Unextractable fossil fuels in a 1.5 °C world.

Parties to the 2015 Paris Agreement pledged to limit global warming to well below 2 °C and to pursue efforts to limit the temperature increase to 1.5 °C relative to pre-industrial times1. However, fossil fuels continue to dominate the global energy system and a sharp decline in their use must be realized to keep the temperature increase below 1.5 °C (refs. 2-7). Here we use a global energy systems model8 to assess the amount of fossil fuels that would need to be left in the ground, regionally and globally, to allow for a 50 per cent probability of limiting warming to 1.5 °C. By 2050, we find that nearly 60 per cent of oil and fossil methane gas, and 90 per cent of coal must remain unextracted to keep within a 1.5 °C carbon budget. This is a large increase in the unextractable estimates for a 2 °C carbon budget9, particularly for oil, for which an additional 25 per cent of reserves must remain unextracted. Furthermore, we estimate that oil and gas production must decline globally by 3 per cent each year until 2050. This implies that most regions must reach peak production now or during the next decade, rendering many operational and planned fossil fuel projects unviable. We probably present an underestimate of the production changes required, because a greater than 50 per cent probability of limiting warming to 1.5 °C requires more carbon to stay in the ground and because of uncertainties around the timely deployment of negative emission technologies at scale.

Aging, generational shifts, and energy consumption in urban China.

China is recognized as the largest energy consumer and is also the country with the largest and fastest-aging population. Ongoing demographic changes may reshape China's household-based energy consumption patterns because of the large gap in consumption behavior between the elderly and the young as well as varying attitudes toward the environment among generations. However, when the impact of China's aging population on energy consumption is projected, the heterogeneous cognitive norms of generations in the process of demographic transition are not well understood. In this study, we assessed the future impact of China's demographic transition on energy consumption using a proposed theoretical framework to distinguish between age and generational effects. Specifically, we used age-period-cohort (APC) detrended analysis to estimate age and generational effects based on China's urban household survey data from 1992 to 2015. The results indicated large differences in energy use propensity across ages and generations. The elderly and younger generations tended to be energy-intensive consumers, resulting in higher energy consumption in this aging society. Our results consequently show that future changes in China's elderly population will result in a substantial increase in energy consumption. By 2050, the changing consumption share of the elderly population will account for ∼17 to 26% of total energy consumption in the residential sector, which is close to 115 million tons of standard coal (Mtce). These findings highlight the need to interlace environmental education policies and demographic transitions to promote energy conservation behavior in children and youth for low-carbon, sustainable development.

Improved walking energy efficiency might persist in presence of simulated full weight regain after multidisciplinary weight loss in adolescents with obesity: the POWELL study.

AIM: Weight loss leads to a reduction of the energy cost of walking but the respective implications of the metabolic and mechanic changes remain unknown. The present study compares the post-weight loss energy cost of walking (Cw) with and without a total reload of the induced weight reduction in adolescents with obesity. METHODS: Energy cost of walking and substrate use were evaluated during a graded walking exercise (4×6-min at 0.75, 1, 1.25, 1.5 m.s-1) before (V1) and after a 12-week intervention in 21 adolescents with obesity (11 girls; 13.8 ± 1.4 y). After weight loss, the walking exercise was randomly repeated once without weight reload (V2) and once with a loading corresponding to the total induced weight loss during the program (V2L). Body composition was assessed before and after the intervention. RESULTS: Body weight and fat mass decreased in response to the 12-week intervention (p < 0.001), while FFM did not change. The absolute gross Cw (ml.m-1) was higher on V1 compared with V2 at every speed. The absolute net Cw (ml.m-1) was higher on V1 compared to V2L at 0.75 m.s-1 (p = 0.04) and 1 m.s-1 (p = 0.02) and higher on V2L compared with V2 at 1.5 m.s-1 (p = 0.03). Net Cw (ml.m-1.kg-1) on V1 being higher than V2 (p < 0.001), and V2L higher than V2 (p = 0.006). The absolute CHO oxidation (mg.min-1) did not show any condition effect (p = 0.12) while fat utilization was higher on V1 compared to V2 and V2L (p < 0.001). Relative to body weight CHO oxidation was lower on V1 compared to V2 (p = 0.04) and V2L (p = 0.004) while relative to body weight fat oxidation was higher on V1 than V2 (p = 0.002). CONCLUSION: Adolescents with obesity might not show an entire rise back to pre-weight loss values of their metabolic cost of walking when weight gain is simulated. These new findings suggest metabolic and physiological adaptations to weight loss of the energy metabolism that remain to be clarified.

Essential lipid autacoids rewire mitochondrial energy efficiency in metabolic dysfunction-associated fatty liver disease.

BACKGROUND AND AIM: Injury to hepatocyte mitochondria is common in metabolic dysfunction-associated fatty liver disease. Here, we investigated whether changes in the content of essential fatty acid-derived lipid autacoids affect hepatocyte mitochondrial bioenergetics and metabolic efficiency. APPROACH AND RESULTS: The study was performed in transgenic mice for the fat-1 gene, which allows the endogenous replacement of the membrane omega-6-polyunsaturated fatty acid (PUFA) composition by omega-3-PUFA. Transmission electron microscopy revealed that hepatocyte mitochondria of fat-1 mice had more abundant intact cristae and higher mitochondrial aspect ratio. Fat-1 mice had increased expression of oxidative phosphorylation complexes I and II and translocases of both inner (translocase of inner mitochondrial membrane 44) and outer (translocase of the outer membrane 20) mitochondrial membranes. Fat-1 mice also showed increased mitofusin-2 and reduced dynamin-like protein 1 phosphorylation, which mediate mitochondrial fusion and fission, respectively. Mitochondria of fat-1 mice exhibited enhanced oxygen consumption rate, fatty acid ß-oxidation, and energy substrate utilization as determined by high-resolution respirometry, [1- 14 C]-oleate oxidation and nicotinamide adenine dinucleotide hydride/dihydroflavine-adenine dinucleotide production, respectively. Untargeted lipidomics identified a rich hepatic omega-3-PUFA composition and a specific docosahexaenoic acid (DHA)-enriched lipid fingerprint in fat-1 mice. Targeted lipidomics uncovered a higher content of DHA-derived lipid autacoids, namely resolvin D1 and maresin 1, which rescued hepatocytes from TNFα-induced mitochondrial dysfunction, and unblocked the tricarboxylic acid cycle flux and metabolic utilization of long-chain acyl-carnitines, amino acids, and carbohydrates. Importantly, fat-1 mice were protected against mitochondrial injury induced by obesogenic and fibrogenic insults. CONCLUSION: Our data uncover the importance of a lipid membrane composition rich in DHA and its lipid autacoid derivatives to have optimal hepatic mitochondrial and metabolic efficiency.

The ripple effects of the energy crisis on academia: Europe's energy crisis deepens concerns about the future of academia and research laboratories are in dire need of sustainable solutions: Europe's energy crisis deepens concerns about the future of academia and research laboratories are in dire need of sustainable solutions.

Rising energy prices and drastic inflation put pressure on research institutions to save energy and money.

Seeing green in conference season.

Researchers and scientific organizations are becoming aware of the greenhouse-gas emissions and waste associated with attending scientific conferences. Fledgling efforts are now underway to address this problem by offering carbon offsets, recycling at conferences, reducing conference travel, or replacing meetings with teleconferences.

LEDs for photons, physiology and food.

Lighting based on light-emitting diodes (LEDs) not only is more energy efficient than traditional lighting, but also enables improved performance and control. The colour, intensity and distribution of light can now be controlled with unprecedented precision, enabling light to be used both as a signal for specific physiological responses in humans and plants, and as an efficient fuel for fresh food production. Here we show how a broad and improved understanding of the physiological responses to light will facilitate greater energy savings and provide health and productivity benefits that have not previously been associated with lighting.

Dynamic role of clean energy and sustainable economic growth in coastal region: Novel observations from China.

China's coastal region is the major geographical unit for the future development of China's industrial sector. The transformation of basic structure to high-class development in China's coastal places is a significant tool for promoting the changes related to quality, power and efficiency in regional economic development. In the 21st century, environmental and energy issues have increased worldwide, and challenges related to environmental pollution, energy crises, and ecological imbalances have emerged. To climate change and energy utilization, the sustainable progress of clean energy is the new route of future energy development. Based on China's non-polluting energy growth process in the last ten years, this article explores China's clean/green energy policies and economic growth development plans. Clean energy utilization is crucial for sustainable development in the context of high-quality economic growth and climate change. However, the monetary evolution and carbon emission are not investigated whole from the clean energy aspects. Using Wind energy sources as the acceptable variable, this paper employs threshold regression and impulse functions to assess the energy consumption and economic growth on carbon emission in 30 Chinese provinces over the 2000 to 2020 period. The Deep Belief Network (DBN) model predicts wind energy utilization and efficiency. The results show that economic development and carbon emissions are connected. Further, growth influences promote the offset of carbon emissions. Green innovation alters the nexus of carbon emissions, and China's economy reduces carbon usage. It provides the decision-making policies for clean energy development.

Collaborative effect of the energy conservation and emission reduction fiscal policy in China.

Government-led national comprehensive demonstration cities for Energy Conservation and Emission Reduction Fiscal Policy (ECERFP) are pivotal for China in addressing environmental governance. Using a panel dataset covering 278 Chinese cities from 2003 to 2019, this study adopts the staggered difference-in-differences (DID) approach to investigate the synergistic impacts of ECERFP on pollution and carbon reduction. The findings indicate that ECERFP contributes to a 3% improvement in pollution reduction performance, a 1.5% enhancement in carbon reduction performance, and a 4% overall increase in combined pollution and carbon reduction efforts. Furthermore, the study examines the heterogeneous effects of ECERFP on environmental performance. ECERFP significantly influences the synergistic efforts in pollution and carbon reduction by fostering green innovation, enhancing energy allocation, and optimizing industrial structures. This study both theoretically and empirically outlines the specific pathways and mechanisms through which "incentive-based" green fiscal policy promotes synergistic pollution and carbon reduction, thus providing a pragmatic foundation for enhancing the role of fiscal policy in environmental governance.

The synergy effect of energy security and carbon-haze collaborative management: From the perspective of biased technological progress.

It is important to ensure energy security and achieve carbon-haze collaborative management for sustainable development. Reducing imported energy dependence is necessary to maintain energy security, while its impact on environmental quality remains unclear. From the perspective of biased technological progress, this paper estimates the level of biased technological progress towards self-sufficient energy by a heterogeneous stochastic frontier analysis (SFA) function, and then empirically examines whether self-sufficient energy biased technological progress has a dampening effect on haze pollution and carbon emissions. It is found that: (1) Self-sufficient energy biased technological progress can effectively reduce haze pollution and carbon emissions, achieving a synergistic effect between energy security and carbon-haze collaborative management. (2) "Efficiency enhancement" and "quality improvement" are the essential mechanisms for the synergistic effect. (3) Environmental regulation, abundant resource and technology endowments can enhance the haze reduction effect. And the lower dependence on foreign trade and stable global economic policy environment are more conducive to achieving carbon-haze collaborative control. (4) In the Eastern and Western regions, self-sufficient energy biased technology can be sped up to alleviate haze pollution. The findings can enrich the research exploring pollution control from the perspective of biased technological progress, and provide policy recommendations for promoting high-quality development.

Reducing the carbon footprint of radiology through automatic workstation shutdown protocols.

AIM: Climate change poses a major threat to human health, with significant contributions from healthcare systems, with the UK National Health Service (NHS) accounting for 4% of national CO2 emissions. Radiology departments, with high energy consumption from heating, ventilation, cooling (HVAC), and scanners, also contribute significantly. Workstations, though less power-intensive than scanners, are numerous and offer an avenue for emission reduction potential. This study investigates the impact of an automatic power-off/on protocol for radiology workstations at an acute hospital trust on energy consumption, carbon emissions, and financial savings. MATERIALS AND METHODS: Data from 88 reporting workstations were collected on power usage, CO2 emissions, and the associated energy cost before and after implementing an automatic shutdown protocol, which ensured workstations were turned off out of hours and over weekends. RESULTS: Average weekly workstation on-time fell from 148 to 75.5 hours, resulting in an annual energy saving of 17 MWh, equivalent to a reduction of 3.4 tonnes CO2 equivalent and a financial saving of £5000. No complaints or issues with workflow disruption were reported. CONCLUSION: This intervention demonstrates a significant reduction in emissions and energy costs without workflow disruption, offering an easy and replicable sustainability measure for radiology departments. While savings are modest compared to HVAC and scanner emissions, the protocol's simplicity and effectiveness in addressing human factors in power management highlight its potential. Broader application across hospital networks could yield substantial environmental and financial benefits. These findings contribute to the ongoing efforts to improve sustainability within radiology and health care.

An energy efficiency assessment of Yttrium-aluminum-garnet laser in vitro.

To study the effect range of the Nd:YAG laser through various levels of cloudy medium for targets with varying grayscale values in vitro. The coated paper cards with grayscale values of 0, 50, 100, and 150 were used as the laser's targets, which were struck straightly with varying energies using three burst modes (single pulse, double pulse, and triple pulse). Six filters (transmittances of 40, 50, 60, 70, 80, and 90) were applied to simulate various levels of cloudy refractive medium. Image J software was used to measure the diameters and regions of the laser spots. The ranges of the Nd:YAG laser spots increased with energy in the same burst mode (P < 0.05). Under the same amount of energy, the ranges of the Nd:YAG laser spot increased with the grayscale value of the targets (P < 0.05). The greater the transmittance of the filters employed, the larger the range of the Nd: YAG laser spots produced. Assuming that the total pulse energy is identical, the effect ranges of multi-pulse burst modes were significantly larger than those of single-pulse burst mode (P < 0.05). The effect range of a Nd:YAG laser grows with increasing energy and the target's grayscale value. A cloudy refractive medium has a negative impact on the effect range of the Nd: YAG laser. The single pulse mode has the narrowest and safest efficiency range.

Unlocking a greener future: The role of digital finance in enhancing green total factor energy efficiency.

The development of digital finance provides new opportunities for improving energy efficiency and promoting green development. This paper calculates green total factor energy efficiency (GTFEE) using the super-efficiency SBM model and examines the impact of digital finance on GTFEE. Digital finance has a significant positive impact on GTFEE. Under a bank-dominated financial structure, the positive impact of digital finance on GTFEE is quite significant. In regions with intense banking competition, a large amount of green credit, and lower resource dependence, digital finance is conducive to enhancing GTFEE. Optimizing the allocation efficiency of production factors is an essential mechanism for digital finance to encourage improvements in GTFEE. Digital finance alleviates distortions in factor markets and enhances the matching of the marginal output and the price of capital, labor, and energy factors, thereby facilitating improvements in GTFEE. Further analysis indicates that digital finance has a significant, positive spatial spillover effect on GTFEE, enhancing GTFEE levels in both local and neighboring regions. This study enriches the research on the relationship between digital finance and energy efficiency and provides theoretical foundations and policy references for how digital finance can better serve the green transition of the economy.

A puzzle on environmental quality and energy security.

i) This study examines the determinants of environmental quality. It is not possible to fully analyze the complex network that emerges from the set of interactions of these determinants, both with each other and with environmental security. Indeed, a number of variables and relationships hidden in the background of the puzzle such as 'game theoretical interactions between economies on energy security', characterize this network. However, this study, which includes energy security and environmental quality simultaneously, may open the door to revealing the key patterns of the current network. ii) This study, which investigates the network between environmental problems and energy security, provides empirical evidence that these two variables may well evolve by positively affecting each other under some conditions. iii) Using the current and sophisticated econometric methods such as CDw + based on Juodis and Reese (2022) test and CS-ARDL Model, over a panel of top 20 energy-using countries in the period 1980-2018, the empirical analysis of the article shows that an increase in energy security risk positively affects environmental quality in aggregate by motivating increased energy efficiency, triggering environmental awareness and regulations, and stimulating research and development activities for clean energy etc. Technologies. Therefore, this study concludes that potential policies and reforms, including reducing fossil fuel consumption, increasing energy efficiency in distribution and consumption, encouraging investments in clean energy are of key importance in making energy security sustainable in the long term by increasing environmental quality.

Do crypto investors care about energy use and climate change? Evidence from Ethereum's transition to proof-of-stake.

This paper analyses the transition of Ethereum (ETH) from the energy-intensive Proof-of-Work (PoW) to the less energy-intensive Proof-of-Stake (PoS). We analyze returns, volatility, return correlations and volume of ETH, ETC and Bitcoin for all events in the lead-up to the actual change from PoW to PoS also labelled "the merge." The analysis suggests that some investors value the less energy-intensive mining mechanism and invest in ETH. However, since the overall effect is weak, we conclude that despite all the media attention and the stated concerns about the high energy-intensity of Bitcoin and PoW, most investors do not react to the change with an increased investment in Ethereum.

The impact of green innovation resilience on energy efficiency: A perspective based on the development of the digital economy.

As digital economy develops, its impact on green innovation and energy efficiency has become the focus of current research. To explore the impact of the current development of the digital economy on the energy industry, this paper selects the parameter of green innovation resilience, analyzes the impact mechanism of green innovation resilience on energy efficiency under the shock of digital economic development, and uses relevant data from 284 cities in China from 2011 to 2019 for empirical testing. It is found that: green innovation resilience promotes energy efficiency; low level of green innovation resilience inhibits the improvement of energy efficiency, while high level of resilience promotes energy efficiency; the initial stage of digital economic development generates resource grabbing and the effect of technological constraints, which weakens the role of green innovation resilience in promoting energy efficiency. The results indicate that the resilience of the green innovation system should be strengthened in order to fully tap the potential for promoting energy efficiency; the policy orientation of "digital greening-energy efficient" should be pursued in the development of digital economy; the rational allocation of resources and the implementation of green standards should be strengthened in the process of digital economic development; and the constraints on energy efficiency improvement in the early stage of digital economic development should be broken through by accelerating the digitalization process.

Renewable energy technology innovation and urban green economy efficiency.

Green economy efficiency is the core-factor of urban economic and environmental development. As a sustainable instruments, renewable energy technology innovation (RETI) not only reflects the low energy-consumption, but also promotes the reasonable and balanced relationship between resources utilization and urban economy. In this regard, this paper selects China's cities to investigate the effect of RETI on urban green economy efficiency from 2004 to 2020 based on theoretical analyses and previous studies. The paper finds that RETI can promote urban green economy efficiency significantly, passing a series of robustness test, and its effect has connected differently with the factor of regional factor, cleaner production level and environment pollution. Meanwhile, RETI promotes urban green economy efficiency by reducing CO2 emission and polluting manufacturing agglomeration. To date, this study has discovered the green economy efficiency improvement effects of RETI, providing theoretical basis and practical recommendations for government, technological agency and urban industries.

Utilizing support vector machines to foster sustainable development and innovation in the clean energy sector via green finance.

As the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. in order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. through the use of Support Vector Machine (SVM) Multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. with wind power base a as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terAs the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. In order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. Through the use of Support Vector Machine (SVM) multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. With Wind Power Base A as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terms of the number of units, internal rate of return within the project, and annual average equivalent utilization hours at Wind Power Base A. The number of units decreases to 5860, with total purchase and transportation costs remaining basically unchanged, the internal rate of return increases to 9.3%, and annual equivalent utilization hours increase to 2044 h. Energy consumption and CO2 emissions are significantly reduced, with energy consumption decreasing by 0.68 × 109 kgce and CO2 emissions decreasing by 1.29 × 109 kg. The optimization effects are mainly concentrated in the production and installation stages, with emission reductions achieved through the recycling and disposal of materials consumed in the early stages. In terms of investment benefits, environmental benefits are enhanced, with a 13.93% reduction in CO2 emissions. Moreover, there is improved energy efficiency, with the energy input-output ratio increasing from 7.73 to 9.31. This indicates that the Wind Power Base A project has significant environmental and energy efficiency advantages in the clean energy industry. This work innovatively provides a comprehensive assessment and optimization scheme for clean energy projects and predicts the profitability of Wind Power Base A using SVM multi-factor models. Besides, this work optimizes key parameters of the project using a bi-level multi-objective approach, thus comprehensively improving the investment return and utilization efficiency of the wind power base. This work provides innovative methods and strong data support for the development of the clean energy industry, which is of great significance for promoting sustainable development under the backdrop of green finance.

Can digital industrialization promote energy conservation development in China? Empirical evidence based on national big data comprehensive pilot zone policy.

Digital industrialization represented by big data provides substantial support for the high-quality development of the digital economy, but its impact on urban energy conservation development requires further research. To this end, based on the panel data of Chinese cities from 2010 to 2019 and taking the establishment of the national big data comprehensive pilot zone (NBDCPZ) as a quasi-natural experiment, this paper explores the impact, mechanism, and spatial spillover effect of digital industrialization represented by big data on urban energy conservation development using the Difference-in-Differences (DID) method. The results show that digital industrialization can help achieve urban energy conservation development, which still holds after a series of robustness tests. Mechanism analysis reveals that digital industrialization impacts urban energy conservation development by driving industrial sector output growth, promoting industrial upgrading, stimulating green technology innovation, and alleviating resource misallocation. Heterogeneity analysis indicates that the energy conservation effect of digital industrialization is more significant in the central region, intra-regional demonstration comprehensive pilot zones, large cities, non-resource-based cities, and high-level digital infrastructure cities. Additionally, digital industrialization can promote energy conservation development in neighboring areas through spatial spillover effect. This paper enriches the theoretical framework concerning the relationship between digital industrialization and energy conservation development. The findings have significant implications for achieving the coordinated development of digitalization and conservation.