Institutions make a difference: assessing the predictors of climate policy stringency using machine learning
Despite the urgent need for ambitious national climate policies to reduce carbon emissions, their implementation lacks stringency. This lack of policy stringency is driven by a complex combination of a country’s numerous politico-economic, institutional and socio-economic characteristics. While exta...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | Environmental Research Letters |
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| Online Access: | https://doi.org/10.1088/1748-9326/ada0cb |
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| author | Angelika von Dulong Achim Hagen |
| author_facet | Angelika von Dulong Achim Hagen |
| author_sort | Angelika von Dulong |
| collection | DOAJ |
| description | Despite the urgent need for ambitious national climate policies to reduce carbon emissions, their implementation lacks stringency. This lack of policy stringency is driven by a complex combination of a country’s numerous politico-economic, institutional and socio-economic characteristics. While extant studies aim at estimating causal effects between a selection of such characteristics and policy stringency, we examine the importance of a comprehensive set of predictors that underlie such empirical models. For this purpose, we employ machine-learning methods on a data set covering 22 potential predictors of policy stringency for 95 countries. Conditional random forests suggest that the most important predictors of policy stringency are of institutional nature: freedom (of press, media, associations, and elections), governmental effectiveness, and control of corruption. Further, accumulated local effects plots suggest that the relationship between some predictors, e.g. freedom or education, and policy stringency is highly non-linear. |
| format | Article |
| id | doaj-art-38b11056eb874bcc873f39687036ca9f |
| institution | Kabale University |
| issn | 1748-9326 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj-art-38b11056eb874bcc873f39687036ca9f2024-12-27T12:56:02ZengIOP PublishingEnvironmental Research Letters1748-93262024-01-0120101405610.1088/1748-9326/ada0cbInstitutions make a difference: assessing the predictors of climate policy stringency using machine learningAngelika von Dulong0Achim Hagen1https://orcid.org/0000-0002-5875-9226Resource Economics Group, Humboldt-Universität zu Berlin , Berlin, Germany; Berlin School of Economics , Berlin, GermanyBerlin School of Economics , Berlin, Germany; PECan Research Group, Humboldt-Universität zu Berlin , Berlin, GermanyDespite the urgent need for ambitious national climate policies to reduce carbon emissions, their implementation lacks stringency. This lack of policy stringency is driven by a complex combination of a country’s numerous politico-economic, institutional and socio-economic characteristics. While extant studies aim at estimating causal effects between a selection of such characteristics and policy stringency, we examine the importance of a comprehensive set of predictors that underlie such empirical models. For this purpose, we employ machine-learning methods on a data set covering 22 potential predictors of policy stringency for 95 countries. Conditional random forests suggest that the most important predictors of policy stringency are of institutional nature: freedom (of press, media, associations, and elections), governmental effectiveness, and control of corruption. Further, accumulated local effects plots suggest that the relationship between some predictors, e.g. freedom or education, and policy stringency is highly non-linear.https://doi.org/10.1088/1748-9326/ada0cbclimate policypolitical economyinstitutions |
| spellingShingle | Angelika von Dulong Achim Hagen Institutions make a difference: assessing the predictors of climate policy stringency using machine learning Environmental Research Letters climate policy political economy institutions |
| title | Institutions make a difference: assessing the predictors of climate policy stringency using machine learning |
| title_full | Institutions make a difference: assessing the predictors of climate policy stringency using machine learning |
| title_fullStr | Institutions make a difference: assessing the predictors of climate policy stringency using machine learning |
| title_full_unstemmed | Institutions make a difference: assessing the predictors of climate policy stringency using machine learning |
| title_short | Institutions make a difference: assessing the predictors of climate policy stringency using machine learning |
| title_sort | institutions make a difference assessing the predictors of climate policy stringency using machine learning |
| topic | climate policy political economy institutions |
| url | https://doi.org/10.1088/1748-9326/ada0cb |
| work_keys_str_mv | AT angelikavondulong institutionsmakeadifferenceassessingthepredictorsofclimatepolicystringencyusingmachinelearning AT achimhagen institutionsmakeadifferenceassessingthepredictorsofclimatepolicystringencyusingmachinelearning |