Socio-economic Planning Sciences最新文献_第6页

Research on the operational strategies of data products under China's data element policy: Subsidizing service providers or manufacturers? 中国数据要素政策下的数据产品运营策略研究：补贴服务提供商还是补贴制造商？

IF 5.4 2区经济学 Q1 ECONOMICS

Socio-economic Planning Sciences

Pub Date : 2025-11-02 DOI: 10.1016/j.seps.2025.102374

Yazhou Liu, Wenxiu Hu, Li Liu, Xin Wang

The divergence in perceived value of data products leads to discrepancies in value assessment by both parties in transactions, limiting the circulation of data as an economic factor. The government attempts to alleviate this issue through transaction subsidies. While existing studies focus on data market design and the incentive effects of subsidies on corporate R&D, there is a lack of systematic exploration on how governments should design effective subsidy policies for data products. To improve subsidy efficiency and optimize data product operations, this study integrates the demand preferences of data product end users (DPEU) into the utility function. It constructs a game model under three scenarios: no subsidy, subsidy to data product manufacturers (DPM), and subsidy to data product service providers (DPSP), analyzing optimal decisions under various subsidy strategies. Results show that without subsidies, DPM and DPSP, constrained by costs, reduce investments in quality and promotion, significantly suppressing market transactions. With subsidies, DPM increases demand by enhancing quality and optimizing prices, while DPSP promotes transactions through intensified marketing, jointly facilitating data product exchanges. Regardless of subsidies, DPEU's quality and promotion preferences consistently drive profits for all parties, with quality preference having a greater impact. Conversely, investments in quality and promotion costs suppress profits, with quality costs having a stronger inhibitory effect. This study provides a theoretical foundation for governments to design targeted subsidy policies for data products and for companies to optimize their data product operational strategies, thereby contributing to the healthy development of the data product market.

数据产品感知价值的差异导致交易双方价值评估的差异，限制了数据作为经济因素的流通。政府试图通过交易补贴来缓解这一问题。现有研究主要集中在数据市场设计和补贴对企业研发的激励作用上，缺乏对政府如何设计有效的数据产品补贴政策的系统探索。为了提高补贴效率，优化数据产品运营，本研究将数据产品终端用户的需求偏好纳入效用函数。构建了不补贴、补贴数据产品制造商（DPM）和补贴数据产品服务商（DPSP）三种场景下的博弈模型，分析了不同补贴策略下的最优决策。结果表明，在没有补贴的情况下，DPM和DPSP受到成本约束，减少了对质量和推广的投资，显著抑制了市场交易。DPM通过补贴提高质量、优化价格来增加需求，DPSP通过强化营销促进交易，共同促进数据产品交换。在不考虑补贴的情况下，DPEU的质量偏好和促销偏好始终为各方带来利润，其中质量偏好的影响更大。相反，对质量和促销成本的投入会抑制利润，其中质量成本的抑制作用更强。本研究为政府制定有针对性的数据产品补贴政策，为企业优化数据产品运营策略提供理论依据，从而促进数据产品市场的健康发展。

{"title":"Research on the operational strategies of data products under China's data element policy: Subsidizing service providers or manufacturers?","authors":"Yazhou Liu, Wenxiu Hu, Li Liu, Xin Wang","doi":"10.1016/j.seps.2025.102374","DOIUrl":"10.1016/j.seps.2025.102374","url":null,"abstract":"<div><div>The divergence in perceived value of data products leads to discrepancies in value assessment by both parties in transactions, limiting the circulation of data as an economic factor. The government attempts to alleviate this issue through transaction subsidies. While existing studies focus on data market design and the incentive effects of subsidies on corporate R&D, there is a lack of systematic exploration on how governments should design effective subsidy policies for data products. To improve subsidy efficiency and optimize data product operations, this study integrates the demand preferences of data product end users (DPEU) into the utility function. It constructs a game model under three scenarios: no subsidy, subsidy to data product manufacturers (DPM), and subsidy to data product service providers (DPSP), analyzing optimal decisions under various subsidy strategies. Results show that without subsidies, DPM and DPSP, constrained by costs, reduce investments in quality and promotion, significantly suppressing market transactions. With subsidies, DPM increases demand by enhancing quality and optimizing prices, while DPSP promotes transactions through intensified marketing, jointly facilitating data product exchanges. Regardless of subsidies, DPEU's quality and promotion preferences consistently drive profits for all parties, with quality preference having a greater impact. Conversely, investments in quality and promotion costs suppress profits, with quality costs having a stronger inhibitory effect. This study provides a theoretical foundation for governments to design targeted subsidy policies for data products and for companies to optimize their data product operational strategies, thereby contributing to the healthy development of the data product market.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"103 ","pages":"Article 102374"},"PeriodicalIF":5.4,"publicationDate":"2025-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145467588","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}

引用次数: 0

Introducing a novel AI-based text mining method illustrated through an analysis of German innovation proposals 通过对德国创新提案的分析，介绍了一种新的基于人工智能的文本挖掘方法

IF 5.4 2区经济学 Q1 ECONOMICS

Socio-economic Planning Sciences

Pub Date : 2025-11-01 DOI: 10.1016/j.seps.2025.102339

Lütje Lange, Alexander Griffiths, Hans-Hennig von Grünberg

In this study, we examine how standard AI tools such as ChatGPT can be used for the structured analysis of large text corpora. To this end, we analysed 482 applications from a specific innovation funding program of the German Federal Ministry of Science using ChatGPT. Thanks to ChatGPT's ability to cluster projects based on their characteristics, complex data sets can be systematically explored and patterns recognized that would have remained hidden in a manual analysis. It turns out that cluster formation controlled in advance by the user via cluster definitions (using prompts), is in some cases more meaningful than the fully automated cluster formation of tools such as BERTopic. The analysis of the 482 funding applications provides detailed insights into the state of innovation in Germany: 83 % of the proposals dealt with topics related to digitalization and social innovation (half each), while the remaining 17 % dealt with sustainability issues. While 77 % of all project activities focus solely on the early concept phases, only 17 % of activities relate to the piloting and validation of applied ideas. Correlation analyses examine the relationships and potential connections between the clusters identified in different categories, in order to uncover patterns and dependencies in the innovation application data. For example, the correlation data can be used to determine the “age” of certain fields of innovation. The study also demonstrates the suitability of the method for classifications with external cluster definitions such as the UN Sustainable Development Goals (SDGs) or the EU program “Horizon Europe” to assess the suitability of research projects, with regard to specific frameworks. This could be particularly useful for scientific funding organizations.

在这项研究中，我们研究了如何将ChatGPT等标准人工智能工具用于大型文本语料库的结构化分析。为此，我们使用ChatGPT分析了来自德国联邦科学部特定创新资助计划的482份申请。由于ChatGPT能够根据项目的特征对其进行聚类，因此可以系统地探索复杂的数据集，并识别出手动分析中隐藏的模式。事实证明，在某些情况下，由用户通过集群定义（使用提示）预先控制的集群形成比BERTopic等工具的全自动集群形成更有意义。对482项资助申请的分析提供了对德国创新状况的详细见解：83%的提案涉及与数字化和社会创新相关的主题（各占一半），其余17%涉及可持续性问题。虽然77%的项目活动只关注早期概念阶段，但只有17%的活动与应用想法的试点和验证有关。相关性分析检查在不同类别中确定的集群之间的关系和潜在联系，以便发现创新应用程序数据中的模式和依赖关系。例如，相关数据可以用来确定某些创新领域的“年龄”。该研究还证明了该方法与外部集群定义（如联合国可持续发展目标（SDGs）或欧盟计划“地平线欧洲”）进行分类的适用性，以评估研究项目在特定框架方面的适用性。这对科学资助组织尤其有用。

{"title":"Introducing a novel AI-based text mining method illustrated through an analysis of German innovation proposals","authors":"Lütje Lange, Alexander Griffiths, Hans-Hennig von Grünberg","doi":"10.1016/j.seps.2025.102339","DOIUrl":"10.1016/j.seps.2025.102339","url":null,"abstract":"<div><div>In this study, we examine how standard AI tools such as ChatGPT can be used for the structured analysis of large text corpora. To this end, we analysed 482 applications from a specific innovation funding program of the German Federal Ministry of Science using ChatGPT. Thanks to ChatGPT's ability to cluster projects based on their characteristics, complex data sets can be systematically explored and patterns recognized that would have remained hidden in a manual analysis. It turns out that cluster formation controlled in advance by the user via cluster definitions (using prompts), is in some cases more meaningful than the fully automated cluster formation of tools such as BERTopic. The analysis of the 482 funding applications provides detailed insights into the state of innovation in Germany: 83 % of the proposals dealt with topics related to digitalization and social innovation (half each), while the remaining 17 % dealt with sustainability issues. While 77 % of all project activities focus solely on the early concept phases, only 17 % of activities relate to the piloting and validation of applied ideas. Correlation analyses examine the relationships and potential connections between the clusters identified in different categories, in order to uncover patterns and dependencies in the innovation application data. For example, the correlation data can be used to determine the “age” of certain fields of innovation. The study also demonstrates the suitability of the method for classifications with external cluster definitions such as the UN Sustainable Development Goals (SDGs) or the EU program “Horizon Europe” to assess the suitability of research projects, with regard to specific frameworks. This could be particularly useful for scientific funding organizations.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"103 ","pages":"Article 102339"},"PeriodicalIF":5.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145569095","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}

引用次数: 0

Impact of rural Return Entrepreneurship pilot policies on agricultural carbon emissions in China's Yangtze river Economic Belt 农村返乡创业试点政策对长江经济带农业碳排放的影响

IF 5.4 2区经济学 Q1 ECONOMICS

Socio-economic Planning Sciences

Pub Date : 2025-10-30 DOI: 10.1016/j.seps.2025.102373

Hanxiang Luo , Zhaoyang Xiang , Tianwei Xie

This paper investigates the environmental impact of China's Rural Return Entrepreneurship Pilot Policy (RREPP) on agricultural carbon emissions in the Yangtze River Economic Belt. Using panel data from 110 prefecture-level cities spanning 2011–2020, we implement a staggered difference-in-differences (DID) approach combined with propensity score matching to identify the causal effect of the policy. Results indicate that RREPP significantly reduces agricultural carbon emissions, with robustness confirmed through placebo tests, sample restrictions, and spatial econometric models. Mechanism analysis suggests that the effect operates through two primary channels: (i) technological innovation, proxied by green patent authorizations, and (ii) knowledge spillovers, captured via a gravity-based spillover index. We further explore heterogeneity across regions and find stronger emission reductions in areas with higher digital infrastructure and lower educational attainment, highlighting the role of local absorptive capacity. Spatial decomposition reveals that policy effects are largely local, with limited diffusion to neighboring counties. This study contributes to the literature by linking human capital reflux with environmental performance and positioning rural entrepreneurship as a policy lever for agricultural decarbonization. Policy implications emphasize the need for regionally adaptive interventions that integrate entrepreneurship support with green technology diffusion and institutional capacity-building.

本文研究了中国农村返乡创业试点政策对长江经济带农业碳排放的环境影响。利用2011-2020年110个地级市的面板数据，我们采用交错差中差（DID）方法结合倾向得分匹配来识别政策的因果效应。结果表明，RREPP显著降低了农业碳排放，并通过安慰剂检验、样本限制和空间计量模型证实了其稳健性。机制分析表明，这种效应通过两个主要渠道发挥作用：(1)以绿色专利授权为代表的技术创新；(2)通过基于引力的溢出指数捕捉到的知识溢出。我们进一步探讨了区域间的异质性，发现数字基础设施水平较高、受教育程度较低的地区减排力度更大，并强调了地方吸收能力的作用。空间分解表明，政策效应主要是局部性的，向周边县的扩散有限。本研究通过将人力资本回流与环境绩效联系起来，并将农村创业定位为农业脱碳的政策杠杆，为文献做出了贡献。政策影响强调需要采取区域适应性干预措施，将支持创业与绿色技术扩散和机构能力建设结合起来。

{"title":"Impact of rural Return Entrepreneurship pilot policies on agricultural carbon emissions in China's Yangtze river Economic Belt","authors":"Hanxiang Luo , Zhaoyang Xiang , Tianwei Xie","doi":"10.1016/j.seps.2025.102373","DOIUrl":"10.1016/j.seps.2025.102373","url":null,"abstract":"<div><div>This paper investigates the environmental impact of China's Rural Return Entrepreneurship Pilot Policy (RREPP) on agricultural carbon emissions in the Yangtze River Economic Belt. Using panel data from 110 prefecture-level cities spanning 2011–2020, we implement a staggered difference-in-differences (DID) approach combined with propensity score matching to identify the causal effect of the policy. Results indicate that RREPP significantly reduces agricultural carbon emissions, with robustness confirmed through placebo tests, sample restrictions, and spatial econometric models. Mechanism analysis suggests that the effect operates through two primary channels: (i) technological innovation, proxied by green patent authorizations, and (ii) knowledge spillovers, captured via a gravity-based spillover index. We further explore heterogeneity across regions and find stronger emission reductions in areas with higher digital infrastructure and lower educational attainment, highlighting the role of local absorptive capacity. Spatial decomposition reveals that policy effects are largely local, with limited diffusion to neighboring counties. This study contributes to the literature by linking human capital reflux with environmental performance and positioning rural entrepreneurship as a policy lever for agricultural decarbonization. Policy implications emphasize the need for regionally adaptive interventions that integrate entrepreneurship support with green technology diffusion and institutional capacity-building.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"103 ","pages":"Article 102373"},"PeriodicalIF":5.4,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145467587","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}

引用次数: 0

Leave me be, make me strong! Growing solo to innovate together. Results from machine learning techniques on the innovation performance in resource-constrained manufacturing industries 别管我，让我坚强起来！独自成长，共同创新。机器学习技术对资源约束型制造业创新绩效的影响

IF 5.4 2区经济学 Q1 ECONOMICS

Socio-economic Planning Sciences

Pub Date : 2025-10-24 DOI: 10.1016/j.seps.2025.102365

Ángel Peiró-Signes , Carlos Labarcés-Ballestas , Marival Segarra-Oña , Óscar Trull-Domínguez

Innovation drives economic growth and competitiveness, particularly for small and medium-sized enterprises; however, firms in countries with low innovation capacity face significant barriers, ranging from limited financial resources to inadequate technological infrastructure. This paper examines the drivers of innovation in a resource-constrained and low-innovation ecosystem by using Colombian manufacturing industry data as a reference case, as it is a context characterized by low levels of innovation activity and underdeveloped innovation ecosystems. Using data from the 2020 Survey on Development and Technological Innovation in the Manufacturing Industry, this study employs machine learning techniques to identify the key internal and external factors influencing innovation outcomes, with results indicating that firms allocating proportionally more labor and investment to scientific, technological, and innovation activities are more likely to achieve innovative outcomes. In contrast to high-innovation regions, collaboration has a less significant effect on innovation, suggesting that the lack of ecosystem support may limit knowledge sharing and resource exchange. These findings highlight the need for targeted policies focused on strengthening financial access, promoting knowledge networks, and building collaborative frameworks. The study concludes by outlining policy recommendations aimed at enhancing innovation in low-innovation contexts and suggests paths for further research on the role of ecosystem support in emerging economies.

创新推动经济增长和竞争力，尤其是对中小企业而言；然而，创新能力低的国家的企业面临着巨大的障碍，从有限的财政资源到不充分的技术基础设施。本文以创新活动水平低、创新生态系统不发达的哥伦比亚制造业数据为参考案例，考察了资源受限和低创新生态系统中的创新驱动因素。本文利用《2020年制造业发展与技术创新调查》数据，运用机器学习技术识别了影响创新成果的关键内外部因素，结果表明企业在科技创新活动中按比例分配更多的劳动力和投资更有可能实现创新成果。与高创新区域相比，协作对创新的影响不那么显著，这表明生态系统支持的缺乏可能会限制知识共享和资源交换。这些发现突出表明，需要制定有针对性的政策，重点是加强融资渠道、促进知识网络和建立合作框架。最后，本研究概述了在低创新背景下加强创新的政策建议，并为进一步研究生态系统支持在新兴经济体中的作用提出了路径。

{"title":"Leave me be, make me strong! Growing solo to innovate together. Results from machine learning techniques on the innovation performance in resource-constrained manufacturing industries","authors":"Ángel Peiró-Signes , Carlos Labarcés-Ballestas , Marival Segarra-Oña , Óscar Trull-Domínguez","doi":"10.1016/j.seps.2025.102365","DOIUrl":"10.1016/j.seps.2025.102365","url":null,"abstract":"<div><div>Innovation drives economic growth and competitiveness, particularly for small and medium-sized enterprises; however, firms in countries with low innovation capacity face significant barriers, ranging from limited financial resources to inadequate technological infrastructure. This paper examines the drivers of innovation in a resource-constrained and low-innovation ecosystem by using Colombian manufacturing industry data as a reference case, as it is a context characterized by low levels of innovation activity and underdeveloped innovation ecosystems. Using data from the 2020 Survey on Development and Technological Innovation in the Manufacturing Industry, this study employs machine learning techniques to identify the key internal and external factors influencing innovation outcomes, with results indicating that firms allocating proportionally more labor and investment to scientific, technological, and innovation activities are more likely to achieve innovative outcomes. In contrast to high-innovation regions, collaboration has a less significant effect on innovation, suggesting that the lack of ecosystem support may limit knowledge sharing and resource exchange. These findings highlight the need for targeted policies focused on strengthening financial access, promoting knowledge networks, and building collaborative frameworks. The study concludes by outlining policy recommendations aimed at enhancing innovation in low-innovation contexts and suggests paths for further research on the role of ecosystem support in emerging economies.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"103 ","pages":"Article 102365"},"PeriodicalIF":5.4,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145419007","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}

引用次数: 0

From investment to impact: Exploring socio-economic prospect of hydrogen investment in Tees Valley, UK 从投资到影响：探索英国蒂斯谷氢投资的社会经济前景

IF 5.4 2区经济学 Q1 ECONOMICS

Socio-economic Planning Sciences

Pub Date : 2025-10-24 DOI: 10.1016/j.seps.2025.102371

Vahid Ghorbani Pashakolaie , Bjarnhedinn Gudlaugsson , Tariq G. Ahmed

Financial viability is fundamental for investment success, however, long run sustainable investment relies on delivering tangible socio-economic benefits that foster societal acceptance, enhancing community welfare and well-being. This study developed a quantitative model to evaluate the socio-economic impact of a proposed 1 GW green and 2 GW blue hydrogen investment in Tees Valley, UK, from 2027 to 2035. We introduced the socio-economic impact (SEI) ratio, defined as the ratio of socio-economic impact to the Levelized Cost of Hydrogen (LCOH), to illustrate the significance of socio-economic impact beyond financial returns.

Findings indicate that the cumulative environmental and economic impact of green hydrogen amounted to £1.5 ± 0.5 bn, and £1.35 ± 0.27 bn, respectively, with an employment impact of £269 ± 28 mn. In contrast, the proposed blue hydrogen investment is expected to deliver £2.9 ± 0.9 bn environmental impact, £1.84 ± 0.37 bn economic impact, and £212 ± 26 mn employment social impact. The SEI ratio of green hydrogen was found to range between 48 % and 62 %, and 60 %–79 % for blue hydrogen, suggesting overall SEI ratio of approximately 60 % for combined green and blue investment. Sensitivity analysis using Monte Carlo simulation revealed that the results are particularly sensitive to the Gross Value Added (GVA), emission, and employment factors. These findings highlight the importance of integrating socio-economic considerations into hydrogen planning, investment strategies, and decision-making to optimise environmental, societal, and economic outcomes.

财务可行性是投资成功的基础，然而，长期可持续投资依赖于提供切实的社会经济效益，以促进社会接受，提高社区福利和福祉。本研究开发了一个定量模型，以评估2027年至2035年英国蒂斯谷拟议的1吉瓦绿色和2吉瓦蓝色氢投资的社会经济影响。我们引入了社会经济影响（SEI）比率，定义为社会经济影响与氢的平化成本（LCOH）的比率，以说明经济回报之外的社会经济影响的重要性。研究结果表明，绿色氢的累积环境和经济影响分别为15±5亿英镑和1.35±2.7亿英镑，就业影响为269±28百万英镑。相比之下，拟议的蓝色氢投资预计将带来29±9亿英镑的环境影响，18.4±3.7亿英镑的经济影响，以及2.12±26亿英镑的就业社会影响。绿色氢的SEI比率在48%至62%之间，蓝色氢的SEI比率在60%至79%之间，这表明绿色和蓝色组合投资的总体SEI比率约为60%。利用蒙特卡罗模拟的敏感性分析表明，结果对总增加值（GVA）、排放和就业因素特别敏感。这些发现强调了将社会经济因素纳入氢规划、投资战略和决策以优化环境、社会和经济成果的重要性。

{"title":"From investment to impact: Exploring socio-economic prospect of hydrogen investment in Tees Valley, UK","authors":"Vahid Ghorbani Pashakolaie , Bjarnhedinn Gudlaugsson , Tariq G. Ahmed","doi":"10.1016/j.seps.2025.102371","DOIUrl":"10.1016/j.seps.2025.102371","url":null,"abstract":"<div><div>Financial viability is fundamental for investment success, however, long run sustainable investment relies on delivering tangible socio-economic benefits that foster societal acceptance, enhancing community welfare and well-being. This study developed a quantitative model to evaluate the socio-economic impact of a proposed 1 GW green and 2 GW blue hydrogen investment in Tees Valley, UK, from 2027 to 2035. We introduced the socio-economic impact (SEI) ratio, defined as the ratio of socio-economic impact to the Levelized Cost of Hydrogen (LCOH), to illustrate the significance of socio-economic impact beyond financial returns.</div><div>Findings indicate that the cumulative environmental and economic impact of green hydrogen amounted to £1.5 ± 0.5 bn, and £1.35 ± 0.27 bn, respectively, with an employment impact of £269 ± 28 mn. In contrast, the proposed blue hydrogen investment is expected to deliver £2.9 ± 0.9 bn environmental impact, £1.84 ± 0.37 bn economic impact, and £212 ± 26 mn employment social impact. The SEI ratio of green hydrogen was found to range between 48 % and 62 %, and 60 %–79 % for blue hydrogen, suggesting overall SEI ratio of approximately 60 % for combined green and blue investment. Sensitivity analysis using Monte Carlo simulation revealed that the results are particularly sensitive to the Gross Value Added (GVA), emission, and employment factors. These findings highlight the importance of integrating socio-economic considerations into hydrogen planning, investment strategies, and decision-making to optimise environmental, societal, and economic outcomes.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"103 ","pages":"Article 102371"},"PeriodicalIF":5.4,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145517511","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}

引用次数: 0

How to enhance students’ well-being in Spain: A multidimensional study based on multiobjective optimization 如何提高学生在西班牙的幸福感：基于多目标优化的多维研究

IF 5.4 2区经济学 Q1 ECONOMICS

Socio-economic Planning Sciences

Pub Date : 2025-10-24 DOI: 10.1016/j.seps.2025.102352

Andrea Orozco-Villodres , Ana B. Ruiz , Mariano Luque

This paper addresses the multidimensional nature of the concept of student well-being from a multiple criteria decision making perspective, specifically differentiating between the social, physical, and mental dimensions. The main purpose is to analyze how the well-being levels among Spanish students could be improved. Using PISA 2022 data, three indexes of student well-being are constructed: social, physical and mental. Using econometric tools, a multiobjective optimization model is developed to simultaneously maximize the three proposed indexes. The resulting analysis makes it possible to evaluate the real possibilities for improving the three well-being dimensions and the scenarios that would make it possible (i.e., socio-educational context for the students). It allows foreseeing the impact of the change in one of the indexes on the other two. In addition, existing correlations between students' socio-educational characteristics are introduced into the model to represent the student's context as realistically as possible. The results indicate that students who seek to optimize their well-being from the triple perspective adopted (physical, social, and mental) must make trade-offs between the indicators in order to achieve a balanced solution.

本文从多标准决策的角度阐述了学生幸福概念的多维性，特别是区分了社会、身体和心理维度。主要目的是分析如何提高西班牙学生的幸福水平。利用PISA 2022数据，构建了学生幸福感的三个指标：社会、身体和心理。利用计量经济学工具，建立了一个多目标优化模型，以同时最大化这三个指标。由此产生的分析可以评估改善三个福祉维度的实际可能性以及使之成为可能的情景（即学生的社会教育背景）。它允许预测其中一个指数的变化对其他两个指数的影响。此外，在模型中引入了学生社会教育特征之间存在的相关性，以尽可能真实地代表学生的情境。结果表明，从所采用的三重视角（身体，社会和心理）寻求优化幸福感的学生必须在指标之间进行权衡，以实现平衡的解决方案。

{"title":"How to enhance students’ well-being in Spain: A multidimensional study based on multiobjective optimization","authors":"Andrea Orozco-Villodres , Ana B. Ruiz , Mariano Luque","doi":"10.1016/j.seps.2025.102352","DOIUrl":"10.1016/j.seps.2025.102352","url":null,"abstract":"<div><div>This paper addresses the multidimensional nature of the concept of student well-being from a multiple criteria decision making perspective, specifically differentiating between the social, physical, and mental dimensions. The main purpose is to analyze how the well-being levels among Spanish students could be improved. Using PISA 2022 data, three indexes of student well-being are constructed: social, physical and mental. Using econometric tools, a multiobjective optimization model is developed to simultaneously maximize the three proposed indexes. The resulting analysis makes it possible to evaluate the real possibilities for improving the three well-being dimensions and the scenarios that would make it possible (i.e., socio-educational context for the students). It allows foreseeing the impact of the change in one of the indexes on the other two. In addition, existing correlations between students' socio-educational characteristics are introduced into the model to represent the student's context as realistically as possible. The results indicate that students who seek to optimize their well-being from the triple perspective adopted (physical, social, and mental) must make trade-offs between the indicators in order to achieve a balanced solution.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"103 ","pages":"Article 102352"},"PeriodicalIF":5.4,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145419009","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}

引用次数: 0

Prediction of the gender inequality index based on data-driven interpretable ensemble learning methods 基于数据驱动的可解释集成学习方法的性别不平等指数预测

IF 5.4 2区经济学 Q1 ECONOMICS

Socio-economic Planning Sciences

Pub Date : 2025-10-22 DOI: 10.1016/j.seps.2025.102366

Mehmet Hakan Özdemir , Batin Latif Aylak , Celal Cakiroglu , Mahmut Bağcı

Gender inequality is acknowledged as a major hindrance to human development, evident in multiple social, political, economic, and cultural aspects. Therefore, identifying the factors contributing to gender inequality and quantifying them is crucial for enhancing societal progress. A new index, the gender inequality index (GII), was introduced in the 2010 Human Development Report to quantify and compare gender inequalities among different countries. Multiple indicators are used to calculate the GII, which involves complex analytical calculations. This study utilizes these indicators as input features to predict the GII using XGBoost, CatBoost, Extra Trees, LightGBM, Ridge, and Lasso regression models. These regressors are trained for predicting the GII as a function of maternal mortality ratio, adolescent birth rate, share of seats in parliament, female population with at least some secondary education, male population with at least some secondary education, female labour force participation rate, and male labour force participation rate. It is observed that XGBoost, CatBoost, Extra Trees and LightGBM predictors have

R^{2}

score greater than 0.98, while the Ridge and Lasso regressors have

R^{2}

score less than 0.90. The highest average accuracy is obtained by the CatBoost model while the XGBoost model has the greatest computational speed. Furthermore, the Shapley additive explanations methodology is utilized to detect the impact of different input features on the model predictions, and this information allows for more precise calculation of the GII. Thus, the proposed machine learning procedure enables both simplicity and flexibility for the GII prediction and provides more effective use of the GII.

性别不平等被认为是人类发展的主要障碍，体现在社会、政治、经济和文化的多个方面。因此，确定造成性别不平等的因素并加以量化，对于促进社会进步至关重要。2010年人类发展报告中引入了一个新的指数——性别不平等指数（GII），用于量化和比较不同国家之间的性别不平等。全球创新指数的计算使用了多个指标，涉及复杂的分析计算。本研究利用这些指标作为输入特征，使用XGBoost、CatBoost、Extra Trees、LightGBM、Ridge和Lasso回归模型预测GII。对这些回归量进行了训练，以预测全球创新指数作为产妇死亡率、青少年出生率、议会席位份额、至少接受过一些中等教育的女性人口、至少接受过一些中等教育的男性人口、女性劳动力参与率和男性劳动力参与率的函数。观察到，XGBoost、CatBoost、Extra Trees和LightGBM预测因子的R2值大于0.98，而Ridge和Lasso回归因子的R2值小于0.90。CatBoost模型的平均精度最高，XGBoost模型的计算速度最快。此外，Shapley加性解释方法被用来检测不同输入特征对模型预测的影响，这些信息允许更精确地计算GII。因此，提出的机器学习过程使GII预测既简单又灵活，并提供了更有效的GII使用。

{"title":"Prediction of the gender inequality index based on data-driven interpretable ensemble learning methods","authors":"Mehmet Hakan Özdemir , Batin Latif Aylak , Celal Cakiroglu , Mahmut Bağcı","doi":"10.1016/j.seps.2025.102366","DOIUrl":"10.1016/j.seps.2025.102366","url":null,"abstract":"<div><div>Gender inequality is acknowledged as a major hindrance to human development, evident in multiple social, political, economic, and cultural aspects. Therefore, identifying the factors contributing to gender inequality and quantifying them is crucial for enhancing societal progress. A new index, the gender inequality index (GII), was introduced in the 2010 Human Development Report to quantify and compare gender inequalities among different countries. Multiple indicators are used to calculate the GII, which involves complex analytical calculations. This study utilizes these indicators as input features to predict the GII using XGBoost, CatBoost, Extra Trees, LightGBM, Ridge, and Lasso regression models. These regressors are trained for predicting the GII as a function of maternal mortality ratio, adolescent birth rate, share of seats in parliament, female population with at least some secondary education, male population with at least some secondary education, female labour force participation rate, and male labour force participation rate. It is observed that XGBoost, CatBoost, Extra Trees and LightGBM predictors have <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> score greater than 0.98, while the Ridge and Lasso regressors have <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> score less than 0.90. The highest average accuracy is obtained by the CatBoost model while the XGBoost model has the greatest computational speed. Furthermore, the Shapley additive explanations methodology is utilized to detect the impact of different input features on the model predictions, and this information allows for more precise calculation of the GII. Thus, the proposed machine learning procedure enables both simplicity and flexibility for the GII prediction and provides more effective use of the GII.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"103 ","pages":"Article 102366"},"PeriodicalIF":5.4,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145365493","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}

引用次数: 0

The role of climate risk in shaping longevity dynamics: Extending stochastic mortality models 气候风险在塑造寿命动力学中的作用：扩展随机死亡率模型

IF 5.4 2区经济学 Q1 ECONOMICS

Socio-economic Planning Sciences

Pub Date : 2025-10-20 DOI: 10.1016/j.seps.2025.102353

Imma Lory Aprea , Francesca Perla , Raffaele Clemente Petrella , Mariafortuna Pietroluongo , Salvatore Scognamiglio

This paper investigates the impact of climate risk on mortality dynamics across different populations. We introduce extensions of the Lee–Carter model that integrate climate risk data, specifically Annual Temperature Anomalies levels, to improve mortality projections. A three-stage calibration strategy, based on the Ordinary Least Squares estimator, is proposed to estimate the model parameters. Numerical experiments, conducted on a sample of 42 populations, demonstrate that incorporating climate risk information enhances forecasting accuracy. Additionally, further improvements in forecasting performance are observed when climate data is combined with economic indicators such as GDP.

本文研究了气候风险对不同种群死亡率动态的影响。我们引入了李-卡特模型的扩展，该模型整合了气候风险数据，特别是年温度异常水平，以改进死亡率预测。提出了一种基于普通最小二乘估计量的三阶段校正策略来估计模型参数。在42个人口样本中进行的数值试验表明，纳入气候风险信息可以提高预测的准确性。此外，当气候数据与GDP等经济指标相结合时，预测效果进一步改善。

引用次数: 0

Temporal analysis of the clustering and hypothesized social contagion of mass killing events in the United States 美国大规模杀戮事件聚类和假设社会传染的时间分析

IF 5.4 2区经济学 Q1 ECONOMICS

Socio-economic Planning Sciences

Pub Date : 2025-10-18 DOI: 10.1016/j.seps.2025.102349

Zachary T. Hornberger , Douglas M. King , Sheldon H. Jacobson

Mass killings (e.g., 2016 Pulse nightclub shooting, 2017 Las Vegas shooting) are tragedies that devastate the victims’ families and harm the local communities and the nation at large. Amid an increase in mass-killing research, the idea that these events may be contagious has emerged among scholars and been publicized in popular media. This paper implements a three-phase methodology to evaluate the social contagion hypothesis for US mass killings that (a) detects prominent contagion effects, (b) identifies mass killing clusters, and (c) detects subtle contagion effects. Evidence of a prominent contagion effect was not found, utilizing a periodically-observed time-homogeneous Poisson process framework. It is shown that the occurrence of family mass killings and the occurrence of felony mass killings were homogeneous and temporally random between 2006 and 2023, whereas the rate of public mass killings during this timeframe approximately doubled starting in late 2015. The occurrence of public mass killings was homogeneous and temporally random when separated at this arrival rate changepoint. Event clusters were identified and compared to Poisson bursts with respect to three attributes: number of clusters, duration, and surprise. The relationship between event notoriety and the time until the subsequent event was also evaluated. Analysis of the relationship between event notoriety and the time until the next mass killing revealed some irregularities that, while not consistent with a subtle contagion effect, invite future qualitative research investigating specific clusters for evidence of behavioral transmission. The ten highest-density clusters for each mass killing type are reported to facilitate future research.

大规模杀戮（如2016年Pulse夜总会枪击案、2017年拉斯维加斯枪击案）是摧毁受害者家庭、伤害当地社区和整个国家的悲剧。随着大规模杀戮研究的增多，这些事件可能会传染的观点在学者中出现，并在大众媒体上得到宣传。本文采用了一种三阶段方法来评估美国大规模杀戮的社会传染假说，该方法(a)检测出显著的传染效应，(b)识别出大规模杀戮集群，(c)检测出微妙的传染效应。利用周期性观察的时间均匀泊松过程框架，没有发现显著传染效应的证据。研究表明，在2006年至2023年期间，家庭大规模杀戮和重罪大规模杀戮的发生是同质的，在时间上是随机的，而从2015年底开始，这段时间内公共大规模杀戮的发生率大约翻了一番。当在这个到达率变化点分开时，公共大规模杀戮的发生是同质的，并且在时间上是随机的。事件集群被识别，并与泊松爆发在三个属性方面进行了比较：集群数量、持续时间和惊讶度。事件恶名与事件发生前的时间之间的关系也被评估。对事件恶名与下一次大规模杀戮发生时间之间关系的分析揭示了一些不规律，尽管与微妙的传染效应不一致，但未来的定性研究将对特定群体进行调查，以寻找行为传播的证据。报告了每种大规模杀戮类型的十个密度最高的集群，以促进未来的研究。

{"title":"Temporal analysis of the clustering and hypothesized social contagion of mass killing events in the United States","authors":"Zachary T. Hornberger , Douglas M. King , Sheldon H. Jacobson","doi":"10.1016/j.seps.2025.102349","DOIUrl":"10.1016/j.seps.2025.102349","url":null,"abstract":"<div><div>Mass killings (e.g., 2016 Pulse nightclub shooting, 2017 Las Vegas shooting) are tragedies that devastate the victims’ families and harm the local communities and the nation at large. Amid an increase in mass-killing research, the idea that these events may be <em>contagious</em> has emerged among scholars and been publicized in popular media. This paper implements a three-phase methodology to evaluate the social contagion hypothesis for US mass killings that (a) detects prominent contagion effects, (b) identifies mass killing clusters, and (c) detects subtle contagion effects. Evidence of a prominent contagion effect was not found, utilizing a periodically-observed time-homogeneous Poisson process framework. It is shown that the occurrence of family mass killings and the occurrence of felony mass killings were homogeneous and temporally random between 2006 and 2023, whereas the rate of public mass killings during this timeframe approximately doubled starting in late 2015. The occurrence of public mass killings was homogeneous and temporally random when separated at this arrival rate changepoint. Event clusters were identified and compared to Poisson bursts with respect to three attributes: number of clusters, duration, and surprise. The relationship between event notoriety and the time until the subsequent event was also evaluated. Analysis of the relationship between event notoriety and the time until the next mass killing revealed some irregularities that, while not consistent with a subtle contagion effect, invite future qualitative research investigating specific clusters for evidence of behavioral transmission. The ten highest-density clusters for each mass killing type are reported to facilitate future research.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"103 ","pages":"Article 102349"},"PeriodicalIF":5.4,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145517449","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}

引用次数: 0

Blockchain barriers in public organizations: A systematic literature review 公共组织中的壁垒：系统的文献综述

IF 5.4 2区经济学 Q1 ECONOMICS

Socio-economic Planning Sciences

Pub Date : 2025-10-18 DOI: 10.1016/j.seps.2025.102364

Angelo Murano, Bruna Bruno, Vincenzo Vespri

Blockchain technology has garnered attention for its potential to revolutionize public sector services by enhancing transparency, efficiency, and trust. However, adoption remains hindered by critical organizational and technical barriers, which are underexplored in current literature, particularly from an economic-organizational perspective. This paper systematically reviews recent studies to identify sector-specific challenges, including interoperability issues and a shortage of technical expertise. By addressing these barriers, the findings propose tailored strategies to facilitate effective implementation, offering practical insights for public sector stakeholders and laying a robust foundation for future research.

区块链技术因其通过提高透明度、效率和信任来彻底改变公共部门服务的潜力而受到关注。然而，采用仍然受到关键的组织和技术障碍的阻碍，这些障碍在目前的文献中没有得到充分的探讨，特别是从经济组织的角度来看。本文系统地回顾了最近的研究，以确定特定部门的挑战，包括互操作性问题和技术专长的短缺。通过解决这些障碍，研究结果提出了促进有效实施的量身定制战略，为公共部门利益相关者提供了实际见解，并为未来的研究奠定了坚实的基础。

引用次数: 0