Greenhouse emissions and air pollutants pose a global threat to the environment and human health. Emission inventories are a valuable tool in understanding emission sources and their overall impact on the environment. Most cities and countries do not include non-road mobile machinery (NRMM) when compiling emission inventories. Furthermore, little effort has been made to understand better the impact of this source of emissions on the environment. For these reasons, this research examines the data from the existing NRMM emission inventories and other studies concerning NRMM emissions. After careful literature review, three main problems in creating a national NRMM emission inventory are identified and reviewed: lack of a comprehensive list of NRMM and their activity data, lack of emission factor data, and lack of research. The data from the existing inventories show that compared to some emissions, NRMM has a three times larger proportion of emissions compared to the proportion of energy consumption. Furthermore, there are significant differences in total emissions when comparing the same pollutants among different countries. A general lack of data is the common denominator for all these problems and can only be solved by creating national NRMM databases operated by a designated institution. This institution must be able to annually update relevant NRMM data through questionnaires and experimental research on the existing NRMM.

In recent years, in addition to laboratory tests to determine emissions from road motor vehicles, tests in real driving conditions RDE (Real Driving Emissions) with the use of portable measuring equipment PEMS (Portable Emissions Measurement System) have been conducted. The paper presents the results of the emission research conducted on a vehicle Dacia Duster 1.0 TCe 100 ECO-G fuelled by petrol Eurosuper 95 and liquefied petroleum gas (LPG). The aim of the research was to determine the emissions of harmful substances and carbon dioxide, i.e. fuel consumption, in accordance with the prescribed RDE procedure and their comparison for the two types of motor vehicle fuel. Results clearly showed that the method used can differentiate between fuel types. The results correspond to the RDE emissions public data, which secures that the methodology used is in accordance with the procedure for measuring emissions in real driving conditions.

Agricultural tractor emissions have a notable influence on total emissions and climate change. This is especially important when developing an agricultural machinery emission inventory since tractors are the most widely used agricultural machinery. However, as with other agricultural machinery, there is a significant lack of data concerning tractors. This study combined two large-scale datasets to develop a Tier 2 tractor emission inventory for the Republic of Croatia. The first dataset, which was collected via a large-scale survey with 8895 respondents, includes data on fuel consumption by machine and engine type. The second dataset, which is official data of the Ministry of the Interior, includes data on machinery age. Since there is no direct link between datasets, data on fuel consumption were calculated for each of 21 Croatian counties and were then linked to the corresponding municipalities’ tractor age and power rating of the vehicle fleet. The result was used to calculate tractor emissions by with a Tier 2 approach using the method listed in the 2019 EMEP/EEA emissions guidebook. The calculations were made for five exhaust emission components: carbon dioxide (CO₂), carbon monoxide (CO), hydrocarbon (HC), nitrogen oxide (NO_X) and particulate matter (PM). The continental counties had the highest emissions for all emission types, especially the Osječko-baranjska and Vukovarsko-srijemska counties. The Tier 2 emission inventory was compared to data calculated using the Tier 1 method on a county level. There were notable differences between the inventories for HC and PM emissions, which is mainly due to Tier 2 approach having different emission factors depending on vehicle age. The end result was compared to the already existing Tier 1 emission inventory on a county level.

The automotive industry continues to face numerous challenges that demand ongoing process improvement and system optimization. Incidents reported in the media have highlighted the relevant impact regarding companies’ sustainability and brand image and consequently, the urgent need for stronger traceability mechanisms and faster response capabilities throughout the supply chain. This paper focuses on enhancing a key logistic process within automotive companies – the incoming material inspection – by applying the DMAIC (Define, Measure, Analyse, Improve, Control) methodology. This process is critical, as it directly influences production flow, product quality, and overall supply chain efficiency. The DMAIC approach was selected due to its proven effectiveness in driving systematic process improvements, reducing defects, and establishing measurable control standards across manufacturing and logistics operations. Each phase of the methodology provides a structured framework with clearly defined objectives, control mechanisms, and deadlines to ensure consistent progress and accountability. Implementation of the DMAIC methodology resulted in a more transparent, standardized, and efficient inspection process. Quantitative and qualitative outcomes demonstrated significant improvements in process control and traceability. Furthermore, the initial project objectives were fully achieved and, in several instances, surpassed – validating the robustness of the applied methodology. This analysis highlights the critical role of structured quality improvement frameworks, such as DMAIC, in managing increasingly complex logistics operations. In an industry shaped by digital transformation and sustainability imperatives, adopting data-driven methodologies fosters agility, consistency, and a culture of continuous improvement is essential. Beyond enhancing operational efficiency, such approaches strengthen collaboration, accountability, and long-term competitiveness within the automotive sector.