(254ag) Environmental and Economic Efficiency Assessment of EU Manufacturing Sectors Via Input-Output Tables and Data Envelopment Analysis

Authors: 
Guillén-Gosálbez, G., Imperial College of Science, Technology and Medicine
Zurano-Cervelló, P., Universitat Rovira i Virgili
Mateo-Sanz, J. M., Universitat Rovira i Virgili
Jiménez, L., Universitat Rovira i Virgili
The EU manufacturing sectors contribute significantly to its environmental footprint in a wide variety of categories, including global warming. Given the urgent need to reduce the impact of human activities in order to avoid exceeding the planetary boundaries, it is imperative to identify among these sectors the ones that contribute significantly towards the total impact but marginally in terms of economic wealth, as this information can assist in the design of more effective environmental regulations. Unfortunately, in todayâ??s globalized market, trade activities between sectors and countries makes it difficult to estimate their â??trueâ? footprint, as a wide variety of impacts are embodied in the goods and services exchanged between them.

In this article, the manufacturing EU sectors are assessed according to their impact in the global warming, acidification, and tropospheric ozone formation potential damage categories, and considering as well their contribution towards the EUâ??s gross domestic product. To integrate all these metrics into a single meaningful score and facilitate their benchmark, we follow an approach that combines environmentally-extended multi-regional input-output tables (EEMRIO) and data envelopment analysis (DEA). EEMRIO tables are used to determine the production-based and consumption-based impact of each sector, while DEA is employed to identify efficient and inefficient sectors and establish improvement targets for those found to be inefficient.

A preliminary analysis is first carried out to identify the most polluting sectors and determine net flows of different impacts exported and imported via trade. The DEA linear programming models are next run in order to identify efficient and inefficient sectors and define targets for the latter through projection onto the efficient frontier that, if achieved, would make them efficient. Numerical results show that the most inefficient sectors are those related with minerals, metals, chemicals and petroleum products. Our approach provides valuable insight for public policy makers that seek to reduce anthropogenic impacts without sacrificing the competitiveness of the economic sectors.