Just Accepted
ORIGINAL ARTICLES
Evaluating progress in achieving the SDGs at sub-national level in Spain: a multicriteria analysis
Luisa Paolotti1,*, Ignacio Melendez Pastor2, Elena Ricciolini1, Lucia Rocchi1, Asunción Maria Agulló Torres3, Antonio Boggia1
1 Department of Agricultural, Food and Environmental Sciences, University of Perugia, Italy
2 Department of Agrochemistry and Environment, Miguel Hernández University of Elche, Spain
3 Department of Agri-environmental Economics, Miguel Hernández University of Elche, Spain
Accepted: 2025-01-11 | Published Online: 2025-01-24
DOI: 10.36253/aestim-17200
ABSTRACT
The UN 2030 Agenda is the current reference point for achieving sustainable development at the international level. Focusing on the implementation effort and monitoring the progress of SDGs are crucial aspects for achieving the Goals by 2030. The evaluation and achievement of sustainability at the sub-national level is fundamental, as sustainable development is considered achievable if it originates on the local level. Given that, the objective of this research was to assess sustainable development related to the 2030 Agenda considering the 17 regions (autonomous communities) of Spain. The analysis was carried out through the Spatial Sustainability Assessment Model (SSAM), set up as a plug-in of QGIS, which integrates multi-criteria analysis with the geographical tool. The region datasets referred to years 2019 and 2020 to observe a comparison of pre and post-COVID framework and to assess possible changes due to pandemic impacts. Results showed that, both in 2019 and 2020, for the environmental dimension the majority of the regions obtained very low or low results, showing a generally scarce environmental situation. A general decline for the majority of the indices was observed and a decrease in sustainability from north to south was detected, both for the social and the global sustainability dimensions. The social dimension in most cases was the one marking the global ordination of the communities.
Valuing cultural ecosystem services: an application to forest areas in Marche Region, Italy
Danilo Gambelli*, Alice Dappozzo, Andrea Cameli, Carlo Urbinati, Alessandro Vitali
Department of Agricultural, Food and Environmental Sciences (D3A), Università Politecnica delle Marche, Italy.
Accepted: 2024-12-19 | Published Online: 2025-01-03
DOI: 10.36253/aestim-16391
ABSTRACT
This article investigates the perceived relevance of cultural ecosystem services (ES) in two forest areas in the Marche region (Italy) and how users, tourists and locals value them economically. Two surveys were used to collect data from visitors to the two areas. Through a performance analysis (IPA), we assessed visitors’ satisfaction with cultural ES in the two areas under investigation. The economic appraisal of the ecosystem services in the two forest areas was based on a contingent valuation model (CVM) using a double-bounded approach to estimate visitors’ willingness to pay (WTP). This type of research, merging qualitative and monetary evaluation of ES can contribute to defining policy management by identifying aspects and activities in natural areas that require specific intervention. Evidence for the economic value delivered by a broader range of ES may support the definition of more effective policy measures for forest areas and create the basis for the definition of payment schemes for ES.
Interpretable Machine Learning for the German residential rental market – shedding light into model mechanics
Severin Bachmann
Nuremberg Research Institute for Cooperative Studies, Nuremberg, Germany.
Accepted: 2024-12-04 | Published Online: 2024-12-20
DOI: 10.36253/aestim-16351
ABSTRACT
We compare the drivers in Machine learning models and give insights into their strengths and weaknesses predicting rental prices. The study employs SHAP values to measure feature importance. The study aims to investigate linear regression, decision tree and XGBoost algorithms. The research is unique in its application of IML methods to a large dataset of over 2.4 million observations in the German rental market and its application of comparative statistics using aggregate SHAP values. Main results are the superiority of XGB and LR showing higher SHAP values overall and thus explaining its lower predictive efficacy. DT models capture intricate interactions among variables with fewer features, while XGB accommodates more variables, emphasizing its higher complexity and thus superior performance. The top ten features for DT and XGB models show significant overlap, indicating robust concordance. Specific features are identified that distinguish the models, suggesting that a more complex model, like XGB, handles dummy variables more adeptly.