Carbon Dioxide Emissions In China Research Articles

The pressure of coal consumption on China's carbon dioxide emissions: A spatial and temporal perspective

As the world's largest coal consumer, China is facing the dual challenge of implementing strict coal reduction policies while heavily relying on coal. It is crucial to comprehend the pressures exerted on carbon dioxide emissions from coal consumption as China strives to transition towards a carbon-neutral era. This study defines and classifies the pressure of carbon dioxide emissions resulting from coal consumption, referred to as "carbon-coal pressure", in 30 provinces (including municipalities and autonomous regions) from 1997 to 2019. This classification enriches the study of the pressure on carbon dioxide emissions by specific energy types. By calculating the centre of gravity of the carbon-coal pressure and its evolution trends, the spatial pattern of the carbon-coal pressure and the evolution characteristics of the centre of gravity of the pressure are revealed. The results demonstrate that, despite the continuous growth in total coal consumption and carbon dioxide emissions in China, the carbon-coal pressure index exhibits a decreasing trend in certain regions, with significant inter-regional differences. Most provinces fall into the high-pressure and higher-pressure categories. The number of high-pressure provinces has decreased by 33% from 18 to 8, while the number of low-pressure provinces has risen from 0 to 1, and both higher and medium pressure types have increased. The overall stress index decreases from 0.79 to 0.7. The pressure centre displays a similar spatial trend to the overall changes in coal consumption and carbon dioxide emissions centre, albeit with a smaller magnitude of change. Beijing stands out as the only province with low pressure.

Prediction method of regional carbon dioxide emissions in China under the target of peaking carbon dioxide emissions: A case study of Zhejiang

AbstractAll provinces of China respond to the central government, predict future carbon dioxide emissions, and formulate action plans detailing how the province intends to fulfill its target of carbon emission peaking before 2030. Based on the bottom‐up energy consumption prediction and top‐down goal verification, this paper constructs a set of regional carbon dioxide emission prediction methods. Compared to the traditional bottom‐up prediction method, this method could simplify the parameters while improving the prediction accuracy. This model is used to predict and analyze the process of carbon dioxide emission peaking in Zhejiang. The results show that the mean absolute percentage error of the retrospective prediction value is only 1.56%. Zhejiang will reach carbon dioxide emission peaking around 2029–2030, and the peak value will be 569.7 million tons. Different factors have different effects on the process of carbon dioxide emission peaking. There is a strong correlation between the peak time of carbon dioxide emission and the production time of major energy‐consuming projects in Zhejiang. Meanwhile, if the 16 nuclear reactors are not put into operation, Zhejiang will not be able to achieve the goal of carbon dioxide emission peaking. Besides, the basic data used in this model is mainly from the local statistical departments of the region. Thus, it can be applied to other provinces and regions conveniently.

Study on general industrial solid waste and carbon reduction in China: Coupling coordination model, life cycle assessment and environmental safety control

The production of general industrial solid waste (GISW) which has grown rapidly along with the economy, has become a bottleneck that is impeding the sustainable and healthy growth of society and the economy. At the same time, China, as the country with the largest carbon emissions at present, has set a double carbon target, so China is faced with the dual goal of promoting the synergy of GISW pollution and carbon reduction. This paper systematically reviews the actual production data, literature reports, standard specifications, etc. of GISW and carbon dioxide emission in China, and processes the data with synergistic effect model. Both GISW and carbon emission in China show an obvious spatial aggregation effect, and the coupling and coordination index of both grows from moderate maladjustment to senior coordination stage, and the synergistic effect of different regions gradually increases. Selecting typical pollutants from key provinces analyzed based on coupling coordination degree, and the life cycle assessment was performed using SimaPro 9.0 software, the results show that fly ash is more destructive to the environment and causes higher carbon emissions than the other two GISW, with carbon emission equivalent being about 1.8 times that of tailings and 1.6 times that of EMR. Compared with the carbon emission equivalent of 1.12 × 103 kg CO2 eq in the preparation of traditional cementitious materials, the preparation of coal-based cementitious materials generates less greenhouse gases with carbon emission equivalent of about 1.06 × 103 kg CO2 eq, which has a significant solid waste consumption effect as well as carbon reduction effect. Meanwhile, from the GISW storage process, a disposal environmental safety control system is established mainly for heavy metals, volatile organic compounds and atmospheric pollutants by comprehensively referring to literature and standard specifications.

Does Higher Institutional Quality Result in Environmental Sustainability? A Comparative Study of India and China

There is a lack of comprehensive research comparing India and China in terms of institutional quality and environmental degradation. This special issue aims to make threefold distinct contributions. By comparing India and China, we can learn from each other's experiences and gain insight into the shared difficulties both nations face when trying to achieve environmental sustainability in the face of high-quality institutional frameworks. The impact of corruption, economic development, ecological risk, and renewable energy sources on carbon dioxide emissions in China and India is investigated in this study. For this reason, we also employ the ARDL model and pairwise Granger causality to prove that something caused another. Applying the ARDL long-run bound test, the empirical results reveal a long-run connection between variables in both China and India. In the case of China and India, the estimated results also reveal a negative correlation between carbon emissions and the usage of renewable energy sources, but a positive correlation between carbon emissions and economic growth, corruption, and environmental risk. Improving institutional quality is the goal, and the present theoretical and applied discourse on comparative environmental sustainability is a good place to start. As a result, policymakers in China and India should oppose unfair practices that undermine competition laws and policies by instituting stringent anti-corruption measures and environmental rules. Energy efficiency measures that reduce carbon emissions without slowing economic growth should also be the government's primary focus.

Prediction intervals for carbon dioxide emissions in china by extreme learning machine ensemble based on particle swarm optimization

<p>The emission of carbon dioxide is the major cause of the greenhouse effect, which has a negative impact on human survival and sustainable economic development; therefore, it is very significant for discovering the underlying influential factors of carbon dioxide emissions. In this paper, an extreme learning machine ensemble based on particle swarm optimization approach(PSO-ELMEnsemble) are applied to predict the emission of carbon dioxide, which provide estimated values as well as the corresponding reliability. In addition, the particle swarm optimization approach is used to optimize the connection weights of extreme learning machine. The performance of the proposed PSO-ELMEnsemble is experimentally validated by carbon dioxide emissions data during the period 1978-2016 by using state-of-the-art comparative method, where the proposed approach outperforms the others in achieving the best trade-off between accuracy and simplicity.</p>

The environmental cost of cryptocurrency: Assessing carbon emissions from bitcoin mining in China

This study estimates the environmental impacts of Bitcoin mining. Employing a top-down measurement approach, this paper assesses the carbon footprint of Bitcoin mining in China from 2017 to 2021. The findings reveal that mining activities during this period contributed to a total of 77.84 million tons of carbon dioxide emissions in China. By utilizing data at the provincial level, we find that the seasonal migration of Bitcoin mining pools will lead to regional power demand shocks in China. Additionally, this study predicts future carbon emissions from Bitcoin mining in China, projecting cumulative carbon dioxide emissions of 76.40 million tons and 722.18 million tons by 2030 and 2060 respectively, in the absence of any policy interventions. Based on these findings, this paper posits that governments worldwide should make efforts to restrict the carbon emissions from Bitcoin mining and opt for environmentally friendly technological methods to fundamentally alleviate Bitcoin's reliance on energy. The implication for central banks is that carbon emission should be taken into consideration when designing the central bank digital currencies (CBDCs).

Insights into the efficiency of China's green energy policies

Given the global inclination of economies toward green solutions, the current study aims to explore the association between green energy transition, adjusted net saving, energy intensity, and carbon dioxide emission in China. To achieve this, a novel approach utilizing Wavelet analysis is employed to examine the dynamic association among these variables from 1979 to 2020. The analysis utilized continuous wavelet, wavelet coherence, and partial and multiple wavelet coherence techniques, offering a comprehensive understanding beyond the limitations of prior research by investigating the relationships over various time frames. The findings from the wavelet coherence analysis yielded intriguing insights, indicating that an increase in energy transition is likely to result in higher energy intensity and increased CO2 emissions. Surprisingly, the study observed a negative impact of adjusted savings on energy transition in the medium run, whereas in the long term, an increase in adjusted net savings was associated with higher carbon emissions in the Chinese economy. This highlights the intricate interplay between energy transition, adjusted savings, and environmental impacts, with different time scales playing a significant role in shaping these connections. Policymakers should carefully consider these dynamics to formulate effective strategies for sustainable development and carbon emission reduction in China. Furthermore, employing partial and multiple coherence analysis, the outcomes has demonstrated a strong interconnected correlations between energy intensity, green energy transition, CO2 emissions, and adjusted net savings across short-run, medium-run, and long-run time frequencies. Overall, the study concludes the positive link between energy and carbon emissions in China, while the effectiveness of the green energy transition remains vulnerable. The findings offer valuable insights for informed decision-making and policy implications, facilitating the adoption of appropriate measures to promote a greener and more sustainable future in China.

Research on the Relationship Between Provincial OFDI And Carbon Emissions Under Dual Carbon Targets

With the development of economic globalization and China's continuous high-quality development, China's global status continues to rise, driving more and more companies to "go out" to conduct foreign direct investment overseas in order to seek a larger market, in the face of the two major tasks of "economic development" and "achieving dual carbon goals", studying the impact of foreign direct investment on my country's carbon dioxide emissions has a very important reality for whether our country can achieve economic transformation and fulfill its emission reduction commitments. and guiding significance. Therefore, this article based on the panel data of energy consumption in 30 provinces and regions in China from 2004 to 2019 . At the same time, per capita GDP, technological level, industrial structure, etc. were selected as control variables, a fixed effect model was established, and an empirical analysis was conducted on the carbon emission effects of my country's foreign direct investment by region and time period. Empirical analysis results show that: The increase in the scale of OFDI will promote the increase of carbon dioxide emissions in China, but there are significant differences in the carbon emission effects of various regions. The carbon emission effect of foreign direct investment in the eastern and western regions is significantly higher than that in the central region. Over time, the expansion of foreign direct investment will reduce the increase in carbon dioxide emissions it brings. In addition, the improvement of per capita GDP and industrial structure will increase carbon dioxide emissions, while the improvement of technological level will significantly reduce carbon dioxide emissions. Finally, reasonable policy suggestions are given based on the results of empirical analysis and the current situation of foreign direct investment and domestic carbon dioxide emissions.

Evolution Characteristics and Main Influencing Factors of Carbon Dioxide Emissions in Chinese Cities from 2005 to 2020

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.

Analysis of the impacts of carbon dioxide emission on climate change

In recent years, the global excess carbon dioxide emission had caused severe climate change and increase in global temperature since the start of Industrial Revolution. To be specific, extreme weathers have been observed all over the world due to global warming. Therefore, it is of vital importance that the causes of excess carbon dioxide emission should be studied along with respective mitigating strategies. On this basis, this study aims to study and investigate the global history and causes of excess carbon dioxide emission, as well as the physical chemical mechanism of greenhouse effect. In detail, case analysis of excess carbon dioxide emission in China is provided with the relevant negative impact of climate change in China. According to the analysis, policies adopted by the Chinese government and their efficiencies and corresponding suggestions are also discussed in the meantime. Overall, these results shed light on guiding further exploration of carbon dioxide emission on climate change.

Influencing the Variable Selection and Prediction of Carbon Emissions in China

In order to study the changing rule of carbon dioxide emissions in China, this paper systematically focused on their current situation, influencing factors, and future trends. Firstly, the current situations of global carbon dioxide emissions and China’s carbon dioxide emissions were presented via a visualization method and their characteristics were analyzed; secondly, the random forest regression model was used to screen the main factors affecting China’s carbon emissions. Considering the different aspects of carbon emissions, 29 influencing factors were determined and 6 main influencing factors were determined according to the results of the random forest regression model. Then, a prediction model for carbon emissions in China was established. The BP neural network model, multi-factor LSTM time series model, and CNN-LSTM model were compared on the test set and all of them passed the test. However, the goodness of fit of the CNN-LSTM model was about 0.01~0.02 higher than the other two models and the MAE and RMSE of the CNN-LSTM model were about 0.01~0.03 lower than those of the other two models. Thus, it was selected to predict China’s carbon dioxide emissions. The predicted results showed that the peak of China’s carbon emissions will be around 2027 and the peak of these emissions will be between 12.9 billion tons and 13.2 billion tons. Overall, the paper puts forward reasonable suggestions for China’s low-carbon development and provides a reference for an adjustment plan of energy structure.

Investigating the Direct and Spillover Effects of Urbanization on Energy-Related Carbon Dioxide Emissions in China Using Nighttime Light Data

Cities are the main emission sources of the CO2 produced by energy use around the globe and have a great impact on the variation of climate. Although the implications of urbanization and socioeconomic elements for carbon emission have been extensively explored, previous studies have mostly focused on developed cities, and there is a lack of research into naturally related elements due to the limited data. At present, remote sensing data provide favorable conditions for the study of large-scale and long-time series. Also, the spillover mechanism of urbanization effects on the discharge of carbon has not been fully studied. Therefore, it is necessary to distinguish the types of influence that various urbanization factors have on emissions of CO2. Firstly, this study quantifies the urban CO2 emissions in China by utilizing nighttime lighting images. Then, the spatio-temporal variations and spatial dependence modes of CO2 emissions are explored for 284 cities in China from 2000–2018. Finally, the study further ascertains that multi-dimensional urbanization, socio-economic and climate variables affect the discharge of carbon using spatial regression models. The results indicate that CO2 emissions have a remarkable positive spatial autocorrelation. Urbanization significantly increases CO2 emissions, of which the land urbanization contribution towards CO2 emissions is the most important in terms of spillover effects. Specifically, the data on urbanization’s direct effects reveal that CO2 emissions will increase 0.066%when the urbanization level of a city rises 1%, while the spillover effect indicates that an 0.492% emissions increase is associated with a 1% rise of bordering cities’ average urbanization level. As for the socio-economic factors, population density suppresses CO2 emissions, while technological levels boost CO2 emissions. The natural control factors effect a remarkable impact on CO2 emissions by adjusting energy consumption. This study can provide evidence for regional joint prevention in urban energy conservation, emission reduction, and climate change mitigation.

Role of green energy usage in reduction of environmental degradation: A comparative study of East Asian countries

Global environmental sustainability is a key concern, where green energy usage is considered an important indicator. This paper aims to empirically investigate the impact of green energy usage on environmental degradation. A sample of East Asian countries is selected from 1990 to 2021. For this purpose, comparative analysis and pooled analysis are undertaken. East Asian region is chosen for study as these countries are pioneers of clean green energy usage worldwide. The impact of green energy usage on environmental sustainability is analysed along with controlling the effect of health, outcome, population, and financial development. The study's findings indicate that green energy significantly decreased environmental degradation by reducing carbon dioxide emissions in China and Japan. However, Mongolia is not a sovereign nation in producing and consuming green energy; thus, renewable energy consumption could not reduce environmental degradation there. Seemingly, pooled analysis also proved that green energy is crucial for sustainable environment in East Asia and helps policymakers globally take sustainable environmental measures, which becomes a crucial requirement for many other activities that enhance growth.

Retracted: Investigating the Impact of Transportation Infrastructure and Tourism on Carbon Dioxide Emissions in China.

[This retracts the article DOI: 10.1155/2022/8421756.].

A novel grey Verhulst model with four parameters and its application to forecast the carbon dioxide emissions in China

In the context of dual carbon targets, a reliable prediction of China's carbon dioxide emissions is of great significance to the design and formulation of emission reduction policies by Chinese government. To this end, a novel grey Verhulst model with four parameters is proposed in this paper according to the evolution law and the data characteristics of China's carbon dioxide emissions. The new model solves the defect of poor structural adaptability of the traditional grey Verhulst model by introducing a nonlinear correction term. Besides, the range of values for the order of the grey generation operator of the new model is expanded from a positive real number to any real number (r ∈ R+ → r ∈ R) by expanding the value range of the Gamma function. The new model is used to simulate China's carbon dioxide emissions, and its comprehensive mean relative percentage error is only 0.65 %, which is better than that of the other three grey models (2.39 %, 2.34 %, 2.35 % respectively). It shows that the proposed new model has better modeling ability. Finally, the new model is applied to predict China's carbon dioxide emissions, and the results show that it will still increase year by year, reaching 13,687 million tons by 2028 (only 11,420 million tons in 2021). Therefore, some countermeasures and suggestions are proposed to control China's carbon dioxide emissions in this paper.

The effect of automation on firms’ carbon dioxide emissions of China

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.

Modelling and forecasting non-renewable energy consumption and carbon dioxide emissions in China using a PSO algorithm-based fractional non-linear grey Bernoulli model

In China, the consumption of non-renewable energy increases not only in general economic growth but also in large amounts of carbon dioxide (CO2) emissions which cause disasters and catastrophic damages to the environment. To alleviate environmental pressure, it is neccessary to forecast and model the relationship between energy consumption and CO2 emissions. In this study, a fractional non-linear grey Bernoulli (FANGBM(1,1)) model based on particle swarm optimization is proposed to forecast and model non-renewable energy consumption and CO2 emissions in China. Firstly, based on the FANGBM(1,1) model, non-renewable energy consumption in China is predicted. The comparison results of several competitive models show that the FANGBM(1,1) model has the best predictive performance. Then, the relationship between non-renewable energy consumption and CO2 emissions is modeled. On this basis, China's future CO2 emissions are effectively predicted based on the established model. The forecast results show that the growth trend of China's CO2 emissions will continue to grow to 2035, while the prediction results in different scenarios also show that that the different growth rates of renewable energy share lead to different times to peak CO2 emissions. In the end, relevant suggestions are proposed to support China's dual carbon goals.

Prediction of carbon dioxide emissions in China using a novel grey model with multi-parameter combination optimization

With the rapid development of industrialization and urbanization in China, the carbon dioxide emissions keep increasing for a long time. It is of great significance to reasonably predict the carbon dioxide emissions to realize “carbon peaking and carbon neutrality” goals in China. To this end, based on the new structure grey model, the variable orders are defined and optimized differentially, and the smoothing generation operator is introduced to variously design and optimize the driving term. Thus, a new multivariable grey prediction model with the combination optimization of multi-parameter is constructed. The new model is applied to predict the carbon dioxide emissions in China, and its comprehensive error is only 0.085%, which is far superior to the traditional similar grey prediction models (3.274%, 7.713%). This study provides a new modeling method for carbon dioxide emissions prediction, and has positive significance for enriching and improving the structural and methodological systems of grey prediction models.

Impact of Innovation in Solar Photovoltaic Energy Generation, Distribution, or Transmission-Related Technologies on Carbon Dioxide Emissions in China

Impact of Innovation in Solar Photovoltaic Energy Generation, Distribution, or Transmission-Related Technologies on Carbon Dioxide Emissions in China

Green finance, renewable energy development, and climate change: evidence from regions of China

In this study, using data from 2010 to 2021, and by utilizing the stochastic impacts by regression on population, affluence, and technology (STIRPAT) theory, and system generalized method of moments, the effect of green financing and deployment of renewable energy on carbon dioxide emissions in China and its provinces were analyzed. The results show that green financing reduces environmental pollution at the country level. Moreover, with a 1% increase in renewable energy consumption, carbon dioxide emission can be expected to decrease by 0.103%. It also demonstrates that green financing has a statistically significant coefficient only in provinces located in the eastern and western regions. Chinese policymakers should incentive policies for provinces in the eastern region of China in order to have a cleaner environment. The central region should be under supportive and pressure policies to move faster along the path to sustainable development.