The purpose of this work is to carry out an accurate and extensive environmental analysis of the steel production occurring in in the largest integrated EU steel mill, located in the city of Taranto in southern Italy. The end goal is that of highlighting the steelworks’ main hot spots and identifying potential options for environmental improvement. The development for such an analysis is based on a Life Cycle Assessment (LCA) of steel production with a cradle to casting plant gate approach that covers the stages from raw material extraction to solid steel slab production. The inventory results have highlighted the large solid waste production, especially in terms of slag, which could be reused in other industries as secondary raw materials. Other reuses, in accordance with the circular economy paradigm, could encompass the energy waste involved in the steelmaking process. The most burdening lifecycle phases are the ones linked to blast furnace and coke oven operations. Specifically, the impact categories are influenced by the energy consumption and also by the toxicity of the emissions associated with the lifecycle of steel production. A detailed analysis of the toxicity impacts indicates that LCA is still not perfectly suitable for toxicity assessments and should be coupled with other more site specific studies in order to understand such aspects fully. Overall, the results represent a first step to understanding the current levels of sustainability of the steelworks, which should be used as a starting point for the development both of pollution control measures and of symbiotic waste reutilization scenarios needed to maintain the competitiveness of the industrial plant.

Purpose: For the development of any life cycle assessment study, the practitioner frequently integrates primary data collected on-field, with background data taken from various life cycle inventory databases which are part of most commercial LCA software packages. However, such data is often not generally applicable to all product systems since, especially concerning the agri-food sector, available datasets may not be fully representative of the site specificity of the food product under examination. In this context, the present work investigates the background, sources and methodological aspects that characterise the most known commercial databases containing agri-food data, with a focus on four agri-food supply chains (olive oil, wine, wheat products and citrus fruit), which represent an important asset for the Italian food sector. Methods: Specifically, the paper entails a review of currently available LCI databases and their datasets with a twofold scope: firstly, to understand how agri-food data is modelled in these databases for a coherent and consistent representation of regional scenarios and to verify whether they are also suitable for the Italian context and, secondly, to identify and analyse useful and relevant methodological approaches implemented in the existing LCI databases when regional data are modelled. Results: Based on the aforementioned review, it is possible to highlight some problems which may arise when developing an LCI pertaining to the four Italian agri-food supply chains, namely: 1. The need for specific inventory datasets to tackle the specificities of agri-food product systems. 2. The lack of datasets, within the existing DBs, related to the Italian context and to the abovementioned supply chains. In fact, at present, in the currently available LCI DBs, there are very few (or in some cases none) datasets related to Italian wine, olive oil, wheat-based products and citrus fruit. The few available datasets often contain some data related to the Italian context but also approximate data with that of product systems representing other countries. Furthermore, the present study allowed to identify and discuss the main aspects to be used as starting elements for modelling regional data to be included in a future Italian LCI database of the abovementioned four supply chains. Conclusions: The results of the present study represent a starting point for the collection of data and its organisation, in order to develop an Italian LCI agri-food database with datasets which are representative of the regional specificities of four agri-food supply chains which play an important role in the Italian economy.