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Influencing factors of carbon emissions and their trends in China and India: a machine learning method

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Abstract

China and India are the largest coal consumers and the most populated countries in the world. With industrial and population growth, the need for energy has increased, which has inevitably led to an increase in carbon dioxide (CO2) emissions because both countries depend on fossil fuel consumption. This paper investigates the impact of energy consumption, financial development (FD), gross domestic product (GDP), population, and renewable energy on CO2 emissions. The study applies the long short-term memory (LSTM) method, a novel machine learning (ML) approach, to examine which influencing driver has the greatest and smallest impact on CO2 emissions; correspondingly, this study builds a model for CO2 emission reduction. Data collected between 1990 and 2014 were analyzed, and the results indicated that energy consumption had the greatest effect and renewable energy had the smallest impact on CO2 emissions in both countries. Subsequently, we increased the renewable energy coefficient by one and decreased the energy consumption coefficient by one while keeping all other factors constant, and the results predicted with the LSTM model confirmed the significant reduction in CO2 emissions. Finally, this study forecasted a CO2 emission trend, with a slowdown predicted in China by 2022; however, CO2 emission’s reduction is not possible in India until 2023. These results suggest that shifting from nonrenewable to renewable sources and lowering coal consumption can reduce CO2 emissions without harming economic development.

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Source: Authors’ construct

Fig. 2

Source: Authors’ construct

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Source: Authors’ construct

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Abbreviations

ANN:

Artificial neural network

ARDL:

Autoregressive distributed lag

BRICKS:

Brazil, Russia, India, China, and South Africa

CO2 :

Carbon dioxide

\({i}_{t}\) :

Input gate

\({f}_{t}\) :

Forget gate

FD:

Financial development

GDP:

Gross domestic product

GW:

Gigawatts

kg:

Kilogram

MSE:

Mean squared error

MAPE:

Mean absolute percentage error

ML:

Machine learning

Mtce:

Metric tons carbon equivalent

\({O}_{t}\) :

Output gate

OPEC:

Organization of the Petroleum Exporting Countries

ReLU:

Rectifier function

LSTM :

Long short-term memory

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Authors and Affiliations

  1. School of Economics and Management, China University of Geosciences, Wuhan, China

    Mansoor Ahmed & Chuanmin Shuai

  2. School of Geography and Information Engineering, China University of Geosciences, Wuhan, China

    Maqsood Ahmed

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  1. Mansoor Ahmed
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Mansoor Ahmed: conceptualization, data curation, methodology, software, writing original draft. Chuanmin Shuai: formal analysis, supervision, review, and editing. Maqsood Ahmed: methodology, software, visualization.

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Ahmed, M., Shuai, C. & Ahmed, M. Influencing factors of carbon emissions and their trends in China and India: a machine learning method. Environ Sci Pollut Res 29, 48424–48437 (2022). https://doi.org/10.1007/s11356-022-18711-3

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