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Purpose: Based on the micro-electro-mechanical system (MEMS) technology, acoustic emission sensors have gained popularity owing to their small size, consistency, affordability and easy integration. This study aims to provide direction for the advancement of MEMS acoustic emission sensors and predict their future potential for structural health detection of microprecision instruments. Design/methodology/approach: This paper summarizes the recent research progress of three MEMS acoustic emission sensors, compares their individual strengths and weaknesses, analyzes their research focus and predicts their development trend in the future. Findings: Piezoresistive, piezoelectric and capacitive MEMS acoustic emission sensors are the three main streams of MEMS acoustic emission sensors, which have their own advantages and disadvantages. The existing research has not been applied in practice, and MEMS acoustic emission sensor still needs further research in the aspects of wide frequency/high sensitivity, good robustness and integration with complementary metal oxide semiconductor. MEMS acoustic emission sensor has great development potential. Originality/value: In this paper, the existing research achievements of MEMS acoustic emission sensors are described systematically, and the further development direction of MEMS acoustic emission sensors in the future research field is pointed out. It provides an important reference value for the actual weak acoustic emission signal detection in narrow structures. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this paper is to estimate the energy saving potential in China's paper industry by determining energy intensity under different scenarios. Cointegration model and stability test are applied to formulate the equilibrium equation. Results show that energy price, industry structure, profit margin and technology have significant impact as they are negatively correlated to changes in energy intensity. Three scenarios (BAU (business as usual), intermediate and active) are designed to estimate the future trend of energy intensity in paper industry. In 2010, the energy intensity (energy consumption per unit of value added) was 4 tce/10,000 RMB, and by the scenarios it is expected to considerably decline. According to the BAU (business as usual) scenario, the energy intensity will decrease to 2.56, 1.43 and 0.70 tce/10,000 RMB by 2015, 2020 and 2025, respectively. With respect to the intermediate scenario it is expected to drop further to 0.44 tce/10,000 RMB. Yet by the active scenario, the energy intensity is expected to considerably decline to 0.36 tce/10,000 RMB by 2025. Using the BAU forecast as a baseline, the quantity of energy savings is estimated to be 185.6 billion tce by 2025. Considering this important potential, we also provided some policy suggestions. [Copyright &y& Elsevier]

China, situated in the circum-Pacific seismic belt, experiences frequent seismic activity and faces diverse geological conditions, making structural stability of paramount importance, especially under seismic conditions. The majority of current earthquake generation methods do not consider the nonstationary nature of earthquakes. This paper introduces a spectral representation-random function model for generating nonstationary earthquakes, effectively simulating stochastic seismic ground motion. Furthermore, traditional slope stability analysis methods are deterministic and incapable of providing probabilistic assessments of slope instability. Therefore, this paper proposes a unified framework for static and dynamic structural reliability analysis based on the direct probability integration method, quantifying the impact of stochastic seismic ground motion on the dynamic reliability of slope stability. Finally, the proposed methods are applied to an excavation slope in Nanjing, using sliding displacement and safety factors as evaluation criteria to study the reliability of the slope under the influence of stochastic seismic events. [ABSTRACT FROM AUTHOR]

This paper investigates the long-run and short-run relationship between money supply and inflation in Pakistan, utilizing annual data spanning from 1981 to 2021. The key objective is to assess the impact of monetary policy, specifically money supply, on inflation dynamics in the country. To achieve this, the Autoregressive Distributed Lag (ARDL) bounds testing approach is employed, which is suitable for analyzing cointegration among variables with mixed integration orders. The results reveal both short and long-run cointegration between inflation, money supply, unemployment, and interest rates. Notably, unemployment demonstrates a negative correlation with inflation, while money supply and interest rates exhibit a positive relationship. These findings underscore the importance of dedicated policy measures to manage inflation effectively. The paper concludes by recommending the establishment of a policy implementation body and collaboration between the government and the central bank to ensure financial stability and control inflation through well-calibrated monetary and fiscal policies. [ABSTRACT FROM AUTHOR]

Summary: Cointegration has shown useful for modeling non‐stationary data with long‐run equilibrium relationships among variables, with applications in many fields such as econometrics, climate research and biology. However, the analyses of vector autoregressive models are becoming more difficult as data sets of higher dimensions are becoming available, in particular because the number of parameters is quadratic in the number of variables. This leads to lack of statistical robustness, and regularisation methods are paramount for obtaining valid estimates. In the last decade, many papers have appeared suggesting different penalisation approaches to the inference problem. Here, we make a comprehensive review of different penalisation methods adapted to the specific structure of vector cointegrated models suggested in the literature, with relevant references to software packages. The methods are evaluated and compared according to a range of error measures in a simulation study, considering combinations of low and high dimension of the system and small and large sample sizes. [ABSTRACT FROM AUTHOR]

In this paper we give an affirmative answer to the Euclidean analogue of a question of Bourgain and Brezis concerning the optimal Lorentz estimate for a Div–Curl system: If F ∈ L 1 (R 3 ; R 3) satisfies div F = 0 in the sense of distributions, then the function Z = curl (- Δ) - 1 F satisfies curl Z = F div Z = 0 and there exists a constant C > 0 such that ‖ Z ‖ L 3 / 2 , 1 (R 3 ; R 3) ≤ C ‖ F ‖ L 1 (R 3 ; R 3) . Our proof relies on a new endpoint Hardy–Littlewood–Sobolev inequality for divergence free measures which we obtain via a result of independent interest, an atomic decomposition of such objects. [ABSTRACT FROM AUTHOR]

This paper explores the knowledge network structure of foreign research literature by applying the qualitative comparative analysis (QCA) method to the field of information science and library science (ISLS) from the perspective of the cocitation of social network actors such as authors, institutions, countries, and literature, and it further reveals the future application trends of this method. [Method/process] Based on 86 journals in the ISLS field that were downloaded from the Web of Science using the QCA method, the social network analysis (SNA) method and the visual analysis tool Gephi are used to analyse the author cooperation network, the research institution cooperation network, the national cooperation network, the cocitation network, the cutting-edge trends, etc., of journal papers. The analysis shows that the QCA method covers a wide range within the field of ISLS, but the research topics involved in this field are not concentrated, and the author cooperation network has scale-free characteristics. The application of the QCA method is still dominant in European and American countries, and China, the USA, and Italy all play key roles in the national cooperation network. Finally, the institutional cooperation network has certain small group attributes. [ABSTRACT FROM AUTHOR]

Supply chain instabilities and inflated material prices have had a disruptive impact on cost estimating of construction projects. While several research efforts used national macroeconomic indicators to forecast the prices of domestically produced construction materials, none of the existing studies investigated whether the lagged macroeconomic indicators of the main trading partners could enhance the predictability of the prices of cement, steel, and lumber in the US construction sector. This paper fills this knowledge gap. The authors adopted a multi-step methodology that included: (1) collecting data on the target variables and the candidate leading indicators; (2) identifying the structural breaks in the collected data sets; (3) conducting causality tests to identify short-term associations and cointegration tests to examine long-term relationships; (4) developing vector error correction (VEC) models to forecast the prices in the short and long terms; and (5) evaluating the performance of the proposed models against existing forecasting models in the literature. Results of the Granger test and Johansen test indicate that Canada's overall producer price index (PPI) is a consistent leading indicator of the prices of cement, and Mexico's overall PPI is a consistent leading indicator of the prices of steel. Findings indicate no statistical evidence to suggest that neither Canada's PPI nor Mexico's PPI can be leading indicators of lumber prices. Over an 18-month ahead of sample horizon, the presented VEC models of cement and steel prices outperformed existing models, particularly beyond the 1-year-ahead forecasts. Utilization of the proposed forecasting models can significantly enhance the accuracy of cost estimates and feasibility studies of construction projects. This provides proactive financial planning for construction contractors and project owners through improved short- and long-term forecasting of the prices of main construction materials. [ABSTRACT FROM AUTHOR]

The profound changes brought about by informatization and digitalization have given rise to the user-centered innovation concept, and value co-creation by enterprises has become an inevitable trend. It has become a pressing issue for scholars to analyze the mechanism of consumer participation in the value co-creation of innovative enterprises. In this paper, by establishing an evolutionary game model between consumers and innovative enterprises, we analyze in depth the mechanism of consumer participation in the value co-creation of innovative enterprises. The results show that the initial cooperation probability between consumers and innovative enterprises directly affects their strategic choices; the establishment of reward mechanisms makes consumers more inclined to choose active participation in value co-creation strategies; as the probability of non-cooperation between the two parties being reported increases, the probability of consumers and innovative enterprises choosing cooperation also increases. Studying the mechanism of consumer participation in the value co-creation of innovative enterprises has essential theoretical and practical significance for enterprises to achieve value creation, enhance competitiveness, and promote innovation. This study not only enriches and develops relevant theories but also provides guidance and support for the practice of enterprises, promoting sustainable development and successful co-creation. [ABSTRACT FROM AUTHOR]

The life expectancy rate is an essential yardstick for measuring people's well–being. The healthier the population, the better the economic rate of productivity. Not many studies have investigated the asymmetric link between energy use and life expectancy. Thus, this paper addresses the gap by evaluating the impact of fossil fuel (FFEC) and renewable energy consumption (RNEW) on life expectancy (LEXP) in Nigeria from 1965–2019 using the Autoregressive Distributed Lag (ARDL) and Non‐linear ARDL methods. The bounds test to cointegration confirmed the presence of long‐run association. According to the findings of the ARDL technique, FFEC and RNEW improve LEXP in the long and short‐run. In addition, the NARDL result confirms that a positive shock in FFEC and RNEW improves LEXP in the long and short‐run. This study suggests that cleaner technologies should be applied to fossil fuel sources, while the importance and use of RNEW sources should be continually promoted. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between the gross value added (GVA) of Greece's agricultural sector and the GVAs of the other sectors. The research considers both the relationship between value levels and the cycles of GVAs. Dynamic analysis using ARDL modeling shows that there is no cointegration between agricultural GVA and the other GVAs. However, there is an estimated cointegrating relationship between business cycles of agriculture and those of the rest of the economic sectors, with the cycles of services being the significant variable. Moreover, econometric analysis using NARDL modeling shows that there is a cointegrating relationship between the levels of GVAs as well, when asymmetricity—with respect to GVA changes of the services sector—is introduced. [ABSTRACT FROM AUTHOR]

The "traditional view" on the historical decline of child labour has emphasised the role of the approval of effective child labour (minimum working age) laws. Since then, the importance of alternative key driving factors such as schooling, demography, household income or technology has been highlighted. While historically leading countries such as England and industrial labour have been studied, peripheral Europe and a full participation rate also including agriculture and services have received limited research attention. The contribution of this paper is to provide a first empirical explanation for the child labour decline observed in a European peripheral country like Portugal using long historical yearly data. For doing so, we use long series of Portugal's child labour participation rate and several candidate explanatory factors. We implement cointegration techniques to relate child labour with its main drivers. We find that not only factors related to the "traditional view" were important for the Portuguese case. In fact, a mixture of legislation, schooling, demography, income, and technological factors seem to have contributed to the sustainable fall of Portugal's child labour. Hence, explanations for observed child labour decline seem to differ by country and context, introducing a more nuanced view of the existing literature. [ABSTRACT FROM AUTHOR]

In this paper, we examine how foreign direct investment inflows in agriculture affect the agricultural exports of developing countries. The main objective of our analysis is to identify and quantify the export effects of foreign direct investment in the sector, as well as the effects of other relevant export determinants, and to explore the possible long-run relationship between the variables. Using panel data on 80 developing countries observed in the period 2005-2017, we employ the system-generalised method of moments and the method of quasi-maximum likelihood to estimate a dynamic agricultural exports model. Additionally, we test for the cointegration between exports and foreign direct investment using the Pedroni and Kao approaches. We find that foreign direct investment inflows have a positive impact on agricultural exports in the analysed countries in both the short run and the long run. Thereby, we identified the significance of both direct and spillover export effects. This implies that the liberalisation and promotion of foreign investment in agriculture could alleviate the problems of capital scarcity and constraints in developing countries and increase their agricultural export competitiveness. [ABSTRACT FROM AUTHOR]

In order to improve the economic growth efficiency of industrial and commercial enterprises in coastal cities and realize the GDP growth of coastal cities, this paper studies the economic growth factors of industrial and commercial enterprises in coastal cities based on the unexpected super efficiency model. Based on the research and analysis of the previous economic growth theories, this paper finds out the main factors that affect the economic growth of industrial and commercial enterprises in coastal cities, and uses the advanced econometric method to establish the relevant test model to analyze the correlation between the time series of economic growth factors and the time series of coastal cities, so as to realize the economic growth factors of industrial and commercial enterprises in coastal cities Element study. The empirical results show that the main factors affecting the economic growth of industrial and commercial enterprises in coastal cities are capital and labor force, with labor force as the main body; Technical and institutional factors also contribute to the GDP of industrial and commercial enterprises in coastal cities, but the impact is not significant and needs further improvement. In general, these factors can promote the economic growth of industrial and commercial enterprises in coastal cities. The time series and time series of each factor variable are first-order non-stationary series with long-term cointegration relationship. [ABSTRACT FROM AUTHOR]

Pollution haven hypothesis (PHH) assumes that polluting industries will move to regions with lesser rigorous environmental regulations. On the other hand, pollution halo hypothesis presumes that industries transfer their clean technologies through FDI inflows to the host countries. Following these theoretical perceptions, this paper empirically examines how foreign direct investment (FDI) affects pollution (CO2 emissions) for four selected Asian countries (Malaysia, Philippines, Singapore, Thailand) over the period 1971-2014. This is done by applying the autoregressive distributed lags (ARDL) model of Pesaran et al.. The ARDL model is employed under two scenarios: without and with structural breaks. The long-run findings, for both scenarios, suggest support for the pollution haven hypothesis (PHH) for the Philippines only. Whereas, the findings lend support for the pollution halo hypothesis for Malaysia and Singapore. In addition, the paper explores the causality direction between FDI and pollution (CO2 emissions) using the Vector Error Correction model (VECM). The results show mixed long- and short-run Granger causality findings. [ABSTRACT FROM AUTHOR]

Bitcoin has attracted incessant attentions in recent times. Studies have completed models to examine the relationship between Bitcoin and other multiple attendant variables. This paper considers a simple and direct price-volume relation. The paper offers causality evidence according to the dynamic asymmetric causality test. Based on available monthly data spanning 2010:M7-2022:M10, the paper shows that Bitcoin price and volume are integrated, both been I(0)'s. Moreover, the paper discloses the short- and long-term price-volume behaviors of Bitcoin using the cointegration test and vector error correction model (VECM). Taken together, the study first confirms long run relations and presents the estimates of the parsimonious VECM. The results show short run evidence of positive price-volume relations, and in the long run, the disequilibria are as well corrective and mean reversing. The outcomes of the Hatemi-J's causality testing suggest likely evidence of bidirectional causality between the positive and negative fragments of the shocks of Bitcoin price and volume during the periods. [ABSTRACT FROM AUTHOR]

Summary: The modular multilevel converter (MMC) is a novel prospective multilevel converter topology for high‐voltage or high‐power applications. This paper presents the 3‐ Φ model integrated into a linear model in transient analysis and the design of current controller for MMC. This paper analyzes the control interactions of MMC on nearby turbine generator. The eigenvalue analysis is used to evaluate the impact of different controller modes of MMC. The impact of converter control with nonlinear switches is analyzed through the transient simulation. The eigenvalue analysis and controller design are carried out on a linear model, and the transient simulation analysis is performed on both the D‐Q model and a three‐phase model of MMC using MATLAB‐Simulink. The results show that the transient simulation of three‐phase model on the proposed approach is consistent with the transient simulation of D‐Q model. Also, the transient simulation results show a satisfactory response with the current controller parameters of the MMC at various operating cases. To validate the results of MATLAB‐Simulink model, the proposed system is implemented in hardware‐in‐the‐loop (HIL) simulation with an integrated subsystem built in two real‐time simulators. The subsystem of mathematical model is implemented in dSPACE, and the subsystem of 3‐ Φ MMC with controller is implemented in Typhoon. The HIL simulation results show a satisfactory response and consistent with MATLAB‐Simulink model results. [ABSTRACT FROM AUTHOR]

Purpose: This paper empirically investigates the effect of economic policy uncertainty (EPU) on the UK money demand stability during the inter-war period (1920–1938). Both a narrow definition (M0) and a broad definition (M3) of money are investigated. Design/methodology/approach: The empirical investigation is conducted by employing the autoregressive distributed lag (ARDL) bounds testing approach to cointegration. Findings: Results presented indicate a stable demand for both definitions of money only when EPU is included as one of the determinants of demand function. The EPU imposes a negative effect on the demand for both definitions of money. The causality test results further indicate long- and short-term causality from the determinants (including EPU) to both forms of money demand. Practical implications: Significant presence of the economic uncertainty weakens the effects of the monetary policy on the economy. Originality/value: This is a historical economics paper. Given the turmoil and uncertainty associated with the inter-war period, an empirical investigation of UK money demand is an interesting exercise. This is the first such paper. [ABSTRACT FROM AUTHOR]

The paper investigates the relationship between tourism, energy consumption, trade openness, economic growth, and CO2 emissions for 20 economies of the APEC region from 1995 to 2017. This paper employs cross-sectional dependence with heterogeneous panel estimation techniques. The data confirms cross-sectional dependence, and the CIPS panel unit root test shows that the variables are stationary at their first differences. The Westerlund panel cointegration test affirms a long-run relationship among the variables. Tourism and trade openness have significant positive effects on CO2 emissions while economic growth and energy consumption adversely affect CO2 emissions in the long-run. The panel non-causality test reveals that there is a one-way causality running from tourism to CO2 emissions and economic growth to CO2 emissions. [ABSTRACT FROM AUTHOR]

The causal relationship between economic growth and defence spending has attracted considerable attention and has been the subject of many empirical studies. Hoping to contribute to the existing pool of literature, this paper examines the relationship between military expenditure and growth in the case of Cyprus, a small island economy, for the period 1964-99. The findings reported suggest the presence of bi-directional instantaneous causality between defence spending and economic growth. [ABSTRACT FROM AUTHOR]

This paper deals with the analysis of the temperatures in a group of 36 African countries. By looking at the maximum, minimum and the range (the difference between the maximum and the minimum) and using a long memory model based on fractional integration and cointegration, we first show that all series display a long memory pattern, with a significant positive time trend in 29 countries for the maximum temperatures and in 33 for the minimum ones. Looking at the range, the estimated value for the order of integration is smaller than the one based on maximum or minimum temperatures in 17 countries. Performing fractional cointegration tests between the maximum and minimum temperatures, our results indicate that the two series cointegrate in the classical sense (i.e., with a short memory equilibrium relationship) in a group of 11 countries, and there is another group of eight countries displaying cointegration in the fractional sense. The remaining 17 countries with no evidence of cointegration are therefore at a very high risk of climate change due to the absence of long-term co-movement in their maximum and minimum temperatures. Findings in this paper are of tremendous interpretations and relevance for the analysis and climate projections in Africa. [ABSTRACT FROM AUTHOR]

This methodology paper introduces a collective, team-based approach to constructing narratives in narrative research. The goal of the larger study was to explore the pedagogical belief and practices of engineering faculty members. The newly formed team of researchers ranged from novices to experts in the field of qualitative research, and this space created a unique opportunity to reflect on and explore the co-construction of Cody's narrative, the first narrative that the team constructed. The narrative was smoothed and constructed in a way that reduces some of the limitations inherent in narrative smoothing, through a deliberate and intentional negotiation process. We hope that this deeper exploration of our methods is helpful for other narrative researchers who are interested in team-based approaches to co-construction of narratives. [ABSTRACT FROM AUTHOR]

The environmental Kuznets curve (EKC) hypothesis assumes the nonlinear relationship between production and its environmental effects, suggesting that the negative effect of production on the natural environment is reversed at higher levels of development. In contrast to the majority of the studies, this paper considers the possibility of non‐linear effects of per capita output on agricultural land use (cropland), as one of the natural resource stocks, and additionally examines the influence of demographic and productivity variables on the agricultural land use. The study draws on panel data from 143 economies over the 1990–2019 period and employs a series of panel econometrics techniques (including panel unit root and cointegration tests, panel ordinary least squares, generalized method of moments, and panel quantile models). The results unequivocally indicate the inverted U‐shaped relationship between the GDP per capita and cropland use, as per EKC hypothesis. The results are robust with respect to employing different techniques, the identified turning points correspond to low and low‐middle‐income levels, and the effects of agricultural productivity and population density on land use are respectively negative and positive. [ABSTRACT FROM AUTHOR]

This paper examines the sustainability of the current account deficit (CAD) and validity of the intertemporal budget constraint of India for the period of 1980 to 2019. The cointegration results show that exports and imports are not cointegrated and the current account (CA) series is not mean reverting during the study period. Therefore, the results refute the sustainability of CAD and the validity of the intertemporal budget constraint in the Indian context. Furthermore, the study also emphasised the testing of the disaggregated CA and found that while the goods account is not sustainable, the services account found to be sustainable in the long run. Our findings thus indicate the potential for aggregation bias to have an impact on the overall CA sustainability results. Therefore, policies that help in achieving self‐sufficiency on the one hand and export promotion in goods on the other may eventually contribute to CA sustainability. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has significantly impacted international trade, benefiting national and international economies. It has increased trade and financial liberalization in many countries. Ecological and environmental protection have become critical issues due to the fast mobility of money and commodities. The purpose of this paper is to find a way to balance foreign trade growth with environmental protection. We aim to achieve this by using IoT to establish a co-integration model that takes into account the comprehensive environmental index, imports and exports, and the capital-labor ratio. We have collected data from Quanzhou City between 2011 and 2019, based on various environmental pollution indicators, import and export trade volume, capital-labor ratio, and other factors. Our analysis includes an examination of the empirical model of foreign trade and the ecological environment, as well as the impulse response. Additionally, we used prediction variance decomposition and the Granger causality test to understand the coordinated relationship between foreign trade and the ecological environment. Our results demonstrate that when the volume of exports (EX) increases by 1 percent, the ecological environment in Quanzhou City is negatively impacted by about 0.06%. On the other hand, when the volume of imports (IM) increases by 1 percent, there is a 0.26% positive effect on the ecological environment of the city. Finally, when the capital-to-labor ratio (K) increases by 1 percent, there is a 0.40% deterioration of the city's ecological environment. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between innovative green (public) efforts and short and long-term environmental degradation, through a cointegration panel with 18 countries selected for the period 1995-2018. The ARDL Panel for cointegration was specified with variable effects of scale, composition and technological change. The results obtained that higher levels of public expenditure on resources directed towards environmental R&D (green innovative efforts), with the aim of green technologies, have a negative relationship with the growth of environmental impacts, i.e., that is. such investments have a good expectation of reducing environmental degradation. [ABSTRACT FROM AUTHOR]

Welfare measures and their assessment involve a set of indicators that, as economic thought evolves, are subject to critique. Nonetheless, the ultimate debate focuses on the quality of life and the conditions fostered by each economy. Using data from the period 1993 to 2019, this paper presents an analysis of the current account and terms of trade, highlighting the significance of these variables, as they encompass important flows and contribute to shaping market conditions and the resulting benefits. The study employs both Vector Error Correction (VECM) and Structural Vector Error Correction (SVECM) models to explore the interplay between external dynamics and their effects on the economy. The findings reveal that an export price shock has an expansive effect on investment, yet its impact on savings becomes marginally significant over time. Unexpected shocks on investment are roughly 38% reliant on external factors, of which an average of 32% is attributable to export prices, underscoring the tight correlation between investment and international market dynamics. Notably, the results indicate that external dynamics primarily exert short-term effects. Consequently, despite periods of robust growth, reduced unemployment, and poverty reduction, these indicators have not proven to be sustainable in Peru. [ABSTRACT FROM AUTHOR]

This study estimates the long-term impact of Chinese RMB exchange rate movements on the trade balance between China and the Eastern African Belt and Road Initiative countries. The Pedroni cointegration analysis and the fully modified and dynamic OLS techniques are applied to the dataset from UNComtrade and China Statistical Yearbook 2019. Some specification novelties are the use of imports and exports as independent variables. By so doing, the paper proposes an alternative measure of the trade balance. We find that the RMB exchange rate movements are stable during the sample period and slightly affect the trade balance between China and the Eastern African BRI countries. Additionally, the study does not find evidence supporting the J-curve effect in China-East Africa trade balance. The empirical results suggest that the Eastern African BRI countries would gain much more trading collectively with China than individually. Therefore, the study strongly recommends that the East African Community accelerate its integration process and establish Export Quotas for trade outside the bloc. [ABSTRACT FROM AUTHOR]

This paper presents a novel estimator for a nonstandard restriction to both symmetry and low rank in the context of high-dimensional cointegrated processes. Furthermore, we discuss rank estimation for high-dimensional cointegrated processes by restricted bootstrapping of the Gaussian innovations. We demonstrate that the classical rank test for cointegrated systems is prone to underestimating the true rank and demonstrate this effect in a 100-dimensional system. We also discuss the implications of this underestimation for such high-dimensional systems in general. Also, we define a linearized Kuramoto system and present a simulation study, where we infer the cointegration rank of the unrestricted p\times p system and successively the underlying clustered network structure based on a graphical approach and a symmetrized low rank estimator of the couplings derived from a reparametrization of the likelihood under this unusual restriction. [ABSTRACT FROM AUTHOR]

In this paper, using empirical and statistical research, we analyze the impact of the external sector on economic growth in Paraguay during the 1994-2020 period. The results enabled the assessment of the validity of the income elasticities of imports calculated in the long term, attributed to the presence of cointegration among the series. Consequently, the economic growth rate associated with the external balance was obtained, aligning with Thirlwall's Law. The empirical analysis and validation of Thirlwall's Law suggest that the growth dynamics of the Paraguayan economy can be explained by the Thirlwall model. Thus, it was determined that the external sector has had an impact on Paraguay's economic growth. [ABSTRACT FROM AUTHOR]

The objective of this research paper is to apply a mathematical model to estimate and predict the economic growth of the Caspian countries in the period from 1995 to 2022. We use multiple regression by applying the OLS method to estimate the impact of global oil price, energy resource production per capita, trade, and renewable energy on GDP per capita. The mathematical approach uses fixed and random effects models to assess the overall impact of the independent variables on economic growth in this region and over the period analysed. This study also aims to investigate whether the explanatory variables are cointegrated in the long run; as such, we carry out several mathematical cointegration tests, namely the Pedroni and Johansen tests. The mathematical analysis is completed by the estimation of short- and long-run parameters using the stochastic VAR/VEC models, the impulse response function, and the causality test to assess economic growth in this region. This study's main finding is that GDP per capita is increasingly influenced by its previous values, which is confirmed by considering lag 1 and lag 2. The results of the Granger causality tests identify several bidirectional relationships between GDP per capita and oil and gas production. These relationships are clearly positive evidence of the growth trend and progress of economic activity in the Caspian region. The practical implications of the study aim to promote and support the use of renewable energy sources. In this sense, policymakers in the Caspian countries should create favourable conditions for the transition to a green economy. An important aspect is the efforts of the government authorities to make their policies more environmentally friendly, as decarbonisation is a good practice in the current context of sustainability and related choices. As the Caspian countries are heavily dependent on conventional energy production, it is essential for them to increase their export earnings from energy resources via diversifying and strengthening new energy opportunities and partnerships. [ABSTRACT FROM AUTHOR]

Digital elevation models (DEMs) are currently one of the most widely used data sources in glacier thickness change research, due to the high spatial resolution and continuous coverage. However, raw DEM data are often misaligned with each other, due to georeferencing errors, and a co-registration procedure is required before DEM differencing. In this paper, we present a comparative analysis of the two classical co-registration methods proposed by Nuth and Kääb (2011) and Rosenholm and Torlegard (1988). The former is currently the most commonly used method in glacial studies, while the latter is a seminal work in the photogrammetric field that has not been extensively investigated by the cryosphere community. Furthermore, we also present a new residual correction method using a generalized additive model (GAM) to eliminate the remaining systematic errors in DEM co-registration results. The performance of the two DEM co-registration methods and three residual correction algorithms (the GAM-based method together with two parametric-model-based methods) was evaluated using multiple DEM pairs from the Greenland Ice Sheet and mountain glaciers, including Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEMs, ZiYuan-3 (ZY-3) DEMs, the Shuttle Radar Topography Mission (SRTM) DEM, and the Copernicus DEM. The experimental results confirm our theoretical analysis of the two co-registration methods. The method of Rosenholm and Torlegard has a greater ability to remove DEM misalignments (an average of 4.6 % and 13.7 % for the test datasets from Greenland Ice Sheet and High Mountain Asia, respectively) because it models the translation, scale, and rotation-induced biases, while the method of Nuth and Kääb considers translation only. The proposed GAM-based method performs statistically better than the two residual correction methods based on parametric regression models (high-order polynomials and the sum of the sinusoidal functions). A visual inspection reveals that the GAM-based method, as a non-parametric regression technique, can capture complex systematic errors in the DEM co-registration residuals. [ABSTRACT FROM AUTHOR]

The prevailing research on forecasting surface deformations within mining territories predominantly hinges on parameter-centric numerical models, which manifest constraints concerning applicability and parameter reliability. Although Multi-Temporal InSAR (MT-InSAR) technology furnishes an abundance of data, the underlying information within these data has yet to be fully unearthed. Consequently, this paper advocates a novel methodology for prognosticating mining area surface deformation by integrating ensemble learning with MT-InSAR technology. Initially predicated upon the MT-InSAR monitoring outcomes, the target variables for the ensemble learning dataset were procured by melding distance-based features with spatial autocorrelation theory. In the ensuing phase, spatial stratified sampling alongside mutual information methodologies were deployed to select the features of the dataset. Utilizing the MT-InSAR monitoring data from the Zixing coal mine in Hunan, China, the relationship between fault slippage and coal extraction in the study area was rigorously analyzed using Granger causality tests and Johansen cointegration assays, thereby acquiring the dataset requisite for training the Bagging model. Subsequently, leveraging the Bagging technique, ensemble models were constructed employing Decision Trees, Support Vector Regression, and Multi-layer Perceptron as foundational estimators. Furthermore, the Tree-structured Parzen Estimator (TPE) optimization algorithm was applied to the Bagging model, resulting in an optimal model for predicting fault slip in mining areas. In comparison with the baseline model, the performance increased by 25.88%, confirming the effectiveness of the data preprocessing method outlined in this study. This result also demonstrates the innovation and feasibility of combining ensemble learning with MT-InSAR technology for predicting mining area surface deformation. This investigation is the first to integrate TPE-optimized ensemble models with MT-InSAR technology, offering a new perspective for predicting surface deformation in mining territories and providing valuable insights for further uncovering the hidden information in MT-InSAR monitoring data. [ABSTRACT FROM AUTHOR]

The objective of the paper is to verify hypotheses regarding integration and cointegration (relation) of mean apartment prices on the primary and secondary market in Szczecin. Both transaction prices as well as offer prices of apartments were investigated. The analysis period encompasses the years of 2006 – 2022 (quarterly data). An ADF test was employed to examine the integration of time series, taking into consideration a deterministic component in the form of a quadratic function. Only the time series of mean offer prices and transaction prices on the primary market proved to be integrated in the first degree. The time series of mean offer prices and transaction prices on the secondary market were not integrated, they occurred to be trend stationary series. A two-step Engle-Granger test was employed to analyze the cointegration of time series, which confirmed the cointegration of mean offer prices and transaction prices on the primary market. The relations between individual price types were examined with the use of a procedure which entailed analyzing (with an ADF test) difference stationarity between prices. From the empirical studies it arises that, in Szczecin, transaction and offer prices on the primary market follow one another. On the secondary market, offer and transaction prices are trend stationary and they converge. On the other hand, prices on the primary market diverge from prices on the secondary market (the primary market diverges from the secondary market). This concerns both offer prices as well as transaction prices. [ABSTRACT FROM AUTHOR]

This paper examines the impact of green finance on environmental performance in developing countries. Using panel cointegration analysis and panel vector error correction model (VECM) to a sample of 76 developing economies from 2010 to 2019, this study confirms that carbon emissions (CO 2) is cointegrated with green finance, real gross domestic product, urbanization, and renewable energy under different specifications and therefore we estimate the long-run relationships among these variables. Specifically, both green finance and renewable energy are found to exert significant and inhibitory effects on CO 2 emissions, reaffirming the necessity to promote green finance and renewable energy. Results from the paper also document the existence of the Environmental Kuznets Curve and the increasing effects of urbanization on CO 2 emissions. Several policy implications and recommendations are drawn from this study. • We examine impact of green finance on environmental performance. • 76 developing economies from 2010 to 2019 are considered. • Green finance and renewable energy exert inhibitory effects on CO 2 emissions. • Results support the existence of the Environmental Kuznets Curve. [ABSTRACT FROM AUTHOR]

As Turkiye celebrates its 100th year, planning for sustainable and inclusive economic growth for Turkiye's future is crucial. In this context, this paper investigates the association between economic evolution and CO2 releases, energy usage, and gross fixed capital formation; utilizing the Bounds Cointegration test and Granger-causality test during the period spanning from 1990 to 2021. The results validate the long-run relationship between economic evolution and CO2. Moreover, the findings endorse the neutrality hypothesis. For policymakers, it's crucial to champion investments in renewable energy platforms, potentially by offering incentives such as tax breaks. Concurrently, the correlation between economic progress and growing CO2 releases signals the importance of pushing for cleaner industrial processes and enhanced energy efficiency. Moreover, given the varied findings of several studies, it's necessary to perpetuate ongoing research to base policies on the latest and most exact data present. [ABSTRACT FROM AUTHOR]

Usually developing and transitional countries are characterised by foreign exchange and capital scarcities and hence resort to stringent trade and capital control policies. This might become counterproductive and provide incentives to the international traders and investors to go for corrupt practices. This paper investigates how do these tight policies might encourage illegal or hidden capital movements across borders. By presenting both a theoretical and an empirical analysis, where traders and investors rationally misreport to evade stringent trade and investment barriers, we first show that illegal capital outflow takes place through trade misreporting and interestingly, export and import misreporting are cointegrated. Secondly and more importantly illegal capital inflow might take place through overreporting of FDI values and illegal capital outflow and inflow are cointegrated too. Based on the thorough investigation of the BRICS – USA bilateral trade and FDI data, we propose that a less regulated trade and investment regime might benefit these countries more as tight and restrictive policies seem to be self-defeating. Our study comes up with policy conclusions that might minimise the cross-border illegal capital movements. [ABSTRACT FROM AUTHOR]

The paper aims to analyze the contagion effect coming from the developed stock markets of the US and Germany to the emerging CEE stock markets of Romania, Czech Republic, Hungary, and Poland using daily data for the period April 2005–April 2021. The paper also captures the level of integration of these emerging stock markets by analyzing the volatility spillover phenomenon. The quantification of the contagion effect coming from the developed to the emerging stock markets consisted of an empirical analysis based on the DCC-GARCH (Dynamic Conditional Correlation) model. Through this multivariate model, the time-varying conditional correlations were analyzed, both in periods of normal economic development and in times of economic instability, when there was a significant increase in the correlation coefficients between developed and emerging stock market indices. Furthermore, the level of connectedness between these markets has been analyzed using the volatility spillover index developed by Diebold and Yilmaz. The empirical results surprised the high level of integration of the analyzed stock markets in Central and Eastern Europe, with the intensity of volatility transmission between these markets increasing significantly during times of crisis. All stock market indices analyzed show periods during which they transmit net volatility and periods during which they receive net volatility, indicating a bidirectional volatility spillover phenomenon. Mostly, the BET, PX, and WIG indices are net transmitters of volatilities, whereas the BUX index is net recipient, except during the COVID-19 crisis, when it transmitted net volatility to the other three indices. Finally, using a Markov switching-regime VAR approach with two regimes, we explored the contagion effect between emerging CEE and developed stock markets during the COVID-19 pandemic. The empirical results proved a shift around the outbreak of the health crisis, after which the high volatility regime dominates the CEE markets. The contagion effects from developed stock markets to emerging CEE markets significantly increased during the first stage of the health crisis. [ABSTRACT FROM AUTHOR]

Night light data can reflect the economic activity in a specific area, which is closely related to GDP. But within the different cities and areas, the relationships show different performances. This paper uses the data of long time series multi-source night lights to solve the limitation of DMSP-OLS and NPP-VIIRS data sources. According to the situation of Shanghai, we studied the relationship between average night light intensity and GDP in Shanghai through correlation analysis and linear regression methods. The results show that there is a significant positive relationship between the Shanghai average night light intensity and GDP, also the average night light intensity has a significant positive effect on GDP in Shanghai. In this paper, the new attempt is made in mathematical calculation method and constructing model, and a better result is obtained, which has good theoretical significance and practical value for the scientific application of night light data. [ABSTRACT FROM AUTHOR]

At present, China's equipment manufacturing industry and producer service industry are gradually ushered in the leapfrog high-quality development of the rising period. It is particularly important how Shanxi, as the vanguard of the transformation and development, positively responds to the national call to break the shackles of the current integrated development of equipment manufacturing and producer services, for Shanxi Province and even the country's future industrial integration, transformation, and upgrading to accumulate "Shanxi Experience." Therefore, this paper measures and analyzes the integration level of equipment manufacturing and production services between 2012 and 2017 using the input-output method. Then, by constructingthe model of the factors influencing the integration of Shanxi's equipment manufacturing industry and producer service industry, selecting the relevant indicators and bringing in the relevant data from 2009 to 2019, by using the principal component regression analysis method, and thus clarifies the main factors that influence the convergence of the equipment manufacturing industry and production service industry in Shanxi. Finally, this paper has provided targeted countermeasures and suggestions to further promote the integration of the two industries in Shanxi Province. [ABSTRACT FROM AUTHOR]

This paper is aim to find out the relationship among stock markets of BRICS Countries during the COVID-19. International investors want different portfolio while investing in other countries and policy makers needs information about the economies of the countries so this paper help them to get the relationship among BRICS countries. To find out the Cointegration among these secondary data has been used from 31st December 2019 to 31st December 2020. ADF and PP Test used to check the stationarity in the data set and VAR Model used to find out the cointegration between these countries and Granger Causality test used to find out the Cause relationship among the stock prices of the BRICS Countries. After analysis this paper finds that a significant integration among the BRICS Countries. [ABSTRACT FROM AUTHOR]

Money neutrality has complex consequences for small, open, and dollarized economies. For instance, it can cause external debt, when used as a stabilizer of the stock of circulating money in the face of negative exogenous shocks, not to contribute to economic growth in times of stagnation. The purpose of this paper is to describe this problem for the Ecuadorian case using a cointegration methodology and a vector error correction (VEC) model estimated over the 2000-2019 period (quarterly). The resulting impulse-response functions of the VEC model present evidence in favor of the money neutrality hypothesis, and the simulation of an exogenous negative shock in oil prices qualitatively describes the potential interaction between circulating currency, external debt, and production in dollarization. The paper concludes by presenting multiple empirical monetary-real patterns that may be useful for future research on dollarization. [ABSTRACT FROM AUTHOR]

• Impacts of export quality on economic progress are examined. • The role of economic complexity for economic growth is investigated. • 28 OECD country's data is utilized from 1990 to 2019. • Export quality and economic complexity induce sustainable growth. • Institutional quality is a significant determinant for economic growth. It is important to unearth the role of structural change factors for sustainable growth in developed economies. Economic complexity, export product quality, and institutional indicators represent the overall structural change and transformation in an economy. This paper probed the influence of export product quality and economics of complexity on economic growth for 28 OECD (Organization for Economic Co-operation and Development) countries using the data from 1990 to 2019. In doing so, the study employs three-panel data econometric models to investigate the role of economic complexity, export quality, and institutional quality on economic development. The paper performs the empirical tests, including the cross-sectional related dependency methods and cointegration techniques. As per the results emerging from fully modified OLS (FMOLS) and dynamic OLS (DOLS) estimation, export quality and financial development positively and significantly strongly impact growth in the long run. The empirical conclusions report detailed discussion on sustainable growth and discuss novel implications concerning product quality and institutional performance. The conclusions stress the practical implications for sustainable economic growth of OECD economies. [ABSTRACT FROM AUTHOR]

Globalization is a phenomenon that has significant effects in many areas such as socio-economic, political, cultural etc. of the last century. With globalization, consumption habits may also change. As a matter of fact, it can be stated that the effects of globalization on food security are also a subject that needs to be addressed and examined. From this point of view, the aim of this paper is to investigate the effect of globalization and economic growth on food security for MIST countries between 1970 and 2019. Food production index was taken as a food safety indicator. In empirical analysis, CADF unit root test, Durbin-Hausman cointegration test, and AMF coefficient estimator were used. As a result of the analysis, a cointegration relationship was found between globalization, economic growth and food security in MIST countries. Furthermore, while economic growth has a positive and statistically significant effect on food security, globalization has a statistically significant but negative effect on food security. [ABSTRACT FROM AUTHOR]

Since 1999, Environment and Climate Change Canada (ECCC) has been coordinating a multi-laboratory comparison of measurements of long-lived greenhouse gases in whole air samples collected at the Global Atmosphere Watch (GAW) Alert Observatory located in the Canadian high Arctic (82°28' N, 62°30' W). In this paper, we evaluate the measurement agreement of atmospheric CO2, CH4, N2O, SF6, and stable isotopes of CO2 (δ13C, δ18O) between leading laboratories from 7 independent international institutions. The measure of success is linked to target goals for network compatibility outlined by the World Meteorological Organization's (WMO) GAW greenhouse gas measurement community. Overall, based on ~8000 discrete flask samples, we find that the co-located atmospheric CO2 and CH4 measurement records from Alert by CSIRO, MPI-BGC, SIO, UHEI-IUP, ECCC, and NOAA are generally consistent with the WMO compatibility goals of ±0.1 ppm CO2 and ±2 ppb CH4 over the 17-year period (1999 - 2016), although there are periods where differences exceed target levels and persist as systematic bias for months or years. Consistency with the WMO goals for N2O, SF6, and stable isotopes of CO2 (δ13C, δ18O) has not been demonstrated. Additional analysis of co-located comparison measurements between CSIRO, SIO, and NOAA at other geographical sites suggests that the findings at Alert for CO2, CH4, N2O and δ13C-CO2 could be extended across the CSIRO, SIO, and NOAA observing networks. Two approaches are carried out to determine the level of agreement as a collective for the 7 individual laboratories (1) pooling the differences of individual laboratories over the entire sampling records from a designated reference laboratory and determining the 95th percentile range of these data points and (2) averaging the 2 standard deviations (2-sigma) of the means for all flask samples taken in each individual sampling episode over the entire sampling record. For CO2, from 5691 samples, we derive a measurement agreement level of-0.51 to +0.53 ppm using the 95th percentile range of the differences from NOAA measurements. Similarly, we derive a corresponding value of ± 0.37 ppm using the mean of 2-sigma values from 923 individual weekly sampling episodes. For CO2 isotopes using INSTAAR measurements as a reference, we derive measurement agreement values of -0.09 to +0.07 and ± 0.06 ‰ for δ13C and -0.50 to +0.58 and ±0.31 %o for δ18O, for the 95th percentile ranges and the mean of the 2-sigma values, respectively. For other gases, the corresponding values for both approaches are -4.86 to +6.16 and ± 3.62 ppb for CH4, -0.75 to +1.20 and ± 0.64 ppb for N2O, and -0.14 to +0.09 and ± 0.09 ppt for SF6. These upper and lower limits represent our best estimate of the measurement agreement at the 95% confidence level for these individual laboratories, providing more confidence for using these datasets in various scientific applications (e.g., long-term trend analysis). [ABSTRACT FROM AUTHOR]

This paper deals with a kind of nonconvex optimistic bilevel optimization programs. In some process of dealing this kind of bilevel programs, difficulties are essentially moved to estimating for coderivative of the solution map. To deal with these difficulties, we use value function of the lower level problem and its modifications as implicit functions to describe the solution map. By applying techniques in variational analysis, we give estimates for coderivative of the solution map. Then, we will show the applications in optimality conditions for these bilevel programs which we derived by using the extremal principle. [ABSTRACT FROM AUTHOR]

The movement of rock formations caused by mining eventually leads to the mining subsidence damage of the surface. In order to accurately and efficiently predict the surface subsidence caused by mining, an object-oriented method combined with the classical probability integration method is introduced in this paper, and an object-oriented probability integration prediction model framework is established. MATLAB2019 is used to develop the application program of the prediction model, the reliability of the prediction model is tested by engineering cases, and the main influencing factors of mining subsidence are analyzed at the same time. The results show that the predicted results of the model are basically consistent with the case. The prediction model developed in this paper using the object-oriented method combined with the probability integration method is reliable. It is feasible to apply the object-oriented method to mining subsidence prediction. [ABSTRACT FROM AUTHOR]

For Internet information services, it is very important to closely monitor a large number of key time series data generated by core business for anomaly detection. Although there have been many anomaly detection models in recent years, its practical application is still a big challenge. The model usually needs repeated iteration and parameter adjustment; and for different types of time series data, we need to select different models. Therefore, this paper proposes an anomaly detection model based on time series. The model first designs the statistical features, fitting features, and time-frequency domain features for the time series, and then uses the random forest integration model to automatically select the appropriate features for anomaly classification. In addition, this paper presents an anomaly evaluation index ADC score with timeliness window, which adds the time delay factor of anomaly detection on the basis of F1-score. We use the KPI time series, a representative key performance index in the industry, as the experimental data. It is found that the ADC score of the anomaly detection model in this paper reaches the level of 0.7–0.8, which can meet the needs of practical application. [ABSTRACT FROM AUTHOR]

Urbanization is the accompaniment of industrialization and modernization, a symbol of the progress of human civilization, and it has always been the focus of social attention. With the transfer of rural population to cities, the secondary and tertiary industries continue to produce agglomeration effects, and the urbanization process is accelerated. The level of urbanization has gradually become an important indicator to measure the degree of regional development, which has led to the exploration of the inner dynamics of urbanization. Finance is the core of modern economy, and urbanization is an important driving force for modern economic and social development. Research on the relationship between financial development and urban scale should also receive widespread attention. On the basis of reviewing and sorting out the related research results of urbanization and financial development, this paper makes an in-depth analysis of urbanization and financial development, respectively, aiming to have a systematic, comprehensive, and objective analysis of the interaction between urbanization and financial development. On this basis, combined with the current situation of our country's financial development and urbanization, the internal relationship between the two is discussed. Based on the mutual information algorithm, this paper selects indicators that are representative of urbanization level, financial development, and economic development. By using the mutual information theory to analyze and rank the social and economic factors related to financial factors, it reveals the most relevant and accurate financial forecast. Then, it also discusses whether there is a cointegration relationship between urbanization and financial development and economic growth in our country and the causal relationship between them and analyzes the reasons on this basis. [ABSTRACT FROM AUTHOR]