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This paper constructs the money demand function including the variable of electronic payments while adopting the Autoregressive Distributed Lag (ARDL) bounds testing approach to investigate the relationship between electronic payments and money demand in China. The empirical results show that there is the cointegration among the variables in the constructed function with stability, and the e-payment variable is negatively correlated with the demand for money. According to the findings of this paper, in the long run, for every 1 percent increase in the e-payment variable, the demand for M1 will decrease by approximately 0.01 percent. In addition, the money demand function that contains the e-payment variable performs better than the one that does not in the forecast evaluation. [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]

Our paper examines and compares the influence of oil prices, international geopolitical risks, and government expenditures on Saudi Arabia's economic diversification during 1970–2020. We employ the bounds testing approach to cointegration to estimate the parameters of the Autoregressive Distributed Lag model to attain the paper's goal. Our results show that oil prices and international geopolitical risk harm the KSA diversification process in the short run. The destructive effect of oil prices continues in the long run, while the impact of the international geopolitical risk does not persist. On the other hand, government expenditures encourage the diversification attitude in the short and long run. This outcome highlights the capability of the KSA government to stimulate the diversification process. It is a positive sign of the state capitalism doctrine's impact on the KSA economic diversification mechanism. From a policy implication perspective, our paper suggests prioritizing economic diversification clearly on the KSA government's agenda. Simultaneously, strengthening the KSA institutional framework and developing new forms of the social contract are critical to motivating the diversification process. • Do oil prices, geopolitical risk, and government spending affect diversification. • We employ a time series autoregressive distributed lag model. • Oil prices and geopolitical risk discourage economic diversification. • Government expenditures encourage economic diversification. • The impact of oil prices and government expenditures prevails to the long run. [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]

• 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]

Renewable energy adaptation can play a critical role to halt climate change, although many researchers in the past tried to estimate its effect toward the environment, but the findings are still unclear and complex due to limitation of data and adopted methodologies. Therefore, to overcome the weaknesses, this paper aims to re-investigate the role of renewable energy adaptation, financial development and globalization toward environmental quality and economic progress in leading economies, we call it big five economies (B5) of the world. These economies (USA, China, UK, Germany, and Japan) were chosen based on ranking by the IMF. In this paper, we applied panel techniques for data analysis including panel data unit roots test, padroni cointegration analysis and panel data FMOLS and panel DOLS. Further, to explore country specific results and to ensure the robustness of findings, frequency domain causality test was applied. Results of estimation reveal that renewable energy adaptation mitigate the environmental impact to improve environmental quality and reinforce economic progress in B5 countries. Although, financial development, capital formation, natural resources and globalization improve economic progress, but these factors create adverse consequence for the environmental quality. Similarly, it can be stated that it's a tradeoff between economy and the environment. Further, the role of trade is found as constructive for environmental quality and economic progress in B5 economies. We believe that the conclusions of our study hold promise for key insights for the policy development. • This paper adopted panel of Big five economies. • Renewable energy adaptation holds a critical role for the environment. • Financial development in selected economies boost the economic output. • Resources utilization reinforce economic progress and environmental degradation. [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]

This paper aims to quantify the relation between real GDP, CO2 emissions, renewable and nonrenewable energy consumption, tourism development and labor force for France and Germany as these two countries are pioneer countries pushing Paris agreement within and outside the European Union. The time spans have been used for the period of 1995–2015 according to data availability. In the presence of cross-sectional dependency, several first and second-generation unit root tests confirm the unit root problem while Pedroni and Westernlund confirm the cointegration. Results reveal an inverted U-shape relation between CO2 emissions and real GDP in long run confirming the validity of environmental Kuznets curve for the group of France and Germany. Results declare that renewable energy significantly reduces carbon emissions while nonrenewable energy consumption adds to carbon emissions. Tourism sector was found helpful in carbon reduction that appeals to attract more tourists for the development of sustainable tourism industry by taking energy conservation and emissions reduction in relevant industries in the full consideration. • This paper explores the determinants of carbon emissions for France and Germany. • An inverted U-shape relation was found between carbon emissions and real GDP in long run. • Results reveal renewable energy helps in carbon emissions reduction. • Nonrenewable energy consumption was adding in CO2 emissions. • Tourism sector was found supportive in carbon reduction. [ABSTRACT FROM AUTHOR]

• This paper explores long-run and short-run relationships of bitcoin implied volatility with VIX and implied volatilities derived from gold and oil. • The NARDL model revealed a long-run relationship among bitcoin volatility and others. • Kyrtsou-Labys nonlinear symmetric and asymmetric causality tests established pair-wise causality between implied volatilities under study. • Our findings highlight the integration of bitcoin markets with global financial markets, indicating the coming of age of the bitcoin market. Bitcoin Implied Volatility, derived from the recently launched options on Bitcoin, provides a new forward-looking measure of uncertainty in financial markets. This paper explores its long-run and short-run relationships with the implied volatilities of equity, gold, and oil. The NARDL model reveals a long-run relationship among Bitcoin volatility and others. Kyrtsou-Labys Nonlinear Causality tests established pair-wise causality between the implied volatilities under study. Our findings highlight the integration of Bitcoin markets with global financial markets, indicating the coming of age of the Bitcoin market. [ABSTRACT FROM AUTHOR]

Data-driven models have been recognized as powerful tools to support Structural Health Monitoring (SHM). This paper contributes to the literature by exploring two data-driven approaches to detect damage through changes in a set of variables that assess the condition of the structure, and accommodates the challenge that may arise due to the influence of environmental and operational variabilities. This influence is reflected in the response of the structure and can reduce the probability of detecting damage in a structure or increase the probability of signaling false positives. This paper conducts a comparative study between a machine learning detection approach (supported by linear regression, random forest, support vector machine, and neural networks) and a cointegration approach, with the aim of detecting damage as early as possible. This study also contributes to the literature by evaluating the merits of the damage detection methods using real data collected from a small-scale structure. The structure is analyzed in a reference state and a perturbed state in which damage is emulated. The results show that both approaches are able to detect damage within the first 24 h, without ever signaling false positives. The cointegration based approach can notably detect damage after 10 h and 15 minutes, while the machine learning approach takes 20 h 30 m to detect damage. • Comparison of Machine Learning and Cointegration in SHM. • Consideration of damages and false alarms. • Usage of a reduced scale structure to reproduce the behavior of a bridge column. • Cointegration is the fastest to detect damage. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between financial development and access to electricity for rural and urban India by incorporating economic growth, trade, and foreign direct investment in energy production function. In doing so, we have applied autoregressive distributed lag bounds testing to examine long run cointegration between the variables by accommodating structural break(s) stemming in the series. Our empirical results indicate the presence of cointegration association between access to electricity and its determinants. We note that financial development enhances access to electricity in rural India but declines it in urban India. Economic growth has positive effect on access to electricity. Trade openness and foreign direct investment lower access to electricity in rural India. In urban India, trade openness and foreign direct investment increase access to electricity. The association between financial development and access to electricity is inverted-U shaped for rural and urban India. This paper would present new policy direction for Indian government to enhance access to electricity by using financial development as an economic tool. • Financial development and access to electricity have a strong linkage. • In India financial development enhances access to electricity in rural area but has adverse effect in urban area. • Rise in economic growth of India has increased the access to electricity. • Growth of Trade Openness and Foreign Direct investment increase access to electricity. [ABSTRACT FROM AUTHOR]

This paper focuses on an important empirical and methodological research question, namely possibly asymmetric and hence nonlinear cointegrating relationships between variables. It extends the Granger and Yoon (2002) method on hidden cointegration for time series data to a panel data framework. Solutions are provided for transforming the panel data variables with deterministic as well as stochastic trend parts into partial cumulative sums for positive and negative components. The transformed data can then be used to test for the long run relationship between the underlying components. The proposed method is applied to a small panel of three Scandinavian countries examining the presence of a long-term relationship between government consumption and economic growth based on quarterly data. First, the standard method that does not allow for asymmetry was implemented. The results do not provide evidence of a long-term relationship between the two variables. However, the results based on the tests suggested in this paper indicate that the underlying variables are indeed related in the long run. Thus, it might be important to separate the impact of positive shocks from the negative ones when the long run relationships between panel data variables are investigated. [ABSTRACT FROM AUTHOR]

Taking the living space of the Historic Centre of Macao as an example, by field measurement of the midweek and weekend data of people and traffic flow in major blocks with remote sensors technology, this paper comprehensively analyzes the association and impact of multi-level spatial networks on the urban vitality of historic blocks, evaluate and verify the close correlation and goodness of fit between the analysis of spatial syntax and the field test data. This paper selects three levels: metropolitan level, block level and neighborhood level, and uses information sensors to conduct correlation analysis on the number of standardized stores and spatial syntactic parameters for the living space in the Historic Centre of Macao, verifying the role and effect of integration and through a degree in analyzing multi-level spatial time and space. In the subsequent development of the Historic Centre, the service sector with a small radius should be protected and renewed, and the guidelines and orientation for street renewal and revitalization should also be enhanced with the help of information actuators. • The paper analyzes the association and impact of multi-level spatial networks on city. • Analyze standardized stores and spatial syntactic parameters of living space of Macao. • Guidelines and orientation for street renewal and revitalization should be enhanced. [ABSTRACT FROM AUTHOR]

In contemporary times, where most academic research mainly focuses on the factors of economic and environmental sustainability and emissions reduction. Yet, very little attention has been paid to the identification of the factors of renewable energy, which requires appropriate policy-level attention. Consequently, this research investigated two developed economies, i.e., Germany and the USA, from 1991 to 2021 where, the objective of the study includes using novel and robust empirical methods to test the causal relationship between renewable energy and CO2 emissions, economic growth, technological innovations and oil prices. Using the normality and unit root estimators, this study observed that non-normal data distribution, yet all the variables are stationary. Using time-series and panel cointegration tests, the results validate the cointegration between economic growth, oil prices, carbon emission, technological innovation, and renewable energy adoption in the United States whereas Germany does not show cointegration between the variables. This study employ 's the Morlet-Wavelet approaches and key findings show that all these variables have a significant role in improving renewable energy adoption in both the region. Furthermore, results show a unidirectional and bidirectional causal association between the variables via the panel-stacked Granger causality test. This study recommends effective policy ramifications concerning improved investment in technological innovation, improved low-carbon production, and diverting economic growth to renewable energy transition. Use of improved new time-series method of wavelet coherence show the key contribution in this paper with new evidence of time frequency analysis on how external variables affect renewable energy consumption in developed countries of US and Germany. The objective includes understanding the effects of CO2 emissions, economic growth, technological innovations and oil prices on renewable energy which would give evidence to policy makers and environmentalists on how developed countries should improve clean energy adoption. [ABSTRACT FROM AUTHOR]

• Developed a comprehensive approach that integrates theoretical analysis, finite element modeling, and experimental validation to achieve uniform deformation of forged rings and minimize defects. • Introduced the isothermal field method for designing ring preforms of irregular cross-section rings. • Employed a combination of isothermal field method, finite element simulation, and intelligent optimization algorithms to determine the reasonable size range of preforms. • Through comprehensive consideration of various forming preforms, rational ring preforms were obtained through redesign. One of the main challenges faced by the profiled ring rolling (PRR) process is to ensure the deformation uniformity of the ring, which is closely related to the design of the ring preform. The paper explores the application of the isothermal field method (IFM) in designing preforms for PRR processes, targeting the aerospace sector's high-end equipment manufacturing. Through the integration of advanced computational models and practical design principles, a comprehensive approach involving IFM, finite element simulation, support vector machine modeling, and genetic algorithm optimization is employed to determine the geometric dimensions of rational preforms. The material flow behavior during the PRR process is analyzed, and the effects of different isotherms combinations on strain, stress and temperature distribution are discussed. The findings demonstrate that adjusting the preform's shape during the section forming stage can optimize the uniformity of strain, stress, and temperature within the forging. The stable ring rolling process revealed no macroscopic or microscopic defects, validating the design method of ring preform based on IFM is reasonable. The research contributes to advancing near-net shape forming technologies, offering a novel approach to preform design that marries theoretical analysis with practical manufacturability considerations. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the effect of balance of payments statistical leakages (Net errors and omissions) on the external solvency of net foreign asset positions in the EU28 sample over the period 1995–2019. The Bohn (2007) error-correction reaction function approach adapted to a dynamic panel framework (pooled mean group estimator) yields evidence of a substantial improvement in the sustainability of external positions in one out of four analyzed countries (compared to only one to three countries reporting deterioration), once the extent of hypothesized trade and non-interest income misreporting is accounted for. These findings may reflect the presence of a possible association between statistical omissions and current account transactions linked to remittances flows or tax optimization behavior, as the statistical leakages are sensitive to changes in corporate tax rates and international migrant stock. [ABSTRACT FROM AUTHOR]

Using survey forecast data, this paper studies whether professional forecasters utilize long-run cointegration relationships among macroeconomic variables to forecast the future, as postulated in stochastic growth models. Significant heterogeneity exists among forecasters. The majority of the forecasters do not use these long-run relationships. The results are robust across different groups, to addressing the multiple testing problem and to allowing for structural break. [ABSTRACT FROM AUTHOR]

This paper deals with the analysis of the relationship between cryptocurrencies and gold prices. In particular, we use fractional integration and cointegration techniques to examine the degree of persistence of the series and the possibility of short and long run equilibrium relationships between them. Our results indicate that there is evidence of mean reversion in gold prices and also in some of the cryptocurrencies; however, cointegration is only found in a few cases with a very small degree of cointegration in the long run relationship. Testing the hypothesis of convergence throughout the ratios, again we only found evidence of mean reversion in the cases of Bytecoin, Dash, Ether, Monero and Ether. • The paper analyses the relationship between cryptocurrencies and gold prices. • Fractional integration and cointegration is used to examine the degree of persistence. • Results indicate that there is evidence of mean reversion in gold prices. • Also in some of the cryptocurrencies. • Cointegration is found in a few cases with a very small degree of cointegration. [ABSTRACT FROM AUTHOR]

The growing concern for the environment and imperative need for technology-enabled energy efficient freight transportation have led to the development of dedicated freight corridors (DFCs) in many countries like Germany, Netherlands, Austria, and India. The rapid expansion of DFCs is needed to improve supply chain efficiency. DFCs have potential to decongest existing rail networks, reduce the modal share of road freight transportation, and decarbonize freight transport sector. This study attempts to use transition management theory for identifying enablers of DFC for the transition from road to rail freight transportation. Exploratory factor analysis (EFA) is employed to confirm the three-level transition management framework for these enablers. This paper also proposes an eight-level hierarchical total interpretive structural modeling (TISM) based model for explicating the inter-relationships among the enablers of DFC to decarbonize freight transportation and achieve sustainability in the freight transport sector. Eventually, this paper provides empirical evidence to identify and validate the enablers of DFC through the lens of transition management theory. This paper also highlights the role of 'adoption of advanced technologies' to achieve sustainability in freight transportation. The outcomes of this study also facilitate policy-makers in understanding and managing the key enablers of DFCs to decarbonize freight transport sector. • Pinpoints 14 enablers of dedicated freight corridor for the road to rail transition • Empirically validates the proposed three-level transition management framework • Strategic level enablers explain the highest proportion of the variance. • Advanced technology usage helps to attain sustainability in freight transportation. • Strategic level enablers have higher factor loading and form the base of TISM model. [ABSTRACT FROM AUTHOR]

Abstract Accumulation of public debt in Sri Lanka is raised significantly since the independence. It exceeded 100% of gross domestic products (GDP) in the late 1980s and the early 2000s. Although it has been declined in the recent past and becomes 79.3% of GDP in 2016; the high level of debt in a weak fiscal position of the small economy Sri Lanka is an issue of concern. In this backdrop, this paper examines the impact of public debt and foreign aid on income, price level and interest rate in Sri Lanka for the post-reform period. It is found that public debt in general and foreign debt, in particular, depresses income and stimulates price level. Domestic debt has some impact on the price level. On the other hand, foreign aid has a deleterious effect on both the income and price level. The foreign debt and aid have raised interest rate both in the short-run and in the long-run, while no significant impact of the domestic debt on interest rate is found. Based on these findings, the paper elaborates on some long-term measures for reducing the dependence on debt and aid in Sri Lanka. [ABSTRACT FROM AUTHOR]

Abstract The problem of local damage detection for rotating machines is widely studied in the literature. In order to extract the information of the damage, the approach based on the vibration signal analysis is usually applied. Very often classical methods suited for impulsiveness analysis are not sufficient, therefore it is proposed to analyse the signal in terms of its periodicity features. In this paper the cointegration approach is applied to vibration signal in the context of local damage detection in gearbox. The goal of this approach is to recognize if the examined signal comes from healthy or damaged machine. Firstly, we assume periodic correlation of given signal and measure its period. Afterwards, signal is restructured and divided into sub-signals according to the discovered period. Finally, we check if sub-signals are integrated and calculate the cointegrating vector by using the least squares method. In the paper we present details of the method and benefits of using our procedure. To validate our methodology we used simulated vibration signal based on typical gearbox used in mining industry, as well as real data from the gearbox. In the paper authors present a concept of the complete procedure that uses cointegration interpretation as a diagnostic measure. [ABSTRACT FROM AUTHOR]

Abstract To overcome the limitations of the traditional approach which uses linear causality to examine whether the supply-leading and demand-following theories hold. As certain countries will be found not to follow the theory by using the traditional approach, this paper first suggests using all the proxies of financial development and economic growth as well as both multivariate and bivariate linear and nonlinear causality tests to analyze the relationship between financial development and economic growth. The multivariate nonlinear test not only takes into consideration both dependent and joint effects among variables, but is also able to detect a multivariate nonlinear deterministic process that cannot be detected by using any linear causality test. We find five more countries in which the supply-leading hypothesis and/or demand-following hypothesis hold true than with the traditional approach. However, there is still one country, Pakistan, for which no linear or nonlinear causality is found between its financial development and economic growth. To overcome this limitation, this paper suggests including cointegration in the analysis. This leads us to conclude that either supply-leading or demand-following hypotheses or both hold for all countries without any exception. There will be some types of relationships between economic growth and financial development in any country such that either they move together or economic growth causes financial development or financial development causes economic growth without any exception. The finding in our paper is useful for governments, politicians, and other international institutions in their decision making process for the development of the countries and reducing poverty. Highlights • First to use cointegration and (non)linear causality to study financial development and economic growth. • Financial development and economic growth are moving together in some developing countries. • Both demand-following and supply-leading theories hold for all of the countries studied in our paper. • Including nonlinear test allow us to detect causality in five more countries. • Our finding helps in the decision making in the development of the countries and reducing poverty. [ABSTRACT FROM AUTHOR]

Abstract This paper studies the optimal level of housing wealth theoretically and empirically. It first develops a housing model based on the wealth effects on the optimal growth path model of Kurz (1968) and shows that there is an optimal ratio between the housing wealth and income (h-y ratio), housing wealth and consumption (h-c ratio), and housing wealth and total wealth (h-w ratio). The paper next shows that the calibrated h-y, h-c and h-w ratios are markedly consistent with the US data, the benchmark economy. In particular, it confirms that they are about 1.5, 2.2, and 0.35, respectively. Finally, the paper econometrically investigates the long run relationship between h-y, h-c, and h-w in the US economy, using the cointegration approach with structural shift. The empirical analyses show that (i) there is a long-run cointegration relation for the variables of interest, (ii) the estimated long-run response of housing wealth to income, to consumption and to total wealth are consistent with stylized values, and (iii) the time varying responses of housing wealth adequately reflect the effects of some major shocks in the US economy. As a policy implication, we argue that an economy having h-y, h-c, and h-w ratios substantially higher than the optimal values may be considered to indicate a bubble in housing, which may imply waste of its productive resources invested in a non-productive asset, and hence intervention of policy maker with contractionary policies. [ABSTRACT FROM AUTHOR]

Highlights • The enterprise needs to become a focal center of stakeholders knowledge integration. • The process of knowledge management could be structured on the bases of value chain. • The integration of knowledge should be carried out as part of special registers. Abstract In this paper, based on stakeholder theory and value chain concept integration, a new approach to the corporate knowledge management in a Ramp-up is introduced. With set theory use, the types of knowledge the development of which is in the interest of the stakeholders are conceptually defined. The paper also includes the description of the approach to definition of responsibility centers for knowledge management, highlights their functionality, provides examples of specific practices and knowledge management tools. [ABSTRACT FROM AUTHOR]

• The empirical evidence supports the hypothesis of positive effects of trade surplus on profitability. • The positive effects of investment rate on profitability are not confirmed, while the effects of fiscal activism on profitability are demonstrably negative. • A strong positive influence of profit rate on the pace of capital accumulation (investment) is indicated. • The results are robust across sub-periods and panels with smaller cross-sectional dimension. The paper considers several determinants of profitability (investment rate, trade surplus, and budget deficit) in the OECD economies in the 1960–2014 period, using the Kalecki's profits equation framework. We estimate and utilise the aggregate profit rate measures for the total economy, employ a series of panel unit root and panel cointegration tests, and apply panel vector autoregressive (PVAR) model to examine the relationship between the profit rate and its determinants in the short- and long-run. The empirical evidence supports the hypothesis of positive effects of trade surplus on profitability, in line with Kaleckian views concerning the cyclical and secular dynamics of profits and profit restoration forces in the capitalist economies. The positive effects of investment rate on profitability are not confirmed, while the effects of fiscal activism on profitability are demonstrably negative; in contrast, a strong positive influence of profit rate on the pace of capital accumulation (investment) is indicated. The results are robust across sub-periods and panels with smaller cross-sectional dimension. Empirical results highlight the divergence between real investment and profitability, capital formation slowdown, and fiscal policy implementation problems in OECD economies over recent decades. [ABSTRACT FROM AUTHOR]

This “white paper” is a concise perspective based on a session during FIPSE 3, held in Rhodes, Greece, June 20–23, 2016. This was the third conference in the series “Future Innovation in Process Systems Engineering” (http://fi-in-pse.org), which takes place every other year in Greece, with a limited number of participants and just three topics/sessions whose objective is to pose and discuss open research challenges in Process Systems Engineering. This specific session comprised invited talks by Sigurd Skogestad and Iiro Harjunkoski, followed by short presentations by the participants and extensive discussions. The paper does not intend to provide a comprehensive review on the subject, or a detailed exposition of the concepts and problems. Its aim is to highlight open problems and directions for future research. [ABSTRACT FROM AUTHOR]

This paper analyses the stochastic properties of the US real estate market in the long term (1987–2023). For this purpose, we rely on fractionally integrated methods to fill the gap left by studies focusing on possible cointegration that use linear unit root tests. Both original and log transformed data of house price indices have been used with and without taking inflation into account. Even though several studies mentioned mean reversion in the short term, in the long term the real estate market shows a clear pattern of high persistence and no reversion to the mean. The effect of inflation seems to add correlation to real prices and conversely, an increase in the integration factor. Structural breaks reveal that recent values of this integration factor are greater, thus prices are becoming more persistent. • Analysis of the stochastic properties of the US real estate market (1987-2023) with fractionally integrated methods • The real estate market prices shows a clear pattern of high persistence and no reversion to the mean • Inflation seems to add correlation to real prices and conversely, an increase in the integration factor • Structural breaks reveal that recent values of the integration factor are becoming explosive (d>2) in nominal price series • Recent values of real time series show are d<2, but the I(2) null hypothesis cannot be rejected in the confidence interval [ABSTRACT FROM AUTHOR]

The sensitivity of structural health monitoring systems to environmental and operational conditions poses a significant challenge due to their inherent susceptibility to outliers. This paper proposes an effective model-updating-based optimization algorithm that can alleviate the impact of outliers associated with field and operational fluctuations. The proposed method addresses the influence of uncertainties from sources such as white noise, colored noise, and measurement errors, which can introduce outliers in datasets. The approach comprises a hybrid procedure in which a Gaussian smoothing technique is first employed to smooth out measured data to reduce the impact of irregularities. Next, Johansen cointegration is employed for raw data fusion to further enhance the signature of shared patterns. A novel optimization algorithm based on the Reptile Search Algorithm (RSA), named Improved RSA (IRSA), is proposed to solve the objective function based on the concept of mutual information. This algorithm provides a superior solution with much improved computational speed and accuracy compared to RSA. The new hybrid method was validated by several numerical and experimental damage detection studies. Furthermore, it was compared to other state-of-the-art methods described in the literature. The results clearly demonstrate the superior performance of the newly developed method. • Applying Gaussian smoothing to handle high-frequency noise while preserving more information and maintaining functionality. • Introducing novel application of Johansen cointegration as a data fusion technique, effectively handling outliers in FRF signals. • Proposing a new objective function based on Mutual Information, Johansen cointegration, and Gaussian smoothing. • Proposing a new and robust optimization algorithm based on the concept of the Reptile Search Algorithm (RSA), named Improved RSA (IRSA). • Demonstrating the robustness of the proposed method with numerical and experimental evaluations. [ABSTRACT FROM AUTHOR]

There is a growing interest in developing Inter-Plant Heat Integration (IPHI) methods for reducing energy consumptions and emissions beyond that of independent plants. However, practical IPHI problems present complexities as boundaries for simultaneous intra- and inter-plant heat integration are considered. Pinch Analysis (PA) can mitigate these complexities by elucidating design insights before developing more detailed inter-plant Heat Exchanger Networks (HENs). In this paper, a novel screening algorithm based on PA and the theoretical maximum inter-plant heat recovery potential Q r e c max is proposed for practical IPHI problems. By using this screening algorithm, a large-scale IPHI problem including multiple plants is divided into several smaller sections with each section including two or three plants. Utilizing this algorithm allows for easier calculation and improved total heat recovery potentials approaching to Q r e c max . The final inter-plant HEN configuration(s) for the segregated IPHI scheme(s) are easily developed separately using PA or Mathematical Programming (MP). Before these two steps, the selection of participating streams for IPHI from individual plants is also recommended. Finally, a mathematical model is used to determine the inter-plant HEN configuration(s) in the companion paper of this study. [ABSTRACT FROM AUTHOR]

One-third of global energy demand as well as CO 2 emissions is accounted to industrial processes. In particular, over 30% of total industrial energy consumption is attributed to the iron and steel, cement and refining industry. The aim of the present paper is to review crosscutting technologies and measures adopted to improve energy efficiency in the main Italian industrial sectors regulated by the recent EU legislation on integrated pollution prevention and control (IPPC-IED). In fact, 2010/75/EU Directive on industrial emissions, a general improvement on the entire environmental performance of the industrial installations is pursued by taking into account relevant environmental issues, such as emissions into water, air, soil as well as energy efficiency, waste production, recovering and recycling, restoration of the site upon definitive cessation of activities. Moreover, according to 2010/75/EU Directive, IPPC permits should be based on Best Available Techniques (BAT) aimed to reach an adequate level of environmental protection and should establish all the necessary measures including operating conditions, emission limit values for relevant polluting substances as well as monitoring requirements. Thus, in the present paper an assessment of Best Available Techniques (BAT) related to energy efficiency in the main Italian industrial installations is carried out on the base of the analysis on the IPPC permits released by the Italian Ministry for the Environment, Land and Sea, and made available on its web site. As a result a significant improvement in energy efficiency was assessed. In fact, many process units were upgraded, by major revamping or even by replacing the process completely with one showing better performances. Finally, since energy efficiency is at its best at the beginning of the life cycle of the plant, operators, whenever necessary, periodically monitor and measure the actual energy performance of their own installations for process control and maintenance purposes. [ABSTRACT FROM AUTHOR]

In a co-simulation context, interconnected systems of differential equations are solved separately but they regularly communicate data to one another during these resolutions. Iterative co-simulation methods have been developed in order to enhance both stability and accuracy. Such methods imply that the systems must integrate one or more times per co-simulation step (the interval between two consecutive communications) in order to find the best satisfying interface values for exchanged data (according to a given coupling constraint). This requires that every system involved in the modular model is capable of rollback: the ability to re-integrate a time interval that has already been integrated with different input commands. In a paper previously introduced by Eguillon et al. in 2022, the COSTARICA process is presented and consists in replacing the non-rollback-capable systems by an estimator on the non-last integrations of the iterative process. The MISSILES algorithm, introduced in this paper, consists in applying the COSTARICA process on every system of a modular model simulated with the IFOSMONDI-JFM iterative co-simulation method (introduced by Eguillon et al. in 2021). Indeed, in this case, the iterative part on the estimators of each system can be avoided as the global resolution on a co-simulation step can be written as a single global linear system to solve. Consequently, MISSILES is a non-iterative method that leads to the same solution than the IFOSMONDI-JFM iterative co-simulation method applied to systems using the COSTARICA process to emulate the rollback. [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]

The paper examines the integration between electricity and renewable energy certificate (REC) markets for India. The empirical investigation covers the factors influencing solar and non-solar REC markets, using an autoregressive distributed lag (ARDL) modeling approach. The variables used in this study are the traded volume of solar and non-solar RECs, the traded volume of electricity, wholesale electricity price, and prices of solar and non-solar RECs. The dataset contains monthly time series for the period January 2013–July 2020. The ARDL bounds test results show that the variables are cointegrated. The long-run ARDL estimates indicate that the traded volume of solar REC is influenced by the traded volume of electricity (positive effect), wholesale electricity price (negative effect), the traded volume of non-solar REC (positive effect), and price of solar REC (negative effect). However, the traded volume of non-solar REC is influenced by only traded volume (positive effect) and price (positive effect) of solar REC. Based on the findings of this study, few suggestions are discussed to strengthen the REC market in India. [Display omitted] • Impact of electricity market on renewable energy certificate (REC) market is examined for India. • Autoregressive distributed lag model is employed on a monthly time series data over January 2013–July 2020. • Only the traded volume of solar REC is influenced by traded volume and price of electricity. • Traded volume of solar and non-solar RECs is positively related to each other. • An increase in the price of solar REC raises traded volume of non-solar REC. [ABSTRACT FROM AUTHOR]

This study explores the effect of geopolitical risk on renewable energy deployment, assuming that geopolitical uncertainty stimulates nations to be independent and rely on their renewable energy sources in order to reduce the geopolitical risk concomitant to fossil fuel inflows. The current paper's data sample covers 10 net crude oil importer countries during the period 1985–2017. It employs panel cointegration analysis and estimate an autoregressive distributed lag model. The results show that geopolitical risk has a significant and positive effect on renewable energy diffusion. Therefore, this work determines that it is an incentive, not an obstacle, to renewable energy deployment. The present study's policy implication is that renewable energy development is expected to increase if the geopolitical risk rises, all else being equal. [ABSTRACT FROM AUTHOR]

The environmental control system of an aircraft is a complex energy intensive system and the most important non-propellant consumer of energy among all aircraft systems. Considering the highly competitive market for conventional aircraft, meaningful analysis methods for evaluation during conceptual design of ECS and effective modelling and simulation tools became more important. Hence, this paper focuses on energy and exergy analyses applied to the conventional aircraft environmental control system combined with the model-based design approach. The reported results are related to the different analysis methods for aircraft environmental control systems and cover the model-based design approach. The exergy method is applied to a model of a conventional air generation unit of a commercial aircraft. This model was developed using a model-based design framework. Within this framework, the exergy analysis was performed for four phases of a standard flight mission: Take-off, cruise, landing and taxi. The results obtained from the detailed exergy analysis show highly varying performances for the components at the different simulation cases. The exergy efficiencies range from 1% to 88%. A discussion about the special requirements for aircraft environmental control system analysis methods concludes the paper. [ABSTRACT FROM AUTHOR]

The paper empirically examines the cointegrating relationship between carbon emissions, energy consumption, trade openness and financial development in Pakistan using ARDL bounds test for cointegration procedure. Annual time series data is used for the period 1971–2011. The results reveal an inverted U-shaped relationship between carbon emission and energy consumption with a maximum threshold value of energy consumption per capita 640 kg of oil equivalent. Currently, the economy is operating below this level and therefore it is expected that carbon emission will continue to rise gradually over some time until the threshold level is reached. The lower than threshold level of energy consumption implies that scale and composition effects dominate the technology effect in terms of energy use. Further, the long-run results indicate that one percent increase in trade openness and financial development will increase carbon emission by 0.247% and 0.165%, respectively. The short-run elasticities are 0.122% and 0.087% for trade openness and financial development, respectively. The Granger causality results indicate a unidirectional causality from energy consumption, trade openness and financial development to carbon emission; and a bi-directional causality between energy consumption and financial development. In line with the results and given the growing focus on climate change effects in Pakistan, the paper discusses some policy issues for consideration and highlights the need to interpret elasticities with caution. [ABSTRACT FROM AUTHOR]

A forecasting method, easily understood and applied by administrators/policy-makers, is developed in this paper. Based on a simple, but quite realistic assumption, that production in a given region ‘pulls’ or ‘shapes’ the volume of production nationally a co-integration forecasting method is proposed in the paper. Using data from administrative regions, the empirical analysis suggests that the property of co-integration can be used to forecast agricultural production in the short-run. [ABSTRACT FROM AUTHOR]

In order to improve the effect of corporate financial management, this paper analyzes the integration path of management accounting and financial accounting combined with big data technology. Moreover, this paper uses SVM, LR, and DT as the base classifiers to build an integrated model based on Bagging, Boosting, and Stacking methods, and uses mixed data composed of MD&A and financial indicator data and financial indicator data to conduct financial early warning research. In addition, this paper applies a variety of integration methods to the research of this paper, and discusses the applicability of the integration model constructed by different integration methods on the research data. Through experimental simulation, it can be seen that the management accounting and financial accounting integration path based on big data analysis proposed in this paper is better, which can effectively promote the effect of corporate financial management. [ABSTRACT FROM AUTHOR]

The paper aims at investigation of the nexus between Industry 4.0 (I4.0) and environmental sustainability in addition to exploring the long-run and short-run effects of Industry 4.0 on CO 2 emissions. For this end, energy consumption, internet and communication technology (ICT) exports, research and development (R&D), artificial intelligence (AI), ICT technology patents and bitcoin are taken as control variables of Industry 4.0 for a panel of 9 countries that contribute to 62% of the total CO 2 emissions in the world. For this purpose, the paper follows two approaches. First, the paper proposes utilization of AI and ICT technology patents, technology-related R&Ds and ICT exports as variables of I4.0 in addition to investigating the effects of economic growth, energy consumption and bitcoin. Second, to control structural changes and nonlinearity in the cointegrating relations and existence of degenerate cointegration, Fourier panel bootstrap autoregressive distributed lag model (FPBARDL) is utilized. Afterwards, FPBARDL based long, short and strong causality analyses are conducted. The empirical findings revealed positive impacts of all I4.0-related variables on emissions in the long-run. Though I4.0 related AI and ICT innovation has no direct effect in the short-run, its effects are determined through increased energy consumption towards emissions. The strong positive effects of energy consumption and positive effects of economic growth, ICT exports and R&D are observed both in the short and long-run. In addition to positive impacts of I4.0 on environment, the findings favor insufficiency of policies focusing on lowering emissions in I4.0 context.Policy recommendations include strong commitment to energy efficiency and renewable energies and trade policies with environmental concerns. [ABSTRACT FROM AUTHOR]

This paper aims at examining the impacts of shale gas revolution on industrial production in the US. To this end, this paper, first, throughout literature review, exposes the features of shale gas revolution in the US in terms of energy technology and energy markets. However, the potential influences of shale gas extraction on the US economy are not explicit in the existing literature. Thus, considering mainly the output of shale gas revolution on the US economy in this research, later, the paper conducts econometric models to reveal if there exists significant effect(s) of shale gas revolution on the US economy. Therefore, the paper employs unit root tests and cointegration tests by following relevant US monthly data from January 2008 to December 2013. Then, this paper observes long run impact of shale gas production on industrial production in the US through dynamic ordinary least squares estimation with dummy structural breaks and conducts Granger causality test based on vector error correction model. The dynamic ordinary least squares estimator explores that shale gas production has a positive effect on industrial production. Besides, the Granger causality test presents that shale gas production Granger causes industrial production in the long run. Based on the findings of the long run estimations, the paper yields that industrial production is positively related to shale gas production. Eventually, upon its findings, this paper asserts that (i) the shale gas revolution in the US has considerable positive effects on the US economy within the scope of the validity of the growth hypothesis, (ii) new technologies might be developed to mitigate the possible negative environmental effects of shale gas production, (iii) the countries having shale gas reserves, as in US, may follow energy policies to utilize their shale reserves more in the future to meet their energy demand and to increase their economic welfare. [ABSTRACT FROM AUTHOR]

In the complex and dynamic nature of financial markets where increasingly sophisticated investment strategies are required, deep reinforcement learning (DRL) has proven successful in generating real-time investment strategies, outperforming classical models. Alongside this, statistical arbitrage strategies exploit temporary market inefficiencies to generate returns. In this regard, a novel DRL-based arbitrage method has been developed. This paper presents a unified framework that combines classical statistical arbitrage theory with DRL framework to generate investment strategies. The framework addresses the challenges of identifying similar asset portfolios, extracting signals indicating temporary price deviations, and determining optimal trading rules given market conditions. The proposed methodology involves constructing arbitrage portfolios based on cointegration relationships, removing signals from price series and portfolios, and using a DRL agent to make optimal decisions within a fixed time horizon. The empirical analysis focuses on the cryptocurrency market, known for its volatility and risk. Results demonstrate that DRL agents can generate strategies with positive returns in out-of-sample periods 79.52% to 112.82% with no transaction cost, outperforming market benchmark Bitcoin 32.51% return, the best performing over the period Litecoin with 57.11% return and the worst performing Solana with a 35.70% loss. Moreover, these strategies effectively reduce risk, achieving higher risk-adjusted returns on individual assets. The strategies maintain positive returns when considering transaction costs, with the DRL agent outperforming the standard arbitrage strategy. The best-performing strategy is based on a Deep Q-Network (DQN) agent with a return of 18.39%, annualized volatility of 12.22%, and an annualized Sharpe ratio of 2.43. At the same time, Bitcoin holds an annualized volatility of 44.13% and a Sharpe ratio of 1.08. Their randomness and coherence are studied to verify the robustness of the agents' decisions. The actions generated by the agents are not random but based on well-founded policies, which align with the obtained results. • The proposed Framework combines statistical arbitrage theory with deep reinforcement learning. • The proposed Framework generates investment strategies. • Optimal trading rules generated given cryptocurrency market conditions. • The proposed Framework generated strategies that outperformed market benchmarks. • Randomness and coherence verified the robustness of the agents' decisions. [ABSTRACT FROM AUTHOR]

In this paper, we proposed three nonparametric kernel estimators for the overlapping Weitzman measure Δ. Due to the difficulty of finding a general expression for Δ when the nonparametric kernel method is adopted, we suggest using the numerical integration method as the first stage of our estimation process. In particular, three numerical integration rules are considered, which are known as, trapezoidal and Simpson rules. The statistical properties of the resulting estimators are studied and investigated by using the simulation technique and their performances are also compared with some existing nonparametric kernel estimators developed by Eidous and Al-Talafhah (2020). The numerical results demonstrated the superiority and usefulness of the proposed technique over that suggested by Eidous and Al-Talafhah in estimating the overlapping measure Δ. [ABSTRACT FROM AUTHOR]

This study investigates the role of foreign direct investment (FDI) in facilitating the transition toward low carbon societies in Latin America from 1990 to 2022, in order to investigate the effects of FDI among the green transition process and contribute to the design of new ad hoc green FDI policies. Additionally, we introduce the Low Carbon Transition Curve hypothesis as a framework for understanding the relationship between income and the transition to green economies in the selected countries. Through the application of cointegration regression analysis, preceded by the examination of unit roots and cointegration, we uncover notable findings. Our results indicate that the impact of FDI on the consumption of renewable energy exhibits heterogeneity across Latin American countries, as evidenced by the lack of statistical significance in the renewable/fossil fuel mix consumption of energy. However, it is important to note that FDI has a positive and significant effect on the promotion of renewable energy consumption. Furthermore, our analysis confirms the statistical significance of the income-low carbon transition hypothesis across all regression models, suggesting that income per capita plays a pivotal role as a driving factor in countries' transition to green economies. In light of these findings, it is recommended that countries utilize FDI to reduce their dependency on fossil fuels by offering incentives for green FDI, which can contribute significantly to the transition process. These practical implications can guide decision-making processes and support the implementation of measures that contribute to a more sustainable and environmentally conscious energy sector in Latin America. • This paper explores the role of foreign direct investment (FDI) in promoting the transition to low carbon societies. • We introduce the Low Carbon Transition Curve hypothesis to conceptualize the relationship between income and the green transition. • We employed fully modified ordinary least squares and canonical cointegration relationship estimation. • Estimates produced evidence that the FDI contributes to a more intensive use of renewable energy. • The analysis supports the significance of the income-low carbon transition hypothesis. [ABSTRACT FROM AUTHOR]

Landsat orthorectified products use Ground Control Points (GCPs) and Digital Elevation Models (DEM) to improve the geolocation accuracy and temporal consistency, and to account for the relief displacements due to the sensor-target geometry. In Collection-2, to improve the geometric harmonization between Landsat and Sentinel-2 (S2) orthorectified products, the Landsat GCP's absolute and relative accuracies were improved using the S2 Global Reference Image (GRI) dataset through a continent-level bundle adjustment method. The GRI is a highly accurate global image dataset that was developed by the European Space Agency (ESA) to improve the S2 multi-temporal geolocation accuracy. Since late August 2021, ESA has been using the GRI dataset in the geometric refinement process to generate S2 terrain-corrected (L1C) products. This paper presents the co-registration accuracy between the Landsat-8 (L8) Collection-2 terrain-corrected products and the S2 L1C products that were processed with and without the use of the GRI dataset. The image-to-image registration (I2I) analysis performed between the L8 and S2 data products over a set of globally distributed tiles shows a significant improvement in their co-registration accuracy when GRI is used in the S2 L1C product generation. The co-registration error is estimated to be <6 m circular error at 90% probability (CE90) when GRI is used, and >12 m CE90 when GRI is not used in the S2 product generation process. A similar I2I analysis was conducted between S2 L1C products, L8 L1TP products, and L8 and Landsat 9 (L9) L1TP products. The analysis shows that the S2 L1C products are co-registered with each other temporally to better than 5.1 m CE90 when GRI is used. The L8 L1TP products and L8 versus L9 L1TP products are both co-registered temporally to better than 3 m CE90. • L8 and S2 products are registered to better than 6 m CE90 when GRI is used. • Continent-specific bias was not observed between L8 and S2 products. • Sentinel-2 products are co-registered to better than 6 m CE90. • Landsat (L8 and L9) products are co-registered to better than 3 m CE90. • A spatial normalization method was developed to resample the S2 10 m band to 15 m. [ABSTRACT FROM AUTHOR]

Globally, the interaction among innovation, tourism, and climate change has become a point of focus recently. This study examines the impact of innovation and tourism development on the sustainable development of the G7 economies. Panel data from 2000 to 2019 was analyzed for cross-sectional dependency, and a series of panel unit root tests were used to confirm the variables' unit root properties. Pedroni and Kao's tests confirm the cointegration relationship among the variables. The fully modified OLS and dynamic OLS results show that higher economic prosperity and lower environmental pollution are associated with innovation as measured in scientific publications and patents, highlighting the importance of innovation. The results highlight the positive significant impact of tourism arrivals on pollution reduction and economic expansion, which mark the steps for sustainable development. The study finds further evidence that asylum refugees did not positively contribute to economic prosperity, nor were they active in the environmental correction plans of the countries. The level of education provides strong evidence supporting sustainable development by encouraging economic growth and discouraging environmental degradation. Overall, the results recommend raising the level of innovation, tourism development, and higher technical education for the sustainable development of the G7 economies. These findings offer important contributions to firms, policymakers, and UN sustainable development agendas. [Display omitted] • The paper assesses the impact of innovation and tourism on sustainability. • The results show that innovation helps improve environmental quality. • The results reveal that innovation helps foster economic prosperity. • The paper finds that tourism helps in pollution reduction and economic expansion. • The results show that the level of education is important for sustainability. [ABSTRACT FROM AUTHOR]

• A bi-level model is proposed to foster the integration of Distributed Generation in distribution systems. • The volatility of the market price is modeled using the discrete probability density function. • A coordinated planning is proposed to optimize the performance of the system considering all stakeholders. • A robust model of tariff, distribution network and distributed generation is proposed in a robust mix-integer linear model. This paper aims to find an optimal economic signal to foster Distributed Generator (DG) owners to improve the performance of Distribution System. The proposed model considers all agents involved including Distribution Network Operators (DNOs), retailers, consumer, and prosumers. The relationship between all agents, the impact in the tariff and the Policy Maker (PM) role is described and modelled. The specific incentive studied here is the price paid to DG owners for the energy delivered to the network. A coordinated planning approach (CPA) is proposed to find the optimal size and location of DGs in order to minimize the total cost of distribution system using a stochastic bi-level mix-integer linear programming model subject to network constraints taking into account the volatility of the market price. A realistic case study (16-bus UK generic medium voltage distribution system) is used to demonstrate the effectiveness of the proposed method. Additionally, this paper explores the impact of DG integration on network cost and DG energy price on customers' and prosumers' payments, modeling tariff capacity and energy charges. The results show that optimal integration of DG can improve the performance of the distribution system up to 15% and reduce consumers' payments by 13%. [ABSTRACT FROM AUTHOR]

Following the 2005 Kyoto protocol, developed countries made commitments to reduce the emission of greenhouse gases, mostly by integrating renewable energy technologies into their power production systems. It is a more challenging procedure for third world countries, including Lebanon, that have limited means and potential to achieve such goals. This paper assesses the status of renewable energy systems in developing countries, and concentrates on the solar photovoltaic energy production due to its abundant availability in these countries relatively to other clean energy production methods. Comparisons of developing countries׳ achievements and goals each according to their economical, political and social considerations are conducted. Projects ranging from small scale standalone systems such as microgrids and minigrids to large scale energy production stations will be presented by dividing the countries into categories that share similar constraints and limitations in the integration of such installations. Furthermore, the paper analyzes the state of energy generation based on photovoltaic systems in Lebanon in contrast to other developing countries. [ABSTRACT FROM AUTHOR]

Demand response (DR) is regarded as an important method in the economic and secure operation of the power system, which has attracted great attentions from both industry and academia. With increasing integration of distributed energy resources (DER), demand-side resources become more active and diversified. Moreover, developments of the smart grid (SG) technology provide better environment for DR application. This paper presents an overview of demand-side resource developments from controllable loads to generalized demand-side resources (GDR) including distributed generation (DG) and electric energy storage (EES). Besides, aggregation technologies such as virtual power plant (VPP) and load aggregator (LA) are summarized here. Advantages of GDR application and its development prospects are presented at the end of the paper. [ABSTRACT FROM AUTHOR]

There is a global consensus on making full use of renewable energy because of human concern for energy security and environmental deterioration. Power generation with large-scale renewable energy such as solar and wind energy has become the trend of modern power systems, and the resulting impacts on power systems are more and more prominent. Therefore, there is great concern from academic and engineering areas of all countries. In China, as the fastest growing country in the photovoltaic (PV) industry in the world, these problems are particularly outstanding. This paper extensively reviews the present research and application status of large-scale PV (LSPV) power generation in China, discusses the modelling and simulation techniques of LSPV, explores impacts of LSPV integration on dynamic and static characteristics of power systems, and describes key technologies about LSPV power generation delivery and consumption from the perspective of power system planning, simulation, dispatching and control. Additionally, this paper also refines academic and engineering problems, and recommendations are proposed for future research and development of LSPV power generation in China. [ABSTRACT FROM AUTHOR]