Inner Mongolia Autonomous Region is a big province of energy, variety complete and reserve sufficient. However, with the continuous growth of energy freight volume, the imbalance of energy freight varieties and the unreasonable structure of energy industry in Inner Mongolia Autonomous Region are becoming more and more serious. The energy SAM table was compiled based on the latest official input-output table of Inner Mongolia, and the CGE model of regional energy freight volume and GAMS program were constructed to simulate and predict the total energy freight volume of Inner Mongolia Autonomous Region in 2025-2035. The results show that the average accuracy of the prediction model is 2.27% according to the error terms between the prediction results and the actual results. The conclusions can provide reference for stabilizing economic growth, developing regional green logistics and strengthening technological innovation and transformation.

Linlin Zhao, Zhu Wen. Research on Inner Mongolia freight volume forecast model based on computable general equilibrium. Academic Journal of Business & Management (2023) Vol. 5, Issue 7: 141-149. https://doi.org/10.25236/AJBM.2023.050721.

[1] Lu Hua, Yuan Min, Wang Libiao, du Zhiping. A study on the benefits of System dynamics end-shared delivery system -- a case study of Tongzhou District, Beijing [J]. Journal of Beijing Jiaotong University (Social Sciences), 2021, 20(04): 135-145.

[2] Xu Fei, Ren Shuang. Research on railway freight demand forecast based on decomposition-integration [J]. Operations and management, 2021, 30(08): 133-138. 

[3] Alexandru-eugen S T Tescu. Statistical analysis of investments in transport infrastructure, passenger numbers, and energy consumption by modes of transport in Romania [J]. Proceedings of the International Conference on Applied Statistics, January 1, 2019. 

[4] Chiao Hee-Ho. Integration of urban land use model and freight transport model -- a case study of British freight transport model [J/OL]. International urban planning: 1-12[2022-10-22]. 

[5] Amaya Johanna, Delgado-Lindeman Maira, Arellana Julian, Allen Jaime. Urban freight logistics: What do citizens perceive? [J]. Transportation Research Part E, 2021, 152:21 -30. 

[6] Qi Mingyao, Yang Kunhe, Miao lixin. Freight volume forecasting model based on computable general equilibrium [J]. Industrial technology economy, 2013, 32(03): 3-10. 

[7] Guo Zhengquan. Simulation analysis of our low-carbon economic development policy based on CGE model [D]. China University of Mining and technology (Beijing), 2011. 

[8] Lu Chunhua, Li Chenguang, Li Hong. Study on promoting low-carbon development of Jiangsu province by carbon tax policy based on dynamic CGE model [J]. Scientific decision-making, 2022(06): 54-72. 

[9] Wei L , Gao C , Zhang S . Research on the GM(1,1)-Markov chain model of highway freight volume forecasting[C]// 2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS). IEEE, 2015.

[10] Tang X H ,  Yan Y G . Research on the Scale of Logistics Park Land Based on the Forecast of Freight Volume[J]. Logistics Sci-Tech, 2016.

[11] Wang Jun. Compilation of Sam Table of Chinas social accounting matrix based on CGE model of energy economy [J]. Statistics & decision making, 2019, 35(20): 5-10. 

[12] Su Limin. The compilation of Macro Sam in 2017 and the selection of the optimal balance method [J]. Statistical theory and practice, 2020(05): 33-37. 

[13] Tu Taotao, Ma Qiang. Study on the balance method of social accounting matrix -- rediscussion on the least square cross-entropy method [J]. Quantitative economics, 2021, 38(06): 166-180. 

[14] Zhang Yang, Martin. China municipality social accounting matrix data set [J]. Journal of Global Change Data, 2020, 4(01): 46-51 + 46-51. 

[15] Godfrey Ho, Liu Lanjuan. The improvement and comparative study of Gras method based on the adjustment of social accounting matrix and the updating of input-output table [J]. Quantitative Economics, technical economics, 2018, 35(11): 142-161. 

[16] Wang Jia, Wu Xian, Ye Jun. Regional CGE model of Liaoning province [C]. Civil Engineering Construction Management: Selected Papers of Building Construction Professional Committee of Liaoning Institute of Civil Architecture. 2012:150-154. 

[17] Hu Ming, Shao Xuefeng. The impact of new information infrastructure construction on Chinas economic transition: An analysis based on the dynamic recursive CGE model [J]. Economic Questions, 2022(10): 12-18. 

[18] Freire-González Jaume, Ho Mun S. Policy strategies to tackle rebound effects: A comparative analysis [J]. Ecological Economics, 2022, 193.

[19] Wojciech Kamiński. Comparison of forecasting methods for freight transportation by Polish railways [J]. Pollack Periodica, 2019, 14(2).

[20] Zhang Zuomin, Ma Yiwen, Guo Huifang. Optimal structural tax reduction based on a computable general equilibrium model [J]. Finance and Economics, 2021(5): 12. 

[21] Tian Shengdan. The impact of the COVID-19 epidemic and its policy on our macro-economy: An analysis based on the computable General Equilibrium Model [J]. Consumer economy, 2020, 36(3): 11. 

[22] Hossain Syed Shoyeb, Delin Huang. Measuring economic impact in Korea, Japan, India, China, and ASEAN considering agricultural sectors: a dynamic CGE approach based on GAMS [J]. Review of World Economics, 2021(prepublish).