Calculation of global carbon dioxide emissions: Review of emission factors and a new approach taking fuel quality into consideration

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.

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.

The rapid urbanization of China has increased pressure on its environmental and ecological well being. In this study, the temporal and spatial profiles of China’s carbon dioxide emissions are analyzed by taking heterogeneities into account based on an integration of the extended stochastic impacts using a geographically and temporally weighted regression model on population, affluence, and technology. Population size, urbanization rate, GDP per capita, energy intensity, industrial structure, energy consumption pattern, energy prices, and economy openness are identified as the key driving factors of regional carbon dioxide emissions and examined through the empirical data for 30 provinces during 2006‒2010. The results show the driving factors and their spillover effects have distinct spatial and temporal heterogeneities. Most of the estimated time and space coefficients are consistent with expectation. According to the results of this study, the heterogeneous spatial and temporal effects should be taken into account when designing policies to achieve the goals of carbon dioxide emissions reduction in different regions.

Greenhouse gas emission factor for the energy sector in Mauritius

Climate change will become the priority for the air quality management. It focuses more on carbon dioxide emission. Indonesia which has power generation dominated by coal combustion is expected to develop the national CO 2 emission factor. Due to the high variation in Indonesia coal rank and its growing magnitude in CO 2 emission caused by the future coal-fired power plant development, the characteristic emission value becomes an important concern. The method used in this study is developed from the IPCC Guidelines for Energy. The conversion unit plays an important role in the calculation method. The result shows that the higher in C content, the lower in its CO 2 emission factor. It means that coal classified as high C content or high heating value will produce low carbon dioxide emission factor. The average CO 2 emission factor obtained in Indonesian coal is 99,718 kg CO 2 /TJ with an average value of carbon content 27.2 kg C/GJ, and NCV equal to 19.8 TJ/Gg. Coal rank is categorized as lignite to subbituminous or bituminous.

Primary energy-related carbon dioxide emissions in China

Using a back propagation neural network based on improved particle swarm optimization to study the influential factors of carbon dioxide emissions in Hebei Province, China

The estimation of the carbon dioxide emission and driving factors in China based on machine learning methods

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.

Dynamic analysis of carbon dioxide emissions in China's petroleum refining and coking industry

In agricultural systems, soil carbon dioxide emissions and physical properties are thought to depend largely on management practices. This field study was carried out in a semi-arid region of eastern Tunisia to evaluate the effects of tillage management on soil carbon dioxide emissions and related physical properties; bulk density (BD), penetration resistance (PR), total porosity (TP) and air-filled porosity (AFP). Tillage management treatments included plowing with a moldboard plow or a disk plow to different depths, described here as shallow (10 cm), medium (15 cm) and deep (25 cm). No-tillage was also considered as a control plot. Correlation analysis was used to explore how soil carbon dioxide emissions (CO2) were related to the other studied properties. The results showed higher carbon dioxide (CO2) emissions (p < .05) from tilled soil compared to no-till (NT), regardless of the tillage management. No significant differences in carbon dioxide (CO2) emissions were found between moldboard and disk plow tillage at the same tillage depth. Soil carbon dioxide release was the highest after deep tillage (moldboard = 0.101 t ha−1 and disk plow = 0.107 t ha−1) suggesting that deeper tillage to 25 cm promoted higher carbon dioxide (CO2) emissions. Significant differences with tillage were observed in bulk density (BD) and penetration resistance (PR) compared to no-tillage. Correlations of carbon dioxide emissions to soil physical properties across all the tillage treatments indicated significant negative relationships between carbon dioxide (CO2) emissions and soil bulk density (BD) and penetration resistance (PR) and significant positive relationships between carbon dioxide (CO2) and total porosity (TP) and air-filled porosity (WFP) suggesting that these soil attributes are important controlling factors of carbon dioxide (CO2) emissions.

The key obstacles to attaining the prevailing goal of viable development are environmental degradation and climate change. Copious efforts have been made in this field, but still, the policy embraced and the empirical connection among the factors of carbon dioxide emissions are not evident. The connection between the variables of the analysis is subject to a theoretical and statistical inconsistency in the research. Through this research work, the association between carbon dioxide emissions and its determinants such as economic growth, energy use, financial development and technical progress is examined in Malaysia for the period from 1985 to 2019. The auto-regressive distributed lag method is employed to estimate long-run parameters. The results indicate that during the research period TI has an inverse but negligible impact on pollution in Malaysia. The analysis further shows that higher growth of economy increases long-term environmental efficiency and is consistent with the Kuznets environmental hypothesis. Also, the findings show that financial sector development would minimize emissions of carbon dioxide, thereby enhancing environmental quality in Malaysia. The short-term findings do not validate the EKC hypothesis. The Granger causality results reveal a two-way causality ranging from the growth of the economy to carbon dioxide emissions and TI to carbon dioxide emissions.Keywords: CO2 emissions, Technological Growth, GDP, MalaysiaJEL Classifications: O3, Q3DOI: https://doi.org/10.32479/ijeep.11888

The urgency to mitigate carbon dioxide emissions (‘carbon emissions’) has gained significant societal attention as the consequences of climate change continue to unfold. There is a notable scarcity of studies conducted on carbon emissions for construction operations. Hence, the aim is to identify the key influence factor of carbon emissions to develop a carbon dioxide reduction framework for construction operations. A survey was conducted with 297 Malaysian construction stakeholders to analyse 18 factors. Descriptive and Rasch analyses were used. The results revealed the reliability and validity of the model, which could help explore ways to reduce carbon emissions during the construction phase. The findings indicate that a gradual transformation process is necessary to expedite the transition towards low carbon dioxide construction. The research enhances the existing literature by emphasising the key factors for stakeholders who aim to manage carbon emissions efficiently during a construction project. There are different ideas on how to estimate and manage carbon emissions in construction projects, but there are no standardised ways. This study looks into the main factors that contribute to the generation of carbon emissions in construction, to identify ways to reduce them, evaluating these factors from the perspective of different stakeholders.

China has made remarkable achievements in reducing carbon emissions in recent years. However, there is still much reduction room before achieving carbon neutrality. In Beijing, the capital of China, it is a strategic choice to respond to global climate change by promoting green and low-carbon development. This paper calculates the carbon dioxide emissions of key industries in Beijing and analyzes the temporal evolution trend of carbon emissions. Carbon dioxide emissions in Beijing before 2030 are predicted based on the grey prediction GM (1,1) and BP neural network model. The effects of factors of carbon dioxide emissions are discussed using the threshold regression model under different economic conditions. The results show that energy consumption intensity, GDP per capita, and the ownership of civil cars have a positive impact on carbon dioxide emissions, while the number of permanent residents and urban green space areas have a negative impact on carbon dioxide emissions. These findings of carbon emission prediction and influencing factors contribute to carbon reduction path design. Related policy implications on carbon emission reduction are put forward from the aspects of promoting industrial upgrading, accelerating the construction of advanced economic structures, optimizing transportation structures, and strengthening green building development.

China's transportation industry is entering a stage of high-quality development. Carbon emissions and environmental protection issues have put pressure on the construction of a green and low-carbon transportation system, and the transportation industry has become one of the industries with the fastest growth in carbon emissions. Therefore, it is of great significance to study the influencing factors of carbon dioxide emissions in the transportation industry and predict its carbon emissions. This article first thoroughly analyzes the main sources of carbon emissions in the transportation industry, including nine major energy consumption sources such as coal, gasoline, and diesel, and obtains the carbon emission values ​​from 2000 to 2017. Secondly, a linear regression analysis was performed on 13 pre-selected influencing factors and CO2 emissions in the transportation industry. In order to obtain the potential similarities between the factors, factor 13 is divided into four categories: economic scale, population size, transportation structure, and energy consumption. Each category and factor analysis is divided into four potential factors. Third, a training model was established based on the data from 2000 to 2012. Four algorithms, neural network (BP), extreme learning machine (ELM), genetic algorithm optimized neural network (GA-BP), and genetic algorithm optimized extreme learning machine (GA-ELM) are used to predict 2013 to 2017 and compare the predicted value of its respective algorithm with the actual value. Finally, the results show that the genetic algorithm optimized extreme learning machine (GA-ELM) hybrid heuristic algorithm has the highest degree of fit between the predicted value and the true value, which further illustrates the carbon emissions of the hybrid heuristic algorithm in the transportation industry. For the superiority of the prediction, the study also shows that the four influencing factors seriously affect the carbon emissions of the transportation industry. Therefore, accelerating the upgrading of the transportation structure and changing the proportion of energy consumption will be important measures for the transportation sector to control carbon emissions in the next step, so as to promote the sustainable development of the transportation system.