Improvement of the method for calculating agricultural energy efficiency based on the three-stage Data Envelopment Analysis
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摘要:
精准测算是提高农业能源效率的基础, 有助于识别能源使用的瓶颈和潜力, 优化农业能源结构, 突破能源与环境双重约束, 进而有力有效地推进乡村全面振兴。概念辨析发现, 传统的农业能源效率测算结果实质是包含能源的农业生产效率。为科学合理地测算农业能源效率, 本文提出了一种基于三阶段数据包络分析(DEA)模型的改进计算方法, 并以中国30个省(自治区、直辖市, 不包括香港、澳门、台湾和西藏)的面板数据为样本进行测算, 对比原有方法的分析结果以验证改进方法的可靠性。结果表明: 1)随机前沿(SFA)分析可知, 环境变量和随机因素对能源效率影响显著, 说明该方法能够剔除生产因素对农业能源效率的影响, 从而规避部分测算结果高于实际值的问题; 2)与传统方法对比, 改进方法的估算结果与中国农业经济发展趋势更相符, 波动节点与相应政策出台时间更契合; 3)传统方法的估算结果会受物价和成本影响, 与真实农业能源效率产生较大偏离, 其中北京、上海和青海最为明显。综上所述, 改进的三阶段DEA农业能源效率测算方法明显优于传统方法, 可为企业及政府在农业节能减排方面提供更加准确的决策依据。
Abstract:Energy is the basis for the development of modern society and an important guarantee for comfortable rural living and successful agricultural production. With industrialization and urbanization occurring rapidly in China, the demand for efficient energy in agricultural modernization will inevitably increase. In the face of increasingly severe global issues such as limited resources, environmental calamities, and food insecurity, accurate measurement is key to improving agricultural energy efficiency, facilitating users in identifying bottlenecks and potential in energy usage, optimizing the agricultural energy structure, and breaking through the dual constraints of energy and the environment, which in turn will effectively promote comprehensive rural revitalization. Previous concept exploration has revealed the existence of a conceptual intersection between conventional agricultural energy efficiency and agricultural production efficiency, where the calculation output of the former is in fact the latter including energy. To scientifically and logically calculate agricultural energy efficiency, we proposed an improved algorithm based on a three-stage Data Envelopment Analysis (DEA) model. Based on the conventional one-stage calculation method, this algorithm applied a second-stage Stochastic Frontier Approach (SFA) and third-stage DEA analysis. Panel data from 30 provinces (municipalities and autonomous regions, not including Hong Kong, Macao, Taiwan and Xizang) in China were used as a sample to test the updated algorithm. The reliability of the model was tested by comparing its calculated results with those obtained using the conventional method. The following observations were made: 1) Outputs from the second-stage SFA showed that the likelihood-ratio (LR) values of all input slack variables were greater than 10.501 (P<1%). The impacts of environmental variables and random factors on energy efficiency were significant, indicating that the SFA analysis is necessary and effective and can eliminate the effect of production factors on agricultural energy efficiency, thereby avoiding the problem of some calculated results being higher than the observed values. 2) Compared with the gap of approximately 0.1 derived from the conventional method of calculating agricultural energy efficiency in the past 20 years, the final (third-stage) efficiency value from the improved model increased from 0.240 in 2003 to 0.541 in 2018, demonstrating that the estimated result was more appropriate for the trend in China’s agricultural economy development. The fluctuation node was more consistent with the periods when the corresponding policies were introduced, such as the severe agricultural blow resulting from natural disasters at the end of the 20th century, the first China’s No. 1 central document with the theme of “Agriculture, Rural Areas, and Farmers” issued in 2004, the international economic and financial crisis in 2008, and other important nodes. 3) The estimates from the conventional method were greatly biased from actual values owing to price and cost influences, especially in Beijing, Qinghai, and Shanghai, where the differences between the traditional and improved models were 0.95, 0.87, and 0.77, respectively. In summary, the improved three-stage DEA method for calculating agricultural energy efficiency is superior to the conventional method, and can provide a more accurate decision-making basis for enterprises and governments in the fields of agricultural energy conservation and emissions reduction.
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图 1 改进模型(第三阶段)的2002—2021年中国农业能源效率均值趋势
Figure 1. Trends of average agricultural energy efficiencies of China based on the improved model (third stage) from 2002 to 2021
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图 3 基于改进模型测算的2002—2021年5省农业能源效率均值
Figure 3. Average agricultural energy efficiencies of 5 provinces in China based on the improved model from 2002 to 2021
表 1 农业能源效率评价指标体系
Table 1 Evaluation index system of agricultural energy efficiency
变量类型 Variable type 指标名称 Index name 投入变量
Input variable能源投入
Energy input直接能源投入
Direct energy input /MJ农业能源终端消费量 Total final consumption of primary industry 农业畜力能量投入量 Input of animal energy input for agriculture production 间接能源投入
Indirect energy input /MJ农药能量投入量 Input of pesticide energy for agriculture production 农用化肥能量投入量 Input of fertilizer energy for agriculture production 农用塑料薄膜能量投入量 Input of agirultural plastic film energy for agriculture production 机械投入
Machinery input /(×104 kW)农业机械总动力 Total power of agricultural machinery 劳动力投入
Labor input /(×104 person)农业劳动力 Labor for agriculture production 土地投入
Land input /(×103 hm2)农作物总播种面积 Total sown area of crops 资本投入
Capital input /(×108 ¥)农业资本存量 Agricultural capital stock 产出变量
Output variable期望产出
Desirable output /(×108 ¥)农业总产值 Total agricultural output value 非期望产出
Undesirable output /(×104 t)农业碳排放量 Agricultural carbon emissions 环境变量
Environment variable农业技术水平
Agricultural technique level /%研究与试验发展中农业技术人员占农业劳动力比例
Proportion of the agricultural R&D staffs to total labor for agriculture production财政农业支出
Fiscal expenditures on agriculture /%农林水事务支出占一般公共预算支出比例
Proportion of the budgetary expenditure for agriculture, forestry and water conservancy to public budgetary expenditure受灾率 Disaster rate /% 受灾面积占农作物总播种面积比例
Proportion of the disaster-affected area to the total sown area of crops
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表 2 广义农业能源投入能量折标值
Table 2 Equivalent energy values for the input of energy in broad sense of agriculture
种类 Category 能量折标值 Equivalent energy value 农业生物能量投入
Agricultural input of biological energy畜力 Animal energy /(MJ∙h−1) 4.50 农业化学能量投入
Agricultural input of chemical energy农用化肥
Agricultural fertilizer /(MJ∙kg−1)氮肥 Nitrogen fertilizer 64.40 磷肥 Phosphate fertilizer 11.96 钾肥 Potash fertilizer 6.70 复合肥 Compound fertilizer 28.05 农药 Pesticide /(MJ∙kg−1) 208.00 农用塑料薄膜 Agricultural plastic film /(MJ∙kg−1) 46.89
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表 3 2002—2021年中国30个省(直辖市、自治区, 不包括香港、澳门、台湾和西藏)第一阶段农业能源效率
Table 3 Agricultural energy efficiencies of 30 provinces (municipalities and autonomous regions, excluding Hong Kong, Macao, Taiwan and Xizang) of China based on the first-stage model from 2002 to 2021
省(直辖市、自治区)
Province (municipality, autonomous region)2002 2007 2012 2015 2016 2017 2018 2019 2020 2021 均值
Mean排序
Rank北京 Beijing 1.000 0.689 0.605 0.660 0.695 0.741 0.892 1.000 1.000 0.640 0.757 9 天津 Tianjin 0.820 1.000 0.413 0.420 0.435 0.420 0.415 0.421 0.505 0.526 0.567 18 河北 Hebei 1.000 0.750 0.478 0.445 0.438 0.431 0.492 0.500 0.617 0.636 0.614 13 山西 Shanxi 0.372 0.351 0.293 0.250 0.277 0.311 0.317 0.332 0.415 0.447 0.332 30 内蒙古 Inner Mongolia 0.800 0.469 0.440 0.425 0.428 0.392 0.483 0.502 0.588 0.657 0.498 23 辽宁 Liaoning 1.000 0.646 0.615 0.611 0.618 0.606 0.618 0.632 0.653 0.672 0.669 10 吉林 Jilin 1.000 0.571 0.531 0.481 0.452 0.421 0.451 0.485 0.535 0.544 0.567 17 黑龙江 Heilongjiang 0.589 0.451 0.515 0.556 0.544 0.533 0.532 0.589 0.594 0.599 0.523 20 上海 Shanghai 1.000 0.941 0.913 0.868 1.000 0.877 0.913 0.877 1.000 0.768 0.939 2 江苏 Jiangsu 1.000 0.845 1.000 0.827 0.786 0.766 0.752 0.767 0.796 0.815 0.857 7 浙江 Zhejiang 0.744 1.000 1.000 0.806 0.767 1.000 0.970 0.883 1.000 1.000 0.876 6 安徽 Anhui 0.644 0.546 0.511 0.479 0.469 0.475 0.465 0.493 0.583 0.597 0.526 19 福建 Fujian 1.000 0.915 0.809 0.869 1.000 1.000 0.983 1.000 1.000 1.000 0.910 4 江西 Jiangxi 0.773 0.569 0.495 0.544 0.552 0.536 0.530 0.560 0.680 0.715 0.569 16 山东 Shandong 1.000 0.627 0.623 0.565 0.561 0.547 0.578 0.575 0.616 0.694 0.648 11 河南 Henan 0.672 0.542 0.457 0.422 0.429 0.417 0.423 0.435 0.595 0.608 0.499 22 湖北 Hubei 1.000 0.619 0.554 0.512 0.538 0.534 0.527 0.535 0.587 0.623 0.597 15 湖南 Hunan 0.757 0.591 0.466 0.391 0.385 0.379 0.374 0.405 0.509 0.505 0.501 21 广东 Guangdong 1.000 0.865 0.714 0.673 0.711 0.707 0.692 0.716 0.812 0.864 0.777 8 广西 Guangxi 1.000 0.670 0.587 0.520 0.525 0.521 0.529 0.572 0.626 0.661 0.634 12 海南 Hainan 1.000 0.870 0.879 1.000 0.964 1.000 0.967 1.000 1.000 1.000 0.939 3 重庆 Chongqing 0.679 0.416 0.353 0.429 0.488 0.467 0.475 0.497 0.552 0.563 0.453 27 四川 Sichuan 1.000 0.680 0.584 0.551 0.552 0.529 0.613 0.523 0.616 0.592 0.614 14 贵州 Guizhou 0.384 0.356 0.464 0.488 0.472 0.428 0.397 0.402 0.484 0.474 0.420 28 云南 Yunnan 0.459 0.496 0.496 0.455 0.456 0.455 0.472 0.517 0.567 0.586 0.476 26 陕西 Shaanxi 0.485 0.494 0.489 0.477 0.505 0.506 0.514 0.533 0.580 0.584 0.494 24 甘肃 Gansu 0.533 0.449 0.367 0.370 0.389 0.405 0.428 0.454 0.497 0.550 0.416 29 青海 Qinghai 1.000 1.000 0.821 0.752 0.706 0.803 0.788 0.827 1.000 1.000 0.878 5 宁夏 Ningxia 1.000 0.368 0.410 0.490 0.440 0.445 0.480 0.482 0.737 0.778 0.483 25 新疆 Xinjiang 1.000 0.929 0.812 0.954 0.847 0.826 0.919 1.000 1.000 1.000 0.947 1 平均 Average 0.824 0.657 0.590 0.576 0.581 0.583 0.600 0.617 0.691 0.690 0.633 —
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表 4 第二阶段SFA回归分析结果
Table 4 Regression results of SFA on the second-stage model
投入松弛变量
Input slack variable常数
Constant农业技术水平
Agricultural technique
level财政农业支出
Fiscal expenditures
on agriculture受灾率
Disaster rateσ2 γ LR单边检验
LR one-sided test能源投入 Energy input 2.843 5×107*** −5.212 2×108*** −3.370 3×106*** 2.005 7×107*** 1.221 4×1015 0.368 3 46.35 机械投入 Machinery input 4.512 6×103*** −1.201 2×105*** −1.466 1×103*** −2.563 5×103*** 6.033 7×106 0.870 3 579.16 劳动力投入 Labor input 9.793 8×102*** −4.202 6×104*** −3.217 7×102 −1.344 4×102** 1.252 5×105 0.786 1 395.09 土地投入 Land input 6.192 1×103*** −1.151 2×105*** −3.647 1×103*** −1.576 7×103*** 8.243 2×106 0.879 2 590.60 资本投入 Capital input 6.916 1×103*** 1.636 2×103*** −5.749 2×103*** −6.822 9×103*** 9.337 5 ×1060.297 7 43.08 LR为单边广义似然比统计量, 在1%显著性水平下的临界值为10.501[43]。**和***表示在P<5%和P<1%水平显著相关。LR is the one-sided generalized likelihood ratio statistic with a critical value of 10.501[43] at 1% significance level. ** and *** represent significant correlations at P<5% and P<1% levels, respectively.
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表 5 2002—2021年中国30个省(直辖市、自治区, 不包括香港、澳门、台湾和西藏)第三阶段农业能源效率
Table 5 Agricultural energy efficiencies of 30 provinces (municipalities and autonomous regions, excluding Hong Kong, Macao,Taiwan and Xizang) of China based on the third-stage model from 2002 to 2021
省(直辖市、自治区)
Province (municipality, autonomous region)2002 2007 2012 2015 2016 2017 2018 2019 2020 2021 均值
Mean排序
Rank北京 Beijing 0.055 0.060 0.073 0.063 0.058 0.052 0.047 0.043 0.041 0.042 0.059 30 天津 Tianjin 0.048 0.060 0.077 0.083 0.086 0.080 0.073 0.074 0.079 0.081 0.072 27 河北 Hebei 0.303 0.608 0.650 0.680 0.706 0.704 0.755 0.764 0.797 0.803 0.650 4 山西 Shanxi 0.088 0.139 0.250 0.267 0.266 0.273 0.276 0.292 0.327 0.347 0.215 24 内蒙古 Inner Mongolia 0.135 0.266 0.424 0.446 0.449 0.442 0.492 0.515 0.545 0.591 0.367 18 辽宁 Liaoning 0.230 0.381 0.536 0.565 0.545 0.540 0.550 0.568 0.581 0.599 0.463 12 吉林 Jilin 0.151 0.276 0.401 0.411 0.394 0.380 0.400 0.433 0.490 0.494 0.343 20 黑龙江 Heilongjiang 0.166 0.297 0.602 0.671 0.668 0.678 0.683 0.706 0.730 0.742 0.498 10 上海 Shanghai 0.060 0.064 0.072 0.069 0.065 0.064 0.062 0.060 0.058 0.050 0.064 28 江苏 Jiangsu 0.346 0.540 0.708 0.847 0.884 0.882 0.877 0.890 0.887 0.906 0.678 3 浙江 Zhejiang 0.239 0.300 0.430 0.458 0.471 0.476 0.480 0.491 0.509 0.511 0.393 16 安徽 Anhui 0.248 0.364 0.534 0.631 0.639 0.664 0.663 0.700 0.730 0.751 0.508 8 福建 Fujian 0.227 0.324 0.461 0.503 0.511 0.514 0.516 0.517 0.524 0.523 0.411 15 江西 Jiangxi 0.178 0.280 0.402 0.467 0.485 0.485 0.485 0.499 0.500 0.506 0.371 17 山东 Shandong 0.661 1.000 1.000 1.000 1.000 0.980 1.000 0.984 1.000 1.000 0.981 1 河南 Henan 0.483 0.813 0.929 1.000 1.000 0.947 0.950 0.931 1.000 1.000 0.882 2 湖北 Hubei 0.231 0.406 0.643 0.667 0.709 0.725 0.724 0.737 0.776 1.000 0.571 7 湖南 Hunan 0.248 0.407 0.533 0.569 0.589 0.596 0.602 0.649 0.748 0.737 0.507 9 广东 Guangdong 0.306 0.447 0.608 0.683 0.713 0.728 0.712 0.782 1.000 1.000 0.600 6 广西 Guangxi 0.183 0.351 0.542 0.606 0.634 0.644 0.661 0.687 0.706 0.722 0.489 11 海南 Hainan 0.085 0.125 0.198 0.229 0.233 0.235 0.231 0.233 0.232 0.238 0.177 25 重庆 Chongqing 0.109 0.162 0.250 0.290 0.318 0.316 0.326 0.333 0.358 0.367 0.235 22 四川 Sichuan 0.280 0.486 0.685 0.752 0.780 0.773 0.774 0.882 1.000 1.000 0.635 5 贵州 Guizhou 0.104 0.141 0.275 0.383 0.400 0.403 0.403 0.406 0.426 0.406 0.261 21 云南 Yunnan 0.162 0.279 0.435 0.528 0.559 0.577 0.583 0.641 0.691 0.713 0.416 14 陕西 Shaanxi 0.121 0.215 0.402 0.466 0.483 0.491 0.506 0.523 0.548 0.562 0.352 19 甘肃 Gansu 0.089 0.158 0.239 0.292 0.301 0.317 0.333 0.363 0.389 0.430 0.234 23 青海 Qinghai 0.020 0.035 0.067 0.077 0.081 0.086 0.092 0.097 0.108 0.110 0.062 29 宁夏 Ningxia 0.027 0.051 0.097 0.115 0.117 0.120 0.129 0.131 0.149 0.156 0.088 26 新疆 Xinjiang 0.132 0.241 0.461 0.564 0.581 0.590 0.631 0.656 0.686 0.749 0.422 13 平均 Average 0.203 0.332 0.460 0.514 0.531 0.533 0.541 0.565 0.614 0.637 0.432 —
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表 6 2018年中国部分省和直辖市的农产品零售价格
Table 6 Retail prices of agricultural products in selected province and municipalities of China in 2018
¥∙kg−1 地区 Area 粳米 Rice 猪肉 Pork 牛肉 Beef 羊肉 Mutton 水产品 Aquatic product 蔬菜 Vegetable 鸡蛋 Egg 北京 Beijing 6.04 30.45 66.41 73.43 — 6.25 9.57 上海 Shanghai — 27.96 — — 36.50 7.82 12.00 天津 Tianjin — 28.02 59.66 69.05 19.04 6.16 8.69 青海 Qinghai 6.54 27.01 57.87 56.39 50.39 6.19 10.61 平均 Average 5.56 21.90 64.30 63.64 17.33 6.12 10.10 全国平均数据来源于2019年的《中国农产品价格调查年鉴》, 北京数据来源于北京市价格监测中心, 上海数据来源于上海市农业委员会信息中心, 青海数据来源于青海省发展和改革委员会。The national averages are derived from China Yearbook of Agricultural Price Survey in 2019; the Beijing data are derived from the Beijing Price Monitoring Center; the Shanghai data are derived from the Information Center of the Shanghai Municipal Agriculture Commission; and the Qinghai data are derived from the Development and Reform Commission of QingHai Province.
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