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The purpose of this paper is to examine significant steps taken by the Pennsylvania State University (Penn State) to account for both energy cost savings and greenhouse gas (GHG) emissions reduction goals through strategic investments in energy conservation measures (ECMs) in campus buildings. Through an analysis of multiple years of investment in facility upgrades across the university, the impacts of ECMs of various types are characterized by building type. The standards and criteria for ECMs investments are also evaluated with the goal to develop a predictive tool to support decision making pertaining to an annual investment in a portfolio of ECMs that will maintain a trajectory to achieve both financial return on investment as well as GHG reduction goals.

This study is comprised of three main parts: analyzing the energy costs saving and GHG emissions reduction contribution of various building types in which ECMs were conducted, analyzing costs saving and GHG emissions reduction contribution of each ECM while considering the average annual investments made in them and estimating the impact of upgrading Penn State’s steam plants from firing a mixture of coal and natural gas to natural gas only on the GHG emissions.

These analyses help identify which types of buildings and ECMs would have larger savings and emissions reduction contributions. A calculator is also created to enable forecasting of costs saving and GHG emissions reduction of investment distribution strategy among ECMs. This study demonstrates that the calculator based on data from previous years will benefit decision makers in more wisely configuring the investment portfolio.

This paper fulfills an identical need to couple energy efficiency strategies coupled with the environmental impacts associated with different fossil fuel energy sources.

Priorities of decarbonizing the mining sector together with an availability of cost-effective technological solutions lead renewable energy (RE) to become an attractive energy source for the mining industry. Several pilot projects are run as hybrid systems, providing additional capacity to traditional energy systems. The purpose of this paper is to develop a mathematical model as a decision-making tool. The decision refers to a replacement of the fossil fuel system contains by the hybrid system in the sense of no return.

Four systems are considered. System one contains only a diesel plant. System two consists of a hybrid energy system with a photovoltaic (PV) part and a genset as back-up. System three includes a conventional natural gas combined cycle (CGCC) plant. Finally, system four covers a hybrid energy system with a PV part and CGCC turbine. The mathematical model is based upon the well-known concept of levelized cost of electricity.

The scenarios account for the degradation rate of PV modules, the PV yields of mines in different locations and the greenhouse gas emissions impact. The results show the break-even times of each scenario and the years of no return for the four systems in each scenario.

The solution of the model is performed for two case-studies. Case study 1 compares the diesel and hybrid PV-diesel systems. Case study 2 compares the CGCC and hybrid PV-natural gas systems.

This model can be generalized to all mining settings, with specific practical implications for off-grid mines.

The results of this paper bring a valuable contribution to carbon dioxide emissions reduction.

The paper aims to enhance the attention of decision-makers on fossil fuel and RE technologies increase the attractiveness of RE in powering mining operations.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐17; Journal of Property Investment & Finance Volumes 8‐17; Property Management Volumes 8‐17; Structural Survey Volumes 8‐17.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐17; Journal of Property Investment & Finance Volumes 8‐17; Property Management Volumes 8‐17; Structural Survey Volumes 8‐17.

The purpose of this paper is to present and discuss the methodological approach and the results of the investigation of greenhouse gas emission abatement costs in Upper Austria.

The assessment covers the quantification of marginal abatement costs (MACs) of greenhouse gas (GHG) emissions and the emission reduction potentials of various energy efficiency and fuel switch measures with a special emphasis on the heat, electricity and transport sectors in Upper Austria during the period from 2010 to 2030.

The expert‐based assessment in Upper Austria shows negative abatement costs for 19 of 56 evaluated strategies. While these measures are very efficient from an economic point of view, the remaining 37 measures are associated with higher costs. The evaluation reveals a significant reduction potential of 5.2 million tons CO₂e (which represent 21 per cent) of the current GHG emissions in Upper Austria for the examined period.

MACs are generally limited to a certain time frame. Furthermore, the expert‐based approach is based on several assumptions and neglects behavioural and learning aspects.

This contribution uses a multi‐criteria approach that reveals the economic efficiency and the ecological effectiveness of the considered strategies/technologies with regard to greenhouse gas emission reductions, the improvement of the overall energy efficiency, and the competitiveness of a fuel switch towards renewable energy sources. Drawing upon the findings of this study, policy recommendations can be elaborated and the necessary improvements of the regulative framework can be implemented.

Providing and maintaining safe and comfortable conditions in sport centres raises many issues, particularly cost. The paper gives an overview of the factors associated with sport centre servicing and attempts to highlight the governing factors associated with this, particularly energy costs. A total of 19 sport centres in the City of Liverpool in the UK are investigated, using data elicited from the Liverpool Leisure Services Directorate. The energy operating costs were analysed using statistical methods. Six models were developed to predict total energy costs. Testing and validation results showed a high level of model accuracy. The models would be of use to professionals involved in feasibility studies at the design stage.

Ethiopia’s energy sector faces critical challenges to meeting steadily increasing energy demand given limited infrastructure, heavy reliance on hydroelectric power and underdevelopment of alternative energy resources. The purpose of this paper was to identify optimal least cost investment decisions for integrated energy source diversification. The authors seek to contribute to the relevant literature by paying particular attention to the role of public policy for promoting renewable energy investment and to better understand future energy security implications of various sources of uncertainty.

The authors created a dynamic linear programming model using General Algebraic Modelling System software to explore the national energy security implications of uncertainties associated with increasing technological advances and efficiency, and climate change scenarios.

To cope with the impacts of drought expected from future climate change on hydroelectric power production, Ethiopia would need to invest in the development of alternative energy resources. Such investment would not only enhance the sustainability and reliability of energy production but also increase costs. Greater rates of technological and efficiency innovations, however, were found to improve electricity diversification and reduce production costs and shadow prices or resource scarcity, and are thus key for enhancing energy security and reducing the risks posed by drought.

The dynamic linear programming model by the authors represents a flexible sector modelling tool for exploring the sustainability and efficiency of energy resource development pathways and evaluating the effects of different sources of uncertainty on the energy sector.

The paper aims to explore the potential impact that the introduction of the UK's carbon reduction commitment (CRC) energy efficiency scheme will have on: energy use in the tenanted commercial built environment; and the idea of the net lease.

The paper reviews various background documents preceding the implementation of CRC in order to identify the abatement incentives established. The common structure of commercial leases and the early property market reaction to CRC are also considered in order to explore how effective the CRC scheme is likely to be in achieving the twin goals of carbon saving and landlord‐tenant collaboration.

Key to the success of the CRC scheme will be the way in which the financial and reputational drivers established by the CRC scheme incentivise landlords and tenants to make technological and social changes to reduce energy consumption. Given the variety of ways that energy is supplied to tenanted commercial property, the complexity of the CRC scheme, the traditionally adversarial relations between landlords and tenants and the “split‐incentive” of commercial leases, energy abatement opportunities are found to be significantly more limited in the leasehold context than for owner‐occupied properties. Nonetheless, the paper notes that the introduction of the CRC scheme has begun an important conversation and is an important step towards tackling energy efficiency.

The paper brings together understandings of the legal framework of commercial leases, of the property market and practice, and the landlord and tenant relationship – to consider how the CRC scheme will help to deliver the UK's goal of reducing carbon emissions.

The purpose of this paper is to explore the financial and economic aspects of the renewable energy sector aiming to develop and present a project feasibility analysis model that allows the public sector to master plan socially beneficial infrastructure projects and to find financially viable options for private investments. This paper develops a general frame that can be harmonized to a certain project by applying relevant country specific schemes.

The cost-benefit analysis (CBA) approach is used to develop relevant formulas aiming to compare the economic internal rate of return (EIRR) and financial internal rate of return (FIRR) of the possible investment options. The IRR method is used for the development of a platform that will allow comparing different project alternatives and choosing an optimal model for both public and private partners. A case study approach from Uzbekistan is used to highlight the implementation possibilities of the model based on a certain country example.

This paper develops a decision-making frame allowing the public sector to find organizational options that provide economically viable projects and at the same time attract private investors in the latter. The designed map of possible benefits, costs and revenue mechanisms allows practitioners to analyze the economic and financial viability of the existing combinations by using the developed model.

The developed model will allow the public sector to use the needed data on different possible design models in the developed formulas in order to identify the EIRR and FIRR of each option. Nevertheless, the application of the model will be possible after considering country specific options needed for CBA. The private sector can use the model to identify the financially acceptable options for the investments.

The paper provides the decision makers with a sound tool to identify the possible combinations of the options to conduct a relevant project with private investments in the renewable energy sector and to choose the model that generates the highest social welfare.

Maintenance and running costs contribute significantly to the total cost of running facilities. Almost half of the energy consumed in the UK is used in buildings. Energy consumption can be attributed to many factors. Describes the fundamentals associated with modelling running costs in leisure centres and then investigates 19 sport centres in the city of Liverpool, using data elicited from the Liverpool Leisure Services Directorate. The energy operating costs were analysed using statistical techniques and artificial intelligence methods. Three types of modelling, linear/non‐linear regression, neural networks and neurofuzzy were developed to predict energy cost. Testing and validation of the results showed that neural network models outperformed both regression and neurofuzzy techniques. However, all the models showed a high level of accuracy. The models would be of use to professionals involved in feasibility studies.