Carbon Footprint of IT-Services – A comparative Study of energy consumption for Offline and Online Storage Usage

This paper focusses on the Carbon Footprint of IT-Services (CFIS) by presenting a comparative study of energy consumption for Offline and Online Storage. We therefore conducted a case study with an IT-Service provider as well as experimental simulation of customer’s ICT hardware. Based on literature review, we initially present related work and describe underlying concepts e.g. Carbon Footprint of Products, Life Cycle Assessment (LCA) as well as ICT energy and performance measurement. The paper proposes a methodological framework for CFIS based on the phases of LCA. Geared towards the framework we present a comparison of ICT-related energy consumptions for Offline and Online Storage as well as allocation and calculation approaches. Finally, presented carbon footprint results are discussed in terms of limitations and further research directions. The CFIS is an inevitable step to advance Green IS/IT research, since it quantifies dependencies between IT-Services, ICT energy consumption and related greenhouse gas emissions

Evaluation of the carbon footprint of the Study and Information Centre of the University of Szeged

Measuring the CO2 emission to the atmosphere has become significantly important due to the monitoring demand of pollutant emission based on the directives of the Kyoto Protocol. The carbon quota system has created strict regulations for measuring the CO2 emission in certain industries, internalizing the negative external effect of pollution created by human activity. As the built infrastructure is responsible for 40% of CO2 emission, this study focuses on the evaluation of the carbon footprint of the Study and Information Centre, which is one of the largest and most frequently visited main buildings of the University of Szeged [1]. The data collection used for the evaluation was conducted in the first quarter of 2020 and contains information for all three scopes (fuel combustion, company vehicles, fugitive emission – purchased electricity, heat and steam – purchased goods and services, business travel, waste disposal, transportation, investments). In the process of data collection, the eating habits, selective waste collection and travelling methods were covered in a visitor/employee survey as well. The results highlighted in this paper will provide a basis for further carbon reduction investments, protocols and events held for shaping the visitors’ and employees’ consciousness after the COVID 19 pandemic. Keyword: Environmental impact, carbon footprint, Bilan Carbone, higher education, travel, meal

Comparative Analysis of Energy Intensity and Profitability in Emerging E-Grocery Retail Models

The supermarkets account for approximately 3% - 4% of the electricity consumption in the industrialised countries which makes them one of the main contributors to climate change. Food retail, similarly to other energy intensive industries requires deep changes that would reduce its negative environmental footprint. Online grocery (e-grocery), recording three-digit growth globally in the times of COVID-19 pandemic, has the potential to disrupt the market and bring opportunities for energy intensity reduction. Brick & mortar retailers adapting to this trend not only experience technical challenges with order fulfilment and last-mile logistics, but also they struggle to achieve profitability of e-grocery. Therefore, there is a need for guidance in this transformation. This thesis aims to help retailers choose the least energy intensive and profitable e-grocery configuration by comparing the emerging fulfilment models (in-store, omni store, dark store) and last-mile delivery options (click & collect, home delivery). The scope of this thesis includes a literature and market review, where an overview of e-grocery market, logistics, technologies, energy intensity and economics is given. The review is followed by methodology explaining the tools and assumptions used for analyses. Then, energy intensity analysis is performed where energy intensity per order is calculated using CyberMart software for three fulfilment models and two delivery options. After that, the operating costs and profitability of e-grocery models is analysed. Finally, the results from analyses are discussed, concluded and recommendations for the retailers are given. The results of this thesis suggest that e-grocery may indeed reduce energy intensity of food retail but the energy consumption has little impact on the operational costs of the e-grocers. Labour fulfilment and order delivery costs optimisation play the biggest role in achieving profitability of online retail. Thus, it is recommended that the retailers, along with the growing penetration of e-grocery, develop automated fulfilment and click & collect solutions that would reduce the operational costs and allow for an incremental, yet future-proof adaptation to the e-grocery revolutionStormarknaderna står för cirka 3% - 4% av elförbrukningen i de industrialiserade länderna, vilket gör dem till en av de främsta orsakerna till klimatförändringarna. Livsmedelsbutiker kräver, på liknande sätt som andra energikrävande industrier, stora förändringar som kan minska dess negativa miljöpåverkan. E-handel av livsmedel, som har registrerat tresiffrig tillväxt globalt under tiderna för COVID-19-pandemin, har potential att störa marknaden och ge möjligheter till minskning av energiintensitet. Traditionell verksamhet som anpassar sig till denna trend upplever inte bara tekniska utmaningar med orderhantering och leverans till slutkund, utan de kämpar också för att uppnå lönsamhet. Därför finns det behov av vägledning i denna omvandling. Denna avhandling syftar till att hjälpa återförsäljare att välja de minst energikrävande och lönsamma alternativen för e-handel av livsmedel genom att jämföra de nya modellerna för orderhantering (in-store, omni store, dark store) och leveransalternativ till slutkund (click & collect, hemleverans). Avhandlingen omfattar en litteratur- och marknadsstudie, där en överblick ges över e-handel av livsmedel, logistik, teknik, energiintensitet och ekonomi. Studien följs av ett avsnitt om metodik som förklarar verktyg och antaganden som används för analysen. Därefter utförs en analys av energiintensitet där energiintensitet per order beräknas med hjälp av programvaran CyberMart för tre modeller för orderhantering och två leveransalternativ. Därefter analyseras driftskostnaderna och lönsamheten för modeller för e-handel av livsmedel. Slutligen diskuteras resultaten från analyserna och rekommendationerna till detaljhandlarna presenteras. Resultaten av denna avhandling tyder på att e-handel av livsmedel verkligen kan minska energianvändningen i livsmedelsindustrin men energiförbrukningen har liten inverkan på driftskostnaderna för e-handlarna. Att optimera arbetet för orderhantering och kostnaden för leverans spelar störst roll för att uppnå lönsamhet för e-handeln. Det rekommenderas därför att detaljhandlarna, tillsammans med den växande andelen av e-handel, utvecklar automatiserad lösningar för orderhantering och click & collect som skulle minska driftskostnaderna och möjliggöra en stegvis, men ändå framtidssäker anpassning till e-handeln av livsmede

Corporate Social Responsibility in the Information Technology Industry

Information technology companies contribute to greenhouse gas emissions via data servers, office administrative operations, and manufacturing of hardware components. Corporate Social Responsibility programs are a voluntary method of tracking and reducing negative environmental impacts. The 2019 Corporate Social Responsibility data from four Bay Area (California, USA) information technology companies (Adobe, Cisco, Salesforce, and Nvidia) all listed in sustainable index funds were compared to determine best reporting standards. The data analysis also allowed for research into how carbon emissions are categorized and reported on, and which emissions companies pay attention to through Corporate Social Responsibility projects. The physical ownership responsibility for CO2 emissions are titled Scope 1 (company’s own emissions), Scope 2 (energy used in operations), and Scope 3 (suppliers and stakeholders’ emissions). Most of the company’s emissions come from Scope 3 operations, but most of the Corporate Social Responsibility projects address emissions in Scope 1. Without industrywide key performance indicators or standard reporting frameworks, sustainable actions cannot be easily compared. Adding regulatory requirements, such as those proposed by US Securities and Exchange Commission, will improve corporate CO2 reporting standardization and transparency, allowing for easier comparisons and highlighting scalable environmental improvements. Companies in the information technology industry can reduce their carbon emissions by improving energy efficiency at data centers, implementing sustainable software design, and prolonging the lifespan of hardware components by making products modular and repairable

The SusTEACH Methodology: Assessment of the environmental impacts of Higher Education Teaching Models and development of an Environmental Appraisal toolkit

Sustainable, low carbon Higher Education (HE) teaching systems are part of the carbon reduction strategies needed to meet the targets set for HE institutions. Significant changes in the use of Information and Communication Technologies (ICTs) have led to new methods in teaching and learning, blending conventional and ICT-based teaching models. Little is known about their environmental impacts. Few studies have considered the whole system carbon-based environmental impacts of different systems of delivering Higher Education. One notable exception was the Factor 10 Visions study ‘Towards Sustainable Higher Education’, which offered an exemplar methodology for conducting an environmental impact audit/assessment of HE courses/modules. Building on this, the SusTEACH project examined the transformative effect of ICTs on HE teaching models, and developed a methodology to assess the main carbon-based environmental impacts of HE courses/modules, and to provide estimations of the energy and carbon impacts associated with different HE teaching models. This methodology as outlined here has supported the SusTEACH project research analysis of over thirty HE courses and modules, and the SusTEACH toolkit available from OpenLearn Module: The environmental impact of teaching and learning

An assessment of the sustainability of E-fulfilment models for the delivery of fast moving consumer goods to the home

Online retail sales are growing rapidly and have captured a significant proportion of the retail market in many countries. Although companies are under mounting pressure to reduce their environmental impact, the environmental effect of the different online distribution strategies remains unclear. Most previous studies of this subject have only included partial effects and consequences. To enable a more holistic understanding, this study proposes a more inclusive framework of environmental assessment based on life cycle analysis. This was applied to fast moving consumer goods (FMCG). Previous studies have shown that the last mile delivery contributes significantly to the environmental impact of online retailing, mainly because of the nature of the home delivery operations, including narrow time windows and short order lead times. If consumers were to buy products online on a subscription basis and give the supplier more control over the replenishment process there might be less need for fast deliveries, creating opportunities to improve the efficiency of home deliveries and reduce their environmental impact. The study classified different forms of subscription arrangement, assessed their relative attractiveness to consumers and examined their likely impact on the supply chain. Consumer views on subscriptions were surveyed by means of focus group discussions and interviews. To assess the likely supply chain impacts of subscriptions, the literature on vendor-managed inventory was consulted. A Life-Cycle Assessment (LCA) model was built to quantify and compare the environmental impact of various e-fulfilment models for FMCG products in the United Kingdom. This study reveals that the method of execution have a large influence on the environmental impact. In store-based retailing, the energy consumption within the supermarket is a significant contributor to the total greenhouse gas emissions. On the other hand, some forms of home delivery, involving for example the use of parcel networks with no pre-agreed time-slots and relatively high rates of delivery failure and customer collection, are also carbon-intensive. This contribution of consumer trips to the total footprint is much smaller in case of van-based deliveries where pre-agreed time-windows are used. Regardless of the business model, the total carbon footprint per item depends heavily on the number of items per delivery. Consequently, companies or consumers looking to decrease the environmental impact of online shopping should maximise the number of items per delivery. The study concludes with an assessment of the strengths, weaknesses and possible environmental improvements of each of the efulfilment methods, taking account of the possible role of subscriptions

Energy sustainability of next generation cellular networks through learning techniques

The trend for the next generation of cellular network, the Fifth Generation (5G), predicts a 1000x increase in the capacity demand with respect to 4G, which leads to new infrastructure deployments. To this respect, it is estimated that the energy consumption of ICT might reach the 51% of global electricity production by 2030, mainly due to mobile networks and services. Consequently, the cost of energy may also become predominant in the operative expenses of a Mobile Network Operator (MNO). Therefore, an efficient control of the energy consumption in 5G networks is not only desirable but essential. In fact, the energy sustainability is one of the pillars in the design of the next generation cellular networks. In the last decade, the research community has been paying close attention to the Energy Efficiency (EE) of the radio communication networks, with particular care on the dynamic switch ON/OFF of the Base Stations (BSs). Besides, 5G architectures will introduce the Heterogeneous Network (HetNet) paradigm, where Small BSs (SBSs) are deployed to assist the standard macro BS for satisfying the high traffic demand and reducing the impact on the energy consumption. However, only with the introduction of Energy Harvesting (EH) capabilities the networks might reach the needed energy savings for mitigating both the high costs and the environmental impact. In the case of HetNets with EH capabilities, the erratic and intermittent nature of renewable energy sources has to be considered, which entails some additional complexity. Solar energy has been chosen as reference EH source due to its widespread adoption and its high efficiency in terms of energy produced compared to its costs. To this end, in the first part of the thesis, a harvested solar energy model has been presented based on accurate stochastic Markov processes for the description of the energy scavenged by outdoor solar sources. The typical HetNet scenario involves dense deployments with a high level of flexibility, which suggests the usage of distributed control systems rather than centralized, where the scalability can become rapidly a bottleneck. For this reason, in the second part of the thesis, we propose to model the SBS tier as a Multi-agent Reinforcement Learning (MRL) system, where each SBS is an intelligent and autonomous agent, which learns by directly interacting with the environment and by properly utilizing the past experience. The agents implemented in each SBS independently learn a proper switch ON/OFF control policy, so as to jointly maximize the system performance in terms of throughput, drop rate and energy consumption, while adapting to the dynamic conditions of the environment, in terms of energy inflow and traffic demand. However, MRL might suffer the problem of coordination when finding simultaneously a solution among all the agents that is good for the whole system. In consequence, the Layered Learning paradigm has been adopted to simplify the problem by decomposing it in subtasks. In particular, the global solution is obtained in a hierarchical fashion: the learning process of a subtask is aimed at facilitating the learning of the next higher subtask layer. The first layer implements an MRL approach and it is in charge of the local online optimization at SBS level as function of the traffic demand and the energy incomes. The second layer is in charge of the network-wide optimization and it is based on Artificial Neural Networks aimed at estimating the model of the overall network.Con la llegada de la nueva generación de redes móviles, la quinta generación (5G), se predice un aumento por un factor 1000 en la demanda de capacidad respecto a la 4G, con la consecuente instalación de nuevas infraestructuras. Se estima que el gasto energético de las tecnologías de la información y la comunicación podría alcanzar el 51% de la producción mundial de energía en el año 2030, principalmente debido al impacto de las redes y servicios móviles. Consecuentemente, los costes relacionados con el consumo de energía pasarán a ser una componente predominante en los gastos operativos (OPEX) de las operadoras de redes móviles. Por lo tanto, un control eficiente del consumo energético de las redes 5G, ya no es simplemente deseable, sino esencial. En la última década, la comunidad científica ha enfocado sus esfuerzos en la eficiencia energética (EE) de las redes de comunicaciones móviles, con particular énfasis en algoritmos para apagar y encender las estaciones base (BS). Además, las arquitecturas 5G introducirán el paradigma de las redes heterogéneas (HetNet), donde pequeñas BSs, o small BSs (SBSs), serán desplegadas para ayudar a las grandes macro BSs en satisfacer la gran demanda de tráfico y reducir el impacto en el consumo energético. Sin embargo, solo con la introducción de técnicas de captación de la energía ambiental, las redes pueden alcanzar los ahorros energéticos requeridos para mitigar los altos costes de la energía y su impacto en el medio ambiente. En el caso de las HetNets alimentadas mediante energías renovables, la naturaleza errática e intermitente de esta tipología de energías constituye una complejidad añadida al problema. La energía solar ha sido utilizada como referencia debido a su gran implantación y su alta eficiencia en términos de cantidad de energía producida respecto costes de producción. Por consiguiente, en la primera parte de la tesis se presenta un modelo de captación de la energía solar basado en un riguroso modelo estocástico de Markov que representa la energía capturada por paneles solares para exteriores. El escenario típico de HetNet supondrá el despliegue denso de SBSs con un alto nivel de flexibilidad, lo cual sugiere la utilización de sistemas de control distribuidos en lugar de aquellos que están centralizados, donde la adaptabilidad podría convertirse rápidamente en un reto difícilmente gestionable. Por esta razón, en la segunda parte de la tesis proponemos modelar las SBSs como un sistema multiagente de aprendizaje automático por refuerzo, donde cada SBS es un agente inteligente y autónomo que aprende interactuando directamente con su entorno y utilizando su experiencia acumulada. Los agentes en cada SBS aprenden independientemente políticas de control del apagado y encendido que les permiten maximizar conjuntamente el rendimiento y el consumo energético a nivel de sistema, adaptándose a condiciones dinámicas del ambiente tales como la energía renovable entrante y la demanda de tráfico. No obstante, los sistemas multiagente sufren problemas de coordinación cuando tienen que hallar simultáneamente una solución de forma distribuida que sea buena para todo el sistema. A tal efecto, el paradigma de aprendizaje por niveles ha sido utilizado para simplificar el problema dividiéndolo en subtareas. Más detalladamente, la solución global se consigue de forma jerárquica: el proceso de aprendizaje de una subtarea está dirigido a ayudar al aprendizaje de la subtarea del nivel superior. El primer nivel contempla un sistema multiagente de aprendizaje automático por refuerzo y se encarga de la optimización en línea de las SBSs en función de la demanda de tráfico y de la energía entrante. El segundo nivel se encarga de la optimización a nivel de red del sistema y está basado en redes neuronales artificiales diseñadas para estimar el modelo de todas las BSsPostprint (published version