targetfev / AutoGluon ↗Global Sovereign Carbon Dioxide (CO2) Emissions
Covers annual sovereign carbon dioxide (CO2) emissions across 191 nations spanning 60 historical years at yearly cadence. Evaluates a 5-year forecast horizon to project sovereign decarbonization trajectories and national emissions reduction targets.
Business Objective: Global Sovereign Carbon Dioxide (CO2) Emissions
01Business Context
National environmental agencies, international treaty bodies (UNFCCC), sovereign wealth funds, and ESG risk committees require multi-year emissions projections to calibrate climate transition risk, verify Nationally Determined Contributions (NDCs), and assess global carbon budgets on an annual cycle.
02Analytical Objective
Forecast territorial carbon dioxide emissions for 191 sovereign states over a 5-year forward horizon (H=5) using six decades of annual historical emission dynamics.
03Economic & Decision Impact
Under-forecasting emissions obscures structural transition bottlenecks, leaving governments exposed to treaty penalties, underfunded carbon capture infrastructure, and unmitigated climate liability. Over-forecasting misallocates capital toward stranded decarbonization assets and penalizes sovereign credit ratings through over-stringent regulatory burdens.
Due to smooth non-seasonal annual trends across 60 time steps, classical statistical baselines like AutoARIMA and damped Holt-Winters ETS deliver stable linear drift extrapolations but struggle with structural economic decoupling. Gradient boosted trees (LightGBM lag models) capture non-linear sovereign economic shifts when cross-country features are shared, while zero-shot time series foundation models (such as Mistral's Timestral and MOIRAI) excel at generalizing multi-country macroeconomic transition curves without overfitting short series lengths.
Target & Feature Column Definitions
Exhaustive business definitions, measurement units, roles, and target variables across all 3 columns.
| Col | Variable / Feature Name | Role | Type | Unit / Scale | Description & Business Meaning |
|---|---|---|---|---|---|
| C | targetAnnual Territorial CO2 Emissions | TARGET | NUM | kt CO2 | Total annual territorial carbon dioxide emissions produced from fossil fuel combustion and industrial processes. |
| A | idCountry / Sovereign Entity | SERIES ID | CAT | — | Unique sovereign state, country, or territorial identifier for each time series. |
| B | timestampReporting Year | TIME | TIME | year | Annual reporting timestamp marking the end of each calendar observation period from 1960 onwards. |
Interactive Data Table
Explore rows, feature values, and target labels for Global Sovereign Carbon Dioxide (CO2) Emissions.
Foundation Models vs Traditional ML & Baselines (41 Methods)
Empirical evaluation directly from the AutoGluon fev benchmark suite evaluating zero-shot foundation models against deep learning, gradient-boosted trees, and statistical baselines across 100 evaluation tasks.
| Rank | Method / Model | Family | Organization | SQL Win Rate?Average win rate (%) against all competing models across the benchmark tasks under Scaled Quantile Loss. | SQL Skill?Normalized skill score relative to the Seasonal Naive baseline (0% = baseline, higher is better). | MASE Win Rate?Average win rate (%) under Mean Absolute Scaled Error. | Latency?Median end-to-end execution time per 100 time series in seconds. | Link |
|---|---|---|---|---|---|---|---|---|
| #1 | Timestral-209M | Foundation Model | Mistral AI | 87.7% | +50.4% | 86.0% | 0.51s | Repo ↗ |
| #2 | Timestral-123M (Carla Engine) | Foundation Model | Mistral AI | 86.8% | +50.2% | 85.0% | 0.32s | Repo ↗ |
| #3 | TimeRSI | Closed-Source API | GaTech & SamayLabs | 84.8% | +51.2% | 83.8% | 2512s | Repo ↗ |
| #4 | t0-gamma | Foundation Model | The Forecasting Company | 84.4% | +49.4% | 81.8% | 4.38s | Repo ↗ |
| #5 | Timestral-28M | Foundation Model | Mistral AI | 79.2% | +48.4% | 77.0% | 0.11s | Repo ↗ |
| #6 | timesfm-3 | Foundation Model | 78.5% | +49.0% | 77.0% | 3.41s | Repo ↗ | |
| #7 | ChakraTS | Baseline | Community | 78.2% | +48.1% | 79.5% | 14.22s | Repo ↗ |
| #8 | Lingjiang2.0 | Baseline | Community | 73.9% | +47.0% | 71.3% | 8.89s | Repo ↗ |
| #9 | Chronos-2 | Foundation Model | AWS | 70.4% | +47.3% | 68.4% | 0.84s | Repo ↗ |
| #10 | timeh2o-v1-tiny | Foundation Model | H2O.ai | 69.9% | +45.5% | 67.0% | 0.19s | Repo ↗ |
| #11 | Timestral-5M | Foundation Model | Mistral AI | 69.0% | +46.2% | 67.7% | 40ms | Repo ↗ |
| #12 | Toto-2.0-2.5B | Baseline | Community | 67.5% | +44.4% | 65.7% | 5.58s | Repo ↗ |
| #13 | t0-beta | Foundation Model | The Forecasting Company | 67.4% | +46.6% | 64.5% | 2.29s | Repo ↗ |
| #14 | Toto-2.0-1B | Baseline | Community | 66.4% | +44.4% | 65.5% | 2.48s | Repo ↗ |
| #15 | TiRex-2 | Foundation Model | NX-AI | 65.8% | +45.5% | 63.4% | 0.27s | Repo ↗ |
| #16 | Toto-2.0-313m | Baseline | Community | 65.1% | +44.2% | 63.5% | 1.06s | Repo ↗ |
| #17 | TiRex | Foundation Model | NX-AI | 58.0% | +42.6% | 54.1% | 0.24s | Repo ↗ |
| #18 | Toto-2.0-22m | Baseline | Community | 57.6% | +42.9% | 56.0% | 0.25s | Repo ↗ |
| #19 | TimesFM-2.5 | Baseline | Community | 54.6% | +42.2% | 53.5% | 1.87s | Repo ↗ |
| #20 | TabPFN-TS-3 | Foundation Model | Prior Labs | 52.4% | +43.1% | 51.3% | 235s | Repo ↗ |
| #21 | TS-ICL | Foundation Model | EDF-Lab | 51.2% | +43.1% | 49.8% | 2.49s | Repo ↗ |
| #22 | FlowState | Foundation Model | IBM | 49.3% | +39.9% | 49.9% | 2.29s | Repo ↗ |
| #23 | citras-fm | Foundation Model | Hitachi | 48.8% | +41.2% | 46.9% | 0.60s | Repo ↗ |
| #24 | Toto-1.0 | Baseline | Community | 47.5% | +40.8% | 45.9% | 22.35s | Repo ↗ |
| #25 | TabPFN-TS | Foundation Model | Prior Labs | 47.3% | +41.5% | 45.4% | 109s | Repo ↗ |
| #26 | Toto-2.0-4m | Baseline | Community | 46.2% | +40.7% | 43.6% | 0.20s | Repo ↗ |
| #27 | Moirai-2.0 | Baseline | Community | 41.9% | +39.3% | 40.5% | 0.35s | Repo ↗ |
| #28 | Chronos-Bolt | Foundation Model | AWS | 41.4% | +38.9% | 39.3% | 0.26s | Repo ↗ |
| #29 | TFT | Deep Learning | — | 31.7% | +32.2% | 29.2% | 829s | Repo ↗ |
| #30 | Stat. Ensemble | Baseline | Community | 27.5% | +20.7% | 30.6% | 147s | Repo ↗ |
| #31 | Sundial-Base | Foundation Model | Tsinghua University | 27.4% | +33.4% | 33.6% | 8.01s | Repo ↗ |
| #32 | PatchTST | Deep Learning | — | 26.8% | +30.1% | 25.1% | 664s | Repo ↗ |
| #33 | DeepAR | Deep Learning | — | 26.1% | +29.1% | 23.6% | 1113s | Repo ↗ |
| #34 | AutoARIMA | Statistical Baseline | — | 23.3% | +21.3% | 21.5% | 20.14s | Repo ↗ |
| #35 | AutoETS | Statistical Baseline | — | 20.3% | -26.8% | 22.5% | 3.48s | Repo ↗ |
| #36 | CatBoost | Tree Ensemble | — | 19.1% | +23.0% | 35.1% | 29.48s | Repo ↗ |
| #37 | LightGBM | Tree Ensemble | — | 17.8% | +21.0% | 33.1% | 3.03s | Repo ↗ |
| #38 | AutoTheta | Statistical Baseline | — | 15.6% | +5.5% | 19.8% | 3.28s | Repo ↗ |
| #39 | Seasonal Naive | Baseline | Community | 10.3% | 0.0% | 11.8% | 0.48s | Repo ↗ |
| #40 | Naive | Statistical Baseline | — | 6.7% | -45.4% | 10.9% | 0.47s | Repo ↗ |
| #41 | Drift | Statistical Baseline | — | 6.1% | -45.8% | 10.0% | 0.45s | Repo ↗ |
Variable Schema & Summary Distributions
Observed numerical ranges, category cardinalities, missing rates, and sample values across 11,460 rows.
| Col | Variable Name | Type | Missing | Distinct | Summary Stats / Distribution | Sample Values |
|---|---|---|---|---|---|---|
| C | targetTARGET | NUM | 0 | 8,292 | [-80.67400360107422, 34344008] μ=992162 σ=3049003 | 414.3710021972656491.37799072265625689.39599609375 |
| A | id | CAT | 0 | 191 | — | AfghanistanAfghanistanAfghanistan |
| B | timestamp | CAT | 0 | 60 | — | 1960-12-31T00:00:001961-12-31T00:00:001962-12-31T00:00:00 |
Frequently Asked Questions: Global Sovereign Carbon Dioxide (CO2) Emissions
Common questions regarding the Global Sovereign Carbon Dioxide (CO2) Emissions dataset, machine learning task formulations, and in-browser tabular inference.
What is the forecasting horizon for Global Sovereign Carbon Dioxide (CO2) Emissions?
The standardized forecasting horizon is 5 years (H=5) at an annual cadence, matching international multi-year climate policy review cycles and sovereign NDC updates.
Can I run zero-server forecasts on this dataset in Google Sheets?
Yes, Carla enables zero-server in-context time series forecasting directly in spreadsheets using modern foundation models such as Mistral's Timestral, requiring no remote API round-trips or server infrastructure.
Which models perform best on Global Sovereign Carbon Dioxide (CO2) Emissions?
Zero-shot foundation models like Mistral's Timestral and MOIRAI excel on multi-entity yearly series by leveraging cross-country macro trends, while tuned LightGBM and AutoARIMA baselines provide strong competitive performance on smooth, monotonic trajectories.
Test TabICLv2 on Global Sovereign Carbon Dioxide (CO2) Emissions Yourself
Open the pre-loaded Google Sheet and let Carla configure the target and task for local, zero-cloud tabular machine learning.
Launch Carla
Click once to open the spreadsheet and Carla side panel together.
Review the Setup
Carla selects the dataset target and task from this page automatically.
Evaluate & Predict
Run predictions and compute metrics with zero server uploads.
Provenance & Attribution
Friedlingstein et al., Global Carbon Budget 2023, Earth System Science Data, 15, 5301–5369, 2023.