Guidance: Tourism and Recreation

Overview

Tourism and recreation in coastal areas is a major component of thriving coastal communities and a measure of how much people value ocean systems. A score of 100 means a region sustainably realizes its full recreational potential.

This goal is not about the revenue or livelihoods that are generated by tourism and recreation (that is captured in other goals) but instead captures the value that people have for experiencing and enjoying coastal areas.

Practial Guidance

This goal can take many different directions and draw from a wide range of data sources. This goal can benefit from creating thinking, as demonstrated by many OHI+ assessments (see below for ideas)! For example, in the Brazil Assessment the density of hotel employees per state was used as a proxy of how well visited coastal areas were.

A good starting point is to consider how people use and enjoy the ocean in your region and then try to determine the information that could capture this.

For example, you may be able to find information describing the number of visitors to surf areas, beaches, hiking trails, etc. Or, the number of people buying fishing licences or parking passes.

Other types of information that could be useful, include: boat rentals, resort registries, whale watching and bird watching tours. Social media posts could be a rich data source, but will take considerable skill to harvest and analyze. General information such as whether people have access to beaches, boating areas, or to surfing spots could be useful.

The best way to represent the sustainability term for this goal is to use a local indicator or a measure of tourism sustainability or competitiveness. Otherwise, use the TTCI value from the Global 2013 assessment for the study area (applied evenly across all regions).

The reference point will depend on the type of data. Does your country have industry growth rate targets? Do you want to increase tourism, or ensure it does not decline?

Examples of the Approach

Assessment	Developing the Model	Setting the Reference Point	Other Considerations
Global 2012	This goal measured the number of international tourists arriving by airline to coastal regions, accounting for their average length of stay, and adjusting by population size. The data were found through international airline arrivals and the Tourism Competitiveness Index (TTCI) from the World Economic Forum.	This study used a spatial comparison reference point that compares each region to the best performing regions. To compare regions, arrivals were divided by the region’s population.	There were data limitations that were comprehensive data available on a global scale. This approach did not account for domestic tourism.
Global 2013 - 2015	The study used the direct employment in the tourism industry relative to total labor force and used the TTCI.	The reference point was the best scoring region across all years and rescaled all other regions across all years to that score. All regions above this score received a status score of 100.	A new model was developed using employment in the tourism sector as a proxy for the total number of people engaged in coastal tourism and recreation. It involved assumptions, but these data were of better quality and closer to what this goal is trying to capture than those used in Global 2012.
Brazil 2014	The model developed for Global 2012 was changed to use information on hotel employees for each coastal municipality. The status was measured for each coastal state as the density of hotel jobs in coastal areas.	The reference value used was the highest status value across all states over the time series, which was Rio de Janeiro in 2011.	The goal model assumes that the majority of coastal hotels are located in proximity to the shoreline, and that the number of hotel employees is directly proportional to the volume of tourists an area receives.
U.S. West Coast 2014	There were data available for changes in participation in 19 different marine and coastal specific recreational activities over time. These observations were used to produce a predictive model that was employed to estimate participation rates in recent years.	The reference point was temporal, compared to 2000.	The approach took advantage of time-series data. Participation rates more closely matched the intent of this goal and were a more robust proxy than international tourist arrivals data, and the reference point was spatial instead of temporal.
Israel 2014	The amount of coastal park visits and hotel occupancies were used as as a proxy for the number of people actually engaged in coastal tourism. Status of the two indicators were calculated separately and then weighted to calculate the status of this goal (ie. 1/3 for hotels and 2/3 for parks).	Hotels reference points were taken from official planning targets for year 2020.	N/A
Ecuador-Gulf of Guayaquil 2015	The approach is similar to Global 2012 assessment. The number of domestic and international tourists in each region were the product of country-wide data and the fraction of tourists per region.	The reference points were the number of domestic visitors and international visitors estimated for 2019. These values are calculated by province assuming an annual growth of 6.9% proposed by Integral Tourism Marketing Plan of Ecuador -PIMTE- 2014 for inbound tourism, and a growth of 2.44% for domestic tourism.	No local data were available for other indicators such as activities, hotel employments, etc. But we were able to add domestic tourism data to supplement international tourism data.
China 2015	Status model is based on the ratio of visitors and coastal area.	The spatial reference point was the region with the highest ratio across all years.	The number of visitors included both domestic and international visitors. Travel and Tourism Competitive Index (TTCI) was also incorporated.