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The recreation opportunity spectrum (ROS) has been widely recognized as an effective tool for the inventory and planning of outdoor recreational resources. However, its applications have been primarily focused on forest-dominated settings with few studies being conducted on all land types at a regional scale. The creation of a ROS is based on physical, social, and managerial settings, with the physical setting being measured by three criteria: remoteness, size, and evidence of humans. One challenge to extending the ROS to all land types on a large scale is the difficulty of quantifying the evidence of humans and social settings. Thus, this study, for the first time, developed an innovative approach that used night lights as a proxy for evidence of humans and points of interest (POI) for social settings to generate an automatic ROS for Hunan Province using Geographic Information System (GIS) spatial analysis. The whole province was classified as primitive (2.51%), semi-primitive non-motorized (21.33%), semi-primitive motorized (38.60%), semi-developed natural (30.99%), developed natural (5.61%), and highly developed (0.96%), which was further divided into three subclasses: large-natural (0.63%), small natural (0.27%), and facilities (0.06%). In order to implement the management and utilization of natural recreational resources in Hunan Province at the county (city, district) level, the province’s 122 counties (cities, districts) were categorized into five levels based on the ROS factor dominance calculated at the county and provincial levels. These five levels include key natural recreational counties (cities, districts), general natural recreational counties (cities, districts), rural counties (cities, districts), general metropolitan counties (cities, districts), and key metropolitan counties (cities, districts), with the corresponding numbers being 8, 21, 50, 24, and 19, respectively.
Wenjing Zeng; Yongde Zhong; Dali Li; Jinyang Deng. Classification of Recreation Opportunity Spectrum Using Night Lights for Evidence of Humans and POI Data for Social Setting. Sustainability 2021, 13, 7782 .
AMA StyleWenjing Zeng, Yongde Zhong, Dali Li, Jinyang Deng. Classification of Recreation Opportunity Spectrum Using Night Lights for Evidence of Humans and POI Data for Social Setting. Sustainability. 2021; 13 (14):7782.
Chicago/Turabian StyleWenjing Zeng; Yongde Zhong; Dali Li; Jinyang Deng. 2021. "Classification of Recreation Opportunity Spectrum Using Night Lights for Evidence of Humans and POI Data for Social Setting." Sustainability 13, no. 14: 7782.
This manuscript examines the driving forces of carbon emissions in China’s tourism industry. Tourism carbon emissions are estimated by constructing China’s Economic-Environmental Accounts (EEA). Analysis is divided into five-time intervals and specifically examines intensity, scale, structure, and technology. Following index and structural decomposition methods, changes in tourism carbon emissions were segmented into sixteen economy-wide and tourism-specific driving forces. Results demonstrate that direct and total tourism carbon emissions compose 0.7% and 2.7% of total carbon emissions in China. Analysis revealed the positive driver of tourism emissions was domestic tourists, representing 140.4% increase in direct and 263.4% increase in total tourism carbon emissions. Modelling identified energy intensity as the main negative driver in total and direct tourism carbon emissions, especially for national economic sectors (−208.6%) and non-transport tourism sectors (−33.8%). Future research should focus on the measurement and implementation of mitigation policies for domestic tourism emissions.
Fen Luo; Brent D. Moyle; Char-Lee J. Moyle; Yongde Zhong; Shengyi Shi. Drivers of carbon emissions in China’s tourism industry. Journal of Sustainable Tourism 2019, 28, 747 -770.
AMA StyleFen Luo, Brent D. Moyle, Char-Lee J. Moyle, Yongde Zhong, Shengyi Shi. Drivers of carbon emissions in China’s tourism industry. Journal of Sustainable Tourism. 2019; 28 (5):747-770.
Chicago/Turabian StyleFen Luo; Brent D. Moyle; Char-Lee J. Moyle; Yongde Zhong; Shengyi Shi. 2019. "Drivers of carbon emissions in China’s tourism industry." Journal of Sustainable Tourism 28, no. 5: 747-770.
Forest landscape plays a critical role in the resource management and recreational planning of forest destinations. An assessment of forest landscape quality (FLQ) could reflect the distribution of landscape resources, hence identifying the hotpots and areas with high visual quality and protection values. The objective of this study is to propose, for the first time, a methodology for assessing FLQ at the national level. Based on China’s forestry inventory database, the paper identified landform patterns and vegetative patterns as determinants (including 12 indicators) to establish an evaluation index system, and further implemented and mapped FLQ using the ArcGIS Engine platform. Results show high mountain ranges and tropical areas in China often have a high-quality forest landscape, while low FLQ scores are found in low mountains and foothills. The distribution of the four FLQ levels indicates most forest areas are featured with mediocre- or low- quality landscape values, and the differences of FLQ among different forest types are obvious. Furthermore, there is a relatively low correlation between the total forest area and the area of high-quality forest landscape. Overall, this study could contribute to enriching the existing assessment system for FLQ and to guiding the planning, policy development, and decision-making for China’s forestry administration.
Jiangzhou Wu; Yongde Zhong; Jinyang Deng. Assessing and Mapping Forest Landscape Quality in China. Forests 2019, 10, 684 .
AMA StyleJiangzhou Wu, Yongde Zhong, Jinyang Deng. Assessing and Mapping Forest Landscape Quality in China. Forests. 2019; 10 (8):684.
Chicago/Turabian StyleJiangzhou Wu; Yongde Zhong; Jinyang Deng. 2019. "Assessing and Mapping Forest Landscape Quality in China." Forests 10, no. 8: 684.