Purpose: This study aims to examine the various climate change practices adopted by firms and develop a set of corporate indexes that measure the level of climate change corporate commitment, climate change risk management integration and climate change strategies adoption. Moreover, this study examines the relationship between the aforementioned indexes. The authors claim that there is a positive relationship between the adoption of climate change strategies, corporate commitment and risk management integration. The aforementioned indexes have been used to assess the largest companies in the oil and gas sectors.Design/methodology/approach: To assess this study’s sample companies, a content analysis of their carbon disclosure project (CDP) reports for the years 2012-2015 was conducted. Finally, weights were assigned to the content analysis data based on the results of a survey regarding the difficulty of implementing each climate change practice included in the respective index. The survey sample included climate change experts who are either currently employed in companies that are included in the Financial Times Global 500 (FT 500) list, or work as external partners with these companies.Findings: The present study results highlight the need for developing elaborate corporate indexes, as the various climate change practices have different degrees of difficulty regarding their implementation. Additionally, a general trend in adopting climate change strategies is observed, especially in the field of carbon reduction strategies, which mainly involve the implementation of low carbon technologies. Finally, a positive and significant relationship was found between carbon reduction targets, risk management integration and climate change strategies.Practical implications: Although international research has extensively examined the importance of managers’ perceptions on environmental issues as an enabling factor in developing environmental strategies, according to the results of our survey, corporations must go beyond top management commitment towards climate change to be able to successfully implement climate change strategies. Incorporation of climate change risk management procedures into a company’s core business activities as well as the establishment of precise carbon reduction targets can provide the basis on which successful climate change strategies are implemented.Originality/value: Most studies address the issue of climate change management in terms of environmental or sustainability management. Furthermore, research on climate change and its relationship with business management is mainly theoretical, and climate change corporate performance is measured with aggregate indexes. This study focuses on climate change which is examined from a five-dimensional perspective: top management commitment, carbon reduction targets, risk management integration, carbon reduction and carbon compensation strategies. This allows us to conduct an in-depth analysis of the various climate change practices of firms.

The need to rapidly reduce greenhouse gases (GHGs) has accelerated the adoption of circular economy (CE) practices. However, this has proved challenging for small and medium enterprises (SMEs), who lack the financial, organizational, and informational capabilities to adopt circular business models. This paper highlights some of the SMEs’ challenges in adopting CE practices. It focuses on the need to calculate their carbon footprint (CF) by drawing on the literature examining the effect of information technologies (IT) on adopting CE strategies. This study aims to present a CF calculation tool used to calculate the CF of SMEs. The tool’s design is based on the Life Cycle Assessment (LCA) methodology, which assesses the environmental impact of a particular service or product over the different stages of its life cycle. The tool was tested in a small cheese factory in northern Greece, an SME representative of the country’s average SME. The production process was mapped, a GHG inventory was created, and the total emissions related to the production of a specific product were estimated. Our results show that adopting such CF calculation tools can have various implications regarding the organizational structure of SMEs, leading to cost reductions and reducing potential environmental impacts. The final aim is to test this tool at a large scale.

A novel method of portfolio selection is provided with further higher moments, filtering with fundamentals in intelligent computing resources. The Evolutional Portfolio Optimization System (EPOS) evaluates unobtrusive relations from a vast amount of accounting and financial data, excluding hoax and noise, to select the optimal portfolio. The fundamental question of Free Will, limited in investment selection, is answered through a new philosophical approach.

We evolved our past Portfolio Yield Reactive (PYR) model to provide a competitive system with infiltration of categorical information and fundamentals into advanced higher-order moments that support more objective portfolio selection aided by intelligent computing. The system of the PYR model searches for hidden corporate performance prototypes in big data from accounting and financial statements. The PYR model restricts malicious patterns, such as hoaxes, noise, and manipulation, incorporated into a novel optimal portfolio selection method.

The optimal portfolio selection problem is investigated in fundamentals of higher order moments. The returns behavior frequently skewed and in excess kurtosis, along with investors’ preferences set new grounds of discussion. Higher order moments, than the kurtosis, will offer further information on investors. A more complex problem arises, of higher flexibility, non-convexity, in unlimited scale fitted to portfolio optimization. The principal problem of Free Will is thus answered, with emphasis on investors. We discuss the OPSI model introducing three hybrid neuro-genetic models of numerous topologies and one regression. Firstly the Radial Basis Function Networks-RBF are in 40 hybrid forms and 10 RBF Neural Nets whilst the results are compared to 50 Time-Lag Recurrent Network-TLRN Hybrids topologies, 10 on the MultiLayer Perceptron-MLP Neural Nets, and the Bayesian Logistic Regression-BLR, to define the most competitive methods in asset allocation and corporate evaluation. New solutions are offered under specific hybrids whilst portfolio efficiency is either evolutionary or intelligent. Introducing the parameters of financial health, we propose the advanced expected utility function filtering noise. The problem of wealth maximisation is transformed to a preferential combination on gain and loss. The TLRN hybrid networks are a very efficient and reliable model on portfolio selection. The OPSI model offers a competitive approach in efficient portfolio selection, protecting the investor from systematic exposure. In the investors Free Will problem, the answer is that Logic is dynamic linearly but adjusting to the environment overrides new challenges of superior potentials than the linear series of events. It is consistent to the maximisation of utility and investors’ welfare.