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Ensuring energy security and controlling the share of energy in climate change are the two main challenges of the energy sector in the path of a sustainable future. This study has designed the dynamic model of Iran’s electrical energy supply system based on the water-food-energy-climate change nexus in order to identify sustainable policies for the supply of electrical energy resources and adaptation to Iran’s climate change process.

For this purpose, first, the system dynamics model was designed with the participation of policymakers and the supply and demand data of water-food-energy resources and the trend of climate change and economic growth in Iran. After validation, the model is simulated in the 30-year horizon (2020–2050), and according to the results of the Monte Carlo sensitivity analysis, Iran’s electric energy supply policies are based on four strategies, including (1) Electric energy supply based on electric energy supply management from non-renewable sources, (2) Development of electrical energy supply based on management of energy supply from renewable sources, (3) Electrical energy supply based on electrical energy demand management and (4) Electrical energy supply based on adaptation to climate changes. By identifying and applying the policies of each strategy separately, the model was tested and the results were compared.

Based on the implementation of the combination of selected policies in the model, the following policies have been proposed: 16% development of nuclear power plants, 18% reduction in the ratio of production of gas power plants to the total production of non-renewable power plants, and an increase in the production of combined cycle power plants through conversion of gas to combined cycle, energy aggregation and development of heat recovery systems in industrial units by 32%, a decrease of 5% Energy transmission and distribution losses, per capita reduction of energy consumption from 0.926 to the global average of 0.182 (MW) per year, management of water demand in the food sector by increasing irrigation efficiency to about 85%, a 27% increase in the area of land under the irrigation network, and reducing losses Food amounting to the global average of 0.9 m tons per year.

The proposed model is an application of system dynamics in the field of policymaking to ensure the security of electrical energy resources, taking into account the water-food-energy-climate changes nexus. The model is a valuable tool for policymakers in planning the sustainable management of resources in the path of adapting to climate change.

Increased dietary salt content is one of the effective factors of hypertension and a major public health challenge globally. Although the positive effects of dietary salt reduction on health are universally accepted, people can hardly reduce their salt intake. The purpose of this study is to identify the inhibitory factors of dietary salt reduction among 20–65-year-old women in Yazd City, Iran.

This study was conducted using a deductive content analysis approach based on the communication for the behavioral impact (COMBI) framework. The purposeful sampling method was applied with maximum variation in terms of different educational levels, age groups, occupational status and residential areas to select the participants. Snowball sampling was used to select health-care professionals. Furthermore, semi-structured interviews and focus-group discussions were conducted with 31 local women and 11 health-care professionals working in the City until data saturation was achieved. Data were analyzed using Graneheim and Landsman’s method.

After data analysis, 617 initial codes were extracted over the perceived barriers. After merging similar codes, 223 codes were extracted. The barriers were classified into five main categories of family, personal, organizational, educational and socio-cultural barriers.

Based on the COMBI framework, the results demonstrated that the most important barriers for reducing salt intake were negative attitude toward restrictions on dietary salt intake, insufficient and incorrect beliefs about the health risk of salt, lack of family support, inadequate health literacy and low self-efficacy in Yazd City. Among these barriers, lack of family support was considered as the most effective factor in reducing salt consumption. So, by focusing on this area and providing the community with the required education, the amount of salt consumed by families can be reduced.

Researchers have devoted considerable attention to ecolabel products and their purchase intention. However, empirical research often presents relatively unpredictable and uneven results. Thus, the relationship between the antecedents and outcome variables among ecolabelling studies, such as purchase intention, remains ambiguous. To address this gap in the literature, this study combines the Theory of Planned Behaviour (TPB) and Stimulus, Organism, Response (SOR) theory within a meta-analytic framework, consolidating existing literature on the purchase intention of eco-labelled products to analyse concrete relationships between antecedents and purchase intention.

We conducted a comprehensive analysis of 37 studies and a total sample size of 16,672 participants. The analysis employed a MASEM technique, and the findings of the analysis offer empirical support for the significance of all the proposed relationships within the provided conceptual framework.

The results revealed that environmental advertising significantly impacts green attitude and later substantially influences consumers' intention to make environmentally conscious purchases. The present study also has examined the potential inclusion of different recommended moderators, such as time period and sample size.

The current study focuses on core variables consistently utilized in previous research. Apart from these, additional variables have also been considered in the ecolabelling literature but have not been included in the scope of this study. Future research endeavours may incorporate additional moderators, such as cultural differences and gender dominance, to further enhance the understanding of the subject matter.

Notably, it stands out as one of the initial meta-analyses on ecolabelling, which also incorporated the examination of several moderators.

Earned value management (EVM)–based models for estimating project actual duration (AD) and cost at completion using various methods are continuously developed to improve the accuracy and actualization of predicted values. This study primarily aimed to examine natural gradient boosting (NGBoost-2020) with the classification and regression trees (CART) base model (base learner). To the best of the authors' knowledge, this concept has never been applied to EVM AD forecasting problem. Consequently, the authors compared this method to the single K-nearest neighbor (KNN) method, the ensemble method of extreme gradient boosting (XGBoost-2016) with the CART base model and the optimal equation of EVM, the earned schedule (ES) equation with the performance factor equal to 1 (ES1). The paper also sought to determine the extent to which the World Bank's two legal factors affect countries and how the two legal causes of delay (related to institutional flaws) influence AD prediction models.

In this paper, data from 30 construction projects of various building types in Iran, Pakistan, India, Turkey, Malaysia and Nigeria (due to the high number of delayed projects and the detrimental effects of these delays in these countries) were used to develop three models. The target variable of the models was a dimensionless output, the ratio of estimated duration to completion (ETC(t)) to planned duration (PD). Furthermore, 426 tracking periods were used to build the three models, with 353 samples and 23 projects in the training set, 73 patterns (17% of the total) and six projects (21% of the total) in the testing set. Furthermore, 17 dimensionless input variables were used, including ten variables based on the main variables and performance indices of EVM and several other variables detailed in the study. The three models were subsequently created using Python and several GitHub-hosted codes.

For the testing set of the optimal model (NGBoost), the better percentage mean (better%) of the prediction error (based on projects with a lower error percentage) of the NGBoost compared to two KNN and ES1 single models, as well as the total mean absolute percentage error (MAPE) and mean lags (MeLa) (indicating model stability) were 100, 83.33, 5.62 and 3.17%, respectively. Notably, the total MAPE and MeLa for the NGBoost model testing set, which had ten EVM-based input variables, were 6.74 and 5.20%, respectively. The ensemble artificial intelligence (AI) models exhibited a much lower MAPE than ES1. Additionally, ES1 was less stable in prediction than NGBoost. The possibility of excessive and unusual MAPE and MeLa values occurred only in the two single models. However, on some data sets, ES1 outperformed AI models. NGBoost also outperformed other models, especially single models for most developing countries, and was more accurate than previously presented optimized models. In addition, sensitivity analysis was conducted on the NGBoost predicted outputs of 30 projects using the SHapley Additive exPlanations (SHAP) method. All variables demonstrated an effect on ETC(t)/PD. The results revealed that the most influential input variables in order of importance were actual time (AT) to PD, regulatory quality (RQ), earned duration (ED) to PD, schedule cost index (SCI), planned complete percentage, rule of law (RL), actual complete percentage (ACP) and ETC(t) of the ES optimal equation to PD. The probabilistic hybrid model was selected based on the outputs predicted by the NGBoost and XGBoost models and the MAPE values from three AI models. The 95% prediction interval of the NGBoost–XGBoost model revealed that 96.10 and 98.60% of the actual output values of the testing and training sets are within this interval, respectively.

Due to the use of projects performed in different countries, it was not possible to distribute the questionnaire to the managers and stakeholders of 30 projects in six developing countries. Due to the low number of EVM-based projects in various references, it was unfeasible to utilize other types of projects. Future prospects include evaluating the accuracy and stability of NGBoost for timely and non-fluctuating projects (mostly in developed countries), considering a greater number of legal/institutional variables as input, using legal/institutional/internal/inflation inputs for complex projects with extremely high uncertainty (such as bridge and road construction) and integrating these inputs and NGBoost with new technologies (such as blockchain, radio frequency identification (RFID) systems, building information modeling (BIM) and Internet of things (IoT)).

The legal/intuitive recommendations made to governments are strict control of prices, adequate supervision, removal of additional rules, removal of unfair regulations, clarification of the future trend of a law change, strict monitoring of property rights, simplification of the processes for obtaining permits and elimination of unnecessary changes particularly in developing countries and at the onset of irregular projects with limited information and numerous uncertainties. Furthermore, the managers and stakeholders of this group of projects were informed of the significance of seven construction variables (institutional/legal external risks, internal factors and inflation) at an early stage, using time series (dynamic) models to predict AD, accurate calculation of progress percentage variables, the effectiveness of building type in non-residential projects, regular updating inflation during implementation, effectiveness of employer type in the early stage of public projects in addition to the late stage of private projects, and allocating reserve duration (buffer) in order to respond to institutional/legal risks.

Ensemble methods were optimized in 70% of references. To the authors' knowledge, NGBoost from the set of ensemble methods was not used to estimate construction project duration and delays. NGBoost is an effective method for considering uncertainties in irregular projects and is often implemented in developing countries. Furthermore, AD estimation models do fail to incorporate RQ and RL from the World Bank's worldwide governance indicators (WGI) as risk-based inputs. In addition, the various WGI, EVM and inflation variables are not combined with substantial degrees of delay institutional risks as inputs. Consequently, due to the existence of critical and complex risks in different countries, it is vital to consider legal and institutional factors. This is especially recommended if an in-depth, accurate and reality-based method like SHAP is used for analysis.