On the driving factors of China's provincial carbon emission from the view of periods and groups

Study on carbon emission driving factors and carbon peak forecasting in power sector of Shanxi province

The realisation of the low-carbon transition of the energy system in resource-intensive regions, as embodied by Shanxi Province, depends on a thorough understanding of the factors impacting the power sector's carbon emissions and an accurate prediction of the peak trend. Because of this, the power industry's carbon emissions in Shanxi province are measured in this article from 1995 to 2020 using data from the Intergovernmental Panel on Climate Change (IPCC). To obtain a deeper understanding of the factors impacting carbon emissions in the power sector, factor decomposition is performed using the Logarithmic Mean Divisia Index (LMDI). Second, in order to precisely mine the relationship between variables and carbon emissions, the Sparrow Search Algorithm (SSA) aids in the optimisation of the Long Short-Term Memory (LSTM). In order to implement SSA-LSTM-based carbon peak prediction in the power industry, four development scenarios are finally built up. The findings indicate that: (1) There has been a fluctuating upward trend in Shanxi Province's total carbon emissions from the power industry between 1995 and 2020, with a cumulative growth of 372.10 percent. (2) The intensity of power consumption is the main factor restricting the rise of carbon emissions, contributing -65.19%, while the per capita secondary industry contribution factor, contributing 158.79%, is the main driver of the growth in emissions. (3) While the baseline scenario and the rapid development scenario fail to peak by 2030, the low carbon scenario and the green development scenario peak at 243,991,100 tonnes and 258,828,800 tonnes, respectively, in 2025 and 2028. (4) Based on the peak performance and the decomposition results, resource-intensive cities like Shanxi's power industry should concentrate on upgrading and strengthening the industrial structure, getting rid of obsolete production capacity, and encouraging the faster development of each factor in order to help the power sector reach peak carbon performance.

Spatial-temporal pattern and spatial convergence of carbon emission intensity of rural energy consumption in China

Based on the panel data of 30 provinces (municipalities and autonomous regions) in China from 2005 to 2019, this paper uses Gini coefficient decomposition and kernel density estimation to investigate the regional differences and dynamic evolution trend of rural energy carbon emission intensity in China. Then, the convergence model is used to analyze the convergence characteristics and influencing factors of carbon emission intensity. The study found the following: (1) During the observation period, the carbon emissions of coal energy and oil energy were much higher than those of gas energy. The carbon emissions of rural energy consumption experienced three stages of development, and the carbon emission intensity showed a downward trend as a whole. The spatial distribution pattern of total carbon emissions present an "adder" distribution, and the spatial agglomeration phenomenon gradually strengthens with the passage of time. (2) The Gini coefficient of China's rural energy consumption carbon emission intensity shows a trend of "Inverted N-shaped." The Gini coefficient of carbon emission intensity in the eastern and northeastern regions shows an increasing trend, while the Gini coefficient of carbon emission intensity in the western and central regions shows a downward trend. The super variable density is the main source of carbon emission intensity difference. The peak value of the main peak of the nuclear density curve of the carbon emission intensity increased significantly, the bimodal form evolved into a single peak form, and the density center moved to the left. (3) The carbon emission intensity of rural energy consumption in the whole, central, and western regions of China has the characteristic of σ convergence, while the carbon emission intensity in the eastern and northeastern regions does not have the characteristic of σ convergence. There is a significant spatial positive correlation in the carbon emission intensity, there is also a significant β convergence characteristic, the speed of conditional β convergence is significantly higher than that of absolute β convergence, and the spatial interaction will further improve the convergence speed. Industrial structure, industrial agglomeration, and energy efficiency will increase the convergence speed. In terms of sub-regions, the conditional convergence rate of carbon emission intensity in the four regions shows a decreasing trend in the northeast, central, eastern, and western regions.

CO2 emissions in China's power industry by using the LMDI method

WIth the introduction of "carbon peak and neutrality" targets, China's power industry is under enormous pressure to reduce carbon dioxide (CO₂) emissions, as it produces more than 40% of emissions. In response, China's power industry is actively reducing the investment in thermal energy and gradually shifting toward non-fossil energy sources. However, the CO₂ reduction effect of these measures is still unknown. This study aims to analyze CO₂ emissions from China's power industry from 2009 to 2018 from an entire lifecycle perspective, considering that CO₂ emissions also exist in non-fossil power generation. The logarithmic mean Divisia index (LMDI) method is employed to identify the factors influencing CO₂ emissions. Then, the modified STochastic Impacts by Regression on Population, Affluence and Technology model is used for comparative validation. The results show that (1) CO₂ emissions from China's power industry increased significantly, from 276.5 million tons of CO₂ equivalent (Mtce) in 2009 to 436.44 Mtce in 2018; (2) the investment intensity, investment structure, and emission intensity dampen CO₂ emissions, with cumulative contribution rates of - 28.88%, - 11.89%, and - 3.16%, respectively. The investment efficiency, economic development level, and population size contribute to CO₂ emissions, with cumulative contribution rates of 29.76, 24.68, and 1.07%, respectively; and (3) Investment into the hydropower contributes the least to CO₂ emissions, followed by wind, nuclear, photovoltaic, and thermal power. These research findings suggest that the power industry should improve its investment decision-making capabilities and pay particular attention to the hydropower-led non-fossil energy sector.

Analysis of the decoupling state and driving forces of China's construction industry under the carbon neutrality target

The essential to achieving the 2060 carbon neutrality target in China lies in the performance of the construction industry. Decoupling economic development from CO₂ emissions is the main strategy for reducing emissions in the construction industry. This paper is based on panel data for China and its 30 provinces during 2009-2019. A Tapio decoupling model is constructed to analyze the decoupling state of economic development and CO₂ emissions in the construction industry. The logarithmic mean Divisia index model is constructed to continue the decomposition of the drivers of the decoupling state and CO₂ emissions. The results show that (1) the economic development level of most provinces is positively correlated with their CO₂ emissions; (2) Beijing and Jiangsu reach the ideal strong decoupling state, and Heilongjiang has the worst decoupling state. The same type of decoupling state shows a certain aggregation phenomenon in space; (3) economic output plays a critical role in promoting CO₂ emissions and decoupling of the construction industry in China and the provinces. The main driver of decoupling is indirect carbon intensity; (4) energy intensity has a greater impact on CO₂ emissions reduction in regions with more developed economic levels. Understanding the drivers of the decoupling state in China's construction industry provides a valuable basis for energy efficiency and emission reduction efforts in China and other countries.

Exploration of Spatio-Temporal Characteristics of Carbon Emissions from Energy Consumption and Their Driving Factors: A Case Analysis of the Yangtze River Delta, China

For the Yangtze River Delta (YRD) region of China, exploring the spatio-temporal characteristics of carbon emissions from energy consumption (CEECs) and their influencing factors is crucial to achieving carbon peaking and carbon neutrality as soon as possible. In this study, an improved LMDI decomposition model based on the Tapio model and Kaya's equation was proposed. Combined with the improved LMDI and k-means cluster analysis methods, the energy structure, energy intensity, unit industrial output value and population size were selected as the driving factors, and the contribution of each driving factor to the CEECs of prefecture-level cities was quantitatively analyzed. Our study found that: (1) By 2020, the total amount of CEECs in the 26 prefecture-level cities in the YRD will stabilize, while their intensity has shown a downward trend in recent years. (2) The decoupling relationship between CEECs and economic development generally showed a trend from negative decoupling to decoupling. The dominant factor in decoupling was generally the shift of DEL values towards urbanization rate and energy intensity and the open utilization of energy technologies. (3) From 2000 to 2010, the dominant factors affecting CEECs in 26 cities were energy intensity and energy structure, followed by industrial output value and urbanization rate. In general, the promotion effect of economic development on carbon emissions in the YRD region was greater than the inhibitory effect. After 2010, the restrictive effect of various factors on CEECs increased significantly, among which the role of gross industrial output was crucial. The research results can provide a scientific policy basis for the subsequent spatial management and control of carbon emission reduction and carbon neutrality in the YRD region at a finer scale.

Key mitigation regions and strategies for CO2 emission reduction in China based on STIRPAT and ARIMA models

China is facing increasing pressure to reduce CO₂ emissions from energy consumption. Given this issue, understanding the characteristics, influencing factors, and trends can provide adequate information for decision-makers to solve the CO₂ emission problem. This study analyzes the characteristics of CO₂ emissions from energy consumption in 30 regions of China from 2005 to 2018 and applies the STIRPAT model to identify the impact of the influencing factors. Combined with the CO₂ emission trend in 2030 as predicted by the ARIMA model, the key mitigation regions and strategies reduction have been determined. Results indicate that CO₂ emissions have been increasing from 2005 to 2018 in China, thus showing the characteristic of the east being larger than the west spatially. Under the baseline scenario, these emissions will continue to rise in 2030. Carbon emissions intensity is declining, and the gap between provinces with the highest and lowest per capita CO₂ emissions is widening. Although per capita GDP is significantly positively correlated with provinces, population is the key factor influencing more provinces, followed by the proportion of the secondary industry and urbanization rate. To achieve low-carbon sustainable development, Shandong, Shanxi, Inner Mongolia, Guangdong, Shaanxi, Xinjiang, and Ningxia are considered the key regions of concern for emission reduction. The heterogeneity of CO₂ emission characteristics and influencing factors among regions provides a direction for the development of targeted and differentiated regional emission reduction strategies.

Changes in carbon emission performance of energy-intensive industries in China

As the major energy consumers, energy-intensive industries are the key players in achieving carbon emission reduction targets. The paper builds a super slack-based model (SBM) considering this undesirable output and calculates the carbon emission efficiency. Then, the meta-frontier Malmquist-Luenberger productivity index (MF-MLPI) is constructed to dynamically analyze the growth rate changes of the carbon emission efficiency and the regional differences in energy-intensive industries. Furthermore, the carbon emission reduction potential of the energy-intensive industries in various economic regions of China is discussed, and the conclusions are as follows: there is a big difference in the carbon emission technology gap ratios (TGRs) of the energy-intensive industries in different economic regions; the growth rate of the carbon emission efficiency of energy-intensive industries shows a trend of first declining and then slowly recovering, while the carbon reduction potential generally shows a trend of decreasing and then rising; and the carbon emission reduction potential in the eastern region keeps decreasing.

Impact of uncertainty on regional carbon peak paths: an analysis based on carbon emissions accounting, modeling, and driving factors

Regional carbon emission paths have an important impact on the realization of China's carbon emission peak target. Due to the uncertainty of future development model, the change of carbon emissions will also face uncertainty, which will make achieving the peak target challenging. Taking Shandong, Henan, and Guangdong, three of China's most populous provinces, as examples, this study analyzed the impacts of uncertainties in carbon accounting principles, driving factors, and simulation mechanism on achieving the peak target. The results show that (1) under the baseline scenario, the accounting principles based on primary energy consumption and IPCC sector consumption will make the peaking time of Guangdong be evaluated as 2018 and 2030, respectively, and the simulation based on IPCC sector accounting will advance the peaking time of Shandong by at least 5 years, while Henan will be less affected. (2) When considering the impact of the energy structure, Guangdong and Henan are estimated to peak in 2011 and 2018, while without considering the impact of the energy structure, the peak in the two provinces may be after 2035. Energy structure has no effect on the estimation of peaking time for Shandong. In addition, the k value in the ridge regression method also has no effect on the peaking time for the three provinces; it only affects the simulations of annual carbon emissions. This study also presented the carbon emission trajectory under different scenarios; from the simulation results, environmental regulation measures such as accelerating industrial structure transformation and increasing energy consumption intensity may help to achieve the peak carbon emission target as soon as possible. It also suggests that uncertainty should be included in future carbon assessments to present a more complete carbon emission trajectory.