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"pref" : "Climate change mitigation requires policies that are both effective and politically feasible, making policy design a crucial focus of comparative research. However, systematic cross-national data on design features have been scarce due to the resource-intensive nature of manual coding. This dataset, ClimateGen, addresses this gap by providing a comprehensive, harmonized, event-based classification of climate policy outputs. Building on the International Energy Agency (IEA) ‘Policies and Measures’ database, ClimateGen covers over 3,400 climate policy expansions across 23 affluent democracies from 1990 to 2022. To process the extensive unstructured textual data, the dataset leverages an innovative, reproducible pipeline powered by generative artificial intelligence. Specifically, it employs the Large Language Model GPT-4o, utilizing an iteratively refined prompt architecture rigorously validated against human expert coding. By employing multiple classification trials and leveraging the model’s parametric knowledge and emergent reasoning capabilities, the pipeline systematically infers latent distributive effects from sparse textual policy records. The core contribution of ClimateGen is its systematic categorization of climate policies along three theoretically grounded design dimensions: (1) Instrument choice, distinguishing between fiscal support and regulatory measures; (2) Target groups, separating consumer-oriented policies (highly salient to households) from broader economy-oriented measures; and (3) Distributive incidence, identifying whether consumer policies allocate costs and benefits in a progressive or regressive manner across income strata. By quantifying these micro-level design choices, ClimateGen provides a unique empirical yardstick. It enables researchers to benchmark national climate policy portfolios, examine the trade-offs between political feasibility and equity, and empirically test theories of distributive politics, policy feedback, policy expansion, and climate policy sequencing.",
"de" : "Climate change mitigation requires policies that are both effective and politically feasible, making policy design a crucial focus of comparative research. However, systematic cross-national data on design features have been scarce due to the resource-intensive nature of manual coding. This dataset, ClimateGen, addresses this gap by providing a comprehensive, harmonized, event-based classification of climate policy outputs. Building on the International Energy Agency (IEA) ‘Policies and Measures’ database, ClimateGen covers over 3,400 climate policy expansions across 23 affluent democracies from 1990 to 2022. To process the extensive unstructured textual data, the dataset leverages an innovative, reproducible pipeline powered by generative artificial intelligence. Specifically, it employs the Large Language Model GPT-4o, utilizing an iteratively refined prompt architecture rigorously validated against human expert coding. By employing multiple classification trials and leveraging the model’s parametric knowledge and emergent reasoning capabilities, the pipeline systematically infers latent distributive effects from sparse textual policy records. The core contribution of ClimateGen is its systematic categorization of climate policies along three theoretically grounded design dimensions: (1) Instrument choice, distinguishing between fiscal support and regulatory measures; (2) Target groups, separating consumer-oriented policies (highly salient to households) from broader economy-oriented measures; and (3) Distributive incidence, identifying whether consumer policies allocate costs and benefits in a progressive or regressive manner across income strata. By quantifying these micro-level design choices, ClimateGen provides a unique empirical yardstick. It enables researchers to benchmark national climate policy portfolios, examine the trade-offs between political feasibility and equity, and empirically test theories of distributive politics, policy feedback, policy expansion, and climate policy sequencing.",
"en" : "Climate change mitigation requires policies that are both effective and politically feasible, making policy design a crucial focus of comparative research. However, systematic cross-national data on design features have been scarce due to the resource-intensive nature of manual coding. This dataset, ClimateGen, addresses this gap by providing a comprehensive, harmonized, event-based classification of climate policy outputs. Building on the International Energy Agency (IEA) ‘Policies and Measures’ database, ClimateGen covers over 3,400 climate policy expansions across 23 affluent democracies from 1990 to 2022. To process the extensive unstructured textual data, the dataset leverages an innovative, reproducible pipeline powered by generative artificial intelligence. Specifically, it employs the Large Language Model GPT-4o, utilizing an iteratively refined prompt architecture rigorously validated against human expert coding. By employing multiple classification trials and leveraging the model’s parametric knowledge and emergent reasoning capabilities, the pipeline systematically infers latent distributive effects from sparse textual policy records. The core contribution of ClimateGen is its systematic categorization of climate policies along three theoretically grounded design dimensions: (1) Instrument choice, distinguishing between fiscal support and regulatory measures; (2) Target groups, separating consumer-oriented policies (highly salient to households) from broader economy-oriented measures; and (3) Distributive incidence, identifying whether consumer policies allocate costs and benefits in a progressive or regressive manner across income strata. By quantifying these micro-level design choices, ClimateGen provides a unique empirical yardstick. It enables researchers to benchmark national climate policy portfolios, examine the trade-offs between political feasibility and equity, and empirically test theories of distributive politics, policy feedback, policy expansion, and climate policy sequencing."
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"pref" : "The used data listed below can either be downloaded from the original study pages or can be downloaded here: \r\n- Landesdatenbank NRW (https://www.landesdatenbank.nrw.de/ldbnrw/online/) \r\n- Bundesnetzagentur - Kraftwerksliste (https://www.bundesnetzagentur.de/DE/Sachgebiete/ElektrizitaetundGas/Unternehmen_Institutionen/Versorgungssicherheit/Erzeugungskapazitaeten/Kraftwerksliste/start.html) \r\n- Statistik der Kohlenwirtschaft e.V. (https://kohlenstatistik.de/) \r\n- Wegweiser Kommune (https://www.wegweiser-kommune.de/; „Datenlizenz Deutschland – Namensnennung – Version 2.0\" (www.govdata.de/dl-de/by-2-0) \r\n- Bundesinstitut für Bau-, Stadt- und Raumforschung (https://www.bbsr.bund.de/BBSR/EN/home/_node.html) \r\n- Inequality Barometer 2020 (https://doi.org/10.7802/2740)\r\n- Inequality Barometer 2022\r\n- Politbarometer Gesamtkumulation (https://doi.org/10.4232/1.13299)\r\n- Politbarometer West 2007 (https://doi.org/10.4232/1.4803)",
"de" : "The used data listed below can either be downloaded from the original study pages or can be downloaded here: \r\n- Landesdatenbank NRW (https://www.landesdatenbank.nrw.de/ldbnrw/online/) \r\n- Bundesnetzagentur - Kraftwerksliste (https://www.bundesnetzagentur.de/DE/Sachgebiete/ElektrizitaetundGas/Unternehmen_Institutionen/Versorgungssicherheit/Erzeugungskapazitaeten/Kraftwerksliste/start.html) \r\n- Statistik der Kohlenwirtschaft e.V. (https://kohlenstatistik.de/) \r\n- Wegweiser Kommune (https://www.wegweiser-kommune.de/; „Datenlizenz Deutschland – Namensnennung – Version 2.0\" (www.govdata.de/dl-de/by-2-0) \r\n- Bundesinstitut für Bau-, Stadt- und Raumforschung (https://www.bbsr.bund.de/BBSR/EN/home/_node.html) \r\n- Inequality Barometer 2020 (https://doi.org/10.7802/2740)\r\n- Inequality Barometer 2022\r\n- Politbarometer Gesamtkumulation (https://doi.org/10.4232/1.13299)\r\n- Politbarometer West 2007 (https://doi.org/10.4232/1.4803)",
"en" : "The used data listed below can either be downloaded from the original study pages or can be downloaded here: \r\n- Landesdatenbank NRW (https://www.landesdatenbank.nrw.de/ldbnrw/online/) \r\n- Bundesnetzagentur - Kraftwerksliste (https://www.bundesnetzagentur.de/DE/Sachgebiete/ElektrizitaetundGas/Unternehmen_Institutionen/Versorgungssicherheit/Erzeugungskapazitaeten/Kraftwerksliste/start.html) \r\n- Statistik der Kohlenwirtschaft e.V. (https://kohlenstatistik.de/) \r\n- Wegweiser Kommune (https://www.wegweiser-kommune.de/; „Datenlizenz Deutschland – Namensnennung – Version 2.0\" (www.govdata.de/dl-de/by-2-0) \r\n- Bundesinstitut für Bau-, Stadt- und Raumforschung (https://www.bbsr.bund.de/BBSR/EN/home/_node.html) \r\n- Inequality Barometer 2020 (https://doi.org/10.7802/2740)\r\n- Inequality Barometer 2022\r\n- Politbarometer Gesamtkumulation (https://doi.org/10.4232/1.13299)\r\n- Politbarometer West 2007 (https://doi.org/10.4232/1.4803)"
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"pref" : "The published Stata syntax and data files can be used to replicate the results reported in Schmelz, K. & Bowles, S. (2025):\r\n\r\nConventional approaches to policy design often fail to recognize the plasticity of citizens’ beliefs and values upon which policy effectiveness and political sustainability depends. By way of illustration, we show that environmental policies may fail this test, crowding out pre-existing green values. Our representative survey of 3,306 Germans finds that enforced restrictions to promote carbon-neutral lifestyles would trigger strong negative responses because they “restrict freedom.” This is true even among those who would adopt green lifestyles when voluntary, possibly undermining support for green political movements. These results combined with the long-term political consequences of the polarizing reactions to covid mandates motivate a new approach to climate policy design. We set aside the conventional economic model assuming self-interested citizens, in which there could be no green values to crowd out. Instead, we propose a dynamic approach recognizing that a) to succeed, essential policies including bans, carbon taxes and the promotion of new technologies must be both implementable and politically sustainable, entailing b) a critical role for citizens’ green values, which c) may be either diminished or cultivated, depending on policy design.",
"de" : "The published Stata syntax and data files can be used to replicate the results reported in Schmelz, K. & Bowles, S. (2025):\r\n\r\nConventional approaches to policy design often fail to recognize the plasticity of citizens’ beliefs and values upon which policy effectiveness and political sustainability depends. By way of illustration, we show that environmental policies may fail this test, crowding out pre-existing green values. Our representative survey of 3,306 Germans finds that enforced restrictions to promote carbon-neutral lifestyles would trigger strong negative responses because they “restrict freedom.” This is true even among those who would adopt green lifestyles when voluntary, possibly undermining support for green political movements. These results combined with the long-term political consequences of the polarizing reactions to covid mandates motivate a new approach to climate policy design. We set aside the conventional economic model assuming self-interested citizens, in which there could be no green values to crowd out. Instead, we propose a dynamic approach recognizing that a) to succeed, essential policies including bans, carbon taxes and the promotion of new technologies must be both implementable and politically sustainable, entailing b) a critical role for citizens’ green values, which c) may be either diminished or cultivated, depending on policy design.",
"en" : "The published Stata syntax and data files can be used to replicate the results reported in Schmelz, K. & Bowles, S. (2025):\r\n\r\nConventional approaches to policy design often fail to recognize the plasticity of citizens’ beliefs and values upon which policy effectiveness and political sustainability depends. By way of illustration, we show that environmental policies may fail this test, crowding out pre-existing green values. Our representative survey of 3,306 Germans finds that enforced restrictions to promote carbon-neutral lifestyles would trigger strong negative responses because they “restrict freedom.” This is true even among those who would adopt green lifestyles when voluntary, possibly undermining support for green political movements. These results combined with the long-term political consequences of the polarizing reactions to covid mandates motivate a new approach to climate policy design. We set aside the conventional economic model assuming self-interested citizens, in which there could be no green values to crowd out. Instead, we propose a dynamic approach recognizing that a) to succeed, essential policies including bans, carbon taxes and the promotion of new technologies must be both implementable and politically sustainable, entailing b) a critical role for citizens’ green values, which c) may be either diminished or cultivated, depending on policy design."
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"pref" : "Employing new and original survey data collected in three waves (May and November 2020 as well as May 2021) in Germany, this paper studies the dynamics of individual-level support for additional health care spending. A first major finding is that, so far, health care spending preferences have not radically changed during the Covid-19 pandemic, at least at the aggregate level. A more detailed analysis reveals, secondly, that individual-level support for additional spending on health care is strongly conditioned by performance perceptions and, to a lesser extent, general political trust. Citizens who regard the system as badly (well) prepared to cope with the crisis are more likely to support (oppose) additional spending. Higher levels of political trust are also positively associated with spending support, but to a lesser degree. The paper concludes by discussing the implications of these findings for policy-making and welfare state politics in the post-pandemic era.
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\nThis data set provides the data and Stata code used for the article. A detailed description of the variables is available from the corresponding publication. Please cite the paper if you use the data.
",
"de" : "Employing new and original survey data collected in three waves (May and November 2020 as well as May 2021) in Germany, this paper studies the dynamics of individual-level support for additional health care spending. A first major finding is that, so far, health care spending preferences have not radically changed during the Covid-19 pandemic, at least at the aggregate level. A more detailed analysis reveals, secondly, that individual-level support for additional spending on health care is strongly conditioned by performance perceptions and, to a lesser extent, general political trust. Citizens who regard the system as badly (well) prepared to cope with the crisis are more likely to support (oppose) additional spending. Higher levels of political trust are also positively associated with spending support, but to a lesser degree. The paper concludes by discussing the implications of these findings for policy-making and welfare state politics in the post-pandemic era.
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",
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\nThis data set provides the data and Stata code used for the article. A detailed description of the variables is available from the corresponding publication. Please cite the paper if you use the data.
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\r\nThe survey program took the form of several online surveys conducted in 2020 and 2021. The surveys were organized in two series, Survey A and B. For each serie, three cross-sectional surveys werde conducted that included a sample of respondents who where surveyed repeatedly.
\r\nThis dataset is the survey that took place in November 2020. It is the second Wave of Survey A.
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\r\nThe other surveys can also be found in the GESIS repository (https://doi.org/10.7802/2116 , https://doi.org/10.7802/2118)"
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