Provincial heterogeneity effects of electrification on carbon dioxide emissions in China and the moderating effect of power supply mix

Purpose: China is confronting with tremendous pressure in carbon emission reduction. While logistics industry seriously relies on fossil fuel, and emits greenhouse gas, especially carbon dioxide. The aim of this article is to estimate the carbon dioxide emission in China ’ s logistics sector, and analyze the causes for the change of carbon dioxide emission, and identify the critical factors which mainly drive the change in carbon dioxide emissions of China ’ s logistics industry . Design/methodology/approach: The logarithmic mean Divisia index (LMDI) method has often been used to analyze decomposition of energy consumption and carbon emission due to its theoretical foundation, adaptability, ease of use and result interpretation. So we use the LMDI method to analyze the changes in carbon dioxide emission in China ’ s logistics industry in this paper . Findings: By analyzing carbon dioxide emission of China ’ s logistics, the results show that the carbon dioxide emission of logistics in China has increased by 21.5 times, from 45.1 million tons to 1014.1 million tons in the research period. The highway transport is the main contributor to carbon dioxide emission in logistics industry. The energy intensity and carbon dioxide emission factors were contributing to the reduction of carbon dioxide emission in China ’ s logistics industry in overall study period. Originality/value: Although there are a lot of literature analyzed carbon dioxide emission in many industry sectors, for example manufacturing, iron and steel , pulp and paper, cement, glass industry, and so on. However, few scholars researched on carbon dioxide emission in logistics industry. This the first study is in the context of carbon dioxide emission of China ’ s logistics industry.

This paper empirically analyzes the impact of automation upon firms’ carbon dioxide emissions (CO2 emissions) of China by using data for the period 1998–2009. Our research yields a few findings. First, we find that automation as measured by the robot density can reduce firms’ CO2 emissions intensity. Specifically, 1% increase in the robot density leads to a 0.018% decrease in CO2 emissions intensity. Second, we find that automation reduces firms’ CO2 emissions intensity by promoting firms’ technological innovation and improving management efficiency. Finally, we find that automation exerts a greater impact on reducing CO2 emissions intensity for firms in industries with high CO2 emissions intensity rather than low CO2 emissions intensity, and for firms in capital-intensive industries rather than non-capital-intensive industries, as well as firms in industries with high servitization of manufacturing rather than low servitization of manufacturing. Moreover, the mitigating effects of automation have been given greater play on firms’ CO2 emissions intensity after the global financial crisis.

An analysis of the driving forces of energy-related carbon dioxide emissions in China’s industrial sector

97/00606 The Chinese energy system: Implications for future carbon dioxide emissions in China

Commercial department assumes the vital part in energy conservation and carbon dioxide emission mitigation of China. This paper applies the time-series data covering 2001-2015 and introduces the STIRPAT method to research the factors of commercial department's carbon dioxide emissions in China. The combination of STIRPAT method and ridge regression is first adopted to research carbon dioxide emissions of commercial department in China. Potential influencing factors of carbon dioxide emission, including economic growth, level of urbanization, aggregate population, energy intensity, energy structure and foreign direct investment, are selected to establish the extended stochastic impacts by regression on population, affluence and technology (STIRPAT) model, where ridge regression is adopted to eliminate multicollinearity. The estimation consequences show that all forces were positively related to carbon dioxide emissions in China's commercial department except for energy structure. Energy structure is the only negative factor and aggregate population is the maximal influencing factor of carbon dioxide emissions. The economic growth, urbanization level, energy intensity and foreign direct investment all positively contribute to carbon dioxide emissions of commercial department. The findings have significant implications for policy-makers to enact emission reduction policies in commercial sector. Therefore, the paper ought to take into full consideration these different impacts of above influencing factors to abate carbon dioxide emissions of commercial sector.

Influencing factors of the carbon dioxide emissions in China's commercial department: A non-parametric additive regression model

Drivers of China's carbon dioxide emissions: Based on the combination model of structural decomposition analysis and input-output subsystem method

This paper explores the dynamic relationship among bank credit, house prices and carbon dioxide emissions in China by systematically analyzing related data from January 2000 to December 2019 with the help of the time-varying parameter vector autoregression with stochastic volatility (TVP-SV-VAR) model and the Bayesian DCC-GARCH model. Empirical results show the expansion of bank credit significantly drives up house prices and increases carbon dioxide emissions in mosttimes. The rise in house prices inhibits the expansion of bank credit but increases carbon dioxide emissions and aggravates environment pollution, and that the increase in carbon dioxide is helpful to stimulate bank credit expansion and house price rise. In addition, bank credit and house prices are most relevant, followed by bank credit and carbon dioxide emissions, then by house prices and carbon dioxide emissions. Therefore, we believe that in order to stabilize skyrocketing house prices, restrain carbon dioxide emissions, and secure a stable and healthy macro-economy, the government should strengthen management of bank credit, and effectively control its total volume.

Primary energy-related carbon dioxide emissions in China

The nexus between digital economy and carbon dioxide emissions in China: The moderating role of investments in research and development

The Chinese government set a goal in 2009 to cut carbon emissions by 40–45 percent of 2005 GDP per unit by 2020. The role of fiscal decentralization reform in strengthening environmental governance has gained importance. This paper explored the impact of fiscal decentralization reform from 2010 to 2019 on carbon dioxide emissions in China. We utilized the first-order differential dynamic panel econometrics model to examine the correlation between fiscal decentralization and carbon dioxide emission under fiscal imbalance and transfer indirect effects. The findings revealed that 1) fiscal imbalance reduced CO2 emissions due to the decentralization of revenue, and expenditure asymmetry undermined CO2 emissions control. 2) The central government’s transfer payments offset the negative consequences of a fiscal imbalance. The fiscal decentralization of the government caused a difference between regional income and expenditures in the budget. However, it could affect local government expenditure on carbon emission control through central transfer payments, which could restrain carbon emissions and control environmental pollution. 3) The impact of fiscal decentralization on carbon dioxide emissions was influenced by the industrial structure with the U-Shape effect. This was because the adjustment of the industrial structure was cross-term. In the early stage of the industrial structure adjustment, there was a significant decline in coal consumption demand and carbon emissions reduced. However, as the proportion of the secondary industry increased, there was a significant positive correlation between the secondary sector and carbon dioxide emissions in China. Our findings have important policy implications. First, while the promotion of Chinese officials is based on local GDP performance, locals may introduce green GDP as the criterion for rating governments’ performance. Second, local governments should improve environmental governance by increasing technical, environmental protection, and innovation investment. All in all, the findings provide a theoretical basis for relevant research and policy suggestions for China.

Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector autoregression model

ABSTRACTThe aim of this paper is to examine the transitional dynamics of carbon dioxide emission in China by using a prefecture-level database. Convergence analysis is conducted and mobility probability plots (MPPs) are employed to examine the distribution dynamics of 286 cities from 2002 to 2011. The empirical investigation is conducted in three steps. In the first step, the research is carried out at the national level, and it provides an overall view of the evolution of carbon dioxide emission in China. In step two, the database is divided into smaller spatial groupings so as to investigate the relationship between carbon dioxide emission and geographical location. In the third step, the evolution of carbon dioxide emission in the key cities and in other non-key cities is examined, along with its implications for environmental protection. This study offers valuable information on convergence of carbon dioxide emission in China.

Industrial structural transformation and carbon dioxide emissions in China

Based on the carbon emission database of the China Urban Greenhouse Gas Working Group, this paper analyzed the spatiotemporal evolution characteristics and main influencing factors of urban carbon dioxide emissions in China using ArcGIS spatial analysis and SPSS statistical analysis methods, in order to provide a reference for the formulation of the national “double-carbon” strategy and the construction of low-carbon urbanization. The results showed that (1) the urban carbon dioxide emissions in China exhibit a “point-line-area” spreading spatial grid. Carbon dioxide emissions form a planar emission pattern surrounded by the Beijing–Tianjin–Hebei urban agglomeration, Yangtze River Delta urban agglomeration, and Central Plains urban agglomeration. A high per capita and high-intensity emission belt from Xinjiang to Inner Mongolia has been formed. (2) The proportion of industrial emissions continues to decrease, and the range of high industrial emissions has gradually crossed the “Hu Huan-yong Line”, spreading from eastern China to the whole country. The emissions from transportation, the service industry, and households have become new growth points, and high-value emissions from households have also shown a nationwide spreading trend. (3) The main factors influencing the spatial distribution of carbon dioxide emissions are urbanization, the economy, industry, investment, and household energy consumption.