Spatiotemporal variations of cultivated land use efficiency in the Yangtze River Economic Belt based on carbon emission constraints

doi:10.1007/s11442-020-1741-8

Abstract

Abstract:

In this study, the carbon emissions (CEs) from cultivated land (CL) were included as an undesirable output in the utilization efficiency of such land. A slack-based model was used to calculate the CL use efficiency (CLUE) for 11 provinces and cities in the Yangtze River Economic Belt (YREB) from 2007 to 2016, and then a kernel density estimation map was drawn to analyze the spatiotemporal variations of CLUE. The Tobit model was also employed to analyze the factors affecting the CLUE. The results show the following. 1) In the YREB, the CEs from CL showed a rising and then a slowly decreasing trend. In this paper, we calculate CEs by carbon emission factors and major carbon sources, and the CEs from CL in the YREB totaled 25.2354 million tons in 2007. By 2014, the value had increased gradually to 28.4400 million tons, and by 2016 it had declined to 27.8922 million tons, suggesting that the carbon-emission reduction measures of the government had an impact. 2) The CLUE of various provinces and cities in the YREB showed an upward trend in the time dimension, while for the spatial dimension, the kernel density was high in the east and low in the west, and the areas with high kernel density were mainly located in the Yangtze River Delta. 3) The per capita gross domestic product, the primary industrial output, and the number of agricultural technicians per 10,000 people had positive effects on the CLUE. The CL area per capita and the electrical power per hectare for agricultural machinery had significant negative impacts on CLUE. In addition, every 1% increase in the number of agricultural technicians increased the CLUE by 0.057%.

Key words: Yangtze River Economic Belt, carbon emissions, cultivated land use efficiency, Tobit model

LUO Xiang, AO Xinhe, ZHANG Zuo, WAN Qing, LIU Xingjian. Spatiotemporal variations of cultivated land use efficiency in the Yangtze River Economic Belt based on carbon emission constraints[J].Journal of Geographical Sciences, 2020, 30(4): 535-552.

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References 52

[1]	Banker R D, Charnes A, Cooper W W , 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9):1078-1092.
[2]	Battese G E, Coelli T J , 1992. Frontier production functions, technical efficiency and panel data: With application to paddy farmers in India. Journal of Productivity Analysis, 3:153-169.
[3]	Brown L R , 1995. Who Will Feed China? Wake-up Call for a Small Planet. London: England Earthscan Publications.
[4]	Buyanovsky G A, Wagner G H , 1998. Carbon cycling in cultivated land and its global significance. Global Change Biology, 4(2):131-141.
[5]	Chabi-Olaye A, Nolte C, Schulthess F et al., 2005. Relationships of intercropped maize, stem borer damage to maize yield and land-use efficiency in the humid forest of Cameroon. Bulletin of Entomological Research, 95(5):417-427.
[6]	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
[7]	Chen Y J, Xiao B L, Fang L N et al., 2011. The quality analysis of cultivated land in China. Scientia Agricultura Sinica, 44(17):3557-3564. (in Chinese) doi: doi: 10.3864/j.issn.0578-1752.2011.17.008
[8]	Chen Y S, Zhang S H, Huang D S et al., 2017. The development of China’s Yangtze River Economic Belt: How to make it in a green way? Science Bulletin, 62(9):648-651. doi: 10.1016/j.scib.2017.04.009
[9]	Dalgaard T, Halberg N, Porter J R , 2001. A model for fossil energy use in Danish agriculture used to compare organic and conventional farming. Agriculture Ecosystems and Environment, 87(1):51-65.
[10]	Deng X Z, Huang J K, Rozelle S et al., 2005. Cultivated land conversion and potential agricultural productivity in China. Land Use Policy, 23(4):372-384. doi: 10.1016/j.landusepol.2005.07.003
[11]	Deng X Z, Huang J K, Rozelle S et al., 2015. Impact of urbanization on cultivated land changes in China. Land Use Policy, 45:1-7.
[12]	Feng T, Zhang F R, Li C et al., 2014. Spatial distribution of prime farmland based on cultivated land quality comprehensive evaluation at county scale. Transactions of the Chinese Society of Agricultural Engineering, 30(1):200-210, 293. (in Chinese)
[13]	Gregg J S, Andres R J, Marland G , 2008. China: Emissions pattern of the world leader in CO₂ emissions from fossil fuel consumption and cement production. Geophysical Research Letters, 35(8):135-157.
[14]	Guo J, Zhang Z K, Meng L , 2012. China’s provincial CO₂ emissions embodied in international and interprovincial trade. Energy Policy, 42(C):486-497.
[15]	Huang J K, Deng X Z, Rozelle S , 2004. Cultivated land conversion and bio-productivity in China. Proceedings of SPIE - The International Society for Optical Engineering, 5544.
[16]	Jin G, Chen K, Wang P et al., 2019. Trade-offs in land-use competition and sustainable land development in the North China Plain. Technological Forecasting and Social Change, 141:36-46.
[17]	Jin G, Deng X Z, Chu X et al., 2017. Optimization of land-use management for ecosystem service improvement: A review. Physics and Chemistry of the Earth, 101:70-77.
[18]	Jin G, Deng X Z, Zhao X D et al., 2018. Spatiotemporal patterns in urbanization efficiency within the Yangtze River Economic Belt between 2005 and 2014. Journal of Geographical Sciences, 28(8):1113-1126.
[19]	Jin G, Li Z H, Deng X Z et al., 2019. An analysis of spatiotemporal patterns in Chinese agricultural productivity between 2004 and 2014. Ecological Indicators, 105:591-600.
[20]	Jin J J, Jiang C, Li L , 2013. The economic valuation of cultivated land protection: A contingent valuation study in Wenling City, China. Landscape and Urban Planning, 119:158-164.
[21]	Johnson M F, Franzluebbers A J, Weyers S L et al., 2007. Agricultural opportunities to mitigate greenhouse gas emissions. Environmental Pollution, 150(1):107-124.
[22]	Kaoru T , 2001. A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3):498-509.
[23]	Lee J, Six J, King A P , et al., 2006. Tillage and field scale controls on greenhouse gas emissions. Journal of Environmental Quality, 35(3):714-725. doi: 10.2134/jeq2005.0337
[24]	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)
[25]	Li H, Shi J F , 2014. Energy efficiency analysis on Chinese industrial sectors: An improved Super-SBM model with undesirable outputs. Journal of Cleaner Production, 65:97-107. doi: 10.1016/j.jclepro.2013.09.035
[26]	Li S C, Bing Z L, Jin G , 2019. Spatially explicit mapping of soil conservation service in monetary units due to land use/cover change for the Three Gorges Reservoir Area, China. Remote Sensing, 11(4):1-21. doi: 10.3390/rs11010001
[27]	Li Z J, Yu X K, Gong C J , 2013. Intensity change in cultivated land use in Shandong province from 1980 to 2010. Information Engineering Research Institute, 5:140-146.
[28]	Lin H C Hülsbergen, Kurt-Jürgen, , 2017. A new method for analyzing agricultural land-use efficiency, and its application in organic and conventional farming systems in southern Germany. European Journal of Agronomy, 83:15-27. doi: 10.1016/j.eja.2016.11.003
[29]	Liu H G, Liu W D, Fan X M , et al., 2015. Carbon emissions embodied in value added chains in China. Journal of Cleaner Production, 103:362-370. doi: 10.1016/j.jclepro.2014.09.077
[30]	Liu X W, Zhao C L, Song W , 2017. Review of the evolution of cultivated land protection policies in the period following China’s reform and liberalization. Land Use Policy, 67:660-669. doi: 10.1016/j.landusepol.2017.07.012
[31]	Lyle G, Bryan B A, Ostendorf B , 2015. Identifying the spatial and temporal variability of economic opportunity costs to promote the adoption of alternative land uses in grain growing agricultural areas: An Australian example. Journal of Environmental Management, 155:123-135. doi: 10.1016/j.jenvman.2015.02.006
[32]	Pan J H, Phillips J, Chen Y , 2008. China’s balance of emissions embodied in trade: Approaches to measurement and allocating international responsibility. Oxford Review of Economic Policy, 24(2):354-376. doi: 10.1093/oxrep/grn016
[33]	Paustian K, Cole C V, Sauerbeck D , et al., 1998. CO₂ mitigation by agriculture: An overview. Climatic Change, 40(1):135-162. doi: 10.1023/A:1005347017157
[34]	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
[35]	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
[36]	Skevas T, Stefanou S E, Oude Lansink A , 2014. Pesticide use, environmental spillovers and efficiency: A DEA risk-adjusted efficiency approach applied to Dutch arable farming. European Journal of Operational Research, 237(2):658-664. doi: 10.1016/j.ejor.2014.01.046
[37]	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
[38]	Tobin J , 1958. Estimation of relationships for limited dependent variables. Econometrica, 26(1):24-36. doi: 10.2307/1907382
[39]	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
[40]	Wang G G, Liu Y S, Li Y R et al., 2015. Dynamic trends and driving forces of land use intensification of cultivated land in China. Journal of Geographical Sciences, 25(1):45-57. doi: 10.1007/s11442-015-1152-4
[41]	Wang K Y , 2013. The research on impact factors and characteristic of cultivated land resources use efficiency: Take Henan province, China as a case study. Information Engineering Research Institute, 5:2-9.
[42]	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 and Environment, 91(1-3):217-232. doi: 10.1016/S0167-8809(01)00233-X
[43]	Wu F L, Li L, Zhang H L et al., 2007. Effects of conservation tillage on net carbon flux from farmland ecosystems. Chinese Journal of Ecology, 26(12):2035-2039. (in Chinese)
[44]	Xu X B, Hu H Z, Tan Y et al., 2019. Quantifying the impacts of climate variability and human interventions on crop production and food security in the Yangtze River Basin, China, 1990-2015. Science of The Total Environment, 665:379-389. doi: 10.1016/j.scitotenv.2019.02.118
[45]	Yang H, Li X B , 2000. Cultivated land and food supply in China. Land Use Policy, 17(2):73-88. doi: 10.1016/S0264-8377(00)00008-9
[46]	Yang S, Li S P, Luo L , 2011. Study on the farmland use efficiency and its influencing factors in Shanxi province. China Land Science, 25(2):47-54. (in Chinese)
[47]	Yao X, Zhou H C, Zhang A Z et al., 2015. Regional energy efficiency, carbon emission performance and technology gaps in China: A meta-frontier non-radial directional distance function analysis. Energy Policy, 84:142-154. doi: 10.1016/j.enpol.2015.05.001
[48]	Ye H, Pu L J , 2011. Study on the cultivated land use efficiency between different regions of China and its convergence. Journal of Natural Resources, 26(9):1467-1474. (in Chinese) doi: 10.11849/zrzyxb.2011.09.002
[49]	Zhang J R, Zeng W H, Wang J N , et al., 2017. Regional low-carbon economy efficiency in China: Analysis based on the Super-SBM model with CO₂ emissions. Journal of Cleaner Production, 163:202-211. doi: 10.1016/j.jclepro.2015.06.111
[50]	Zhang L, Zhang F R, An P L , et al., 2008. Comparative study of cultivated land use intensive degree and its change law at different economic levels. Transactions of the Chinese Society of Agricultural Engineering, 24(1):108-112. (in Chinese)
[51]	Zhang N, Gao Z Q, Wang X M et al., 2010. Modeling the impact of urbanization on the local and regional climate in Yangtze River Delta, China. Theoretical and Applied Climatology, 102(3/4):331-342. doi: 10.1007/s00704-010-0263-1
[52]	Zhang R T, Jiao H F , 2015. Spatial-temporal pattern differentiation and its mechanism analysis of using efficiency for provincial cultivated land in China. Transactions of the Chinese Society of Agricultural Engineering, 31(2):277-287. (in Chinese)

Source	Coefficient	Unit	Reference
Tillage	312.6	kg/km²	Wu et al., 2007
Machinery	0.18	kg/kW	West et al., 2002
Fertilizers	0.8956	kg/kg	West et al., 2002
Pesticides	4.9341	kg/kg	Post et al., 2000
Plastic sheets	5.18	kg/kg	Li et al., 2011
Irrigation	25	kg/hm²	Li et al., 2011

	Indicators	Data sources
Input	I₁	China Rural Statistical Yearbook (2008-2017)
	I₂	China Rural Statistical Yearbook (2008-2017)
	I₃	China Rural Statistical Yearbook (2008-2017)
	I₄	China Rural Statistical Yearbook (2008-2017)
	I₅	China Rural Statistical Yearbook (2008-2017)
	I₆	China Rural Statistical Yearbook (2008-2017)
	I₇	China Rural Statistical Yearbook (2008-2017)
Output	O₁	China Rural Statistical Yearbook (2008-2017)
	O₂	China Rural Statistical Yearbook (2008-2017)
	O₃	$E=\sum{{{E}_{i}}}=\sum{{{T}_{i}}\cdot {{\delta }_{i}}}$, where T_i and δ_i are the values of each carbon source and the CE coefficient, respectively.
Influencing factors	PC	Land Survey Results Sharing Application Service Platform
	PG	Statistical yearbooks of the provinces and cities in the YREB from 2008 to 2017
	PP	Statistical yearbooks of the provinces and cities in the YREB from 2008 to 2017
	MP	Statistical yearbooks of the provinces and cities in the YREB from 2008 to 2017
	AT	EPS data platform
	PI	China Environmental Protection Database

Regions	Year										Average
Regions	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	Average
Shanghai	28.29	28.55	26.09	25.63	25.11	23.36	22.82	22.02	20.98	19.60	24.24
Jiangsu	408.22	408.29	415.04	414.50	412.49	409.01	405.91	404.10	396.96	389.90	406.44
Zhejiang	145.38	147.53	149.13	148.23	149.11	150.71	151.63	147.96	145.89	139.67	147.52
Anhui	374.26	379.77	386.80	398.38	410.56	416.34	425.00	426.57	423.85	410.36	405.19
Jiangxi	189.09	195.18	199.31	206.38	207.07	209.46	209.83	209.38	210.11	207.22	204.30
Hubei	373.63	401.05	413.84	425.33	429.67	429.97	422.50	420.44	406.07	397.02	411.95
Hunan	295.49	301.53	311.39	318.65	325.22	335.36	337.70	337.73	336.21	334.77	323.40
Chongqing	103.84	108.08	113.40	114.42	119.03	119.84	120.78	121.96	122.94	121.38	116.57
Sichuan	304.20	310.62	319.02	321.84	328.94	332.38	330.97	331.29	331.91	331.06	324.23
Yunnan	201.27	216.21	222.93	238.92	257.23	274.89	285.33	296.04	302.46	307.35	260.26
Huizhou	99.88	109.56	112.01	107.26	117.48	122.94	122.89	127.33	130.09	130.90	118.03
Total	2523.54	2606.35	2668.97	2719.56	2781.90	2824.26	2835.34	2844.84	2827.47	2789.22	2742.14

Region	2007		2010		2013		2016
Region	CCR	SBM	CCR	SBM	CCR	SBM	CCR	SBM
Shanghai	0.9135	0.6728	0.9844	0.8853	1	1	1	1
Jiangsu	1	1	0.9822	0.7922	0.9986	0.9338	1	1
Zhejiang	0.6957	0.4185	0.7997	0.5749	0.9321	0.7798	1	1
Anhui	0.9006	0.5860	0.9113	0.6419	0.8974	0.6633	0.9527	0.7483
Jiangxi	1	1	0.9653	0.7987	1	1	1	1
Hubei	0.8922	0.5689	0.9017	0.5717	0.9576	0.7586	1	1
Hunan	0.9487	0.7318	0.9482	0.7720	0.9401	0.7738	1	1
Chongqing	0.9905	0.9267	0.9824	0.9087	0.9974	0.9319	1	1
Sichuan	1	1	0.9701	0.8881	0.9784	0.9371	1	1
Yunnan	0.7185	0.4993	0.6519	0.4753	0.7441	0.5431	0.7821	0.5461
Huizhou	1	1	0.9819	0.8595	0.8672	0.7141	1	1

Variables	Coefficient	Std. Err.	Z	Significance
PC	-0.0004409	0.0001838	-2.4	0.016
PG	3.14E-06	1.08E-06	2.9	0.004
PP	0.0000704	0.0000279	2.52	0.012
MP	-0.0208301	0.0070656	-2.95	0.003
AT	0.0573215	0.0258476	2.22	0.027
PI	-1.16E-06	0.0000392	-0.03	0.976