Association for Public Policy Analysis & Management

A major challenge in environmental governance is managing the tradeoffs inherent in major infrastructure projects. The complex dependencies between water, energy, and food infrastructure pose political and social tradeoffs that extend beyond mechanistic engineering considerations, reflecting judgments about the value of public and private goods. These values emerge during infrastructure planning processes, when stakeholders are invited to provide input on proposed projects. In theory, stakeholder involvement should shape what tradeoffs are deemed acceptable in the approved version of the project. However, the length of planning documents and the breadth of public comments makes systematic assessment of public values quite difficult, and the impact of these comments on the final distribution of cross-resource tradeoffs is unclear.

Our project focuses on the case of trade-offs identified during the environmental impact assessment process for water and energy projects across California. We use automated text mining approaches to systematize the process of identifying the distribution of impacts identified during the environmental impact assessment process, measuring the distribution of public sentiment about those tradeoffs voiced in written comments, and observing whether those values were associated with reduced tradeoffs between the draft and final assessments. We address two primary research questions: (1) What is the balance of social and political tradeoffs associated with water and energy infrastructure development and management?; and (2) How does stakeholder involvement--in terms of volume, intensity, and particular issues raised--drive changes in the trade-offs posed by infrastructure siting and management plans?

To aggregate social indicators that speak to key water-energy-food nexus tradeoffs and that may play a role in reducing negative side-effects associated with new infrastructure, this research applies text-mining approaches to documents produced in the siting and permitting of water capture and storage infrastructure, energy generation facilities, and irrigation projects. Our approach takes advantage of Environmental Impact Statement (EIS) administrative procedures--a draft EIS, followed by a public comment period, and subsequently a final EIS--to examine how responsive these analyses are to public preferences regarding water-energy-food nexus tradeoffs. We will first use natural language processing (NLP) to categorize projected impacts from these projects on related infrastructures by analyzing draft EIS. We will then use text mining to extract information on stakeholder preferences regarding system tradeoffs from written comments and minutes from public meetings. Finally, we will reapply POS tagging to the final EIS to re-categorize projected impacts and summarize how the revised project plan balances impacts on related infrastructures. By comparing impacts identified from the draft and final EIS, we will identify changes in the distribution of impacts and whether any trade-offs were reduced. Further, by linking these changes to tradeoffs identified in public comments and meeting minutes, we will assess how issues raised in comments and meetings drove tradeoff adjustments in the final EIS.