Pub Date : 2026-06-01Epub Date: 2026-01-28DOI: 10.1016/j.seps.2026.102427
Di Su, Guogang Wang
This study addresses the need for systematic evaluation of rural industry development quality (RIDQ) in China’s rural revitalization strategy. Drawing on systems theory and value theory, we develop a conceptual framework defining RIDQ as the degree to which objective characteristics meet societal requirements, and construct a “three dimensions, seven categories, and sixteen indicators (3D7C16I)” evaluation system. Using multiple weighting methods (AHP-EWM, ridge regression, machine learning), 1967 county-level units in 2013, 2017, and 2022 are analyzed.
Findings: (1) RIDQ shows ”high in the east, low in the west” gradient with strong spatial autocorrelation. (2) Temporally, RIDQ grows rapidly first then differentiates. (3) High/low-level regions are stable, while middle-tier regions fluctuate. (4) Neighbor environments create poverty traps (low-level), gradual optimization (medium-level), or siphoning effects (high-level). These provide empirical basis for differentiated rural revitalization policies.
{"title":"The quality of rural industry development: Conceptual connotation, logical construction and measurement evaluation","authors":"Di Su, Guogang Wang","doi":"10.1016/j.seps.2026.102427","DOIUrl":"10.1016/j.seps.2026.102427","url":null,"abstract":"<div><div>This study addresses the need for systematic evaluation of rural industry development quality (RIDQ) in China’s rural revitalization strategy. Drawing on systems theory and value theory, we develop a conceptual framework defining RIDQ as the degree to which objective characteristics meet societal requirements, and construct a “three dimensions, seven categories, and sixteen indicators (3D7C16I)” evaluation system. Using multiple weighting methods (AHP-EWM, ridge regression, machine learning), 1967 county-level units in 2013, 2017, and 2022 are analyzed.</div><div>Findings: (1) RIDQ shows ”high in the east, low in the west” gradient with strong spatial autocorrelation. (2) Temporally, RIDQ grows rapidly first then differentiates. (3) High/low-level regions are stable, while middle-tier regions fluctuate. (4) Neighbor environments create poverty traps (low-level), gradual optimization (medium-level), or siphoning effects (high-level). These provide empirical basis for differentiated rural revitalization policies.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"105 ","pages":"Article 102427"},"PeriodicalIF":5.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Agricultural carbon emission efficiency (ACEE) is crucial for advancing global carbon neutrality goals. However, existing research at the national level often overlooks the function of agricultural carbon sinks and exhibits deficiencies in analyzing the driving mechanisms of ACEE and making precise predictions. To address this, this paper constructs a more comprehensive ACEE measurement system and introduces machine learning techniques to thoroughly analyze the spatio-temporal dynamics, driving factors, and future trends of global ACEE. Firstly, by incorporating agricultural carbon sinks as an ecological output, this study develops an ACEE measurement system covering 162 countries, overcoming the limitations of previous studies that were often confined to regional levels or neglected carbon sinks. Measurements based on the global super-efficiency Epsilon-Based Measure model reveal that from 1995 to 2021, ACEE generally increased across countries, but spatial differentiation intensified, exhibiting a significant Matthew effect. Secondly, this study combines interpretable machine learning and geographically and temporally weighted regression to unveil the driving mechanisms of ACEE from socio-economic, agricultural, and climatic dimensions. Agricultural production level is the primary driver for enhancing ACEE, and economic development level also demonstrates a significant promoting role. However, rainfall intensity and agrochemical use intensity are the main inhibiting factors. Urbanization level, industrial structure, and agricultural trade openness negatively affect ACEE in most countries, while the positive effects of technological progress have been diminishing annually. Finally, to enhance prediction accuracy, this study employs an optimized backpropagation neural network model to predict ACEE for different country groups from 2025 to 2035. The ACEE gap between high- and low-level country groups is projected to further widen, and the global divergence trend will become more pronounced.
{"title":"Global agricultural carbon emission efficiency: Using machine learning techniques to reveal driving factors and forecast future trends","authors":"Wei Wang , Xiaodong Pei , Hongtao Jiang , Mumah Edwin , Yangfen Chen","doi":"10.1016/j.seps.2026.102428","DOIUrl":"10.1016/j.seps.2026.102428","url":null,"abstract":"<div><div>Agricultural carbon emission efficiency (ACEE) is crucial for advancing global carbon neutrality goals. However, existing research at the national level often overlooks the function of agricultural carbon sinks and exhibits deficiencies in analyzing the driving mechanisms of ACEE and making precise predictions. To address this, this paper constructs a more comprehensive ACEE measurement system and introduces machine learning techniques to thoroughly analyze the spatio-temporal dynamics, driving factors, and future trends of global ACEE. Firstly, by incorporating agricultural carbon sinks as an ecological output, this study develops an ACEE measurement system covering 162 countries, overcoming the limitations of previous studies that were often confined to regional levels or neglected carbon sinks. Measurements based on the global super-efficiency Epsilon-Based Measure model reveal that from 1995 to 2021, ACEE generally increased across countries, but spatial differentiation intensified, exhibiting a significant Matthew effect. Secondly, this study combines interpretable machine learning and geographically and temporally weighted regression to unveil the driving mechanisms of ACEE from socio-economic, agricultural, and climatic dimensions. Agricultural production level is the primary driver for enhancing ACEE, and economic development level also demonstrates a significant promoting role. However, rainfall intensity and agrochemical use intensity are the main inhibiting factors. Urbanization level, industrial structure, and agricultural trade openness negatively affect ACEE in most countries, while the positive effects of technological progress have been diminishing annually. Finally, to enhance prediction accuracy, this study employs an optimized backpropagation neural network model to predict ACEE for different country groups from 2025 to 2035. The ACEE gap between high- and low-level country groups is projected to further widen, and the global divergence trend will become more pronounced.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"105 ","pages":"Article 102428"},"PeriodicalIF":5.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-06-01Epub Date: 2026-01-29DOI: 10.1016/j.seps.2026.102429
Marianna Siino, Stefano Iezzi, Mario Gara
This paper leverages open data from Italy's Central Anti-Corruption Authority (Autorità Nazionale Anticorruzione, ANAC) and relevant literature to propose a multi-layered system of risk indicators for detecting potential corruptive conducts in public procurement. The development and use of such indicators are widespread among national and international organisations as public procurement is particularly vulnerable to corruption. This vulnerability is particularly critical in Italy, where corruption is reportedly connected to criminal infiltration. Moreover, the European institutions are currently disbursing to Italy's government an unprecedented amount of funds for infrastructures and structural reforms, which makes Italian procurement all the more attractive to criminals. The relevant literature suggests a wide array of indicators and red flags, each tackling a specific vulnerability in procurement procedures liable to be exploited for illicit ends. This paper offers a system of auction-specific individual indicators offering a wide-ranging view on all such vulnerabilities, and at the same time taking into account the actual availability of data. Due focus is drawn on missing information, held as an indicator of opaqueness in itself. Based on these indicators, we compute a composite risk measure at the auction level and, by further aggregation, develop an indicator at the level of contracting authorities. A significant contribution of this work is the use of confidential data from Italy's Financial Intelligence Unit (Unità di Informazione Finanziaria per l’Italia, UIF) on firms potentially linked to organised crime to validate these indicators, providing evidence of their effectiveness. The potential applications of these indicators include monitoring public tenders, risk-ranking of awarding authorities and contractors, prioritising investigative and anti-money laundering activities.
本文利用意大利中央反腐败局（autorit Nazionale Anticorruzione， ANAC）的公开数据和相关文献，提出了一个多层次的风险指标体系，用于发现公共采购中潜在的腐败行为。由于公共采购特别容易受到腐败的影响，这些指标的开发和使用在国家和国际组织中普遍存在。这种脆弱性在意大利尤为严重，据报道，腐败与犯罪渗透有关。此外，欧洲机构目前正在向意大利政府支付前所未有的资金，用于基础设施和结构改革，这使得意大利采购对犯罪分子更具吸引力。相关文献提出了一系列广泛的指标和危险信号，每一项都针对采购程序中可能被用于非法目的的特定脆弱性。本文提供了一个拍卖特定的个人指标系统，提供了对所有此类漏洞的广泛看法，同时考虑到数据的实际可用性。对缺失的信息给予了应有的关注，这本身就是不透明的一个指标。基于这些指标，我们在拍卖层面计算了一个综合风险度量，并通过进一步汇总，在签约当局层面开发了一个指标。这项工作的一个重要贡献是使用意大利金融情报股（unitedi Informazione Finanziaria per l 'Italia， UIF）关于可能与有组织犯罪有关的公司的机密数据来验证这些指标，为其有效性提供证据。这些指标的潜在应用包括监督公开招标、对授予当局和承包商进行风险排名、确定调查和反洗钱活动的优先次序。
{"title":"Corruption risk indicators in public procurement: Definition and evaluation with organised crime data","authors":"Marianna Siino, Stefano Iezzi, Mario Gara","doi":"10.1016/j.seps.2026.102429","DOIUrl":"10.1016/j.seps.2026.102429","url":null,"abstract":"<div><div>This paper leverages open data from Italy's Central Anti-Corruption Authority (Autorità Nazionale Anticorruzione, ANAC) and relevant literature to propose a multi-layered system of risk indicators for detecting potential corruptive conducts in public procurement. The development and use of such indicators are widespread among national and international organisations as public procurement is particularly vulnerable to corruption. This vulnerability is particularly critical in Italy, where corruption is reportedly connected to criminal infiltration. Moreover, the European institutions are currently disbursing to Italy's government an unprecedented amount of funds for infrastructures and structural reforms, which makes Italian procurement all the more attractive to criminals. The relevant literature suggests a wide array of indicators and red flags, each tackling a specific vulnerability in procurement procedures liable to be exploited for illicit ends. This paper offers a system of auction-specific individual indicators offering a wide-ranging view on all such vulnerabilities, and at the same time taking into account the actual availability of data. Due focus is drawn on missing information, held as an indicator of opaqueness in itself. Based on these indicators, we compute a composite risk measure at the auction level and, by further aggregation, develop an indicator at the level of contracting authorities. A significant contribution of this work is the use of confidential data from Italy's Financial Intelligence Unit (Unità di Informazione Finanziaria per l’Italia, UIF) on firms potentially linked to organised crime to validate these indicators, providing evidence of their effectiveness. The potential applications of these indicators include monitoring public tenders, risk-ranking of awarding authorities and contractors, prioritising investigative and anti-money laundering activities.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"105 ","pages":"Article 102429"},"PeriodicalIF":5.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-06-01Epub Date: 2026-02-11DOI: 10.1016/j.seps.2026.102448
Xiao Xu , Tao Pang , Hongjun Peng , Wenting Sun
In this paper, we study a forestry operator facing financial constraints and a financial institution that provides pledge financing, using the expected revenue rights from forestry carbon sinks and the management rights of transferred forestland as collateral. Output uncertainty in forestry carbon sinks and government subsidies are explicitly considered. The problem is formulated as a Stackelberg game. We derive the forestry operator’s optimal decisions on forest quality and the financial institution’s optimal pledge rate, and investigate the effects of key factors such as the probability of deforestation disasters and the carbon sink price. Numerical analyses are also presented. The main findings are as follows. First, infrequent deforestation disasters do not affect forest quality or the pledge rate, whereas frequent disasters reduce incentives for forest management and increase financing difficulty. Second, expanding the scale of forest management increases profits, but may lead to lower forest quality and a reduced pledge rate. Third, an increase in the economic value of transferred forestland management rights does not affect forest quality but reduces forestry profits. Moreover, the impact of this economic value on the pledge rate depends on the carbon sink price. Finally, when the probability of deforestation disasters is relatively low or very high, pledge financing can improve forest quality. In addition, under certain conditions, pledge financing can also enhance the forestry operator’s profits.
{"title":"Can forestry carbon sink pledge financing improve the quality of forest management?","authors":"Xiao Xu , Tao Pang , Hongjun Peng , Wenting Sun","doi":"10.1016/j.seps.2026.102448","DOIUrl":"10.1016/j.seps.2026.102448","url":null,"abstract":"<div><div>In this paper, we study a forestry operator facing financial constraints and a financial institution that provides pledge financing, using the expected revenue rights from forestry carbon sinks and the management rights of transferred forestland as collateral. Output uncertainty in forestry carbon sinks and government subsidies are explicitly considered. The problem is formulated as a Stackelberg game. We derive the forestry operator’s optimal decisions on forest quality and the financial institution’s optimal pledge rate, and investigate the effects of key factors such as the probability of deforestation disasters and the carbon sink price. Numerical analyses are also presented. The main findings are as follows. First, infrequent deforestation disasters do not affect forest quality or the pledge rate, whereas frequent disasters reduce incentives for forest management and increase financing difficulty. Second, expanding the scale of forest management increases profits, but may lead to lower forest quality and a reduced pledge rate. Third, an increase in the economic value of transferred forestland management rights does not affect forest quality but reduces forestry profits. Moreover, the impact of this economic value on the pledge rate depends on the carbon sink price. Finally, when the probability of deforestation disasters is relatively low or very high, pledge financing can improve forest quality. In addition, under certain conditions, pledge financing can also enhance the forestry operator’s profits.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"105 ","pages":"Article 102448"},"PeriodicalIF":5.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-06-01Epub Date: 2026-02-03DOI: 10.1016/j.seps.2026.102424
Yanyue (Lillian) Ding, Jonathan F. Bard
This paper presents a new mixed-integer linear programming model for managing the size and composition of a workforce that provides home healthcare services. Decisions center around hiring, training, and downgrading in the face of high resignation rates and a fluctuating imbalance between supply and demand. Novel features of the model include a workforce that is characterized by hierarchical skills and various levels of experience, both affecting individual productivity and operational costs. The optimization problem is to determine a weekly hiring, training, and downgrading plan over the long-term to minimize the weighted sum of costs. Constraints include meeting demand, assuring that patients can be assigned the most appropriate caregivers, and maintaining a target level of skills and experience among the staff. Complications concern an annual turnover rate that exceeds 60% as well as uncertain demand. To validate the model, extensive tests were conducted using data provided by a U.S. home health agency. The results show that optimal solutions can be obtained in a few minutes or less for most instances, depending on the number of patients and caregivers. A major insight gained from the study is that it is possible to derive hiring rules that are simple to implement and closely match optimal plans.
{"title":"Long-term workforce planning for home healthcare1","authors":"Yanyue (Lillian) Ding, Jonathan F. Bard","doi":"10.1016/j.seps.2026.102424","DOIUrl":"10.1016/j.seps.2026.102424","url":null,"abstract":"<div><div>This paper presents a new mixed-integer linear programming model for managing the size and composition of a workforce that provides home healthcare services. Decisions center around hiring, training, and downgrading in the face of high resignation rates and a fluctuating imbalance between supply and demand. Novel features of the model include a workforce that is characterized by hierarchical skills and various levels of experience, both affecting individual productivity and operational costs. The optimization problem is to determine a weekly hiring, training, and downgrading plan over the long-term to minimize the weighted sum of costs. Constraints include meeting demand, assuring that patients can be assigned the most appropriate caregivers, and maintaining a target level of skills and experience among the staff. Complications concern an annual turnover rate that exceeds 60% as well as uncertain demand. To validate the model, extensive tests were conducted using data provided by a U.S. home health agency. The results show that optimal solutions can be obtained in a few minutes or less for most instances, depending on the number of patients and caregivers. A major insight gained from the study is that it is possible to derive hiring rules that are simple to implement and closely match optimal plans.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"105 ","pages":"Article 102424"},"PeriodicalIF":5.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122550","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: 2026-01-04DOI: 10.1016/j.seps.2025.102413
Yanmei Wang , Enhui Sun , Wenying Yan
As climate change intensifies and ocean resource exploitation continues, the marine environment has gained increasing societal attention. Marine environmental monitoring technologies are crucial for ocean conservation. Collaborative innovation among interdisciplinary organizations is pivotal to technological advancement. However, the mechanisms underlying marine organizational collaborative innovation remain underexplored. This study constructs a collaborative innovation network using Chinese joint patent application data related to marine environmental monitoring buoy technologies. By employing visualization tools, we trace the evolutionary paths of the network and apply the Temporal Exponential Random Graph Model (TERGM) to examine the relationships between key factors and the network's formation and evolution. The findings underscore the roles of endogenous structures, node attributes, external conditions, and time dependence on network formation and evolution. The study also reveals the growing tendency for organizations to collaborate with those possessing similar technological knowledge structures. Identifying these key factors enables environmental advocates and policymakers to tailor strategies effectively in support of marine sustainable development.
{"title":"Marine organizational collaborative network: Enhancing technological innovation for environmental monitoring","authors":"Yanmei Wang , Enhui Sun , Wenying Yan","doi":"10.1016/j.seps.2025.102413","DOIUrl":"10.1016/j.seps.2025.102413","url":null,"abstract":"<div><div>As climate change intensifies and ocean resource exploitation continues, the marine environment has gained increasing societal attention. Marine environmental monitoring technologies are crucial for ocean conservation. Collaborative innovation among interdisciplinary organizations is pivotal to technological advancement. However, the mechanisms underlying marine organizational collaborative innovation remain underexplored. This study constructs a collaborative innovation network using Chinese joint patent application data related to marine environmental monitoring buoy technologies. By employing visualization tools, we trace the evolutionary paths of the network and apply the Temporal Exponential Random Graph Model (TERGM) to examine the relationships between key factors and the network's formation and evolution. The findings underscore the roles of endogenous structures, node attributes, external conditions, and time dependence on network formation and evolution. The study also reveals the growing tendency for organizations to collaborate with those possessing similar technological knowledge structures. Identifying these key factors enables environmental advocates and policymakers to tailor strategies effectively in support of marine sustainable development.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102413"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925790","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: 2026-01-29DOI: 10.1016/j.seps.2026.102422
Mohammad Ali Hassanabadi , Ata Allah Taleizadeh
The increasing adoption of blockchain technology (BT) in supply chain management has led to the emergence of co-opetition strategies among competing firms, aimed at enhancing the efficiency of consortium blockchain-based platforms (BBPs). We develop an evolutionary game theoretical model with two competing manufacturers and customers to analyze long-term interactions and firms’ adherence to co-opetition strategies, incorporating the dual impact of BT on supply chain circularity and resilience. By considering the effects of BBP traceability and transparency on enhancing customer trust, operational efficiency of take-back systems, and mitigating disruption costs, the conditions necessary for fully realizing the long-term operational, economic, and environmental benefits of BBPs are investigated. Additionally, we introduce a trust mechanism to evaluate both performance and relationship management in long-term interactions. Our model analysis reveals key conditions under which competitive firms are more inclined to engage in co-opetition: (i) in the presence of severe disruptions or strong control over disruptive events enabled by BBP efficiency enhancements, (ii) when BBP efficiency improvements lead to a more optimized take-back system, (iii) when customers highly value the traceability and transparency provided by BBPs, and (iv) under high trust coefficients or significant punitive costs within the trust mechanism. Furthermore, our findings suggest that the trust mechanism can drive the supply chain toward greater circularity, particularly when customers are sensitive to transparency and traceability features. A numerical example illustrates these insights, and the study provides managerial implications that lay the groundwork for the practical implementation of blockchain-driven co-opetition strategies in competitive supply chains.
{"title":"Blockchain-driven co-opetition in circular and resilient supply chains: A long-term evaluation perspective using trust mechanism","authors":"Mohammad Ali Hassanabadi , Ata Allah Taleizadeh","doi":"10.1016/j.seps.2026.102422","DOIUrl":"10.1016/j.seps.2026.102422","url":null,"abstract":"<div><div>The increasing adoption of blockchain technology (BT) in supply chain management has led to the emergence of co-opetition strategies among competing firms, aimed at enhancing the efficiency of consortium blockchain-based platforms (BBPs). We develop an evolutionary game theoretical model with two competing manufacturers and customers to analyze long-term interactions and firms’ adherence to co-opetition strategies, incorporating the dual impact of BT on supply chain circularity and resilience. By considering the effects of BBP traceability and transparency on enhancing customer trust, operational efficiency of take-back systems, and mitigating disruption costs, the conditions necessary for fully realizing the long-term operational, economic, and environmental benefits of BBPs are investigated. Additionally, we introduce a trust mechanism to evaluate both performance and relationship management in long-term interactions. Our model analysis reveals key conditions under which competitive firms are more inclined to engage in co-opetition: (i) in the presence of severe disruptions or strong control over disruptive events enabled by BBP efficiency enhancements, (ii) when BBP efficiency improvements lead to a more optimized take-back system, (iii) when customers highly value the traceability and transparency provided by BBPs, and (iv) under high trust coefficients or significant punitive costs within the trust mechanism. Furthermore, our findings suggest that the trust mechanism can drive the supply chain toward greater circularity, particularly when customers are sensitive to transparency and traceability features. A numerical example illustrates these insights, and the study provides managerial implications that lay the groundwork for the practical implementation of blockchain-driven co-opetition strategies in competitive supply chains.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102422"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172639","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.2026.102430
Yixin He , Siwei Xiao , Marios Kremantzis , Aniekan Essien , Umair Tanveer , Ali Emrouznejad , Shamaila Ishaq
This study evaluates the operational and revenue generation efficiency of English Premier League (EPL) clubs from 2014/15 to 2023/24 using a novel Dynamic Network Data Envelopment Analysis (DNDEA) model under the Variable Returns to Scale (VRS) assumption. By integrating dynamic and network structures, the model decomposes club performance into sequential stages, specifically operational conversion and revenue generation, and traces the intertemporal transmission of economic, sporting, and social factors. Unlike traditional static DEA models that treat efficiency as time-invariant and “black-boxed”, the DNDEA model provides a temporal-diagnostic lens that detect shifts in efficiency trajectories and inter-stage feedback across seasons. Results reveal two enduring archetypes: high-performing clubs that achieve competitive outcomes with limited financial inputs through effective resource management, and financial giants that excel in revenue generation but struggle to translate investments into consistent sporting success. Dynamic analysis shows system efficiency peaked in 2014/15 before declining sharply in 2015/16 and 2018/19, respectively, driven by imbalances between escalating financial investments and stagnating on-field performance. Operational inefficiencies in Stage 1 (resource conversion) were more critical than those in Stage 2 (revenue generation), underscoring challenges in aligning short-term investments with long-term sustainability. The study advances sports analytics by providing a holistic framework for evaluating football club efficiency, emphasizing actionable strategies such as optimizing talent acquisition, prioritizing dual-return investments, and leveraging fan engagement.
{"title":"Dynamic interplay of sports, social, and economic factors in the English Premier League: A network DEA approach","authors":"Yixin He , Siwei Xiao , Marios Kremantzis , Aniekan Essien , Umair Tanveer , Ali Emrouznejad , Shamaila Ishaq","doi":"10.1016/j.seps.2026.102430","DOIUrl":"10.1016/j.seps.2026.102430","url":null,"abstract":"<div><div>This study evaluates the operational and revenue generation efficiency of English Premier League (EPL) clubs from 2014/15 to 2023/24 using a novel Dynamic Network Data Envelopment Analysis (DNDEA) model under the Variable Returns to Scale (VRS) assumption. By integrating dynamic and network structures, the model decomposes club performance into sequential stages, specifically operational conversion and revenue generation, and traces the intertemporal transmission of economic, sporting, and social factors. Unlike traditional static DEA models that treat efficiency as time-invariant and “black-boxed”, the DNDEA model provides a temporal-diagnostic lens that detect shifts in efficiency trajectories and inter-stage feedback across seasons. Results reveal two enduring archetypes: high-performing clubs that achieve competitive outcomes with limited financial inputs through effective resource management, and financial giants that excel in revenue generation but struggle to translate investments into consistent sporting success. Dynamic analysis shows system efficiency peaked in 2014/15 before declining sharply in 2015/16 and 2018/19, respectively, driven by imbalances between escalating financial investments and stagnating on-field performance. Operational inefficiencies in Stage 1 (resource conversion) were more critical than those in Stage 2 (revenue generation), underscoring challenges in aligning short-term investments with long-term sustainability. The study advances sports analytics by providing a holistic framework for evaluating football club efficiency, emphasizing actionable strategies such as optimizing talent acquisition, prioritizing dual-return investments, and leveraging fan engagement.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102430"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172644","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: 2025-12-27DOI: 10.1016/j.seps.2025.102411
Zahra Abbasi , Mohammad Afzalinejad , Ali Asghar Foroughi
Data Envelopment Analysis (DEA) is a prominent tool used to assess the efficiency of decision-making units (DMUs). While static DEA models measure performance without considering time dependency, dynamic DEA incorporates time as a factor in the modeling. In recent years, environmental concerns have become a significant focus for the world community. In the context of DEA, these concerns are often expressed as undesirable outputs of the production process. Therefore, in addition to evaluating operational efficiency, it is essential to consider environmental efficiency to obtain a comprehensive measurement of DMUs’ performance. This paper presents the assessment of operational and environmental efficiency and their integration into a unified efficiency measure within the dynamic DEA framework. The time dependency of efficiency is taken into account and the links between consecutive time periods are categorized as either good or bad types. Additionally, static environmental models are established to enable comparison of dynamic and static efficiency. The proposed models are used to evaluate the performance of twenty-one countries in the agriculture sector. In this study, GHG emissions and cumulative agricultural loss due to disasters are selected as undesirable factors. Environmental efficiency generally improves over 2016–2018; however, while the static assessment shows steady progress, the dynamic assessment rises until 2017 and then slightly declines in 2018. The number of efficient countries in the operational dimension is much more than those in the environmental dimension, which shows that the economic dimension has a higher priority among countries.
{"title":"Dynamic evaluation of operational, environmental, and unified efficiencies: A DEA application in agriculture","authors":"Zahra Abbasi , Mohammad Afzalinejad , Ali Asghar Foroughi","doi":"10.1016/j.seps.2025.102411","DOIUrl":"10.1016/j.seps.2025.102411","url":null,"abstract":"<div><div>Data Envelopment Analysis (DEA) is a prominent tool used to assess the efficiency of decision-making units (DMUs). While static DEA models measure performance without considering time dependency, dynamic DEA incorporates time as a factor in the modeling. In recent years, environmental concerns have become a significant focus for the world community. In the context of DEA, these concerns are often expressed as undesirable outputs of the production process. Therefore, in addition to evaluating operational efficiency, it is essential to consider environmental efficiency to obtain a comprehensive measurement of DMUs’ performance. This paper presents the assessment of operational and environmental efficiency and their integration into a unified efficiency measure within the dynamic DEA framework. The time dependency of efficiency is taken into account and the links between consecutive time periods are categorized as either good or bad types. Additionally, static environmental models are established to enable comparison of dynamic and static efficiency. The proposed models are used to evaluate the performance of twenty-one countries in the agriculture sector. In this study, GHG emissions and cumulative agricultural loss due to disasters are selected as undesirable factors. Environmental efficiency generally improves over 2016–2018; however, while the static assessment shows steady progress, the dynamic assessment rises until 2017 and then slightly declines in 2018. The number of efficient countries in the operational dimension is much more than those in the environmental dimension, which shows that the economic dimension has a higher priority among countries.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102411"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884089","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.2026.102421
Yunsu Du , Qianqian Chen , Huaping Sun , Zhenhua Zhang , Denis Nikolaevich Sidorov
With the rapid diffusion of industrial robots due to the aging of the global population, their implications for carbon emissions have increasingly become salient. Using a comprehensive industry-level dataset covering manufacturing sectors in 40 countries, this study provides novel empirical evidence on the impact of robot adoption on industrial carbon emission intensity. Results show that robot adoption significantly reduces carbon emission intensity in manufacturing industries. This finding remains robust after several robustness checks, including the estimation of instrumental variables and alternative measures of robot adoption. Mechanism analyses reveal that the carbon-reducing effect of robot adoption primarily operates through improvements in total factor productivity. Furthermore, a significant ripple effect is identified, whereby robot adoption in upstream industries amplifies downstream carbon emission reductions through interindustry linkages. From a policy perspective, these results underscore the relevance of promoting productivity-enhancing robot adoption and leveraging supply-chain interactions to support global low-carbon economic development.
{"title":"Robot adoption and carbon emission reduction: Mechanism and ripple effect analysis","authors":"Yunsu Du , Qianqian Chen , Huaping Sun , Zhenhua Zhang , Denis Nikolaevich Sidorov","doi":"10.1016/j.seps.2026.102421","DOIUrl":"10.1016/j.seps.2026.102421","url":null,"abstract":"<div><div>With the rapid diffusion of industrial robots due to the aging of the global population, their implications for carbon emissions have increasingly become salient. Using a comprehensive industry-level dataset covering manufacturing sectors in 40 countries, this study provides novel empirical evidence on the impact of robot adoption on industrial carbon emission intensity. Results show that robot adoption significantly reduces carbon emission intensity in manufacturing industries. This finding remains robust after several robustness checks, including the estimation of instrumental variables and alternative measures of robot adoption. Mechanism analyses reveal that the carbon-reducing effect of robot adoption primarily operates through improvements in total factor productivity. Furthermore, a significant ripple effect is identified, whereby robot adoption in upstream industries amplifies downstream carbon emission reductions through interindustry linkages. From a policy perspective, these results underscore the relevance of promoting productivity-enhancing robot adoption and leveraging supply-chain interactions to support global low-carbon economic development.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102421"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172636","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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