Research Article
Modelling Analysis of Forestry Input-Output Elasticity in China
Table 2
Local Moran’s index based on space weight matrix
as well as test results.
| Province | | |
| Beijing | 0.3633 | 0.27 | Tianjing | 0.3745 | 0.23 | Hebei | 0.0130 | 0.48 | Shanxi | 0.2943 | 0.29 | Neimenggu | 0.3060 | 0.14 | Liaoning | −0.0611 | 0.39 | Jilin | 0.0189 | 0.31 | Heilongjiang | 0.0534 | 0.41 | Shanghai | −0.0002 | 0.03 | Jiangsu | −0.0382 | 0.04 | Zhejiang | 0.0251 | 0.06 | Anhui | 1.2997 | 0.01 | Fujian | −0.1454 | 0.01 | Jiangxi | 0.7301 | 0.02 | Shandong | 0.6203 | 0.11 | Henan | 0.3067 | 0.24 | Hubei | −0.2512 | 0.49 | Hunan | 0.3949 | 0.1 | Guangdong | 2.3471 | 0.04 | Guangxi | 0.6460 | 0.07 | Hainan | 0.0000 | 0.01 | Chongqing | −0.9785 | 0.46 | Sichuan | 0.3683 | 0.01 | Guizhou | 0.0096 | 0.31 | Yunnan | 0.2826 | 0.45 | Xizang | 1.0362 | 0.22 | Shaanxi | 0.4459 | 0.15 | Gansu | 0.4282 | 0.09 | Qinghai | 0.1428 | 0.04 | Ningxia | 0.2303 | 0.09 | Xinjiang | 0.4537 | 0.01 |
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Note: is local Moran’s index; is the adjoint probability of .
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