Development of indicators to assess economic vulnerabilities to changes in ecosystem services: case study of counties in Maryland, USA.
We develop indicators showing the relative environmental burdens that human activities place on locales for a given level of economic benefits. The main purpose is to develop tools that allow us to examine the potential vulnerabilities within economies to changes in resource conditions. The indicators of pollution emission or resource consumption per job can be used to identify potential challenges to resource and industry managers and to compare areas in terms of their ability to adapt to change. For example, if a large number of area jobs are dependent on abundant water, this indicates a vulnerability to a reduction in water availability for industrial use. We develop a case study for 23 counties and 1 city in Maryland to examine the usefulness and limitations of the indicators. Our case study demonstrates that the indicators provide an informative view into patterns of local economic activity and use of an area's environmental goods and services. In contrast to patterns for total environmental burdens (e.g., total SO2 emissions) that are typically reported, the rates of environmental burden per job are not simply correlated with high or low economic output. Thus, the indicators represent distinct patterns of environmental burdens per job that reflect reliance on environmental services. The indicators have some limitations when used at this fine scale because they can misrepresent conditions in counties in which economic sectors are dominated by one or a few businesses. For this reason, the indicators are best used as a regional screening tool.
Wainger
Lisa A
LA
Center for Environmental Science, University of Maryland, PO Box 38, Solomons, Maryland 20688, USA. wainger@cbl.umces.edu
King
Dennis M
DM
Cantrell
Joyce A
JA
Bird
Sandra L
SL
D000383:Agriculture; D003132:Commerce; D003247:Conservation of Natural Resources; D003362:Cost-Benefit Analysis; D017753:Ecosystem; D004784:Environmental Monitoring / Q000706:statistics & numerical data*; D004787:Environmental Pollution / Q000191:economics*; D008396:Maryland; D012307:Risk Factors