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Industrial structure optimizing oriented by consuming preference pattern

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  • Yongbin Zhu
  • Yajuan Shi
  • Zheng Wang

Abstract

Industrial structure adjustment is one of major routes to upgrading the industries and reducing the carbon emissions. Where the industrial structure will evolve that performs the best for the well-being of the social members, and how the energy consumption will change due to the industrial structure change is our concern here. we built a Multi-sector Inter-temporal Dynamic Optimization (MIDO) model by combining the Ramsey-type neo-classical optimal growth model with the general equilibrium model, where the foresight property of consumers and the input-output relationship of each sector are considered. In the optimization objective, we extended the conventional social utility of aggregated consumption in Ramsey model by summing up a weighed utility from consuming goods/services of each sector. The weight in utility function refers to the consuming preference of each sector. Furthermore, the simulation scenarios are built on the presumption that the consuming preference pattern might change with its development stage. So we assumed one scenario follows the present preference of China and three alternative scenarios based on the consuming preference of US, EU and Japan, to study the consumer preference oriented industrial structure evolution for China and the according energy demand trends in each scenario. Our simulation can give us a detailed trend of all economic variables, such as the GDP, output, energy demand, consumption, investment and so forth of each sector during the whole planning period. The preliminary results indicate that the share of Agriculture as well as the Food & Clothes sectors will shrink, while the Other Services sector sees its share enlarging under all four preference patterns. In the Scen-China scenario, if the preference pattern of Chinese consumers applies to Chinese economy, the shares of Heavy-Manufacture, Transport, Chemicals and Metals sectors continue to increase, while those high energy-exhaustive sectors witness their shares decreasing if the preference pattern of American consumers applies to Chinese economy in the Scen-US scenario. Otherwise, the shares of Transport and Chemicals in Scen-EU scenario will decline slightly if the preference pattern of EU consumers applies to Chinese economy; and in Scen_JP scenario the Transport sector’s share will rise and the Metals’ share will drop if the preference pattern of Japanese consumers applies to Chinese economy. It will manifest an inversed-U shape trend of the aggregated energy use of China, due to industrial structure change and energy efficiency improvement. The gross energy use in the Scen-China, Scen-EU, Scen-JP and Scen-US will considerably decline from 2810 Mtoe to 2166 Mtoe for the peak value of energy use. Therefore, the preference pattern of US consumers is relatively better than that of JP consumers, than that of EU consumers and that of Chinese consumers from the perspective of energy conservation and emission reduction.

Suggested Citation

  • Yongbin Zhu & Yajuan Shi & Zheng Wang, 2015. "Industrial structure optimizing oriented by consuming preference pattern," EcoMod2015 8297, EcoMod.
  • Handle: RePEc:ekd:008007:8297
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    References listed on IDEAS

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    1. David Cass, 1965. "Optimum Growth in an Aggregative Model of Capital Accumulation," Review of Economic Studies, Oxford University Press, vol. 32(3), pages 233-240.
    2. Alcantara, Vicent & Duarte, Rosa, 2004. "Comparison of energy intensities in European Union countries. Results of a structural decomposition analysis," Energy Policy, Elsevier, vol. 32(2), pages 177-189, January.
    3. X. Q. Liu & B. W. Ang & H.L. Ong, 1992. "The Application of the Divisia Index to the Decomposition of Changes in Industrial Energy Consumption," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 161-178.
    4. Ang, B. W., 1995. "Multilevel decomposition of industrial energy consumption," Energy Economics, Elsevier, vol. 17(1), pages 39-51, January.
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    Keywords

    China; Modeling: new developments; Optimization models;

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