• 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.

The outbreak of the COVID-19 epidemic has greatly hindered the development of the tourism industry. It is urgent for the city to improve the quality of public service in scenic spots, so as to attract more tourists and achieve sustainable development. With a literature review and reference to some guidance, the evaluation indicator system of public service satisfaction of scenic spots was constructed based on the analytic hierarchy process. Then, we distributed two questionnaires to complete the study. The first is the expert questionnaire for the evaluation indicator system. We used YAAHP software to process the questionnaire data and calculated the weight of each indicator, which provided a basis for the following analysis. The second is the questionnaire distributed to tourists of Xiamen. Then, we used the fuzzy comprehensive evaluation method to analyze the satisfaction of public services in scenic spots. The outcomes show that tourists are overall satisfied with the public services of scenic spots in Xiamen. However, there are still some problems, such as lagging information service, inadequate security, and supervision in the scenic spot. Therefore, the city and scenic spots should improve the level of smart tourism service, strengthen the construction of hardware and software facilities, and focus on the protection of tourists’ rights and interests.

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.

When the World Health Organization (WHO) analyzed the culprits of global warming, it was found that in developed countries with relatively few high-polluting heavy industries, the medical sector is one of the sources of high-density carbon emissions. Therefore, the medical industry has a noticeable impact on the environment. Amid the current COVID-19 epidemic, this study adopts the theory of planned behavior (TPB), widely used in decision-making science. We selected a regional teaching hospital with 339 employees in Taiwan to obtain valid questionnaire data. We explore the comparative analysis of different intra-organizational stakeholders’ “attitudes,” “subjective norms,” and “perceived behavioral control” on the hospital’s behavioral intention to promote green healthcare. The results show that the TPB model has reliable explanatory power. All three factors have a positive and significant effect on promoting green hospital behavior. Among them, perceived behavioral control was the most notable. A comparative analysis of the differences among stakeholders in the research model shows that “medical administrators” and “nursing staff” have a higher proportion of significant influence effects in various hypotheses, highlighting the critical roles of these two groups in promoting green hospitals. This research policy suggests that the cross-departmental staff in the hospital put forward green innovation ideas, strengthen internal environmental education and management, establish a good incentive system for front-line nursing staff, and implement the sustainable development strategy of the hospital.

The COVID-19 pandemic affects all of society and hinders day-to-day activities from a straightforward perspective. The pandemic has an influential impact on almost everything and the characteristics of the pandemic remain unclear. This ultimately leads to ineffective strategic planning to manage the pandemic. This study aims to elucidate the typical pandemic characteristics in line with various temporal phases and its associated measures that proved effective in controlling the pandemic. Besides, an insight into diverse country's approaches towards pandemic and their consequences is provided in brief. Understanding the role of technologies in supporting humanity gives new perspectives to effectively manage the pandemic. Such role of technologies is expressed from the viewpoint of seamless connectivity, rapid communication, mobility, technological influence in healthcare, digitalization influence, surveillance and security, Artificial Intelligence (AI), and Internet of Things (IoT). Furthermore, some insightful scenarios are framed where the full-fledged implementation of technologies is assumed, and the reflected pandemic impacts in such scenarios are analyzed. The framed scenarios revolve around the digitalized energy sector, an enhanced supply chain system with effective customer-retailer relationships to support the city during the pandemic scenario, and an advanced tracking system for containing virus spread. The study is further extended to frame revitalization strategies to highlight the expertise where significant attention needs to be provided in the post-pandemic period as well as to nurture sustainable development. Finally, the current pandemic scenario is analyzed in terms of occurred changes and is mapped into SWOT factors. Using Fuzzy Technique for Order of Preference by Similarity to Ideal Solution based Multi-Criteria Decision Analysis, these SWOT factors are analyzed to determine where prioritized efforts are needed to focus so as to traverse towards sustainable cities. The results indicate that the enhanced crisis management ability and situational need to restructure the economic model emerges to be the most-significant SWOT factor that can ultimately support humanity for making the cities sustainable.

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.