Explore the vast range of titles available.

PurposeTo estimate the discounted money value of human lives lost (DMVHL) due to COVID-19 in Spain.Design/methodology/approachThe study employs the human capital approach to estimate the DMVHL (assuming Spain's life expectancy of 83 years and a 3% discount rate) of the 20,453 human lives lost in Spain from COVID-19 as of 19 April 2020. Sensitivity analysis was conducted alternately assuming (a) 5% and 10% discount rate; and (b) global life expectancy of 72 years, and the world's highest life expectancy of 87.1 years.FindingsThe 20,453 human lives lost due to COVID-19 had a total DMVHL of Int$ 9,629,234,112, and an average of Int$ 470,798 per human life lost. Alternate re-estimation of the economic model with a 5% and 10% discount rates led to 19.8% and 47.4% reductions in the DMVHL, respectively. Re-calculation of the economic model using the global life expectancy of 72 years, while holding the discount rate constant at 3%, diminished the DMVHL by 41%. While the re-run of the same model using the world's highest life expectancy of 87.1 years instead, it increased the DMVHL by 18%.Research limitations/implicationsThe study omits the value of health systems inputs used in preventing, diagnosing and treating COVID-19 cases; and the negative impact of COVID-19 on the agriculture, education, finance, manufacturing, travel, tourism, and trade sectors.Social implicationsThere is a need to use this kind of evidence to advocate for increased investments into the strengthening of the national health system, IHR capacities, and coverage of safe water and sanitation facilities.Originality/valueIn Spain, no other study had attempted to estimate the net present value of human lives lost from COVID-19.

Energy and emission data are crucial to climate change research and mitigation efforts. The accuracy of energy statistics is essential for mitigation strategies and evaluating the performance of low carbon energy transition efforts. This study provides the most up‐to‐date emission inventories for China and its provinces for 2018 and 2019. We also update the carbon dioxide (CO2) emission inventories of China and 30 provinces since 2012 based on the newly revised energy statistics. The inventories are compiled in a combined accounting approach of scope 1 (Intergovernmental Panel on Climate Change territorial emissions from 17 types of fossil fuel combustion and cement production by 47 socioeconomic sectors) and scope 2 (emissions from purchased electricity and heat consumption). The most recent energy revision led to an increase in reported national CO2 emissions by an average of 0.3% from 2014 to 2017. The results show that data revisions raised China's carbon intensity mitigation baseline (in 2005) by 5.1%–10.8% and thus made it more challenging to fulfill the mitigation pledges. However, the 2020 carbon intensity mitigation target was achieved ahead of schedule in 2018. A preliminary estimate of China's national emissions for 2020 shows that the COVID‐19 pandemic and lockdown was not able to offset China's annual increase in CO2 emissions. These emissions inventories provide an improved evidence base for China's policies toward net‐zero emissions. Key Points The most up‐to‐date emission inventories for China and its provinces A combined accounting approach of scope 1 and 2 emissions China's 2020 carbon intensity mitigation target was achieved ahead of schedule

This study tries to find new insights of implementations of carbon tax policy as a suitable way to reach the long-term zero-carbon plan. This paper explores how carbon tax can affect the macroeconomy in Japan through the structural vector autoregression (S-VAR) technique conducted for the quarterly data throughout 2005–2020. A theoretical general equilibrium model backs the empirical analysis. The major findings reveal that any increase in energy price from the carbon tax will lead to an increase in interest rate, exchange rate, and consumer price index while there is a negative relationship between energy price increase from carbon tax and real gross domestic product (GDP) in Japan. Carbon Policy Refolution (Reform + Evolution), refunding carbon tax revenues, and adaptation of long-term policy of net zero GHG emissions by 2050 with the current situation of Japan’s power sectors are the major practical policies of this study.

This paper presents interrelated indicators for tracking progress towards the Paris Agreement. Findings show broad consistency with keeping warming below 2 °C, but technological advances are needed to achieve net-zero emissions. Current emission pledges to the Paris Agreement appear insufficient to hold the global average temperature increase to well below 2 °C above pre-industrial levels 1 . Yet, details are missing on how to track progress towards the ‘Paris goal’, inform the five-yearly ‘global stocktake’, and increase the ambition of Nationally Determined Contributions (NDCs). We develop a nested structure of key indicators to track progress through time. Global emissions 2 , 3 track aggregated progress 1 , country-level decompositions track emerging trends 4 , 5 , 6 that link directly to NDCs 7 , and technology diffusion 8 , 9 , 10 indicates future reductions. We find the recent slowdown in global emissions growth 11 is due to reduced growth in coal use since 2011, primarily in China and secondarily in the United States 12 . The slowdown is projected to continue in 2016, with global CO 2 emissions from fossil fuels and industry similar to the 2015 level of 36 GtCO 2 . Explosive and policy-driven growth in wind and solar has contributed to the global emissions slowdown, but has been less important than economic factors and energy efficiency. We show that many key indicators are currently broadly consistent with emission scenarios that keep temperatures below 2 °C, but the continued lack of large-scale carbon capture and storage 13 threatens 2030 targets and the longer-term Paris ambition of net-zero emissions.

The Intergovernmental Panel on Climate Change (IPCC) Special Report on Global Warming of 1.5°C points to the need for carbon neutrality by mid‐century. Achieving this in the United States in only 30 years will be challenging, and practical pathways detailing the technologies, infrastructure, costs, and tradeoffs involved are needed. Modeling the entire U.S. energy and industrial system with new analysis tools that capture synergies not represented in sector‐specific or integrated assessment models, we created multiple pathways to net zero and net negative CO2 emissions by 2050. They met all forecast U.S. energy needs at a net cost of 0.2–1.2% of GDP in 2050, using only commercial or near‐commercial technologies, and requiring no early retirement of existing infrastructure. Pathways with constraints on consumer behavior, land use, biomass use, and technology choices (e.g., no nuclear) met the target but at higher cost. All pathways employed four basic strategies: energy efficiency, decarbonized electricity, electrification, and carbon capture. Least‐cost pathways were based on >80% wind and solar electricity plus thermal generation for reliability. A 100% renewable primary energy system was feasible but had higher cost and land use. We found multiple feasible options for supplying low‐carbon fuels for non‐electrifiable end uses in industry, freight, and aviation, which were not required in bulk until after 2035. In the next decade, the actions required in all pathways were similar: expand renewable capacity 3.5 fold, retire coal, maintain existing gas generating capacity, and increase electric vehicle and heat pump sales to >50% of market share. This study provides a playbook for carbon neutrality policy with concrete near‐term priorities. Plain Language Summary We created multiple blueprints for the United States to reach zero or negative CO2 emissions from the energy system by 2050 to avoid the most damaging impacts of climate change. By methodically increasing energy efficiency, switching to electric technologies, utilizing clean electricity (especially wind and solar power), and deploying a small amount of carbon capture technology, the United States can reach zero emissions without requiring changes to behavior. Cost is about $1 per person per day, not counting climate benefits; this is significantly less than estimates from a few years ago because of recent technology progress. Models with more detail than used in the past revealed unexpected synergies, counterintuitive results, and tradeoffs. The lowest‐cost electricity systems get >80% of energy from wind and solar power but need other resources to provide reliable service. Eliminating fossil fuel use altogether is possible but higher cost. Restricting biomass use and land for renewables is possible but could require nuclear power to compensate. All blueprints for the United States agree on the key tasks for the 2020s: increasing the capacity of wind and solar power by 3.5 times, retiring coal plants, and increasing electric vehicle and electric heat pump sales to >50% of market share. Key Points The United States can reach zero net CO2 emissions from energy and industry in 2050 at a net cost of 0.2–1.2% of GDP, not counting climate benefits Multiple feasible pathways exist, all based on energy efficiency, clean electricity, electrification, and carbon capture for use or storage Least‐cost electricity systems obtain >80% of their energy from wind and solar, with existing types of thermal generation for reliability

Ecosystems generate a wide range of benefits for humans, including some market goods as well as other benefits that are not directly reflected in market activity 1 . Climate change will alter the distribution of ecosystems around the world and change the flow of these benefits 2 , 3 . However, the specific implications of ecosystem changes for human welfare remain unclear, as they depend on the nature of these changes, the value of the affected benefits and the extent to which communities rely on natural systems for their well-being 4 . Here we estimate country-level changes in economic production and the value of non-market ecosystem benefits resulting from climate-change-induced shifts in terrestrial vegetation cover, as projected by dynamic global vegetation models (DGVMs) driven by general circulation climate models. Our results show that the annual population-weighted mean global flow of non-market ecosystem benefits valued in the wealth accounts of the World Bank will be reduced by 9.2% in 2100 under the Shared Socioeconomic Pathway SSP2-6.0 with respect to the baseline no climate change scenario and that the global population-weighted average change in gross domestic product (GDP) by 2100 is −1.3% of the baseline GDP. Because lower-income countries are more reliant on natural capital, these GDP effects are regressive. Approximately 90% of these damages are borne by the poorest 50% of countries and regions, whereas the wealthiest 10% experience only 2% of these losses. Country-level changes in economic production and the value of non-market ecosystem benefits show unequal impacts on the global values of natural capital resulting from climate-change-induced shifts in terrestrial vegetation cover.

As long as economic growth is a major political goal, decoupling growth from resource use and emissions is a prerequisite for a sustainable net-zero emissions future. However, empirical evidence for absolute decoupling, i.e. decreasing resource use and emissions at the required scale despite continued economic growth, is scarce and scattered across different research streams. In this two-part systematic review, we assess how and to what extent decoupling has been observed and what can be learnt for addressing the sustainability and climate crisis. Based on a transparent approach, we systematically identify and screen more than 11 500 scientific papers, eventually analyzing full texts of 835 empirical studies on the relationship between economic growth (GDP), resource use (materials and energy) and greenhouse gas emissions. Part I of the review examines how decoupling has been investigated across three research streams: energy, materials and energy, and emissions. Part II synthesizes the empirical evidence and policy implications (Haberl et al 2020 Environ. Res. Lett. 15 065003). In part I, we examine the topical, temporal and geographical scopes, methods of analysis, institutional networks and prevalent conceptual angles. We find that in this rapidly growing literature, the vast majority of studies-decomposition, 'causality' and Environmental Kuznets Curve analysis-approach the topic from a statistical-econometric point of view, while hardly acknowledging thermodynamic principles on the role of energy and materials for socio-economic activities. A potentially fundamental incompatibility between economic growth and systemic societal changes to address the climate crisis is rarely considered. We conclude that the existing wealth of empirical evidence merits braver conceptual advances than we have seen thus far. Future work should focus on comprehensive multi-indicator long-term analyses, conceptually grounded on the fundamental biophysical basis of socio-economic activities, incorporating the role of global supply chains as well as the wider societal role and preconditions of economic growth.

The Stock Market is one of the most active research areas, and predicting its nature is an epic necessity nowadays. Predicting the Stock Market is quite challenging, and it requires intensive study of the pattern of data. Specific statistical models and artificially intelligent algorithms are needed to meet this challenge and arrive at an appropriate solution. Various machine learning and deep learning algorithms can make a firm prediction with minimised error possibilities. The Artificial Neural Network (ANN) or Deep Feed‐forward Neural Network and the Convolutional Neural Network (CNN) are the two network models that have been used extensively to predict the stock market prices. The models have been used to predict upcoming days' data values from the last few days' data values. This process keeps on repeating recursively as long as the dataset is valid. An endeavour has been taken to optimise this prediction using deep learning, and it has given substantial results. The ANN model achieved an accuracy of 97.66%, whereas the CNN model achieved an accuracy of 98.92%. The CNN model used 2‐D histograms generated out of the quantised dataset within a particular time frame, and prediction is made on that data. This approach has not been implemented earlier for the analysis of such datasets. As a case study, the model has been tested on the recent COVID‐19 pandemic, which caused a sudden downfall of the stock market. The results obtained from this study was decent enough as it produced an accuracy of 91%.

Kazakhstan has a relatively high level of overall gender development, as well as of female employment in its energy industries. Diverse views and backgrounds are necessary to address the challenges of curbing emissions in Kazakhstan, a major fossil fuel producer and exporter. However, our analysis of the Labor Force Survey indicates that female representation among energy sector managers and overall workforce has been falling over time. Moreover, we find that women in Kazakhstan’s coal mining, petroleum extraction, and power industries are concentrated in low-skilled and non-core occupations. Next, by analyzing data on labor compensation within energy occupations, we discover signs of persistent vertical discrimination, which may reduce incentives for women to upgrade their skills. Finally, we find that major shocks, such as the COVID-19 pandemic, may stall or reverse prior progress in increasing the energy sector’s gender diversity. Our findings contribute to raising gender awareness among the stakeholders in Kazakhstan’s energy sector in order to facilitate evidence-based gender mainstreaming.

Economic growth is an important metric for the sustainable development of any region or country. Central and Eastern Europe members of the European Union are important players of the single market, which implements regional policies to mitigate socio-economic differences between its newer and established members. The present study examines the factors that shape the phenomenon of economic growth across 62 NUTS 2 regions from 11 countries in Central and Eastern Europe during the period 2011–2020. The study investigates determinants related to education level, involvement of young people in the labor market, household net income, high-speed internet facilities and overall hours spent at work during a year. Three panel data models estimated with first-differenced generalized method of moments showed that regional economic growth was significantly influenced mainly by income, the rate of young employees and educational attainment level. Relevant insights and policy implications for regions in CEE countries are addressed.