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Article

Analysis of Forest Utilization Patterns to Improve Life Satisfaction and Policy Directions

by
Mijin Lee
and
Jeonghee Lee
*
Forest Human Service Division, Future Forest Strategy Department, National Institute of Forest Science, Seoul 02455, Republic of Korea
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(8), 3689; https://doi.org/10.3390/su17083689
Submission received: 27 February 2025 / Revised: 15 April 2025 / Accepted: 15 April 2025 / Published: 18 April 2025
(This article belongs to the Special Issue The Convergence of Recreation, Green Tourism and Environmental Sustainability)
Versions Notes

Abstract

Interest in the balance between work and life and improving the quality of life is expected to steadily increase the number of users of forest welfare facilities. In this study, the “2023 Forest Recreation and Welfare Activity Survey” was used to analyze how characteristics of forest visits, such as visit frequency, the number of visited areas, expenditures, and the presence of companion animals, affect life satisfaction based on three visit types: daily (less than 4 h), day (more than 4 h), and overnight (more than 1 night). A hierarchical regression of 9437 respondents revealed that age, occupation, education, spouse, children, household income, and type of residence, excluding sex, were significantly correlated with life satisfaction. A hierarchical regression analysis revealed that for daily visitors, both visit frequency and companion animals increased life satisfaction. For day visitors, visit frequency, the number of visited areas, expenditures, and companion animals enhanced life satisfaction. For overnight visitors, visit frequency and expenditures positively impacted life satisfaction. Across all visit types, the frequency of forest visits consistently increased life satisfaction. This study statistically clarified how the characteristics of each visit type affect life satisfaction. The findings provide foundational data for future policies and research on forest recreation and welfare.

1. Introduction

In recent years, the importance of promoting health and preventing disease has been emphasized [1,2,3,4], and people’s interest in personal happiness [5,6,7] and the realization of the importance of work and life balance has increased [8,9]. Happiness is measured in various ways, such as quality of life [10], life satisfaction [11], and subjective well-being [12]. However, Statistics Korea measures the quality of life of Korean citizens by referring to the subjective well-being scale of the Organization for Economic Co-operation and Development (OECD) [13]. The integration of life satisfaction and positive and negative emotions provides an indicator of satisfaction with one’s current overall life. Although Korea’s life satisfaction score has slightly increased since 2013, it still ranks 35th out of 38 OECD member countries [13]. Therefore, the Korea Forest Service is trying to improve the quality of life of Koreans through the establishment of forest welfare facilities and various policies. In this study, the term “forest welfare” refers to a range of economic, social, and emotional support services based on forest resources, aimed at enhancing the well-being of the public. This concept is informed by Korea’s national forest welfare policy, which promotes the use of forests to improve quality of life through recreational, therapeutic, and educational programs.
Forest welfare facilities can contribute to stress relief and mental and physical stability, improve health, and strengthen social bonds through various activities in natural environments [14,15,16,17,18,19]. Many studies have reported that visiting forests, such as parks and forested areas, helps improve quality of life, with relaxation and activities in natural environments promoting psychological stability and quality of life [20,21,22,23,24,25]. Thus, the number of people who visit forest welfare facilities to obtain the various positive benefits of forests is expected to increase until 2050. Subsequently, the overall demand for visits will decrease due to the impact of population decline, although the demand for forest welfare experiences by individuals will continue to increase until 2070 [26].
The facilities and time utilized vary depending on the purpose of the forest visit and type of activity [27,28]. Forest visitation activities are characterized by health promotion, education, learning, relaxation, recreation, and reporting. Forest welfare facilities are established and managed according to the purpose of visitation [29,30]. Forest visits can be categorized into daily, day, and overnight visits. Daily visits are frequent and include relatively short activities near home or the workplace, with each lasting less than four hours. Day visits take up a considerable amount of time and last longer than four hours. Overnight visits last for at least one night. Visit and activity characteristics affect destination choice and satisfaction, and satisfaction is a major factor in repeat visits. Therefore, research on visitor characteristics is important [29,31].
Previous studies have primarily analyzed the relationship between forest visits and life satisfaction by focusing on visit frequency and broadly categorizing visits into day trips and overnight stays [32]. However, there is a lack of research that distinguishes between daily, day, and overnight visits and examines how their distinct characteristics influence life satisfaction. Therefore, this study aimed to categorize forest visit characteristics into three types (daily, day, and overnight) and analyze the impact of each type on life satisfaction. The study also analyzed how specific characteristics—such as the number of visits, variety of areas visited, total expenditure, and presence of a companion animal—affect life satisfaction, while controlling for demographic characteristics. In doing so, this study contributes to the literature by offering a more nuanced understanding of forest usage patterns and their relationship to well-being, thereby providing useful insights for future forest welfare policies and sustainable nature-based interventions.
Based on this objective, the study addresses the following research questions:
(1)
Do demographic characteristics (gender, age, type of residence, monthly household income, education, etc.) influence life satisfaction?
(2)
How do forest visit characteristics of daily (less than 4 h) visitors (e.g., visit frequency, travel distance, and presence of companion animals) affect life satisfaction?
(3)
How do forest visit characteristics of day (more than 4 h) visitors (e.g., diversity of visited locations, visit frequency, expenditure, and presence of companion animals) affect life satisfaction?
(4)
How do forest visit characteristics of overnight (more than 1 night) visitors (e.g., diversity of visited locations, number of nights, visit frequency, expenditure, and presence of companion animals) affect life satisfaction?

2. Materials and Methods

2.1. Survey Overview

This study used data from the “2023 Forest Recreation and Welfare Activities Survey” conducted by the Korea Forest Service. The survey period lasted approximately three months from August to October 2023, and the participants were people aged 15 years and older and living in 17 regions across the country. The survey was conducted using proportional sampling according to sex and age. In total, 11,457 responses were collected, and 9437 individuals who had visited a forest in the past year (January 2022 to December 2022) were assessed.

2.2. Survey Instrument

A structured questionnaire was used to collect information on respondents’ forest visit behaviors and their life satisfaction. The data used in this study were drawn from the 2023 Forest Recreation and Welfare Activity Survey, which was conducted by the Korea Forest Welfare Institute under the supervision of the Korea Forest Service. This nationwide survey was designed to assess the status of public engagement in forest-based recreational and welfare activities, including walking, forest-bathing, education, and other nature-based experiences offered in forest settings. It provided large-scale, representative data useful for informing forest-related policy and research in Korea. Life satisfaction, defined as an individual’s overall evaluation of their life as a whole, was measured using an 11-point Likert scale, as recommended by the OECD guidelines [33], where 0 indicates “not at all satisfied” and 10 indicates “completely satisfied”. The specific question used was the following: “Overall, how satisfied are you with your life nowadays?”. Participants also provided information on their demographic characteristics (gender, age, education, income, and region) and detailed forest visit characteristics, depending on the type of visit. Forest visit types were defined as follows: daily visits (less than 4 h), day visits (more than 4 h), and overnight visits (more than one night). Daily visitors answered questions about the number of visits, travel distance, and presence of companion animals. Day and overnight visitors were asked about the number of visited areas, number of visits, total expenditure, and presence of companion animals. Overnight visitors additionally reported the number of nights stayed. The “total expenditures” variable referred to the total spending per person per trip, recorded in Korean won (KRW).

2.3. Statistical Analysis

A frequency analysis was conducted to examine demographic characteristics. Independent t-tests and an analysis of variance (ANOVA) were used to analyze the influence of personal characteristics on life satisfaction. Subsequently, hierarchical regression analysis was performed to control for the effects of demographic variables and to assess the impact of forest visit characteristics on life satisfaction. This method allowed for the comparison of contributions from newly added independent variables while controlling for those that had been previously included [34]. Categorical variables such as age group, type of residence, and monthly household income were converted into dummy variables for analysis. All data were analyzed using IBM SPSS Statistics version 24.0 (SPSS Inc., Chicago, IL, USA), and the significance level was set at p < 0.05.

3. Results

3.1. Demographic Characteristics

A total of 9437 respondents who had visited a forest and their demographic characteristics are presented in Table 1. Males accounted for 4297 of the respondents (45.5%), and females accounted for 54.5%. The age distribution of respondents was as follows: 3.4% were in their teens, 9.4% in their 20s, 14.9% in their 30s, 14.8% in their 40s, 20.9% in their 50s, and 36.7% were aged 60 or older. Regarding monthly household income, 6.8% of respondents earned less than KRW 2 million, 13.7% earned between KRW 2 million and 4 million, 14.8% earned between KRW 4 million and 6 million, and 64.6% earned more than KRW 6 million. In terms of type of residence, 21.4% lived in single-family houses, 60.3% in apartments, 16.7% in townhouse or rowhouses, and 1.6% in other housing types. Regarding occupation, 32.6% participants were employed, 67.4% were unemployed, 47.0% had less than a high school degree, and 53.0% had a college degree or higher. Additionally, 35.0% had a spouse, 65.0% had no spouse, 80.8% had children, and 19.2% had no children.

3.2. Impact of Personal Characteristics on Life Satisfaction

To analyze the impact of personal demographic characteristics on life satisfaction, independent t-tests were performed for sex, occupation, education, spouse, and children, while an ANOVA was conducted for age, monthly household income, and type of residence. According to the t-test results (Table 2), life satisfaction did not differ significantly by sex; however, life satisfaction significantly differed between occupations, education levels, spouses, and children. Respondents with an occupation, a university degree or higher, a spouse, and children reported significantly higher life satisfaction than those without these characteristics. The ANOVA results (Table 3) indicated that life satisfaction was highest among respondents in their 20s and 30s, those with a household income between KRW 4 million and 6 million, and those living in apartments. Therefore, we controlled for demographic variables except for sex in the hierarchical regression analysis.

3.3. Impact of Forest Visitation Type on Life Satisfaction

A hierarchical regression analysis was conducted to analyze the impact of forest visit characteristics on life satisfaction. Forest visitation types were categorized as daily (less than 4 h), day (more than 4 h), and overnight (more than 1 night). For daily visitors, the variables included the number of forest visits per year, travel time to the forest, and presence of a companion animal. Day visitors were analyzed by the number of areas visited, number of forest visits per year, total expenditures, and the presence of a companion animal, whereas overnight visitors were analyzed based on the period of stay.

3.3.1. Characteristics of Forest Visits by Type

The 9437 people who visited forests were surveyed according to the type of visit (Table 4). A total of 6241 participants reported making daily visits to forests, with an average of 115.91 visits per year and an average travel time of 16.41 min. Of these, 1037 (16.6%) visited the forest with companion animals. A total of 4870 day visitors visited forests 3.34 times per year and an average of 2.1 locations per visit. An average of KRW 65,930 (USD 44.98) was spent per person per visit, and 362 people (7.4%) visited the forest with companion animals. The number of overnight visitors was 2456, and they visited the forest 1.84 times per year, staying an average of 3.94 days and visiting an average of 1.46 locations. An average of KRW 183,043 (USD 124.87) was spent per person per visit, and 201 people (8.2%) visited the forest with companion animals.

3.3.2. Characterization of Daily Visitors

To analyze the impact of daily forest visitations on life satisfaction, we conducted hierarchical regression analyses using data from the daily visitor group (Table 5). After controlling for demographic characteristics, hierarchical regression analyses were conducted to analyze the impact of forest visit characteristics on life satisfaction. Both Models 1 and 2 were statistically significant, and the explanatory ability of the model increased by 0.8%. The Variance Inflation Factor (VIF) ranged from 1.038 to 5.872, indicating that multicollinearity was not a problem, and the Durbin–Watson score was 1.632, indicating that the regression model was appropriate.
The analysis of the visit characteristics of daily visitors showed that the number of forest visits and presence of companion animals had a significant positive effect on life satisfaction. A higher number of forest visits and the presence of companion animals were associated with higher life satisfaction. The average travel time to the forest did not have a significant effect on life satisfaction. For daily visitors, the forest visit characteristic that had the greatest impact on life satisfaction was the number of forest visits (β = 0.086).

3.3.3. Characterization of Day Visitors

For day visitors, an analysis was conducted on visitors who visited the forest for more than 4 h per day (Table 6). Both Models 1 and 2 were statistically significant, increasing the explanatory ability of the model by 1.8%. The VIF ranged from 1.019 to 6.175, indicating that multicollinearity was not a problem, and the Durbin–Watson score was 1.833, indicating that the regression model was suitable.
Regarding the visit characteristics of day visitors, the diversity of areas visited, the number of forest visits, total expenditures, and the presence of companion animals had a significant positive effect on life satisfaction. A higher diversity of areas visited, a higher number of forest visits, higher expenditures, and the presence of a companion animal corresponded to higher life satisfaction. For day visitors, the forest visit characteristic that had the greatest impact on life satisfaction was the variety of areas visited (β = 0.089).

3.3.4. Characterization of Overnight Visitors

The characteristics of overnight forest visits were analyzed for visitors who stayed for at least one night (Table 7). Both Models 1 and 2 were statistically significant, increasing the explanatory ability of the model by 1.3%. The VIF ranged from 1.247 to 6.250, indicating that there was no multicollinearity problem. The Durbin–Watson coefficient was 1.894, indicating that the regression model was appropriate.
The analysis of the characteristics of overnight visitors showed that the number of forest visits and total expenditure had a significant positive effect on life satisfaction. A higher number of forest visits and greater total expenditure were associated with higher life satisfaction. In contrast, the number of nights stayed had a negative effect on life satisfaction. The diversity of the visited areas and presence of companion animals did not significantly affect life satisfaction. For overnight visitors, the number of visits had the greatest impact (β = 0.088).

4. Discussion

The Korea Forest Service is promoting the Forest Welfare Promotion Plan to establish a comprehensive forest welfare policy in response to growing public interest in health and quality of life [35]. The Second Forest Welfare Promotion Plan aims to provide various forest welfare services based on the vision of “forest welfare for all”. Against this backdrop, this study was conducted to analyze the relationship between forest welfare activity participation and life satisfaction using the 2023 Forest Recreation and Welfare Activities Survey, with the aim of offering insights that may inform future forest welfare policies. To this end, forest visits were categorized into daily, day, and overnight types, and the characteristics of each type were examined in relation to life satisfaction. Based on the findings, this study proposes several suggestions that may serve as reference points for forest welfare policy development according to visit type.
First, nearby forest spaces should be created for daily visitors. In the analysis of daily visitors, the frequency of forest visits and the presence of companion animals positively impacted life satisfaction, with the frequency of forest visits having the strongest effect. This result aligns with previous findings suggesting that proximity to green spaces promotes frequent use and is positively associated with physical and mental health outcomes [36,37,38]. Such evidence supports the idea that enhancing the accessibility of nearby forests can play an important role in promoting well-being. This finding suggests that increasing the availability of nearby forests and green spaces is important [39,40,41]. The distance to accessible forests has been shown to influence the frequency of forest visits [42,43], with people who live closer to forests visiting them more frequently [44,45,46]. Therefore, policy efforts should focus on increasing the accessibility of living forests and intentionally creating urban green spaces that allow residents to use forests on a daily basis. Such efforts can improve the mental health and emotional well-being of urban residents. This is consistent with the restoration theory and stress reduction theory, which posit that repeated, low-effort exposure to natural environments—such as nearby forests—can facilitate recovery from mental fatigue and reduce physiological stress by engaging parasympathetic nervous system responses and promoting emotional regulation [47,48].
Second, diversity and accessibility must be increased for day visitors. Day visitors’ life satisfaction was influenced by the diversity of areas visited, the number of forest visits, total expenditures, and the presence of companion animals. Among these, visiting a greater variety of forest areas had the strongest effect. This supports findings by Rantakokko’s [49], who emphasized that exposure to diverse green spaces contributes significantly to perceived quality of life. Similarly, Keniger [50] reported that the psychological benefits of nature are enhanced when individuals engage with a variety of natural settings, rather than repeating visits to the same location [51]. This observation is further supported by the attention restoration theory. The theory explains that natural environments help restore depleted attentional capacity by providing softly fascinating stimuli. These stimuli engage the mind effortlessly without demanding focused effort, allowing cognitive resources to recover. As a result, mental fatigue is reduced and attentional clarity is improved, which may enhance perceived life satisfaction following exposure to diverse forest settings [47]. This suggests that visitor satisfaction can be improved by offering different themed experience programs in nearby forest areas or by developing region-specific forest welfare content. Furthermore, accessibility to various areas must be improved by developing transportation infrastructure and enhancing public transportation connectivity [52]. For example, shuttle buses between major cities, nearby forest welfare facilities, and day packages with forest experiences may be useful alternatives. These efforts will increase the satisfaction of day visitors and contribute to improving their quality of life by further strengthening the accessibility of forest welfare benefits.
Third, a systematic program for overnight visitors should be introduced. For overnight visitors, the main variables were the number of forest visits and total expenditures, with the number of visits having a greater impact on life satisfaction. This is in line with studies showing that the frequency of nature engagement has a greater effect on well-being than the duration of a single visit [53,54,55]. One unexpected finding was the negative association between the number of nights stayed and life satisfaction. This result may reflect travel-related fatigue, unmet expectations during extended forest stays, or stress arising from the logistics of longer trips. These interpretations are consistent with the findings of Morris and Guerra [56], who reported that longer trip durations tend to increase fatigue and stress, ultimately leading to lower mood and reduced satisfaction. Further research is needed to better understand these dynamics. Therefore, short-term overnight programs and the creation of natural recreational forests are needed to encourage frequent visits rather than long stays for overnight visitors. For example, introducing themed forest experience courses or membership programs for overnight visitors could encourage repeat visits. These efforts will increase the satisfaction of overnight visitors while strengthening the sustainability of forest welfare services. From a restorative perspective, frequent overnight visits allow individuals to detach from daily routines and stressors. This deeper immersion in nature creates opportunities for sustained psychological recovery. Unlike brief visits that may offer short-term stress relief, overnight stays can promote longer-term improvements in mood and overall well-being [47].
In all three visit types, the number of forest visits showed a statistically significant positive association with life satisfaction, suggesting that frequent forest use may contribute to greater subjective well-being. These results are consistent with those of previous studies that reported the positive effects of visiting forests in living areas or green spaces in residential neighborhoods on life satisfaction, suggesting that frequent visits to forests are an important factor in increasing life satisfaction [32,57,58,59]. Therefore, policies to increase the number and diversity of forest visits are required. Specialized forest welfare facilities and services are required for each type of visit, which can help forests increase life satisfaction. In particular, policies are required to expand forest spaces in close proximity for daily visitors, improve infrastructure to encourage day visitors to visit different areas, and provide places and overnight programs to increase the frequency of overnight visits. Thus, policies that promote diversity in the access to and use of forests will be effective in increasing the life satisfaction of each type of visitor.
Fourth, the role of companion animals in forest visitation requires more cautious interpretation. In our analysis, the presence of companion animals was significantly associated with life satisfaction for daily and day visitors, although the effect sizes were small. No significant relationship was found for overnight visitors. While some previous studies have reported positive associations between pet ownership and life satisfaction, others have found no significant effects [60,61,62,63]. These mixed results suggest that the relationship between pets and forest-based well-being warrants further investigation. In recent years, research on companion animals and well-being has continued to expand, with growing evidence pointing to potential psychological and social benefits of pet ownership. In particular, pet-related activities such as dog walking have been linked to increased physical activity and a greater sense of community and belonging [64,65]. These findings suggest that forest environments may serve as meaningful spaces not only for recreation but also for enhancing well-being through shared experiences with pets. According to the Ministry of Agriculture, Food, and Rural Affairs, as of 2022, approximately 25.4% of Korean households own pets, representing around 15 million individuals [66]. In this study, only 16.6% of daily visitors, 7.4% of day visitors, and 8.2% of overnight visitors brought companion animals, indicating that this subgroup represents a relatively small portion of the total sample. Nonetheless, as the pet-owning population continues to grow, it may be worthwhile to consider how forest welfare services can be adapted to accommodate pet-related needs. Future development of designated trails, shelters, and pet-friendly accommodations could improve the forest experience for this demographic and potentially encourage broader participation in forest welfare programs.
For both day and overnight visits, the total expenditures on forest visits significantly affected life satisfaction. This finding contrasts with some previous studies that have found little to no relationship between expenditure and subjective well-being in nature-based activities [32], suggesting that the role of spending may vary depending on context. These results differ from those of previous studies; thus, further research is required. However, Choung et al. [67] report that spending on leisure is significantly related to life satisfaction, indicating that investing in new experiences away from daily routines may offer psychological benefits. To better understand this relationship, insights from other domains such as marine leisure activities can be informative. A study on marine leisure sports found that factors such as product (program variety and stability), place (facilities and cleanliness), and facilitation (promotion, follow-up, and communication) had a greater impact on participation satisfaction than price, ultimately influencing users’ intention to revisit [68]. Similarly, studies in tourism and leisure have shown that satisfaction with travel experiences enhances overall quality of life and increases the likelihood of repeat participation [69,70]. These findings suggest that beyond monetary cost, the overall experience and satisfaction derived from forest welfare activities may play a more critical role in promoting life satisfaction. Therefore, future research should explore how specific aspects of forest recreation services such as program quality, environment, and service delivery contribute to satisfaction and long-term engagement. This could inform the development of tailored services designed to maximize user satisfaction and promote the sustained use of forest welfare programs.
This study analyzed the relationship between forest welfare activities and life satisfaction, identifying the impact of factors such as the frequency of forest visits and the presence of companion animals. Hierarchical regression models were used to examine whether specific demographic and forest visit characteristics significantly influence life satisfaction. Although the models revealed statistically significant associations, the overall explanatory power was relatively low, with R² values of approximately 0.05, indicating that the included variables accounted for only about 5% of the variance in life satisfaction. Such low R2 values are not uncommon in social and behavioral research, particularly in studies involving complex psychological constructs such as life satisfaction. As noted by Ozili [71], low R2 values can still offer meaningful insights when the identified relationships are statistically significant and theoretically grounded. Life satisfaction is influenced by a wide array of factors beyond the scope of this study, including psychological traits, interpersonal relationships, health status, and cultural context. Therefore, while our results should be interpreted with caution, the statistically significant patterns observed in this study still contribute valuable knowledge to the field of forest welfare and quality-of-life research.
Moreover, although multicollinearity was not statistically problematic, we observed moderate correlations among certain predictors, especially among overnight visitors. For instance, the number of visits and number of nights stayed were strongly correlated, suggesting that these variables may reflect overlapping aspects of forest engagement. This conceptual proximity was considered during interpretation to avoid overstating the independent contribution of each factor.
The results also reveal differences in life satisfaction across various types of forest visits, which can serve as a basis for designing tailored forest welfare policies. Furthermore, these findings offer meaningful directions for improving the accessibility and sustainability of forest welfare services. Since the primary goal of this study was to examine whether certain variables significantly influence life satisfaction, rather than to maximize predictive power, the relatively low explanatory power should be acknowledged but does not diminish the significance of the findings. We believe that these results can still contribute meaningfully to the sustainable development of forest welfare. Future studies should consider additional variables that may better explain life satisfaction, such as perceived health status, psychological resilience, social support, and accessibility to green spaces. Incorporating these dimensions could improve the explanatory power of predictive models and deepen our understanding of the multifaceted nature of well-being.

5. Conclusions

This study examined the effects of forest visit characteristics on life satisfaction by categorizing forest visit types into daily, day, and overnight visits. The results revealed statistically significant associations, with differences observed across visit types. In particular, visit frequency was found to be a significant factor that positively influences life satisfaction across all visit types. These findings suggest that forest welfare services have the potential to enhance quality of life and may inform the development of more tailored and accessible policies. However, several limitations should be acknowledged. The explanatory power of the models was relatively low, so the results must be interpreted carefully. The sample was also drawn from a specific national context, which may affect the generalizability of the results to other populations or regions. Despite these limitations, the study contributes valuable insights to the field of forest welfare and provides a foundation for future research. Expanding the range of explanatory variables—including psychological, social, and cultural factors—and utilizing longitudinal designs would help strengthen the explanatory power of future models and deepen understanding of how forest-based activities contribute to well-being.

Author Contributions

Conceptualization, M.L. and J.L.; methodology, J.L.; software, M.L.; validation, M.L. and J.L.; formal analysis, M.L.; investigation, M.L. and J.L.; resources, J.L.; data curation, J.L.; writing—original draft preparation, M.L.; writing—review and editing, J.L.; visualization, M.L.; supervision, J.L.; project administration, J.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Ethical review and approval were waived for this study because it used secondary data from the 2023 Forest Recreation and Welfare Activities Survey, which is a nationally approved statistical dataset administered by the Korea Forest Welfare Institute under the authority of the Korea Forest Service.

Informed Consent Statement

Informed consent was waived because this study used anonymized secondary data collected through a national survey.

Data Availability Statement

The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Haskell, W.L.; Blair, S.N.; Hill, J.O. Physical activity: Health outcomes and importance for public health policy. Prev. Med. 2009, 49, 280–282. [Google Scholar] [CrossRef] [PubMed]
  2. McGinnis, J.M.; Williams-Russo, P.; Knickman, J.R. The case for more active policy attention to health promotion. Health Aff. 2002, 2, 78–93. [Google Scholar] [CrossRef] [PubMed]
  3. Kaminsky, L.A.; German, C.; Imboden, M.; Ozemek, C.; Peterman, J.E.; Brubaker, P.H. The importance of healthy lifestyle behaviors in the prevention of cardiovascular disease. Prog. Cardiovasc. Dis. 2022, 70, 8–15. [Google Scholar] [CrossRef] [PubMed]
  4. Ali, A.; Katz, D.L. Disease prevention and health promotion: How integrative medicine fits. Am. J. Prev. Med. 2015, 49, S230–S240. [Google Scholar] [CrossRef]
  5. Joshanloo, M.; Weijers, D. Aversion to happiness across cultures: A review of where and why people are averse to happiness. J. Happiness Stud. 2014, 15, 717–735. [Google Scholar] [CrossRef]
  6. Mangra, G.I.; Mangra, M.G.; Bocean, C.G.; Vărzaru, A.A. Assessing the Relationships between Physical Activity, a Healthy Life, and Personal Happiness in European Union Countries. Healthcare 2024, 12, 1941. [Google Scholar] [CrossRef]
  7. Lee, B.C. A Study on Relationship between Work Life Balance and a Sense of Happiness of Leisure Sports Participants. Korean J. Phys. Educ. 2016, 55, 549–557. [Google Scholar]
  8. Kelliher, C.; Richardson, J.; Boiarintseva, G. All of work? All of life? Reconceptualising work-life balance for the 21st century. Hum. Resour. Manag. J. 2019, 29, 97–112. [Google Scholar] [CrossRef]
  9. Gragnano, A.; Simbula, S.; Miglioretti, M. Work–life balance: Weighing the importance of work–family and work–health balance. Int. J. Environ. Res. Public Health 2020, 17, 907. [Google Scholar] [CrossRef]
  10. Huete-Alcocer, N.; López-Ruiz, V.R.; Alfaro-Navarro, J.L.; Nevado-Peña, D. European Citizens’ happiness: Key factors and the mediating effect of quality of life, a PLS approach. Mathematics 2022, 10, 367. [Google Scholar] [CrossRef]
  11. Diener, E.D.; Emmons, R.A.; Larsen, R.J.; Griffin, S. The satisfaction with life scale. J. Personal. Assess. 1985, 49, 71–75. [Google Scholar] [CrossRef] [PubMed]
  12. Diener, E.; Sim, J.H. Happiness/Subjective well-being. In Emotion Theory: The Routledge Comprehensive Guide; Routledge: New York, NY, USA, 2024; pp. 199–217. [Google Scholar]
  13. Statistics Research Institute. Quality of Life Indicators in Korea 2023; Statistics Research Institute: Daejeon, Republic of Korea, 2024. [Google Scholar]
  14. Hansmann, R.; Hug, S.M.; Seeland, K. Restoration and stress relief through physical activities in forests and parks. Urban For. Urban Green. 2007, 6, 213–225. [Google Scholar] [CrossRef]
  15. Grilli, G.; Sacchelli, S. Health benefits derived from forest: A review. Int. J. Environ. Res. Public Health 2020, 17, 6125. [Google Scholar] [CrossRef]
  16. Karjalainen, E.; Sarjala, T.; Raitio, H. Promoting human health through forests: Overview and major challenges. Environ. Health Prev. Med. 2010, 15, 1–8. [Google Scholar] [CrossRef]
  17. Townsend, M. Feel blue? Touch green! Participation in forest/woodland management as a treatment for depression. Urban For. Urban Green. 2006, 5, 111–120. [Google Scholar] [CrossRef]
  18. Morita, E.; Fukuda, S.; Nagano, J.; Hamajima, N.; Yamamoto, H.; Iwai, Y.; Nakashima, T.; Ohira, G.; Shirakawa, T. Sychological effects of forest environments on healthy adults: Shinrin-yoku (forest-air bathing, walking) as a possible method of stress reduction. Public Health 2007, 121, 54–63. [Google Scholar] [CrossRef] [PubMed]
  19. Ochiai, H.; Ikei, H.; Song, C.; Kobayashi, M.; Miura, T.; Kagawa, T.; Li, Q.; Kumeda, S.; Imai, M.; Miyazaki, Y. Physiological and psychological effects of a forest therapy program on middle-aged females. Int. J. Environ. Res. Public Health 2015, 12, 15222–15232. [Google Scholar] [CrossRef]
  20. Ulrich, R.S. Visual landscapes and psychological well-being. Landsc. Res. 1979, 4, 17–23. [Google Scholar] [CrossRef]
  21. Brymer, E.; Davids, K.; Mallabon, L. Understanding the psychological health and well-being benefits of physical activity in nature: An ecological dynamics analysis. Ecopsychology 2014, 6, 189–197. [Google Scholar]
  22. Wolsko, C.; Lindberg, K. Experiencing connection with nature: The matrix of psychological well-being, mindfulness, and outdoor recreation. Ecopsychology 2013, 5, 80–91. [Google Scholar] [CrossRef]
  23. Sung, J.D.; Woo, J.M.; Kim, W.; Lim, S.K.; Chung, E.J. The effect of cognitive behavior therapy-based “forest therapy” program on blood pressure, salivary cortisol level, and quality of life in elderly hypertensive patients. Clin. Exp. Hypertens. 2012, 34, 1–7. [Google Scholar] [CrossRef] [PubMed]
  24. Han, S.J.; Koo, C.D. Effects of healing forest walks for short breaks on mood state, quality of life, and stress reduction of university hospital workers. J. People Plants Environ. 2018, 21, 423–431. [Google Scholar] [CrossRef]
  25. Takayama, N.; Morikawa, T.; Bielinis, E. Relation between psychological restorativeness and lifestyle, quality of life, resilience, and stress-coping in forest settings. Int. J. Environ. Res. Public Health 2019, 16, 1456. [Google Scholar] [CrossRef] [PubMed]
  26. National Institute of Forest Science. A Commentary on Forest Through the Eyes of the People Vol 4.: Use the 2022 Forest Welfare Statistics Report; National Institute of Forest Science: Seoul, Republic of Korea, 2023. [Google Scholar]
  27. Výbošťok, J.; Pichlerová, M.; Lamatungga, K.E.; Tamatam, D.; Önkal, D.; Halaj, D.; Pichler, V. Preferences for woodland activities and forest features as predictors of well-being after forest visits: Evidence from a nationally representative survey in Slovakia. Ambio 2024, 53, 795–807. [Google Scholar] [CrossRef]
  28. Agimass, F.; Lundhede, T.; Panduro, T.E.; Jacobsen, J.B. The choice of forest site for recreation: A revealed preference analysis using spatial data. Ecosyst. Serv. 2018, 31, 445–454. [Google Scholar] [CrossRef]
  29. Jang, Y.S.; Yoo, R.H.; Lee, J.H. The characteristics of forest leisure activities and demographic factors influencing visitor preference. J. Korean Soc. For. Sci. 2020, 109, 231–242. [Google Scholar]
  30. Kim, D.H.; Kang, B.H.; Lee, E.J.; Kang, H.J.; Lee, J.H. An Analysis the Relationship between User Characteristics by Forest Welfare Facilities: Focusing on Correspondence Analysis. J. Korean Soc. For. Sci. 2024, 113, 1–13. [Google Scholar]
  31. Jee, B.G. Influence of Tourists’ Selective Inclination of Destination on their Tour Satisfaction, Revisit and Public Relations. J. Korea Contents Assoc. 2009, 9, 417–425. [Google Scholar] [CrossRef]
  32. Jang, Y.S.; Lee, J.H.; Yoo, R.H. The effects of forest visit characteristics on the individual life satisfaction. J. Korean Inst. For. Recreat. 2020, 24, 17–27. [Google Scholar] [CrossRef]
  33. OECD. OECD Guidelines on Measuring Subjective Well-Being; OECD Publishing: Paris, France, 2013. [Google Scholar]
  34. Jung, S.H.; Seo, D.G. Assessing Mediated Moderation and Moderated Mediation: Guidelines and Empirical Illustration. Korean J. Psychol. Gen. 2016, 35, 257–282. [Google Scholar] [CrossRef]
  35. Korea Forest Service. 2nd Forest Welfare Promotion Plan (2023~2027); Korea Forest Service: Daejeon, Republic of Korea, 2023. [Google Scholar]
  36. Hamer, M.; Stamatakis, E.; Steptoe, A. Dose-response relationship between physical activity and mental health: The Scottish Health Survey. Br. J. Sports Med. 2009, 43, 1111–1114. [Google Scholar] [CrossRef] [PubMed]
  37. Mitchell, R.; Astell-Burt, T.; Richardson, E.A. A comparison of green space indicators for epidemiological research. J. Epidemiol. Community Health 2011, 65, 853–858. [Google Scholar] [CrossRef]
  38. Korpela, K.M.; Ylén, M.; Tyrväinen, L.; Silvennoinen, H. Favorite Green, Waterside and Urban Environments, Restorative Experiences and Perceived Health in Finland. Health Promot. Int. 2010, 25, 200–209. [Google Scholar] [CrossRef]
  39. Grahn, P.; Stigsdotter, U.A. Landscape planning and stress. Urban For. Urban Green. 2003, 2, 1–18. [Google Scholar] [CrossRef]
  40. Maas, J.; Verheij, R.A.; Groenewegen, P.P.; De Vries, S.; Spreeuwenberg, P. Green space, urbanity, and health: How strong is the relation? J. Epidemiol. Community Health 2006, 60, 587–592. [Google Scholar] [CrossRef]
  41. Korcz, N.; Kamińska, A.; Ciesielski, M. Is the Level of Quality of Life Related to the Frequency of Visits to Natural Areas? Forests 2024, 15, 2257. [Google Scholar] [CrossRef]
  42. Xu, S.; Yuan, S.; Li, J.; Gao, X.; Hu, J. Urban park green space use analysis based on trajectory big data: Experience from a medium–sized city in China. Heliyon 2024, 10, e26445. [Google Scholar] [CrossRef]
  43. Roovers, P.; Hermy, M.; Gulinck, H. Visitor profile, perceptions and expectations in forests from a gradient of increasing urbanisation in central Belgium. Landsc. Urban Plan. 2002, 59, 129–145. [Google Scholar] [CrossRef]
  44. Neuvonen, M.; Sievänen, T.; Tönnes, S.; Koskela, T. Access to green areas and the frequency of visits–A case study in Helsinki. Urban For. Urban Green. 2007, 6, 235–247. [Google Scholar] [CrossRef]
  45. Kim, J.; Won, Y.; Kim, Y.; Corley, E. Preferring Local over Non-Local Parks? Green Space Visit Patterns by Urban Residents in Desert Cities, Arizona. Landsc. Urban Plan. 2025, 256, 105292. [Google Scholar] [CrossRef]
  46. Hoehner, C.M.; Ramirez, L.K.B.; Elliott, M.B.; Handy, S.L.; Brownson, R.C. Perceived and objective environmental measures and physical activity among urban adults. Am. J. Prev. Med. 2005, 28, 105–116. [Google Scholar] [CrossRef] [PubMed]
  47. Kaplan, S. The restorative benefits of nature: Toward an integrative framework. J. Environ. Psychol. 1995, 15, 169–182. [Google Scholar] [CrossRef]
  48. Ulrich, R.S.; Simons, R.F.; Losito, B.D.; Fiorito, E.; Miles, M.A.; Zelson, M. Stress recovery during exposure to natural and urban environments. J. Environ. Psychol. 1991, 11, 201–230. [Google Scholar] [CrossRef]
  49. Rantakokko, M.; Keskinen, K.E.; Kokko, K.; Portegijs, E. Nature diversity and well-being in old age. Aging Clin. Exp. Res. 2018, 30, 527–532. [Google Scholar] [CrossRef]
  50. Keniger, L.E.; Gaston, K.J.; Irvine, K.N.; Fuller, R.A. What are the benefits of interacting with nature? Int. J. Environ. Res. Public Health 2013, 10, 913–935. [Google Scholar] [CrossRef] [PubMed]
  51. Bruley, E.; Locatelli, B.; Lavorel, S. Nature’s contributions to people: Coproducing quality of life from multifunctional landscapes. Ecol. Soc. 2021, 26, 12. Available online: https://hal.science/cirad-03920160/ (accessed on 1 March 2025). [CrossRef]
  52. Tu, X.; Huang, G.; Wu, J.; Guo, X. How do travel distance and park size influence urban park visits? Urban For. Urban Green. 2020, 52, 126689. [Google Scholar] [CrossRef]
  53. Hong, S.K.; Lee, S.W.; Jo, H.K.; Yoo, M.Y. Impact of frequency of visits and time spent in urban green space on subjective well-being. Sustainability 2019, 11, 4189. [Google Scholar] [CrossRef]
  54. Kamitsis, I.; Francis, A.J. Spirituality mediates the relationship between engagement with nature and psychological wellbeing. J. Environ. Psychol. 2013, 36, 136–143. [Google Scholar] [CrossRef]
  55. Mitchell, R. Is physical activity in natural environments better for mental health than physical activity in other environments? Soc. Sci. Med. 2013, 91, 130–134. [Google Scholar] [CrossRef]
  56. Morris, E.A.; Guerra, E. Are we there yet? Trip duration and mood during travel. Transp. Res. Part F Traffic Psychol. Behav. 2015, 33, 38–47. [Google Scholar] [CrossRef]
  57. Triguero-Mas, M.; Donaire-Gonzalez, D.; Seto, E.; Valentín, A.; Martínez, D.; Smith, G.; Hurst, G.; Carrasco-Turigas, G.; Masterson, D.; Berg, M.v.D.; et al. Natural outdoor environments and mental health: Stress as a possible mechanism. Environ. Res. 2017, 159, 629–638. [Google Scholar] [CrossRef]
  58. Jiang, Y.; Huang, G. Urban residential quarter green space and life satisfaction. Urban For. Urban Green. 2022, 69, 127510. [Google Scholar] [CrossRef]
  59. Jang, Y.S.; Yoo, R.H.; Lee, J.H. The effects of visit characteristics in neighborhood forest on individual life satisfaction. J. People Plants Environ. 2019, 22, 677–690. [Google Scholar] [CrossRef]
  60. Kim, J.H.; Chun, B.C. Association between companion animal ownership and overall life satisfaction in Seoul, Korea. PLoS ONE 2021, 16, e0258034. [Google Scholar] [CrossRef] [PubMed]
  61. Scoresby, K.J.; Strand, E.B.; Ng, Z.; Brown, K.C.; Stilz, C.R.; Strobel, K.; Barroso, C.S.; Souza, M. Pet ownership and quality of life: A systematic review of the literature. Vet. Sci. 2021, 8, 332. [Google Scholar] [CrossRef] [PubMed]
  62. Teo, J.T.; Thomas, S.J. Psychological mechanisms predicting wellbeing in pet owners: Rogers’ core conditions versus Bowlby’s attachment. Anthrozoös 2019, 32, 399–417. [Google Scholar] [CrossRef]
  63. Bock, J.L.; Nesbitt, L.; Mavoa, S.; Meitner, M.J. Attributes and benefits of urban green space visits–Insights from the City of Vancouver. Urban For. Urban Green. 2024, 98, 128399. [Google Scholar] [CrossRef]
  64. Toohey, A.M.; Rock, M.J.; O’Brien, C. Dog-Walking and Sense of Community in Neighborhoods: Implications for Promoting Regular Physical Activity in Adults 50 Years and Older. Health Place 2013, 22, 75–81. [Google Scholar] [CrossRef]
  65. McConnell, A.R.; Brown, C.M.; Shoda, T.M.; Stayton, L.E.; Martin, C.E. Friends with Benefits: On the Positive Consequences of Pet Ownership. J. Pers. Soc. Psychol. 2011, 101, 1239–1252. [Google Scholar] [CrossRef]
  66. Ministry of Agriculture, Food and Rural Affairs. 2022 Public Awareness Survey of Animal Protection Report; Ministry of Agriculture, Food and Rural Affairs: Sejong, Republic of Korea, 2022. [Google Scholar]
  67. Choung, Y.; Pak, T.Y.; Chatterjee, S. Consumption and life satisfaction: The Korean evidence. Int. J. Consum. Stud. 2021, 45, 1007–1019. [Google Scholar] [CrossRef]
  68. Yu, H.S.; Ha, S.Y. influence of marketing mix factors of service quality on participation-satisfaction and re-purchase intention in marine Leisure sports. Korean J. Phys. Educ. 2007, 46, 485–494. [Google Scholar]
  69. Jeon, J.R.; Jeon, H.M. The Relationships among Tourism Experience, Tourism Satisfaction, Quality of Life, and Revisit Intention in Historical and Culture Tourism Sites—Focused on MZ Generation. Foodserv. Ind. J. 2022, 18, 243–256. [Google Scholar]
  70. Rittichainuwat, B.N.; Qu, H.; Mongknonvanit, C. A study of the impact of travel satisfaction on the likelihood of travelers to revisit Thailand. J. Travel Tour. Mark. 2002, 12, 19–43. [Google Scholar] [CrossRef]
  71. Ozili, P.K. The Acceptable R-Square in Empirical Modelling for Social Science Research. In Social Research Methodology and Publishing Results: A Guide to Non-Native English Speakers; IGI Global: Hershey, PA, USA, 2023; pp. 134–143. [Google Scholar]
Table 1. Demographic characteristics of the participants (N = 9437).
Table 1. Demographic characteristics of the participants (N = 9437).
VariablesN%
SexMale429745.5
Female514054.5
Age15–193173.4
20–298909.4
30–39140814.9
40–49139314.8
50–59196920.9
Over 60346036.7
Monthly Household Income
(million won)		Under 2006446.8
200–400129413.7
400–600140014.8
Over 600609964.6
Type of ResidenceSingle-Family House202121.4
Apartment569560.3
Townhouse/Rowhouse157416.7
Other1471.6
OccupationHave307532.6
Not636267.4
Education<University443147.0
>University500653.0
SpouseWith330335.0
Without613465.0
ChildrenWith762680.8
Without181119.2
Total9437100
N: number of people.
Table 2. Differences in life satisfaction according to sex, occupation, education, spouses, and children.
Table 2. Differences in life satisfaction according to sex, occupation, education, spouses, and children.
VariablesMeanSDTp
SexMale6.701.3781.8390.066
Female6.641.399
OccupationHave6.741.3626.9370.000 **
Not6.521.435
Education<University6.471.37713.2700.000 **
>University6.851.377
SpouseWith6.691.3772.2530.024 *
Without6.621.412
ChildrenWith6.721.368−6.8940.000 **
Without6.461.458
* p < 0.05, ** p < 0.01, SD: Standard Deviation.
Table 3. Difference in life satisfaction with age, monthly household income, and type of residence.
Table 3. Difference in life satisfaction with age, monthly household income, and type of residence.
VariablesMeanSDFP
Age15–196.571.43827.9360.000 **
20–296.871.368
30–396.881.367
40–496.751.328
50–596.741.363
Over 606.461.412
Monthly Household Income
(million won)		Under 2006.141.48749.0700.000 **
200–4006.481.397
400–6006.781.364
Over 6006.741.367
Type of ResidenceSingle-Family House6.571.3938.4310.000 **
Apartment6.721.400
Townhouse/Rowhouse6.581.331
Other6.721.451
** p < 0.01, SD: Standard Deviation.
Table 4. Characteristics by forest visit type.
Table 4. Characteristics by forest visit type.
Daily (Mean ± SD)Day (Mean ± SD)Overnight (Mean ± SD)
Number of visits (year)115.91 ± 85.823.34 ± 8.531.84 ± 1.58
Distance (minutes)16.41 ± 12.50--
Number of visited areas (year)-2.10 ± 1.091.46 ± 0.75
Number of nights stayed (year)--3.94 ± 2.89
Expenditure (KRW)-65,930 ± 43,799183,043 ± 128,750
Companion animal (yes)16.62%7.43%8.18%
Total624148702456
SD: Standard Deviation.
Table 5. Analysis of the hierarchical regression for daily visitors.
Table 5. Analysis of the hierarchical regression for daily visitors.
Independent VariableModel 1Model 2
SEβt-Value (p-Value)SEβt-Value (p-Value)
Constant0.156 41.474 (0.000 **)0.162 38.406 (0.000 **)
Number of Visits 0.0000.0866.288 (0.000 **)
Distance 0.001−0.003−0.254 (0.799)
Companion Animal 0.0480.0272.148 (0.032 *)
Control VariableAge Over 60
15–190.1280.0473.369 (0.001 *)0.1290.0614.349 (0.000 **)
20–290.0930.0513.207 (0.001 *)0.0940.0704.311 (0.000 **)
30–390.0710.0291.782 (0.075)0.0720.0392.396 (0.017 *)
40–490.0640.0070.440 (0.660)0.0640.0161.065 (0.287)
50–590.0530.0281.804 (0.071)0.0530.0342.215 (0.027 *)
Type of Residence Other
Single-Family House0.138−0.067−1.623 (0.105)0.138−0.061−1.487 (0.137)
Apartment0.135−0.019−0.413 (0.680)0.134−0.014−0.306 (0.760)
Townhouse/Rowhouse0.140−0.051−1.421 (0.155)0.140−0.048−1.360 (0.174)
Monthly Household Income (million won)Over 600
Under 2000.092−0.071−3.777 (0.000 **)0.092−0.076−4.047 (0.000 **)
200–4000.066−0.053−3.126 (0.002 *)0.066−0.051−3.042 (0.002 *)
400–6000.057−0.002−0.131 (0.896)0.057−0.001−0.089 (0.929)
Occupation0.0430.0412.815 (0.005 *)0.0430.0513.498 (0.000 **)
Education0.0460.0935.728 (0.000 **)0.0460.0996.117 (0.000 **)
Spouse0.0490.0603.737 (0.000 **)0.0490.0654.004 (0.000 **)
Children0.065−0.023−1.168 (0.243)0.065−0.016−0.822 (0.411)
StatisticR2 = 0.039, adj R2 = 0.037,
F = 16.871, p = 0.000 **		R2 = 0.047, adj R2 = 0.044,
F = 16.971, p = 0.000 **
Durbin-Watson = 1.632
* p < 0.05, ** p < 0.01, SE: Standard Error.
Table 6. Analysis of the hierarchical regression for day visitors.
Table 6. Analysis of the hierarchical regression for day visitors.
Independent VariableModel 1Model 2
SEβt-Value (p-Value)SEβt-Value (p-Value)
Constant0.186 37.491 (0.000 **)0.189 34.712 (0.000 **)
Number of Visited Areas 0.0170.0895.711 (0.000 **)
Number of Visits 0.0060.0362.290 (0.022 *)
Expenditure 0.0050.0815.532 (0.000 **)
Companion Animal 0.0690.0322.195 (0.028 *)
Control VariableAgeOver 60
15–190.1250.0201.190 (0.234)0.1250.0241.385 (0.166)
20–290.0860.0894.018 (0.000 **)0.0850.1024.625 (0.000 **)
30–390.0660.0552.575 (0.010 *)0.0660.0582.757 (0.006 *)
40–490.0590.0180.937 (0.349)0.0590.0231.194 (0.233)
50–590.0510.0371.960 (0.050*)0.0510.0361.930 (0.054)
Type of ResidenceOther
Single-Family House0.170−0.062−1.080 (0.280)0.168−0.066−1.163 (0.245)
Apartment0.167−0.082−1.179 (0.238)0.165−0.085−1.235 (0.217)
Townhouse/Rowhouse0.170−0.135−2.426 (0.015*)0.169−0.141−2.552 (0.011 *)
Monthly Household Income (million won)Over 600
Under 2000.127−0.024−1.387 (0.165)0.126−0.020−1.180 (0.238)
200–4000.063−0.066−3.689 (0.000 **)0.062−0.060−3.410 (0.001 **)
400–6000.051−0.042−2.588 (0.010 *)0.051−0.038−2.359 (0.018 *)
Occupation0.0450.0643.810 (0.000 **)0.0440.0543.202 (0.001 **)
Education0.0440.0955.184 (0.000 **)0.0440.0784.237 (0.000 **)
Spouse0.0520.0432.080 (0.038 *)0.0250.0341.671 (0.095)
Children0.064−0.072−3.723 (0.000 **)0.064−0.059−3.081 (0.002 **)
StatisticR2 = 0.032, adj R2 = 0.029,
F = 10.070, p = 0.000 **		R2 = 0.050, adj R2 = 0.046,
F = 12.582, p = 0.000 **
Durbin-Watson = 1.833
* p < 0.05, ** p < 0.01, SE: Standard Error.
Table 7. Analysis of the hierarchical regression for overnight visitors.
Table 7. Analysis of the hierarchical regression for overnight visitors.
Independent VariableModel 1Model 2
SEβt-Value (p-Value)SEβt-Value (p-Value)
Constant0.257 28.415 (0.000 **)0.264 27.407 (0.000 **)
Number of Visited Areas 0.0510.0451.496 (0.135)
Number of Nights Stayed 0.023−0.207−4.392 (0.000 **)
Number of Visits 0.0430.0881.970 (0.049 *)
Expenditure 0.0020.0492.289 (0.022 *)
Companion Animal 0.097−0.010−0.512 (0.609)
Control VariableAgeOver 60
15–190.1590.0020.088 (0.930)0.1590.0180.693 (0.488)
20–290.1230.0130.376 (0.707)0.1220.0270.788 (0.431)
30–390.0980.0481.443 (0.149)0.0980.0561.706 (0.088)
40–490.0940.0270.916 (0.360)0.0930.0341.145 (0.252)
50–590.0880.0120.441 (0.659)0.0880.0210.769 (0.442)
Type of ResidenceOther
Single-Family House0.228−0.077−1.106 (0.269)0.227−0.062−0.899 (0.369)
Apartment0.222−0.147−1.703 (0.089)0.221−0.133−1.541 (0.124)
Townhouse/Rowhouse0.227−0.143−1.970 (0.049 *)0.226−0.128−1.768 (0.077)
Monthly Household Income
(million won)		Over 600
Under 2000.204−0.057−2.532 (0.011 *)0.203−0.055−2.426 (0.015 *)
200–4000.093−0.082−3.627 (0.000 **)0.092−0.079−3.477 (0.001 **)
400–6000.078−0.014−0.643 (0.520)0.078−0.011−0.475 (0.635)
Occupation0.0690.0401.615 (0.106)0.0690.0421.689 (0.091)
Education0.0700.0853.303 (0.001 **)0.0710.0883.410 (0.001 **)
Spouse0.075−0.015−0.511 (0.609)0.074−0.010−0.356 (0.722)
Children0.097−0.069−2.879 (0.004 **)0.096−0.064−2.654 (0.008 **)
StatisticR2 = 0.027, adj R2 = 0.021,
F = 4.376, p = 0.000 **		R2 = 0.040, adj R2 = 0.032,
F = 4.969, p = 0.000 **
Durbin-Watson = 1.894
* p < 0.05, ** p < 0.01, SE: Standard Error.
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Lee, M.; Lee, J. Analysis of Forest Utilization Patterns to Improve Life Satisfaction and Policy Directions. Sustainability 2025, 17, 3689. https://doi.org/10.3390/su17083689

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Lee, Mijin, and Jeonghee Lee. 2025. "Analysis of Forest Utilization Patterns to Improve Life Satisfaction and Policy Directions" Sustainability 17, no. 8: 3689. https://doi.org/10.3390/su17083689

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Lee, M., & Lee, J. (2025). Analysis of Forest Utilization Patterns to Improve Life Satisfaction and Policy Directions. Sustainability, 17(8), 3689. https://doi.org/10.3390/su17083689

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