electricsheepafrica/africa-world-bank-climate-change-indicators-for-gabon

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- climate-weather
- indicators
- gab
pretty_name: "Gabon - Climate Change"
dataset_info:
splits:
- name: train
num_examples: 1199
- name: test
num_examples: 299
---

# Gabon - Climate Change

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-climate-change-indicators-for-gabon) · **License:** `cc-by` · **Updated:** 2026-03-27

---

## Abstract

Contains data from the World Bank's [data portal](http://data.worldbank.org/). There is also a [consolidated country dataset](https://data.humdata.org/dataset/world-bank-combined-indicators-for-gabon) on HDX.

Climate change is expected to hit developing countries the hardest. Its effects—higher temperatures, changes in precipitation patterns, rising sea levels, and more frequent weather-related disasters—pose risks for agriculture, food, and water supplies. At stake are recent gains in the fight against poverty, hunger and disease, and the lives and livelihoods of billions of people in developing countries. Addressing climate change requires unprecedented global cooperation across borders. The World Bank Group is helping support developing countries and contributing to a global solution, while tailoring our approach to the differing needs of developing country partners. Data here cover climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use. Other indicators relevant to climate change are found under other data pages, particularly Environment, Agriculture & Rural Development, Energy & Mining, Health, Infrastructure, Poverty, and Urban Development.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: **GAB**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Food security and nutrition |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 1,499 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 1,199 rows |
| **Test split** | 299 rows |
| **Geographic scope** | GAB |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2026-03-27 |

---

## Variables

**Geographic** — `country_name` (Gabon), `country_iso3` (GAB), `year` (range 1960.0–2024.0).

**Outcome / Measurement** — `value` (range -435710000.0–369790000.0).

**Identifier / Metadata** — `indicator_name` (Urban population (% of total population), Urban population, Population, total), `indicator_code` (SP.URB.TOTL.IN.ZS, SP.URB.TOTL, SP.POP.TOTL), `esa_source` (HDX), `esa_processed` (2026-04-11).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-climate-change-indicators-for-gabon")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `country_name` | object | 0.0% | Gabon |
| `country_iso3` | object | 0.0% | GAB |
| `year` | int64 | 0.0% | 1960.0 – 2024.0 (mean 1998.018) |
| `indicator_name` | object | 0.0% | Urban population (% of total population), Urban population, Population, total |
| `indicator_code` | object | 0.0% | SP.URB.TOTL.IN.ZS, SP.URB.TOTL, SP.POP.TOTL |
| `value` | float64 | 0.0% | -435710000.0 – 369790000.0 (mean -781068.6222) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-11 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1960.0 | 2024.0 | 1998.018 | 2000.0 |
| `value` | -435710000.0 | 369790000.0 | -781068.6222 | 42.4 |

---

## 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`. 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 World Bank Group 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/world-bank-climate-change-indicators-for-gabon) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_world_bank_climate_change_indicators_for_gabon,
title = {Gabon - Climate Change},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-climate-change-indicators-for-gabon},
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.*

搜集汇总

数据集介绍

构建方式

在气候变化研究领域，数据集的构建往往依赖于权威机构的长期监测与系统化整合。本数据集源于世界银行集团的气候变化指标数据库，原始数据通过人道主义数据交换平台获取，并由Electric Sheep Africa团队进行专业化处理。构建过程涉及从CKAN API下载原始数据，随后进行格式转换与标准化清洗，包括统一列名为蛇形命名法、规范化缺失值标记为NaN。为确保机器学习任务的适用性，数据被划分为训练集与测试集，采用固定随机种子实现80/20的比例分割，最终以Snappy压缩的Parquet格式存储，兼顾了数据完整性与处理效率。

特点

该数据集聚焦于加蓬这一非洲国家，涵盖了1960年至2024年间的气候相关指标，体现了时间跨度的纵深性。其核心特征在于以国家层面的聚合数据为观测单元，包含城市人口比例、总人口等关键社会经济与气候变量，共计8个字段，其中数值型与分类型变量分布均衡。数据规模适中，总计1499条记录，已预先分割为训练集与测试集，便于直接应用于建模任务。此外，数据集经过精心清洗与标准化，缺失值处理一致，且所有字段均无空值，确保了较高的数据质量与机器学习流程的顺畅性。

使用方法

在气候建模与政策分析的应用场景中，该数据集可直接服务于监督学习任务，如基于历史指标的回归预测或分类研究。使用者可通过Hugging Face的datasets库便捷加载，利用提供的Python代码片段快速导入数据并转换为Pandas DataFrame进行探索。数据已预分割为训练集与测试集，用户可在此基础上构建特征工程，例如利用年份、指标代码等字段进行时序分析或交叉验证。需要注意的是，数据源自世界银行，虽经清洗但未独立验证，建议结合原始方法论说明进行结果解读，以确保分析结论的稳健性与可解释性。

背景与挑战

背景概述

加蓬世界银行气候变化指标数据集由世界银行集团于2026年发布，并由Electric Sheep Africa机构进行机器学习格式的整理与发布。该数据集聚焦于加蓬这一非洲发展中国家，旨在系统性地量化气候变化对该国社会经济与自然环境的多维度影响。其核心研究问题在于通过国家层面的聚合数据，揭示气候变化在人口分布、城市化进程及能源消耗等关键领域的长期趋势与潜在风险。作为气候变化经济学与可持续发展交叉领域的重要实证资源，该数据集为政策制定者与研究人员提供了评估气候脆弱性、设计适应性策略的量化基础，对推动全球南方的气候韧性研究具有显著的学术与实用价值。

当前挑战

该数据集致力于应对气候变化影响评估这一复杂领域问题，其核心挑战在于如何从有限的国家级宏观指标中，精准捕捉气候变化与多维社会经济变量间的非线性动态关联。具体而言，数据集所涵盖的指标如城市人口比例与总人口数量，虽能反映长期结构性变化，却难以直接量化气候冲击的即时效应与空间异质性。在构建过程中，数据整合面临原始数据报告不一致、定义标准随时间演变以及缺失值处理等难题。此外，自动化清洗流程虽能统一数据格式，却无法修正源数据中可能存在的报告偏差或方法论差异，这要求使用者必须审慎解读数据背后的统计假设与收集局限。

常用场景

经典使用场景

在气候变化研究领域，该数据集为加蓬国家层面的气候指标提供了结构化时序数据，涵盖城市化率、人口总量等关键变量。研究者通常将其用于构建回归或分类模型，以预测气候变化对加蓬社会经济指标的长远影响，例如通过分析历史数据模拟未来城市化进程与气候脆弱性的关联。这类应用有助于揭示发展中国家在气候压力下的动态适应机制。

实际应用

在实际层面，该数据集被政府机构与国际组织用于制定加蓬的国家适应计划与可持续发展政策。例如，结合城市化与人口数据，规划者可以优化基础设施布局以应对海平面上升风险；能源部门则可依据历史排放趋势设计低碳转型路径。这些应用直接助力于提升加蓬的气候韧性和实现《巴黎协定》目标。

衍生相关工作

围绕该数据集衍生的经典工作包括基于机器学习的加蓬气候脆弱性图谱构建，以及利用面板数据模型分析非洲国家气候指标异质性的比较研究。例如，研究者将其与其他非洲国家数据集整合，开发了区域气候风险预测框架；亦有工作聚焦于数据价值插补方法，以提升小国时序数据的完整性，推动了发展经济学与环境科学的交叉创新。