Data and Code for “City-Level Carbon Responsibility, Embodied Carbon Transfer, and Mitigation Responses in the Peanut Oil Supply Chain: Evidence from Eight Provinces and Municipalities in China”

Description

This dataset contains the processed input data, city-level MRIO matrices, derived carbon-accounting results, sensitivity analyses, and reproducibility code supporting the manuscript “City-Level Carbon Responsibility, Embodied Carbon Transfer, and Mitigation Responses in the Peanut Oil Supply Chain: Evidence from Eight Provinces and Municipalities in China”. The dataset covers 87 cities and five supply-chain stages (cultivation, transportation, processing, storage, and retail) over 2004–2024. It includes processed data used to construct the 435 × 435 intermediate transaction matrices and 435 × 87 final-demand matrices, annual production- and consumption-based carbon-accounting results, embodied carbon-flow outputs, LMDI decomposition results, clustering outputs, mitigation-scenario results, and robustness analyses. The accompanying code reproduces the principal results, tables, and figures reported in the manuscript and Supporting Information. Public statistical inputs are derived from the sources cited in the manuscript. Certain enterprise-derived source records are subject to confidentiality restrictions; only de-identified or processed model inputs are included where permitted.

Steps to reproduce

Extract the Peanut_Oil_MRIO_Data_Code.zip archive . The dataset contains the processed inputs and code required to construct the city-level MRIO framework and reproduce the principal results reported in the manuscript. Users should retain the original ordering of cities and supply-chain stages when running the scripts. The workflow covers MRIO construction, production- and consumption-based carbon accounting, embodied carbon-flow analysis, LMDI decomposition, clustering, mitigation-scenario analysis, and robustness checks. Output files generated by the scripts correspond to the main results, tables, and figures reported in the manuscript and Supporting Information.

Institutions

• Beijing Technology and Business University

  Beijing, Beijing

Categories

Funders

• National Key R&D Program of China

  Grant ID: No. 2023YFE0104900
• European Commission

  Grant ID: No. 101081776
• UK Research and Innovation (UKRI)
• Swiss State Secretariat for Education, Research and Innovation (SERI)

Licence