Does social contact lessen perceived cultural distance? Evidence from tourist–host social contact

2016-2017 > Academic research: refereed > Publication in refereed journal201804_a bcm

Analyzing the Economic Sustainability of Tourism Development: Evidence from Hong Kong

Despite increased concerns about the negative economic impacts of tourism on host communities, insufficient attention has been paid to assess tourism economic sustainability. This paper aims to develop and validate a framework for assessing economic sustainability from the perspective of local stakeholders. In-depth interviews with 12 major stakeholders and a telephone survey with 1839 Hong Kong citizens were conducted. The tourism economic sustainability construct consisted of three dimensions: economic positivity, development control and individual welfare. The reliability and validity of the dimensions were confirmed by the data of sub-samples. The links between socio-demographic characteristics and attitudes toward tourism economic sustainability were evaluated. This paper enhanced our understanding of tourism economic sustainability by expanding the measurement from the macro level to micro level. Using the study setting in Hong Kong, it transcends previous analysis by providing a context to learn from ongoing controversies about the effects of tourism on host community

Net exchanges of CO2, CH4, and N2O between China's terrestrial ecosystems and the atmosphere and their contributions to global climate warming

Author Posting. © American Geophysical Union, 2011. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 116 (2011): G02011, doi:10.1029/2010JG001393.China's terrestrial ecosystems have been recognized as an atmospheric CO2 sink; however, it is uncertain whether this sink can alleviate global warming given the fluxes of CH4 and N2O. In this study, we used a process-based ecosystem model driven by multiple environmental factors to examine the net warming potential resulting from net exchanges of CO2, CH4, and N2O between China's terrestrial ecosystems and the atmosphere during 1961–2005. In the past 45 years, China's terrestrial ecosystems were found to sequestrate CO2 at a rate of 179.3 Tg C yr−1 with a 95% confidence range of (62.0 Tg C yr−1, 264.9 Tg C yr−1) while emitting CH4 and N2O at rates of 8.3 Tg C yr−1 with a 95% confidence range of (3.3 Tg C yr−1, 12.4 Tg C yr−1) and 0.6 Tg N yr−1 with a 95% confidence range of (0.2 Tg N yr−1, 1.1 Tg N yr−1), respectively. When translated into global warming potential, it is highly possible that China's terrestrial ecosystems mitigated global climate warming at a rate of 96.9 Tg CO2eq yr−1 (1 Tg = 1012 g), substantially varying from a source of 766.8 Tg CO2eq yr−1 in 1997 to a sink of 705.2 Tg CO2eq yr−1 in 2002. The southeast and northeast of China slightly contributed to global climate warming; while the northwest, north, and southwest of China imposed cooling effects on the climate system. Paddy land, followed by natural wetland and dry cropland, was the largest contributor to national warming potential; forest, followed by woodland and grassland, played the most significant role in alleviating climate warming. Our simulated results indicate that CH4 and N2O emissions offset approximately 84.8% of terrestrial CO2 sink in China during 1961–2005. This study suggests that the relieving effects of China's terrestrial ecosystems on climate warming through sequestering CO2 might be gradually offset by increasing N2O emission, in combination with CH4 emission.This study has been supported by NASA LCLUC Program (NNX08AL73G_S01) , NASA IDS Program (NNG04GM39C), and China’s Ministry of Science and Technology (MOST) 973 Program (2002CB412500)

The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom : 1990-2017

Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27 C UK). We integrate recent emission inventory data, ecosystem process-based model results and inverse modeling estimates over the period 1990-2017. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported to the UN climate convention UNFCCC secretariat in 2019. For uncertainties, we used for NGHGIs the standard deviation obtained by varying parameters of inventory calculations, reported by the member states (MSs) following the recommendations of the IPCC Guidelines. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model-specific uncertainties when reported. In comparing NGHGIs with other approaches, a key source of bias is the activities included, e.g., anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011-2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 TgCH(4) yr (-1) (EDGAR v5.0) and 19.0 TgCH(4) yr(-1) (GAINS), consistent with the NGHGI estimates of 18.9 +/- 1.7 TgCH(4) yr(-1). The estimates of TD total inversions give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher-resolution atmospheric transport models give a mean emission of 28.8 TgCH(4) yr(-1). Coarser-resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 TgCH(4) yr(-1)) and surface network (24.4 TgCH(4) yr (-1)). The magnitude of natural peatland emissions from the JSBACH-HIMMELI model, natural rivers and lakes emissions, and geological sources together account for the gap between NGHGIs and inversions and account for 5.2 TgCH(4) yr(-1). For N2O emissions, over the 2011-2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 TgN(2)Oyr(-1), respectively, agreeing with the NGHGI data (0.9 0.6 TgN(2)Oyr(-1)). Over the same period, the average of the three total TD global and regional inversions was 1.3 +/- 0.4 and 1.3 +/- 0.1 TgN(2)Oyr(-1), respectively. The TD and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at the EU CUK scale and at the national scale.Peer reviewe

The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom : 1990-2019

Funding Information: We thank Aurélie Paquirissamy, Géraud Moulas and the ARTTIC team for the great managerial support offered during the project. FAOSTAT statistics are produced and disseminated with the support of its member countries to the FAO regular budget. Annual, gap-filled and harmonized NGHGI uncertainty estimates for the EU and its member states were provided by the EU GHG inventory team (European Environment Agency and its European Topic Centre on Climate change mitigation). Most top-down inverse simulations referred to in this paper rely for the derivation of optimized flux fields on observational data provided by surface stations that are part of networks like ICOS (datasets: 10.18160/P7E9-EKEA , Integrated Non-CO Observing System, 2018a, and 10.18160/B3Q6-JKA0 , Integrated Non-CO Observing System, 2018b), AGAGE, NOAA (Obspack Globalview CH: 10.25925/20221001 , Schuldt et al., 2017), CSIRO and/or WMO GAW. We thank all station PIs and their organizations for providing these valuable datasets. We acknowledge the work of other members of the EDGAR group (Edwin Schaaf, Jos Olivier) and the outstanding scientific contribution to the VERIFY project of Peter Bergamaschi. Timo Vesala thanks ICOS-Finland, University of Helsinki. The TM5-CAMS inversions are available from https://atmosphere.copernicus.eu (last access: June 2022); Arjo Segers acknowledges support from the Copernicus Atmosphere Monitoring Service, implemented by the European Centre for Medium-Range Weather Forecasts on behalf of the European Commission (grant no. CAMS2_55). This research has been supported by the European Commission, Horizon 2020 Framework Programme (VERIFY, grant no. 776810). Ronny Lauerwald received support from the CLand Convergence Institute. Prabir Patra received support from the Environment Research and Technology Development Fund (grant no. JPMEERF20182002) of the Environmental Restoration and Conservation Agency of Japan. Pierre Regnier received financial support from the H2020 project ESM2025 – Earth System Models for the Future (grant no. 101003536). David Basviken received support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (METLAKE, grant no. 725546). Greet Janssens-Maenhout received support from the European Union's Horizon 2020 research and innovation program (CoCO, grant no. 958927). Tuula Aalto received support from the Finnish Academy (grants nos. 351311 and 345531). Sönke Zhaele received support from the ERC consolidator grant QUINCY (grant no. 647204).Peer reviewedPublisher PD

Tourist typology in social contact: An addition to existing theories

Tourist-host social contact, as a unique type of social contact, is not getting sufficient attention in tourism academia considering its remarkable impacts on tourists’ travel attitudes, behaviors and long-term perceptions. The objectives of the current study are to explore the dimensions of tourist-host social contact and to contribute to the theory of tourist typology according to their dynamic nature in tourist-host social interaction. Forty-five in-depth interviews were conducted to generate insightful information. The software of NVivo 10 was applied to examine and code the transcripts. As a result, six dimensions were adopted to describe tourist-host social contact, which are purposes, determinants, activities, intensity, impacts and attitudes. Five types of tourists were pinpointed and theoretical and practical contributions of the study were discussed

Dream Destination: A concept of perception of leisure travellers

This exploratory study adopted grounded theory approach to investigate the concept of dream destination and its perception by travelers. The data were collected through three methods: Online travel forum, in-depth interview and focus group. Triangulation of data collected from the above three methods were used to establish the trustworthiness of the study. Based on the iteration of data analysis, the dream destination was classified into six categories and the conceptual framework of dream destination was generated