European Directive 85/337/EC introduced Environmental Impact Assessment (EIA) as a process governed by administrative rules with the aim of reducing environmental degradation and associated health problems generated by projects. Generally, the EIA process involves analyses and evaluations of the potential impacts that human activities may have on the environment, by considering approaches such as the precautionary principle, prevention of conflicts, loss of natural resources, and environmental degradation. EIA influences decision-making at local, national, and transboundary levels, with the following overall objectives: potentially screen out environmentally harmful projects, predict significant adverse impacts, suggest measures to reduce or prevent major impacts, identify feasible alternatives, and engage communities or individuals potentially affected by the implementation of the project. Several issues obstructing proper implementation of the EIA process are common in developing countries: low quality of assessment reports, lack of public participation, insufficient equipment, and trained staff, inadequate institutional framework, and low cooperation between policymakers, researchers, and stakeholders. The number of research studies focused on the investigation of EIA collaboration process through network analysis and multilevel adaptive models is worryingly limited, considering that the implementation of EIA procedures is deficient in most developing countries, and the contribution of science that envisages the collaboration between the actors involved to the process is minor at best. The aim of this paper is to use Multilevel Network Reification to create Higher-order Adaptive Network Models. The results of our multilevel network analysis will contribute to reshape impact assessment procedures and create opportunities for better communication and transparency between practitioners, researchers, policymakers, and other stakeholders. Therefore, integrating Multilevel Adaptive Models in EIA helps to raise the policy efficiency and define the dynamic interplay between EIA actors and diagnose the organizational structures that strongly influence this procedure. Thus, by using an adaptive computational network model we try to understand the roles of each actor, and the connections established in different EIA networks. Our findings will provide innovative information to find solutions and design a collaborative EIA procedure to improve projects under evaluation considering the current threats to society and the environment.
|Title of host publication||Proc. of the 5th International Conference on Computational Methods in Systems and Software, CoMeSySo'21|
|Publisher||Springer Nature Switzerland AG|
|Publication status||Published - 3 Aug 2021|
|Name||Lecture Notes in Networks and Systems|