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AbstractAttaining continuous economic growth entails special consideration of energy sector and the environment. Compliance with this purpose may be more intricate in the uncertain milieu of developing countries. The present paper examines the nature of causality between energy consumption, environment pollution, and economic growth in 8 contiguous developing countries, considering GDP per capita, CO2 emissions, energy use, labour force, total population, urban population, capital formation, financial development, and trade openness. The author applied spatial simultaneous equations for random effects panel data to investigate the spatial interactions of adjacent countries over the period from 1998 to 2011. The findings reveal that energy consumption, environment degradation, and economic growth of a country influence those of its neighbours. Additionally, the results document bidirectional causal relationship between economic growth and environment pollution, as well as between environment pollution and energy consumption. Thus, there is a bidirectional relationship between energy use and economic growth. Fossil fuels replacement with renewable energy and usage of tax instruments to reduce greenhouse gas are recommended.

Option price prediction has been an important issue in the finance literature within recent years. Affected by numerous factors, option price forecasting remains a challenging problem. In this study, a novel hybrid model for forecasting option price consisting of parametric and non-parametric methods is presented. This method is composed of three stages. First, the conventional option pricing methods such as Binomial Tree, Monte Carlo, and Finite Difference are used to primarily calculate the option prices. Next, the author employs an Adaptive Neuro-Fuzzy Inference System (ANFIS) in which the parameters are trained with particle swarm optimization to minimize the prediction errors associated with parametric methods. To select the best input data for the ANFIS structure, which has high mutual information associated with the future option price, the proposed method uses an entropy approach. Experimental examples with data from the Australian options market demonstrate the effectivity of the proposed hybrid model in enhancing the prediction accuracy compared to another method.

Cultural productions are considered as a sign of civilization in modern societies. Theater is known as an important type of cultural productions, playing important role in the cultural economy of a society. Due to complexities of socio-economic interactions, this sector needs dynamic investigation to illuminate different aspects of possible potentials and threats. The present paper tries to find relationships between Iran public theater economy and production structure based on a dynamic model including all economic stages, namely production, distribution, and consumption to achieve a solid perception of Iran theater position. The authors use System Dynamics to create a model that can explain or mimic the behavior of the system in order to evaluate policies. Since Tehran City Theater complex is the sole place for the public theater in Iran, the authors assess it over the period 2012-2015 and predict its behavior to 2022. On the other hand, the investigation in this context is being directed in accordance with microeconomics principles. The results indicate that the position of Iran public theater is undesired due to vague managerial policy. Also, the findings offer insights into the problems and suggest practical solutions.