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Integrating Multilevel Adaptive Models to Develop Systematic, Transparent, and Participatory EIA Practice

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Data Science and Intelligent Systems (CoMeSySo 2021)


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 the 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. This paper aims to use Multilevel Network Reification to create Higher-order Adaptive Network Models. The results of 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 aim to understand the roles of each actor and the connections established in different EIA networks. The 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.

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AN and LR were supported by a grant of the Romanian National Authority for Scientific Research (, PN-III-P1-1.1-TE-2019-1039.

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Correspondence to Andreea Nita .

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Appendix: Specification of the Adaptive Model by Role Matrices

Appendix: Specification of the Adaptive Model by Role Matrices

In this section, the full specification of the model is provided in terms of role matrices, which is the standard format used for design of a model and which can also be used as input for the software environment. Each role matrix has three color blocks that match the colors in the graphical representation used earlier: pink for the base level, blue for the first-order self-model states and purple for the second-order self-model states.

Connectivity Characteristics Role Matrices

The first role matrix mb (see Table 3) represents the base connections between all the states, as presented in the graphical representation in Fig. 1. For example, row 4 with state NGO (also indicated by X4) has five incoming connections from X4, X1, X2, X7, X9, which are NGO itself, D, PA, LP, and ME. Role matrix mcw (see Table 4) shows the weights ω of the connections presented in role matrix mb. There is a difference in nonadaptive (green) and adaptive connections (peach-red). For example, in row 2 for state PA with ten incoming connections, there are two non-adaptive connection weights with vales (0.5 for the connection from X1 and 1 for the connection from X21, as can be seen in role matrix mb) and 8 adaptive connection weights. The latter 8 ones are represented by states X13 (i.e., WD,PA, a first-order self-model state), and similarly, the other self-model states X14 to X20.

Table 3. Role matrix mb for base connectivity.

Aggregation Characteristics Role Matrices

Role matrix mcfw (see Table 5) specifies which combination function is used for each state. For example, states X1 to X10 and X21 use the combination function alogistic, which is indicated by a combination function weight γ of 1 in the first column. Role matrix mcfp (see Table 6) defines the exact parameters used for each state and function.

Table 4. Role matrix mcw for base connectivity.
Table 5. Role matrix mcfw for combination function weights.
Table 6. Role matrix mcfp for combination function weights.

Timing Characteristics Role Matrix and Initial Values

The speed factors ηY are specified in role matrix ms; see Table 7.

Table 7. Role matrix ms for speed factors.

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Nita, A., Treur, J., Rozylowicz, L. (2021). Integrating Multilevel Adaptive Models to Develop Systematic, Transparent, and Participatory EIA Practice. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Data Science and Intelligent Systems. CoMeSySo 2021. Lecture Notes in Networks and Systems, vol 231. Springer, Cham.

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