DSpace Collection:
https://hdl.handle.net/10171/53623
2024-03-29T02:19:32ZTheorizing the principles of sustainable production in the context of circular economy and industry 4.0
https://hdl.handle.net/10171/65804
Title: Theorizing the principles of sustainable production in the context of circular economy and industry 4.0
Abstract: The concept of Sustainable Production is evolving with changes triggered by the emergence of new economic and
industrial models such as Circular Economy and Industry 4.0. However, most studies that currently link these
concepts are based on the principles of Sustainable Production defined 20 years ago. Therefore, the primary
aim of this study is to redefine the principles that should govern Sustainable Production operations in the tran-
sition towards a Circular Economy and smart industry models. To this end, an initial proposal of 11 principles
was shared with 11 world-class experts (academics and practitioners) and a consensus proposal was sought
through a Delphi Panel. Ten principles emerged from this study, which were evaluated by experts according to
criteria of significance, parsimony, semantic consistency and empirical adequacy. Additionally, to study the rela-
tionships between the ten principles, the Interpretative Structural Model (ISM) technique was applied. The ISM
technique identified which principles are independent of or dependent on each other and established relation-
ships between the principles. The findings suggest that Principle 5 (“Prioritize employees' well-being”), Principle
6 (“Enhance management commitment to sustainability”), Principle 9 (“Measure and optimize sustainable pro-
cesses”) and Principle 10 (“Boost the use of sustainable technologies”) help to establish an ideal context to enhance
the development of the rest of the principles that characterize Sustainable Production. The presentation of the ten
principles opens new possibilities for researchers while helping managers to better understand sustainability in
terms of production and, therefore contribute to achieving SDG 12.2022-01-01T00:00:00ZAre cities aware enough? A framework for developing city awareness to climate change
https://hdl.handle.net/10171/65283
Title: Are cities aware enough? A framework for developing city awareness to climate change
Abstract: Cities are growing and becoming more complex, and as they continue to do so, their capacity to deal with foreseen and unforeseen challenges derived from climate change has to adapt accordingly. In the last decade, an effort has been made to build city resilience and improve cities’ capacity to respond to, recover from and adapt to climate change. However, certain city stakeholders’ lack of proactive behavior has resulted in less effective city resilience-building strategies. In this sense, the importance of developing stakeholders’ awareness of climate change in order to ensure proactivity is documented in the literature. However, there is a lack of studies that define how, when and what should be done to develop stakeholders’ climate change awareness at a city scale. This paper presents a framework to develop stakeholders climate change awareness as a result of a systematic literature review and a co-creation process with the participation of 47 experts through a focus group and a Delphi study. The framework defines a four-step process and includes nine policies that seek to develop stakeholders’ climate change awareness. The framework concludes determining the responsibilities of each stakeholder by defining the policies they should implement, and the effect one policy might cause on other stakeholders and among policies.2020-01-01T00:00:00ZA new mindset for circular economy strategies: case studies of circularity in the use of water
https://hdl.handle.net/10171/64960
Title: A new mindset for circular economy strategies: case studies of circularity in the use of water
Abstract: In a circular economy (CE) environment, it is important to make good and efficient use of resources and consider that the waste generated in production processes can be a valuable resource. However, the tools and methodologies conventionally used to analyze and evaluate production systems are based on techniques focused on linear production management models, where the primary purpose is to reduce the treatment and management of waste as much as possible and where productive and environmental efficiency are not evaluated simultaneously. Changing the paradigm from a linear to a circular economy requires that a new strategy for production systems be defined, one that makes production processes simultaneously circular and efficient (in terms of quality and productivity). In this context, a holistic vision is needed when implementing CE strategies. Therefore, the main aim of this paper is to provide evidence, through two real case studies in the use of water, that the management of this resource without considering systemic thinking may not be the most circular solution. Main results showed that improvements based on the traditional approach of reducing resource use cannot provide the best results if they are supported only by current process consumption without considering the circularity of resources.2020-01-01T00:00:00ZRobust active learning with binary responses
https://hdl.handle.net/10171/63880
Title: Robust active learning with binary responses
Abstract: We introduce a method of Robust Learning (‘robl’) for binary data, and propose its use in situations where Active Learning is appropriate, and where sampling the predictors is easy and cheap, but learning the responses is hard and expensive. We seek robustness against both modelling errors and the mislabelling of the binary responses. Thus we aim to sample effectively from the population of predictors, and learn the responses only for an ‘influential’ sub-population. This is carried out by probability weighted sampling, for which we derive optimal ‘unbiased’ sampling weights, and weighted likelihood estimation, for which we also derive optimal estimation weights. The robustness issues can lead to biased estimates and classifiers; it is somewhat remarkable that our weights eliminate the mean of the bias – which is a random variable as a result of the sampling – due to both types of errors mentioned above. These weights are then tailored to minimize the mean squared error of the predicted values. Simulation studies indicate that when bias is of significant concern, robl allows for substantial reductions, relative to Passive Learning, in the prediction errors. The methods are then illustrated in real-data
analyses.2022-01-01T00:00:00Z