جستجوی مقالات مرتبط با کلیدواژه "kalman filter" در نشریات گروه "محیط زیست"

 Recent studies have identified social capital as an important and influential factor in improving the management of environmental issues as it facilitates and strengthens the cooperation of environmental activists to solve problems in the field of the environment. This paper aimed to examine the role of social and human capital in the ways that environmental investments affect the quality of the environment in Iran.

 In this research, using the Kalman Filter approach and data from 1974 to 2016, the trend of changes in the effecting coefficient of environmental investment on the quality of the environment in Iran was estimated using the Eviews software. Then, in the framework of the Johansson cointegration method, while examining the stationarity of the variables by augmented Dickey Fuller unit root test and determining the number of convergence relationships using the effects and maximum eigenvalues tests, long-term relationships between social capital, human capital, and effecting coefficient of environmental investment was estimated using Maximum Likelihood method in Microfit software. Impulse response functions were also used to examine how the effecting coefficient of environmental investment on the quality of the environment reacts to changes in social and human capital.

 The results of estimating the effecting coefficient of environmental investment on the quality of the environment in Iran using the Kalman filter method showed that this coefficient has experienced a fluctuating trend in the studied period. The results of the estimation of long-term cointegration relationships suggest that there was a significant relationship between fluctuations of this coefficient and fluctuations of social and human capital. This means that with the increase of social capital in a society, the effecting coefficient of environmental investment on the quality of the environment will be strengthened and vice versa. The findings of the impulse response functions showed that applying a shock in social capital in a given period leads to stimulation and reaction of the affecting coefficient of environmental investment on the quality of the environment and, ultimately, the formation of a different level of equilibrium for this coefficient in the next periods. The results of the estimation of error correction vectors also indicate that, on average, 26% of each period nonequilibrium in environmental investment impact on the quality of the environment is moderated in the next period.

 Based on the findings of this research, the effectiveness and efficiency of environmental investments in improving the quality of the environment depend, to a large extent, on the knowledge and trust of the community in relation to the programs and activities defined in this regard, as well as the level of their effective participation in the definition and implementation of these programs and activities. In other words, the one-way and top-down approach of the government in the field of environmental protection is not enough to guarantee the achievement of the goals set for environmental management. It is necessary to design and provide the mechanisms required to attract the participation and trust of individuals and different stakeholders involved in the environmental management process at all levels.

One of the main concerns of industrialization is the corresponding environmental effects. Industrialization has affected the entire environment with its severe downside problems. The formation of massive pollution made by industries are the result of the constant need and greed of the human being. These industries, including transportation and manufacturing, are exhausting the earth’s resources and are causing tremendous stress on the environment and the ecological system. Water, soil and air pollution, are defined as the negative results of industrial development and industrialization. Global warming and greenhouse effects are the result, which is a massive impact of industrialization on the environment. The degradation of the entire environment and ecological system, is inclined to become permanent and tends to cause several negative effects on the economy, by causing human losses, ill health of the employee’s at large costs to governments, manufacturing and society. Therefore, it is very important to find necessary solutions to control such effects to facilitate the conditions for achieving sustainable industrial growth. This requires to identify the exact effects of industrialization on environment and the factors and solution which can mitigate this kind of bad effects of industrialization. According to the literature, there are a wide variety of variables that can help policymakers to control the side effects of industrialization. Social capital is one of these factor. Social capital comprises relations of trust, reciprocity, common rules, norms and sanctions, and connectedness in institutions. Social capital is necessary for sustainable and equitable solutions to natural resource management. This paper is to investigate the contribution of social capital in moderating the environmental effects of industrialization.
Data for variables included in the estimated econometric models in this paper- including per capita carbon dioxide emission(as environment quality index), per capita income, per capita industrial value added (as industrialization index), energy use intensity, urban population, human capital and social capital - is gathered from Iran central bank data center during the period 1967-2015. Augmented dickey fuller test is used to investigate the stationary of variables included in the econometrics models of the study. In order to analysis the moderating role of social capital as well as human capital in the reaction of environment quality to industrialization in Iran, two steps were taken as follows: at the first step, the effect of industrialization on environment quality was estimated using Kalman filtering approach during the period 1967-2015. At the second step, the moderating role of social capital is tested using Johnson co integration approach. Sohwarz and Quinn criteria were used to determine the optimal lag numbers in vector autoregressive model. The number of co-integration vectors was determined using maximum eigenvalue and trace tests. Vector error correction model is used to test and analyze the partial short-run adjustments.
Discussion of Augmented dickey fuller test shows that the order of integration of all variables is one and so these variables are not stationary series at level. This results lead the research team to use co integration test to analysis the relationship between variables. Estimation of the environment quality function, by Kalman filter approach estimation, shows that per capita carbon dioxide emission (environment quality index) is positively influenced by first power of per capita income, per capita industrial value added(industrialization index), energy use intensity and urban population. This finding is in line with the theoretical expectation and many other empirical researches in this field. The coefficient of the first power of per capita income in estimated model is 0.1659 and indicates that if the first power of per capita income increase one unit, per capita carbon dioxide emission increase 0.1659 unit. The coefficient of the energy use intensity in estimated model is 0.0005 and indicates that if energy use intensity increase one unit, per capita carbon dioxide emission increase 0.0005 unit. The coefficient of the urban population in estimated model is 0.0027 and indicates that if urban population increase one unit, per capita carbon dioxide emission increase 0.0027 unit. The coefficient of per capita industrial value added(industrialization index) in estimated model is 0.8176 and indicates that if industrialization index increase one unit, per capita carbon dioxide emission increase 0.8176 unit. There is a positive and statistically significant relationship between per capita industrial value added and per capita carbon dioxide emission. Estimation of industrialization coefficient in environment quality function using Kalman filtering reveals that this coefficient has experienced an increasing trend during the period 1982 -2015. This finding shows that the degree of industrialization side effects on environmental quality has been intensified during the period. Co integration test based on eigenvalue and trace statistics in this paper confirm the presence of almost one co integration vectors between the model variables. According to the estimated coefficients of restricted vectors, there is a long term negative relationship between social capital and the coefficient of industrialization in environment quality function. The coefficient of social capital in restricted vector is estimated around -12.1956. This shows that, in long term, as social capital improve or increase around one unit, the coefficient of industrialization in environment quality function decrease and mitigate around 12.19 unit. According to the estimated vector error correction model, social capital and human capital does not significantly affect the degree of industrialization effects on environment quality.
Based on the negative and significant coefficient of social capital in environment quality function estimated in this paper, it is inferred that the fraying trend of social capital has intensified the negative environmental side effects of industrialization in Iran during last decades. At the same time, improving the people’s awareness of environmental supportive activities, has mitigated the harmful effects of industrialization. According to the findings, related organizations and institutions like industrial organization, environmental organization, and other cultural institution are strongly recommended to have a close cooperation in order to design and implement the comprehensive and integrated program to manage the environmental side effects of industrialization process in Iran. Top – down approaches and policies are not enough to ensure the applicability of measures in field of environmental protection. People participation in environmental activities is the main precondition to enhance the effectiveness and efficiency of environmental side effects management in industrial projects and activities. This requires the proper level and structure of trust, networking and social capital in the society.

Transportation and traffic flows are the main factors of air pollution in Iran. Therefore it is essential to investigate their effects on the air quality of urban areas to forecast more accurately and manage better traffic and pollution. Unfortunately, not much research has been conducted on this issue in Iran. Identification of pollutant sources is the most important and time-consuming stage of air pollution modelling. We cannot consider only one variable for air pollution modelling in a single region. Hence, different variables should be taken into account, studied and planned. Some measures make significant changes in the air pollution of a metropolis. Hence, undertaking a series of measures can reduce the air pollution, and adopting new methods to evaluate the air pollution is one of these measures. The main goal of this research is to offer a smart model by which concentration of pollutants such as CO can be estimated with the appropriate accuracy and, by examining the causes of these pollutants and predicting the air pollution, the necessary actions to manage and control the air pollution can be planned (Hassan and Croether, 1998).

In this paper, a neural network model and a nonlinear state space model were designed based on urban traffic in Shiraz. In these models the concentrations of CO, NO2 and SO2 pollutants were analyzed and also estimated using a Kalman Filter for a 24 hour cycle. The models are based on the correlation between the volume of pollution, traffic, initial pollution and meteorological information. The extended Kalman Filter algorithm was used to analyse and predict the air pollution in Shiraz over a 24 hour period. A key factor of the proposed system is its adaptation with the short time pollution changes (Safavi, 2008; Brown et al., 2007).
Result and

In this research, traffic and pollution data caused by pollutant concentrations has been studied, then an attempt was made to match these air pollution data with significant parts in Shiraz city and many traffic and pollution data were excluded due to a mismatch in terms of location. Finally, modelling was updated based on this data and the result was adapted to real data. This nonlinear model structure offers the advantage of being evolutionary and sufficiently flexible, in the sense that the overall evaluation of the model performance can easily be undertaken by excluding or adding input variables. On the other hand, if the corresponding data of each new station is available, the study can be extended to other parts of Shiraz city. So, if traffic data is available in some parts of the city, the pollution can be extended to some other parts and be reduced in critical areas using certain traffic strategies.

The neural network method and the Kalman Filter were tested on Shiraz pollution data which revealed that the models, specially the Kalman Filter, work reasonably well.