中国粮食生产碳排放动态演进及驱动效应

doi:10.11821/dlxb202309005

地理学报 ›› 2023, Vol. 78 ›› Issue (9): 2186-2208.doi: 10.11821/dlxb202309005

• 土地利用与“双碳”研究 • 上一篇下一篇

中国粮食生产碳排放动态演进及驱动效应

张青青¹^,²(), 曲衍波¹^,²^,³(), 展凌云¹^,², 苏德胜²^,³, 韦川辰²^,³

1.山东财经大学经济学院，济南 250014
2.山东财经大学土地科学与政策研究院，济南 250014
3.山东财经大学公共管理学院，济南 250014

收稿日期:2022-10-24 修回日期:2023-06-06 出版日期:2023-09-25 发布日期:2023-09-28
通讯作者: 曲衍波(1982-), 男, 山东龙口人, 博士, 教授, 博士生导师, 研究方向为土地系统优化、国民经济运行与低碳绿色发展。E-mail: yanboqu2009@126.com
作者简介:张青青(1995-), 女, 河南濮阳人, 博士生, 研究方向为粮食安全与低碳绿色发展。E-mail: qingqing_zhang2022@126.com
基金资助:
国家自然科学基金项目(42077434);国家自然科学基金项目(41771560);山东省高校青年创新团队发展计划(2019RWG016)

Dynamic evolution and driving effects of carbon emissions from grain production in China

ZHANG Qingqing¹^,²(), QU Yanbo¹^,²^,³(), ZHAN Lingyun¹^,², SU Desheng²^,³, WEI Chuanchen²^,³

1. School of Economics, Shandong University of Finance and Economics, Jinan 250014, China
2. Institute of Land Science and Policy, Shandong University of Finance and Economics, Jinan250014, China
3. School of Public Administration and Policy, Shandong University of Finance and Economics, Jinan 250014, China

Received:2022-10-24 Revised:2023-06-06 Published:2023-09-25 Online:2023-09-28
Supported by:
National Natural Science Foundation of China(42077434);National Natural Science Foundation of China(41771560);Shandong Provincial Colleges and Universities Youth Innovation Team Development Plan Project(2019RWG016)

摘要/Abstract

摘要：

保障“双碳”目标下的粮食安全是中国应对气候变化和构建人类生命共同体的重要课题。本文采用生命周期评价法测算1997—2020年全国及31个省份（数据暂未含港澳台）粮食生产碳排放量，利用基尼系数、标准差椭圆和核密度等方法探析其动态演进特征，从整体、时段、区域分解的角度识别中国粮食生产碳排放的驱动因素及其时空效应。研究发现：① 考察期内中国粮食生产碳排放总量呈增长态势，年均增长率1.30%，物质资料投入和秸秆燃烧的贡献最大，2017年以来呈现下降趋势，2020年碳排放总量为49478.19万t。② 在三大粮食生产功能区和6个粮食作物种植制度区层面，考察期内粮食主产区、长江中下游和华北地区的省份粮食生产碳排量一直处于前列，粮食主销区的碳排放量呈现缩减趋势。③ 考察期内中国粮食生产碳排放量的绝对差异扩大，整体上呈现高水平收敛和区域间差异扩大趋势。④ 中国粮食生产碳排放受经济、社会、技术、人口和自然等因素共同作用，农业总产值和粮食总产量作为关键影响因素表现出负向空间溢出效应，地区经济结构、劳动节约型技术和和农业生产结构与其相反；阶段效应以“十一五”时期为节点，驱动因素趋向简单化；区域效应中驱动因素趋向复杂化。本研究为推进“双碳”战略下粮食绿色生产提供了理论和方法依据。

关键词: 粮食生产碳排放, 生命周期评价法, 动态演进, 空间溢出效应

Abstract:

Ensuring food security under the "double carbon" goal is an important issue for China to tackle climate change and build a community of human life. This paper employs the life cycle assessment method to gauge the grain production carbon emissions in China and its 31 provincial-level regions from 1997 to 2020, and uses the Dagum Gini coefficient, standard deviation ellipse and kernel density methods to explore the dynamic evolution characteristics. It further identifies the underlying factors of China's grain production carbon emissions, as well as their spatio-temporal impacts, under a comprehensive, period-based, and regional-level decomposition perspective. The results show that: (1) During the inspection period, China's total carbon emissions from grain production showed an upward trend, with an annual growth of 1.30%, yet since 2017 it has been in a continuous downward trajectory. Material inputs and straw burning account for the lion's share of emissions. The total carbon emissions in 2020 amounted to 494.78 million tons. (2) With regard to the three major grain production functional areas and six grain crop planting system areas, the carbon emissions of grain production in the main grain production areas, the middle and lower reaches of the Yangtze River and North China provinces have been in the forefront during the investigation period, while those in the main grain sales areas have displayed a diminishing trend. (3) The absolute difference of China's grain production carbon emissions enlarged manifesting as a tendency of high level convergence and widening of regional disparities. (4) China's grain production carbon emissions are jointly affected by economic, social, technological, demographic and natural factors. With gross agricultural output value and gross grain output serving as the key influential factors, they display negative spatial spillover effects; meanwhile, regional economic structure, labor saving technology, and agricultural production composition demonstrate diametrically opposite effects. The stage effect takes the 11th Five-Year Plan period (2006-2010) as the node, and the driving factors tend to be simplified. In the regional effect, the driving factors from the main sales area, the main production area to the balanced production and marketing area tend to be complex. This study provides a theoretical and methodological basis for promoting green grain production under the "dual carbon" strategy.

Key words: grain production carbon emission, life cycle assessment, dynamic evolution, spatial spillover effect

张青青, 曲衍波, 展凌云, 苏德胜, 韦川辰. 中国粮食生产碳排放动态演进及驱动效应[J]. 地理学报, 2023, 78(9): 2186-2208.

ZHANG Qingqing, QU Yanbo, ZHAN Lingyun, SU Desheng, WEI Chuanchen. Dynamic evolution and driving effects of carbon emissions from grain production in China[J]. Acta Geographica Sinica, 2023, 78(9): 2186-2208.

图/表 12

表1

变量选择及描述性统计

一级指标	二级指标	符号	指标描述	单位	均值	标准差	选择依据
粮食生产碳排放	粮食生产碳排放	C_C	测算方法详见上文	万t	1431.21	1136.05
经济发展水平	经济增长	EG	人均地区生产总值	元	32415.12	28383.29	[13-14]
	农业总产值	TAOV	以1997年为基期，采用农产品生产者价格指数对农业总产值进行折算	亿元	1095.67	1064.93	[10]
	地区经济结构	RES	农业增加值/地区生产总值	%	7.51	4.39	[10]
	政府财政投入	GFI	农林水事务支出/地方一般公共预算支出	%	9.54	3.59	[7]
人民生活水平	农村居民人均可支配收入	AID	以1997年为基期，采用农村居民消费价格指数分别对农村居民人均可支配收入和农村居民人均消费支出进行折算	元	7275.58	5880.83	[10, 12, 15, 17]
	农村居民人均消费支出	FC	以1997年为基期，采用农村居民消费价格指数分别对农村居民人均可支配收入和农村居民人均消费支出进行折算	元	5665.57	4609.77	[10, 14]
	城乡收入差距	IG	城镇居民人均可支配收入/农村居民人均纯收入	%	2.82	0.63	[14, 17]
	非农兼业水平	NAI	农村居民经营净收入/农村居民可支配总收入	%	51.31	18.15	非农兼业水平代表农村居民从事非农产业收入占比
技术进步水平	劳动节约型技术	LST	农业机械总动力/乡村第一产业就业人员数量	kW/人	3.26	2.11	劳动节约型技术意味着机械化的使用节约的人力资本
	个体技术水平	ITL	以农村劳动力平均受教育年限来表示，不识字或识字很少、小学程度、初中程度、高中程度、大专或大专以上程度的受教育年限分别定义为1 a、6 a、9 a、12 a和16 a	a	7.32	0.93	[12]
	科技创新水平	STI	IPC中A01类中的发明专利申请量和实用新型专利申请量之和，来表示粮食生产方面的绿色专利量，即科技创新水平	个	106.56	205.35	分类标准见国家知识产权局(www.cnipa.gov.cn/)
人口规模	农村总人口	TRP	农村年末总人口数	万人	2223.33	1625.99	[10, 12]
	人口密度	PD	农村总人口/地区总人口	%	50.61	15.85	[10]
	农村老龄化	RA	以乡村老年抚养比表示，即农村老年人口与15~64岁人口的比例	%	13.92	5.07
	城镇化率	UB	区域城镇人口/总人口	%	49.37	16.67	[14]
自然条件	农业生产结构	APS	粮食作物播种面积/农作物总播种面积	%	66.34	12.69	[15]
	粮食总产量	TGO	地区粮食总产量水平	万t	1765.63	1480.10	[10]
	自然灾害	ND	农作物受灾面积/农作物播种面积	%	23.75	16.33	[20]

表1

图1

表2

时空演进方法介绍

方法名称	模型公式	公式释义	参考文献
Dagum基尼系数	$G = ∑ j = 1 k ∑ h = 1 k ∑ i = 1 n h ∑ r = 1 n h y j i - y h r 2 n 2 y -$ , $y - h ≤ … ≤ y - j ≤ … ≤ y - k$ $G j j = 1 2 y - j ∑ i = 1 n j ∑ r = 1 n j y j i - y j r n j 2$ , $G w = ∑ j = 1 k G j j P j S j G j h = ∑ i = 1 n j ∑ r = 1 n h y j i - y h r n j n h (y - j + y - h)$ $G n b = ∑ j = 2 k ∑ h = 1 j - 1 G j h (p j s h + p h s j) D j h$ $G t = ∑ j = 2 k ∑ h = 1 j = 1 G j h (p j s h + p h s j) (1 - D j h)$ , $G = G w + G n b + G t$ $s . t . p j = n j y -, s j = n j y - j n y -, j = 1,2, …, k$	$G$ 为总体基尼系数； $y - h 、 y - j 、 y - k$ 为h、j、k地区内各省份粮食生产碳排放均值； $G j j$ 和 $G w$ 分别为j地区内的基尼系数和区域内差异贡献； $G j h$ 和 $G n b$ 分别为j和h地区间的基尼系数和地区间净差值差异贡献； $G t$ 为超变密度， $y j i$ (y_hr)为j(h)地区中i(r)省份的研究对象值，n为省份个数，k为地区个数， $n j (n h)$ 为j(h)地区内的省份个数。	[37]
标准差椭圆	$X - w = ∑ i = 1 n w i x i ∑ i = 1 n w i$ , $Y - w = ∑ i = 1 n w i y i ∑ i = 1 n w i$ $δ x = ∑ i = 1 n (w i x - i c o s θ - w i y - i s i n θ) 2 ∑ i = 1 n w i 2 δ y = ∑ i = 1 n (w i x - i s i n θ - w i y - i c o s θ) 2 ∑ i = 1 n w i 2 t a n θ = (∑ i = 1 n w i 2 x - i 2 - ∑ i = 1 n w i 2 y - i 2) + ∑ i = 1 n w i 2 x - i 2 - ∑ i = 1 n w i 2 y - i 2 2 + 4 ∑ i = 1 n w i 2 x - i 2 y - i 2 2 ∑ i = 1 n w i 2 x - i y - i$	$x - i$ 和 $y - i$ 空间区位( $x i$ , $y i$ )距离分布重心的相对坐标； $w i$ 为权重，以粮食生产碳排放水平来替代；θ为标准差椭圆的方位角，即正北方向顺时针旋转与标准差椭圆长轴形成的夹角； $δ x$ 和 $δ y$ 为x轴和y轴上标准差。	[38⇓-40]
核密度	$f x = 1 N h ∑ i = 1 N K x i - X - h$ , $K x = 1 2 π e x p - x 2 2$ $l i m x → ∞ K x ∙ x = 0 K x ≥ 0, ∫ - ∞ + ∞ K x d x = 1 s u p K x < + ∞, ∫ - ∞ + ∞ K 2 (x) d x = 1$	$f x$ 为随机变量X在点x的概率密度公式； $K ∙$ 为高斯核函数；N为样本观测值个数； $x i$ 和 $X -$ 分别为独立同分布的观测值和均值； $h$ 为带宽。	[40-41]

表2

图2

图3

图4

图5

图6

图7

表3

表4

分阶段效应分解

变量	“九五”时期		“十五”时期		“十一五”时期		“十二五”时期		“十三五”时期
变量	直接效应	间接效应	直接效应	间接效应	直接效应	间接效应	直接效应	间接效应	直接效应	间接效应
EG	0.0131^**(1.87)	0.0092(0.60)	-0.0053(-1.76)	0.0096(1.67)	-	-	-0.0040(-1.66)	0.0039(0.52)	-	-
TAOV	0.7478^***(7.04)	1.0737^***(2.61)	0.3762^***(8.59)	0.2756^**(2.20)	0.2419^**(7.38)	-0.0531(-0.54)	0.1981^***(6.76)	-0.1769(-1.43)	0.0745^**(2.07)	-0.0369(-0.35)
RES	0.1351(0.03)	2.3720(0.18)	-	-	-	-	-68.2388^***(-6.20)	16.6843(0.50)	-4.4609(-0.69)	-12.1253(-0.76)
GFI	0.2257(0.12)	4.5439(0.87)	6.6406^**(1.97)	-14.2396^**(-2.11)	-	-	22.4611^***(3.67)	-2.2172(-0.13)	11.5515^**(2.10)	2.0072(0.11)
AID	-0.2187^***(-3.79)	0.3336^*(1.95)	9.85e-07(0.00)	0.0775^*(1.06)	-0.0089(-0.46)	0.0740^*(1.75)	-	-	-	-
FC	0.1295^**(2.41)	-0.1967(-1.37)	-0.0125(-0.35)	-0.0937(-1.55)	-	-	-0.0133(-1.18)	-0.0019(-0.07)	-0.0042(-0.31)	0.0096(0.30)
IG	-10.9165^*(-1.88)	-35.5993(-0.56)	-25.1003^*(-2.23)	-19.5517(-0.23)	-77.7230^*(-1.73)	11.4164(0.10)	45.7370(1.22)	132.4146(1.38)	-	-
NAI	1.4880(0.97)	-0.9994(-0.25)	0.6116(0.66)	5.2679(1.26)	-	-	-	-	0.2694(0.09)	-0.0262(-0.18)
LST	20.6416(1.28)	42.6954(0.96)	4.7857(0.60)	-57.3295(-1.11)	-8.5334(-0.55)	93.8520^***(3.19)	6.6337(0.64)	85.4482^**(2.36)	-16.6534(-1.45)	28.7742^**(1.25)
ITL	-19.2751(-1.49)	16.3759(0.57)	-	-	-	-	47.5075^*(1.86)	-12.7284(-0.18)	-64.2875^*(-1.79)	-27.9906(-0.28)
STI	-0.5373(-0.74)	-5.6656^***(-2.82)	0.3386(0.90)	-1.1444^*(-1.53)	-0.0981(-1.13)	0.5323^**(2.29)	-0.0886^**(-2.16)	0.1596(1.35)	0.0507(1.19)	-0.1524(-1.18)
TRP	-0.1496^**(-2.57)	0.1861(0.97)	-0.2965^***(-5.08)	0.1212(0.64)	-0.0561^*(-1.86)	-0.0459(-0.71)	0.7776^***(7.48)	0.2535(0.50)	-	-
PD	-0.7939(-0.28)	22.7967^**(2.16)	-3.2678(-0.88)	-9.8060(-0.80)	-	-	-8.4094(-0.81)	-59.2798^*(-1.88)	-17.8929^**(-2.22)	-23.6108^*(-1.20)
RA	-0.7393(-0.14)	-21.7477^*(-1.84)	-5.4746^**(-2.23)	-6.2712^*(-0.97)	-1.4658(-0.36)	-6.9139^*(-0.77)	-0.2741(-0.10)	-3.9315(-0.61)	-	-
UB	-	-	-	-	-2.2332(-0.41)	-2.6759(-0.20)	14.1162(1.09)	-46.1311(-1.02)	-2.1586(-0.22)	-19.5559(-0.80)
APS	1.0030(0.46)	6.9806(1.17)	5.1533^***(2.60)	-0.7874(-0.21)	4.1566^*(1.96)	-6.1748(-1.24)	-3.2038(-0.92)	-2.9667(-0.41)	0.6122(0.19)	0.7105(0.10)
TGO	0.2733^***(8.57)	-0.3458^***(-3.64)	0.1497^***(4.65)	-0.0311(-0.40)	0.2508^***(6.74)	-0.0137(-0.20)	0.4931^***(8.92)	-0.1979(-1.01)	0.2145^***(5.08)	0.0270(0.37)
ND	-	-	-	-	-	-	-	-	-	-

表4

表5

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变量	SDM模型(点估计)	效应分解
变量	SDM模型(点估计)	直接效应	间接效应	总效应
EG	0.0006(0.70)	0.0006(0.68)	0.0024(1.07)	0.0030(1.18)
TAOV	0.1877(11.27)^***	0.1871(13.2)^***	-0.0703(-1.73)^*	0.1168(2.96)^***
RES	-15.7691(-4.49)^***	-15.6667(-4.14)^***	14.9481(1.70)^*	-0.7186(-0.07)
FC	-0.0334(-4.14)^***	-0.0329(-3.86)^***	-0.0096(-0.43)	-0.0425(-1.83)^*
IG	-55.2664(-3.77)^***	-57.5456(-4.23)^***	72.1915(1.12)	14.6459(0.22)
NAI	-3.1822(-2.74)^***	-3.5015(-2.53)^**	5.4475(1.63)	1.9459(0.60)
LST	-18.9970(-3.19)^***	-19.4826(-3.42)^***	71.3746(4.76)^***	51.8920(3.60)^***
ITL	15.9622(0.78)	14.7096(0.70)	65.7949(1.38)	80.5045(1.61)
STI	-0.1150(-2.99)^***	-0.1205(-3.32)^***	0.0756(0.85)	-0.0449(-0.55)
TRP	0.0876(2.99)^***	0.0844(3.03)^***	-0.0554(-0.61)	0.0290(0.32)
PD	3.9570(2.83)^***	4.0173(2.91)^***	2.8347(0.75)	6.8520(1.75)^*
UB	1.6554(2.57)^***	1.6782(2.74)^***	2.4773(1.47)	4.1554(2.18)^**
APS	-1.2948(1.99)^*	-1.3282(-1.91)^*	-1.5478(-1.63)^*	-2.8761(1.05)^*
TGO	0.3434(19.35)^***	0.3449(19.44)^***	-0.1640(-3.35)^***	0.1809(3.81)^***
Spatial rho	-0.0781(-0.97)
R²	0.7936

变量	主产区		主销区		产销平衡区
变量	直接效应	间接效应	直接效应	间接效应	直接效应	间接效应
EG	0.0018 (1.40)	-0.0004 (-0.18)	-0.0006 (-2.02)	-0.0008 (-2.54)	-0.0006 (-0.31)	0.0119 (1.50)
TAOV	-	-	0.0207^* (1.65)	0.0307 (1.45)	0.3359^*** (14.87)	-0.2312^*** (-2.82)
RES	-11.7711^*** (-2.72)	31.7285^*** (3.55)	-5.3779^** (-1.57)	4.8057^** (0.89)	-13.0367^*** (-2.74)	46.1485^*** (3.97)
GFI	6.4114 (1.30)	10.2832 (1.09)	2.1765 (1.31)	0.6488 (0.23)	-	-
AID	-	-	-	-	0.0180 (0.96)	-0.2084^*** (-3.21)
FC	-0.0311^* (-1.11)	0.0984^*** (3.41)	-	-	-0.0248^* (-0.73)	-0.1113^** (-1.98)
IG	46.7902 (1.04)	129.3354 (1.25)	9.8614 (0.46)	56.6290 (1.86)	12.6662 (1.00)	-47.7325 (-0.76)
NAI	-1.8931^* (-2.93)	8.2315^** (2.55)	-0.4092 (-0.70)	-4.6811^*** (-6.75)	0.7816 (0.73)	3.7739 (0.86)
LST	-22.8996^** (-2.46)	31.8371^** (1.50)	-4.1095 (-1.24)	-3.7142 (-0.94)	-13.2169^** (-1.62)	133.1993^*** (4.11)
ITL	-23.6301 (-0.74)	24.9214 (0.43)	-	-	-14.8211 (-0.86)	10.3733 (-0.18)
STI	-0.1871^*** (-4.44)	0.1791^*** (2.87)	-	-	-	-
TRP	0.2417^*** (6.61)	0.1925 (1.57)	0.0056 (0.38)	-0.0176 (-0.82)	0.4282^*** (6.41)	0.0488 (0.57)
PD	7.3935^*** (2.89)	-9.7461 (-1.55)	-2.6106^*** (-3.06)	-0.5226 (-0.35)	3.1176 (0.75)	17.6796^*** (2.90)
RA	-19.4179^*** (-5.28)	-0.8790 (-0.10)	-1.1176^* (-1.69)	0.1424 (0.17)	-1.9680^* (-1.79)	-15.5187 (-1.31)
UB	0.4190 (0.39)	-0.1931 (-0.13)	-0.2149 (-0.37)	-0.8702 (-1.05)	-0.9621 (-1.44)	-3.9512^** (-2.38)
APS	-8.5440^*** (-3.95)	-18.4176^*** (-3.70)	-1.1756^*** (-2.84)	-1.2379^** (-2.23)	-7.2307^*** (-3.36)	-12.5795^* (-1.72)
TGO	0.3584^*** (18.52)	-0.1640^*** (-4.35)	0.3850^*** (12.79)	-0.0922^* (-1.87)	0.6825^*** (8.49)	-0.0980^* (-1.37)
ND	-	-	-	-	2.3395^*** (5.90)	1.0089 (0.94)

中国粮食生产碳排放动态演进及驱动效应

Dynamic evolution and driving effects of carbon emissions from grain production in China

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