Pub Date : 2026-04-01Epub Date: 2026-01-05DOI: 10.1016/j.seps.2026.102416
Xu Zhang , Zhongmin Yan , Abdul Rauf
In the wave of digital transformation, whether artificial intelligence (AI) can drive disruptive innovation in small and medium-sized enterprises (SMEs) has become an important research question. Using data on China's “Specialized, Refined, Distinctive, and Innovative” (SRDI) enterprises from 2014 to 2024, this paper measures the penetration level of AI in enterprises based on large language models (LLMs) text analysis methods, and constructs a large-scale patent text corpus to derive a disruptive innovation index. Results show that the AI adoption significantly enhances the disruptive innovation level of SRDI enterprises, and the conclusion still holds true after robustness tests. Mechanism analysis reveals that AI promotes disruptive innovation by optimizing human capital structures, increasing R&D investment, and facilitating access to policy support. The positive effect of AI on disruptive innovation is stronger for enterprises in eastern regions and high-technology sectors. This study deepens understanding of how AI drives disruptive innovation and provides implications for intelligent manufacturing development.
{"title":"Does artificial intelligence promote disruptive innovation in SRDI enterprises: Evidence from LLM-based text analysis","authors":"Xu Zhang , Zhongmin Yan , Abdul Rauf","doi":"10.1016/j.seps.2026.102416","DOIUrl":"10.1016/j.seps.2026.102416","url":null,"abstract":"<div><div>In the wave of digital transformation, whether artificial intelligence (AI) can drive disruptive innovation in small and medium-sized enterprises (SMEs) has become an important research question. Using data on China's “Specialized, Refined, Distinctive, and Innovative” (SRDI) enterprises from 2014 to 2024, this paper measures the penetration level of AI in enterprises based on large language models (LLMs) text analysis methods, and constructs a large-scale patent text corpus to derive a disruptive innovation index. Results show that the AI adoption significantly enhances the disruptive innovation level of SRDI enterprises, and the conclusion still holds true after robustness tests. Mechanism analysis reveals that AI promotes disruptive innovation by optimizing human capital structures, increasing R&D investment, and facilitating access to policy support. The positive effect of AI on disruptive innovation is stronger for enterprises in eastern regions and high-technology sectors. This study deepens understanding of how AI drives disruptive innovation and provides implications for intelligent manufacturing development.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102416"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976773","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 : 2026-04-01Epub Date: 2026-01-19DOI: 10.1016/j.seps.2026.102420
Tailong Li , Jinmeng Shi
This paper develops a theoretical model to analyze how artificial intelligence (AI) reshapes inter-industry wage inequality and how data protection influences the reshape. Moving beyond skill- and task-based models, we conceptualize production as an instruction-based process using machines, data, and labor. By introducing a novel taxonomy of personal- and enterprise-data-intensive sectors, we demonstrate that the ratio of data costs between these sectors is the primary driver of wage inequality, rather than the relative labor supply. This “data cost effect” can explain several puzzling phenomena in the labor market, including the wage divergence among similarly skilled workers and the unexpected resilience of certain low-skill services. Furthermore, we show that stringent data protection and privacy legislation naturally increases the cost of personal data, thereby suppressing wages in sectors that rely on it. Our study establishes a theoretical connection between data governance and wage inequality, offering a new framework for understanding income distribution in the era of AI.
{"title":"The era of AI: Technological change, data protection, and inter-industry wage inequality","authors":"Tailong Li , Jinmeng Shi","doi":"10.1016/j.seps.2026.102420","DOIUrl":"10.1016/j.seps.2026.102420","url":null,"abstract":"<div><div>This paper develops a theoretical model to analyze how artificial intelligence (AI) reshapes inter-industry wage inequality and how data protection influences the reshape. Moving beyond skill- and task-based models, we conceptualize production as an instruction-based process using machines, data, and labor. By introducing a novel taxonomy of personal- and enterprise-data-intensive sectors, we demonstrate that the ratio of data costs between these sectors is the primary driver of wage inequality, rather than the relative labor supply. This “data cost effect” can explain several puzzling phenomena in the labor market, including the wage divergence among similarly skilled workers and the unexpected resilience of certain low-skill services. Furthermore, we show that stringent data protection and privacy legislation naturally increases the cost of personal data, thereby suppressing wages in sectors that rely on it. Our study establishes a theoretical connection between data governance and wage inequality, offering a new framework for understanding income distribution in the era of AI.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102420"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022390","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 : 2026-04-01Epub Date: 2025-11-20DOI: 10.1016/j.seps.2025.102392
Margarida R. Santos , Sofia Kalakou , Fernando A.F. Ferreira
Urban Air Mobility (UAM) is a promising component of future mobility systems. To ensure its smooth and viable implementation, it is crucial that authorities, organizations, public services and stakeholders in general consider not only economic aspects but also environmental, safety and socio-economic factors through a holistic approach. However, the current literature primarily focuses on specific subtopics of UAM individually, failing to address the topic in an integrated and comprehensive manner. This study aims to overcome this limitation by conducting a Systematic Literature Review (SLR) on UAM, analyzing a database of 129 articles published between 2017 and 2023. Specifically, a bibliographic coupling analysis and a Multiple Correspondence Analysis (MCA) were performed. The results include a list of 150 indicators used to assess environmental, safety and socio-economic impacts of UAM, as well as the identification of four core thematic clusters: (1) UAM Technology and its Sustainability; (2) Environmental Assessment; (3) Traffic Management for the Airspace Industry; and (4) Passenger Transport and Demand Management. The findings of this research complement existing literature and contribute to the development of the field by shedding light on UAM’s key stakeholders, impacts and the indicators used to assess these impacts.
{"title":"Mapping research achievements on urban air mobility: A systematic literature review","authors":"Margarida R. Santos , Sofia Kalakou , Fernando A.F. Ferreira","doi":"10.1016/j.seps.2025.102392","DOIUrl":"10.1016/j.seps.2025.102392","url":null,"abstract":"<div><div>Urban Air Mobility (UAM) is a promising component of future mobility systems. To ensure its smooth and viable implementation, it is crucial that authorities, organizations, public services and stakeholders in general consider not only economic aspects but also environmental, safety and socio-economic factors through a holistic approach. However, the current literature primarily focuses on specific subtopics of UAM individually, failing to address the topic in an integrated and comprehensive manner. This study aims to overcome this limitation by conducting a Systematic Literature Review (SLR) on UAM, analyzing a database of 129 articles published between 2017 and 2023. Specifically, a bibliographic coupling analysis and a Multiple Correspondence Analysis (MCA) were performed. The results include a list of 150 indicators used to assess environmental, safety and socio-economic impacts of UAM, as well as the identification of four core thematic clusters: (1) <em>UAM Technology and its Sustainability</em>; (2) <em>Environmental Assessment</em>; (3) <em>Traffic Management for the Airspace Industry</em>; and (4) <em>Passenger Transport and Demand Management</em>. The findings of this research complement existing literature and contribute to the development of the field by shedding light on UAM’s key stakeholders, impacts and the indicators used to assess these impacts.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102392"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790464","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 : 2026-04-01Epub Date: 2025-12-08DOI: 10.1016/j.seps.2025.102407
Moslem Savari , Bagher Khaleghi
The deforestation phenomenon increases every year all over the world due to human and natural factors and sometimes leaves irreparable negative consequences. Therefore, the majority of countries and related researchers and policy-makers are looking for solutions to prevent further damages to them. In the meantime, in Iran, as a country with limited forest area, they are also being destroyed on a large scale due to local communities being heavily reliant on the forests for their livelihoods and the absence of sustainable resource management. In this regard, this research was aimed at discovering the factors affecting the forest conservation behavior (FCB) in northwestern Iran. Here, Health Belief Model (HBM) was employed as the research theoretical framework. The study utilized questionnaire survey method, and data analysis was conducted using structural equation modeling (SEM). The statistical population was all local people residing on the margins and inside the Arasbaran forests in northwestern Iran. The findings indicated that HBM is an efficient theory in this regard, so that its components including Perceived Susceptibility (PS), Perceived Severity (PSV), Perceived Benefit (PB), Perceived Barriers (PBR), Cue to Action (CU) and Self-Efficacy (SE) were able to explain 61 % of the FCB variance. The results of this effort, while filling the gaps in the research literature in this field, can help the relevant policy-makers and decision-makers in promoting safe behavior in the natural environment and forest sustainability.
{"title":"Perceptions and beliefs of local Iranian communities towards forest protection","authors":"Moslem Savari , Bagher Khaleghi","doi":"10.1016/j.seps.2025.102407","DOIUrl":"10.1016/j.seps.2025.102407","url":null,"abstract":"<div><div>The deforestation phenomenon increases every year all over the world due to human and natural factors and sometimes leaves irreparable negative consequences. Therefore, the majority of countries and related researchers and policy-makers are looking for solutions to prevent further damages to them. In the meantime, in Iran, as a country with limited forest area, they are also being destroyed on a large scale due to local communities being heavily reliant on the forests for their livelihoods and the absence of sustainable resource management. In this regard, this research was aimed at discovering the factors affecting the forest conservation behavior (FCB) in northwestern Iran. Here, Health Belief Model (HBM) was employed as the research theoretical framework. The study utilized questionnaire survey method, and data analysis was conducted using structural equation modeling (SEM). The statistical population was all local people residing on the margins and inside the Arasbaran forests in northwestern Iran. The findings indicated that HBM is an efficient theory in this regard, so that its components including Perceived Susceptibility (PS), Perceived Severity (PSV), Perceived Benefit (PB), Perceived Barriers (PBR), Cue to Action (CU) and Self-Efficacy (SE) were able to explain 61 % of the FCB variance. The results of this effort, while filling the gaps in the research literature in this field, can help the relevant policy-makers and decision-makers in promoting safe behavior in the natural environment and forest sustainability.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102407"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737728","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 : 2026-04-01Epub Date: 2026-01-30DOI: 10.1016/j.seps.2026.102426
Augusto César da Cunha Assumpção , Renata Albergaria de Mello Bandeira , Orivalde Soares da Silva Júnior , Yesus Emmanuel Medeiros Vieira , Letícia Caldas , Matheus Meirim , Rafael Martinelli
Emergency Water Transport (EWT) is the reactive response most commonly adopted by developing countries to cope with droughts. As droughts become more frequent, severe, and prolonged due to climate change, this operation becomes an important factor in public policies to address the problem of water shortage. However, when applied on a large scale to geographically dispersed rural populations, EWT requires the use of a significant fleet of vehicles, leading to high operating costs. Thus, it is crucial to propose solutions that guarantee the financial and technical viability of EWT. Therefore, this work proposes an algorithm to solve the water distribution problem in drought scenarios, which is modeled as an Inventory Routing Problem (IRP), to optimize the weekly vehicle routes and the inventory at each demand point for a period of one month. We explore the use of a hybrid metaheuristic of Iterated Local Search with Randomized Variable Neighborhood Descent (ILS-RVND) to search for efficient solutions. The algorithm was applied to 798 classical IRP benchmark instances and to a real case of water distribution in the semi-arid region of Brazil. Comparing our results with those from the DIMACS Challenge revealed improvements in 12 previously best-known solutions. The results for the real case are compared with the actual routes adopted in Brazilian water distribution, with solutions using routes obtained by solving the Capacitated Vehicle Routing Problem (CVRP). The proposed application reduces travel costs by up to 45.9% and improves equity in water distribution, offering a scalable solution for humanitarian logistics.
{"title":"Inventory routing for humanitarian water distribution in drought-affected regions","authors":"Augusto César da Cunha Assumpção , Renata Albergaria de Mello Bandeira , Orivalde Soares da Silva Júnior , Yesus Emmanuel Medeiros Vieira , Letícia Caldas , Matheus Meirim , Rafael Martinelli","doi":"10.1016/j.seps.2026.102426","DOIUrl":"10.1016/j.seps.2026.102426","url":null,"abstract":"<div><div>Emergency Water Transport (EWT) is the reactive response most commonly adopted by developing countries to cope with droughts. As droughts become more frequent, severe, and prolonged due to climate change, this operation becomes an important factor in public policies to address the problem of water shortage. However, when applied on a large scale to geographically dispersed rural populations, EWT requires the use of a significant fleet of vehicles, leading to high operating costs. Thus, it is crucial to propose solutions that guarantee the financial and technical viability of EWT. Therefore, this work proposes an algorithm to solve the water distribution problem in drought scenarios, which is modeled as an Inventory Routing Problem (IRP), to optimize the weekly vehicle routes and the inventory at each demand point for a period of one month. We explore the use of a hybrid metaheuristic of Iterated Local Search with Randomized Variable Neighborhood Descent (ILS-RVND) to search for efficient solutions. The algorithm was applied to 798 classical IRP benchmark instances and to a real case of water distribution in the semi-arid region of Brazil. Comparing our results with those from the DIMACS Challenge revealed improvements in 12 previously best-known solutions. The results for the real case are compared with the actual routes adopted in Brazilian water distribution, with solutions using routes obtained by solving the Capacitated Vehicle Routing Problem (CVRP). The proposed application reduces travel costs by up to 45.9% and improves equity in water distribution, offering a scalable solution for humanitarian logistics.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102426"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172637","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 : 2026-04-01Epub Date: 2026-02-03DOI: 10.1016/j.seps.2026.102434
Hung Pham , Tuan Le , Nhi Huynh , Tuan Chu , Hiep Pham , Huy Truong Quang , Son Dao
This study addresses the optimization of medical waste collection (MWC) in Ho Chi Minh City, Vietnam, where increasing waste volumes pose challenges for efficiency and sustainability. Using real-world operational data from Citenco, we formulate the problem as a capacitated vehicle routing problem (CVRP) with stochastic demand, solved through a combination of constraint programming and chance-constrained programming. The proposed model reduces total travel distance by 22%, travel time by 10%, and increases vehicle load utilization by 6%, while lowering the number of daily trips to treatment facilities. Sensitivity analysis confirms robustness under varying service levels and expanded coverage. These results provide evidence-based insights for policymakers and public waste management agencies, supporting sustainable decision-making in urban medical waste collection.
{"title":"Optimizing medical waste collection in urban systems: A constraint programming approach for sustainable public sector decision-making","authors":"Hung Pham , Tuan Le , Nhi Huynh , Tuan Chu , Hiep Pham , Huy Truong Quang , Son Dao","doi":"10.1016/j.seps.2026.102434","DOIUrl":"10.1016/j.seps.2026.102434","url":null,"abstract":"<div><div>This study addresses the optimization of medical waste collection (MWC) in Ho Chi Minh City, Vietnam, where increasing waste volumes pose challenges for efficiency and sustainability. Using real-world operational data from Citenco, we formulate the problem as a capacitated vehicle routing problem (CVRP) with stochastic demand, solved through a combination of constraint programming and chance-constrained programming. The proposed model reduces total travel distance by 22%, travel time by 10%, and increases vehicle load utilization by 6%, while lowering the number of daily trips to treatment facilities. Sensitivity analysis confirms robustness under varying service levels and expanded coverage. These results provide evidence-based insights for policymakers and public waste management agencies, supporting sustainable decision-making in urban medical waste collection.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102434"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172640","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 : 2026-04-01Epub Date: 2026-01-23DOI: 10.1016/j.seps.2026.102425
Jia-hao Wu, Yuhuan Zhao, Jingzhi Zhu
Rapid improvements in urban energy efficiency () are essential for achieving climate and sustainable development goals, yet the roles of artificial intelligence (AI) and green finance in this process remain insufficiently understood. This study develops a theoretical model that links AI to through technological innovation and industrial structure adjustment, and examines the role of green finance. Then, using panel data for 282 Chinese cities from 2012 to 2023, we conduct an empirical analysis to tests the theoretical framework. The main findings are as follows. (1) AI significantly improves and this finding holds following a series of robustness and endogeneity tests. The positive effect is not universal but is primarily observed in the cities with greater location, industry conditions, and government attention. (2) Green technological innovation as well as the rationalization and advancement industrial structure are key channels through which AI improves . (3) Green finance amplifies the benefits of AI by easing financing constraints, and exhibits a nonlinear threshold effect whereby the marginal contribution of AI to increases once green finance exceeds a critical level. (4) Further analysis reveals that AI exhibits positive spatial spillovers, does not induce an energy rebound effect, and reduces urban carbon emission intensity. We also found that human-machine collaboration plays a crucial role on . This study provides theoretical and empirical evidence for policymakers to develop AI and energy strategies in city level.
{"title":"Artificial intelligence, green finance and urban energy efficiency: Evidence from Chinese 282 cities","authors":"Jia-hao Wu, Yuhuan Zhao, Jingzhi Zhu","doi":"10.1016/j.seps.2026.102425","DOIUrl":"10.1016/j.seps.2026.102425","url":null,"abstract":"<div><div>Rapid improvements in urban energy efficiency (<span><math><mrow><mi>U</mi><mi>E</mi><mi>E</mi></mrow></math></span>) are essential for achieving climate and sustainable development goals, yet the roles of artificial intelligence (AI) and green finance in this process remain insufficiently understood. This study develops a theoretical model that links AI to <span><math><mrow><mi>U</mi><mi>E</mi><mi>E</mi></mrow></math></span> through technological innovation and industrial structure adjustment, and examines the role of green finance. Then, using panel data for 282 Chinese cities from 2012 to 2023, we conduct an empirical analysis to tests the theoretical framework. The main findings are as follows. (1) AI significantly improves <span><math><mrow><mi>U</mi><mi>E</mi><mi>E</mi></mrow></math></span> and this finding holds following a series of robustness and endogeneity tests. The positive effect is not universal but is primarily observed in the cities with greater location, industry conditions, and government attention. (2) Green technological innovation as well as the rationalization and advancement industrial structure are key channels through which AI improves <span><math><mrow><mi>U</mi><mi>E</mi><mi>E</mi></mrow></math></span>. (3) Green finance amplifies the benefits of AI by easing financing constraints, and exhibits a nonlinear threshold effect whereby the marginal contribution of AI to <span><math><mrow><mi>U</mi><mi>E</mi><mi>E</mi></mrow></math></span> increases once green finance exceeds a critical level. (4) Further analysis reveals that AI exhibits positive spatial spillovers, does not induce an energy rebound effect, and reduces urban carbon emission intensity. We also found that human-machine collaboration plays a crucial role on <span><math><mrow><mi>U</mi><mi>E</mi><mi>E</mi></mrow></math></span>. This study provides theoretical and empirical evidence for policymakers to develop AI and energy strategies in city level.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102425"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078050","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: 2025-11-20DOI: 10.1016/j.seps.2025.102391
Jingwen Li , Xiang Zhang , Fengxin Dai , Liang Tang , Sitong Liu
Emergency resource location decision is strategic due to its high cost and long-lasting implications. There exist multiple potential uncertainties in disaster events that could lead to the current optimal location decision becoming suboptimal in the future, so it is crucial to consider possible emergency resource allocation as recourse decisions. This paper addresses the emergency resource location and allocation issue under uncertain demand, uncertain transportation cost, and disruption risk and proposes a two-stage robust framework. Especially, we categorize emergency resources into personnel and materials and consider their coupling relationship. The two-stage robust framework is reformulated utilizing duality, Karush-Kuhn-Tucker condition, and linearization methods. In addition, we develop an improved column-and-constraint generation algorithm to solve the proposed models. The experiments illustrate that the developed two-stage framework surpasses the established single-stage robust framework, and the presented improved column-and-constraint generation algorithm exhibits superior performance in comparison to the benders-dual cutting plane algorithm. Furthermore, the results reveal that increased fixed costs of opening supply points and uncertainty levels lead to higher total costs and computational time for the proposed seven models considering combinations of different uncertainties, with disruption risk significantly impacting model performance. To enhance resilience, it is recommended that emergency logistics decision-makers prioritize investments in transportation infrastructure and storage capacities at key supply points while adjusting uncertain budget parameters and disturbance ratios to optimize location and resource allocation, ensuring timely delivery of essential resources to disaster-affected areas.
{"title":"Robust optimization of emergency resource location and coupling allocation considering multiple uncertainties","authors":"Jingwen Li , Xiang Zhang , Fengxin Dai , Liang Tang , Sitong Liu","doi":"10.1016/j.seps.2025.102391","DOIUrl":"10.1016/j.seps.2025.102391","url":null,"abstract":"<div><div>Emergency resource location decision is strategic due to its high cost and long-lasting implications. There exist multiple potential uncertainties in disaster events that could lead to the current optimal location decision becoming suboptimal in the future, so it is crucial to consider possible emergency resource allocation as recourse decisions. This paper addresses the emergency resource location and allocation issue under uncertain demand, uncertain transportation cost, and disruption risk and proposes a two-stage robust framework. Especially, we categorize emergency resources into personnel and materials and consider their coupling relationship. The two-stage robust framework is reformulated utilizing duality, Karush-Kuhn-Tucker condition, and linearization methods. In addition, we develop an improved column-and-constraint generation algorithm to solve the proposed models. The experiments illustrate that the developed two-stage framework surpasses the established single-stage robust framework, and the presented improved column-and-constraint generation algorithm exhibits superior performance in comparison to the benders-dual cutting plane algorithm. Furthermore, the results reveal that increased fixed costs of opening supply points and uncertainty levels lead to higher total costs and computational time for the proposed seven models considering combinations of different uncertainties, with disruption risk significantly impacting model performance. To enhance resilience, it is recommended that emergency logistics decision-makers prioritize investments in transportation infrastructure and storage capacities at key supply points while adjusting uncertain budget parameters and disturbance ratios to optimize location and resource allocation, ensuring timely delivery of essential resources to disaster-affected areas.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"103 ","pages":"Article 102391"},"PeriodicalIF":5.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614546","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}
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.
{"title":"The role of climate risk in shaping longevity dynamics: Extending stochastic mortality models","authors":"Imma Lory Aprea , Francesca Perla , Raffaele Clemente Petrella , Mariafortuna Pietroluongo , 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":"2026-02-01","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}
Pub Date : 2026-02-01Epub Date: 2025-11-12DOI: 10.1016/j.seps.2025.102380
Yanchao Feng , Tong Yan , Jia Guo
In the digital era, artificial intelligence (AI) is reshaping economies and energy systems. This paper assesses the global effect of AI on energy resilience (ER), emphasizing the moderating role of financial inclusion (FI). Using panel data from 64 countries over the period from 2000 to 2019, this study employs the IV-GMM, difference-in-differences, and panel quantile regression models to ensure robust results. Findings show that a 1 % increase in AI contributes to roughly a 0.04 %–0.13 % increase in ER, and that FI strengthens this effect. Mediation analysis reveals that per capita GDP, technological progress, and human capital mediate the artificial intelligence-energy resilience relationship. Heterogeneity analysis indicates that while AI improves ER in low-resilience contexts, it may reduce it in high-income countries, and has no significant effect in middle-income ones. These results underline the importance of tailoring AI and FI strategies to national contexts. Policymakers should focus on advancing AI-enabled energy management and expanding access to inclusive finance services to build more resilient energy systems worldwide.
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