Filters

20203

More filters

2021 - 20223
Reset filters

Settings

End date: 2020 × Start date: 2020 × DDC: 500 × Version: publishedVersion × Subject: France ×

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Paris Climate Agreement passes the cost-benefit test
    ([London] : Nature Publishing Group UK, 2020) Glanemann, Nicole; Willner, Sven N.; Levermann, Anders
    The Paris Climate Agreement aims to keep temperature rise well below 2 °C. This implies mitigation costs as well as avoided climate damages. Here we show that independent of the normative assumptions of inequality aversion and time preferences, the agreement constitutes the economically optimal policy pathway for the century. To this end we consistently incorporate a damage-cost curve reproducing the observed relation between temperature and economic growth into the integrated assessment model DICE. We thus provide an inter-temporally optimizing cost-benefit analysis of this century’s climate problem. We account for uncertainties regarding the damage curve, climate sensitivity, socioeconomic future, and mitigation costs. The resulting optimal temperature is robust as can be understood from the generic temperature-dependence of the mitigation costs and the level of damages inferred from the observed temperature-growth relationship. Our results show that the politically motivated Paris Climate Agreement also represents the economically favourable pathway, if carried out properly.
  • Thumbnail Image
    Item
    Taking stock of national climate policies to evaluate implementation of the Paris Agreement
    ([London] : Nature Publishing Group UK, 2020) Roelfsema, Mark; van Soest, Heleen L.; Harmsen, Mathijs; van Vuuren, Detlef P.; Bertram, Christoph; den Elzen, Michel; Höhne, Niklas; Iacobuta, Gabriela; Krey, Volker; Kriegler, Elmar; Luderer, Gunnar; Riahi, Keywan; Ueckerdt, Falko; Després, Jacques; Drouet, Laurent; Emmerling, Johannes; Frank, Stefan; Fricko, Oliver; Gidden, Matthew; Humpenöder, Florian; Huppmann, Daniel; Fujimori, Shinichiro; Fragkiadakis, Kostas; Gi, Keii; Keramidas, Kimon; Köberle, Alexandre C.; Aleluia Reis, Lara; Rochedo, Pedro; Schaeffer, Roberto; Oshiro, Ken; Vrontisi, Zoi; Chen, Wenying; Iyer, Gokul C.; Edmonds, Jae; Kannavou, Maria; Jiang, Kejun; Mathur, Ritu; Safonov, George; Vishwanathan, Saritha Sudharmma
    Many countries have implemented national climate policies to accomplish pledged Nationally Determined Contributions and to contribute to the temperature objectives of the Paris Agreement on climate change. In 2023, the global stocktake will assess the combined effort of countries. Here, based on a public policy database and a multi-model scenario analysis, we show that implementation of current policies leaves a median emission gap of 22.4 to 28.2 GtCO2eq by 2030 with the optimal pathways to implement the well below 2 °C and 1.5 °C Paris goals. If Nationally Determined Contributions would be fully implemented, this gap would be reduced by a third. Interestingly, the countries evaluated were found to not achieve their pledged contributions with implemented policies (implementation gap), or to have an ambition gap with optimal pathways towards well below 2 °C. This shows that all countries would need to accelerate the implementation of policies for renewable technologies, while efficiency improvements are especially important in emerging countries and fossil-fuel-dependent countries.
  • Thumbnail Image
    Item
    Learning from urban form to predict building heights
    (San Francisco, California, US : PLOS, 2020) Milojevic-DupontI, Nikola; Hans, Nicolai; Kaack, Lynn H.; Zumwald, Marius; Andrieux, François; de Barros Soares, Daniel; Lohrey, Steffen; PichlerI, Peter-Paul; Creutzig, Felix
    Understanding cities as complex systems, sustainable urban planning depends on reliable high-resolution data, for example of the building stock to upscale region-wide retrofit policies. For some cities and regions, these data exist in detailed 3D models based on real-world measurements. However, they are still expensive to build and maintain, a significant challenge, especially for small and medium-sized cities that are home to the majority of the European population. New methods are needed to estimate relevant building stock characteristics reliably and cost-effectively. Here, we present a machine learning based method for predicting building heights, which is based only on open-access geospatial data on urban form, such as building footprints and street networks. The method allows to predict building heights for regions where no dedicated 3D models exist currently. We train our model using building data from four European countries (France, Italy, the Netherlands, and Germany) and find that the morphology of the urban fabric surrounding a given building is highly predictive of the height of the building. A test on the German state of Brandenburg shows that our model predicts building heights with an average error well below the typical floor height (about 2.5 m), without having access to training data from Germany. Furthermore, we show that even a small amount of local height data obtained by citizens substantially improves the prediction accuracy. Our results illustrate the possibility of predicting missing data on urban infrastructure; they also underline the value of open government data and volunteered geographic information for scientific applications, such as contextual but scalable strategies to mitigate climate change.