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

---
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
- zwe
pretty_name: "Zimbabwe - Climate Change"
dataset_info:
splits:
- name: train
num_examples: 1268
- name: test
num_examples: 317
---

# Zimbabwe - Climate Change

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-climate-change-indicators-for-zimbabwe) · **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-zimbabwe) 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: **ZWE**.

*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)** | 1,585 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 1,268 rows |
| **Test split** | 317 rows |
| **Geographic scope** | ZWE |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2026-03-27 |

---

## Variables

**Geographic** — `country_name` (Zimbabwe), `country_iso3` (ZWE), `year` (range 1960.0–2025.0).

**Outcome / Measurement** — `value` (range -0.4523–1608360000.0).

**Identifier / Metadata** — `indicator_name` (Population in urban agglomerations of more than 1 million (% of total population), Urban population (% of total population), Urban population), `indicator_code` (EN.URB.MCTY.TL.ZS, SP.URB.TOTL.IN.ZS, SP.URB.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-zimbabwe")
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% | Zimbabwe |
| `country_iso3` | object | 0.0% | ZWE |
| `year` | int64 | 0.0% | 1960.0 – 2025.0 (mean 1998.2984) |
| `indicator_name` | object | 0.0% | Population in urban agglomerations of more than 1 million (% of total population), Urban population (% of total population), Urban population |
| `indicator_code` | object | 0.0% | EN.URB.MCTY.TL.ZS, SP.URB.TOTL.IN.ZS, SP.URB.TOTL |
| `value` | float64 | 0.0% | -0.4523 – 1608360000.0 (mean 3771246.2229) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-11 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1960.0 | 2025.0 | 1998.2984 | 2000.0 |
| `value` | -0.4523 | 1608360000.0 | 3771246.2229 | 32.6776 |

---

## 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-zimbabwe) for the publisher's own methodology notes and caveats.

---

## Citation

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

在气候变化的全球背景下，该数据集由世界银行集团发布，聚焦于津巴布韦的气候变化指标。原始数据通过HDX平台的CKAN API获取，经过Electric Sheep Africa的精心处理，统一了列名格式为蛇形命名法，并将常见的缺失值标记标准化为NaN。随后，数据被转换为Parquet格式，并采用固定随机种子按80/20的比例划分为训练集和测试集，确保了数据在机器学习任务中的可用性和一致性。

特点

该数据集以国家层面的聚合数据为核心，涵盖了从1960年至2025年的长期观测，共包含1,585条记录和8个变量。其特点在于整合了气候系统、气候影响暴露度、韧性、温室气体排放及能源使用等多维指标，如城市人口比例和城市集聚人口等具体测量值。数据经过清洗和标准化处理，所有字段均无缺失，为研究者提供了结构清晰、可直接用于表格分类或回归分析的高质量资源。

使用方法

在机器学习应用中，用户可通过Hugging Face的datasets库轻松加载该数据集，并利用其预划分的训练集和测试集进行模型开发。数据以Pandas DataFrame形式呈现，便于进行探索性分析和特征工程。研究者可基于年份、指标代码等变量，构建预测模型以评估气候变化趋势或政策影响，但需注意数据源自世界银行，使用时应参考原始方法论说明以确保分析的严谨性。

背景与挑战

背景概述

在气候变化对全球可持续发展构成严峻挑战的背景下，世界银行集团于2026年发布了针对津巴布韦的气候变化指标数据集，旨在系统量化该国气候系统的演变轨迹、环境暴露度及社会韧性。该数据集由Electric Sheep Africa机构进行机器学习友好型重构，聚焦于国家层面的聚合数据，涵盖了从1960年至2025年的城市化进程、人口分布及能源消耗等多维度指标。其核心研究问题在于揭示发展中国家在气候脆弱性框架下的长期趋势与结构性特征，为政策制定者与研究人员提供了评估气候适应策略与减缓措施成效的实证基础，对非洲区域的气候治理与可持续发展研究具有重要的参考价值。

当前挑战

该数据集致力于应对气候变化影响评估中的复杂挑战，特别是在数据稀疏且异质性高的区域环境中，如何准确量化气候脆弱性与社会经济指标间的动态关联。构建过程中面临多重障碍，包括原始数据中存在的报告不一致性、定义差异以及潜在的采样偏差，这些因素可能影响时间序列分析的可靠性。此外，自动化清洗流程难以纠正源数据中的误报数值或方法论局限，要求使用者必须结合世界银行的方法论说明进行审慎解读。数据集虽经标准化处理，但其国家层面的聚合性质限制了微观层面的深入探究，在构建高精度预测模型或因果推断时需警惕生态学谬误的风险。

常用场景

经典使用场景

在气候变化研究领域，该数据集为分析津巴布韦的气候变化指标提供了结构化基础。研究者通常利用其时间序列数据，构建回归模型预测城市化进程与气候变化之间的动态关联，例如通过城市人口比例等指标评估气候脆弱性。这类分析有助于揭示长期趋势，为政策制定提供量化依据，尤其在探讨发展中国家气候适应策略时，数据集成为验证假设的关键资源。

实际应用

在实际应用中，该数据集支持政府机构与非营利组织制定气候适应和减缓政策。例如，基于城市人口聚集指标，规划者可以评估基础设施投资优先级，以应对极端天气事件。同时，能源部门可借助排放数据优化清洁能源部署，助力津巴布韦实现国家自主贡献目标。这些应用强化了数据驱动决策在提升社区韧性与促进公平转型中的实践价值。

衍生相关工作

围绕该数据集衍生的经典工作包括气候脆弱性指数构建与机器学习驱动的风险预测模型。学者们常将其与卫星遥感或社会经济数据融合，开发区域尺度的气候影响评估框架。此外，该数据集也激励了针对非洲国家的比较研究，例如在城市化与碳排放关联分析中，为全球南方气候治理提供了可复现的基准案例。