Pub Date : 2023-08-01DOI: 10.1080/15567249.2023.2241455
Zeynep Ceylan
ABSTRACT The lockdown measures implemented to contain the COVID-19 pandemic have had a considerable effect on the consumption of natural gas, which is closely linked to the economic growth of countries. Accurately forecasting natural gas demand is critical for making informed decisions in unprecedented and unexpected situations. This study aims to compare artificial learning-based algorithms and classical statistical time series models in predicting natural gas demand during the pandemic, using Turkey as a case study. Common time series prediction methods, including Autoregressive Integrated Moving Average (ARIMA), Nonlinear Autoregression Neural Network (NARNN), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM), were utilized for this purpose. The impact of the pandemic on natural gas demand was analyzed by including 2-year natural gas consumption data since its onset. Root mean square error (RMSE), correlation coefficient (R), and mean absolute error (MAE) criteria were used as performance evaluation metrics to select the best model. The results confirmed that the deep-learning-based LSTM model provided better prediction accuracy than time-series benchmark models, with the lowest RMSE (9.442) and the highest R (0.997) values in the test dataset. Furthermore, the results were validated by statistical analysis using the Diebold-Mariano and Nemenyi tests.
{"title":"Comparative analysis of deep learning and classical time series methods to forecast natural gas demand during COVID-19 pandemic","authors":"Zeynep Ceylan","doi":"10.1080/15567249.2023.2241455","DOIUrl":"https://doi.org/10.1080/15567249.2023.2241455","url":null,"abstract":"ABSTRACT The lockdown measures implemented to contain the COVID-19 pandemic have had a considerable effect on the consumption of natural gas, which is closely linked to the economic growth of countries. Accurately forecasting natural gas demand is critical for making informed decisions in unprecedented and unexpected situations. This study aims to compare artificial learning-based algorithms and classical statistical time series models in predicting natural gas demand during the pandemic, using Turkey as a case study. Common time series prediction methods, including Autoregressive Integrated Moving Average (ARIMA), Nonlinear Autoregression Neural Network (NARNN), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM), were utilized for this purpose. The impact of the pandemic on natural gas demand was analyzed by including 2-year natural gas consumption data since its onset. Root mean square error (RMSE), correlation coefficient (R), and mean absolute error (MAE) criteria were used as performance evaluation metrics to select the best model. The results confirmed that the deep-learning-based LSTM model provided better prediction accuracy than time-series benchmark models, with the lowest RMSE (9.442) and the highest R (0.997) values in the test dataset. Furthermore, the results were validated by statistical analysis using the Diebold-Mariano and Nemenyi tests.","PeriodicalId":51247,"journal":{"name":"Energy Sources Part B-Economics Planning and Policy","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78319693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-31DOI: 10.1080/15567249.2023.2240784
D. Vo, Chi Minh Ho, A. Vo
ABSTRACT The renewable energy-economic growth-environment nexus has been generally considered an important puzzle that needs to be addressed for the following phrases of sustainable economic growth and development globally. The existing literature confirms the potential role of trade openness, financial development, and urbanization in this nexus. However, these roles have largely been ignored in empirical studies. This study uses the second-generation macro econometrics regarding panel unit root tests, cointegration tests, long-run estimations, and Granger homogeneous non-causality tests to investigate these impacts on the important nexus. First, the findings reveal that economic growth increases CO2 emissions, whereas renewable energy usage supports economic growth and mitigates CO2 emissions. There appears to be a bidirectional causality relationship between renewable energy and economic growth, which Granger-cause CO2 emissions unidirectionally. Second, trade openness increases CO2 emissions and renewable energy usage. We also find that financial development positively contributes to economic growth, whereas urbanization has no impact on the nexus in the long run. Third, trade openness Granger causes economic growth bidirectionally, and unidirectional causality from financial development to economic growth and CO2 emissions is found. Our results confirm a bidirectional causality relationship between urbanization, economic growth, and CO2 emissions and a unidirectional causality relationship from renewable energy to urbanization. Policy implications have emerged for countries globally, including emerging markets, which are the main emitters. Increased renewable energy supply and usage in total energy and extended financial systerms will sustain economic development and limit CO2 emissions.
{"title":"Trade openness, financial development, and urbanization in the renewable energy-growth-environment nexus","authors":"D. Vo, Chi Minh Ho, A. Vo","doi":"10.1080/15567249.2023.2240784","DOIUrl":"https://doi.org/10.1080/15567249.2023.2240784","url":null,"abstract":"ABSTRACT The renewable energy-economic growth-environment nexus has been generally considered an important puzzle that needs to be addressed for the following phrases of sustainable economic growth and development globally. The existing literature confirms the potential role of trade openness, financial development, and urbanization in this nexus. However, these roles have largely been ignored in empirical studies. This study uses the second-generation macro econometrics regarding panel unit root tests, cointegration tests, long-run estimations, and Granger homogeneous non-causality tests to investigate these impacts on the important nexus. First, the findings reveal that economic growth increases CO2 emissions, whereas renewable energy usage supports economic growth and mitigates CO2 emissions. There appears to be a bidirectional causality relationship between renewable energy and economic growth, which Granger-cause CO2 emissions unidirectionally. Second, trade openness increases CO2 emissions and renewable energy usage. We also find that financial development positively contributes to economic growth, whereas urbanization has no impact on the nexus in the long run. Third, trade openness Granger causes economic growth bidirectionally, and unidirectional causality from financial development to economic growth and CO2 emissions is found. Our results confirm a bidirectional causality relationship between urbanization, economic growth, and CO2 emissions and a unidirectional causality relationship from renewable energy to urbanization. Policy implications have emerged for countries globally, including emerging markets, which are the main emitters. Increased renewable energy supply and usage in total energy and extended financial systerms will sustain economic development and limit CO2 emissions.","PeriodicalId":51247,"journal":{"name":"Energy Sources Part B-Economics Planning and Policy","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85293683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-20DOI: 10.1080/15567249.2023.2233968
Salih Çam, Muhammed Ali Kağızman
ABSTRACT This study investigates the effects of several contextual variables, including renewable energy intensity, capital stock per labor, natural resource rent, the share of imported energy in total energy consumption, the ratio of carbon emissions to GDP, population, and energy production on energy efficiency in EU countries. While Tobit regression is used to examine the effects of contextual variables on energy efficiency, Data Envelopment Analysis (DEA), Slack-Based Data Envelopment Analysis (SBM-DEA), and Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) are used to calculate energy efficiency values of countries. Four different Tobit regression models are estimated as a function of the censored value for the energy efficiency series. The results show that renewable energy intensity, the ratio of carbon emissions to GDP, and population size have negative effects on energy efficiency. In contrast, regardless of the efficiency level, the share of imported energy in total energy consumption, total energy production, capital stock per labor, and technological progress have positive effects on energy efficiency.
{"title":"Investigating the energy efficiency determinants in EU countries by using multi-criteria decision analysis and the Tobit regression model","authors":"Salih Çam, Muhammed Ali Kağızman","doi":"10.1080/15567249.2023.2233968","DOIUrl":"https://doi.org/10.1080/15567249.2023.2233968","url":null,"abstract":"ABSTRACT This study investigates the effects of several contextual variables, including renewable energy intensity, capital stock per labor, natural resource rent, the share of imported energy in total energy consumption, the ratio of carbon emissions to GDP, population, and energy production on energy efficiency in EU countries. While Tobit regression is used to examine the effects of contextual variables on energy efficiency, Data Envelopment Analysis (DEA), Slack-Based Data Envelopment Analysis (SBM-DEA), and Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) are used to calculate energy efficiency values of countries. Four different Tobit regression models are estimated as a function of the censored value for the energy efficiency series. The results show that renewable energy intensity, the ratio of carbon emissions to GDP, and population size have negative effects on energy efficiency. In contrast, regardless of the efficiency level, the share of imported energy in total energy consumption, total energy production, capital stock per labor, and technological progress have positive effects on energy efficiency.","PeriodicalId":51247,"journal":{"name":"Energy Sources Part B-Economics Planning and Policy","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75165349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-16DOI: 10.1080/15567249.2023.2234905
L. Aidoo, H. Khobai, E. Kleynhans
ABSTRACT The past decade has seen an increase in renewable energy sources in the energy mix due to global environmental and supply security concerns. This paper aimed to investigate renewable energy effects on Gross Domestic Product (GDP) in the Southern African Power Pool (SAPP). The investigation is from 1988 to 2018, using the autoregressive distributed lag (ARDL), Nonlinear ARDL (NARDL), and fully modified OLS (FMOL) techniques. Based on empirical analyses of ARDL and FMOLS, it was found that both renewable and nonrenewable energies have a positive impact on economic growth in the Southern African power pool. However, the NARDL estimation indicates that neither renewable nor nonrenewable energy has a significant effect on economic growth in Southern African power pool. Therefore, based on the findings from both ARDL and FMOLS, the growth hypothesis is supported regarding the connection between renewable energy and economic growth in the Southern Africa power pool. Centered on the results, some policy implications were drawn for SAPP for sustainable economic growth, which includes measures to increase renewable energy in the energy mix, and also to encourage the optimization of cross-border connections where energy production from countries within SAPP that are not restricted by natural resources can be relied on to export sustainable energy. Additionally, building a more sustainable integrated power system in the region will assist SAPP with sustainable energy access and reduce the dependence on nonrenewable energy within the interconnected countries soon.
{"title":"The impact of renewable energy on economic growth in the Southern African Power Pool (SAPP)","authors":"L. Aidoo, H. Khobai, E. Kleynhans","doi":"10.1080/15567249.2023.2234905","DOIUrl":"https://doi.org/10.1080/15567249.2023.2234905","url":null,"abstract":"ABSTRACT The past decade has seen an increase in renewable energy sources in the energy mix due to global environmental and supply security concerns. This paper aimed to investigate renewable energy effects on Gross Domestic Product (GDP) in the Southern African Power Pool (SAPP). The investigation is from 1988 to 2018, using the autoregressive distributed lag (ARDL), Nonlinear ARDL (NARDL), and fully modified OLS (FMOL) techniques. Based on empirical analyses of ARDL and FMOLS, it was found that both renewable and nonrenewable energies have a positive impact on economic growth in the Southern African power pool. However, the NARDL estimation indicates that neither renewable nor nonrenewable energy has a significant effect on economic growth in Southern African power pool. Therefore, based on the findings from both ARDL and FMOLS, the growth hypothesis is supported regarding the connection between renewable energy and economic growth in the Southern Africa power pool. Centered on the results, some policy implications were drawn for SAPP for sustainable economic growth, which includes measures to increase renewable energy in the energy mix, and also to encourage the optimization of cross-border connections where energy production from countries within SAPP that are not restricted by natural resources can be relied on to export sustainable energy. Additionally, building a more sustainable integrated power system in the region will assist SAPP with sustainable energy access and reduce the dependence on nonrenewable energy within the interconnected countries soon.","PeriodicalId":51247,"journal":{"name":"Energy Sources Part B-Economics Planning and Policy","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2023-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83813197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ABSTRACT Carbon neutrality is one of the key issues in mitigating global climate change. The carbon neutrality process accelerates the global energy transformation from fossil energy to renewable energy. Among the main indicators related to carbon neutrality proposed by the Chinese government, those directly related to energy include carbon emission intensity, the proportion of non-fossil energy consumption, and the total installed capacity of wind and solar power generation. It can be seen that the transformation of energy development is crucial for realizing carbon neutrality. Meanwhile, the correlation between carbon neutrality and economic development is not antagonistic. On the contrary, carbon neutrality can not only enhance the quality and efficiency of traditional industries but also improve the overall development of society. Therefore, the coordinated and sustainable developments of the energy - economy- environment (3E) system is the priority for achieving the target of carbon neutrality. However, the impact of carbon neutrality on regional development requires long-term consideration, which increases the difficulties of comprehensive planning. In this study, a long-term comprehensive programming model of regional energy and economic development under carbon peaking and carbon neutrality targets was established. A carbon neutrality assessment indicator (CNAI) framework was constructed to assess the effect of carbon neutrality actions in different regions. Meanwhile, a LEAP model was developed to simulate energy demand and carbon emissions. The multi-target weights determined by assessment results of CNAI and the parameters range set by LEAP model were the basis of the multi-objective optimization model to support regional development. Furthermore, the proposed method was applied to Shaanxi province in China, which offered quantitative targets and suggestions for energy structure transformation and industrial structure adjustment. The results show that coal and oil will account for 6.25% of the total energy consumption and the proportion of secondary industry will drop to 25.40% by 2060. The method established in this study provides a new scientific approach for resolving multi-objective trade-offs in long-term programming and determining a reasonable optimization range for regional development under climate mitigation policies.
{"title":"Long-term energy-environment-economic programming under carbon neutrality target: a study on China’s regional energy transition pathways and CO2 mitigation strategies","authors":"Menglin Liu, Jiangtao Wu, Zhikai Lang, Xianyang Meng","doi":"10.1080/15567249.2023.2229321","DOIUrl":"https://doi.org/10.1080/15567249.2023.2229321","url":null,"abstract":"ABSTRACT Carbon neutrality is one of the key issues in mitigating global climate change. The carbon neutrality process accelerates the global energy transformation from fossil energy to renewable energy. Among the main indicators related to carbon neutrality proposed by the Chinese government, those directly related to energy include carbon emission intensity, the proportion of non-fossil energy consumption, and the total installed capacity of wind and solar power generation. It can be seen that the transformation of energy development is crucial for realizing carbon neutrality. Meanwhile, the correlation between carbon neutrality and economic development is not antagonistic. On the contrary, carbon neutrality can not only enhance the quality and efficiency of traditional industries but also improve the overall development of society. Therefore, the coordinated and sustainable developments of the energy - economy- environment (3E) system is the priority for achieving the target of carbon neutrality. However, the impact of carbon neutrality on regional development requires long-term consideration, which increases the difficulties of comprehensive planning. In this study, a long-term comprehensive programming model of regional energy and economic development under carbon peaking and carbon neutrality targets was established. A carbon neutrality assessment indicator (CNAI) framework was constructed to assess the effect of carbon neutrality actions in different regions. Meanwhile, a LEAP model was developed to simulate energy demand and carbon emissions. The multi-target weights determined by assessment results of CNAI and the parameters range set by LEAP model were the basis of the multi-objective optimization model to support regional development. Furthermore, the proposed method was applied to Shaanxi province in China, which offered quantitative targets and suggestions for energy structure transformation and industrial structure adjustment. The results show that coal and oil will account for 6.25% of the total energy consumption and the proportion of secondary industry will drop to 25.40% by 2060. The method established in this study provides a new scientific approach for resolving multi-objective trade-offs in long-term programming and determining a reasonable optimization range for regional development under climate mitigation policies.","PeriodicalId":51247,"journal":{"name":"Energy Sources Part B-Economics Planning and Policy","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83326608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-27DOI: 10.1080/15567249.2023.2229320
M. Bozorgi, Animesh Dutta, S. Mahmud, S. Tasnim
ABSTRACT Solar thermal and biomass hybridization combine two energy sources that complement one another seasonally and diurnally, overcoming their respective disadvantages. In the current research, the feasibility of locating a new medium capacity (5–150 Mwe) hybrid concentrated solar biomass power plant is investigated in Alberta and Ontario. To address the above-mentioned goal, three critical criteria are considered to find the most suitable region. The amount of direct normal irradiation and the biomass feedstock in 50 km and 100 km radii are analyzed in different regions in Ontario and Alberta. The third factor is the distance to the substations. According to the results, although it is difficult to justify developing a hybrid concentrated solar biomass power plant in Ontario, at least five locations in Alberta meet the aforementioned criteria. By concentrating on substations located near natural gas power plants, Calgary, with four natural gas power plants, can be regarded as the most suitable region. According to the findings, we identified that by establishing a 100 Mwe hybrid concentrated solar biomass power plant that requires 359,478 tons of forestry biomass and costs between 3.7 and 4.9 mCAD/Mwe, about 5% of Calgary’s total electricity consumption would be met while reducing CO2 emissions by 32 tons.
{"title":"Identifying optimal geographic locations for hybrid concentrated solar biomass (HCSB) power plants in Alberta and Ontario, Canada","authors":"M. Bozorgi, Animesh Dutta, S. Mahmud, S. Tasnim","doi":"10.1080/15567249.2023.2229320","DOIUrl":"https://doi.org/10.1080/15567249.2023.2229320","url":null,"abstract":"ABSTRACT Solar thermal and biomass hybridization combine two energy sources that complement one another seasonally and diurnally, overcoming their respective disadvantages. In the current research, the feasibility of locating a new medium capacity (5–150 Mwe) hybrid concentrated solar biomass power plant is investigated in Alberta and Ontario. To address the above-mentioned goal, three critical criteria are considered to find the most suitable region. The amount of direct normal irradiation and the biomass feedstock in 50 km and 100 km radii are analyzed in different regions in Ontario and Alberta. The third factor is the distance to the substations. According to the results, although it is difficult to justify developing a hybrid concentrated solar biomass power plant in Ontario, at least five locations in Alberta meet the aforementioned criteria. By concentrating on substations located near natural gas power plants, Calgary, with four natural gas power plants, can be regarded as the most suitable region. According to the findings, we identified that by establishing a 100 Mwe hybrid concentrated solar biomass power plant that requires 359,478 tons of forestry biomass and costs between 3.7 and 4.9 mCAD/Mwe, about 5% of Calgary’s total electricity consumption would be met while reducing CO2 emissions by 32 tons.","PeriodicalId":51247,"journal":{"name":"Energy Sources Part B-Economics Planning and Policy","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73013871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-27DOI: 10.1080/15567249.2023.2229315
Fuyong Yang, Qingsong Xu, Xueqin Wang, Qing Liu, Wenming Shi
ABSTRACT Carbon dioxide (CO2) emission mitigation has recently been a critical policy-making concern in the financial area. To address this concern, we examine whether and to what extent financial development reduces CO2 emissions in China from a spatial dependence and heterogeneity perspective. The empirical results of applying a random effects eigenvector spatial filtering (RE-ESF) model and a RE-ESF with non-spatially varying coefficients (RE-ESF-SNVC) model to a provincial panel dataset over the period 1997 − 2017 indicate better goodness-of-fit due to the consideration of spatial effects. The role of financial development in mitigating CO2 emissions is confirmed at both the global level by using the RE-ESF model and the local level by using the RE-ESF-SNVC model. Moreover, the contributions of financial development display clear regional disparities, suggesting the inclusion of spatial heterogeneity when developing financial policies for CO2 emission mitigation. However, such contributions are restricted by financial constraints which increase CO2 emissions globally and locally, requiring a balance between financial development and financial constraints when expanding bank loans. Additionally, the one-way analysis of variance reveals the existence of a geographically dividing line approximately connecting the easternmost tip of Xinjiang and the easternmost tip of Fujian. It is observed that provinces above the line generally have stronger impacts of financial development on carbon emission reduction, while impacts in provinces below the line are generally weaker. These findings encourage the implementation of proactive fiscal and monetary policies to promote financial development. Considering financial constraints, cross-provincial and province-specific financial policies should be balanced when allocating more financial resources to provinces with greater contributions of financial development to CO2 emission mitigation.
{"title":"Uncovering the mystery of financial development in mitigating CO2 emissions in China: A spatial dependence and heterogeneity perspective","authors":"Fuyong Yang, Qingsong Xu, Xueqin Wang, Qing Liu, Wenming Shi","doi":"10.1080/15567249.2023.2229315","DOIUrl":"https://doi.org/10.1080/15567249.2023.2229315","url":null,"abstract":"ABSTRACT Carbon dioxide (CO2) emission mitigation has recently been a critical policy-making concern in the financial area. To address this concern, we examine whether and to what extent financial development reduces CO2 emissions in China from a spatial dependence and heterogeneity perspective. The empirical results of applying a random effects eigenvector spatial filtering (RE-ESF) model and a RE-ESF with non-spatially varying coefficients (RE-ESF-SNVC) model to a provincial panel dataset over the period 1997 − 2017 indicate better goodness-of-fit due to the consideration of spatial effects. The role of financial development in mitigating CO2 emissions is confirmed at both the global level by using the RE-ESF model and the local level by using the RE-ESF-SNVC model. Moreover, the contributions of financial development display clear regional disparities, suggesting the inclusion of spatial heterogeneity when developing financial policies for CO2 emission mitigation. However, such contributions are restricted by financial constraints which increase CO2 emissions globally and locally, requiring a balance between financial development and financial constraints when expanding bank loans. Additionally, the one-way analysis of variance reveals the existence of a geographically dividing line approximately connecting the easternmost tip of Xinjiang and the easternmost tip of Fujian. It is observed that provinces above the line generally have stronger impacts of financial development on carbon emission reduction, while impacts in provinces below the line are generally weaker. These findings encourage the implementation of proactive fiscal and monetary policies to promote financial development. Considering financial constraints, cross-provincial and province-specific financial policies should be balanced when allocating more financial resources to provinces with greater contributions of financial development to CO2 emission mitigation.","PeriodicalId":51247,"journal":{"name":"Energy Sources Part B-Economics Planning and Policy","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78701651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-14DOI: 10.1080/15567249.2023.2223202
M. Arnone, A. Canova, S. Balocco, P. Lazzeroni, I. Mariuzzo, A. Portoraro, M. Repetto
ABSTRACT The current EU regulatory framework is forcing the need for a substantial and significant reduction of greenhouse gas emissions in all the main economic sectors since climate change effects have been more tangible in recent years. The adoption of actions and measures to reduce CO2 emissions are thus essential for preventing global warming. For this reason, a simplified bottom-up approach is presented here to estimate the potential environmental benefit achievable, in terms of CO2 emissions saving, when a set of proposed energy efficiency measures were adopted in relevant economic sectors of the UNESCO area of Vineyard Landscape in Piedmont Region in Italy. Specifically, representative case studies were assumed for each economic sector being studied to identify the energy efficiency measures to be adopted. Then, progressive scenarios with different territorial diffusion of the proposed measures were performed. The analysis shows that a CO2 emission reduction from 7.1% to 32.4% can be potentially obtained by considering different diffusion pathways of the identified energy efficiency measures in the residential, accommodation, private transport, and food industry sectors, so that the UNESCO area can effectively contribute to contrast global warming. The proposed simplified approach can be replicated in other similar context to obtain rough estimation of emission savings due to territorial diffusion of energy efficiency measures considering available regional and local databases.
{"title":"Environmental perspective of decarbonization actions in the Italian UNESCO site of the Vineyard landscape of Piedmont Region","authors":"M. Arnone, A. Canova, S. Balocco, P. Lazzeroni, I. Mariuzzo, A. Portoraro, M. Repetto","doi":"10.1080/15567249.2023.2223202","DOIUrl":"https://doi.org/10.1080/15567249.2023.2223202","url":null,"abstract":"ABSTRACT The current EU regulatory framework is forcing the need for a substantial and significant reduction of greenhouse gas emissions in all the main economic sectors since climate change effects have been more tangible in recent years. The adoption of actions and measures to reduce CO2 emissions are thus essential for preventing global warming. For this reason, a simplified bottom-up approach is presented here to estimate the potential environmental benefit achievable, in terms of CO2 emissions saving, when a set of proposed energy efficiency measures were adopted in relevant economic sectors of the UNESCO area of Vineyard Landscape in Piedmont Region in Italy. Specifically, representative case studies were assumed for each economic sector being studied to identify the energy efficiency measures to be adopted. Then, progressive scenarios with different territorial diffusion of the proposed measures were performed. The analysis shows that a CO2 emission reduction from 7.1% to 32.4% can be potentially obtained by considering different diffusion pathways of the identified energy efficiency measures in the residential, accommodation, private transport, and food industry sectors, so that the UNESCO area can effectively contribute to contrast global warming. The proposed simplified approach can be replicated in other similar context to obtain rough estimation of emission savings due to territorial diffusion of energy efficiency measures considering available regional and local databases.","PeriodicalId":51247,"journal":{"name":"Energy Sources Part B-Economics Planning and Policy","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81392311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-09DOI: 10.1080/15567249.2023.2219676
M. Jahangir, Mohammad Salehi, Hamed Alimoradiyan
ABSTRACT In order to evaluate the hybrid renewable configurations in diverse applications, this inquiry tries to propose a novel decision-making procedure. The open-source WEC dynamics simulator, exergetic assessment, and Hybrid Optimization Model for Electric Renewables Pro software are integrated. In this innovative method, the exergy of the component is calculated using MATLAB software. Technical analysis for the goal of rural electrification is examined in this work. Thus, several integrated energy systems are technically researched. This study deals with the exergy, environmental, and exergoenvironmental analyses of the hybrid renewable energy system, to assess its performance and environmental impact when operating on Siri Island. In the photovoltaic solar panels, wind turbines, wave energy converters, diesel generators, and batteries set up, the total exergy efficiency and exergy destruction rate were 18.4% and 7.24 106 kWh/year, respectively. Additionally, it was discovered that the wave energy converter was responsible for 5% of the total energy wasted. Photovoltaic solar panels, wind turbines, and wave energy converters all have mean annual exergy efficiencies calculated to be 11.7%, 13.1%, and 20.8%, respectively. Environmental and exergoenvironmental analyses illustrate that the off-grid scenario decreases CO2 emission by 95% compared to the on-grid scenario and utilizing diesel generators increases the exergy stability factor of the energy system. The suggested decision-making procedure can be a potent and reliable instrument for analyzing any hybrid renewable arrangement from a technological standpoint.
{"title":"Exergy and environmental analysis of oscillating wave energy converter hybrid with other renewable energy resources: A case study","authors":"M. Jahangir, Mohammad Salehi, Hamed Alimoradiyan","doi":"10.1080/15567249.2023.2219676","DOIUrl":"https://doi.org/10.1080/15567249.2023.2219676","url":null,"abstract":"ABSTRACT In order to evaluate the hybrid renewable configurations in diverse applications, this inquiry tries to propose a novel decision-making procedure. The open-source WEC dynamics simulator, exergetic assessment, and Hybrid Optimization Model for Electric Renewables Pro software are integrated. In this innovative method, the exergy of the component is calculated using MATLAB software. Technical analysis for the goal of rural electrification is examined in this work. Thus, several integrated energy systems are technically researched. This study deals with the exergy, environmental, and exergoenvironmental analyses of the hybrid renewable energy system, to assess its performance and environmental impact when operating on Siri Island. In the photovoltaic solar panels, wind turbines, wave energy converters, diesel generators, and batteries set up, the total exergy efficiency and exergy destruction rate were 18.4% and 7.24 106 kWh/year, respectively. Additionally, it was discovered that the wave energy converter was responsible for 5% of the total energy wasted. Photovoltaic solar panels, wind turbines, and wave energy converters all have mean annual exergy efficiencies calculated to be 11.7%, 13.1%, and 20.8%, respectively. Environmental and exergoenvironmental analyses illustrate that the off-grid scenario decreases CO2 emission by 95% compared to the on-grid scenario and utilizing diesel generators increases the exergy stability factor of the energy system. The suggested decision-making procedure can be a potent and reliable instrument for analyzing any hybrid renewable arrangement from a technological standpoint.","PeriodicalId":51247,"journal":{"name":"Energy Sources Part B-Economics Planning and Policy","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90600069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-07DOI: 10.1080/15567249.2023.2219681
Liangui Peng, Ying Li
The dual-credit policy for auto manufacturers and the subsidy policy for charging infrastructure operators have become important policies to guide and promote the development of the automotive industry. However, few studies consider the impact of mixed policies on the market strategies of auto manufacturers and charging infrastructure operators considering consumer green preferences. To fill the gap, this article establishes optimization models to analyze and compare the effects of mixed policies on the decision-making of auto manufacturers and charging infrastructure operators under the cooperative and noncooperative modes considering consumer preference. The results reveal four main insights: (1) Two types of policies all have direct positive effects on the market diffusion of electric vehicles. Under the noncooperative mode, the dual-credit policy has no spillover effect. Under cooperative mode, two types of policies all have spillover effects. The dual-credit policy and charging infrastructure subsidy policy have positive superposition effects on the optimal number of charging piles, and the optimal demands for electric vehicles. (2) When the credit price is less than a certain threshold, there is a substitutive effect between the charging infrastructure subsidy policy and the dual-credit policy on promoting the market diffusion of electric vehicles. (3) There is a substitutive effect between high-level consumers’ green preference and policies on promoting the market diffusion of electric vehicles. (4) Under the cooperative mode, the direct and spillover effects of policies are more than that are under the noncooperative mode. These insights demonstrate the effectiveness and limitations of policies and the importance of providing complementary policies for consumers. The government should encourage market cooperation between auto manufacturers and charging infrastructure operators, maintain a stable price level for credits, and introduce incentive policies for green consumption.
{"title":"Effects of mixed policies on the cooperative and noncooperative strategies of auto manufacturers and charging infrastructure operators considering consumer preferences","authors":"Liangui Peng, Ying Li","doi":"10.1080/15567249.2023.2219681","DOIUrl":"https://doi.org/10.1080/15567249.2023.2219681","url":null,"abstract":"The dual-credit policy for auto manufacturers and the subsidy policy for charging infrastructure operators have become important policies to guide and promote the development of the automotive industry. However, few studies consider the impact of mixed policies on the market strategies of auto manufacturers and charging infrastructure operators considering consumer green preferences. To fill the gap, this article establishes optimization models to analyze and compare the effects of mixed policies on the decision-making of auto manufacturers and charging infrastructure operators under the cooperative and noncooperative modes considering consumer preference. The results reveal four main insights: (1) Two types of policies all have direct positive effects on the market diffusion of electric vehicles. Under the noncooperative mode, the dual-credit policy has no spillover effect. Under cooperative mode, two types of policies all have spillover effects. The dual-credit policy and charging infrastructure subsidy policy have positive superposition effects on the optimal number of charging piles, and the optimal demands for electric vehicles. (2) When the credit price is less than a certain threshold, there is a substitutive effect between the charging infrastructure subsidy policy and the dual-credit policy on promoting the market diffusion of electric vehicles. (3) There is a substitutive effect between high-level consumers’ green preference and policies on promoting the market diffusion of electric vehicles. (4) Under the cooperative mode, the direct and spillover effects of policies are more than that are under the noncooperative mode. These insights demonstrate the effectiveness and limitations of policies and the importance of providing complementary policies for consumers. The government should encourage market cooperation between auto manufacturers and charging infrastructure operators, maintain a stable price level for credits, and introduce incentive policies for green consumption.","PeriodicalId":51247,"journal":{"name":"Energy Sources Part B-Economics Planning and Policy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135409284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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