TY - RPRT A1 - Khanna, Tarun A1 - Baiocchi, Giovanni A1 - Callaghan, Max W. A1 - Creutzig, Felix A1 - Bogdan Guias, Horia A1 - Haddaway, Neal A1 - Hirth, Lion A1 - Javaid, Aneeque A1 - Koch, Nicolas A1 - Laukemper, Sonja A1 - Loeschel, Andreas A1 - Del Mar Zamora, Maria A1 - Minx, Jan C. T1 - Reducing carbon emissions of households through monetary incentives and behavioral interventions: a meta-analysis N2 - Despite the importance of evaluating all mitigation options so as to inform policy decisions addressing climate change, a systematic analysis of household-scale interventions to reduce carbon emissions is missing. Here, we address this gap through a state-of-the-art machine-learning assisted meta-analysis to comparatively assess the effectiveness of a range of monetary and behavioral interventions in energy demand of residential buildings. We identify 122 studies and extract 360 effect sizes representing trials on 1.2 million households in 25 countries. We find that all the studied interventions reduce energy consumption of households. Our meta-regression evidences that monetary incentives are on an average more effective than behavioral interventions, but deploying the right combinations of interventions together can increase overall effectiveness. We estimate global cumulative emissions reduction of 8.64 Gt CO2 by 2040, though deploying the most effective packages and interventions could result in greater reduction. While modest, this potential should be viewed in conjunction with the need for de-risking mitigation with energy demand reductions and realizing substantial co-benefits.  Y1 - 2021 U6 - https://doi.org/10.21203/rs.3.rs-124386/v1 ER - TY - JOUR A1 - Minx, Jan C. A1 - Callaghan, Max W. A1 - Lamb, William F. A1 - Garard, Jennifer A1 - Edenhofer, Ottmar T1 - Learning about climate change solutions in the IPCC and beyond JF - Environmental science & policy Y1 - 2017 U6 - https://doi.org/10.1016/j.envsci.2017.05.014 SN - 1462-9011 N1 - In Press, Corrected Proof When the final article is assigned to volumes/issues of the Publication, the Article in Press version will be removed and the final version will appear in the associated published volumes/issues of the Publication. The date the article was first made available online will be carried over. PB - Elsevier CY - Amsterdam [u.a.] ER - TY - JOUR A1 - Müller-Hansen, Finn A1 - Callaghan, Max W. A1 - Lee, Yuan Ting A1 - Leipprand, Anna A1 - Flachsland, Christian A1 - Minx, Jan C. T1 - Who cares about coal? Analyzing 70 years of German parliamentary debates on coal with dynamic topic modeling JF - Energy Research & Social Science N2 - Despite Germany’s Paris Agreement pledge and coal exit legislation, the political debate around carbon-intensive coal remains heated. Coal power and mining have played an important, yet changing role in the history of German politics. In this paper, we analyze the entire parliamentary debate on coal in the German parliament (Bundestag) from its inception in 1949 to 2019. For this purpose we extract the more than 870,000 parliamentary speeches from all protocols in the history of the Bundestag. We identify the 9167 speeches mentioning coal and apply dynamic topic modeling – an unsupervised machine learning technique that reveals the changing thematic structure of large document collections over time – to analyze changes in parliamentary debates on coal over the past 70 years. The trends in topics and their varying internal structure reflect how energy policy was discussed and legitimized over time: Initially, coal was framed as a driver of economic prosperity and guarantee of energy security. In recent years, the debate evolved towards energy transition, coal phase-out and renewable energy expansion. Germany’s smaller and younger parties, the Greens and the Left Party, debate coal more often in the context of the energy transition and climate protection than other parties. Our results reflect trends in other countries and other fields of energy policy. Methodologically, our study illustrates the potential of and need for computational methods to analyze vast corpora of text and to complement traditional social science methods. KW - Coal transition KW - Energy policy KW - Parliamentary debates KW - Topic modeling KW - Computational text analysis Y1 - 2021 U6 - https://doi.org/10.1016/j.erss.2020.101869 VL - 72 ER -