Socio-economic Planning Sciences最新文献

{"title":"Quantitative assessment of the synergistic effect of pollution reduction and carbon reduction in the planting industry from the perspective of marginal abatement costs - Evidence from the main producing areas in the Yangtze River Basin of China","authors":"Yangyang Zhu ,&nbsp;Xicong Wang ,&nbsp;Zhenhong Qi","doi":"10.1016/j.seps.2025.102376","DOIUrl":"10.1016/j.seps.2025.102376","url":null,"abstract":"<div><div>Promoting the synergistic effects of pollution reduction and carbon reduction (PRCR) in the planting industry is a key path to achieving green, low-carbon, and high-quality agricultural development. Although existing studies have focused on PRCR synergistic effects, few have evaluated it from the perspective of marginal abatement cost, and there is currently no research analyzing the driving factors of PRCR synergistic effects specifically in the planting industry. This study uses the non-radial directional distance function to quantitatively assess the PRCR synergistic effects in the main agricultural production areas of the Yangtze River Basin (YRB) from 2000 to 2023, systematically analyzing its temporal and spatial evolution, regional differences, dynamic development characteristics, driving factors, and the paths for synergistic emission reduction. The study finds that: ①Carbon emissions (PCE) and surface source pollution (PSP) show an inverted “U” shape over time, with PRCR synergistic effects fluctuating upward, and the carbon reduction effect being dominant. ②PRCR synergistic effects exhibit spatial heterogeneity, with high values concentrated in the upstream areas and gradually extending to the midstream, while the midstream remains at a low to medium-high level and the downstream remains low over the long term. The marginal abatement cost of joint emission reduction is the highest in the upstream areas. ③Regional differences show a fluctuating convergence trend, with differences mainly attributed to the contribution of hypervariable density, and a clear catch-up effect between regions. Strengthening PSP control in the downstream areas could accelerate convergence. ④PRCR synergistic effects only show stable aggregation at both high and low levels. The PRCR synergistic effects at various levels are relatively stable, especially the carbon reduction effect. ⑤Optimizing planting structures, promoting agricultural technological progress, increasing financial support for agriculture, and advancing urbanization all contribute to enhancing PRCR synergistic effects. The main agricultural production areas of the YRB (especially the upstream) are better suited to use carbon reduction in the planting industry as the main strategy to achieve higher levels of PRCR synergistic effects, while the midstream regions should balance food security, carbon reduction, and surface source pollution control.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"103 ","pages":"Article 102376"},"PeriodicalIF":5.4,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145517448","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":"The conjoint use of the dynamic factor analysis and weighted forecasts: an application on inclusiveness in Europe","authors":"Paolo Mariani,&nbsp;Andrea Marletta,&nbsp;Piero Quatto","doi":"10.1016/j.seps.2025.102372","DOIUrl":"10.1016/j.seps.2025.102372","url":null,"abstract":"<div><div>The study of short- and medium-term forecasts has been the subject of numerous contributions from both a methodological and an applicative point of view. The augmented complexity in the representation of phenomena increasingly suggests the joint use of multiple indicators through multivariate techniques for reducing the size of variables. This contribution proposes a combined use of well-known methods of dynamic factor analysis together with a new forecasting approach in order to obtain future forecasts. This technique is particularly efficient in the case of short time series and is based on a different weighting of the most recent observations, exploiting the concept of velocity and acceleration. In particular, from an application point of view, the object of the study is inclusiveness in Europe, understood as the relationship between macroeconomic variables and employment rates obtained from the labor force survey. The proposed method also provided forecast intervals in order to visualize a measure of forecast error.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"103 ","pages":"Article 102372"},"PeriodicalIF":5.4,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145517450","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":"Research on the operational strategies of data products under China's data element policy: Subsidizing service providers or manufacturers?","authors":"Yazhou Liu,&nbsp;Wenxiu Hu,&nbsp;Li Liu,&nbsp;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&amp;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}

{"title":"Introducing a novel AI-based text mining method illustrated through an analysis of German innovation proposals","authors":"Lütje Lange,&nbsp;Alexander Griffiths,&nbsp;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}

{"title":"Impact of rural Return Entrepreneurship pilot policies on agricultural carbon emissions in China's Yangtze river Economic Belt","authors":"Hanxiang Luo ,&nbsp;Zhaoyang Xiang ,&nbsp;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}

{"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 ,&nbsp;Carlos Labarcés-Ballestas ,&nbsp;Marival Segarra-Oña ,&nbsp;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}

{"title":"From investment to impact: Exploring socio-economic prospect of hydrogen investment in Tees Valley, UK","authors":"Vahid Ghorbani Pashakolaie ,&nbsp;Bjarnhedinn Gudlaugsson ,&nbsp;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}

{"title":"How to enhance students’ well-being in Spain: A multidimensional study based on multiobjective optimization","authors":"Andrea Orozco-Villodres ,&nbsp;Ana B. Ruiz ,&nbsp;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}

{"title":"Prediction of the gender inequality index based on data-driven interpretable ensemble learning methods","authors":"Mehmet Hakan Özdemir ,&nbsp;Batin Latif Aylak ,&nbsp;Celal Cakiroglu ,&nbsp;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}

{"title":"The role of climate risk in shaping longevity dynamics: Extending stochastic mortality models","authors":"Imma Lory Aprea ,&nbsp;Francesca Perla ,&nbsp;Raffaele Clemente Petrella ,&nbsp;Mariafortuna Pietroluongo ,&nbsp;Salvatore Scognamiglio","doi":"10.1016/j.seps.2025.102353","DOIUrl":"10.1016/j.seps.2025.102353","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"103 ","pages":"Article 102353"},"PeriodicalIF":5.4,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145365550","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}