Modeling and Simulation of Carbon Emission Related Issues
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...
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| Format: | Book Chapter |
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MDPI - Multidisciplinary Digital Publishing Institute
2019
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| Online Access: | Get Fullteks DOAB: description of the publication |
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| LEADER | 05421naaaa2201273uu 4500 | ||
|---|---|---|---|
| 001 | doab_20_500_12854_53689 | ||
| 005 | 20210211 | ||
| 020 | |a books978-3-03921-312-2 | ||
| 020 | |a 9783039213122 | ||
| 020 | |a 9783039213115 | ||
| 024 | 7 | |a 10.3390/books978-3-03921-312-2 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 100 | 1 | |a Tsai, Wen-Hsien |4 auth | |
| 245 | 1 | 0 | |a Modeling and Simulation of Carbon Emission Related Issues |
| 260 | |b MDPI - Multidisciplinary Digital Publishing Institute |c 2019 | ||
| 300 | |a 1 electronic resource (420 p.) | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |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. | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by-nc-nd/4.0/ |2 cc |4 https://creativecommons.org/licenses/by-nc-nd/4.0/ | ||
| 546 | |a English | ||
| 653 | |a shale gas | ||
| 653 | |a n/a | ||
| 653 | |a Tapio's model | ||
| 653 | |a 1)) | ||
| 653 | |a tea | ||
| 653 | |a VARIMAX-ECM model | ||
| 653 | |a wave energy converter | ||
| 653 | |a error correction mechanism model | ||
| 653 | |a low-carbon agriculture | ||
| 653 | |a hybrid ship power systems | ||
| 653 | |a greenhouse gas emissions | ||
| 653 | |a STIRPAT model | ||
| 653 | |a textile industry | ||
| 653 | |a carbon tax | ||
| 653 | |a refined oil distribution | ||
| 653 | |a pushback control | ||
| 653 | |a takeoff rate | ||
| 653 | |a economic growth | ||
| 653 | |a generalized regression neural network (GRNN) | ||
| 653 | |a Industry 4.0 | ||
| 653 | |a HOMER software | ||
| 653 | |a population growth | ||
| 653 | |a Markov forecasting model | ||
| 653 | |a household consumption | ||
| 653 | |a life cycle assessment | ||
| 653 | |a green quality management | ||
| 653 | |a agricultural-related sectors | ||
| 653 | |a non-energy uses of fossil fuels | ||
| 653 | |a investment under uncertainty | ||
| 653 | |a CO2 emissions forecasting | ||
| 653 | |a decoupling analysis | ||
| 653 | |a CO2 emissions | ||
| 653 | |a quotas allocation | ||
| 653 | |a carbon price fluctuation | ||
| 653 | |a final energy consumption | ||
| 653 | |a ethylene supply | ||
| 653 | |a household CO2 emissions (HCEs) | ||
| 653 | |a green transportation | ||
| 653 | |a Li-ion battery | ||
| 653 | |a Activity-Based Costing (ABC) | ||
| 653 | |a decoupling elasticity | ||
| 653 | |a causal factors | ||
| 653 | |a renewable energy | ||
| 653 | |a per capita household CO2 emissions (PHCEs) | ||
| 653 | |a shipping | ||
| 653 | |a input-output model | ||
| 653 | |a carbon intensity target | ||
| 653 | |a climate change | ||
| 653 | |a Monte Carlo method | ||
| 653 | |a CLA Model | ||
| 653 | |a energy intensity | ||
| 653 | |a total carbon emissions | ||
| 653 | |a mathematical programming | ||
| 653 | |a sustainable development | ||
| 653 | |a Generalized Divisia Index | ||
| 653 | |a carbon trading | ||
| 653 | |a influence factor | ||
| 653 | |a tire industry | ||
| 653 | |a socio-economic scenarios | ||
| 653 | |a hybrid genetic algorithm | ||
| 653 | |a economic growth and the environment | ||
| 653 | |a non-linear programming | ||
| 653 | |a environmental impact | ||
| 653 | |a capacity expansion | ||
| 653 | |a product-mix decision model | ||
| 653 | |a influencing factors | ||
| 653 | |a scenario forecast | ||
| 653 | |a energy structure | ||
| 653 | |a China | ||
| 653 | |a carbon emissions | ||
| 653 | |a inventory routing problem | ||
| 653 | |a green manufacturing | ||
| 653 | |a fairness | ||
| 653 | |a power industry | ||
| 653 | |a activity-based costing (ABC) | ||
| 653 | |a aircraft | ||
| 653 | |a electric power industry | ||
| 653 | |a taxi time | ||
| 653 | |a real options analysis | ||
| 653 | |a carbon footprint | ||
| 653 | |a LT-ARIMAXS model | ||
| 653 | |a carbon intensity | ||
| 653 | |a gray model (GM (1 | ||
| 653 | |a reducing carbon emissions | ||
| 653 | |a sustainable agriculture | ||
| 653 | |a long-term | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/1501 |7 0 |z Get Fullteks |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/53689 |7 0 |z DOAB: description of the publication |