Peaking process and decoupling analysis of carbon emissions of crop production in China
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摘要: 判断种植业碳达峰进程, 可为温室气体减排提供农业领域的数据支撑。考虑农用物资、水稻种植、土壤管理和秸秆燃烧4类排放源, 本文对2000—2020年中国30省(市、自治区)种植业碳排放进行核算, 分类别、分量级对达峰进程展开初步探索, 利用Tapio脱钩指数探讨种植业碳排放与农业产值之间的关系。结果显示: 全国种植业碳排放量年均为23 326.860万t, 在2015年达到峰值26 264.777万t, 达峰后年均变化率为−1.560%, 尚处于平台期。根据达峰进程, 可将30省(市、自治区)分为下降期(北京、天津等13地)、平台期(山西、重庆等10地)、达峰期(河南、安徽等7地)。从全国层面来看, 种植业碳排放与农业产值的长期关系表现为弱脱钩, 短期关系已由弱脱钩转变为强脱钩。就省域层面而言, 短期关系自多种类型并存格局演化为强脱钩主导的极化态势。应根据达峰阶段及特点, 分区域、分类型制定全局减排策略, 加快我国种植业碳排放达峰转降进程。Abstract: Exploring the peak process of carbon emissions from crop production provides a basis for mitigating greenhouse gas emissions. Previous studies found that carbon emissions from crop production in China reached an inflection point in 2015. Nonetheless, determining whether a peak has been reached is unreliable without verifying the specific peaking process using statistical approaches. To better understand the peaking process, this study calculated the carbon emissions from crop production in 30 Chinese provinces from 2000 to 2020, considering four carbon sources: agricultural materials, rice paddies, soil management, and straw burning. The peak carbon emissions process was then explored at the national and provincial levels. The Tapio decoupling index was used to verify the relationship between carbon emissions and economic output. The results showed that: (1) The total carbon emissions from crop production in China had an annual average of 233.269 Mt, increasing from 200.020 Mt to 242.819 Mt during the study period, peaking at 262.648 Mt in 2015. The average annual rate of change after reaching the peak was −1.560%, indicating that emissions entered a plateau. Over time, agricultural materials became the primary emissions source (34.6%), whereas soil management contributed the least (11.6%) in 2020. (2) Carbon emissions from crop production were positively correlated with the cropping scale. Only two provinces, Hunan and Henan, had the highest emissions of over 20 Mt; five provinces, such as Hubei and Shandong, had the highest emissions distribution of 15–20 Mt, and other five provinces, like Jiangxi and Sichuan, had the highest emissions ranging from 10 to 15 Mt. In contrast, the highest emissions in 18 provinces were less than 10 Mt, especially in Beijing, Tianjin, and Qinghai, with emission peaks below 1 Mt. As far as the peaking process, the carbon emissions in 13 provinces, including Beijing and Tianjin, were in a state of decline, those of 10 provinces, such as Shanxi and Chongqing, entered a plateau, and those of seven provinces like Henan and Anhui had not met their peak yet. (3) At the national level, the long-term relationship between carbon emissions and economic output showed weak decoupling, whereas the short-term relationship changed from weak to strong decoupling. At the provincial level, the short-term relationship evolved from multi-type coexistence to strong decoupling. Consequently, it is recommended that the emission mitigation of crop production in China should be accelerated by source and phase based on the peaking process and emission magnitude. Provinces with emissions in peaking and plateauing states require additional attention, as their subsequent developments determined the overall emission reduction. In comparison, flexible space for emission mitigation can be provided to the provinces in declining states, as many of them are accompanied by low emissions and optimistic momentum. However, three high-emission provinces, Hubei, Jiangxi, and Shandong, also reached their peak emissions and began to decline, which may serve as examples of provinces with similar conditions. These findings provide local solutions for accelerating the peaking process of carbon emissions from crop production in China.
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图 1 基于指标变化率与弹性值的种植业经济产出的碳排放Tapio脱钩类别划分(ε为脱钩指数, ΔE/E表示种植业碳排放变化率, ΔA/A表示农业产值变化率)
Figure 1. Classification of Tapio decoupling states of carbon emission of crop production based on the changing rate and elasticity (ε is decoupling index, ΔE/E is the change rate of carbon emissions of crop production, ΔA/A is the change rate of agricultural output value)
图 2 2000—2020年中国种植业碳排放演进过程
Figure 2. Evolution of carbon emissions of crop production in China from 2000 to 2020
图 3 2000—2020年中国30省(市、自治区)种植业碳排放总量、结构及达峰进程
纵轴表示种植业碳排放, 横轴表示年份, 从左至右为2000—2020年。每幅堆积柱状图的纵轴区间统一标注于最左侧, 图幅下方依次呈现对应省份、基期(2000年)排放量(×104 t)→峰值排放量(×104 t)→末期(2020年)排放量(×104 t)和达峰后年均变化率。The vertical axis denotes carbon emissions of crop production, and the horizontal axis denotes time, from 2000 to 2020. The range of the vertical axis of the line graph by province is marked on the left side, and the corresponding provinces, emissions (×104 t) in 2000 → peak emissions (×104 t) → emissions (×104 t) in 2020, and annual average change rates after peaking are presented below each bar graph.
Figure 3. Amount, composition, and peaking process of carbon emissions of crop production in 30 Chinese provinces between 2000 and 2020
图 4 基于种植业碳排放和农业产值的省域类型划分
1: 湖南; 2: 河南; 3: 安徽; 4: 江苏; 5: 山东; 6: 湖北; 7: 黑龙江; 8: 江西; 9: 广东; 10: 广西; 11: 四川; 12: 河北; 13: 吉林; 14: 云南; 15: 浙江; 16: 辽宁; 17: 福建; 18: 内蒙古; 19: 新疆; 20: 陕西; 21: 山西; 22: 重庆; 23: 贵州; 24: 甘肃; 25: 海南; 26: 宁夏; 27: 上海; 28: 天津; 29: 北京; 30: 青海。1: Hunan; 2: Henan; 3: Anhui; 4: Jiangsu; 5: Shandong; 6: Hubei; 7: Heilongjiang; 8: Jiangxi; 9: Guangdong; 10: Guangxi; 11: Sichuan; 12: Hebei; 13: Jilin; 14: Yunnan; 15: Zhejiang; 16: Liaoning; 17: Fujian; 18: Inner Mongolia; 19: Xinjiang; 20: Shaanxi; 21: Shanxi; 22: Chongqing; 23: Guizhou; 24: Gansu; 25: Hainan; 26: Ningxia; 27: Shanghai; 28: Tianjin; 29: Beijing; 30: Qinghai.
Figure 4. Provincial classification based on carbon emissions of crop production and agricultural output value
图 5 2000—2020年中国种植业碳排放与农业产值的脱钩状态
Figure 5. Decoupling states between carbon emissions of crop production and agricultural output value in China between 2000 and 2020
表 1 2000—2020年中国30省(市、自治区)种植业碳排放与农业产值的脱钩状态
Table 1. Decoupling states between carbon emissions of crop production and agricultural output value in 30 provinces (city or autonomous region) from 2000 to 2020 in China
省份
Province2000—2005 2005—2010 2010—2015 2015—2020 ΔE/E ΔA/A ε s ΔE/E ΔA/A ε s ΔE/E ΔA/A ε s ΔE/E ΔA/A ε s 北京 Beijing −0.197 0.066 −2.973 a −0.011 0.130 −0.088 a −0.279 −0.103 2.713 c −0.427 −0.359 1.190 e 天津 Tianjin 0.137 −0.020 −6.745 g 0.088 0.199 0.440 b −0.051 0.207 −0.245 a −0.175 0.167 −1.048 a 河北 Hebei 0.308 0.313 0.985 d −0.016 0.228 −0.072 a 0.058 0.205 0.285 b −0.160 0.170 −0.940 a 山西 Shanxi 0.226 0.084 2.682 f 0.181 0.227 0.800 b 0.123 0.237 0.520 b −0.045 0.184 −0.243 a 内蒙古 Inner Mongolia 0.400 0.213 1.878 f 0.473 0.205 2.314 f 0.360 0.373 0.963 d −0.013 0.146 −0.088 a 辽宁 Liaoning 0.222 0.341 0.652 b 0.171 0.144 1.189 d 0.050 0.445 0.112 b −0.071 0.057 −1.252 a 吉林 Jilin 0.310 0.410 0.757 b 0.192 0.290 0.662 b 0.295 0.329 0.898 d −0.035 0.259 −0.134 a 黑龙江 Heilongjiang 0.136 0.478 0.285 b 0.738 0.255 2.890 f 0.278 0.356 0.781 b −0.069 0.192 −0.362 a 上海 Shanghai −0.282 −0.090 3.150 c −0.005 0.012 −0.456 a −0.116 −0.087 1.330 c −0.205 −0.216 0.950 e 江苏 Jiangsu −0.016 0.164 −0.095 a 0.062 0.197 0.317 b 0.023 0.210 0.108 b −0.034 0.108 −0.314 a 浙江 Zhejiang −0.187 0.113 −1.656 a −0.084 0.167 −0.505 a −0.091 0.111 −0.817 a −0.087 0.168 −0.517 a 安徽 Anhui 0.057 0.024 2.414 f 0.163 0.276 0.589 b 0.151 0.250 0.606 b −0.042 0.156 −0.272 a 福建 Fujian −0.082 0.187 −0.435 a −0.052 0.224 −0.232 a −0.050 0.237 −0.213 a −0.136 0.212 −0.643 a 江西 Jiangxi 0.124 0.209 0.590 b 0.105 0.175 0.604 b 0.054 0.262 0.204 b −0.093 0.247 −0.376 a 山东 Shandong 0.112 0.186 0.604 b 0.059 0.189 0.311 b 0.032 0.218 0.145 b −0.082 0.214 −0.381 a 河南 Henan 0.175 0.267 0.658 b 0.243 0.279 0.869 d 0.111 0.241 0.460 b −0.030 0.249 −0.121 a 湖北 Hubei 0.051 0.144 0.352 b 0.121 0.216 0.559 b 0.078 0.240 0.324 b −0.116 0.231 −0.501 a 湖南 Hunan 0.006 0.218 0.026 b 0.099 0.241 0.410 b 0.055 0.210 0.264 b −0.089 0.192 −0.461 a 广东 Guangdong −0.078 0.254 −0.308 a 0.003 0.196 0.013 b 0.007 0.222 0.031 a −0.060 0.272 −0.221 a 广西 Guangxi 0.085 0.308 0.276 b −0.016 0.289 −0.055 a 0.015 0.296 0.051 b −0.074 0.347 −0.212 a 海南 Hainan 0.018 0.354 0.050 b 0.220 0.422 0.522 b 0.001 0.348 0.003 a −0.203 0.302 −0.672 a 重庆 Chongqing 0.036 0.199 0.181 b 0.021 0.327 0.064 b 0.029 0.253 0.116 b −0.037 0.239 −0.155 a 四川 Sichuan −0.002 0.113 −0.016 a 0.052 0.167 0.309 b 0.023 0.226 0.101 b −0.065 0.350 −0.186 a 贵州 Guizhou 0.041 0.135 0.301 b 0.056 0.188 0.296 b 0.122 0.396 0.309 b −0.178 0.456 −0.391 a 云南 Yunnan 0.113 0.245 0.461 b 0.102 0.324 0.315 b 0.177 0.350 0.506 b −0.130 0.400 −0.324 a 陕西 Shaanxi 0.055 0.372 0.148 b 0.223 0.349 0.641 b 0.136 0.321 0.425 b −0.063 0.261 −0.239 a 甘肃 Gansu 0.206 0.353 0.585 b 0.340 0.320 1.065 d 0.334 0.317 1.056 d −0.201 0.304 −0.659 a 青海 Qinghai −0.046 0.182 −0.250 a 0.280 0.372 0.754 b 0.198 0.274 0.721 b −0.236 0.252 −0.936 a 宁夏 Ningxia 0.157 0.402 0.390 b 0.279 0.473 0.589 b 0.042 0.274 0.155 b −0.030 0.190 −0.156 a 新疆 Xinjiang 0.257 0.270 0.954 d 0.465 0.392 1.188 d 0.469 0.420 1.118 d −0.027 0.306 −0.087 a ΔE/E表示种植业碳排放变化率, ΔA/A表示农业产值变化率, ε表示脱钩指数, s表示脱钩状态, a为强脱钩, b为弱脱钩, c为衰退脱钩, d为增长连接, e为衰退连接, f为扩张负脱钩, g为强负脱钩。ΔE/E is the change rate of carbon emissions of crop production, ΔA/A is the change rate of agricultural output value, ε is the decoupling index. s denotes the decoupling states, a denotes the strong decoupling, b denotes the weak decoupling, c denotes the recessive decoupling, d denotes the expansive coupling, e denotes the recessive coupling, f denotes the expansive negative decoupling, and g denotes the strong negative decoupling. 
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