Purpose: – The purpose of this paper is to propose a generic model of Integrated Management System of Quality, Environment and Safety (IMS-QES) that can be adapted and progressively to assimilate various Management Systems, of which highlights: ISO 9001 for Quality; ISO 14001 for Environment; OHSAS 18001 for Occupational Health and Safety. Design/methodology/approach: – The model was designed in the real environment of a Portuguese Organization and 160 employees were surveyed. The rate response was equal to 86 percent. The conceived model was implemented in a first phase for the integration of Quality, Environment and Safety Management Systems. Findings: – Among the main findings of the survey the paper highlights: the elimination of conflicts between individual systems with resources optimization; creation of added value to the business by eliminating several types of wastes; the integrated management of sustainability components in a global market; the improvement of partnerships with suppliers of goods and services; reducing the number of internal and external audits. Originality/value: – This case study is one of the first Portuguese empirical researches about IMS-QES and the paper believes that it can be useful in the creation of a Portuguese guideline for integration, namely the Quality Management Systems; Environmental Management Systems and Occupational Health and Safety Management Systems among others.

This paper presents and explores the different Earth Observation approaches and their contribution to the achievement of United Nations Sustainable Development Goals. A review on the Sustainable Development concept and its goals is presented followed by Earth Observation approaches relevant to this field, giving special attention to the contribution of Machine Learning methods and algorithms as well as their potential and capabilities to support the achievement of Sustainable Development Goals. Overall, it is observed that Earth Observation plays a key role in monitoring the Sustainable Development Goals given its cost-effectiveness pertaining to data acquisition on all scales and information richness. Despite the success of Machine Learning upon Earth Observation data analysis, it is observed that performance is heavily dependent on the ability to extract and synthesise characteristics from data. Hence, a deeper and effective analysis of the available data is required to identify the strongest features and, hence, the key factors pertaining to Sustainable Development. Overall, this research provides a deeper understanding on the relation between Sustainable Development, Earth Observation and Machine Learning, and how these can support the Sustainable Development of countries and the means to find their correlations. In pursuing the Sustainable Development Goals, given the relevance and growing amount of data generated through Earth Observation, it is concluded that there is an increased need for new methods and techniques strongly suggesting the use of new Machine Learning techniques.