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Background: Carbon footprint is a widely used tool to measure the impact of human activities on global warming. The lockdowns caused by the COVID-19 pandemic have significantly changed energy consumption forms and decreased CO2 emissions worldwide. This research is a trial to elaborate the effect of COVID-19 pandemic on the carbon footprint of the High Institute of Public Health (HIPH). Objective(s): The present study aimed at measuring the amount of water, electricity, fuel, and paper consumption by HIPH before and during the emergence of COVID-19 and assessing the carbon footprint of the HIPH population inside the building through the same period. Methods: A cross-sectional survey was done using a pre-designed questionnaire targeting 10 % of the HIPH population before and during the pandemic. Bills of water, electricity, paper, and fuel consumption were used to calculate the carbon footprint for one year before and one year during COVID-19. Results: Online responses increased during COVID-19 emergence (69.2% during COVID-19 pandemic versus 44.1% before COVID-19 pandemic). Females were more than 2/3 of the respondents (70.6%). There was a significant difference in traveling outside Egypt before and during COVID-19 (X2=12.794, p-value=0.002). A significant reduction in the average time spent in front of the computer at HIPH was found during the emergence of COVID-19 as most of the work became from home (X2= 18.443, p-value= 0.001). Significant reduction in the consumption of hot drinks, cold drinks, bottled water and food inside the HIPH was noticed (X2=50.219, p-value<0.0001; X2=12.030, p-value=0.017; X2=15.945, p-value=0.004; X2=72.929, p-value<0.0001 respectively). The carbon footprint of HIPH in the period from July 2018 to June 2019 was 79.43MT of CO2e. In the period from July 2020 to June 2021, it was 59.85MT of CO2e with a 25% reduction in the emission. Conclusion: The carbon footprint of HIPH was reduced during the lockdown period compared to that before the epidemic. So, efforts should be gathered to hasten the reduction of carbon footprint through encouraging online teaching and changing lifestyle. [ABSTRACT FROM AUTHOR]

As there is a constant trade-off between carbon dioxide emissions against economic growth for every government, carbon efficiency is a key indicator to guide sustainable development. However, the energy crisis and COVID-19 recovery (declined cases of COVID-19 infection, flight recovery, manufacturing restart, and increasing import and export trading) could affect carbon efficiency. Therefore, this paper combines the fuzzy regression discontinuity and random forest algorithm (RF-FRD method) to estimate the discontinuity of the energy crisis and COVID-19 recovery on carbon efficiency. The findings show that discontinuity points in carbon efficiency were induced by the energy crisis and COVID-19 recovery. The positive treatment effect at the first discontinuity point proves that the "zero-tolerance" policies effectively promote carbon efficiency. Besides, the negative treatment effect at the second discontinuity point proves that electricity rationing has not always improved carbon efficiency during the energy crisis. [ABSTRACT FROM AUTHOR]

The EU emissions trading system's (ETS) invalidation rule implies that shocks and overlapping policies can change cumulative carbon emissions. This paper explains these mechanisms and simulates the effect of COVID-19, the European Green Deal, and the recovery stimulus package on cumulative EU ETS emissions and allowance prices. Our results indicate that the negative demand shock of the pandemic should have a limited effect on allowance prices and rather translates into lower cumulative carbon emissions. Aligning EU ETS with the 2030 reduction target of −55% might increase allowance prices to 45–94 €/ton CO2 today and reduce cumulative carbon emissions to 14.2–18.3 GtCO2 compared to 23.5–33.1 GtCO2 under a −40% 2030 reduction target. Our results crucially depend on when the waterbed will be sealed again, which is an endogenous market outcome, driven by the EU ETS design, shocks and overlapping climate policies such as the recovery plan. This paper finds that the EU's 2030 reduction target of -55% might correspond to EU ETS allowance prices between 45 and 94 e/ton CO2 today, while the invalidation rule reduces carbon emissions to 14.2 to 18.3 GtCO2 over the EU ETS' remaining lifetime. [ABSTRACT FROM AUTHOR]

This paper analyzes the dynamics of CO2 emissions over the last decade for a large group of 25 European countries, an issue that is at the center of the ecological transition project involving various commitments (COP21, COP26, G20 meetings, etc.). To this end, our model measures the repercussions of energy price shocks (oil, gas, coal) for carbon emissions, as well as changes in industrial production and sustainable development in the context of the coronavirus pandemic. Using annual data for 23 EU countries, together with Russia and the UK, our findings show that CO2 emissions reacted significantly to oil and coal price shocks and vary with industrial production cycles. We quantified this reaction while computing the related elasticities. Further, while a significant reduction in CO2 emissions was observed during the COVID-19 pandemic, the stepping up of investment in sustainable development and renewable energy consumption also had a negative impact on CO2 emissions. This suggests that the key driver to reducing the risk of climate change and lowering high carbon dioxide (CO2) emissions involves significant commitment to sustainable development. [ABSTRACT FROM AUTHOR]

Smart tech offers much promise for tourism recovery from the COVID-19 crisis and the broader issue of decarbonising tourism. This paper investigates how accommodation managers engaged with smart tech during the crisis, and the journey of learning how to use numerical data output to drive sustainability actions. Interviews with nine accommodation managers/owners at six sites uncovers a 'tech journey' that extends prior to, and beyond, the acquisition and installation of the smart technology itself. The journey is explained by a new framework that recognises the need for users (here, the accommodation managers) of smart tech to 'make it their own', integrating it into their decisions, workflows and finally, identity. This can only happen through a capacity to understand what the data (i.e. numerical outputs) mean, and a process of transforming data into actions. Understanding these processes of 'data domestication' and 'data clotting' addresses key gaps in how to achieve potentially radical changes in resource use. Only one case study site had reached this final stage of the journey. The theoretical framework uses the findings from each case to propose early diagnostic questions/tools that can help identify where smart system may need assistance to move from data to action. [ABSTRACT FROM AUTHOR]

The demand of online remote working from home significantly increased in 2020/21 due to the Covid-19 pandemic. This unforeseen situation has forced individuals and organisations to rapidly train employees and adopt the use of on-line working styles, seeking to maintain the same level of productivity as working from the office. The paper outlines a survey conducted amongst people working from home to identify the challenges and opportunities this change in workstyle offers. At the beginning of the pandemic, many employees faced difficulties adapting to using online tools and combining their working hours with daily routines and family commitments. However, the results show that within a short period of time the respondents had managed to develop the necessary experience and knowledge for digital working utilising tools such as collaboration platforms and video conferencing. A large proportion of respondents recognised the advantage of eliminating travelling time when working remotely from home which also has a positive impact on the environment and CO2 emissions. However, some drawbacks have been identified such as the lack of face-to-face discussion and informal meetings during working days. The Self-Determination Theory is discussed within the context of this paper and it has been found that the theory could provide an explanation of the efficient and rapid adaptation of the technology be employees. [ABSTRACT FROM AUTHOR]

The exploration of the dependency structure of the Chinese and EU carbon trading markets is crucial to the construction of a globally harmonized carbon market. In this paper, we studied the characteristics of structural interdependency between China's major carbon markets and the European Union (EU) carbon market before and after the launch of the national carbon emissions trading scheme (ETS) and the occurrence of the new coronavirus (COVID-19) by applying the C-vine copula method, with the carbon trading prices of the EU, Beijing, Shanghai, Guangdong, Shenzhen and Hubei as the research objects. The study shows that there exists a statistically significant dependence between the EU and the major carbon markets in China and their extremal dependences and dependence structures are different at different stages. After the launch of the national carbon ETS, China has become more independent in terms of interdependency with the EU carbon market, and is more relevant between domestic carbon markets. Most importantly, we found that the dependence between the EU and Chinese carbon markets has increased following the outbreak of COVID-19, and tail dependency structures existed before the launch of the national carbon ETS and during the outbreak of the COVID-19. The results of this study provide a basis for the understanding of the linkage characteristics of carbon trading prices between China and the EU at different stages, which in turn can help market regulators and investors to formulate investment decisions and policies. [ABSTRACT FROM AUTHOR]

The outbreak and ongoing evolution of the COVID-19 pandemic have dramatically impacted economic development and CO 2 emissions. China is under simultaneous pressure to recover from the outbreak and meet its carbon reduction targets, and the government is endeavouring to stimulate economic recovery through fiscal and monetary policies. This paper uses a computable general equilibrium model to measure the impact on China's economic recovery and carbon emissions by incorporating the pandemic shock and related economic recovery policies of loan prime rate (LPR) and value-added tax (VAT) reduction. The study found that COVID-19 led to a simultaneous shock on China's supply and demand sides in which GDP dropped by 2.62% and carbon emissions fell by 2.53%, compared to the period prior to COVID-19. Although the LPR and VAT reduction effectively mitigated economic loss, the combined LPR and VAT reduction had a more substantial effect on boosting GDP than the single policies. The VAT cut expands production and was used to overcome supply-side shocks, while lowering LPR mitigates the damage of demand-side shocks. Compared to the VAT reduction policy, reduced LPR has smaller carbon emissions per unit of GDP output. Consequently, we recommend that the government concentrate on a combination of policies to navigate pandemic shocks, as the two economic stimulus policies are confirmed to complement one another in terms of strengths and shortcomings. [ABSTRACT FROM AUTHOR]

Owing to the lockdowns associated with the Coronavirus Disease 2019(COVID-19) pandemic, carbon emissions were significantly reduced. However, the accurate impacts on the personal transport sector since then remain unclear. To further investigate the influence of sudden public health emergencies on actual carbon emissions from personal electric vehicles, this paper examined the travel patterns and corresponding carbon emissions of plug-in hybrid electric vehicles (PHEVs) operating in Chongqing, China, before and after COVID-19. The results revealed that the pandemic has reshaped the travel patterns of vehicle drivers, with a 9 % reduction in the postpandemic fleet average daily travel mileage. Currently, the total daily carbon emissions of a PHEV with a range of 80 km (PHEV80) are 6.24 kg, which is 13 % lower than emissions from conventional vehicles and 32 % higher than those from electric battery-powered vehicles before the pandemic. Since COVID-19, there has been a 24 % decrease in carbon emissions from PHEV80 vehicles for the fleet and a 30 % maximum increase for individuals. Furthermore, considering the integration of 50 % renewable energy into China's power grid by 2025, PHEVs can better mitigate the fluctuations in carbon emissions associated with sudden public health emergencies compared with conventional vehicles. [ABSTRACT FROM AUTHOR]

This paper investigates and proposes applications for emerging, off-the-shelf technologies for vertical transportation (VT) equipment, with a view to tackling some of the changes in social, economic, and environmental requirements of key stakeholders for tall buildings. COVID-19 has presented opportunities for IoT technologies to be used within VT systems, to measure air quality and maintain the peace of mind of tenants returning to the office. Sensors have been used to measure a range of in-car air quality metrics, the analysis of which illustrate how Internet-of-Things (IoT) devices can provide stakeholders with accessible, transparent information about the environment in their lifts. IoT devices can also be used to monitor energy usage of VT equipment and make strategic decisions to save energy and reduce carbon emissions. [ABSTRACT FROM AUTHOR]

Incentivizing businesses to lower carbon emissions and trade back excess carbon allowances paved the way for rapid growth in carbon credit ETFs. The use of carbon allowances as a hedging alternative fueled this rally further, causing a shift to speculation and forming repetitive bubbles. Speculative bubbles are born from euphoria, yet, they are relatively predictable, provided their pattern matches the log periodic power law (LPPL) with specific stylized facts. A "Minsky moment" identifies a clear speculative bubble as a signal of financial system instability, while a "Social bubble" is regarded as relatively positive, increasing in the long run, infrastructure spending and development. The aim of this paper is to investigate whether various carbon credit bubbles during the pandemic period caused financial instability or had a positive impact ("Minsky" or "Social"). Particularly, we investigate the carbon credit bubble behavior in the ETF prices of KRBN, GRN (Global Carbon Credit tracking ETFs), and the SOLCARBT index during the COVID-19 pandemic period by adopting the log-periodic power law model (LPPL) methodology, which has been widely used, over the past decade, for detecting bubbles and crashes in various markets. In conclusion, these bubbles are social and propelled by the newfound interest in carbon credit trading, for obvious reasons. [ABSTRACT FROM AUTHOR]

Purpose: The stringency policy and economic support policy in response to and to address the coronavirus disease 2019 have become a significant concern since the end of 2019. The motivation that led to this study is that, the selection of the stringency policy and the economic support policy appear to have brought about the opposite effects of the environmental costs of carbon dioxide emissions. The study's objective is to examine the contradictory impacts of these stringency and economic support policies on carbon dioxide emissions. Design/methodology/approach: This study applies panel data for the top four countries responsible for carbon dioxide emission, namely China, the United States of America, India and Russia. A fully modified ordinary least squares estimator and dynamic ordinary least squares estimator are employed to determine the long-run parameters. Findings: The results indicate that the effect of reduced carbon dioxide emissions due to a one-unit increase in the stringency policy is greater than the effect of increased carbon dioxide emissions caused by a one-unit increase in the economic support policy. Hence, if the two policies are implemented simultaneously, a positive net effect on environmental costs will be gained. Research limitations/implications: The study investigates in a general scope, the impact these response policies have on the environment. Future researchers may enhance the research on environmental impact in different sectors due to the implementation of both policies to enrich the analytical perspective. Practical implications: The results have provided implications for policymakers to emphasize more on stringency-oriented policies while giving economic support to the low-income or unemployed households in order to reduce carbon dioxide emissions. Originality/value: Despite the foreseen effects of the stringency policy and economic support policy, there has hardly been any studies that have explored empirically the nexus between both policies with carbon dioxide emissions in one empirical model. Furthermore, the paper uses the high-frequency data in determining the contradictory impacts of stringency policy and economic support policy on CO2 emissions. [ABSTRACT FROM AUTHOR]

This paper provides a picture of how societies in the G7 countries have responded to the COVID-19 pandemic. Our point of departure is to examine the effects of the pandemic in terms of four fundamental normative sources for well-being: Solidarity (S; willingness for social cooperation), Agency (A; empowerment to shape one's prospects through one's own efforts), GDP (G), and Environmental Performance (E)--SAGE for short. The normative foundations of SAGE are communitarianism, classical liberalism, materialistic utilitarianism, and ecoethics. We find that although G and E responded predictably and uniformly to the pandemic (such as G declining and carbon emissions improving), the societal responses were strikingly different. Societies that are cohesive and empowered (high S and A) may be expected to cope with the pandemic better than those that are fragmented and disempowered (low S and A). Furthermore, the pandemic has had diverse effects on S and A; while some societies became cohering and empowering (rising S and A), others became fragmenting and disempowering (falling S and A), and yet others became fragmenting and empowering. We also show that most G7 countries experienced greater tribalization (measured as the difference between inward S and outward S) during the pandemic. These trends are a matter of concern since they suggest that the willingness and perceived ability to address collective challenges collectively have waned. The analysis also suggests that governments' social policies may have an important role to play alongside economic and health policies in coping with the pandemic. [ABSTRACT FROM AUTHOR]

This paper offers a quantitative assessment of the impacts of the COVID-19 pandemic-induced lockdown and government fiscal plan, containing 'green' elements on the economy and the environment of South Africa. The analysis uses a dynamic computable general equilibrium model operationalised using a social accounting matrix coupled with a greenhouse gas balance and emissions data. We find that while the economy is harshly impacted by the pandemic in the short term, the government fiscal package ameliorates and cushions the negative effects on poor households. Importantly, an adaptation of the fiscal package towards a 'greener' policy achieves the same economic outcome and reduces unemployment. Carbon dioxide emissions decrease in the short run due to economic slowdown. This improvement persists until 2030. These results can be used as decision support for policy makers on how to orient the post COVID-19 policies to be pro-poor and pro-environment, and thus, 'build back better and fairer'. [ABSTRACT FROM AUTHOR]

Carbon and pollutant emissions drive climate change and affect health and daily life. The Covid-19 pandemic created an unprecedented opportunity to understanding the impact of carbon and pollutant emission. In this paper were identified Telecommunications records-derived general mobility indices as contributors to the PM10 pollutant concentration development in industry-intensive Sisak-Moslavina County, Croatia, during the Covid19 outbreak in 2020. Time-series of daily mean PM10 concentrations and mobility changes indicators are analysed for their statistical properties identification, decomposed, and the decomposing components examined for association using Tailored software developed in the R environment for statistical computing. [ABSTRACT FROM AUTHOR]

The aviation industry has become one of the top ten greenhouse gas emission industries in the world. China's aviation carbon emissions continue to increase, but the analysis of its influencing factors at the provincial level is still incomplete. This paper firstly uses Stochastic Impacts by Regression on Population, Affluence and Technology model (STIRPAT) model to analyze the time series evolution of China's aviation carbon emissions from 2000 to 2019. Secondly, it uses the Logarithmic Mean Divisia Index (LDMI) model to analyze the influencing characteristics and degree of four factors on China's aviation carbon emissions, which are air transportation revenue, aviation route structure, air transportation intensity and aviation energy intensity. Thirdly, it determines the various factors' influencing direction and evolution trend of 31 provinces' aviation carbon emissions in China (not including Hong Kong, Macao, Taiwan of China due to incomplete data). Finally, it derives the decoupling effort model and analyzes the decoupling relationship and decoupling effort degree between air carbon emissions and air transportation revenue in different provinces. The study found that from 2000 to 2019, China's total aviation carbon emissions continued to grow, while the growth rate of aviation carbon emissions showed a fluctuating downward trend. Air transportation revenue and aviation route structure promote the growth of total aviation carbon emissions, and air transportation intensity and aviation energy intensity have a restraining effect on the growth of total aviation carbon emissions. The scope of negative driving effect of air transportation revenue and air transportation intensity on total aviation carbon emissions in various provinces has increased. While the scope of positive driving influence of aviation route structure on total aviation carbon emissions of various provinces has increased, aviation energy intensity mainly has negative driving influence on total aviation carbon emissions of each province. Overall, the emission reduction trend in the areas to the west and north of the Qinling-Huaihe River Line is obvious. The decoupling mode between air carbon emissions and air transportation revenue in 31 provinces is mainly expansion negative decoupling. The air transportation intensity effect shows strong decoupling efforts in most provinces, the decoupling effort of aviation route structure effect and aviation energy intensity effect is not prominent. [ABSTRACT FROM AUTHOR]

Government policies during the COVID-19 pandemic have drastically altered patterns of energy demand around the world. Many international borders have been closed and populations have been confined to their homes, which has reduced transport and changed consumption patterns. A decrease in CO2 emissions and pollution has thus been recorded during forced confinements. This has offered a glimpse of how the world might look like with a drastic reduction of human impact. The present paper explores how facts are framed in news discourse and investigates the recontextualization of the environmental issue in news media. The study analyses a collection of news articles about the consequences of coronavirus on the environment published on the websites of the major news channels in the UK, namely BBC News and Sky News. This CDA investigation studies how the UK news outlets re-mediated scientific knowledge about climate change to emphasise or minimise the positive consequences of confinement on the planet. [ABSTRACT FROM AUTHOR]

A sustainable development agenda has always been at the core of BRICS cooperation. However, the progress has been limited for many reasons, including economic diversity, over-reliance on Western technologies and capital, lack of own sources of financing, and common strategy. At the same time, accounting for over 40% of the global population, over 20% of the world's GDP, and contributing to more than 40% of global CO2 emissions, BRICS countries are among the world's most important players in sustainability and climate. Therefore, finding organic ways of sustainable growth in these countries is crucial to global efforts in achieving sustainable development goals (SDGs). Using a qualitative data analysis, a review of the literature and reports of international organizations, this paper aims to analyze the current trends, risks, and opportunities in the advancement of BRICS cooperation on SDGs while placing a special emphasis on impact investment as a way to bring additional finance to BRICS countries. [ABSTRACT FROM AUTHOR]

Tourism is one of the fastest-growing sectors in the world with a shift from mass tourism to personalized travel. Nevertheless, it generates significant environmental impacts. The current events associated with quarantine measures generated by COVID-19 represent, however, a risk for this sector. It is hence necessary to create strategies that allow efficient decision-making for all echelons and actors for a rapid recovery. Tourists are key actors, which makes necessary to facilitate tourism trip planning according to tourists' preferences as a complex process. In this paper, we propose a novel model of tourist trip planning for heterogeneous preferences in a tourist group and selection of transport modes, in the first instance, while a second step seeks at minimizing the level of CO2 emissions. A comparison of the two models is made considering the objectives associated with individual tourist benefits and group profit equity, in contrast to the inclusion of the cost of CO2 emissions. A numerical comparison is carried out with a total of 546 data sets. Results illustrate the conflict between those objectives by generating an inverse relationship between the individual and group profit equity of tourists, in addition to individual benefit and emission minimization. [ABSTRACT FROM AUTHOR]

The economic recovery packages launched by governments around the world in the post-COVID-19 period have raised concerns that green development could be shelved amid recovery. Although the idea of 'green recoveryJ gained strong support worldwide, questions like whether green recovery is inevitably accompanied by economic sacrifice and what challenges it faces need to be thoroughly pondered before actions being taken. Research for these questions is not only qualitatively but also quantitatively crucial for the formulation of economic recovery policies. Based on the latest information on travel data and epidemic control measures an Adaptive Regional Input-Output Model is applied to the simulation of post-COVID-19 economic recovery processes, assessing the influence of different recovery plans on economy, employment and carbon emissions. The findings show that green recovery plans targeting clean energy and digital economy lead to comparable or even higher economic growth than the traditional ones characterized by fossil energy and heavy industry investment. Nevertheless, the challenge of reemployment faced by the green recovery may slow down the economic recovery process. Compared to the traditional plan, the 4 green recovery scenarios lead to economic growth by 0.3% 〜 14. 8% and job growth by - 4% 〜3%. But it requires trans-sectoral reemployment for nearly 100 million people (about 15% of the initial labor market scale) 96% of whom are middle-and low-skilled workers who are most seriously affected by the pandemic. The barriers to reemployment, like the mismatch between skills and jobs, may increase the time and expense of hiring and training the new workforce. Such barriers thus aggravate structural unemployment and social inequity and bring secondary harm to the vulnerable. This paper points out that economic recovery and green development are not mutually exclusive but can achieve win-win results with preconditions. It is also crucial to bear in mind that solely focusing on the potential mutual benefits of economic growth and job creation, while ignoring the preconditions for benefits realization could lead to misleading decisions. Increasing labor market flexibility and promoting social justice and equity are forward-looking strategies to strengthen the resilience of the economic system and to achieve win-win results for economic recovery and green development. [ABSTRACT FROM AUTHOR]

Conditions governing industrial activities during and after global shocks with societal and economic transformations such as the COVID-19 pandemic have led to the loss of effectiveness of conventional approaches to dealing with uncertainties. The occurrence of sharp fluctuations in the essential parameters has left decision-makers in an unpredictable situation. Therefore, proactive efforts should be made to develop current approaches for adapting to new conditions. This paper establishes a strategic, tactical, and operational decision-making framework under the COVID-19 outbreak by developing a new uncertainty type called deep dynamic uncertainty. In the first step, a Mixed-Integer Linear Programming (MILP) model is proposed for the green and reliable closed-loop supply chain network design. The proposed model allows the decision-maker (DM) to manage and control co 2 emissions and e-waste generation. In the second step, a new three-step algorithm called Augmented Adjustable Column-Wise Robust Optimization (AACWRO) is first proposed. Then, by combining the proposed column-wise uncertainty with multi-stage stochastic programming (MSSP) approach, deep dynamic uncertainty is theorized for modeling the demand uncertainty under pandemic conditions. The model's performance under deep dynamic uncertainty has been carefully investigated based on the real ventilator and infusion pump supply chain network in Iran. The model under deep dynamic uncertainty, while maintaining tractability and adjustability, provides flexibility in entering data into the problem and significantly increases the coverage of modeling uncertainties. The results clearly demonstrate the efficiency of the proposed approach. The model under deep dynamic uncertainty at all levels of conservatism has on average 42.96% lower cost and 32% higher stability than the MSSP model. • Green and reliable closed-loop medical device supply chain network are studied. • A mechanism is proposed for the management of co 2 emission and e-waste generation. • Stochastic programming and novel robust optimization are considered simultaneously. • Deep dynamic uncertainty is introduced to deal with COVID-19 uncertainties. • The model efficiency is evaluated based on the ventilator and infusion pump industry. [ABSTRACT FROM AUTHOR]

This paper uses statistical learning theory and big data analysis to study the energy consumption structure of China from qualitative and quantitative aspects during COVID-19. According to the domestic and foreign scholars' research on the optimization of energy consumption structure, the carbon emission factor is considered in the optimization of energy consumption structure. Taking the minimum energy consumption cost and carbon dioxide emission as the objective function, the carbon dioxide emission is taken as the objective function, and the total energy consumption and various energy consumption proportions as the constraint conditions, the multi-objective planning method is used to evaluate the energy consumption structure of China. The optimization model of source consumption structure is analyzed, and the medium and long-term energy transformation path and optimization model under low-carbon constraints are studied. Combined with the experimental algorithms related to big data, it is concluded that China's economic development mainly depends on a large amount of energy consumption during the COVID-19 period. On this basis, some suggestions are put forward to realize the sustainable development of China's economy and energy. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic lockdowns led to a sharp drop in socio-economic activities in China in 2020, including reductions in fossil fuel use, industry productions, and traffic volumes. China's economy suffered a serious negative effect from COVID-19. However, there is a "positive effect" on CO 2 emissions reduction. Here, for the first time, this paper constructs a new model named "Weighted Multi-regional Hypothetical Extraction Method (WMHEM)" based on a multiregional input-output model. It not only solves the problems of traditional HEM methods such as improper use of assumptions, excessive reliance on industry intermediate input, but also accurately reflects the impact of external shocks on the inter-industry linkages. By using the monthly economic data of each provinces in China during COVID-19 (except Hong Kong，Macao and Taiwan) an the latest Multi-regional input-output tables, the "economic negative effect" and "CO 2 emission positive effect" under COVID-19 in China are measured. Results show that COVID-19 lockdown was estimated to have reduced China's CO 2 emissions substantially between January and March in 2020, with the largest reductions in February. With the spread of coronavirus controlled, China's CO 2 emissions rebounded in April. In addition, key emission reduction sectors and key development encouraged sectors are selected by combining "economic negative effect" and "CO 2 emission positive effect" during COVID-19. Therefore, policies recommendations are put forward based on forward and backward linkages respectively which are from two ends of the supply chain to turn pandemic-related CO 2 emissions declines into firm climate action. • COVID-19 has negative effects on economy, but positive effects on CO 2 emissions. • A new model is used to measure economic and CO 2 emission effects during COVID-19. • Hubei had the biggest economic and CO 2 emission effects on China due to COVID-19. • Economic and CO 2 emission effects of COVID-19 are combined to select key sectors. [ABSTRACT FROM AUTHOR]

[Display omitted] • Indian electricity demand reduced by 11 % vis-à-vis 2019 during the lockdown. • Sectoral demand reduction—73 % industrial, remaining 27 % commercial. • Pan India lights-off event—hydropower provides the bulk of the flexibility (60 %). • The plant load factor of coal dropped to a record low of 35 % during phase-1. • Power sector emissions reduced by 16 % (86 MtCO2) compared to 2019. This paper aims to understand the responsiveness of the power sector during the lockdown due to the COVID-19 pandemic beginning from March 25, 2020 and analyses its impact on the demand, operation and supply of electricity in the Indian power system. The role of the C&I load share in reducing the electricity demand of the different states in India has been examined using multi-sectoral regression analysis. The impact of the pan India lights-off event on the short-term operational flexibility response of the power system has also been analysed using high-temporal resolution data. The results indicate that there has been a reduction of nearly 70 TWh of electricity demand during the lockdown period, an 11 % reduction compared to 2019. The top three states recording the highest reduction were Maharashtra, Gujarat and Tamil Nadu at 11 TWh, 10.6 TWh and 8.4 TWh, respectively. Regression analysis revealed that out of the total drop in load demand — 73 % is owed to the industrial sector, while the remaining 27 % is to the commercial sector. This demand drop also impacted the upstream electricity supply mix where 96 % of the reduction in supply was borne by the coal generating units, recording its lowest national plant load factor at 35 % with a 16 % reduction in overall CO 2 emission compared to the same period in 2019. In conclusion, a case study of Maharashtra has been used to analyse the impact of this reduction in electricity demand on the supply mix, hourly load profile and cost of supply under the different lockdown phases. [ABSTRACT FROM AUTHOR]

Achieving the peak of carbon dioxide (CO 2) emissions requires a large amount of green and low-carbon investment. Whether the green finance system can efficiently support the capital need for achieving the CO 2 emissions target in the context of the COVID-19 epidemic is a matter of concern. This paper constructs a system dynamics model (SD model) to illustrate the quantitative relationship between the green finance system and CO 2 emissions and introduce the COVID-19 epidemic as a variable to analyze ten simulation scenarios regarding the carbon emissions commitment realization under different green finance and economic growth status. It is shown that: (1) Regardless of the impact of COVID-19, China can meet its commitment by reaching its CO 2 emissions peak in 2030 and realizing a 20% non-fossil energy proportion in 2025; (2) Under the impact of the epidemic, the goal can not be obtained in all energy consumption scenarios when the government expenditure on the environment is low. The target year of reaching CO 2 emissions peak becomes 2033, 2037, and 2040. The results indicate that reducing government expenditure on environment protection makes the CO 2 emissions peak target less likely to be achieved within a given time frame. We also concluded with important policy implications according to the result of the simulations. Overall, this study makes a reference for other economies and researchers to quantitatively predict the interaction relationship between the green finance system and CO 2 emissions in the context of COVID-19, which provides policymakers with insights into a joint power of energy consumption upgrading and green capital guidance. • Providing empirical studies regarding the mechanism of the inhibitory effect of green finance on CO 2 emissions. • Using systems dynamics model to address the missing data problem. • Making solid suggestions under COVID-19 Epidemic. • Building the relationship between the green finance system and CO 2 emissions and making forecasts for "3060" mission result. [ABSTRACT FROM AUTHOR]

The CNG2020 strategy proposed in 2016 has directly impacted the global aviation industry, and the ensuing COVID-19 pandemic has brought the global aviation industry to a standstill. This paper calculates the Pollution Abatement Costs (PAC) index and regulated profits of the global aviation industry by establishing Data Envelopment Analysis (DEA) model. Then, considering the airlines' different recovery times from COVID-19, we predict the data of 25 international benchmark airlines from 2021 to 2027 based on the actual data during 2012–2019. The regulated profits can help judge whether airlines can achieve a win-win situation of carbon emission reduction and revenue growth in the future recovery process. We have some findings: 1. Air France-KLM has the maximum regulated profits, while EasyJet has the minimal regulated gains. 2. The setting of route conditions impacts whether airlines can achieve a win-win situation. • We select the optimal path for global aviation industry from 30 set paths under the CNG2020 strategy. • The paths contain the recovery time from COVID-19, carbon peak time and development speed after recovery. • We discuss the possibility of the top 25 airlines to achieve carbon reduction and revenue growth simultaneously. • Eight airlines can achieve carbon reduction and revenue growth simultaneously. [ABSTRACT FROM AUTHOR]

In the context of the 2020 world pandemic of COVID-19, many professional and scientific associations have had to change the way that they gather and reorganize their scholarly conferences. For many of them, conferences are a main source of income and important occasions for business meetings and managerial decisions. Yet, with trips and face-to-face encounters banned, the opportunity arrived for going virtual and developing human and technical capacities to provide online environments for virtual conferences. In this article, we analyze the case of an e-conference co-organized by two international organizations at two levels: (a) the decision-making process and (b) the assessment by attendees. Relying on personal, first-hand experience, participant observation, and a post-conference survey, we collected data that allow an examination of the intricacies of the decision to go virtual as well as pros and cons of the experiences of colleagues who presented papers and attended sessions. Consistent with previous studies, we found that scholars value the possibility of having more colleagues participating (providing geographic and career stage diversity), reduction of carbon emissions, lower travel and transportation costs, and ease of watching presentations later (through recordings). On the other hand, academics point to the difficulty of socializing as the main problem, with implications for strengthening academic networks and consolidation of professional careers. [ABSTRACT FROM AUTHOR]

This paper explores people's willingness to reduce travel consumption in support of the transition to a low-carbon pathway beyond COVID-19, using new survey data from UK car drivers and air travellers. Evidence from our study indicates that reductions of 24% - 30% to car use and 20% - 26% to air travel could be sustained in the long term. This potentially could lead to annual reductions of 343–529 kgCO 2 per car driver (20% - 29% of pre-COVID-19 car emissions) and 215–359 kgCO 2 per air traveller (10% - 20% of pre-COVID-19 emissions from flying), suggesting that behavioural change may be a major route to emissions reductions. We find that stated voluntary reductions are greater among those who report having 'more time to do creative things' since the start of the COVID-19 lockdowns. Hence, recovery policies promoting low-carbon leisure time may be a key to consumption reductions. We also find that higher-income travellers consume and pollute substantially more than the rest, and yet there is little difference in relative voluntary reductions across the income distribution. We conclude that behaviour associated with affluence represents a major barrier to a low-carbon transition, and that policies must address over-consumption associated with affluence as a priority. [ABSTRACT FROM AUTHOR]

While the deployment of technological innovation was able to avert a devastating global supply chain fallout arising from the impact of ravaging COronaVIrus Disease 19 (COVID-19) pandemic, little is known about potential environmental cost of such achievement. The aim of this paper is to identify the determinants of logistics performance and investigate its empirical linkages with economic and environmental indicators. We built a macro-level dataset for the top 25 ranked logistics countries from 2007 to 2018, conducting a set of panel data tests on cross-sectional dependence, stationarity and cointegration, to provide preliminary insights. Empirical estimates from Fully Modified Ordinary Least Squares (FMOLS), Generalized Method of Moments (GMM), and Quantile Regression (QR) model suggest that technological innovation, Human Development Index (HDI), urbanization, and trade openness significantly boost logistic performance, whereas employment and Gross Fixed Capital Formation (GFCF) fail to respond in such a desirable path. In turn, an increase in the Logistic Performance Index (LPI) is found to worsen economic growth. Finally, LPI exhibits a large positive effect on carbon emissions, which is congruent with a strand of the literature highlighting that the modern supply chain is far from being decarbonized. Thus, this evidence further suggest that more global efforts should be geared to attain a sustainable logistics. • Examining 25 topmost Logistics Countries on many fronts. • Technological innovation, HDI, urbanization, and trade openness spur logistic performance. • Logistic performance does not improve with employment rate and Gross Fixed Capital Formation. • Logistic performance increases carbon dioxide emissions. [ABSTRACT FROM AUTHOR]

In recent years, as China's peaking carbon dioxide emissions and air pollution control projects have converged, scholars have begun to focus on the synergistic mechanisms of greenhouse gas and pollution gas reduction. In 2020, the unprecedented coronavirus pandemic, which led to severe nationwide blockade measures, unexpectedly provided a valuable opportunity to study the synergistic reduction in greenhouse gases and polluting gases. This paper uses a combination of NO2, O3, and CO2 column concentration products from different satellites and surface concentrations from ground-based stations to investigate potential correlations between these monitoring indicators in four Chinese representative cities. We found that XCO2 decreased in March to varying degrees in different cities. It was witnessed that the largest decrease in CO2, −1.12 ppm, occurred in Wuhan, i.e., the first epicenter of COVID-19. We also analyzed the effects of NO2 and O3 concentrations on changes in XCO2. First, in 2020, we used a top-down approach to obtain the conclusion that the change amplitude of NO2 concentration in Beijing, Shanghai, Guangzhou, and Wuhan were −24%, −18%, −4%, and −39%, respectively. Furthermore, the O3 concentration increments were 5%, 14%, 12%, and 14%. Second, we used a bottom-up approach to obtain the conclusion that the monthly averaged NO2 concentrations in Beijing, Shanghai, and Wuhan in March had the largest changes, changing to −39%, −40%, and −61%, respectively. The corresponding amounts of changes in monthly averaged O3 concentrations were −14%, −2%, and 9%. However, the largest amount of change in monthly averaged NO2 concentration in Guangzhou was found in December 2020, with a value of −40%. The change in O3 concentration was −12% in December. Finally, we analyzed the relationship of NO2 and O3 concentrations with XCO2. Moreover, the results show that the effect of NO2 concentration on XCO2 is positively correlated from the point of the satellite (R = 0.4912) and the point of the ground monitoring stations (R = 0.3928). Surprisingly, we found a positive (in satellite observations and R = 0.2391) and negative correlation (in ground monitoring stations and R = 0.3333) between XCO2 and the O3 concentrations. During the epidemic period, some scholars based on model analysis found that Wuhan's carbon emissions decreased by 16.2% on average. Combined with satellite data, we estimate that Wuhan's XCO2 fell by about 1.12 ppm in February. At last, the government should consider reducing XCO2 and NO2 concentration at the same time to make a synergistic reduction. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic resulted in stay-at-home policies and other social distancing behaviors in the United States in spring of 2020. This paper examines the impact that these actions had on emissions and expected health effects through reduced personal vehicle travel and electricity consumption. Using daily cell phone mobility data for each U.S. county, we find that vehicle travel dropped about 40% by mid-April across the nation. States that imposed stay-at-home policies before March 28 decreased travel slightly more than other states, but travel in all states decreased significantly. Using data on hourly electricity consumption by electricity region (e.g., balancing authority), we find that electricity consumption fell about 6% on average by mid-April with substantial heterogeneity. Given these decreases in travel and electricity use, we estimate the county-level expected improvements in air quality, and, therefore, expected declines in mortality. Overall, we estimate that, for a month of social distancing, the expected premature deaths due to air pollution from personal vehicle travel and electricity consumption declined by approximately 360 deaths, or about 25% of the baseline 1500 deaths. In addition, we estimate that CO2 emissions from these sources fell by 46 million metric tons (a reduction of approximately 19%) over the same time frame. [ABSTRACT FROM AUTHOR]

Increased clean energy production from household solar panels is a potentially vital component of a sustainable energy transition and decarbonisation that can reduce carbon dioxide emissions from many countries. This paper investigates multiple channels of wealth effects on household solar-panel uptake. There is evidence of impacts of both financial and non-financial assets on solar-panel uptake, but the evidence for financial assets is much more robust. Compared to the highest-asset quartile, proportional solar-panel uptake is three percentage points lower for the second-highest quartile of households based on financial assets, all else equal. This gap grows to six percentage points for households in the lowest quartile for financial assets. The results are robust across many models using probit, logit and linear probability formats. Knowledge of the relative magnitudes of impacts of wealth channels is important for policymakers who are considering supporting solar-panel uptake, particularly following the COVID-19 pandemic, when efficiency of public spending will be crucial. Our results reveal that means testing for solar policy support should be based on financial-asset thresholds, rather than non-financial assets or income. These are globally important policy lessons in creating a viable climate change adaptation strategy through solar electrification. [Display omitted] • Financial assets are more important than non-financial assets for residential solar uptake. • Two components of financial assets, private pensions and other assets, have similar impacts. • Liabilities also have a positive association with household solar-panel uptake. • In contrast, income is not a significant contributor to residential solar uptake. • Means testing for policy support should be based on financial-asset thresholds. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic resulted in stay-at-home policies and other social distancing behaviors in the United States in spring of 2020. This paper examines the impact that these actions had on emissions and expected health effects through reduced personal vehicle travel and electricity consumption. Using daily cell phone mobility data for each U.S. county, we find that vehicle travel dropped about 40% by mid-April across the nation. States that imposed stay-at-home policies before March 28 decreased travel slightly more than other states, but travel in all states decreased significantly. Using data on hourly electricity consumption by electricity region (e.g., balancing authority), we find that electricity consumption fell about 6% on average by mid-April with substantial heterogeneity. Given these decreases in travel and electricity use, we estimate the county-level expected improvements in air quality, and, therefore, expected declines in mortality. Overall, we estimate that, for a month of social distancing, the expected premature deaths due to air pollution from personal vehicle travel and electricity consumption declined by approximately 360 deaths, or about 25% of the baseline 1500 deaths. In addition, we estimate that CO2 emissions from these sources fell by 46 million metric tons (a reduction of approximately 19%) over the same time frame.