Table 4 Efficiency values and slack values in the 31 provinces of China in 2015
Provinces | Overall efficiency | Technical efficiency | scale efficiency | Type of scale efficiency | S1− | S2− | S3− | S1+ | S2+ | S3+ | Relatively efficiency status |
|---|---|---|---|---|---|---|---|---|---|---|---|
Beijing | 1.000 | 1.000 | 1.000 | − | 0 | 0 | 0 | 0 | 0 | 0 | Efficient |
Tianjin | 1.000 | 1.000 | 1.000 | − | 0 | 0 | 0 | 0 | 0 | 0 | Efficient |
Hebei | 0.806 | 0.817 | 0.986 | DRS | 37,342 | 62,436 | 97,330 | 0 | 0 | 4.378 | Inefficient |
Shanxi | 0.588 | 0.591 | 0.994 | IRS | 26,227 | 74,891 | 120,527 | 0 | 0 | 9.331 | Inefficient |
Inner Mongolia | 0.647 | 0.653 | 0.990 | IRS | 12,732 | 46,397 | 73,638 | 0 | 0 | 12.519 | Inefficient |
Liaoning | 0.762 | 0.775 | 0.984 | DRS | 11,938 | 66,044 | 78,509 | 0 | 0 | 3.887 | Inefficient |
Jilin | 0.706 | 0.709 | 0.995 | IRS | 8444 | 42,010 | 62,271 | 0 | 0 | 7.537 | Inefficient |
Heilongjiang | 0.759 | 0.761 | 0.998 | IRS | 4965 | 53,164 | 68,410 | 18,611,938 | 0 | 7.441 | Inefficient |
Shanghai | 1.000 | 1.000 | 1.000 | − | 0 | 0 | 0 | 0 | 0 | 0 | Efficient |
Jiangsu | 0.967 | 1.000 | 0.967 | DRS | 0 | 0 | 0 | 0 | 0 | 0 | Weakly efficient |
Zhejiang | 0.943 | 1.000 | 0.943 | DRS | 0 | 0 | 0 | 0 | 0 | 0 | Weakly efficient |
Anhui | 0.990 | 1.000 | 0.990 | DRS | 0 | 0 | 0 | 0 | 0 | 0 | Weakly efficient |
Fujian | 0.882 | 0.885 | 0.997 | IRS | 10,158 | 19,895 | 32,351 | 0 | 0 | 7.816 | Inefficient |
Jiangxi | 1.000 | 1.000 | 1.000 | − | 0 | 0 | 0 | 0 | 0 | 0 | Efficient |
Shandong | 0.797 | 0.939 | 0.848 | DRS | 4696 | 43,217 | 180,217 | 18,959,586 | 0 | 3.852 | Inefficient |
Henan | 0.843 | 0.953 | 0.884 | DRS | 4034 | 23,184 | 77,957 | 67,288,228 | 0 | 0 | Inefficient |
Hubei | 0.979 | 1.000 | 0.979 | DRS | 0 | 0 | 0 | 0 | 0 | 0 | Weakly efficient |
Hunan | 1.000 | 1.000 | 1.000 | − | 0 | 0 | 0 | 0 | 0 | 0 | Efficient |
Guangdong | 1.000 | 1.000 | 1.000 | − | 0 | 0 | 0 | 0 | 0 | 0 | Efficient |
Guangxi | 1.000 | 1.000 | 1.000 | − | 0 | 0 | 0 | 0 | 0 | 0 | Efficient |
Hainan | 0.904 | 0.915 | 0.988 | IRS | 428 | 3285 | 15,726 | 1,167,618 | 0 | 3.761 | Inefficient |
Chongqing | 1.000 | 1.000 | 1.000 | − | 0 | 0 | 0 | 0 | 0 | 0 | Efficient |
Sichuan | 0.935 | 1.000 | 0.935 | DRS | 0 | 0 | 0 | 0 | 0 | 0 | Weakly efficient |
Guizhou | 0.951 | 0.951 | 1.000 | − | 4273 | 9532 | 12,576 | 11,209,602 | 0 | 6.534 | Inefficient |
Yunnan | 0.992 | 1.000 | 0.992 | DRS | 0 | 0 | 0 | 0 | 0 | 0 | Weakly efficient |
Tibet | 1.000 | 1.000 | 1.000 | − | 0 | 0 | 0 | 0 | 0 | 0 | Efficient |
Shannxi | 0.798 | 0.800 | 0.998 | IRS | 12,444 | 42,438 | 70,079 | 0 | 0 | 5.008 | Inefficient |
Gansu | 0.815 | 0.817 | 0.997 | IRS | 14,526 | 23,418 | 33,263 | 0 | 0 | 3.031 | Inefficient |
Qinghai | 0.958 | 0.988 | 0.970 | IRS | 1428 | 4712 | 581 | 3,718,718 | 0 | 5.194 | Inefficient |
Ningxia | 1.000 | 1.000 | 1.000 | − | 0 | 0 | 0 | 0 | 0 | 0 | Efficient |
Xinjiang | 0.995 | 1.000 | 0.995 | DRS | 0 | 0 | 0 | 0 | 0 | 0 | Weakly efficient |
Mean | 0.904 | 0.921 | 0.982 | / | 4956 | 16,601 | 29,788 | 3,901,796 | 0 | 3 | / |