electricsheepafrica/africa-cog-climate-trace

--- annotations_creators: - no-annotation language_creators: - found language: - en license: cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - tabular-classification - tabular-regression task_ids: [] tags: - africa - humanitarian - hdx - electric-sheep-africa - climate-weather - environment - points-of-interest-poi - cog pretty_name: "Congo: Greenhouse Gas and Air Pollutant Emissions" dataset_info: splits: - name: train num_examples: 8992 - name: test num_examples: 2248 --- # Congo: Greenhouse Gas and Air Pollutant Emissions **Publisher:** Climate TRACE · **Source:** [HDX](https://data.humdata.org/dataset/cog-climate-trace) · **License:** `cc-by` · **Updated:** 2026-03-30 --- ## Abstract Climate TRACE is a non-profit coalition of organizations building a timely, open, and accessible inventory of exactly where greenhouse gas emissions are coming from. Climate TRACE estimates greenhouse gas (GHG) and air pollutant emissions for over 2.7 million sources (from over 744 million assets), and every single country globally. The Climate TRACE emissions inventory includes: - Annual country-level emissions by sub-sector and by gas beginning in 2015 - Monthly source-level emissions by sub-sector and gas beginning in 2021 and confidence - Emissions source ownership where and when available. Each row in this dataset represents time-series observations. Data was last updated on HDX on 2026-03-30. Geographic scope: **COG**. *Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).* --- ## Dataset Characteristics | | | |---|---| | **Domain** | Climate and environment | | **Unit of observation** | Time-series observations | | **Rows (total)** | 11,241 | | **Columns** | 13 (4 numeric, 9 categorical, 0 datetime) | | **Train split** | 8,992 rows | | **Test split** | 2,248 rows | | **Geographic scope** | COG | | **Publisher** | Climate TRACE | | **HDX last updated** | 2026-03-30 | --- ## Variables **Geographic** — `year` (range 2024.0–2026.0), `emissionsquantity` (range 0.0–36904.0683). **Temporal** — `month` (range 1.0–12.0). **Identifier / Metadata** — `full_name` (Congo, Kouilou Region, COG, Brazzaville Region, COG), `id` (COG, COG.5_1, COG.2_1), `level_0_id` (COG), `level_1_id` (COG.5_1, COG.2_1, COG.9_1), `name` (Congo, Kouilou Region, Brazzaville Region) and 2 others. **Other** — `level` (range 0.0–1.0), `sector` (agriculture, waste, transportation), `gas` (ch4). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-cog-climate-trace") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `full_name` | object | 0.0% | Congo, Kouilou Region, COG, Brazzaville Region, COG | | `id` | object | 0.0% | COG, COG.5_1, COG.2_1 | | `level` | int64 | 0.0% | 0.0 – 1.0 (mean 0.91) | | `level_0_id` | object | 0.0% | COG | | `level_1_id` | object | 9.0% | COG.5_1, COG.2_1, COG.9_1 | | `name` | object | 0.0% | Congo, Kouilou Region, Brazzaville Region | | `year` | int64 | 0.0% | 2024.0 – 2026.0 (mean 2024.6217) | | `month` | int64 | 0.0% | 1.0 – 12.0 (mean 6.6977) | | `sector` | object | 0.0% | agriculture, waste, transportation | | `gas` | object | 0.0% | ch4 | | `emissionsquantity` | float64 | 0.0% | 0.0 – 36904.0683 (mean 291.6092) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-04 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `level` | 0.0 | 1.0 | 0.91 | 1.0 | | `year` | 2024.0 | 2026.0 | 2024.6217 | 2025.0 | | `month` | 1.0 | 12.0 | 6.6977 | 7.0 | | `emissionsquantity` | 0.0 | 36904.0683 | 291.6092 | 0.1355 | --- ## Curation Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 1 column(s) with >80% missing values were removed: `level_2_id`. 10,885 exact duplicate rows were removed. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet. --- ## Limitations - Data originates from Climate TRACE and has not been independently validated by ESA. - Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection. - Refer to the [original HDX dataset page](https://data.humdata.org/dataset/cog-climate-trace) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_cog_climate_trace, title = {Congo: Greenhouse Gas and Air Pollutant Emissions}, author = {Climate TRACE}, year = {2026}, url = {https://data.humdata.org/dataset/cog-climate-trace}, note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)} } ``` --- *[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*annotations_creators: - 无注释 language_creators: - 爬取获取 language: - 英语 license: CC-BY-4.0 multilinguality: - 单语言 size_categories: - 10000<n<100000 source_datasets: - 原创 task_categories: - 表格分类 - 表格回归 task_ids: [] tags: - 非洲 - 人道主义 - HDX（人道主义数据交换） - Electric Sheep Africa - 气候与天气 - 环境 - 兴趣点(POI) - COG（刚果共和国） pretty_name: "刚果：温室气体与大气污染物排放" dataset_info: splits: - name: train num_examples: 8992 - name: test num_examples: 2248 # 刚果：温室气体与大气污染物排放 **发布方**：Climate TRACE · **来源**：[HDX（人道主义数据交换）](https://data.humdata.org/dataset/cog-climate-trace) · **许可证**：`cc-by` · **更新时间**：2026-03-30 --- ## 摘要 Climate TRACE 是由多家机构组成的非营利性联盟，旨在构建及时、开放且可获取的温室气体排放来源精准清单。该联盟针对全球所有国家的超270万个排放源（源自超7.44亿个资产），估算了温室气体（GHG）与大气污染物的排放量。 Climate TRACE 排放清单包含以下内容： - 2015年起按子行业与气体类型划分的年度国家级排放数据 - 2021年起按子行业、气体类型划分的月度排放源级数据及置信度信息 - 可获取时的排放源所有权信息。 本数据集的每一行均代表一组时间序列观测值。数据最后于2026-03-30在HDX（人道主义数据交换）平台更新。地理覆盖范围：**COG（刚果共和国）**。 *由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为适配机器学习的Parquet（帕库）格式。* --- ## 数据集特征 | 类别 | 详情 | |---|---| | **应用领域** | 气候与环境 | | **观测单元** | 时间序列观测值 | | **总行数** | 11241 | | **列数** | 13列（4列数值型、9列分类型、0列日期时间型） | | **训练集划分** | 8992行 | | **测试集划分** | 2248行 | | **地理覆盖范围** | COG（刚果共和国） | | **发布方** | Climate TRACE | | **HDX最后更新时间** | 2026-03-30 | --- ## 变量说明 **地理类变量** — `year`（取值范围：2024.0–2026.0）、`emissionsquantity`（取值范围：0.0–36904.0683）。 **时间类变量** — `month`（取值范围：1.0–12.0）。 **标识符/元数据类变量** — `full_name`（示例值：刚果、奎卢地区、COG（刚果共和国）、布拉柴维尔地区、COG（刚果共和国））、`id`（示例值：COG（刚果共和国）、COG.5_1、COG.2_1）、`level_0_id`（示例值：COG（刚果共和国））、`level_1_id`（示例值：COG.5_1、COG.2_1、COG.9_1）、`name`（示例值：刚果、奎卢地区、布拉柴维尔地区）及另外2个变量。 **其他类变量** — `level`（取值范围：0.0–1.0）、`sector`（示例值：农业、废弃物处理、交通运输）、`gas`（示例值：ch4）。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-cog-climate-trace") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() --- ## 数据结构 | 列名 | 数据类型 | 缺失率 | 取值范围/示例值 | |---|---|---|---| | `full_name` | object | 0.0% | 刚果、奎卢地区、COG（刚果共和国）、布拉柴维尔地区、COG（刚果共和国） | | `id` | object | 0.0% | COG（刚果共和国）、COG.5_1、COG.2_1 | | `level` | int64 | 0.0% | 0.0 – 1.0（均值：0.91） | | `level_0_id` | object | 0.0% | COG（刚果共和国） | | `level_1_id` | object | 9.0% | COG.5_1、COG.2_1、COG.9_1 | | `name` | object | 0.0% | 刚果、奎卢地区、布拉柴维尔地区 | | `year` | int64 | 0.0% | 2024.0 – 2026.0（均值：2024.6217） | | `month` | int64 | 0.0% | 1.0 – 12.0（均值：6.6977） | | `sector` | object | 0.0% | 农业、废弃物处理、交通运输 | | `gas` | object | 0.0% | ch4 | | `emissionsquantity` | float64 | 0.0% | 0.0 – 36904.0683（均值：291.6092） | | `esa_source` | object | 0.0% | HDX（人道主义数据交换） | | `esa_processed` | object | 0.0% | 2026-04-04 | --- ## 数值型变量统计摘要 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `level` | 0.0 | 1.0 | 0.91 | 1.0 | | `year` | 2024.0 | 2026.0 | 2024.6217 | 2025.0 | | `month` | 1.0 | 12.0 | 6.6977 | 7.0 | | `emissionsquantity` | 0.0 | 36904.0683 | 291.6092 | 0.1355 | --- ## 数据整理流程 原始数据通过CKAN API从HDX（人道主义数据交换）平台下载，并转换为Parquet（帕库）格式。将列名统一转为小写并标准化为蛇形命名法（snake_case）。将常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）统一替换为`NaN`。移除了1列缺失率超过80%的变量`level_2_id`，并删除了10885条完全重复的行。本数据集使用固定随机种子（42）按80/20的比例划分为训练集与测试集，并以Snappy压缩的Parquet（帕库）格式存储。 --- ## 数据集局限性 - 本数据集源自Climate TRACE，未经过Electric Sheep Africa（以下简称ESA）的独立验证。 - 自动化清洗流程无法修正原始数据集中的错报值、定义不一致问题或采样偏差。 - 如需了解发布方的方法说明与免责声明，请参阅[原始HDX（人道主义数据交换）数据集页面](https://data.humdata.org/dataset/cog-climate-trace)。 --- ## 引用格式 bibtex @dataset{hdx_africa_cog_climate_trace, title = {Congo: Greenhouse Gas and Air Pollutant Emissions}, author = {Climate TRACE}, year = {2026}, url = {https://data.humdata.org/dataset/cog-climate-trace}, note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)} } --- *[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — 非洲机器学习数据集基础设施。尼日利亚拉各斯。*