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The 21st Conference of the Parties (CoP21) to the United Nations Framework Convention on Climate Change (UNFCCC) shifted the nature of the political economy challenge associated with achieving a global emissions trajectory that is consistent with a climate. The shifts generated by CoP21 place country decision-making and country policies at centre stage. Under moderately optimistic assumptions concerning the vigour with which CoP21 objectives are pursued, nearly every country in the world will set about to design and implement the most promising and locally relevant policies for achieving their agreed contribution to global mitigation. These policies are virtually certain to vary dramatically across countries. In short, the world stands at the cusp of an unprecedented era of policy experimentation in driving a clean energy transition. This book steps into this new world of broad-scale and locally relevant policy experimentation. The chapters focus on the political economy of clean energy transition with an emphasis on specific issues encountered in both developed and developing countries. Lead authors contribute a broad diversity of experience drawn from all major regions of the world, representing a compendium of what has been learned from recent initiatives, mostly (but not exclusively) at country level, to reduce GHG emissions. As this new era of experimentation dawns, their contributions are both relevant and timely.

ghg emissions --- global emissions --- political economy --- policy experimentation --- clean energy transition --- climate change --- cop21 --- unfccc

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The main function of the sensory systems is the transducing of external stimuli into bioelectrical signals, which are conducted through afferent pathways from sensory epithelia to the brain. However, it is known that descending projections are ubiquitous in the different sensory modalities, and in the case of auditory efferents connect the cerebral cortex with sensory receptor cells. Several functions have been attributed to the efferent system, including protection to acoustic trauma, unmasking of auditory stimuli in background noise, balance of interaural sensitivity and some cognitive functions like modulation of cochlear sensitivity during selective attention to auditory or visual stimuli. In addition there is evidence of a possible involvement of the efferent system in the etiology or treatment of some clinical pathologies like tinnitus. In this e-book, entitled “Auditory Efferent System: New Insights from Cortex to Cochlea”, we aimed to give an overview of the advances concerning the descending projections from the auditory cortex to subcortical nuclei and the olivocochlear system. In addition, different theoretical proposals of efferent functions are presented. We think that this e-book is an important contribution to the understanding of the efferent system in mammals, merging auditory-cortex literature with studies performed in the olivocochlear system.

Olivocochlear --- auditory efferent --- corticofugal --- descending projections --- alpha-9 nicotinic receptors --- otoacoustic emissions --- Contralateral acoustic stimulation

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Achieving the ambitious greenhouse gas (GHG) reduction targets set up by German legislation requires political incentives to reduce GHG emissions of energy intensive industries without hampering their international competitiveness. This dissertation therefore analyses the influence of different political instruments (ETS, EEG, EnergieStG, StromStG) on induced emission reductions as well as on relevant economic parameters. Thereby, the focus is set on the metal production.

Metallerzeugung, Klimapolitik, Treibhausgasemissionen, EnergieeffizienzMetal production, climate policy, greenhouse gas emissions, energy efficiency

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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.

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

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Electric power systems around the world are changing in terms of structure, operation, management and ownership due to technical, financial, and ideological reasons. Power systems keep on expanding in terms of geographical areas, asset additions, and the penetration of new technologies in generation, transmission, and distribution. The conventional methods for solving the power system design, planning, operation, and control problems have been extensively used for different applications, but these methods suffer from several difficulties, thus providing suboptimal solutions. Computationally intelligent methods can offer better solutions for several conditions and are being widely applied in electrical engineering applications. This Special Issue represents a thorough treatment of computational intelligence from an electrical power system engineer’s perspective. Thorough, well-organised, and up-to-date, it examines in detail some of the important aspects of this very exciting and rapidly emerging technology, including machine learning, particle swarm optimization, genetic algorithms, and deep learning systems. Written in a concise and flowing manner by experts in the area of electrical power systems who have experience in the application of computational intelligence for solving many complex and difficult power system problems, this Special Issue is ideal for professional engineers and postgraduate students entering this exciting field.

defect detection --- glass insulator --- localization --- self-shattering --- spatial features --- particle swarm optimization --- particle update mode --- inertia weight --- reactive power optimization --- Combustion efficiency --- NOx emissions constraints --- boiler load constraints --- least square support vector machine --- differential evolution algorithm --- model predictive control --- incipient cable failure --- VMD --- feature extraction --- CNN --- economic load dispatch --- emission dispatch --- combined economic emission/environmental dispatch --- particle swarm optimization --- genetic algorithm --- penalty factor approach --- long short term memory (LSTM) --- genetic algorithm (GA) --- short term load forecasting (STLF) --- electricity load forecasting --- multivariate time series --- grid observability --- active distribution system --- meter allocation --- parameter estimation