مجله اقتصاد و توسعه کشاورزی
سال سی و سوم شماره 3 (پیاپی 44، پاییز 1398)

Global warming is an important issue for all people in the world. Once greenhouse gases (GHGs) are generated, they accumulate in the atmosphere for a very long period. For this reason, the scope of their impact is not only limited to the present generation, but also will continue to affect coming generations. Due to these long lasting effects, global warming must be dealt with seriously in order to achieve environmental and economic sustainability. Among the six dominant greenhouse gases (GHGs) mentioned by the UNFCCC, carbon dioxide emissions (CO2) are the main contributor to the bulk of accumulated GHG emissions and showing the highest growth rates over time. These national climate action plans, communicated by 189 participating countries to date, will not be sufficient to meet the level required to stay well below 2°C. In order to achieve the long term goals contained in the Agreement, governments will regularly set or update their emissions reductions targets. Hence, the international community has taken some measures to solve this problem it includes the Kyoto Protocol and the Paris Agreement.

In this paper, we empirically investigate the impact of the Kyoto Protocol and Paris Agreement on CO2 emissions using a sample of 139 countries.To analyze the effect of the Kyoto Protocol on the emission of CO2, the period 2005-2012 are considered and in Paris agreement the years of 2014-2017 are included. We propose the use of a difference-in-difference (DiD) regression and a propensity score matching (PSM) methods to address the endogeneity of the policy variable, namely Kyoto and Paris commitments. Countries are matched according to observable characteristics to create a suitable counterfactual. We correspondingly estimated a panel data model for the whole sample and the matched sample and compared the results to those obtained using a Covariates variable. The model proposed to estimate the effects of the Kyoto Protocol on CO2 emissions includes GDP, Foreign direct investment (FDI), The proportion of urban population to total (UP), The share of value added in industry (AVI) as main drivers of emissions. A differences-in-difference estimator has been proposed, in which rather than evaluating the effect on the outcome variable, evaluating the effect on the change in the outcome variable before and after the intervention is done. Our inference in difference in difference method is based on the differences between committed and non-committed countries over two time periods: a pre-treatment period of 2014-2015 and a post-treatment year of 2016-2017.

As the coefficient for facing future commitments from Kyoto protocol and Paris Agreement is statistically significant, we conclude that the Kyoto protocol and Paris agreement effect are due to pre-ratification differences in emissions. In Kyoto protocol the results of the difference in difference method indicate that countries that face emission commitments emit on average 1.89 percent less CO2 compared to the control group of countries, which face similar conditions in terms of GDP, FDI, and AVI and UP, but do not have to cut emissions. In propensity score matching result illustrate CO2 emissions has reduced by 1.76 percent. Similar to other studies estimating the Kyoto effect, we also obtain that ratifying Kyoto has a negative and significant effect on emissions. In particular, our results show that a country with emission commitments emits on average 1.8 percent less CO2 than a country without reduction commitments. The results of the difference in difference method indicate that countries with emission commitments from the Paris agreement has reduced on average about 1.21 percent less CO2 than similar countries that did not ratify the agreement and according to the PSM method, in the commitment countries in Paris agreement, it has dropped by 1.45 percent.

According to the result, the impact of the Kyoto Protocol and the Paris Agreement based on two approaches are different, but it is obviously clear that these international agreements have been successful in reducing CO2 emissions. Yet, in order to stabilize global warming at 2 degrees Celsius, much more serious measures would have to be taken. Although emissions from the developed countries with reduction commitments have declined and some countries achieved their targets, the decline in emissions is unlikely to be enough to stabilize levels of GHGs in the atmosphere. The main policy recommendation derived from this study is that policy makers should actively work towards finding a way of extending the international agreement to a wider range of countries, including the so-called new industrialized nations, which indeed should be renamed ‘already’ industrialized countries.

Organic farming plays an important role in protecting the environment, maintaining non-renewable resources, improving the food quality, reducing the production of unnecessary products, and promoting market- oriented agricultural sector. In fact, organic farming make a significant contribution in improving the quality of the environment and natural resources, and also it has a positive effect on the quality of food supply and the promotion of public health. Given the many benefits of organic products, the market for these products has been increasingly considered by researchers, government officials and consumers. First step in developing the market for organic products is to meet the needs and demands of consumers. Recognizing consumer behavior and investigating the factors affecting it contributes significantly in success of any economic system. Besides, in advanced marketing studies, the process of identifying consumer choice is very crucial. Contrary to economists' views, consumers give little weight to benefits and costs in their decision making, and their choices are based on people's behavior, habits and other factors that may speed up the decision making. Consumer preferences for organic products depend on many factors and the importance of each of these factors varies among different consumers. Therefore, the main aim of this study is to rate and evaluate factors affecting the consumer preferences for organic products (fruitage, vegetables and cucurbits) in Mashhad city.

Many marketing researchers use regression models to evaluate consumer decisions. In these models, decision variables are definitive part of utility function which is used to calculate how to choose a product. Linearity of utility function is the vital hypothesis. To specify a non-linear model, it is necessary to use variables that can show non-linear effects (For example, including the quadratic term of variables). However, this requires the insertion of assumptions about the nature of the utility function which ultimately leads to specification bias, and subsequently misinterpretation and unreasonable applications in marketing studies. Modeling complex processes is one of the advantages of artificial neural networks, and in this approach, it is not necessary to specify a mathematical relationship between the variables. The nonlinear and complex interactions can be considered between system variables using artificial neural network model. In this study in order to rate and evaluate factors affecting consumers preferences for organic products (fruitage, vegetables and cucurbits) an artificial neural network has been used that is consist of three dependent or target variables. Also, in order to evaluate the importance of the explanatory variables of the artificial neural network, partial derivatives approach has been used. Therefore, the use of three output variables on artificial neural networks simultaneously and partial derivative approach was distinctive features of this study compared with previous ones. Data is collected through questionnaires from a total of 175 households living in Mashhad. Age, gender, education, household size, number of household members under 10 years, number of household members over 65 years, price, having information on organic products, product appearance, having information on the supply of organic products, nutritional values, ease of access, the supply of organic products during the year and having labels were the input variables of artificial neural network. Consumer preferences for the purchase of organic fruitage, vegetables and cucurbits were the target variables of the artificial neural network.

The results indicate that price has the greatest influence on willingness to consume organic products among all other factors. The price effect on willingness to consume organic products is different among individual consumers, and it's independent of the product. This finding suggested that the price of organic products had a significant impact on consumer purchasing decisions in comparison with other marketing mix elements.

The adoption and implementation of marketing strategies based on price play a very important role in the growth of organic products markets. The results of the study indicate that, for each consumer and each product, the price had almost the similar effects on willingness to choose. Hence, it is recommended that the similar pricing strategies be used for these three organic products.

The creation of agricultural processing and complementary industries is one of the ways to reduce poverty and unemployment in rural areas. Therefore, in order to encourage economic agents to create such industries, it is necessary to identify the affective factors on their decision. Information of the Agricultural Jihad Organization of Mazandaran province shows that the concentration of these industries in cities of Amol, Babol and Sari is more than other cities. In addition, the distribution rate of exploited processing industries is different from year to year. By considering the different rate of exploitation in years and locations, this question arises: Does location and time have any effect on exploitation of the agricultural processing and complementary industries in Mazandaran province? What is the contribution of these factors to the exploitation of processing industries?

To answer research questions, the data of 2572 exploited and unexploited units are collected from the Agricultural Jihad Organization of Mazandaran province. The status of exploitation of processing industries is a qualitative variable with two values ​​of zero and one. To determine the effect time in exploitation, the data are divided according to the Location and the commencing time of unit construction. The two-level Logit model is used for estimating model. In the estimated multilevel model, only intercept component is considered as a random component. Also, in this study, the Variance Partition Coefficient (Vpc) and the Likelihood ratio test is used to evaluate the multilevel model

The value of the likelihood ratio statistic in commencing time of construction model is 330/72 that it indicates that two-level logit model is more suitable than the logit model for estimating data. The Variance Partition Coefficient shows that classifying processing and complementary industries based on the time of construction in Mazandaran province, can explain 28.37 percent of the observed deviation on average, which is not explained by independent variables in the model. While 1.2% of the observed deviation is explained by classifying the industries of Mazandaran province according to construction site. Also the share of location in the exploitation of the processing industries in Mazandaran province varies from 0.2 to 4.6%. In other words, the share of worst and the best location in terms of spatial characteristics are 0.2 and 4.6%, respectively. While the impact of the commencing time of construction in the exploitation of the processing industries of Mazandaran province varies from 19% to 40%. The results of the estimation model show that variables of cooperative ownership, planned capacity, unit area, establishment in the industrial parks, the amount of capital and type of activity effect on creation and exploitation the agricultural processing and complementary industries. Marginal effect of Horticultural activity variables, establishment in industrial parks and livestock activity, are more than other variables.

The results show unlike the high emphasis on location factor in locational studies, the share of this factor in construction and exploitation of the processing and complementary industries varies from 0.2 to 2.4 percent. One of the most important reasons for low share of the location factor can be attributed to the right choice of place for construction by entrepreneurs. The results of the two-level logit model indicate that the start time of construction unit explains 28.37 percent of the variation in the exploitation of the agricultural processing in the Mazandaran province. The best and worst time to start construction is 42% and 19% respectively in the exploitation of the processing industries. The high share of start time shows that if an economic agent starts a single unit at different times, the chance of exploiting from the unit varies in different times. The variability of the conditions creates a concept that is called time rendering, in which the success of the construction of processing industries is a function of the conditions of the start date and not the characteristics of applicants for these designs. Therefore, it is recommended to condition creation of new units and exploiting from existing half-unit (including inflation rates, exchange rates, facility rates, facility quantity and rules) to be equal over time. Which this goal would be achieved by adopting to policies and doing activities that are matched with time, trustees and authorities of country.

Fishery is an important activity in terms of trade, income, livelihoods, food and nutrition security especially to fishing communities living close to the coast. Therefore, Fishery is an essential part of sustainable development goals. High-level experts emphasize the enormous potential of oceans and seas for wielding so much influence in offering solution to one of the greatest humanitarian crises such as rapid population growth and meeting their basic needs (High Level Panel of Experts on Food Security). The concepts of sustainability are greatly connected to "sustainable development"; as it is the development of resources for human use that modifies natural ecosystems. One of the scientific and practical ways of achieving sustainability, as first step, is evaluating or measuring sustainability. In this article, a participatory multi-criteria approach is used to evaluate the sustainability of Iranian fisheries in the Persian Gulf as a case study.

Given that the fishery is a multidimensional human activity, and for the purpose of decision-making and management, sustainability assessment is necessary in all its dimensions. Among scientific approaches, multi-criteria decision-making methods have been evaluated as a formal and scientific method for assessing sustainability. Rapfish introduces a rapid appraisal technique to evaluate the sustainability status of fisheries, with a multidimensional view at the issue of sustainability in fisheries and based on a multi-criteria approach.
 

The importance of each of dimensions for Persian Gulf is expressed by weights for three groups of stakeholders separately. Weights are obtained based on a pairwise comparison between dimensions using the AHP method for each groups. Finally, the overall weight is calculated using the arithmetic mean. For example, for the researchers groups, the ecological weight 0.43 show that ecological dimension is more important than other dimensions, while the managers groups consider the management dimension (0.35) and technological (0.22) have more weight than other dimensions. For fishermen, as the exploiters of the Persian Gulf, the economic dimension is more important which has weight of 0.41 in first priority compared to other dimensions, followed by technological dimension (0.22) and social dimension (0.21). Overall weight indicates the importance of dimensions in terms of three groups of stakeholders. For Persian Gulf, ecological and economic dimensions are equally important and dimensions of social (0.19), technological (0.17) and management (0.18) are of almost the same importance. The average score for each dimension was obtained according to the mean value of the sustainability score for each system. Based on this ecological dimension, with an average sustainability score of 37.8% in a less sustainable situation, social and management dimensions with less than 50% are also less stable(sustainable). Regarding the average sustainability score, the activities of the fishing systems in the Persian Gulf are in a sustainable state in the economic (65%) and technical (64%) areas. 

Considering the importance of the issue of fishery sustainability in the Persian Gulf, this study identify the exact dimensions of sustainability based on the fundamental studies conducted in the world and the views of local stakeholders in Iran. The evaluation is carried out in five dimensions: ecological, economic, social, technological, and management, and the significance of each of these dimensions is measured from the perspective of three different groups of stakeholders. The importance of each of these dimensions is measured from the perspective of three different groups of stakeholders. The results indicate the importance of all five dimensions, and three ecological, economic, and social dimensions are prominent. Based on this result, it is suggested that managers, policymakers and stakeholders in the south of Iran pay attention to all dimensions and also policies and plans should cover all of them. Since without a holistic view, it is doubtful to move towards sustainable fishery in Persian Gulf. In the next step, using conventional methods, the importance of indicators for each dimension is determined based on local stakeholders' opinion. In fact, these dimensions and indicators are validated and localized for Iran.

cooperatives play a pivot role in creating sustained society. The objective of cooperative movement in developing countries is not only to renew old economical methods, but also to create fair economic and social conditions. For example, in the agriculture sector of most of these countries, small and even moderate agricultural units do not have acceptable productivity and efficiency and are not able to have production with reasonable prices. Therefore, one of the most effective ways for reaching to a successful agriculture development pattern and finally realizing the large-scale objectives of development is to organize people in groups known as “agricultural production cooperatives (APCs)”. Hereby, economic growth, production increase and fair distribution of incomes can be achieved by practical cooperation of rural population in various civil and social activities. Indeed, these cooperatives pave the way for boosting the production and fair distribution of interests by providing a favorable ground for institutionalizing the cooperation of rural population. All the cooperatives do not act in the same way even if they are located in the same country and are governed by the same laws. Studies showed that successful Iranian cooperatives have longer organizational history and more members, grant greater credits and inputs to their individual members, earn greater income, get more benefits, and incorporate more small scale farmers.

in this study, Questionnaire has been used for information collection. The questionnaire is validated after some revisions following consulting with professionals and experts. The reliability of the questionnaire is confirmed by Cronbach's Alpha (α = 0.971) and its validity is confirmed by professional and experts view. Finally, an interpretative equation (ISM) is used to analyze the results of the findings and presenting a sustainable development model for agricultural production cooperatives. ISM is an interactive learning process. In this technique, a set of different directly and indirectly related elements are structured into a comprehensive systematic model. The so formed model portrays the structure of a complex issue or problem in a carefully designed pattern implying graphics as well as words. In other words, interpretive structural modeling (ISM) is a well-established methodology for identifying relationships among specific items, which define a problem or an issue8. For any complex problem under consideration, a number of factors may be related to an issue or problem. However, the direct and indirect relationships between the factors describe the situation far more accurate than the individual factor taken into isolation. Therefore, ISM develops insights into collective understandings of these relationships.

Based on the findings and factors affecting development of cooperatives (economic, social-cultural, environmental, legal, educational, managerial, marketing, infrastructure, individual and cluster characteristics), variables are classified in 5 different levels and according to relationships, the ISM chart is drawn. After analyzing the MICMAC, the variables are divided into three groups: intrusive, independent and dependent variables. Since the definition of relationships between variables and types of variables can lead to better understanding of the subject and appropriate decision, it is recommended to managers of agricultural production cooperatives to pay attention to each component in order to create sustainable cooperatives.

Based on the findings from theoretical literature and interviews with managers and experts of cooperatives, components that affect sustainable development of agricultural production cooperatives are identified. Then, by using expert opinion and structural-interpretation equation approach, 10 components as the main objectives for sustainable development of agricultural production cooperatives are selected. The results of the analysis show that economic, social-cultural, environmental, legal, educational, managerial, marketing, institutional-infrastructural, personality-individual and clustering factors are effective on sustainable development of agricultural cooperatives. Also, it is important to specify the exact relationship between variables and their ranking in order to provide a model for sustainable development of agricultural production cooperatives. In this regard, structural-interpretive equations have been used. The results show that economic, socio-cultural, environmental and infrastructure-institutional factors are classified as intrusive variables. Legal factors and clustering cooperatives are classified as independent variables. Finally, marketing, educational, managerial, and individual factors are classified as dependent variables. According to the results, economic, institutional- infrastructural, and environmental factors are among most basic factors in the sustainable development of agricultural production cooperatives in East Azerbaijan province. Therefore, it is suggested that strategic action be taken to improve the status quo.

Providing food for people is one of the most important concerns of planners and policymakers in different communities. Protein is among the food needed for humans and lack of it in diet can lead to malnutrition and thus endanger human health. Parallel to population growth and changing consumption patterns towards more protein substances consumption, as well as the relative expensiveness of red meat in recent decades, poultry industry has been considered in Iran and in the world. Chicken meat is one of the most important sources of protein. Thus, reducing the cost as well as increasing its production can improve nutrition level and have an effective role in human health. It is assumed in economy that the producer's goal is to maximize profits. Producers of broiler chicken are no exception to this, and although they may pursue other goals, such as their own employment and their families, they seek to maximize the profits of their assets, like any other producer. Among the most important ways of achieving this goal are the proper use of production inputs as well as optimal scale production, which is used to determine the proximity of a decision maker unit to the optimal scale. Optimal scale refers to the amount of production in which the elasticity of scale equals one, and also there is a constant return to scale in production. Hence, it should be considered that scale efficiency measures average productivity while production takes place in optimal scale. If a firm exceeds the optimal scale, it has a decreasing return to scale, and if it runs at a level lower than the optimal scale, it will have an increasing return to scale. In agricultural economics, scale efficiency is usually nonparametric and is estimated in the framework of data envelopment analysis (DEA). Although many studies have attempted to estimate technical efficiency using parametric and nonparametric methods, scale efficiency is estimated exclusively in the framework of the nonparametric technique of data envelopment analysis. Ray (1998) presented a parametric method in which the scale efficiency is calculated by estimating a production function under the variable returns-to-scale hypothesis and from the estimated scale elasticity. Regarding the technical efficiency and scale efficiency of broiler and laying chicken breeding units, numerous studies have been conducted on the mentioned subject. But all of these studies, particularly in Iran, have used the data envelopment analysis method to measure the scale efficiency. In the present study, the scale efficiency of broiler chicken production units is calculated by parametric method through estimating the stochastic frontier function for the first time in Iran.

In this study, the parametric method of estimating the scale efficiency, first presented by Ray (1998), is used to calculate the scale efficiency of broiler chickens breeding units in Khuzestan province. First, the frontier production function introduced by Lovell and Schmidt (1977) and Miocene and Von den Berg (1977) is evaluated after comparing Cobb-Douglas and Translog functional forms in the framework of a translog production function. Initially, the technical efficiency of studied units is estimated by imposing the necessary assumptions and then, the scale elasticity of different production units is estimated and, the scale efficiency is computed through relation provided by Ray (1998).

Based on the results, the average age of the owners of the studied production units was 46.5 years, their average experience was 18 years and their average education level was 12 years. The average capacity of the investigated poultry was 21600 pieces, the average breeding period 4 times in each year, and the length of the breeding period was about 48 days. The calculation of the production elasticity showed that broiler chicken producers use labor, food, number of chicks and water inputs in the economic area of the production and medicine, electricity and fuel inputs in the third region of production. The average scale elasticity in the units under study was 1.12, indicating that the broiler chicken producers have an increasing return to scale on average. The average scale efficiency of broiler chicken breeding units was 72% and the average technical efficiency of the producers was 88%, indicating that there was the possibility of increasing technical efficiency up to 12% with existing technology on average. The results also demonstrated that the cities of Dezful and Behbahan with an average of 81% had the highest scale efficiency and the Ramhormoz city with an average efficiency of 60% had the lowest scale efficiency. In terms of technical efficiency, Baghmalek city with an average of 95% had the highest and Shadegan city with an average technical efficiency of 76% had the lowest technical efficiency among the studied cities.

Estimating the Recreational Value of Rudkhan Castel Forest Park: Application of One and One-half Bound (OOHD) Dichotomous Choice Contingent Valuation