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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.
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.
Advances and Technology Development in Greenhouse Gases: Emission, Capture and Conversion is a comprehensive seven-volume set of books that discusses the composition and properties of greenhouse gases, and introduces different sources of greenhouse gases emission and the relation between greenhouse gases and global warming. The comprehensive and detailed presentation of common technologies as well as novel research related to all aspects of greenhouse gases makes this work an indispensable encyclopedic resource for researchers in academia and industry.Volume 7 titled Process Modelling and Simulation reviews process modelling and simulation. The book reviews modeling studies of GHGs emissions and surveys the details of carbon capture modelling with several well-developed processes such as absorbers, swing technologies, and microstructures. It addresses modelling of geological and ocean storage, and reviews simulation studies of the chemical conversion of carbon dioxide to any valuable materials. The book summarizes essential information required in the simulation and modelling of the processes which are beneficial in carbon capture, storage, or conversion. Introduces modeling and simulation methods of carbon and methane emission Describes modeling and simulation procedures of producing chemicals from carbon as well as methane Discusses modeling and simulation of various technologies for carbon capture
The progression of models developed to analyze the global carbon cycle in resolving the CO/sub 2//climate issue serves as an example of the changing character of models, depending on the immediate role they play in environmental decision making. The dominant and successful role served by models in the carbon cycle problem demonstrates to some degree the desirability of this flexible approach. The pattern of model development will be different for each application of modeling to environmental decision making; however, some general simularities frequently emerge. Simple models, heavily based in measurements, are often the most useful in assisting decision makers to establish priorities among a set of environmental issues. When the decision to allocate resources to the analysis of an environmental problem has been reached, structurally detailed models which are not necessarily sophisticated in the representation of each process are useful in establishing the relative importance of system interactions. Models in which structure is condensed but sophistication in representing processes is emphasized are used in actually developing policy. Finally, the design of management practices to mitigate an environmental impact requires the most complex models which usually can only be developed when the insight from earlier stages of analysis has been gained.
The purpose of this Special Issue is to investigate topics related to sustainability issues in the new era, especially in Industry 4.0 or other new manufacturing environments. Under Industry 4.0, there have been great changes with respect to production processes, production planning and control, quality assurance, internal control, cost determination, and other management issues. Moreover, it is expected that Industry 4.0 can create positive sustainability impacts along the whole value chain. There are three pillars of sustainability, including environmental sustainability, economic sustainability, and social sustainability. This Special Issue collects 15 sustainability-related papers from various industries that use various methods or models, such as mathematical programming, activity-based costing (ABC), material flow cost accounting, fuel consumption model, artificial intelligence (AI)-based fusion model, multi-attribute decision model (MADM), and so on. These papers are related to carbon emissions, carbon tax, Industry 4.0, economic sustainability, corporate social responsibility (CSR), etc. The research objects come from China, Taiwan, Thailand, Oman, Cyprus, Germany, Austria, and Portugal. Although the research presented in this Special Issue is not exhaustive, this Special Issue provides abundant, significant research related to environmental, economic, and social sustainability. Nevertheless, there still are many research topics that require our attention to solve problems of sustainability.
Environmental issues is one of the significantly important subjects to be solved on a global scale for attaining sustainable developments in the new century. A major feature of environmental issues is that studies on environments are interdisciplinary and should deal with complex and heterogeneous systems, which include chemical, physical and biological phenomena and further socio-economical activities and technology developments for energy saving and CO 2 emission reduction. The workshop provided the forum for presentation of new developments in the important interdisciplinary field of environmental systems involving the application of concepts, methods and techniques of modeling and control. The aim was to review state-of-the-art systems and control approaches, exchange new results in interdisciplinary fields and look at feasible developments to the future. The scope of this workshop covered various aspects of systems and control approaches to environmental issues: Environmental assessment; Systems approaches to environmental management and strategies; Modeling of global warming and impact analysis on socio-structure; Modeling of ecological systems; Control and optimization of environmental quantities; System reform and integration for environments; Advanced control technology for reduction of energy consumption and CO 2 emission; Cogeneration systems; Control systems approaches to clean energy; Fuel cells and their applications; Systems Integration technologies in urban planning; Systems modeling and data analysis in life cycle assessment; Manufacturing in symbiosis with environments; Modeling and control of waste incineration plant; Water quality control and waste water management; Other related topics.
The roles and applications of various modeling approaches, aimed at improving the usefulness of energy policy models in public decision making, are covered by this book. The development, validation, and applications of system dynamics and agent-based models in service of energy policy design and assessment in the 21st century is a key focus. A number of modeling approaches and models for energy policy, with a particular focus on low-carbon economic development of regions and states are covered. Chapters on system dynamics methodology, model-based theory, fuzzy system dynamics frame-work, and optimization modeling approach are presented, along with several chapters on future research opportunities for the energy policy modeling community. The use of model-based analysis and scenarios in energy policy design and assessment has seen phenomenal growth during the past several decades. In recent years, renewed concerns about climate change and energy security have posed unique modeling challenges. By utilizing the validation techniques and procedures which are effectively demonstrated in these contributions, researchers and practitioners in energy systems domain can increase the appeal and acceptance of their policy models.
This paper examines the interrelationship of learning e ffects and emission control on the diffusion of carbon capture and storage (CCS). We introduce a dynamic model in which an electricity producer maximizes pro ts subject to emissions control. All technologies are characterized by specific linear marginal costs and CO2 emissions which need to be covered by limited emission permits. The fossil fuel-based plants can be replaced by the CCS technology which is associated with higher capital costs and a lower system efficiency. Both parameters improve due to learning effects if the technology is applied. The model is formulated as a non-linear optimization problem, and solved using GAMS. Given the assumed technological data, the results for a data set of Germany show that CCS is essential to reach carbon emission goals. However, particularly in the case of learning, CCS can help renewable technologies to become competitive.
This paper examines the interrelationship of learning effects and emission control on the diffusion of carbon capture and storage (CCS). We introduce a dynamic model in which an electricity producer maximizes profits subject to emissions control. All technologies are characterized by specific linear marginal costs and CO2 emissions which need to be covered by limited emission permits. The fossil fuel-based plants can be replaced by the CCS technology which is associated with higher capital costs and a lower system efficiency. Both parameters improve due to learning effects if the technology is applied. The model is formulated as a non-linear optimization problem, and solved using GAMS. Given the assumed technological data, the results for a data set of Germany show that CCS is essential to reach carbon emission goals. However, particularly in the case of learning, CCS can help renewable technologies to become competitive.