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210512 ||| eng |
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|a 9783039213122
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|a books978-3-03921-312-2
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|a 9783039213115
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| 100 |
1 |
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|a Tsai, Wen-Hsien
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| 245 |
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|a Modeling and Simulation of Carbon Emission Related Issues
|h Elektronische Ressource
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| 260 |
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|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2019
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| 300 |
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|a 1 electronic resource (420 p.)
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| 653 |
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|a long-term
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| 653 |
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|a CO2 emissions forecasting
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| 653 |
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|a input-output model
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| 653 |
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|a influencing factors
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| 653 |
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|a Industry 4.0
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| 653 |
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|a aircraft
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| 653 |
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|a Markov forecasting model
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| 653 |
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|a energy intensity
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| 653 |
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|a LT-ARIMAXS model
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| 653 |
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|a carbon emissions
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| 653 |
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|a CLA Model
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| 653 |
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|a green manufacturing
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| 653 |
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|a carbon tax
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| 653 |
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|a hybrid ship power systems
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| 653 |
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|a error correction mechanism model
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| 653 |
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|a socio-economic scenarios
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| 653 |
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|a greenhouse gas emissions
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| 653 |
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|a HOMER software
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| 653 |
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|a generalized regression neural network (GRNN)
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| 653 |
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|a Monte Carlo method
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| 653 |
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|a Generalized Divisia Index
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| 653 |
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|a carbon trading
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| 653 |
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|a sustainable agriculture
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| 653 |
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|a life cycle assessment
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| 653 |
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|a tire industry
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| 653 |
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|a power industry
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| 653 |
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|a population growth
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| 653 |
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|a carbon intensity target
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| 653 |
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|a product-mix decision model
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| 653 |
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|a economic growth and the environment
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| 653 |
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|a causal factors
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| 653 |
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|a Tapio's model
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| 653 |
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|a final energy consumption
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| 653 |
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|a economic growth
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| 653 |
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|a China
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| 653 |
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|a non-energy uses of fossil fuels
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| 653 |
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|a Activity-Based Costing (ABC)
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| 653 |
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|a climate change
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| 653 |
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|a taxi time
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| 653 |
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|a reducing carbon emissions
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| 653 |
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|a tea
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| 653 |
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|a carbon intensity
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| 653 |
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|a pushback control
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| 653 |
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|a sustainable development
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| 653 |
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|a total carbon emissions
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| 653 |
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|a shipping
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| 653 |
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|a renewable energy
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| 653 |
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|a agricultural-related sectors
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| 653 |
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|a takeoff rate
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| 653 |
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|a hybrid genetic algorithm
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| 653 |
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|a n/a
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| 653 |
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|a investment under uncertainty
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| 653 |
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|a fairness
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| 653 |
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|a STIRPAT model
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| 653 |
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|a low-carbon agriculture
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| 653 |
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|a influence factor
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| 653 |
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|a shale gas
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| 653 |
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|a VARIMAX-ECM model
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| 653 |
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|a mathematical programming
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| 653 |
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|a per capita household CO2 emissions (PHCEs)
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| 653 |
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|a textile industry
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| 653 |
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|a 1))
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| 653 |
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|a decoupling elasticity
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| 653 |
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|a carbon footprint
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| 653 |
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|a wave energy converter
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| 653 |
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|a refined oil distribution
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| 653 |
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|a carbon price fluctuation
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| 653 |
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|a gray model (GM (1
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| 653 |
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|a real options analysis
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| 653 |
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|a household CO2 emissions (HCEs)
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| 653 |
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|a energy structure
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| 653 |
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|a decoupling analysis
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| 653 |
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|a electric power industry
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| 653 |
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|a ethylene supply
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| 653 |
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|a non-linear programming
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| 653 |
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|a household consumption
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| 653 |
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|a quotas allocation
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| 653 |
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|a green quality management
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| 653 |
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|a green transportation
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| 653 |
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|a scenario forecast
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| 653 |
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|a environmental impact
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| 653 |
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|a inventory routing problem
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| 653 |
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|a activity-based costing (ABC)
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| 653 |
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|a Li-ion battery
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| 653 |
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|a CO2 emissions
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| 653 |
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|a capacity expansion
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by-nc-nd/4.0/
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| 028 |
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|a 10.3390/books978-3-03921-312-2
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/53689
|z DOAB: description of the publication
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| 856 |
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|u https://www.mdpi.com/books/pdfview/book/1501
|7 0
|x Verlag
|3 Volltext
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|a 363
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|a 500
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|a 330
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|a 551.6
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|a 333
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|a 380
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|a 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.
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