growing number of EVs on the road and the rising focus on life-cycle battery management.

OriginalThis study contributes to promoting EV battery collection in a market-driven environment where the success of battery recycling and reuse depends on market dynamics. The specific contributions of this study are summarized as follows.

OriginalFirst, the findings of this study assist companies considering EV battery collection in designing effective battery collection programs. The signs of estimated coefficients provide fundamental insights into consumer preferences and key factors influencing participation.

OriginalBased on these insights, companies can determine whether to offer additional services, contracts, or information. Second, companies can develop strategies to reduce program costs and promote consumer participation.

OriginalSince MWTA helps companies understand trade-offs between rewards and other attributes, they can calculate the savings in rewards instead of providing additional services, such as a pickup service or information provision.

OriginalThird, the uncertainty analyses in this study suggests that companies need to focus on consumer sensitivity to a pickup service and information provision. While the results indicate that consumers prefer these services, their preferences may change over time.

OriginalSince changes in consumer preferences can influence both the rewards they receive and companies' profitability, companies should understand these preference structures and closely monitor consumer sensitivity to convenience and the information provided by programs.

OriginalIn particular, providing environmental information can encourage consumer participation at minimal additional cost if companies understand the types of information consumers seek.

OriginalFourth, the findings of this study also assist companies in forecasting program trends. They can predict consumers' return timing, the number of collected EV batteries, and reward costs.

OriginalIn particular, if companies sign obligatory contracts, forecasting the number of collected EV batteries becomes easier. Although this study examined consumer preferences for battery collection programs, it did not focus on how individual consumer characteristics influence these preferences.

OriginalFuture research can enhance the comprehensiveness of collection strategies by estimating the effects of these characteristics and tailoring programs to different consumer segments.

OriginalAdditionally, consumers' MWTA may vary depending on their familiarity with the programs and background knowledge. For instance, as consumers' awareness of the programs and their need to improve environmental quality increase, their willingness to participate in such programs may also rise.

OriginalFurthermore, applying these findings to other countries requires caution, as this study focused on South Korean consumers. This is particularly relevant in e-waste management, where developed and developing countries exhibit significant differences in regulatory frameworks, governance, and financial support (Kosai et al., 2022). Further studies should consider these contextual differences when designing and implementing collection programs.

OriginalCRediT authorship contribution statement Hwarang Lee: Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Jongdae Kim: Writing – original draft, Validation, Investigation, Data curation, Conceptualization.

OriginalDeclaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

OriginalAcknowledgments This work was conducted under the framework of Research and Development Program of the Korea Institute of Energy Research (KIER) (C2-2443-01).

OriginalAppendix A.

OriginalSupplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.jenvman.2025.124853.

OriginalData availability Data will be made available on request.