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Research on the Application of Conjoint Analysis in Carbon Tax Pricing for the Sustainable Development Process of China

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  • Jiahao Zhang

    (Faculty of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, China)

  • Chaolin Li

    (Faculty of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, China)

  • Xiangnan Ji

    (Faculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu 802-8577, Japan)

  • Li Zhang

    (School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China)

  • Yanjun Chen

    (The Institute of Sustainable Development, Macau University of Science and Technology, Macau, China)

Abstract

This study aims to evaluate the preferences of Shanghai residents for four key attributes of carbon tax policies via the choice-based conjoint analysis method, with the goal of understanding the potential application of carbon tax policies in China. The findings indicate that the most critical concern for Shanghai residents is the annual cost of the ca trbonax (48.87%), followed by policy transparency (24.72%), revenue allocation (16.68%), and policy implementers (9.73%). The average willingness to pay is CNY 1435.28 annually, indicating a relatively high acceptance of carbon tax policies. Furthermore, increasing public awareness of carbon tax policies significantly enhances willingness to pay, which in turn helps increase tax revenue and promotes the development of low-carbon technologies. This study provides valuable insights for policymakers on carbon tax implementation, contributing to China’s transition to a low-carbon economy.

Suggested Citation

  • Jiahao Zhang & Chaolin Li & Xiangnan Ji & Li Zhang & Yanjun Chen, 2024. "Research on the Application of Conjoint Analysis in Carbon Tax Pricing for the Sustainable Development Process of China," Sustainability, MDPI, vol. 16(21), pages 1-24, October.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:21:p:9407-:d:1509594
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    References listed on IDEAS

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