Factors affecting carbon dioxide (CO2) emissions in China's transport sector: a dynamic nonparametric additive regression model

Developing the electric vehicle (EV) industry is generally considered to be an effective way of easing the imbalance between the supply and demand of oil, and, in addition, the pressure to reduce environmental pollution. Developed countries and most developing countries including Brazil, Russia, India, and China (so-called ‘BRIC’ countries) are actively promoting the development of EVs. By studying different types of widely-used gasoline internal combustion engine vehicles (ICEVs) and EVs, we compare the effect on the environment of utilizing EVs in both developed and developing countries. This is achieved by using a ‘well-to-wheel’ method. The results show that compared to gasoline ICEVs, EVs have a significant effect on CO2 emission reduction. However, the corresponding air pollution due to SO2, PM10, NOx, etc. for a given EV varies substantially in different countries because of the influence of several factors (electrical power structure, line loss rate, and so on). As developing countries use larger proportions of thermal power or present high line loss rates, pollutant emission produced by a certain EV is much higher than that in developed countries. Taking China as a typical developing country as an example, this research dynamically predicts the environmental effects expected in 2020 and 2025 due to a developing EV industry. Predictions are based on a method of Monte Carlo simulation and consider the government’s development plan for energy. Finally, according to the results obtained, policies and suggestions for the development of the EV industry in developing countries are proposed.

Can the development of electric vehicles reduce the emission of air pollutants and greenhouse gases in developing countries

Review of CO2 injection techniques for enhanced shale gas recovery: Prospect and challenges

Shale gas is becoming an increasingly promising alternative energy resource because of its high efficiency and environment-friendly characteristic. The amount of adsorbed gas on the shale matrix surfaces and dissolved gas in the shale matrix bulk is the dominant factor in the long-term productivity of shale reservoir. Although experimental measurements have been extensively carried out to investigate the gas adsorption and diffusion properties in the shale matrix, they cannot provide the detailed information on the microscopic transport mechanism of shale gas during the gas production process. Molecular simulation can accurately visualize the gas adsorption/desorption and diffusion processes in the shale matrix. In the present study, the recent research advances of molecular simulation on gas adsorption/desorption and diffusion in the shale matrix are reviewed. Firstly, the density functional theory (DFT) for shale gas molecule desorption/adsorption on the surface of the matrix crystal is illustrated. Then, the grand canonical Monte Carlo (GCMC) method predicting the amount of shale gas desorption/adsorption in the shale matrix crystal is introduced. Finally, molecular dynamics simulation (MD) for gas diffusion in the shale matrix is elucidated. Further developments of the molecular simulation method in shale gas production are also discussed.

Review of Molecular Simulation Method for Gas Adsorption/desorption and Diffusion in Shale Matrix

Optimized transesterification reaction for efficient biodiesel production using Indian oil sardine fish as feedstock

Shale gas is a significant source of unconventional energy With the largest recoverable reserves in the world, China has 361 trillion cubic meters of shale gas, which will contribute to ensuring its national energy security and promoting the diversity of energy supply Chinese government spares no effort to support the utilization and development of shale gas However, the development of shale gas remains deeply troubled; thus the authors would like to reveal the factors influencing shale gas development in China Through the study of various literature and carrying out interviews with experts about shale gas development, the authors collected 206 valid questionnaires from experts, scholars or researchers in shale gas field and then used Structural Equation Modeling to analyze the data, illustrate the relationships among environment, technology, resource, market and shale gas development and finally establish a shale gas development implementation model The research findings would help Chinese government advance shale gas development, and improve further studies in shale gas area

The situation analysis of shale gas development in China-based on Structural Equation Modeling

The purpose of the research is to study consumers׳ attitudes towards biodiesel implementation in China. In the paper, authors analyzed the future trends of diesel demand and revealed the prospects of biodiesel in China. And a comprehensive survey has been conducted for 226 private diesel vehicle owners (drivers) in Beijing. The questionnaire survey method is used to collect information, including respondents׳ viewpoints on fuels selection, biodiesel quality, biodiesel price and related policies. Furthermore, authors applied Structured Equation Modeling (SEM) to analyze the collected data. The results indicate that the most important factor affecting consumers’ selection of biodiesel is price. Other significant factors are policy, government incentives and biodiesel quality. Besides, the results also showed that policy and government incentives could affect the price and quality of biodiesel. These results give rise to serious consideration about consumers’ preference for and awareness of biodiesel. The research findings will help government and corporations to understand the consumer׳s energy consumption behaviors and improve the further implementation for biodiesel in Beijing.

Analyzing the factors that influence Chinese consumers׳ adoption of the biodiesel: The private vehicles owner׳s investigating in Beijing

The escalating issue of air pollution in China’s rapidly developing urban areas has prompted increased attention to the role of meteorological conditions in PM2.5 pollution. This study examines the spatiotemporal distribution of PM2.5 concentrations and their relationship with meteorological factors in six major Chinese urban agglomerations from 2017 to 2020, using daily average data. Statistical and spatial analysis techniques are employed, alongside the construction of eight machine learning models for prediction purposes. The study also compares the feature importance of various meteorological factors impacting PM2.5 concentrations. Results reveal significant regional differences in both average PM2.5 levels and meteorological influences. The Multilayer Perceptron (MLP) model demonstrates the highest prediction accuracy for PM2.5 concentrations. According to the MLP model’s feature importance identification, temperature is the most significant factor affecting PM2.5 concentrations across all urban agglomerations, while wind speed and precipitation have the least impact. Contributions from air pressure and dew point temperature, however, vary among different urban agglomerations. This research considers the impact of urban agglomerations and meteorological conditions on PM2.5 and also offers valuable artificial intelligence-based insights into the key meteorological factors influencing PM2.5 concentrations in diverse regions, thereby informing the development of effective air pollution control policies.

/pdf/pm2-5-concentration-prediction-in-six-major-chinese-urban-19kniyqa.pdf

PM2.5 Concentration Prediction in Six Major Chinese Urban Agglomerations: A Comparative Study of Various Machine Learning Methods Based on Meteorological Data

Population urbanization and economic development are highly correlated, and the degree of coordination between the two has gradually become one of the important issues for the healthy development of the region. With the advancement of high-quality integration in the Yangtze River Delta, it is particularly important to clarify the coupling and coordination process and spatial differentiation between population urbanization and economic development for the future integration and sustainable development of the Yangtze River Delta region. Given this, we establish a comprehensive evaluation index system of population urbanization and economic development based on the period of 2009–2020. Taking the Yangtze River Delta urban agglomeration as the research object, the entropy method and the coupling coordination model are used to carry out Spatio-temporal coupling analysis. The results show that (1) the level of population urbanization and economic development of the Yangtze River Delta urban agglomeration is continuously rising, while economic development lags behind population urbanization development, and the economic development level needs to be further improved. The degree of coupling coordination of population urbanization and economic development has continued to increase, and the synergistic development effect of the two has been improved, (2) the comprehensive level of population urbanization and economic development of each city in the Yangtze River Delta urban agglomeration shows an obvious inter-regional unbalanced development trend, and (3) there is an obvious polarization in the coupling coordination level between population urbanization and economic development in the Yangtze River Delta urban agglomeration.

Spatio-temporal Coupling Analysis of Population Urbanization and Economic Development in Yangtze River Delta Urban Agglomeration

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Chi Chen

Author Tools

Chat about Author

Papers

The situation analysis of shale gas development in China-based on Structural Equation Modeling

Analyzing the factors that influence Chinese consumers׳ adoption of the biodiesel: The private vehicles owner׳s investigating in Beijing

PM2.5 Concentration Prediction in Six Major Chinese Urban Agglomerations: A Comparative Study of Various Machine Learning Methods Based on Meteorological Data

Spatio-temporal Coupling Analysis of Population Urbanization and Economic Development in Yangtze River Delta Urban Agglomeration