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Although it is a widely accepted fact that climate change can negatively impact on tourism demand and affect the economies at the socio-economic level, empirical studies on the climate change tourism development nexus has been quite scant, especially for the case of island economies that are heavily dependent on tourism. This study aims to supplement the literature on climate change and tourism by empirically assessing the relationship between climate change and tourist arrivals for the case of 18 small island developing states over the period from 1989 to 2016.

This paper uses dynamic panel data techniques, namely, a panel vector autoregressive framework, which accounts for dynamic and endogeneity issues.

The results from the analysis confirm the existence of a significant relationship between climate change and tourism demand in both the long-run and short run. Further analysis shows a bi-directional causality between climatic change and tourism demand while the study also confirms the tourism led growth hypothesis.

This research supplements the literature on the tourism-environment link, especially for tourism dependent island economies.

Results from this study are important to policymakers who should spare no effort to mitigate the effect of adverse climatic change in the context of tourism development.

This study is built on a unique data set for a sample of island economies and interestingly adopts dynamic panel data analysis to account for dynamics and endogenity in the climate change-tourism development nexus.

Based on a panel vector error correction model (PVECM), this study aims to investigate the impact of foreign direct investment (FDI) on tourism development in a selected group of 17 small island economies during 1995-2018. In the long run, a positive and direct relationship was found between foreign investment and tourists’ arrival. Moreover, economic performance and tourists’ income were also found to be key determinants of tourism development. It is further observed that there is bidirectional causality between the two variables. Hence, one can argue that FDI is a key element for tourism development. So, if the countries can attract more FDI and grow economically, these elements will contribute positively to the sector in the future.

This work uses rigorous dynamic time series analysis, namely, a dynamic PVECM, which takes into account dynamism and endogeneity issues in tourism modelling. Furthermore, the PVECM is also appropriately suited for integrating short- and long-run analysis.

The results confirm that FDI has been an important ingredient in the tourism development of the island economies in the long run. Interestingly, a bidirectional causality between FDI and tourism development is validated. Moreover, growth will as well be important. So, if the country can attract more FDI and grow economically, these elements will attract the tourists of the future.

Relatively few studies have rigorously studied the relationships between FDI and tourism development, particularly with respect to developing countries and small island states which rely heavily on tourism as well as FDI. As such existing research has neglected dynamic and reverse causality analysis in their respective FDI-tourism modelling. This study thus attempts to address the above and supplement the literature by investigating the direct and indirect relationship between FDI and tourism development for the case of small island economies over the period 1995-2018. Moreover, the implication of foreign capital inflows on tourism futures will as well be developed.

The present study investigates the extent to which technological progress influences trade in the Common Market for Eastern and Southern Africa (COMESA) region over the period 1990–2017.

Methodologically, this study uses a rigorous dynamic analysis namely a dynamic vector error correction model (PVECM) to carry out the proposed investigation. Such a procedure ensures that the dynamic behaviour under consideration is properly captured, while simultaneously catering for causality issues.

The results show that technological progress has had a positive and significant effect on trade for the sample of countries in the COMESA region over the years of studies. Also, the long-run results show that local investment and economic growth have a positive impact on international trade. Furthermore, the short-run estimates allowed us to make further analysis of the results. For instance, it is observed that trade as well results in technological progress as per the study. Hence, there is reverse causation or bi-directional causality between trade and technological progress.

Very few research studies have been conducted on the link between technological progress and trade in a macroeconomy. The analysis thus is believed to supplement the dwarf literature on the technological progress and trade nexus by bringing additional evidence from COMESA.

Using panel data for the Regional Comprehensive Economic Partnership (RCEP) member states, the present study explored the role of RCEP negotiations on tourism development.

A dynamic econometric model, namely the panel autoregressive dynamic lag model (PARDL) has been used. To test for panel causality, Dumitrescu–Hurlin panel causality tests were used.

Through the use of a dynamic econometric model, namely the PARDL, the results show that the RCEP negotiations, growth rates, as well as international trade contribute towards tourism development. Furthermore, the Dumitrescu–Hurlin panel causality tests confirm the existence of a bidirectional causal link between tourism development and RCEP negotiations. Finally, a unidirectional causal link is observed between tourism development and international trade.

This existing evidence on the topic seems to be very scant and limited to specific regions and particular regional trade agreements. This paper thus fills an important gap in the literature by advancing evidence about the effects of the RCEP on international tourism flows across member countries.

This study aims at evaluating the effect of the COVID-19 pandemic on the export trade system for Mauritius during the first half of 2020 (January 2020–June 2020).

An initial analysis of the monthly export time series data proves that on the whole, the series have diverged from their actual trends after the outbreak of the COVID-19 pandemic: observed values are less than those predicted by the selected optimal forecast models. The authors subsequently employ the Bayesian structural time series (BSTS) framework for causal analysis to estimate the impact of the COVID-19 pandemic on the island's export system.

Overall, the findings show that the COVID-19 pandemic has a statistically significant and negative impact on the Mauritian export trade system, with the five main export trading partners and sectors the most affected. Despite that the impact in some cases is not apparent for the period of study, the results indicate that total exports will surely be affected by the pandemic in the long run. Nevertheless, this depends on the measures taken both locally and globally to mitigate the spread of the pandemic.

This study thus contributes to the growing literature on the economic impacts of the COVID-19 pandemic by focussing on a small island economy.