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The earth is experiencing unprecedented global warming and this has an adverse impact on the health of the planet, people and economies. A major contributor to the degradation of planetary health is CO2 emissions that are contributed by the transportation sector. The brunt of the environmental degradation is experienced by developing countries, where the transportation sector is dependent on cheaper fossil-fuels. This study examines the causal interface between transportation infrastructure development, economic growth, institutional quality, and CO2 emissions in 79 developing countries over the period 2005 to 2022. A quantile-on-Quintile regression (QQR) and vector error-correction model (VECM) are deployed to study the causal relationships between the above-mentioned variables. The empirical findings show strong endogenous relationships between the variables in the short-run and long-run. The concerning trend is that, while the transportation sector and improvement in the institutions contribute positively to the economic growth of these countries, CO2 emissions (from the transportation sector) have a detrimental, and perhaps larger, impact on economic growth. A key insight from these findings is that there is a need to reduce CO2 emissions by ensuring greater use of renewable energy to power the transportation sector. A more environmentally-friendly transportation sector will be critical in enabling these countries to transition towards sustainable economic growth trajectories. [Display omitted] • Exploring the interface between institutional quality, transportation, economic growth and CO2. • The sample comprises developing countries from 2005 to 2022. • The study uncovers uni- and bidirectional nexus between these variables. [ABSTRACT FROM AUTHOR]

East/West (E/W) vertical bifacial photovoltaic (PV) modules can achieve higher profits than the conventional North/South (N/S) tilted configuration depending on the design choices and external conditions. In this study a model based on 2D view factor concept is developed to estimate the power generated by a large-scale bifacial PV farm, considering the non-uniformity of the incident irradiance and the spectral impact. A validation using measured data is performed, focusing on the non-uniformity of the rear irradiance. This model is used to compare the profitability between E/W vertical and N/S tilted PV farm configurations, considering higher prices during noon with respect to morning/evening periods. The results identify the ratio between these two price values as the key variable that influences the comparison between the PV farm configurations. Specifically, a sufficiently high price ratio ensures the higher profitability of E/W vertical modules, however, the exact value is dependent on the location and the design variables. In general, higher row-to-row distance and lower diffuse fraction enhance the profitability of the E/W vertical over the N/S tilted configuration. On the other hand, elevation of the modules, curtailment strategies and hybrid solutions have a minor influence. • The model simulates the performance of bifacial photovoltaic farms. • The profitability of vertical bifacial modules depends on the design parameters. • The electricity market conditions influence the profitability of vertical modules. • Large daily price differences are favourable for vertical modules. • Vertical modules are favourable in locations characterized by low diffuse fraction. [ABSTRACT FROM AUTHOR]

This study examines the link between marine resources exports and their impact on economic development in Pakistan by using Vector Error-Correction Models (VECM). The data of 57 years from 1960 to 2017 were used to analyse the impact among variables. The findings of this research were obtained as there is no long-run association discovered between the fisheries exports and economic development. However, we discovered that there is a short-run relationship sustained between them. In addition, the results from the Ordinary Least Squares (OLS) regression also validates that there is a robust relationship between GDP and exports of fisheries products. The findings indicated that the sub-sector (Fisheries) of the agriculture sector is playing an extensive role in the economic growth of Pakistan. [ABSTRACT FROM AUTHOR]

This paper introduces a method of combining phase space reconstruction and symbolic dynamics to study the causality between urbanization and economic growth at different regional levels in Shandong Province and finds that there is a strong positive relationship between urbanization and economic growth from China, indicating that the development of urbanization can drive the economic growth. Then, according to the results of correlation analysis between respective subvariables belonging to urbanization and economic growth and the principle of "deleting strong and reserving weak," the paper selects the independent variable and dependent variable to explore the hidden causal mechanisms that drive the developing of urbanization and economic growth from China. The results show that (1) the pattern causality between the independent variable and the dependent variable is dominated by dark causality at the provincial level; (2) the pattern causality between the independent variable and the dependent variable is dominated by dark causality at the Jinan economic circles and the Lunan economic circles, but the positive causality is dominated at the Jiaodong economic circles; (3) the types of causality between the same evaluation index and PU in different regions are different, and furthermore the degrees of positive, negative, and dark causality are different at two levels and three regions. The conclusion shows that although there is an obvious positive interaction between urbanization and economic growth, the influences of many factors are neither positive nor negative causality, but dark causality, which plays an important role in developing urbanization and economic growth. This work is useful for studying the law of causality between urbanization and economic growth, and this interesting result can be extended to other economic events. [ABSTRACT FROM AUTHOR]

This particular study investigated the possibility of modelling the exchange rate volatility of the USD/LKR currency pair and analysed whether macroeconomic factors influence the exchange rate. To model the exchange rate volatility, a combination of Autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) family models were used. The ARDL model was utilized to explore the presence of dynamic short-run and long-run relationships between the exchange rate and macroeconomic variables. The ARDL model empirical findings inferred that a long-run relationship does not exist between any of the examined macroeconomic variables and the exchange rate. In contrast, a short-run relationship exists between exchange rate lag one, exchange rate lag two, inflation, and merchandising trade balance. Thereby, as per the findings improving the merchandising trade balance and minimising inflation would minimise volatility in the exchange rate. All stakeholders who are exposed to foreign exchange volatility including policymakers, importers, exporters, and financial institutions can benefit from this study's findings. This research focused on the most recent economic phenomena of Sri Lanka and used Gross official reserve as a variable that was rarely used in existing literature on Sri Lankan exchange rate. [ABSTRACT FROM AUTHOR]

This study analyzes the dynamic effects of overall globalization and its three sub-dimensions (economic globalization, political globalization, and social globalization) on renewable energy consumption in Turkey using annual data from 1980 to 2017. As an estimation technique, the dynamic autoregressive distributed lag simulation (dynamic ARDL) developed by Jordan and Philips (2018) is employed. The results indicate that the overall globalization process, both in the short-run and in the long run, and its dimensions of economic and political globalization, only in the long-run, have positive and significant effects on Turkey's renewable energy consumption. However, social globalization does not have any significant effect on renewable energy consumption either in the short-run and in the long run. Additionally, increases in Turkey's human capital stock and economic growth rate appear to cause more renewable energy consumption. Based on those results, some important policy implications encouraging the globalization and economic growth processes and enriching the human capita stock are suggested. [ABSTRACT FROM AUTHOR]

This paper focuses on climate change in Turkey caused by energy consumption using an Autoregressive Distributed Lag and Toda-Yamamoto causality analysis. The motivation and aim are: Finding evidence of causality for the relationship between energy consumption, growing economies and climate change depending on parameters that vary over time, which are observed and argued through political implications. Temperature and precipitation are the dependent variables for climate change; energy types and Gross Domestic Product per capita are the independent variables for economic determiners. Data was collected annually from various institutions between 1980 and 2019. According to the Toda-Yamamoto test, a negative relationship is determined between renewable energy consumption and temperature in both the short and long term. The results reveal that a 1% increase in renewable energy reduces the temperature by 0.031%. The increase of renewable energy may help in decreasing temperature. Precipitation and non-renewable energy consumption have a positive relationship in both the short and long term, with a 1% increase in non-renewable energy consumption causing a 0.175% increase in precipitation, indicating a negative effect on climate change. Encouraging renewable energy consumption through government incentives can be a powerful solution to decrease the negative effects of climate change in Turkey. • This study examines the relationships between climate change and energy consumption. • It conducts an ARDL analysis and Toda-Yamamoto causality tests in a growing economy. • Renewable energy consumption has a positive effect (reduces) temperature. • Non-renewable energy consumption has a negative effect (increases) precipitation. • Renewable energy consumption is critical mitigating global warming in Turkey. [ABSTRACT FROM AUTHOR]

The growing apprehension of global warming, greenhouse gas emissions and increasing use of fossil fuel due to emergent economic activity signifies that for the sustainability of the environment cleaner energy use is important. In the same pursuit, this study aims to examine the energy, environment and economic growth nexus of Bangladesh, incorporating both renewable and non‐renewable energy consumption with forest depletion. We have employed the Two‐Stage Least Square approach to avoid likely simultaneity bias and possible endogeneity of the variables arising from the ordinary least squares. Result postulates non‐renewable energy consumption and forest depletion have a significant impact on CO2 emission, whereas renewable energy shows no significant consequence. The issue which arises in this regard is that renewable energy consumption has not yet reached a stage from where it could play a substantial role in emission reduction of the country. Therefore, countries should adopt policies that would provide a conducive environment for affordable energy use. [ABSTRACT FROM AUTHOR]

This paper made an attempt of econometric analysis of the investment situation in Pakistan for the period 1990-2017. The variables including domestic credit, national savings, lagged investment (IV), imports, and the real exchange rate were found to be the major factors affecting investment in Pakistan. Among all, the savings coefficient is positive and insignificant because of savings in Pakistan had no healthy pattern. Exchange rate has adversely affected the investment because of unidirectional causality. Specification and analytical tests supported reliability of the investment does not affect investment model from key econometric issues. The stability test shows that investment fluctuated during the 1990-2017 periods. OLS and GMM results are comparable. OLS estimation results are reliable since these variables do not suffer from Endogeneity problem. [ABSTRACT FROM AUTHOR]

Understanding the determinants of the shadow economy plays a vital role in formulating policies for economic growth and development, particularly for the Southeast Asian countries–a new economic force for a global economy. The key drivers of a shadow economy, such as institutional quality, taxation, government expenditure, are widely examined. However, the effect of national intellectual capital, which affects macroeconomic indicators, on the shadow economy has largely been ignored in the existing literature. Our paper examines this critical link and its causality relationship for eight Southeast Asian countries from 2000 to 2017. This paper uses the dynamic ordinary least squares (DOLS) and fully modified ordinary least squares (FMOLS), which allow cross-sectional dependence and slope homogeneity in panel data analysis. Empirical findings from this paper indicate that national intellectual capital impacts negatively and significantly the shadow economy size. This finding implies that enhancing national intellectual capital reduces the shadow economy size. These two forces lead to enhanced economic growth. Our Granger causality tests confirm a bi-directional relationship between national intellectual capital and the shadow economy. As a result, policies targeted to reduce the shadow economy size can now include the accumulation of national intellectual capital, particularly for Southeast Asian Countries. [ABSTRACT FROM AUTHOR]

This paper investigates the causal relationship between bank development and economic growth in the US before and after the financial crisis in 2008. Based on the quarterly panel data of US states for 2002Q1-2012Q4, this paper uses two-step system GMM method to test the long-term and short-term impact of bank on growth. In addition, panel Granger causality test is used to verify the causality between bank and growth in different periods. The study finds that banks have played a significant role in the long-run economic growth process and there exists a bidirectional causality between bank and growth among US states. In the short term, the financial crisis has corrected the short-run relationship between bank and growth. The relationship between bank and growth has changed from one-way causality before the crisis to two-way causality after the crisis. Before the crisis, economic growth promotes bank credit, but bank development does not promote growth. After the crisis, banks play the role of economic driver through credit, savings and services, and economic growth drives bank savings and scale expansion. The financial intermediary service functions of banks, especially credit to private sector and absorbing savings to transfer loans, determine the role of bank development in economic growth. Policy makers should support bank development in the long run to establish a mature financial intermediary system and promote regional economic development. [ABSTRACT FROM AUTHOR]

With 2.5 global hectares (gha) per capita against 2.7 gha per capita, China's ecological footprint is desirably below the world's average ecological footprint per capita. Undesirably, the country's per person ecological footprint outweighs the world's average biocapacity per person of 1.7 gha, thus signifying an enormous pressure on the country's ecological capacity. This reason accounts for the motivation to explore the dynamics of ecological footprint for China over the period 1971–2016 by employing a series of empirical techniques that include quantile-on-quantile regression (QQR), spectral Granger causality (SGC), and quantile regression. Indicatively, the empirical findings are in folds. First, from the QQR, economic growth exerts a positive effect on (i) ecological footprint especially in the middle quantile (0.4–0.7) and (ii) all quantiles (0.01–0.95) of economic growth. Second, both fossil fuel and primary energy utilization exert a positive impact on (i) all quantiles (0.01–0.95) of ecological footprint and (ii) all quantiles (0.01–0.95) of the two energy profiles. Third, it is surprising to see renewable energy utilization exerting a positive effect on ecological footprint at the lower tail (0.1–0.40) and on renewable energy use at the higher tail (0.70–0.95). Additionally, the SGC result revealed Granger causality from primary energy use and economic growth to the ecological footprint in the long-run without reverse. Additionally, without reverse, there is a Granger causality from renewable energy use to the ecological footprint in the short-, medium-, and long-term. Importantly, the overall policy implication suggests a more drastic decoupling of the country's growth from the supply side (ecological pressure and environmental deprivation). [ABSTRACT FROM AUTHOR]

To test for the existence of long run relationship, a variety of null of cointegration tests have been developed in literature. This study is aimed at comparing these tests on basis of size and power using stringency criterion: a robust technique for comparison of tests as it provides with a single number representing the maximum difference between a test's power and maximum possible power in the entire parameter space. It is found that in general, asymptotic critical values tends to produce size distortion and size of test is controlled when simulated critical values are used. The simple LM test based on KPSS statistic is the most stringent test at all sample sizes for all three specifications of deterministic component, as it has the maximum difference approaching to zero and lesser than 20% for the entire parameter space. [ABSTRACT FROM AUTHOR]

This study examines the relationships between information and communication technology (ICT) usage and CO2 emissions considering economic growth, trade openness, and renewable electricity in the Organization for Economic Co-operation and Development countries for the period 1990–2018. It adopts pooled mean group (PMG) estimation based on the autoregressive distributed lag model. The PMG estimates indicate that although the coefficient value is small, ICT progress acts as a factor in increasing CO2 emissions in the long run. However, there is no significant short-run relationship between these two variables. Furthermore, economic growth increases CO2 emissions in the short and long run. The expansion of renewable electricity and trade openness reduces CO2 emissions in the long run. To mitigate the CO2 emissions originating from ICT, energy-saving technologies that use ICT as an energy management system should be further enhanced. The expansion of renewable electricity and the promotion of trade openness will also contribute to the mitigation of CO2 emissions in this region. [ABSTRACT FROM AUTHOR]

The current study employs a Granger causality test within a Quantile approach investigating CO2 emission determinants in China. Results show urbanization, financial development and openness to trade are leading determinants of CO2 emissions in China. These results highlight climate change issues while taking advantage of a new methodology to fill a gap in the current literature. Our findings show key implications for PRC government policy related to pollutant reduction policy. [ABSTRACT FROM AUTHOR]

Understanding the relationship between electricity consumption and economic growth is crucial for formulating efficient energy policies for all sectors in general, and for agriculture in particular. To study this, an empirical examination of the long-run co-movement and the causal relationship between electricity consumption and real gross state domestic product (GSDP) from agriculture and its allied sectors is attempted. The agricultural sector involves the use of different input technologies that are further influenced by electricity consumption. To account for this technology-enabling effect of electricity, we further take up an analysis of the relationship between electricity consumption and agricultural technology factors: fertilizer consumption, the share of irrigation, area under cereal, and the extent of mechanization. We use both state (data from 17 states for the period 1993–2017 for the electricity–GSDP relationship) and country-level data (for the period 1980–2018 for the electricity–technology factors relationship) in the analysis. Since short-period time series datasets analysis may yield unreliable and inconsistent results, we employed modern heterogeneous panel co-integration and panel-based error correction model techniques for analyzing the energy–growth linkage. When the heterogeneous states effect is taken into account, the empirical results fully support a positive long-run co-integrated connection between GSDP and electricity consumption. We could detect both long-run and short-run unidirectional causality running from electricity consumption to agricultural growth. Further electricity consumption was also found to augment the use of technology factors in agriculture. This calls for implementing policies and strategies for achieving higher electricity use in agriculture and improved efficiency in its utilization simultaneously. [ABSTRACT FROM AUTHOR]

This study empirically investigates the spillover between exchange rate risk and sectoral returns in Pakistan over the period of 1992–2017 using high-frequency data. Building on the Wavelet-multi-resolution-extended dynamic conditional correlation GARCH (MRA-EDCC GARCH) model, our findings suggest that exchange rate risk transmission to sectoral stock returns is scale-dependent. The extent of mean and volatility spillover prevalence varies across different scales. Hence, homogenous trading strategies for short-term and long-term investors may not be favorable. The finding further reveals that the risk transmissions are notable over the short run. In the context of cross spillover, there is greater evidence of exchange rate volatility impact on sectoral returns, whereas, few sectoral returns are found to affect exchange rate. Our results are robust to alternative methods of causality and short-run volatility spillover through GARCH-in-mean SVAR model. Our findings provide useful implications for risk mitigation of portfolio managers and speculators. [ABSTRACT FROM AUTHOR]

Nowadays, the increasing environmental pollution and efforts to alleviate the negative effects of global warming have raised the problem of using energy resources that cause CO2 emissions. Economic development andfurther economic growth accelerate the use of energy resources. This process causes a rise in the level of CO2 emission as well as the depletion of energy reserves. Therefore, it is necessary to formulate the optimal level of these three variables together in terms of decreasing CO2 emission, preserving energy, and improving economic performance. Therefore, this study aims to investigate whether there is a relationship between CO2 emission, energy consumption, and economic growth using data set from developed countries groups (the EU19, EU28, G7, G20, OECD-Europe, OECD-Total, BRICS, OECD-America and Eastern Europe, Caucasus and Central Asia). For this purpose, using the 2000-2018 data of developed countries, the preliminary estimation tests were used for panel unit root, Westerlund panel co-integration, and cross-section dependency. According to the unit root test results, variables that are not stationary at the level are found stationary taking their first differences. Westerlund (2007) panel cointegration test results show that there is cointegration among the variables and they move together in the long run. Further, Dimutrescu- Hurlin (2012) panel causality test results show that there is a bidirectional causality relationship between CO2 emissions and growth, and a unidirectional causality relationship between energy consumption and growth, from growth to energy. The results of this study suggest prioritizing the improvement of energy efficiency in developed countries in terms of technology and making the implementation of joint action plans in environmentally friendly policies a priority for policymakers. [ABSTRACT FROM AUTHOR]

Energy is one of the most influential factors on economic growth and development. With the effect of population growth, industrialization and urbanization, the need for energy is constantly increasing around the world. In addition to the need for energy in all areas of life, energy has an extremely important place especially in large-scale industrial countries. On the other hand, energy has made many contributions to economic growth. Therefore, energy is the main factor in the basis of developed countries. In this context, there are many studies on energy consumption and economic growth in the literature. However, it is also known that in some countries, energy production cannot meet the energy consumption. In this context, it is seen that natural resources, whose supply is limited, have a very important place in economic growth analysis. For this purpose, it is aimed to examine whether there is a causal relationship between the variables in selected OECD countries (Belgium, Spain, France, Italy, Austria, Poland, Portugal, Sweden, Norway, United Kingdom) using energy consumption and economic growth data between 2000-2019. In this context, analyzes were made with the data obtained from the World Bank with the STATA econometric analysis package program. As a result of the analysis, it can be said that a one-way causality relationship between economic growth and energy consumption has been determined. Considering the Westerlund cointegration test result, the hypothesis that there is no cointegration at the 5% significance level is rejected. [ABSTRACT FROM AUTHOR]

We aim at examining the asymmetric relationship between foreign direct investment (FDI) and economic growth by adding renewable and non-renewable energy consumption, labor, and capital for Turkey in 1987-2015 period. For this purpose, non-linear ARDL test is employed for the first time to analyze the link amongst the variables in the context of Turkey. Our results confirm that all variables included into model have positive effects on economic growth. For example, while 1 % increase of positive shock in FDI increases economic growth by 0.053 %, 1 % increase in negative shock decreases economic growth around 0.057 % in Turkey in the long-run. Furthermore, since non-renewable energy contributes to economic growth nearly two times higher than renewable energy, Turkish economy is dependent on imported fossil fuels rather than renewable energy sources. Our study highlights the importance of energy efficiency and productivity issues in Turkey. Policy implications of obtained results are presented at the end of the study. [ABSTRACT FROM AUTHOR]

In recent times, technological innovations among nations are the most powerful instrument for higher economic growth rates and development. A higher level of achievement in the diffusion/adoption of technology can create more effective economic growth. Given this motivation, the study aims to examine the impact of technological achievements on economic growth, gross capital formation, medium and high-tech exports, and employment in chosen 72 countries over the period of1990 - 2020. The unit root tests of the cross-section augmented Im-Pesaran-Shin (CIPS) test of Pesaran and also covariate Augmented Dickey-Fuller (CADF) test of Hansen, the Pedroni cointegration test, and then Pesaran ARDL model has been applied in the analysis of the data. The ARDL model results reveal a statistically significant causality and positive relationships between the technology achievement index and GDP growth, gross capital formation, medium and high-tech exports, and employment significance at 1 percent level in the long run according to Pooled Mean Group (PMG) estimator. Consequently, technological innovations are linked with economic growth and macroeconomic factors, that is to say, to get higher growth needs to grow up adaptation of technology and also to produce and trade technology-specific products. [ABSTRACT FROM AUTHOR]

The objective of this paper is to ascertain the impact of Chinese FDI on economic growth in Pakistan. This study documents the exploration of the determinants of economic growth in Pakistan by emphasizing the significant role played by Chinese FDI and investments in renewable energy in particular. This paper employs time series data analysis to examine the relationship between GDP and Chinese FDI, inflation, trade openness, exchange rates, interest rates, remittances, and renewable energy consumption from 1990 to 2019. The study involved performing the ARDL bounds test, and it was determined that the dependent and independent variables are linked in the long term. Furthermore, the error correction model is negative and noteworthy, which checks the long-run relationship between variables. According to the findings of the autoregressive distributed lag (ARDL) model, Chinese FDI has a substantial favorable effect on Pakistan's economic growth. Furthermore, renewable energy usage has a long-term favorable and significant association with Pakistan's economic growth. This study established that FDI, and particularly renewable energy, will stimulate the economic growth of Pakistan. Our research has substantial policy implications, especially when it comes to the relationship between FDI and renewable energy. [ABSTRACT FROM AUTHOR]

In order to minimize interruptions to recording, geomagnetic observatories usually use a back-up instrument operating simultaneously with the primary instrument in order to obtain comparative observations. Based on the correction parameter calculation method established in the previous work, we focused on the effects of temperature and instrument drift on the comparative geomagnetic vector observations. The linear influence of temperature on the comparative data was shown to be variable. The relative temperature coefficient changed around the temperature inflection point and showed a V-type distribution in a scatter plot. This conclusion was verified in laboratory experiments. The long-term time drift between the comparative instruments exhibits a linear pattern, and the fitness of the correction model can be evaluated by the degree to which the residual distribution of the fitted straight line conforms to the normal distribution. However, the absolute value of the long-term time drift between variometers with the same type of probe is very small. Therefore, long-term time drift correction should be carried out with care. The associated analysis and conclusions have the potential to benefit data agreement correction of long-term comparative geomagnetic vector observations and comparative testing of the performance of vector instruments. [ABSTRACT FROM AUTHOR]

English expression, language characteristics and usage norms are quite special, which is quite different from Chinese. This has special requirements for auxiliary teaching tools that use computer technology for English text processing. Based on neural network algorithm, this paper combines the actual needs of English grammar error correction to construct an English grammar error correction model based on neural network. In data processing, after feature selection, logistic regression model is used to analyze the influence of different features on article error correction. The article error correction incorporating word vector features mainly explores how to effectively express the features in English grammar error correction. In addition, this paper proposes two methods to optimize the feature representation in article error correction. One is to directly use the word vector corresponding to the word as a feature, replacing the original One-hot encoding, and the other uses a clustering method to compress the article features. Finally, this paper designs experiments to study the performance of the model constructed in this paper. The results obtained show that the model constructed in this paper has a certain effect. [ABSTRACT FROM AUTHOR]

In causal relationship studies, the latent variables may summarize the phenotypes in theoretical traits according to their phenotypic correlations, improving the understanding of causal relationships between broilers phenotypes. In this study, we aimed to investigate potential causal relationships among latent variables in broilers using a structural equation model in the context of genetic analysis. The data used in this study comprised 14 traits in broilers with 2,017 records each, and 104,154 animals in pedigree. Four latent variables (WEIGHT, LOSSES, COLOUR, and VISCERA) were defined and validated using Bayesian Confirmatory Factor Analysis. Subsequently, a search for causal linkage structures was performed, obtaining a single causal link structure between the latent variables. Then, this information was used to fit the structural equation model (SEM). The results from the SEM indicated positive causal effects of the variables WEIGHT and LOSSES on the variables VISCERA and COLOUR, respectively, with structural coefficient estimates of 1.006 and 0.040, respectively. On the other hand, an antagonist causal effect of the variable WEIGHT on the variable LOSSES was verified, with a structural coefficient estimate of −4.333. These results highlight the causal relationship between performance and meat quality traits, which may be associated with the natural processes involved in the conversion of muscle into meat and the structural changes in muscle tissues due to intense selection for high growth rates in broilers. [ABSTRACT FROM AUTHOR]

This paper investigates the association among energy consumption, carbon‐dioxide emission, and India's economic growth along with supplemental variables including foreign direct investment, population density, inflation, and agricultural land. An autoregressive distributive lag model is applied over annual time‐series data from 1985 to 2019. Further, the robustness and novelty of the model are confirmed by applying fully modified ordinary‐least‐square, dynamic ordinary‐least‐square, canonical co‐integrating regression, variance decomposition, and impulse response function. The analysis results indicate that a cointegration association exists among variables. In the short run, the profound determinants of economic development are energy consumption, carbon‐dioxide emission, inflation, and agricultural land. In the long run, both inflation and agricultural land significantly negatively influence economic growth while carbon emission drives a positive impact. Interestingly, a one‐way Granger causality runs from economic growth towards energy consumption, carbon emission, and agricultural land. The new research presents a comprehensive understanding of economic growth indicators, aiding policymakers, and advancing existing literature. Consequently, as a recommendation, the policymakers should constitute explicit goals for renewable energy consumption to stimulate sustainable economic growth with carbon emission reduction. [ABSTRACT FROM AUTHOR]

This study tries to assess the Pakistani rice producer’s behaviour under the exchange rate volatility. The exchange rate variability is a risk which may force the producers to change their production preferences. The Cobb-Douglas production function used to examine the functional association between exchange rate variability and behaviour of Pakistani rice producers. The Exchange rate volatility is measured through the Moving Average Standard Deviation and time-series data from 1981 to 2018 collected from various sources. This study uses the Johansen Co-integration and Vector error correction model to assess the short run and long run association between the exchange rate variability and rice production in Pakistan. The results confirm that the appreciation in the exchange rate will adversely affect rice production in Pakistan. This negative and significant relationship confirms the risk-averse behaviour of Pakistani rice producers. Parameters stability is checked by the Cusum and Cusumq statistics. Policymakers, when designing policies to promote rice production, should consider the exchange rate volatility. [ABSTRACT FROM AUTHOR]

A significant corridor under the "Belt and Road Initiative" is announced by the government of China for numerous transport and energy projects. China focuses greatly on the sustainability of these projects, in accordance with Green Investment Principles. For this, environmental, social, governance, and technological considerations are taken into account. This paper offers a novel assessment of the impact of green sustainability under BRI and the current emission scenario in Pakistan. The question that needs to be addressed here is whether the projects under Belt and Road Initiative are as environment friendly as they claim. In the first phase of this study, we review theories establishing evidence based on existing literature to determine the influence of Belt and Road on economic development. In the next phase, we use Environment Kuznets Curve and apply estimates from Johansson's Cointegration Test to quantify the present environmental scenario of Pakistan and probable environmental risk portrayed by the China-Pakistan Economic Corridor (CPEC). This paper helps in determining the relationship between carbon emission and three major variables including energy consumption, economic growth, and foreign direct investment (FDI). Cointegration analysis is applied on time series data comprising of a 40 years' period from 1979 to 2019. The paper finds that (i) the belt and road corridors could substantially improve trade, foreign investment, and living conditions for citizens in participating countries; (ii) moreover, this study confirms the presence of one cointegrated equation suggesting the presence of a long term relationship between the CO2 and independent variables and a short run association running from EnC to CO2. The study also proposes a Green BRI Model which formulates a body that devises environmental performance standards, reviews procedures, and ensures that they get implemented on all CPEC projects. The Belt and Road Initiative increases emissions among participating countries. China and corridor economies need to adopt strict policy reforms that increase transparency, expand trade, improve debt sustainability, and mitigate environmental, social, and corruption risks. [ABSTRACT FROM AUTHOR]

Economic development leads to the widespread use of energy, which results in carbon emissions. In order to determine the correlation between different urban carbon emissions and fiscal growth, a coupling and coordination model of urban carbon emissions and economic growth based on discrete Fourier transform (DFT) is constructed. According to the coupling and coordinated development characteristics of economic growth and carbon emissions, the evaluation index system of the regional economy and carbon emissions is constructed. This paper examines the proportion of primary energy consumption and the influencing factors of carbon emission in a city and constructs the economic growth computational intelligence model and urban carbon emission model. The coupling degree among economic growth, carbon emission, and energy consumption is studied; the coupling standard between economic growth and energy consumption carbon emission is determined; and the carbon emission factor under the method of DFT is introduced. The coupling coordination model between economic growth and carbon emission is constructed, and the interaction mechanism between carbon emission, economic development, and environmental protection is determined. The experimental findings demonstrate that when energy consumption intensity and carbon emission are relatively low, the model's change in the trend of coupling coordination index is comparable with the real/actual condition and the model is more reliable. [ABSTRACT FROM AUTHOR]

Carbon emissions constitute a large portion of greenhouse gases that are responsible for global warming and climate change. This study examines the impact of export variety on carbon emissions along with foreign direct investment (FDI) and technological development as determinants of environmental degradation in Pakistan. Moreover, this study is conducted in the context of the environmental Kuznets curve hypothesis (EKC). This study applies dynamic ordinary least squares and error correction models for long-term and short-term estimates, respectively. The results indicate that the EKC hypothesis is valid in the long term. This implies that Pakistan's economy reached the threshold level of income, after which an increase in income was not responsible for environmental degradation. Export variety restrains environmental degradation in the short term and is not a significant factor in the long term. Energy consumption has aggravated environmental degradation, while FDI and technological development are restraining environmental degradation. Policy measures are recommended to curb environmental degradation in Pakistan. [ABSTRACT FROM AUTHOR]

Covid-19 pandemic has adversely affected the different industrial sectors of Indian economy. It has thus adversely disturbed the parameters of the sustainable development in this country. This paper shows how different macroeconomic sectors of India have reacted to the adverse impact in the Indian Economy differently. Some sectors are highly affected while some sectors are not appreciably affected by the pandemic. Few major sectors have been affected badly by the pandemic. For example the highest impact on industrial growth during the pandemic has been assessed for the sectors like, mining -26.9%, manufacturing -66.6%, infrastructure -37.9%, steel - 65.2%, cement - 40%, crude oil -5.22% etc. I have studied in this paper the impact of Covid-19 pandemic on different sectors and got different findings for different sectors. The study involved with the Cointegration and Vector Error Correction Estimation of Time Series Econometrics. It may be noted that the pandemic has been going on for one and half year and therefore it is expected to be a transitory phenomenon. We, therefore, cannot expect a very long run relationship between pandemic and other sectors of the economy. That's why, the Cointegration study has been indirectly established through the estimation of Vector Error Correction model. In the estimated VEC model it has been found that there is a strong relation between changes in macroeconomic parameters and the pandemic in the short run. I have found that the growth rate in manufacture, infrastructure and mining etc have been affected adversely by the pandemic. Growth rate of different sectors have been significantly related to the 1st and 2nd lag variation of the Covid-19 dataset. The Covid-19 dataset have been found to exert negative impact on the sectors under study. [ABSTRACT FROM AUTHOR]

Climate change is the hotspot of every political and economic debate around the world. Its impacts are severe, and developing countries are highly vulnerable. Greenhouse gas emissions are growing because of economic expansion and an ever-expanding population. Using a fully modified OLS estimator, this study evaluated the link between sulfur emissions (SO2) and economic indices in SAARC nations. In addition, the study included panel data from SAARC nations from 1975 to 2018. For the long-run connection between variables, the study used panel unit root and cointegration tests. The study also included a trend analysis to comprehend the dataset's monotone tendency. The findings signify that the GDP growth has negatively influenced SO2 emissions. Therefore, foreign direct investment, trade openness, electric energy production, and population growth positively relate to SO2 emissions. The SAARC countries will promote sustainable economic growth because GDP growth is not influencing greenhouse gases. The demand for energy in SAARC countries is growing by with increasing population and economic growth by integrating different economic corridors in the Asia region, which affects environmental quality through increased economic activities. All the nations need to increase renewable resources for energy generation; otherwise, the problem of the environment remains unsolved. SAARC countries need to change the goods mix in international trade and avoid dirty imports and exports to adopt market-based policies. [ABSTRACT FROM AUTHOR]

The improper allocation of economic and environmental resources damages the United Nations sustainable development Agenda, which remains a challenge for policymakers to stop the rot through efficient governance mechanisms. The study designed an efficient environmental governance framework by extending the different governance factors linked to the environmental sustainability ratings in the cross-section of 67 countries. The results of the two-regime based estimator show that environmental corruption (regime-1), environmental politics (regime-2), and environmental laws (regime-2) negatively correlated with the environmental sustainability rating, whereas environmental democracy (regime-1 & 2) positively correlated with the environmental sustainability agenda across countries. The government effectiveness and the country's per capita income both escalates environmental sustainability ratings. The results align with the Demopolis theory, the effective regulatory theory, and the theory of law and politics. The causality estimates show that environmental corruption and government effectiveness causes environmental politics and economic growth. In contrast, environmental democracy and environmental regulations cause a country's per capita income. The bidirectional causality is found between environmental regulations and environmental corruption on the one hand, while environmental regulations and environmental politics Granger cause each other on the other hand. The results show the importance of environmental regulations in managing ecological corruption and politics across countries. The variance decomposition analysis suggested that environmental politics likely influenced the environmental sustainability agenda, followed by government effectiveness and environmental democracy for the next ten years. The study emphasized the need to design an efficient environmental governance framework that minimizes environmental corruption and enables them to move towards environmental democracy, stringent environmental laws, and regulations. Government effectiveness would mainly be linked to reducing corruption and political instability to achieve clean, green and sustainable development. [ABSTRACT FROM AUTHOR]

Demand is an important factor in supply chain network design (SCND). Among the many factors that affect demand, the impact of price cannot be underestimated. This study, for the first time, provides enterprises with direct multi-period pricing and demand decision solutions in SCND by simultaneously considering the impact of price-sensitive demand and incremental quantity discounts on the network. In this way, a mixed-integer nonlinear programming model is established based on the relationships between price and demand functions to maximize the overall profit of the supply chain. Due to the nonlinear characteristics of the model, this study proves the existence of the optimal solution of the problem and the feasibility of implementing the following two algorithms by analyzing the nature of the problem. The first algorithm is based on the second-order cone programming (SOCP) method, and the second algorithm is based on the outer-approximation method. The results of numerical experiments at different scales show that the SOCP method can obtain the optimal solution for small and medium-scale experiments. In contrast, the outer-approximation method can find approximate optimal solutions within a reasonable time for large-scale experiments. The results confirm that considering incremental quantity discount can significantly enhance the profitability of supply chain networks in long-term planning. Finally, some future research directions in the field of SCND are discussed. [ABSTRACT FROM AUTHOR]

The sustainability of the blue economy (BE) may be impeded by the adverse consequences of human activities, such as greenhouse gas (GHG) emissions, unsustainable economic expansion, and engagement in environmentally harmful trading practices. Therefore, this study aims to determine the impact of GHG emissions, trade, and economic growth on the BE. To achieve this, we used a panel dataset of 25 selected countries surrounding the Indian Ocean, covering the period 2002–2019. As a process of estimation, we employed the Westerlund error correction model to detect cointegration. To determine the long-term relationship among the variables, we adopted the cross-sectional autoregressive distributed lag (CS-ARDL) approach, which revealed a significant outcome in the long run but an insignificant outcome in the short run. In response to this divergence, the panel corrected standard error (PCSE) model was applied to correct the autocorrelation problem. Subsequently, a two-step generalized methods of moment (GMM) test was conducted to check robustness. Based on the results of PCSE and GMM, this study reveals that trade has a negative effect on the BE, while GHG emissions and economic growth have a positive ascendance. From a policy standpoint, all governments must integrate ocean-based solutions into their national climate pledges to fulfill the objectives outlined in the Sustainable Development Goals (SDGs), particularly SDG 14 (Life Below Water). [ABSTRACT FROM AUTHOR]

This paper investigates the effects of Chongqing's rural and urban residents and total resident population on economic development based on the residents' consumption structure and analyses of economic development theories concerned by using the input-output table of Chongqing during 2002–2017 and SDA (Structure Decomposition Analysis) model. The study found that, compared with the previous years, the direct consumption of the primary industry's unit output to the industrial products has decreased significantly in 2017, while the direct consumption to the tertiary industry has increased significantly; The direct consumption per unit output of the second industry is basically equal to that of the products of the industry, while the direct consumption of the products of the third industry has increased; The direct consumption per unit output value of the tertiary industry is basically equal to that of the primary industry. In the long run, the changes in consumption structure of rural and urban residents and total resident population and the increase in proportion of tertiary industry accelerate the transformation and upgrading of industrial structure. However, the effect of consumption structure on GDP (Gross Domestic Product) varies from year to year. On the whole, the changes of residents' consumption have a positive effect on GDP (Gross Domestic Product). [ABSTRACT FROM AUTHOR]

Despite the importance of cost of capital and expected returns from investing on renewable electricity generation in Africa on one hand and the implications of such investments on economic, social and environmental factors on the other. These concepts are not well understood by non-finance and finance actors respectively. Studies that analyze renewable energy-growth nexus at regional and global level are lacking. Instead, existing studies emphasize on individual countries or panel of countries. The paper undertakes comparisons of costs and returns of selected renewable energy technologies in Africa and presents estimates of its impact on Africa's economic growth between 1990 and 2014. Even with abundant renewable resources and well performing technologies measured using levelized cost of electricity and expected internal rate of return, investment on renewable electricity generation is quite low. Findings from Autoregressive Distributed Lag approach and cointegration established a long run relationship among hydro power generation, carbon emissions and electricity consumption. Error Correction Model results suggest that investment in renewable generations excluding hydro power generation is boosted by economic growth, so long as development of such renewables corresponds with lesser carbon emissions. To accelerate renewable development, SSA countries will need to deploy initiatives that enhances national economic growth. [ABSTRACT FROM AUTHOR]

This empirical study examines the causal links among renewable energy consumption (REC) and non‐renewable energy consumption (NREC) and economic growth in Germany, the UK and France between 1980 and 2019. To this purpose, we use two different approaches: Toda and Yamamoto (Journal of Econometrics, 66, 1995, 225) time causality test and Lemmens et al. (International Journal of Forecasting, 24, 2008, 414) frequency causality test. For UK and France, the results show no significant causality between Gross Domestic Product (GDP) and REC, which supports the neutrality hypothesis. Regarding NREC, similar findings are obtained. However, for Germany, the time approach indicates bi‐directional causality between REC and GDP, while the frequency approach shows uni‐directional causality from REC to GDP in the long‐run. Therefore, there is a consensus on the growth hypothesis in Germany. Furthermore, the frequency analysis displays also one way causal nexus running from Germany's GDP to NREC in long horizons. The policymakers in UK and France should take into account that renewables and energy conservation policies are neutral in respect to economic growth. As for Germany, measures of reducing NREC would not significantly damage GDP, and renewable energies promotion plan would be beneficial for economic growth. [ABSTRACT FROM AUTHOR]

In the context of recent environmental debates related to the impact of economic development on environment quality, this paper's aim is to explain the GHG emissions in few EU New Member States (Bulgaria, Slovakia, Slovenia, Czech Republic, Hungary, Romania and Poland) in the period 1990–2019 using a panel data approach (panel threshold and dynamic panel models) and a time series approach (vector error correction models). The importance of this study is related to the identification of paths to reduce pollution in order to manage climate challenges. The results indicate similarities and differences in terms of the impact of various indicators on the total GHG emissions and in the agricultural sector. There is a negative correlation between human development index/GDP and total GHG emissions when GDP growth is below and above 0.83%. Above this threshold estimate, there is a more strong and negative correlation between human development index and GHG emissions than below the threshold value. Below 1.41%, human development index and growth of value-added in agriculture do not influence pollution in agriculture. Above 1.41%, economic growth and change in value-added in agriculture positively influence the pollution in this sector. The relationship between GDP and GHG is inverted N-shaped for Bulgaria, Hungary, Czech Republic, Slovakia, Slovenia and Romania (sample countries), while the relationship between value-added in agriculture and GHG is N-shaped. This research confirms the U-shaped RKC at the national level and for agriculture in the sample countries, while an inversely U pattern was observed for Poland. Renewable energy consumption reduces total GHG emissions in the sample countries. Some policy proposals are indicated to promote a sustainable development in these countries with less pollution. [Display omitted] [ABSTRACT FROM AUTHOR]

Although it is widely recognized that Foreign Direct Investment (FDI) inflows have a dominant effect on economic growth of host countries, the determinants of FDI inflows are still unclear. Especially, about the effect of exchange rate on FDI inflow, the results reached by scholars vary across countries or regions. It is of great practical and theoretical significance to explore the influencing effects of exchange rate on FDI inflow and identify the mechanisms that underlie them in close association with regional economic characters so as to help local government implement targeted government policies to achieve sustainable FDI inflow and sustainable economic growth. For this purpose, the influencing effects and the influencing mechanisms of the exchange rate on FDI inflows are investigated for Zhejiang province, China, over 1985–2019 by employing the co-integration tests, vector error correction models, Granger causality tests, and impulse response tests. Empirical results indicate that there are long-term stable and unidirectional causal relationship between the exchange rate and FDI inflow. Continuous appreciation of RMB against USD discourages FDI inflow. The mechanism which underlies the long-term relationship is the wealth effect, rather than the cost effect or the demand effect. By contrast, in the short run, neither the exchange rate nor the three influencing mechanism has a significant impact on FDI inflow. These results suggest policy recommendations for improving FDI by accumulating human capital and improving infrastructure. These findings are also applicable for other countries or regions with similar economic characters. [ABSTRACT FROM AUTHOR]

Since the beginning of 2018, Sino-US trade frictions have been escalating to the fields of science and technology, finance, and geography. Especially in the financial field, the United States has forcibly identified China as a "currency manipulator." In order to analyze the impact of Sino-US trade on the RMB exchange rate, based on the Sino-US import and export trade data under the quarterly HS classification from 2003 to 2019 and the RMB real effective exchange rate, this article carries out the traditional time series test, seasonal unit root test, and cointegration test and further constructs the seasonal error correction model to explore the long-term and short-term dynamic impact of Sino-US import and export trade structure on RMB real effective exchange rate. The results shows that the upgrading and optimization of the overall trade structure between China and the United States will increase the appreciation pressure of RMB real effective exchange rate. There are seasonal and long-term trends between RMB real effective exchange rate and different types of import and export trade structures between China and the United States. Therefore, this article not only strongly refutes the "theory of RMB appreciation" and puts forward policy suggestions to effectively deal with the negative impact of Sino-US trade friction but also provides a research framework for global trade, especially the decoupling of trade structure and exchange rate between developing and developed countries. [ABSTRACT FROM AUTHOR]

The study aims to shed new lights on the lead-lag relationships between the financial sector (RFSI) and economic growth (GDP) in the midst of global economic policy uncertainty (GEPU) shocks for BRICS economies. Hence, the bivariate, partial, and wavelet multiple correlations techniques are employed. From the bivariate analysis, we document positive bi-directional causality between the RFSI and economic growth over the sample period. The partial wavelet reveals that GEPU shocks distort the significance and directional comovements between the RFSI and GDP. Moreover, the outcome from the wavelet multiple cross correlations (WMCC) indicates that the RFSI is a first mover at most time scales for the BRICS economies. This is followed by GEPU which either leads or lags for most scales, especially for South Africa. The impact of GEPU on RFSI and GDP is worst for South Africa in about four cases in the medium-, and long-terms. This signifies that South Africa's financial markets and economic growth are vulnerable to GEPU. However, the impetus for GEPU to drive the comovements between the financial sector and economic activity was less pronounced in the pre-COVID analysis conducted with the WMCC. The study supports both the supply-leading and demand-following hypotheses. Our findings also underscore the need for policymakers, investors and academics alike to incessantly observe the dynamics between finance and growth across time and periodicity while considering adverse shocks from global economic policy uncertainty in tandem. [ABSTRACT FROM AUTHOR]

Transport infrastructure has often been observed to influence urbanization which is again associated with economic development and improvement in quality of life. However, the direction of causality between the two is unclear as it may depend on the particular context which is very much determined by the socio-economic conditions, political policy decisions, etc. Also, the role of different transport sectors (e.g. road, rail, port, etc.) may change over different phases of development and historic conditions. This study, thus, attempts to explore the causal direction between transport infrastructure and urbanization during the post-liberalization era in India (1991–2011). It has used eleven variables to represent various transport sectors and also categorizes the Indian states into leading, intermediate and lagging groups to find out how the role of various transport sectors changes with various phases of development. The study has used dynamic time series models such as Vector Error-Correction (VEC) and Vector Auto-Regression (VAR) for this purpose, which is absent in the present literature. Results showed support in favor of modernization theory of urbanization in most of the cases. • Causality between transport infrastructure and urbanization is explored. • The study focuses on the post-liberalization period in India. • Relationship changes across regions and sub-sectors of transport infrastructure. • Support in favor of modernization theory has been obtained. • Urbanization causes urban bias (in road infrastructure), mainly in lagging region. [ABSTRACT FROM AUTHOR]

In recent years, wind turbine yaw misalignment that tends to degrade the turbine power production and impact the blade fatigue loads raises more attention along with the rapid development of large-scale wind turbines. The state-of-the-art correction methods require additional instruments such as laser imaging detection and ranging to provide the ground truths and are not suitable for long-term operation and large-scale implementation due to the high costs. In the present study, we propose a framework that enables the effective and efficient detection and correction of static and dynamic yaw errors by using only turbine supervisory control and data acquisition data, suitable for a low-cost regular inspection for large-scale wind farms in onshore, coastal, and offshore sites. This framework includes a short-period data collection of the turbine operating under multiple static yaw errors, a data mining correction for the static yaw error, and ultra-short-term dynamic yaw error forecasts with machine learning algorithms. Three regression algorithms, i.e., linear, support vector machine, and random forest, and a hybrid model based on the average prediction of the three, have been tested for dynamic yaw error prediction and compared using the field measurement data from a 2.5 MW turbine. For the data collected in the present study, the hybrid method shows the best performance and can reduce the total yaw error by up to 85% (on average of 71%) compared to the cases without static and dynamic yaw error corrections. In addition, we have tested the transferability of the proposed method in the application of detecting other static and dynamic yaw errors. [ABSTRACT FROM AUTHOR]

The positive impact of the tourism industry on economic growth, revenues, infrastructure, employment, social inclusion and poverty reduction, although widely recognised, has been lately weighted against the appearance and exacerbation of several problems, such as: environmental footprint, increase of income inequality, cost increases related to solid waste collection, energy consumption, increased global CO2 emissions. On the other hand, the tourism sector is not just an active economic, societal, or environmental change agent; in turn, the tourism sector supports or is highly influenced by various factors, such as climate change, economic, political, or social factors. More recently, this industry has been highly impacted by the pandemic, technological developments and cultural trends. In this article we examined both the short and long-run relationship between tourism development and economic growth, CO2 emissions and energy consumption in European Union member states (EU27), by using the Principal Component Analysis (PCA) technique and autoregressive distributed lag (ARDL) model for panel data. The findings suggest that economic growth and energy consumption have a statistically significant impact on the tourism index both in the short and long-run, whereas CO2 emissions only have a significant impact upon the tourism index on the long run. [ABSTRACT FROM AUTHOR]

Due to the limitation of the satellite attitude measurement accuracy and the system servo control error of the payload scanning mechanism, an optimal use of Micro-Wave Radiation Imager (MWRI) observations requires high geolocation accuracy. In the operational system, the MWRI geolocation accuracy reaches 1 pixel, and there still exists room for improvement. In this article, we improve upon the coastline inflection point method (CIM) and propose to assign the accurate correspondence by employing a nonrigid point set registration method. First, the method identifies a set of latent variables to recognize outliers and then applies nonparametric geometric constraints to the correspondence as a priori distribution. Second, the maximum a posteriori (MAP) estimation is applied by the expectation–maximization (EM) algorithm to obtain correct inliers. The comparison with other methods demonstrates that the proposed method can provide more accurate estimation of geolocation bias. In addition, the pixel error and changes in spacecraft attitude with the long-term geolocation data in FY-3C MWRI before and after correction were analyzed during the period from April 1 to August 30, 2018. The results have shown that the geolocation errors are reduced from [0.50, 0.60] pixels to [0.20, 0.33] pixels in the along- and cross-track directions after the attitude correction. In addition, the reduction of the standard deviation shows that the geolocation quality of MWRI is improved. [ABSTRACT FROM AUTHOR]

We examine the effects of transport infrastructure on house prices in the OECD countries over the period 1870 to 2016. We employ both parametric and non-parametric panel data techniques that account for the non-linear and time-varying relationship between transport infrastructure and house prices. Our parametric results, which are based on newly developed panel data models that incorporate interactive fixed effects, suggest that transport infrastructure is positively associated with house prices. The non-parametric estimates suggest a time-varying relationship and that the average positive effect of transport infrastructure has been the case for most of the period we study. We also examine three channels through which transport infrastructure could influence house prices – economic growth, employment, and crime – and find that economic growth is a mechanism through which transport infrastructure transmits to house prices. • We examine the effects of transport infrastructure on house prices in the OECD countries over the period 1870 to 2016. • We employ both parametric and non-parametric panel data techniques. • Transport infrastructure is positively associated with house prices. • Economic growth is a mechanism through which transport infrastructure transmits to house prices. [ABSTRACT FROM AUTHOR]

This study investigates the indirect effect of large-scale climate oscillations and the corresponding teleconnection with lake water level (WL) oscillations. For this, the effect of the Southern Oscillation Index (SOI), and North Atlantic Oscillation (NAO) on the Lake Urmia WL during 1966–2016 is investigated using cross-correlation, cross-wavelet (XWT), wavelet-coherence (CWT), and nonlinear multivariate generalized autoregressive conditional heteroscedasticity (GARCH) models. Based on the XWT and CWT analyses, a temporal phase of WL leading by the SOI and an anti-phase of WL-NAO linkage within 11 years is evident. The models also depict a long-term persistence and effectiveness of the conditional covariance on both SOI–WL and NAO–WL links. It is concluded that the SOI and NAO have no immediate impact on the WL while the magnitude of the impact is exacerbated after the year 2000; however, the establishment of the SOI–WL link is found to be more relevant than that of the NAO–WL link. [ABSTRACT FROM AUTHOR]

In the paper "A New Generalized Taylor-Like Explicit Method for Stiff Ordinary Differential Equations", El-Zahar et al. [[1]] present a new generalized Taylor-like explicit method for stiff ordinary differential equations. In the paper "Stability Switches and Double Hopf Bifurcation Analysis on Two-Degree-of-Freedom Coupled Delay van der Pol Oscillator" [[11]], Chen and Qian study two-degrees-of-freedom coupled van der Pol oscillators with time delay feedback using the normal form and central manifold theories. The modeling and control of nonlinear dynamic systems is challenging in mathematics and engineering. [Extracted from the article]

The size of a partly observed population is often estimated with the capture-recapture model. An important assumption of this chat model is that sources can be perfectly linked. This assumption is of relevance if the identification of records is not obtained by some perfect identifier (such as an id code) but by indirect identifiers (such as name and address). In that case, the perfect linkage assumption is often violated, which in general leads to biased population size estimates. Initial suggestions to solve this use record linkage probabilities to correct the capture-recapture model. In this article we provide a general framework, based on the standard log-linear modelling approach, that generalises this work towards the inclusion of additional sources and covariates. We show that the method performs well in a simulation study. [ABSTRACT FROM AUTHOR]