The availability of open government data has expanded considerably in recent years. This expansion is expected to generate significant benefits not just for increasing government transparency, but also for the economy. The aim of this study is to illustrate the use of open government data in estimating personal income levels for all 3181 municipalities, towns, and communes in Romania. The novelty of our work comes from the high granularity of the estimates obtained. We use tax revenues collected by local governments in Romania on vehicles and buildings owned by natural persons, as well as data on energy subsidies. The classification is conducted using the k-means clustering algorithm. We find three distinct clusters of communities, which we map. The results can benefit both businesses and policymakers. The former can use the income level estimates for market intelligence purposes, while for the latter, these may aid in determining the financial sustainability of local governments and a better allocation of central government resources at the subnational level.

Over the past few years the global oil and gas industry has been going through a severe market downturn. Despite recent signs of stabilization, oil prices have a long history marked by volatility. In this context, it is imperative for oil companies to optimize their capital allocation, as this might support risk mitigation. The purpose of this paper is to offer a tool that might support the strategic decision-making process for companies operating in the oil industry. Our model uses Markowitz’ portfolio selection theory to construct the efficient frontier for currently producing fields and a set of investment projects. These relate to oil and gas exploration projects and projects aimed at enhancing current production. The net present value is obtained for each project under a set of usersupplied scenarios. For the base-case scenario we also model oil prices through Monte Carlo simulation. We run the model for a combination of portfolio items which include both currently producing assets and new exploration projects, using data characteristics of a mature region with a high number of low-production fields. Our objective is to find the vector of weights (equity stake in each project) which minimizes portfolio risk, given a set of expected portfolio returns. The model is of particular interest for companies operating in Eastern Europe, or in any other mature region. It can also support divestment and acquisition decisions since these may place the company’s portfolio closer or farther away from the efficient frontier. The model is highly versatile and can be implemented on any software with an optimization package such as Microsoft Excel.

The latest European Union measures for combating climate adopted in the “Fit for 55 package” envisage the extension of the Emissions Trading System, the first “cap-and-trade” system in the world created for achieving climate targets, which limits the amount of greenhouse gas emissions by imposing a price on carbon. In this context, our study provides an integrated assessment of carbon price risk exposure of all economic sectors in the European Union Member States, thus supporting decision making in determining the energy transition risk. We propose a novel approach in assessing carbon risk exposure using the Value at Risk methodology to compute the carbon price under the EU ETS, based on historical price simulation for January–August 2021 and ARMA-GARCH models for the October 2012–August 2021 period. We further built a value erosion metric, which allowed us to establish each sector’s exposure to risk and to identify differences between Eastern and Western EU countries. We find that the refining sector appears to be highly vulnerable, whereas there is higher potential for large losses in the energy supply and chemical sectors in Eastern EU Member States, given a different pace of industry restructuring.

Humanitarian workers operate in complex environments with various challenges and demanding working conditions. These challenges put aid workers in a range of risks and under the pressure. However, human resources are crucial for success of humanitarian operations in general. At the same time, each humanitarian operation is reliant on logistics and logistics activities are always connected with logistic staff. Understanding what motivates logisticians to join the humanitarian sector is essential information for humanitarian organizations and for recruiters within. Also, knowing which factors influence motivation and job satisfaction of humanitarian logisticians could help the organizations to struggle with the extremely turnover they have to face. Up to this moment, needed skills and the performance of humanitarian logisticians were examined. Also, the motivators of humanitarian workers are covered in previous research. Therefore, the additional aim of this research is to extend the knowledge about the human resources in humanitarian sector as well.

The impact of natural resource exploitation has been a controversial topic, subject to intense debate. The literature has traditionally focused on its consequences on national socioeconomic development. More recently, scholars concentrated on local effects following greater availability of data at the subnational and project level. We add to the literature by concentrating on Romanian oil and gas operations, a mature region with a long history of hydrocarbon activities. Such regions have seldom been studied and we argue that in light of the ongoing energy transition these should garner greater interest, particularly those located within the European Union where environmental pressure is significant. Our methodology consists of testing the ability of the random forest classification algorithm to distinguish between local communities with oil and gas operations present and those without on a number of indicators which could be broadly considered developmental. The algorithm fails to accurately classify hydrocarbon-intensive communities, indicating that there are no significant differences between these and the rest. We argue that this is likely due to the limited tax collection powers of local governments, with royalties going directly to the central government with no specific distribution provision at the local level. Another potential explanation may be the diversification of local economies and existing related manufacturing and services activities.