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2023 Vol.41, Issue 6 Preview Page

Article

A Preference Analysis for User Incentives and Suggestions for the Future Direction toward Carbon Neutrality in Transportation Domains

탄소중립을 위한 수송분야 이용자 인센티브 선호도 분석과 방향성 연구

Sujeong HONG1
,
Chansik CHOI2
,
Soobeom LEE3
,
Jihye BYUN4*
홍 수정1
,
최 찬식2
,
이 수범3
,
변 지혜4*
1Ph.D. Candidate, Department of Transportation Engineering, University of Seoul, Seoul 02504, Korea
2M.S. Student, Department of Transportation Engineering, University of Seoul, Seoul 02504, Korea
3Professor, Department of Transportation Engineering, University of Seoul, Seoul 02504, Korea
4Assistant Professor, Department of Transportation Engineering, University of Seoul, Seoul 02504, Korea
1서울시립대학교 교통공학과 박사수료
2서울시립대학교 교통공학과 석사과정
3서울시립대학교 교통공학과 교수
4서울시립대학교 교통공학과 조교수
*Corresponding Author
31 December 2023. pp. 657-673
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Abstract

Considering the recent climate anomalies, domestic initiatives towards carbon neutrality have been launched, underscoring the ongoing efforts to achieve carbon-neutral goals. Among global carbon emissions, the transportation sector contributes 24%, with passenger vehicles accounting for 45% of that share. While the government is currently offering various incentives to reduce carbon emissions in the transportation sector, the need for effective strategies to mitigate carbon emissions from passenger vehicles remains paramount. Therefore, this study aimed to analyze user preferences for incentive systems in the transportation sector to reduce carbon emissions effectively. By utilizing the Best-Worst Scaling (BWS) technique, user preferences were analyzed, and a Balanced Incomplete Block Design (BIBD) was employed to enhance response reliability. Our results are as follows: First, there is a general preference for public transportation-related incentives, and there is a limited willingness to reduce carbon emissions by decreasing individual car usage. Second, respondents residing in metropolitan areas prefer public transportation-related incentives, while those in non-metropolitan areas favor incentives related to purchasing hydrogen electric vehicles and low-carbon vehicles. Third, respondents who own private vehicles show a particular disinterest in incentives related to walking and cycling, which are modes of public transportation. Finally, respondents who predominantly rely on own vehicles also exhibit the highest preference for incentives concerning the purchase of hydrogen electric and low-carbon vehicles. Consequently, there is a need to customized incentive systems according to regional transportation environments, and recommendations for incentive systems should consider the reality that car owners are less likely to transition to alternative modes of transportation. Based on the results of preference analysis, future research can provide direction and guidelines for new incentive systems, which can also be applied to assess preferences for new incentive programs and their utilization destinations.

Keywords
BWS
carbon-neutral
incentive system
preference analysis
transportation sector

최근 이상기후가 발생함에 따라 우리나라도 21년 탄소중립 시나리오를 발표하며, 목표 달성을 위한 다양한 노력을 진행 중에 있다. 국제에너지기구(IEA)에 따르면 전 세계적인 탄소배출량 중 수송부문의 탄소배출량은 24%, 그중 45%는 승용차가 차지하고 있다. 정부에서는 수송 분야의 탄소배출량 감축을 위하여 다양한 인센티브 제도를 시행 중에 있으나, 승용차에서 발생하는 탄소배출량을 감축할 수 있는 효과적이고 구체적인 방안이 필요한 상황이다. 따라서 본 연구에서는 이용자들의 인센티브 제도 선호도를 분석을 통해 수송 분야의 효율적인 탄소배출량 감소 방향성을 제안해보고자 한다. 이에 따라 BWS 기법을 활용해 이용자들의 선호도를 분석하였으며, 균형불완비블록설계 방식을 통해 응답의 신뢰성을 높이고자 하였다. 그 결과는 다음과 같다. 첫째, 전반적으로 대중교통 관련 인센티브를 선호하였으며, 자동차 주행거리를 줄여 탄소배출량을 줄이려는 의지는 낮았다. 둘째, 자가용을 보유한 응답자는 대중교통 이용 인센티브 중 특히 도보, 자전거 이용에 관련한 인센티브를 선호하지 않았다. 마지막으로 자가용을 주요 이동 수단으로 사용하는 응답자는 또한 수소전기차 및 저탄소차 구매 지원금 관련 인센티브를 가장 선호하였다. 결론적으로 지자체별 교통환경에 따라 인센티브 제도를 상이하게 구성할 필요성이 있으며, 승용차를 보유한 이용자가 수단 전환할 가능성이 적은 실정을 대비하는 인센티브 제도의 제안이 필요하다. 향후 선호도 분석 결과를 바탕으로 신규 인센티브 제도의 방향성과 가이드라인을 제시할 수 있으며, 신규 인센티브 제도와 인센티브 사용처에 대한 선호도 분석에도 활용할 수 있을 것으로 판단된다.

키워드
BWS 기법
탄소중립
인센티브 제도
선호도 분석
수송 분야
References
1
Byun B.S., Chae E.J., Park S.Y. (2014), An Analysis on the Characteristics of Energy Consumption from the Households in Carbon Point Program and Adequacy in Standards for Incentive Offerings, Journal of Environmental Policy and Administration, 22(1), 95-112. 10.15301/jepa.2014.03.22.1.95
2
Chang J.B. (2018a), Evaluation of Consumer Preferences for General Food Values in Korea: Best-worst Scaling Approach, Korean Journal of Agricultural Science, 45(3), 547-559.
3
Chang J.B. (2018b), Strategies and Priorities for Developing Beef Cattle Industry, Korean Journal of Agricultural Management and Policy, 45(2), 315-332. 10.30805/KJAMP.2018.45.2.315
4
Chang J.B., Lee Y.H. (2015), Determination of Priorities in the Forest Carbon Sink Enhancement Policies in the Republic of Korea, Journal of Agriculture and Life Science, 39(2), 128-146.
5
Cho Y.H., Ko S.M., Jeong G.C. (2022), Analysis of Relative Importance and Preference of Artificial Intelligence Medical products and Services Using BWS Method, Journal of Industrial Innovation, 38(3), 55-65.
6
Choi J.S. (2017), Research on Priorities of Purchasing Factors of Local Food Using Best-Worst Scaling Method, Journal of Regional Studies and Development, 26(3), 127-151. 10.18350/ipaid.2017.26.3.127
7
Choi J.S., Yang Y.R. (2019), Priority of Educational Aid Preference of High School Teachers in Cambodia Rural Areas: Using Best-Worst Scaling, Journal of the Korean Society of International Agriculture, 31(1), 90-97. 10.12719/KSIA.2019.31.1.90
8
Han J.Y. (2019), Study on Mode Choice Modeling for Mobility as a Service(MaaS): Stated Preference Survey, Hongik University, Master Dissertation.
9
Hu S.M. (2019), A Study on the Development Strategy of Xinjiang Ski Tourism Industry Based on SWOT-AHP Analysis, Hanyang University, Master Dissertation.
10
Jeong G.C. (2022), Korea Public Preferences for Healthcare Policy Priority Setting Using Best-Worst Scaling, Innovation studies, 17(1), 105-122. 10.46251/INNOS.2022.2.17.1.105
11
Jeong U.B. (2021), An Analysis on Hotel Employees' Preference System for Choosing a great Workplace and Estimating the Value of Key Attributes of the Workplace based on Willingness-to-pay, Sejong University, Ph.D Dissertation.
12
Kim J.I. (2022), Issues of Carbon Neutrality in the Transportation Sector and Mobility Transition, The Korea Transport Institute, Sejong, South Korea.
13
Kim K. S., Shin M. S., Koo J. K. (2010), A Study on Carbon Incentive System Based on Investigation of Energy Consumption in Korean Universities, Hwankyungkyoyuk, 23(2), 65-81.
14
Kim K. U., Kang W. S., Lee H.J. (2021), Planning Low-Carbon Behaviors(LCBs) Incentive Programs Towards Seoul as a Carbon Neutral City, The Seoul Institute, 1-150.
15
Kim K.S. (2001), Stated Preference Design and Analysis (the first phase), The Korea Transport Institute, Sejong, South Korea.
16
Kim K.S. (2002), Stated Preference Design and Analysis (the second phase), The Korea Transport Institute, Sejong, South Korea.
17
Kim Y.H. (2018), A Study on the Preference of Government Policy of SMEs Using AHP Analysis from the respective of its demanding and supplying groups, Daejeon University, Master Dissertation.
18
Kim Y.H. (2018), A Study on the Preference of Government Policy of SMEs Using AHP Analysis from the respective of its demanding and supplying groups, Daejeon University, Master Dissertation.
19
Ko C.N., Park D.J. (2023), AHP Analysis of Factors of Selecting Public Transport Modes When Building Carbon-neutral MaaS, Journal of Korean Society for Urban Railway, 11(1), 1419-1431. 10.24284/JKOSUR.2023.3.11.1.1419
20
Lee H.S., Lee S.B., Lim J.B. (2022), Analysis of Preference for UAM of Public Transportation Users Following UAM Adoption(Incheon Airport - Incheon Gil Medical Center Line), Journal of The Korea Institute of Intelligent Transport Systems, 21(5), 1-14. 10.12815/kits.2022.21.5.1
21
Loose S.M., Lockshin L. (2013). Testing the robustness of best worst scaling for cross-national segmentation with different numbers of choice sets, Food Quality and Preference, 27(2), 230-242. 10.1016/j.foodqual.2012.02.002
22
Marley A.A., Louviere J.J. (2005), Some Probabilistic Models of Best, Worst, and Best-worst Choices, Journal of Mathematical Psychology, 49(6), 464-480. 10.1016/j.jmp.2005.05.003
23
Nunes F., Madureira T., Oliveira J.V., Madureira H. (2016). The consumer trail: Applying best-worst scaling to classical wine attributes, Wine Economics and Policy, 5(2), 78-86. 10.1016/j.wep.2016.10.002
24
Song S.S. (2022), Policy Tasks to Achieve Carbon Neutrality in The Transportation Sector, Public Policy, 204, 18-20.
Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 41
  • No :6
  • Pages :657-673
  • Received Date : 2023-09-26
  • Revised Date : 2023-10-16
  • Accepted Date : 2023-11-28
  • DOI :https://doi.org/10.7470/jkst.2023.41.6.657
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