A spatial causal network approach for multi-stressor risk analysis and mapping for environmental impact assessments

Abstract

Environmental impact assessment (EIA) relies on rigorous scientific assessment of all potential causal pathways by which large-scale developments may impact on valued assets in a region. Despite their importance to informed decision-making, many EIAs are flawed by incomplete analysis of causal pathways, limited spatial assessment and a lack of transparency about how risks have been evaluated across the region.To address these, we describe an EIA methodology based on network analysis of potential causal pathways in a given region. This network approach is coupled with a systematic evaluation of the likelihood, consequence and mitigation options for each causal pathway from one or more human activities to multiple valued assets. The method includes analysis of the confidence in these evaluations, recognizing where knowledge gaps constrain assessments of risks to particular assets.The causal network approach is complemented by a spatially explicit analysis of the region that allows residual risk (i.e. risk remaining after all feasible mitigations) to be mapped for all valued assets. This identifies which activities could lead to potential impacts of varying concern (rated from ‘very low’ to ‘very high’), their likely pathways, which valued assets are at risk and where these residual risks are greatest. The output maps reveal ‘risk hotspots’ where more detailed local-scale assessments and monitoring should focus.The method is demonstrated by application to potential impacts on 8 valued assets (aquifers, ecosystems and protected species) due to unconventional gas resource development in the Cooper Basin, central Australia. Results show which activities and causal pathways are of potential concern to different valued assets and where residual risk is greatest for particular species and ecosystems.This spatial causal network provides a systematic, consistent and transparent assessment of potential impacts, improving the quality of decision-making about planned developments and their environmental risks.