Tourism expenditure of EU-27 regions under the global economic crisis

This study focuses on the underpinnings of the households’ tourism expenditure decisions during the global economic crisis in 2009. In particular, this study tests if during an economic crisis, decisions on tourism expenditure depend on climate conditions of the place of origin, GDP and GDP growth, among other well-known determinants. It should be noted that cutback decisions on tourism expenditure are not independent of destination choice, and for that reason the model requires the estimation of both decisions simultaneously. The methodology proposed in this paper represents a new way of analyzing the impacts of an economic crisis on tourism expenditure. Two levels of analysis can be considered. On the one hand, macroeconomic data of tourism expenditure is usually explored. On the other hand, the microeconomic analysis of the household and regional variables of their environment that may enrich the analysis. If the econometric model takes into account all these variables simultaneously, then the linkage between GDP changes and tourists´ behavior is enriched and it may be estimated more accurately. As far as we know, this paper is the first study that models the cutback decision on tourism expenditure. Modeling such decision is a challenge because it is not independent of the destination choice. For instance, households that travel domestically may not be as sensitive to the crisis as those who travel abroad. For this purpose, the econometric model employed is a simultaneous system of cutback decision and destination choice. More precisely, Simultaneous Semi-Ordered Bivariate Probit has proved to be the most useful econometric model for the estimation because it deals with the simultaneity of the cutback and destination choice decisions as well as the endogeneity. This research has proved that during an economic crisis, households react cutting back their tourism expenditure depending on GDP, GDP growth, and climate in their place of origin. In that sense, there are marked differences between North-European and Mediterranean regions. It is interesting to note that regions with bad climate are less likely to cut back than those households located in regions with good climate.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Tourism expenditure under the global economic crisis: the role of climate in the place of residence

Tourists from different European regions have reacted heterogeneously during the Global Economic Crisis. Such variability is due to different preferences and willingness to pay for tourism. This paper explores the underpinnings behind such heterogeneity. Regional variables and household socioeconomic variables are gathered to understand tourists' expenditure cutback decision. Since the cutback decision is not independent of the destination choice, a Simultaneous Semi-Ordered Bivariate Probit model is specified, which deals with the simultaneous estimation of both decisions and endogeneity. Post-estimation results are based on GIS, contours and non-parametric analysis. They prove that during an economic crisis, tourists' cutback decisions on tourism expenditure depend on climate conditions of the place of origin, GDP and GDP growth.Universidad de Málaga. Campus de Excelencia Internacional de Andalucí

Quantifying the impact of airlines exit in tourism destinations. The cases of Monarch and Thomas Cook

We analyze the impact on tourists’ arrivals after the bankruptcies of Monarch Airlines and Thomas Cook. It draws on arrivals from the UK to Canary Islands and it employs a univariate and multivariate structural time series with level interventions. More interestingly, after Monarch exit, the policymakers applied a laissez faire strategy, which resulted in a 93.04% net loss of their level of traffic in Tenerife. However, after Thomas Cook exit, the policymakers intervened in the market proposing incentives to the incumbent airlines to cover the loss. It resulted in a 34.72% net loss of Thomas Cook level of traffic. Moreover, we provide details about the redistribution of the traffic among the incumbent airlines that diminished the net loss of passengers

¿Cómo impulsa el turismo la economía local? Algunas experiencias y herramientas de análisis

Se trata en primer lugar el papel del turismo como motor de desarrollo, el vínculo entre el turismo y el PIB, así como el papel de la demanda y del gasto agregado. En la segunda parte se exponen las herramientas y experiencias de análisis en evaluación de proyectos ex-ante: Barbados, China y Uruguay; y proyectos ex-post: Ecuador, Etiopía y Málaga.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Estimating the spatial and time decay impacts of a local event

This paper studies the spatial and temporal decay impacts of a local event on tourism accommodation in a region. The results show that the day of the event, the number of occupied rooms, ADR and revenue reach a marked peak. Moreover, it shows the presence of a time decay impact on revenue, which is asymmetric in favour of the days before the event. A spatial panel data regression method has been employed. The case study concerns Ironman Triathlon event and its impact on the Airbnb listings in the Spanish region of Vitoria-Gasteiz in 2019

Understanding tourists´ economizing strategies during the global economic crisis

This paper explores how tourists from 165 regions of EU-27 countries cut back their tourism expenditure during the global economic crisis in 2009. This study disentangles the cutback tourism expenditure in two mutually related decisions: First, it takes into account whether the tourist has had to cut back on tourism expenditure due to the crisis and second, how they decided to cut back according to six alternatives: “fewer holidays”, “reduced length of stay”, “cheaper means of transport”, “cheaper accommodation”, “travel closer to home” or “change the period of travel”. The econometric model able to deal with such simultaneous decisions is an adaptation of the Heckman model in generalized structural equations modeling. This methodology permits to control by sample selection bias and correlations between equations. This paper highlights the existence of patterns in the cut back alternatives depending on the socioeconomic characteristics of the household and the climate conditions in origin.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Safe disposal of solid wastes generated during arsenic removal in drinking water

Wastes generated during the treatment of water containing arsenic were mixed with Portland cement in 3 : 1 volume ratio, respectively, to produce mortars that were then used to manufacture monolithic bricks. Two different wastes, containing 1.0 × 103 and 2.0 × 103 mg As per kg of dried waste, were generated in experiments of aqueous trivalent arsenic ([As(III)] = 50 mg L-1) removal in columns filled with a mixture of zero-valent iron and sand (1%, w/w of ZVI). The mechanical tests indicated that the wastecontaining bricks showed a decrease in the compression tests, while no significant differences were found in the flexural tests. Studies on arsenic leaching indicated that, in normal conditions, the amount of released arsenic is not significant, as extreme conditions are required to exceed the maximum allowable limit for non-hazardous waste. Even though the quality of the resulting mortar is lower, it is still well suited to make bricks for use in the construction of foundations or for final disposal in landfills.Fil: de Seta, Elizabeth Graciela. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; ArgentinaFil: Reina, Fernando Damián. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Mugrabi, Fernando Isaac. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; ArgentinaFil: Lan, Luis Eugenio. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; ArgentinaFil: Guerra, Juan Pablo. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; ArgentinaFil: Porcel Laburu, Aitor. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; ArgentinaFil: Domingo, Esteban José. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; ArgentinaFil: Meichtry, Jorge Martin. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Soil organic carbon stocks in native forest of Argentina: a useful surrogate for mitigation and conservation planning under climate variability

Background The nationally determined contribution (NDC) presented by Argentina within the framework of the Paris Agreement is aligned with the decisions made in the context of the United Nations Framework Convention on Climate Change (UNFCCC) on the reduction of emissions derived from deforestation and forest degradation, as well as forest carbon conservation (REDD+). In addition, climate change constitutes one of the greatest threats to forest biodiversity and ecosystem services. However, the soil organic carbon (SOC) stocks of native forests have not been incorporated into the Forest Reference Emission Levels calculations and for conservation planning under climate variability due to a lack of information. The objectives of this study were: (i) to model SOC stocks to 30 cm of native forests at a national scale using climatic, topographic and vegetation as predictor variables, and (ii) to relate SOC stocks with spatial–temporal remotely sensed indices to determine biodiversity conservation concerns due to threats from high inter‑annual climate variability. Methods We used 1040 forest soil samples (0–30 cm) to generate spatially explicit estimates of SOC native forests in Argentina at a spatial resolution of approximately 200 m. We selected 52 potential predictive environmental covariates, which represent key factors for the spatial distribution of SOC. All covariate maps were uploaded to the Google Earth Engine cloud‑based computing platform for subsequent modelling. To determine the biodiversity threats from high inter‑annual climate variability, we employed the spatial–temporal satellite‑derived indices based on Enhanced Vegetation Index (EVI) and land surface temperature (LST) images from Landsat imagery. Results SOC model (0–30 cm depth) prediction accounted for 69% of the variation of this soil property across the whole native forest coverage in Argentina. Total mean SOC stock reached 2.81 Pg C (2.71–2.84 Pg C with a probability of 90%) for a total area of 460,790 km2, where Chaco forests represented 58.4% of total SOC stored, followed by Andean Patagonian forests (16.7%) and Espinal forests (10.0%). SOC stock model was fitted as a function of regional climate, which greatly influenced forest ecosystems, including precipitation (annual mean precipitation and precipitation of warmest quarter) and temperature (day land surface temperature, seasonality, maximum temperature of warmest month, month of maximum temperature, night land surface temperature, and monthly minimum temperature). Biodiversity was influenced by the SOC levels and the forest regions. Conclusions In the framework of the Kyoto Protocol and REDD+, information derived in the present work from the estimate of SOC in native forests can be incorporated into the annual National Inventory Report of Argentina to assist forest management proposals. It also gives insight into how native forests can be more resilient to reduce the impact of biodiversity loss.EEA Santa CruzFil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.Fil: Peri, Pablo Luis. Universidad Nacional de la Patagonia Austral; Argentina.Fil: Peri, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Gaitan, Juan José. Universidad Nacional de Luján. Buenos Aires; Argentina.Fil: Gaitan, Juan José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Mastrangelo, Matias Enrique. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Grupo de Estudio de Agroecosistemas y Paisajes Rurales; Argentina.Fil: Mastrangelo, Matias Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Nosetto, Marcelo Daniel. Universidad Nacional de San Luis. Instituto de Matemática Aplicada San Luis. Grupo de Estudios Ambientales; Argentina.Fil: Nosetto, Marcelo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Villagra, Pablo Eugenio. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA); Argentina.Fil: Villagra, Pablo Eugenio. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias; Argentina.Fil: Balducci, Ezequiel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Yuto; Argentina.Fil: Pinazo, Martín Alcides. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Montecarlo; Argentina.Fil: Eclesia, Roxana Paola. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná; Argentina.Fil: Von Wallis, Alejandra. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Montecarlo; Argentina.Fil: Villarino, Sebastián. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Grupo de Estudio de Agroecosistemas y Paisajes Rurales; Argentina.Fil: Villarino, Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Alaggia, Francisco Guillermo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Campo Anexo Villa Dolores; Argentina.Fil: Alaggia, Francisco Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Gonzalez-Polo, Marina. Universidad Nacional del Comahue; Argentina.Fil: Gonzalez-Polo, Marina. Consejo Nacional de Investigaciones Científicas y Técnicas. INIBIOMA; Argentina.Fil: Manrique, Silvana M. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Energía No Convencional. CCT Salta‑Jujuy; Argentina.Fil: Meglioli, Pablo A. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA); Argentina.Fil: Meglioli, Pablo A. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias; Argentina.Fil: Rodríguez‑Souilla, Julián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas (CADIC); Argentina.Fil: Mónaco, Martín H. Ministerio de Ambiente y Desarrollo Sostenible. Dirección Nacional de Bosques; Argentina.Fil: Chaves, Jimena Elizabeth. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas (CADIC); Argentina.Fil: Medina, Ariel. Ministerio de Ambiente y Desarrollo Sostenible. Dirección Nacional de Bosques; Argentina.Fil: Gasparri, Ignacio. Universidad Nacional de Tucumán. Instituto de Ecología Regional; Argentina.Fil: Gasparri, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Alvarez Arnesi, Eugenio. Universidad Nacional de Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario; Argentina.Fil: Alvarez Arnesi, Eugenio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe; Argentina.Fil: Barral, María Paula. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Grupo de Estudio de Agroecosistemas y Paisajes Rurales; Argentina.Fil: Barral, María Paula. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Von Müller, Axel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Esquel Argentina.Fil: Pahr, Norberto Manuel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Montecarlo; Argentina.Fil: Uribe Echevarría, Josefina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Quimilí; Argentina.Fil: Fernandez, Pedro Sebastian. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Famaillá; Argentina.Fil: Fernandez, Pedro Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ecología Regional; Argentina.Fil: Morsucci, Marina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA); Argentina.Fil: Morsucci, Marina. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias; Argentina.Fil: Lopez, Dardo Ruben. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Campo Anexo Villa Dolores; Argentina.Fil: Lopez, Dardo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Cellini, Juan Manuel. Universidad Nacional de la Plata (UNLP). Facultad de Ciencias Naturales y Museo. Laboratorio de Investigaciones en Maderas; Argentina.Fil: Alvarez, Leandro M. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA); Argentina.Fil: Alvarez, Leandro M. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias; Argentina.Fil: Barberis, Ignacio Martín. Universidad Nacional de Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe; Argentina.Fil: Barberis, Ignacio Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe; Argentina.Fil: Colomb, Hernán Pablo. Ministerio de Ambiente y Desarrollo Sostenible. Dirección Nacional de Bosques; Argentina.Fil: Colomb, Hernán. Administración de Parques Nacionales (APN). Parque Nacional Los Alerces; Argentina.Fil: La Manna, Ludmila. Universidad Nacional de la Patagonia San Juan Bosco. Centro de Estudios Ambientales Integrados (CEAI); Argentina.Fil: La Manna, Ludmila. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Barbaro, Sebastian Ernesto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Cerro Azul; Argentina.Fil: Blundo, Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ecología Regional; Argentina.Fil: Blundo, Cecilia. Universidad Nacional de Tucumán. Tucumán; Argentina.Fil: Sirimarco, Marina Ximena. Universidad Nacional de Mar del Plata. Grupo de Estudio de Agroecosistemas y Paisajes Rurales (GEAP); Argentina.Fil: Sirimarco, Marina Ximena. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Cavallero, Laura. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Campo Anexo Villa Dolores; Argentina.Fil: Zalazar, Gualberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA); Argentina.Fil: Zalazar, Gualberto. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias; Argentina.Fil: Martínez Pastur, Guillermo José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas (CADIC); Argentina

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Comparison of seven prognostic tools to identify low-risk pulmonary embolism in patients aged <50 years

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