Neoclassical economics is based on the assumptions that people have rational preferences among outcomes. In this case, individuals maximize utility while firms strive to maximize profits. Neoclassical economics poses several theories to describe how scares resources are allocated given that individuals act independently with full and relevant information. The need for massive social justice especially when addressing issues such as equity and justice as opposed to economic efficiency to build a sustainable economy in practice rather than in abstract theory is what prompted the formulation of green economics model. Green economics is concerned with the incorporation of moral values into economic decisions while considering their ethical implications such as the rights of future generations and treatment of the poor (Kennet & Heinemaan, 2005). However, many of these issues are relevant to the green economy. It shifts focus from economic growth towards a steady-state economy. Thus the green economics focuses on the human interactions through increased involvement for mutual and community-based economic activity through a regulated international environmental and social framework.
Ecological and neoclassical economics are similar in that both interdependent as they offer a greater measure and evaluation using techniques. Both models integrate ideas and theories that are fashioned to end the systemic and institutional causes of poverty and inequality.
Differences between the theories.According to Kennet & Heinemaan (2005), green economics differs from neoclassical economics in that ecological economics is more academic and scientific in nature that favor sustainability physical capital cannot substitute the natural capital. Neoclassical environmental economics on the other hand has a goal of weak sustainability in that technology leads to accumulation of physical capital which replaces the natural capital, ecological economics provides for a steady state economy with zero growth. However, neoclassical economics emphasize on preservation of human capital of natural ecosystems that provide goods and services to people.
Treatment methods for reducing matter emissions
Use of efficient vehicles. One of the most effective ways of curbing noise and air emissions regulating the number of vehicles on the roads. Vehicle owners ought to use efficient vehicles which do not emit fumes. Unroadworthy vehicles which emit pollutant fumes should be serviced immediately. The government should ensure that all the road users use efficient vehicles and those who break such rules should be punished severely. It is also the onus of the government to see to it that roads are tarmacked. The government can also impose restrictions such as regulating the weight or vehicle type and help cut on the emissions caused by motorized traffic.
Increasing the moisture content. Application of deliquescent salts can also help control emissions. Consistent, light watering is efficient than less frequent, heavy watering (Chil, 2011). The moisture content can be increased by spreading water or application of salts which helps attract salt. Calcium chloride for example helps to significantly cut emissions of dust and is effective through the summer season without causing environmental problems (EPA, 2010).
Treatment methods for reducing nitrogen dioxide emissions
Gas scrubbing Gas scrubbing is currently the most common form of reducing nitrogen dioxide. Sodium hydroxide is used as the scrubbing medium. It is basically the simplest method to use as it adds no contaminations to the scrubbing solution and allows for the recovery of commercial products in the process (Gurjar, Molina & Ojha, 2010)
Efficient manure management techniques. Emissions can be reduced by cutting down the usage of nitrogen-based fertilizer applications and applying fertilizers more efficiently (U.S. Department of State, 2007). Reducing mobile fuel consumption can also help cut the emissions of the nitrogen dioxide. Introduction of pollution control technologies such as catalytic converters can also help reduce exhaust pollutants and reduce emissions significantly (EPA, 2010). Switching to effective technologies serves as the best aid to use in curbing nitrogen dioxide emissions (EPA, 2005).
Gaussian process is a stochastic process in statistics which considers time. The Gaussian process entails inputs and outputs whereby each point in input space exhibits a normally distributed random variable. Caline4 is one of the Gaussian diffusion model which is used in categorizing pollutant dispersion in roads by applying and employs a mixing zone concept. Hiway2 on the other hand is used to forecast the saturation level of non-active pollutants in highway traffic (Petersen, 1980). Aeropol is also another steady state dispersion model that uses metrological information and some stability function such as dispersion parameters, and mixing height as well as wind profile to the interpreted area sources (Kaasik, 2000). Apart from supporting regulatory modeling programs, Aermod is used in assessing air quality and deposition fields of up to 30km (Szepesi, Fekete & Lee, 2005). Additionally, this model is essential in computing concentration values, dry, wet as well as deposition rates.
Each Gaussian model plays and unique function. Caline4 for example plays the role of assessing the quality of air impacts near transportation facilities such as parking bays and street canyons. Additionally, it helps predict pollutant concentrations for receptors located within 500 meters of the roadway. Hiway2 on the other hand is the model to use when determining the concentration at receptor locations downwind of at-grade and cut section highways located in relatively uncomplicated terrain (Petersen, 1980). However the wind direction, highway orientation and receptor location is required. With Aeropol, the concentration near the underlying surface, dry and wet deposition flux serves as the specimen for use. With the help of aeropol, gridded vertical profile of potential temperatures which are generated by taking into account the exponential decay of pollutant (Kaasik, 2000).
Modeling methods used in artificial neural network models and fuzzy logic based models
Artificial neural networks analyze and forecast functions large inputs which unknown values. They contain simple mathematical models of interconnected neurons that help define distribution over x or both x and y. Neural network techniques (ANN) contain several layers of many computing components. These layers are called nodes. Each node receives an input signal from an external input, processes the signals through a transfer function, and gives outputs which are the final results. Essentially, artificial neural networks are grouped into two main categories:
Feed-forward networks The most commonly used cost estimation model is the feed forward multi-layer perception with back propagation learning algorithm. In these networks, no loops occur in the path. A good example of a feed-forward network is the multi Perceptron (MLP). In this ANN model, all nodes and layers are arranged in a feed forward manner. The first node lies at the lowest layer. This layer is called input layer and it is the layer in which external information is received. The last layer is usually the highest layer. In this layer, all the networks produce the model solution. It also has a hidden layer used in the identification of complex patterns in the data. This layer is also called the output layer.
Feedback networks Feedback networks is the second type of artificial neural networks. Unlike the Feed-forward networks have recursive loops that occur in the path.
As hybrids of artificial neural networks and fuzzy logic, Neuro-fuzzy techniques are intelligent systems that combining the human-like reasoning style of fuzzy systems with the learning and connectionist structure of neural networks.
How to check Statistical adequacy of the models
Validation of models is paramount in construction of a sequence. Model validation involves checking on the goodness of fit of the data series. Some of the models to use include the numerical models, graphical models. In a way, graphical models are simple to use and readily illustrate particular aspects of the data. Numerical models on the other hand encompass the computation of the r2 which is essentially a measure the variability of the model. When the goodness of fit is equal to 1, it implies the data is perfectly fit for use as it is accurate, concise and relevant.
Weather Research and Forecasting and Chilean ozone modeling conclusions.
WRF stands for Weather Research and Forecasting (WRF). Information on WRF-Chem model, as well as its organization, practical applications as well as WRF announcements, can be obtained from the main homepage website site (wrf-model.org).
The modeling described in the book used 115*127 grids with 15 second time steps. Use of 115x127 grids gives about 14,605 cells. By using 330x254 grid 83,820 cells would be obtained. Given that the physical distance is the same, the results are not likely to be more precise. Use of 130*254 grids is the same as using 115*127 grids twice. The time doubled since the grids used also doubled.
Conclusions of Chilean ozone modeling study
Oxidation of hydrocarbons and nitrogen oxides contributes to high ozone levels. This has essentially been the case in Chile. During spring, summer and autumn, Chile experiences high ozone levels. In a study conducted to investigate on the reason behind this occurrence, variables including the maximum temperature, type of day, VOCs, Nox AND VOCs/Nox ratio were investigated. It was found out that there was a strong positive correlation between high temperatures and ozone levels concentration. The highest and strongest correlation existed between the maximum daily ozone concentration and daily maximum temperature. In a study of days, the weekends recorded reduced nitrogen dioxide emissions and the measured ozone levels had week correlation with the type of day. Though the presence of the weekend effect was present, it did not have an effect with the ozone levels. Hypothetically, it was said that there was reduced nitrogen dioxide emissions in the weekend thus the name the weekend effect. However, irrespective of the day, the ozone maxima was the same. The implication is that the weekend effect was negligible in with increase in the VOCs/Nox ratios.
Atmospheric stability refers to the lack of resilience of the atmosphere to vertical motion. In measuring the atmospheric stability several factors such as the buoyant lift, wind velocity as well as the lapse need to be understood. Buoyant lift refers to the manner in which the air is lifted. After becoming warmer than the surrounding, air parcel located at the earths surface becomes less dense than the surrounding air. The resultant effect is the rise in the hotter and less dense air. The process of rising up is referred to as buoyancy. The process continues and the air continues to rise. As the air continues to rise, it cools in the process. This is the rate at which the air cools as it ascends is referred to as the lapse rate. With a given lapse rate, static stability is what will determine whether the air will remain buoyant. Assuming that the air is initially at rest, which in this case will be referred to as static stability, the air will keep rising and with a slight upward push it will keep on rising which in this case, it will be referred to as static instability. In case the air remains at that position, it is referred to as neutral. Depending on the Celsius per kilometer, there are two types of instability which includes absolute unstable and c...
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