Pub 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":"2026-02-03","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 : 2026-01-23DOI: 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":"2026-01-23","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 : 2026-01-19DOI: 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":"2026-01-19","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 : 2026-01-16DOI: 10.1016/j.seps.2026.102418
Lukáš Frýd, Ondřej Sokol
Data envelopment analysis (DEA) is one of the two primary estimators of technical efficiency and is widely applied in policy evaluations within agricultural, environmental, and other domains. In the two-stage efficiency analysis, the DEA efficiency scores are estimated in the first stage, followed by an assessment of the influence of selected policy variables on these scores in the second stage. This paper demonstrates that two-stage efficiency DEA analyses are not robust to variations in the measurement of fundamental input variables, even when the correlation between alternative input measures exceeds 0.9. This lack of robustness is reflected in substantial heterogeneity in both statistical significance and the signs of parameters that capture the effects of environmental variables on efficiency. Consequently, by selecting seemingly interchangeable inputs, it is possible to obtain results that align with prior expectations, raising serious concerns about the reliability of DEA-based policy analyses. We argue that, given the nature of the problem, robustness cannot be achieved through methodological refinements of the DEA itself. Rather, the only viable strategy is to explicitly assess the robustness of the results with respect to alternative input specifications.
{"title":"Hidden heterogeneity in measuring production factors: Implications for two-stage efficiency analysis","authors":"Lukáš Frýd, Ondřej Sokol","doi":"10.1016/j.seps.2026.102418","DOIUrl":"10.1016/j.seps.2026.102418","url":null,"abstract":"<div><div>Data envelopment analysis (DEA) is one of the two primary estimators of technical efficiency and is widely applied in policy evaluations within agricultural, environmental, and other domains. In the two-stage efficiency analysis, the DEA efficiency scores are estimated in the first stage, followed by an assessment of the influence of selected policy variables on these scores in the second stage. This paper demonstrates that two-stage efficiency DEA analyses are not robust to variations in the measurement of fundamental input variables, even when the correlation between alternative input measures exceeds 0.9. This lack of robustness is reflected in substantial heterogeneity in both statistical significance and the signs of parameters that capture the effects of environmental variables on efficiency. Consequently, by selecting seemingly interchangeable inputs, it is possible to obtain results that align with prior expectations, raising serious concerns about the reliability of DEA-based policy analyses. We argue that, given the nature of the problem, robustness cannot be achieved through methodological refinements of the DEA itself. Rather, the only viable strategy is to explicitly assess the robustness of the results with respect to alternative input specifications.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102418"},"PeriodicalIF":5.4,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022388","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 : 2026-01-13DOI: 10.1016/j.seps.2026.102417
Xuelu Xu , Binxin Yang , Peiming He , Mengyao Tao , Litai Chen
Regional logistics integration (RLI) has emerged as a pivotal driver of regional integration (RI), playing a critical role in fostering regional coordinated development. However, research on RLI operational mechanism has not been systematically explored, which limits the proper assessment of RLI level under various policy scenarios, thereby hindering the effective implementation of relevant policies. To address this gap, this study analyzes empirical data from western China through a dual-validation framework, employing system dynamics (SD) modeling for scenario simulation and utilizing the gravity model alongside historical data for validation, thereby enabling systematic examination of RLI dynamic evolution under diverse policy scenarios. First, the RLI level is assessed through a comprehensive indicator system and gravity model, which serves for dual validation purposes in the subsequent SD modeling. Second, a system framework for RLI is developed based on core-periphery theory to elucidate the causal relationships among related variables. Then, a SD model is constructed and optimized to simulate RLI changes in western China up to 2035. Finally, both single-policy and combined-policy scenarios are examined, with RLI in western China being enhanced through adjustments to endogenous variables. The results indicate that the impact of single logistics soft policies on RLI becomes more significant in the later stages of the study, while the benefits of single logistics hard policies are more pronounced in the earlier stages. However, combined policies produce effects that diverge from a mere linear aggregation of single policies impacts. Notably, the systematic integration of the three types of policies is most conducive to the long-term development of RLI. These findings provide valuable insights for policymakers aiming to improve RLI. The proposed RLI model incorporates rich information, enabling policymakers to adjust the model parameters to reflect changes in complex environments, thereby facilitating the formulation of optimal RLI policies.
{"title":"System dynamics modelling for improving regional logistics integration: A case study of western China","authors":"Xuelu Xu , Binxin Yang , Peiming He , Mengyao Tao , Litai Chen","doi":"10.1016/j.seps.2026.102417","DOIUrl":"10.1016/j.seps.2026.102417","url":null,"abstract":"<div><div>Regional logistics integration (RLI) has emerged as a pivotal driver of regional integration (RI), playing a critical role in fostering regional coordinated development. However, research on RLI operational mechanism has not been systematically explored, which limits the proper assessment of RLI level under various policy scenarios, thereby hindering the effective implementation of relevant policies. To address this gap, this study analyzes empirical data from western China through a dual-validation framework, employing system dynamics (SD) modeling for scenario simulation and utilizing the gravity model alongside historical data for validation, thereby enabling systematic examination of RLI dynamic evolution under diverse policy scenarios. First, the RLI level is assessed through a comprehensive indicator system and gravity model, which serves for dual validation purposes in the subsequent SD modeling. Second, a system framework for RLI is developed based on core-periphery theory to elucidate the causal relationships among related variables. Then, a SD model is constructed and optimized to simulate RLI changes in western China up to 2035. Finally, both single-policy and combined-policy scenarios are examined, with RLI in western China being enhanced through adjustments to endogenous variables. The results indicate that the impact of single logistics soft policies on RLI becomes more significant in the later stages of the study, while the benefits of single logistics hard policies are more pronounced in the earlier stages. However, combined policies produce effects that diverge from a mere linear aggregation of single policies impacts. Notably, the systematic integration of the three types of policies is most conducive to the long-term development of RLI. These findings provide valuable insights for policymakers aiming to improve RLI. The proposed RLI model incorporates rich information, enabling policymakers to adjust the model parameters to reflect changes in complex environments, thereby facilitating the formulation of optimal RLI policies.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102417"},"PeriodicalIF":5.4,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022389","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 : 2026-01-10DOI: 10.1016/j.seps.2026.102415
Raffaele Mattera , Philip Hans Franses
We propose a new spatio-temporal hierarchical clustering approach that is suitable for clustering African countries based on Gross Domestic Product under measurement error. To accommodate for measurement error, we use slave trade as an instrument. Furthermore, we extend our method to allow for a range of macroeconomic indicators, instead of just GDP. We document that our findings largely agree on the degree of convergence.
{"title":"Analyzing convergence across African economies while allowing for measurement errors","authors":"Raffaele Mattera , Philip Hans Franses","doi":"10.1016/j.seps.2026.102415","DOIUrl":"10.1016/j.seps.2026.102415","url":null,"abstract":"<div><div>We propose a new spatio-temporal hierarchical clustering approach that is suitable for clustering African countries based on Gross Domestic Product under measurement error. To accommodate for measurement error, we use slave trade as an instrument. Furthermore, we extend our method to allow for a range of macroeconomic indicators, instead of just GDP. We document that our findings largely agree on the degree of convergence.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102415"},"PeriodicalIF":5.4,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976772","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 : 2026-01-05DOI: 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":"2026-01-05","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 : 2026-01-04DOI: 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":"2026-01-04","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}
The real estate industry encompasses sequential sub-processes in operations, including land acquisition, house construction, and house sales and rentals. Investigating the sub-process structure of real estate operations is essential to demystifying and improving the overall operational performance. This study proposes an additive network DEA model to estimate the process-oriented performance of urban real estate operations and capture hidden sub-process performance. The sequential linear programming method is used to address the model's nonlinearity. We further explore the impact of operational performance on housing prices to identify the main underlying driver of China's booming real estate market. The proposed model is applied to assess the operational performance of Chinese urban real estate markets over the past decade. The empirical findings reveal that: (1) performance losses may stem from weaknesses in the housing construction process, with significant improvement potential in overall operational and sub-process performance in most cities. (2) Enhanced performance in the construction process can fuel short-term housing prices increases during market booms. (3) Higher real estate operational performance may initially raise housing prices but ultimately inhibit them in the long term due to limited market demand. Our proposed method framework proves to be an effective tool for policymakers to design wise operational plans for improving real estate operational performance.
{"title":"Operational performance of urban real estate in China: An additive network DEA model","authors":"Hao Zhang , Wattanaporn Nalinrat , Rong Xiang , Anyu Yu , Yue Gao","doi":"10.1016/j.seps.2025.102414","DOIUrl":"10.1016/j.seps.2025.102414","url":null,"abstract":"<div><div>The real estate industry encompasses sequential sub-processes in operations, including land acquisition, house construction, and house sales and rentals. Investigating the sub-process structure of real estate operations is essential to demystifying and improving the overall operational performance. This study proposes an additive network DEA model to estimate the process-oriented performance of urban real estate operations and capture hidden sub-process performance. The sequential linear programming method is used to address the model's nonlinearity. We further explore the impact of operational performance on housing prices to identify the main underlying driver of China's booming real estate market. The proposed model is applied to assess the operational performance of Chinese urban real estate markets over the past decade. The empirical findings reveal that: (1) performance losses may stem from weaknesses in the housing construction process, with significant improvement potential in overall operational and sub-process performance in most cities. (2) Enhanced performance in the construction process can fuel short-term housing prices increases during market booms. (3) Higher real estate operational performance may initially raise housing prices but ultimately inhibit them in the long term due to limited market demand. Our proposed method framework proves to be an effective tool for policymakers to design wise operational plans for improving real estate operational performance.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102414"},"PeriodicalIF":5.4,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976771","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 : 2025-12-29DOI: 10.1016/j.seps.2025.102408
Sergio Hoffmann , Rita Laura D’Ecclesia
Environmental, Social, and Governance (ESG) metrics have become central to sustainability assessment, yet the link between national conditions and composite ESG performance remains largely unexplored. We develop a bottom-up national ESG rating by aggregating the distribution of listed firms’ ESG scores for twelve developed economies between 2013 and 2022. Several aggregation schemes—mean, median, Sen’s inequality-adjusted index, and a dispersion-adjusted mean—are benchmarked, and the resulting rankings prove highly consistent, supporting the median as the headline measure. National ratings are then compared with World Bank indicators of environmental efficiency, social welfare, and governance quality through panel fixed-effects regressions and four machine-learning models (Random Forest, Gradient Boosting, Support Vector Regression, and CatBoost), assessed via cross-validation and explainability tools. CatBoost achieves the highest predictive accuracy and balanced use of predictors. Energy intensity and under-five mortality consistently act as dominant negative drivers, while gender representation and demographic maturity contribute positively. A pillar-level (E, S, G) panel-VAR analysis reveals strong within-pillar persistence and asymmetric cross-effects led by the social dimension. Overall, the framework provides a transparent bridge from firm-level data to national ESG performance, delivering robust and interpretable evidence for policy evaluation and sustainable investment screening.
{"title":"Measuring national sustainability: ESG scores from corporate data","authors":"Sergio Hoffmann , Rita Laura D’Ecclesia","doi":"10.1016/j.seps.2025.102408","DOIUrl":"10.1016/j.seps.2025.102408","url":null,"abstract":"<div><div>Environmental, Social, and Governance (ESG) metrics have become central to sustainability assessment, yet the link between national conditions and composite ESG performance remains largely unexplored. We develop a bottom-up national ESG rating by aggregating the distribution of listed firms’ ESG scores for twelve developed economies between 2013 and 2022. Several aggregation schemes—mean, median, Sen’s inequality-adjusted index, and a dispersion-adjusted mean—are benchmarked, and the resulting rankings prove highly consistent, supporting the median as the headline measure. National ratings are then compared with World Bank indicators of environmental efficiency, social welfare, and governance quality through panel fixed-effects regressions and four machine-learning models (Random Forest, Gradient Boosting, Support Vector Regression, and CatBoost), assessed via cross-validation and explainability tools. CatBoost achieves the highest predictive accuracy and balanced use of predictors. Energy intensity and under-five mortality consistently act as dominant negative drivers, while gender representation and demographic maturity contribute positively. A pillar-level (E, S, G) panel-VAR analysis reveals strong within-pillar persistence and asymmetric cross-effects led by the social dimension. Overall, the framework provides a transparent bridge from firm-level data to national ESG performance, delivering robust and interpretable evidence for policy evaluation and sustainable investment screening.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102408"},"PeriodicalIF":5.4,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884088","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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