Besides, this course gives course followers an understanding of the key challenges and pathways to sustainable development in the Arctic region.

This course is unique as it gathers the world’s experts to speak about the Arctic. Their respective inputs from different academic perspectives and disciplines offer a relevant and complete assessment of the Arctic region and its connection to the rest of the planet. At the end of the day, the course intends to offer in full scope the keys to understanding as the Arctic plays as a mirror of the human and geological dynamics.

Towards sufficiency in housing: Agent-based model and transition scenarios

Margarita Agriantoni

Research problem: Housing is responsible for a considerable part of global resource consumption. In Switzerland, it accounts for almost one third of the country's CO2 emissions and half of the total energy demand. Thus, housing plays an important role in the transition to sustainable development. Housing has been thoroughly studied from a disciplinary perspective, e.g., economics, social sciences, psychology, engineering. However, little attention has been paid to housing as a system of interrelated actors, e.g., developers, owners, tenants and state, whose actions might be described by different disciplines. Aim: The aim of this thesis is to investigate how a change in the construction practices of property owners in combination to a more space-efficient allocation of dwellings to households and a behavioural change of tenants can achieve a substantial reduction in the environmental impact of housing. This dissertation focuses on the interrelations of housing actors, concentrating mainly on the property owners and explores which measures and to what extent can lead to a reduction of space consumption of housing.Methods: Housing is characterised by a high level of complexity, therefore a transdisciplinary approach is adopted. Firstly, the Swiss housing market and its unique attributes are studied. However, in this thesis, we study a subset of the Swiss rental housing market; it comprises two housing cooperatives: ABZ in Zurich and SCHL in Lausanne as well as the insurer and asset manager Swiss Mobiliar. Their housing stocks, of approximately 10,000 dwellings, are examined in depth and are used as a case study. Extensive data gathering allowed to create an exhaustive inventory of the building stock of the three property owners. The data on tenants was limited, thus, we conducted a tenant survey. Furthermore, a series of workshops with both tenants and property owners were organised to study their decision-making processes. A bottom-up agent-based model (ABM) is built, which portrays the case study housing system. The main actors of the rental housing market (property owners and households) are represented in the model by agents; the owner agents make decisions about their building stock and tenants, while the household agents choose a dwelling based on their needs and housing preferences. The dynamic evolution of both owners and households and the interaction between them is simulated in the model and allows the study of emergent phenomena. The present thesis attempts the assessment of the environmental impact of housing through the lens of housing sufficiency. Space consumption, in terms of dwelling surface and floor area per capita, is considered as the largest determinant of resource consumption.Results: The simulation results show that measures taken to decrease the environmental impact of housing and space consumption are inadequate if owners do not change their construction practices and households do not actively change their housing behaviour. Most measures explored in this thesis, if implemented individually, succeed in reducing the rate of increase in space consumption but fail to reverse the upward trend. A more space-efficient allocation of dwellings to households proves to have a certain effect. However, only when combined with other measures, such as the densification of the existing building stock and a change in construction practices, a significant decrease of environmental impact is observed.

EPFL2022

Reducing Greenhouse Gas emissions is not the only solution

Justine Brun

With the increasing strength and frequency of climate change events, the urgency to mitigate climate change impact is ever so important. Canada reported his Nationally Determined Contribution (NDC) and submitted its ambition to reduce his Greenhouse Gas (GHG) emissions by 40-45% by 2030 compared to 2005 and reach net-zero emissions by 2050. Interest for energy system modelling has been increasing for planning future energy systems, as a result of growing concern for sustainable development and transition towards renewable energies. Nevertheless, planning a decarbonization of energy system can potentially lead to environmental burden shifting, and can increase impacts on other environmental impacts such as ecosystem quality or human health. Thus, this project aims towards integrating Life Cycle Analysis (LCA) and more specifically Life Cycle Impact Assessment (LCIA) within Energy Systems Modelling, in order to minimize not only climate change impacts, but human health and ecosystem quality. In addition, a dual spatial resolution was integrated in the model in order to increase data precision and computational efficiency. The results show that a low-carbon energy system, based on the optimization of GHG emissions allows to greatly reduce all the environmental impacts under analysis, compared to an all economic- based energy system, but a multi-objective optimization allows to simultaneously reduce impacts on ecosystem quality and human health, while reducing GHG emissions and keeping good economical and technical performances. The results also show that it is theoretically possible for Canada to keep on track with their emission reduction ambition by 2030, by deploying more renewable energies. This project does not allow assessing Canada’s potential to reach net-zero emissions, as environmental impacts from carbon capture technologies are still to be characterized and integrated in the model.

2022

Technology Strategy in Dynamic Environments: A Computational Analysis of the Automation of Routines, the Organization of AI, and the Evaluation of Technology Risk

Maximilian Wieland Hofer

In this dissertation, I develop theory and evidence to argue that new technologies are central to how firms organize to create and capture value. I use computational methods such as reinforcement learning and probabilistic topic modeling to investigate three topics: the automation of routines, the organization of artificial intelligence (AI), and the evaluation of technology risk. Overall, I argue that new technologies are not a panacea for the firm but require deliberate strategic planning to manage the potential downsides of myopic automation, AI interdependencies, and the disclosure of technology risks.In the first essay, I argue that while automation can increase productivity by reducing the costs of coordinating individuals, the automation of routines can also incur an indirect opportunity cost due to slow adaptation to environmental change. I develop a reinforcement learning simulation to model the impact of automation on the returns from the division of labor in dynamic environments and to show how automation incurs opportunity costs through lost learning and slow adaptation. Moreover, automation can be suboptimal when it brings about myopic behavior, i.e. high returns from the division of labor in the short term, but negative returns in the long term. Given the simulation results, I argue that firms need dynamic routines to simultaneously balance learning and automation. I open-source the simulation platform as OrgSim-RL on GitHub.In the second essay, I argue that a data-driven culture - what I define as a Data Clan - can help to coordinate complex interdependencies between AI components within a firm. I analyze in-depth semi-structured interview data with a hierarchical stochastic block model (hSBM) and hand-coding to find that managers focus primarily on building a strong culture and establishing high-quality data assets when allocating resources to AI initiatives. Given the results, I inductively develop implications for theory and argue that the emergence of a Data Clan can be a governance mechanism to reduce coordination frictions and build a competitive advantage in the age of AI.In the third essay, I argue that investors require a higher initial return to take on more technology risk disclosure during an IPO. I quantify the magnitude of disclosed risk and the risk disclosure topics based on a latent Dirichlet allocation (LDA) topic model of IPO prospectus text and find a return-for-risk association between text-based technology risk disclosure and underpricing. The study also finds evidence that owning granted patents is associated with a lower return-for-risk association, suggesting that intellectual property allows the disclosure of risk without losing the competitive advantage. I open-source the code for quantifying risk disclosure as RiskyData-LDA on GitHub.In summary, this dissertation develops theory and finds evidence across three essays to argue that leveraging new technologies requires deliberate strategic planning to manage potential downsides of new technologies, such as the opportunity costs of automation, coordination costs, and costs associated with raising capital. The results suggest three mitigating solutions: dynamic routines to balance learning and automation, a Data Clan to improve coordination, and disclosure through patents to reduce underpricing.

EPFL2022

Technology Strategy in Dynamic Environments: A Computational Analysis of the Automation of Routines, the Organization of AI, and the Evaluation of Technology Risk

Maximilian Wieland Hofer

EPFL2022

Towards sufficiency in housing: Agent-based model and transition scenarios

Margarita Agriantoni

EPFL2022

Global Arctic

Towards sufficiency in housing: Agent-based model and transition scenarios

Reducing Greenhouse Gas emissions is not the only solution

Technology Strategy in Dynamic Environments: A Computational Analysis of the Automation of Routines, the Organization of AI, and the Evaluation of Technology Risk

Technology Strategy in Dynamic Environments: A Computational Analysis of the Automation of Routines, the Organization of AI, and the Evaluation of Technology Risk

Towards sufficiency in housing: Agent-based model and transition scenarios

Reducing Greenhouse Gas emissions is not the only solution