(562d) Statistical Analysis of Global Environmental Impact Patterns Using a World Multi-Regional Input Output Database

Authors: 
Pascual-González, J., Universitat Rovira i Virgili
Guillén-Gosálbez, G., University Rovira i Virgili
Jiménez Esteller, L., Universitat Rovira i Virgili
Mateo-Sanz, J. M., Universitat Rovira i Virgili

In the recent past, the study of the international channels through which impacts are traded at a global scale has gained wider interest. In this context, environmentally extended multi-regional input-output tables (EEMRIO) are widely used to assess the impact of economic activities on the environment.These models are a valuable tool to attribute pollution or resources depletion to the final demand of a product or service following a consistent approach, which makes them very useful in the development of environmental policies.

A key point in their use and, more generally, in the area of environmental engineering, concerns how the environmental performance is assessed. A plethora of environmental indicators are available for quantifying the anthropogenic damage in different categories. Among them, those based on Life Cycle Assessment (LCA) principles have recently become the prevalent approach. Many methods for impact assessment have been presented so far (in general), and some of them have been incorporated into IO models (in a more specific field). In contrast, the relationships between impacts in different damage categories are poorly understood at both, the local and global scales. Particularly, at a global scale, little effort has been devoted to the study of the environmental impact patterns of nations. The analysis of these global impact fingerprints, however, could assist in the development of more effective environmental policies in several ways.

In this work, we apply a multivariate statistical analysis to data retrieved from a multi-regional input-output (IO) table covering 69 environmental indicators classified into 5 main categories: energy, emissions, material, water and land; and 41 countries.

We find that damages in different categories and also within the same one are highly correlated. In addition, our statistical analysis reveals that countries show a very similar environmental impact pattern. While the environmental fingerprint of nations is similar, the intensity is different depending on the level of development. Our findings might help to develop more effective environmental regulations.