Open access peer-reviewed chapter

# The Tourism Impacts of Lake Erie Hazardous Algal Blooms

Written By

Matthew Bingham and Jason Kinnell

Submitted: November 11th, 2019 Reviewed: August 18th, 2020 Published: September 17th, 2020

DOI: 10.5772/intechopen.93625

From the Edited Volume

## Inland Waters

Chapter metrics overview

View Full Metrics

## Abstract

Nutrient loading and warming waters can lead to hazardous algal blooms (HABs). Policymakers require cost-effective valuation tools to help understand impacts and prioritize adaptation measures. This chapter evaluates the tourism impacts of HABs in Western Lake Erie based on HABs that occurred in 2011 and 2014, both through a unique temporal and spatial specification of HAB severity as well as input/output analysis and decomposition of trips and profitability.

### Keywords

• hazardous algal blooms
• HABs
• socioeconomic
• benefits transfer
• Lake Erie
• input/output
• tourism

## 3. HAB scenarios studied in this effort

HABs of varying levels of severity are likely to recur in Lake Erie. Their size and location are difficult to predict, but mitigation may allow for the avoidance of potentially large and far-reaching economic effects. Consequently, when considering the immediate (i.e., within-year) effects, this study uses past HABs to predict the economic effects that would accompany reductions in future HABs.

This study focuses on the most damaging recent HABs in 2011 and 2014, and the consequent service reductions for those years. While information about beach closures is available, there are no data specifically analyzing reductions in tourism, or quantitative analyses of the impacts of these two HABs While visual data showing reductions in ecological service (such as contaminated shorelines or clogged marines) are readily available, a lack of quantitative or written analysis hinders precise analysis of the date, location, and severity of past HABs.

Given this limitation, this study uses news reports and satellite images to create a scale of HAB severity [4]. Since most overhead images of Lake Erie’s algal blooms are not precisely dated, the study relies on date-stamped satellite images from NOAA such as that depicted below (Figure 3﻿﻿).

For several years up to 2012, NOAA posted Medium-Spectral Resolution Imaging Spectrometer (MERIS) imagery of Lake Erie. Since then, NOAA has posted images of Lake Erie HABs from the Moderate Resolution Imaging Spectro-Radiometer (MODIS) on the AQUA satellite. Both MERIS and MODIS imagery are dated at least weekly [6]. An example satellite view is depicted above. This study uses a scale ranging from 0 to 1 to quantify HAB severity in a given area of Lake Erie.

This study uses the finest degree possible of both temporal specificity—weekly analysis—and spatial specificity—county-level for mainland shorelines in addition to three island groupings. Severity ratings by week and month were developed for 2011 and 2014 from July through October. Table 1﻿﻿ below analyzes July of 2011.

Location2011 July weeks
1st2nd3rd4th
Essex mainland000.250
Pelee Island0000
Wayne (southern tip)0000
Monroe000.500.50
Lucas000.500.25
Ottawa mainland0000
Bass Islands0000
Sandusky000.500.25
Erie mainland0000
Kelleys Island, Erie County0000

### Table 1.

Severity rating for HABs in the Western Basin of Lake Erie, July 2011. Sources: [6, 7, 8, 9].

This information was incorporated into the evaluation of effects to tourism.

## 4. Tourism and commerce

Since tourism, business demand, and commercial property values are all closely related, by affecting tourism HABs can in turn negatively impact all three economic sectors in areas close to western Lake Erie. For example, a well-publicized HAB event would almost certainly reduce tourism, in turn lowering revenue for businesses such as local restaurants, hotels, and charter boat operators. As these businesses lose revenue, they would likely purchase fewer supplies, affecting other businesses upstream in the supply chain. Finally, since these businesses would be expected to purchase less labor due to lower demand, either by hiring less or through layoffs, the local economy suffers as a result of lost local wages.

Ultimately, these sorts of effects would be reflected in business balance sheets as reduced revenues and profitability. Additionally, since affected businesses’ values are most likely tied to their assets and the real estate they occupy (for example, a marina is not easily converted to some other use), on-going balance sheet effects would ultimately lead to reductions in commercial real estate values.

There are many challenges to understanding the implications of changes in tourism from HABs. The clearest challenge obstructing a precise analysis of these impacts is a lack of data either on the amount of tourism at risk or the specific impact of HABs on tourism. For example, while county-level data exists for total expenditures on tourism, this includes tourism which would not be interrupted by HABs or other discouraging factors.

An additional challenge relates to the distinction between economic benefits (willingness to pay) and economic impacts (expenditures), and the measurement of the economic benefits that arise from economic impacts (profits). For example, consider a restaurant owner who loses $10,000 in revenue because of a HAB. The owner’s willingness to pay to recover that revenue; is (roughly speaking) the lost profit on that revenue. This is more difficult to identify than lost revenue. Understanding the negative effects of HABs upstream in a supply chain requires knowing what expenditures were foregone, which depends on the operation’s variable cost situation with respect to employees (salaried or not) already purchased foodstuffs (perishable or not) and utilities. To address this issue, the study identifies expenditure changes and then characterizes benefits associated with those changes. An additional issue is that changes in tourism may represent changed rather than lost trips. A tourist who does not go to the western basin because of HABs might instead go to the central basin, or somewhere else. As a result, changes in demand in one area have an opposite effect in other areas. To address this, we limit the geographical scope of the study to a region affected by HABs. Finally, because commercial property values tend to be linked to business profitability, evaluating both risks double-counting. This study focuses on business profitability. The remainder of this chapter presents the detailed methods and results. Counties studied are United States counties depicted below (Figure 4﻿﻿). Due to differences in available data, slightly different methods are applied for Ohio, and Michigan. ### 4.1 Ohio tourism As different sorts of information are available by region, varying approaches are applied. This sub-section explores potential effects in Lucas, Ottawa, Sandusky, and Erie counties. The approach relies on estimates of expenditures per trip. Expenditure and trip data in Ohio are collected from [3, 10] which indicate$110 per Ohio day visitor in 2013. This is 57.4% of total visitor spending and 80% of total Ohio visitors. Some 33% are from Toledo and Cleveland.

Spending from overnighters in 2013 was estimated at $335 per day—42.6% of total Ohio visitor spending. These visitors were 20% of total visitors. Of these, 20% are with relatives and friends. Average of nights per trip was 3.2 nights per trip and that of members per party was 3.4. Eighteen percent of these visitors went to a beach at a lake. Consumers spend the most on transportation, as well as food and beverage, since both day and overnight visitors spend money in these categories. Lodging only accounts for 11% of spending, while retail and recreation expenditures are almost one-third of Ohio visitor spending. These expenditure rates can be subdivided based on trip type and expenditures. For example, day visitors spend$110 per visitor with none of that being for air travel or lodging. Overnight visitors’ costs vary depending on if visitors stay with friends/family or in commercial lodging. For the purposes of this study, we presume overnight visitors who stay with friends and family do not spend money on lodging and overnight visitors who stay with friends and family spend an average of $244. Those who stay in commercial lodging places spend about 10% more on food and beverages than overnight visitors who stay with friends and family. On average this is$358 per day for each overnight visitor who pays for lodging.

Per-day expenditures vary by type of visit. In order to capture the full effect of changes in tourism using available tourism information, the effect of consumer expenditures must be extrapolated in terms of their implications for expenditures in other parts of the supply chain. To do so, we apply a mathematical-economic technique called input/output analysis [11]. Input/output analysis can be used to assess the effects of direct changes in expenditures through indirect impacts which arise in supplying industries and induced impacts which result from changes in local employment impacts to local expenditures.

Impacts are estimated using IMPLAN [12] with equations and data from ZIP codes on the shoreline of Lake Erie in Lucas County, Ohio. IMPLAN contains detailed input-output information on more than 500 economic sectors at the national, state, county, and ZIP code level.

Expenditures are apportioned over these sectors at the rate that they appear in the IMPLAN data and then simulations are conducted using IMPLAN. The sum of per-trip indirect and induced effects is a fraction of direct effects.

The approach for estimating tourist trips and dollars at risk in Ohio begins with estimates of by county tourism economic impacts in 2013. These are available from [3].

## Acknowledgments

Underlying efforts were funded by the International Joint Commission. The authors are grateful for assistance from Frank Lupi and Sanjiv Sinha.

## Conflict of interest

There are no conflicts of interest.

## References

1. 1. Bingham M, Sinha S, Lupi F. Economic Benefits of Reducing Harmful Algal Blooms in Lake Erie. Submitted to the International Joint Commission. Ann Arbor, Michigan: Environmental Consulting & Technology, Inc.; 2015. p. 66
2. 2. U.S. Geological Survey. Lake Erie Waters at the Beach with Buoy, Public Domain Image [Internet]. 2014. Available from:https://www.usgs.gov/media/images/lake-erie-waters-beach-buoy[Accessed: 12 February 2020]
3. 3. Tourism Economics. The economic impact of tourism in Ohio. In: Presented at the TourismOhio Advisory Board’s 2014 Symposium on the Future of Tourism in Ohio. Wayne, Pennsylvania: Tourism Economics; 2014
4. 4. Ohio Department of Health, Ohio Environmental Protection Agency, and Ohio Department of Natural Resources. State of Ohio Harmful Algal Bloom Response Strategy for Recreational Waters [Internet]. 2012. Available from:http://www.epa.ohio.gov/portals/35/hab/HABResponseStrategy.pdf[Accessed: 30 April 2015]
5. 5. U.S. Geological Survey. Lake Erie Algal Bloom, Public Domain Image [Internet]. 2014. Available from:https://www.usgs.gov/media/images/lake-erie-algal-bloom[Accessed: 12 February 2020]
6. 6. National Oceanic and Atmospheric Administration Great Lakes Environmental Research Laboratory. Harmful Algal Blooms in Lake Erie—Experimental HAB Bulletin Archive [Internet]. 2015. Available from:http://www.glerl.noaa.gov/res/waterQuality/lakeErieHABArchive/[Accessed: 04 May 2015]
7. 7. International Joint Commission Working Committee 2, Land Use and Management. Final Report [Internet]. 1993. Available from:http://www.ijc.org/files/publications/ID1243.pdf[Accessed: 13 May 2015]
8. 8. Michalak A, Anderson E, Beletsky D, Boland S, Bosch N, Bridgeman T, et al. Record-setting algal bloom in Lake Erie caused by agricultural and meteorological trends consistent with expected future conditions. Proceedings of the National Academy of Sciences of the United States of America. 2013;110(16):6448-6452
9. 9. Shuchman R, Sayers M, Raymer Z, Grimm A. Multi-Satellite Harmful Algal Bloom Observation Summary 1997-2014 Western Basin Lake Erie [Internet]. 2015. Available from:http://www.glerl.noaa.gov/res/waterQuality/docs/Table_Update_Jan2015.pdf[Accessed: 13 May 2015]
10. 10. Longwoods International. TourismOhio. In: Presented at the TourismOhio Advisory Board’s 2014 Symposium on the Future of Tourism in Ohio; 26 June 2014; Columbus, Ohio [Internet]. 2014. Available from:http://discoverohio.com/admin/new/Uploads/MeetingMinutes/3bce4ace-8fbc-4144-bddb-a231a162780fLongwoods%20I.pdf[Accessed: 30 March 2015]
11. 11. Leontief W. Input-Output Economics. New York: Oxford University Press; 1986
12. 12. IMPLAN Group, LLC. IMPLAN system (Lucas County, Ohio 2013 Data). Huntersville, North Carolina: IMPLAN Group, LLC; 2014
13. 13. National Restaurant Association. Restaurant operations report [Internet]. 2015. Available from:http://www.restaurant.org/News-Research/Research/Operations-Report[Accessed: 01 May 2015]
14. 14. Locsin A. The Average Profit Margin for a Restaurant [Internet]. 2015. Available from:http://smallbusiness.chron.com/average-profit-margin-restaurant-13477.html[Accessed: 19 May 2015]
15. 15. Monroe County Planning Department and Commission. Monroe County, Michigan: 2013 Comprehensive Economic Development Strategy Second Draft. Monroe, Michigan: Monroe County Planning Department and Commission; 2013. Available from:https://www.co.monroe.mi.us/docs/13_CEDS_2ndDraft_complete.pdf[Accessed: 30 April 2015]
16. 16. D.K. Shifflet & Associates Ltd. Year-End 2010 Visitor Profile: An Inside Look at the Leisure Travel Market in Michigan. McLean, Virginia: D.K. Shifflet & Associates Ltd. p. 2011

Written By

Matthew Bingham and Jason Kinnell

Submitted: November 11th, 2019 Reviewed: August 18th, 2020 Published: September 17th, 2020