Showing 1 - 4 results of 4 for search '"Intergovernmental Panel on Climate Change"', query time: 0.03s Refine Results
  1. 1

    Mainstreaming precast and block hempcrete—a carbon sequestering solution for the built environment by Pandwe Gibson

    Published 2024-11-01
    “…We explore how lime, CO2, and structural components can increase commercial viability and create the scale necessary for the United Nations Intergovernmental Panel on Climate Change’s (IPCC) call for nations to maintain the global temperature increase below 1.5°C and net zero by 2050 while tackling the global housing crisis. …”
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  2. 2

    Considerations for determining warm-water coral reef tipping points by P. Pearce-Kelly, A. H. Altieri, J. F. Bruno, C. E. Cornwall, M. McField, A. I. Muñiz-Castillo, J. Rocha, R. O. Setter, C. Sheppard, R. M. Roman-Cuesta, C. Yesson

    Published 2025-02-01
    “…These impacts may drive coral ecosystems past critical thresholds, beyond which the system reorganises, often abruptly and potentially irreversibly; this is what the Intergovernmental Panel on Climate Change (IPCC, 2022) define as a tipping point. …”
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  3. 3

    Analysis of drought and extreme precipitation events in Thailand: trends, climate modeling, and implications for climate change adaptation by José Francisco de Oliveira-Júnior, David Mendes, Helder Dutra Porto, Kelvy Rosalvo Alencar Cardoso, José Augusto Ferreira Neto, Emannuel Bezerra Cavalcante da Silva, Marlúcia de Aquino Pereira, Monica Cristina Damião Mendes, Bernardo Bruno Dias Baracho, Punyawi Jamjareegulgarn

    Published 2025-02-01
    “…The climate indices used were Consecutive Dry Days (CDD), Maximum Number of Consecutive Summer Days (CSU), Consecutive Wet Days (CWD), Warm Spell Duration Index (WSDI), and Maximum Number of Consecutive Wet Days (WW) derived from simulations of an ensemble composed of six models from the Intergovernmental Panel on Climate Change (IPCC) via the Coupled Model Intercomparison Project Phase 6 (CMIP6) using Artificial Neural Networks (ANN) with the backpropagation method. …”
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  4. 4

    Research on Characteristics and Influencing Factors of High Temperature Disaster Risk in Wuhan Based on Local Climate Zone by Shujing GUO, Li ZHANG

    Published 2025-01-01
    “…Mapping the high temperature disaster risk in the urban development area of Wuhan and analyzing the high temperature disaster risk and influencing factors thereof at the local scale can provide an important basis for the prevention of high temperature disasters in the city.MethodsBased on the “hazard – exposure – vulnerability” high temperature disaster risk assessment framework proposed by the Intergovernmental Panel on Climate Change, this research constructed an assessment system by utilizing multi-source data, and then pre-processes all relevant indicators to make them dimensionless. …”
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