Socio-economic Planning Sciences最新文献

{"title":"Beyond efficiency: Quality of Government and eco-productivity convergence in the EU","authors":"Marc Aliana,&nbsp;Maria Teresa Balaguer-Coll,&nbsp;Diego Prior,&nbsp;Emili Tortosa-Ausina","doi":"10.1016/j.seps.2026.102569","DOIUrl":"10.1016/j.seps.2026.102569","url":null,"abstract":"<div><div>This paper examines eco-productivity convergence across European Union regions while explicitly incorporating institutional quality within a multilevel governance framework. Using a panel of 216 NUTS-2 regions over the period 2010–2023, we analyse whether regions converge in their ability to generate economic output while limiting environmental pressures, and whether the estimated convergence patterns differ once both national and regional Quality of Government (QoG) are taken into account. Eco-productivity is measured using a nonparametric frontier approach based on Data Envelopment Analysis, with labour and capital as inputs, GDP as a desirable output, and Greenhouse Gas (GHG) emissions, transformed into an output-oriented ‘GHG savings’ indicator. The results show that average eco-productivity levels are around 8% higher when QoG is accounted for, cross-regional dispersion is considerably lower, and <span><math><mi>β</mi></math></span>-convergence is consistently stronger and statistically significant across all subperiods. Overall, eco-productivity convergence is associated with QoG, suggesting that sustainable regional catch-up is more likely where green investment is matched by improvements in institutional quality.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"106 ","pages":"Article 102569"},"PeriodicalIF":5.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148736201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The quality of rural industry development: Conceptual connotation, logical construction and measurement evaluation","authors":"Di Su,&nbsp;Guogang Wang","doi":"10.1016/j.seps.2026.102427","DOIUrl":"10.1016/j.seps.2026.102427","url":null,"abstract":"<div><div>This study addresses the need for systematic evaluation of rural industry development quality (RIDQ) in China’s rural revitalization strategy. Drawing on systems theory and value theory, we develop a conceptual framework defining RIDQ as the degree to which objective characteristics meet societal requirements, and construct a “three dimensions, seven categories, and sixteen indicators (3D7C16I)” evaluation system. Using multiple weighting methods (AHP-EWM, ridge regression, machine learning), 1967 county-level units in 2013, 2017, and 2022 are analyzed.</div><div>Findings: (1) RIDQ shows ”high in the east, low in the west” gradient with strong spatial autocorrelation. (2) Temporally, RIDQ grows rapidly first then differentiates. (3) High/low-level regions are stable, while middle-tier regions fluctuate. (4) Neighbor environments create poverty traps (low-level), gradual optimization (medium-level), or siphoning effects (high-level). These provide empirical basis for differentiated rural revitalization policies.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"105 ","pages":"Article 102427"},"PeriodicalIF":5.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global agricultural carbon emission efficiency: Using machine learning techniques to reveal driving factors and forecast future trends","authors":"Wei Wang ,&nbsp;Xiaodong Pei ,&nbsp;Hongtao Jiang ,&nbsp;Mumah Edwin ,&nbsp;Yangfen Chen","doi":"10.1016/j.seps.2026.102428","DOIUrl":"10.1016/j.seps.2026.102428","url":null,"abstract":"<div><div>Agricultural carbon emission efficiency (ACEE) is crucial for advancing global carbon neutrality goals. However, existing research at the national level often overlooks the function of agricultural carbon sinks and exhibits deficiencies in analyzing the driving mechanisms of ACEE and making precise predictions. To address this, this paper constructs a more comprehensive ACEE measurement system and introduces machine learning techniques to thoroughly analyze the spatio-temporal dynamics, driving factors, and future trends of global ACEE. Firstly, by incorporating agricultural carbon sinks as an ecological output, this study develops an ACEE measurement system covering 162 countries, overcoming the limitations of previous studies that were often confined to regional levels or neglected carbon sinks. Measurements based on the global super-efficiency Epsilon-Based Measure model reveal that from 1995 to 2021, ACEE generally increased across countries, but spatial differentiation intensified, exhibiting a significant Matthew effect. Secondly, this study combines interpretable machine learning and geographically and temporally weighted regression to unveil the driving mechanisms of ACEE from socio-economic, agricultural, and climatic dimensions. Agricultural production level is the primary driver for enhancing ACEE, and economic development level also demonstrates a significant promoting role. However, rainfall intensity and agrochemical use intensity are the main inhibiting factors. Urbanization level, industrial structure, and agricultural trade openness negatively affect ACEE in most countries, while the positive effects of technological progress have been diminishing annually. Finally, to enhance prediction accuracy, this study employs an optimized backpropagation neural network model to predict ACEE for different country groups from 2025 to 2035. The ACEE gap between high- and low-level country groups is projected to further widen, and the global divergence trend will become more pronounced.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"105 ","pages":"Article 102428"},"PeriodicalIF":5.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust interactive and interpretable hierarchical PCA: Applications to crime data","authors":"Marco Tolla ,&nbsp;Marco Riani ,&nbsp;Paolo Mariani ,&nbsp;Corrado Crocetta","doi":"10.1016/j.seps.2026.102514","DOIUrl":"10.1016/j.seps.2026.102514","url":null,"abstract":"<div><div>In a context characterized by increasing institutional complexity and growing constraints on available resources, this paper proposes an interpretable framework for the construction of composite indicators aimed at supporting strategic decision making in public administration with specific emphasis on financial crime data. Robust statistical methods provide valuable support for analysing how and where resources should be deployed, enabling data-driven decisions aimed at improving the performance of Public Administrations for the benefit of the community. However, such analyses must produce results that are interpretable, clearly quantify the contribution of each unit, and remain robust in the presence of atypical observations. Furthermore, the inclusion of interactive tools is essential to allow end users to easily explore the characteristics and relative positions of units within the analytical space. Motivated by these requirements, this paper describes the interactive and interpretable framework based on robust hierarchical principal component analysis for optimizing resources and operational strategies within the Guardia di Finanza (GdF - Italian Economic and Financial Police).</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"105 ","pages":"Article 102514"},"PeriodicalIF":5.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence and socio-economic transformation","authors":"Jun Liu,&nbsp;Shunfeng Song","doi":"10.1016/j.seps.2026.102510","DOIUrl":"10.1016/j.seps.2026.102510","url":null,"abstract":"<div><div>This special issue provides a systematic review and summary of 20 artificial intelligence (AI)-related articles. These studies span multiple dimensions, including micro-level corporate governance, meso-level industrial synergy, and macro-level economic development. Through a comprehensive synthesis, the special issue finds that AI not only serves as a core tool for enhancing total factor productivity, driving disruptive innovation, and mitigating operational risks but also demonstrates significant governance efficacy in sustainability issues such as curbing ESG greenwashing and improving energy efficiency. Furthermore, this special issue explores the profound impacts of AI on labor market structures, wage equity, and regional inclusive growth. The special issue aims to provide theoretical support and empirical evidence for understanding the multifaceted roles of intelligent transformation in economic and social development.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"105 ","pages":"Article 102510"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148349545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Invest in African countries? Evaluation of risk based on the cloud model","authors":"Min An,&nbsp;Bo Qiu,&nbsp;Jiahao Zhang,&nbsp;Mingyue Teng,&nbsp;Shanshan Xie","doi":"10.1016/j.seps.2026.102419","DOIUrl":"10.1016/j.seps.2026.102419","url":null,"abstract":"<div><div>In the background of economic globalization, Africa has become a hot destination for foreign investment by its vast market and rich resource. However, risks such as political instability and weak economic foundation in African countries have brought great uncertainties to the effectiveness of investment. To assess the investment risk objectively and scientifically in Africa, the statistical data of 13 key African countries in the period of 2016–2020 are selected from the recent African Attractiveness Survey Report. Secondly, this paper establishes an investment risk index system covering politics, economy, law, society, and environment. Then use the coefficient of variation method to determine the degree of influence of each indicator from both temporal and spatial perspectives. Finally, apply the cloud model which can consider the fuzziness and randomness to measure the investment risk in Africa from the temporal and spatial perspectives. The results show that: (1) from the perspectives of time and space, the weights of indicators in the economic and social dimensions are 0.19 and 0.27, and 0.36 and 0.24, respectively. (2) The level of investment risk varies significantly among 13 African countries, with Tanzania and Kenya having a total score higher than 0.600 and being the top targets for investment. (3) The investment risk of key African countries fluctuated down from 2016 to 2019, but increased significantly in 2020, with the total score between 0.300 and 0.500.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"105 ","pages":"Article 102419"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148565488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new distributional based indicator to measure energy poverty in Italy","authors":"Alfonso Carfora ,&nbsp;Gloria Polinesi ,&nbsp;Maria Cristina Recchioni ,&nbsp;Francesca Mariani ,&nbsp;Mariateresa Ciommi","doi":"10.1016/j.seps.2026.102511","DOIUrl":"10.1016/j.seps.2026.102511","url":null,"abstract":"<div><div>Energy poverty refers to a situation where households struggle to access or afford essential energy services needed for daily living-such as heating, cooling, lighting, and cooking. Traditionally, this issue is evaluated using a fixed lower bound for the energy expenditure, commonly set at 10% of household income. In response to these concerns, the current study questions the effectiveness of fixed thresholds and introduces an alternative, data-driven methodology. This technique enables the identification of endogenous threshold points that accurately reflect the heterogeneity in household energy spending distributions. Furthermore, we enhance the estimation of energy poverty by combining these endogenous thresholds with a measure of economic vulnerability. This statistical framework enables the definition of a new energy poverty indicator defined as the joint probability that energy expenditure exceeds the endogenous threshold and non-energy expenditure falls below 60% of the national median. This offers a probabilistic assessment of energy poverty. Using data from the Italian National Institute of Statistics (ISTAT), specifically the Household Budget Survey (HBS), for the years 2019-2023, the analysis of energy indicator reveals that energy poverty is prevalent mainly in Southern regions of Italy. Furthermore, a spatial analysis covering the period 2019-2022 is conducted to explore the regional drivers of energy poverty.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"105 ","pages":"Article 102511"},"PeriodicalIF":5.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corruption risk indicators in public procurement: Definition and evaluation with organised crime data","authors":"Marianna Siino,&nbsp;Stefano Iezzi,&nbsp;Mario Gara","doi":"10.1016/j.seps.2026.102429","DOIUrl":"10.1016/j.seps.2026.102429","url":null,"abstract":"<div><div>This paper leverages open data from Italy's Central Anti-Corruption Authority (Autorità Nazionale Anticorruzione, ANAC) and relevant literature to propose a multi-layered system of risk indicators for detecting potential corruptive conducts in public procurement. The development and use of such indicators are widespread among national and international organisations as public procurement is particularly vulnerable to corruption. This vulnerability is particularly critical in Italy, where corruption is reportedly connected to criminal infiltration. Moreover, the European institutions are currently disbursing to Italy's government an unprecedented amount of funds for infrastructures and structural reforms, which makes Italian procurement all the more attractive to criminals. The relevant literature suggests a wide array of indicators and red flags, each tackling a specific vulnerability in procurement procedures liable to be exploited for illicit ends. This paper offers a system of auction-specific individual indicators offering a wide-ranging view on all such vulnerabilities, and at the same time taking into account the actual availability of data. Due focus is drawn on missing information, held as an indicator of opaqueness in itself. Based on these indicators, we compute a composite risk measure at the auction level and, by further aggregation, develop an indicator at the level of contracting authorities. A significant contribution of this work is the use of confidential data from Italy's Financial Intelligence Unit (Unità di Informazione Finanziaria per l’Italia, UIF) on firms potentially linked to organised crime to validate these indicators, providing evidence of their effectiveness. The potential applications of these indicators include monitoring public tenders, risk-ranking of awarding authorities and contractors, prioritising investigative and anti-money laundering activities.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"105 ","pages":"Article 102429"},"PeriodicalIF":5.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measuring the effectiveness of fiscal policy in multidimensional poverty reduction","authors":"Maria Emma Santos ,&nbsp;Nora Lustig ,&nbsp;Maximiliano Miranda-Zanetti","doi":"10.1016/j.seps.2026.102489","DOIUrl":"10.1016/j.seps.2026.102489","url":null,"abstract":"<div><div>In this paper, we propose indicators of Impact Effectiveness (IE) and Spending Effectiveness (SE) for fiscal interventions aimed at reducing multidimensional poverty extending Enami's (2018, 2022) unidimensional indicators. IE compares the observed reduction in poverty with the maximum reduction that could have been achieved under an optimal allocation of resources. SE compares the actual budget used with the minimum budget required to achieve the same reduction under an optimal allocation. We introduce an overarching framework with two alternative optimal criteria for resource allocation. The first criterion maximizes the reduction in any given multidimensional poverty index (MPI) with the structure of the <span><math><mrow><msub><mi>M</mi><mn>0</mn></msub></mrow></math></span> measure of the Alkire and Foster (2011) family, also called the Adjusted Headcount Ratio. The second is a lexicographic Prioritarian criterion that sequentially prioritizes households from the multidimensionally poorest—those with the highest deprivation score— to the least intensely poor. This approach is equivalent to sequentially maximizing the reduction in an MPI with subsequent sufficiently high poverty cutoffs. Implementing the methodology requires three types of data: household survey data, estimates of the cost of eliminating each deprivation at the household level, and information on planned or executed public spending in the dimensions under study. The framework can be applied ex-post to evaluate effectiveness, or ex-ante to guide the design of multidimensional poverty reduction strategies. This approach provides policymakers with a transparent and rigorous tool to assess and improve the effectiveness of multidimensional poverty spending in line with the 2030 Agenda for Sustainable Development.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"105 ","pages":"Article 102489"},"PeriodicalIF":5.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can forestry carbon sink pledge financing improve the quality of forest management?","authors":"Xiao Xu ,&nbsp;Tao Pang ,&nbsp;Hongjun Peng ,&nbsp;Wenting Sun","doi":"10.1016/j.seps.2026.102448","DOIUrl":"10.1016/j.seps.2026.102448","url":null,"abstract":"<div><div>In this paper, we study a forestry operator facing financial constraints and a financial institution that provides pledge financing, using the expected revenue rights from forestry carbon sinks and the management rights of transferred forestland as collateral. Output uncertainty in forestry carbon sinks and government subsidies are explicitly considered. The problem is formulated as a Stackelberg game. We derive the forestry operator’s optimal decisions on forest quality and the financial institution’s optimal pledge rate, and investigate the effects of key factors such as the probability of deforestation disasters and the carbon sink price. Numerical analyses are also presented. The main findings are as follows. First, infrequent deforestation disasters do not affect forest quality or the pledge rate, whereas frequent disasters reduce incentives for forest management and increase financing difficulty. Second, expanding the scale of forest management increases profits, but may lead to lower forest quality and a reduced pledge rate. Third, an increase in the economic value of transferred forestland management rights does not affect forest quality but reduces forestry profits. Moreover, the impact of this economic value on the pledge rate depends on the carbon sink price. Finally, when the probability of deforestation disasters is relatively low or very high, pledge financing can improve forest quality. In addition, under certain conditions, pledge financing can also enhance the forestry operator’s profits.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"105 ","pages":"Article 102448"},"PeriodicalIF":5.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}