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In our modern world, energy keeps the global economy running, and economic growth concerns are profoundly interrelated with environmental quality issues. Interestingly, scientists engage with empirical research to identify the impacts and causalities at the interface of economic activities, energy supply, and demand. The importance of the present study lies in a discussion of all contemporary research efforts bridging two strands of empirical literature in environmental economics: developments in energy growth nexus discussion and the environmental Kuznets curve. Furthermore, it highlights the inclusion of untested explanatory variables and the impacts on environmental degradation levels. In the context of the EKC hypothesis, the most popular indicators are greenhouse gas emissions (GHGs) and carbon dioxide emissions in conceptualizing environmental degradation. A review of relevant empirical studies disclosed additional research opportunities that can consider currently untested and less visible proxies of economic growth. For both strands in the literature, results differ based on the group of countries investigated, the econometric models adopted, the format of data, e.g., time series or panel analyses, the time frames due to data availability, and the proxies used to conceptualize energy, environmental degradation, and economic growth. Practical implications indicate that environmental degradation can be avoided or significantly limited within sustainable economic growth to reduce carbon dioxide emissions and increase the use of renewables in the energy mix. Furthermore, one particular implication is the concept of energy efficiency to reduce relevant demand to produce the same outcome or task. [ABSTRACT FROM AUTHOR]

The article concerns the verification of links between the energy intensity of public urban transport, regional economic development and urbanisation in the light of sustainable development challenges and club convergence processes. Three research questions were formulated in the article: (1) Does the regional economic development of voivodeships affect the energy intensity of public urban transport? (2) Does urbanisation shape energy consumption patterns in public urban transport (and are there agglomeration effects)? (3) Does the level of urbanisation of voivodeships depend on their regional level? The study covered 16 subregions of Poland from 2010 to 2020. A spatial dynamic exploration of the relationships between energy intensity patterns of public urban transport and the phenomenon of urbanisation and regional economic development was carried out. Panel models of vector autoregression and panel causality testing were used. The influence of the economic development of regions and the level of urbanisation on energy intensity patterns of public urban transport was confirmed. The link between economic development and the process of urbanisation was identified. An additional effect was noticed (novelty knowledge), i.e., there is a delayed club convergence between voivodeships in the area of energy intensity of public urban transport, regional economic development and urbanisation (polyconvergence). [ABSTRACT FROM AUTHOR]

This study examines the dynamic connection between green energy, employment, fossil fuel energy, and human development index including additional variables such as education, life expectancy, and poverty in panel data of 30 developing countries from 1990–2017. Previous studies analyzed the total energy consumption on the human development index, whereas the differential effect of green and fossil fuel energy with employment and human development index has not been examined. The empirical exercise is based on the panel co-integration test, panel fully modified ordinary least squares, dynamic ordinary least squares, and vector error correction estimation approaches. The results reveal that all variables are co-integrated. The results of regression analysis indicate that green energy, education, life expectancy, and employment increase the human development index, but fossil fuel energy and poverty decrease the human development index by −0.016 and 0.023%. In addition, the vector error correction model designates that there is bidirectional causality between green energy consumption and the human development index. Therefore, for developing countries, the development and utilization of green energy sources (wind, solar, geothermal, etc.) are needed. Moreover, these countries should rely less on fossil fuel energy because it causes a decrease in the HDI. [ABSTRACT FROM AUTHOR]

The development of a country's economy is directly related to the use of energy in that country's economic sectors. Therefore, the energy–environmental Kuznets curve (EEKC) is often used when analysing a country's potential and challenges in sustainable development, green economy, and green growth. This hypothesis tests whether there is an inverse "U"-shaped relationship between energy use and economic growth and is especially important when analysing developing countries to assess if, at a certain point, energy use begins to drop, resulting in fewer greenhouse gas emissions, environmental degradation, and the consumption of fossil-based fuels. This study aims to examine the relationship between energy consumption and economic growth in the Baltic States from 1995 to 2019, with a focus on the agriculture sector. The study uses the non-linear autoregressive distributed lag (NARDL) model for individual and panel time series. Total energy use, as well as electricity use, is included in the study, whereas gross value added is employed as a measure of economic growth. Research data analysis reveals that energy use in all three Baltic countries stabilises as gross value added increases. However, there is insufficient evidence to show that after a certain point, energy use begins to drop; thus, the hypothesis for the inverse "U"-shaped energy–environmental Kuznets curve (EEKC) is rejected. Research results have important practical implications regarding countries' policies toward energy, including the use of electricity and sustainable development. [ABSTRACT FROM AUTHOR]

Electricity consumption and government policy are two vital elements for economic growth. Thus, this study explores the roles of electricity use and government policy in the economic growth of the selected four South Asian countries over the period from 1980 to 2014. The study includes the government policy variable in the extended Cobb–Douglas production function of the electricity driven growth model, which was absent in earlier studies. The pooled mean group-based panel autoregressive distributed lag (P-ARDL) method is used for empirical investigation, while fully modified ordinary least squares (FMOLS) and dynamic ordinary least square (DOLS) methods are used for checking the sensitivity of the P-ARDL estimates. Our results reveal that the effects of electricity, government spending, financial development and capital formation have significant positive effects on the economic growth of South Asia. However, exports and imports are found to have detrimental effects. Causality test reveals a unidirectional causality from electricity consumption to economic growth that supports the growth hypothesis. Following the findings, important policy recommendations are made to foster the economic growth in the South Asian countries. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between geothermal energy consumption, economic growth, and foreign direct investments in countries where geothermal energy production is possible. Panel Fourier Granger causality and panel Fourier Toda–Yamamoto causality tests (2020–2021) were applied, which can take into account smooth transitional structural breaks with trigonometric functions using quarterly data for the period 2016 Q1–2020 Q3. Data were obtained from the International Energy Agency (IEA), Federal Reserve Economic Data (FRED), and the OECD official website. According to the results obtained based on panels, there is one-way causality from economic growth to geothermal energy and one-way causality from geothermal energy consumption to foreign direct investments. The results obtained based on individual countries indicate that one-way causality from foreign direct investment to geothermal energy consumption was found for Mexico and Portugal, and one-way causality from geothermal energy consumption to economic growth was found for Italy and Mexico. On the other hand, causality from economic growth to geothermal energy consumption was observed for Germany, Japan, and USA. No significant results were found for Turkey and New Zealand, and it is understood that the macroeconomic structures of these countries are not affected by geothermal energy. The difference in the results reveals that the application recommendations on this subject should also be different. [ABSTRACT FROM AUTHOR]

Energy price fluctuations pose a significant risk and uncertainty to financial investments for new developments in conventional power and freshwater cogeneration facilities. This study attempts to address the problem of making robust valuation for low-carbon energy project investments subject to multi-dimensional price risk, particularly looking at some key research questions: (a) how does the correlation structure, or independence, between the price risks affect the project value; and (b) does adding flexibility in investment enhance or worsen the project valuation, given (a). This study identified three price factors with significant fluctuations that impact conventional power generation, namely: wholesale electricity spot price, natural gas spot price, and CO2 market price. The price factors were used to construct a multidimensional risk model and evaluate investment decisions for cogeneration project expansion in the future based on a low-carbon energy mix. To this end, five cogeneration configurations using combined-cycle gas turbine (CCGT) integrated with solar photovoltaics (PV) and carbon capture and storage (CCS) technologies were assessed. A combined price risk was initially estimated by transforming the given price factors representing maximum covariance using principal component analysis (PCA). The trend and volatilities in the major principal component scores (the combined price risk indicator) were modelled using the geometric Brownian motion stochastic process, whose parameters were determined and then used to perform time-series simulation and generate multiple realisations of the principal component. A back transformation was then applied to obtain the simulated values representing future uncertainties in the price factors. The effect of price risk and uncertainties were subsequently evaluated using a recombining binomial lattice model for real options analysis (ROA). There were financial gains when PV was mixed with conventional natural gas-fired technology. Investment in cogeneration configurations with (a) 25% PV share provided a 53% gain in the extended net present value (e–NPV); and (b) 50% PV share provided a 124% e–NPV gain when compared to the baseline cogeneration system with no PV shares. The analyses demonstrate that PV technology is a better hedging option than CCS against future market uncertainty and price volatility. [ABSTRACT FROM AUTHOR]

This study examined the nexus between economic growth, energy consumption, and the environment with the moderating role of agricultural value addition and forest in Africa based on data sourced from 1980 to 2019. We employed both the time domain and frequency domain panel Granger causality estimation techniques to compare results across the different horizons. Extant literature suggests the inability of time domain estimation techniques to account for causality at different frequencies. The study also accounts for the nexus among our variables both at the single-country and multi-country levels. The results at the single-country level are at best mixed. The results of the panel Granger causality at the frequencies domain suggest that a bi-directional relationship exists between energy consumption and economic growth, and that energy consumption Granger causes carbon emissions in Africa. The results align with the feedback hypothesis on the one hand but contradict the conservation hypothesis on the other hand. The study has some policy implications. [ABSTRACT FROM AUTHOR]

Energy prices (EPs) play an imperative role in South Asian Country (SAC) Gross Domestic Product (GDP). This research empirically examines the influence of sustainable energy price shocks (EPSs) on macroeconomic indicators. The study is to forecast the impact of EPS on macroeconomic indicators from 1980 to 2020. The analysis is carried out by employing the Vector Auto-Regression (VAR) approach. Impulse Response Functions (IRFs) results indicate that EPS decreases Gross Domestic Product (GDP). They exist in the short run and the long run. This research study's overall findings suggest that high EPSs have a negative impact on GDP. The study implies that policymakers should develop, adopt, and initiate some imperatives to control the unanticipated volatility and movements in EP. The study highlights that policy should be designed to prevent fluctuations in sustainable EP and plan conservative energy policies that motivate discovering alternative energy sources to meet increasing energy demand and improve economic growth. [ABSTRACT FROM AUTHOR]

The key goal of this research was to figure out the short and long run relationship between environmental degradation caused by carbon dioxide (CO2) emissions and energy consumption, the level of GDP economic growth, and urbanization in the Visegrad Region countries (V4). The study used data from the years 1996–2020. In the methodological area, ARDL bound test, and ARDL and ECM models were used to determine the directions and strength of interdependence. The results show that in the case of some V4 countries (Poland, Slovakia, and Hungary), changes in the urbanization rate affect CO2 emissions. Moreover, it was confirmed that the phenomenon of urbanization influences the enhanced energy consumption in the studied countries. In the case of individual countries, these relationships were varied, both unidirectional and bidirectional. Their nature was also varied—there were both long and short-term relationships. These findings suggest that the V4 countries should increase renewable and ecological energy sources. It is also recommended to enhancement energy savings in the areas of both individual and industrial consumption by promoting low-emission solutions. This should be done while considering changes in urbanization. [ABSTRACT FROM AUTHOR]

The countries of Central and Eastern Europe, from the non-euro area, have completed the process of economic transition before joining the European Union. Achieving a certain level of economic development and membership in the European Union have generated their involvement in a new transition process, namely the energy transition. Concerns about promoting the low carbon economy have become increasingly complex for those countries that are interested in the environmental impact of economic activity. This study aims to analyze the process of energy transition in the countries of Central and Eastern Europe on the basis of the causality relationship among specific variables for the period 1990–2018. The study is based on cross-sectional panel data and the panel vector error correction model (PVECM). The efforts made by these countries by joining the European Union have generated economic development, with positive effects being recorded on the protection of the environment, a fact due to the strict regulations adopted and rigorous implementation at the national level. Foreign capital had a positive impact on the transition to a low carbon economy because most of the FDI flows attracted by the non-euro countries in the CEE come from Western Europe, i.e., from EU member countries, located either among the founders or among the countries that joined during the first waves of union expansion. Membership in the European Union facilitates the energy transition process for the non-euro countries of Central and Eastern Europe, but the new geopolitical events generate the reconfiguration of the European strategy of considering the need to ensure energy security. [ABSTRACT FROM AUTHOR]

The motivation behind the study is continuous fluctuations in energy prices in Pakistan, so this study aims to investigate the role of a twin deficit, urbanization, climate change, energy production from oil and gas, and the exchange rate in energy inflation. This study utilized oil prices and electricity prices to capture energy inflation using time series data from 1972 to 2021, from World Development Indicators (WDI) and the Census of Electricity Establishment (CEE). This study utilized the vector auto-regressive (VAR) model to investigate the short-run and long-run estimates. This study found that the twin deficit and the exchange rate have a significant and positive association with energy inflation. However, the size impact of the twin deficit is greater on oil prices as compared to electricity prices. Furthermore, urbanization, climate change, and energy production from oil and gas have a positive and significant long-run association with electricity prices. Moreover, the results of the variance decomposition test indicate that the relative contribution of the budget deficit in electricity prices (Model 1) is greater than other modeled variables, while the relative contribution of the budget deficit and climate change is greater in oil prices (Model 2), as compared to other exogenous variables. This study helps policymakers to devise policies to control energy inflation, which affects the well-being of society. [ABSTRACT FROM AUTHOR]

This research investigates the relationships among renewable energy consumption, economic growth, and financial development in five sub-Saharan African nations utilizing panel data from 2000 to 2020. Econometric methods are used to ascertain the existence or absence of cross-sectional dependence and the short-run and long-run connections between the following factors: Pesaran cross-sectional dependence (CD) and cross-sectionally augmented IPS (CIPS) unit root tests, pooled mean group (PMG), and dynamic ordinary least squares (DOLS) estimations. The presence of cross-sectional dependence is found and represented with the CIPS unit root test. No significant short-run relationship is found between the variables of the study, yet a significant long-run relationship is present among them. A positive relationship exists between CO2 emissions and financial development, while financial development and renewable energy consumption are found to have negative relationships with CO2 emissions. The study also supports the scale effect of the environmental Kuznets curve hypothesis. Additionally, no causality is found among the variables, and impulse response and variance decomposition estimation are carried out to recommend future effects. Policy implications of findings are discussed, with accompanying suggestions. [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]

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]

Radiation forecast accounting for daily and instantaneous variability was pursued by means of a new bi-parametric statistical model that builds on a model previously proposed by the same authors. The statistical model is developed with direct reference to the Liu-Jordan clear sky theoretical expression but is not bound by a specific clear sky model; it accounts separately for the mean daily variability and for the variation of solar irradiance during the day by means of two corrective parameters. This new proposal allows for a better understanding of the physical phenomena and improves the effectiveness of statistical characterization and subsequent simulation of the introduced parameters to generate a synthetic solar irradiance time series. Furthermore, the analysis of the experimental distributions of the two parameters' data was developed, obtaining opportune fittings by means of parametric analytical distributions or mixtures of more than one distribution. Finally, the model was further improved toward the inclusion of weather prediction information in the solar irradiance forecasting stage, from the perspective of overcoming the limitations of purely statistical approaches and implementing a new tool in the frame of solar irradiance prediction accounting for weather predictions over different time horizons. [ABSTRACT FROM AUTHOR]

Pakistan is facing an energy crisis and is also severely affected by climate change. Moreover, Pakistan is not doing well as far as health outcome indicators are concerned. The causal nexus among energy, environment, and health outcomes is well-established in literature. Besides, financial development also grabs the attention of health outcome literature as financial development can play a significant role in improving health outcomes. Thus, this study was conducted to test the causal nexus among energy consumption, environmental degradation, financial development, and health outcomes in the case of Pakistan. This study proxies health outcomes with life expectancy and infant mortality. Time series data have been analyzed through different econometric techniques, such as unit root tests, cointegration techniques, causality techniques, and cointegration regressions. Moreover, this study not just discovers the causal direction among variables but also determines the strength of causality through variance decomposition. Results of the study confirm that all variables of the study are cointegrated in the long run. The causality analysis reveals that unidirectional causality is running from energy consumption and environmental degradation to health outcomes, whereas bidirectional causality is found between financial development and health outcomes in the long run. Besides, this study also determines the effect of energy, environmental degradation, and financial development in the health outcome model and finds that energy and financial development can help Pakistan to improve health outcomes. Policy implications are recommended for Pakistan. [ABSTRACT FROM AUTHOR]

This paper investigates the nexus between CO2 emissions (CO2E), GDP, energy use (ENU), and population growth (PG) in India from 1980–2018 by comparing the "vector error correction" model (VECM) and "auto regressive distributed lag" (ARDL). We applied the unit root test, Johansen multi-variate cointegration, and performed a Variance decomposition analysis using the Cholesky approach. The VECM and ARDL-bound testing approaches to cointegration suggest a long-term equilibrium nexus between GDP, energy use, population growth and CO2E. The empirical outcomes show the existence of a long-term equilibrium nexus between the variables. The Granger causality results show that short-term bi-directional causality exists between GDP and ENU, while a uni-directional causality between CO2E and GDP, CO2E and ENU, CO2E and PG, and PG and ENU. Evidence from variance decomposition indicates that 58.4% of the future fluctuations in CO2E are due to changes in ENU, 2.8% of the future fluctuations are due to changes in GDP, and 0.43% of the future fluctuations are due to changes in PG. Finally, the ARDL test results indicate that a 1% increase in PG will lead to a 1.4% increase in CO2E. Our paper addresses some important policy implications. [ABSTRACT FROM AUTHOR]

Power forecasting models offer valuable insights on the electricity consumption patterns of clients, enabling the development of advanced strategies and applications aimed at energy saving, increased energy efficiency, and smart energy pricing. The data collection process for client consumption models is not always ideal and the resulting datasets often lead to compromises in the implementation of forecasting models, as well as suboptimal performance, due to several challenges. Therefore, combinations of elements that highlight relationships between clients need to be investigated in order to achieve more accurate consumption predictions. In this study, we exploited the combined effects of client similarity and causality, and developed a power consumption forecasting model that utilizes ensembles of long short-term memory (LSTM) networks. Our novel approach enables the derivation of different representations of the predicted consumption based on feature sets influenced by similarity and causality metrics. The resulting representations were used to train a meta-model, based on a multi-layer perceptron (MLP), in order to combine the results of the LSTM ensembles optimally. This combinatorial approach achieved better overall performance and yielded lower mean absolute percentage error when compared to the standalone LSTM ensembles that do not include similarity and causality. Additional experiments indicated that the combination of similarity and causality resulted in more performant models when compared to implementations utilizing only one element on the same model structure. [ABSTRACT FROM AUTHOR]

The relationships between crude oil prices and exchange rates have always been of interest to academics and policy analysts. There are theoretical transmission channels that justify such links; however, the empirical evidence is not clear. Most of the studies on causal relationships in this area have been restricted to a linear framework, which can omit important properties of the investigated dependencies that could be exploited for forecasting purposes. Based on the nonlinear Granger causality tests, we found strong bidirectional causal relations between crude oil prices and two currency pairs: EUR/USD, GBP/USD, and weaker between crude oil prices and JPY/USD. We showed that the significance of these relations has changed in recent years. We also made an attempt to find an effective strategy to forecast crude oil prices using the investigated exchange rates as regressors and vice versa. To this aim, we applied Support Vector Regression (SVR)—the machine learning method of time series modeling and forecasting. [ABSTRACT FROM AUTHOR]

This empirical study assesses the effect of CO2 emissions, urbanization, energy consumption, and agriculture on Thailand's economic growth using a dataset between 1970 and 2018. The ARDL and the frequency domain causality (FDC) approaches were applied to assess these interconnections. The outcome of the bounds test suggested a long-term association among the variables of investigation. The ARDL outcomes reveal that urbanization, agriculture, energy consumption, and CO2 emissions positively trigger Thailand's economic growth. Additionally, the frequency domain causality test was used to detect a causal connection between the series. The main benefit of this technique is that it can detect a causal connection between series at different frequencies. To the understanding of the authors, this is the first study in the case of Thailand that will apply the FDC approach to capture the causal linkage between GDP and the regressors. The outcomes of the causality test suggested that CO2 emissions, urbanization, energy consumption, and agriculture can predict Thailand's economic growth in the long term. These outcomes have far-reaching implications for economic performance and Thailand's macroeconomic indicators. [ABSTRACT FROM AUTHOR]

Developing countries, including Pakistan, need a considerable effort to withstand economic growth; however, these countries have to cope with greenhouse gases emission and other environmental concerns. Financial advancement gives rise to modern, sometimes even innovative and energy-efficient technologies and, thus, contributes to a decline in energy usage among market entities: organizations and households. The current study explores the nonlinear asymmetric relationship between economic growth (Y) and the selected exogenous variables in Pakistan by incorporating time series data spanning from 1971 to 2016. Economic growth was considered as a target variable, while energy consumption (EC), electric power consumption (EPC), financial development (FD), and energy imports (EM) were considered independent variables. To investigate cointegration among the given variables, a nonlinear ARDL bound testing approach was employed. BDS independence test was used to check the nonlinearity, and a structural break unit root test was used for testing data stationarity. The findings confirm the presence of co-integration in the selected variables. A symmetric unidirectional significant causality exists running from EPC to Y, while a bidirectional symmetric causality was found between FD and Y. In contrast, any negative shocks in EPC, FD, and EM were found to have a positive asymmetric effect on Y. Meanwhile, a neutral effect was found between EC and Y. The outcomes of this study can provide guidelines for future researchers and policymakers. [ABSTRACT FROM AUTHOR]

The environmental performance index was developed to protect public health, and to sustain and manage the ecological vitality that is a crucial factor in countries' social and economic development. The increase in CO2 emissions has been threatening environmental and human health. The main objective of this study is to evaluate the impact of economic growth, energy consumption, energy management, the urban population, trade openness, and financial development on CO2 emissions in the OECD countries that have a high ranking in the environmental performance index by utilizing the panel data analysis method for the years spanning 1990–2014. This assessment finds positive relationships between economic growth, energy consumption, and the urban population, and CO2 emissions. Moreover, it is put forward that a negative and significant relationship between financial development and CO2 emissions exists. Despite displaying a similar negative correlation, the relationship between trade openness and CO2 emissions is insignificant. In the Dumitrescu-Hurlin panel causality test conducted, it was seen that a two-way causality is prevalent between energy consumption and CO2 emissions. In addition, interrelations where CO2 emissions cause trade openness, and the urban population is an explanatory variable of the former relationship, were discovered. [ABSTRACT FROM AUTHOR]

Blue Economy represents a new and interesting concept on a global level, both from the economic potential but also by the fact that it can be used to reduce environmental degradation. The main goal of this research is to identify the causality relations between the greenhouse gas emissions, the Blue Economy and economic growth based on a panel of annual data from the 28 countries that are members of the European Union (EU) over the 2009–2018 period. After applying stationarity and cointegration tests, the long term cointegration coefficients shall be determined with the help of the fully modified ordinary least squares (FMOLS) estimator. Granger causality estimation based on the vector error correction model (VECM) was applied to identify the causality relationship between the variables and to detect the direction of causality. Based on the identified causality relations, the Blue Economy has a significant influence on greenhouse gas emissions in the long run. Unidirectional causality relations were identified from the economic growth of greenhouse gas emissions in the long term, as well as from the greenhouse gas emissions on economic growth in the short term. [ABSTRACT FROM AUTHOR]

The rapid economic growth observed in Central European countries in the last thirty years has been the result of profound political changes and economic liberalization. This growth is partly connected with reducing carbon dioxide (CO2) emissions. However, the problem of CO2 emissions seems to remain unresolved. The aim of this paper is to test whether the Environmental Kuznets Curve (EKC) hypothesis holds true for Central European countries in an annual sample data that covers 1995–2016 in most countries. We examine cointegration by applying the Autoregressive Distributed Lag bound testing. This is the first study examining the relationship between CO2 emissions and economic growth in individual Central European countries from a long-run perspective, which allows the results to be compared. We confirmed the cointegration, but our estimates confirmed the EKC hypothesis only in Poland. It should also be noted that in all nine countries, energy consumption leads to increased CO2 emissions. The long-run elasticity ranges between 1.5 in Bulgaria and 2.0 in Croatia. We observed exceptionally low long-run elasticity in Estonia (0.49). Our findings suggest that to solve the environmental degradation problem in Central Europe, it is necessary to individualize the policies implemented in the European Union. [ABSTRACT FROM AUTHOR]

The aim of this study is to examine the empirical cointegration, long-run and short-run dynamics and causal relationships between carbon emissions, energy consumption and economic growth in 14 Danube region countries over the period of 1990–2019. The autoregressive distributed lag (ARDL) bounds testing methodology was applied for each of the examined variables as a dependent variable. Limited by the length of the time series, we excluded two countries from the analysis and obtained valid results for the others for 26 of 36 ARDL models. The ARDL bounds reliably confirmed long-run cointegration between carbon emissions, energy consumption and economic growth in Austria, Czechia, Slovakia, and Slovenia. Economic growth and energy consumption have a significant impact on carbon emissions in the long-run in all of these four countries; in the short-run, the impact of economic growth is significant in Austria. Likewise, when examining cointegration between energy consumption, carbon emissions, and economic growth in the short-run, a significant contribution of CO2 emissions on energy consumptions for seven countries was found as a result of nine valid models. The results contribute to the information base essential for making responsible and informed decisions by policymakers and other stakeholders in individual countries. Moreover, they can serve as a platform for mutual cooperation and cohesion among countries in this region. [ABSTRACT FROM AUTHOR]

This paper examines the environmental Kuznets curve (EKC) in Vietnam between 1977 and 2019. Using the autoregressive distributed lag (ARDL) approach, we find an inverted N-shaped relation between economic growth and carbon dioxide emissions in both the long- and short-run. The econometric results also reveal that energy consumption and urbanization statistically positively impact pollution. The long-run Granger causality test shows a unidirectional causality from energy consumption and economic growth to pollution while there is no causal relationship between energy consumption and economic growth. These suggest some crucial policies for curtailing emissions without harming economic development. In the second step, we also employed the back-propagation neural networks (BPN) to compare the work of econometrics in carbon dioxide emissions forecasting. A 5-4-1 multi-layer perceptron with BPN and learning rate was set at 0.1, which outperforms the ARDL's outputs. Our findings suggest the potential application of machine learning to notably improve the econometric method's forecasting results in the literature. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused a drop-in economic activity and energy consumption of the United States. This work aims to investigate the spillover effects of the United States' COVID-19 economic recession on economic growth and energy consumption in other nations using a global vector autoregressive (GVAR) approach and quarterly data between 1990 and 2013 from 41 major countries/regions. On the one hand, the simulation results indicate that the US COVID-19 recession has a negative impact on other countries' economic growth through trade ties, reducing the economic growth of other countries, especially for countries which have a close trade relationship with the US. In addition, the spillover effects of the US economic recession have different impacts on other countries' energy consumption. Countries with the closest trade ties to the US are most affected, such as Japan and China. In addition, the impact of the US' economic shock on energy consumption in developing countries is significant in the short term, while its impact on developed countries is significant in the long term. On the other hand, the simulation results of energy spillover effects indicate a reduction in US energy consumption slightly reduces economic growth in other nations. In addition, a reduction in energy consumption in the US does not have a significant negative impact on energy consumption in other developed countries. Furthermore, the spillover effect of declining energy consumption in the US on energy consumption in developing countries is significant in the short term. However, the spillover effects of falling energy consumption in the US on developing countries are different. The spillover effect of the decline in energy consumption in the US causes a slight decline in energy consumption in China and Brazil, whereas the spillover effect of the decline in energy consumption in the US does not cause a decline in energy consumption in India and Brazil. [ABSTRACT FROM AUTHOR]

The purpose of this study is to analyze the evolution of the scientific research regarding the relationship between energy and economic growth, in order to reveal preferred topics and less approached themes. We conducted an occurrence and cluster analysis, followed by a correspondence analysis using articles published between 1979 and 2019 in journals indexed in the Web of Science. The analysis was split into three periods taking into account the major economic and energetic milestones. The analysis focused on distribution of the topics studied both by years and by journals. The research revealed some major trends: there has been an explosive increase in studies based on Asian countries over the three periods as concerns for sustainable development intensified, and environmental issues were associated with research on the relationship between energy and economic growth. Even if the journals cover different scientific areas, during the last 10 years they contain articles with very similar topics. [ABSTRACT FROM AUTHOR]

The strong interdependency between economic growth and conventional energy consumption have led to significant environmental impact, especially with respect to greenhouse gas emissions. Conventional energy-intensive industries release increasing quantities every year, which has prompted global leaders to consider new approaches based on sustainable consumption. The main purpose of this research is to propose a new energy index that accounts for the complexity and interdependences between the research variables. The methodology is based on Principal Component Analysis (PCA) and combines the key components determined into a score that allows for both temporal and cross-country comparisons. All data analyses were performed using IBM SPSS Statistics 25™. The main findings show that most countries improved their economic performance since 2014, but the speed of the improvement varies a lot from one country to another. The final score determined reflects the complex changes taking place in each country and the efficiency of the governmental measures for sustainable economic growth based on low energy consumption and low environmental pollution. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact of the COVID-19 pandemic on economic growth and electricity consumption and investigates the hypothesis of the influence of this consumption on the gross domestic product (GDP) for Romania. Using time series on monthly electricity consumption and quarterly GDP and a multi-linear regression model, we performed an analysis of the evolution of these indicators for 2007–2020, a comparison between their behavior during the financial crisis vs. COVID-19 crisis, and empirically explore the relationships between GDP and electricity consumption or some of its components. The results of the analysis confirm that the shock of declining activity due to the COVID-19 pandemic had a severe negative impact on electric energy consumption and GDP in the first half of 2020, followed by a slight recovery. By using a linear regression model, long-term relationships between GDP and domestic and non-household electricity consumptions were found. The empirically estimated elasticity coefficients confirm the more important impact of non-household electricity consumption on GDP compared to the one of domestic electricity consumption. In the context of the COVID-19 pandemic, the results of the study could be useful for optimizing energy and economic growth policies at the national and European levels. [ABSTRACT FROM AUTHOR]

The purpose of this study is to examine the relationship between renewable energy sources and economic growth of the South Asian Association for regional cooperation (SAARC) countries. This study uses three main renewable energy sources, namely geothermal, hydro, and wind.This study collects data set from SAARC countries from 1995 to 2018 and applies a fixed effect test and panel vector error correction model (PVECM) for data analysis. The overall results show that all three renewable energy sources have a positive significant impact on economic development among SAARC countries' economies. Moreover, hydropower renewable energy has more effects and influences on economic growth as compared to the other two individual sources of renewable energy. [ABSTRACT FROM AUTHOR]

This study investigates the asymmetric cointegration and causal relationships between economic growth, carbon emissions, and energy consumption in the next eleven (11) countries over the period 1972–2013. The nonlinear autoregressive distributed lag (NARDL) bounds testing approach and nonpragmatic Granger causality tests are employed. This research's empirical results have entrenched vital relationships that have significant policy implications. We affirm nonlinear cointegration among the variables in Bangladesh, Iran, Turkey, and Vietnam. The long-run asymmetric effect outcomes indicate a definite boom in economic growth, significantly increases carbon emission in Turkey, and a decline in Vietnam. Additionally, a positive shock to energy consumption significantly increases the carbon emission in Bangladesh, Iran, and Turkey, but a decrease in emissions in Vietnam. Findings from the Wald test reveal a long-run asymmetric effect between carbon emission and economic growth in Bangladesh, Iran, and Vietnam, and for Iran, an asymmetric short-run impact. Long-run and short-run asymmetric effects between carbon emission and energy consumption in Bangladesh and Iran. In terms of asymmetric causality results, bidirectional causality between carbon emission and economic growth was noted in Bangladesh and Turkey, and a unidirectional causality from economic growth to carbon emission in Egypt and South Korea. Energy consumption causes carbon emission in Bangladesh, Egypt, Pakistan, South Korea, and not vice versa. We determined a bidirectional asymmetric causality relationship between carbon emission and energy consumption in Vietnam and a unidirectional causality link from carbon emissions to Turkey's energy consumption. [ABSTRACT FROM AUTHOR]

In this contemporary era, environmental problems spread at different levels in all countries of the world. Economic growth does not just depend on prioritizing the environment or improving the environmental situation. If the foreign direct investment is directed to the polluting industries, they will increase pollution and damage the environment. The purpose of the study is to consider the relationship between foreign direct investment in Kazakhstan and Uzbekistan and economic growth and renewable energy consumption. The study is based on data obtained from 1992 to 2018. The results show that there is a two-way link between foreign direct investment and renewable energy consumption in the considered two countries. The Granger causality test approach is applied to explore the causal relationship between the variables. The Johansen co-integration test approach is also employed to test for a relationship. The empirical results verify the existence of co-integration between the series. The main factors influencing renewable energy are economic growth and electricity consumption. To reduce dependence on fuel-based energy sources, Kazakhstan and Uzbekistan need to attract energy to renewable energy sources and implement energy efficiency based on rapid progress. This is because renewable energy sources play the role of an engine that stimulates the production process in the economy for all countries. [ABSTRACT FROM AUTHOR]

The present study is in line with the United Nations Sustainable Development Goals (UN-SDGs) that address pertinent global issues. This study focuses on the need for access to clean and affordable energy consumption, responsible energy consumption, sustainable economic growth, and climate change mitigation. To this end, this paper evaluates the relevance of the renewable energy sector on the environmental Kuznets curve (EKC) framework in Portugal, Italy, Greece, and Spain for the period 1995–2015. As an econometric strategy, we adopt the use of panel data over the highlighted countries. In the first step, we apply the unit root test recommended by Levin, Lin, and Chu in conjunction with ADF-Fisher, and Phillips-Perron for robustness and consistency. We found that the variables used in this study are integrated I (1) in the first difference. In the second step, we apply the Pedroni cointegration test, and Kao Residual cointegration test, and we observe that the variables are cointegrated in the long run. The generalized least squares (GLS), the panel fully modified least squares (FMOLS), ordinary least squares robust (OLS), and panel quantile regression are considered in this research. The econometric results validate the assumption of the environmental Kuznets curve, i.e., and there is a positive correlation between income per capita and a negative effect of squared income per capita on carbon dioxide emissions. In contrast, we observe that renewable energy reduces CO2 emissions. Finally, we also find a direct connection between the urban population and the environmental degradation in the examined blocs. These results show that in Portugal, Italy, Greece, and Spain, more is required to achieve environmental sustainability in the respective countries growth trajectory. Further policy prescriptions are appended in the concluding section of this study. [ABSTRACT FROM AUTHOR]

Southeast Asian region is fast growing in terms of economy with rapid population growth, high energy consumption, and pollution. Understanding these linkages are crucial to guidance of appropriate policy. This study aims to examine the causal relationship between energy consumption with economic growth and CO2 emissions of the four selected Asian countries, namely Indonesia, Malaysia, Philippines, and Thailand between the years 1971–2017 using Johansen cointegration method combined with Granger causality model. The results found the evidence of cointegration in all countries implying a long-run relationship among energy consumption, economy and pollution exists. The causality main results show the evidence of unidirectional causality running from economic growth to energy consumption in Indonesia, Malaysia, and Thailand, while the opposite direction was found in Philippines. The results of Indonesia, Malaysia, and Thailand support "conservative hypothesis" suggesting that energy conservation policies could be adopted in these countries as it would not constrain growth of the economies. Whereas the results of Philippines appear to support "growth hypothesis" implying that energy is a key driver to stimulate economy. Limiting the use of energy could affect the economy. Instead, the policies, therefore, should focus on promoting other alternative energy source such as renewable energy in order to maintain sustainable growth. [ABSTRACT FROM AUTHOR]

The European Green Deal considers the increase in the share of renewable energy in final energy consumption (REFEC) among the main targets for achieving sustainable EU economies. In this context, the main aim of this paper is to provide an empirical evaluation of the relationship between GDP, global competitiveness index (GCI) and renewable energy consumption. According to panel data models based on the fully modified ordinary least squares method (FMOLS), there is a positive effect of renewable energy consumption progress on GDP and GCI growth, and also a positive influence of economic growth on renewable energy consumption in the period 2007–2019 in the EU countries. The energy consumption is more influenced by economic growth rather than economic competitiveness. Few scenarios were proposed for economic growth and share of renewable sources (RESs) in the final consumption using as forecasting method the proposed panel data models. The cluster analysis suggested two groups of countries according to RES share in gross final energy consumption (GFEC). The first group includes six countries (Sweden, Denmark, Finland, Latvia, Portugal and Austria) that fixed a target of 30% or more, while the second one refers to countries with lower targets. Some policy recommendations are provided for the EU countries to enhance the utilization of renewable energy. [ABSTRACT FROM AUTHOR]

This article analyzed the relationship between financial development, renewable energy consumption, economic growth, and energy prices in Azerbaijan by employing time series data for the time span of 1993–2015. The autoregressive distributed lagged (ARDL) technique was applied in empirical estimations, because it performs better than all the alternative techniques in small samples, which was the case here in this article. The results of estimation found that there is a positive and statistically significant influence of financial development and economic growth on renewable energy consumption, whereas the prices of energy proxied by CPI have an adverse impact on renewable energy consumption in Azerbaijan. Also, estimation results demonstrated that a 1% rise in financial development, proxied by domestic credit as a percentage of GDP, and economic growth increase renewable energy consumption by 0.16% and 0.60%, respectively. The different financial development impacts on renewable energy consumption and related policy implications were also introduced. [ABSTRACT FROM AUTHOR]

Using a panel cointegration model developed based on the data extracted from the World Bank indicators, this study quantified the relationship between carbon emissions, energy consumption, economic growth, and trade openness in sub-Saharan African countries. It discovered from our analysis that there exists a long-run causality association amongst CO 2 emissions, energy consumption, economic growth, and trade openness. The study noted the existence of the Environmental Kuznets Curve (EKC) in the panel using the square term for trade openness; it was found to have a negative impact, thus trade in the long run will somewhat decrease the environmental pollution in this region. The study results imply that there should be stringent policies and rigorous enforcement in sub-Saharan African to ensure sustainable growth without associative environmental issues. [ABSTRACT FROM AUTHOR]

Energy consumption is of great significance to the sustainable development of the economy. Due to the spatial heterogeneity of low-carbon growth in regional economies, the relationship between energy consumption and economic growth is complicated. However, a few researches have been published about spatial spillover effects and non-linearity of energy consumption and financial development on regional economic growth in China. Based on the panel data of 30 provinces in China from 2007 to 2017, this paper analyzes the spatial spillover effects and threshold effects of energy consumption and financial development on regional economic growth by using spatial and nonlinear econometric methods. The main conclusions are as follows. Spatial econometric methods show that financial development and energy consumption are two factors of production input to promote China's economic growth. Meanwhile, energy consumption and financial development have spillover effects on regional economic growth. Additionally, the nonlinear econometric method finds that with increasing financial development, the impact of energy consumption on economic growth is segmented. Therefore, relevant policies should be implemented to enhance the role of finance in energy consumption to promote low-carbon growth of China's economy. [ABSTRACT FROM AUTHOR]

This paper evaluates the link between economic growth, renewable energy, tourism arrivals, trade openness, and carbon dioxide emissions in the European Union (EU-28). As an econometric strategy, the research uses panel data. In the first step, we apply the unit root test, and the results demonstrated that the variables used in this study are integrated I (1) in the first difference. In the second step, we apply the Pedroni cointegration test, and Kao Residual cointegration test, and we observe that the variables are cointegrated in the long run. The panel fully modified least squares (FMOLS), panel dynamic least squares (DOLS), and generalized moments system (GMM-System) estimator are considered in this research. The econometric results proved that trade openness and renewable energy decreased climate change and environmental degradation. The empirical study also found a positive effect of economic growth on carbon dioxide emissions. Moreover, tourism arrivals are negatively correlated with carbon dioxide emissions, showing sustainability practices of the tourism sector on the environment. Furthermore, carbon dioxide emissions in the long run present a positive impact, indicating that climate change increases. In this study, we also consider the recent methodology of Dumitrescu–Hurlin to observe the causality and the relationship between renewable energy, trade openness, economic growth, tourism arrivals, and carbon dioxide emissions. [ABSTRACT FROM AUTHOR]

We propose the use of wavelet-based semiparametric models for forecasting the value-at-risk (VaR) and expected shortfall (ES) in the crude oil market. We compared the forecast outcomes across different time scales for three semiparametric models, three nonparametric, distribution-based, generalized, autoregressive, conditional, heteroskedasticity (GARCH) models, and three rolling-window models. We found that the GARCH model estimated by the Fissler and Ziegel (FZ) zero loss minimization (GARCH-FZ) model performs the best at forecasting the VaR and ES in the short term, whereas the hybrid model performs the best for mid- and long-term time scales. Thus, long-term investors should consider the hybrid model and short-term investors should employ the GARCH-FZ model in their risk management processes. Overall, our proposed wavelet-based semiparametric models outperform the other models tested for all time scales and market conditions. As such, we suggest that these models are considered for the management of crude oil price risk and in the development of energy policy. [ABSTRACT FROM AUTHOR]

This article examines the asymmetric relationship between electric consumption, economic growth, and carbon dioxide emission in 15 countries over the period 1971–2014. We employed a nonlinear auto-regressive distribution Lag (NARDL) model approach to investigate the asymmetric cointegration between variables. Additionally, we applied the asymmetric causality approach to determine the causal relationship between variables. Results confirm nonlinear cointegration between variables in Cameroon, Congo Republic, Zambia, Canada, and the UK. The Wald test results confirm a long-run asymmetric link between electricity consumption, economic growth, and carbon emission in Canada and Cameroon, while a short-run asymmetric effect in the Congo Republic and the UK. Findings from the granger causality test are volatile across variables. The result provides strong support for the symmetric relationship between electric consumption, economic growth, and carbon emission in the short and long run. This study provides new evidence for policymakers to formulate country-specific policies to obtain better environmental quality while achieving sustainable economic growth. [ABSTRACT FROM AUTHOR]

This study aims to explore the casual relationship between agricultural production, economic growth and carbon dioxide emissions in Pakistan. An autoregressive distributed lag (ARDL) model is applied to examine the relationship between agricultural production, economic growth and carbon dioxide emissions using time series data from 1960 to 2014. The Augmented Dickey–Fuller (ADF), Phillips–Perron (PP) and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests are used to check the stationarity of variables. The results show both short-run and long-run relationships between agricultural production, gross domestic product (GDP) and carbon dioxide emissions in Pakistan. From the short-run estimates, it is found that a 1% increase in barley and sorghum production will decrease carbon dioxide emissions by 3% and 4%, respectively. The pairwise Granger causality test shows unidirectional causality of cotton, milled rice, and sorghum production with carbon dioxide emissions. Due to the aforementioned cause, it is essential to manage the effects of carbon dioxide emissions on agricultural production. Appropriate steps are needed to develop agricultural adaptation policies, improve irrigation facilities and introduce high-yielding and disease-resistant varieties of crops to ensure food security in the country. [ABSTRACT FROM AUTHOR]

Carbon price forecasting is significant to both policy makers and market participants. However, since the complex characteristics of carbon prices are affected by many factors, it may be hard for a single prediction model to obtain high-precision results. As a consequence, a new hybrid model based on multi-resolution singular value decomposition (MRSVD) and the extreme learning machine (ELM) optimized by moth–flame optimization (MFO) is proposed for carbon price prediction. First, through the augmented Dickey–Fuller test (ADF), cointegration test and Granger causality test, the external factors of the carbon price, which includes energy and economic factors, are selected in turn. To select the internal factors of the carbon price, the carbon price series are decomposed by MRSVD, and the lags are determined by partial autocorrelation function (PACF). MFO is then used for the optimization of ELM parameters, and external and internal factors are input to the MFO-ELM. Finally, to test the capability and effectiveness of the proposed model, MRSVD-MFO-ELM and its comparison models are used for carbon price forecast in the European Union (EU) and China, respectively. The results show that the performance of the model is significantly better than other models. [ABSTRACT FROM AUTHOR]

As one of the leading types of energy, crude oil plays a crucial role in the global economy. Understanding the movement of crude oil prices is very attractive for producers, consumers and even researchers. However, due to its complex features of nonlinearity and nonstationarity, it is a very challenging task to accurately forecasting crude oil prices. Inspired by the well-known framework "decomposition and ensemble" in signal processing and/or time series forecasting, we propose a new approach that integrates the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), differential evolution (DE) and several types of ridge regression (RR), namely, ICEEMDAN-DE-RR, for more accurate crude oil price forecasting in this paper. The proposed approach consists of three steps. First, we use the ICEEMDAN to decompose the complex daily crude oil price series into several relatively simple components. Second, ridge regression or kernel ridge regression is employed to forecast each decomposed component. To enhance the accuracy of ridge regression, DE is used to jointly optimize the regularization item, the weights and parameters of each single kernel for each component. Finally, the predicted results of all components are aggregated as the final predicted results. The publicly available West Texas Intermediate (WTI) daily crude oil spot prices are used to validate the performance of the proposed approach. The experimental results indicate that the proposed approach can achieve better performance than some state-of-the-art approaches in terms of several evaluation criteria, demonstrating that the proposed ICEEMDAN-DE-RR is very promising for daily crude oil price forecasting. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of jumps in forecasting co-volatility in the presence of leverage effects for daily crude oil and gold futures. We use a modified version of the jump-robust covariance estimator of Koike (2016), such that the estimated matrix is positive definite. Using this approach, we can disentangle the estimates of the integrated co-volatility matrix and jump variations from the quadratic covariation matrix. Empirical results show that more than 80% of the co-volatility of the two futures contains jump variations and that they have significant impacts on future co-volatility but that the impact is negligible in forecasting weekly and monthly horizons. [ABSTRACT FROM AUTHOR]