Multi-dimensional research and quantitative evaluation of export potential of Dezhou smes to Central Asia based on multi-level regression model under the background of China-Kyrgyzstan-Uzbekistan Railway
Sep 25, 2025
About this article
Published Online: Sep 25, 2025
Received: Jan 05, 2025
Accepted: May 02, 2025
DOI: https://doi.org/10.2478/amns-2025-1015
Keywords
© 2025 Xingyuan Sun, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Figure 1.

Figure 2.

Figure 3.

Test of variance inflation factor
Variables | VIF | 1/VIF |
---|---|---|
1.524 | 0.656 | |
1.798 | 0.556 | |
1.915 | 0.522 | |
2.382 | 0.420 | |
3.263 | 0.306 | |
1.241 | 0.806 | |
2.518 | 0.397 | |
2.065 | 0.484 | |
1.859 | 0.538 | |
1.752 | 0.571 | |
1.603 | 0.624 | |
1.731 | 0.578 |
The top five categories of products exported by China to the Central Asian countries
Country | 2013 | 2022 | ||||||
---|---|---|---|---|---|---|---|---|
Product category quantity | Product code | Amount ($100 million) | Share (%) | Product category quantity | Product code | Amount ($100 million) | Share (%) | |
Kazakhstan | 3442 | 870423 | 1.88 | 1.65 | 3410 | 620193 | 3.82 | 2.16 |
730420 | 2.10 | 1.85 | 847193 | 3.82 | 2.16 | |||
730519 | 2.48 | 2.18 | 950390 | 5.09 | 2.88 | |||
640299 | 5.06 | 4.45 | 620293 | 6.41 | 3.63 | |||
847120 | 6.57 | 5.78 | 852520 | 6.63 | 3.76 | |||
Kyrgyzstan | 2608 | 611030 | 1.41 | 3.24 | 2725 | 620462 | 5.14 | 4.64 |
630260 | 1.62 | 3.72 | 611030 | 5.72 | 5.17 | |||
540752 | 1.85 | 4.25 | 620193 | 6.81 | 6.15 | |||
600192 | 2.04 | 4.68 | 640299 | 6.87 | 6.20 | |||
521019 | 2.37 | 5.44 | 620293 | 10.49 | 9.47 | |||
Tajikistan | 1483 | 630260 | 0.48 | 2.59 | 2659 | 940540 | 0.44 | 1.85 |
730830 | 0.49 | 2.64 | 621210 | 0.53 | 2.23 | |||
640510 | 0.52 | 2.81 | 870899 | 0.62 | 2.61 | |||
610423 | 0.60 | 3.24 | 721070 | 0.71 | 2.99 | |||
701339 | 0.64 | 3.45 | 640299 | 0.95 | 4.00 | |||
Turkmenistan | 1398 | 841510 | 0.29 | 2.59 | 1624 | 510820 | 0.17 | 2.35 |
842139 | 0.35 | 3.12 | 852810 | 0.21 | 2.90 | |||
848180 | 0.39 | 3.48 | 401120 | 0.24 | 3.32 | |||
847120 | 0.44 | 3.92 | 300220 | 0.28 | 3.87 | |||
730420 | 0.96 | 8.56 | 860210 | 0.36 | 4.97 | |||
Uzbekistan | 2232 | 721049 | 0.68 | 2.61 | 3341 | 852810 | 0.95 | 1.19 |
730420 | 0.68 | 2.61 | 870323 | 0.96 | 1.20 | |||
721070 | 0.75 | 2.88 | 390760 | 1.15 | 1.44 | |||
730511 | 0.76 | 2.92 | 870390 | 1.48 | 1.85 | |||
730519 | 1.94 | 7.46 | 852520 | 2.66 | 3.33 |
Regression result
Variable | OLS | RE | SFA | |
---|---|---|---|---|
Time invariant | Time-varying | |||
1.174*** | 0.895*** | 1.132*** | 0.984*** | |
0.886*** | 1.114*** | 0.998*** | 0.875*** | |
-11.208 | -12.319*** | -11.526*** | -13.914*** | |
1.257** | 1.406** | 0.683** | 0.878*** | |
-0.643*** | -0.347 | -0.012 | -0.514*** | |
1.625*** | 1.434*** | 1.538*** | 1.296*** | |
cons | 231.742 | 265.839 | 273.824*** | 274.521*** |
- | - | 0.615 | 1.498 | |
0.862 | 1.157 | 1.483 | 2.106 | |
- | - | - | 0.024*** | |
- | - | 0.857 | 0.915 | |
Logarithmic likelihood value | - | - | -501.365 | -582.419 |
LR | 154.257 | |||
Sample size | 800 | 800 | 800 | 800 |
Parameter estimation result
Variable | Coefficient | T-statistic | |
---|---|---|---|
Random frontier gravity model | 261.415* | 28.436 | |
1.048*** | -8.82 | ||
0.756*** | 26.034 | ||
-12.035*** | -15.87 | ||
0.765*** | -2.538 | ||
-0.514*** | 4.885 | ||
0.736** | -18.059 | ||
0.021*** | 24.958 | ||
Trade inefficiency model | 1.026*** | -1.105 | |
3.247*** | -1.245 | ||
-1.466*** | -2.034 | ||
-0.518*** | -2.653 | ||
-3.525*** | -4.516 | ||
-0.067*** | -4.948 | ||
-1.965** | -5.137 | ||
-1.829* | -15.073 | ||
Reference quantity | 1.584 | ||
0.675 | |||
Logarithmic likelihood value | -1185.639 | ||
LR | 117.241 |
Results of SFA model applicability test
Original hypothesis | Constraint model | Unconstrained model | LR | 1% critical value | Conclusion |
---|---|---|---|---|---|
There are no trade inefficiencies | -1542.36 | -1076.43 | 985.92 | 9.34 | Refuse |
Non-efficient terms do not have time variability | -571.24 | -502.49 | 120.45 | 10.61 | Refuse |
The variable |
-471.28 | -505.64 | -82.47 | 10.48 | Refuse |
The variable |
-541.35 | -516.03 | 57.24 | 10.48 | Can’t refuse |
China’s exports to the five Central Asian countries from 2013 to 2023
Country | 2013 | 2015 | 2017 | 2019 | 2021 | 2023 |
---|---|---|---|---|---|---|
Kazakhstan | 113.67 | 80.09 | 102.35 | 114.35 | 130.87 | 248.34 |
53.36% | 48.58% | 51.44% | 46.60% | 48.22% | 40.21% | |
Kyrgyzstan | 43.58 | 35.94 | 50.24 | 58.63 | 63.76 | 197.46 |
20.46% | 21.80% | 25.25% | 23.89% | 23.49% | 31.97% | |
Tajikistan | 18.54 | 18.06 | 13.27 | 15.16 | 15.85 | 37.39 |
8.70% | 10.96% | 6.67% | 6.18% | 5.84% | 6.06% | |
Turkmenistan | 11.22 | 8.41 | 3.89 | 4.24 | 5.01 | 9.88 |
5.27% | 5.10% | 1.95% | 1.73% | 1.85% | 1.60% | |
Uzbekistan | 26.01 | 22.36 | 29.23 | 53.1 | 55.93 | 124.47 |
12.21% | 13.56% | 14.69% | 21.60% | 20.60% | 20.16% | |
Total | 213.02 | 164.86 | 198.98 | 245.39 | 271.42 | 617.54 |
100.00% | 100.00% | 100.00% | 100.00% | 100.00% | 100.00% |
Applicability test results of trade inefficiency model
Original hypothesis | Constraint model | Unconstrained model | LR | 1% critical value | Conclusion |
---|---|---|---|---|---|
The variable |
-178.48 | -505.64 | 78.95 | 10.48 | Refuse |
The variable |
-178.48 | -516.03 | 132.16 | 10.48 | Refuse |
The variable |
-178.48 | -140.57 | 74.59 | 10.48 | Refuse |
The variable |
-178.48 | -129.46 | 97.34 | 10.48 | Refuse |
The variable |
-178.48 | -87.39 | 181.21 | 10.48 | Refuse |
The variable |
-178.48 | -116.58 | 124.68 | 10.48 | Refuse |
The variable |
-178.48 | -114.58 | 128.92 | 10.48 | Refuse |
Potential value of export trade and potential improvement space
Country | Kazakhstan | Uzbekistan | Kyrgyzstan | Tajikistan | Turkmenistan | |||||
---|---|---|---|---|---|---|---|---|---|---|
Year | ||||||||||
2013 | 466.72 | 330.47 | 325.59 | 285.13 | 807.17 | 637.94 | 102.44 | 91.28 | 56.32 | 50.54 |
2014 | 501.49 | 356.45 | 268.05 | 247.18 | 778.91 | 618.18 | 169.38 | 145.79 | 56.33 | 44.94 |
2015 | 442.25 | 297.93 | 410.38 | 347.55 | 1170.64 | 899.65 | 307.71 | 260.92 | 53.37 | 51.01 |
2016 | 542.16 | 350.4 | 350.75 | 299.96 | 1620.68 | 1204.2 | 642.69 | 536.83 | 98.66 | 89.15 |
2017 | 538.15 | 319.11 | 463.53 | 408.53 | 1586.11 | 1156.74 | 285.62 | 239.51 | 101.41 | 88.91 |
2018 | 543.71 | 309.68 | 477.53 | 409.69 | 1539.82 | 1079.04 | 180.18 | 141.5 | 92.3 | 79.96 |
2019 | 578.77 | 285.51 | 355.39 | 302.99 | 1603.54 | 1014.14 | 409.39 | 332.24 | 104.83 | 79.89 |
2020 | 631.22 | 282.64 | 408.13 | 349.44 | 1301.5 | 838.87 | 201.12 | 158.71 | 115.81 | 102.25 |
2021 | 549.31 | 213.71 | 280.24 | 239.62 | 1255.95 | 765.72 | 117.58 | 85.79 | 91.16 | 79.21 |
2022 | 596.3 | 190.87 | 361.06 | 307.2 | 625.3 | 352.61 | 124 | 97.92 | 99.99 | 91.28 |
2023 | 582.85 | 136.19 | 386.9 | 319.95 | 949.51 | 499.5 | 321.43 | 238.77 | 78.15 | 71.12 |