Journal of Resources and Ecology ›› 2021, Vol. 12 ›› Issue (2): 292-301.DOI: 10.5814/j.issn.1674-764x.2021.02.015
• Ecosystem Services and Ecological Risks of Land Resource • Previous Articles Next Articles
LI Yue1,3, WANG Jijun1,2,*(), HU Xiaoning4,*(
), ZHAO Xiaocui1
Received:
2020-05-26
Accepted:
2020-10-25
Online:
2021-03-30
Published:
2021-05-30
Contact:
WANG Jijun,HU Xiaoning
Supported by:
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.
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URL: http://www.jorae.cn/EN/10.5814/j.issn.1674-764x.2021.02.015
Fig. 2 Sequence diagram of the Grain for Green Program (GGP), agroecosystem, agroeconomic system, and agrosocial system. Note: “Weights” means the weights of Grain for Green Program and the agricultural eco-economic social system. GGP=Grain for Green Program, STXT=Agroecosystem, JJXT=Agroeconomic system, SHXT=Agrosocial system.
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* |
Table 1 Selection results of optimal lag orders
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 |
Table 2 Results of Granger causality test
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 |
Fig. 4 (A) Impact of the impulse response function of the GGP impact on the agroecosystem and (B) Impact of the impulse response function of the GGP impact on the agroeconomic system
Fig. 5 (A) Impact of the impulse response function of the GGP on the agrosocial system and (B) impact of the impulse response function of the agroecosystem on GGP
Fig. 6 (A) Impact of the impulse response function of the agroeconomic system on GGP and (B) impact of the impulse response function of the agrosocial system on GGP
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 |
Table 3 Results of variance decomposition of GGP
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 |
Period | GGP | STXT | JJXT | SHXT |
---|---|---|---|---|
1 | 34.03854 | 65.96146 | 0.000000 | 0.000000 |
2 | 24.16985 | 47.34999 | 26.74597 | 1.734188 |
3 | 22.66621 | 45.31722 | 29.97800 | 2.038576 |
4 | 23.87240 | 44.77885 | 29.48483 | 1.863920 |
5 | 20.98296 | 39.30572 | 37.74605 | 1.965261 |
6 | 21.01156 | 39.24508 | 37.71735 | 2.026013 |
7 | 18.12831 | 30.99178 | 47.64984 | 3.230058 |
8 | 16.21747 | 27.36007 | 52.56165 | 3.860810 |
9 | 16.48018 | 27.01160 | 52.57408 | 3.934142 |
10 | 16.88979 | 27.14635 | 52.10232 | 3.861549 |
11 | 17.20576 | 27.26732 | 51.69244 | 3.834473 |
12 | 18.57390 | 26.91517 | 50.71937 | 3.791569 |
13 | 18.39318 | 25.52646 | 52.21594 | 3.864421 |
14 | 18.18169 | 25.01597 | 52.90871 | 3.893628 |
15 | 18.16849 | 24.98703 | 52.95221 | 3.892270 |
16 | 18.02933 | 24.71425 | 53.35900 | 3.897418 |
17 | 18.48797 | 24.60392 | 53.03599 | 3.872119 |
18 | 19.04771 | 24.21242 | 52.86298 | 3.876884 |
19 | 19.06914 | 23.86512 | 53.14646 | 3.919277 |
20 | 19.02169 | 23.94952 | 53.09804 | 3.930749 |
Table 4 Results of variance decomposition of STXT
Period | GGP | STXT | JJXT | SHXT |
---|---|---|---|---|
1 | 34.03854 | 65.96146 | 0.000000 | 0.000000 |
2 | 24.16985 | 47.34999 | 26.74597 | 1.734188 |
3 | 22.66621 | 45.31722 | 29.97800 | 2.038576 |
4 | 23.87240 | 44.77885 | 29.48483 | 1.863920 |
5 | 20.98296 | 39.30572 | 37.74605 | 1.965261 |
6 | 21.01156 | 39.24508 | 37.71735 | 2.026013 |
7 | 18.12831 | 30.99178 | 47.64984 | 3.230058 |
8 | 16.21747 | 27.36007 | 52.56165 | 3.860810 |
9 | 16.48018 | 27.01160 | 52.57408 | 3.934142 |
10 | 16.88979 | 27.14635 | 52.10232 | 3.861549 |
11 | 17.20576 | 27.26732 | 51.69244 | 3.834473 |
12 | 18.57390 | 26.91517 | 50.71937 | 3.791569 |
13 | 18.39318 | 25.52646 | 52.21594 | 3.864421 |
14 | 18.18169 | 25.01597 | 52.90871 | 3.893628 |
15 | 18.16849 | 24.98703 | 52.95221 | 3.892270 |
16 | 18.02933 | 24.71425 | 53.35900 | 3.897418 |
17 | 18.48797 | 24.60392 | 53.03599 | 3.872119 |
18 | 19.04771 | 24.21242 | 52.86298 | 3.876884 |
19 | 19.06914 | 23.86512 | 53.14646 | 3.919277 |
20 | 19.02169 | 23.94952 | 53.09804 | 3.930749 |
Period | GGP | STXT | JJXT | SHXT |
---|---|---|---|---|
1 | 1.182166 | 31.04439 | 67.77345 | 0.000000 |
2 | 10.99956 | 21.93295 | 58.37821 | 8.689267 |
3 | 11.62723 | 29.13453 | 45.50244 | 13.73580 |
4 | 6.480872 | 11.59514 | 67.78314 | 14.14085 |
5 | 4.166707 | 7.265975 | 73.83517 | 14.73215 |
6 | 3.222419 | 5.346392 | 76.93101 | 14.50018 |
7 | 2.470198 | 4.137165 | 79.50457 | 13.88807 |
8 | 2.786844 | 3.767963 | 80.35815 | 13.08704 |
9 | 4.136604 | 3.577964 | 79.91008 | 12.37535 |
10 | 5.712280 | 3.458045 | 78.95150 | 11.87818 |
11 | 7.744052 | 3.304099 | 77.51841 | 11.43344 |
12 | 11.05443 | 3.362066 | 74.66244 | 10.92106 |
13 | 13.87833 | 3.315876 | 72.26145 | 10.54434 |
14 | 16.14107 | 3.224412 | 70.37941 | 10.25511 |
15 | 17.99782 | 3.146222 | 68.82362 | 10.03234 |
16 | 19.33046 | 3.086780 | 67.69297 | 9.889791 |
17 | 20.01023 | 3.068669 | 67.07638 | 9.844721 |
18 | 20.33374 | 3.048619 | 66.75265 | 9.864993 |
19 | 20.44495 | 2.996728 | 66.62714 | 9.931181 |
20 | 20.27283 | 2.931656 | 66.76228 | 10.03323 |
Table 5 Results of variance decomposition of JJXT
Period | GGP | STXT | JJXT | SHXT |
---|---|---|---|---|
1 | 1.182166 | 31.04439 | 67.77345 | 0.000000 |
2 | 10.99956 | 21.93295 | 58.37821 | 8.689267 |
3 | 11.62723 | 29.13453 | 45.50244 | 13.73580 |
4 | 6.480872 | 11.59514 | 67.78314 | 14.14085 |
5 | 4.166707 | 7.265975 | 73.83517 | 14.73215 |
6 | 3.222419 | 5.346392 | 76.93101 | 14.50018 |
7 | 2.470198 | 4.137165 | 79.50457 | 13.88807 |
8 | 2.786844 | 3.767963 | 80.35815 | 13.08704 |
9 | 4.136604 | 3.577964 | 79.91008 | 12.37535 |
10 | 5.712280 | 3.458045 | 78.95150 | 11.87818 |
11 | 7.744052 | 3.304099 | 77.51841 | 11.43344 |
12 | 11.05443 | 3.362066 | 74.66244 | 10.92106 |
13 | 13.87833 | 3.315876 | 72.26145 | 10.54434 |
14 | 16.14107 | 3.224412 | 70.37941 | 10.25511 |
15 | 17.99782 | 3.146222 | 68.82362 | 10.03234 |
16 | 19.33046 | 3.086780 | 67.69297 | 9.889791 |
17 | 20.01023 | 3.068669 | 67.07638 | 9.844721 |
18 | 20.33374 | 3.048619 | 66.75265 | 9.864993 |
19 | 20.44495 | 2.996728 | 66.62714 | 9.931181 |
20 | 20.27283 | 2.931656 | 66.76228 | 10.03323 |
Period | GGP | STXT | JJXT | SHXT |
---|---|---|---|---|
1 | 18.87098 | 6.760140 | 70.84797 | 3.520917 |
2 | 26.76893 | 9.248055 | 60.95672 | 3.026292 |
3 | 27.11706 | 14.34652 | 55.47644 | 3.059981 |
4 | 25.22953 | 14.47205 | 56.99748 | 3.300943 |
5 | 27.17946 | 15.90058 | 53.79811 | 3.121845 |
6 | 28.83369 | 15.67092 | 52.44756 | 3.047826 |
7 | 28.17519 | 15.86272 | 52.85195 | 3.110131 |
8 | 27.65035 | 15.82999 | 53.31729 | 3.202368 |
9 | 27.11451 | 15.50274 | 54.21229 | 3.170458 |
10 | 27.29663 | 15.57717 | 54.00622 | 3.119978 |
11 | 26.90601 | 15.32391 | 54.48380 | 3.286285 |
12 | 25.86527 | 14.93348 | 55.65069 | 3.550558 |
13 | 25.11885 | 14.50791 | 56.65721 | 3.716026 |
14 | 25.24391 | 14.68999 | 56.33951 | 3.726583 |
15 | 25.23326 | 14.68276 | 56.34445 | 3.739534 |
16 | 25.51067 | 14.83210 | 55.93534 | 3.721885 |
17 | 25.55084 | 14.69503 | 56.01712 | 3.737012 |
18 | 25.41625 | 14.43714 | 56.38646 | 3.760148 |
19 | 25.49555 | 14.37880 | 56.36660 | 3.759053 |
20 | 25.49843 | 14.36618 | 56.37778 | 3.757611 |
Table 6 Results of variance decomposition of SHXT
Period | GGP | STXT | JJXT | SHXT |
---|---|---|---|---|
1 | 18.87098 | 6.760140 | 70.84797 | 3.520917 |
2 | 26.76893 | 9.248055 | 60.95672 | 3.026292 |
3 | 27.11706 | 14.34652 | 55.47644 | 3.059981 |
4 | 25.22953 | 14.47205 | 56.99748 | 3.300943 |
5 | 27.17946 | 15.90058 | 53.79811 | 3.121845 |
6 | 28.83369 | 15.67092 | 52.44756 | 3.047826 |
7 | 28.17519 | 15.86272 | 52.85195 | 3.110131 |
8 | 27.65035 | 15.82999 | 53.31729 | 3.202368 |
9 | 27.11451 | 15.50274 | 54.21229 | 3.170458 |
10 | 27.29663 | 15.57717 | 54.00622 | 3.119978 |
11 | 26.90601 | 15.32391 | 54.48380 | 3.286285 |
12 | 25.86527 | 14.93348 | 55.65069 | 3.550558 |
13 | 25.11885 | 14.50791 | 56.65721 | 3.716026 |
14 | 25.24391 | 14.68999 | 56.33951 | 3.726583 |
15 | 25.23326 | 14.68276 | 56.34445 | 3.739534 |
16 | 25.51067 | 14.83210 | 55.93534 | 3.721885 |
17 | 25.55084 | 14.69503 | 56.01712 | 3.737012 |
18 | 25.41625 | 14.43714 | 56.38646 | 3.760148 |
19 | 25.49555 | 14.37880 | 56.36660 | 3.759053 |
20 | 25.49843 | 14.36618 | 56.37778 | 3.757611 |
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. |
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