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Purpose – The accounting literature has demonstrated increased concern over issues of sustainability. One of the most critical issues that corporations face at the moment is climate change. This especially concerns companies that are substantial consumers of materials and energy. Corporations have increasingly acknowledged that to address this issue the existing ways of managing business must be changed.

This study examines the challenges posed by measurement of energy efficiency performance in an energy-intensive industry. It covers the challenges encountered with respect to the energy efficiency indicator and its use as a management tool.

Methodology – The case study method was used to conduct the research, which took place in a single firm in an energy-intensive process industry.

Findings – The results indicate that the current energy efficiency indicator, specific energy consumption (SEC), does not meet the criteria for good performance measurement. In particular, the controllability of energy efficiency seems problematic. Another major challenge is target setting. Measurable targets are needed to identify and prioritise areas where consumption and emission can be reduced.

Practical implications – The study provides practical knowledge on what is happening in organisations that pursue sustainable development and in particular, environmental efficiency.

Originality/value – Although the conceptual challenges in energy efficiency measurement are well known in the technical literature, discussions dealing with its management have been few in number. This study is a cross-disciplinary work and combines technical energy efficiency literature and management accounting research on performance management.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

The contemporary dynamics impose companies to both innovate and internationalize at the same time while remaining competitive in the international marketplace. With this context in mind, Industry 4.0 technologies have the potential to increase the competitiveness of companies, leading to a new era of “Manufacturing Renaissance.” Recently, conceptual studies have speculated on possible impacts of the adoption of Industry 4.0 technologies in terms of international business. However, empirical studies on this topic are still lacking. Through a multiple case study approach, this study presents exploratory qualitative research investigating the relationship between Industry 4.0 and the internationalization of companies. The analysis of 16 Italian manufacturing exporting companies, which have adopted some of these technologies, has revealed a more intriguing relationship compared to the one presented in current literature, and thus has opened avenues for future research on this issue.

Global warming is a process that takes place 11,500 years after the end of the last Ice Age. The main identified reason is the increased emissions of greenhouse gases (GHGs). Since the nineteenth century, GHG evolution has recorded a quantum leap from the previous linear development. Human is the main factor behind this evolution, through industrialization and the exponential increase of population. Based on these, the chapter’s primary goal was to highlight an original method of predicting the future evolution of GHG emissions in the domains of Energy (including Transportation), Industry Processes and Product Use, Agriculture, and Waste Management. The novelty of the research consisted of testing several variants of functions (power, exponential, inverse trigonometric) to identify, from a group of variants. This optimal function would generate those predictions, which are closest to the real values. The causes that create GHG emissions in each of the four domains were the foundation for the analysis. This chapter focuses on two main subjects: first, the identification of a smooth function to predict the evolution of GHG emissions, and second, the function’s use to estimate the projections of GHG emissions in the coming years for the four domains: Energy (including Transportation), Industry Processes and Product Use, Agriculture, and Waste Management. An observation was that the weights of these four domains remain relatively the same despite the reductions in the total GHG emissions.

Electric cars represent the most energy efficient technical option available for passenger cars, compared to conventional combustion engine cars and vehicles based on fuel cells. However, this requires an efficient charging infrastructure and low carbon electricity production as well. Combustion engine cars which were converted to electric cars decreased lifecycle CO₂-equivalent emissions per passenger-km travelled down to one third of before, when powered by green electricity. However, through an analysis of 78 scientific reports published since 2010 for life cycle impacts from 18 aggregated impact categories, this chapter finds that the results are mixed. Taken together, however, the reduced environmental impacts of electric cars appear advantageous over combustion engine cars, with further room for improvement as impacts generated during the production phase are addressed. When it comes to battery components, Cobalt (Co) stands out as critical. Assessing the impact of electric cars on the local air quality, they are not ‘zero emission vehicles’. They emit fine dust due to tyre and brake abrasion and to dust resuspension from the street. These remaining emissions could be easily removed by adding an active filtration system to the undercarriage of electric vehicles. If electric cars are operated with electricity from fossil power plants nearby, the emissions of these plants need to be modelled with respect to possibly worsening the local air quality.

We present empirical evidence on the productive efficiency of electric utilities in the United States from 1990 to 2004. This period is marked by major attempts to introduce deregulation with an expectation that it will lead to improved operating efficiency and ultimately to lower consumer prices. However, relying on improved techniques of estimating productive efficiency, we find that firms in jurisdictions that adopted deregulation have in fact lower productive efficiency, and have also experienced decreases in efficiency over time. In particular, the vertical separation of generation, a hallmark of an effort to deregulate the industry, is associated with an adverse impact on productive efficiency.

The global financial meltdown of late 2008 threatened the survival of many banks, insurance companies, automakers, and other institutions, further contributing to the economic slowdown already underway in the United States and abroad. The ensuing recession has negatively impacted on the airline industry in the United States with losses running into billions. In this chapter, we illustrate the use of data envelopment analysis (DEA), an operations research technique, to analyze the operating efficiency of the US airline industry by benchmarking a set of ratios that assess the operating efficiency of a firm against its peers. DEA clearly brings out the airline(s) that is (are) operating more efficiently in comparison to other airlines in the industry, and points out the areas that poorly performing airlines need to improve.