The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Climate policies that achieved major emission reductions: Global evidence from two decades
Abstract
Meeting the Paris Agreement’s climate targets necessitates better knowledge about which climate policies work in reducing emissions at the necessary scale. We provide a global, systematic ex post evaluation to identify policy combinations that have led to large emission reductions out of 1500 climate policies implemented between 1998 and 2022 across 41 countries from six continents. Our approach integrates a comprehensive climate policy database with a machine learning–based extension of the common difference-in-differences approach. We identified 63 successful policy interventions with total emission reductions between 0.6 billion and 1.8 billion metric tonnes CO 2 . Our insights on effective but rarely studied policy combinations highlight the important role of price-based instruments in well-designed policy mixes and the policy efforts necessary for closing the emissions gap.
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Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927
Difference-in-differences with variation in treatment timing
Goodman-Bacon, A. (2021) · Journal of Econometrics · doi:10.1016/j.jeconom.2021.03.014 · Cited by 8,046
The adaptive lasso and its oracle properties
Zou, H. (2006) · Journal of the American Statistical Association · doi:10.1198/016214506000000735 · Cited by 7,470
Net-zero emissions energy systems
Davis, S. J., Lewis, N. S., Shaner, M., Aggarwal, S., Arent, D., Azevedo, I. L., Benson, S. M., Bradley, T., Brouwer, J., Chiang, Y.-M., et al. (2018) · Science · doi:10.1126/science.aas9793 · Cited by 2,391
Statistics for High-Dimensional Data: Methods, Theory and Applications
Bühlmann, P., & van de Geer, S. (2011) · Springer Series in Statistics · doi:10.1007/978-3-642-20192-9 · Cited by 1,680
Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects
Abadie, A. (2021) · Journal of Economic Literature · doi:10.1257/jel.20191450 · Cited by 1,486
Making sense of sensitivity: extending omitted variable bias
Cinelli, C., & Hazlett, C. (2020) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/rssb.12348 · Cited by 1,088
Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models
Xu, Y. (2017) · Political Analysis · doi:10.1017/pan.2016.2 · Cited by 938
Lasso-type recovery of sparse representations for high-dimensional data
Meinshausen, N., & Yu, B. (2006) · Cited by 744
Metadata
- Authors
- Stechemesser, A., Koch, N., Mark, E., Dilger, E., Klösel, P., Menicacci, L., Nachtigall, D., Pretis, F., Ritter, N., Schwarz, M., Vossen, H., & Wenzel, A.
- Venue
- Science
- Published
- 2024-08-23
- Volume
- 385
- Issue
- 6711
- Pages
- 884-892
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W4401751997
- Cited by
- 322
- References
- 52
- FWCI
- 114.91
- Type
- journal article
- Language
- en
- Open access
- closed
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- Stechemesser, A., Koch, N., Mark, E., Dilger, E., Klösel, P., Menicacci, L., Nachtigall, D., Pretis, F., Ritter, N., Schwarz, M., Vossen, H., & Wenzel, A. (2024). Climate policies that achieved major emission reductions: Global evidence from two decades. Science. https://doi.org/10.1126/science.adl6547
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