Spatial Effects Research Articles

Study on the impact of air pollution on residents' health expenditures in Guangdong, Hong Kong, and Macao Greater Bay Area

The Guangdong, Hong Kong, and Macao Greater Bay Area (GHMA) has experienced economic development and rapid growth of transportation infrastructure in recent years. However, the economic advancement is also accompanied by serious atmospheric pollution, which threatens the health of the residents, thus, it is of great significance to explore the impact of atmospheric pollution on the health expenditures of residents in the GHMA. The article establishes a spatial econometric model to study the impact of atmospheric pollution on residents' health expenditure in the GHMA based on panel data from 2014 to 2021, using nine prefectures in the GHMA as research objects. The results show that: (1) Atmospheric pollution in the GHMA has an obvious spatial agglomeration phenomenon and spatial spillover effect, and the impact of atmospheric pollution on the health of the residents is still very serious; (2) PM2.5 emissions are positively and significantly related to the actual health care cost per person, and the rise in air pollution is the main reason for the rise in public health spending; (3) Other factors also have different impacts on residents' health expenditures. Based on the above research, the article puts forward corresponding policy recommendations.

Spatial effect of urban morphology on land surface tempature from the perspective of local climate zone

Urban expansion results in the intrusion of artificial surfaces into natural environments, giving rise to phenomena such as urban heat islands and heightened temperatures within inhabited areas. This study examined the dynamic evolution of urban morphology and land surface temperature through the lens of local climate zone. Furthermore, the spatial relationship between urban morphology indices and land surface temperature was analyzed within the context of loops. The investigation yielded several noteworthy findings: (1) LCZ8, LCZ6, and LCZD were the most significant transfer-in and transfer-out classes within the study area. (2) The heat contribution of residential LCZs (LCZ 2,3,5) decreased over time, ranging from 0.415 to 0.171, while that of industrial LCZs (LCZ 8,10) increased within the range of 0.348–0.553. (3) Urban heat island mitigation can be achieved by regulating building height and surface fraction. Establishing a judicious threshold for reducing LST is imperative for sky view factor and impervious surface fraction factors. Surface albedo exhibited a substantial inhibitory effect in 2020. The observation of urban morphological changes and long-term local climate zone considerations integrate the responsibility of mitigating urban heat islands into urban planning.

High coke resistance Ni-based CH4/CO2 reforming catalysts with strong spatial confinement effect: Effect of CeO2 shell thickness

Ni-based catalysts show promise as candidates for the dry reforming of methane (DRM), yet the susceptibility to sintering and carbon deposition is a major obstacle to industrialization. This work demonstrates a mesoporous Ni-based catalyst with a thickness-tailorable CeO2 shell for enhanced spatial confinement effect for the DRM. The Ni-MCM-41@xCeO2 catalysts are prepared at various CeO2 shell thickness through a two-step hydrothermal reaction. The kinetic studies have shown that the Ni-MCM-41@2CeO2 catalyst has the lowest activation energy, producing a high conversion of CH4 and CO2 as high as around 80 % at 700 °C. Our characterizations reveal that the Ni core is tightly confined in the mesoporous skeleton of MCM-41 and within a CeO2 shell. The Ni-MCM-41@2CeO2 catalyst is able to sustain high activity for more than 10 h of operation, with a remarkably reduced carbon deposition (0.28 %) as compared with a conventional Ni-Ce/MCM-41 catalyst (8.77 %). Furthermore, the density functional theory (DFT) calculation supports that the CeO2 shell layer significantly reduces dissociation potential barrier for CH4 and CO2, hence enhancing the catalytic activity of the DRM.

The influence of two and three-dimensional spatial characteristics of industrial parks on the emotional well-being of employees: A case study of Shenzhen

Enhancing the emotions of the workers within industrial parks is crucial for achieving the sustainable development goal (SDG). However, evidence on the impact of industrial park environmental layouts on emotions is limited due to the low attention on the workers in the industrial park. Based on a framework of two-dimensional (2D) industrial park morphology indicators and three-dimensional (3D) spatial objective perception, this study aims to compare and analyze the effect of environmental characteristics in Shenzhen's industrial parks on workers' emotions by using emotional geography and social media data. The potential mechanism that environmental characteristic variables influence the positive emotion index (PEI) is also explored. Using the natural language processing (NLP) to analyze the Weibo check-in data, the study captures the daily emotions of workers in industrial parks. By adopting the linear and spatial regression models, this research reveals the complex relationship between the environmental layout of the total 425 industrial parks in Shenzhen city and the corresponding emotions of the workers. The conclusions are as follows: (1) There is a significant correlation between the environmental layout of the industrial parks and the emotions of the workers, with the correlation among 3D variables being higher than that among 2D variables considering spatial effects. (2) There is a certain degree of coordination and contradiction between the morphological indicators of the industrial parks and the spatial objective perception variables, necessitating the planners to consider both macro-scale and micro-perception environmental characteristics to enhance the workers' emotions. (3) In addition to higher accessibility and more public transportation options, the safer, wider, relatively more open streets, more perceivable green spaces, and richer street life can promote the positive emotions among the workers. On the contrary, the enclosed environments, the dense and overwhelming buildings, and the excessive road density may lead to negative emotions. This study contributes to establish the environmental layouts for the industrial parks that foster positive worker emotions and offer insights for the sustainable development of the industrial parks.

Regulating ligand length to improve the PL QY and stability of CsPbCl0.9Br2.1 nanocrystals for LEDs and photoelectric applications

Perovskite luminescent materials have been extensively investigated due to their comprehensive applications for full-color display, lighting, and communication. However, the development of blue-emissive perovskites has seriously lagged behind that of green and red counterparts, primarily because of their low photoluminescence quantum yield (PL QY) and poor stability. Here, we prepared blue-emissive CsPbCl0.9Br2.1 perovskite nanocrystals (PeNCs) and enhanced their QY from 61.3 % to 90.4 % by regulating the chain lengths of ligands. Post-treated PeNCs also exhibit good stability, their PL intensity is maintained at about 90 % after storage at ambient conditions for 10 days. The exciton dynamics results obtained from time-resolved fluorescence spectra and transient absorption spectra show that dodecyldimethylammonium bromide (DDAB), featuring double 12-hydrocarbon chains, is more capable of passivating surface defects and inhibiting non-radiative composites than the other double 8- or double 16-carbon chain ligands, which greatly improved the probability and rate of radiative recombination. Subsequently, the mechanism of ligand chain length regulation on the PL properties and stability of PeNCs was analyzed in terms of ligand-NC surface interaction, ligand polarity, hydrophobicity, and spatial effects. Furthermore, the device based on DDAB-CsPbCl0.9Br2.1 demonstrated stable EL and long operation lifetime, indicating its significant potential as an efficient blue emitter for backlighting in display technologies.

Carbon Abatement Effect of Low-Carbon City Pilot Policy: Insights from a Time-Varying DID Model

The low-carbon city pilot (LCCP) policy constitutes a crucial element in China’s endeavors to attain its carbon peak target. Thoroughly analyzing the carbon abatement effects and mechanisms of the LCCP policy is essential for supporting China’s goals of achieving low-carbon sustainability. We use panel data from 1997 to 2019, covering 249 Chinese cities, for a quasi-natural experiment. This study assesses the effect of the LCCP project on urban carbon emission intensity (CEI) using a difference-in-difference (DID) model. Furthermore, the article conducts analyses on policy coordination effects, impact mechanisms, and spatial spillover effects. Our research findings indicate that the LCCP policy substantially decreases urban CEI, a result that withstands a battery of robustness tests. The carbon abatement effect of LCCP policies is dynamic and typically endures for three years. Subsequent investigations unveil a cooperative impact between the LCCP policy and the carbon trading pilot policy. The decrease of urban CEI under the LCCP policy is primarily attributed to command and market tools, although the efficacy of voluntary tools remains unrealized. From the standpoint of adjacent nonpilot cities, our conclusions do not endorse the presence of spatial spillover effect for the LCCP project. The insights derived from our findings offer crucial guidance for policymakers exploring low-carbon models and pathways.

Spatial Dynamic Interaction Effects and Formation Mechanisms of Air Pollution in the Central Plains Urban Agglomeration in China

Accurately identifying the dynamic interaction effects and network structure characteristics of air pollution is essential for effective collaborative governance. This study investigates the spatial dynamic interactions of air pollution among 30 cities in the Central Plains Urban Agglomeration using convergent cross mapping. Social network analysis is applied to assess the overall and node characteristics of the spatial interaction network, while key driving factors are analyzed using an exponential random graph model. The findings reveal that air pollution levels in the Central Plains Urban Agglomeration initially increase before they decrease, with heavily polluted cities transitioning from centralized to sporadic distribution. Among the interactions, Heze’s air pollution impact on Kaifeng was the strongest, while Xinxiang’s impact on Changzhi was the weakest. The emission and receiving effects peaked during 2010–2012. The air pollution interactions among cities exhibit significant network characteristics, with block model results indicating that emitting and receiving relationships are primarily concentrated in the bidirectional spillover plate. Natural factors such as temperature and precipitation significantly influence the spatial interaction network. Economic and social factors like economic level and industrial sector proportion also have a significant impact. However, population density does not influence the spatial interaction network. This study contributes to understanding the spatial network of air pollution, thereby enhancing strategies for optimizing regional collaborative governance efforts to address air pollution.

Temperature, relative humidity and elderly type 2 diabetes mortality: A spatiotemporal analysis in Shandong, China

BackgroundThe mortality of type 2 diabetes mellitus (T2DM) can be affected by environmental factors. However, few studies have explored the effects of environmental factors across diverse regions over time. Given the vulnerability observed in the elderly group in previous research, this research applied Bayesian spatiotemporal models to assess the associations in the elderly group. MethodsData on T2DM death in the elderly group (aged over 60 years old) at the county level were collected from the National Death Surveillance System between 1st January 2013 and 31st December 2019 in Shandong Province, China. A Bayesian spatiotemporal model was employed with the integrated Nested Laplace Approach to explore the associations between socio-environmental factors (i.e., temperatures, relative humidity, the Normalized Difference Vegetation Index (NDVI), particulate matter with a diameter of 2.5 μm or less (PM2.5) and gross domestic product (GDP)) and T2DM mortality. ResultsT2DM mortality in the elderly group was found to be associated with temperature and relative humidity (i.e., temperature: Relative Risk (RR) = 1.41, 95% Credible Interval (CI): 1.27–1.56; relative humidity: RR = 1.05, 95% CI:1.03–1.06), while no significant associations were found with NDVI, PM2.5 and GDP. In winter, significant impacts from temperature (RR = 1.18, 95% CI: 1.06–1.32) and relative humidity (RR = 0.94, 95% CI: 0.89–0.99) were found. Structured and unstructured spatial effects, temporal trends and space-time interactions were considered in the model. ConclusionsHigher mean temperatures and relative humidities increased the risk of elderly T2DM mortality in Shandong Province. However, a higher humidity level decreased the T2DM mortality risk in winter in Shandong Province. This research indicated that the spatiotemporal method could be a useful tool to assess the impact of socio-environmental factors on health by combining the spatial and temporal effects.

Spatial Spillover Effects of Digital Finance on Corporate ESG Performance

Spatial Spillover Effects of Digital Finance on Corporate ESG Performance

Internal catalysis significantly promotes the bond exchange of covalent adaptable polyurethane networks

Self-healing covalent adaptable networks (CANs) are not only of fundamental interest but also of practical importance for achieving carbon neutrality and sustainable development. However, there is a trade-off between the mobility and cross-linking structure of CANs, making it challenging to develop CANs with excellent mechanical properties and high self-healing efficiency. Here, we report the utilization of a highly dynamic four-arm cross-linking unit with an internally catalyzed oxime-urethane group to obtain CAN-based ionogel with both high self-healing efficiency (>92.1%) at room temperature and superior mechanical properties (tensile strength 4.55 MPa and toughness 13.49 MJ m-3). This work demonstrates the significant potential of utilizing the synergistic electronic, spatial, and topological effects as a design strategy for developing high-performance materials.

Co-abatement of greenhouse gas and air pollutants in Shanghai, China: Spatial hotspots identification, effects assessment and policy implication

Driving co-abatement of Greenhouse Gas (GHGs) and Air Pollutants (APs) in the city level is crucial for fostering societal green and low-carbon transitions, yet comprehensive and refined researches at this level remain limited. To facilitate urban fine management of GHGs control and APs reduction, this study targeted nine categories of anthropogenic emission sources in Shanghai, a typical megacity of China, analyzing the co-benefits of three types of GHGs (CO2, CH4, N2O) and seven types of APs (SO2, NOx, CO, VOCs, NH3, PM2.5, PM10) via emissions flow, spatial distribution, hotspot regions identification, and scenario prediction. Results highlighted the source heterogeneity of different types and significant contributions of energy consumption. CO2 emissions showed a strong spatial correlation with SO2, NOx, and CO, followed by VOCs and PM. Hotspot regions for CO2-VOCs, CO2-NOx and CO2-SO2 co-abatement included power plants, petrochemical enterprises and chemical industrial parks in the southern coastal areas, iron and steel enterprises and power plants in the northern coastal areas, and airport areas in the central and eastern coastal areas, presenting great potential maximum reduction benefits. Achieving positive co-benefits in industrial sector would depend on the steady decline of CO2 emissions in power generation and steel industries. Introducing carbon capture devices and improving energy efficiency would be more beneficial to CO2 emission reduction, while increasing the share of clean energy and phasing out outdated vehicles, machinery, or production capacities are more effective in reducing APs. These mitigation measures could achieve 68.8 % and 47.6 % reduction for CO2 and APs by 2050, respectively, and the co-effect of CO2 and APs emission reduction would gradually increase with the continuous implementation of these measures.

The dynamic spatial effects of education investment on carbon emissions: heterogeneous analysis based on north-south differences in China

This article is based on panel data from 266 cities in China from 2009 to 2020. Classical economics theory and Marxist political economy theory are used as the theoretical basis for the study. Static spatial Durbin model regression and dynamic spatial Durbin model regression are conducted to analyze the impact of education investment on carbon dioxide emissions. The results show that education investment can significantly reduce carbon dioxide emissions, not only through spatial spillover effects, but also through dynamic effects over time. The implementation of education investment in one city can have a positive impact on surrounding cities, thereby achieving a certain degree of reduction in carbon emissions. Additionally, the inhibitory effect of education investment on carbon emissions is more significant in the short term. Furthermore, the study found that there is significant heterogeneity in the effect of education investment on carbon dioxide emissions between northern and southern cities, with education investment in southern cities having a more significant effect. This may be related to the industrial structure and climate of southern cities. The conclusions of this study provide useful reference for the development of education policies and environmental protection policies. In the future, the government can increase education investment appropriately to achieve the goal of reducing carbon dioxide emissions. And research has shown that education investment can suppress carbon emissions by influencing changes in energy and industrial structures. Therefore, when using measures to reduce emissions in education investment, we can focus on these two aspects.

Spatial network analysis and driving forces of urban carbon emission performance: Insights from Guangdong Province

As the primary contributor to carbon emissions, how cities enhance their carbon emission performance and mitigate emissions is crucial for achieving low-carbon urban environments in China. However, existing research often overlooks the spatial interconnectedness of carbon emission performance, neglecting reciprocal influences among cities. This study examines the network structure of carbon emission performance among Guangdong's cities from 1997 to 2019, using a super-efficient SBM model and social network analysis, and measures spatial impacts of network factors with the spatial Durbin model. Findings reveal that: (1) The overall network of carbon emission performance is relatively loose with minimal changes in connectivity and efficiency but shows significant local clustering. (2) Shenzhen, Guangzhou, and Zhuhai have high centrality, dominating carbon emission performance resources and acting as key transmission nodes, while most other regions have low centrality, indicating network polarization and potential vulnerabilities. (3) Enhancing a region's centrality, economic development, industrial structure, openness, and attraction of talent and technology can boost local carbon emission performance, but may also lead to the displacement of emissions to neighboring areas and outflow of low-carbon and innovative elements, negatively affecting surrounding regions through spatial spillover effects. This research advances regional carbon emission reduction strategies by highlighting the interplay between spatial networks and carbon emission performance, fostering synergies in reduction efforts.

Spatial Pattern of Host Tree Size, Rather than of Host Tree Itself, Affects the Infection Likelihood of a Fungal Stem Disease.

The spatial pattern of diseased forest trees is a product of the spatial pattern of host trees and the disease itself. Previous studies have focused on describing the spatial pattern of diseased host trees, and it remains largely unknown whether an antecedent spatial pattern of host/nonhost trees affects the infection pattern of a disease and how large the effect sizes of the spatial pattern of host/nonhost trees and host size are. The results from trivariate random labeling showed that the antecedent pattern of the host ash tree, Fraxinus mandshurica, but not of nonhost tree species, impacted the infection pattern of a stem fungal disease caused by Inonotus hispidus. To investigate the effect size of the spatial pattern of ash trees, we employed the SADIE (Spatial Analysis by Distance IndicEs) aggregation index and clustering index as predictors in the GLMs. Globally, the spatial pattern (vi index) of ash trees did not affect the infection likelihood of the focal tree; however, the spatial pattern of DBH (diameter at breast height) of ash trees significantly affected the infection likelihood of the focal tree. We sampled a series of circular plots with different radii to investigate the spatial pattern effect of host size on the infection likelihood of the focal tree locally. The results showed that the location (patch/gap) of the DBH of the focal tree, rather than that of the focal tree itself, significantly affected its infection likelihood in most plots of the investigated sizes. A meta-analysis was employed to settle the discrepancy between plots of different sizes, which led to results consistent with those of global studies. The results from meta-regression showed that plot size had no significant effects.

Impact of pathogenic variants of the Ras-mitogen-activated protein kinase pathway on major white matter tracts in the human brain.

Noonan syndrome and neurofibromatosis type 1 are genetic conditions linked to pathogenic variants in genes of the Ras-mitogen-activated protein kinase signalling pathway. Both conditions hyper-activate signalling of the Ras-mitogen-activated protein kinase pathway and exhibit a high prevalence of neuropsychiatric disorders. Further, animal models of Noonan syndrome and neurofibromatosis type 1 and human imaging studies show white matter abnormalities in both conditions. While these findings suggest Ras-mitogen-activated protein kinas pathway hyper-activation effects on white matter, it is unknown whether these effects are syndrome-specific or pathway-specific. To characterize the effect of Noonan syndrome and neurofibromatosis type 1 on human white matter's microstructural integrity and discern potential syndrome-specific influences on microstructural integrity of individual tracts, we collected diffusion-weighted imaging data from children with Noonan syndrome (n = 24), neurofibromatosis type 1 (n = 28) and age- and sex-matched controls (n = 31). We contrasted the clinical groups (Noonan syndrome or neurofibromatosis type 1) and controls using voxel-wise, tract-based and along-tract analyses. Outcomes included voxel-wise, tract-based and along-tract fractional anisotropy, axial diffusivity, radial diffusivity and mean diffusivity. Noonan syndrome and neurofibromatosis type 1 showed similar patterns of reduced fractional anisotropy and increased axial diffusivity, radial diffusivity, and mean diffusivity on white matter relative to controls and different spatial patterns. Noonan syndrome presented a more extensive spatial effect than neurofibromatosis type 1 on white matter integrity as measured by fractional anisotropy. Tract-based analysis also demonstrated differences in effect magnitude with overall lower fractional anisotropy in Noonan syndrome compared to neurofibromatosis type 1 (d = 0.4). At the tract level, Noonan syndrome-specific effects on fractional anisotropy were detected in association tracts (superior longitudinal, uncinate and arcuate fasciculi; P < 0.012), and neurofibromatosis type 1-specific effects were detected in the corpus callosum (P < 0.037) compared to controls. Results from along-tract analyses aligned with results from tract-based analyses and indicated that effects are pervasive along the affected tracts. In conclusion, we find that pathogenic variants in the Ras-mitogen-activated protein kinase pathway are associated with white matter abnormalities as measured by diffusion in the developing brain. Overall, Noonan syndrome and neurofibromatosis type 1 show common effects on fractional anisotropy and diffusion scalars, as well as specific unique effects, namely, on temporoparietal-frontal tracts (intra-hemispheric) in Noonan syndrome and on the corpus callosum (inter-hemispheric) in neurofibromatosis type 1. The observed specific effects not only confirm prior observations from independent cohorts of Noonan syndrome and neurofibromatosis type 1 but also inform on syndrome-specific susceptibility of individual tracts. Thus, these findings suggest potential targets for precise, brain-focused outcome measures for existing medications, such as MEK inhibitors, that act on the Ras-mitogen-activated protein kinase pathway.

Comprehensive performance analysis of deep integration and innovative development of logistics and manufacturing industries: A comparison analysis between coastal and inland regions in China

In today's industry development, the integration of manufacturing and logistics industries has received increasing attention from the international community. The Chinese government has set a strategic agenda for the deep integration and innovative development of logistics and manufacturing industries (DIIDLMI). However, it is still unclear what impact DIIDLMI has on the development of two industries. Especially with significant differences in resource endowments, it is interesting to see whether such impacts vary between coastal and inland regions. Therefore, this study focuses on the regional collaborative development mechanism and analyzes the performance of DIIDLMI by using the Spatial Durbin Model with provincial data in China. The analysis result indicates that, first, DIIDLMI has positive local effect and positive spatial spillover effect on digital intelligence technology development of the two industries, positive spatial spillover effect on low-carbon transformation and profit level of the logistics industry, positive local effect on low-carbon transformation of the manufacturing industry, and positive local effect on profit level of the two industries. Second, we also obtain some counterintuitive results, i.e., DIIDLMI has no local effect on low-carbon transformation of the logistics industry, no spatial spillover effect on low-carbon transformation and profit level of the manufacturing industry. Third, compared to coastal regions, DIIDLMI in inland regions has a more positive local effect on digital intelligence technology development of the logistics industry, a more positive spatial spillover effect on low-carbon transformation of the logistics industry, a more positive local effect on low-carbon transformation of the manufacturing industry, and a more positive local effect on profit level of the manufacturing industry. Finally, based on these results, policy recommendations are presented for promoting the related industrial development.

The spatial spillover impact and transmission mechanisms of logistics agglomeration on eco-efficiency: A case study in China

This study employs spatial durbin model (SDM) and spatial mediation model to empirically investigate the relationship between logistics agglomeration (LA) and logistics eco-efficiency (LEE), as well as the transmission mechanism role of technological innovation (TI) and advanced industrial structure (AIS), using panel data on inter-provincial logistics in China from 2008 to 2020. The findings indicate a U-shaped relationship between LA and LEE. The early development of LA in China was accompanied by environmental sacrifices. Further analysis of the curves reveals that LA has spatial spillover effect on LEE. That is, insufficient concentration of LA has a siphon effect on the LEE of surrounding areas, whereas appropriate concentration of LA has a radiation effect on the LEE of surrounding areas. Spatial mediation analysis indicates that enhancing TI and developing AIS are essential channels through which LA bolsters LEE. Additionally, this study innovatively incorporates topography ruggedness and altitude as instrumental variables to account for potential endogeneity issues in the empirical model. Theoretical and practical significance is attributed to this research for China's logistics industry, particularly shedding light on the optimization of efficiency and long-term improvement of LEE through spatial reform via agglomeration zones.

Can Tourism Economy Enhance the Level of Urban Green Development? A Study Based on Spatial Effects and Threshold Characteristics

Can Tourism Economy Enhance the Level of Urban Green Development? A Study Based on Spatial Effects and Threshold Characteristics

Vertical fiscal imbalances, environmental regulations and resource allocation — in the Chinese context

ABSTRACT Vertical fiscal imbalances (VFI) in China have created significant challenges for resource allocation and environmental governance. This study investigates the impact of VFI on resource allocation in Chinese cities. Results show that VFI intensifies resource misallocation, particularly at the provincial compared to the municipal level. And VFI influences resource allocation via informal and formal environmental regulations, with public and administrative regulations mitigating effects, while supervisory regulations exacerbate them. The negative impact of VFI is especially pronounced in declining cities, non-industrial hubs, and regions with severe resource misallocation. Additionally, VFI and resource misallocation demonstrate negative spatial spillover effects. Our findings provide valuable insights into the influence of VFI across different administrative levels in China, highlighting the need for policy reforms to address these fiscal disparities.

Yesterday's solution: Do national economic and technological development zones promote industrial development in typical developing regions of China?

With the proliferation of special economic zones (SEZs) among emerging economies, their mixed performance has sparked discussions on contextual heterogeneity and underlying driving mechanisms. Characterized by an outward-oriented economy and industry-city interaction, the cross-stage establishment practice of China's national economic and technological development zones (ETDZs) is conducive to expanding existing research focused on cross-country analyses of SEZs. This study constructs difference-in-differences models to analyze the influence of national ETDZs on regional industrial development in the Yellow River basin, a typical developing region in China, from 2003 to 2019, evaluating their performance under a regional balanced development strategy and a within a lagging economic backdrop. The results indicate that (1) national ETDZs do not contribute to industrial growth and even exert a negative effect; (2) while they fail to perform better over time, their spatial spillover effect slightly benefits neighboring cities; (3) an optimal economic environment and proactive policy promotion can respectively mitigate and exacerbate their negative effects; (4) the inability to attract external enterprises and achieve industrial restructuring is the primary cause of the negative effect, highlighting a mismatch and discord between the economic and policy attributes of national ETDZs and evolving contexts. Based on these findings, recommendations are proposed to cultivate SEZs as suitable policy platforms and effective economic spaces.