Pub Date : 2026-02-01Epub Date: 2025-11-04DOI: 10.1016/j.seps.2025.102372
Paolo Mariani, Andrea Marletta, Piero Quatto
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.
{"title":"The conjoint use of the dynamic factor analysis and weighted forecasts: an application on inclusiveness in Europe","authors":"Paolo Mariani, Andrea Marletta, 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":"2026-02-01","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}
Pub Date : 2026-02-01Epub Date: 2025-11-19DOI: 10.1016/j.seps.2025.102389
Quan Sun, Minjie Huang
Artificial intelligence (AI) has the potential to transform productivity across industries, yet firm-level empirical evidence remains limited in emerging economies. This paper examines the impact of AI adoption on firm-level output growth and total factor productivity, using data from Chinese listed companies between 2009 and 2021. We measure AI investment through firm-level spending on software, cloud services, intellectual property, and advanced digital technologies disclosed in financial statements. This measurement approach reflects AI’s role as a general-purpose technology increasingly embedded in digital infrastructure and business processes—features that are not well captured by traditional proxies such as industrial robot usage. To address endogeneity, we employ a nonparametric production function estimation with instrumental variables, using regional variation in digital economy policy as an exogenous source. Results show that AI investment significantly boosts output, particularly in light manufacturing, chemicals, and high-tech sectors. While intermediate materials remain the primary input, AI’s contribution to aggregate output growth has steadily increased. AI adoption also enhances firms’ resilience during downturns, though the benefits are uneven—large firms gain substantially, whereas small and medium enterprises see more modest effects. Further analysis reveals short-run implementation costs that can temporarily reduce productivity, though persistent AI adoption yields divergent long-run outcomes across industries. Quantile regressions show that lower-productivity firms often realize initial gains that fade or reverse, while frontier firms enjoy sustained improvements. Finally, we identify strong positive spillovers: AI investments by nearby firms generate external productivity gains, highlighting the importance of innovation clusters. Overall, our findings position AI as a key driver of output and productivity in emerging economies, and emphasize the need for targeted, inclusive policy frameworks to support its widespread and equitable adoption.
{"title":"Firm-level evidence on AI-driven output expansion and productivity in China","authors":"Quan Sun, Minjie Huang","doi":"10.1016/j.seps.2025.102389","DOIUrl":"10.1016/j.seps.2025.102389","url":null,"abstract":"<div><div>Artificial intelligence (AI) has the potential to transform productivity across industries, yet firm-level empirical evidence remains limited in emerging economies. This paper examines the impact of AI adoption on firm-level output growth and total factor productivity, using data from Chinese listed companies between 2009 and 2021. We measure AI investment through firm-level spending on software, cloud services, intellectual property, and advanced digital technologies disclosed in financial statements. This measurement approach reflects AI’s role as a general-purpose technology increasingly embedded in digital infrastructure and business processes—features that are not well captured by traditional proxies such as industrial robot usage. To address endogeneity, we employ a nonparametric production function estimation with instrumental variables, using regional variation in digital economy policy as an exogenous source. Results show that AI investment significantly boosts output, particularly in light manufacturing, chemicals, and high-tech sectors. While intermediate materials remain the primary input, AI’s contribution to aggregate output growth has steadily increased. AI adoption also enhances firms’ resilience during downturns, though the benefits are uneven—large firms gain substantially, whereas small and medium enterprises see more modest effects. Further analysis reveals short-run implementation costs that can temporarily reduce productivity, though persistent AI adoption yields divergent long-run outcomes across industries. Quantile regressions show that lower-productivity firms often realize initial gains that fade or reverse, while frontier firms enjoy sustained improvements. Finally, we identify strong positive spillovers: AI investments by nearby firms generate external productivity gains, highlighting the importance of innovation clusters. Overall, our findings position AI as a key driver of output and productivity in emerging economies, and emphasize the need for targeted, inclusive policy frameworks to support its widespread and equitable adoption.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"103 ","pages":"Article 102389"},"PeriodicalIF":5.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614544","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}
Pub Date : 2026-02-01Epub Date: 2025-10-30DOI: 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.
{"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":"2026-02-01","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}
Pub Date : 2026-02-01Epub Date: 2025-11-14DOI: 10.1016/j.seps.2025.102375
Yanju Chen , Yan Liu , Yuhan Wang
Agricultural waste, a widely available resource, has long been plagued by issues of dispersion and inefficient collection. Its improper handling poses serious threats to human health, ecological environment, and economic development. Waste collection and recycling are among the effective measures currently being implemented in many regions to promote sustainable and green development. Waste management is a complex planning issue in an uncertain environment. Designing a more comprehensive and practical agricultural waste collection and transport (AWCT) network is crucial for effective agricultural waste management. This location–allocation–routing problem (LARP) aims to optimize the locations of collection centers (CCs) and processing centers (PCs), effectively allocate waste collected demands, and determine the optimal vehicle routes. This paper focuses on designing a robust AWCT network which can deal with the challenge of uncertainty due to incomplete distribution information. Firstly, two ambiguity sets are proposed to characterize the amount of agricultural waste collected and the operating cost of CC under partial distribution information. Then a distributionally robust optimization (DRO) model is proposed and transformed into a computable mixed-integer linear programming (MILP) form equivalently. Furthermore, a Benders decomposition (BD) algorithm is developed for solving the MILP model. Finally, this method is applied to a case in Shenyang, Liaoning Province, China, to demonstrate the effectiveness of the proposed model and algorithm. The main experimental results show that: (1) The network designed by the proposed model can withstand the influence of uncertainty in the amount of agricultural waste collected and the operating cost of CC at a relatively small robustness price; (2) Managers can adjust the parameters according to their own preferences to achieve a balance between total cost and robustness. The proposed AWCT network design model has certain application prospects. It can provide practical decision-making support for formulating targeted policies and strategies, so as to promote sustainable development and resource utilization, and offer a comprehensive framework for agricultural waste management practices.
{"title":"Designing an agricultural waste collection and transport network based on robust optimization","authors":"Yanju Chen , Yan Liu , Yuhan Wang","doi":"10.1016/j.seps.2025.102375","DOIUrl":"10.1016/j.seps.2025.102375","url":null,"abstract":"<div><div>Agricultural waste, a widely available resource, has long been plagued by issues of dispersion and inefficient collection. Its improper handling poses serious threats to human health, ecological environment, and economic development. Waste collection and recycling are among the effective measures currently being implemented in many regions to promote sustainable and green development. Waste management is a complex planning issue in an uncertain environment. Designing a more comprehensive and practical agricultural waste collection and transport (AWCT) network is crucial for effective agricultural waste management. This location–allocation–routing problem (LARP) aims to optimize the locations of collection centers (CCs) and processing centers (PCs), effectively allocate waste collected demands, and determine the optimal vehicle routes. This paper focuses on designing a robust AWCT network which can deal with the challenge of uncertainty due to incomplete distribution information. Firstly, two ambiguity sets are proposed to characterize the amount of agricultural waste collected and the operating cost of CC under partial distribution information. Then a distributionally robust optimization (DRO) model is proposed and transformed into a computable mixed-integer linear programming (MILP) form equivalently. Furthermore, a Benders decomposition (BD) algorithm is developed for solving the MILP model. Finally, this method is applied to a case in Shenyang, Liaoning Province, China, to demonstrate the effectiveness of the proposed model and algorithm. The main experimental results show that: (1) The network designed by the proposed model can withstand the influence of uncertainty in the amount of agricultural waste collected and the operating cost of CC at a relatively small robustness price; (2) Managers can adjust the parameters according to their own preferences to achieve a balance between total cost and robustness. The proposed AWCT network design model has certain application prospects. It can provide practical decision-making support for formulating targeted policies and strategies, so as to promote sustainable development and resource utilization, and offer a comprehensive framework for agricultural waste management practices.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"103 ","pages":"Article 102375"},"PeriodicalIF":5.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145569098","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}
Pub Date : 2026-02-01Epub Date: 2025-11-14DOI: 10.1016/j.seps.2025.102383
Tianyang Cai, Yusen Ye
Motivated by the logistical challenges of distributing fresh produce during prolonged, government-enforced lockdowns, we investigate a novel problem: the Post-Pandemic Pickup and Distribution Problem with Time Constraints (PP-PDPTC). In the post-pandemic phase, supply and demand stabilize, yet food distribution remains constrained by rigid time windows and institutional rules. This setting requires a careful balance of effectiveness (demand fulfilment), equity (fair distribution), and efficiency (timely delivery). We propose two models for equity-aware food allocation. The first assumes that decision-makers possess prior knowledge of acceptable inequality levels (e.g., a maximum Gini coefficient) and treats equity as a constraint; the second embeds equity directly in the objective function, enabling dynamic trade-offs. We further devise a Transfer and Deprivation strategy that reallocates surplus food from overserved areas or trims excess allocations to improve equity. Coupled with an Adaptive Large Neighborhood Search algorithm for routing, this yields a two-stage heuristic framework. We test the models on a real-world case from Shanghai's Omicron lockdown in March 2022. Results reveal key trade-offs: pursuing equity can raise food waste under oversupply, increasing fleet size chiefly boosts effectiveness, whereas expanding vehicle capacity benefits equity. The heuristic consistently surpasses commercial solvers in solution quality and runtime, offering a practical tool for post-pandemic fresh-produce distribution under institutional constraints.
{"title":"Delivering fresh produce during a lockdown: The post-pandemic pickup and distribution problem with time constraints","authors":"Tianyang Cai, Yusen Ye","doi":"10.1016/j.seps.2025.102383","DOIUrl":"10.1016/j.seps.2025.102383","url":null,"abstract":"<div><div>Motivated by the logistical challenges of distributing fresh produce during prolonged, government-enforced lockdowns, we investigate a novel problem: the Post-Pandemic Pickup and Distribution Problem with Time Constraints (PP-PDPTC). In the post-pandemic phase, supply and demand stabilize, yet food distribution remains constrained by rigid time windows and institutional rules. This setting requires a careful balance of effectiveness (demand fulfilment), equity (fair distribution), and efficiency (timely delivery). We propose two models for equity-aware food allocation. The first assumes that decision-makers possess prior knowledge of acceptable inequality levels (e.g., a maximum Gini coefficient) and treats equity as a constraint; the second embeds equity directly in the objective function, enabling dynamic trade-offs. We further devise a Transfer and Deprivation strategy that reallocates surplus food from overserved areas or trims excess allocations to improve equity. Coupled with an Adaptive Large Neighborhood Search algorithm for routing, this yields a two-stage heuristic framework. We test the models on a real-world case from Shanghai's Omicron lockdown in March 2022. Results reveal key trade-offs: pursuing equity can raise food waste under oversupply, increasing fleet size chiefly boosts effectiveness, whereas expanding vehicle capacity benefits equity. The heuristic consistently surpasses commercial solvers in solution quality and runtime, offering a practical tool for post-pandemic fresh-produce distribution under institutional constraints.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"103 ","pages":"Article 102383"},"PeriodicalIF":5.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145569094","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}
Socio-economic development (SED) remains a critical priority for policymakers aiming to foster inclusive growth and drive national progress. This study presents a comprehensive multi-criteria assessment of regional SED across 16 Indian states, focusing on the influence of innovation (INV) performance and foreign direct investment (FDI) on achieving sustainable development goals (SDGs). A new multi-criteria decision-making (MCDM) method, called Preference using Root Value based on Aggregated Normalisations (PROVAN), is introduced in this paper to enhance decision accuracy by integrating five different normalization techniques. Criteria weights are determined using an extended version of Weights by ENvelope and SLOpe (WENSLO) method, which incorporates multiple normalization strategies to improve robustness. The evaluation considers nine SED and seven INV criteria derived from secondary data sources. The causal relationships are statistically analyzed using Somer's δ test, and the model's reliability is confirmed through comparative and sensitivity analyses. Results reveal that Maharashtra emerges as the top-performing state in both SED (1.5572) and INV (1.5473), followed by Tamil Nadu and Karnataka, indicating strong performance across socio-economic and innovation indicators. The findings highlight significant inter-state disparities and confirm that states with stronger innovation capabilities tend to achieve better socio-economic outcomes. FDI is shown to positively influence sustainable economic development, reinforcing the strategic importance of attracting capital to advance SDGs. The proposed PROVAN-WENSLO framework offers a robust and adaptable tool for regional development planning and policy formulation.
{"title":"Preference using Root Value based on Aggregated Normalizations (PROVAN): A data-driven method for socio-economic and innovation assessment","authors":"Sanjib Biswas , Nibir Khawash , Prasenjit Chatterjee , Edmundas Kazimieras Zavadskas","doi":"10.1016/j.seps.2025.102343","DOIUrl":"10.1016/j.seps.2025.102343","url":null,"abstract":"<div><div>Socio-economic development (SED) remains a critical priority for policymakers aiming to foster inclusive growth and drive national progress. This study presents a comprehensive multi-criteria assessment of regional SED across 16 Indian states, focusing on the influence of innovation (INV) performance and foreign direct investment (FDI) on achieving sustainable development goals (SDGs). A new multi-criteria decision-making (MCDM) method, called Preference using Root Value based on Aggregated Normalisations (PROVAN), is introduced in this paper to enhance decision accuracy by integrating five different normalization techniques. Criteria weights are determined using an extended version of Weights by ENvelope and SLOpe (WENSLO) method, which incorporates multiple normalization strategies to improve robustness. The evaluation considers nine SED and seven INV criteria derived from secondary data sources. The causal relationships are statistically analyzed using Somer's δ test, and the model's reliability is confirmed through comparative and sensitivity analyses. Results reveal that Maharashtra emerges as the top-performing state in both SED (1.5572) and INV (1.5473), followed by Tamil Nadu and Karnataka, indicating strong performance across socio-economic and innovation indicators. The findings highlight significant inter-state disparities and confirm that states with stronger innovation capabilities tend to achieve better socio-economic outcomes. FDI is shown to positively influence sustainable economic development, reinforcing the strategic importance of attracting capital to advance SDGs. The proposed PROVAN-WENSLO framework offers a robust and adaptable tool for regional development planning and policy formulation.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"103 ","pages":"Article 102343"},"PeriodicalIF":5.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145323618","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}
Pub Date : 2026-02-01Epub Date: 2025-10-18DOI: 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.
{"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":"2026-02-01","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}
Pub Date : 2026-02-01Epub Date: 2025-10-24DOI: 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.
{"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":"2026-02-01","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}
Pub Date : 2026-02-01Epub Date: 2025-11-24DOI: 10.1016/j.seps.2025.102386
Grammatoula Papaioannou, Victor V. Podinovski
{"title":"Foreword to the Special Issue “Data envelopment analysis: Novel models and methodologies for efficiency and performance assessment of public organizations”","authors":"Grammatoula Papaioannou, Victor V. Podinovski","doi":"10.1016/j.seps.2025.102386","DOIUrl":"10.1016/j.seps.2025.102386","url":null,"abstract":"","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"103 ","pages":"Article 102386"},"PeriodicalIF":5.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736689","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}
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