Spatiotemporal evolution and influencing factors of agricultural carbon emissions in Hebei Province at the county scale
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摘要: 县域是农业碳排放基本单元, 研究县域农业碳排放的时空演变与驱动因素对制定区域差异化农业减排政策有重要意义。本文测算了2009—2019年河北省168个县农业碳排放量, 基于探索性空间统计与空间计量方法, 研究了县域农业碳排放的时空演变与影响因素并讨论了空间溢出的边界。研究表明: 河北省农业碳排放整体呈下降趋势, 农业碳排放中土地管理、畜禽肠道发酵和粪便管理的碳排放分别占33.00%、42.57%和24.33%, 县域尺度农业碳排放呈现高度空间集聚的特点。农业碳排放的热点分布与农业产业结构有密切关系, 土地管理引发的农业碳排放热点在冀东南的深州、武强、饶阳等县, 冀北丰宁、围场、滦平和隆化县为畜牧业排放热点。县域农业碳排放有显著的空间溢出效应, 邻近地区农业碳排放对本地区碳排放有正向作用。农业经济发展是农业碳排放增加的主要驱动力, 农业产业结构、机械化程度、化肥施用强度、农村能源消费和农民收入是驱动农业碳排放增长的重要因素。城镇化率对农业碳排放有反向影响。农业碳排放受空间外溢与边界效应双重影响, 碳排放空间溢出范围大概在6~8个邻近县。本研究为建立区域农业碳减排机制提供了政策依据和定量研究工具。Abstract: Global climate change, caused by greenhouse gas emissions, is a common challenge for human society. Counties are the smallest administrative units covered by statistics in China, and are also the basic unit at which agricultural carbon emissions data are collected. Studying the spatiotemporal evolution and drivers of agricultural carbon emissions in counties is important for improving the inventory of agricultural carbon emission data, establishing agricultural carbon emission monitoring systems, and formulating regional emission reduction policies. A county-level greenhouse gas emission inventory was established by combining the agricultural greenhouse gas inventory with the characteristics of county data in this study. The agricultural carbon emissions of 168 counties in Hebei Province from 2009 to 2019 were measured firstly, and the spatial and temporal evolution and drivers of agricultural carbon emissions in counties were analyze then from the perspective of spatial spillover using exploratory spatial statistics and spatial measurement methods. The boundary effects of spatial spillover were investigated finally by using a dynamic spatial model to reveal the regular changes induced by the spatial spillover of agricultural carbon emissions in counties with increasing distance. The study results showed that agricultural carbon emissions in Hebei Province were decreasing during the study duration, with land management, livestock and poultry enteric fermentation, and manure management accounting for 33.00%, 42.57%, and 24.33% of agricultural carbon emissions, respectively. A high spatial agglomeration of agricultural carbon emissions at the county scale was found, and the distribution of agricultural carbon emissions hotspots was closely related to the structure of the agricultural industry. The hotspots of agricultural carbon emissions caused by land management were in Shenzhou, Wuqiang, and Raoyang counties in the south of Hebei, whereas the hotspots of livestock emissions were in Fengning, Weichang, Luanping, and Longhua counties in the north of Hebei. County agricultural carbon emissions had a significant spatial spillover effect, and agricultural carbon emissions in neighboring areas increased the overall carbon emissions in the region. Agricultural economic development was the main driver for the increase in agricultural carbon emissions. The agricultural industry structure, mechanization, fertilizer application intensity, rural energy consumption, and farmers’ income were important factors that increased the agricultural carbon emissions. The urbanization rate had an inverse effect on agricultural carbon emissions. Agricultural carbon emissions were affected by both spatial and boundary factors. The spatial spillover of agricultural carbon emissions in the county showed an increasing trend within 30 km, and a decreasing trend within 30–85 km, and the spatial spillover boundary of agricultural carbon emissions was thus approximately 30 km. The spatial spillover of carbon emissions occurred in approximately 6–8 neighboring counties. This study provides a basis and data foundation for establishing regional agricultural carbon emission reduction policies.
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Key words:
- County scale /
- Agricultural carbon emissions /
- Spatial layout /
- Spatial panel model
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图 1 县域农业碳排放模型空间权重矩阵W1−W5
W1: 邻近空间权重矩阵; W2: 固定距离空间权重矩阵; W3: K临近空间权重矩阵; W4: 自然临近空间权重矩阵; W5: 反距离空间权重矩阵。W1: adjacent space weight matrix; W2: fixed distance space weight matrix; W3: K-adjacent space weight matrix; W4: natural-adjacent space weight matri; W5: inverse distance space weight matrix.
Figure 1. Spatial weight matrix W1-W5 of the county agricultural carbon emission model
表 1 农业碳源与温室气体排放系数
Table 1. Agricultural carbon sources and greenhouse gas emission factors
碳排放源
Carbon source碳排放系数
Carbon emission factor数据来源
Data source土地管理
Land management化肥 Fertilizer 895.6 kg∙t−1 美国橡树岭国家实验室 Oak Ridge National Laboratory, USA 农药 Pesticides 4934 kg∙t−1 美国橡树岭国家实验室 Oak Ridge National Laboratory, USA 农膜 Farm film 5180 kg∙t−1 南京农业大学 Nanjing Agricultural University 翻耕 Plowing 312.6 kg∙hm−2 中国农业大学 China Agricultural University 灌溉 Irrigation 266.48 kg∙hm−2 HuaPing Duan 农业机械
Agricultural machinery0.18 kg∙kW−1 Dubey 肠道发酵
Intestinal fermentation
(CH4)牛 Cow 80.46 kg∙head−1∙a−1 中国省级温室气体清单编制指南
Guidelines for the Preparation of Provincial Greenhouse Gas
Inventories in China猪 Pig 1 kg∙head−1∙a−1 羊 Sheep 8.23 kg∙head−1∙a−1 禽畜粪便
Livestock manure
(CH4, N2O)牛 Cow 5.14 kg∙head−1∙a−1, 中国省级温室气体清单编制指南
Guidelines for the Preparation of Provincial Greenhouse Gas
Inventories in China1.29 kg∙head−1∙a−1 猪 Pig 3.12 kg∙head−1∙a−1, 0.093 kg∙head−1∙a−1 羊 Sheep 0.16 kg∙head−1∙a−1, 0.227 kg∙head−1∙a−1 水稻种植
Rice cultivation华北单季稻
North China single-season rice234 kg∙hm−2 中国省级温室气体清单编制指南
Guidelines for the Preparation of Provincial Greenhouse Gas
Inventories in China《中国省级温室气体清单编制指南》中猪、羊、牛的碳排放系数不同的饲养规模存在差异, 计算时取其均值。The carbon emission factors of pig, sheep and cattle in the “Guidelines for the Preparation of Provincial Greenhouse Gas Inventories in China” differ for different feeding scales and are calculated by taking their average values. 表 2 县域农业碳排放影响因素变量选取与描述性统计
Table 2. Variable selection and descriptive statistics of factors influencing agricultural carbon emissions in counties
变量
Variable符号
Symbol变量解释
Explanation均值
Mean标准差
Std. deviation人均农业生产总值
Agricultural GDP per capita (¥)AGDP 农业生产总值与农村总人口之比
Ratio of gross agricultural product to total rural population9870 5059 城镇化率
Urbanization rate (%)Urban 参考河北农村统计年鉴
Reference to Hebei Rural Statistical Yearbook46.7 18.9 农村居民收入
Income of rural residents (¥)Income 参考河北农村统计年鉴
Reference to Hebei Rural Statistical Yearbook9632 7616 农业产业结构
Agricultural industry structure (%)Str 种植业产值与农业总产值之比
Ratio of plantation output to total agricultural output0.52 0.15 农业机械化程度
Agricultural mechanization level (%)Tech 机耕面积与农作物播种面积之比
Ratio of machine cultivated area to crop sown area0.64 0.23 化肥施用强度
Fertilizer application intensity (%)Fer 化肥使用量与农作物播种面积之比
Ratio of fertilizer use to crop sown area0.39 0.20 农村用电量
Energy consumption (×108 kWh)Energy 参考河北省农村统计年鉴
Reference to Hebei Rural Statistical Yearbook34 724 62 574 表 3 河北省2009—2019年县域农业碳排放Moran’I指数
Table 3. Morans’ I of agricultural carbon emissions in counties of Hebei Province from 2009 to 2019
年份
Year农业碳
排放
Agricultural
carbon emission畜牧业
碳排放
Carbon emissions
from livestock
farming人均碳排
放强度
Carbon emission
intensity per
capita2009 0.287 0.306 0.617 2010 0.272 0.273 0.599 2011 0.308 0.225 0.568 2012 0.308 0.221 0.623 2013 0.298 0.260 0.638 2014 0.324 0.251 0.622 2015 0.304 0.136 0.506 2016 0.311 0.135 0.412 2017 0.310 0.289 0.420 2018 0.344 0.355 0.316 2019 0.320 0.361 0.326 表 4 河北省县域农业碳排放影响因素回归结果
Table 4. Regression results of factors influencing agricultural carbon emissions in counties of Hebei Province counties
普通面板
模型
Common panel data model空间误差模型
Spatial error model空间滞后模型
Spatial lag model空间通用模型
Spatial general model固定效应
Fixed effect随机效应
Random effect固定效应
Fixed effect随机效应
Random effect固定效应
Fixed effect随机效应
Random effect农业人均生产总值 Agricultural GDP per capita 0.96***(0.03) 0.92***(0.03) 0.89***(0.03) 0.88***(0.03) 0.79***(0.03) 0.89***(0.03) 0.81***(0.03) 城镇化率 Urbanization rate −0.64***(0.12) −0.46***(0.11) −1.31***(0.07) −0.52***(0.11) −1.19***(0.05) −0.50***(0.11) −1.20***(0.05) 农村居民收入 Income of rural residents 0.05***(0.01) 0.05***(0.01) 0.03*(0.01) 0.05***(0.01) 0.03*(0.01) 0.05***(0.01) 0.03*(0.01) 农业产业结构 Agricultural industry structure 0.12**(0.04) 0.13***(0.04) 0.10**(0.04) 0.10**(0.03) 0.07*(0.03) 0.11**(0.04) 0.08*(0.03) 农业机械化率 Agricultural mechanization rate 0.15***(0.03) 0.14***(0.03) 0.14***(0.03) 0.15***(0.03) 0.14***(0.03) 0.15***(0.03) 0.14***(0.03) 化肥施用强度 Fertilizer application intensity 0.08*(0.03) 0.11***(0.03) 0.12***(0.03) 0.08**(0.03) 0.07*(0.03) 0.09**(0.03) 0.09**(0.03) 农村能源消耗 Rural energy consumption 0.43***(0.02) 0.42***(0.02) 0.40***(0.02) 0.39***(0.02) 0.37***(0.02) 0.39***(0.02) 0.37***(0.02) 常数项 Constant 11.45***(0.39) 5.86***(0.32) 6.37***(0.33) 空间滞后系数 Spatial lag coefficient 0.29***(0.03) 0.34***(0.03) 0.26***(0.04) 0.30***(0.03) 空间误差系数 Spatial error coefficient 0.29***(0.03) 0.36***(0.04) 0.06(0.05) 0.09*(0.05) 样本量 Observations 1680 1680 1680 1680 1680 1680 1680 空间LM检验
Spatial LM test3304*** 空间LM1检验
Spatial LM1 test57*** 空间LM2检验
Spatial LM2 test10*** 空间LM-Lambda
检验
Spatial LM-Lambda test8*** 空间LM-Mu检验
Spatial LM-Mu test56*** 空间豪斯曼检验
Hausman test for spatial modelschisq=36, df=7, P-value=9e−06 *、**和***分别表示回归系数在P<10%、P<5%和P<1%的显著水平, 括号内为标准差。*, ** and *** denote the significance levels of the regression coefficients of P<10%, P<5%, and P<1%, respectively. Date in parentheses are the corresponding standard deviations. 表 5 稳健性检验结果
Table 5. Robustness test results
W1 W4 W2 农业人均生产总值 Agricultural GDP per capita 0.94***(0.03) 0.94***(0.03) 0.90***(0.03) 城镇化率
Urbanlization rate−0.61***(0.11) −0.60***(0.11) −0.51***(0.11) 农村居民收入
Income of rural residents0.05***(0.01) 0.05*** (0.01) 0.04***(0.02) 农业产业结构
Agricultural industry structure0.12**(0.04) 0.10**(0.04) 0.11**(0.04) 农业机械化水平
Agricultural mechanization rate0.16***(0.03) 0.15***(0.03) 0.15***(0.03) 化肥施用强度
Fertilizer application intensity0.08**(0.03) 0.08**(0.03) 0.08**(0.03) 农村能源消费
Rural energy consumption0.41***(0.02) 0.42***(0.02) 0.40***(0.02) 空间滞后系数
Spatial lag coefficient0.20***(0.03) 0.20***(0.03) 0.14***(0.04) 空间误差系数
Spatial error coefficient0.03(0.05) −0.003(0.05) 0.07(0.05) W1: 邻近空间权重矩阵; W4: 自然临近空间权重矩阵; W2: 固定距离空间权重矩阵。*、**、***分别表示回归系数在P<10%、P<5%、P<1%水平显著, 括号内为标准差。W1: adjacent space weight matrix; W4: natural-adjacent space weight matrix; W2: fixed distance space weight matrix. *, **, *** denote the significance levels of the regression coefficients of P<10%, P<5%, and P<1%, respectively. Date in parentheses are the corresponding standard deviations. -
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