Investors’ Limited Attention: Evidence from REITs

The Journal of Real Estate Finance and Economics - This paper examines the degree to which the market prices of publicly traded firms reflect and respond to new information regarding the economic...     

Do wood fibers from a pulp mill affect the distribution of total and methyl mercury in river sediments?

The St. Lawrence River near Cornwall Ontario is affected by industrial mercury contamination of sediments and biota. It has been suggested that pulp and paper mill effluents may stimulate bacterial mercury methylation in these sediments, leading to contamination of aquatic biota. To test this hypothesis, we examined sediment–porewater dynamics of total mercury (THg) and methyl mercury (MeHg) at a site with high concentrations of wood fibers from a pulp and paper mill effluent and a nearby reference site with low wood fiber content. Dissolved phase THg (THg_diss) and MeHg (MeHg_diss) in porewater profiles showed that 38 ± 30.9% (SD) of THg in porewaters was in the methylated form regardless of wood fiber content. MeHg_diss and THg_diss concentrations were homogeneous between porewater and overlying water, indicating (a) that there is minimal net diffusion of MeHg_diss and THg_diss and (b) that redox-dependent processes such as sulfate reduction and Fe reduction were not associated with MeHg_diss distribution in these sediment profiles. MeHg and THg in solid phase showed coincident subsurface peaks at depths > 40 cm suggesting either that historical deposits of MeHg on particles (MeHg_(p)) are preserved in deep sediments, or that Hg methylation is active in deep sediments.     

Control,Knowledge, and Persuasive Power in Advertising Creativity: An Ethnographic Practice Theory Approach

This article reports findings from an extended multimethod ethnographic study into the social practices of advertising creativity. The study was conducted in a major Iranian creative advertising agency that has many international clients and earns annual billings equivalent to more than USD $100 million. Findings focus on three sets of overlapping, aggregated social practices—labeled control power, knowledge power, and persuasive power—that serve to work around tensions over creative output to accomplish the dual goals of creating good, effective work and persuading the client to buy in to the creative strategy. We conclude with implications for future practice and research.     

Inequality,public good provision and the composition of trade

This paper investigates the effect of trade liberalization on the provision of public goods and shows that inequality also plays a vital role here. Public goods help enhance the productivity of firms, lower prices and raise profitability. The provision of public goods has different effects in closed and open economies. In an open economy, the impact of productive spending on increasing profit is stronger. Consequently, the opening up of the economy shifts the benefits of productive public goods from consumers to firms. As the median voters’, share of the firm’s profit rises, public goods become more appealing and flourish. Consequently, the manufacturing export is boosted by a rise in productivity.     

Risk Management of Drinking Water Supply in Critical Conditions Using Fuzzy PROMETHEE V Technique

Water crisis management in drinking water supply systems is a crucial issue. Many different natural and unnatural disasters cause great damages to these systems. Prioritizing effective strategies by expert decision-makers before any incidence can greatly reduce this damage. Moreover, cost minimization in the provision of these strategies is very essential as the urban crisis management organizations are financially limited. Therefore, this study provided a model for the selection of the most appropriate drinking water supply strategies in crisis conditions, considering possible limitations. Fuzzy PROMTHEE V, a multi-criteria decision-making method, is a new approach developed in this research to help the decision-makers in selecting a set of possible alternatives for drinking water supply management, which are ranked based on five criteria determined by decision-makers (water supply reliability, implementation speed and simplicity, implementation cost, social satisfaction, water quality) and budget limitation as the constraint for the optimization problem. This model was applied in a case study of Tehran city in Iran. Due to the uncertainties in expert opinions and parameters needed for drinking water supply risk management, this model was solved with three fuzzy methods and one non-fuzzy method, and the results were compared. Findings showed that strengthening passive defense in water supply, transportation, and distribution systems; providing water consumption management; encouraging people to save water for emergency conditions and planning to exploit popular forces and performing maneuvers are top-ranked alternatives.

     

An Extended PROMETHE II Multi-Criteria Group Decision Making Technique Based on Intuitionistic Fuzzy Logic for Sustainable Energy Planning

The implementations of Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) category to complex multi-criteria group decision making (MCGDM) scenarios have been included in thousands areas. Outranking methods such as PROMETHEE II are also greatly employed in energy planning application. In MCGDM methods if decision makers (DMs) are not able to treat precise data in order to define their preferences, the intuitionistic fuzzy set (IFS) theory enables them. IFS attributes are connected with the degree of membership and non-membership, and can be used to draw uncertainty in group decision-making situations. In this paper, a new version of the PROMETHEE II method is proposed, aiming at solving MCGDM problems. Linguistic variables are expressed in the membership function and non-membership function of IFS which are used to assess the weights of all criteria and the ratings of each alternative with respect to each criteria. Conditional normalized Euclidean distance measure is adopted to measure deviations between alternatives on intuitionistic fuzzy set. Then, a ranking algorithm is applied to indicate the order of superiority of alternatives. Finally, a practical example is given to an application of sustainable energy planning to verify our proposed method. Additionally, a comparative analysis is done among the proposed PROMETHEE II method and the intuitionistic fuzzy technique for order preference by similarity to ideal solution (IF-TOPSIS) method and elimination and choice translating reality method (IF-ELECTRE).     

Sustainable Water Supply and Demand Management in Semi-arid Regions: Optimizing Water Resources Allocation Based on RCPs Scenarios

Climate as one of the key factors in water resources management affects the amount of water in the hydrological cycle, which subsequently impacts the level of water availability. Considering the challenges that the South Alborz Region, Iran is currently facing in supplying water for various consumers; in this study, the climate change adaptation scenarios are investigated for sustainable water supply and demand. This study uses a procedure in which five different adaptation approaches, under RCPs scenarios, were established using the WEAP model to assess the impacts of various adaptation strategies on increasing the balance between water supply and demand over current and 2020s accounts. The findings suggest an imbalance between supply and demand in the current situation with the greatest imbalance in domestic use while the minimum in the industrial sector. The results of assessing adaptive scenarios show that various scenarios have different effects on balancing the water supply and demand by different consumers; on the other hand, the scenarios that directly affect domestic water demand have the greatest effect on minimizing the gap between supply and demand in the region; therefore, the options for decreasing the population demand along with diminishing the losses in the domestic water distribution network are the most effective alternatives for balancing supply and demand under all of the climate scenarios. The findings of this research indicate that adaptive management with the focus on restricting demand helps actively management of water resources in the regions with scarce water resources.

     

An Artificial Intelligence Approach for the Stochastic Management of Coastal Aquifers

Aquifer recharge rates and patterns are often uncertain, especially in arid areas due to sporadic and erratic rainfall. Therefore, determining the optimal groundwater abstraction using classical approaches such as Monte Carlo Simulation (MCS) requires a large number of groundwater simulations and exorbitant computational efforts. The problem becomes even more complex and time consuming for regional coastal aquifers whose domains must be discretized using high-resolution meshes. In fact, even fast evolutionary multi-objective optimization techniques generally require a large number of simulations to determine the Pareto-front among the objectives. This study explores the performance of a Decision Tree (DT) approach for the generation of the Pareto optimal solutions of groundwater extraction. This paper applies the DTs for the optimal management of the Al-Khoud coastal aquifer in Oman. The learning process of the developed DT-based model uses the output of a numerical simulation model to assess the aquifer response based on different abstraction policies. The trained DT network then utilizes the NSGA-II to determine the Pareto-optimal solutions. The simulation show that the general flux pattern in the study area is toward the sea and the hydraulic head following a similar pattern in both best and worst recharging scenarios downstream of the studied recharging dam. Statistical tests showed a good correlation between the DT-based and simulation-based results and demonstrate the capability of the DT approach to obtain high-quality solutions by incorporating a large number of recharge scenarios. Moreover, the required runtime of the DT-based approach is extremely low (5 min) compared to that of the simulation-based method (several days). This means that including additional Monte-Carlo simulations can be readily done in few minutes using the obtained DTs, instead of the long computational time needed by the simulation-based approach.     

New Approach for Sediment Yield Forecasting with a Two-Phase Feedforward Neuron Network-Particle Swarm Optimization Model Integrated with the Gravitational Search Algorithm

Water Resources Management - Predicting sediment yield is an important task for decision-makers in environmental monitoring and water management since the benefits of applying non-linear,...     

Resource‐driven victory

The vast popularity of distributive policies in many resource‐rich countries coincided with the oil and gas price hike of 2004. However, following the sharp decline of price in the late 2014, this popularity started waning and the tendency toward more productive policies gained traction. Using a theoretical model, this paper studies the optimal composition of public spending and demonstrates that, for a sufficiently low amount of any exogenous revenue, for example, resource revenue, investing revenue in productive public good is preferable. The representative agent prefers more distributive policies as resource revenue increases. A key determinant of the optimal composition of public spending is the inherited level of productivity. Countries with too low or too high productivity both find distributive policies more appealing even for a small amount of resource revenue. Yet, they have an essential difference. Resource revenue eradicates individuals' incentive to work in countries with low initial productivity while individuals always prefer to work in highly productive countries.