This study primarily aims to analyze the relationship between selected board characteristics and the extent of environmental disclosure in annual reports of Turkish companies, using a sample of 62 non-financial firms listed on the BIST-100 index at the end of 2011. The content analysis is used to measure the extent of environmental disclosure. Four board characteristics, namely board size, board independence, board gender diversity and audit committee independence, are considered as the independent variables that may have an impact on the extent of the environmental disclosures of Turkish companies. According to the results of the regression analysis, only board size has a statistically significant and positive relationship with the extent of environmental disclosure. This result implies that firms with larger boards disclose more environmental information than firms with smaller boards. On the other hand, the rest of the independent variables are found to be unrelated to the extent of environmental disclosure. The low degree of independence and gender diversity on the boards of the sample companies for the time period analyzed in the study could be one possible explanation for this result.

Firms worldwide have been facing an increasing pressure to disclose their Greenhouse Gas (GHG) emissions since GHG emissions are seen as the main source of global warming which is one of the most challenging problems that the world is faced with. For this reason, voluntary GHG disclosure represents a growing area of research interest. However, the existing research generally focuses on developed countries. In this sense, the present paper aims to contribute to the existing GHG disclosure literature by analyzing the determinants of voluntary disclosure of firms operating in a developing country, Turkey. The effects of both financial characteristics and board structures of firms on voluntary disclosure decisions are analyzed as the possible determinants of GHG disclosures of Turkish firms. We use two proxies for assessing the firms’ GHG disclosures. The first proxy, “sensitiveness tendency”, indicates the response behavior of firms to the Carbon Disclosure Project (CDP) survey. The second proxy, namely, “transparence tendency”, represents the disclosure behavior of firms. Using logistic regression models with a sample of 84 listed Turkish companies which were included in the Carbon Disclosure Project survey in 2014, 2015 and 2016, we find that firm size, institutional ownership and market value are positively related to the sensitivity of sampled firms, while board size is negatively related. On the other hand, our results indicate that firm size, profitability and institutional ownership have positive impacts on the transparency of Turkish listed firms.

Purpose The purpose of this paper is to analyze the impact of logistics performance on the carbon (CO2) emissions of Balkan countries. Design/methodology/approach Fixed-effects panel regression analysis is used to estimate the causal relationship between CO2 emissions and logistic performances of Balkan countries. Logistics performance is measured by logistics performance index (LPI) which was published by the World Bank in 2007, 2010, 2012, 2014 and 2016 and used for ranking countries by means of their logistics performance. LPI is based on six main indicators: customs procedures, logistics costs and the quality of the infrastructure for overland and maritime transport. As a measure of carbon emissions of sampled countries, the natural logarithm of carbon dioxide emission per capita is used in this study. Findings The results obtained reveal that there is a positive and significant relationship between logistics performance and CO2 performances of the sampled Balkan countries. Research limitations/implications This study is based on only 11 Balkan countries. In this sense, the data used in the analysis is limited. Originality/value Considering the important geostrategic position of the Balkan region, logistics sector has an important role for the development of the countries in that region. In this sense, the findings of this study may provide useful insights for policymakers to achieve sustainable economic development. Furthermore, as far as the authors know, this is the first study that focuses on the relationship between logistics performance and carbon emissions of Balkan countries.

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Environmental information disclosure has been received special attention from both academic researchers and companies due to the increasing awareness of environmental issues worldwide. However, there is little empirical evidence on the environmental information disclosure practices in the developing countries. In this context, this study contributes to the existing literature by investigating the status of the environmental disclosures of companies operating in a developing country, Turkey. Our sample consists of 62 non-financial firms listed on the BIST-100 index in the financial year 2011. The annual reports of sample firms for the years of 2010 and 2011 are analyzed through content analysis, which is widely used in the research of this topic. Although there is a decrease in the proportion of companies that disclosed environmental information from 2010 to 2011, we found that there is a significant increase in the level of environmental disclosure by Turkish listed companies. The results of the study also show that Turkish companies disclose mostly narrative information and the level of disclosure of environmental information varies across sectors.

Multi-criteria decision-making (MCDM) techniques are increasingly being used for the problem of location selection, which directly affects the long-term success of a company. Besides these techniques, with the advantage of handling both spatial and non-spatial data, geographic information systems (GIS) also represent a useful method for selecting the appropriate location for different kinds of facilities and sites. In this respect, this study aims to compare the results of a MCDM technique, fuzzy technique for order preference by similarity to ideal solution (TOPSIS), and GIS for the location selection of shopping malls in Turkey. According to the results of both fuzzy TOPSIS and GIS, the Marmara region was determined as the best alternative for shopping malls in Turkey.Sustainability 2019, 11, 3837 2 of 22 sense, it can be easily said that location selection represents a typical multi-criteria decision-making (MCDM) problem [3][4][5][9][10][11].As an advanced and a widely used subdiscipline of operations research, MCDM basically represents a decision-making technique that can be used to evaluate a number of alternatives by taking into account multiple and usually conflicting criteria that may be quantitative or qualitative, and aims to provide support to the decision maker for making the choice between alternatives [9,[12][13][14]. In this framework, MCDM can be defined as the process of evaluating alternatives for the purpose of selection or ranking via utilizing a number of qualitative and/or quantitative criteria that have different measurement units [15].MCDM involves a set of techniques such as min-max, max-min, ELECTRE, PROMETHEE, TOPSIS, fuzzy TOPSIS, compromise programming, analytic hierarchy process (AHP), fuzzy AHP, data envelopment analysis, and goal programming, that can be used for comparing and prioritizing multiple alternatives and finally selecting the best-fit choice [16]. Among these techniques, for the solutions to location problems that contain vague and incomplete data and linguistic variables, fuzzy decision-making techniques have been attracting growing attention [12,17].On the other hand, it should be stated that these techniques are not suitable for spatial data [18]. In this context, with the advantage of handling both spatial and non-spatial data, geographic information systems (GIS) have been applied for solving location problems [18][19][20]. GIS represent computer-based tools that can be used for maintaining, managing, integrating and analyzing spatial data from different sources. GIS allow us to store, edit, manipulate and analyze geographically referenced data to generate interpretive maps and related statistics relevant for decision making [11,14,21]. In this sense, one could say that although MCDM and GIS have developed independently, they actually support one another and their combination generates more useful and reliable information that makes it possible to reach accurate decisions, especially in the case of location selection [11,14,22].Starting from this point of view, thi...

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