Environmental Impact Assessment (EIA) is a mandatory decision-support tool in every country of the world, developed 50 years ago to ensure the consideration of the environmental consequences of development decisions prior to approval decisions being made. Specifically, the aim of developing an EIA system was to make decision-making affecting the environment more accountable, through the use of objective scientific evidence. It remains the project decision-support tool of choice despite considerable research efforts failing to provide convincing evidence that it achieves this aim. Here we explain this apparent paradox by arguing that EIA supports neoliberal agendas by facilitating economic development. We present arguments based on a neo-Gramscian perspective that explains how apparent advances in the practice of EIA are sanctioned because they actually maintain the political nature of the EIA system, which continues to undermine attempts to use evidence objectively. We use a narratives of change approach to support this perspective. We conclude that EIA can only make decision-making more accountable if strategies to depoliticise it are employed, including emphasising substantive outcomes.

Quality is much sought after in, and a basic foundation for, good impact assessment (IA). However, the term is rarely defined, has an uncertain relationship with IA effectiveness, and it means different things to different stakeholders, which can lead to debates over the legitimacy associated with an IA process. Thus, IA quality needs conceptualising to position research and practice within broader understandings. This paper contributes to this conceptualisation by identifying nine dimensions of quality through a process of literature review drawing on three fields of study in which quality and quality management have already been debated and conceptualised: education; health care; and business. This approach sidesteps the plural views on quality existing within the field of IA itself which might otherwise bias the identification of quality dimensions. We therefore propose that the dimensions of IA quality are: Efficiency; Optimacy; Conformance; Legitimacy; Equity; Capacity Maintenance; Transformative Capacity; and Quality Management. A literature review of IA research and practice confirms the relevance of the identified quality dimensions to IA. We identify, to an extent, the relationship between quality and effectiveness. Quality aligns with procedural and transactive effectiveness, partly aligns with normative effectiveness and is distinct from, but helps to deliver, substantive effectiveness

Biodiversity is under significant threat globally and therefore the biodiversity input to environmental impact assessment (EIA) is important. The quality of biodiversity inputs needs to be high if biodiversity is to be protected, especially in areas with high biodiversity value. Here, we follow-up quality reviews of biodiversity inputs to EIA reports, through interviews with the biodiversity specialists who authored the biodiversity inputs, in order to find explanations for the quality results. This is the first quality review research to systematically engage with biodiversity specialists in this way. The biodiversity specialists highlighted professional registration as a key factor supporting strengths around professional conduct and gathering of baseline information. Weaknesses identified relate to review areas dealing with alternatives, public participation, prediction, as well as management actions and monitoring arrangements, which seem to be the result of a lack of understanding and/or agreement on the role of the biodiversity specialists in the EIA process. The research results suggest that ideally biodiversity inputs should not be seen as a one-off contribution but rather as an iterative contribution during different stages of the EIA process.

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