Exploring public preferences for ecosystem service improvements regarding nature reserve restoration: A choice experiment study

Shi, Hengtong; Sun, Han; Ali, Muhammad Abu Sufyan; Mao, Hui

doi:10.1016/j.ecolind.2023.111121

Cited by 3 publications

(3 citation statements)

References 80 publications

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“…Real-world scenarios of static resource allocation include land use planning (Y. Wang et al, 2023aWang et al, , 2023b, water resource allocation (Pan et al, 2023), and natural reserve management (Shi et al, 2023), among others. These scenarios are based on the principles of partitioning service spaces and scheduling services, primarily focusing on the rational partitioning or arrangement of demand areas to maximize the service efficiency of resources within a limited space.…”

Section: Reinforcement Learning In Static Resource-based Resource All...mentioning

confidence: 99%

A survey on applications of reinforcement learning in spatial resource allocation

Zhang,

Wang,

Mango

et al. 2024

Comput.Urban Sci.

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The challenge of spatial resource allocation is pervasive across various domains such as transportation, industry, and daily life. As the scale of real-world issues continues to expand and demands for real-time solutions increase, traditional algorithms face significant computational pressures, struggling to achieve optimal efficiency and real-time capabilities. In recent years, with the escalating computational power of computers, the remarkable achievements of reinforcement learning in domains like Go and robotics have demonstrated its robust learning and sequential decision-making capabilities. Given these advancements, there has been a surge in novel methods employing reinforcement learning to tackle spatial resource allocation problems. These methods exhibit advantages such as rapid solution convergence and strong model generalization abilities, offering a new perspective on resolving spatial resource allocation problems. Despite the progress, reinforcement learning still faces hurdles when it comes to spatial resource allocation. There remains a gap in its ability to fully grasp the diversity and intricacy of real-world resources. The environmental models used in reinforcement learning may not always capture the spatial dynamics accurately. Moreover, in situations laden with strict and numerous constraints, reinforcement learning can sometimes fall short in offering feasible strategies. Consequently, this paper is dedicated to summarizing and reviewing current theoretical approaches and practical research that utilize reinforcement learning to address issues pertaining to spatial resource allocation. In addition, the paper accentuates several unresolved challenges that urgently necessitate future focus and exploration within this realm and proposes viable approaches for these challenges. This research furnishes valuable insights that may assist scholars in gaining a more nuanced understanding of the problems, opportunities, and potential directions concerning the application of reinforcement learning in spatial resource allocation.

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Section: Reinforcement Learning In Static Resource-based Resource All...mentioning

confidence: 99%

A survey on applications of reinforcement learning in spatial resource allocation

Zhang,

Wang,

Mango

et al. 2024

Comput.Urban Sci.

View full text Add to dashboard Cite

show abstract

“…Similar to this paper, some scholars have introduced the choice experiment method into studies about the evaluation of recreational resources and consumer preferences in national parks and nature reserves. Shi et al explored the public's preferences for the improvement of ecosystem service in nature reserves with the choice experiment [13]; Mzek et al investigated tourists' preferences and willingness to pay for Malaysian tiger conservation in Malaysian National Park [14]. Cheng et al analyzed the factors influencing the value of recreational resources in wetland reserves under different states in Fujian Province, China [15]; Juutinen et al took Finland Oulanka National Park as an example and explored the relationship between ecology and recreation in the construction of national parks.…”

Section: Introductionmentioning

confidence: 99%

Tourists’ Preferences and Willingness to Pay for Biodiversity, Concession Activity and Recreational Management in Wuyishan National Park in China: A Choice Experiment Method

Liu,

Wu,

Jiang

et al. 2024

Forests

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It is necessary for national parks to exert multiple forms of influence. One innovative function of national parks is leisure and recreation. By optimizing the recreation function, national parks can enhance public participation and tourists’ willingness to pay to achieve sustainable development. This paper surveys 435 tourists in Wuyishan National Park with a discrete choice experiment, applies mixed logit models to explore their preferences for recreational attributes from three dimensions and analyzes the heterogeneity. The findings indicate that attributes such as the number of concession activities, transportation variety, the density of recreational facilities, biodiversity, sightseeing zone and combined ticket fee significantly impact tourists’ willingness to pay. Higher-educated, younger and willing-to-revisit tourists are more likely to pay for the park and hope to optimize the facilities and activities. The estimated recreational value of the park is RMB 164.51 (about USD 22.75) per person. Here, heterogeneity exist: higher-educated tourists ask for lager sightseeing zone, younger ones prefer more diverse activities and those who are willing to revisit incline to variety means of transportation.

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“…Reviewing the existing research, as quality of life and environmental satisfaction are related, many studies have been conducted which examined residents' environmental satisfaction (Ma et al, 2018;Lee, 2021, Frey, 2010Ferreira et al, 2006). Various studies were performed in each environmental sector, including residential or life satisfaction based on air or water pollution, satisfaction with forest-related ecosystem services in the natural ecology sector, and satisfaction with management systems and improvement plans in the soil and waste sector (MacKerron and Mourato, 2009;Kruize and Ruysbroek, 2004;Wakuma, 2019;Luechinger, 2009;Kim and Kang, 2016;Frey et al, 2010;Rehdanz and Maddison, 2008;Nadeem, 2020;Shi et al, 2023;Wang et al, 2024;Olander et al, 2018). Recently, due to the COVID-19 outbreak, studies have reported that environmental improvement and health should be prioritized (Anessi-Pessina et al, 2020;Tracker, 2020;Dzigbede et al, 2020).…”

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confidence: 99%

Factors Affecting the Willingness to Pay for Environmental Improvements during the COVID-19 Pandemic in Taean-gun, Chungcheongnam-do

Lee

2024

J. People Plants Environ

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Background and objective: This study aimed to assess satisfaction with environmental factors during the COVID-19 pandemic and key factors affecting the willingness to pay (WTP) for environmental improvements using logistic regression analysis.Methods: Cross-sectional survey data of 504 residents of Taean-gun in Chungcheongnam-do (South Chungcheong Province), taken from July to August 2020, were used.Results: The assessment of their satisfaction with five environmental factors found that their satisfaction with the natural ecology had the highest score, while their satisfaction with the waste factor had the lowest score. With respect to overall environmental satisfaction, it was found that regardless of gender, age, and income, most residents were highly satisfied with the overall environment of Taean-gun (Taean County). In addition, the WTP for environmental improvements was slightly higher among males than among females. Residents in their 60s or older and with a monthly income of 4 - 4.99 million won were most likely to express WTP for environmental improvements. A binominal logit analysis found that increased environmental concerns (EC), satisfaction with environmental health (EHS) and income were crucial factors in determining the WTP for environmental health. The variables of increased EC and income, have a positive (+) relationship with WTP, while the variable of EHS has a significantly a negative (-) relationship with WTP.Conclusion: In other words, the higher the EC, the lower the EHS, and the higher the income, the more likely residents were to express WTP for environmental improvements. This study has significance in that it can provide primary data for sustainable local environmental plans to improve public environmental awareness for sustainability, and environmental strategies to prepare for post-pandemic life.

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Exploring public preferences for ecosystem service improvements regarding nature reserve restoration: A choice experiment study

Cited by 3 publications

References 80 publications

A survey on applications of reinforcement learning in spatial resource allocation

A survey on applications of reinforcement learning in spatial resource allocation

Tourists’ Preferences and Willingness to Pay for Biodiversity, Concession Activity and Recreational Management in Wuyishan National Park in China: A Choice Experiment Method

Factors Affecting the Willingness to Pay for Environmental Improvements during the COVID-19 Pandemic in Taean-gun, Chungcheongnam-do

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