Abstract
Although energy-related factors, such as energy intensity and energy consumption, are well recognized as major drivers of carbon dioxide emission in China, little is known about the time-varying impacts of other macrolevel nonenergy factors on carbon emission, especially those from macroeconomic, financial, household, and technology progress indicators in China. This paper contributes to the literature by investigating the time-varying predictive ability of 15 macrolevel indicators for China’s carbon dioxide emission from 1982 to 2017 with a dynamic model averaging (DMA) method. The empirical results show that, firstly, the explanatory power of each nonenergy predictor changes significantly with time and no predictor has a stable positive/negative impact on China’s carbon emissions throughout the whole sample period. Secondly, all these predictors present a distinct predictive ability for carbon emission in China. The proportion of industry production in GDP (IP) shows the greatest predictive power, while the proportion of FDI in GDP has the smallest forecasting ability. Interestingly, those Chinese household features, such as Engel’s coefficient and household savings rate, play very important roles in the prediction of China’s carbon emission. In addition, we find that IP are losing its predictive power in recent years, while the proportion of value-added of the service sector in GDP presents not only a leading forecasting weight, but a continuous increasing prediction power in recent years. Finally, the dynamic model averaging (DMA) method can produce the most accurate forecasts of carbon emission in China compared to other commonly used forecasting methods.
Highlights
As an important part of the atmosphere, greenhouse gases, i.e., carbon dioxide (CO2), nitrous oxide, and methane, act just like a blanket, can absorb infrared radiation, and prevent it from escaping into outer space, maintaining the temperature of Earth’s atmosphere and surface
Many research studies have proved the impacts of energy-related factors, such as energy consumption per capita, total energy consumption, fossil fuel energy consumption, renewable energy consumption, nuclear energy consumption, and coal consumption, on CO2 emission. is paper, pays attention to those nonenergy indicators from
We find that the CC OLS and equal-weighted averaging models cannot survive in the model confidence set (MCS) tests under many statistical criteria with p values smaller than 0.1. is means that, on the one hand, constant coefficient (CC) model can rarely describe the true relationships between carbon emission in China and those commonly used explanatory variables and cannot provide accurate predictions for it
Summary
As an important part of the atmosphere, greenhouse gases, i.e., carbon dioxide (CO2), nitrous oxide, and methane, act just like a blanket, can absorb infrared radiation, and prevent it from escaping into outer space, maintaining the temperature of Earth’s atmosphere and surface. Since the beginning of the Industrial Revolution in the early 1800s, the concentration of greenhouse gases, especially CO2, in the atmosphere, has greatly increased because of the great consumptions of fossil fuels. Discrete Dynamics in Nature and Society macroeconomy (especially from finance sectors), household wealth conditions, and technical progress level, which have not been investigated in a comprehensive framework in previous research studies. Patent number is commonly used as a proxy of technical progress in extant research studies. These patent data are not available for China in the early 1980s. Erefore, in our research, we use the ratios of total R&D to GDP as well as the number of college students to China’s population as the other two proxies to measure the technical progress condition in China These patent data are not available for China in the early 1980s. erefore, in our research, we use the ratios of total R&D to GDP as well as the number of college students to China’s population as the other two proxies to measure the technical progress condition in China
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