• Energy Research

The proper development of transportation constitutes the basis for an effectively functioning economy at the national and global levels. On the other hand, transportation significantly impacts the environment and climate. Sustainable transportation management should therefore include both economic, social and environmental aspects. The article aims to comprehensively assess the economic–energy–environmental efficiency of the 27-road freight transport sector in EU countries in 2019. The research was conducted using the non-parametric Data Envelopment Analysis (DEA) method. The Slacks-Based Measure–Data Envelopment Analysis (SBM-DEA) model was used, taking into account unwanted (undesirable) effects. As non-energy inputs in the DEA model used the labor in the road freight transport sector, stock of registered goods vehicles, and the length of the road network. Moreover, the energy consumption by the road freight transport sector was used as energy inputs in the DEA model. Desirable outputs were taken as road freight transport sector revenues and freight work performed by the sector. GHG emissions expressed in CO2 equivalent were treated as undesirable outputs. The research also adopts energy productivity and GHG emission efficiency indicators. The eco-efficiency of the road freight transport sector in EU countries varies. Ten countries have efficient road freight transport sectors. The efficient road freight transport group included Denmark, Germany, Belgium, Lithuania, Luxembourg, the Netherlands, Poland, Portugal, Slovenia and Bulgaria. They efficiently transformed the inputs into outputs. Five countries were recognized as eco-efficiency followers, including Italy, Finland, Slovakia, Sweden and Romania, and 12 countries were characterized by an inefficient road freight transport sector. Based on benchmarking principles for inefficient road freight transport sectors, the changes in input and output levels were proposed to improve efficiency. The relationship between the economic development of EU countries and the eco-efficiency of the road freight transport sector was also analyzed, indicating a positive relationship between the variables but with weak strength. The main contributions of this article are an extension of previous DEA works that assesses the efficiency of the road freight transport sector, also considering undesirable variables. Research conclusions are particularly important for policymakers in the context of management sustainable transportation development in the EU.

The analysis of the economic efficiency of agriculture has been the subject of numerous studies. An economically efficient agricultural sector is not always environmentally efficient. Agriculture is a large emitter of greenhouse gases. The Intergovernmental Panel on Climate Change states that food production and agriculture are responsible for 21–37% of total global CO2 emissions. Due to the comprehensive assessment of the agricultural efficiency, it is worthwhile to apply to its measurement an integrated approach based on economic, energy and environmental aspects. These aspects were the main reasons for undertaking this research. The purpose of the study was to determine the economic, energy and environmental efficiency of agriculture in the EU Member States in 2019. The environmental analyses relate to the period 1990–2019. A total of 26 member states of the European Union (excluding Malta and Luxembourg) were selected for research. The sources of materials were Eurostat and the European Environmental Agency. This study was based on the Data Envelopment Analysis method, and used the DEA model focused on minimizing inputs. The research also adopts energy productivity and greenhouse gas emission efficiency indicators. The DEA model features the following variables: one effect (value of agricultural production) and four inputs (land, labour, use of fertilizers and use of energy). It was found that seven out of the 26 studied EU countries have efficient agriculture. The efficient agriculture group included The Netherlands, Denmark, Greece, Cyprus, the United Kingdom, Italy and Ireland. Based on the DEA method, benchmarks have been defined for countries with inefficient agriculture. On the basis of these benchmarks for inefficient agricultural sectors, it was possible to determine how they could improve efficiency to achieve the same results with fewer inputs. This issue is particularly important in the context of sustainable agricultural development. In the next stage of the research, the analysis of economic and energy efficiency was combined with the analysis of GHG emission efficiency in agriculture. Four groups of countries have been distinguished: eco-efficiency leaders, eco-efficiency followers, environmental slackers, eco-efficiency laggards. The leaders of the classification were The Netherlands, Italy, Greece, Cyprus and Portugal.

Article concerns economic and energy efficiency of agriculture in European Union countries. The study period concerned 2016. For analysis and presentation of materials, descriptive, tabular and graphic methods and the Data Envelopment Analysis (DEA) method - CCR (Charnes, Cooper and Rhodes) model focused on input-oriented minimisation were used. An assessment of the socio-economic development of the EU countries was made using the following measures: Human Development Index (HDI) and Gross Domestic Product (GDP) per capita (USD per inhabitant). Modern agriculture depends on industrial energy sources and as the socio-economic development changes into more and more energy-intensive production technologies. After presenting the introduction and review of the literature, the economic and energy efficiency of agriculture in the EU countries in 2016 was examined, which was at a high level - the DEA reached 0.67. Then, the correlation between the socio-economic development of countries and their economic and energy efficiency was analysed. It was also found that along with socio-economic development in the EU countries, the economic and energy efficiency of agriculture is increasing.

This paper aims to indicate the linkages between crude oil prices and selected food price indexes (dairy, meat, oils, cereals, and sugar) and provide an empirical specification of the direction of the impact. This paper reviews the fuel–food price linkage models with consideration to the time series literature. This study adopts several methods, namely the Augmented Dickey–Fuller test, Granger causality test, the cointegration test, the vector autoregression model, and the vector error correction model, for studying the price transmission among the crude oil and five selected food groups. The data series covers the period between January 1990 and September 2020. The empirical results from the paper indicate that there are long-term relationships between crude oil and meat prices. The linkage of crude oil prices occurred with food, cereal, and oil prices in the short term. Furthermore, the linkages between the analyzed variables increased in 2006–2020.

Road freight transport involves many adversities, along with the growing effect of carbon dioxide transmitted by vehicles on the natural environment, greenhouse gas emissions, or extensive energy use. Within the smart mobility concept, the acknowledged management of enterprises’ relationships with customers within their service is profoundly determined by the deployment of compelling Intelligent Transportation Systems (ITSs) applications in forming united cooperation with the customers. The paper proposes selected ITS applications as an advancement of logistics customer service in road freight transport enterprises that is divided into a group of six applications which are critical within the area of vehicle support, improving the energy efficiency of transport and reducing the negative impact of transport on the natural environment, reducing transport time but increasing connectivity and comfort, as well as a group of ten different applications chosen as crucial for general management support and increasing accessibility, cohesiveness, and control in management processes in road freight transport enterprises. The study also presents the effect of ITS applications on logistics customer service as dependent on an alignment between the group of ITS applications and logistics resources of road freight transport enterprises. The model proposed in the paper was analyzed based on the survey data obtained from 164 road freight transport enterprises in Poland.