Preprint Article Version 1 This version is not peer-reviewed

Rebound Effect and Technical Learning of Energy-environmental Evaluation in Manufacturing Industry

Version 1 : Received: 5 August 2019 / Approved: 6 August 2019 / Online: 6 August 2019 (12:57:09 CEST)

How to cite: Wang, J.; Wang, K.; Wang, L. Rebound Effect and Technical Learning of Energy-environmental Evaluation in Manufacturing Industry. Preprints 2019, 2019080080 (doi: 10.20944/preprints201908.0080.v1). Wang, J.; Wang, K.; Wang, L. Rebound Effect and Technical Learning of Energy-environmental Evaluation in Manufacturing Industry. Preprints 2019, 2019080080 (doi: 10.20944/preprints201908.0080.v1).

Abstract

We introduce a total energy and environmental evaluation method in the manufacturing industry. The method gives us a series of descriptive indexes to assess the overall environmental effect level on materials, energy, wastes and products in the life cycle process. Meanwhile, the method uses partial indexes, environmental effect factors, and correction offsets into the quantitative model to analyze the learning process and rebound effect on the energy and environment at each procedure. In this work, we choose S-shaped learning curves to describe how to decrease energy consumption and improve the technical learning by doing for recent 30 years. Also, we draw the different rebound effect curves of the total energy-environmental evaluation with technical learning method, which use the annual industrial production growth rate to show that it's significant to estimate its effect on technology changes. The ideas about the interaction of energy and production environment from material flows to energy consumption, direct us to build an example to estimate quantitatively the results with different condition factors, and realize the process improvement and develop new products.

Subject Areas

energy-environmental evaluation; cleaner production; descriptive index; technical learning curve; rebound effect; life cycle

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