In order to measure progress in achieving the Sustainable Development Goals (SDGs) by 2030, 169 targets have been approved globally. Even though interest in implementing these goals is high, many states have not yet established a set of subnational indicators to measure the implementation of the SDGs and have not completed their own assessment of progress in achieving these global goals. This study aims to measure the progress toward achieving the SDG at local and regional level in Romania by calculating the SDG Index. For the calculation of the SDG Index at subnational level, we propose an integrated approach based on 90 indicators, stored and processed in a PostgreSQL object‐relational database. The results show the concentration of the highest performances of sustainable development in some specific geographical areas. The rural areas and the extended peripheral regions in the eastern and southern part of the country are the poorest performers.

Night-time lights satellite images provide a new opportunity to measure regional inequality in real-time by developing the Night Light Development Index (NLDI). The NLDI was extracted using the Gini coefficient approach based on population and night light spatial distribution in Romania. Night-time light data were calculated using a grid with a 0.15 km² area, based on Defense Meteorological Satellite Program (DMSP) /Operational Linescan System (OLS satellite imagery for the 1992–2013 period and based on the National Polar-orbiting Partnership–Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) satellite imagery for the 2014–2018 period. Two population density grids were created at the level of equal cells (0.15 km²) using ArcGIS and PostgreSQL software, and census data from 1992 and 2011. Subsequently, based on this data and using the Gini index approach, the Night Light Development Index (NLDI) was calculated within the MATLAB software. The NLDI was obtained for 42 administrative counties (nomenclature of territorial units for statistics level 3 (NUTS-3 units)) for the 1992–2018 period. The statistical relationship between the NLDI and the socio-economic, demographic, and geographic variables highlighted a strong indirect relationship with local tax income and gross domestic product (GDP) per capita. The polynomial model proved to be better in estimating income based on the NLDI and R² coefficients showed a significant improvement in total variation explained compared to the linear regression model. The NLDI calculated on the basis of night-time lights satellite images proved to be a good proxy for measuring regional inequalities. Therefore, it can play a crucial role in monitoring the progress made in the implementation of Sustainable Development Goal 10 (reduced inequalities).

This paper investigates local-scale social vulnerability to flood hazards in Romania, aiming to identify the most vulnerable social and demographic groups across a wide range of geographical locations by considering three dimensions: demographic, socioeconomic, and the built environment. The purpose of the paper is threefold: first, it strives to improve the Social Vulnerability model (SoVI^®) by applying a different weighting method adapted to the Romanian context, taking into consideration the municipalities exposed to flood movements. Second, it aims to develop an assessment model for the most vulnerable communities by measuring the heterogeneity according to local indicators related to disaster risks. Third, it aims to facilitate emergency managers to identify community sub-groups that are more susceptible to loss and to increase the resilience of local communities. To perform local-level vulnerability mapping, 28 variables were selected and three aggregated indexes were constructed with the help of the ArcGIS software. Moreover, a model of Geographically Weighted Regression (GWR) between communities directly affected by floods and localities with high- and very high values of the Local Social Vulnerability Index (LoSoVI) was used to explore the spatial relationship among them and to compare the appropriateness of Ordinary Least Square (OLS) and GWR for such modelling. The established GWR model has revealed that the negative effects of flood hazards are often associated with communities with a high degree of social vulnerability. Thus, the analysis is able to provide a more comprehensive picture on communities in desperate need of financial resources in order to have the ability to diminish the negative impacts of flood hazards and to provide a more sustainable society.

The aim of the paper is to develop a model for the real-time estimation of local level income data by combining machine learning, Earth Observation, and Geographic Information System. More exactly, we estimated the income per capita by help of a machine learning model for 46 cities with more than 50,000 inhabitants, based on the National Polar-orbiting Partnership–Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime satellite images from 2012–2018. For the automation of calculation, a new ModelBuilder type tool was developed within the ArcGIS software called EO-Incity (Earth Observation–Income city). The sum of light (SOL) data extracted by means of the EO-Incity tool and the observed income data were integrated in an algorithm within the MATLAB software in order to calculate a transfer equation and the average error. The results achieved were subsequently reintegrated in EO-Incity and used for the estimation of the income value at local level. The regression analyses highlighted a stable and strong relationship between SOL and income for the analyzed cities. The EO-Incity tool and the machine learning model proved to be efficient in the real-time estimation of the income at local level. When integrated in the information systems specific for smart cities, they can serve as a support for decision-making in order to fight poverty and reduce social inequalities.

This research aims to improve the existing methodology by quantifying the effects of climate change on social vulnerability by developing a set of vulnerability indicators. It delimits for the first time in Romania the most vulnerable areas from this point of view and facilitates the decision-making processes and planning efforts targeting the increase of resilience and adaptive capacity of local communities. We selected 35 variables and constructed four aggregated indexes by applying the principal component analysis, and then the Climate-Related Social Vulnerability index (CleSoVI) index was derived. It pointed out that the most significant impact on the vulnerability of settlements in the test region (Cluj County) can be attributed to the lack of adaptive capacity and increased poverty. The most vulnerable areas are located in the northern and south-eastern parts of the county. From a socio-economic point of view, local authorities’ efforts should reduce the vulnerability of these regions and prepare them to cope with and adapt to the impact of climate change.

This paper aims to examine what can predict the adoption of agro-biomass as an innovative heating solution in rural areas. It explores the social acceptance of agro-biomass such as agriculture byproducts for heating in a rural context. The objectives are threefold: (1) to examine if there is a causal relationship between awareness and knowledge of agro-biomass applications for heating and intention to adopt this solution; (2) to investigate the extent to which perceived local drivers as well as barriers associated with agro-biomass utilization influence the intention to adopt this heating solution; and (3) to determine if knowledge about agro-biomass energy use, as well as political barriers to agro-biomass adoption for heating, play a mediating and/or moderating role in the established causal relationships. This paper uses primary data collected from rural residents located in different geographic areas in Romania. The 673 valid responses were analysed through multiple regression and mediation and moderation tests. The results confirm that awareness, knowledge, and perception of local drivers and of political barriers are positive predictors of intention to adopt agro-biomass as an innovative solution for heating. They also confirm the mediating role of knowledge and the moderating role of political barriers in the established relationships.