Reprint
Modeling and Simulation of Carbon Emission Related Issues
Edited by
August 2019
420 pages
- ISBN978-3-03921-311-5 (Paperback)
- ISBN978-3-03921-312-2 (PDF)
This book is a reprint of the Special Issue Modeling and Simulation of Carbon Emission Related Issues that was published in
Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary
Carbon emissions reached an all-time high in 2018, when global carbon dioxide emissions from burning fossil fuels increased by about 2.7%, after a 1.6% increase in 2017. Thus, we need to pay special attention to carbon emissions and work out possible solutions if we still want to meet the targets of the Paris climate agreement. This Special Issue collects 16 carbon emissions-related papers (including 5 that are carbon tax-related) and 4 energy-related papers using various methods or models, such as the input–output model, decoupling analysis, life cycle impact analysis (LCIA), relational analysis model, generalized Divisia index model (GDIM), forecasting model, three-indicator allocation model, mathematical programming, real options model, multiple linear regression, etc. The research studies come from China, Taiwan, Brazil, Thailand, and United States. These researches involved various industries such as agricultural industry, transportation industry, power industry, tire industry, textile industry, wave energy industry, natural gas industry, and petroleum industry. Although this Special Issue does not fully solve our concerns, it still provides abundant material for implementing energy conservation and carbon emissions reduction. However, there are still many issues regarding the problems caused by global warming that require research.
Format
- Paperback
License
© 2019 by the authors; CC BY-NC-ND license
Keywords
household consumption; total carbon emissions; CLA Model; influence factor; STIRPAT model; carbon emissions; influencing factors; decoupling elasticity; Generalized Divisia Index; Tapio’s model; household CO2 emissions (HCEs); per capita household CO2 emissions (PHCEs); input–output model; refined oil distribution; inventory routing problem; hybrid genetic algorithm; carbon emissions; carbon tax; decoupling analysis; greenhouse gas emissions; carbon footprint; low-carbon agriculture; causal factors; CO2 emissions forecasting; VARIMAX-ECM model; sustainable development; economic growth; population growth; carbon price fluctuation; renewable energy; real options analysis; investment under uncertainty; carbon emissions; carbon tax; activity-based costing (ABC); capacity expansion; green quality management; product-mix decision model; mathematical programming; long-term; final energy consumption; LT-ARIMAXS model; sustainable development; economic growth and the environment; error correction mechanism model; activity-based costing (ABC); mathematical programming; textile industry; green manufacturing; Industry 4.0; carbon emissions; Activity-Based Costing (ABC); carbon emissions; tire industry; carbon trading; mathematical programming; quotas allocation; carbon emissions; electric power industry; fairness; agricultural-related sectors; carbon emissions; carbon tax; China; power industry; carbon emissions; Generalized Divisia Index; scenario forecast; Monte Carlo method; wave energy converter; life cycle assessment; energy intensity; carbon intensity; aircraft; taxi time; takeoff rate; pushback control; green transportation; carbon emissions; reducing carbon emissions; carbon intensity target; energy structure; gray model (GM (1, 1)); generalized regression neural network (GRNN); Markov forecasting model; non-linear programming; ethylene supply; shale gas; non-energy uses of fossil fuels; socio-economic scenarios; climate change; tea; climate change; sustainable agriculture; environmental impact; carbon footprint; CO2 emissions; HOMER software; hybrid ship power systems; Li-ion battery; shipping; n/a