A model to control environmental performance of project execution process based on greenhouse gas emissions using earned value management

In response to recent climate change, which is believed to be attributed to the release of greenhouse gas (GHG) emissions, many countries are placing CO2 abatement programs such as carbon tax and cap-and-trade. Projects do have a significant share in GHGs and therefore their environmental performance, like their schedule and cost performance, should be monitored and controlled. Although many large projects would pass an environmental assessment in the project evaluation phase, the issue of environmental performance monitoring during the project execution phase has not been addressed in project management methodologies. The objective of this paper is to develop a model to estimate project GHG emissions, and to measure project GHG performance using the developed metrics, which can be used at any point in time over the life of a project. A comprehensive study is conducted to collect information on GHG emission factors of various project activity data (such as material use, energy and fuel consumption, transportation, etc.), and a user form interface is developed to calculate the total GHG of an activity. Also, a breakdown structure is proposed which supports managing all the project GHG accounts. The monitoring and control model is formulated based on the logic used in earned value management (EVM) methodology. The proposed model is then implemented to a work package of a real construction project. The results present the project initial GHG plan and show that the model is able to calculate project GHG variance by the reporting date and predict project final GHG based on a project GHG performance index. The method presented in this paper is general and can be applied to any type of projects in an organization that aims to reduce its carbon footprint. The same structure can be applied to monitor and control any other environmental impact associated with project execution process.     

Eco-SpaCE: An object-oriented, spatially explicit model to assess the risk of multiple environmental stressors on terrestrial vertebrate populations

Wildlife organisms are exposed to a combination of chemical, biological and physical stressors. Information about the relative impact of each stressor can support management decisions, e.g., by the allocation of resources to counteract those stressors that cause most harm. The present paper introduces Eco-SpaCE; a novel receptor-oriented cumulative exposure model for wildlife species that includes relevant ecological processes such as spatial habitat variation, food web relations, predation, and life history. A case study is presented in which the predicted mortality due to cadmium contamination is compared with the predicted mortality due to flooding, starvation, and predation for three small mammal species (Wood mouse, Common vole, and European mole) and a predator (Little owl) living in a lowland floodplain along the river Rhine in The Netherlands. Results indicated that cadmium is the principal stressor for European mole and Little owl populations. Wood mouse and Common vole population densities were mainly influenced by flooding and food availability. Their estimated population sizes were consistent with numbers reported in literature. Predictions for cadmium accumulation and flooding stress were in agreement with field data. The large uncertainty around cadmium toxicity for wildlife leads to the conclusion that more species-specific ecotoxicological data is required for more realistic risk assessments. The predictions for starvation were subject to the limited quantitative information on biomass obtainable as food for vertebrates. It is concluded that the modelling approach employed in Eco-SpaCE, combining ecology with ecotoxicology, provides a viable option to explore the relative contribution of contamination to the overall stress in an ecosystem. This can help environmental managers to prioritize management options, and to reduce local risks.     

A model framework to assess the effect of dairy farms and wild fowl on microbial water quality during base-flow conditions

There is concern regarding microbial water quality in many pastoral catchments in New Zealand which are home to numerous livestock and wild animals. Information on microbial impacts on water quality from these animals is scarce. A framework is needed to summarise our current knowledge and identify gaps at the scale of an individual farm. We applied a Monte Carlo modelling approach to a hypothetical dairy farm based on the extensive data sets available for the Toenepi Catchment, Waikato, New Zealand. The model focused on quantifiable direct inputs to the stream from ducks, cows and farm dairy effluent (FDE) during base-flow conditions. Most of the inputs of Escherichia coli from dairy farms occur sporadically and, therefore, have little effect on the expected median stream concentrations. These sporadic inputs do however, have a strong influence on extrema such as 95th percentile values. Current farm mitigations of fencing streams and using improved management practices for applying FDE to land, such as low application rate deferred FDE irrigation systems, would appreciably reduce faecal microbial inputs to the stream. However, the concentrations of E. coli in rural streams may not reduce as much as expected as wild fowl living in streams would have a larger effect on water quality than a farm in which environmental mitigations are widely implemented.     

Evaluating the potential impact of global warming on the UAE residential buildings – A contribution to reduce the CO2 emissions

There is significant evidence that the world is warming. The International Panel of Climate Change stated that there would be a steady increase in the ambient temperature during the end of the 21st century. This increase will impact the built environment, particularly the requirements of energy used for air-conditioning buildings. This paper discusses issues related to the potential impact of global warming on air-conditioning energy use in the hot climate of the United Arab Emirates. Al-Ain city was chosen for this study. Simulation studies and energy analysis were employed to investigate the energy consumption of buildings and the most effective measures to cope with this impact under different climate scenarios. The paper focuses on residential buildings and concludes that global warming is likely to increase the energy used for cooling buildings by 23.5% if Al-Ain city warms by 5.9 °C. The net CO₂ emissions could increase at around 5.4% over the next few decades. The simulation results show that the energy design measures such as thermal insulation and thermal mass are important to cope with global warming, while window area and glazing system are beneficial and sensitive to climate change, whereas the shading devices are moderate as a building CO₂ emissions saver and insensitive to global warming.