Pub Date : 2023-03-31DOI: 10.1177/13548166231167045
Hongwei Liu, Chenchen Gao, Henry Tsai
This paper measures tourism carbon efficiency (TCE) in China by incorporating energy consumption and carbon dioxide (CO2) emissions into an efficiency assessment framework, and to further investigate the determinants of TCE by considering the spatial spillover effects. To do this, a bootstrap slacks-based measure (SBM) model was applied to assess the TCE in 30 provincial-level administrative regions of China from 2008 to 2019. Next, the Moran’s index and spatial Durbin model (SDM) were adopted to explore the spatial distribution and determinants of TCE. The results indicate that regional differences affect the level of China’s TCE, as do spatial spillover effects. In addition, technology innovation, urbanization rate, and government support positively affect TCE. In contrast, economic growth negatively affects TCE. Educational attainment, green infrastructure, and government support have a negative spatial spillover effect on TCE. Transportation infrastructure has a negative total effect on TCE.
{"title":"Spatial spillover and determinants of tourism efficiency: A low carbon emission perspective","authors":"Hongwei Liu, Chenchen Gao, Henry Tsai","doi":"10.1177/13548166231167045","DOIUrl":"https://doi.org/10.1177/13548166231167045","url":null,"abstract":"This paper measures tourism carbon efficiency (TCE) in China by incorporating energy consumption and carbon dioxide (CO2) emissions into an efficiency assessment framework, and to further investigate the determinants of TCE by considering the spatial spillover effects. To do this, a bootstrap slacks-based measure (SBM) model was applied to assess the TCE in 30 provincial-level administrative regions of China from 2008 to 2019. Next, the Moran’s index and spatial Durbin model (SDM) were adopted to explore the spatial distribution and determinants of TCE. The results indicate that regional differences affect the level of China’s TCE, as do spatial spillover effects. In addition, technology innovation, urbanization rate, and government support positively affect TCE. In contrast, economic growth negatively affects TCE. Educational attainment, green infrastructure, and government support have a negative spatial spillover effect on TCE. Transportation infrastructure has a negative total effect on TCE.","PeriodicalId":23204,"journal":{"name":"Tourism Economics","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49228423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-21DOI: 10.1177/13548166231161879
Wei Guo, Jing Wang, Yue Kang
The existing literature has not fully explored the polarization of tourism employment and the causes of this polarization, and this article makes contributions to addressing this issue. Using data on tourism-related occupations from the China General Social Survey , the article finds that current tourism employment is polarized in an inverted “U” shape, with the top of the polarization occurring in the middle-skilled occupational area. This differs significantly from the existing literature, which concludes that there is a “U” shaped employment polarization. The article finds that workers’ use of the Internet is the main cause of the inverted U-shaped polarization of tourism employment. This is reflected in the relatively low proportion of employment in the low-skilled and high-skilled occupational groups and the relatively high and stable proportion of employment in the middle-skilled occupational groups. However, dynamically, the employment of low-skilled groups tends to increase, and high-employment groups tend to decrease. These results reveal new findings in the tourism labor market and have important implications for current research on tourism employment.
{"title":"Internet use and inverted U-shaped employment polarization in tourism occupations","authors":"Wei Guo, Jing Wang, Yue Kang","doi":"10.1177/13548166231161879","DOIUrl":"https://doi.org/10.1177/13548166231161879","url":null,"abstract":"The existing literature has not fully explored the polarization of tourism employment and the causes of this polarization, and this article makes contributions to addressing this issue. Using data on tourism-related occupations from the China General Social Survey , the article finds that current tourism employment is polarized in an inverted “U” shape, with the top of the polarization occurring in the middle-skilled occupational area. This differs significantly from the existing literature, which concludes that there is a “U” shaped employment polarization. The article finds that workers’ use of the Internet is the main cause of the inverted U-shaped polarization of tourism employment. This is reflected in the relatively low proportion of employment in the low-skilled and high-skilled occupational groups and the relatively high and stable proportion of employment in the middle-skilled occupational groups. However, dynamically, the employment of low-skilled groups tends to increase, and high-employment groups tend to decrease. These results reveal new findings in the tourism labor market and have important implications for current research on tourism employment.","PeriodicalId":23204,"journal":{"name":"Tourism Economics","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47883912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-14DOI: 10.1177/13548166231163206
Charbel Bassil, Ghialy Yap
We estimate the impact of political instability and the population of immigrants in Australia on the flow of international tourist arrivals. We hypothesize that political instability has a short-term negative effect while the population of immigrants in Australia may have a positive or negative effect depending on time span. Moreover, we postulate that the population of immigrants resorbs in the short run partially or totally the adverse effect of political instability. Our empirical strategy takes into consideration potential heterogeneity among cross-sections and differentiates between short-term and long-term effects. For this purpose, we use the Pooled Mean Group estimator in a panel Autoregressive Distributed Lag model. Findings from the pooled estimations suggest that, in the short run, the population of immigrants in Australia reduces the negative effect of political instability on international tourism flows. However, its effect is negative in the long run. We also find evidence for heterogeneity across countries.
{"title":"Can immigration moderate the adverse effects of political instability on international tourism? A case study of Australia","authors":"Charbel Bassil, Ghialy Yap","doi":"10.1177/13548166231163206","DOIUrl":"https://doi.org/10.1177/13548166231163206","url":null,"abstract":"We estimate the impact of political instability and the population of immigrants in Australia on the flow of international tourist arrivals. We hypothesize that political instability has a short-term negative effect while the population of immigrants in Australia may have a positive or negative effect depending on time span. Moreover, we postulate that the population of immigrants resorbs in the short run partially or totally the adverse effect of political instability. Our empirical strategy takes into consideration potential heterogeneity among cross-sections and differentiates between short-term and long-term effects. For this purpose, we use the Pooled Mean Group estimator in a panel Autoregressive Distributed Lag model. Findings from the pooled estimations suggest that, in the short run, the population of immigrants in Australia reduces the negative effect of political instability on international tourism flows. However, its effect is negative in the long run. We also find evidence for heterogeneity across countries.","PeriodicalId":23204,"journal":{"name":"Tourism Economics","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45398863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-06DOI: 10.1177/13548166231158705
Jianwei Bi, T. Han, Yanbo Yao
Compared with coarse-grained forecasting, fine-grained tourism demand forecasting is a more challenging task, but research on this issue is very scarce. To address this issue, a decomposition ensemble deep learning model is proposed by integrating CEEMDAN, CNNs, LSTM networks, and AR models. The CEEMDAN can decompose complex tourism demand data into multiple components with simpler characteristics, thereby reducing the complexity of forecasting. The CNNs and LSTM networks can fully capture the locally recurring patterns and the long-term dependencies of the components obtained by CEEMDAN. The AR model can capture the scale of tourism demand data, which can overcome the problem that the output scale of the deep neural networks (i.e., CNNs and LSTM networks) is not sensitive to the scale of the inputs. The effectiveness of the proposed model is verified by comparing with five benchmark models using real-time data on tourist volumes at two attractions.
{"title":"Fine-grained tourism demand forecasting: A decomposition ensemble deep learning model","authors":"Jianwei Bi, T. Han, Yanbo Yao","doi":"10.1177/13548166231158705","DOIUrl":"https://doi.org/10.1177/13548166231158705","url":null,"abstract":"Compared with coarse-grained forecasting, fine-grained tourism demand forecasting is a more challenging task, but research on this issue is very scarce. To address this issue, a decomposition ensemble deep learning model is proposed by integrating CEEMDAN, CNNs, LSTM networks, and AR models. The CEEMDAN can decompose complex tourism demand data into multiple components with simpler characteristics, thereby reducing the complexity of forecasting. The CNNs and LSTM networks can fully capture the locally recurring patterns and the long-term dependencies of the components obtained by CEEMDAN. The AR model can capture the scale of tourism demand data, which can overcome the problem that the output scale of the deep neural networks (i.e., CNNs and LSTM networks) is not sensitive to the scale of the inputs. The effectiveness of the proposed model is verified by comparing with five benchmark models using real-time data on tourist volumes at two attractions.","PeriodicalId":23204,"journal":{"name":"Tourism Economics","volume":"29 1","pages":"1736 - 1763"},"PeriodicalIF":4.4,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45964845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-22DOI: 10.1177/13548166231158439
N. Kumar
A recent critique of the tourism-growth literature raises concerns about whether tourism leads to permanent economic growth. We argue that tourism can be linked to total factor productivity. Tourism demand spurs efficiency gains in the tourism sector through learning by doing. However, because tourism is demand-led, asymmetric effects may arise in the tourism-productivity association. To model the permanent growth effects of tourism, we use panel asymmetric ARDL models with annual data from 94 countries over the period 1995–2018. The finding implies that tourism has permanent but asymmetric growth effects.
{"title":"Modelling the asymmetric productivity effects of tourism demand","authors":"N. Kumar","doi":"10.1177/13548166231158439","DOIUrl":"https://doi.org/10.1177/13548166231158439","url":null,"abstract":"A recent critique of the tourism-growth literature raises concerns about whether tourism leads to permanent economic growth. We argue that tourism can be linked to total factor productivity. Tourism demand spurs efficiency gains in the tourism sector through learning by doing. However, because tourism is demand-led, asymmetric effects may arise in the tourism-productivity association. To model the permanent growth effects of tourism, we use panel asymmetric ARDL models with annual data from 94 countries over the period 1995–2018. The finding implies that tourism has permanent but asymmetric growth effects.","PeriodicalId":23204,"journal":{"name":"Tourism Economics","volume":"29 1","pages":"1764 - 1789"},"PeriodicalIF":4.4,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42035432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-19DOI: 10.1177/13548166231159520
Apostolos Ampountolas, Giuseppina Menconi, G. Shaw
One of the sectors with the fastest growth rates worldwide is the hospitality and tourism industry, although the pandemic caused losses and setbacks for the industry. The potential of the metaverse and virtual travel could lead to the emergence of a brand-new sector. Thus, traditional online travel agencies may need to change their utility model in the future to accommodate the new technology. This virtual transformation in the metaverse entails more flexible travel options, customized consumer services, and improved entertainment. The employment of augmented and virtual reality technology in the metaverse may enable seamless interaction between users in real and simulated surroundings. As such future developments in metaverse technology may enable fully immersive experiences. Customers can now get real-time price changes, availability, and promotions bypassing third-party distribution platforms. This research note aims to introduce the metaverse’s enormous potential and to define three research proposals for additional investigation.
{"title":"Metaverse research propositions: Online intermediaries","authors":"Apostolos Ampountolas, Giuseppina Menconi, G. Shaw","doi":"10.1177/13548166231159520","DOIUrl":"https://doi.org/10.1177/13548166231159520","url":null,"abstract":"One of the sectors with the fastest growth rates worldwide is the hospitality and tourism industry, although the pandemic caused losses and setbacks for the industry. The potential of the metaverse and virtual travel could lead to the emergence of a brand-new sector. Thus, traditional online travel agencies may need to change their utility model in the future to accommodate the new technology. This virtual transformation in the metaverse entails more flexible travel options, customized consumer services, and improved entertainment. The employment of augmented and virtual reality technology in the metaverse may enable seamless interaction between users in real and simulated surroundings. As such future developments in metaverse technology may enable fully immersive experiences. Customers can now get real-time price changes, availability, and promotions bypassing third-party distribution platforms. This research note aims to introduce the metaverse’s enormous potential and to define three research proposals for additional investigation.","PeriodicalId":23204,"journal":{"name":"Tourism Economics","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44495790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-09DOI: 10.1177/13548166231153908
Jianwei Bi, T. Han, Yanbo Yao
To forecast the tourism demand across a set of tourist attractions with spatial dependence, a new model is proposed, which has three stages: tourist attraction selection, base predictor generation, and base predictor combination. In stage 1, a method for selecting associated attractions based on multi-dimensional scaling is used to determine the strength of the spatial dependence between each pair of attractions. In stage 2, a hybrid base predictor based on LSTM networks and Autoregressive model is developed, where the LSTM networks are used to capture the spatial dependence among attractions, and the Autoregressive model is used capture the scale of tourist volume at each attraction. In stage 3, a strategy for combining these base predictors is proposed; it can alleviate the overfitting problem of LSTM and improve the stability of forecasts. Finally, the superiority of the model is verified through the data on tourist volumes at 77 attractions in Beijing.
{"title":"Collaborative forecasting of tourism demand for multiple tourist attractions with spatial dependence: A combined deep learning model","authors":"Jianwei Bi, T. Han, Yanbo Yao","doi":"10.1177/13548166231153908","DOIUrl":"https://doi.org/10.1177/13548166231153908","url":null,"abstract":"To forecast the tourism demand across a set of tourist attractions with spatial dependence, a new model is proposed, which has three stages: tourist attraction selection, base predictor generation, and base predictor combination. In stage 1, a method for selecting associated attractions based on multi-dimensional scaling is used to determine the strength of the spatial dependence between each pair of attractions. In stage 2, a hybrid base predictor based on LSTM networks and Autoregressive model is developed, where the LSTM networks are used to capture the spatial dependence among attractions, and the Autoregressive model is used capture the scale of tourist volume at each attraction. In stage 3, a strategy for combining these base predictors is proposed; it can alleviate the overfitting problem of LSTM and improve the stability of forecasts. Finally, the superiority of the model is verified through the data on tourist volumes at 77 attractions in Beijing.","PeriodicalId":23204,"journal":{"name":"Tourism Economics","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47467706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-06DOI: 10.1177/13548166231155301
Jiafeng Gu
A comprehensive and accurate assessment of the policy effect of national tourism days is of great significance to further promote urban inbound tourism. Based on the panel data of 59 cities in China from 2000 to 2017, this paper evaluates the local and spatial spillover effects of the China Tourism Day policy on urban inbound tourism using a spatial difference-in-differences model based on the establishment of the China Tourism Day as a quasi-natural experiment. The study found that the China Tourism Day policy significantly increased the number of foreign tourists in cities but significantly reduced their average length of stay, with a positive spatial spillover effect on inbound tourism in surrounding cities.
{"title":"The impact of national tourism day festivals on inbound tourism: A spatial difference-in-differences approach","authors":"Jiafeng Gu","doi":"10.1177/13548166231155301","DOIUrl":"https://doi.org/10.1177/13548166231155301","url":null,"abstract":"A comprehensive and accurate assessment of the policy effect of national tourism days is of great significance to further promote urban inbound tourism. Based on the panel data of 59 cities in China from 2000 to 2017, this paper evaluates the local and spatial spillover effects of the China Tourism Day policy on urban inbound tourism using a spatial difference-in-differences model based on the establishment of the China Tourism Day as a quasi-natural experiment. The study found that the China Tourism Day policy significantly increased the number of foreign tourists in cities but significantly reduced their average length of stay, with a positive spatial spillover effect on inbound tourism in surrounding cities.","PeriodicalId":23204,"journal":{"name":"Tourism Economics","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48322021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-06DOI: 10.1177/13548166231155535
Anil Bolukoglu, Tugce Gozukucuk
The intergovernmental institutions recommend tourism development as a policy goal for reducing gender inequality through employment channels. However, such approaches ignore the indirect and induced effects of tourism sector development on women’s employment through male dominated forward and backward linkages. Adverse spillover effects of tourism-related sectors limit the impact of tourism on generating women’s employment. With this goal in mind, the study estimates the spillover effect of tourism sector development on the gender gap in selected employment indicators of 27 European Union countries between 2008 and 2019. Results show that development in the tourism sector, measured by the number of tourists per active population, improves the gender gap in labor force participation, employment, and unemployment among EU countries. Considering the precarious working conditions of the tourism sector, results only reflect a limited aspect of women’s empowerment.
{"title":"Tourism development and women employment: A study on the European union countries","authors":"Anil Bolukoglu, Tugce Gozukucuk","doi":"10.1177/13548166231155535","DOIUrl":"https://doi.org/10.1177/13548166231155535","url":null,"abstract":"The intergovernmental institutions recommend tourism development as a policy goal for reducing gender inequality through employment channels. However, such approaches ignore the indirect and induced effects of tourism sector development on women’s employment through male dominated forward and backward linkages. Adverse spillover effects of tourism-related sectors limit the impact of tourism on generating women’s employment. With this goal in mind, the study estimates the spillover effect of tourism sector development on the gender gap in selected employment indicators of 27 European Union countries between 2008 and 2019. Results show that development in the tourism sector, measured by the number of tourists per active population, improves the gender gap in labor force participation, employment, and unemployment among EU countries. Considering the precarious working conditions of the tourism sector, results only reflect a limited aspect of women’s empowerment.","PeriodicalId":23204,"journal":{"name":"Tourism Economics","volume":"1 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65470818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-01DOI: 10.1177/13548166231154314
Elena Prodi, V. Fasone, M. R. Di Tommaso
Selective industrial policies have been increasingly used by governments to achieve desired normative goals. However, they have been revealed to be complex and vulnerable interventions, demanding robust tools able to justify choices and mitigate potential ‘government failures’. In light of the emerging challenges and potential disruptions that might threaten our economies and societies, we contend that postshock industry resilience can be a valuable analytical framework to understand how different sectors react to unforeseen shocks. Accordingly, we present a methodology that measures postshock industry resilience and apply it to the Italian case in the aftermath of the 2008 shock. Particular attention is devoted to tourism-related industries. Main findings show that the industries reacted heterogeneously to the 2008 shock. For tourism-related industries, the results suggest following an ad hoc approach to the analysis of each tourism-focused industry to avoid generalizations that might lead to incorrect policy interpretations.
{"title":"Does industry resilience matter for postshock industrial policy? A focus on tourism-related industries","authors":"Elena Prodi, V. Fasone, M. R. Di Tommaso","doi":"10.1177/13548166231154314","DOIUrl":"https://doi.org/10.1177/13548166231154314","url":null,"abstract":"Selective industrial policies have been increasingly used by governments to achieve desired normative goals. However, they have been revealed to be complex and vulnerable interventions, demanding robust tools able to justify choices and mitigate potential ‘government failures’. In light of the emerging challenges and potential disruptions that might threaten our economies and societies, we contend that postshock industry resilience can be a valuable analytical framework to understand how different sectors react to unforeseen shocks. Accordingly, we present a methodology that measures postshock industry resilience and apply it to the Italian case in the aftermath of the 2008 shock. Particular attention is devoted to tourism-related industries. Main findings show that the industries reacted heterogeneously to the 2008 shock. For tourism-related industries, the results suggest following an ad hoc approach to the analysis of each tourism-focused industry to avoid generalizations that might lead to incorrect policy interpretations.","PeriodicalId":23204,"journal":{"name":"Tourism Economics","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48935117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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