Whether international tourism can lead to economic growth is an important macroeconomic question for both policy makers and investors. We extend the literature by investigating if tourism development is an additional determinant of income in the presence of the standard income determinants (such as capital accumulation), or if the effects of tourism development on economic growth work through the standard income determinants, instead. Empirically, we develop a tourism-growth model that is an extension of Solow (1956) and estimate our model with a cross-section of 109 countries. Our findings indicate that investments in tourism in and of itself appear to be insufficient for economic growth. Instead, tourism’s contribution to the long-term growth of an economy comes through its role as an integral part of a broader development strategy that is more generally focused on standard income determinants.

We implement a fast and efficient algorithm to compute qualitatively constrained smoothing and regression splines for quantile regression, exploiting the sparse structure of the design matrices involved in the method. In a previous implementation, the linear program involved was solved using a simplex-like algorithm for quantile smoothing splines. The current implementation uses the Frisch-Newton algorithm, recently described by Koenker and Ng (2005b). It is a variant of the interior-point algorithm proposed by Portnoy and Koenker (1997), which has been shown to out perform the simplex method in many applications. The current R implementation relies on the R package S parse M of Koenker and Ng (2003) which contains a collection of basic linear algebra routines for sparse matrices to exploit the sparse structure of the matrices involved in the linear program to further speed up computation and save memory usage. A small simulation illustrates the superior performance of the new implementation.

Tourism at China’s cultural heritage sites has developed rapidly in recent years. These sites have encountered many challenging problems such as poor tourist experience, over-commercialization, and loss of cultural authenticity. This study took the coordinative green development of tourist experience and commercialization of tourism (CGDTECT) at cultural heritage sites as the research objective, using two UNESCO World Heritage Sites—the Ancient City of Pingyao and West Lake Cultural Landscape of Hangzhou—and the Confucius Temple in the historic district of Nanjing as case studies. As such, we attempted to construct an indicator system for CGDTECT at cultural heritage sites. We adopted a fuzzy comprehensive evaluation method and importance-performance analysis (IPA) method to analyze the importance and performance of CGDTECT from a tourist’s perspective. Our findings show that tourists thought the coordinative green development and experience at the three cultural heritage sites were more important; they were basically satisfied with CGDTECT at the three case studies, so a continuous promotion strategy could be taken. The findings of this study can provide scientific theoretical guidance and practical reference for CGDTECT at cultural heritage sites, and contribute to the scientific protection of cultural heritage sites and the sustainable development of tourism.

Carbon emissions play an important role in sustainable tourism development at heritage sites. The study takes the Wulingyuan Scenic and Historic Interest Area (WSHIA) as an example, and primary and secondary data sources are used to measure and estimate the carbon emissions of tourist attractions from 1979 to 2014. The temporal and spatial evolution of carbon emissions and their influencing factors for tourist attractions at heritage tourist destinations are analyzed. The results show that there are great differences in carbon emissions per visitor across the different types of tourism attractions at the heritage tourist destination, and there are significant monthly and interannual differences in the carbon emissions of the tourism attractions in the WSHIA. The main influencing factors include tourism seasonality, the rapid growth of China’s tourism market, and the rising popularity of heritage tourism. The spatial evolution of carbon emissions of the tourist attractions can be divided into three stages, and its main influencing factors include functional zoning and environmental regulation at the heritage sites and diversified evolution of the heritage tourist attractions. The findings of this study could enrich theories of low-carbon tourism and provide the low-carbon development measures of sustainable tourism in heritage tourist destinations for policymakers.