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Impact of Financial Subsidy Schemes on Climate Goals in the Residential Building Sector

Title data

Wiethe, Christian:
Impact of Financial Subsidy Schemes on Climate Goals in the Residential Building Sector.
In: Journal of Cleaner Production. Vol. 344 (2022) . - No. 131040.
ISSN 0959-6526
DOI: https://doi.org/10.1016/j.jclepro.2022.131040

Official URL: Volltext

Project information

Project title:
Project's official titleProject's id
Projektgruppe WI Digital FinanceNo information

Abstract in another language

The international Paris agreement climate goals regarding the residential building sector were mainly incorporated into national legislation as CO2 emission reduction levels for specific years (e.g., 80% CO2 emission reduction until 2035). Financial subsidy schemes incentivizing early retrofitting can lead to lock-in effects, not realizing energy savings potential from technological advancements in the long run and potentially failing emission reduction goals. However, early retrofitting leads to CO2 emission reductions over longer periods, minimizing the combined total CO2 emissions. Depending on which of these two conflicting goals is pursued, differing subsidy schemes are suitable to incentivize respective retrofits. Knowledge about the effects of these subsidy schemes is relevant to setting correct incentives. We, therefore, investigate the difference in CO2 emission reductions of time-dependent subsidy schemes per monetary unit invested. In doing so, this study is the first to investigate time-dependent subsidy schemes. We apply an agent-based building stock model for a case study to the German residential building stock using an extensive real-world dataset. Results indicate that prioritizing early retrofits reduces the probability of achieving emission reduction goals while simultaneously minimizing total CO2 emissions. Total CO2 emission reductions per monetary unit invested differ up to 675% compared to static subsidy schemes. We conclude that political incentive mechanisms should not be designed to
meet the climate goals but instead minimize total CO2 emissions.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Building stock model; Energetic retrofitting; Energy policy; Machine learning algorithms; Agent-based; Risk
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Professor Information Systems and Digital Energy Management
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Professor Information Systems and Digital Energy Management > Professor Information Systems and Digital Energy Management - Univ.-Prof. Dr. Jens Strüker
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Fraunhofer Project Group Business and Information Systems Engineering
Research Institutions > Affiliated Institutes > FIM Research Center Finance & Information Management
Faculties
Faculties > Faculty of Law, Business and Economics
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 23 Mar 2022 08:53
Last Modified: 30 Jun 2022 13:09
URI: https://eref.uni-bayreuth.de/id/eprint/68973