Driving Factors of CO2 Emissions: Further Study Based on Machine Learning

Abstract

Greenhouse gases, especially carbon dioxide (CO2) emissions, are viewed as one of the core causes of climate change, and it has become one of the most important environmental problems in the world. This paper attempts to investigate the relation between CO2 emissions and economic growth, industry structure, urbanization, research and development (R&D) investment, actual use of foreign capital, and growth rate of energy consumption in China between 2000 and 2018. This study is important for China as it has pledged to peak its carbon dioxide emissions (CO2) by 2030 and achieve carbon neutrality by 2060. We apply a suite of machine learning algorithms on the training set of data, 2000–2015, and predict the levels of CO2 emissions for the testing set, 2016–2018. Employing rmse for model selection, results show that the nonlinear model of k-nearest neighbors (KNN) model performs the best among linear models, nonlinear models, ensemble models, and artificial neural networks for the present dataset. Using KNN model, sensitivity analysis of CO2 emissions around its centroid position was conducted. The findings indicate that not all provinces should develop its industrialization. Some provinces should stay at relatively mild industrialization stage while selected others should develop theirs as quickly as possible. It is because CO2 emissions will eventually decrease after saturation point. In terms of urbanization, there is an optimal range for a province. At the optimal range, the CO2 emissions would be at a minimum, and it is likely a result of technological innovation in energy usage and efficiency. Moreover, China should increase its R&D investment intensity from the present level as it will decrease CO2 emissions. If R&D reinvestment is associated with actual use of foreign capital, policy makers should prioritize the use of foreign capital for R&D investment on green technology. Last, economic growth requires consuming energy. However, policy makers must refrain from consuming energy beyond a certain optimal growth rate. The above findings provide a guide to policy makers to achieve dual-carbon strategy while sustaining economic development.

The Prediction of Medium- and Long-Term Trends in Urban Carbon Emissions Based on an ARIMA-BPNN Combination Model

“…This is different from most previous studies [34]. Although a decrease in energy consumption per unit of GDP indicates high energy utilization efficiency, it does not necessarily mean that carbon emissions will Energies 2024, 17, 1856 9 of 19 decrease, as the amount of carbon emissions also depends on factors such as total energy consumption [35], carbon emission intensity [4], and energy structure [36]. Due to Suzhou's energy structure being mainly composed of high-carbon energy, with a large total energy consumption and a high carbon emission intensity, even if the energy consumption per unit of GDP is reduced, carbon emissions may still increase.…”

“…The selection of appropriate influencing factors is the foundation for establishing carbon emission prediction models. The current research mostly considers factors such as population size, economic scale, energy structure, energy intensity, industrial structure, and urbanization level to establish carbon emission prediction models, including carbon emission assessments globally across six continents (Europe, North America, South America, Asia, Africa, and Oceania) [4] 2 of 19 and in the Beijing-Tianjin-Hebei region [5], as well as Guangdong Province [6]. Meanwhile, the land use change rate, forest protection situation, biomass energy utilization efficiency, transportation mode, and renewable energy utilization rate were taken into account for Brazil's carbon emissions by Pedreira [7].…”

Analysis of environmental sustainability and economic development from electricity consumption based on the modified spatial Durbin model

“…According to the analysis, the relationship between space and surrounding CO 2 releases is now shaped like an upside-down U. The federal, state, and municipal governments have implemented laws addressing environmental contamination, which should promote the utilization of foreign investments for R&D projects involving green technology [ 34 ]. Geological linkage was strongest between 2013 and 2021, as shown in Fig.…”

The Spatiotemporal Distribution and Drivers of Urban Carbon Emission Efficiency: The Role of Technological Innovation

“…(4) Economic development level (EDL): Economic growth directly affects carbon emissions [ 57 ]. Economic development generates a continuous increase in carbon emissions through resource consumption while leading to energy-saving with the advancement of the development process [ 58 ].…”