Pub 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":"2026-01-28","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":"2026-01-27","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 : 2026-01-23DOI: 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":"2026-01-23","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}
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}
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