What is it about?

The purpose of this paper was to find out the best-fit curves between production & consumption-based emissions and vital socio-economic factors of the world's topmost carbon emitter China. Until now most of the literature takes in to account only direct emissions while ignoring these main two accounting approaches for GHG emissions. This paper will not only be helpful specifically to Chinese policymakers but also to other scholars and governments concerned with environmental mitigation.

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Why is it important?

This paper is novel in several aspects first of all non-linear regression analysis has rarely been applied to understand the relationship between different driving factors and carbon emissions of a country. Secondly, in general, there is not much literature available where the relationship between a country‘s direct (production-based) and carbon footprint (consumption-based) emissions and significant driving factors have been analyzed. Finally, to the best of author‘s knowledge, there is no such attempt has been made to graphically model the relationship between both the direct and consumption-based emissions and important socioeconomic factors of China (which is the largest carbon emitter under both the approaches). In this study, the author has tried to fill this gap. The author estimated best-fit curves for both production and consumption-based emissions with selective key driving factors from 1970 to 2015. A comprehensive presentation of key driving factors temporal relationship with both type of emissions will help policymakers to devise better informed future mitigation policies for both PBA and CBA based emissions of China.

Perspectives

My first article base on various non-linear regression methods.

Dr. Muhammad Jawad Sajid
Xuzhou Institute of Technology

Read the Original

This page is a summary of: Modelling best fit-curve between China’s production and consumption-based temporal carbon emissions and selective socio-economic driving factors, IOP Conference Series Earth and Environmental Science, February 2020, Institute of Physics Publishing, DOI: 10.1088/1755-1315/431/1/012061.
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