Tarefa causcibench_real.q116

Veredito

Pergunta feita ao analista

How much of global total economic damages due to extreme weather is attributable to climate change? (in thousands)

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Verificações

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Trajetória

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1. 0Início

   How much of global total economic damages due to extreme weather is attributable to climate change? (in thousands)

   newman_global_costs (agent)

   The study examines the global economic costs of extreme weather events attributable to climate change using Event Attribution methodology. It analyzes 185 extreme weather events from 2000-2019 across multiple countries, combining data from climate attribution studies with socio-economic impact data from the Emergency Management Database (EM-DAT). The methodology uses climate models to compare observed extreme weather events with counterfactual scenarios without anthropogenic greenhouse gas emissions. This generates Fraction of Attributable Risk (FAR) estimates quantifying what portion of each event's risk is attributable to human-induced climate change. These FAR values are then applied to observed mortality and economic damages to calculate climate change-attributable costs globally. Variable description: - Event Identifier: Identifies the specific extreme weather event - Year: Year of event - Country/region: Country of event - Continent: Continent where event happened - Disaster Type: One of Flood, Heatwave, Wildfire, Drought, Storm, Cold wave - FAR Best Estimate: FAR value = 1 - p_1/p_0, where p_1 is the probability of event in the real world, and p_0 in counterfactual world without climate change. - Total Deaths: Total number of deaths caused by event - Total Affected - Total Damage (US$ Thousands): Amount of damage caused in thousands of US dollars per event - Insured Loss (US$ Thousands): Amount of economic damage insured - Uninsured damages: Difference of previous two - Total Damage (US$ Millions): Amount of damage caused in millions of US dollars per event - Insured Loss (US$ Millions): Amount of economic damage insured (in millions)

   file b5566c38a3ac6ecba6cc014aea2af8aaa376b6fed658c546a704080cb4a8240d · frame —
2. 1Turno do modelo
   call_df3e237dreferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   causcibench_metadata_rule/1: ols

   Chamadas pedidas

   • infer.ols

     {
       "controls": [],
       "dataset": "newman_global_costs",
       "x": [
         "FAR Best Estimate"
       ],
       "y": "Total Damage (US$ Thousands)"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.olsv0.1.0call_a24ccc8ddecidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: ols

   Argumentos

   {
     "cluster": null,
     "controls": [],
     "dataset": "newman_global_costs",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "FAR Best Estimate"
     ],
     "y": "Total Damage (US$ Thousands)"
   }

   Figuras call_a24ccc8d

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.FAR Best Estimate Coefficient on FAR Best Estimate in the regression of Total Damage (US$ Thousands)	2.485.720	2485720.7916303845	2.238.3702238368.5876999022	-1.948.470 – 6.919.910

   Diagnósticos

   • n_obs = 116116
   • k = 22
   • r2 = 0,007724940.007724937614798977
   • df_resid = 114114
   • se_type = "HC1"
   • n_clusters = —

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 5116862.193457595,
         "ci_low": 1376350.1531758988,
         "coef": 3246606.173316747,
         "p": 0.0008167544203470741,
         "se": 944100.601527751,
         "t": 3.438834980152606,
         "term": "Intercept"
       },
       {
         "ci_high": 6919911.723894987,
         "ci_low": -1948470.1406342182,
         "coef": 2485720.7916303845,
         "p": 0.2691180557229439,
         "se": 2238368.5876999022,
         "t": 1.110505573250854,
         "term": "FAR Best Estimate"
       }
     ]
   }

   • 75 rows were dropped because one of 'Total Damage (US$ Thousands)', 'FAR Best Estimate' was missing

   Digests dos dados

   newman_global_costs 6d5cad51d45205e9a2becb995681807ee23c75f78a49e411ace6a31ff6cc0d24

   sha256 do resultado

   727f6b5d6a01f795d493816306055c6b082a636fbbc3da35929d816610b65cc2

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_5dcbdba4referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa (infer.ols): coef.FAR Best Estimate = 2485720.",
       "caveats": [],
       "figure_refs": [
         "call_a24ccc8d:coef.FAR Best Estimate"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
5. 4Guarda
   resposta aceita

   Números encontrados

   2485720

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
6. 5Resposta

   Estimativa (infer.ols): coef.FAR Best Estimate = 2485720.

   Figuras citadas: call_a24ccc8d:coef.FAR Best Estimate · Parada: answered

   Figura da resposta: call_a24ccc8d:coef.FAR Best Estimate = 2.485.7202485720.7916303845 (se 2.238.3702238368.5876999022)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximafalhouobrigatória2485720.792 vs -1413836.076

Épocas × verificações

Tentativas

Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.

• época 1, tentativa 1falhoupontuada(exibindo)

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