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Journal of Arid Land  2024, Vol. 16 Issue (3): 396-414    DOI: 10.1007/s40333-024-0094-z
Research article     
Spatiotemporal characteristics of cultivated land use eco-efficiency and its influencing factors in China from 2000 to 2020
LI Shaoting1,2, MU Na1,2, REN Yanjun1,2,3,*(), Thomas GLAUBEN3
1College of Economics and Management, Northwest A&F University, Yangling 712100, China
2Sino-German Center for Agricultural and Food Economics, Northwest A&F University, Yangling 712100, China
3Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Halle 06120, Germany
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Improving cultivated land use eco-efficiency (CLUE) can effectively promote agricultural sustainability, particularly in developing countries where CLUE is generally low. This study used provincial-level data from China to evaluate the spatiotemporal evolution of CLUE from 2000 to 2020 and identified the influencing factors of CLUE by using a panel Tobit model. In addition, given the undesirable outputs of agricultural production, we incorporated carbon emissions and nonpoint source pollution into the global benchmark-undesirable output-super efficiency-slacks-based measure (GB-US-SBM) model, which combines global benchmark technology, undesirable output, super efficiency, and slacks-based measure. The results indicated that there was an upward trend in CLUE in China from 2000 to 2020, with an increase rate of 2.62%. The temporal evolution of CLUE in China could be classified into three distinct stages: a period of fluctuating decrease (2000-2007), a phase of gradual increase (2008-2014), and a period of rapid growth (2015-2020). The major grain-producing areas (MPAs) had a lower CLUE than their counterparts, namely, non-major grain-production areas (non-MPAs). The spatial agglomeration effect followed a northeast-southwest strip distribution; and the movement path of barycentre revealed a "P" shape, with Luoyang City, Henan Province, as the centre. In terms of influencing factors of CLUE, investment in science and technology played the most vital role in improving CLUE, while irrigation index had the most negative effect. It should be noted that these two influencing factors had different impacts on MPAs and non-MPAs. Therefore, relevant departments should formulate policies to enhance the level of science and technology, improve irrigation condition, and promote sustainable utilization of cultivated land.

Key wordscultivated land use eco-efficiency (CLUE)      slacks-based measure (SBM) model      barycentre model      standard deviation ellipse (SDE)      panel Tobit model      carbon emissions      nonpoint source pollution     
Received: 22 September 2023      Published: 31 March 2024
Corresponding Authors: *REN Yanjun (E-mail:
Cite this article:

LI Shaoting, MU Na, REN Yanjun, Thomas GLAUBEN. Spatiotemporal characteristics of cultivated land use eco-efficiency and its influencing factors in China from 2000 to 2020. Journal of Arid Land, 2024, 16(3): 396-414.

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Fig. 1 Connotation of cultivated land use eco-efficiency (CLUE) in this study
Indicator Definition Mean SD
Input Land Total sown area of crops (×103 hm2) 5154.20 3733.38
Labor Labor was calculated as the employee numbers in the primary industry multiplied by the proportion of total agricultural output value to the gross output value of primary industry (×104 persons). 496.30 389.30
Agricultural machinery Total power of agricultural machinery (×104 kW) 2763.18 2687.36
Pesticide Usage of pesticide (×104 t) 5.06 4.22
Chemical fertilizer Net amount of chemical fertilizer usage (×104 t) 169.62 138.49
Agricultural Film Usage of agricultural film (×104 t) 6.78 6.35
Desirable output Total agricultural output value Total agricultural output value (×108 CNY) 769.98 630.59
Total grain output Total grain output (×104 t) 1818.10 1587.47
Undesirable output Carbon emissions Total carbon emissions (×104 t) 255.10 194.22
Nonpoint source pollution Total loss of fertilizer nitrogen and phosphorus, pesticide, and agricultural film (×104 t) 20.84 16.27
Table 1 Evaluation indicator system of cultivated land use eco-efficiency (CLUE) in this study
Classification Carbon source Coefficient Reference
The first source Pesticide (kg) 4.9341 kg/kg Post and Kwon (2000)
Chemical fertilizer (kg) 0.8956 kg/kg West and Marland (2002)
Agricultural film (kg) 5.1800 kg/kg Li et al. (2011)
The second source Diesel fuel (kg) 0.5927 kg/kg Li et al. (2011)
The third source Irrigation (hm2) 25.0000 kg/hm2 Dubey and Lal (2009)
The fourth source Plowing (hm2) 3.1260 kg/hm2 Wu et al. (2007)
Table 2 Carbon emission coefficient of each kind of carbon source evaluated by this study
Dimension Variable Description Mean SD Minimum Maximum
Natural condition MCI Proportion of total sown area of crops to total cultivated area (%) 1.25 0.39 0.48 2.29
Regional economic development level GPC Gross domestic product (GDP) per capita (×104 CNY/person) 2.13 1.44 0.27 8.57
Agricultural production condition II Proportion of effective irrigated area to total cultivated area (%) 0.52 0.23 0.14 1.18
Science and technology level STI Proportion of local expenditures on science and technology to general budgetary expenditures of local governments (%) 1.83 0.01 0.30 7.20
Agricultural business scale SAL Total sown area of crops divided by agricultural labour (hm2/person) 1.17 0.59 0.47 4.41
Table 3 Selected influencing factors of cultivated land use eco-efficiency (CLUE) by this study
Fig. 2 Boxplot of CLUE in China from 2000 to 2020. The upper and lower boundaries of box indicate the 75% and 25% quantiles, respectively. The line within the box represents the median and the cross represents the average value. The upper boundary of error bar is equal to the 75th percentile plus 1.5 times interquartile range, while the lower boundary of error bar is equal to the 25th percentile minus 1.5 times interquartile range.
Fig. 3 Temporal change in CLUE of each province, autonomous region, and municipality in China from 2000 to 2020
Fig. 4 Spatial distribution of CLUE in China in 2000 (a), 2007 (b), 2014 (c), and 2020 (d). Note that this map is based on the standard map (No. GS(2019)1823) of the Map Service System ( marked by the Ministry of Natural Resources of the People's Republic of China, and the base map has not been modified.
Fig. 5 Spatial evolution of CLUE in China from 2000 to 2020. Note that this map is based on the standard map (No. GS(2019)1823) of the Map Service System ( marked by the Ministry of Natural Resources of the People's Republic of China, and the base map has not been modified
Year Latitude Longitude Location Moving direction Moving distance (km) Moving speed (km/a)
2000 32°36′00′′N 110°06′00′′E Shiyan City, Hubei Province - - -
2007 34°11′24′′N 110°58′12′′E Sanmenxia City, Henan Province 69°22′12′′
east by north
196.09 28.01
2014 34°36′00′′N 112°52′12′′E Zhengzhou City, Henan Province 19°03′00′′
east by north
178.14 25.45
2020 33°56′24′′N 111°18′36′′E Luoyang City, Henan Province 32°00′00′′
west by south
159.88 26.65
Table 4 Barycentre parameters of CLUE in China from 2000 to 2020
Year Area (×105 km2) XStdDist (km) YStdDist (km) Azimuth (°) y/x-axis
2000 47.45 1339.96 1127.28 55.24 0.84
2001 49.38 1393.16 1128.21 48.43 0.81
2002 49.68 1451.66 1089.45 49.08 0.75
2003 49.72 1420.75 1114.04 58.76 0.78
2004 46.23 1381.06 1065.29 50.24 0.77
2005 44.79 1338.47 1064.88 54.06 0.80
2006 46.50 1373.37 1077.54 50.75 0.78
2007 47.55 1395.04 1084.99 67.01 0.78
2008 48.55 1439.49 1073.64 57.05 0.75
2009 44.91 1311.08 1090.46 58.88 0.83
2010 47.61 1342.38 1129.05 61.64 0.84
2011 46.47 1354.24 1092.38 49.93 0.81
2012 46.12 1339.03 1096.46 51.52 0.82
2013 45.67 1343.34 1082.25 48.35 0.81
2014 44.86 1325.58 1077.22 46.40 0.81
2015 43.99 1060.93 1319.96 44.42 1.24
2016 41.73 1017.41 1305.56 38.24 1.28
2017 42.14 1025.77 1307.82 41.30 1.27
2018 42.04 1050.66 1273.60 43.19 1.21
2019 42.86 1053.97 1294.60 42.60 1.23
2020 45.31 1278.14 1128.47 60.01 0.88
Table 5 Standard deviation ellipse (SDE) parameters of CLUE in China from 2000 to 2020
Variable Regression coefficient
Whole country MPAs Non-MPAs
MCI 0.0980±0.0442** 0.0306±0.0554 0.1130±0.0587*
GPC 0.0788±0.0074*** 0.0671±0.0110*** 0.0765±0.0101***
II -0.2240±0.0783*** -0.3020±0.1010*** -0.2180±0.1030**
STI 2.0760±0.8870** 3.8660±1.2210*** 1.1420±1.2700
SAL 0.0931±0.0202*** 0.0944±0.0256*** 0.1100±0.0263***
Constant 0.1870±0.0549*** 0.3020±0.0673*** 0.1740±0.0692**
Wald χ2 test 341.69*** 158.98*** 155.41***
Likelihood ratio test 302.29*** 154.48*** 137.96***
n 651 273 378
Table 6 Regression results of the panal Tobit model for CLUE in China from 2000 to 2020
[1]   Benjamin D. 1992. Household composition, labor markets, and labor demand: Testing for separation in agricultural household models. Econometrica, 60(2): 287-322.
doi: 10.2307/2951598
[2]   Bommarco R, Kleijn D, Potts S G. 2013. Ecological intensification: Harnessing ecosystem services for food security. Trends in Ecology & Evolution, 28(4): 230-238.
doi: 10.1016/j.tree.2012.10.012
[3]   Bonfiglio A, Arzeni A, Bodini A. 2017. Assessing eco-efficiency of arable farms in rural areas. Agricultural Systems, 151: 114-125.
doi: 10.1016/j.agsy.2016.11.008
[4]   Chai C Q, Zhang B B, Li Y Y, et al. 2023. A new multi-dimensional framework considering environmental impacts to assess green development level of cultivated land during 1990 to 2018 in China. Environmental Impact Assessment Review, 98: 106927, doi: 10.1016/j.eiar.2022.106927.
[5]   Charnes A, Cooper W W, Rhodes E. 1978. Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6): 429-444.
doi: 10.1016/0377-2217(78)90138-8
[6]   Chen J. 2007. Rapid urbanization in China: A real challenge to soil protection and food security. Catena, 69(1): 1-15.
doi: 10.1016/j.catena.2006.04.019
[7]   Coluccia B, Valente D, Fusco G, et al. 2020. Assessing agricultural eco-efficiency in Italian Regions. Ecological Indicators, 116: 106483, doi: 10.1016/j.ecolind.2020.106483.
[8]   Deng X Z, Gibson J. 2019. Improving eco-efficiency for the sustainable agricultural production: A case study in Shandong, China. Technological Forecasting and Social Change, 144: 394-400.
doi: 10.1016/j.techfore.2018.01.027
[9]   Djihouessi M B, Degan A, Yekanbessoun N M, et al. 2022. Inventory of agroecosystem services and perceptions of potential implications due to climate change: A case study from Benin in West Africa. Environmental Impact Assessment Review, 95: 106792, doi: 10.1016/j.eiar.2022.106792.
[10]   Duan J K, Ren C C, Wang S T, et al. 2021. Consolidation of agricultural land can contribute to agricultural sustainability in China. Nature Food, 2: 1014-1022.
doi: 10.1038/s43016-021-00415-5 pmid: 37118257
[11]   Dubey A, Lal R. 2009. Carbon footprint and sustainability of agricultural production systems in Punjab, India, and Ohio, USA. Journal of Crop Improvement, 23: 332-350.
doi: 10.1080/15427520902969906
[12]   FAO (Food and Agriculture Organization of the United Nations), IFAD (International Fund for Agricultural Development), UNICEF (United Nations Children's Fund), et al. 2021. The State of Food Security and Nutrition in the World 2021: Transforming Food Systems for Food Security, Improved Nutrition and Affordable Healthy Diets for All. Roma: FAO, 7-38.
[13]   Gao J J, Gai Q G, Liu B B, et al. 2021. Farm size and pesticide use: Evidence from agricultural production in China. China Agricultural Economic Review, 13(4): 912-929.
doi: 10.1108/CAER-11-2020-0279
[14]   Garnett T, Appleby M C, Balmford A, et al. 2013. Sustainable intensification in agriculture: Premises and policies. Science, 341(6141): 33-34.
doi: 10.1126/science.1234485 pmid: 23828927
[15]   Glackin S, Trubka R, Dionisio M R. 2016. A software-aided workflow for precinct-scale residential redevelopment. Environmental Impact Assessment Review, 60: 1-15.
doi: 10.1016/j.eiar.2016.04.002
[16]   Griffith D A. 1984. Theory of Spatial Statistics and Models. Dordrecht: Reidel Publishing Company, 3-15.
[17]   Han H B, Zhang X Y. 2020. Exploring environmental efficiency and total factor productivity of cultivated land use in China. Science of the Total Environment, 726: 138434, doi: 10.1016/j.scitotenv.2020.138434.
[18]   Heidenreich A, Grovermann C, Kadzere I, et al. 2022. Sustainable intensification pathways in Sub-Saharan Africa: Assessing eco-efficiency of smallholder perennial cash crop production. Agricultural Systems, 195: 103304, doi: 10.1016/j.agsy.2021.103304.
[19]   Hou X H, Liu J M, Zhang D J, et al. 2021. Effect of landscape-scale farmland fragmentation on the ecological efficiency of farmland use: a case study of the Yangtze River Economic Belt, China. Environmental Science and Pollution Research, 28: 26935-26947.
doi: 10.1007/s11356-021-12523-7
[20]   Huang C B, Zhao D Y, Fan X, et al. 2022. Landscape dynamics facilitated non-point source pollution control and regional water security of the Three Gorges Reservoir area, China. Environmental Impact Assessment Review, 92: 106696, doi: 10.1016/j.eiar.2021.106696.
[21]   Huang J H, Yang X G, Cheng G, et al. 2014. A comprehensive eco-efficiency model and dynamics of regional eco-efficiency in China. Journal of Cleaner Production, 67: 228-238.
doi: 10.1016/j.jclepro.2013.12.003
[22]   Huang Q Q, Rozelle S, Lohmar B, et al. 2006. Irrigation, agricultural performance and poverty reduction in China. Food Policy, 31(1): 30-52.
doi: 10.1016/j.foodpol.2005.06.004
[23]   Huang Z H, Du X J, Castillo C S Z. 2019. How does urbanization affect farmland protection? Evidence from China. Resources, Conservation and Recycling, 145: 139-147.
doi: 10.1016/j.resconrec.2018.12.023
[24]   Jawaduddin M, Memon S, Bheel N D, et al. 2019. Synthetic grey water treatment through FeCl3-activated carbon obtained from cotton stalks and river sand. Civil Engineering Journal, 5(2): 340-348.
doi: 10.28991/cej-2019-03091249
[25]   Kuang B, Lu X H, Zhou M, et al. 2020. Provincial cultivated land use efficiency in China: Empirical analysis based on the SBM-DEA model with carbon emissions considered. Technological Forecasting and Social Change, 151: 119874, doi: 10.1016/j.techfore.2019.119874.
[26]   Lambin E F, Gibbs H K, Ferreira L, et al. 2013. Estimating the world's potentially available cropland using a bottom-up approach. Global Environmental Change, 23(5): 892-901.
doi: 10.1016/j.gloenvcha.2013.05.005
[27]   Lefever D W. 1926. Measuring geographic concentration by means of the standard deviational ellipse. American Journal of Sociology, 32(1): 88-94.
doi: 10.1086/214027
[28]   Li B, Zhang J B, Li H P. 2011. Research on spatial-temporal characteristics and affecting factors decomposition of agricultural carbon emission in China. China Population, Resources and Environment, 21(8): 80-86. (in Chinese)
[29]   Li Y R, Fan P C, Liu Y S. 2019. What makes better village development in traditional agricultural areas of China? Evidence from long-term observation of typical villages. Habitat International, 83: 111-124.
doi: 10.1016/j.habitatint.2018.11.006
[30]   Liu Y S, Zou L L, Wang Y S. 2020. Spatial-temporal characteristics and influencing factors of agricultural eco-efficiency in China in recent 40 years. Land Use Policy, 97: 104794, doi: 10.1016/j.landusepol.2020.104794.
[31]   Luo X, Ao X H, Zhang Z, et al. 2020. Spatiotemporal variations of cultivated land use efficiency in the Yangtze River Economic Belt based on carbon emission constraints. Journal of Geographical Sciences, 30: 535-552.
doi: 10.1007/s11442-020-1741-8
[32]   Mamuse A, Porwal A, Kreuzer O, et al. 2009. A new method for spatial centrographic analysis of mineral deposit clusters. Ore Geology Reviews, 36(4): 293-305.
doi: 10.1016/j.oregeorev.2009.06.001
[33]   McMillan J, Whalley J, Zhu L. 1989. The impact of China's economic reforms on agricultural productivity growth. Journal of Political Economy, 97(4): 781-807.
doi: 10.1086/261628
[34]   National Bureau of Statistics of China. 2001-2021a China Statistical Yearbook. Beijing: China Statistical Press. (in Chinese)
[35]   National Bureau of Statistics of China. 2001-2021b. China Rural Statistical Yearbook. Beijing: China Statistical Press. (in Chinese)
[36]   Newbold T, Hudson L N, Hill S L L, et al. 2015. Global effects of land use on local terrestrial biodiversity. Nature, 520: 45-50.
doi: 10.1038/nature14324
[37]   Nitsch H, Osterburg B, Roggendorf W, et al. 2012. Cross compliance and the protection of grassland-Illustrative analyses of land use transitions between permanent grassland and arable land in German regions. Land Use Policy, 29(2): 440-448.
doi: 10.1016/j.landusepol.2011.09.001
[38]   Niu S D, Fang B. 2019. Cultivated land protection system in China from 1949 to 2019: Historical evolution, realistic origin exploration and path optimization. China Land Science, 33(10): 1-12. (in Chinese)
[39]   Nkansah M A, Donkoh M, Akoto O, et al. 2019. Preliminary studies on the use of sawdust and peanut shell powder as adsorbents for phosphorus removal from water. Emerging Science Journal, 3(1): 33-40.
doi: 10.28991/esj-2019-01166
[40]   Pastor J T, Lovell C A K. 2005. A global Malmquist productivity index. Economics Letters, 88(2): 266-271.
doi: 10.1016/j.econlet.2005.02.013
[41]   Post W M, Kwon K C. 2000. Soil carbon sequestration and land-use change: processes and potential. Global Change Biology, 6(3): 317-327.
doi: 10.1046/j.1365-2486.2000.00308.x
[42]   Potts S G, Biesmeijer J C, Kremen C, et al. 2010. Global pollinator declines: trends, impacts and drivers. Trends in Ecology & Evolution, 25(6): 345-353.
doi: 10.1016/j.tree.2010.01.007
[43]   Qi X X, Vitousek P M, Liu L M. 2015. Provincial food security in China: A quantitative risk assessment based on local food supply and demand trends. Food Security, 7(3): 621-632.
doi: 10.1007/s12571-015-0458-5
[44]   Quaye A K, Hall C A S, Luzadis V A. 2010. Agricultural land use efficiency and food crop production in Ghana. Environment, Development and Sustainability, 12(6): 967-983.
doi: 10.1007/s10668-010-9234-z
[45]   Ray D K, Ramankutty N, Mueller N D, et al. 2012. Recent patterns of crop yield growth and stagnation. Nature Communications, 3: 1293, doi: 10.1038/ncomms2296.
pmid: 23250423
[46]   Sabiha N E, Salim R, Rahman S. 2017. Eco-efficiency of high-yielding variety rice cultivation after accounting for on-farm environmental damage as an undesirable output: An empirical analysis from Bangladesh. Australian Journal of Agricultural and Resource Economics, 61(2): 247-264.
doi: 10.1111/ajar.2017.61.issue-2
[47]   Schaltegger S, Sturm A. 1990. Ecological rationality: starting points for the design of ecology-oriented management instruments. Operation, 44(4): 273-290. (in German)
[48]   Simar L, Wilson P W. 2007. Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136(1): 31-64.
doi: 10.1016/j.jeconom.2005.07.009
[49]   Song W, Pijanowski B C. 2014. The effects of China's cultivated land balance program on potential land productivity at a national scale. Applied Geography, 46: 158-170.
doi: 10.1016/j.apgeog.2013.11.009
[50]   State Council of China. 2009. China Pollution Source Census. Beijing: China Statistical Press. (in Chinese)
[51]   Sun J T, Pan L L, Tsang D C W, et al. 2018. Organic contamination and remediation in the agricultural soils of China: A critical review. Science of the Total Environment, 615: 724-740.
doi: 10.1016/j.scitotenv.2017.09.271
[52]   Tang L P, Ke X L, Zhou T, et al. 2020. Impacts of cropland expansion on carbon storage: A case study in Hubei, China. Journal of Environmental Management, 265: 110515, doi: 10.1016/j.jenvman.2020.110515.
[53]   Tobin J. 1958. Estimation of relationships for limited dependent variables. Econometrica, 26(1): 24-36.
doi: 10.2307/1907382
[54]   Todorovic M, Mehmeti A, Scardigno A. 2016. Eco-efficiency of agricultural water systems: Methodological approach and assessment at meso-level scale. Journal of Environmental Management, 165: 62-71.
doi: S0301-4797(15)30263-2 pmid: 26413800
[55]   Toma P, Miglietta P P, Zurlini G, et al. 2017. A non-parametric bootstrap-data envelopment analysis approach for environmental policy planning and management of agricultural efficiency in EU countries. Ecological Indicators, 83: 132-143.
doi: 10.1016/j.ecolind.2017.07.049
[56]   Tone K. 2001. A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3): 498-509.
doi: 10.1016/S0377-2217(99)00407-5
[57]   Tone K. 2002. A slacks-based measure of super-efficiency in data envelopment analysis. European Journal of Operational Research, 143(1): 32-41.
doi: 10.1016/S0377-2217(01)00324-1
[58]   Tone K. 2004. Dealing with undesirable outputs in DEA: A slacks-based measure (SBM) approach. National Graduate Institute for Policy Studies (GRIPS). Tokyo, Japan, 44-45.
[59]   van Uytvanck J, van Noyen A, Milotic T, et al. 2010. Woodland regeneration on grazed former arable land: A question of tolerance, defence or protection? Journal for Nature Conservation, 18(3): 206-214.
doi: 10.1016/j.jnc.2009.10.001
[60]   van Vliet J, de Groot H L F, Rietveld P, et al. 2015. Manifestations and underlying drivers of agricultural land use change in Europe. Landscape and Urban Planning, 133: 24-36.
doi: 10.1016/j.landurbplan.2014.09.001
[61]   Vanhulsel M, Beckx C, Janssens D, et al. 2011. Measuring dissimilarity of geographically dispersed space-time paths. Transportation, 38(1): 65-79.
doi: 10.1007/s11116-010-9286-9
[62]   Wang K Y, Zhang P Y. 2013. The research on impact factors and characteristic of cultivated land resources use efficiency-take Henan Province, China as a case study. IERI Procedia, 5: 2-9.
doi: 10.1016/j.ieri.2013.11.062
[63]   Wang J Y, Zhang Z W, Liu Y S. 2018. Spatial shifts in grain production increases in China and implications for food security. Land Use Policy, 74: 204-213.
doi: 10.1016/j.landusepol.2017.11.037
[64]   Wang J Y, Su D, Wu Q, et al. 2023. Study on eco-efficiency of cultivated land utilization based on the improvement of ecosystem services and emergy analysis. Science of the Total Environment, 882: 163489, doi: 10.1016/j.scitotenv.2023.163489.
[65]   Wang L J, Li H. 2014. Cultivated land use efficiency and the regional characteristics of its influencing factors in China: By using a panel data of 281 prefectural cities and the stochastic frontier production function. Geographical Research, 33(11): 1995-2004. (in Chinese)
[66]   Wang X B, Herzfeld T, Glauben T. 2007. Labor allocation in transition: Evidence from Chinese rural households. China Economic Review, 18(3): 287-308.
doi: 10.1016/j.chieco.2007.02.004
[67]   WBCSD (World Business Council for Sustainable Development). 1996. Eco-efficient: Leadership for Improved Economic and Environmental Performance. Antwerp: WBCSD, 1-16.
[68]   Weltin M, Zasada I, Piorr A, et al. 2018. Conceptualising fields of action for sustainable intensification - A systematic literature review and application to regional case studies. Agriculture, Ecosystems & Environment, 257: 68-80.
doi: 10.1016/j.agee.2018.01.023
[69]   West T O, Marland G. 2002. A synthesis of carbon sequestration, carbon emissions, and net carbon flux in agriculture: Comparing tillage practices in the United States. Agriculture, Ecosystems & Environment, 91(1-3): 217-232.
doi: 10.1016/S0167-8809(01)00233-X
[70]   Wong D W S. 1999. Several fundamentals in implementing spatial statistics in GIS: Using centrographic measures as examples. Geographic Information Sciences, 5(2): 163-174.
[71]   Wu F L, Li L, Zhang H L, et al. 2007. Effects of conservation tillage on net carbon flux from farm land ecosystems. Chinese Journal of Ecology, 26(12): 2035-2039. (in Chinese)
[72]   Xiao Y P, Ma D L, Zhang F T, et al. 2023. Spatiotemporal differentiation of carbon emission efficiency and influencing factors: From the perspective of 136 countries. Science of the Total Environment, 879: 163032, doi: 10.1016/j.scitotenv.2023.163032.
[73]   Xie H L, Chen Q R, Wang W, et al. 2018. Analyzing the green efficiency of arable land use in China. Technological Forecasting and Social Change, 133: 15-28.
doi: 10.1016/j.techfore.2018.03.015
[74]   Yadav D, Wang J Y. 2017. Modelling carbon dioxide emissions from agricultural soils in Canada. Environmental Pollution, 230: 1040-1049.
doi: S0269-7491(17)31159-4 pmid: 28764120
[75]   Yang B, Wang Z Q, Zou L, et al. 2021. Exploring the eco-efficiency of cultivated land utilization and its influencing factors in China's Yangtze River Economic Belt, 2001-2018. Journal of Environmental Management, 294: 112939, doi: 10.1016/j.jenvman.2021.112939.
[76]   Yang B, Zhang Z, Wu H. 2022. Detection and attribution of changes in agricultural eco-efficiency within rapid urbanized areas: A case study in the urban agglomeration in the middle reaches of Yangtze River, China. Ecological Indicators, 144: 109533, doi: 10.1016/j.ecolind.2022.109533.
[77]   Yang J, Huang Z H, Zhang X B, et al. 2013. The rapid rise of cross-regional agricultural mechanization services in China. American Journal of Agricultural Economics, 95(5): 1245-1251.
doi: 10.1093/ajae/aat027
[78]   Yang J, Wang H, Jin S Q, et al. 2016. Migration, local off-farm employment, and agricultural production efficiency: Evidence from China. Journal of Productivity Analysis, 45(3): 247-259.
doi: 10.1007/s11123-015-0464-9
[79]   Yin Y Q, Hou X H, Liu J M, et al. 2022. Detection and attribution of changes in cultivated land use ecological efficiency: A case study on Yangtze River Economic Belt, China. Ecological Indicators, 137: 108753, doi: 10.1016/j.ecolind.2022.108753.
[80]   Yue T X, Fan Z M, Liu J Y. 2005. Changes of major terrestrial ecosystems in China since 1960. Global and Planetary Change, 48(4): 287-302.
doi: 10.1016/j.gloplacha.2005.03.001
[81]   Zhang C Z, Su Y Y, Yang G Q, et al. 2020. Spatial-temporal characteristics of cultivated land use efficiency in major function-oriented zones: A case study of Zhejiang Province, China. Land, 9(4): 114, doi: 10.3390/land9040114.
[82]   Zhang F T, Tan H M, Zhao P, et al. 2022. What was the spatiotemporal evolution characteristics of high-quality development in China? A case study of the Yangtze River economic belt based on the ICGOS-SBM model. Ecological Indicators, 145: 109593, doi: 10.1016/j.ecolind.2022.109593.
[83]   Zhang J, Fu X L, Yan S P. 2017a. Symposium: Structural change, industrial upgrading and China's economic transformation. Economic Systems, 41(2): 163-164.
[84]   Zhang J F, Fang H, Wang H X, et al. 2017b. Energy efficiency of airlines and its influencing factors: A comparison between China and the United States. Resources, Conservation and Recycling, 125: 1-8.
doi: 10.1016/j.resconrec.2017.05.007
[85]   Zhang R R, Ma W M, Liu J J. 2021. Impact of government subsidy on agricultural production and pollution: A game-theoretic approach. Journal of Cleaner Production, 285: 124806, doi: 10.1016/j.jclepro.2020.124806.
[86]   Zhao Q Y, Bao H X H, Zhang Z L. 2021. Off-farm employment and agricultural land use efficiency in China. Land Use Policy, 101: 105097, doi: 10.1016/j.landusepol.2020.105097.
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