Loading...
Provides datasets on global and regional CO2 emissions from fossil fuels and land use, carbon sinks in oceans and land, and net carbon flux data. Used in data engineering for climate analytics pipelines, carbon accounting systems, and building greenhouse gas monitoring dashboards in Python.
The Global Carbon Atlas provides downloadable CSV and NetCDF files. Engineers use `pandas.read_csv()` for country-level emissions data and `xarray` with `netCDF4` for gridded atmospheric concentration datasets, enabling spatial carbon flux analysis.
Global Carbon Atlas data trains AI climate models for emission prediction and carbon budget forecasting. RAG systems built on this dataset help LLMs answer 'Which countries are responsible for the largest share of global CO2 emissions?' with authoritative carbon science data.
# pip install pandas
import pandas as pd
# Download CSV from globalcarbonatlas.org
url = "https://globalcarbonatlas.org/wp-content/uploads/2023/12/GCB2023v1.1_FF_emissions_territorial_countries.csv"
df = pd.read_csv(url, index_col=0)
# Latest year emissions
latest = df.iloc[:, -1].sort_values(ascending=False)
print("Top 10 emitters:", latest.head(10))Official dataset source
More datasets used by Python data engineers.
The OECD provides macroeconomic, social, and environmental datasets for 38 member countries including GDP, employment, health spending, education attainment, and trade. Used in data engineering for building country comparison dashboards, policy analysis pipelines, and economic indicator tracking systems in Python.
Access 16,000+ development indicators from the World Bank covering GDP, poverty, health, education, infrastructure, and environment for 200+ countries. Used in data engineering for building global development dashboards, time-series analysis pipelines, and cross-country economic comparison systems in Python.
The World Economic Forum publishes datasets and reports covering global competitiveness, gender parity, energy transition, and digital economy indicators across 140+ countries. Used in data engineering for economic research pipelines, policy benchmarking systems, and building global indicator dashboards in Python.