Sectoral Aggregation Bias in the Accounting of Emissions Embodied in Trade and Consumption

Conference Proceedings Paper
  • China Energy & Climate Project
Sectoral Aggregation Bias in the Accounting of Emissions Embodied in Trade and Consumption
Zhang, D., J. Caron, N. Winchester and V. Karplus (2013)
Conference Proceedings, GTAP 16th Annual Conference on Global Economic Analysis (Shanghai, June 12–14) GTAP Paper 4095

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Abstract/Summary:

Computable general equilibrium (CGE) models seeking to evaluate the impacts of electricity policy face difficulties incorporating detail on the variable nature of renewable energy resources. To improve the accuracy of modeling renewable energy and climate policies, detailed scientific and engineering data are used to inform the parameterization of wind electricity in a new regional CGE model of China. Wind power density (WPD) in China has been constructed using boundary layer flux data from the Modern Era Retrospective-analysis for Research and Applications (MERRA) dataset with a 0.5° latitude by 0.67° longitude spatial resolution. Wind resource data are used to generate production cost functions for wind at the provincial level and offshore, incorporating technological parameters and geographical constraints. With these updated wind production cost data to parameterize the wind electricity option in a CGE model, an illustrative policy analysis of the current feed-in tariff (FIT) for wind electricity is performed. Assuming a generous penetration rate and no interprovincial interconnection, we find that the contribution of wind to total electricity generation is 215 TWh, reducing CO2 emissions by 3.5%. We discuss the relative merits of the FIT by province. Our analysis shows how wind electricity resource can be differentiated based on location and quality in a CGE model and applied to a analyze climate and energy policies.

Citation:

Zhang, D., J. Caron, N. Winchester and V. Karplus (2013): Sectoral Aggregation Bias in the Accounting of Emissions Embodied in Trade and Consumption. Conference Proceedings, GTAP 16th Annual Conference on Global Economic Analysis (Shanghai, June 12–14) GTAP Paper 4095 (https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=4095)

Superseded by:

Reprint 2018-7. Sectoral aggregation error in the accounting of energy and emissions embodied in trade and consumption (Journal of Industrial Ecology, 2018)
  • Conference Proceedings Paper
China Project
Sectoral Aggregation Bias in the Accounting of Emissions Embodied in Trade and Consumption

Zhang, D., J. Caron, N. Winchester and V. Karplus

Conference Proceedings
GTAP 16th Annual Conference on Global Economic Analysis (Shanghai, June 12–14) GTAP Paper 4095

Abstract/Summary: 

Computable general equilibrium (CGE) models seeking to evaluate the impacts of electricity policy face difficulties incorporating detail on the variable nature of renewable energy resources. To improve the accuracy of modeling renewable energy and climate policies, detailed scientific and engineering data are used to inform the parameterization of wind electricity in a new regional CGE model of China. Wind power density (WPD) in China has been constructed using boundary layer flux data from the Modern Era Retrospective-analysis for Research and Applications (MERRA) dataset with a 0.5° latitude by 0.67° longitude spatial resolution. Wind resource data are used to generate production cost functions for wind at the provincial level and offshore, incorporating technological parameters and geographical constraints. With these updated wind production cost data to parameterize the wind electricity option in a CGE model, an illustrative policy analysis of the current feed-in tariff (FIT) for wind electricity is performed. Assuming a generous penetration rate and no interprovincial interconnection, we find that the contribution of wind to total electricity generation is 215 TWh, reducing CO2 emissions by 3.5%. We discuss the relative merits of the FIT by province. Our analysis shows how wind electricity resource can be differentiated based on location and quality in a CGE model and applied to a analyze climate and energy policies.

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