Comprehensive optimisation of China’s energy prices, taxes and subsidy policies based on the dynamic computable general equilibrium model

Abstract Under the condition of increasingly serious environmental pollution, rational energy policy plays an important role in the practical significance of energy conservation and emission reduction. This paper defines energy policies as the compilation of energy prices, taxes and subsidy policies. Moreover, it establishes the optimisation model of China’s energy policy based on the dynamic computable general equilibrium model, which maximises the total social benefit, in order to explore the comprehensive influences of a carbon tax, the sales pricing mechanism and the renewable energy fund policy. The results show that when the change rates of gross domestic product and consumer price index are ±2%, ±5% and the renewable energy supply structure ratio is 7%, the more reasonable carbon tax ranges from 10 to 20 Yuan/ton, and the optimal coefficient pricing mechanism is more conducive to the objective of maximising the total social benefit. From the perspective of optimising the overall energy policies, if the upper limit of change rate in consumer price index is 2.2%, the existing renewable energy fund should be improved.

[1]  Aliasghar Montazar,et al.  Optimize of all Effective Infiltration Parameters in Furrow Irrigation Using Visual Basic and Genetic Algorithm Programming , 2012 .

[2]  Boqiang Lin,et al.  A revisit of fossil-fuel subsidies in China: Challenges and opportunities for energy price reform , 2014 .

[3]  Sen Guo,et al.  Review on the costs and benefits of renewable energy power subsidy in China , 2014 .

[4]  H. Shayeghi,et al.  Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVM based scheme , 2013 .

[5]  Alireza Talaei,et al.  Predicting oil price movements: A dynamic Artificial Neural Network approach , 2014 .

[6]  M. Valipour,et al.  Parameters Estimate of Autoregressive Moving Average and Autoregressive Integrated Moving Average Models and Compare Their Ability for Inflow Forecasting , 2012 .

[7]  Joao P. S. Catalao,et al.  Electricity prices forecasting by a hybrid evolutionary-adaptive methodology , 2014 .

[8]  Ehsan Rezaei,et al.  Air, Water, and Soil Pollution Study in Industrial Units Using Environmental Flow Diagram , 2012 .

[9]  Tahsin Koroglu,et al.  Current status of wind energy and wind energy policy in Turkey , 2013 .

[10]  Bruce Morley,et al.  Environmental Taxes and Economic Growth:Evidence from Panel Causality Tests , 2014 .

[11]  Mohammad Valipour,et al.  A comprehensive study on irrigation management in Asia and Oceania , 2015 .

[12]  Hai-ying He,et al.  Electricity demand price elasticity in China based on computable general equilibrium model analysis , 2011 .

[13]  Mohammad Ebrahim Banihabib,et al.  Monthly Inflow Forecasting using Autoregressive Artificial Neural Network , 2012 .

[14]  Birol Kılkış,et al.  An energy source policy assessment using analytical hierarchy process , 2012 .

[15]  Selma Şekercioğlu,et al.  Renewable energy perspectives in the frame of Turkey's and the EU's energy policies. , 2012 .

[16]  Reza Valipour,et al.  Deal with Environmental Challenges in Civil and Energy Engineering Projects Using a New Technology , 2013 .

[17]  Mohammad Valipour,et al.  Future of agricultural water management in Africa , 2015 .

[18]  M. Valipour,et al.  Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir , 2013 .

[19]  Mohammad Valipour,et al.  Land use policy and agricultural water management of the previous half of century in Africa , 2015, Applied Water Science.

[20]  Yi-Ming Wei,et al.  Platform for China Energy & Environmental Policy Analysis: A general design and its application , 2014, Environ. Model. Softw..

[21]  D. Willenbockel.  Structural Effects of a Real Exchange Rate Revaluation in China: A CGE Assessment , 2006 .

[22]  Aliasghar Montazar,et al.  Sensitive analysis of optimized infiltration parameters in SWDC model , 2012 .

[23]  Lei Zhu,et al.  Optimal carbon taxes in carbon-constrained China: A logistic-induced energy economic hybrid model , 2014 .

[24]  T. Hertel,et al.  Impacts of the Doha Development Agenda on China: The Role of Labor Markets and Complementary Education Reforms , 2005 .

[25]  M. Valipour.  Future of the area equipped for irrigation , 2014 .

[26]  Ching-Chih Chang,et al.  Energy source options for the generation of electrical power in Taiwan , 2014 .

[27]  Michael A. Martin,et al.  Using neural networks and extreme value distributions to model electricity pool prices: Evidence from the Australian National Electricity Market 1998-2013 , 2014 .

[28]  Mohammad Kazem Sheikh-El-Eslami,et al.  Price forecasting of day-ahead electricity markets using a hybrid forecast method , 2011 .

[29]  Ramin Fazlola,et al.  Agricultural water management in the world during past half century , 2015 .

[30]  Chao Qing-chen,et al.  The Summary of Carbon Capture and Storage Technology and Its Impact on China , 2006 .

[31]  Joao P. S. Catalao,et al.  Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach , 2011 .

[32]  Duc Khuong Nguyen,et al.  Asymmetric and nonlinear pass-through of crude oil prices to gasoline and natural gas prices , 2014 .