Growth Of CO2 Emissions In China Research Articles

Synergy among finance, energy and CO2 emissions in a dynamic setting: Measures to optimize the carbon peaking path

Financial development and energy consumption are expected to exert a significant effect on CO2 emission changes. However, there remains a research gap in optimizing pathways to carbon peaking under financial and energy drivers. This paper constructs a novel model covering finance and energy consumption using the generalized Divisia Index (GDI) method. The GDI allows to quantify the drivers behind the CO2 emission change in China during 2005–2020. Furthermore, it combines a Monte Carlo simulation with scenario analysis to forecast the path of CO2 emissions during 2021–2030. The results demonstrate that energy structure and carbon factor remain the principal drivers for CO2 emission growth in China. Thus, it is still pivotal to optimize energy saving and carbon reduction policies. Financial energy saving has enormous potential for reducing CO2 emissions over time, even though the immediate influence on CO2 emission is not significant. China has a substantial probability of reaching the peaking target in 2026 under the technology breakthrough scenario, whereas in the other two scenarios the target cannot be achieved.

Regional inequality in the carbon emission intensity of public buildings in China

Driven by urbanization and the growth of residents’ income, CO2 emissions from energy consumption in public buildings have gradually become an important source of CO2 emission growth in China. There is an obvious phenomenon of carbon emission inequality, and there is still a research gap. This study explores the inequality in the carbon emission intensity of public buildings and its drivers on two scales—carbon emissions per capita and per unit of floor area—using provincial panel data for public buildings in China from 2010 to 2019 by Kaya-Theil model. The following results are obtained: (1) The carbon emissions per capita increased from 415 kg to 605 kg, those per unit of floor area decreased from 70.67 kg to 62.16 kg, and those from public buildings in hot summer and cold winter regions and cold regions were significantly higher than those in other regions. (2) The overall regional variation in the carbon emission intensity of public buildings mainly consists of intra-regional variations, ranging mainly from 70% to 90%. The role of building climate zones in the spatial difference in carbon emissions per capita is gradually increasing. (3) The added value of the tertiary sector per capita is the main factor contributing to the inequality in the carbon intensity of public buildings, with a contribution of approximately 70% (±5%), and energy consumption per unit of floor area gradually contributes to the increase in carbon inequality. Our findings provide practical guidance for reducing the carbon emission intensity, thereby further promoting the development of global low-carbon goals.

How to achieve China’s CO2 emission reduction targets by provincial efforts? – An analysis based on generalized Divisia index and dynamic scenario simulation

China has set a series of periodical CO2 emission reduction targets, with a goal of peaking CO2 emission by 2030. Due to China’s large size and imbalanced development, the achievement of such targets depends on the performances of China’s provinces. Under such a circumstance, this study aims to identify the driving forces and predict the trajectories of China’s overall and provincial energy-related CO2 emissions. The generalized Divisia index decomposition analysis results show that investment sale expansion is the major driver of CO2 emission growth in China and most Chinese provinces, which is partly offset by the efficiency gains. Our dynamic scenario simulation results show that most Chinese provinces will achieve their CO2 intensity reduction targets but have difficulties in achieving their CO2 emission peak targets. Further integration of both efficiency and technology improvement is one effective approach toward peaking CO2 emissions. Provincial disparities exist, resulting in that different factors have different impacts in different Chinese provinces. Consequently, it is crucial to set up appropriate province-specific emission peak targets and raise province-specific emission reduction policies by considering the local realities.

Decomposition and scenario analysis of CO2 emissions in China’s power industry: based on LMDI method

The power industry is a major contributor to CO2 emissions in China and thus plays a critical role in achieving the targets of CO2 emission reduction. This study analyzes the historical trajectory and feature of CO2 emissions in China’s power industry, explores the driving factors of CO2 emission change using LMDI method, and develops two emission reduction scenarios to evaluate the reduction potential of CO2 emissions. Results show the following: (1) China’s power industry has experienced a significant but unstable increase in CO2 emissions from 343.18 Mt in 1985 to 3447.57 Mt in 2013, a growth rate of 904.60%. (2) Industrial-scale effect plays a dominant role in promoting CO2 emission growth in China’s power industry, and the corresponding contribution degree reaches 111.73%. Energy intensity effect contributes most to the decrease in CO2 emissions, with a contribution degree of −16.82%. Capital productivity effect is another important factor leading to the increase in CO2 emissions. (3) The aggregate CO2 emission reduction in China’s power industry would reach 18,031.62 Mt in the ideal scenario and 15,466.03 Mt in the current policy scenario during 2014–2030. Finally, this study provides policy implications for energy-saving and CO2 emission reduction in China’s power industry.

Identifying the Determinants of CO2 Emission Change in China’s Power Sector

Power sector is significantly important for China to achieve the CO2 emission reduction targets. In this study, we analyze the features of CO2 emissions and environment effect in China’s power sector, investigate the driving factors of CO2 emission change based on the logarithmic mean Divisia index (LMDI) method, and evaluate the mitigation potential of CO2 emissions in China’s power sector. Results show that CO2 emissions in China’s power sector increased rapidly from 492.00 Mt in 1990 to 3049.88 Mt in 2014 while CO2 emission intensity experienced an unsteady downward trend during the study period. Industrial scale effect is the key contributor to CO2 emission growth in China’s power sector, and its contribution degree reaches 123.97%. Energy intensity effect contributes most to the decrease in CO2 emissions, with a contribution degree of −20.01%. Capital productivity effect is another important factor leading to CO2 emissions increase. The aggregate CO2 emission reduction would reach 17973.86 million tons (Mt) during 2015–2030 in the ideal emission reduction scenario. Finally, policy recommendations are made for future energy-saving and CO2 emission reduction in China’s power sector.

A spatio-temporal decomposition analysis of energy-related CO2 emission growth in China

This paper presented a coupled decomposition analysis of CO2 emissions due to energy consumption in China. The logarithmic mean divisia index (LMDI) approach was adopted to decompose the driving factors of CO2 emissions in eight sub-periods over 1995 to 2011. Decomposition and heterogeneity analyses of CO2 emission over multiple spatial series were also conducted for the 29 provinces in China. Twelve driving factors in view of four categories of effects were decomposed. The results indicated that (a) driving directions of the factors were highly consistent. All the factors exhibited consistently positive and negative effects both spatially and temporally. Among these factors, the average labor productivity (ALP) was the dominant positive driving factor and energy intensity of production sector (EIP) was the dominant negative driving factor, (b) over multiple temporal scales, China experienced the largest energy-related CO2 emission growth in the sub-periods during 2001–2003 and 2003 to 2005. Additionally, the change in the CO2 emissions from 1997 to 1999 was negative, unlike the other 7 sub-periods, and (c) over multiple spatial scales, contribution values of the factors in different provinces varied significantly, where in general the positive driving effects outweighed the negative inhibiting effects. In total, CO2 emissions in eastern provinces increased to the largest degrees, followed by central and western ones. It is suggested that China should coordinate and balance the relationships between economic development and CO2 emission reduction, further decrease energy intensity of many production sectors, gradually adjust the economic and energy structures, and formulate CO2 emission reduction policies to accommodate regional disparities.