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<p>Numerous studies have explored the accumulation of coastal litter, where the understanding of pro-environmental behavior of beachgoers traditionally relied on questionnaire-based methods. However, the outcomes of these studies on the basis of questionnaire methods have been subjective and limited. Therefore, this study presents a novel and practical methodology to assess pro-environmental behaviors by analyzing real-time litter disposal patterns through strategically placed recycling and nonrecycling bins at No. 1 Bathing Beach (NB) in Qingdao, China. Data were collected from 20 paired bins, positioned at regular intervals along beach during both summer and winter seasons, offering a reliable and direct approach to understanding waste disposal behavior. The results showed significant variations in seasonal accumulations in beach litter, with higher in summer (1.78 ± 2.87 items/meter) than in winter (0.60 ± 0.95 items/meter), driven by increased tourist activity and local sources. Plastic litter dominated the composition, accounting for 54% in summer and 49% in winter, consistent with global trends. Pollution levels were evaluated using Plastic Abundance Index (PAI), Clean Coast Index (CCI), Hazardous Items Index (HII), and Clean Environmental Index (CEI), revealing high plastic abundance and hazardous waste, classifying NB as “extremely dirty.” The average efficiency of recycling bins was 57.73% in summer and 50.27% in winter, while nonrecycling bins showed 48.70% and 45.50% efficiency during same periods, indicating a shift from moderate to poor performance. These findings reveal frequent bin misuse by beachgoers, emphasizing the need for awareness campaigns, educational programs, and improved bin placement. This study offers a more reliable, real-time framework for understanding pro-environmental behavior, providing actionable data to enhance coastal waste management and sustainable tourism initiatives.</p>

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Assessment of tourist littering behavior using an innovative monitoring technique at No. 1 bathing Beach, Qingdao, China

  • 期刊论文

  • 发表日期：

    2025年11月13日

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  • Rashid Pervez,
  • Chao Jia,
  • Marium Sardar,
  • Zafar Ullah Jattak,
  • Jawad Ali,
  • Muhammad Fahad Sardar,
  • Zhongping Lai

摘要

Numerous studies have explored the accumulation of coastal litter, where the understanding of pro-environmental behavior of beachgoers traditionally relied on questionnaire-based methods. However, the outcomes of these studies on the basis of questionnaire methods have been subjective and limited. Therefore, this study presents a novel and practical methodology to assess pro-environmental behaviors by analyzing real-time litter disposal patterns through strategically placed recycling and nonrecycling bins at No. 1 Bathing Beach (NB) in Qingdao, China. Data were collected from 20 paired bins, positioned at regular intervals along beach during both summer and winter seasons, offering a reliable and direct approach to understanding waste disposal behavior. The results showed significant variations in seasonal accumulations in beach litter, with higher in summer (1.78 ± 2.87 items/meter) than in winter (0.60 ± 0.95 items/meter), driven by increased tourist activity and local sources. Plastic litter dominated the composition, accounting for 54% in summer and 49% in winter, consistent with global trends. Pollution levels were evaluated using Plastic Abundance Index (PAI), Clean Coast Index (CCI), Hazardous Items Index (HII), and Clean Environmental Index (CEI), revealing high plastic abundance and hazardous waste, classifying NB as “extremely dirty.” The average efficiency of recycling bins was 57.73% in summer and 50.27% in winter, while nonrecycling bins showed 48.70% and 45.50% efficiency during same periods, indicating a shift from moderate to poor performance. These findings reveal frequent bin misuse by beachgoers, emphasizing the need for awareness campaigns, educational programs, and improved bin placement. This study offers a more reliable, real-time framework for understanding pro-environmental behavior, providing actionable data to enhance coastal waste management and sustainable tourism initiatives.