Synergic Relationship between the Grain for Green Program and the Agricultural Eco-economic System in Ansai County based on the VAR model

doi:10.5814/j.issn.1674-764x.2021.02.015

Abstract

Abstract:

Understanding the synergic relationship between the Grain for Green Program (GGP) and the agricultural eco-economic system is important for designing an optimized agricultural eco-economic system and developing a highly efficient structure of an agricultural industry chain and a resource chain. This study used Ansai County time series data from 1995 to 2014, applied vector autoregressive (VAR) models and used tools such as Granger causality, impulse response analysis and variance decomposition, to explore the synergy between the GGP and the agricultural eco-economic system. The results revealed a synergic and reciprocal relationship between the GGP and the agroeconomic system. The contribution of the GGP to the agroecosystem reached 34%, which was significantly higher than either its largest contribution to the agroeconomic system (20.8%) or its peak contribution to the agrosocial system (26.7%). The agroeconomic system had the most prominent influence on the GGP, with a year-round stable contribution of up to 55.3%. These results were consistent with reality. However, the impact of the GGP on the agricultural eco-economic system was weaker than the effect of the agricultural eco-economic system on the GGP. The lag of variable stationarity after the shock was relatively short, indicating that optimal coupling had not formed between the GGP and the agricultural eco-economic system. On the basis of enhancing the ecological functions, we should construct the agricultural industry-resource chain such that it focuses on promoting the effective utilization of resources in the region. In addition, the development of a carbon sink industry can be used to manifest the ecological values of ecological functions.

Key words: Grain for Green Program (GGP), agroecosystem, agroeconomic system, agrosocial system, collaborative analysis, vector autoregressive model, Ansai County

LI Yue, WANG Jijun, HU Xiaoning, ZHAO Xiaocui. Synergic Relationship between the Grain for Green Program and the Agricultural Eco-economic System in Ansai County based on the VAR model[J]. Journal of Resources and Ecology, 2021, 12(2): 292-301.

Figures/Tables 12

References 22

1	Bennett M T . 2008. China’s sloping land conversion program: Institutional innovation or business as usual? Ecological Economics, 65(4): 699‒711.
2	Gao L, Yang X K, Hu H Z , et al. 2019. Analysis on ecological and economic benefits of implementing the Project of Returning Farmlands to Forests in Chongqing City. Research of Soil and Water Conservation, 26(6): 353‒358. (in Chinese)
3	Gutiérrez R L, Hogarth N J, Zhou W , et al. 2016. China’s conversion of cropland to forest program: A systematic review of the environmental and socioeconomic effects. Environmental Evidence, 5:21. DOI: 10.1186/s13750-016-0071-x.
4	Hosseini S D, Verma M . 2017. A Value-at-Risk (VAR) approach to routing rail hazmat shipments. Transportation Research Part D: Transport and Environment, 54(7): 191‒211.
5	Jindal R, Swallow B, Kerr J . 2008. Forestry-based carbon sequestration projects in Africa: Potential benefits and challenges. Natural Resources Forum, 32(2):116‒130.
6	Li Q R, Amjath-Babu T S, Sieber S , et al. 2018a. Assessing divergent consequences of payments for ecosystem serviceson rural livelihoods: A case-study in China’s Loess Hills. Land Degradation & Development, 29(10): 3549‒3570.
7	Li Q R, Liu Z, Zander P , et al. 2016. Does farmland conversion improve or impair household livelihood in smallholder agriculture system? A case study of Grain for Green Project impacts in China’s Loess Plateau. World Development Perspectives, 2: 43‒54.
8	Li Y, Wang J J, Liu P L , et al. 2018b. Study on the synergic relationship between grain for green and agricultural eco-economic social system—A case study in Ansai County. Journal of Natural Resources, 33(7): 1179‒1190. (in Chinese)
9	Lyu C H, Xu Z Y . 2020. Crop production changes and the impact of Grain for Green Program in the Loess Plateau of China. Journal of Arid Land. 12(1): 18‒28.
10	National Forestry and Grassland Administration of China. 2017. China forestry development report(2016). Beijing, China: China Forestry Publishing House. (in Chinese?)
11	Silver W L, Ostertag R, Lugo A E . 2000. The potential for carbon sequestration through reforestation of abandoned tropical agricultural and pasture lands. Restoration Ecology, 8(4): 394‒407.
12	Sims C A . 1972. Money, income, and causality. American Economic Review, 62(4): 540‒552.
13	Sims C A . 1980. Macroeconomics and reality. Econometrica, 48(1):1-48.
14	Tiwari A K, Cai Y F, Chang T . 2019. Monetary shocks to macroeconomic variables in China using time-vary VAR model. Applied Economics Letters, 26(20): 1664‒1669.
15	Wang J J, Guo M C, Jiang Z D , et al. 2010. The construction and application of an agricultural ecological-economic system coupled process model. Acta Ecologica Sinica, 30(9): 2371‒2378. (in Chinese)
16	Wang S, Yue X M . 2017. The Grain-for-Green Project, non-farm employment, and the growth of farmer income. Economic Research Journal, 52(4): 106‒119.
17	Wang X H, Shen J X, Zhang W . 2014. Emergy evaluation of agricultural sustainability of Northwest China before and after the Grain-for-Green Policy. Energy Policy, 67: 508‒516.
18	Wang Y J, Jiang Z D, Wang J J . 2015. Impact of sloping land conversion program on coupling of agricultural ecological system. Systems Engineering — Theory & Practice, 35(12): 3155‒3163.(in Chinese)
19	Wang Y. 2018. Green development strategy and challenges in agriculture.http://www.h2ochina.com/news/282165.html. [2019-02-15].
20	Wei X H, Zheng J, Liu G H , et al. 2015. The concept and application of carbon sequestration potentials in plantation forests. Acta Ecologica Sinica, 35(12):3881-3885. (in Chinese)
21	Zhang Y Q . 2012. On the identification issues of the structural VAR models: A review. Journal of Applied Statistics and Management, 31(5): 805‒812. (in Chinese)
22	Zinda J A, Trac C J, Zhai D L , et al. 2017. Dual-function forests in the returning farmland to forest program and the flexibility of environmental policy in China. Geoforum, 78: 119‒132.

Lag	ln L	LR	FPE	AIC	SC	HQ
0	95.4200	NA	2.51E-10	‒10.7553	‒10.5593	‒10.7358
1	162.4732	94.6633	6.63E-13	‒16.7616	‒15.7813	‒16.6641
2	196.2529	31.7924	1.23E-13	‒18.8533	‒17.0888	‒18.6779
3	268.7519	34.1172^*	6.90E-16^*	‒25.5002^*	‒22.9516^*	‒25.2469^*

Lag	ln L	LR	FPE	AIC	SC	HQ
0	95.4200	NA	2.51E-10	‒10.7553	‒10.5593	‒10.7358
1	162.4732	94.6633	6.63E-13	‒16.7616	‒15.7813	‒16.6641
2	196.2529	31.7924	1.23E-13	‒18.8533	‒17.0888	‒18.6779
3	268.7519	34.1172^*	6.90E-16^*	‒25.5002^*	‒22.9516^*	‒25.2469^*

Excluded	Chi-sq	df	Prob.
STXT is not a Granger cause of GGP	30.24180	3	0.0000
JJXT is not a Granger cause of GGP	14.37835	3	0.0024
SHXT is not a Granger cause of GGP	24.83216	3	0.0000
None of the three is a Granger cause of GGP	162.7878	9	0.0000
GGP is not a Granger cause of STXT	0.205770	3	0.0047
GGP is not a Granger cause of JJXT	13.55359	3	0.0036
GGP is not a Granger cause of STXT	24.04031	3	0.0000

Excluded	Chi-sq	df	Prob.
STXT is not a Granger cause of GGP	30.24180	3	0.0000
JJXT is not a Granger cause of GGP	14.37835	3	0.0024
SHXT is not a Granger cause of GGP	24.83216	3	0.0000
None of the three is a Granger cause of GGP	162.7878	9	0.0000
GGP is not a Granger cause of STXT	0.205770	3	0.0047
GGP is not a Granger cause of JJXT	13.55359	3	0.0036
GGP is not a Granger cause of STXT	24.04031	3	0.0000

Period	GGP	STXT	JJXT	SHXT
1	100.0000	0.000000	0.000000	0.000000
2	46.97864	0.275452	49.99727	2.748641
3	36.93287	0.189110	59.72816	3.149855
4	43.82030	5.560389	48.10528	2.514029
5	41.47807	6.651222	49.29604	2.574670
6	40.36725	7.864319	49.24901	2.519428
7	40.45021	7.857298	49.18690	2.505590
8	41.92192	7.802659	47.72272	2.552702
9	41.10650	7.647614	48.40435	2.841536
10	36.85136	6.952093	52.65719	3.539356
11	36.46208	6.884060	52.88802	3.765845
12	35.97839	6.968095	53.10793	3.945588
13	35.60862	6.868993	53.39047	4.131913
14	34.46051	6.724904	54.42927	4.385313
15	33.55404	6.487351	55.38504	4.573565
16	32.90078	6.334881	56.07234	4.691998
17	32.72247	6.247984	56.27963	4.749917
18	33.17266	6.238183	55.84694	4.742213
19	33.58538	6.178893	55.49491	4.740820
20	33.84172	6.066398	55.33573	4.756155