Exploring carbon dioxide emissions forecasting in China: A policy-oriented perspective using projection pursuit regression and machine learning models

Lei Chang, Muhammad Mohsin, Amir Hasnaoui and Farhad Taghizadeh-Hesary

Technological Forecasting and Social Change, 2023, vol. 197, issue C

Abstract: Achieving a balance between future greenhouse gas reduction and sustained economic growth is of utmost importance. This study leverages machine learning (ML), specifically projection pursuit regression (PPR), to evaluate the key factors that influence CO2 emission predictions in China. The analysis notably identifies the escalating electricity consumption as a primary influencing factor. Based on empirical findings, it is evident that building electricity consumption will continue to rise steadily until 2050 unless new restrictions or technological advancements are implemented. Relying solely on the reduced carbon intensity of electricity will not enable China to achieve carbon neutrality. Therefore, there is a pressing need for more energy-efficient building retrofits and technologies to reduce power consumption in both residential and commercial properties. This policy-oriented study underscores its practical implications, offering valuable insights to policymakers for developing targeted CO2 reduction strategies that align with sustainable development and climate goals.

Keywords: Forecasting carbon emission; Machine learning model; Emission intensity (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:197:y:2023:i:c:s0040162523005577

DOI: 10.1016/j.techfore.2023.122872

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