A multisectoral decomposition analysis of Beijing carbon emissions

Factors that influence carbon emissions due to energy consumption in China: Decomposition analysis using LMDI

Decoupling economic growth from carbon dioxide emissions in China: A sectoral factor decomposition analysis

The emission of CO2 from major sectors and key industries are the predominant sources of regional CO2 emissions. It is the prerequisite to promote sectoral carbon emissions reduction, to cla-rify their influencing factors and investigate their relationship with regional economic growth. It is also of great significance for the implementation of regional total carbon emissions control. Using the Logarithmic mean Divisia index method (LMDI) and the Tapio decoupling model, we analyzed the driving factors, and decoupling status with economic growth of 13 major carbon emissions industries in Fujian Province from 1997 to 2017. The results showed that the electricity and heat production and supply industry was the major source of CO2 emissions in Fujian Province, with an increase of 101.74 Mt (from 18.89 Mt to 120.63 Mt) during the period 1997 to 2017. The top three industries with the fastest annual growth rate in CO2 emissions were non-ferrous metal smelting and rolling processing industry (18.1%), textile industry (12.1%), and ferrous metal smelting and rolling processing industry (12.1%). Among the influence factors for the changes in carbon emissions in 13 major industries, economic growth effect and population scale effect were the main positive driving factors, while the restraining effects of energy structure, energy intensity, and industrial structure were continuously increasing. In terms of decoupling relationship, the decoupling index between economic growth and industry-related CO2 emissions showed a downward trend on the whole. Since the 11th Five-Year-Plan period, some industries had begun to show strong decoupling to some extent. The farming, forestry, animal husbandry, fishery and water conservancy industry exhibited expansive negative decoupling, whereas the electricity and heat production and supply industry exhibited weak negative decoupling during 13th Five-Year Plan period. The effects of energy structure and energy intensity had substantial impacts on the decoupling with economic growth for various industries. The industrial structure effect had a smaller impact on the decoupling with economic growth, while the population scale effect had almost no impact.

China can be regarded as a group of disparate economies, so the responsibilities of reduction have to be decided by considering different development stages over the provinces as well as reaching fairness of allocation. This study analyzed factors that influenced carbon dioxide emission changes due to energy-related consumption of 30 mainland provinces in China from 2005 to 2011, which was to promote carbon emission reduction and allocate carbon emission allowance. First, the Logarithmic Mean Divisia Index (LMDI) technique was adopted to decompose the changes in carbon emissions at the provincial level into five effects that were carbon coefficient, energy structure, energy intensity, economic output and population-scale effect. Next, according to the LMDI decomposition results, the overall contributions of various decomposition factors were calculated and applied to distribute carbon emission allowance over 30 provinces in China in 2020. The total effects of economic output, population-scale effect and energy structure on carbon emissions were positive, whereas the overall effect of energy intensity was negative. The allocation of carbon emission allowance can facilitate decision makers to reconsider the emission reduction targets and some related policies.

As the global ambition is directed at net-zero 2050 amidst energy intensity-efficiency targets, the advanced economies, such as the United States of America (USA) has been consistently charged with more target-driven commitments. Considering this, the current study finds the influence of commercial, industrial, and household energy intensities on both the economic and environmental indicators. A set of cointegration approaches was employed to evaluate the long-run and short-run relationship between covariates and carbon emission over the period 1974–2019. Empirical findings reveal that all the covariates are positive and significantly related to carbon emissions. For instance, the emission of carbon dioxide is worsened by economic growth in both the short- and long-run. Additionally, intense use of energy across the commercial, household, and industrial sectors is responsible for an increase in environmental degradation arising from the emission of carbon emission. Importantly, environmental degradation that is attributed to energy intensity is far more (twice) in the commercial sector and household sector, than in the industrial sector. Regarding the economic aspects, there is statistical evidence that research and development expenditure in energy efficiency improves economic growth while higher energy intensities in the commercial and industrial sectors are detrimental to economic expansion. As a policy, the study suggests that the share of renewable or clean energy technology in the country’s energy mix should be significantly increased to over-turn the undesirable economic, environmental, and global warming-related issues in the United States. Other few directions for policy implication were addressed.

Carbon emissions and the rising effect of trade openness and foreign direct investment: Evidence from a threshold regression model

Previous research has investigated the connections between foreign direct investment (FDI), carbon emissions (CO2), and foreign trade openness. However, these past studies did not specifically focus on industrial sectors in China and their carbon emissions, thus leaving a gap in understanding this relationship. In our study, we aim to contribute to the existing body of knowledge by employing a threshold regression model with a threshold variable. This model calculates how strongly carbon emissions are produced to assess the impact of the industrial sector on carbon emissions. Our findings reveal that foreign trade openness and FDI have a threefold threshold impact on industrial carbon emissions. The effect of FDI on carbon emissions in the industrial sector shows a pattern of initially lowering and then increasing the emissions, indicating potential harm. Conversely, the impact of foreign trade openness on carbon emissions exhibits both positive and negative effects. While foreign trade openness exacerbates carbon emissions in economic sectors with lower carbon intensity, it helps mitigate emissions in sectors with high- carbon emission levels. Furthermore, our study identifies that the intensity of economic activity, per capita GDP, and the number of employees all significantly influence the industrial sector’s carbon emissions. By employing the latest cutting-edge methodology, our research opens the door for extrapolating these findings to other nations for a comprehensive analysis.

Driving forces for carbon emissions changes in Beijing and the role of green power

South China’s Guangdong Province, the Chinese largest provincial economy and the global 14th biggest economy, has been facing a huge challenge of achieving economic growth without emission growth. Developing new strategy for making economic growth compatible carbon reduction requires better understanding of the decoupling carbon emission from economic growth. In this paper, we conduct a comprehensive decoupling and decomposition analysis of carbon emission from economic output in Guangdong Province from a sector perspective. We firstly calculate carbon emission in six sectors based on the energy consumption of each sector and carbon coefficient of 13 types of fuels during 2000–2014, and then quantify the decoupling status between CO2 emissions and economic growth in those six sectors by using the Tapio decoupling index, finally, investigate the influencing factors of emissions by using the decomposition techniques. The modeling results show that agricultural sector has strong decoupling, industrial, transport and others sectors are weak decoupling; construction and trade sectors are expansive negative decoupling. We also find that energy intensity and economic output are the major factors influencing carbon emission, also the effects of energy structure and emission factor among six sectors are studied. Some policy recommendations finally are put forward.

As a matter of fact, carbon emission is a hot topic in contemporary society. This paper focuses on the carbon emission problem in Anhui Province, and adopts the IPCC method to measure the industrial carbon emission and total carbon emission in each region of Anhui Province, and analyses the decoupling of GDP and carbon emission in each region of the province from 2011 to 2020 based on the Tapio decoupling model. In addition, this study constructs a prediction model for the trend of carbon emission in the industrial sector of Anhui Province by using the STIRPAT model, and sets up three different scenarios for the indicators of the size of the industrial economy, the industrial output value per capita, the energy structure, the energy intensity, and the intensity of the industrial sector's carbon emission in Anhui, including the energy saving, the baseline, and the aggressive scenarios. According to the analysis, weakly decoupled relationship between GDP and carbon emissions in various regions of Anhui Province. In addition, the peak carbon emissions are 459.27 million tons, 493.25 million tons, and 562.48 million tons in 2030, 2035, and 2040 in the three different scenarios, namely, energy conservation, baseline, and aggressive scenarios, respectively.

Convergence of carbon dioxide emissions in different sectors in China

The primary objective of this study is to utilize the K-Nearest Neighbor Algorithm (KNN) to investigate the relationship between energy intensity and energy consumption across the Residential, Commercial, Industrial, Transportation, and electric power sectors. The paper approved the KNN Algorithm as more accurate than the remaining algorithms. The most influential factors affecting energy intensity are the Power Sector (61.89%), the Industrial Sector (24.62%), the Transportation Sector (8.5%), the Residential Sector (3.6%), and the Commercial Sector (1.3%). Consequently, the Industrial, Electric Power, and industrial Sectors have the most significant influence on energy intensity. Thus, enhancing the energy performance of these sectors can reduce energy intensity and maximize efficiency, leading to improved environmental sustainability.

Cities play a major role in decoupling economic growth from carbon emission for their significant role in climate change mitigation from national level. This paper selects Beijing (economic center and leader of emission reduction in China) as a case to examine the decoupling process during the period 2000–2015 through a sectoral decomposition analysis. This paper proposes the decoupling of carbon emission from economic growth or sectoral output by defining the Tapio decoupling elasticity, and combined the decoupling elasticity with decomposition technique such as Logarithmic Mean Divisia Index approach. The results indicate that agriculture and industrial sectors presented strong decoupling state, and weak decoupling is detected in construction and other industrial sectors. Meanwhile, transport sector is in expansive negative decoupling while trade industry shows expansive coupling during the study period. Per-capita gross domestic product, industrial structure, and energy intensity are the most significant effects influencing the decoupling process. Agriculture and industry are conducive to decoupling of carbon emissions from economic output, while transport and trade are detrimental to the realization of strong decoupling target between 2000 and 2015. However, construction and other industrial sectors exerted relatively little minor impact on the whole decoupling process. Improving and promoting energy-saving technologies in transport sector and trade sector should be the key strategy adjustments for Beijing to reduce carbon emissions in the future. The study aims to provide effective policy adjustments for policy makers to accelerate the decoupling process in Beijing, which, furthermore, can lay a theoretical foundation for other cities to develop carbon emission mitigation polices more efficiently.

To effectively address climate change, it is necessary to quantify the carbon emissions in high energy-consuming regions, analyze driving factors, and explore effective pathways for achieving green development. Therefore, this paper takes Liaoning Province as research area, using extended Kaya identity and LMDI method to analyze the driving factors of carbon emissions from energy consumption in five major industries and the residential consumption sector from 2011 to 2020 in Liaoning Province. Furthermore, this paper uses the Tapio model to explore the decoupling relationship between carbon emissions and economic development. The results show that: 1) From 2011 to 2020, total carbon emissions from energy consumption in five major industries showed a trend of initially declining and then rising, while carbon emissions from the residential consumption sector exhibited an upward trend. 2) For carbon emissions from the industrial sector, economic output and industrial structure are the primary factors that promote and inhibit carbon emissions respectively. The inhibitory effects of energy structure and energy intensity are not significant. Population scale has a certain promoting effect on carbon emissions. For residential energy consumption carbon emissions, Household consumption expenditure, residential energy structure, and residential population scale are driving factors that promote the growth of carbon emissions, while residential energy intensity restrains the growth of carbon emissions. 3) From 2011 to 2018, carbon emissions from the industrial sector have been decoupled from economic output, and the decoupling state is dominated by weak decoupling. However, carbon emissions are once again correlated with economic development in 2019–2020. Carbon emissions from residential energy consumption have not yet decoupled from consumption expenditure, and its decoupling state is unstable and has no obvious change rule.

Impact of labor and energy allocation imbalance on carbon emission efficiency in China's industrial sectors