Modeling carbon emission trajectory of China, US and India

Wang, Qiang; Li, Shuyu; Pisarenko, Zhanna

doi:10.1016/j.jclepro.2020.120723

Cited by 90 publications

(27 citation statements)

References 53 publications

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“…2 It has been further unveiled that primary energy use by urban China exceeded 80% of the total primary energy use in China. 3 The gradual upsurge in urban agglomeration has led to rapid land agglomeration in China, which is the agglomeration of the built environment. To this end, the infrastructure industry played a key role in the economy.…”

Section: Introductionmentioning

confidence: 99%

Are the intensity of energy use, land agglomeration, CO₂ emissions, and economic progress dynamically interlinked across development levels?

Ahmad

Anser

et al. 2020

Energy & Environment

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Although urban agglomerations have introduced substantial contributions to the economies around the globe, it has also led to the serious environmental challenges. However, this situation may vary across the development levels. The existing knowledge offers a gap in terms of both theoretical and empirical grounds. The Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) is previously not known to incorporate land agglomeration and the intensity of energy use. Besides, the investigation of linkages among the variables of interest across the development levels within a country is not known to be considered by the existing knowledge. This study systematically investigates the heterogeneous dynamic causality among the intensity of energy use, land agglomeration, carbon dioxide emissions (CO2), and economic progress across the development levels in the Chinese economy, considering 29 provinces for the period 2000 to 2018. To this end, a long-term co-integration association is tested and found existent among the variables of interest. A dynamic common correlated effects mean group approach is applied for impact analysis. The key findings include: The impacts of economic progress and land agglomeration on CO2 are found positive and significant in the country panel and western zone of China (WZC). It turned to be neutral in the case of the central zone of China (CZC) and significantly negative in the eastern zone of China (EZC). To this end, economic progress presented a ‘development ladder-based CO2 mitigation effect,’ while the land agglomeration exposed the ‘land agglomeration ladder-based CO2 mitigation effect’. Further, the causalities extracted are: first, economic progress is found in positive bilateral linkages with the intensity of energy use and land agglomeration for all the panels. Second, a positive and unilateral causal bridge is found operating from land agglomeration to the intensity of energy use and from the intensity of energy use to CO2. Third, a unilateral linkage of mixed nature is exposed to exist from land agglomeration to CO2, with positive causal links for country panel and WZC, negative causal links for EZC, while a neutral linkage is found for CZC. Fourth, a bidirectional link with mixed causalities appeared in the country panel and WZC. Economic progress increased CO2 in WZC. Next, a negative bilateral link is observed between the two variables in EZC. Additionally, this link remained neutral in CZC. Based on empirics, it is revealed that the development level matters in determining the links among the variables of interest.

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Section: Introductionmentioning

confidence: 99%

Are the intensity of energy use, land agglomeration, CO₂ emissions, and economic progress dynamically interlinked across development levels?

Ahmad

Anser

et al. 2020

Energy & Environment

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“…By improving the traditional EKC theory based on the endogenous economic growth model-the Moon-Sonn model, Luo et al predicts the future economic growth pattern of China and the trend of the total carbon emissions generated by China's future energy consumption, finding that under the current level of technological development, China would reach the peaks of carbon emissions and energy consumptions in 2040 and 2043, respectively [19]. Wang et al used carbon emission data and Gross Domestic Product (GDP) data in the past 30 years as a benchmark and a prediction model of discrete second-order difference equation to predict carbon emissions and GDP in 2020 [20]. Wang et al used a Logistic model to simulate the carbon emission of each province in China from 2010 to 2020 [21].…”

Section: Research On Carbon Emission Predictionmentioning

confidence: 99%

“…By summarizing carbon emission prediction models and methods worldwide, Wang et al (2020) have created a discrete second-order difference equation prediction model to predict China’s carbon emissions in 2020, and the results suggested that China’s carbon emissions’ growth rate would continue to grow in the next ten years and reducing carbon emissions per unit of GDP is of great significance for reducing carbon emissions [ 25 ]. After a quantitative analysis of carbon emissions’ driving factors in China, the United States, Japan and Europe, Wang et al (2020) find that in the past 20 years, the indicators of population and economic development had a very noticeable impact on the hike of China’s carbon emissions, while the indicators of carbon emission intensity and energy intensity had a depressing effect on carbon emissions [ 26 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of direct carbon emissions of Chinese provinces using artificial neural networks

Jin

2021

PLoS ONE

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Closely connected to human carbon emissions, global climate change is affecting regional economic and social development, natural ecological environment, food security, water supply, and many other social aspects. In a word, climate change has become a vital issue of general concern in the current society. In this study, the carbon emission data of Chinese provinces in 1999–2019 are collected and analyzed, so as to identify the carbon emission of direct consumption per 10,000 residents in each province (including each municipal city and autonomous region) and the entire nation based on population data. The Arc Geographic Information Science Engine (ArcGIS Engine) and C#.NET platform are employed to call the MATLAB neural network toolbox. A model is selected and embedded in the prediction system to develop the entire system. This study demonstrates that the carbon emissions per resident in Northern China are significantly higher than those in Southern China, with the rate of carbon emissions continuing to increase over time. Compared with other models, the Elman neural network has a higher carbon emission prediction accuracy, but with more minor errors. For instance, its accuracy and prediction performance are improved by 55.93% and 19.48%, respectively, compared with the Backpropagation Neural Network (BPNN). The prediction results show that China is expected to reach its peak carbon emission in around 2025–2030. The above results are acquired based on the concept of carbon emissions and neural network model theories, supported by GIS component technology and intelligent methods. The feasibility of BPNN, Radial Basis Function (RBF) and Elman neural network models for predicting residential carbon emissions is analyzed. This study also designs a comprehensive, integrated and extensible visual intelligent platform, which is easy to implement and stable in operation. The trend and characteristics of carbon emission changes from 2027 to 2032 are explored and predicted based on the data about direct carbon emissions of Chinese provincial residents from 1999 to 2019, purposed to provide a scientific basis for the control and planning of carbon emissions.

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“…Carbon emission is the biggest problem faced by developing countries like India. It is followed by China in CO 2 emission (Wang et al , 2020). The majority of India’s power demand is met by fossil-based coal-fired power plants.…”

Section: Carbon Emissionsmentioning

confidence: 99%

Current scenario of wind power in India, government policies, initiatives, status and challenges

Satpute

Kumar

2020

IJESM

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Purpose This paper aims to review the role of government initiatives for the development of wind power industries in India, to provide better and benevolent policies in the production of wind energy density and to maximize the use of the renewable source of energy which permits to reduce carbon emission from the coal-based power plant and to curtail tackle need of society and mitigate poverty. Design/methodology/approach The present study is carried out on the current position of wind power generation in India. Government policies for promoting clean energy and associated problems are also analysed herein detail. However, secondary approaches are opted in terms of alertness of caring for the environment hazardous and reduced the major economies aspects by fulfilling the schema of Kyoto Protocol and Paris Agreement, United Nations Framework Convention on Climate Change. Findings The prospective of wind energy generation is huge, as an ancient source of energy, wind can be used both as a source of electricity and for agricultural, irrigation uses. The study of wind turbine blades and its features showed how it can be properly fabricated and used to extract the maximum power, even at variable and low wind speeds. Research limitations/implications Although India has achieved a remarkable advancement in wind power sectors, it needs to eradicate all the loopholes to evolve as super power in wind energy sector leaving behind its rivalry China. To do this, it is required to develop in many fields such as skilled manpower, advancement in research and development, grid and turbine installation, proper distribution, smooth land acquisition, modern infrastructure, high investment and above all industry friendly government policy. Practical implications The present study finds out effects of wind power energy as a source of renewable energy to mitigate energy crisis. Social implications As a source of renewable energy and cost effectiveness, wind power can be evolved as a potential means enhance social life. Originality/value The present paper caries out critical analysis for the active use of renewable energy in the present and forthcoming days. Such unique analysis must help India as a developing nation to balance its energy crisis.

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Modeling carbon emission trajectory of China, US and India

Cited by 90 publications

References 53 publications

Are the intensity of energy use, land agglomeration, CO₂ emissions, and economic progress dynamically interlinked across development levels?

Are the intensity of energy use, land agglomeration, CO₂ emissions, and economic progress dynamically interlinked across development levels?

Prediction of direct carbon emissions of Chinese provinces using artificial neural networks

Current scenario of wind power in India, government policies, initiatives, status and challenges

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Modeling carbon emission trajectory of China, US and India

Cited by 90 publications

References 53 publications

Are the intensity of energy use, land agglomeration, CO2 emissions, and economic progress dynamically interlinked across development levels?

Are the intensity of energy use, land agglomeration, CO2 emissions, and economic progress dynamically interlinked across development levels?

Prediction of direct carbon emissions of Chinese provinces using artificial neural networks

Current scenario of wind power in India, government policies, initiatives, status and challenges

Contact Info

Product

Resources

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Are the intensity of energy use, land agglomeration, CO₂ emissions, and economic progress dynamically interlinked across development levels?

Are the intensity of energy use, land agglomeration, CO₂ emissions, and economic progress dynamically interlinked across development levels?