In order to improve environmental performance, the participation of effective and transparent citizens and governments that help counteract corruption in environmental matters is crucial. In this sense, this work focuses on exploring relationships between e-participation, e-government, the corruption index and environmental performance indicators. To this end, a sample comprising 116 countries from varying geographic regions is used in conjunction with indicators of environmental performance, e-participation, e-government and the corruption index. Through the use of the HJ-biplot and STATIS multivariate statistical techniques, it will be possible to observe the role that these variables play in countries’ behavioural patterns with respect to environmental performance. The results show a correlation between the indicator ‘perception of corruption’ and environmental performance; therefore, the lower the level of corruption, the higher the environmental performance index. We conclude that countries that exhibit more e-participation, lower levels of corruption and better level income are more likely to follow policies and programmes aimed at achieving better environmental performance.

This paper aims to categorize countries by their e-participation index, according to political, capacity, and governmental environment factors; examine how they are projected based on these factors; and analyze whether this projection corresponds to the current state of e-participation development. It is the first study to provide an overview of the e-participation level using multivariate analysis techniques for three-way data analysis, specifically, the X-STATIS methodology and cluster analysis. These techniques enable the simultaneous representation of countries, factors, conditions, trajectories, and groupings, taking into account national conditions in the evolution of e-participation from 2008 to 2016. The results show that when the conditions of each country interact with the level of e-participation development, and depending on the economic development, 7% of countries are lagging behind in e-participation evolution, given their institutional and political capacity. This delay is particularly relevant in countries that enjoy a higher level of socioeconomic status. Meanwhile, 38% are above the level they would correspond to.

Multi-set multivariate data analysis methods provide a way to analyze a series of tables together. In particular, the STATIS-dual method is applied in data tables where individuals can vary from one table to another, but the variables that are analyzed remain fixed. However, when you have a large number of variables or indicators, interpretation through traditional multiple-set methods is complex. For this reason, in this paper, a new methodology is proposed, which we have called Sparse STATIS-dual. This implements the elastic net penalty technique which seeks to retain the most important variables of the model and obtain more precise and interpretable results. As a complement to the new methodology and to materialize its application to data tables with fixed variables, a package is created in the R programming language, under the name Sparse STATIS-dual. Finally, an application to real data is presented and a comparison of results is made between the STATIS-dual and the Sparse STATIS-dual. The proposed method improves the informative capacity of the data and offers more easily interpretable solutions.