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中国设施农业的减碳增汇效应分析基于1828个县域面板数据的实证研究

花匠小妙招
2024-11-03 10:35

摘要: 设施农业作为中国现代农业的重要标志, 揭示其时空格局演变及减碳增汇效应对实现“双碳”目标、促进绿色转型发展具有重要意义。本文应用空间经济计量方法, 采用2013—2017年中国1828个县域面板数据, 分析中国县域碳排放、固碳量及设施农业的时空演变特征, 在此基础上实证分析了设施农业的减碳增汇效应, 并从区域和粮食产区角度进行了异质性分析。结果表明: 1)中国县域碳排放总体呈东高西低的空间格局, 固碳量则显示出西高东低的特点, 设施农业面积空间格局也为东高西低, 三者均具有显著的空间正相关性及高-高空间集聚特征。2)设施农业面积对碳排放的直接和间接效应都呈“U”型, 存在显著减碳效应, 但设施农业面积对固碳量的直接和间接效应都显著为负, 增汇效应不明显。3)设施农业面积对于减碳增汇的作用存在显著的异质性。就区域层面而言, 设施农业面积显著促进东部和东北部地区碳减排, 显著抑制中部和西部地区碳汇; 就粮食产区而言, 设施农业面积对粮食主产区和主销区存在显著的减碳效应, 对三大粮食产区的增汇效应均不明显。研究认为, 各县应当加快推进设施农业绿色发展, 因地制宜发展低碳农业, 加强区域间减碳增汇合作交流, 以此有力地推进设施农业绿色可持续发展, 实现减碳增汇的愿景。

关键词: 设施农业 / 碳排放 / 碳汇 / 粮食产区

Abstract: Agriculture is not only a significant source of carbon emissions but also a massive carbon sink system. Reducing carbon emissions and increasing carbon sinks are essential directions and goals for achieving the green and high-quality development of modern agriculture. Protected agriculture is a crucial symbol of modern agriculture in China, revealing its spatiotemporal pattern evolution and the effect of reducing carbon emissions and increasing carbon sink is of great significance for achieving the “double carbon” goal and promoting green transformational development. However, there is limited research on reducing carbon emissions and increasing carbon sinks in protected agriculture, and the spatial correlation among protected agriculture, carbon emissions, and carbon sinks has not received enough attention. Research on reducing carbon emissions and increasing carbon sinks in protected agriculture should be conducted using a more microscopic county-level sample and should consider the heterogeneity of location and grain-production areas. Based on a comprehensive review of the existing literature, this study applies spatial econometric methods and utilizes panel data from 1828 counties in China from 2013 to 2017 for statistical analysis and empirical research. First, this study analyzed the temporal characteristics and spatial correlations of carbon emissions, carbon sinks, and protected agriculture at county level. Subsequently, it empirically analyzed the impact of protected agriculture on carbon emissions and sinks. Based on this, further analyses were conducted from the perspective of regional and grain-production area heterogeneity. The results showed that 1) carbon emissions in Chinese counties exhibited an overall pattern of higher levels in the east and lower levels in the west, while carbon sinks showed the opposite pattern. The spatial pattern of protected agriculture area followed the same trend as carbon emissions, and all three variables exhibited significant positive spatial correlations. A high-high agglomeration of carbon emissions was concentrated in the eastern region, with the center of gravity leaning towards the northwest. A high-high agglomeration of carbon sinks was concentrated in the western, northeastern and southwestern regions, whereas a high-high agglomeration of protected agriculture was concentrated in the Huang-Huai-Hai Region. 2) Protected agriculture area had a “U”-shaped direct and indirect effect on carbon emissions, indicating a significant carbon reduction effect. However, both the direct and indirect effects of protected agriculture area on carbon sinks were significantly negative, suggesting an insignificant sequestration effect. 3) The roles of protected agriculture in carbon reduction and sequestration exhibited significant heterogeneity. In terms of regional heterogeneity, protected agriculture significantly promoted carbon emission reduction in the eastern and northeastern regions, while significantly inhibiting carbon sinks in the central and western regions. Regarding the heterogeneity in grain-production areas, protected agriculture area had a significant carbon reduction effect in the major grain-production and sales areas, but its sequestration effect was not significant in the three grain-production areas. This study suggests that counties should accelerate the promotion of green development in protected agriculture, develop low-carbon agriculture according to local conditions, strengthen cooperation and communication on carbon reduction and sequestration between regions, effectively promote the green and sustainable development of protected agriculture, and achieve the goals of carbon reduction and sequestration.

图 1 中国县域碳排放量空间分布(2013—2017年)

CE: 碳排放量, ×104 t(CO2eq)。审图号: GS(2024)1028。CE: carbon emissions, ×104 t(CO2eq). Review number is GS(2024)1028.

Figure 1. The spatial distribution of carbon emissions at county level in China from 2013 to 2017

图 2 中国县域固碳量空间分布(2013—2017年)

CS: 固碳量, ×104 t(CO2eq)。审图号: GS(2024)1028号。CS: carbon sink, ×104 t(CO2eq). Review number is GS(2024)1028.

Figure 2. The spatial distribution of carbon sink at county level in China from 2013 to 2017

图 3 中国县域设施农业空间分布(2013—2017年)

PAA: 设施农业面积, hm2。审图号: GS(2024)1028。PAA: protected agriculture area, hm2. Review number is GS(2024)1028.

Figure 3. The spatial distribution of protected agriculture at county level in China from 2013 to 2017

图 4 2013—2017年中国县域碳排放、固碳量和设施农业面积的全局Moran’s I值

CE: 碳排放量; CS: 固碳量; PAA: 设施农业面积。CE: carbon emissions; CS: carbon sink; PAA: protected agriculture area.

Figure 4. The Global Moran’s I of carbon emissions, carbon sink and protected agriculture area at country level in China from 2013 to 2017

图 5 中国碳排放量(CE)、固碳量(CS)和设施农业(PAA) LISA图

H-H: 高-高集聚; H-L: 高-低集聚; L-H: 低-高集聚; L-L: 低-低集聚。审图号: GS(2024)1028号。H-H: high-high agglomeration; H-L: high-low agglomeration; L-H: low-high agglomeration; L-L: low-low agglomeration. Review number is GS(2024)1028.

Figure 5. The LISA value maps of carbon emissions (CE), carbon sink (CS) and protected agriculture area (PAA) at county level in China

表 1 设施农业对碳排放和固碳量影响的估计结果

Table 1 Estimated results of the impact of protected agriculture on carbon emissions and carbon sink

变量
Variable 碳排放量 Carbon emissions 固碳量 Carbon sink OLS SLM SEM SDM SDM lnPAA −0.0075***
(−2.7485) −0.0071***
(−3.5578) −0.0066***
(−3.2849) −0.0063***
(−3.1703) −0.0032***
(−3.4563) (lnPAA)2 0.0006***
(2.6051) 0.0007***
(3.7690) 0.0006***
(3.4511) 0.0006***
(3.2495) lnGDP 0.0221***
(3.3408) 0.1783***
(3.6824) 0.0218***
(4.0978) 0.0183***
(3.2024) 0.0095
(1.4341) lnPIS 0.0537***
(9.0372) 0.0242***
(5.5616) 0.0217***
(4.6394) 0.0176***
(3.7848) lnPD 0.0420***
(3.0515) 0.0257**
(2.5579) 0.0193*
(1.8927) 0.0084
(0.8391) 0.0295**
(2.2060) lnGE −0.0247***
(−4.9563) 0.0138***
(3.7638) 0.0151***
(3.6823) 0.0146***
(3.5461) 0.0151***
(2.7988) lnGCP — −0.0001
(−0.0440) lnFC — −0.0815**
(−2.1893) W×lnPAA −0.1420***
(−4.2178) −0.0946***
(−7.7651) W×(lnPAA)2 0.0172***
(6.1496) W×lnGDP −0.3086***
(−7.4568) 0.0096
(0.2632) W×lnPIS 0.2514***
(5.6439) W×lnPD 2.7880***
(15.8044) 1.7539***
(8.2997) W×lnGE −0.0132
(−0.5151) −0.1355***
(−5.6721) W×lnGCP 0.2766***
(6.6997) W×lnFC 0.2578***
(4.2254) 常数项
Constant term 5.2022***
(45.3186) ρ、λ 0.9928***
(312.2870) 0.9928***
(313.9317) 0.9909***
(246.2671) 0.9970***
(753.6668) 豪斯曼检验
Hausman test 1746.6819*** LR检验
LR test 262.0914*** 279.5325*** 样本量
Sample size 9140 9140 9140 9140 9140 　　PAA: 设施农业面积; GDP: 经济发展水平; PIS: 产业结构; PD: 人口密度; GE: 政府财政支出; GCP: 粮食产量; FC: 森林覆盖面积; W: 空间权重矩阵; ρ: 空间自回归系数; λ: 误差项的空间自回归系数; OLS: 普通最小二乘法; SLM: 空间滞后模型; SEM: 空间误差模型; SDM: 空间杜宾模型; LR: 似然比。括号中为Z统计量; *, **和***分别代表在10%、5%和1%水平显著。PAA: protected agriculture area; GDP: level of economic development; PIS: production industrial structure; PD: population density; GE: government expenditure; GCP: grain production; FC: forest area; W: spatial weighting matrix; ρ: spatial autoregressive parameter; λ: spatial autoregressive parameter of the error term; OLS: Ordinary Least Square; SLM: Spatial Lag Model; SEM: Spatial Error Model; SDM: Spatial Durbin Model; LR: likelihood ratio. The value within parentheses represents the Z-statistic; *, ** and *** represent significant correlation at 10%, 5%, and 1% levels, respectively.

表 2 区域异质性和粮食产区异质性下设施农业对碳排放影响的估计结果

Table 2 Estimated results of the impact of protected agriculture on carbon emissions due to regional and grain production regional heterogeneity

变量
Variable 区域异质性
Regional heterogeneity 粮食产区异质性
Grain production regional heterogeneity 东部地区
Eastern
area 中部地区
Central
area 西部地区
Western
area 东北地区
Northeast
area 粮食主产区
Major grain-production
area 粮食主销区
Major grain-sales
area 产销平衡区
Production and sales balance
area lnPAA −0.0127***
(−3.32) −0.0004
(−0.11) −0.0042
(−1.43) −0.0150*
(−1.70) −0.0062**
(−2.46) −0.0083*
(−1.65) 0.0003
(0.09) (lnPAA)2 0.0011***
(3.39) 0.0001
(0.18) 0.0003
(1.07) 0.0013*
(1.89) 0.0006***
(2.89) −0.0007*
(1.69) −0.0001
(−0.29) lnGDP 0.0495***
(3.39) 0.0771***
(5.59) 0.0099
(1.25) 0.0170**
(2.03) 0.0510***
(6.92) −0.0713**
(−2.29) −0.0068
(−0.81) lnPIS −0.0066
(−0.48) 0.0083
(0.74) 0.0318***
(5.34) 0.0137*
(1.95) −0.0148**
(−2.18) 0.0065
(0.28) 0.0308***
(5.11) lnPD 0.0287*
(1.86) 0.0238
(1.37) −0.0666***
(−3.19) 0.0714***
(2.70) 0.0313**
(2.39) 0.0067
(0.37) −0.0592**
(−2.52) lnGE 0.0476***
(4.76) 0.0316***
(3.38) 0.0011
(0.1880) −0.0002
(−0.03) 0.0062
(1.25) 0.0777***
(4.92) 0.0043
(0.63) W×lnPAA −0.0651*
(−1.66) 0.0915**
(2.26) −0.1172***
(−2.99) −0.1186**
(−2.06) −0.1180***
(−3.59) −0.0428
(−0.90) −0.0023
(−0.04) W×(lnPAA)2 0.0052*
(1.76) −0.0065*
(−1.69) 0.0121***
(2.96) 0.0074
(1.59) 0.0106***
(4.03) 0.0028
(0.82) 0.0029
(0.51) W×lnGDP −0.0657
(−0.93) −0.0565
(−0.75) −0.1205***
(−3.10) −0.0438
(−1.09) -0.1971***
(−4.70) −0.0370
(−0.22) −0.2165***
(−4.99) W×lnPIS 0.2164**
(2.12) −0.1370**
(−2.03) 0.0699*
(1.92) −0.0222
(−0.55) 0.1430***
(3.16) 0.5332***
(3.61) 0.0882**
(2.54) W×lnPD 0.7186***
(4.22) 0.4052
(1.57) 0.4602***
(2.62) 0.3565*
(1.84) 1.9034***
(11.78) 0.4633**
(2.44) 0.6746***
(3.43) W×lnGE −0.0517
(−1.5398) −0.2299***
(−5.19) 0.1550***
(4.39) 0.0043
(0.15) −0.1177***
(−4.65) 0.0832*
(1.74) 0.0584
(1.57) ρ 0.9705***
(74.72) 0.9339***
(33.97) 0.9846***
(149.37) 0.9731***
(82.60) 0.9805***
(115.43) 0.9626***
(58.46) 0.9827***
(133.58) 样本量
Sample size 2385 2430 3605 720 5115 990 3035 　　PAA: 设施农业面积; GDP: 经济发展水平; PIS: 产业结构; PD: 人口密度; GE: 政府财政支出; W: 空间权重矩阵; ρ: 空间自回归系数; 括号中为 Z 统计量; *, **和***分别代表在10%、5%和1%水平显著。PAA: protected agriculture area; GDP: level of economic development; PIS: production industrial structure; PD: population density; GE: government expenditure; W: spatial weighting matrix; ρ: spatial autoregressive parameter. The value within parentheses represents the Z-statistic; *, ** and *** represent significant correlation at 10%, 5%, and 1% levels, respectively.

表 3 区域异质性和产区异质性下设施农业对固碳量影响的估计结果

Table 3 Estimated results of the impact of protected agriculture on carbon sink due to regional and grain production regional heterogeneity

变量
Variable 区域异质性
Regional heterogeneity 产区异质性
Grain production regional heterogeneity 东部地区
Eastern
area 中部地区
Central
area 西部地区
Western
area 东北地区
Northeast
area 粮食主产区
Major grain-production
area 粮食主销区
Major grain-sales
area 产销平衡区
Production and sales balance
area lnPAA −0.0009
(−0.60) −0.0025*
(−1.72) −0.0043***
(−3.06) −0.0048
(−1.30) −0.0031***
(−2.55) −0.0007
(−0.69) −0.0018
(−1.20) lnGDP −0.0488***
(−3.23) 0.0793***
(4.59) −0.0128
(−1.45) 0.0002
(0.02) 0.0224**
(2.37) −0.0262
(−1.56) −0.0177**
(−2.07) lnPD 0.0331*
(1.94) 0.0371*
(1.65) −0.0107
(−0.40) −0.0319
(−0.75) 0.0397**
(2.00) 0.0081
(0.77) −0.0002
(−0.01) lnGE 0.0424***
(3.85) 0.0021
(0.17) 0.0009
(0.13) −0.0266**
(−2.49) 0.0146**
(1.97) 0.0257***
(2.86) 0.0059
(0.77) lnGCP 0.0029
(0.38) 0.0341**
(2.30) 0.0041
(1.25) 0.0205*
(1.70) −0.0010
(−0.15) 0.0014
(0.25) −0.0011
(−0.34) lnFC −0.1164*
(−1.71) −0.6921***
(−5.98) 0.1439*
(1.93) 2.9391***
(2.85) 0.1930***
(−3.38) −0.2176***
(−3.08) −0.1347**
(−2.06) W×lnPAA −0.0342**
(−2.53) 0.0029
(0.17) −0.0577***
(−5.12) 0.0313
(1.12) −0.0263*
(−1.82) −0.0552***
(−8.22) −0.0571***
(−4.97) W×lnGDP 0.0490
(0.62) −0.2617***
(−3.90) 0.1992***
(4.72) 0.0114
(0.30) 0.0253
(0.65) −0.3503***
(−3.86) −0.0469
(−1.35) W×lnPD 1.0941***
(5.87) 0.0392
(0.13) −0.7192***
(−3.02) −0.2691
(−0.65) 2.2437***
(9.33) 0.3679***
(3.39) −0.6769***
(−3.05) W×lnGE −0.0582**
(−2.36) −0.0004
(−0.01) 0.1549***
(3.96) −0.0451
(−0.91) −0.2069***
(−7.06) 0.1078***
(5.21) 0.1944***
(4.86) W×lnGCP 0.2181***
(5.02) 0.8207***
(10.02) −0.0505*
(−1.76) 0.0510
(1.06) 0.7604***
(11.06) 0.1537***
(7.20) 0.1168***
(4.50) W×lnFC −0.0015
(−0.02) 1.0852***
(7.33) −0.0531
(−0.51) −2.9562**
(−2.56) 0.3583***
(4.77) 0.6167***
(4.28) 0.3275***
(3.55) ρ 0.9935***
(339.69) 0.9911***
(250.66) 0.9920***
(277.33) 0.9671***
(67.16) 0.9953***
(477.15) 0.9889***
(199.40) 0.9918***
(272.71) 样本量
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