Envisioning green solutions for reducing the ecological footprint of a university campus

This paper aims to report strategies towards a green campus project at Politecnico di Torino University, a 33,000-students Italian higher education institution (HEI), and estimate the avoided ecological footprint (EF) of different scenarios accounted for open spaces. Design/methodology/approach A consumption-based study has been developed to analyse the current EF of the main campus site. Data were collected from different departments and administrative units to identify the measure of the pressure exerted by the campus activities on the ecosystem. Then, possible scenarios were accounted for open spaces along five different design layers: energy, water, landscape, food and mobility. Acting on the spaces by means of biophilic design and user-driven design requires complex considerations on university’s anticipated future needs and a wide-ranging evaluation of the most appropriate pathways forward according to all university stakeholders, far beyond the mere accounting of avoided EF. Findings A reduction of the 21 per cent of the current EF can be achieved through the solutions envisaged in the green campus project along the open space layers. Moreover, universities have the opportunity to not only improve the sustainability of their facilities but also demonstrate how the built environment can be designed to benefit both the environment and the occupants. Research limitations/implications The acknowledgement of predicted behavioural change effects is a question left open to further researchers on methods and indicators for social impact accounting and reporting in truly sustainable university campuses. Originality/value This is the first research that estimates the EF of an Italian HEI. The research represents also an innovative approach integrating the EF reduction scenarios in the design process of the new masterplan of open spaces, trying to identify the connection between environmental impact reduction and improvement in users’ perception

Greening information management: final report

As the recent JISC report on ‘the ‘greening’ of ICT in education [1] highlights, the increasing reliance on ICT to underpin the business functions of higher education institutions has a heavy environmental impact, due mainly to the consumption of electricity to run computers and to cool data centres. While work is already under way to investigate how more energy efficient ICT can be introduced, to date there has been much less focus on the potential environmental benefits to be accrued from reducing the demand ‘at source’ through better data and information management. JISC thus commissioned the University of Strathclyde to undertake a study to gather evidence that establishes the efficacy of using information management options as components of Green ICT strategies within UK Higher Education environments, and to highlight existing practices which have the potential for wider replication

Sustainable consumption: towards action and impact. : International scientific conference November 6th-8th 2011, Hamburg - European Green Capital 2011, Germany: abstract volume

This volume contains the abstracts of all oral and poster presentations of the international scientific conference „Sustainable Consumption – Towards Action and Impact“ held in Hamburg (Germany) on November 6th-8th 2011. This unique conference aims to promote a comprehensive academic discourse on issues concerning sustainable consumption and brings together scholars from a wide range of academic disciplines. In modern societies, private consumption is a multifaceted and ambivalent phenomenon: it is a ubiquitous social practice and an economic driving force, yet at the same time, its consequences are in conflict with important social and environmental sustainability goals. Finding paths towards “sustainable consumption” has therefore become a major political issue. In order to properly understand the challenge of “sustainable consumption”, identify unsustainable patterns of consumption and bring forward the necessary innovations, a collaborative effort of researchers from different disciplines is needed

Carbon footprint assessment tool for universities: CO2UNV

Universities, as organisations engaged in education, research and community services, play an important role in promoting sustainability and should be an example of a sustainable organisation. The Carbon Foot- print (CF) is a very useful decision-making tool that allows organisations to measure and communicate the effect of their activities on the environment. To do so, it is necessary to have tools capable of calcu- lating, tracking and reporting their greenhouse gas (GHG) emissions, as well as guiding the actions for reducing and offsetting them. The aim of this article is to present a tool specifically designed to calcu- late the carbon footprint of universities, called CO2UNV. This tool is able to quantify the CO 2 equivalent (CO 2 e) emissions for scopes 1 (direct GHG emissions), 2 (electricity indirect GHG emissions) and 3 (other indirect GHG emissions), for a university as a whole and for the different buildings/units that it is made up of. It includes, by default, the typical emission sources of an education centre and their corresponding emission factors. However, it is totally adaptable to any other type of organisation thanks to the possibil- ity of including new emission sources and of updating all the emission factors (by default and new). It is also capable of evaluating the evolution of the CF over time, and the CO 2 e offsets achie ved by contribut- ing to offset projects. The results report includes input data and the graphical representation of results. Finally, CO2UNV is applied to calculate and offset the CF of the Universitat Jaume I (Spain), and the study concludes with its validation according to applicability and accuracy criteria.Funding for open access charge: CRUE-Universitat Jaume

A greenability evaluation sheet for AI-based systems

El auge de los sistemas de machine learning (ML), la mejora de sus capacidades y el mayor tamaño de los sistemas, ha incrementado el impacto medioambiental de los modelos ML. Sin embargo, la información sobre cómo se mide, comunica y evalúa la huella de carbono de los modelos de ML es escasa. Este proyecto, basado en un análisis de 1.417 modelos de ML y conjuntos de datos asociados en Hugging Face, el repositorio más popular para modelos de ML preentrenados, tiene como objetivo proporcionar una solución integrada para comprender, informar y optimizar la eficiencia de carbono de los modelos de ML. Además, implementamos una aplicación web que genera etiquetas de eficiencia energética para modelos de ML y permite visualizar sus emisiones de carbono. Con menos del 1% de los modelos en Hugging Face proporcionando información sobre las emisiones de carbono, el proyecto subraya la necesidad de mejorar las prácticas de reporte energético y la promoción del desarrollo de modelos eficientes en carbono dentro de la comunidad Hugging Face. Para abordar esta cuestión, ofrecemos una herramienta web que produce etiquetas de eficiencia energética para modelos de ML, una contribución que fomenta la transparencia y el desarrollo de modelos sostenibles dentro de la comunidad de ML. Permite la creación de etiquetas energéticas, al tiempo que proporciona valiosas visualizaciones de los datos de emisiones de carbono. Esta solución integrada constituye un paso importante hacia prácticas de IA más sostenibles medioambientalmente.The rise of machine learning (ML) systems has increased their environmental impact due to the enhanced capabilities and larger model sizes. However, information about how the carbon footprint of ML models is measured, reported, and evaluated remains scarce and scattered. Aims: This project, based on an analysis of 1,417 ML models and associated datasets on Hugging Face, the most popular repository for pretrained ML models, aims to provide an integrated solution for understanding, reporting, and optimizing the carbon efficiency of ML models. Moreover, we implement a web-based application that generates energy efficiency labels for ML models and visualizes their carbon emissions. With less than 1% of models on Hugging Face currently reporting carbon emissions, the project underscores the need for improved energy reporting practices and the promotion of carbon-efficient model development within the Hugging Face community. To address this, we offer a web-based tool that produces energy efficiency labels for ML models, a contribution that encourages transparency and sustainable model development within the ML community. It enables the creation of the energy labels, while also providing valuable visualizations of carbon emissions data. This integrated solution serves as an important step towards more environmentally sustainable AI practices

Reconsidering the calculation and role of environmental footprints

Following the recent Copenhagen Climate Change conference, there has been discussion of the methods and underlying principles that inform climate change targets. Climate change targets following the Kyoto Protocol are broadly based on a production accounting principle (PAP). This approach focuses on emissions produced within given geographical boundaries. An alternative approach is a consumption accounting principle (CAP), where the focus is on emissions produced globally to meet consumption demand within the national (or regional) economy1. Increasingly popular environmental footprint measures, including ecological and carbon footprints, attempt to measure environmental impacts based on CAP methods. The perception that human consumption decisions lie at the heart of the climate change problem is the impetus driving pressure on policymakers for a more widespread use of CAP measures. At a global level of course, emissions accounted for under the production and consumption accounting principles would be equal. It is international trade that leads to differences in emissions under the two principles. This paper, the second in this special issue of the Fraser Commentary, examines how input-output accounting techniques may be applied to examine pollution generation under both of these accounting principles, focussing on waste and carbon generation in the Welsh economy as a case study. However, we take a different focus, arguing that the ‘domestic technology assumption’, taken as something of a mid-point in moving between production and consumption accounting in the first paper, may actually constitute a more useful focus for regional policymakers than full footprint analyses

The electricity generation mix in Scotland : the long and windy road?

This article reports on research funded by the Engineering and Physical Sciences Research Council (EPSRC) at the University of Strathclyde