Explaining International Differences in Genetically Modified Food Labeling Policies*

Many countries have adopted labeling policies for genetically modified (GM) food, and the regulations vary considerably across countries. We evaluate the importance of political‐economic factors implicit in the choice of GM food labeling regulations. Using an analytical model, we show that production and trade‐related interests play a prominent role in labeling decision‐making. This conclusion is validated by an empirical analysis of GM food labeling policy choices. We find that countries producing GM crops are more likely to have less stringent labeling policies. Food and feed exporters to the European Union (EU) and Japan are more likely to have adopted stricter labeling policies. Labeling regulations in Asia and Europe are similar to those of Japan and the EU. Countries with no labeling policies are less developed, with important rural sectors and are more likely to have ratified the Cartagena Protocol on Biosafety.     

Democracy and environmental quality

We develop and estimate an econometric model of the relationship between several local and global air pollutants and economic development while allowing for critical aspects of the socio-political-economic regime of a State. We obtain empirical support for our hypothesis that democracy and its associated freedoms provide the conduit through which agents can exercise their preferences for environmental quality more effectively than under an autocratic regime, thus leading to decreased concentrations or emissions of pollution. However, additional factors such as income inequality, age distribution, education, and urbanization may mitigate or exacerbate the net effect of the type of political regime on pollution, depending on the underlying societal preferences and the weights assigned to those preferences by the State.     

Measuring Vulnerability to U.S. Foreign Economic Sanctions

Economic sanctions are an important instrument of U.S. foreign policy. While politicians look favorably on unilateral economic sanctions as a policy instrument, many scholars attribute significant long-term economic costs to the United States and have doubts about their effectiveness. We outline a simple approach to assess the vulnerability of target countries to sanctions in order to develop focused sanctions and reduce unnecessary costs to U.S. business, avoid the imposition of sanctions on countries when sanctions are unlikely to have the desired effects, and determine what other countries must join the United States in imposing sanctions if they are to be effective.JEL Classification K330,F190     

Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange

A new method for forecasting the trend of time series, based on mixture of MLP experts, is presented. In this paper, three neural network combining methods and an Adaptive Network-Based Fuzzy Inference System (ANFIS) are applied to trend forecasting in the Tehran stock exchange. There are two experiments in this study. In experiment I, the time series data are the Kharg petrochemical company’s daily closing prices on the Tehran stock exchange. In this case study, which considers different schemes for forecasting the trend of the time series, the recognition rates are 75.97%, 77.13% and 81.64% for stacked generalization, modified stacked generalization and ANFIS, respectively. Using the mixture of MLP experts (ME) scheme, the recognition rate is strongly increased to 86.35%. A gain and loss analysis is also used, showing the relative forecasting success of the ME method with and without rejection criteria, compared to a simple buy and hold approach. In experiment II, the time series data are the daily closing prices of 37 companies on the Tehran stock exchange. This experiment is conducted to verify the results of experiment I and to show the efficiency of the ME method compared to stacked generalization, modified stacked generalization and ANFIS.     

An expert system for predicting shear stress distribution in circular open channels using gene expression programming

The shear stress distribution in circular channels was modeled in this study using gene expression programming (GEP). 173 sets of reliable data were collected under four flow conditions for use in the training and testing stages. The effect of input variables on GEP modeling was studied and 15 different GEP models with individual, binary, ternary, and quaternary input combinations were investigated. The sensitivity analysis results demonstrate that dimensionless parameter y/P, where y is the transverse coordinate, and P is the wetted perimeter, is the most influential parameter with regard to the shear stress distribution in circular channels. GEP model 10, with the parameter y/P and Reynolds number (Re) as inputs, outperformed the other GEP models, with a coefficient of determination of 0.7814 for the testing data set. An equation was derived from the best GEP model and its results were compared with an artificial neural network (ANN) model and an equation based on the Shannon entropy proposed by other researchers. The GEP model, with an average RMSE of 0.0301, exhibits superior performance over the Shannon entropy-based equation, with an average RMSE of 0.1049, and the ANN model, with an average RMSE of 0.2815 for all flow depths.     

Evaluation of suspended load transport rate using transport formulas and artificial neural network models （Case study： Chelchay Catchment）＊

Accurate estimation of sediment load or transport rate is very important to a wide range of water resources projects. This study was undertaken to determine the most appropriate model to predict suspended load in the Chelchay Watershed, northeast of Iran. In total, 59 data series were collected from four gravel bed-rivers and a sand bed river and two depth integrating suspended load samplers to evaluate nine suspended load formulas and feed forward backpropagation Artificial Neural Network (ANN) structures. Although the Chang formula with higher correlation coefficient (r = 0.69) and lower Root Mean Square Error (RMSE = 0.013) is the best suspended load predictor among the nine studied formulas, the ANN models significantly outperform traditional suspended load formulas and show their superior performance for all statistical parameters. Among different ANN structures two models including 4 inputs, 4 hidden and one output neurons, and 4 inputs, 4 and one hidden and one output neurons provide the best simulation with the RMSE values of 0.0009 and 0.001, respectively.     

Conditions for Sustainable Optimal Economic Development

This paper shows that, for dynamic optimizing economies with different types of natural resource, environmental, and human‐made capital stocks, a necessary and sufficient condition for permanently sustaining an optimal utility/consumption level is the stationarity of the current‐value Hamiltonian. For economies whose development is not exogenously and directly affected by time (i.e., time‐autonomous economies), this stationarity condition generalizes Dixit et al.’s (1980 ) “zero‐net‐aggregate‐investment” rule of sustain‐ability, which in turn generalizes Solow‐Hartwick’s sustainability rule. For non‐autonomous economies, the stationarity condition is not generally fulfilled, and the current‐value Hamiltonian under (over) estimates the true welfare level by an amount equal to the discounted value of the net “pure time effect.” For the non‐autonomous case of a time‐dependent utility discount rate, a general condition on the discount rate function (of which the hyperbolic discount rate function is a special case) upholds the results obtained for autonomous cases. The paper concludes with a discussion of policies that promote both optimality and sustainability objectives.     

A COMPLETE THEORY OF COMPARATIVE STATICS FOR DIFFERENTIABLE OPTIMIZATION PROBLEMS

A new comparative statics formalism using generalized compensated derivatives is presented that, in contrast to existing methodologies, directly yields constraint‐free semidefiniteness results for any differentiable, constrained optimization problem. The formalism provides a natural and powerful method of constructing comparative statics results, free of constraints and unrestricted in scope. New results on envelope relations, invariance conditions, rank inequalities and non‐uniqueness are derived that greatly extend their utility and reach. The methodology is illustrated by deriving the comparative statics of multiple linear constraint utility maximization models and the principal‐agent problem with hidden actions, both highly nontrivial and hitherto unsolved problems.     

Multi-Objective Surface Water Resource Management Considering Conflict Resolution and Utility Function Optimization

In the present research, a multi-objective model is developed for surface water resource management in the river basin area which is connected to the lake. This model considers different components of sustainable water resource management including economic, social and environmental aspects, and simultaneously tries to resolve conflicts between different stakeholders by means of non-symmetric Nash bargaining, which is linked to the multi-objective optimization method. This study proposes a new methodology to improve Nash Conflict Resolution through finding the optimum degree of the utility function. The proposed model is examined in the Zarrineh River basin in Iran. The results show that the amount of available resources or volume of reservoirs play a significant role in determining the optimal degree of the utility function and efficiency of the proposed method in such a way that the higher amount of resources or the larger reservoirs will result in the higher optimal degree of the utility function. In the proposed multi-objective model, two different amounts of surface water inflow are considered. The first assumed amount is the long-term average flow rate and the second one is equal to 80% of the first mode, which is reduced based on the estimated impacts of climate changes. This multi-objective allocation model could supply 100 and 97.5% of the environmental demand of Lake Urmia in the first and second situations, respectively.