@techreport{KhannaBaiocchiCallaghanetal., type = {Working Paper}, author = {Khanna, Tarun and Baiocchi, Giovanni and Callaghan, Max W. and Creutzig, Felix and Bogdan Guias, Horia and Haddaway, Neal and Hirth, Lion and Javaid, Aneeque and Koch, Nicolas and Laukemper, Sonja and Loeschel, Andreas and Del Mar Zamora, Maria and Minx, Jan C.}, title = {Reducing carbon emissions of households through monetary incentives and behavioral interventions: a meta-analysis}, doi = {10.21203/rs.3.rs-124386/v1}, abstract = {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. }, language = {de} } @article{MinxCallaghanLambetal., author = {Minx, Jan C. and Callaghan, Max W. and Lamb, William F. and Garard, Jennifer and Edenhofer, Ottmar}, title = {Learning about climate change solutions in the IPCC and beyond}, series = {Environmental science \& policy}, journal = {Environmental science \& policy}, publisher = {Elsevier}, address = {Amsterdam [u.a.]}, issn = {1462-9011}, doi = {10.1016/j.envsci.2017.05.014}, language = {en} } @article{MuellerHansenCallaghanLeeetal., author = {M{\"u}ller-Hansen, Finn and Callaghan, Max W. and Lee, Yuan Ting and Leipprand, Anna and Flachsland, Christian and Minx, Jan C.}, title = {Who cares about coal? Analyzing 70 years of German parliamentary debates on coal with dynamic topic modeling}, series = {Energy Research \& Social Science}, volume = {72}, journal = {Energy Research \& Social Science}, doi = {10.1016/j.erss.2020.101869}, abstract = {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.}, language = {en} }