Analysis of Dynamic Changes in Carbon Footprints of Agricultural Production in the Middle and Lower Reaches of the Yangtze River

Taking six provinces and one city in the middle and lower reaches of the Yangtze River as the main research object, this study investigated the carbon footprint of agricultural production in the region and promoted the development of agricultural carbon reduction. This study used the internationally mainstream IPCC emission factor method to calculate the carbon footprint of agricultural production, and selected indicators such as rural population, crop planting area, rural per capita GDP, and urbanization rate to analyze the influencing factors of agricultural carbon footprint in various provinces in the middle and lower reaches of the Yangtze River based on an extensible STIRPAT model. Due to differences in agricultural production conditions, the carbon footprint per unit area and unit yield vary among provinces and cities in the middle and lower reaches of the Yangtze River. From the 15 year average, the carbon footprint per unit area is synchronized with the carbon footprint per unit yield, with Zhejiang Province having the highest (9830.48 kg (CO2 eq)/hm2, 0.65 kg (CO2 eq)/kg), Hubei Province in the middle (5017.90 kg (CO2 eq)/hm2, 0.54 kg (CO2 eq)/kg), and Jiangxi Province having the lowest (3446.181 kg (CO2 eq)/hm2, 0.46 kg (CO2 eq)/kg). From the perspective of emission structure, the carbon footprint generated by agricultural resource inputs accounts for the largest proportion, with fertilizer and fuel use being the main contributors to emissions. In the analysis of influencing factors, the indicators that mainly promote the carbon footprint of agricultural production include the following: rural population (R), ratio of agricultural value added to GDP(Z), total sown area of crops (B), level of agricultural technology (total power of agricultural machinery) (J), and degree of agricultural mechanization (N). The indicators that mainly inhibit the carbon footprint of agricultural production include the per capita disposable income of rural residents (P), rural GDP per capita (G), and urbanization rate (C). Other indicators have a relatively weak impact on carbon footprint. Overall, optimizing agricultural resource input, improving mechanized productivity, and reasonably controlling fertilizers are important ways of reducing carbon emissions from agricultural production. In the middle and lower reaches of the Yangtze River, it is still necessary to formulate emission reduction measures tailored to different ecological environment characteristics to achieve sustainable agricultural development.

Authors

Institutions

Publication Details

Journal
Agriculture
Published
2025-02-26
DOI
https://doi.org/10.3390/agriculture15050508
Primary Topic
Regional Development and Environment
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analysis of Dynamic Changes in Carbon Footprints of Agricultural Production in the Middle and Lower Reaches of the Yangtze River

Zonggui He, Cuicui Jiao, L.‐T. Ou
Agriculture
Regional Development and Environment
article

Analysis of Dynamic Changes in Carbon Footprints of Agricultural Production in the Middle and Lower Reaches of the Yangtze River

Zonggui He, Cuicui Jiao, L.‐T. Ou
article en

Abstract

Taking six provinces and one city in the middle and lower reaches of the Yangtze River as the main research object, this study investigated the carbon footprint of agricultural production in the region and promoted the development of agricultural carbon reduction. This study used the internationally mainstream IPCC emission factor method to calculate the carbon footprint of agricultural production, and selected indicators such as rural population, crop planting area, rural per capita GDP, and urbanization rate to analyze the influencing factors of agricultural carbon footprint in various provinces in the middle and lower reaches of the Yangtze River based on an extensible STIRPAT model. Due to differences in agricultural production conditions, the carbon footprint per unit area and unit yield vary among provinces and cities in the middle and lower reaches of the Yangtze River. From the 15 year average, the carbon footprint per unit area is synchronized with the carbon footprint per unit yield, with Zhejiang Province having the highest (9830.48 kg (CO2 eq)/hm2, 0.65 kg (CO2 eq)/kg), Hubei Province in the middle (5017.90 kg (CO2 eq)/hm2, 0.54 kg (CO2 eq)/kg), and Jiangxi Province having the lowest (3446.181 kg (CO2 eq)/hm2, 0.46 kg (CO2 eq)/kg). From the perspective of emission structure, the carbon footprint generated by agricultural resource inputs accounts for the largest proportion, with fertilizer and fuel use being the main contributors to emissions. In the analysis of influencing factors, the indicators that mainly promote the carbon footprint of agricultural production include the following: rural population (R), ratio of agricultural value added to GDP(Z), total sown area of crops (B), level of agricultural technology (total power of agricultural machinery) (J), and degree of agricultural mechanization (N). The indicators that mainly inhibit the carbon footprint of agricultural production include the per capita disposable income of rural residents (P), rural GDP per capita (G), and urbanization rate (C). Other indicators have a relatively weak impact on carbon footprint. Overall, optimizing agricultural resource input, improving mechanized productivity, and reasonably controlling fertilizers are important ways of reducing carbon emissions from agricultural production. In the middle and lower reaches of the Yangtze River, it is still necessary to formulate emission reduction measures tailored to different ecological environment characteristics to achieve sustainable agricultural development.

AgricultureVol. 15(5)
Sichuan University of Science and Engineering (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 96%
Regional Development and Environment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.