The environmental Kuznets curve (EKC) hypothesizes that the income-pollution relationship has an inverted U shape, implying that pollution is increasing in income up to a turning point beyond which pollution decreases. The empirical literature has concentrated on estimation of this relationship at the mean, employing longitudinal data on countries or U.S. states and measures of per-capita income and emissions of pollutants like N O x (nitrogen oxide) and SO 2 (sulfur dioxide). The typical …nding supports an inverted U shaped relationship. Estimation at the mean, however, likely masks heterogeneities that can be present at higher and/or lower quantiles of the emissions'distribution. In addition, mean estimation methods are sensitive to the presence of outliers. This study applies methods for conditional-quantile panel …xed e¤ects models to the estimation of the income-emissions relationship on U.S. state-level data on N O x and SO 2 pollutants over the period 1929-1994. Our results indicate that methods that focus on the conditional mean provide too optimistic estimates about emissions reduction of N O x , as conditional-quantile methods suggest that the turning point of the relationship occurs at higher values of income; while the opposite is found for SO 2 . An important lesson we draw from the application of conditional-quantile techniques is that the income-environmental degradation relationship is sensitive to the presence of outliers in the data.
Numerous studies have demonstrated the tourism industry to be especially sensitive to weather and climate variability. Snow-related tourism, being largely dependent on climatic resources, is particularly affected by climate change. Our study provides a new index to reflect the climatic suitability of a given destination for snow-related tourism activities, focusing on resorts with usually limited snowfall. The proposed Skiing Utility Index (SUI) is based purely on the weather preferences of skiers, extracted by questionnaires distributed at the Parnassos ski center (Greece). The index incorporates four different weather variables considered to be the most influential for this type of tourism. The ideal temperature for skiing was found to be close to 0 °C, the ideal wind speed between 0–3.3 m/s, the ideal cloud cover between 0–25% and the snowfall duration between 1–2 h, with the latter found to be the most important variable for skiing. For each climatic variable, a mean utility score profile was developed from all respondents. Following, a utility function was fitted via linear regression to the above-mentioned utility score. All four utility functions were aggregated into one total SUI score. When combined with climate projections, the SUI can support the assessment of climate change risks for snow-related tourism destinations.
Several climate indices have been developed to analyze the relationship between climatic variables and tourist comfort at different destinations, although, none of the indices applied so far in cities have been informed by empirical data collected exclusively at urban tourist destinations. The present paper aims to cover this gap by developing an “Urban Climate Comfort Index” (UCCI) that integrates critical climate variables for urban tourism and is informed by empirical data from an in-situ survey conducted in southern Europe, namely, in close proximity to the Acropolis Museum in Athens, Greece. The survey provided input on the ideal and unacceptable climatic conditions as perceived by urban tourists and on the relevant weight of the selected climatic parameters. Tourist preferences were then translated into a numerical scale by assigning utility scores of 0% and 100% to the “unacceptable” and “ideal” values while using a linear change for the intermediate values. Hence, a best-fitting utility function for each climatic variable was created, and all utility functions were then aggregated through their relative weights to form the UCCI index. The new index can be applied to other similar urban tourist destinations and assist impact assessment studies and tourism management measures, including climate change adaptation.
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