Spatially Disaggregated Energy Consumption and Emissions in End-use Sectors for Germany and Spain

TL;DR

This paper presents a scalable, open-data framework for generating high-resolution energy consumption and emissions data at local levels in Germany and Spain, aiding targeted climate policies.

Contribution

It introduces a novel spatial disaggregation method using XGBoost for imputing missing data, enhancing local climate action planning.

Findings

Successful generation of detailed local energy data

Application of XGBoost improves data imputation accuracy

Framework is reproducible and scalable for municipalities

Abstract

High-resolution energy consumption and emissions datasets are essential for localized policy-making, resource optimization, and climate action planning. They enable municipalities to monitor mitigation strategies and foster engagement among governments, businesses, and communities. However, smaller municipalities often face data limitations that hinder tailored climate strategies. This study generates detailed final energy consumption and emissions data at the local administrative level for Germany and Spain. Using national datasets, we apply spatial disaggregation techniques with open data sources. A key innovation is the application of XGBoost for imputing missing data, combined with a stepwise spatial disaggregation process incorporating district- and province-level statistics. Prioritizing reproducibility, our open-data approach provides a scalable framework for municipalities to develop actionable climate plans. To ensure transparency, we assess the reliability of imputed values and assign confidence ratings to the disaggregated data.