Why China Cannot Decarbonise

[...]it is often argued—as by Yifei Li and Judith Shapiro, for example—that China's dictatorship should be an advantage in this context: ‘Given the limited time that remains to mitigate climate change and protect millions of species from extinction, we need to consider whether a green authoritarianism can show us the way' (Li and Shapiro 2020, quoted from the publisher's book description). Since CCP bosses do not have to contend with public hearings, environmental studies, recalcitrant legislatures, labour unions, a critical press, and so on, Xi should be able to force state-owned polluters to stop polluting or else, and ram through his promised transition to renewable energy (see Smith 2017, 2020c). Climate Action Tracker estimates that in 2021 China's emissions increased by 3.4 per cent to 14.1 gigatonnes of carbon dioxide equivalent (GtCO2e)—nearly triple those of the United States (4.9 GtCO2e) with a gross domestic product just three-fourths as large (CAT n.d.;EIA 2022). Since 2019, China's emissions have exceeded those of all developed countries combined and presently account for 33 per cent of total global emissions (Larsen et al. 2021;IEA 2021). In the first half of 2021, rebounding from the first wave of Covid-19, China's carbon dioxide emissions surged past pre-pandemic levels to reach an all-time high 20 per cent increase in the second quarter before dropping back in late 2021 and the first half of 2022 as the real estate collapse, Omicron lockdowns, and drought-induced hydropower reductions slashed economic growth to near zero in the summer (Hancock 2021;Myllyvirta 2022a;Riordan and Hook 2022). China promised to stop building coal-fired power plants abroad, but it is building more than 200 new coal-fired plants at home in a drive to boost economic growth, maintain jobs in coal-dependent regions, and ensure energy self-sufficiency—locking the country into coal reliance for many decades to come, derailing the transition to renewables, and dooming Xi's UN pledge to transition to a green and low-carbon mode of development (Xie 2020).

Marginal Cost of Energy Volatility in the Brazilian Interconnected Power System

From 2018 to 2022, on average, 70% of the Brazilian effective electric generation was produced by hydropower, 10% by wind power, and 20% by thermal power plants. Over the last five years, Brazil suffered from a series of severe droughts. As a result, hydropower generation was reduced, but demand growth was also declined as results of the COVID-19 pandemic and economic recession. From 2012 to 2022, the Brazilian reservoir system operated with, on average, only 40% of the active storage, but storage recovered to normal levels in the first three months of 2022. Despite large capacity of storage reservoirs, high volatility of the marginal cost of energy was observed in recent years. In this paper, we used two optimization models, NEWAVE and HIDROTERM for our study. These two models were previously developed for mid-range planning of the operation of the Brazilian interconnected power system. We used these two models to optimize the operation and compared the results with observed operational records for the period of 2018-2022. NEWAVE is a stochastic dual dynamic programming model which aggregates the system into four subsystems and 12 equivalent reservoirs. HIDROTERM is a nonlinear programming model that considers each of the 167 individual hydropower plants of the system. The main purposes of the comparison are to assess cooperation opportunities with the use of both models and better understand the impacts of increasing uncertainties, seasonality of inflows and winds, demand forecasts, decisions about storage in reservoirs, and thermal production on energy prices. © World Environmental and Water Resources Congress 2023.All rights reserved

A Review on Solar Energy Policy and Current Status: Top 5 Countries and Kazakhstan

The article describes the world's experience in developing the solar industry. It discusses the mechanisms of state support for developing renewable energy sources in the cases of five countries that are the most successful in this area—China, the United States, Japan, India, and Germany. Furthermore, it contains a brief review of state policy in producing electricity by renewable energy facilities in Kazakhstan. This paper uses statistical information from the International Renewable Energy Agency (IRENA), the International Energy Agency (IEA), British Petroleum (BP), and the Renewable Energy Network (REN21), and peer-reviewed sources. The research methodology includes analytical research and evaluation methods to examine the current state of solar energy policy, its motivators and incentives, as well as the prospects for its development in Kazakhstan and in the world. Research shows that solar energy has a huge development potential worldwide and is sure to take its place in gross electricity production. This paper focuses on the selected economic policies of the top five countries and Kazakhstan, in what may be considered a specific research limitation. Future research suggestions for the expansion of Renewable Energy (RE) in Kazakhstan could include analysing the impact of introducing dedicated policies and incentives for solar systems and exploring the benefits and challenges of implementing large RE zones with government–business collaboration.

Dual-stage day-ahead optimized performance of renewable-based microgrids

In distributed networks, wind turbine generators (WTGs) are to be optimally sized and positioned for cost-effective and efficient network service. Various meta-heuristic algorithms have been proposed to allocate WTGs within microgrids. However, the ability of these optimizers might not be guaranteed with uncertainty loads and wind generations. This paper presents novel meta-heuristic optimizers to mitigate extreme voltage drops and the total costs associated with WTGs allocation within microgrids. Arithmetic optimization algorithm (AOA), coronavirus herd immunity optimizer, and chimp optimization algorithm (ChOA) are proposed to manipulate these aspects. The trialed optimizers are developed and analyzed via Matlab, and fair comparison with the grey wolf optimization, particle swarm optimization, and the mature genetic algorithm are introduced. Numerical results for a large-scale 295-bus system (composed of IEEE 141-bus, IEEE 85-bus, IEEE 69-bus subsystems) results illustrate the AOA and the ChOA outperform the other optimizers in terms of satisfying the objective functions, convergence, and execution time. The voltage profile is substantially improved at all buses with the penetration of the WTG with satisfactory power losses through the transmission lines. Day-ahead is considered generic and efficient in terms of total costs. The AOA records costs of 16.575M$/year with a reduction of 31% compared to particle swarm optimization. © 2023 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

Multilevel governance, community and emergency management during the pandemic: migrants in Japan

PurposeBuilding on perspectives from the study of multilevel governance, migrants' inclusion and emergency management, this article asks how differences across national regulations for foreign residents, work eligibility and access to national emergency supports intersected with local approaches in responding to migrants.Design/methodology/approachThis article examines national policy adjustments and parallel subnational governance early in the pandemic for three groups of foreign residents: international students, technical interns and co-ethnics with long-term visas, primarily Brazilians and Peruvians. It uses Japanese-language documents to trace national policy responses. To grasp subnational governance, the article analyzes coverage in six Japanese regional newspapers from northern, central and western Japan, for the period of April 1 to October 1, 2020.FindingsNational policies obstructed or enabled migrants' treatment as members of the local community but did not dictate this membership, which varied according to migrant group. Migrants' relationship to the community affected available supports.Originality/valueThe article brings together perspectives on multilevel governance, emergency management and migrants' inclusion. It exposes how different migrant groups' ties to the local community affected access to supports.

Tourism of Ukraine as a Type of Business before and during the War

Ukrainian tourism as a type of entrepreneurship before and during the war was analyzed in the article. It has been noted that Ukraine is a hospitable country, endowed with incredible nature, ancient culture and has delicious food. But since February 24, 2022, the country has completely lost inbound tourist traffic, business tourism came to a halt, and local tourism turned into the migration of internally displaced persons. It has been determined that the longer the war in Ukraine continues, the more Europe suffers from it as well. Accordingly, the slogan "Stand with Ukraine", which sounds like the call for help, was proposed to be transformed into "to visit and help". It has been studied that great opportunities will open up for inbound tourism after the war. At first, routes related to the war and remarkable post-war locations will be relevant. At the same time, the beauty and hospitality of Ukraine will attract tourists. After the victory, Ukraine will be among the top five countries in the world attractive to visit. The sky is open, or at least nominally safe.

The War in Ukraine: How Putin's War in Ukraine Has Ruined Russia

The first two months of war alone turned the Russian clock back decades, undoing thirty years of post-Soviet economic gains and reducing the country to an international pariah state. Three decades after the collapse of the Soviet empire, Russians are being dragged back in time to when Soviet citizens lived isolated from the rest of the world, in a bubble of failed ideology and misinformation. That system fell apart under just the kind of autarky and autocracy that Putin hopes to reimpose. Just as the Soviet system collapsed, Putin is also failing Russia, erasing the gains of the postcommunist period in a feckless attempt to rebuild a doomed empire.

A Modified Bottom Up Approach to Estimate Expected Demand Reduction During a Rare Event

Indian power system witnessed its largest very short-time demand ramping during light off event conducted to express solidarity with COVID-19 volunteers. 32 GW demand ramping was observed within 25 minutes and recorded as the highest ramping event across the globe. System operator has taken precautions and successfully handled the event with the help of hydro generation. However, system experienced severe frequency and voltage deviations due to unexpected consumer behaviour. A systematic study and an in-depth analysis of such a severe event would help system operators and planners to prepare for similar events. This paper presents a critical analysis of the activity and conducted a survey to understand consumer response during that event. It also proposes a modified Bottom-Up Approach to estimate Expected Demand Reduction (EDR) for such critical events. Proposed model is validated using data collected from the conducted survey. Proposed EDR estimation model offers better results than the Top Down and Bottom-up approach models used by system operator. © 2022 IEEE.

THE POSITION OF THE GOVERNMENT AND SOCIETY OF THE CZECH REPUBLIC TOWARDS THE WAR IN UKRAINE

The aim of the article is to present the position of the Czech government and the society of this country towards the war in Ukraine. The text is an attempt to conduct a comparative analysis of the position of the Czech government and the part of society that has been opposing the government's policy towards Ukraine for some time. The author presented the main consequences of the Czech government's support for Ukraine, which are related to the post-pandemic crisis. These include, above all, problems with the supply of oil, natural gas and nuclear fuel for Czech nuclear power plants. Social problems include high inflation, rising interest rates and rising prices ofbasic commodities. In the summary, synthetic conclusions were drawn that confirm the discrepancies between the government's policy and the position of that part of society.

PCTran-Cloud: an Accident Simulator for Nuclear Power Plants Based on Cloud Technology

In the current environment where COVID-19 is serious, the space, place and resources required for teaching nuclear power plants are restricted to a great extent. To solve such problems and improve the utilization of education resources, this study improved an accident simulator for nuclear power plants based on the concept of cloud technology. We build the Browser / Server architecture so that the platform has successfully implemented multiterminal, multiplatform and multiuser simultaneous applications. Through the simulation results of the Small Break Loss of Coolant Accident (SBLOCA) and the test results of platform performance by PCTran-Cloud, the correctness of PCTran-Cloud in the accident simulation function and results were verified. In general, PCTran-Cloud has the characteristics of high scalability, high concurrency and high security. The platform can provide an environment for the training and education of nuclear power professionals. © 2022 IEEE.

Estimation of NO2 emission strengths over Riyadh and Madrid from space from a combination of wind-assigned anomalies and a machine learning technique

Nitrogen dioxide (NO2) air pollution provides valuable information for quantifying NOx (NOx = NO + NO2) emissions and exposures. This study presents a comprehensive method to estimate average tropospheric NO2 emission strengths derived from 4-year (May 2018–June 2022) TROPOspheric Monitoring Instrument (TROPOMI) observations by combining a wind-assigned anomaly approach and a machine learning (ML) method, the so-called gradient descent algorithm. This combined approach is firstly applied to the Saudi Arabian capital city of Riyadh, as a test site, and yields a total emission rate of 1.09×1026 molec. s-1. The ML-trained anomalies fit very well with the wind-assigned anomalies, with an R2 value of 1.0 and a slope of 0.99. Hotspots of NO2 emissions are apparent at several sites: over a cement plant and power plants as well as over areas along highways. Using the same approach, an emission rate of 1.99×1025 molec. s-1 is estimated in the Madrid metropolitan area, Spain. Both the estimate and spatial pattern are comparable with the Copernicus Atmosphere Monitoring Service (CAMS) inventory.Weekly variations in NO2 emission are highly related to anthropogenic activities, such as the transport sector. The NO2 emissions were reduced by 16 % at weekends in Riyadh, and high reductions were found near the city center and in areas along the highway. An average weekend reduction estimate of 28 % was found in Madrid. The regions with dominant sources are located in the east of Madrid, where residential areas and the Madrid-Barajas airport are located. Additionally, due to the COVID-19 lockdowns, the NO2 emissions decreased by 21 % in March–June 2020 in Riyadh compared with the same period in 2019. A much higher reduction (62 %) is estimated for Madrid, where a very strict lockdown policy was implemented. The high emission strengths during lockdown only persist in the residential areas, and they cover smaller areas on weekdays compared with weekends. The spatial patterns of NO2 emission strengths during lockdown are similar to those observed at weekends in both cities. Although our analysis is limited to two cities as test examples, the method has proven to provide reliable and consistent results. It is expected to be suitable for other trace gases and other target regions. However, it might become challenging in some areas with complicated emission sources and topography, and specific NO2 decay times in different regions and seasons should be taken into account. These impacting factors should be considered in the future model to further reduce the uncertainty budget.

Smart Meter Data Analytics for Building Monitoring System: A Case Study

Decarbonization, decentralization, and digitalization are the prominent paths for the energy sector in the future. The rise of smart meters across consumers, and industries led to a massive collection of fine-grained energy and electricity consumption-related data. A data science challenge is to analyze the Smart Meter data for the benefit of both the energy providers and the consumers. In this paper, An attempt has been made to analyze the smart meter collected from the IIT Hyderabad campus and presented the analysis into descriptive, predictive, and prescriptive analytics. The data collected from more than 50 meters over a period of one year have been analyzed and results obtained. Interesting trends such as the impact of COVID-19 on campus energy consumption have been examined. The framework for energy data analytics presented in this paper will be useful for any campus in general, and the recommendations presented will save energy expenses. © 2023 IEEE.

Investment in new coal-fired power plants after the COVID-19 pandemic: experts expect 170–270 GW of new coal

To avoid dangerous climate change, the global community has committed to phasing down coal at COP26 in Glasgow. Since policies and pledges currently implemented in the power sector are not sufficient to meet mitigation targets, countries are expected to increase their ambition over time within the UNFCCC process. Furthermore, some countries are bilaterally negotiating support packages to speed up the phase-out of coal through ‘Just Energy Transition Partnerships'. Yet, to assess those ratcheting up efforts an understanding of the current baseline is pivotal. Here, we quantify the probability that currently planned coal plants will be built, based on an in-depth expert elicitation consisting of interviews with 29 experts from ten countries. We analyze the most important factors influencing the realization of the coal pipeline, isolate the effect of the COVID-19 pandemic, and compare the experts' forecasts with mitigation pathways scenarios. We find that globally 170 GW–270 GW of new coal-fired power plants are likely to be built in the upcoming years. Future negotiations for joint partnerships can use the results of this elicitation as a baseline to determine ambitious coal phase-out plans.

Coal Is a Priority for Energy Security, until It Is Not: Coal Phase-Out in the EU and Its Persistence in the Face of the Energy Crisis

In the wake of the COVID-19 pandemic and the Russian invasion of Ukraine, many countries see coal as the easiest solution to their energy sector challenges, despite the consequences for climate goals. Several countries of the European Union started to re-evaluate their coal policies vis-à-vis the current energy crisis and, although such a change is expected to be short-term in nature, it nevertheless has negative consequences for the Union's 2050 climate goal. However, most of the EU countries did not revise their phase-out goals. This paper examines Slovakia as a country that embarked on a coal phase-out trajectory only a few years before the pandemic broke out and stayed firmly on this path despite benefits stemming from the continued use of domestic coal. Domestic coal used to be considered a safeguard of energy security in Slovakia, especially after the 2009 gas crisis. However, a decision was made in 2018 to phase out coal by 2023, and this has not changed despite increased focus on domestic energy sources as energy security guarantors during the current energy crisis. This paper explains the decision in favour of a coal phase-out and its support vis-à-vis the energy crisis using the concept of ‘financial Europeanisation', which stresses the importance of EU funds for the development of the domestic policies of EU member states. While the expected funds serve as a catalyst for the coal phase-out needed to reach climate goals, short-term advantages of revising a coal phase-out were outweighed by long-term benefits provided by EU funds.

System Threat during a Pandemic: How Conspiracy Theories Help to Justify the System

During the COVID-19 pandemic, many people have endorsed conspiracy theories about foreign governments yet shown increased trust and support for their own government. Whether there is a potential correlation between these social phenomena and the psychological mechanisms behind them is still unclear. Integrating insights from the existential threat model of conspiracy theories and system justification theory, two experimental studies were conducted to investigate whether belief in out-group conspiracy theories can play a mediating role in the effects of system threat on people's system justification beliefs against the background of the pandemic. The results show that system threat positively predicts individuals' system-justifying belief, and belief in out-group conspiracy theories mediated this relationship.

Research on the emission reduction effects of carbon trading mechanism on power industry: plant-level evidence from China

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.

Causal Analysis of India's Response to Maritime Security Debate at the UNSC

UNSC debate on the maritime security-initiated discussion on response and reaction plan not only for particular countries and regions overall. The Eastern part of the Indian Ocean (IO) has been a boiling cauldron due to the race of maritime hegemony, maritime alliances for economic cum security dimensions and choking important points of maritime trade in crisis. The outbreak of Covid heavily restricted global supply chains. Security cooperation should focus on 'reassurance' rather than 'deterrence' to create the desired security order in which multilateral frameworks may be formed. In this scenario, security interactions would help nation-states converge their national interests and minimize a zero-sum security situation. UNSC debate on maritime security demands for cooperative security approach through a framework of likeminded states. Pakistan foresees this debate as an increasing challenge by constructing the role of the Pakistan Navy to perform and ensure its capacities for maritime security in the long run. The paper presents the response to Indian Duplicities in the wake of the Russian proposal for maritime security management at UNSC in 2021. Strategic analysis with qualitative research method and exploratory approach is adopted in this research with pertinent and feasible findings.

PI control based on parameter space approach supported metaheuristic optimization algorithms and ANFIS in a natural gas combined cycle power plant

In this article, proportional‐integral (PI) control to ensure stable operation of a steam turbine in a natural gas combined cycle power plant is investigated, since active power control is very important due to the constantly changing power flow differences between supply and demand in power systems. For this purpose, an approach combining stability and optimization in PI control of a steam turbine in a natural gas combined cycle power plant is proposed. First, the regions of the PI controller, which will stabilize this power plant system in closed loop, are obtained by parameter space approach method. In the next step of this article, it is aimed to find the best parameter values of the PI controller, which stabilizes the system in the parameter space, with artificial intelligence‐based control and metaheuristic optimization. Through parameter space approach, the proposed optimization algorithms limit the search space to a stable region. The controller parameters are examined with Particle Swarm Optimization based PI, artificial bee colony based PI, genetic algorithm based PI, gray wolf optimization based PI, equilibrium optimization based PI, atom search optimization based PI, coronavirus herd immunity optimization based PI, and adaptive neuro‐fuzzy inference system based PI (ANFIS‐PI) algorithms. The optimized PI controller parameters are applied to the system model, and the transient responses performances of the system output signals are compared. Comparison results of all these methods based on parameter space approach that guarantee stability for this power plant system are presented. According to the results, ANFIS‐ PI controller is better than other methods.

Intelligent Image Processing for Monitoring Solar Photovoltaic Panels

Despite the COVID-19 pandemic, the global photovoltaic (PV) market grew significantly again in 2021, further enhancing the vital role of solar power in the battle against global climate change. One of the main reasons for the rapid growth of this market is that PV panels are almost maintenance-free after deployment, thereby low Levelized cost of solar power. However, this does not mean that PV panels will not fail in service. In fact, they may suffer from performance degradation, structural failure, or even complete loss of power generation capacity during operation. If these problems cannot be detected and solved in time, they may also bring significant economic losses to the operators. However, a large-scale solar power plant will contain hundreds of thousands of PV panels. How to quickly identify those defective ones from so many PV panels is a quite challenging issue. The research of this paper is to address this issue with the aid of intelligent image processing technology. In this study, an intelligent PV panel condition monitoring technique is developed using machine learning algorithms. It can rapidly process, analyze and classify the thermal images of PV panels collected from solar power plants. Therefore, it not only can quickly identify those defective PV panels but also can accurately diagnose the defect types of the PV panels. It is deemed that the successful development of such a technology will be of great significance to further strengthen the scientific management of solar power assets. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Design and implementation of cloud platform for nuclear accident simulation

Introduction: To meet the multi-user, cross-time-and-space, cross-platform online demand of work, and professional training teaching in nuclear reactor safety analysis under the normalization of Coronavirus Disease 2019. Method: Taking the nuclear accident simulation software PCTRAN as an example, this study adopts cloud computing technology to build the NasCloud, a nuclear accident simulation cloud platform based on Browser/Server architecture, and successfully realizes multi-user, cross-time-and-space, cross-platform applications. Targeting the AP1000, a pressurized water reactor nuclear power plant, the simulation of cold-leg Small Break Loss of Coolant Accident and cold-leg Large Break Loss of Coolant Accident were carried out to verify the correctness of the NasCloud's accident simulation function. Results: The result shows that the simulation functions and results of the NasCloud in multi-terminal are consistent with the single version of PCTRAN. At the same time, the platform has high scalability, concurrency and security characteristics. Discussion: Therefore, the nuclear accident simulation cloud platform built in this study can provide solutions for the work and training of nuclear reactor safety analysis, and provide reference for other engineering design and simulation software cloud to computing transformation. Copyright © 2023 Chen, Chen, Xie, Xiong and Yu.