Scientia Agricultura Sinica ›› 2025, Vol. 58 ›› Issue (11): 2206-2224.doi: 10.3864/j.issn.0578-1752.2025.11.010

• SOIL & FERTILIZER·WATER-SAVING IRRIGATION·AGROECOLOGY & ENVIRONMENT • Previous Articles     Next Articles

Research on the Spatio-Temporal Evolution and Differentiated Improvement Path of Low-Carbon Utilization Efficiency of Cultivated Land Under the Background of New and Old Kinetic Energy Conversion——A Case Study of Guangdong Province

ZHU QingYing1,2,3(), HE GengYi1, SUN Ping3()   

  1. 1 School of Public Management, South China Agricultural University, Guangzhou 510642
    2 Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Guangzhou 510700
    3 Key Laboratory of Geological Safety of Coastal Urban Underground Space, Ministry of Natural Resources, Qingdao 266101, Shandong
  • Received:2024-07-29 Accepted:2024-09-07 Online:2025-06-01 Published:2025-06-09
  • Contact: SUN Ping

Abstract:

【Objective】 To scientifically reveal the spatio-temporal evolution characteristics of the low-carbon use efficiency of cultivated land under the background of new and old kinetic energy conversion, and deeply analyze the differentiated configuration paths for improving the low-carbon use efficiency of cultivated land, so as to provide decision-making basis for the efficient and sustainable use of cultivated land, the cultivation of new agricultural productive forces, and even the high-quality development of agriculture.【Method】 Based on defining the connotation of the low-carbon use efficiency of cultivated land under the background of new and old kinetic energy conversion and constructing a theoretical framework for its configuration path, using the panel data of 21 prefecture-level cities in Guangdong Province from 2010 to 2021, the Super-SBM model with undesired outputs was adopted to evaluate the low-carbon use efficiency of cultivated land under the background of new and old kinetic energy conversion and depict the spatio-temporal evolution characteristics, and the fuzzy-set qualitative comparative analysis (fsQCA) was used to explore the differentiated configuration paths for improving the low-carbon use efficiency of cultivated land. 【Result】 (1) From the perspective of temporal characteristics, the low-carbon use efficiency of cultivated land in Guangdong Province showed a slow upward trend with fluctuations from 2010 to 2021, with an average value of 0.947; the evolution characteristics of the Pearl River Delta and Northern Guangdong were similar to those at the provincial level, Eastern Guangdong had small fluctuations, while Western Guangdong showed a downward trend with fluctuations, and only the Pearl River Delta had an average efficiency value greater than 1; from the perspective of spatial distribution, the spatial differentiation characteristics of the low-carbon use efficiency of cultivated land in Guangdong Province were significant, showing a multi-core and contiguous distribution of high and low efficiency areas, especially the prominent phenomena of high efficiency in the Pearl River Delta and low efficiency in Northern Guangdong. (2) The paths for improving the low-carbon use efficiency of cultivated land in Guangdong Province could be summarized into three types of configuration paths: single-factor dominated by socio-economic or government support, two-factor dominated by technological empowerment-government support or socio-economic-government support, and multi-factor dominated by resource endowment-socio-economic-government support. (3) The level of socio-economic development and the degree of government support were the most universal factors affecting the low-carbon use efficiency of cultivated land, while the influence of cultivated land resource endowment conditions and the level of agricultural technology investment was relatively weak. These factors could undergo equivalent substitution under specific conditions, and cultivated land resource endowment could be replaced by other three types of factors alone or in combination, forming the "same goal through different paths" for improving the low-carbon use efficiency of cultivated land.【Conclusion】 The low-carbon use of cultivated land is a configuration problem affected by the interaction of factors in the "resource-economy-technology-policy" complex system. In practice, attention should be paid to the overall planning of the design of improvement paths and policy implementation according to local conditions. At present, the quantity of cultivated land is under serious threat, and continuous efforts in socio-economic development, financial support for agriculture, and the level of agricultural production technology should become the long-term direction for promoting the low-carbon and efficient use of cultivated land.

Key words: new and old kinetic energy conversion, low-carbon use efficiency of cultivated land, spatio-temporal evolution characteristics, configuration path, Guangdong Province

Fig. 1

Connotation of low-carbon use efficiency of cultivated land under the background of new and old kinetic energy conversion"

Fig. 2

Configuration path analysis framework for improving low-carbon use efficiency of cultivated land under the background of new and old kinetic energy conversion"

Table 1

The descriptive statistics and calibration results of variables"

集合
Collection
条件和结果
Conditions and results
统计性描述 Statistical description 校准 Calibration
平均值
Mean
标准差
Standard deviation
完全隶属
Fully affiliated
交叉点
Intersection
完全不隶属
Completely not affiliated
结果变量
Variables of results
耕地低碳利用效率
Low-carbon use efficiency of cultivated land
1.053 0.242 1.348 1.041 0.756
资源禀赋
Endowment
耕地面积 Cultivated area (×103 hm2) 128520.74 115248.95 342098.41 104500.00 10644.92
水田面积占比
Proportion of paddy field (%)
0.608 0.264 0.932 0.679 0.018
社会经济
Socio-economic
农村人口数Rural population (×104) 3178326.79 1587428.32 6420402.80 3021550.00 495642.00
农村居民人均可支配收入
Per capita disposable income of rural residents (yuan)
18118.73 8402.38 35473.91 16439.10 7495.65
政府支持
Government support
农林水支出
Agriculture, forestry and water expenditure (×108 yuan)
330946.50 207348.69 692572.60 299667.00 60559.35
农业生产技术
Agricultural production technology
有效灌溉率Effective irrigation rate (%) 0.606 0.231 0.981 0.589 0.174
农业技术人员数量
Number of agricultural technicians
535.37 1029.23 2144.85 164.00 61.00

Table 2

Evaluation index system of low-carbon use efficiency of cultivated land under the background of new and old kinetic energy conversion"

变量类型 Variable type 具体变量 Specific variables
投入指标
Input indicators
旧动能
Old kinetic energy
土地 Land 农作物播种面积Planting area of crops (×103 hm2)
劳动Labor 第一产业从业人员数Employees number of the primary industry (×104)
灌溉 Irrigation 耕地有效灌溉面积 Effective irrigated area of cultivated land (×103 hm2)
机械 Machinery 农业机械总动力 Total power of agricultural machinery (×104 kW)
化肥 Chemical fertilizer 化肥折纯量Fertilizer purity conversion (×103 t)
农药Pesticide 农药使用量 Pesticide usage (t)
农膜 Agricultural film 农用塑料薄膜使用量Agricultural film usage (t)
新动能
New kinetic energy
知识 Knowledge 农村专业技术协会数 Rural professional and technical associations number
信息 Information 农村居民家庭每百户计算机拥有量
Number of computers per 100 households in rural areas
技术 Technology 科研课题投入经费Research project investment funds (×104 yuan)
期望产出指标
Expected output indicators
经济效益Economic benefits 农业总产值Total agricultural output value (×108 yuan)
社会效益 Social benefits 粮食总产量Total grain output (×104 t)
环境效益 Environmental benefits 耕地碳汇总量Total carbon sink of cultivated land (×104 t)
非期望产出指标
Unexpected output indicators
环境损失 Environmental losses 耕地碳排放总量Total carbon emissions of cultivated land (t)

Table 3

Variables of configuration analysis"

变量类别 Variable type 具体变量 Specific variables
结果变量
Outcome variable
耕地低碳利用效率
Low-carbon use efficiency of cultivated land
Super-SBM模型测算值
Calculation value of Super-SBM model
条件变量
Conditional variable
耕地资源禀赋
Endowment of cultivated land
耕地面积
Cultivated area
年末耕地面积
Cultivated land area in end of year (×103 hm2)
水田面积占比
Proportion of paddy field
水田面积/耕地面积
Paddy fields area/Cultivated area
社会经济发展水平
Level of socio-economic development
农村人口总数 Rural population 农村人口数Rural population (×104)
农村居民人均可支配收入
Per capita disposable income of rural residents
农村居民人均可支配收入
Per capita disposable income of rural residents (yuan)
政府支持
Government support
农林水支出
Agriculture, forestry and water expenditure
地方一般预算农林水支出
Local agricultural, forestry and water expenditure budget (×108 yuan)
农业技术投入水平
Agricultural technology investment level
有效灌溉率
Effective irrigation rate
有效灌溉面积/耕地面积
Effective irrigation area/Cultivated land area
农业技术人员人数
Number of agricultural technicians
农业技术人员数量
Number of agricultural technicians

Table 4

Low-carbon use efficiency of cultivated land in 21 prefecture level cities in Guangdong Province from 2010 to 2021"

城市
City
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 均值
Mean
珠三角
The Pearl River Delta
0.935 0.937 0.989 1.002 1.038 1.015 1.069 1.061 1.059 1.013 1.066 1.076 1.022
广州Guangzhou 1.024 1.014 1.123 1.097 1.144 1.157 1.168 1.180 1.103 1.090 1.098 1.134 1.111
珠海Zhuhai 0.905 0.730 0.753 0.965 0.958 1.018 1.049 1.062 1.053 1.071 1.071 1.089 0.977
深圳Shenzhen 1.210 1.379 1.646 1.522 1.728 1.495 1.899 1.782 1.811 1.672 1.928 1.990 1.672
佛山Foshan 0.686 0.723 0.927 0.963 0.968 0.968 0.972 0.969 0.968 0.928 0.873 0.962 0.909
惠州Huizhou 0.901 0.912 0.910 0.909 0.912 0.916 0.927 0.891 0.801 0.651 0.869 0.858 0.871
东莞Dongguan 0.957 0.941 0.941 0.995 1.027 1.051 1.141 1.145 1.205 1.226 1.040 1.112 1.065
中山Zhongshan 1.211 1.216 1.143 1.132 1.170 1.080 1.035 0.990 0.945 0.990 1.116 1.049 1.090
江门Jiangmen 0.908 0.910 0.926 0.919 0.900 0.909 0.918 0.918 0.927 0.918 0.918 0.915 0.916
肇庆Zhaoqing 0.616 0.611 0.530 0.519 0.531 0.537 0.513 0.610 0.716 0.572 0.680 0.572 0.584
粤东East Guangdong 0.906 0.907 0.902 0.901 0.918 0.924 0.915 0.874 0.915 0.888 0.927 0.905 0.907
潮州Chaozhou 0.900 0.900 0.900 0.909 0.900 0.936 0.936 0.756 0.891 0.882 0.889 0.887 0.891
汕头Shantou 1.001 1.012 0.974 0.961 1.029 1.018 0.968 0.998 1.004 0.925 1.021 0.986 0.991
揭阳Jieyang 0.959 0.957 0.966 0.980 0.986 0.992 1.034 0.947 0.959 0.925 0.999 0.975 0.973
汕尾Shanwei 0.763 0.757 0.767 0.754 0.755 0.749 0.720 0.796 0.807 0.821 0.798 0.771 0.772
粤北
Northern Guangdong
0.826 0.820 0.827 0.835 0.834 0.837 0.836 0.852 0.878 0.863 0.840 0.866 0.843
河源Heyuan 0.978 0.973 0.974 0.972 0.975 0.978 0.977 0.975 0.990 0.981 0.978 0.978 0.977
梅州Meizhou 0.842 0.839 0.850 0.876 0.888 0.889 0.877 0.882 0.981 0.965 0.946 0.973 0.901
韶关Shaoguan 0.851 0.845 0.842 0.842 0.838 0.837 0.834 0.851 0.896 0.849 0.851 0.934 0.856
清远Qingyuan 0.680 0.671 0.695 0.712 0.709 0.724 0.730 0.716 0.720 0.711 0.716 0.717 0.708
云浮Yunfu 0.779 0.770 0.773 0.775 0.761 0.756 0.760 0.838 0.802 0.810 0.711 0.729 0.772
粤西
West Guangdong
0.957 1.004 0.980 0.979 0.968 0.962 0.956 0.939 0.934 0.942 0.908 0.903 0.953
湛江Zhanjiang 1.072 1.079 1.017 1.031 1.042 1.039 1.047 1.045 1.031 1.045 0.968 1.040 1.038
茂名Maoming 0.991 0.989 0.986 0.987 0.913 0.911 0.914 0.979 0.968 0.971 0.973 0.950 0.961
阳江Yangjiang 0.807 0.945 0.937 0.919 0.950 0.935 0.908 0.793 0.802 0.810 0.783 0.718 0.859
均值Mean 0.906 0.913 0.932 0.940 0.957 0.948 0.968 0.959 0.970 0.943 0.963 0.968 0.947

Fig. 3

Trend of low-carbon use efficiency of cultivated land in Guangdong Province from 2010 to 2021"

Table 5

Spatial distribution and its changes of low-carbon use efficiency of cultivated land in Guangdong Province from 2010 to 2021"

区域
Region
年份 Year
2010 2014 2018 2022
珠三角
The Pearl River Delta
高效率区(中山市、深圳市)
High efficiency zone (Zhongshan City, Shenzhen City)
较高效率区(广州市)
Higher efficiency zone (Guangzhou City)
中等效率区(东莞市)
Medium efficiency zone (Dongguan City)
较低效率区(珠海市、江门市、
惠州市)
Lower efficiency zone (Zhuhai City, Jiangmen City, Huizhou City)
低效率区(肇庆市、清远市、佛
山市)
Low efficiency zone (Zhaoqing City, Qingyuan City, Foshan City)
高效率区(广州市、中山市、深圳市)
High efficiency zone (Guangzhou City, Zhongshan City, Shenzhen City)
较高效率区(东莞市、佛山市、
珠海市)
Higher efficiency zone (Dongguan City, Foshan City, Zhuhai City)
较低效率区(江门市、惠州市)
Lower efficiency zone (Jiangmen City, Huizhou City)
低效率区(肇庆市)
Low efficiency zone (Zhaoqing City)
高效率区(东莞市、深圳市)
High efficiency zone (Dongguan City, Shenzhen City)
较高效率区(广州市、珠海市)
Higher efficiency zone (Guangzhou City, Zhuhai City)
中等效率区(江门市、中山市、佛山市)
Medium efficiency zone (Jiangmen City, Zhongshan City, Foshan City)
较低效率区(惠州市)
Lower efficiency zone (Huizhou City)
低效率区(肇庆市)
Low efficiency zone (Zhaoqing City)
高效率区(深圳市)
High efficiency zone (Shenzhen City)
较高效率区(广州市、中山市、东莞市、珠海市)
Higher efficiency zone (Guangzhou City, Zhongshan City, Dongguan City, Zhuhai City)
中等效率区(佛山市)
Medium efficiency zone (Foshan City)
较低效率区(江门市、惠州市)
Lower efficiency zone (Jiangmen City, Huizhou City)
低效率区(肇庆市)
Low efficiency zone (Zhaoqing City)
粤东
East Guangdong
较高效率区(汕头市)
High efficiency zone (Shantou City)
中等效率区(揭阳市)
Medium efficiency zone (Jieyang City)
较低效率区(潮州市)
Lower efficiency zone (Chaozhou City)
低效率区(汕尾市)
Low efficiency zone (Shanwei City)
较高效率区(汕头市)
Higher efficiency zone (Shantou City)
中等效率区(揭阳市)
Medium efficiency zone (Jieyang City)
较低效率区(潮州市)
Lower efficiency zone (Chaozhou City)
低效率区(汕尾市)
Low efficiency zone (Shanwei City)
较高效率区(汕头市)
Higher efficiency zone (Shantou City)
中等效率区(揭阳市)
Medium efficiency zone (Jieyang City)
较低效率区(汕尾市、潮州市)
Lower efficiency zone (Shanwei City, Chaozhou City)
中等效率区(揭阳市、汕头市)
Medium efficiency zone (Jieyang City, Shantou City)
较低效率区(潮州市)
Lower efficiency zone (Chaozhou City)
低效率区(汕尾市)
Low efficiency zone (Shanwei City)
粤北
Northern Guangdong
中等效率区(河源市)
Medium efficiency zone (Heyuan City)
较低效率区(云浮市、韶关市、梅州市)
Lower efficiency zone (Yunfu City, ShaoGuan City, Meizhou City)
低效率区(清远市)
Low efficiency zone (Qingyuan City)
中等效率区(河源市)
Medium efficiency zone (Heyuan City)
较低效率区(韶关市、梅州市)
Lower efficiency zone (Shaoguan City, Meizhou City)
低效率区(云浮市、清远市)
Low efficiency zone (Yunfu City, Qingyuan City)
较高效率区(河源市)
Higher efficiency zone (Heyuan City)
中等效率区(梅州市)
Medium Efficiency zone (Meizhou City)
较低效率区(云浮市、韶关市)
Lower efficiency zone (Yunfu City, Shaoguan City)
低效率区(清远市)
Low efficiency zone (Qingyuan City)
中等效率区(韶关市、河源市、梅州市)
Medium efficiency zone (Shaoguan City, Heyuan City, Meizhou City)
低效率区(云浮市、清远市)
Low efficiency zone (Yunfu City, Qingyuan City)
粤西
West Guangdong
较高效率区(湛江市、茂名市)
High efficiency zone (Zhanjiang City, Maoming City)
较低效率区(阳江市)
Lower efficiency zone (Yangjiang City)
较高效率区(湛江市)
Higher efficiency zone (Zhanjiang City)
中等效率区(阳江市)
Medium efficiency zone (Yangjiang City)
较低效率区(茂名市)
Lower efficiency zone (Maoming City)
较高效率区(湛江市)
Higher efficiency zone (Zhanjiang City)
中等效率区(茂名市)
Medium efficiency zone (Maoming City)
较低效率区(阳江市)
Lower efficiency zone (Yangjiang City)
较高效率区(湛江市)
Higher efficiency zone (Zhanjiang City)
中等效率区(茂名市)
Medium efficiency zone (Maoming City)
低效率区(阳江市)
Low efficiency zone (Yangjiang City)

Table 6

Results of necessity test for conditional variables"

条件 Condition 一致性 Consistency 覆盖率 Coverage rate
耕地面积 Cultivated area 0.555 0.582
~耕地面积 ~ Cultivated area 0.801 0.715
水田面积占比 Proportion of paddy field 0.639 0.588
~水田面积占比 ~ Proportion of paddy field 0.717 0.728
农村人口数Rural population 0.691 0.677
~农村人口数 ~ Rural population 0.700 0.665
农村居民人均可支配收入 Per capita disposable income of rural residents 0.738 0.755
~农村居民人均可支配收入 ~ Per capita disposable income of rural residents 0.619 0.565
农林水支出 Agriculture, forestry and water expenditure 0.703 0.706
~农林水支出 ~ Agriculture, forestry and water expenditure 0.654 0.607
有效灌溉率 Effective irrigation rate 0.676 0.618
~有效灌溉率 ~ Effective irrigation rate 0.656 0.669
农业技术人员数量 Number of agricultural technicians 0.578 0.650
~农业技术人员数量 ~ Number of agricultural technicians 0.721 0.609

Table 7

Configuration paths for improving the low-carbon use efficiency of cultivated land under the background of new and old kinetic energy conversion"

条件变量
Conditional variable
单要素主导
Single element dominance
双要素主导
Dual element dominance
多要素主导
Multi element dominance
P1 P2 P3 P4 P5
P1a P1b P1C P2 P3 P4a P4b P4c P5a P5b P5C
耕地资源禀赋
Endowment of cultivated land
耕地面积
Cultivated area
水田面积占比
Proportion of paddy field
社会经济发展水平
Level of socio-economic development
农村人口数
Rural population
农村居民人均可支配收入
Per capita disposable income of rural residents
政府支持程度
Government support
农林水支出
Agriculture, forestry and water expenditure
农业技术投入水平
Agricultural technology investment level
有效灌溉率
Effective irrigation rate
农业技术人员数量
Number of agricultural technicians
一致性 Consistency 0.867 0.876 0.852 0.865 0.855 0.864 0.860 0.860 0.857 0.851 0.847
原始覆盖度 Original coverage 0.357 0.275 0.204 0.253 0.213 0.255 0.257 0.269 0.293 0.297 0.270
唯一覆盖度 Unique coverage 0.085 0.034 0.017 0.003 0.012 0.012 0.005 0.009 0.000 0.000 0.000
总体解的一致性 Consistency of overall solution 0.837
总体解的覆盖度 Coverage of the overall solution 0.641

Fig. 4

Substitution of conditional variables in low-carbon use efficiency of cultivated land under the background of new and old kinetic energy conversion"

[1]
刘永健, 耿弘, 孙文华, 李传武, 褚晓潇. 城市建设用地扩张的区域差异及其驱动因素. 中国人口·资源与环境, 2017, 27(8): 122-127.
LIU Y J, GENG H, SUN W H, LI C W, CHU X X. Analysis on the regional differences and driving factors of urban construction land expansion. China Population, Resources and Environment, 2017, 27(8): 122-127. (in Chinese)
[2]
匡丽花, 叶英聪, 赵小敏, 郭熙. 基于改进TOPSIS方法的耕地系统安全评价及障碍因子诊断. 自然资源学报, 2018, 33(9): 1627-1641.

doi: 10.31497/zrzyxb.20170754
KUANG L H, YE Y C, ZHAO X M, GUO X. Evaluation and obstacle factor diagnosis of cultivated land system security in Yingtan city based on the improved TOPSIS method. Journal of Natural Resources, 2018, 33(9): 1627-1641. (in Chinese)
[3]
HUANG Z H, DU X J, CASTILLO C S Z. How does urbanization affect farmland protection? Resources, Conservation and Recycling, 2019, 145: 139-147.
[4]
李玉浩, 王红叶, 崔振岭, 营浩, 曲潇琳, 张骏达, 王新宇. 我国主要粮食作物耕地基础地力的时空变化. 中国农业科学, 2022, 55(20): 3960-3969. doi: 10.3864/j.issn.0578-1752.2022.20.008.
LI Y H, WANG H Y, CUI Z L, YING H, QU X L, ZHANG J D, WANG X Y. Spatial-temporal variation of cultivated land soil basic productivity for main food crops in China. Scientia Agricultura Sinica, 2022, 55(20): 3960-3969. doi: 10.3864/j.issn.0578-1752.2022.20.008. (in Chinese)
[5]
田云, 王梦晨. 湖北省农业碳排放效率时空差异及影响因素. 中国农业科学, 2020, 53(24): 5063-5072. doi: 10.3864/j.issn.0578-1752.2020.24.009.
TIAN Y, WANG M C. Research on spatial and temporal difference of agricultural carbon emission efficiency and its influencing factors in Hubei Province. Scientia Agricultura Sinica, 2020, 53(24): 5063-5072. doi: 10.3864/j.issn.0578-1752.2020.24.009. (in Chinese)
[6]
孟庆雷, 殷宇翔, 王煜昊. 我国农业碳排放的时空演化、脱钩效应及绩效评估. 中国农业科学, 2023, 56(20): 4049-4066. doi: 10.3864/j.issn.0578-1752.2023.20.010.
MENG Q L, YIN Y X, WANG Y H. Spatial-temporal evolution, decoupling effect and performance evaluation of China’s agricultural carbon emissions. Scientia Agricultura Sinica, 2023, 56(20): 4049-4066. doi: 10.3864/j.issn.0578-1752.2023.20.010. (in Chinese)
[7]
张玥, 代亚强, 陈媛媛, 柯新利. 中国耕地低碳利用效率时空演变及其驱动因素. 农业工程学报, 2022, 38(8): 234-243.
ZHANG Y, DAI Y Q, CHEN Y Y, KE X L. Spatial-temporal evolution and driving factors of low-carbon use efficiency of cultivated land in China. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(8): 234-243. (in Chinese)
[8]
匡兵, 卢新海, 韩璟, 张祚. 考虑碳排放的粮食主产区耕地利用效率区域差异与变化. 农业工程学报, 2018, 34(11): 1-8.
KUANG B, LU X H, HAN J, ZHANG Z. Regional differences and dynamic evolution of cultivated land use efficiency in major grain producing areas in low carbon perspective. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(11): 1-8. (in Chinese)
[9]
金贵, 邓祥征, 赵晓东, 郭柏枢, 杨俊. 2005—2014年长江经济带城市土地利用效率时空格局特征. 地理学报, 2018, 73(7): 1242-1252.

doi: 10.11821/dlxb201807005
JIN G, DENG X Z, ZHAO X D, GUO B S, YANG J. Spatio-temporal patterns of urban land use efficiency in the Yangtze River Economic Zone during 2005-2014. Acta Geographica Sinica, 2018, 73(7): 1242-1252. (in Chinese)
[10]
GAO J X, SONG J B, WU L F. A new methodology to measure the urban construction land-use efficiency based on the two-stage DEA model. Land Use Policy, 2022, 112: 105799.
[11]
TAN S, HU B, KUANG B. Regional differences and dynamic evolution of urban land green use efficiency within the Yangtze River Delta, China. Land Use Policy, 2021, 106: 105449.
[12]
刘蒙罢, 张安录, 文高辉. 长江中下游粮食主产区耕地利用生态效率时空格局与演变趋势. 中国土地科学, 2021, 35(2): 50-60.
LIU M B, ZHANG A L, WEN G H. Temporal and spatial pattern and evolution trend of cultivated land use ecological eff iciency in the main grain producing areas in the Lower Yangtze region. China Land Science, 2021, 35(2): 50-60. (in Chinese)
[13]
杨斌, 杨俊, 王占岐, 谭力. 长江经济带耕地绿色低碳利用的时空格局及其成因分析. 中国土地科学, 2022, 36(10): 63-71.
YANG B, YANG J, WANG Z Q, TAN L. Spatial-temporal pattern and attribution of cultivated land green and low-carbon utilization in the Yangtze River economic belt. China Land Science, 2022, 36(10): 63-71. (in Chinese)
[14]
吕添贵, 付舒斐, 胡晗, 汪立, 耿灿. 农业绿色转型约束下耕地绿色利用效率动态演进及其收敛特征研究: 以长江中游粮食主产区为例. 中国土地科学, 2023, 37(4): 107-118.
T G, FU S F, HU H, WANG L, GENG C. Dynamic evolution and convergence characteristics of cultivated land green use efficiency based on the constraint of agricultural green transition: Taking the main grain producing areas in the middle reaches of the Yangtze River as an example. China Land Science, 2023, 37(4): 107-118. (in Chinese)
[15]
FENG L, LEI G P, NIE Y. Exploring the eco-efficiency of cultivated land utilization and its influencing factors in black soil region of Northeast China under the goal of reducing non-point pollution and net carbon emission. Environmental Earth Sciences, 2023, 82(4): 94.
[16]
李长英, 周荣云, 余淼杰. 中国新旧动能转换的历史演进及区域特征. 数量经济技术经济研究, 2021, 38(2): 3-23.
LI C Y, ZHOU R Y, YU M J. Historical evolution and regional characteristics of the conversion of new and old driving force in China. The Journal of Quantitative & Technical Economics, 2021, 38(2): 3-23. (in Chinese)
[17]
裴长洪, 倪江飞. 习近平新旧动能转换重要论述的若干经济学分析. 经济学动态, 2020, 2(5): 3-14.
PEI C H, NI J F. Economic analysis on Xijinping’s important discourses on shifting from old drivers of growth to new ones. Economic Perspectives, 2020, 2(5): 3-14. (in Chinese)
[18]
唐宇, 宋永永, 薛东前, 马蓓蓓, 王莎, 叶昊. 晋陕蒙地区市域新旧动能转换过程与分异机制. 地理科学进展, 2023, 42 (2): 287-300.

doi: 10.18306/dlkxjz.2023.02.007
TANG Y, SONG Y Y, XUE D Q, MA B B, WANG S, YE H. Process and differentiation mechanism of old-new growth driver conversion in the Shanxi-Shaanxi-Inner Mongolia region at city scale. Progress in Geography, 2023, 42(2): 287-300. (in Chinese)

doi: 10.18306/dlkxjz.2023.02.007
[19]
匡兵, 范翔宇, 卢新海. 中国耕地利用绿色转型效率的时空分异特征及其影响因素. 农业工程学报, 2021, 37(21): 269-277.
KUANG B, FAN X Y, LU X H. Spatial-temporal differentiation characteristics of the efficiency of green transformation of cultivated land use and its affecting factors in China. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(21): 269-277. (in Chinese)
[20]
卢新海, 杨喜, 陈泽秀. 中国城市土地绿色利用效率测度及其时空演变特征. 中国人口·资源与环境, 2020, 30(8): 83-91.
LU X H, YANG X, CHEN Z X. Measurement and temporal-spatial evolution characteristics of urban land green use efficiency in China. China Population, Resources and Environment, 2020, 30(8): 83-91. (in Chinese)
[21]
卢新海, 匡兵, 李菁. 碳排放约束下耕地利用效率的区域差异及其影响因素. 自然资源学报, 2018, 33(4): 657-668.

doi: 10.11849/zrzyxb.20170454
LU X H, KUANG B, LI J. Regional differences and its influencing factors of cultivated land use efficiency under carbon emission constraint. Journal of Natural Resources, 2018, 33(4): 657-668. (in Chinese)
[22]
李红波, 杨和平, 王韬, 齐梦娜. 长江经济带耕地利用生态效率时空分异及组态路径研究. 中国土地科学, 2024, 38(1): 114-124.
LI H B, YANG H P, WANG T, QI M N. Spatial-temporal differentiation of ecological efficiency of cultivated land use and its configuration paths in the Yangtze River economic belt. China Land Science, 2024, 38(1): 114-124. (in Chinese)
[23]
臧俊梅, 唐春云, 王秋香, 李宽, 李利番. 基于Super-SBM模型的广东省耕地利用效率空间非均衡性及影响因素研究. 中国土地科学, 2021, 35(10): 64-74.
ZANG J M, TANG C Y, WANG Q X, LI K, LI L F. Research on spatial imbalance and influencing factors of cultivated land use efficiency in Guangdong Province based on Super-SBM model. China Land Science, 2021, 35(10): 64-74. (in Chinese)
[24]
杜运周, 贾良定. 组态视角与定性比较分析(QCA):管理学研究的一条新道路. 管理世界, 2017, 33(6): 155-167.
DU Y Z, JIA L D. Group perspective and qualitative comparative analysis(QCA): A new path for management research. Journal of Management World, 2017, 33(6): 155-167. (in Chinese)
[25]
LIU Y S, LI L Z, WANG Y S. Spatial-temporal characteristics and influencing factors of agricultural eco-efficiency in China in recent 40 years. Land Use Policy, 2020, 97: 104794.
[26]
谭海波, 范梓腾, 杜运周. 技术管理能力、注意力分配与地方政府网站建设——一项基于TOE框架的组态分析. 管理世界, 2019, 35(9): 81-94.
TAN H B, FAN Z T, DU Y Z. Technical management ability, attention distribution and local government website construction. Journal of Management World, 2019, 35(9): 81-94. (in Chinese)
[27]
宋敏, 杨紫雯, 胡银根. 宅基地政策本体建设水平影响因素的组态研究: 基于我国14个首批试点地区的定性比较分析. 中国土地科学, 2022, 36(11): 54-63.
SONG M, YANG Z W, HU Y G. Configurational investigation on the factors influencing the construction level of rural residential land policy ontology: Based on qualitative comparative analysis of 14 pilot areas in China. China Land Science, 2022, 36(11): 54-63. (in Chinese)
[28]
RAGIN C C. Redesigning social inquiry:Fuzzy sets and beyond. Chicago: University of Chicago Press, 2008.
[29]
柯楠, 卢新海, 匡兵, 韩璟. 碳中和目标下中国耕地绿色低碳利用的区域差异与影响因素. 中国土地科学, 2021, 35(8): 67-76.
KE N, LU X H, KUANG B, HAN J. Regional dif ferences and inf luencing factors of green and low-carbon utilization of cultivated land under the carbon neutrality target in China. China Land Science, 2021, 35(8): 67-76. (in Chinese)
[30]
陈丽, 郝晋珉, 王峰, 尹钰莹, 高阳, 段文凯, 杨君. 基于碳循环的黄淮海平原耕地固碳功能研究. 资源科学, 2016, 38(6): 1039-1053.

doi: 10.18402/resci.2016.06.04
CHEN L, HAO J M, WANG F, YIN Y Y, GAO Y, DUAN W K, YANG J. Carbon sequestration function of cultivated land use system based on the carbon cycle for the Huang-Huai-Hai Plain. Resources Science, 2016, 38(6): 1039-1053. (in Chinese)

doi: 10.18402/resci.2016.06.04
[31]
WEST T O, MARLAND G. A synthesis of carbon sequestration, carbon emissions, and net carbon flux in agriculture: Comparing tillage practices in the United States. Agriculture, Ecosystems & Environment, 2002, 91(1/2/3): 217-232.
[32]
POST W M, KWON K C. Soil carbon sequestration and land-use change: Processes and potential. Global Change Biology, 2000, 6(3): 317-327.
[33]
伍芬琳, 李琳, 张海林, 陈阜. 保护性耕作对农田生态系统净碳释放量的影响. 生态学杂志, 2007, 26(12): 2035-2039.
WU F L, LI L, ZHANG H L, CHEN F. Effects of conservation tillage on net carbon flux from farmland ecosystems. Chinese Journal of Ecology, 2007, 26(12): 2035-2039. (in Chinese)
[34]
李波, 张俊飚, 李海鹏. 中国农业碳排放时空特征及影响因素分解. 中国人口·资源与环境, 2011, 21(8): 80-86.
LI B, ZHANG J B, LI H P. Research on spatial-temporal characteristics and affecting factors decomposition of agricultural carbon emission in China. China Population, Resources and Environment, 2011, 21(8): 80-86. (in Chinese)
[35]
马林燕, 张仁慧, 潘子纯, 魏凤. 中国省际耕地利用生态效率时空格局演变及影响因素分析: 基于2000—2019年面板数据. 中国土地科学, 2022, 36(3): 74-85.
MA L Y, ZHANG R H, PAN Z C, WEI F. Analysis of the evolution and influencing factors of temporal and spatial pattern of eco-efficiency of cultivated land use among provinces in China: Based on panel data from 2000 to 2019. China Land Science, 2022, 36(3): 74-85. (in Chinese)
[36]
SCHNEIDER C Q, WAGEMANN C. Standards of good practice in qualitative comparative analysis (QCA) and fuzzy-sets. Comparative Sociology, 2010, 9(3): 397-418.
[1] ZHANG Li,TANG YaFei,LI ZhengGang,YU Lin,LAN GuoBing,SHE XiaoMan,HE ZiFu. Molecular Characteristic of Squash Leaf Curl China Virus (SLCCNV) Infecting Cucurbitaceae Crops in Guangdong Province [J]. Scientia Agricultura Sinica, 2021, 54(19): 4097-4109.
[2] TANG YaFei,PEI Fan,LI ZhengGang,SHE XiaoMan,YU Lin,LAN GuoBing,DENG MingGuang,HE ZiFu. Identification of Viruses Infecting Peppers in Guangdong by Small RNA Deep Sequencing [J]. Scientia Agricultura Sinica, 2019, 52(13): 2256-2267.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!