Analysis of a multi-factor control model for resource productivity in China

Tao Wang of the Center for Industrial Ecology, Department of Chemical Engineering, Tsinghua University, China, constructed an aggregate model of China’s economy for 1980-2010 to retrospectively analyze the sensitivity of resource productivity to variations in investment scenarios.

Tao Wang

Tao Wang

Introduction

Natural resources function as a material foundation for the world economy and have become a critical issue for sustainable development. The simplicity of the GDP output per resource input ratio makes resource productivity an appealing and widespread environmental sustainability indicator. The goal of this project is to construct an adequate aggregate model of China’s economy for 1980-2010 and then carry out retrospective analysis of sensitivity of resource productivity with respect to variations in investment scenarios based on the model.

Data and Methods

Economy-wide material flow analysis (EW-MFA) and derived indicators focusing on the whole economy have been established as the most widespread tools useful for monitoring a vast range of issues related to consumption of materials. Data on EW-MFA indicators of domestic extraction used (DEU) and domestic material consumption (DMC) are from the worldwide database of the Sustainable Europe Research Institute (SERI0) and UN Comtrade for 1980-2010. For modeling, a multi-factor economic growth model was used that had classical production factors such as capital, labor, and technology, and also incorporated natural resource as an additional production factor. Data on labor, R&D expenditures, and GDP were obtained from the China Statistical Yearbook. Data on capital stock are from estimations in the literature.

Results and Conclusions

First, we constructed an adequate aggregate economic growth model of China’s economy for 1980-2010; the model’s mean error in GDP is 5.5%. We assumed that exploitation of natural resources is also related to these classic production factors such as capital stock, labor, and R&D stock. The resource extraction model’s mean error is 3.4%. Second, to increase resource productivity by 1% on average in 1980-2010, investment in capital goods could have been increased by 1.5% or investment in technology by 0.09%; this means that in terms of quantity of money, investment in capital goods and technology could also have been increased by an average of between 400 and 2,400 million yuan (constant 1980 price) per year during this period of time. Third, we clearly saw that resource productivity and resource domestic extraction are both more sensitive to technology investment than to capital investment. Moreover, we found that the potential impact of quantity of resource extraction is subject to this sequence: Non-metals> Metals>> Biomass> Energy in improvement of resource productivity, but the opposite when materials are imported from the rest of the world (ROW).

References

[1] Julia KS, Fridolin K (2011). Material and energy productivity. Environmental Science and Technology 45, 1169-1176.

Supervisor

Arkady Kryazhimskiy, Advanced Systems Analysis, IIASA

Note

Tao Wang of the Center for Industrial Ecology, Department of Chemical Engineering, Tsinghua University, China, is a Chinese citizen. He was funded by IIASA’s Chinese National Member Organization and worked in the Advanced Systems Analysis (ASA) Program during the YSSP.

Please note these Proceedings have received limited or no review from supervisors and IIASA program directors, and the views and results expressed therein do not necessarily represent IIASA, its National Member Organizations, or other organizations supporting the work.


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Last edited: 29 September 2015

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