Agricultural carbon emission intensity (ACEI) in China from 1997 to 2022: Spatiotemporal differences, convergence, and its influence factors

doi:10.1016/j.regsus.2026.100366

Abstract

Abstract:

Exploring the spatiotemporal differences and convergence mechanisms of agricultural carbon emissions in China can contribute to global agricultural carbon emission reduction. Taking 31 provinces, municipalities, and autonomous regions of China (excluding Hong Kong, Macao, and Taiwan due to data unavailability) as the study area, this paper estimated the agricultural carbon emission intensity (ACEI) during 1997-2022 and analyzed its spatiotemporal characteristics, decomposed its regional differences by using the Dagum Gini coefficient, and explored its convergence mechanism by adopting the spatial β-convergence and the Spatial Durbin Model (SDM). The result showed that China’s ACEI has shown a downward trend during 1997-2022, with an average annual reduction rate of 4.71%, and the inter-regional difference continued to widen, with the inter-regional difference of 66.17%. The absolute β-convergence speed was lower than the conditional β-convergence speed, indicating that regional heterogeneity in agricultural production significantly accelerates spatial convergence. This effect was particularly pronounced in the central region, where favorable agricultural conditions contribute to the fastest convergence rate. In this process, social support and environmental regulation, application and innovation of agricultural technologies, as well as agricultural structure and development potential in different regions (eastern, central, and western regions) exhibited distinct spatiotemporal effects. These findings suggest that the hierarchical spatial pattern of ACEI in China results from the interaction of multiple systems, including the environmental, social, economic, and governmental dimensions.

Key words: Agricultural carbon emission intensity (ACEI), Dagum Gini coefficient, Absolute and conditional β-convergence, Spatial Durbin Model (SDM), Spatial convergence

ZOU Lilin, LI Shulin, WANG Yongsheng. Agricultural carbon emission intensity (ACEI) in China from 1997 to 2022: Spatiotemporal differences, convergence, and its influence factors[J]. Regional Sustainability, 2026, 7(4): 100366.

Figures/Tables 9

Fig. 1.

Table 1

Table 2

Fig. 2.

Table 3

Table 4

Table 5

Table 6

Table 7

Spatial effect decomposition of influence factors on ACEI."

Parameter	Whole study area			Eastern region			Central region			Western region
Parameter	Direct effect	Indirect effect	Total effect	Direct effect	Indirect effect	Total effect	Direct effect	Indirect effect	Total effect	Direct effect	Indirect effect	Total effect
β	-0.209^*** (-10.322)	-0.633^*** (-6.476)	-0.843^*** (-8.449)	-0.298^*** (-9.397)	-0.488^*** (-6.257)	-0.785^*** (-9.339)	-0.416^*** (-9.213)	-0.396^*** (-4.256)	-0.812^*** (-8.379)	-0.238^*** (-7.184)	-0.375^*** (-2.882)	-0.613^*** (-4.465)
lnES	-0.123^*** (-2.826)	0.723^*** (3.141)	0.601^** (2.554)	-0.170^** (-2.528)	0.660^*** (3.941)	0.489^*** (2.851)	-0.405^*** (-3.955)	0.787^*** (3.570)	0.383^* (1.870)	-0.060 (-0.694)	1.109^*** (3.373)	1.049^*** (2.980)
lnFS	-0.025^* (-1.891)	-0.191^*** (-4.367)	-0.216^*** (-4.916)	-0.049^** (-2.033)	-0.178^*** (-4.212)	-0.227^*** (-5.902)	-0.029 (-1.091)	-0.173^*** (-3.823)	-0.203^*** (-4.275)	-0.034 (-1.600)	-0.170^*** (-3.098)	-0.203^*** (-3.546)
lnER	-0.009^* (-1.792)	-0.016 (-0.342)	-0.025 (-0.514)	-0.020^** (-2.448)	0.074^* (1.925)	0.054 (1.317)	-0.039^** (-2.546)	-0.011 (-0.204)	-0.049 (-0.847)	-0.005 (-0.684)	0.024 (0.598)	0.019 (0.427)
lnTA	0.038^*** (3.613)	-0.084 (-1.192)	-0.046 (-0.621)	0.076^*** (3.249)	-0.179^** (-2.428)	-0.103 (-1.235)	0.056^** (2.352)	-0.100 (-1.105)	-0.044 (-0.439)	-0.036^* (-1.861)	-0.084 (-1.075)	-0.120 (-1.425)
lnTI	0.015^*** (3.294)	-0.018 (-0.741)	-0.003 (-0.122)	-0.001 (-0.135)	-0.027 (-1.388)	-0.028 (-1.396)	0.030^** (2.445)	-0.036 (-1.549)	-0.007 (-0.243)	0.007 (0.908)	-0.001 (-0.020)	0.006 (0.175)
lnCS	-0.020 (-0.599)	0.451^*** (3.230)	0.431^*** (3.059)	-0.010 (-0.204)	0.374^*** (4.045)	0.364^*** (4.107)	-0.044 (-0.575)	0.722^*** (4.323)	0.678^*** (3.857)	0.073 (1.131)	0.158 (0.668)	0.232 (0.909)
lnDEV	-0.231^*** (-5.301)	0.244 (1.090)	0.014 (0.059)	-0.329^*** (-4.634)	0.266^* (1.764)	-0.063 (-0.412)	-0.560^*** (-5.585)	0.450^** (2.272)	-0.110 (-0.605)	-0.269^*** (-2.871)	0.834^*** (2.598)	0.565^* (1.657)
lnEDU	-0.013 (-1.091)	0.021 (0.709)	0.009 (0.304)	-0.002 (-0.166)	-0.013 (-0.480)	-0.015 (-0.657)	-0.012 (-0.469)	-0.009 (-0.208)	-0.021 (-0.505)	-0.002 (-0.117)	0.035 (0.902)	0.032 (0.918)

Table 7

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Agricultural carbon source	Carbon emission coefficient	Reference	Livestock	CH₄ emission coefficient (kg/(head•a))		N₂O emission coefficient of feces (kg/(head•a))	Reference
Agricultural carbon source	Carbon emission coefficient	Reference	Livestock	Gastrointestinal fermentation	Feces	Feces	Reference
Fertilizer (kg CO₂/kg)	0.896	Oak Ridge National Laboratory	Pig	1.000	3.50	0.53	Wu et al. (2022)
Pesticide (kg CO₂/kg)	4.934	Oak Ridge National Laboratory	Rabbit	0.254	0.08	0.02	Wu et al. (2022)
Film (kg CO₂/kg)	5.180	IAREE	Poultry	0.000	0.02	0.02	Wu et al. (2022)
Diesel (kg CO₂/kg)	0.593	IPCC	Dairy cow	68.000	16.00	1.00	Wu et al. (2022)
Ploughing (kg CO₂/hm²)	312.600	Wu et al. (2007)	Non-dairy cow	51.400	1.50	1.37	Wu et al. (2022)
Irrigation (kg CO₂/hm²)	266.480	Duan et al. (2011)	Horse	18.000	1.64	1.39	Wu et al. (2022)
Rice (kg CH₄/hm²)	2.380-65.420	Min and Hu (2012)	Donkey	10.000	0.90	1.39	Wu et al. (2022)
Winter wheat (kg N₂O/hm²)	1.750	Min and Hu (2012)	Mule	10.000	0.90	1.39	Wu et al. (2022)
Spring wheat (kg N₂O/hm²)	0.400	Min and Hu (2012)	Goat	5.000	0.17	0.33	Wu et al. (2022)
Bean (kg N₂O/hm²)	2.290	Min and Hu (2012)	Sheep	5.000	0.15	0.33	Wu et al. (2022)
Corn (kg N₂O/hm²)	2.532	Min and Hu (2012)	Camel	46.000	1.92	1.39	Wu et al. (2022)
Vegetable (kg N₂O/hm²)	4.944	Min and Hu (2012)
Cotton (kg N₂O/hm²)	0.480	Liu and Liu (2022)
Other crops (kg N₂O/hm²)	0.950	Min and Hu (2012)

Year	Whole country		Eastern region		Central region		Western region
Year	ACEI (t/10⁶ CNY)	AACR (%)	ACEI (t/10⁶ CNY)	AACR (%)	ACEI (t/10⁶ CNY)	AACR (%)	ACEI (t/10⁶ CNY)	AACR (%)
1997	515.38	-2.04	252.65	-2.95	404.86	-2.92	829.90	-1.53
1998	516.10		252.37		414.20		825.79
1999	508.99		246.66		416.24		811.29
2000	504.97		241.23		412.31		808.51
2001	508.14		238.10		392.44		832.81
2002	496.30		229.61		377.38		820.04
2003	471.16		218.18		362.65		775.40
2004	446.06		204.94		329.02		745.11
2005	436.76	-7.21	204.44	-7.01	322.91	-6.89	725.62	-7.36
2006	440.69		205.11		335.05		727.06
2007	375.39		172.66		276.44		627.19
2008	315.92		154.76		227.17		522.81
2009	295.79		147.48		217.12		484.19
2010	287.32		141.13		214.88		469.61
2011	270.44		128.93		199.95		447.15
2012	249.24		119.80		186.92		409.45
2013	234.01		111.44		176.64		384.62
2014	222.70		106.27		169.84		364.68
2015	210.01	-4.32	101.12	-3.98	161.97	-3.72	341.84	-4.61
2016	196.75		97.69		156.28		314.54
2017	194.40		99.30		163.35		302.29
2018	182.70		92.58		158.33		281.55
2019	168.16		82.10		138.47		266.85
2020	157.04		77.42		123.07		252.68
2021	157.06		76.97		126.13		251.11
2022	154.16		76.08		124.20		245.71

Year	Moran’s I	Z	P-value	Year	Moran’s I	Z	P-value
1997	0.2244	3.1128	0.0019	2010	0.1685	2.6678	0.0076
1998	0.2292	3.2648	0.0011	2011	0.1748	2.7983	0.0051
1999	0.2524	3.3851	0.0007	2012	0.1647	2.7754	0.0055
2000	0.2760	3.4752	0.0005	2013	0.1546	2.8071	0.0050
2001	0.2508	3.4441	0.0006	2014	0.1634	2.8874	0.0039
2002	0.2455	3.3787	0.0007	2015	0.1699	2.8688	0.0041
2003	0.2045	3.0929	0.0020	2016	0.1794	2.8080	0.0050
2004	0.1990	3.1189	0.0018	2017	0.1663	2.6903	0.0071
2005	0.2008	3.0984	0.0019	2018	0.1576	2.7024	0.0069
2006	0.2000	3.0664	0.0022	2019	0.1521	2.8881	0.0039
2007	0.1971	2.9853	0.0028	2020	0.1613	2.8418	0.0045
2008	0.1923	2.9512	0.0032	2021	0.1631	2.7351	0.0062
2009	0.2027	2.8503	0.0044	2022	0.1819	2.8525	0.0043

Inspection method	Whole study area		Eastern region		Central region		Western region
Inspection method	Absolute β-convergence	Conditional β-convergence	Absolute β-convergence	Conditional β-convergence	Absolute β-convergence	Conditional β-convergence	Absolute β-convergence	Conditional β-convergence
LM (error)	1346.813^***	1199.725^***	87.938^***	65.426^***	104.460^***	73.359^***	222.569^***	169.221^***
Robust-LM (error)	0.994	0.907	0.020	9.978^***	0.155	2.077	0.005	1.377
LM (lag)	1347.967^***	1267.710^***	88.450^***	80.720^***	104.338^***	87.954^***	223.083^***	188.178^***
Robust-LM (lag)	2.148	68.893^***	0.532	25.272^***	0.033	16.672^***	0.519	20.334^***
Wald (SAR-SDM)	28.870^***	39.850^***	7.590^***	53.430^***	11.070^***	44.650^***	13.890^***	18.700^**
Wald (SEM-SDM)	10.200^***	47.230^***	6.260^**	62.900^***	6.370^**	48.210^***	8.920^***	22.360^***
LR (SAR-SDM)	28.350^***	46.870^***	7.480^***	52.640^***	10.730^***	46.330^***	13.570^***	19.280^**
LR (SEM-SDM)	11.500^***	56.840^***	6.740^***	56.860^***	7.030^***	54.000^***	10.210^***	26.460^**
Hausman test	31.190^***	42.860^***	39.510^***	-5.460	14.280^***	-3.830	14.720^***	42.210^***

Parameter	Whole study area	Eastern region	Central region	Western region
β	-0.089^*** (-5.472)	-0.061^** (-2.566)	-0.127^*** (-3.536)	-0.097^*** (-3.667)
W×β	0.090^*** (5.373)	0.067^*** (2.755)	0.119^*** (3.327)	0.102^*** (3.727)
ρ	0.760^*** (23.092)	0.518^*** (8.250)	0.558^*** (9.736)	0.655^*** (13.380)
sigma2_e	0.004^*** (19.503)	0.004^*** (11.578)	0.005^*** (9.796)	0.004^*** (12.006)
R²	0.023	0.019	0.030	0.033
Log-likelihood	1048.466	373.632	245.951	403.845
Convergence rate (%)	0.36	0.24	0.52	0.39