孙红雨3,,
1.贵州民族大学商学院 贵阳 550025
2.东北林业大学经济管理学院 哈尔滨 150040
3.贵州大学经济学院 贵阳 550025
基金项目: 国家自然科学基金项目71573036
国家社会科学基金重点项目17AZD012
详细信息
作者简介:杨滨键, 研究领域为农业经济学、资源环境经济学、碳会计。E-mail: yangbj919@163.com
通讯作者:研究领域为国际合作理论与政治、生态经济学。E-mail: 287033449@qq.com
中图分类号:F323.22计量
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被引次数:0
出版历程
收稿日期:2020-04-13
录用日期:2020-12-10
刊出日期:2021-03-01
Low carbon performance measurement and dynamic effects: A case study of the planting industry in Shandong Province
YANG Binjian1, 2,,SUN Hongyu3,,
1. Business School of Guizhou Minzu University, Guiyang 550025, China
2. College of Economics and Management, Northeast Forestry University, Harbin 150040, China
3. Economics School of Guizhou University, Guiyang 550025, China
Funds: the National Natural Science Foundation of China71573036
the National Social Science Foundation of China17AZD012
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Corresponding author:SUN Hongyu, E-mail: 287033449@qq.com
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摘要
摘要:温室效应的加剧已经严重影响到人类社会的生存与发展，根据IPCC数据显示，农业温室气体占全球人为排放的13.5%，鉴于山东省农业在我国的重要地位，本文以在农业产值中占比最高的种植业为样本，对山东省低碳绩效展开研究，为山东省种植业低碳发展之路提供参考。本文在测算山东省种植业碳排放量、碳汇量、碳排放强度以及碳排放边际减排成本的基础上，运用DEA-Malmquist模型测算了种植业低碳绩效水平，接着研究了低碳驱动与约束对山东省种植业低碳绩效的动态影响效应。通过研究发现，2000年到2018年山东省种植业碳排放总量和碳汇总量年均增幅分别为0.26%和1.74%，而碳排放强度和减排成本年均降低6.12%和2.10%。低碳绩效指数增长较慢，年均增长速度为3.00%，其主要驱动来源于技术进步。低碳约束目标与低碳驱动手段是种植业低碳绩效变动的直接原因，种植业碳排放强度对种植业低碳绩效具有一定的抑制作用，低碳驱动手段对种植业低碳绩效具有正向促进作用，且低碳驱动手段对种植业的低碳绩效贡献更大。进而提出了制定种植业低碳法律法规与提升财政支持有效性的低碳发展建议。
关键词:减排成本/
低碳绩效/
驱动与约束/
山东/
种植业
Abstract:Increased greenhouse effects have seriously affected the survival and development of human society. Data from the Intergovernmental Panel on Climate Change (IPCC) indicates that agricultural greenhouse gases account for 13.5% of global anthropogenic emissions. This study investigated the planting industry in Shandong Province, China, which accounts for the highest proportion of agricultural output, to provide policy recommendations that should help the planting industry in the province follow a low-carbon development path. This study measured carbon emission, carbon sinks, carbon emission intensity, and the marginal cost of carbon emission reduction; used the DEA Malmquist model to measure the low-carbon performance level; and investigated the dynamic effects of low-carbon driving and constraint on the low-carbon performance of the planting industry in Shandong Province. The results showed that from 2000 to 2018, the carbon emission and carbon sink of planting industry in Shandong Province increased 0.26% and 1.71% averagely every year, while carbon emission intensity and marginal cost of carbon emission reduction decreased 3.12% and 2.10%, respectively. The low-carbon performance index increased slowly with an annual increasing rate of 3.00%, which mainly was driven by the efficiency of technology change. The low-carbon constraint goals and low-carbon drivers were the direct reasons of low-carbon performance change of the planting industry. The carbon emission intensity and low-carbon constraint goals had negative and passive effects on the low-carbon performance of plant industry, respectively. The low-carbon constraint goals played a more important role in the low-carbon performance of plant industry of Shandong Province. The findings of this study suggest that low-carbon development can be achieved by formulating low-carbon laws and regulations and improving the effectiveness of financial support.
Key words:Cost of emission reduction/
Low-carbon performance/
Drive and constraint/
Shandong Province/
Planting industry
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图1山东省种植业低碳绩效PVAR模型解释变量的AR根图
Figure1.AR root graph of explanatory variable of PVAR model for low-carbon performance of planting industry in Shandong Province
下载: 全尺寸图片幻灯片
图2山东省种植业低碳绩效(ML)对净碳汇效应(a)、碳排放强度(b)和低碳驱动手段(c)的脉冲响应
Figure2.Impulse response of low-carbon performance of planting industry (ML) of Shandong Province to the shocks of net carbon sink effect (a), carbon emission intensity (b) and low-carbon driving means (c)
下载: 全尺寸图片幻灯片表1农业种植业不同类型碳源的碳排放系数[9]
Table1.Carbon emission coefficients of different carbon sources of planting industry of agriculture[9]
| 碳源 Carbon source | 碳排放系数 Carbon emission coefficient |
| 农药 Pesticides | 4.9341 kg (C)·kg-1 |
| 农膜 Agricultural film | 5.1800 kg (C)·kg-1 |
| 化肥 Chemical fertilizer | 0.8956 kg (C)·kg-1 |
| 农业灌溉 Agricultural irrigation | 266.4800 kg (C)·hm-2 |
| 农业翻耕 Agricultural ploughing | 312.6000 kg (C)·hm-2 |
| 农用柴油 Agricultural diesel oil | 0.5927 kg (C)·kg-1 |
下载: 导出CSV表2种植业农作物含水量、经济系数与碳吸收率[12]
Table2.Water contents, economic coefficients and carbon absorption rates of crops[12]
| 农作物 Crop | 含水量 Water content | 经济系数 Economic coefficient | 碳吸收率 Carbon absorption rate |
| 小麦 Wheat | 0.12 | 0.40 | 0.485 |
| 稻谷 Unhusked rice | 0.12 | 0.45 | 0.414 |
| 玉米 Corn | 0.13 | 0.40 | 0.471 |
| 谷子 Millet | 0.12 | 0.42 | 0.450 |
| 高粱 Sorghum | 0.12 | 0.35 | 0.450 |
| 薯类 Tubers | 0.70 | 0.70 | 0.423 |
| 棉花 Cotton | 0.08 | 0.10 | 0.450 |
| 花生 Peanut | 0.10 | 0.43 | 0.450 |
| 油菜籽 Rapeseed | 0.10 | 0.25 | 0.450 |
| 烟叶 Tobacco | 0.85 | 0.55 | 0.450 |
| 蔬菜 Vegetables | 0.90 | 0.60 | 0.450 |
下载: 导出CSV表32000—2018年山东省种植业碳排放、碳汇、碳排放强度与碳排放边际减排成本
Table3.Carbon emission, carbon sink, carbon emission intensity and marginal carbon emission reduction cost of Shandong Province in 2000-2018
| 年份 Year | 碳汇总量 Total carbon sink (×104 t) | 碳排放总量 Total carbon emissions (×104 t) | 碳排放强度 Carbon emission intensity [kg×(104￥)-1] | 碳排放边际减排成本 Marginal cost of carbon emission reduction (￥×t-1) |
| 2000 | 5446.46 | 1119.56 | 758.59 | 8.62 |
| 2001 | 5455.56 | 1140.32 | 749.03 | 8.33 |
| 2002 | 5102.54 | 1167.59 | 764.39 | 7.59 |
| 2003 | 5143.65 | 1165.36 | 740.10 | 7.25 |
| 2004 | 5587.27 | 1183.21 | 656.46 | 6.86 |
| 2005 | 6163.10 | 1235.12 | 615.19 | 6.57 |
| 2006 | 6630.01 | 1282.74 | 586.79 | 6.20 |
| 2007 | 6728.39 | 1288.77 | 539.84 | 5.62 |
| 2008 | 7091.25 | 1257.94 | 440.74 | 5.91 |
| 2009 | 7415.33 | 1253.79 | 410.90 | 5.86 |
| 2010 | 7506.08 | 1266.37 | 357.68 | 5.86 |
| 2011 | 7705.21 | 1265.16 | 339.77 | 5.79 |
| 2012 | 7399.18 | 1250.48 | 317.92 | 5.48 |
| 2013 | 6969.86 | 1236.14 | 276.80 | 5.50 |
| 2014 | 7161.82 | 1217.69 | 257.51 | 5.39 |
| 2015 | 7064.99 | 1205.43 | 243.32 | 5.34 |
| 2016 | 6847.59 | 1186.14 | 229.61 | 5.44 |
| 2017 | 7169.86 | 1180.50 | 268.10 | 5.80 |
| 2018 | 7095.17 | 1146.39 | 245.05 | 5.54 |
| 种植业碳排放强度=种植业碳排放量/种植业产值。Carbon emission intensity of planting industry = carbon emission of planting industry / output value of planting industry. | ||||
下载: 导出CSV表42001—2018年山东省种植业低碳绩效动态特征
Table4.Dynamic characteristics of low-carbon performance of planting industry in Shandong Province from 2001 to 2018
| 年份 Year | effch | techch | pech | sech | ML |
| 2001 | 1.001 | 0.982 | 1.000 | 1.001 | 0.983 |
| 2002 | 1.000 | 0.968 | 1.000 | 1.000 | 0.968 |
| 2003 | 1.000 | 1.032 | 1.000 | 1.000 | 1.032 |
| 2004 | 1.000 | 1.056 | 1.000 | 1.000 | 1.056 |
| 2005 | 1.000 | 1.054 | 1.000 | 1.000 | 1.054 |
| 2006 | 1.000 | 1.027 | 1.000 | 1.000 | 1.027 |
| 2007 | 1.000 | 1.074 | 1.000 | 1.000 | 1.074 |
| 2008 | 0.999 | 1.059 | 1.000 | 0.999 | 1.057 |
| 2009 | 1.000 | 1.048 | 0.999 | 1.001 | 1.048 |
| 2010 | 1.001 | 1.083 | 1.001 | 1.001 | 1.085 |
| 2011 | 0.998 | 1.024 | 0.998 | 1.000 | 1.022 |
| 2012 | 1.000 | 1.011 | 1.001 | 0.999 | 1.011 |
| 2013 | 1.000 | 1.054 | 1.000 | 1.000 | 1.054 |
| 2014 | 1.001 | 1.036 | 1.000 | 1.001 | 1.038 |
| 2015 | 1.000 | 1.026 | 1.000 | 1.000 | 1.026 |
| 2016 | 1.000 | 1.012 | 1.000 | 1.000 | 1.012 |
| 2017 | 1.000 | 0.962 | 1.000 | 1.000 | 0.962 |
| 2018 | 1.000 | 1.039 | 1.000 | 1.000 | 1.039 |
| 均值Mean | 1.000 | 1.030 | 1.000 | 1.000 | 1.030 |
| Effch: 技术效率; techch: 技术变化效率; pech: 纯技术效率; sech: 规模效率; ML: 种植业低碳绩效。Effch: technical efficiency; techch: efficiency of technological change; pech: pure technical efficiency; sech: scale efficiency; ML: low-carbon performance of planting industry. | |||||
下载: 导出CSV表5山东省种植业低碳绩效与解释变量面板单位根检验
Table5.Panel unit root test of low-carbon performance and explanatory variables in Shandong Province
| 变量 Variable | LLC-检验 LLC-test | ADF-检验 ADF-test | PP-检验 PP-test | 平稳性 Stationarity | |||||
| 统计量 Statistic | P | 统计量 Statistic | P | 统计量 Statistic | P | ||||
| ML | 0.5368 | 0.7043 | 12.0134 | 0.9998 | 10.1738 | 1.0000 | 非平稳Nonstationary | ||
| DML | -20.4465 | 0.0000 | 280.7420 | 0.0000 | 424.0140 | 0.0000 | 平稳Stable | ||
| PFQD | -3.3001 | 0.0005 | 16.1290 | 0.9960 | 9.8300 | 1.0000 | 非平稳Nonstationary | ||
| DPFQD | -4.9417 | 0.0000 | 57.9853 | 0.0063 | 140.0160 | 0.0000 | 平稳Stable | ||
| ZHXY | -18.5759 | 0.0000 | 36.0732 | 0.3718 | 29.6928 | 0.6788 | 非平稳Nonstationary | ||
| DZHXY | -28.7545 | 0.0000 | 86.5202 | 0.0000 | 143.4370 | 0.0000 | 平稳Stable | ||
| CZZC | -4.9737 | 0.0000 | 31.7795 | 0.5769 | 17.8157 | 0.9899 | 非平稳Nonstationary | ||
| DCZZC | -7.9413 | 0.0000 | 114.6190 | 0.0000 | 229.3080 | 0.0000 | 平稳Stable | ||
| ML: 种植业低碳绩效; DML: 种植业低碳绩效一阶差分; PFQD: 碳排放强度; DPFQD: 碳排放强度一阶差分; ZHXY: 净碳汇; DZHXY: 净碳汇一阶差分; CZZC: 财政支农力度; DCZZC: 财政支农力度一阶差分。ML: low-carbon performance of planting industry; DML: first order difference analysis of low-carbon performance of planting industry; PFQD: carbon emission intensity; DPFQD: first order difference of carbon emission intensity; ZHXY: net carbon sink; DZHXY: first order difference of net carbon sink; CZZC: financial support for agriculture; DCZZC: first order difference of financial support for agriculture. | |||||||||
下载: 导出CSV表6山东省种植业低碳绩效PVAR模型的GMM参数估计表
Table6.GMM parameter estimation table of PVAR model for low-carbon performance of planting industry in Shandong Province
| ML | PFQD | ZHXY | CZZC | |
| L.ML | -0.1226(0.0492)** | -0.2578(0.0823)*** | -0.0565(0.2828) | 3.4689(0.6014)*** |
| L.PFQD | -0.0936(0.0798)** | 1.0632(0.1430)*** | 1.9397(0.4841)*** | -8.3289(1.0770)*** |
| L.ZHXY | 0.0054(0.0079)** | 0.0158(0.0113) | 0.0504(0.0231)** | -0.0141(0.0656) |
| L.CZZC | 0.0202(0.0079)** | -0.0330(0.0139)** | -0.0100(0.0513) | -0.3716(0.0768)*** |
| 括号内数据为标准误差。ML: 种植业低碳绩效; PFQD: 碳排放强度; ZHXY: 净碳汇; CZZC: 财政支农力度; “L.”表示变量滞后1期。*、**和***分别表示在P < 10%、P < 5%和P < 1%水平显著。Data in the bracket is standard error; ML: low-carbon performance of planting industry; PFQD: carbon emission intensity; ZHXY: net carbon sink; CZZC: financial support for agriculture. “L.” indicates that the variable lags one period. *, **, and *** mean significant at the levels of P < 10%, P < 5%, and P < 1%, respectively. | ||||
下载: 导出CSV表7山东省种植业低碳绩效的格兰杰因果关系检验结果
Table7.Granger causality test on low-carbon performance of planting industry in Shandong Province
| 原假设 Original hypothesis | Chi2 | P | 结果 Result |
| PFQD不是ML的格兰杰成因 PFQD is not the Granger cause of ML | 4.375 | 0.041 | 拒绝** Refuse** |
| ML不是PFQD的格兰杰成因 ML is not the Granger cause of PFQD | 9.818 | 0.002 | 拒绝*** Refuse*** |
| ZHXY不是ML的格兰杰成因 ZHXY is not the Granger cause of ML | 5.468 | 0.020 | 拒绝** Refuse** |
| ML不是ZHXY的格兰杰成因 ML is not the Granger cause of ZHXY | 0.040 | 0.842 | 接受 Accept |
| CZZC不是ML的格兰杰成因 CZZC is not the Granger cause of ML | 6.546 | 0.011 | 拒绝** Refuse** |
| ML不是CZZC的格兰杰成因 ML is not the Granger cause of CZZC | 33.268 | 0.000 | 拒绝*** Refuse*** |
| ML: 种植业低碳绩效; PFQD: 碳排放强度; ZHXY: 净碳汇; CZZC: 财政支农力度。***、**和*分别表示在P < 1%、P < 5%和P < 10%水平显著。ML: low-carbon performance of planting industry; PFQD: carbon emission intensity; ZHXY: net carbon sink; CZZC: financial support for agriculture. ***, ** and * mean significance at P < 1%, P < 5% and P < 10% levels, respectively. | |||
下载: 导出CSV表8山东省种植业低碳绩效方差分解分析
Table8.Variance decomposition analysis of low-carbon performance of planting industry in Shandong Province
| 时期 Period | 种植业低碳绩效 ML | 碳排放强度 PFQD | 净碳汇 ZHXY | 财政支农力度 CZZC |
| 1 | 1.0000 | 0.0000 | 0.0000 | 0.0000 |
| 2 | 0.7815 | 0.0334 | 0.0099 | 0.0530 |
| 3 | 0.6984 | 0.0322 | 0.0164 | 0.0845 |
| 4 | 0.6812 | 0.0395 | 0.0161 | 0.0833 |
| 5 | 0.6712 | 0.0389 | 0.0180 | 0.0825 |
| 6 | 0.6624 | 0.0387 | 0.0178 | 0.0814 |
| 7 | 0.6585 | 0.0394 | 0.0177 | 0.0809 |
| 8 | 0.6570 | 0.0399 | 0.0177 | 0.0808 |
| 9 | 0.6564 | 0.0400 | 0.0178 | 0.0807 |
| 10 | 0.6559 | 0.0400 | 0.0177 | 0.0807 |
| 11 | 0.6557 | 0.0400 | 0.0177 | 0.0807 |
| 12 | 0.6555 | 0.0400 | 0.0177 | 0.0808 |
| 13 | 0.6555 | 0.0400 | 0.0177 | 0.0808 |
| 14 | 0.6554 | 0.0400 | 0.0177 | 0.0807 |
| 15 | 0.6553 | 0.0400 | 0.0177 | 0.0807 |
| 16 | 0.6552 | 0.0400 | 0.0177 | 0.0807 |
| ML: low-carbon performance of planting industry; PFQD: carbon intensity; ZHXY: net carbon sink; CZZC: financial support for agriculture. | ||||
下载: 导出CSV参考文献
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