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The purpose of this paper is to compare the ability of popular temperature models, namely, the models given by Alaton et al., by Benth and Benth, by Campbell and Diebold and by Brody et al., to forecast the prices of heating/cooling degree days (HDD/CDD) futures for New York, Atlanta, and Chicago.

To verify the forecasting power of various temperature models, a statistical backtesting approach is utilised. The backtesting sample consists of the market data of daily settlement futures prices for New York, Atlanta, and Chicago. Settlement prices are separated into two groups, namely, “in‐period” and “out‐of‐period”.

The findings show that the models of Alaton et al. and Benth and Benth forecast the futures prices more accurately. The difference in the forecasting performance of models between “in‐period” and “out‐of‐period” valuation can be attributed to the meteorological temperature forecasts during the contract measurement periods.

In future studies, it may be useful to utilize the historical data for meteorological forecasts to assess the forecasting power of the new hybrid model considered.

Out‐of‐period backtesting helps reduce the effect of any meteorological forecast on the formation of futures prices. It is observed that the performance of models for out‐of‐period improves consistently. This indicates that the effects of available weather forecasts should be incorporated into the considered models.

To the best of the author's knowledge this is the first study to compare some of the popular temperature models in forecasting HDD/CDD futures. Furthermore, a new temperature modelling approach is proposed for incorporating available temperature forecasts into the considered dynamic models.

This paper investigates the impact of CO2 vs CO2 “equivalents” (CO2e) by analyzing fugitive emissions, with a particular focus on Fluorinated gases (F-gases), arising from refrigerant leakages in buildings. F-gases are an especially powerful set of GHGs with a global warming potential hundreds to thousands of times greater than that of CO2.

The significant impact of CO2e is tested by means of an empirical study with current consumption data from German food retail warehouses. This evaluation involves the analysis of the Carbon Risk Real Estate Monitor's country- and property-type specific pathway, coupled with a paired samples t-test to examine the hypotheses. The assessment is undertaken by evaluating the type of gas and the amount of leakage reported in the baseline year, subsequently converting these values to CO2e units.

On average, F-gases account for 40% of total building emissions and nearly 45% of cumulative emissions until 2050. In light of ongoing climate change and the rising number of Cooling Degree Days (CDDs), it becomes imperative to assess both the environmental and economic impact of F-gases and to transition toward environmentally friendly refrigerants.

The analysis sheds light on the seldom-addressed threats posed by CO2e emissions stemming from refrigerant losses. By identifying these threats, investors can devise strategies to mitigate potential future costs and carbon risks.

The weather largely affects economic activity, and thus, companies vulnerable to weather risk need to plan ahead to cope with unexpected weather changes, just as they do for changes in interest rates, oil prices, or foreign exchange rates to stabilize their earning stream. Weather derivatives can be a useful tool for weather risk management.

This paper focuses on pricing one of the most popular weather derivatives -HDD/CDD options- and estimating the market price of weather risk (MPR). Historical data are used to construct the stochastic process of temperature, while the current market prices of Chicago and New York HDD futures options are used to extract the implied MPR. The Monte-Carlo Simulation Method is proposed to estimate the price of weather derivatives numerically. In addition, the approximate closed form formula for the options is provided modifying the Alaton, Djehiche, and Stillberg (2002) model. Finally, option price sensitivity to changes in MPR is analyzed to show the important role of the MPR in the weather option pricing model.

The purpose of this paper is to propose a feasible model for the daily average temperatures of Beijing, Shanghai and Shenzhen, in order to price temperature‐based weather derivatives; also to derive analytical approximation formulas for the sensitivities of these contracts.

This study proposes a seasonal volatility model that estimates daily average temperatures of Beijing, Shanghai and Shenzhen using the mean‐reverting Ornstein‐Uhlenbeck process. It then uses the analytical approximation and Monte Carlo methods to price heating degree days and cooling degree days options for these cities. In addition, it derives and calculates the option sensitivities on the basis of an analytical approximation formula.

There exists a strong seasonality in the volatility of daily average temperatures of Beijing, Shanghai and Shenzhen. To model the seasonality Fourier approximation is applied to the squared volatility of daily temperatures. The analytical approximation formulas and Monte Carlo simulation produce very similar prices for heating/cooling degree days options in Beijing and Shanghai, a result that also verifies the convergence of the Monte Carlo and approximation estimators. However, the two methods do not produce converging option prices in the case of HDD options for Shenzhen.

The article provides important insight to investors and hedgers by proposing a feasible model for pricing temperature‐based weather contracts in China and derives analytical approximations for the sensitivities of heating/cooling degree days options.

The purpose of this paper is to investigate the functionality and effectiveness of the Carbon Risk Real Estate Monitor (CRREM tool). The aim of the project, supported by the European Union’s Horizon 2020 research and innovation program, was to develop a broadly accepted tool that provides investors and other stakeholders with a sound basis for the assessment of stranding risks.

The tool calculates the annual carbon emissions (baseline emissions) of a given asset or portfolio and assesses the stranding risks, by making use of science-based decarbonisation pathways. To account for ongoing climate change, the tool considers the effects of grid decarbonisation, as well as the development of heating and cooling-degree days.

The paper provides property-specific carbon emission pathways, as well as valuable insight into state-of-the-art carbon risk assessment and management measures and thereby paves the way towards a low-carbon building stock. Further selected risk indicators at the asset (e.g. costs of greenhouse gas emissions) and aggregated levels (e.g. Carbon Value at Risk) are considered.

The approach described in this paper can serve as a model for the realisation of an enhanced tool with respect to other countries, leading to a globally applicable instrument for assessing stranding risks in the commercial real estate sector.

The real estate industry is endangered by the downside risks of climate change, leading to potential monetary losses and write-downs. Accordingly, this approach enables stakeholders to assess the exposure of their assets to stranding risks, based on energy and emission data.

The CRREM tool reduces investor uncertainty and offers a viable basis for investment decision-making with regard to stranding risks and retrofit planning.

The approach pioneers a way to provide investors with a profound stranding risk assessment based on science-based decarbonisation pathways.

The purpose of this paper is to examine empirically the basis risk and hedging efficiency of temperature‐indexed standardized weather derivatives in hedging weather risks in the US energy industry.

Within the risk minimization framework, using power load and temperature data, this research analyzes both linear and nonlinear hedging strategies using the two most popular types of standardized indexes – city indexes and regional indexes.

The results indicate that the city indexes and regional indexes are not consistently superior to each other and the regional indexes should be a good complement to the current exchange‐listed indexes. The results also document that the basis risk is sufficiently low for the diversified power producers serving the US Northeast or Mid‐Atlantic regions in both the summer and winter seasons and California in the summer season. However, the basis risk is very high for the diversified power producers serving California in hedging the weather risk in the winter season. More discrepancies are observed in the hedging efficiency among the power producers serving the Texas region.

This research provides important implications about the survivability and superiority of current and proposed standardized weather contracts and the design of effective standardized weather derivatives for the extant and potential weather markets.

The application of weather derivatives in hedging crop yield risk is gaining more interest. However, the further development of weather derivatives – particularly exchange-traded – in the agricultural sector has been impeded by concerns over their hedging performance. The purpose of this paper is to develop a new framework to derive the optimal hedging strategy and evaluate hedging effectiveness.

This framework incorporates a stochastic temperature model, a crop yield model, a risk-neutral pricing method and a profit optimization procedure. Based on a large number of simulated scenarios, the authors study crop yield hedge for a future year. The authors allow the hedger to choose from different types of exchange-traded weather derivatives, and examine the impact of various factors on the optimal hedging strategy.

The analysis shows that hedging objective, pricing method and geographical location of the hedged exposure all play important roles in choosing the best hedging strategy and assessing hedging effectiveness.

This framework is forward-looking, because it focusses on the crop yield hedge for a future year rather than on the historical hedging effectiveness often studied in literature. It utilizes the most up-to-date information related to temperature and crop yield, and hence produces a hedging strategy which is more relevant to the year under consideration.

In recent years professional accountancy bodies (e.g., CPA), accreditation institutions (e.g., AACSB) and employers have steadily raised, and continue to raise expectations regarding the need for accounting graduates to demonstrate skills in data analytics. One of the obstacles accounting instructors face in seeking to implement data analytics, however, is that they need access to ample teaching materials. Unfortunately, there are few such resources available for advanced programming languages such as R. While skills in commonly used applications such as Excel are no doubt needed, employers often take these for granted and incremental value is only added if graduates can demonstrate knowledge in using more advanced data analytics tools for decision-making such as coding in programming languages. This, together with the current dearth of resources available to accounting instructors to teach advanced programming languages is what drives motivation for this chapter. Specifically, we develop an intuitive, two-dimensional framework for incorporating R (a widely used open-source analytics tool with a powerful embedded programming language) into the accounting curriculum. Our model uses complexity as an integrating theme. We incorporate complexity into this framework at the dataset level (simple and complex datasets) and at the analytics task level (simple and complex tasks). We demonstrate two-dimensional framework by drawing on authentic simple and complex datasets as well as simple and complex tasks that could readily be incorporated into the accounting curriculum and ultimately add value to businesses. R script programming code are provided for all our illustrations.

On the basis of corporate wholesale and hypermarket stores, this study aims to investigate the relationship between energy consumption, physical building characteristics and operational sales performance and the impact of energy management on the corporate environmental performance.

A very unique dataset of METRO GROUP over 19 European countries is analyzed in a sophisticated econometric approach for the timeframe from January 2011 until December 2014. Multiple regression models are applied for the panel, to explain the electricity consumption of the corporate assets on a monthly basis and the total energy consumption on an annual basis. Using Generalized Additive Models, to model nonlinear covariate effects, the authors decompose the response variables into the implicit contribution of building characteristics, operational sales performance and energy management attributes, under control of the outdoor weather conditions and spatial–temporal effects.

METRO GROUP’s wholesale and hypermarket stores prove significant reductions in electricity and total energy consumption over the analyzed timeframe. Due to the implemented energy consumption and carbon emission reduction targets, the influence of the energy management measures, such as the identification of stores associated with the lowest energy performance, was found to contribute toward a more efficient corporate environmental performance.

In the context of corporate responsibility/sustainability of wholesale, hypermarket and retail corporations, the energy efficiency and reduction of carbon emissions from corporates’ real estate assets is of emerging interest. Besides the insights about the energy efficiency of corporate real estate assets, the role of the energy management, contributing to a more efficient corporate environmental performance, is not yet investigated for a large European wholesale and hypermarket portfolio.

In recent years, there have been a growing number of projects and initiatives to promote the development and market introduction of low and net-zero energy solar homes and communities. These projects integrate active solar technologies to highly efficient houses to achieve very low levels of net-energy consumption. Although a reduction in the energy use of residential buildings can be achieved by relatively simple individual measures, to achieve very high levels of energy savings on a cost effective basis requires the coherent application of several measures, which together optimise the performance of the complete building system. This article examines the design process used to achieve high levels of energy performance in residential buildings. It examines the current design processes for houses used in a number of international initiatives. The research explores how building designs are optimised within the current design processes and discusses how the application of computerised optimisation techniques would provide architects, home-builders, and engineers with a powerful design tool for low and net-zero energy solar buildings.