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Energy poverty is a multifaceted phenomenon that affects many Europeans. Alleviating energy poverty is high in the EU, national, and local policy agendas. Despite the attention the phenomenon has been gaining from a policy perspective, especially after the current energy crisis, there are still some gaps due to the complexity of the issue and its vastly different manifestations across Europe. This manuscript presents the policy implications stemming from the implementation of the POWEPROOR approach in alleviating energy poverty in eight European countries, as co-created with relevant stakeholders in each country. The knowledge gained from empowering energy-poor citizens by promoting behavioural changes and small-scale energy efficiency interventions, as well as by encouraging the uptake of renewable energy sources in the form of collective energy initiatives while leveraging innovative financing schemes, resulted in policy recommendations for national and sub-national governments and lessons for civil society and the private sector.
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The collection and distribution network of ports is the main cause of carbon emissions. The carbon peak is a basic policy in China, and the subsidy policy is one of the common measures used by the government to incentivize carbon reduction. We analyzed the transportation methods and the flow direction of a port and proposed a carbon emission calculation method based on emission factors. Based on the transportation time and the cost, a generalized transportation utility function was constructed, and the logit model was used to analyze the impacts of subsidy policies on transportation, thus calculating the effects of the subsidies on carbon reduction. We used Guangzhou Port as a case study, and calculated the carbon reduction effects in six different subsidy policy scenarios and concluded that the absolute carbon reduction value was proportional to the subsidy intensity. In addition, we constructed a subsidy carbon reduction efficiency index and found that the Guangzhou Port collection and distribution network had higher subsidy carbon reduction efficiency in low-subsidy scenarios. Finally, a sensitivity analysis was conducted on the subsidy parameters, and scenario 8 was found to have the highest subsidy carbon reduction efficiency. This achievement can provide decision support for the carbon emission strategy of the port collection and distribution network.
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PurposeThis study aims to examine the ability of clean energy stocks to provide cover for investors against market risks related to climate change and disturbances in the oil market.Design/methodology/approachThe study adopts the feasible quasi generalized least squares technique to estimate a predictive model based on Westerlund and Narayan's (2015) approach to evaluating the hedging effectiveness of clean energy stocks. The out-of-sample forecast evaluations of the oil risk-based and climate risk-based clean energy predictive models are explored using Clark and West's model (2007) and a modified Diebold & Mariano forecast evaluation test for nested and non-nested models, respectively.FindingsThe study finds ample evidence that clean energy stocks may hedge against oil market risks. This result is robust to alternative measures of oil risk and holds when applied to data from the COVID-19 pandemic. In contrast, the hedging effectiveness of clean energy against climate risks is limited to 4 of the 6 clean energy indices and restricted to climate risk measured with climate policy uncertainty.Originality/valueThe study contributes to the literature by providing extensive analysis of hedging effectiveness of several clean energy indices (global, the United States (US), Europe and Asia) and sectoral clean energy indices (solar and wind) against oil market and climate risks using various measures of oil risk (WTI (West Texas intermediate) and Brent volatility) and climate risk (climate policy uncertainty and energy and environmental regulation) as predictors. It also conducts forecast evaluations of the clean energy predictive models for nested and non-nested models.
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The main aim of the current article is to compare economic and Quality of Life (QL) indices in G7- (Germany, France, United States, United Kingdom, Japan, Italy and Canada) and BRICS-member countries (Brazil, Russia, India, China and South Africa). The research was developed based on selecting indices available in the NUMBEO, UN (United Nations) and OECD (Organization for Economic Cooperation and Development) databases. Results have evidenced that emerging countries belonging to the BRICS bloc have shown lower QL indices than those observed for developed countries in the G7 bloc. With respect to economic data, the USA, China, Japan and Germany were the countries presenting the highest GDP growth. It was possible concluding that countries belonging to the G7 block have better economic and labor indices, which, in their turn, are associated with better QoL indices.
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[...]it was determined that the herd investment was valid for BioNTech and Moderna when the highest index value belonged to BioNTech company. The following study is also related to the financial sector, focused on the Turkish Banking Sector and examines the relationship between financial innovation and economic growth in banks on a regional basis. According to the study results, it was understood that subjective norms and perceived behaviour control had a positive and significant effect on students' recycling behaviours. The last study of this issue, digital household technology ownership analysis, investigated the effects of preferred technology applications in the household and socio-economic and socio-demographic factors by using the generalised sequential logit method with TURKSTAT 2021 Household Information Technologies Usage Research Microdata.
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In this paper, we present a framework for evaluating risk contagion by merging financial networks with machine learning techniques. The framework begins with building a financial network model based on the inter-institutional correlation network, followed by analyzing the structure and overall value changes of the financial network under the stress of a liquidation shock. We then examine the network's evolution over time. We also use three machine learning techniques to assess the abnormal volatility of important financial institutions in the financial network. Finally, we evaluate the spillover effects of risk volatility in financial networks on ESG investments. The findings suggest that the financial network becomes more robust as the connections among financial institutions become more intricate. This leads to an improvement in the ability of the financial network to withstand systemic risk events. Overall, our study provides evidence of the negative impact of risk spillovers in financial networks on ESG investments, highlighting the need for a more sustainable and resilient financial system. This innovative framework combining financial network modeling and machine learning prediction provides a deeper understanding of the evolution of financial networks and a more accurate evaluation of abnormal volatility in financial networks.
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PurposeCarbon trading mechanism has been adopted to foster the green transformation of the economy on a global scale, but its effectiveness for the power industry remains controversial. Given that energy-related greenhouse gas emissions account for most of all anthropogenic emissions, this paper aims to evaluate the effectiveness of this trading mechanism at the plant level to support relevant decision-making and mechanism design.Design/methodology/approachThis paper constructs a novel spatiotemporal data set by matching satellite-based high-resolution (1 × 1 km) CO2 and PM2.5 emission data with accurate geolocation of power plants. It then applies a difference-in-differences model to analyse the impact of carbon trading mechanism on emission reduction for the power industry in China from 2007 to 2016.FindingsResults suggest that the carbon trading mechanism induces 2.7% of CO2 emission reduction and 6.7% of PM2.5 emission reduction in power plants in pilot areas on average. However, the reduction effect is significant only in coal-fired power plants but not in gas-fired power plants. Besides, the reduction effect is significant for power plants operated with different technologies and is more pronounced for those with outdated production technology, indicating the strong potential for green development of backward power plants. The reduction effect is also more intense for power plants without affiliation relationships than those affiliated with particular manufacturers.Originality/valueThis paper identifies the causal relationship between the carbon trading mechanism and emission reduction in the power industry by providing an innovative methodology for identifying plant-level emissions based on high-resolution satellite data, which has been practically absent in previous studies. It serves as a reference for stakeholders involved in detailed policy formulation and execution, including policymakers, power plant managers and green investors.
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PurposeThis study aims to examine the hedge and safe-haven properties of the Sukuk and green bond for the stock markets pre- and during the COVID-19 pandemic period.Design/methodology/approachTo test the hedge and safe-haven characteristics of Sukuk and green bonds for stock markets, the study first uses the methodology proposed by Ratner and Chiu (2013). Next, the authors estimate the hedge ratios and hedge effectiveness of using Sukuk and green bonds in a portfolio with stock markets.FindingsStrong safe-haven features of ethical (green) bonds reveal that adding green bonds into the investment portfolios brings considerable diversification avenues for the investors who tend to take fewer risks in periods of economic stress and turbulence. The hedge ratio and hedge effectiveness estimates reveal that green bonds provide sufficient evidence of the hedge effectiveness for various international stocks.Practical implicationsThe study has significant implications for faith-based investors, ethical investors, policymakers and regulatory bodies. Religious investors can invest in Sukuk to relish low-risk and interest-free investments, whereas green investors can satisfy their socially responsible motives by investing in these investment streams. Policymakers can direct the businesses to include these diversifiers for portfolio and risk management.Originality/valueThe study provides novel insights in the testing hedge and safe-haven attributes of green bonds and Sukuk while using unique methodologies to identify multiple low-risk investors for investors following the uncertain COVID-19 pandemic.
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In particular, the National Science Foundation (NSF) would see its budget increase by nearly 19%, and the Department of Energy's Office of Science - a big investor in the physical sciences - would see an increase of nearly 9% (see 'Biden's budget requests for science in 2024'). "The public-health system has been so underfunded for so long that the truth of the matter is, it's going to take a fair amount of money to make that right, but it's a step in the right direction," he says. Furthermore, the budget would provide $24 billion to help US communities prepare for the rising impacts of climate change, and another $7 billion to help communities that depend on oil, gas and coal extraction to tran-sition to clean energy.
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This study constructs a digital economy (DE) index and explores its impact on environmental quality by utilizing data from China's 287 prefecture-level cities from 2013 to 2019. Unlike past studies, this research examines the indirect effect of DE on environmental pollution through the channels of industrial structure and educational investment. Further, it also analyzes the moderating role of economic globalization and green technology innovation in the nexus between DE and environmental quality. The empirical results indicate that DE significantly and positively enhances environmental quality by mitigating environmental pollution. This outcome remained stable after a series of empirical analyses and stability checks. Secondly, DE positively affects ecological and environmental quality by improving education levels and upgrading industrial structures. Thirdly, green technological innovation and economic globalization positively and significantly moderate the effect of DE development on ecological and environmental quality. Fourthly, associations between the development of DE and environmental quality are heterogeneous in terms of regions and markets, among which the most significant impact exists in the eastern area and the area with higher marketization. Based on the empirical findings, this paper provides comprehensive recommendations for promoting the DE and advancing China's environmental quality. Based on the results, important policy implications are suggested.
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PurposeThe current study investigates the impact of the coronavirus disease 2019 (COVID-19) lockdown restrictions on air quality in an industrial town in Himachal Pradesh (HP) (India) and recommends policies and strategies for mitigating air pollution.Design/methodology/approachThe air quality parameters under study are particulate matter10 (PM10), PM2.5, SO2 and NO2. One-way ANOVA with post-hoc analysis and non-parametric Kruskal–Wallis test, and multiple linear regression analysis are used to validate the data analysis results.FindingsThe findings indicate that the lockdown and post-lockdown periods affected pollutant levels even after considering the meteorological conditions. Except for SO2, all other air quality parameters dropped significantly throughout the lockdown period. Further, the industrial and transportation sectors are the primary sources of air pollution in Paonta Sahib.Research limitations/implicationsFuture research should include other industrial locations in the state to understand the relationship between regional air pollution levels and climate change. The findings of this study may add to the discussion on the role of adopting clean technologies and also provide directions for future research on improving air quality in the emerging industrial towns in India.Originality/valueVery few studies have examined how the pandemic-induced lockdowns impacted air pollution levels in emerging industrial towns in India while also considering the confounding meteorological factors.Graphical abstract
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1. Preface/IntroductionWe are pleased to share with you the scholarly papers that were presented during the 2nd International Conference on Environmental Sustainability and Resource Security (IC-ENSURES 2022), which was held virtually on March 8 and 9 in Johor, Malaysia, at Universiti Teknologi Malaysia. This conference was held concurrently with the International Seminar on Science and Technology (ISSTEC) 2022.Centre for Environmental Sustainability and Water Security (IPASA) organises the IC-ENSURES conference series every two years. To foster information and knowledge exchange on resource security and environmental sustainability, this conference series intends to establish a global forum for professionals and academics. This time around, the theme of the conference is "Green Technology for Sustainable Future: The Next Step”. This theme is pertinent to environmental issues, disaster preparedness, and water security.With the global pandemic happening around the world, we are particularly interested to see how COVID-19 would affect our water and environment. Additionally, natural catastrophes, particularly those associated with climate change, have become more common and severe in recent years due to anthropogenic activities. Putting that in mind, the themes and topics of IC-ENSURES this year were carefully designed so that they associated with the ongoing pandemic and climate change.Engineering-related sessions including discussions on the following two major topics: environmental sustainability and resource security were carried out. These manuscripts were subjected to a rigorous review in compliance with international publication standards. We anticipate that the collection of accepted manuscripts will benefit numerous academicians, researchers, and experts in a given topic.List of Conference or sponsor logo, committees are available in this pdf.
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Electric vehicles are being introduced as a sustainable innovative solution, aimed at eliminating the negative environmental effects by having the ability to reduce carbon emissions, air pollution and fossil fuel dependency. It is therefore important for management, marketers and government to find innovative ways to promote and increase the adoption rate of these vehicles. This paper aims to make use of the theory of consumption values to predict the purchase intention for plug-in hybrid electric vehicles (PHEVs) in Gauteng, South Africa. Using structured questionnaire and convenience sampling, data were collected from 286 respondents. Online data collection was used due to the Corona virus pandemic restrictions, which limited the researchers abilities to explain items in the questionnaire. A partial least squares structural equation modelling was performed to test the proposed hypotheses. Results indicate that functional, social, emotional and conditional values are positively related to customers purchase intention of PHEVs, while the epistemic value is not positively related. This study will provide useful information to electric vehicle manufacturers, car dealerships, marketing managers and the government in developing strategies aimed at encouraging the adoption of PHEVs.
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Purpose>This study aims to investigate the response of green investments of emerging countries to own-market uncertainty, oil-market uncertainty and COVID-19 effect/geo-political risks (GPRs), using the tail risks of corresponding markets as measures of uncertainty.Design/methodology/approach>This study employs Westerlund and Narayan (2015) (WN)-type distributed lag model that simultaneously accounts for persistence, endogeneity and conditional heteroscedasticity, within a single model framework. The tail risks are obtained using conditional standard deviation of the residuals from an asymmetric autoregressive moving average – ARMA(1,1) – generalized autoregressive conditional heteroscedasticity – GARCH(1,1) model framework with Gaussian innovation. For out-of-sample forecast evaluation, the study employs root mean square error (RMSE), and Clark and West (2007) (CW) test for pairwise comparison of nested models, under three forecast horizons;providing statistical justification for incorporating oil tail risks and COVID-19 effects or GPRs in the predictive model.Findings>Green returns responds significantly to own-market uncertainty (mostly positively), oil-market uncertainty (mostly positively) as well as the COVID-19 effect (mostly negatively), with some evidence of hedging potential against uncertainties that are external to the green investments market. Also, incorporating external uncertainties improves the in-sample predictability and out-of-sample forecasts, and yields some economic gains.Originality/value>This study contributes originally to the green market-uncertainty literature in four ways. First, it generates daily tail risks (a more realistic measure of uncertainty) for emerging countries' green returns and global oil prices. Second, it employs WN-type distributed lag model that is well suited to account for conditional heteroscedasticity, endogeneity and persistence effects;which characterizes financial series. Third, it presents both in-sample predictability and out-of-sample forecast performances. Fourth, it provides the economic gains of incorporating own-market, oil-market and COVID-19 uncertainty.
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This study empirically examined the effect of a carbon trading pilot market on export green-sophistication of Chinese listed enterprises by adopting a difference-in-difference method. Findings show that a carbon trading pilot market can improve enterprises’ export green-sophistication after using robustness tests to overcome endogeneity. The impact mechanism test shows that a carbon trading pilot market can improve export green-sophistication by increasing green technology innovation. Further research on the system design of carbon trading pilot markets shows that the greater the total carbon quota allocation, the larger the reduction in the trading volume of Chinese certified emissions. Furthermore, the weaker the punishment for an enterprise’s default in the pilot areas, the less favorable it is for enterprises to improve their export green-sophistication. Compared with the grandfather and historical intensity methods, benchmarking used in the allocation of carbon quotas is conducive to the improvement of the export green-sophistication of enterprises.
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The purpose of this study is to examine and compare different psychological and sociodemographic factors for contracting sweet potato production for farmers with different statuses based upon the theory of planned behavior (TPB). Sustainable production provides contract owners with a sufficient amount of both food crops and a source of bioethanol clean energy. The impact of such factors on potential farmers based on the TPB for a particular contract type is estimated with the data collected in three major sweet potato production cities/counties in Taiwan through the probit model and multinomial logit model. The average size of the surveyed farms is 1.64 ha. The results consistently show that the factors of attitude toward the advantages of contract farming, subjective norms regarding contract farming, perceived contract farming control, and behavior intention have very significant impacts on the selection of contract farming types for professional farmers and brokers. These results indicate that the contract owners will gain the greatest advantage through commanding any factor in TBP for these two groups of farmers, as they have an incentive to manage the sources of sweet potatoes at the best conditions before they have the agreement with the contract owners, either as the supply of bioethanol energy raw materials, supply of food crops, or supply of food processing materials.
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Purpose - The current study investigates the impact of the coronavirus disease 2019 (COVID-19) lockdown restrictions on air quality in an industrial town in Himachal Pradesh (HP) (India) and recommends policies and strategies for mitigating air pollution. Design/methodology/approach - The air quality parameters under study are particulate matter(10) (PM10), PM2.5, SO2 and NO2. One-way ANOVA with post-hoc analysis and non-parametric Kruskal-Wallis test, and multiple linear regression analysis are used to validate the data analysis results. Findings - The findings indicate that the lockdown and post-lockdown periods affected pollutant levels even after considering the meteorological conditions. Except for SO2, all other air quality parameters dropped significantly throughout the lockdown period. Further, the industrial and transportation sectors are the primary sources of air pollution in Paonta Sahib. Research limitations/implications - Future research should include other industrial locations in the state to understand the relationship between regional air pollution levels and climate change. The findings of this study may add to the discussion on the role of adopting clean technologies and also provide directions for future research on improving air quality in the emerging industrial towns in India. Originality/value - Very few studies have examined how the pandemic-induced lockdowns impacted air pollution levels in emerging industrial towns in India while also considering the confounding meteorological factors. [GRAPHICS] .
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Bringing together the resources and members of complementary materials science and engineering groups has long been a defining feature of the Materials Science & Technology (MS&T) conference series. In 2022, MS&T builds on that tradition by welcoming new partners who will bring fresh exhibits symposia, and audiences to the event. As in previous years, MS&T22 will bring together three leading materials societies--TMS, the American Ceramic Society, and the Association for Iron & Steel Technology. To supplement this long-standing partnership, MS&T will welcome Event Partners to expand the exhibition and the Society for Biomaterials to offer additional programming.
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International clean technology diffusion is essential to mitigate and adapt to climate change, while fast and optimal diffusion can be prevented by the paywall of patents. This article explores the deficiency in clean technology diffusion caused by the legal fragmentation and rule complex of international environmental law and intellectual property law. It systematically examines three pathways to foster international clean technology diffusion through: restriction of intellectual property, including imposing external restraints in environmental law;striking internal balancing in maximizing TRIPS flexibilities;and keeping the status quo. It argues that treaty pathways may not work, and an operable pathway to promote clean technology diffusion is to maximize and consolidate TRIPS flexibilities in national laws. This option challenges the popular proposal of a “Doha-like” declaration on TRIPS and climate change due to the paralyzed multilateral trade mechanism, asymmetrical negotiation power of developing countries, prolonged negotiation process, and categorization problem in treaty negotiations.
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Focusing on raising climate concerns and sustaining a clean ecosystem, the current study strives to examine the connectedness of clean energy markets with conventional energy markets and four regional stock markets of Asia, Pacific, Europe, and America for the period spanning January 1, 2004 to August 31, 2021. We employed the volatility connectedness methodology using dynamic conditional correlation (DCC-GARCH) estimates for analysis purposes. There is pronounced within class connectedness of all markets except conventional energy markets, which showed strong disconnection from the network. However, strong inter-class spillovers are reported between clean energy and regional stock markets. Time-varying analysis revealed that intense spillovers are shaped during the Global Financial Crisis, Shale Oil Crisis, and COVID-19 pandemic. Meanwhile, time-varying net connectedness estimates illuminate that world renewable energy and American stock markets are net transmitters, whereas leftover markets are net recipients of spillovers. Further analysis of sub-sample periods during GFC, SOR, and COVID-19 validate that intense spillovers are formed when markets experience unexpected financial, economic, and global health turmoil. We proposed significant implications for regional stock markets of Asia, Pacific, Europe and America to concentrate on the climate-friendly energy markets than conventional energy markets as they service the clean ecosystem motives more specifically.