Research on application of a hybrid heuristic algorithm in transportation carbon emission

Li, Yanmei; Dong, HongKai; Lu, Shuangshuang

doi:10.1007/s11356-021-14079-y

Cited by 24 publications

(6 citation statements)

References 47 publications

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“…While the transportation sector is forecast to grow by nearly 20% by 2030, to meet the Net Zero Scenario, the sector's emissions will need to decline by almost 20% to less than 6 metric tons (Zhao et al, 2022). The findings are in line with Li et al (2021); Jiang et al (2019); Huang et al (2021); Wang et al (2021); and Zhang et al (2021).…”

Section: Resultsmentioning

confidence: 69%

Empirical analysis of R&D spending, transport infrastructure development and CO2 emissions in China

Han

You

Meng

et al. 2023

Front. Environ. Sci.

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Over the past few decades, the transportation sector has been the largest contributor to CO2 emissions in China. Research and Development spending leads to technological innovation in the country and could affect the CO2 emission in the country. Therefore, this study analyzes the nexus between CO2 emissions, transport infrastructure and R&D spending in China. A QARDL approach was used for the data analysis, which revealed Research and Development and Transport infrastructure has a positive impact on CO2 emissions. R&D was only significant in the first 25% quantile, while transportation was significant in almost all quantiles. These results suggest that R&D spending in China is mainly allocated to the sectors that emit the CO2 emission. It is recommended that government should allocate more R&D to carbon-reducing sectors. Furthermore, the government should consider green transportation investments and renewable energy projects in the transportation sector to reduce CO2 emissions in the country.

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Section: Resultsmentioning

confidence: 69%

Empirical analysis of R&D spending, transport infrastructure development and CO2 emissions in China

Han

You

Meng

et al. 2023

Front. Environ. Sci.

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“…Predicting carbon emissions and determining the influencing factors of carbon emissions are beneficial for formulating policies and taking targeted measures. Many studies have shown that machine learning is a reliable tool for achieving the above goals [97,98]. Fields such as prefabrication, transportation, recycling, and machine learning were overlooked in 2022, but they still have research potential.…”

Section: Research Trendsmentioning

confidence: 99%

Scientometric Analysis and Visualization of Carbon Emission Studies in the Construction Industry

Luo,

Yang,

Huang

et al. 2024

Buildings

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The field of carbon emissions in the construction industry has drawn extensive attention from researchers and practitioners due to the issue of global warming. In this study, an in-depth analysis of the research status, trends, and frontiers in the field of carbon emissions in the construction industry was carried out. The CiteSpace tool was used to visualize and analyze relevant papers from 1985 to 2023, to describe the overall knowledge structure in the field of carbon emissions in the construction industry using dual-map overlay analysis, journal co-citation network analysis, and keyword co-occurrence network analysis, to apply cluster analysis and burst detection to identify research trends in the field and the frontiers, and to analyze the scientific collaborations in the field. Further, the core issues in the field of carbon emissions in the construction industry were explored and relevant recommendations were proposed. The results are of great significance in identifying and analyzing knowledge systems and research patterns in the field of carbon emissions in the construction industry and help us to discover and understand the current deficiencies, trends, and frontiers in this field, thus providing useful suggestions and reflections for policymakers, practitioners, researchers, and other stakeholders.

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“…Particle swarm optimization algorithm [15], genetic algorithm [64,65], Flame optimization [66], Manta foraging optimization [41], sparrow search algorithm [20], Gaussian perturbation bat algorithm [67] The primary evaluation criteria for the accuracy of each carbon emission prediction model are the variance (R 2 ), mean square error (RMSE) and mean absolute percentage error (MAPE), as shown in Equations ( 7)-( 9):…”

Section: Elmmentioning

confidence: 99%

A Review of Macroscopic Carbon Emission Prediction Model Based on Machine Learning

Zhao

Liu

et al. 2023

Sustainability

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Under the background of global warming and the energy crisis, the Chinese government has set the goal of carbon peaking and carbon neutralization. With the rapid development of machine learning, some advanced machine learning algorithms have also been applied to the control and prediction of carbon emissions due to their high efficiency and accuracy. In this paper, the current situation of machine learning applied to carbon emission prediction is studied in detail by means of paper retrieval. It was found that machine learning has become a hot topic in the field of carbon emission prediction models, and the main carbon emission prediction models are mainly based on back propagation neural networks, support vector machines, long short-term memory neural networks, random forests and extreme learning machines. By describing the characteristics of these five types of carbon emission prediction models and conducting a comparative analysis, we determined the applicable characteristics of each model, and based on this, future research ideas for carbon emission prediction models based on machine learning are proposed.

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Research on application of a hybrid heuristic algorithm in transportation carbon emission

Cited by 24 publications

References 47 publications

Empirical analysis of R&D spending, transport infrastructure development and CO2 emissions in China

Empirical analysis of R&D spending, transport infrastructure development and CO2 emissions in China

Scientometric Analysis and Visualization of Carbon Emission Studies in the Construction Industry

A Review of Macroscopic Carbon Emission Prediction Model Based on Machine Learning

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