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Development of a Sustainable Systems Engineering Framework for the Design and Assessment of Sustainability Visions.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Development of a Sustainable Systems Engineering Framework for the Design and Assessment of Sustainability Visions./
作者:	Halbe, Johannes.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	367 p.
附註:	Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
Contained By:	Dissertations Abstracts International84-02B.
標題:	Food supply. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29274345
ISBN:	9798841571926

Development of a Sustainable Systems Engineering Framework for the Design and Assessment of Sustainability Visions.
Halbe, Johannes.

Development of a Sustainable Systems Engineering Framework for the Design and Assessment of Sustainability Visions. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 367 p.

Source: Dissertations Abstracts International, Volume: 84-02, Section: B.

Thesis (Ph.D.)--McGill University (Canada), 2021.

This item must not be sold to any third party vendors.

The increasing severity and complexity of environmental problems require new methods and concepts to identify context-specific solution strategies. Three different types of knowledge about complex environmental issues can be generated, including systems knowledge (i.e., How did the issue emerge? What are the characteristics of the problem?), target knowledge (i.e., Which kind of sustainable system state do we want to achieve in the future?) and transformation knowledge (i.e., What measures can be taken to improve the problem and make advances towards our aspired future system state?). Various modeling methods have been developed in the past to generate systems knowledge (e.g., physical models) and transformation knowledge (e.g., explorative scenarios). Modeling methods for generating target knowledge are less developed. In scenario studies, a future system state is often represented as a number of goals, such as reduced water pollution or absence of CO2 emissions. However, a future system state, such as a sustainable energy or food system, is much more complex. Recently, vision modeling has been introduced as a promising method to address the dynamic complexity of visions of a sustainable future system state (i.e., sustainability visions). Current challenges of vision modeling are linked to the lack of empirical data and the normativity of future visions. A lack of data particularly impedes the parametrization and validation of quantitative modeling methods. The normativity of visions requires a participatory approach to deal with the diversity of stakeholders' perceptions of a desirable future, which is based on their values, interests and worldviews.This research has four objectives. The first objective is to identify and further develop systems modeling methods to be applicable for vision design and assessment. These methods should be able to deal with the main challenges of vision modeling, namely a lack of data and the requirement to involve diverse stakeholders. As a second objective, this research aims to develop, test and apply a methodological framework for participatory modeling, which can guide the involvement of stakeholders and an integrated analysis of the participatory process (e.g., analyzing who participated in the different steps of the process). The two remaining objectives aim to develop, test and apply a conceptual and methodological framework for vision design (Objective 3) and vision assessment (Objective 4), which builds upon the systems modeling methods (Objective 1) and a previously developed participatory modeling framework (Objective 2). Qualitative and quantitative modeling methods will be included to allow for a gradual modeling of sustainability visions. In addition, the systematic handling of uncertainties will be addressed to allow for the use of quantitative modeling methods in vision modeling.Functional analysis is a standard method which originates in the field of systems engineering and is further developed in this research to be applicable for vision modeling. As a first step, conceptual work is required to extend the technical focus of functional analysis to include naturebased and social solutions. Based on this new conceptual framework, the functional organization analysis method is further developed to allow for the visualization of alternative system designs, i.e., alternative future system states.

ISBN: 9798841571926Subjects--Topical Terms:

551592
Food supply.

Development of a Sustainable Systems Engineering Framework for the Design and Assessment of Sustainability Visions.
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245 1 0 $a Development of a Sustainable Systems Engineering Framework for the Design and Assessment of Sustainability Visions.
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300 $a 367 p.
500 $a Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
500 $a Advisor: Grunwald, Armin;Adamowski, Jan.
502 $a Thesis (Ph.D.)--McGill University (Canada), 2021.
506 $a This item must not be sold to any third party vendors.
520 $a The increasing severity and complexity of environmental problems require new methods and concepts to identify context-specific solution strategies. Three different types of knowledge about complex environmental issues can be generated, including systems knowledge (i.e., How did the issue emerge? What are the characteristics of the problem?), target knowledge (i.e., Which kind of sustainable system state do we want to achieve in the future?) and transformation knowledge (i.e., What measures can be taken to improve the problem and make advances towards our aspired future system state?). Various modeling methods have been developed in the past to generate systems knowledge (e.g., physical models) and transformation knowledge (e.g., explorative scenarios). Modeling methods for generating target knowledge are less developed. In scenario studies, a future system state is often represented as a number of goals, such as reduced water pollution or absence of CO2 emissions. However, a future system state, such as a sustainable energy or food system, is much more complex. Recently, vision modeling has been introduced as a promising method to address the dynamic complexity of visions of a sustainable future system state (i.e., sustainability visions). Current challenges of vision modeling are linked to the lack of empirical data and the normativity of future visions. A lack of data particularly impedes the parametrization and validation of quantitative modeling methods. The normativity of visions requires a participatory approach to deal with the diversity of stakeholders' perceptions of a desirable future, which is based on their values, interests and worldviews.This research has four objectives. The first objective is to identify and further develop systems modeling methods to be applicable for vision design and assessment. These methods should be able to deal with the main challenges of vision modeling, namely a lack of data and the requirement to involve diverse stakeholders. As a second objective, this research aims to develop, test and apply a methodological framework for participatory modeling, which can guide the involvement of stakeholders and an integrated analysis of the participatory process (e.g., analyzing who participated in the different steps of the process). The two remaining objectives aim to develop, test and apply a conceptual and methodological framework for vision design (Objective 3) and vision assessment (Objective 4), which builds upon the systems modeling methods (Objective 1) and a previously developed participatory modeling framework (Objective 2). Qualitative and quantitative modeling methods will be included to allow for a gradual modeling of sustainability visions. In addition, the systematic handling of uncertainties will be addressed to allow for the use of quantitative modeling methods in vision modeling.Functional analysis is a standard method which originates in the field of systems engineering and is further developed in this research to be applicable for vision modeling. As a first step, conceptual work is required to extend the technical focus of functional analysis to include naturebased and social solutions. Based on this new conceptual framework, the functional organization analysis method is further developed to allow for the visualization of alternative system designs, i.e., alternative future system states.
520 $a La gravite et la complexite croissantes des problemes environnementaux requierent le developpement de nouvelles methodes et de nouveaux concepts afin d'identifier des solutions adaptees au contexte specifique de ces problemes. Trois differents types de connaissances sur les problemes environnementaux complexes peuvent etre generes, y compris la connaissance du systeme (c.-a-d. Comment le probleme est-il apparu? Quelles sont les caracteristiques du probleme?), la connaissance cible (c.-a-d., Quel type d'etat de systeme durable voulons-nous atteindre dans le future?) et les connaissances sur la transformation (c.-a-d., Quelles mesures peuvent etre prises pour ameliorer le probleme et progresser vers le futur systeme desire?). Diverses methodes de modelisation ont ete developpees dans le passe pour generer des connaissances sur les systemes (par exemple, des modeles physiques) et des connaissances sur la transformation (par exemple, des scenarios exploratoires). Par contre, les methodes de modelisation pour generer des connaissances cibles sont moins developpees. Dans les etudes de scenarios, un etat futur d'un systeme est souvent represente par un certain nombre d'objectifs, tels que la reduction de la pollution de l'eau ou l'absence d'emissions de CO2. Cependant, un etat futur d'un systeme, tel qu'un systeme energetique ou alimentaire durable, est beaucoup plus complexe. Recemment, la modelisation de la vision a ete introduite en tant une methode prometteuse pour aborder la complexite dynamique des visions d'un etat futur de systeme durable (c'est-a-dire les visions de la durabilite). Les defis actuels de la modelisation de la vision sont lies au manque de donnees empiriques et a la normativite des visions futures. Le manque de donnees entrave particulierement la parametrisation et la validation des methodes de modelisation quantitative. La normativite des visions requiert une approche participative pour faire face a la diversite des perceptions des parties prenantes d'un avenir souhaitable, qui est base sur leurs valeurs, interets et visions du monde.Cette recherche a quatre objectifs. Le premier objectif est d'identifier et de developper davantage des methodes de modelisation des systemes applicables a la conception et a l'evaluation de la vision. Ces methodes devraient etre en mesure de faire face aux principaux defis de la modelisation de la vision, c'est-a-dire le manque de donnees et la necessite d'impliquer diverses parties prenantes. Le deuxieme objectif vise a developper, tester et appliquer un cadre methodologique pour la modelisation participative pour guider la participation des parties prenantes et une analyse integree du processus participatif (par exemple, analyser qui a participe aux differentes etapes du processus). Les deux autres objectifs visent a developper, tester et appliquer un cadre conceptuel et methodologique pour la conception de la vision (objectif 3) et l'evaluation de la vision (objectif 4), qui s'appuie sur des methodes de modelisation du systeme (objectif 1) et le cadre de modelisation participative (objectif 2) developpe precedemment. Des methodes de modelisation qualitative et quantitative seront incluses pour permettre une modelisation progressive des visions de la durabilite. De plus, le traitement systematique des incertitudes sera aborde pour permettre l'utilisation de methodes de modelisation quantitative dans la modelisation de la vision.
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