Greenhouse gas emissions from agriculture system in China: historical dynamics and key drivers

Abstract

Agriculture is a major source of greenhouse gas (GHG) emissions. To clarify long-term trends and drivers, we estimate provincial agricultural GHG emissions in China from 2000–2020 using a life-cycle assessment (LCA) framework and apply Logarithmic Mean Divisia Index (LMDI) decomposition. Total emissions rose 10.71% (1250.96→1384.99 Mt), peaking in 2017 before declining. Across sources, agri-materials and manure management were the largest contributors in cropping and livestock systems, respectively. LMDI attributes emissions growth primarily to agricultural development, while improvements in emissions intensity offset part of this increase; urbanization generally exerted a smaller positive effect, and labor reductions dampened emissions. Regional heterogeneity is pronounced: northern (Tianjin, Hebei, Shanxi) and central (Henan, Hubei) provinces show fluctuating increases; the north-east exhibits steady growth; and the south-west (Chongqing, Sichuan, Guizhou) shows a fluctuating decline. These results highlight the need for region-specific mitigation strategies, emphasizing input efficiency, manure management, and structural adjustments to advance low-carbon agricultural development.

Graphical abstract

Keywords

agricultural greenhouse gas emissions / life-cycle assessment / LMDI decomposition / China / crop-livestock systems / regional heterogeneity

1 Introduction

Greenhouse gas (GHG) emissions have become a significant concern in the context of climate change mitigation and sustainable development (Ding et al., 2023; Zhao et al., 2024). The agri-food system contributes to 31% of global GHG emissions, with nearly 50 % of global methane (CH₄) and 75 % of nitrous oxide (N₂O) emissions (Crippa et al., 2021; Li et al., 2024). China is a major agricultural producer and its agricultural system is the second largest source of GHG emissions (Chen et al., 2024a). China has committed to achieving carbon neutrality by 2060, which will require a drastic reduction in GHG emissions from agricultural system (Liu et al., 2023). Understanding agricultural GHG emissions and its key drivers is crucial for developing strategies that promote environmentally sustainable agricultural practices (Wang et al., 2021).

The crop production system (CPS) and Livestock production system (LPS) are the main emission sources of agricultural system (Cai et al., 2025). LPS contributes to GHG emissions primarily through the N₂O emission from soil management practices, such as the use of synthetic fertilizers and manure application (Ma et al., 2024). Additionally, agricultural activities can lead to CH₄ emissions, particularly from rice paddies where flooded conditions promote anaerobic decomposition of organic matter by microbes. LPS generates CH₄ emissions through enteric fermentation and manure management (Kharrazi, 2018; Kharrazi et al., 2018; Xing et al., 2022). These emissions not only have environmental implications but also impact the overall sustainability of agricultural systems (Yan et al., 2024).

The agricultural GHG emissions has received considerable attentions. Some studies calculate GHG emissions based on emission sources from agricultural production activities (Shang et al., 2015; Mohareb et al., 2018). For instance, Tian et al. (2022) calculated agricultural GHG emissions at national level based on emission sources including manure management, rice cultivation, fertilizer application, and energy consumption, while Tian and Lin (2021) estimated emissions from rice cultivation, soil management, enteric fermentation, and livestock manure at the inter-provincial level. He et al. (2022) included more emission sources such as agrochemical use, and straw burning in estimation. Another widely used approach is cradle-to-grave life cycle assessment (LCA), which has been widely used to assess the environmental impacts of agricultural product production throughout the entire life cycle. In comparison to accounting methods based on emission sources, LCA constructs material inventories of agricultural production and considers the entire life cycle of agricultural production emissions. For example, Xia and Yan (2020) applied LCA to assess GHG emissions of nearly 10 crops. Tubiello et al. (2022) quantified the emissions from agri-food systems for 196 countries and 40 territories for the period 1990–2019.

Decomposition Analysis is widely used in driving factor analysis due to its significant advantages in exploring the characteristics of changes (Dai et al., 2015). Common decomposition methods include Structural Decomposition Analysis (SDA) and Index Decomposition Analysis (IDA). SDA is based on input-output tables and limited to specific industries and time spans. In comparison, IDA can decompose changes into different factor indicators, quantifying their contribution to the target variable. The Logarithmic Mean Divisia Index (LMDI), a typical IDA method, uses a logarithmic average formula to extend the Kaya identity, addressing issues including residuals, zeros, and negatives, and providing consistent results for change analysis (Goh et al., 2018; Wang et al., 2019; Makutėnienė et al., 2022; Liu et al., 2024). Scholars have identified the driving factors of agriculture GHG emissions and their contributions by conducting LMDI analyses (Lin and Du, 2014; Ma et al., 2018; Zhang et al., 2020; Wu et al., 2021). For example, Li et al. (2022) adapted LMDI to carry out the influence factors of agricultural carbon emissions in Zhejiang province, China. Jia et al. (2024) employed the LMDI and Tapio decoupling model to assess the factors influencing agricultural carbon emissions in the Yellow River Basin. Zhang et al. (2024) employed the LMDI to decompose the changes in GHG emissions intensity in China’s cropland, identifying the contributions of various factors such as climate change, agricultural practices, and technological advancements.

Overall, various studies have been conducted on accounting GHG emissions in China’s agricultural system (Chen et al., 2024b; Xu et al., 2024). However, the current accounting system for GHG emissions in China’s agricultural system is not comprehensive. First, some studies only consider emissions from the perspective of emission sources, focusing only on GHG emissions generated from agricultural inputs without considering emissions generated from the production of agricultural inputs. Secondly, in the existing LCA research, due to the inconsistency of accounting scales and boundaries, the results show great differences. Existing studies are mainly limited to the national scale or a specific region or province, or considered a specific time period or a short time span. The related research conclusions are mostly incomplete and not comparable, making it difficult to analyze the spatial and temporal evolution patterns of GHG emissions at the provincial level. Regarding the decomposition of driving factors, previous studies have explored the factors that impact GHG emissions from the agricultural sector at national, regional, provincial, and municipal levels, revealing the main influencing factors causing changes in carbon emissions. However, few studies have analyzed the factors influencing GHG emissions from the agricultural sector at the provincial level, and proposed specific provincial emission reduction strategies. Therefore, analyzing the influencing factors of GHG emissions from the agricultural sector at the provincial level can provide valuable reference for targeted provincial agricultural GHG emissions reduction policies (Fang et al., 2024).

Overall, previous studies primarily have two limitations. First, most studies focus solely on the crop production system (Guo et al., 2022; Wang et al., 2023), with limited consideration of both crop and livestock systems together. Second, current studies fail to account for inter-provincial disparities and the contributions of economic and social driving factors (Menegat et al., 2022). To address these gaps, this study develops a comprehensive GHG emissions accounting system for both crop production systems (CPS) and livestock production systems (LPS) based on the LCA framework. It estimates provincial-level agricultural GHG emissions from 2000 to 2020 and applies the LMDI decomposition method to identify key driving factors and their contributions across different provinces. By revealing the spatial and structural variations in agricultural GHG emissions over two decades, this study provides valuable insights for designing regionally tailored mitigation strategies.

The structure of this paper is as follows: Section 2 presents Methodology of LCA and LMDI. Section 3 shows the accounting results of CPS and LPS. The main of conclusions are presented in Section 4.

2 Methodology and data

2.1 GHG emissions from crop production system

Figure 1 outlines the system boundaries for the agricultural life cycle assessment (LCA) used in this study. The crop production system (CPS) includes the upstream production of agricultural materials (fertilizers, pesticides, and agricultural films) and on-farm processes such as fertilizer application, crop farming (CH₄ from paddy rice, diesel use, and irrigation electricity), and straw management (returning and burning). Non-CO₂ gases are converted to CO₂-equivalents using their global warming potentials (GWPs). The livestock production system (LPS) encompasses enteric CH₄, manure-management CH₄ and N₂O, and energy use for livestock feeding and processing, as described in Section 2.2.

2.1.1 Emissions from agricultural materials

(1)E_{i,r}^{\mathrm{a}\mathrm{m}}{=A}_{i,r}\times f_{i,r}^{\mathrm{a}\mathrm{m}},

where the superscript n represents crop types; the subscript r represents different provinces; i refers to types of agricultural materials. E_{i,r}^{\mathrm{a}\mathrm{m}} stands for GHG emissions of agricultural material i in province r{=A}_{i,r} stands for the amount of agricultural material i. f_{i,r}^{\mathrm{a}\mathrm{m}} stands for the GHG emissions factor of agricultural materiali in province r.

2.1.2 GHG emissions from crop farming

CH₄ emission from paddy rice farming can be calculated as

(2)E_r^{\mathrm{C}{\mathrm{H}}_4,\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{e}}=S_r^{\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{e}}\times f_r^{\mathrm{C}{\mathrm{H}}_4,\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{e}},

where E_r^{\mathrm{C}{\mathrm{H}}_4,\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{e}} represents CH₄ emission from paddy rice farming in province r, S_r^{\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{e}} represents sown area of paddy rice in province r, f_r^{\mathrm{C}{\mathrm{H}}_4,\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{e}}represents the CH₄ emissions factor from paddy rice field in province r.

GHG emission from diesel consumption for farming machine can be calculated as

(3)E_r^{\mathrm{d}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{e}\mathrm{l}}=A_r^{\mathrm{d}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{e}\mathrm{l}}\times f^{\mathrm{d}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{e}\mathrm{l}},

where E_r^{\mathrm{d}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{e}\mathrm{l}} represents GHG emissions from diesel consumption in province r, A_r^{\mathrm{d}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{e}\mathrm{l}} represents diesel consumption in province r, f^{\mathrm{d}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{e}\mathrm{l}} is the GHG emissions factor of diesel consumption.

GHG emission from electricity consumption for irrigation can be calculated as

(4)E_r^{\mathrm{i}\mathrm{r}\mathrm{r}\mathrm{i}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n},n}=S_r^n\times F^n\times f_r^{\mathrm{i}\mathrm{r}\mathrm{r}\mathrm{i}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}},

E_r^{\mathrm{i}\mathrm{r}\mathrm{r}\mathrm{i}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n},n} stands for GHG emissions from irrigation of crop n in province r. S_r^n stands for sown area of crop n in province r. F^nstands for electric consumption coefficient for irrigation of crop n. f_r^{\mathrm{i}\mathrm{r}\mathrm{r}\mathrm{i}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}is the GHG emission factor for electricity consumption.

2.1.3 GHG emission from fertilizer use

Direct N₂O emissions can be calculated as

(5)E_r^{{\mathrm{N}}_2\mathrm{O},\mathrm{d}\mathrm{r}\mathrm{i}\mathrm{e}\mathrm{c}\mathrm{t}}=\frac{44}{28}\times \left(A_{Nfer,r}^{}+A_{Cfer,r}^{}÷3\right)\times f_r^{{\mathrm{N}}_2\mathrm{O},\mathrm{d}\mathrm{r}\mathrm{i}\mathrm{e}\mathrm{c}\mathrm{t}},

where E_r^{{\mathrm{N}}_2\mathrm{O},\mathrm{d}\mathrm{r}\mathrm{i}\mathrm{e}\mathrm{c}\mathrm{t}} represents direct N₂O emissions from fertilizer use in province r, A_{Nfer,r}^{}represents N fertilizer consumption in province r, A_{Cfer,r}^{} represents compound fertilizer consumption in province r, f_r^{{\mathrm{N}}_2\mathrm{O},\mathrm{d}\mathrm{r}\mathrm{i}\mathrm{e}\mathrm{c}\mathrm{t}} represents direct N₂O emissions factor in province r.

Indirect N₂O emissions can be calculated as

(6)\begin{array}{rl}& E_r^{{\mathrm{N}}_2\mathrm{O},f\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{r}\mathrm{i}\mathrm{e}\mathrm{c}\mathrm{t}}=\frac{44}{28}\times \left(A_{Nfer,r}^{}+\frac{A_{Cfer,r}^{}}{3}\right)\times \\ & \left(B\times f_{}^{{\mathrm{N}}_2\mathrm{O},\mathrm{d}\mathrm{e}\mathrm{p}\mathrm{o}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}+R_{l\wedge r}\times f_{}^{{\mathrm{N}}_2\mathrm{O},\mathrm{d}\mathrm{e}\mathrm{p}\mathrm{o}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}\right),\end{array}

E_r^{{\mathrm{N}}_2\mathrm{O},f\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{r}\mathrm{i}\mathrm{e}\mathrm{c}\mathrm{t}} stands for indirect N₂O emissions from fertilizer use in province r. B stands for fraction of synthetic fertilizer nitrogen that volatilizes as NH₃ and NO_x. f_{}^{{\mathrm{N}}_2\mathrm{O},\mathrm{d}\mathrm{e}\mathrm{p}\mathrm{o}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}} stands for indirect N₂O emissions factor caused by nitrogen deposition. R_{l\wedge r} stands for rate of nitrogen leaching and runoff. f_{}^{{\mathrm{N}}_2\mathrm{O},\mathrm{d}\mathrm{e}\mathrm{p}\mathrm{o}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}} stands for indirect N₂O emissions factor caused by nitrogen leaching and runoff.

2.1.4 GHG emissions from straw management

The amount of collectible straw and discard straw can be calculated as

(7)A_{n,r}^{\mathrm{c}\mathrm{o}\mathrm{l}\mathrm{l}\mathrm{e}\mathrm{c}\mathrm{t}}=A_{n,r}^{\mathrm{y}\mathrm{i}\mathrm{e}\mathrm{l}\mathrm{d}}\times G_{n,r}\times C_n,

(8)A_{n,r}^{\mathrm{d}\mathrm{i}\mathrm{s}\mathrm{c}\mathrm{a}\mathrm{r}\mathrm{d}}=A_{n,r}^{\mathrm{y}\mathrm{i}\mathrm{e}\mathrm{l}\mathrm{d}}\times G_{n,r}\times \left(1-C_n\right),

where A_{n,r}^{\mathrm{c}\mathrm{o}\mathrm{l}\mathrm{l}\mathrm{e}\mathrm{c}\mathrm{t}} stands for the amount of collectible straw of crop n in province r, A_{n,r}^{\mathrm{y}\mathrm{i}\mathrm{e}\mathrm{l}\mathrm{d}} stands for the amount of grain of crop n in province r, G_{n,r} stands for straw-to-grain ratio of crop n in province r, C_nstands for straw collection coefficient of crop n, A_{n,r}^{\mathrm{d}\mathrm{i}\mathrm{s}\mathrm{c}\mathrm{a}\mathrm{r}\mathrm{d}}stands for the amount of discard straw.

N₂O emissions from straw return can be calculated as

(9)E_{n,r}^{{\mathrm{N}}_2\mathrm{O},\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{w}}=\left(A_{n,r}^{\mathrm{c}\mathrm{o}\mathrm{l}\mathrm{l}\mathrm{e}\mathrm{c}\mathrm{t}}\times H_r^{\mathrm{f}\mathrm{e}\mathrm{r}}+A_{n,r}^{\mathrm{d}\mathrm{i}\mathrm{s}\mathrm{c}\mathrm{a}\mathrm{r}\mathrm{d}}\right)\times f_r^{{\mathrm{N}}_2\mathrm{O},\mathrm{d}\mathrm{i}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}}\times A_n,

where E_{n,r}^{{\mathrm{N}}_2\mathrm{O},\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{w}}is the N₂O emission of straw return of crop n in province r, H_r^{\mathrm{f}\mathrm{e}\mathrm{r}} is proportion of straw reused for fertilizer in province r, A_n is nitrogen content of straw.

GHG emissions from straw burning can be calculated as

(10)E_{n,r}^{\mathrm{b}\mathrm{u}\mathrm{r}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g}}=A_{n,r}^{\mathrm{c}\mathrm{o}\mathrm{l}\mathrm{l}\mathrm{e}\mathrm{c}\mathrm{t}}\times H_r^{\mathrm{w}\mathrm{a}\mathrm{s}\mathrm{t}\mathrm{e}\mathrm{d}}\times f_n^{\mathrm{b}\mathrm{u}\mathrm{r}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g}},

where E_{n,r}^{\mathrm{b}\mathrm{u}\mathrm{r}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g}} is the GHG emissions of straw burning, H_r^{\mathrm{w}\mathrm{a}\mathrm{s}\mathrm{t}\mathrm{e}\mathrm{d}} is proportion of straw wasted for open burning, f_n^{\mathrm{b}\mathrm{u}\mathrm{r}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g}}is the GHG emissions factor of straw burning.

2.2 GHG emissions from livestock system

2.2.1 CH₄ emissions from enteric fermentation

(11)E_m^{\mathrm{C}{\mathrm{H}}_4,\mathrm{e}\mathrm{f}}=A_m\times f_m^{\mathrm{C}{\mathrm{H}}_4,\mathrm{e}\mathrm{f}},

where E_m^{\mathrm{C}{\mathrm{H}}_4,\mathrm{e}\mathrm{f}} is the CH₄ emissions from enteric fermentation of livestock m, A_mis the amount of livestock m, f_m^{\mathrm{C}{\mathrm{H}}_4,\mathrm{e}\mathrm{f}} is CH₄ emissions factor of enteric fermentation.

2.2.2 GHG emissions from manure management

(12)E_m^{\mathrm{C}{\mathrm{H}}_4,\mathrm{m}\mathrm{m}}=A_m\times f_m^{\mathrm{C}{\mathrm{H}}_4,\mathrm{m}\mathrm{m}},

(13)E_m^{{\mathrm{N}}_2\mathrm{O},\mathrm{m}\mathrm{m}}=A_m\times f_m^{{\mathrm{N}}_2\mathrm{O},\mathrm{m}\mathrm{m}},

where E_m^{\mathrm{C}{\mathrm{H}}_4,\mathrm{m}\mathrm{m}} stands for CH₄ emissions from manure management of livestock m, E_m^{{\mathrm{N}}_2\mathrm{O},\mathrm{m}\mathrm{m}} stands for N₂O emission from manure management of livestock m, f_m^{\mathrm{C}{\mathrm{H}}_4,\mathrm{m}\mathrm{m}} stands for CH₄ emission factor from manure management, f_m^{{\mathrm{N}}_2\mathrm{O},\mathrm{m}\mathrm{m}} stands for N₂O emission factor from manure management.

2.2.3 GHG emissions from energy consumption

Energy consumption from livestock feeding can be calculated as

(14)E_m^{\mathrm{C}{\mathrm{O}}_2,\mathrm{l}\mathrm{f}}=A_m\times \frac{cos{t}_{m,r}^{\mathrm{e}}}{pric{e}_r^{\mathrm{e}}}\times f_r^{\mathrm{C}{\mathrm{O}}_2,\mathrm{e}}+A_m\times \frac{cos{t}_{m,r}^{\mathrm{c}}}{pric{e}_r^{\mathrm{c}}}\times f_r^{\mathrm{C}{\mathrm{O}}_2,\mathrm{c}},

where E_m^{\mathrm{C}{\mathrm{O}}_2,\mathrm{l}\mathrm{f}} is the CO₂ emission from livestock feeding, cos{t}_r^{\mathrm{e}} is the cost of electricity from livestock m feeding per unit. pric{e}_r^{\mathrm{e}} is the electricity price in province r, cos{t}_{m,r}^{\mathrm{c}} is the cost of coal from livestock m feeding per unit, pric{e}_r^{\mathrm{c}} is the coal price. f_r^{\mathrm{C}{\mathrm{O}}_2,\mathrm{e}} is the CO₂ emission factor of electricity consumption, f_r^{\mathrm{C}{\mathrm{O}}_2,\mathrm{c}} is the CO₂ emission factor of coal consumption.

Energy consumption from livestock production processing can be calculated as

(15)E_m^{\mathrm{C}{\mathrm{O}}_2,\mathrm{l}\mathrm{p}}=A_m\times \frac{M{J}_m}{v}\times f_r^{\mathrm{C}{\mathrm{O}}_2,\mathrm{e}},

where E_m^{\mathrm{C}{\mathrm{O}}_2,\mathrm{l}\mathrm{p}} is the CO₂ emission from livestock production processing. M{J}_m is energy consumption coefficient of livestock production m processing. v is the heat value generated per unit of electricity consumption.

2.2.4 GHG emissions transferred to CO₂ eq

(16)E_{}^{\mathrm{C}{\mathrm{O}}_2\mathrm{e}\mathrm{q}}=E^{\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{l}\mathrm{u}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{t}}\times GW{P}^{\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{l}\mathrm{u}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{t}},

where E_{}^{\mathrm{C}{\mathrm{O}}_2\mathrm{e}\mathrm{q}} is the GHG emission transferred to CO₂ equivalent, E^{\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{l}\mathrm{u}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{t}} is the non-CO₂ GHG emission. GW{P}^{\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{l}\mathrm{u}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{t}} is global warming potential.

2.3 LMDI decomposition

We decompose total GHG emissions into a product of five main drivers based on the expanded Kaya equation:

(17)GHG=\frac{GHG}{GD{P}_{\mathrm{c}\mathrm{r}\mathrm{o}\mathrm{p}}}\times \frac{GD{P}_{\mathrm{c}\mathrm{r}\mathrm{o}\mathrm{p}}}{GD{P}_{\mathrm{a}\mathrm{g}\mathrm{r}}}\times \frac{GD{P}_{\mathrm{a}\mathrm{g}\mathrm{r}}}{Po{p}_{\mathrm{t}}}\times \frac{Po{p}_{\mathrm{t}}}{Po{p}_{\mathrm{r}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{l}}}\times Po{p}_{\mathrm{r}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{l}}.

The 5 driving factors are: \frac{GD{P}_{\mathrm{c}\mathrm{r}\mathrm{o}\mathrm{p}}}{GD{P}_{\mathrm{a}\mathrm{g}\mathrm{r}}} is GHG emissions per agricultural GDP, denoted as Intensity; \frac{GD{P}_{\mathrm{c}\mathrm{r}\mathrm{o}\mathrm{p}}}{GD{P}_{\mathrm{a}\mathrm{g}\mathrm{r}}} is the proportion of cropping GDP to the total GDP; \frac{GD{P}_{\mathrm{a}\mathrm{g}\mathrm{r}}}{Po{p}_{\mathrm{t}}} is agricultural GDP per capita, denoted as Agricultural Development; \frac{Po{p}_{\mathrm{t}}}{Po{p}_{\mathrm{r}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{l}}} is the proportion of agricultural population to the total population, denoted as Urbanization; Po{p}_{\mathrm{r}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{l}} is agricultural population, denoted as Labor.

According to the addition and decomposition method of LMDI decomposition:

(18)\begin{array}{rl}\mathrm{\Delta }GHG=& \mathrm{\Delta }\frac{GHG}{GD{P}_{\mathrm{c}\mathrm{r}\mathrm{o}\mathrm{p}}}+\mathrm{\Delta }\frac{GD{P}_{\mathrm{c}\mathrm{r}\mathrm{o}\mathrm{p}}}{GD{P}_{\mathrm{a}\mathrm{g}\mathrm{r}}}+\mathrm{\Delta }\frac{GD{P}_{\mathrm{a}\mathrm{g}\mathrm{r}}}{Po{p}_{\mathrm{t}}}+\\ & \mathrm{\Delta }\frac{Po{p}_{\mathrm{t}}}{Po{p}_{\mathrm{r}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{l}}}+\mathrm{\Delta }Po{p}_{\mathrm{r}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{l}}.\end{array}

Cumulative effect was calculated and using 2000 as the base period:

(19)\mathrm{\Delta }GH{G}_{\mathrm{f}\mathrm{a}\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{r}}^T={\sum }_{t=1}^T\frac{GH{G}_j^t-GH{G}_j^0}{lnGH{G}_j^t-lnGH{G}_j^0}ln\left(\frac{\mathrm{f}\mathrm{a}\mathrm{c}\mathrm{t}\mathrm{o}{\mathrm{r}}_j^t}{\mathrm{f}\mathrm{a}\mathrm{c}\mathrm{t}\mathrm{o}{\mathrm{r}}_j^0}\right),

where the subscript j represents 5 different driving factors, \mathrm{f}\mathrm{a}\mathrm{c}\mathrm{t}\mathrm{o}{\mathrm{r}}_j^0 is the value of driving factors in 2000, \mathrm{f}\mathrm{a}\mathrm{c}\mathrm{t}\mathrm{o}{\mathrm{r}}_j^t is the value of driving factors t years after 2000.

2.4 Data sources

The data used in this study are sourced from publicly available data sets, including the Statistical Yearbooks, the Easy Professional Superior (EPS) Database, and relevant literature. Specifically, data on fertilizers, pesticides, agricultural films, and total machinery power for agricultural greenhouse gas (GHG) emissions accounting are derived from the China Agricultural Statistical Yearbook and the China Statistical Yearbook. The emission factor used in the GHG emissions accounting process are obtained from the official reports, and relevant literature.

3 Results

3.1 Historical GHG emissions of CPS and LPS from 2000 to 2020

Figure 2 displays the total agricultural greenhouse gas (GHG) emissions at the provincial level in China from 2000 to 2020. The overall trend of GHG emissions shows a fluctuating increase, with the total rising from 1250.96 Mt in 2000 to 1384.99 Mt in 2020, an increase of 10.71%. Specifically, the total GHG emissions increased by 25.60% from 1250.96 Mt to 1571.24 Mt between 2000 and 2017, but there were slight decreases in 2005 and 2016. Since 2017, total GHG emissions have shown a declining trend, decreasing from 1571.24 Mt to 1384.99 Mt, which was a 11.85% decrease. However, in 2019 there was a slight increase. The highest emissions were 1571.24 Mt in 2017, and the lowest emissions were 1250.96 Mt in 2000.

Among all emission sources, the GHG emissions from Agri-materials account for the largest proportion, with an average of 16.85%. From 2000 to 2014, the emissions from agri-materials had increased, reaching a peak in 2014 of 264.67 (Mt CO₂-eq) and then declining annually after that, reaching the lowest emissions of 211.23 in 2020. The GHG emissions from manure management ranks second. Straw management is the third largest source of GHG emissions, with an average contribution rate of about 12.13%. The GHG emission from straw management shows a fluctuating upward trend from 2000 to 2020, with the highest emission in 2019 of 221.98. The GHG emission from fertilizer use accounts for about 11.95% of the total, slightly lower than that from straw management. The GHG emission from fertilizer use shows a general upward trend from 2000 to 2014, reaching a peak of 185.34 in 2014 and then declining yearly from 2014 to 2020. CH₄ from paddy rice and enteric fermentation account for almost the same amount of GHG emissions, each accounting for about 10.79% and 10.39% of the total emissions, respectively. The CH₄ from paddy rice GHG emission decreased continuously from 2000 to 2004, and then it started to rise from 2004. The enteric fermentation GHG emission shows a small fluctuation in the 2000 to 2020 period. The GHG emissions from processing and irrigation are also very close, each contributing about 8.91% and 8.20%, respectively. The GHG emissions from processing shows a fluctuating upward trend from 2000 to 2017, with a small decline in 2005, and then it has shown a declining trend year by year since 2017, and has shown a declining trend overall from 2015 to 2020, with the highest emissions of 141.99 in 2015. The GHG emissions from irrigation show a fluctuating trend from 2000 to 2020, with a decline from 2000 to 2003 and then a fluctuating increase from 2013 to 2020. The GHG emissions from diesel for machine account for 5% of the total emissions. From 2000 to 2015, the GHG emissions from diesel for machine continue to increase, reaching a peak of 82.41 in 2015, but there was a slight decline in 2008. From 2015 to 2020, the GHG emissions from diesel for machine decrease continuously. The GHG emissions from energy consumption account for the smallest proportion with an average of 0.62%. From 2000 to 2020, the fluctuation of energy consumption shows an upward trend, with a significant decline and rise in 2001 and 2002.

According to different emissions sources of the crop production system during the study period, the total GHG emissions from agri-materials were the highest, with an average of 240.66 Mt, accounting for about 1/4 of the total GHG emissions from the cultivated land. Fertilizer and straw management are also the two main sources of GHG emissions, with average emissions of 168.49 Mt and 174.41 Mt, respectively. Diesel for machine had the lowest GHG emissions, with an average of 71.79 Mt, which is less than 10% of the total GHG emissions of crop production system. Among the livestock production system, manure management generated the most GHG emissions, with an average emission of 218.71 Mt, which is nearly half of the total GHG emissions from the livestock industry. The other two major sources of GHG emissions in the livestock industry are enteric fermentation and processing, with emissions of 148.13 Mt and 127.54 Mt, respectively. Energy consumption contributed the least to GHG emissions, with an average of 8.83 Mt, which accounts for only 1.76% of the total GHG emissions from the LPS.

3.2 Regional GHG emissions of crop production system

The GHG emissions from CPS in China’s provinces from 2000 to 2020 are shown in Fig. 3. Taking different time points into account, the GHG emissions of CPS in various provinces show monotonous and fluctuating changes in four modes. Monotonic changes can be divided into two patterns: one is a monotonous increase with time, that is, the GHG emissions of CPS show a monotonous increasing trend from 2000 to 2020, mainly distributed in the North-east and North-east areas, such as Inner Mongolia, Liaoning, Jilin, Heilongjiang, Shaanxi, and Xinjiang; The other is a monotonically decreasing trend with time, mainly distributed in the North, East, and South areas, such as Beijing, Shanghai, Zhejiang, Fujian, and Guangdong. The fluctuating pattern of the provinces with GHG emissions has also been divided into two patterns: one is a fluctuating upward trend, such as Anhui, Jiangxi, Henan, Hubei, Hunan Guangxi, Yunnan, Xizang, Gansu, and Ningxia. The other is a fluctuating downward pattern, such as GHG emissions of Tianjin, Hebei, Shanxi, Jiangsu, Shandong, Hainan, Chongqing, Sichuan, Guizhou and Qinghai.

As for different regions, the dominant pattern of GHG emissions from CPS in each of the seven different regions showed great difference. The predominant pattern in the northern region is fluctuating upward, such as Tianjin, Hebei, Shanxi; while in Beijing, the pattern is declining monotonically, and on the contrary, Inner Mongolia’s pattern is monotonically increasing. In the North-east region, the GHG emissions from CPS in Liaoning, Jilin, and Heilongjiang all show a monotonically increasing pattern. In the eastern region, the predominant pattern of GHG emissions from CPS is monotonically decreasing, such as in Shanghai, Zhejiang, and Fujian. On the other hand, Jiangsu, Fujian, and Shandong show a pattern of fluctuating decline. Anhui, however, exhibits a pattern of fluctuating increase in GHG emissions from CPS. The predominant pattern of GHG emissions from CPS in central China is fluctuating upward, which is manifested in Henan, Hubei, and Hunan. In the southern region, the emission patterns in each province are different. In Guangdong, the pattern is monotonically decreasing. In Guangxi, the pattern is characterized by fluctuating upward. And in Hainan, the pattern is characterized by fluctuating downward. In the south-west region, the predominant pattern of GHG emissions from CPS is fluctuating downward, which is evident in Chongqing, Sichuan, and Guizhou. Conversely, in Yunnan and Xizang, the pattern of GHG emissions is characterized by fluctuating upward. In the north-west region, the GHG emissions pattern from CPS in Shaanxi and Xinjiang is monotonically increasing, while that in Gansu and Ningxia is characterized by fluctuating upward. On the other hand, in Qinghai, the pattern is characterized by fluctuating downward.

3.3 Regional GHG emissions of Livestock Production System

Figure 4 shows GHG emissions from LPS in China by province from 2000 to 2020. On the temporal dimension, the GHG emissions from the LPS in each province show more diverse patterns, with six modes identified: monotonically increasing, monotonically decreasing, increasing first and then decreasing volatility, increasing first and then decreasing volatility, decreasing first and then increasing volatility, and decreasing first and then increasing volatility. Among them, the livestock GHG emissions in Tianjin, Zhejiang, Anhui, Jiangxi, Hainan, Chongqing, Guizhou, and Gansu gradually increased at three time points, showing a monotonically increasing emissions pattern. Conversely, the livestock GHG emissions in Beijing, Hebei, Jiangsu, Guangdong, Qinghai, and Xinjiang gradually decreased at three time points, showing a monotonically decreasing emissions pattern. Many provinces also showed an upward trend followed by a downward trend in their livestock GHG emissions. The emissions of livestock GHG in Fujian, Shandong, Hubei showed a volatile upward pattern from 2000 to 2020. Meanwhile, the emissions of livestock GHG in Inner Mongolia, Liaoning, Jilin, Heilongjiang, Henan, Hunan, Guangxi, Sichuan, Yunnan and Shaanxi showed a volatile downward pattern. Some provinces also showed a downward trend followed by an upward trend in their livestock GHG emissions. The livestock GHG emissions in Shanxi, Shanghai, and Ningxia decreased in 2010 and rose again in 2020, with emissions higher than those in 2000, showing a volatile downward pattern. However, the GHG emissions of Xizang’s livestock industry show a fluctuating declining pattern, with emissions increasing slightly in 2020, but still lower than those in 2000.

On the spatial dimension, the dominant patterns of GHG emissions from the livestock industry varies in seven different regions. In the northern region, the predominant pattern of GHG emissions from the livestock industry is monotonically declining, as evident in the emissions from livestock in Beijing, Hebei, and Tianjin, which have decreased from 2000 to 2020, indicating a monotonically declining pattern. Shanxi’s GHG emissions pattern is characterized by a decline followed by an increase in volatility, while Inner Mongolia’s livestock GHG emissions are characterized by a fluctuating declining mode of first increase and then decrease. The predominant pattern of GHG emissions from the LPS in the north-eastern region is a volatile decline, as evident in the emissions from livestock in Liaoning, Jilin, and Heilongjiang, which increased in 2010, declined in 2020, with emissions in 2020 and lower than those in 2000.

The leading pattern in the eastern region is a monotonically increasing one, such as the GHG emissions from the livestock industry in Zhejiang, Anhui, and Jiangxi gradually increasing from 2000 to 2010 and 2020. Conversely, the GHG emissions from the livestock industry in Jiangsu gradually decreased from 2000 to 2010 and 2020, with a monotonically decreasing pattern. The emission pattern in Fujian and Shandong shows an initial increase followed by a decrease, with a volatile rise. Shanghai’s emission pattern presents a pattern of volatile increase after a period of decline and then an increase.

In the central region, as Henan and Hunan, the GHG emissions from the LPS all show a trend of first increasing and then decreasing, but the predominant pattern is a fluctuating declining pattern of first increase and then decrease. However, the emission pattern in Hubei shows a fluctuating increasing pattern of first increase and then decrease.

The emission patterns of livestock GHG emissions in various provinces in the southern region are different. Guangdong has a monotonically declining emission pattern, while Hainan shows a monotonically increasing emission pattern. Guangxi’s livestock GHG emissions have a pattern of first increasing and then decreasing with a volatile decline.

In the south-west region, the predominant pattern of GHG emissions from the LPS is monotonically increasing, such as in Chongqing, and Guizhou, and an initial rise followed by a drop with a volatile decline, like in Sichuan and Yunnan; in the case of Xizang, the GHG emissions pattern is a fluctuating decreasing pattern of first decrease and then increase.

The emission patterns of livestock GHG emissions in various provinces in the north-west region are also different. Gansu’s livestock GHG emissions show a monotonically increasing pattern, while Qinghai’s emissions show a monotonically decreasing pattern. Shaanxi and Ningxia’s emission patterns are exactly opposite; Shaanxi shows a fluctuating decreasing pattern of first increase and then decrease, while Ningxia exhibits a fluctuating increasing pattern of first decrease and then increase.

3.4 LMDI decomposition

We used the LMDI model to decompose and analyze the driving factors of agricultural GHG emissions at the provincial level in China from 2000 to 2020. The results are shown in Fig. 5. Overall, while the total GHG emissions from the Chinese agricultural system have increased, the growth rate of the total GHG emissions has generally decreased. The growth rate increased slightly from 6.82% (year 2000−2005) to 9.32% (year 2005−2010), and kept declining since then. The national agricultural GHG emissions even decreased by 7.84% between 2015 and 2020. The major contributors of the agricultural GHG emissions changes are the Agricultural development (increasing effect) and Intensity (decreasing effect) during the study period. Moreover, during the period from 2000 to 2020, Agricultural development and Urbanization played an increasing effect role, and Agricultural development was dominant. The contribution rates were more than 40% during the period from 2000 to 2015, playing nearly half the role. However, between 2015 and 2020, there was a significant decline in the contribution rate, from 43.52% to 23.13%. The positive effect of urbanization has continued to strengthen, rising from 4.82% to 17.86%, indicating an increasingly strong contribution to GHG emissions. Intensity and Labor both have a decreasing effect on the GHG emissions of the agricultural system during the period from 2000 to 2020. The decreasing effect of Intensity increased from 29.07% to 56.86% between 2000 and 2005, and its decreasing effect has been declining since then, reaching 27.19% in 2005−2020. On the other hand, the decreasing effect of Labor on GHG emissions has been continuously increasing from 3.17% from 2000 to 2005 to 17.35% from 2015 to 2020. Agricultural structure has both increasing and decreasing effects on GHG emissions. Between 2000 and 2005, it reduced GHG emissions by 11.74%. Between 2005 and 2010, and between 2010 and 2015, the agricultural structure has increased GHG emissions, but the effects were gradually weakened. Lastly, between 2015 and 2020, it led to a 4.31% reduction in GHG emissions.

The contribution of different driving factors on GHG emissions in each province are shown in Fig. 6. The promotion and inhibition factors of agricultural systems in different provinces from 2000 to 2020 vary in size and direction, presenting a relatively obvious regional differentiation feature. Among them, except for Shanghai, Agricultural Development increases GHG emissions, which is the largest contributing factor of the increase of GHG emissions in each province; Intensity has a decreasing effect on GHG emissions, which is the largest contributing factor of the decrease of GHG emissions in each province. Urbanization and Labor are also major factors affecting GHG emissions, which both have increasing effect and decreasing effect. Except for Jilin and Guangxi, Urbanization has a increasing effect on GHG emissions in other provinces, which is less than the driving effect of Agricultural development in most provinces. Labor inhibits GHG emissions from agricultural systems in other provinces except Jilin, Guangxi, and Xizang. In most provinces, the driving effect of Urbanization on GHG emission is stronger than the inhibiting effect of Labor on GHG emission. The effect of Agricultural structure on GHG emission in agricultural system is small and the fluctuation range is not large, which can inhibit GHG emission in agricultural systems in most provinces. Overall, GHG emissions in the central, eastern, and north-eastern regions are more affected by the above five factors than those in the south-west and north-west regions.

The Intensity effect in each province is decreasing GHG emissions, with a decreasing effect on GHG emissions in the agricultural system. The intensity effect is more pronounced in Henan, Shandong, Heilongjiang, Hubei, Hunan, Jiangsu, and Sichuan, which are located in the central and eastern regions of China, and it has a smaller decreasing effect in Xizang, Beijing, and Qinghai.

The Agricultural Development exerts an increasing effect on the GHG emissions in all provinces except Tianjin. Among them, the provinces of Heilongjiang, Henan, Hunan, and Hubei have the strongest increasing effect on GHG emissions in the agricultural system, mainly the central provinces and individual north-eastern provinces. The increasing effect of agricultural development in Shandong, Anhui, and Jiangsu in the eastern region is also strong, and these are all agriculturally developed provinces. Shanxi and Xizang have the weakest increasing effect on GHG emissions in the agricultural system, and Tianjin’s agricultural development has a decreasing effect on the GHG emissions in the agricultural system.

Except for Jilin, Guangxi, and Xizang, labor in all other provinces has a decreasing effect on the GHG emissions in the agricultural system. Provinces with a strong decreasing effect on GHG emissions are mainly located in Heilongjiang, Henan, Jiangsu, Anhui, Shandong, Hunan, Sichuan, Hubei, and Hebei. Among them, Heilongjiang has the strongest decreasing effect on GHG emissions. The decreasing effect on GHG emissions in Beijing, Qinghai, Tianjin, Ningxia, and Hainan is relatively weak.

Except for Jilin and Guangxi, the Urbanization promotes the GHG emissions in the agricultural system in all other provinces. Provinces with a strong increasing effect on GHG emissions are mainly located in Jiangsu, Shandong, Henan, Anhui, Beijing, and Hunan, mainly in the central and eastern regions. Among them, the increasing effect of Jiangsu and Shandong is the strongest. The increasing effect of Xizang, Guizhou, and Shanxi, on GHG emissions is the weakest, and it is also relatively low in Guangdong, Ningxia, Sichuan, Tianjin.

The agricultural structure in each province has both increasing effect and decreasing effect on the GHG emissions in the agricultural system, with relatively small fluctuations and low impact coefficients. Guangxi, Heilongjiang have the strongest increasing effect on GHG emissions, and Shanghai, Henan, Hainan, Shaanxi also promote GHG emissions, but the increasing effect is weaker. Jilin, Anhui, Shandong, Hunan, Hubei have a decreasing effect on GHG emissions. Among them, Jilin, Anhui have the strongest decreasing effect, while Xizang, Tianjin, Qinghai have the weakest decreasing effect on GHG emissions.

4 Conclusions and discussions

4.1 Conclusions

This study calculates the GHG emissions from the agricultural systems at the provincial level from 2000 to 2020 based on the life cycle assessment. We explored the change trends of the GHG emissions from the agricultural systems in different provinces and used the LMDI decomposition to analyze the driving factors of the GHG emissions from the CPS and LPS at provincial level, revealing the contribution of the Agricultural development, Intensity, Labor, Urbanization, and Agricultural structure to the agricultural GHG emissions. Furthermore, the main conclusions are as follows.

1) The total GHG emissions of China’s provincial agricultural systems varied between 2000 and 2020, with a generally upward trend from 2000 to 2017 and a declining trend from 2018 to 2020. During the entire research period, the total GHG emissions of China's agricultural system slightly increased, from 1250.96 Mt to 1384.99 Mt, which represents an increase of 10.71%. In CPS, agri-materials are the largest source of GHG emissions, with an average of 240.66 Mt. In LPS, manure management generated the most GHG emissions, with an average of 218.71Mt. For the entire agricultural system, agri-materials contributed the most to GHG emissions, with an average of 16.84%; energy consumption contributed the least to GHG emissions, with only 0.618%.

2) The results of LMDI decomposition shows the roles of different driving factors of China’s agricultural GHG emissions. Agricultural development and Urbanization are significant increasing factors. The increasing effect of Agricultural development is dominant. The decreasing effect of Urbanization on GHG emissions is constantly strengthening. Intensity shows the most significant decreasing effect on GHG emissions.

3) The GHG emissions of LPS and CPS in 2000, 2010, and 2020 showed different patterns. For CPS, the predominant pattern in the northern and central regions is a fluctuating increase, such as Tianjin, Hebei, Shanxi, Henan, Hubei, Hunan. In the north-east three province, the predominant pattern of GHG emissions was a monotonic increase. In the south-west region, the predominant pattern was a fluctuating decline, such as Chongqing, Sichuan, and Guizhou. The pattern of GHG emissions from LPS was more diverse. In the northern region, the predominant pattern was a monotonous decrease, such as Beijing, Hebei, and Tianjin. In the southern and western regions, the predominant pattern was a monotonic increase, such as Zhejiang, Anhui, and Jiangxi.

4) There are obvious inter-provincial differences in the magnitude and direction of the driving factors. All the provinces have decreasing effect on the GHG emissions, especially in the central (Henan, Hubei, Hunan) and eastern (Shandong) provinces. Except for Tianjin, the increasing effect of Agricultural development in other provinces is positive. The increasing effect is relatively strong in the central and eastern areas with developed agriculture, such as Heilongjiang, Henan, Hunan, and Hubei. Most of the provinces have a decreasing effect of Labor, with Heilongjiang, Henan, and Jiangsu showing an obvious increasing effect. Most of the provinces have a increasing effect of Urbanization, which is relatively strong in central and eastern cities and provinces, such as Jiangsu, Shandong, Henan, Anhui, Beijing, and Hunan, while the increasing effect in Xizang, Guizhou, and Shanxi is the weakest.

4.2 Discussions

China’s agricultural sector has experienced significant changes in greenhouse gas (GHG) emissions from 2000 to 2020. While this study indicates a modest overall increase of 10.71% in GHG emissions during this period, it is essential to consider the broader context and underlying factors influencing these trends. One notable aspect is the substantial contribution of livestock to agricultural GHG emissions, highlighting the need for targeted strategies to mitigate emissions from this sector. Additionally, the overuse of fertilizers in agricultural practices has been identified as a significant driver of GHG emissions (Shao, 2024; Zhang et al., 2024). China consumes about 30% of the world’s nitrogen fertilizers, with a substantial portion used in rice production. This overuse not only contributes to increased emissions but also leads to soil degradation and water pollution, posing challenges for sustainable agricultural development (Penuelas et al., 2023). Furthermore, China’s agricultural GHG emissions are closely linked to macroeconomic policies. Fertilizer inputs, agricultural industry structure, and energy use intensity are significantly positively correlated with carbon emission intensity. Conversely, factors such as urban feedback to rural areas, public investment in agriculture, and large-scale planting have a significant negative correlation with GHG emission intensity.

In conclusion, while this study provides valuable insights into the trends and driving factors of GHG emissions in China’s agricultural systems, it is crucial to address the specific challenges posed by livestock emissions, fertilizer overuse, and the influence of macroeconomic policies. Implementing targeted mitigation strategies is essential for achieving sustainable agricultural development and effectively reducing GHG emissions (Liu and Huang, 2024).

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