In this paper, the analysis of carbon footprint values for children’s footwear was conducted. This group of products is characterized by similar small mass and diversity in the used materials. The carbon footprint is an environmental indicator, which is used to measure the total sets of greenhouse gas (GHG) emissions into the atmosphere caused by a product throughout its entire lifecycle. The complexity of carbon footprint calculation methodology is caused by multistage production process. The probability of emission greenhouse gases exists at each of these stages. Moreover, a large variety of footwear materials – both synthetic and natural, give the possibility of the emission of a lot of waste, sewage and gases, which can be dangerous to the environment. The diversity of materials could be the source of problems with the description of their origins, which make carbon footprint calculations difficult, especially in cases of complex supply chains. In this paper, with use of life cycle assessment, the carbon footprint was calculated for 4 children’s footwear types (one with an open upper and three with full uppers). The life cycles of the product were divided into 8 stages: raw materials extraction (stage 1), production of input materials (stage 2), footwear components manufacture (stage 3), footwear manufacture (stage 4), primary packaging manufacture (stage 5), footwear distribution to customers (stage 6), use phase (stage 7) and product’s end of life (stage 8). On these grounds, it was possible to point out the life cycle stages, where the optimization activities can be implemented in order to reduce greenhouse gases emissions. The obtained results showed that the most intensive corrective actions should be focused on the following stages: 3 (the higher emissivity), 4 and 8.
Urbanization has a far-reaching impact on the environment, economy, political and social processes. Therefore, understanding the spatial distribution and evolution of human settlements is a key element in planning strategies that ensure the sustainable development of urban and rural settlements. Accordingly, it is very important to map human settlements and to monitor the development of cities and villages. Therefore, the problem of settlements has found its reflection in the creation of global databases of urban areas. Global settlement data have extraordinary value. These data allow us to carry out the quantitative and qualitative analyses as well as to compare the settlement network at a regional, national and global scale. However, the possibility of conducting both spatial and attribute analyses of these data would be even more valuable. The article describes how to prepare raster data so that they can be implemented into a vector database. It answers the questions whether it is possible to combine these data with databases available in Poland and what benefits it brings. It presents the methods of data generalization and the optimization of time and disk space. As a result of the study, two vector databases with GUF data were developed. The first database resolution is similar to the original (~12 m resolution) database, the second database contains less detailed (~20 m resolution) data, generalized using mathematical morphology. Both databases have been enriched with descriptive data obtained from the National Geodetic and Cartographic Resource.
In the paper, the research on the process of optimizing the carbon footprint to obtain the low-carbon products is presented. The optimization process and limits were analyzed based on the CFOOD project co-financed by the Polish Research and Development Agency. In the article, the carbon footprint (CF) testing methods with particular emphasis on product life cycle assessment (LCA) are discussed. The main problem is that the energy received from the energy-meters per the production stage is not directly represented in the raw data set obtained from the factory because many production line machines are connected to a single measurement point. In the paper, we show that in some energy-demanding production stages connected with cooling processes the energy used for the same stage and similar production can differ even 25-40%. That is why the energy optimization in the production can be very demanding.