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Trade-induced carbon transfer generates significant impacts on global carbon emissions, leading to carbon inequality (CI). This paper introduces a novel indirect metric, the trade-induced domestic carbon share (TDCS), designed to quantify economies' carbon benefits from international trade. Based on data from 17 manufacturing sectors in 66 major economies, this study compares the impact of developed and developing economies' participation in global value chains (GVCs) on TDCS, offering insights into trade-related carbon inequality. Our analysis reveals that GVCs participation by developed economies generally decrease their TDCS, in contrast to an increase in developing economies, thereby exacerbating carbon inequality. Notably, backward participation in GVCs reduces TDCS across all economies, whereas forward participation in GVCs results in increased TDCS in developing economies but a decrease in developed ones. Additionally, our study indicates that developed economies enhance their position in both forward and backward GVCs but experience a decline in the ratio of domestic value added (DVAR). In contrast, developing economies strengthen their forward GVCs position and DVAR but weaken their backward GVCs position. These findings offer new perspectives on carbon inequality in international trade and provide valuable guidance for policymakers in devising industrial trade and carbon reduction strategies. [ABSTRACT FROM AUTHOR]

• An exergetic approach and a new understanding of the holistic performance of buildings in terms of sustainability and decarbonization. • Taking into account the exergy mismatches among supply and demand points, leading to avoidable yet unaccounted for CO 2 emissions responsibilities. • The green building definitions must be re-examined and upgraded to an exergy basis. • Buildings may not qualify for zero-energy buildings because high exergy of electric power is destroyed by much lower-exergy heating demand. According to the second law of thermodynamics, all human activities cause exergy destructions, adding to additional root causes for carbon dioxide emissions responsibility. It means that current carbon dioxide concentrations are accurately observed, but the root causes and their potential solutions against global warming fall short of achieving the goals of the Paris agreement by almost 45% in terms of decarbonization efforts, as shown in this paper. This result applies to all activities, including the green facility concept. In this respect, the primary aim of this paper is to raise awareness about the essence of the Second Law of Thermodynamics in expanding the green facility concept to reach more effective and sustainable rating methodologies concerning the climate crisis. A new evaluating and rating model with a set of exergy-based green building metrics that relate additional carbon dioxide emissions to irreversible exergy destructions has been developed. Examples about apparently green buildings according to the First Law of Thermodynamics are given by showing that these buildings are not green due to additional carbon dioxide emissions responsibility due to exergy destructions. An airport terminal building case is elaborated. It has been shown that although part of the electricity comes from a third-party wind energy provider, it ends up with carbon dioxide emissions responsibility because it is not entirely used in exergy-rational demand points and compares less favorably with an on-site cogeneration system using natural gas by about 30% more emissions responsibility. The results and derivations of new metrics are discussed, which shed light on adding new criteria to existing green building certification programs. [ABSTRACT FROM AUTHOR]

TanSat-2, the next-generation Chinese greenhouse gas monitoring satellite for measuring carbon dioxide (CO 2 ), has a new city-scale observing mode. We assess the theoretical capability of TanSat-2 to quantify integrated urban CO 2 emissions over the cities of Beijing, Jinan, Los Angeles, and Paris. A high-resolution emission inventory and a column-averaged CO 2 ( X CO 2 ) transport model are used to build an urban CO 2 inversion system. We design a series of numerical experiments describing this observing system to evaluate the impacts of sampling patterns and X CO 2 measurement errors on inferring urban CO 2 emissions. We find that the correction in systematic and random flux errors is correlated with the signal-to-noise ratio of satellite measurements. The reduction in systematic flux errors for the four cities are sizable, but are subject to unbiased satellite sampling and favorable meteorological conditions (i.e., less cloud cover and lower wind speed). The corresponding correction to the random flux error is 19–28%. Even though clear-sky satellite data from TanSat-2 have the potential to reduce flux errors for cities with high CO 2 emissions, quantifying urban emissions by satellite-based measurements is subject to additional limitations and uncertainties. [ABSTRACT FROM AUTHOR]

Existing CO2 emissions reported by city inventories usually lag in real-time by a year or more and are prone to large uncertainties. This study responds to the growing need for timely and precise estimation of urban CO2 emissions to support present and future mitigation measures and policies. We focus on the Paris metropolitan area, the largest urban region in the European Union and the city with the densest atmospheric CO2 observation network in Europe. We performed long-term atmospheric inversions to quantify the citywide CO2 emissions, i.e., fossil fuel as well as biogenic sources and sinks, over 6 years (2016–2021) using a Bayesian inverse modeling system. Our inversion framework benefits from a novel near-real-time hourly fossil fuel CO2 emission inventory (Origins.earth) at 1 km spatial resolution. In addition to the mid-afternoon observations, we attempt to assimilate morning CO2 concentrations based on the ability of the Weather Research and Forecasting model with Chemistry (WRF-Chem) transport model to simulate atmospheric boundary layer dynamics constrained by observed layer heights. Our results show a long-term decreasing trend of around 2 % ± 0.6 % per year in annual CO2 emissions over the Paris region. The impact of the COVID-19 pandemic led to a 13 % ± 1 % reduction in annual fossil fuel CO2 emissions in 2020 with respect to 2019. Subsequently, annual emissions increased by 5.2 % ± 14.2 % from 32.6 ± 2.2 MtCO2 in 2020 to 34.3 ± 2.3 MtCO2 in 2021. Based on a combination of up-to-date inventories, high-resolution atmospheric modeling and high-precision observations, our current capacity can deliver near-real-time CO2 emission estimates at the city scale in less than a month, and the results agree within 10 % with independent estimates from multiple city-scale inventories. [ABSTRACT FROM AUTHOR]

Based on the intertemporal substitution effect, the high inflation expectations can stimulate agents to consume now rather than in the future. However, under the background of global climate policy changes, how inflation expectations affect energy consumption? Using the bootstrap Granger full-sample causality test and sub-sample rolling window tests, this study examines the intertemporal substitution effect of energy consumption in the U.S. The results based on the full-sample data indicate no causality between inflation expectations and energy consumption, which suggests that the intertemporal substitution effect of energy consumption does not exist. Nevertheless, the rolling window method which estimates a time-varying causality identifies a short-lived positive effects of inflation expectations on energy consumption in a distinct sub-period before the global Paris agreement, but disappears since then. Therefore, the intertemporal substitution effect regarding energy consumption does not exist under the background of pressing carbon targets. The effects of energy consumption on inflation expectations can be positive or negative, which tells a cautionary tale about climate policies aiming at engineering lower carbon emissions. [ABSTRACT FROM AUTHOR]

'Belt and Road Initiative' (B&R) countries play critical roles in mitigating global carbon emission under the Paris agreement, but their driving factors and feasibility to reduce carbon emissions remain unclear. This paper aims to identify the main driving factors (MDFs) behind carbon emissions and predict the future emissions trajectories of the B&R countries under different social-economic pathways based on the extended STIRPAT (stochastic impacts by regression on population, affluence, and technology) model. The empirical results indicate that GDP per capita and energy consumption structure are the MDFs that promote carbon emission, while energy intensity improvement is the MDF that inhibits carbon emission. Population, as another MDF, has a dual impact across countries. The carbon emissions in all B&R countries are predicted to increase from SSP1 to SSP3, but emissions trajectories vary across countries. Under the SSP1 scenario, carbon emissions in over 60% of B&R countries can peak or decline, and the aggregated peak emissions will amount to 21.97 Gt in 2030. Under the SSP2 scenario, about half of the countries can peak or decline, while their peak emissions and peak time are both higher and later than SSP1, the highest emission of 25.35 Gt is observed in 2050. Conversely, over 65% of B&R countries are incapable of either peaking or declining under the SSP3 scenario, with the highest aggregated emission of 33.10 Gt in 2050. It is further suggested that decline of carbon emission occurs when the inhibiting effects of energy intensity exceed the positive impacts of other MDFs in most B&R countries. [ABSTRACT FROM AUTHOR]

The International Panel on Climate Change and the 2015 Climate Summit in Paris have recommended that efforts to reduce carbon emissions be coupled with carbon removal from the atmosphere. Carbon negative energy combines net carbon removal with the production of energy products or other revenue-generating products beyond sequestered carbon. Even though both biochemical and thermochemical approaches to carbon negative energy can be envisioned, this paper considers the prospects for the latter including pyrolysis and gasification. The fundamentals of these two processes are described to better understand how they would be integrated with carbon removal. Characteristics of pyrolysis and gasification are related to the kinds of sequestration agents they would produce, the scale of their deployment, the fraction of biomass carbon that could ultimately sequestered, the challenges of effectively sequestering these different forms of carbon and the economics of thermochemical carbon negative energy. [ABSTRACT FROM AUTHOR]

We assess the theoretical capability of the upcoming France–UK MicroCarb satellite, which has a city-scan observing mode, to determine integrated urban emissions of carbon dioxide (CO 2). To achieve this we report results from a series of closed-loop numerical experiments that use an atmospheric transport model with anthropogenic and biogenic fluxes to determine the corresponding changes in atmospheric CO 2 column, accounting for changes in measurement coverage due to cloud loading. We use a maximum a posteriori inverse method to infer the CO 2 fluxes based on the measurements and the a priori information. Using an urban CO 2 inversion system, we explore the relative performance of alternative two-sweep and three-sweep city observing strategies to quantify CO 2 emissions over the cities of Paris and London in different months when biospheric fluxes vary in magnitude. We find that both the two-sweep and three-sweep observing modes are able to reduce a priori flux errors by 20 %–40 % over Paris and London. The three-sweep observing strategy, which generally outperforms the two-sweep mode by virtue of its wider scan area that typically yields more cloud-free observations, can retrieve the total emissions of the truth within 7 % over Paris and 21 % over London. The performance of the limited-domain city-mode observing strategies is sensitive to cloud coverage and particularly sensitive to the prevailing wind direction. We also find that seasonal photosynthetic uptake of CO 2 by the urban biosphere weakens atmospheric CO 2 gradients across both cities, thereby reducing the sensitivity of urban CO 2 enhancements and subsequently compromising the ability of MicroCarb to reduce bias in estimating urban CO 2 emissions. This suggests that additional trace gases co-emitted with anthropogenic CO 2 emissions, but unaffected by the land biosphere, are needed to quantify sub-city scale CO 2 emissions during months when the urban biosphere is particularly active. [ABSTRACT FROM AUTHOR]

Many different emission pathways exist that are compatible with the Paris climate agreement, and many more are possible that miss that target. While some of the most complex Earth System Models have simulated a small selection of Shared Socioeconomic Pathways, it is impractical to use these expensive models to fully explore the space of possibilities. Such explorations therefore mostly rely on one‐dimensional impulse response models, or simple pattern scaling approaches to approximate the physical climate response to a given scenario. Here we present ClimateBench—the first benchmarking framework based on a suite of Coupled Model Intercomparison Project, AerChemMIP and Detection‐Attribution Model Intercomparison Project simulations performed by a full complexity Earth System Model, and a set of baseline machine learning models that emulate its response to a variety of forcers. These emulators can predict annual mean global distributions of temperature, diurnal temperature range and precipitation (including extreme precipitation) given a wide range of emissions and concentrations of carbon dioxide, methane and aerosols, allowing them to efficiently probe previously unexplored scenarios. We discuss the accuracy and interpretability of these emulators and consider their robustness to physical constraints such as total energy conservation. Future opportunities incorporating such physical constraints directly in the machine learning models and using the emulators for detection and attribution studies are also discussed. This opens a wide range of opportunities to improve prediction, robustness and mathematical tractability. We hope that by laying out the principles of climate model emulation with clear examples and metrics we encourage engagement from statisticians and machine learning specialists keen to tackle this important and demanding challenge. Plain Language Summary: Many different emission pathways exist that are compatible with the Paris climate agreement, and many more are possible that miss that target. While some of the most complex Earth System Models have simulated a small selection of possible futures, it is impractical to use these expensive models to fully explore the space of possibilities. Such explorations therefore mostly rely on simple approximations of the global mean temperature response to a given scenario. Here we present ClimateBench—the first benchmarking framework based on a suite of state‐of‐the‐art simulations performed by a full complexity Earth System Model, and a set of baseline machine learning models that emulate its response to a variety of forcers. These emulators can predict annual mean global distributions of temperature, diurnal temperature range and precipitation (including extreme precipitation) given a wide range of emissions and concentrations of carbon dioxide, methane and aerosols, allowing them to efficiently probe previously unexplored scenarios. We also describe a set of evaluation metrics which we hope will entice statisticians and machine learning experts to tackle this important and demanding challenge. Key Points: We introduce the first benchmark for emulation of key spatially resolved climate variables derived from a full complexity Earth System ModelThree baseline emulators are presented which are able to predict regional temperature and precipitation with varying skillEvaluation metrics and areas for future research are presented to encourage further development of trustworthy data‐driven climate emulators [ABSTRACT FROM AUTHOR]

Understanding the role of information communication and technology (ICT) in environmental issues stemming from extensive energy consumption and carbon dioxide emission in the process of economic development is worthwhile both from policy and scholarly fronts. Motivated on this premise, the study contributes to the rising studies associated with the roles of economic growth, institutional quality and information and communication technology (ICT) have on CO2 emission in the framework of the 21st Conference of the Parties (COP21) on climate convention in Paris. Obtaining data from the emerging industrialized seven (E7) economies (China, India, Indonesia, Russia, Mexico, Brazil and Turkey) covering annual frequency from 1995 to 2016 for our analysis achieved significant outcome. From the empirical analysis, economic globalization and renewable energy consumption both reduce CO2 emissions while ICT, institutional quality and fossil fuel contribute to the degradation of the environment. This study affirms the presence of an environmental Kuznets curve (EKC) phenomenon which shows an invented U-shaped curve within the E7 economies. On the causality front, both income and its square have a feedback causal relationship with carbon emissions while economic globalization, institutional quality, ICT and clean energy all have a one-way directional causal relationship with CO2 emissions. Conclusively, the need to reduce environmental degradation activities should be pursued by the blocs such as tree planting activities to mitigate the effect of deforestation. Furthermore, the bloc should shift from the use of fossil-fuel and leverage on ICT to enhance the use of clean energy which is environmentally friendly. [ABSTRACT FROM AUTHOR]

In 2015, the 21st Conference of the Parties reaffirmed the target of keeping the global mean temperature rise below 2 °C or 1.5 °C by 2100 while finding no consensus on how to decarbonize the global economy. In this regard, the speed of decarbonization reflects the (in)flexibility of transforming the energy sector due to engineering, political, or societal constraints. Using economy–energy–climate-integrated assessment models (IAMs), the maximum absolute rate of change in carbon emission allowed from each time step to the next, so-called carbon emission inertia (CEI), governs the magnitude of emission change, affecting investment decisions and economic welfare. Employing the model of investment and endogenous technological development (MIND), we conduct a cost-effectiveness analysis and examine anthropogenic global carbon emission scenarios in line with decarbonizing the global economy while measuring the global mean temperature. We examine the role of CEI as a crucial assumption, where the CEI can vary in four scenarios from 3.7% to 12.6% p.a. We provide what-if studies on global carbon emissions, global mean temperature change, and investments in renewable energy production and show that decarbonizing the global economy might still be possible before 2100 only if the CEI is high enough. In addition, we show that climate policy scenarios with early decarbonization and without negative emissions may still comply with the 2 °C target. However, our results indicate that the 1.5 °C target is not likely to be reached without negative emission technologies. Hence, the window of opportunity is beginning to close. This work can also assist to better interpret existing publications on various climate targets when altering CEI could have played a significant role. [ABSTRACT FROM AUTHOR]

The Paris agreement is a legally binding international treaty on climate change. This study examines the Paris agreement and other explanatory variables such as net energy imports, and consumption of three significant fossil fuel impacts on carbon dioxide (CO2) emissions in three selected European countries, including Germany, Spain, and France. We employed fully modified OLS (FMOLS) and dynamic OLS (DOLS) regression methods to evaluate the effects based on panel data from 1995 to 2019. The results suggest that the impact of the Paris agreement is negligible. According to the FMOLS model, the Paris agreement reduces CO2 emissions by roughly 1%. It seems that the Paris agreement is less effective than it was hoped to be. For consumption of three primary fossil fuels and net energy import effects on the CO2 emissions, the results are as expected to be because any increase in the parameters will grow in carbon dioxide emissions in both models. It is worth mentioning that the most crucial factor in CO2 emissions is the crude oil consumption that has an immense impact on both models. In the other words, a 1% increase in the usage of crude oil will release 0.56% CO2 in the atmosphere. [ABSTRACT FROM AUTHOR]

Buildings have a significant impact on energy consumption and carbon emissions. Smart buildings are deemed to play a crucial role in improving the energy performance of buildings and cities. Managing a smart building requires the modelling of data concerning smart systems and components. While there is a significant amount of research on optimising building energy using the smart building concept, there is a dearth of studies investigating the modelling and management of smart systems' data, which is the starting point for establishing the necessary digital environment for representing a smart building. This study aimed to develop and test a solution for modelling and managing smart building information using an industry foundation classes (IFCs)-based BIM process. A conceptual model expressed in the SysML language was proposed to define a smart building. Five BIM approaches were identified as potential 'prototypes' for representing and exchanging smart building information. The fidelity of each approach is checked through a BIM-based validation process using an open-source visualisation platform. The different prototypes were also assessed using a multi-criteria comparison method to identify the preferred approach for modelling and managing smart building information. The preferred approach was prototyped and tested in a use case focused on building energy consumption monitoring to evaluate its ability to manage and visualise the smart building data. The use case was applied in a real case study using a full-scale demonstrator, namely, the 'Nanterre 3' (N3) smart building located at the CESI campus in Paris-Nanterre. The findings demonstrated that an open BIM format in the form of IFCs could achieve adequate modelling of smart building data without information loss. Future extensions of the proposed approach were finally outlined. [ABSTRACT FROM AUTHOR]

Background: Healthcare aims to promote good health and yet demonstrably contributes to climate change, which is purported to be 'the biggest global health threat of the 21st century'. This is happening now, with healthcare as an industry representing 4.4% of global carbon dioxide emissions. Main body: Climate change promotes health deficits from many angles; however, primarily it is the use of fossil fuels which increases atmospheric carbon dioxide (also nitrous oxide, and methane). These greenhouse gases prevent the earth from cooling, resulting in the higher temperatures and rising sea levels, which then cause 'wild weather' patterns, including floods, storms, and droughts. Particular vulnerability is afforded to those already health compromised (older people, pregnant women, children, wider health co-morbidities) as well as populations closer to equatorial zones, which encompasses many low-and-middle-income-countries. The paradox here, is that poorer nations by spending less on healthcare, have lower carbon emissions from health-related activity, and yet will suffer most from global warming effects, with scant resources to off-set the increasing health care needs. Global recognition has forged the Paris agreement, the United Nations sustainable developments goals, and the World Health Organisation climate change action plan. It is agreed that most healthcare impact comes from consumption of energy and resources, and the production of greenhouse gases into the environment. Many professional associations of medicine and allied health professionals are advocating for their members to lead on environmental sustainability; the Australian Podiatry Association is incorporating climate change into its strategic direction. Conclusion: Podiatrists, as allied health professionals, have wide community engagement, and hence, can model positive environmental practices, which may be effective in changing wider community behaviours, as occurred last century when doctors stopped smoking. As foot health consumers, our patients are increasingly likely to expect more sustainable practices and products, including 'green footwear' options. Green Podiatry, as a part of sustainable healthcare, directs us to be responsible energy and product consumers, and reduce our workplace emissions. [ABSTRACT FROM AUTHOR]

Scenarios avoiding global warming greater than 1.5 or 2 ∘ C, as stipulated in the Paris Agreement, may require the combined mitigation of anthropogenic greenhouse gas emissions alongside enhancing negative emissions through approaches such as afforestation–reforestation (AR) and biomass energy with carbon capture and storage (BECCS). We use the JULES land surface model coupled to an inverted form of the IMOGEN climate emulator to investigate mitigation scenarios that achieve the 1.5 or 2 ∘ C warming targets of the Paris Agreement. Specifically, within this IMOGEN-JULES framework, we focus on and characterise the global and regional effectiveness of land-based (BECCS and/or AR) and anthropogenic methane (CH4) emission mitigation, separately and in combination, on the anthropogenic fossil fuel carbon dioxide (CO2) emission budgets (AFFEBs) to 2100. We use consistent data and socio-economic assumptions from the IMAGE integrated assessment model for the second Shared Socioeconomic Pathway (SSP2). The analysis includes the effects of the methane and carbon–climate feedbacks from wetlands and permafrost thaw, which we have shown previously to be significant constraints on the AFFEBs. Globally, mitigation of anthropogenic CH4 emissions has large impacts on the anthropogenic fossil fuel emission budgets, potentially offsetting (i.e. allowing extra) carbon dioxide emissions of 188–212 Gt C. This is because of (a) the reduction in the direct and indirect radiative forcing of methane in response to the lower emissions and hence atmospheric concentration of methane and (b) carbon-cycle changes leading to increased uptake by the land and ocean by CO2 -based fertilisation. Methane mitigation is beneficial everywhere, particularly for the major CH4 -emitting regions of India, the USA, and China. Land-based mitigation has the potential to offset 51–100 Gt C globally, the large range reflecting assumptions and uncertainties associated with BECCS. The ranges for CH4 reduction and BECCS implementation are valid for both the 1.5 and 2 ∘ C warming targets. That is the mitigation potential of the CH4 and of the land-based scenarios is similar for regardless of which of the final stabilised warming levels society aims for. Further, both the effectiveness and the preferred land management strategy (i.e. AR or BECCS) have strong regional dependencies. Additional analysis shows extensive BECCS could adversely affect water security for several regions. Although the primary requirement remains mitigation of fossil fuel emissions, our results highlight the potential for the mitigation of CH4 emissions to make the Paris climate targets more achievable. [ABSTRACT FROM AUTHOR]

One of the major anthropogenic sources of greenhouse gases is the operation of building stock. Improving its energy efficiency has the potential to significantly contribute to achieving climate change mitigation targets. The purpose of this study was to roughly estimate such potential for the operation of the national building stock of Czechia to steer the national debate on the development of related national plans. The estimation is based on a simplified energy model of the Czech building stock that consists of sub-models of residential and nonresidential building stocks, for which their future energy consumptions, shares of energy carriers and sources, and emission factors were modeled in four scenarios. Uncertainties from the approximation of the emission factors were investigated in a sensitivity analysis. The results showed that the operation of the Czech building stock in 2016 totaled 36.9 Mt CO2, which represented 34.6% of the total national carbon dioxide emissions. The four building stock scenarios could produce reductions in the carbon dioxide emissions of between 28% and 93% by 2050, when also considering on-side production from photovoltaics. The implementation of the most ambitious scenario would represent a drop in national CO2 yearly emissions by 43.2% by 2050 (compared to 2016). [ABSTRACT FROM AUTHOR]