Multifunctional Analysis of Spatially Targeted Environmental Policy

Abstract

Growing tensions between economic priorities and protection of nature highlight the importance of cost-effective environmental policies. Amidst mounting climate impacts and higher inflation, policymakers around the world are working to meet environmental objectives while limiting the burden on taxpayers. There are important spatial dimensions to many critical environmental problems, including air pollution, flooding, and pollinator declines. This thesis demonstrates that adverse incentives may jeopardise the effectiveness of environmental policy when geographic conditions allow firms to export pollutants beyond the regulator’s jurisdiction.
Using a custom air pollution dispersion model, this work calculates the interstate SO2 pollution from coal-fired power plants across the United States between 1997 and 2020. It exploits a natural experiment to show that firms exporting pollutants beyond the regulator’s jurisdiction respond less to a tightening of emission caps.
The following research explores so-called spatially targeted policies that seek to account for heterogeneous policy impacts in different geographies.
The focus of this thesis is environmental land management (ELM) schemes that compensate farms to retire cultivated land. It advances a novel multifunctional cost-effectiveness analysis of hypothetical schemes by combining cost estimates via discrete choice experiments (DCEs) with benefit estimates from hydrological and ecological models.
This thesis shows that tradable and spatially targeted ELM contracts are likely to deliver measurable improvements in both natural flood management and pollinator services. In addition, simulating multiple spatial configurations of ELM features illustrates how small, evenly distributed natural features may cost-effectively circumvent coordination costs among farms.
This thesis demonstrates the value in integrating hypothetical DCEs with spatial simulation models.