Empirical Test Research Articles

Do traumatic events and substance use co-occur during adolescence? Testing three causal etiologic hypotheses.

Why do potentially traumatic events (PTEs) and substance use (SU) so commonly co-occur during adolescence? Causal hypotheses developed from the study of posttraumatic stress disorder (PTSD) and substance use disorder (SUD) among adults have not yet been subject to rigorous theoretical analysis or empirical tests among adolescents with the precursors to these disorders: PTEs and SU. Establishing causality demands accounting for various factors (e.g. genetics, parent education, race/ethnicity) that distinguish youth endorsing PTEs and SU from those who do not, a step often overlooked in previous research. We leveraged nationwide data from a sociodemographically diverse sample of youth (N = 11,468) in the Adolescent Brain and Cognitive Development Study. PTEs and substance use prevalence were assessed annually. To account for the many pre-existing differences between youth with and without PTE/SU (i.e. confounding bias) and provide rigorous tests of causal hypotheses, we linked within-person changes in PTEs and SU (alcohol, cannabis, nicotine) across repeated measurements and adjusted for time-varying factors (e.g. age, internalizing symptoms, externalizing symptoms, and friends' use of substances). Before adjusting for confounding using within-person modeling, PTEs and SU exhibited significant concurrent associations (βs = .46-1.26, ps < .05) and PTEs prospectively predicted greater SU (βs = .55-1.43, ps < .05) but not vice versa. After adjustment for confounding, the PTEs exhibited significant concurrent associations for alcohol (βs = .14-.23, ps < .05) and nicotine(βs = .16, ps < .05) but not cannabis (βs = -.01, ps > .05) and PTEs prospectively predicted greater SU (βs = .28-.55, ps > .05) but not vice versa. When tested rigorously in a nationwide sample of adolescents, we find support for a model in which PTEs are followed by SU but not for a model in which SU is followed by PTEs. Explanations for why PTSD and SUD co-occur in adults may need further theoretical analysis and adaptation before extension to adolescents.

On Trends and Periods in Big History

Over nearly fifty years, Big History has evolved as an interdisciplinary approach, connecting cosmic, geological, biological, and cultural phenomena into a unified narrative of increasing complexity. This paper critically examines various theoretical frameworks within Big History, focusing on their scientific soundness. While progress has been made, challenges persist in establishing a theoretical core and achieving consensus. Commonalities exist, such as the recognition of a trend toward increasing complexity, the division into temporal eras and periods, and the acknowledgment of unique dynamics defining these phases. However, a consensus on the best foundational principles and canonical periods remains elusive. The paper suggests three strategies for theory development: employing cross-disciplinary theories, generalizing discipline-specific theories, or inventing novel theories. Each approach requires further refinement and empirical testing to contribute to consensus building. Big History is argued to have utility based on its ability to contextualize events within a broader framework, but more ambitious rationales and empirical work may be necessary for skeptical audiences. Despite ongoing theoretical debates, immediate progress can be achieved through empirical endeavors, contributing to the discipline's reputation.

Evaluation of selected combustion parameters in a compression-ignition engine powered by hydrogenated vegetable oil (HVO)

The article carries out a detailed analysis and evaluation of indicators related to the combustion process (pressure and temperature in the engine combustion chamber, heat release rate, heat release fraction) in a JCB 444 TA4i compression-ignition engine fuelled with diesel and hydrogenated vegetable oil (HVO). During the empirical tests, the operation of the exhaust gas recirculation (EGR) system was stopped and no other changes were made to the engine settings (factory settings were used). In the first stage, the empirical tests were carried out at speed characteristics on an engine dynamometer. Then, an experiment was carried out at the engine crankshaft speed corresponding to the maximum torque - which consisted of determining the indicators related to the combustion process at a constant mass flow of fuel: diesel and HVO fuel. This provided information on the effect of hydrogenated vegetable oil on the combustion process in relation to the diesel engine feed. The conclusions drawn from the empirical study can be used to develop guidelines to change the operating map of a compression-ignition engine when it is fed with hydrogenated vegetable oil.

A Generalized Residual-Based Test for Fractional Cointegration in Panel Data with Fixed Effects

Asymptotic theories for fractional cointegrations have been extensively studied in the context of time series data, with numerous empirical studies and tests having been developed. However, most previously developed testing procedures for fractional cointegration are primarily designed for time series data. This paper proposes a generalized residual-based test for fractionally cointegrated panels with fixed effects. The test’s development is based on a bivariate panel series with the regressor assumed to be fixed across cross-sectional units. The proposed test procedure accommodates any integration order between [0,1], and it is asymptotically normal under the null hypothesis. Monte Carlo experiments demonstrate that the test exhibits better size and power compared to a similar residual-based test across varying sample sizes.

The Supermultiplier Model and the Role of Autonomous Demand: An Empirical Test for European Countries

ABSTRACT In recent years, Supermultiplier models have attracted rising attention, highlighting the role of autonomous components of demand in shaping the long-run growth path of the economy. This paper aims to empirically test the implications of this approach in selected European economies, grouped according to the literature on welfare model classification. First, we outline the short-run adjustments and long-run relations between autonomous demand growth and output by estimating a vector error-correction model (VECM). Second, we assess if a causal relationship between the variables can be identified by estimating a structural model and computing the orthogonalized impulse response functions, focusing in particular on the effects of a demand shock on output. The empirical results confirm a positive and significant relation between autonomous demand and output in the long run for all countries considered. Moreover, we find that the causal direction runs from the former to the latter, corroborating the idea that autonomous demand shocks permanently affect the growth path of the economies under scrutiny.

Research on the Exposure Risk Analysis of Wildfires with a Spatiotemporal Knowledge Graph

This study focuses on constructions that are vulnerable to fire hazards during wildfire events, and these constructions are known as ‘exposures’, which are an increasingly significant area of disaster research. A key challenge lies in estimating dynamically and comprehensively the risk that individuals are exposed to during wildfire spread. Here, ‘exposure risk’ denotes the potential threat to exposed constructions from fires within a future timeframe. This paper introduces a novel method that integrates a spatiotemporal knowledge graph with wildfire spread data and an exposure risk analysis model to address this issue. This approach enables the semantic integration of varied and heterogeneous spatiotemporal data, capturing the dynamic nature of wildfire propagation for precise risk analysis. Empirical tests are employed for the study area of Xichang, Sichuan Province, using real-world data to validate the method’s efficacy in merging multiple data sources and enhancing the accuracy of exposure risk analysis. Notably, this approach also reduces the time complexity from O (m×n×p) to O (m×n).

Improved Landsat Operational Land Imager (OLI) Cloud and Shadow Detection with the Learning Attention Network Algorithm (LANA)

Landsat cloud and cloud shadow detection has a long heritage based on the application of empirical spectral tests to single image pixels, including the Landsat product Fmask algorithm, which uses spectral tests applied to optical and thermal bands to detect clouds and uses the sun-sensor-cloud geometry to detect shadows. Since the Fmask was developed, convolutional neural network (CNN) algorithms, and in particular U-Net algorithms (a type of CNN with a U-shaped network structure), have been developed and are applied to pixels in square patches to take advantage of both spatial and spectral information. The purpose of this study was to develop and assess a new U-Net algorithm that classifies Landsat 8/9 Operational Land Imager (OLI) pixels with higher accuracy than the Fmask algorithm. The algorithm, termed the Learning Attention Network Algorithm (LANA), is a form of U-Net but with an additional attention mechanism (a type of network structure) that, unlike conventional U-Net, uses more spatial pixel information across each image patch. The LANA was trained using 16,861 512 × 512 30 m pixel annotated Landsat 8 OLI patches extracted from 27 images and 69 image subsets that are publicly available and have been used by others for cloud mask algorithm development and assessment. The annotated data were manually refined to improve the annotation and were supplemented with another four annotated images selected to include clear, completely cloudy, and developed land images. The LANA classifies image pixels as either clear, thin cloud, cloud, or cloud shadow. To evaluate the classification accuracy, five annotated Landsat 8 OLI images (composed of &gt;205 million 30 m pixels) were classified, and the results compared with the Fmask and a publicly available U-Net model (U-Net Wieland). The LANA had a 78% overall classification accuracy considering cloud, thin cloud, cloud shadow, and clear classes. As the LANA, Fmask, and U-Net Wieland algorithms have different class legends, their classification results were harmonized to the same three common classes: cloud, cloud shadow, and clear. Considering these three classes, the LANA had the highest (89%) overall accuracy, followed by Fmask (86%), and then U-Net Wieland (85%). The LANA had the highest F1-scores for cloud (0.92), cloud shadow (0.57), and clear (0.89), and the other two algorithms had lower F1-scores, particularly for cloud (Fmask 0.90, U-Net Wieland 0.88) and cloud shadow (Fmask 0.45, U-Net Wieland 0.52). In addition, a time-series evaluation was undertaken to examine the prevalence of undetected clouds and cloud shadows (i.e., omission errors). The band-specific temporal smoothness index (TSIλ) was applied to a year of Landsat 8 OLI surface reflectance observations after discarding pixel observations labelled as cloud or cloud shadow. This was undertaken independently at each gridded pixel location in four 5000 × 5000 30 m pixel Landsat analysis-ready data (ARD) tiles. The TSIλ results broadly reflected the classification accuracy results and indicated that the LANA had the smallest cloud and cloud shadow omission errors, whereas the Fmask had the greatest cloud omission error and the second greatest cloud shadow omission error. Detailed visual examination, true color image examples and classification results are included and confirm these findings. The TSIλ results also highlight the need for algorithm developers to undertake product quality assessment in addition to accuracy assessment. The LANA model, training and evaluation data, and application codes are publicly available for other researchers.

The Impact of Official Promotion Incentives on Urban Ecological Welfare Performance and Its Spatial Effect

Economic growth, environmental protection, and welfare improvement are closely related to local officials. Therefore, exploring the paths that affect ecological welfare performance (EWP) from the perspective of official promotion incentives has certain practical significance. EWP is an indicator that measures the welfare relationship between ecological resource inputs and outputs, reflecting the sustainable development status of a region. First, considering the background of the dual carbon goals, the EWPs of 284 cities from 2007 to 2020 were measured by constructing an indicator system and using the super-SBM-DEA method. Second, by constructing a theoretical framework of “official promotion incentives-fiscal responsiveness-EWP”, we empirically tested the impact and mechanism of official promotion incentives on urban EWP; finally, based on regional and official age heterogeneity, we explored the differential impacts of official promotion incentives on urban EWPs. The results show that official promotion incentives inhibit the improvement of urban EWP by reducing financial responsiveness; in terms of spatial spillovers, under three types of spatial weight matrices, promotion incentives for officials in neighboring areas can significantly improve the EWPs of local cities. From the perspective of regional differences, official promotion incentives in the eastern region significantly improved urban EWP, official promotion incentives in the western and northeastern regions significantly inhibited urban EWP, and official promotion incentives in the central region had no significant impact on urban EWP. From the perspective of official age, local officials aged 53 and 54 intensified the inhibitory effect of promotion incentives on urban EWP. By constructing a theoretical framework and empirical testing, this paper provides theoretical support and empirical evidence for the impact of official promotion incentives on urban EWP. The results help to understand the relationship between official promotion incentives and sustainable development, and promotes sustainable urban development.

Exploring Positive Psychological Factor on Safety Behavior of Chinese Aircraft Maintenance Technicians: The Interplay of Workplace Well-Being, Work Engagement, and Leadership

The safety behaviors of aircraft maintenance technicians (AMTs) are crucial in ensuring aviation safety. It is, therefore, vital to explore the factors associated with AMT safety behaviors and the underlying mechanisms. This study, drawing on self-determination theory and the broaden-and-build theory, explores the relationship between workplace well-being (WWB) and AMT safety behaviors within the framework of positive psychology. A sample of 593 Chinese AMTs (561 males and 32 females; average age = 31.28, SD = 0.64) anonymously completed questionnaires on demographic variables, WWB, work engagement, safety-specific transformational leadership (SSTL), and safety behavior. We utilized SPSS 26.0 and the PROCESS macro (models 4 and 7), along with Amos 24.0, to conduct an empirical test of a moderated mediation model elucidating the relationship between WWB and safety behavior. The findings reveal a positive association between WWB and AMT safety behaviors, with work engagement acting as a partial mediator. Furthermore, SSTL was found to negatively moderate the positive association between WWB and work engagement, as well as the association between WWB and safety behavior through work engagement. Notably, the positive association between WWB and work engagement, and thus safety behavior, becomes more pronounced under lower SSTL conditions. In conclusion, this study makes theoretical contributions and offers practical insights for improving aircraft maintenance safety management practices.

Does digitalization help green consumption? Empirical test based on the perspective of supply and demand of green products

Green consumption (GC) is an unavoidable choice for mitigating environmental pressures, achieving the “double carbon goal”, and promoting the development of a low-carbon, green society as a whole. With the rapid advancement of digitization (Digi), artificial intelligence, big data, and other associated digital technologies have become powerful driving forces for GC from both the supply and demand perspectives. Therefore, a spatial Durbin model (SDM) was utilized to investigate the local and spatial spillover impacts of Digi on GC based on panel data from 2011 to 2020 from 30 Chinese provinces. It was found that: (1) Digi can influence both the supply and demand aspects of green products, establishing a positive correlation with local GC. It also exerted a favorable incentive effect on GC in neighboring provinces through spatial connections and spatial spillover, within the framework of the imitation mechanism. Several robustness tests revealed that the effects remained significant. (2) the enhanced market mechanisms and a favorable competitive environment offered by the high-level market, in addition to the subsidy and incentive mechanisms within the government's Green Consumption Policy (GCP), were instrumental in fostering the development of GC. Consequently, the government's role in “promotion” and the market's role in “pull” had a regulating effect on the impact of Digi on GC. (3) heterogeneity analysis illustrated that Digi had a more significant promoting effect on GC in the comprehensive big data pilot and large-scale industrial digitization areas. Based on this, we suggest that the state increase investment in Digi to provide a foundation for the research and development of green digital technologies. Additionally, the state should promote the construction and improvement of green consumer markets and formulate more incentive or penalty systems conducive to promoting GC. The research results also provide a theoretical basis for the direct and dynamic effects of Digi, as well as inspiration for promoting GC and green sustainable development.

A Constitutive Model for Stud Connection in Composite Structures

The complexity of finite element analysis for composite structures can be significantly reduced by representing the connector and adjacent concrete as a macroscopic element. Nevertheless, the prevailing macroscopic models for shear connections predominantly employ nonlinear elastic theory. This approach introduces inaccuracies in estimating structural stiffness and load-bearing capabilities, primarily due to its inability to precisely capture the cumulative effects of plastic damage. In response, this study introduces a novel macroscopic elastoplastic model grounded in plasticity theory, aimed at accurately characterizing the nonlinear behavior of stud connections subjected to concurrent shear and tensile forces. This paper meticulously delineates the implementation of the elastoplastic constitutive model using the backward Euler method for numerical integration. It further articulates the derivation of the consistent tangent stiffness, which aligns with the convergence efficiency of the Newton–Raphson iterative approach. The computation of the element stiffness matrix for a two-node element is executed via the governing equation inherent to the finite element method. An exemplar macroelement test conducted in ABAQUS affirms the implicit backward Euler scheme’s stability and consistency across varying tolerances. Validation of the elastoplastic model against empirical test outcomes corroborates its efficacy, demonstrating the model’s precision in predicting the load–displacement behavior of stud connections under the influence of shear and tensile forces.

Market potential, spatial theories and spatial trends

ABSTRACT Previous literature on European regions has shown that structural estimation of New Economic Geography (NEG) wage-type equations obtains results similar to those obtained using old regional economics techniques. I show that this similarity is due to the presence of global spatial trends in the variables (first-order non-stationarity), which produce spurious regressions. Formal tests and graphical models confirm that any variable displaying a core–periphery spatial pattern produces similar predictions for European regional per capita income. Empirical tests of spatial theories should thus pay attention to the geographical features of the administrative units and the global spatial trends of the variables analysed.

Beta diversity and regionalization of the western Atlantic marine biota

AbstractAimUnderstanding the factors determining marine community variations is important for biogeography and conservation. Beta diversity is a metric for mapping species composition variations between communities and regionalizing biota. Ecoregions are commonly used for regionalization, but their empirical testing has been limited. Our aim is to map marine species composition variations in the Western Atlantic, identify variables related to these variations, and regionalize areas based on community distribution. Additionally, we test whether currently proposed ecoregions represent unique biota units and specific environmental conditions.LocationWestern Atlantic Ocean.TaxonVertebrates, invertebrates and algae.MethodsWe constructed a large marine biodiversity database, including vertebrates, invertebrates, and algae, totalling over 4 million records. We used the generalized dissimilarity model (GDM) to identify variables most related to species composition variations and map beta‐diversity variations. We employed an unsupervised classifier for community regionalization. To test if the ecoregion regionalization boundaries are corroborated by species distribution data, we used the Sørensen index. To assess if ecoregions correspond to environmental units, we checked if areas had distinct environmental conditions using a PCA of 134 marine environmental variables.ResultsThe GDM explained a high variation in species composition, 61% in the complete database. Analysing vertebrates, invertebrates and algae separately also yielded relatively high results: 46%, 54%, and 33%, respectively. Coastal areas differed from open sea areas in composition. Environmental variables combined better explained beta diversity than isolated variables. The regionalization based on GDM was not congruent with ecoregion boundaries. Moreover, ecoregions showed no distinction in species composition or environmental conditions.Main ConclusionsThis study's regionalization is crucial for marine biodiversity conservation, focusing on understanding species composition patterns between coastal and open sea areas to develop tailored conservation strategies. Despite sampling limitations, the study advances marine biogeography knowledge by analysing over 4 million species records and 134 environmental variables. This comprehensive approach enhances understanding marine species distribution and diversity and aiding the development of effective conservation measures.

Investigation into the seamless construction for hundred-meter scale super-length raft structure based on magnesia expansive agent concrete

This research introduces an innovative construction method based on magnesia expansive agent concrete for the seamless construction of hundred-meter scale super-length raft structures, corroborated by the on-site test. The basic principle of this construction method is to use the pre compression stress generated by magnesia expansive agent to offset temperature and shrinkage stress. A temperature-strain monitoring system was employed to gather data, affirming the technique’s applicability and safety. Through the examination of temperature and strain dispersion trends in super-length raft structure, recommendations for the configuration of temperature-strain sensors have been put forth. Through the scrutiny of the temporal evolution pattern of temperature, the specific temporal and spatial coordinates that warrant particular vigilance during the surveillance of the raft’s inner-surface temperature difference were identified. Upon evaluating the correlation between strain dispersion and strain-temperature differential in the raft’s thickness dimension, a novel temperature control index (the bottom-surface temperature difference) was introduced. The threshold for this metric was established at 30°C, derived from empirical test outcomes conducted on-site. Furthermore, the critical regions for monitoring the bottom-surface temperature difference were specified.

Analysis of Factors Affecting Voluntourism Network Attention in China: An empirical Test Based on Big Data Analysis

Based on the Baidu index platform, this study takes panel data of 31 provinces, autonomous regions and municipality directly under the central government in China from 2019 to 2023 as a research sample, and and uses big data to empirically analyze the influencing factors of the Voluntourism Network Attention in China. This study chose the Voluntourism Network Attention as the explanatory variable, the seven indicators including the consumption level of the residents, the population above the college and the total tourism revenue of the region as explained variables. Using the STATA15.0 software, the big data model is determined by a descriptive test, a unit root test, a co-integration test, a variance expansion factor (VIF) test, etc. The regression equation of this study was constructed by the generalized least squares method. The empirical study is conducted to determine the influence mode and degree of each factor on the public interest tourism network attention and analyzes the internal causes. The results show that the educational level of the region, the level of economic development and the level of tourism development have a significant positive impact on the Voluntourism Network Attention, and the regression coefficient of the education level and economic development level is the largest. Both of them are the main factors of network the Voluntourism Network Attention. In view of the empirical results, this paper puts forward the following suggestions: First, it is necessary to create more voluntourism routes suitable for college students. Second, it is essential to increase the marketing and promotion of voluntourism activities in economic and tourism developed areas.Last, there is a need to innovate the connotation and form of the voluntourism.

Modelling employee retention in small and medium-sized enterprises and large enterprises in a dynamically changing business environment

Purpose This study aims to explore the holistic context of organisational staff retention in small, medium and large organisations. It also aims to identify the factors affecting the retention of organisations of different sizes. Design/methodology/approach The study implements an empirical test of a model created during previous research with the participation of 511 employees. The responses to the online questionnaire and the modelling were analysed using the partial least squares structural equation modelling method. The models were tested for internal consistency reliability, convergent and discriminant validity, multicollinearity and model fit. Findings Two models were tested by organisation size, which revealed a total of 62 significant correlations between the latent variables tested. Identical correlations were present in both models in 22 cases. After testing the hypotheses, critical variables (nature of work, normative commitment, benefits, co-workers and organisational commitment) were identified that determine employees’ organisational commitment and intention to leave, regardless of the size of the organisation. Research limitations/implications As a result of this research, the models developed are suitable for identifying differences in organisational staffing levels, but there is as yet no empirical evidence on the use of the scales for homogeneous groups of employees. Practical implications The results show that employees’ normative commitment and organisational commitment are critical factors for retention. Of the satisfaction factors examined, the nature of work, benefits and co-workers have a significant impact on retention in organisations, so organisational retention measures should focus on improving satisfaction regarding these factors. Social implications The readers of the journal would appreciate the work, which highlights the significance of employee psychology and retention for organisational success. Originality/value The study is based on primary data and, to the best of the authors’ knowledge, is one of the few studies that take a holistic approach to organisational staff retention in the context of the moderating effect of organisational size. This study contributes to a comprehensive understanding of the phenomenon of employee retention and in contrast to previous research, examines the combined effect of several factors.

Separating insider and informed behavior: Evidence from a natural setting

AbstractPrevious literature has established the need to separate insider from expert trading in both financial and betting markets. This type of separation proves difficult to achieve because experts and insiders co‐exist in most market settings. Utilizing novel betting markets in which expert skill bettors are unable to use their expert knowledge, this study uncouples insider from expert betting, and allows more direct measurement of insider trading than in previous empirical tests. Evidence is presented in favor of some predictions from the highly cited Shin model of insider trading. Price setters charge an extra premium in markets with high proportions of insiders when compared to equivalent markets. The insiders accurately predict event outcomes, and their presence exacerbates a well known bias in betting market prices whereby returns on betting favorites is higher than for bets on longshots.

CSR fit with CEO image: Unpacking its influence on stakeholder engagement and authenticity perception

ABSTRACT This study explores the significant role of Chief Executive Officers (CEOs) in communicating a corporation’s commitment to Social Responsibility (CSR) and their influence on consumer engagement in CSR initiatives. This study introduces a conceptual model that integrates the sense-making approach and social exchange theory within the CSR domain. The model posits a positive correlation between a CEO’s ethical leadership, their personal image congruence with CSR initiatives, and consumer engagement in CSR. Central to this study is the empirical testing of the model, which revealed that CSR authenticity and Consumer-Company Identification (CCI) significantly mediate the relationship between CEO attributes and consumer engagement. Data collected from an online survey with 988 respondents supported that CEOs who are perceived as ethically sound and whose personal image aligns with CSR initiatives tend to foster higher levels of consumer engagement through enhanced perceptions of CSR authenticity and strengthened CCI. These findings offer new insights into the dynamics of CEO influence on CSR communication and consumer engagement.

A wireless neural recording microsystem with operator-based spike detection

We introduce an innovative approach that incorporates operator-based spike detection in wireless microsystems for neural signal processing. Through comparative analyses between simple thresholding and operator-based detection conducted on pre-recorded spike detection experiments, our research emphasizes the superiority of the operator-based spike detection approach. The operator-based spike detection emerges as a promising technique for miniaturized wireless neural signal devices, primarily due to its proficient noise-handling capabilities paired with reduced power consumption. Furthermore, its adaptability across various experimental conditions amplifies its versatility. Empirical tests underscored its low power requisites and compactness, emphasizing practical utility of the detection scheme in the neural microsystems. Collectively, our results mark a significant progression in wireless cerebral signal recording methodologies, paving the way for optimized wireless brain-machine interface (BMI) systems.

Ecological Safety Assessment and Convergence of Resource-Based Cities in the Yellow River Basin

Promoting the sustainable development of resource-based cities is of great significance for the ecological protection and high-quality development of the Yellow River Basin. In order to solve the dilemma of the sustainable development of resource-based cities in the Yellow River Basin, this paper constructs an ecological security evaluation system. It calculates the ecological security level of 30 resource-based cities in the Yellow River Basin from 2006 to 2020 using the TOPSIS model and carries out a classification convergence analysis. The DID empirical test of ecological security factors by sustainable development planning policies is used to distinguish the development characteristics of different resource-based cities. The results show that (1) the ecological security level of resource cities in the Yellow River Basin is generally well developed, and there are differences between different types of resource cities; (2) the resource cities in the Yellow River Basin mainly rely on industrial transformation to improve the ecological security level; and (3) the local governments pay limited attention to environmental protection.