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This study aims to scrutiny the performance of the petrochemical sector and the technological innovation life cycle. Also, the stage of the innovation life cycle for the sector is specified. Then, scenarios are designed to improve the speed of the sector development. For this reason, for synchronizing the petrochemical sector, this study tries to combine two innovation systems (technological and sectoral systems) called “techno-sectoral innovation system” under an integrated model. Furthermore, the “functions and driving motors” are expanded in the proposed model.

By combining two concepts of the innovation systems, the complexity of the system rises to some extent. Also, to model causal relationships in the sector and non-linear connections between variables, system dynamics approach is applied. During this phase, the flow diagram of the model is translated to a simulation programme using Vensim software. Model validation is investigated using a comparison of the actual with simulated values.

The results predict the functions state of the innovation system and detect activation of innovation motors in each stage of the innovation life cycle. Validation shows the acceptable error of the indices. It can be concluded that the sector is relatively in the development state. Four scenarios have been proposed for representing policies that sector uses to motivate its companies. The best scenario is the fourth one that divides resources with different weightings among companies to accelerate switching time between sector’s motors. Finally, the fourth scenario can improve the performance of the petrochemical.

The hybrid approach shows researchers that performance of an industry can be improved based on sectoral and technological at the same time. Thus, this case-based model can contribute to other researchers, as a base model. Also, it could be customized with parameters and the relationship between players and functions. Furthermore, a dynamic switch among the motors has been presented in the model.

Urmia lake water has impressively decreased recently and seriously endangered the lives of the inhabitants. In this paper, the effects of various factors on the reduction of the lake water are investigated and appropriate scenarios are proposed for future improvement. Due to the significant impact of agricultural issues on this crisis, this paper has focused specifically on agriculture. So, this paper aims to forecast and improve the lake water level.

In this paper, a system dynamics (SD) model, which is capable to consider various parameters and variables affecting the lake water level within nonlinear and dynamic relations, is developed.

To show the effectiveness of SD model, real data are used to run the model and the results show that the actual behavior of the lake water is reproduced with high validation (around 98.28 per cent). Also, five different scenarios are proposed to increase lake water volume. The hybrid Scenario 5 (which combines three other scenarios including increasing irrigation efficiency in the agricultural sector, changing cultivation pattern of agricultural products and returning some dams’ water that are consumed in the agricultural sector into the lake) is chosen as the most effective scenario for increasing lake volume about 15 billion m³.

The main contributions of this paper are systemic view to the whole problem, paying attention to the agriculture subject as one of the most important issues, considering many critical variables (e.g. evaporation, salinity and precipitation) and providing improvement policies along with assessing the effects of them.