• Energy Research
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In order to identify ecological relationships of participating countries in the transfer system of ecological capital embodied in global trade, this paper takes the international trade of China and its partners from 2002 to 2017 as a case, and uses the ecological footprint (EF) measured by the optimized product land-use matrix (PLUM) method to quantify ecological capital for the value of macro-ecological resources, then uses the ecological network analysis (ENA) method to construct a complete transfer network of trade-embodied ecological capital and uses a utility analysis to identify ecological relationships between trading countries. Our results show that: (1) Throughout the study period, competition relationships with 61% dominated in the network, and the countries that have a pair-wise competition relationship with China are mainly located in central and western Europe, northeastern Europe, North America, southern Asia and eastern Asia. (2) Indirect utility determines the dominant ecological relationship in system, and it mainly converts dominant ecological relationships from control to competition by transforming exploit into competition. (3) China is looking to creating a more mutually beneficial trading environment at the expense of its own interests. (4) A global crisis event is likely to result in the control of ecological capital in more countries, and in its aftermath, the world is likely to be in a highly competitive environment. Reducing ecological capital consumption by improving energy efficiency and optimizing the global trading environment into a trading system dominated by mutualism relationships can be effective ways for countries around the world to achieve sustainable development post-COVID-19 crisis.

Abstract China has proposed the Renewable Portfolio Standard (RPS) policy to advance the stable development of renewable energy. The RPS requires each province to achieve a stated minimum share of renewable energy power in the total provincial power generation. However, there is an obvious mismatch between the actual capability of generating renewable power and the assigned responsibility for the share of renewable energy power based on the RPS in some provinces. Therefore, this study aims to optimize the renewable power dispatching strategy across provinces for satisfying the RPS requirements in China and to assess the corresponding pressure for each province. A renewable energy power dispatching model is developed, and an economically feasible strategy for dispatching renewable energy power in Chinese provinces in 2020–2022 was obtained. The results indicate that it is necessary to dispatch 395.2 and 140.4 TWh of hydropower and non-hydropower nationwide, respectively, in 2022 to fulfill the RPS target when the COVID-19 is effectively controlled worldwide. If COVID-19 cannot be effectively controlled, 376.6 and 127.8 TWh of hydropower and non-hydropower must be dispatched nationwide to fill the gap. Beijing, Tianjin, Shanghai, and Zhejiang are faced with a relatively high pressure under the RPS target. Finally, a path for each province to achieve its RPS target is proposed.

Considering the important role of energy in modern society, it is imperative to study the current situation and future development of energy under the influence of COVID-19. This paper identifies the current research hotspots, proposes future research directions accordingly, and summarizes the methodologies via a bibliometric analysis. Five research hotspots include COVID-19 and the changes of energy consumption, COVID-19 and the fluctuation of the energy market, COVID-19 and the development of renewable energy, COVID-19 and climate impacts caused by energy consumption, and COVID-19 and the energy policy. According to the influence mechanism of COVID-19 on each hotspot, the pandemic has exerted short-term influencs on energy consumption, energy price, and air pollution. Meanwhile, the pandemic could have a far-reaching impact on the renewable energy sector, climate, and energy policy. In addition, the main methodologies are reviewed, revealing that regression analysis and scenario analysis are commonly used as the quantitative and qualitative methods, respectively. Moreover, given the nonlinear relations between the pandemic and energy, an artificial neural networks model is used to enhance the prediction efficiency of energy demand and price. Finally, policy implications for obtaining clean, low-carbon, safe, and efficient energy in the context of COVID-19 are proposed.

The COVID-19 pandemic caused severe economic contraction and paralyzed industrial activity. Despite a growing body of literature on the impacts of COVID-19 mitigation measures, scant evidence currently exists on the impacts of lockdowns on the economic and industrial activities of developing countries. Our study provides an empirical assessment of lockdown measures using 298,354 data points on daily electricity consumption in 396 sub-industries. To infer causal relationships, we employ difference-in-differences models that compare cities with and without lockdown policies and provide quantitative evidence on whether the long-term gain of lockdowns outweighs the short-term loss. The results show that lockdown policies led to a significant short-term drop in electricity consumption of 15.2% relative to the control group. However, the electricity loss under the no-lockdown scenario is 2.6 times larger than that under the strict lockdown scenario within 4 months of the outbreak. Discrepancies in the impacts among industries are identified, and even within the same industry, lockdowns have heterogeneous effects. The impact of lockdowns on small and medium-sized enterprises in developing countries is seriously underestimated, raising concerns about the distributional impact of subsidy measures. This study serves as a crucial reference for the government when facing public health emergencies and shocks to support better policies.

Electric vehicles are an important part of governments’ environmental policies, and therefore understanding the factors affecting their market share is very important. So, this research is designed to investigate the factors affecting electric vehicle adoption, considering the effects of the COVID-19 pandemic and sustainable development level. Effective factors have been investigated in three categories. One is the characteristics of electric vehicles; the other is the impact of the COVID-19 pandemic on demand for these vehicles; and finally, the impact of the level of sustainable development of countries on adopting electric vehicles. Our analysis method is based on grey econometric and grey regression methods. The results show that vehicle dimensions, battery warranty conditions, battery life, and charging facilities are effective factors in the field of vehicle characteristics that can increase the adoption of electric vehicles. Also, the analysis shows that the COVID-19 pandemic has reduced the adoption of electric vehicles. Finally, we have shown that the market share of electric vehicles is higher in countries with a higher sustainable development level because of better economic, social, and cultural infrastructures.

In recent years, due to the rise in energy prices and the impact of COVID-19, energy shortages have led to unsafe power supply environments. High emissions industries which account for more than 58% of the carbon emissions of Guangdong Province have played an important role in achieving the carbon peak goal, alleviating social energy shortage and promoting economic growth. Controlling high emissions industries will help to adjust the industrial structure and increase renewable energy investment. Therefore, it is necessary to comprehensively evaluate the policies of energy security and the investments of high emission industries. This paper builds the ICEEH-GD (comprehensive assessment model of climate, economy, environment and health of Guangdong Province) model, designs the Energy Security scenario (ES), the Restrict High Carbon Emission Sector scenario (RHS) and the Comprehensive Policy scenario (CP), and studies the impact of limiting high emissions industries and renewable energy policies on the transformation of investment structure, macro-economy and society. The results show that under the Energy Security scenario (ES), carbon emissions will peak in 2029, with a peak of 681 million tons. Under the condition of ensuring energy security, the installed capacity of coal-fired power generation will remain unchanged from 2025 to 2035. Under the Restrict High Carbon Emission Sector scenario (RHS), the GDP will increase by 8 billion yuan compared with the ES scenario by 2035. At the same time, it can promote the whole society to increase 10,500 employment opportunities, and more investment will flow to the low emissions industries. In the Comprehensive Policy scenario (CP), although the GDP loss will reach 33 billion yuan by 2035 compared with the Energy Security scenario (ES), the transportation and service industries will participate in carbon trading by optimizing the distribution of carbon restrictions in the whole society, which will reduce the carbon cost of the whole society by more than 48%, and promote the employment growth of 104,000 people through industrial structure optimization. Therefore, the power sector should increase investment in renewable energy to ensure energy security, limit the new production capacity of high emissions industries such as cement, steel and ceramics, and increase the green transition and efficiency improvement of existing high emissions industries.

AbstractPersonal greenhouse gas (PGHG) emissions were crucial for achieving carbon peak and neutrality targets. The accounting methodology and driving forces identification of PGHG emissions were helpful for the quantification and the reduction of the PGHG emissions. In this study, the methodology of PGHG emissions was developed from resource obtaining to waste disposal, and the variations of Shanghainese PGHG emissions from 2010 to 2020 were evaluated, with the driving forces analysis based on Logarithmic Mean Divisia Index (LMDI) model. It showed that the emissions decreased from 3796.05 (2010) to 3046.87 kg carbon dioxides (CO2) (2014) and then increased to 3411.35 kg CO2 (2018). The emissions from consumptions accounted for around 62.1% of the total emissions, and that from waste disposal were around 3.1%, which were neglected in most previous studies. The PGHG emissions decreased by around 0.53 kg CO2 (2019) and 405.86 kg CO2 (2020) compared to 2018 and 2019, respectively, which were mainly affected by the waste forced source separation policy and the COVID-19 pandemic. The income level and consumption GHG intensity were two key factors influencing the contractively of GHG emissions from consumption, with the contributing rate of 169.3% and − 188.1%, respectively. Energy consumption was the main factor contributing to the growth of the direct GHG emissions (296.4%), and the energy GHG emission factor was the main factor in suppressing it (− 92.2%). Green consumption, low carbon lifestyles, green levy programs, and energy structure optimization were suggested to reduce the PGHG emissions.

The main goal of this study is to evaluate the impact of restrictive measures introduced in connection with COVID-19 on consumption in renewable energy markets. The study will be based on the hypothesis that similar changes in human behavior can be expected in the future with the further spread of COVID-19 and/or the introduction of additional quarantine measures around the world. The analysis also yielded additional results. The strongest reductions in energy generation occurred in countries with a high percentage (more than 80%) of urban population (Brazil, USA, the United Kingdom and Germany). This study uses two models created with the Keras Long Short-Term Memory (Keras LSTM) Model, and 76 and 10 parameters are involved. This article suggests that various restrictive strategies reduced the sustainable demand for renewable energy and led to a drop in economic growth, slowing the growth of COVID-19 infections in 2020. It is unknown to what extent the observed slowdown in the spread from March 2020 to September 2020 due to the policy’s impact and not the interaction between the virus and the external environment. All renewable energy producers decreased the volume of renewable energy market supply in 2020 (except China).