Changing patterns of transportation, land use, water use, and economic activity impact the environment and, ultimately, the quality of human life. Models allow us to predict how human behavior affects the environment, but we do not yet have the ability to predict how changes in the environment affect human behavior and decision making. As a result, we know much more about how the environment responds to human decisions than we do about how humans respond to a changing environment and how these responses drive decision-making. Using the Chesapeake Bay as an example, this project will develop a model to predict how social, economic and policy changes impact water quality, and how changes in water quality influence human behavior and decision-making. These predictions will include several plausible future development scenarios (e.g., smart growth versus business as usual) that will allow us to learn how environmental degradation impacts different communities and how they respond through policies and actions. The model produced by this project will help state and local officials make informed decisions to plan for sustainable growth.
Linked socio-economic and environmental models demonstrate potential for forecasting scenarios of how human behaviors may drive changes in transportation, land use, water quality, and ultimately living resources (e.g., fish habitat and seagrass growth). However, the human responses to changes in environmental quality have not been sufficiently accounted for in most models. This project develops a coupled modeling system to study the complex interrelationships among socio-economic activity, transportation, land use, land cover, and water quality with feedbacks between the human social-economic system and the environmental system. Through the development of a model that links social-economic and environmental models with two-way feedbacks between them, it will be possible to simulate and forecast how human behavior impacts nutrient loading and water quality in Chesapeake Bay and how, in turn, the failure to meet water quality standards feedback to influence human behavior and decision-making.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
