What is it about?

Households play a vital role in producing industrial emissions through final-consumption. Unfortunately, there isn’t much literature on household demand embedded industrial consumption (DEIC) emissions. The aim of this study is to develop a model that can help analyze the impact of key drivers on household DEIC emissions. The model is applied to Chinese urban and rural household DEIC emissions. Additionally, the study also employees Regional Sensitivity Analysis to rank and map the most influential factors of Chinese rural and urban DEIC emissions.

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Why is it important?

First of all, despite the explicit findings on the role of intermediate industrial consumption emissions compared to the inter- mediate production emissions, not many studies have emerged ever since which consider the final demand embedded industrial consumption emissions especially for the China. Second, the explicit evidence on the differences of results obtained under the traditional industrial production and novel consumption embodied approaches requires further work on estimation of impact of different drivers on the industrial consumption embodied final emissions. The general literature on various types of decomposition analyses usually focuses on the drivers of total (direct) and final demand embodied emissions from production. Third, the studies on the estimation of intermediate industrial carbon or pollutant linkages under both the classical and HEM approaches do not consider the drivers of intermediate industrial linkages. Therefore, there is a lack of a general methodology for estimating the impact of intermediate industrial linkages embedded final emissions. This paper modifies the structural decomposition analysis and develops methodology for estimating the impact of different drivers on Chinese rural and urban household embedded industrial consumption emissions. The method named as structural decomposition of intermediate linkages (SDIL) can be used as a reference for future studies for the estimation of the effects of different key factors on intermediate industrial linkages embodied final emissions. Fourth, there is a general lack of studies using uncertainty or sensitivity analysis for input–output modelling. Specifically, there are not many studies on the application of uncertainty and sensitivity analysis to environmentally-extended input–output modelling (Sajid et al., 2020a). This study uses one of the famous GSA methods of Regional Sensitivity Analysis (RSA) to highlight the sensitive factors of the DEIC emission of rural and urban households in China. The ranking and mapping based on the influence (sensitivity) of different factors to Chinese rural and urban households DEIC emissions under the RSA method, will help to determine which factors are important for the cost effective mitigation of Chinese rural and urban household DEIC emissions (where the sensitive factors are more important, because relatively minor changes in their values can lead to significant changes in model output values).


This is my first article in which I used a general sensitivity analysis of the problem of industrial carbon emissions under the input-output model.

Dr. Muhammad Jawad Sajid
Xuzhou Institute of Technology

Read the Original

This page is a summary of: Structural decomposition and Regional Sensitivity Analysis of industrial consumption embedded emissions from Chinese households, Ecological Indicators, March 2021, Elsevier,
DOI: 10.1016/j.ecolind.2020.107237.
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