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Applying Artificial Intelligence and Quantitative Finance for a Successful Heat Transition in the Building Sector.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applying Artificial Intelligence and Quantitative Finance for a Successful Heat Transition in the Building Sector./
Author:	Wiethe, Christian.
Description:	1 online resource (56 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-05, Section: B.
Contained By:	Dissertations Abstracts International84-05B.
Subject:	Energy efficiency. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29805395click for full text (PQDT)
ISBN:	9798352981689

Applying Artificial Intelligence and Quantitative Finance for a Successful Heat Transition in the Building Sector.
Wiethe, Christian.

Applying Artificial Intelligence and Quantitative Finance for a Successful Heat Transition in the Building Sector. - 1 online resource (56 pages)

Source: Dissertations Abstracts International, Volume: 84-05, Section: B.

Thesis (Ph.D.)--Universitaet Bayreuth (Germany), 2022.

Includes bibliographical references

Counteracting global warming requires intensifying decarbonization efforts across all sectors. To this end, the global residential building sector faces an urgent need to progress towards the climate goals, as it accounts for over a sixth of greenhouse gas emissions and over a quarter of energy consumption, most of which are caused by warm water and space heating and cooling. However, many economically and ecologically sensible retrofit measures are not conducted, among other reasons because of high perceived uncertainty regarding financial savings. Against this background, this doctoral thesis aims to contribute to successfully shaping the heat transition in the residential building sector by investigating three main aspects. The first aspect deals with reducing the perceived risk for energetic retrofitting by providing reliable data-driven decision support, as there is currently a research gap regarding long-term (i.e., annual) prediction for residential buildings and the resulting consequences of increased prediction accuracy. The findings in this thesis provide strong evidence that data-driven energy quantification methods reduce prediction errors by about 50% compared to the legally prescribed engineering methods. Assuming rational decision-making and setting up an agent-based building stock model, this increase in prediction accuracy translates into a substantial rise in energetic retrofitting from about 0.98% to 1.68%.Within the model setting, further prediction accuracy gains allow the retrofit rate to eventually exceed the envisaged 2% to successfully shape the heat transition in the residential building sector. The second aspect deals with understanding and managing the remaining risks connected to energetic retrofitting applying concepts from quantitative finance. To this end, this thesis follows literature and differentiates technological and operational risks (first aspect) from contextual and economic risks (second aspect). The findings indicate that risk perception is crucial for evaluating energetic retrofitting. Moreover, the findings provide the theoretical basis and highlight the potential of diversifying and hedging the remaining risk on the financial markets via risk transfer contracts. The third aspect deals with carefully tailored policy measures by constructing spatially and temporally differentiated incentive mechanisms to allocate scarce financial resources efficiently, maximizing the greenhouse gas emission reductions per monetary unit invested. The findings indicate significant influence from regionally differing socio-economic factors on energetic retrofitting. Moreover, time-dependent subsidy schemes incentivizing early retrofitting reduce greenhouse gas emissions substantially. Assuming rational decision-making, greenhouse gas emission reductions per monetary unit invested for time-dependent subsidy schemes exceed the reductions by static subsidy schemes by up to 675%. In summary, this cumulative doctoral thesis comprises seven research articles and aims to contribute to the heat transition in the residential building sector by applying artificial intelligence and concepts from quantitative finance and deriving managerial and policy implications for all focal aspects.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798352981689Subjects--Topical Terms:

3555643
Energy efficiency.
Index Terms--Genre/Form:

542853
Electronic books.

Applying Artificial Intelligence and Quantitative Finance for a Successful Heat Transition in the Building Sector.
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500 $a Source: Dissertations Abstracts International, Volume: 84-05, Section: B.
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504 $a Includes bibliographical references
520 $a Counteracting global warming requires intensifying decarbonization efforts across all sectors. To this end, the global residential building sector faces an urgent need to progress towards the climate goals, as it accounts for over a sixth of greenhouse gas emissions and over a quarter of energy consumption, most of which are caused by warm water and space heating and cooling. However, many economically and ecologically sensible retrofit measures are not conducted, among other reasons because of high perceived uncertainty regarding financial savings. Against this background, this doctoral thesis aims to contribute to successfully shaping the heat transition in the residential building sector by investigating three main aspects. The first aspect deals with reducing the perceived risk for energetic retrofitting by providing reliable data-driven decision support, as there is currently a research gap regarding long-term (i.e., annual) prediction for residential buildings and the resulting consequences of increased prediction accuracy. The findings in this thesis provide strong evidence that data-driven energy quantification methods reduce prediction errors by about 50% compared to the legally prescribed engineering methods. Assuming rational decision-making and setting up an agent-based building stock model, this increase in prediction accuracy translates into a substantial rise in energetic retrofitting from about 0.98% to 1.68%.Within the model setting, further prediction accuracy gains allow the retrofit rate to eventually exceed the envisaged 2% to successfully shape the heat transition in the residential building sector. The second aspect deals with understanding and managing the remaining risks connected to energetic retrofitting applying concepts from quantitative finance. To this end, this thesis follows literature and differentiates technological and operational risks (first aspect) from contextual and economic risks (second aspect). The findings indicate that risk perception is crucial for evaluating energetic retrofitting. Moreover, the findings provide the theoretical basis and highlight the potential of diversifying and hedging the remaining risk on the financial markets via risk transfer contracts. The third aspect deals with carefully tailored policy measures by constructing spatially and temporally differentiated incentive mechanisms to allocate scarce financial resources efficiently, maximizing the greenhouse gas emission reductions per monetary unit invested. The findings indicate significant influence from regionally differing socio-economic factors on energetic retrofitting. Moreover, time-dependent subsidy schemes incentivizing early retrofitting reduce greenhouse gas emissions substantially. Assuming rational decision-making, greenhouse gas emission reductions per monetary unit invested for time-dependent subsidy schemes exceed the reductions by static subsidy schemes by up to 675%. In summary, this cumulative doctoral thesis comprises seven research articles and aims to contribute to the heat transition in the residential building sector by applying artificial intelligence and concepts from quantitative finance and deriving managerial and policy implications for all focal aspects.
520 $a Um dem globalen Klimawandel entgegenzuwirken, mussen die Anstrengungen zur Dekarbonisierung sektorenubergreifend intensiviert werden. Der globale Wohngebaudesektor steht dabei vor der dringenden Notwendigkeit, Fortschritte bei der Erreichung der Klimaziele zu erzielen, da er fur mehr als ein Sechstel der Treibhausgasemissionen und mehr als ein Viertel des Energieverbrauchs verantwortlich ist, die zum grosten Teil durch Warmwasser und Raumheizung und -kuhlung verursacht werden. Viele okonomisch und okologisch sinnvolle Sanierungsmasnahmen werden jedoch nicht durchgefuhrt, u.a. wegen der hohen wahrgenommenen Unsicherheit uber finanzielle Einsparungen. Vor diesem Hintergrund will diese Dissertation einen Beitrag zur erfolgreichen Gestaltung der Warmewende im Wohngebaudebereich leisten, indem sie drei wesentliche Aspekte untersucht. Der erste Aspekt befasst sich mit der Verringerung des wahrgenommenen Risikos fur energetische Sanierungen durch die Bereitstellung zuverlassiger datengestutzter Entscheidungshilfen, da es derzeit eine Forschungslucke hinsichtlich langfristiger (d.h. jahrlicher) Energieverbrauchsprognosen fur Wohngebaude und den resultierenden Konsequenzen einer erhohten Prognosegute gibt. Die Ergebnisse dieser Arbeit zeigen deutlich, dass datengestutzte Methoden zur Energieverbrauchsprognose die Prognosefehler im Vergleich zu den gesetzlich vorgeschriebenen ingenieurtechnischen Methoden um etwa 50 % reduzieren. Unter der Annahme rationaler Entscheider fuhrt diese Erhohung der Vorhersagegenauigkeit innerhalb eines agentenbasierten Gebaudebestandsmodells zu einem deutlichen Anstieg der Sanierungsrate von ca. 0,98% auf 1,68%, wobei die Sanierungsrate durch weitere Erhohung der Prognosegute im Modell schlieslich die anvisierten 2% uberschreitet, welche notwendig sind, um die Warmewende im Wohngebaudebereich erfolgreich zu gestalten. Der zweite Aspekt befasst sich mit dem Verstandnis und dem Management der verbleibenden Risiken im Zusammenhang mit der energetischen Sanierung unter Anwendung finanzmathematischer Konzepte. Dazu werden in Anlehnung an die Literatur technologische und operative Risiken (erster Aspekt) von kontextuellen und wirtschaftlichen Risiken (zweiter Aspekt) unterschieden. Die Ergebnisse zeigen, dass die Risikowahrnehmung fur die Bewertung von energetischen Sanierungen entscheidend ist. Daruber hinaus liefern die Ergebnisse die theoretische Grundlage und zeigen das Potenzial einer Diversifizierung und Absicherung des verbleibenden Risikos auf den Finanzmarkten durch Risikotransfervertrage auf. Der dritte Aspekt befasst sich mit sorgfaltig ausgestalteten politischen Masnahmen, indem raumlich und zeitlich differenzierte Anreizmechanismen konstruiert werden, um die knappen finanziellen Ressourcen effizient zu verteilen und die Treibhausgasemissionsreduktionen pro investierter Geldeinheit zu maximieren. Die Ergebnisse zeigen, dass regional unterschiedliche soziookonomische Faktoren einen signifikanten Einfluss auf die energetische Nachrustung haben. Daruber hinaus reduzieren zeitabhangige Subventionssysteme, die Anreize fur eine fruhzeitige Umrustung schaffen, die Treibhausgasemissionen erheblich. Unter der Annahme rationaler Entscheider ubersteigen die Treibhausgasemissionsreduktionen pro investierter Geldeinheit bei zeitabhangigen Subventionssystemen die Reduktionen durch statische Subventionssysteme um bis zu 675%. Zusammengefasst umfasst diese kumulative Doktorarbeit sieben Forschungsartikel und zielt darauf ab, durch die Anwendung von kunstlicher Intelligenz und finanzmathematischer Konzepte einen Beitrag zur Warmewende im Wohngebaudesektor zu leisten und fur alle Schwerpunktaspekte betriebswirtschaftliche und politische Implikationen abzuleiten.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Energy efficiency. $3 3555643
650 4 $a Emissions. $3 3559499
650 4 $a Decision making. $3 517204
650 4 $a Energy. $3 876794
655 7 $a Electronic books. $2 lcsh $3 542853
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710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Universitaet Bayreuth (Germany). $3 3545581
773 0 $t Dissertations Abstracts International $g 84-05B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29805395 $z click for full text (PQDT)