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To fully explore the potential low-carbon and economic advantages of a virtual power plant (VPP) that aggregates multiple distributed resources, the paper proposes a VPP scheduling model that considers the carbon emission flow (CEF) and demand response (DR), which is characterized by electro-carbon coupling and source-load interaction. First, the electric-carbon characteristics of each distributed resource under VPP are modeled, and the source-load electric-carbon coupling characteristic model is modeled through the CEF theory. On this basis, a load-side multi-type DR model is established to achieve the purpose of source-load synergy to reduce carbon emissions from VPP. To this end, a two-stage scheduling model of VPP considering the source- load electro-carbon coupling relationship is established, and the implementation of the model can reduce power generation costs, carbon emissions and promote clean energy, and the simulation results of the improved IEEE-14 node system verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

This document is a list of academic articles and research papers related to carbon emissions and their effects. The articles cover various topics such as the analysis of carbon emissions in different regions, the driving factors of carbon emissions, and the impact of carbon emissions on the environment. The articles provide valuable insights into the complex issue of carbon emissions and can be useful for library patrons conducting research on this topic. [Extracted from the article]

Classical Bayesian atmospheric inversions process atmospheric observations and prior emissions, the two being connected by an observation operator picturing mainly the atmospheric transport. These inversions rely on prescribed errors in the observations, the prior emissions and the observation operator. At the meso-scale, inversion results are very sensitive to the prescribed error distributions, which are not accurately known. The classical Bayesian framework experiences difficulties in quantifying the impact of mis-specified error distributions on the optimized fluxes. In order to cope with this issue, we rely on recent research results and enhance the classical Bayesian in version framework through a marginalization on all the plausible errors that can be prescribed in the system. The marginalization consists in computing inversions for all possible error distributions weighted by the probability of occurence of the error distributions. The posterior distribution of the fluxes calculated by the marginalization is complicated and not explicitly describable. We then carry out a Monte-Carlo sampling relying on an approximation of the probability of occurence of the error distributions. This approximation is deduced from the well-tested algorithm of the Maximum of Likelihood. Thus, the marginalized inversion relies on an automatic objectified diagnosis of the error statistics, without any prior knowledge about the matrices. It robustly includes the uncertainties on the error distributions, contrary to what is classically done with frozen expert-knowledge error statistics. Some expert knowledge is still used in the method for the choice of emission aggregation pattern and sampling protocol in order to reduce the computation costs of the method. The relevance and the robustness of the method is tested on a case study: the inversion of methane surface fluxes at the meso-scale with real observation sites in Eurasia. Observing System Simulation Experiments are carried out with different transport patterns, flux distributions and total prior amounts of emitted gas. The method proves to consistently reproduce the known "truth" in most cases, with satisfactory tolerance intervals. Additionnaly, the method explicitly provides influence scores and posterior correlation matrices. An in-depth interpretation of the inversion results is then possible. The more objective quantification of the influence of the observations on the fluxes proposed here allows us to evaluate the impact of the observation network on the characterization of the surface fluxes. The explicit correlations between emission regions reveal the mis-separated regions, hence the typical temporal and spatial scales the inversion can analyze. These scales proved to be consistent with the chosen aggregation patterns. [ABSTRACT FROM AUTHOR]

The freight transport industry in Mexico has grown significantly since the establishment of trade agreements in North America, which has brought significant environmental consequences to the main transport corridors. This paper proposes a methodology for the estimation of emissions for freight vehicles on road transportation corridors. The variables included in this analysis allow adequate characterization of the conditions of the vehicle fleet, the geometry and the quality of the road, the environment, and the average annual daily traffic (AADT) of heavy vehicles. The results were structured to show two indicators, the amount of CO2 emissions per kilometer and the amount of emissions per tonne transported. These results will allow establishing a baseline of CO2 emissions through which we can implement actions in the road transport sector to reduce greenhouse gases (GHG) to mitigate climate change and develop parameter values for use in Cost Benefit Analysis. The indicators can also be applied to geospatial modeling of emissions in road transport corridors and forecast its growth. [ABSTRACT FROM AUTHOR]

Enteric fermentation from dairy cows is a major source of methane. Significantly and rapidly reducing those emissions would be a powerful lever to mitigate climate change. For a given productivity level, introducing fodder with high sources of n-3 content, such as grass or linseed, in the feed ration of dairy cows both improves the milk nutritional profile and reduces enteric methane emissions per liter. Changing cows' diet may represent additional costs for dairy farmers and calls for the implementation of payments for environmental services to support the transition. This paper analyzes 2 design elements influencing the effectiveness of a payment conditioned toward the reduction of enteric methane emissions: (1) the choice of emission indicator capturing the effect of farmers' practices, and (2) the payment amount relative to the additional milk production costs incurred. Using representative farm-level economic data from the French farm accountancy data network, we compare enteric methane emissions per liter of milk calculated with an Intergovernmental Panel on Climate Change Tier 2 method, to baseline emissions from a Tier 3 method accounting for diet effects. We also quantify the additional milk production costs of integrating more grass in the fodder systems by estimating variable cost functions for different dairy systems in France. Our results show the relevance of using an emission indicator sensitive to diet effects, and that the significance and direction of the additional costs for producing milk with a diet containing more grass differ according to the production basin and the current share of grasslands in the fodder crop rotation. We emphasize the importance of developing payments for environmental services with well-defined environmental indicators accounting for the technical problems addressed, and the need to better characterize heterogeneous funding requirements for supporting a large-scale adoption of more environment-friendly practices by farmers. [ABSTRACT FROM AUTHOR]

The implementation of pollutant emission control has made initial achievements in the plant power, iron, and steel industries in China. To further improve air quality, it is of great significance to carry out research on zero-impact emissions of the petrochemical industry. Based on the existing concept and practice of zero emissions, this study proposes the concept of zero-impact emissions, taking emission concentration as the constraint. A typical petrochemical enterprise (namely Enterprise A) in Shanghai Chemical Industry Park as the research object, and used the CALPUFF model to simulate the target pollutant emissions (i.e. sulfur dioxide (SO2), nitrogen oxide (NOx), particulate matter (PM), and volatile organic compounds (VOCs)). The current emission standard, spatial distributions, and emission heights of chimneys in Enterprise A was considered as the baseline emission scenario and taking the zero-impact emission as a target for simulation. The results show that the current emission standards of NOx and VOCs (benzene) exceeded the zero-impact emission limits, and needed to be reduced by 22% and 87.5%, respectively. Moreover, the areas that exceeded the zero-impact concentration limits were located northwest of the chimneys and Hangzhou Bay. In terms of seasonal effects, the wind conditions in spring were more adverse for the enterprise to achieve zero-impact emissions. Based on the simulation, the zero-impact emission limits of SO2, NOx, PM, and VOCs (benzene) for Enterprise A were 50 mg/m³, 78 mg/m³, 10 mg/m³, and 0.32 mg/m³, respectively.Implications: Through case study, this paper solves the environmental management issue which is of universal significance for chemical industry park. The concept of zero-impact emissions and the determination method of zero-impact concentration limits proposed in this study could be used as references for related research on cutting emissions. Although the conclusion of this study about the emission limits is not suitable for other enterprises to apply directly, the calculation method of zero-impact emission limit can be used by other enterprises. Furthermore, the zero-impact emission limits on park scale can be determined after comprehensive evaluation based on the calculation results of multiple enterprises. [ABSTRACT FROM AUTHOR]

Parties to the Paris Agreement (PA,) outline their planned contributions towards achieving the PA temperature goal to "hold [...] the increase in the global average temperature to well below 2 ∘ C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 ∘ C" (Article 2.1.a, PA) in their nationally determined contributions (NDCs). Most NDCs include targets to mitigate national greenhouse gas (GHG) emissions, which need quantifications to assess i.a. whether the current NDCs collectively put us on track to reach the PA temperature goals or the gap in ambition to do so. We implemented the new open-source tool " NDCmitiQ " to quantify GHG mitigation targets defined in the NDCs for all countries with quantifiable targets on a disaggregated level and to create corresponding national and global emissions pathways. In light of the 5-year update cycle of NDCs and the global stocktake, the quantification of NDCs is an ongoing task for which NDCmitiQ can be used, as calculations can easily be updated upon submission of new NDCs. In this paper, we describe the methodologies behind NDCmitiQ and quantification challenges we encountered by addressing a wide range of aspects, including target types and the input data from within NDCs; external time series of national emissions, population, and GDP; uniform approach vs. country specifics; share of national emissions covered by NDCs; how to deal with the Land Use, Land-Use Change and Forestry (LULUCF) component and the conditionality of pledges; and establishing pathways from single-year targets. For use in NDCmitiQ , we furthermore construct an emissions data set from the baseline emissions provided in the NDCs. Example use cases show how the tool can help to analyse targets on a national, regional, or global scale and to quantify uncertainties caused by a lack of clarity in the NDCs. Results confirm that the conditionality of targets and assumptions about economic growth dominate uncertainty in mitigated emissions on a global scale, which are estimated as 48.9–56.1 Gt CO2eq. AR4 for 2030 (10th/90th percentiles, median: 51.8 Gt CO2eq. AR4; excluding LULUCF and bunker fuels; submissions until 17 April 2020 and excluding the USA). We estimate that 77 % of global 2017 emissions were emitted from sectors and gases covered by these NDCs. Addressing all updated NDCs submitted by 31 December 2020 results in an estimated 45.6–54.1 Gt CO2eq. AR4 (median: 49.6 Gt CO2eq. AR4, now including the USA again) and increased coverage. [ABSTRACT FROM AUTHOR]

This article addresses a central debate in combatting climate change: whether we should focus on reducing CO2 emissions or on removing the emitted CO2 from the atmosphere. We favor the former by arguing against the economic viability of the carbon dioxide removal (CDR) branch of geoengineering. This is of course not a question of either or, but we argue that the perception of CDR as a viable option reduces the willingness to reduce CO2 emissions. Using the recently developed approach of risk–reward nexus (RRN) in the economics of innovation, we question the economic viability of CDR. The main argument is simple: if one uses the new framework of RRN in evaluating the innovations involved in the CDR branch of geoengineering, not only does one include more areas of risk but also one has to consider a broader base for distributing the rewards. Consequently, from RRN’s point of view, it would be less likely to find investing in CDR economically viable for the investor firms. Although the core argument of the paper concerns the economics of CDR, in a final section the paper tries to show that the economic argument has also ethical implications against relying on CDR. [ABSTRACT FROM AUTHOR]

This paper focuses on the risks associated with “negative emissions” technologies (NETs) for drawing carbon dioxide from the atmosphere through photosynthesis and storing it in land-based sinks or underground. Modelled mitigation pathways for 1.5 °C assume NETs that range as high as 1000 Gt CO2. We argue that this is two to three times greater than the amount of land-based NETs that can be realistically assumed, given critical social objectives and ecological constraints. Embarking on a pathway that assumes unrealistically large amounts of future NETs could lead society to set near-term targets that are too lenient and thus greatly overshoot the carbon budget, without a way to undo the damage. Pathways consistent with 1.5 °C that rely on smaller amounts of NETs, however, could prove viable. This paper presents a framework for assessing the risks associated with negative emissions in the context of equity and sustainable development. To do this, we identify three types of risks in counting on NETs: (1) that NETs will not ultimately prove feasible; (2) that their large-scale deployment involves unacceptable ecological and social impacts; and (3) that NETs prove less effective than hoped, due to irreversible climate impacts, or reversal of stored carbon. We highlight the technical issues that need to be resolved and—more importantly—the value judgements that need to be made, to identify the realistic potential for land-based NETs consistent with social and environmental goals. Given the critical normative issues at stake, these are decisions that should be made within an open, transparent, democratic process. As input, we offer here an indicative assessment of the realistic potential for land-based NETs, based on a precautionary assessment of the risks to their future effectiveness and a provisional assessment of the extent to which they are in conflict with sustainable development goals related to land, food and climate. [ABSTRACT FROM AUTHOR]

Extensive studies on signal noise analysis of Global Navigation Satellite Systems (GNSS) receivers are mainly based on zero and/or short baselines where a pair of receivers is required. This paper develops a new signal noise assessment method for a standalone GNSS receiver. In this new method, the time differenced geometry-free ( $$ \Delta $$ GF) model is formed, where the residual ionospheric delay and multipath remain. In order to eliminate these systematic trend biases, the second-order polynomial fitting is used. Then the mixed autoregressive moving average (ARMA) model is further applied to capture this stationary and time-correlated time series. As a result, the pure random noise of $$ \Delta $$ GF is obtained, which is finally used to assess the precision of receiver signal in terms of the error propagation law. The performance of this new method is numerically tested by using 10 sets of data and compared with the methods of zero and short baselines. The results indicate that the new method is able to assess both the phase and code precision of a standalone receiver, and the conclusions are consistent to those from the methods of zero and short baselines. Since again the new method needs only one receiver, it could be implemented in more applications than the traditional methods, especially in precise point positioning. [ABSTRACT FROM AUTHOR]

Motivation: Creating or extending computational models of complex systems, such as intra- and intercellular biological networks, is a time and labor-intensive task, often limited by the knowledge and experience of modelers. Automating this process would enable rapid, consistent, comprehensive and robust analysis and understanding of complex systems. Results: In this work, we present CLARINET (CLARIfying NETworks), a novel methodology and a tool for automatically expanding models using the information extracted from the literature by machine reading. CLARINET creates collaboration graphs from the extracted events and uses several novel metrics for evaluating these events individually, in pairs, and in groups. These metrics are based on the frequency of occurrence and co-occurrence of events in literature, and their connectivity to the baseline model. We tested how well CLARINET can reproduce manually built and curated models, when provided with varying amount of information in the baseline model and in the machine reading output. Our results show that CLARINET can recover all relevant interactions that are present in the reading output and it automatically reconstructs manually built models with average recall of 80% and average precision of 70%. CLARINET is highly scalable, its average runtime is at the order of ten seconds when processing several thousand interactions, outperforming other similar methods. [ABSTRACT FROM AUTHOR]

Transitioning from a carbon-intensive development path to a low-carbon economy requires understanding of emission sources and its drivers. To achieve Nationally Determined Contribution targets discussed under the Paris Agreement, countries must project emission baselines considering different levels of ambition and sectoral performances and monitor government policies and mitigation actions. This study develops a Monitoring, Reporting, and Verification (MRV) framework based on energy use and carbon emission scenarios for Brazil up to 2030, focusing on transport. Three scenarios are developed based on different political commitments. All scenarios and mitigation actions are discussed under the Brazilian Forum on Climate Change, which is recognized as one of the most important institutional instruments of the National Policy on Climate Change. Results reveal a 41% abatement in CO2e emissions when comparing the most ambitious scenario to the conservative one. MRV framework indicates that Brazil is on track to a low carbon economic transition, although joint action is needed. This is mainly due to the increasing penetration of bioenergy into the transport energy matrix, while electromobility does not expand in Brazil at the pace of major international players such as Europe and China. Special contribution of this paper is on the role of bioenergy in prospective carbon emission scenarios, which is a lack in the literature. [ABSTRACT FROM AUTHOR]

In the present paper, national and externally organized projections of greenhouse gas emissions for Austria were compared to gain insight on the underlying scenario data assumptions. National greenhouse gas emission trends extend until 2030, an assessment of European Union (EU) countries to 2050. In addition, data for 2000-2100 was extracted from the global emission database described by the Representative Concentration Pathways (RCP). By identifying trends in these projections, it was possible to produce (a) a long-term assessment of national scenarios until 2100, (b) an assessment of the ambition level toward national climate strategies, and (c) a standardized method to compare trends across countries. By extracting RCP data, Austrian's methane, nitrous oxide, and carbon dioxide emissions up to 2100 could be projected for all sources as well as specific sectors. With respect to the RCP scenario emission data, national projections did not seem to employ the mitigation potentials available for the most stringent RCP scenario, RCP2.6. Comparing projections that supported the EU Climate Strategy 2030 with national projections revealed similar trends. Because RCP2.6 is the only scenario consistent with a 2 °C global warming target, and it is much more ambitious than any of the national or European projections, further measures will be required if Austria is to adequately contribute to this widely accepted policy goal. [ABSTRACT FROM AUTHOR]

Located far from anthropogenic emission sources, high-altitude mountain stations are considered to be ideal sites for monitoring climatic and environmentally important baseline changes in free tropospheric trace gases and aerosols. In addition, the observations taken at these stations are often used to study the long-range transport of dust as well as anthropogenic and biomass burning pollutants from source regions and to evaluate the performance of global and regional models. In this paper, we summarize the results from past and ongoing field measurements of atmospheric constituents at high-altitude stations across the globe, with particular emphasis on reactive trace species including tropospheric ozone, along with its precursors such as carbon monoxide, nitrogen oxides, total reactive nitrogen, and nonmethane hydrocarbons. Over the past decades, our understanding of the temporal variability and meteorological mechanisms of long-range transport has advanced in tandem with progress in instrumentation and modeling. Finally, the future needs of atmospheric chemistry observations at mountain sites are addressed. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) signals might be affected by various artifacts and noises that have biological and external sources. Baseline wander (BW) is a low-frequency artifact that may be caused by breathing, body movements and loose sensor contact. In this paper, a novel method based on empirical mode decomposition (EMD) for removal of baseline noise from ECG is presented. When compared to other EMD-based methods, the novelty of this research is to reach the optimized number of decomposed levels for ECG BW de-noising using mean power frequency (MPF), while the reduction of processing time is considered. To evaluate the performance of the proposed method, a fifth-order Butterworth high pass filtering (BHPF) with cut-off frequency at 0.5Hz and wavelet approach are applied. Three performance indices, signal-to-noise ratio (SNR), mean square error (MSE) and correlation coefficient (CC), between pure and filtered signals have been utilized for qualification of presented techniques. Results suggest that the EMD-based method outperforms the other filtering method. [ABSTRACT FROM AUTHOR]

This paper investigates the applicability of the Just Transition Transaction (JTT), initially developed as a financial mechanism for South Africa's energy transition, to Southeast Asian (SEA) countries, including Indonesia, Vietnam, and the Philippines, which heavily rely on coal. Utilizing South Africa as a reference case study, we deconstruct the JTT and develop a novel framework of necessary and conducive features for evaluating its suitability for supporting a just energy transition in SEA. Our findings suggest that the JTT is well-suited for Indonesia and Vietnam but not as well suited for the Philippines. Recommendations for specific research avenues in estimating baselines and aligning emissions trajectories are provided. Finally, we propose a tiered JTT model to encourage a supranational transition in SEA and suggest the potential application of our methods for assessing similar mechanisms in other coal-reliant developing countries. • Use South Africa as Case Study to investigate JTT transition finance mechanism • Develop framework of necessary and conducive features of JTT • Use this framework to evaluate JTT's suitability for energy transition in SEA • JTT suitable for Indonesia and Vietnam, not as suitable for Philippines • Tiered JTT a suitable model to encourage transition at supranational level in SEA [ABSTRACT FROM AUTHOR]

The EU has established the world's first cross-border emission-trading systems (ETS) for greenhouse gas (GHG) emissions, currently covering aviation, emission-intensive sectors, and electricity (EITE). The EU Commission has offered to apply emissions trading in new sectors where emissions from maritime transport will be incorporated into the current EU ETS, while a separated emissions trading system will cover emissions from road transport and the building sector. This paper employs a multi-regional multi-sectoral computable general equilibrium (CGE) model with two simultaneous international emission permit markets. After examining the abatement costs for the EU regions, various policy scenarios are implemented to study the welfare effects of forming an ETS covering the sectors other than EITE (NEIT) and its linking with the EITE sectors under two different baselines and four emission reduction targets. The results provide several important insights: (i) Marginal abatement costs in Germany and the Eastern European Union region (EEU) are significantly lower than in the rest of the EU regions. (ii) The carbon price in the emission permit market covering NEIT is significantly higher than the carbon price in the emission permit market covering EITE. (iii) Germany and EEU appear as notable suppliers of emission permits in both markets. (iv) There is a significant aggregate welfare gain under the scenario in which the ETS covering NEIT co-exists parallel with the ETS covering EITE. (v) The aggregate welfare in the EU under the full integration of EITE and NEIT may fall below its value under the scenario with two parallel emission permit markets. [ABSTRACT FROM AUTHOR]

Highlights • Liquefied natural gas cold is recovered to produce different energy carriers. • Cold applications using such energy carriers reduce dependency on electricity. • With these energy carriers, cold applications achieved lower exergy efficiency. • Cold applications are also able to yield lower level of carbon emissions. Abstract Liquefied natural gas releases large amounts of cold energy during the conventional regasification process. Currently, most studies have investigated the opportunities to utilize this waste cold for power cycles but few studies considered using this cold directly on other cold applications. In this paper, different cold recovery approaches are considered and compared depending on the energy carriers (i.e. electricity, liquid carbon dioxide, chilled water, liquid air/nitrogen and latent heat storage) used to support a few cold applications (i.e. air separation units, dry ice production, freezing and district cooling). Using different transportation methods, these energy carriers produced using the recovered cold as part or all of their energy input is coupled to these cold applications with different temperature requirements and located 5 km away from the regasification facilities. This paper investigates the change in overall exergy efficiency and carbon dioxide emissions throughout the whole process from energy carrier generation to their consumption in the cold applications when the cold applications are coupled to different alternative energy carriers, compared with the baseline case. With the availability of these alternative energy carriers, conventional cold applications can be modified to reduce their dependency on electricity and improve their performance. The baseline setup has an overall exergy efficiency of ≈13% while using electricity generated by waste cold assisted power cycles as energy carrier yields overall exergy efficiency of ≈13.2%. Using alternative energy carriers charged with recovered cold, such as liquid carbon dioxide/water, latent heat thermal storage and liquid nitrogen, yields lower overall exergy efficiencies of ≈9.7%, 11.5% and 10.2%, respectively which is largely due to the temperature mismatch and thus large amount of exergy destructions during the heat exchange process. For the carbon dioxide emissions analysis, the baseline setup yields carbon dioxide emissions of ≈22.3 kTPA. Using electricity generated with waste cold assisted power cycle yields improvement on carbon dioxide emissions of ≈18.3% while those using alternative energy carriers yield improvements on carbon dioxide emissions of ≈38.0%, ≈37.0% and ≈6.0%, respectively. [ABSTRACT FROM AUTHOR]

It is central for climate policy to understand, monitor and forecast greenhouse gas (GHG) emissions, generating insights on the key factors and actions with a greater impact to mitigate and adapt to global warming. Focusing on the leading role of cities, it is relevant to build robust and reliable GHG emissions prospects for every city committing to mitigate and adapt to climate change. In this paper, a novel methodology is proposed to build those prospects, presenting a case study with 6231 EU-27 cities and local municipalities taken from the Global Covenant of Mayors (GCoM). Key GHG emissions predictors for the target years of 2020 and 2030 refer to the baseline energy consumption and associated GHG emissions, as well as the population and the national emissions per capita trend. Additionally, other informative predictors are the baseline inventory year, the reduction target, the disaggregated baseline emissions by type of fuel (fossils or renewables), the heating and cooling degree days and the NUTS urban/rural categorization. The proposed methodology allows assessing the cities' achievements based on their expected performance, and it could be further implemented to support new cities willing to commit to a significant reduction of their GHG emissions. • A robust methodology is proposed to predict GHG emissions for cities committing to climate change mitigation and adaptation. • Key predictors refer to local, regional and national sources of information. • A case study is performed with 6231 European cities: 2030 commitments are expected to achieve a 40 % GHG emissions reduction. • The models described in this paper provide useful information to urban policy makers in the climate action planning process. • The results are replicable in the context of transnational initiatives involving cities in the fight against climate change. [ABSTRACT FROM AUTHOR]

Correct ambiguity resolution (AR) is the key for high-precision positioning. In order to reliably resolve ambiguity, an improved single-epoch segmented ambiguity algorithm is proposed in this paper. First, the double-difference wide-lane ambiguities resolved by the Melbourne-Wubbena (MW) method are divided into two categories based on signal-to-noise ratio (SNR). One is the primary ambiguity group that can be resolved easily, and the other is the secondary ambiguity group that is difficult to resolve. Second, the primary ambiguity group is divided into different levels in a (4,1,1…1) pattern based on SNR, and the different levels’ ambiguities are resolved successively, and the method being that the integer ambiguity is selected from two candidate values built by rounding up and down. Third, the resolved primary ambiguity group is used as a constraint to solve the secondary ambiguity group. Finally, the wide-lane observation equation and the basic observation equation are combined to solve the ambiguity of the L1 carrier phase, and the resolved ambiguity can be obtained by directly rounding. The candidate values of the primary ambiguity group is limited to two cycles through this step, and the (4,1,1…1) pattern can reduce ambiguity search times. Since an appropriate selection enables the correct resolution of primary wide-lane ambiguities, the selection of the threshold for SNR is the key to group wide-lane ambiguity, and we use the data from Novatel receiver as an example to discuss how to confirm the SNR threshold in this paper. Static short baseline data and kinematic short baseline that were less than 20 km were collected to validate the proposed algorithm. These results were compared with the commercial software IE8.60. These experiments show that the successful rate reaches 100%, 99.78% and 99.42%, respectively, for the new single-epoch segmented ambiguity method in the three data sets, and the accuracy is similar to that of IE8.60 for the combined double difference. [ABSTRACT FROM AUTHOR]

• Five propulsion systems and six energy carriers for regional trains are analyzed. • Dutch regional railway network with sixteen rail passenger services is considered. • Energy carrier production pathway is the main driver of energy use and emissions. • Battery-electric system running on green electricity offers net-zero emissions. • Hydrogen provides emissions reduction only if produced from green electricity. This paper presents a method for estimating Well-to-Wheel (WTW) energy use and greenhouse gas (GHG) emissions attributed to the advanced railway propulsion systems implemented in conjunction with different energy carriers and their production pathways. The analysis encompasses diesel-electric multiple unit vehicles converted to their hybrid-electric, plug-in hybrid-electric, fuel cell hybrid-electric or battery-electric counterparts, combined with biodiesel or hydrotreated vegetable oil (HVO) as the first and second generation biofuels, liquefied natural gas (LNG), hydrogen and/or electricity. The method is demonstrated using non-electrified regional railway network with heterogeneous vehicle fleet in the Netherlands as a case. Battery-electric system utilizing green electricity is identified as the only configuration leading to emission-free transport while offering the highest energy use reduction by 65–71% compared to the current diesel-powered hybrid-electric system. When using grey electricity based on the EU2030 production mix, these savings are reduced to about 27–39% in WTW energy use and around 68–73% in WTW GHG emissions. Significant reductions in overall energy use and emissions are obtained for the plug-in hybrid-electric concept when combining diesel, LNG, or waste cooking oil-based HVO with electricity. The remaining configurations that reduce energy use and GHG emissions are hybrid-electric systems running on LNG or HVO from waste cooking oil. The latter led to approximately 88% lower WTW emissions than the baseline for each vehicle type. When produced from natural gas or EU2030-mix-based electrolysis, hydrogen negatively affected both aspects, irrespective of the prime mover technology. However, when produced via green electricity, it offers a GHG reduction of approximately 90% for hybrid-electric and fuel cell hybrid-electric configurations, with a further reduction of up to 92–93% if combined with green electricity in plug-in hybrid-electric systems. The results indicate that HVO from waste cooking oil could be an effective and instantly implementable transition solution towards carbon–neutral regional trains, allowing for a smooth transition and development of supporting infrastructure required for more energy-efficient and environment-friendly technologies. [ABSTRACT FROM AUTHOR]

Introduction: Several cities in Malaysia have established plans to reduce their CO2 emissions, in addition to Malaysia submitting a Nationally Determined Contribution to reduce its carbon intensity (against GDP) by 45% in 2030 compared to 2005. Meeting these emissions reduction goals will require a joint effort between governments, industries, and corporations at different scales and across sectors. Methods: In collaboration with national and sub-national stakeholders, we developed and used a global integrated assessment model to explore emissions mitigation pathways in Malaysia and Kuala Lumpur. Guided by current climate action plans, we created a suite of scenarios to reflect uncertainties in policy ambition, level of adoption, and implementation for reaching carbon neutrality. Through iterative engagement with all parties, we refined the scenarios and focus of the analysis to best meet the stakeholders' needs. Results: We found that Malaysia can reduce its carbon intensity and reach carbon neutrality by 2050, and that action in Kuala Lumpur can play a significant role. Decarbonization of the power sector paired with extensive electrification, energy efficiency improvements in buildings, transportation, and industry, and the use of advanced technologies such as hydrogen and carbon capture and storage will be major drivers to mitigate emissions, with carbon dioxide removal strategies being key to eliminate residual emissions. Discussion: Our results suggest a hopeful future for Malaysia's ability to meet its climate goals, recognizing that there may be technological, social, and financial challenges along the way. This study highlights the participatory process in which stakeholders contributed to the development of the model and guided the analysis, as well as insights into Malaysia's decarbonization potential and the role of multilevel governance. [ABSTRACT FROM AUTHOR]

For countries' emission-reduction efforts under the Paris Agreement to be effective, baseline emission/removals levels and reporting must be as transparent and accurate as possible. For Indonesia, which holds among the largest area of tropical peatlands and mangrove forest in the world, it is particularly important for these high-carbon ecosystems to produce high-accuracy greenhouse gas inventory and to improve national forest reference emissions level/forest reference level. Here, we highlight the opportunity for refining greenhouse gas emission factors (EF) of peatlands and mangroves and describe scientific challenges to support climate policy processes in Indonesia, where 55 to 59% of national emission reduction targets by 2030 depend on mitigation in Forestry and Other Land Use. Based on the stock-difference and flux change approaches, we examine higher-tier EF for drained and rewetted peatland, peatland fires, mangrove conversions, and mangrove on peatland to improve future greenhouse gas flux reporting in Indonesia. We suggest that these refinements will be essential to support Indonesia in achieving Forest and Other Land Use net sink by 2030 and net zero emissions targets by 2060 or earlier. [ABSTRACT FROM AUTHOR]

The Geoengineering Model Intercomparison Project (GeoMIP) has proposed multiple model experiments during phases 5 and 6 of the Climate Model Intercomparison Project (CMIP), with the latest set of model experiments proposed in 2015. With phase 7 of CMIP in preparation and with multiple efforts ongoing to better explore the potential space of outcomes for different solar radiation modifications (SRMs) both in terms of deployment strategies and scenarios and in terms of potential impacts, the GeoMIP community has identified the need to propose and conduct a new experiment that could serve as a bridge between past iterations and future CMIP7 experiments. Here we report the details of such a proposed experiment, named G6-1.5K-SAI, to be conducted with the current generation of scenarios and models from CMIP6 and clarify the reasoning behind many of the new choices introduced. Namely, compared to the CMIP6 GeoMIP scenario G6sulfur, we decided on (1) an intermediate emission scenario as a baseline (the Shared Socioeconomic Pathway 2-4.5), (2) a start date set in the future that includes both considerations for the likelihood of exceeding 1.5 °C above preindustrial levels and some considerations for a likely start date for an SRM implementation, and (3) a deployment strategy for stratospheric aerosol injection that does not inject in the tropical pipe in order to obtain a more latitudinally uniform aerosol distribution. We also offer more details regarding the preferred experiment length and number of ensemble members and include potential options for second-tier experiments that some modeling groups might want to run. The specifics of the proposed experiment will further allow for a more direct comparison between results obtained from CMIP6 models and those obtained from future scenarios for CMIP7. [ABSTRACT FROM AUTHOR]

The article presents the authors' reply to Matthew Brander's response to their paper on the necessity of a land-use baseline in attributional LCA where he raised concerns on the appropriateness of attributable LCA (ALCA) to support decision-making. They point out that postponement of natural regeneration can be attributed to any land use regardless of the length occupation and whether the temporal scope includes the initial land transformation.

Savannas constitute the most fire-prone vegetation type on earth and are a significant source of greenhouse gas emissions. Most savanna fires are lit by people for a variety of livelihood applications. 'Savanna burning' is an accountable activity under the Kyoto Protocol, but only Australia, as a developed economy, accounts for emissions from this source in its national accounts. Over the past decade considerable effort has been given to developing savanna burning projects in northern Australia, combining customary indigenous (Aboriginal) approaches to landscape-scale fire management with development of scientifically robust emissions accounting methodologies. Formal acceptance by the Australian Government of that methodology, and its inclusion in Australia's developing emissions trading scheme, paves the way for Aboriginal people to commercially benefit from savanna burning projects. The paper first describes this Australian experience, and then explores options for implementing community-based savanna burning emissions reduction projects in other continental savanna settings, specifically in Namibia and Venezuela. These latter examples illustrate that savanna fire management approaches potentially have broader application for contributing to livelihood opportunities in other fire-prone savanna regions. [ABSTRACT FROM AUTHOR]

Accurately quantifying total greenhouse gas emissions (e.g., methane) from natural systems such as lakes, reservoirs, and wetlands requires the spatial and temporal measurement of both diffusive and ebullitive (bubbling) emissions. Ebullitive emissions exhibit high spatial and temporal variability and as such are difficult to measure. Traditional manual measurement techniques provide only limited localized assessment of methane flux, often introducing significant errors when extrapolated to the whole-of-system. This is further exacerbated when whole-of-region estimates are developed for inclusion in global greenhouse gas inventories. In this paper, we directly address these current sampling limitations by comparing two robot boat-based sampling systems with complementary sensing modalities to directly measure in real time the spatiotemporal release of methane to atmosphere across inland waterways. The first system consists of a single Autonomous Surface Vehicle (ASV) fitted with an Optical Methane Detector with algorithms to exploit the robot's mobility and transect repeatability for the accurate detection and quantification of methane bubbles across whole-of-system. The second system consists of multiple networked ASVs capable of persistent operation and scalable to whole-of-region monitoring. Each ASV carries a novel automated chamber-based gas sampling system to allow simultaneous real-time measurement of methane across the waterway. These ASV systems provide a foundation for persistent large-scale spatiotemporal sampling allowing scientists to develop whole-of-region greenhouse gas estimates and greatly improve global inventory budgets. An overview of the single and multi-robot sampling systems is presented, including their automated methane detection and sampling methodologies for the spatiotemporal quantification of greenhouse gas release to atmosphere. Experimental results are shown demonstrating each system's ability to autonomously navigate, detect, and quantify methane release to atmosphere across an entire inland reservoir. [ABSTRACT FROM AUTHOR]

Recent studies have shown that (three-electrode) electrochemical sensors can be utilised for air quality monitoring and exposure assessment. The long-term performance of these sensors is however, often limited by the effects of ambient meteorological parameters on the sensor baseline, in particular temperature. If electrochemical (EC) sensors are to be adopted for air quality measurement over extended periods (months), this effect must be accounted for. Recent long-term, ambient measurements of CO, NO and NO 2 using EC sensors have revealed that temperature (and relative humidity (RH)) had an effect on the baseline which was more pronounced in the case of NO sensors with coefficient of determination, R 2 of 0.9 when compared to CO and NO 2 with R 2 < 0.2. In this paper we present a correction methodology that quantifies this effect (referred to here as fitted baseline), implementing these correction on the EC measurements. We found that EC sensors corrected for baseline-temperature effect using the method describe in this paper show good agreement when compared with traditional reference instrument. The coefficient of determination R 2 of 0.7–0.8 and gradient of 0.9 was observed for baseline-temperature corrected NO compared to R 2 = 0.02 prior to baseline-temperature correction. Furthermore, the correction methodology was validated by comparing the temperature-baseline with proxy temperature compensating measurements obtained from the fourth electrode of a set of novel four-electrode electrochemical sensors. A good agreement (R 2 = 0.9, with gradients = 0.7–1.08 for NO and 0.5 < R 2 < 0.73 for CO) was observed between temperature fitted baselines and outputs from the fourth electrodes (also known non-sensing/auxiliary electrode). Meanwhile, the long-term stability (calibrated signal output) of temperature-corrected data was evaluated by comparing the change in sensor gain to meteorological parameters including temperature, relative humidity, wind speed and wind direction. The results showed that there was no statistically significant change in sensitivity (two-sided t -test, p = 0.34) of the temperature-corrected electrochemical sensor with respect to these parameters (over several months). This work demonstrates that using the baseline-temperature correction methodology described in this paper, electrochemical sensors can be used for long-term (months), quantitative measurements of air quality gases at the parts per billion volume (ppb) mixing ratio level typical of ambient conditions in the urban environment. [ABSTRACT FROM AUTHOR]

The Ecliptic-poles Stellar Survey (EclipSS) collected far-ultraviolet (FUV: 1160–1420 Å) spectra of 49 nearby (d ≲ 100 pc) F3–K3 main-sequence stars, located at high ecliptic latitudes (north and south), using the Cosmic Origins Spectrograph of the Hubble Space Telescope. The ecliptic poles receive higher exposures from scanning missions like the Transiting Exoplanet Survey Satellite (high-precision optical photometry) and Extended Roentgen Survey with an Imaging Telescope Array (X-ray monitoring), which can deliver crucial contextual information, not otherwise easily secured. The objective was to support theoretical studies of stellar hot outer atmospheres—chromospheres (∼104 K) and coronae (≳1 MK)—which, among other things, can adversely impact exoplanets via host-star "space weather." Flux–flux diagrams (e.g., C ii 1335 Å versus O i 1306 Å) were constructed for the EclipSS stars, solar Cycle 23/24 irradiances, and long-term FUV records of α Cen A (G2 V) and B (K1 V). The EclipSS cohort displays similar minimum ("basal") fluxes to the Sun and solar twin α Cen A, in chromospheric O i 1306 Å. In hotter C ii 1335 Å, a downward slump of the basal fluxes—noted in previous, less controlled surveys—can now be explained as an effect of subsolar abundances. The consistent basal minima in chromospheric and higher temperature species at solar metallicity favor the idea that stellar analogs of the solar supergranulation network provide a baseline of high-energy emissions. The magnetic network is replenished by a "local dynamo" independently of the stellar spin. It can operate even when the starspot-spawning internal dynamo has ceased cycling, as during the Sun's 17th century Maunder Minimum. [ABSTRACT FROM AUTHOR]

In recent years, ammonia has received more and more interest to be used as carbon-free energy. In the present study, a computational model of a burner for the domestic hot water boiler in the building has been developed. The combustion characteristics of propane/ammonia-mixed fuel in the burner were investigated by numerical simulation under different ammonia blending ratios and equivalence ratios. Based on the flue gas concentration distribution of the numerical simulation results, the baseline carbon emission of hot water supply in a community was calculated. The results show that as the ammonia blending ratio increases, the overall combustion temperature decreases. At the outlet of the burner, when the ammonia blending ratio is 0.5, the emission concentration of carbon dioxide can be reduced by 31.4% compared to pure propane combustion. When the ammonia blending ratio increases from 0 to 50%, the carbon baseline emission decreases from 9.89 kg/GJ to 6.87 kg/GJ, and the carbon emission under the baseline decreases from 17.20 T to 11.94 T. The emission of NO pollutants remains basically unchanged due to the decrease of combustion temperature. It indicates that the mixed fuels can effectively reduce carbon emissions in domestic water heaters and have good potential for future application. [ABSTRACT FROM AUTHOR]

Gridded bottom-up inventories of CO 2 emissions are needed in global CO 2 inversion schemes as priors to initialize transport models and as a complement to top-down estimates to identify the anthropogenic sources. Global inversions require gridded datasets almost in near-real time that are spatially and methodologically consistent at a global scale. This may result in a loss of more detailed information that can be assessed by using regional inventories because they are built with a greater level of detail including country-specific information and finer resolution data. With this aim, a global mosaic of regional, gridded CO 2 emission inventories, hereafter referred to as CoCO2-MOSAIC 1.0, has been built in the framework of the CoCO2 project. CoCO2-MOSAIC 1.0 provides gridded (0.1 ∘ × 0.1 ∘) monthly emissions fluxes of CO 2 fossil fuel (CO 2 ff, long cycle) and CO 2 biofuel (CO 2 bf, short cycle) for the years 2015–2018 disaggregated in seven sectors. The regional inventories integrated are CAMS-REG-GHG 5.1 (Europe), DACCIWA 2.0 (Africa), GEAA-AEI 3.0 (Argentina), INEMA 1.0 (Chile), REAS 3.2.1 (East, Southeast, and South Asia), and VULCAN 3.0 (USA). EDGAR 6.0, CAMS-GLOB-SHIP 3.1 and CAMS-GLOB-TEMPO 3.1 are used for gap-filling. CoCO2-MOSAIC 1.0 can be recommended as a global baseline emission inventory for 2015 which is regionally accepted as a reference, and as such we use the mosaic to inter-compare the most widely used global emission inventories: CAMS-GLOB-ANT 5.3, EDGAR 6.0, ODIAC v2020b, and CEDS v2020_04_24. CoCO2-MOSAIC 1.0 has the highest CO 2 ff (36.7 Gt) and CO 2 bf (5.9 Gt) emissions globally, particularly in the USA and Africa. Regional emissions generally have a higher seasonality representing better the local monthly profiles and are generally distributed over a higher number of pixels, due to the more detailed information available. All super-emitting pixels from regional inventories contain a power station (CoCO2 database), whereas several super-emitters from global inventories are likely incorrectly geolocated, which is likely because regional inventories provide large energy emitters as point sources including regional information on power plant locations. CoCO2-MOSAIC 1.0 is freely available at zenodo (10.5281/zenodo.7092358; Urraca et al., 2023) and at the JRC Data Catalogue (https://data.jrc.ec.europa.eu/dataset/6c8f9148-ce09-4dca-a4d5-422fb3682389 , last access: 15 May 2023; Urraca Valle et al., 2023). [ABSTRACT FROM AUTHOR]

China's coal chemical sector uses coal as both a fuel and feedstock and its increasing greenhouse gas (GHG) emissions are hard to abate by electrification alone. Here we explore the GHG mitigation potential and costs for onsite deployment of green H2 and O2 in China's coal chemical sector, using a life-cycle assessment and techno-economic analyses. We estimate that China's coal chemical production resulted in GHG emissions of 1.1 gigaton CO2 equivalent (GtCO2eq) in 2020, equal to 9% of national emissions. We project GHG emissions from China's coal chemical production in 2030 to be 1.3 GtCO2eq, ~50% of which can be reduced by using solar or wind power-based electrolytic H2 and O2 to replace coal-based H2 and air separation-based O2 at a cost of 10 or 153 Chinese Yuan (CNY)/tCO2eq, respectively. We suggest that provincial regions determine whether to use solar or wind power for water electrolysis based on lowest cost options, which collectively reduce 53% of the 2030 baseline GHG emissions at a cost of 9 CNY/tCO2eq. Inner Mongolia, Shaanxi, Ningxia, and Xinjiang collectively account for 52% of total GHG mitigation with net cost reductions. These regions are well suited for pilot policies to advance demonstration projects. The coal chemical sector uses coal to produce chemicals and emits substantial greenhouse gases, which are hard to abate by electrification alone. Deploying green H2 for China's coal chemical plants can reduce ~50% of emissions at a low cost. [ABSTRACT FROM AUTHOR]

(1) Background: Human-generated greenhouse gas (GHG) emissions are the largest contributor to climate change worldwide. Climate change negatively impacts human and planetary health, threatening the existence of life on earth. The healthcare industry is responsible for approximately 8.5% of carbon emissions in the United States. Measuring baseline GHG emissions is the first step in emissions reduction. However, very few models of measurement exist for health care organizations. This project aimed to develop and implement a program to measure and track GHG emissions at a midwestern academic medical center (MAMC) and to educate staff on how to manage the process. (2) Methods: A Plan, Do, Study, Act (PDSA) cycle and Quality Improvement methodology were used to develop, implement, and assess a standardized GHG emission inventory process to measure Scope 1 and Scope 2 baseline emissions and provide virtual training and education to the accountable staff. A pre- and post-survey design was used to measure the knowledge and readiness of the staff after the implementation of the GHG inventory training. (3) Results: The GHG inventory process was validated through an external verification process, and the measurement of Scope 1 and Scope 2 baseline GHG emissions was completed and verified for accuracy through a data comparison review. The pre-post-training survey showed an increase in the knowledge and readiness of staff to maintain a GHG inventory. (4) Conclusions: This work shows the feasibility of obtaining baseline GHG emissions data at large medical centers. It represents the initial phase of the overarching goal to develop site-wide and system-wide carbon reduction strategies and a climate action plan within this health system. [ABSTRACT FROM AUTHOR]

The present paper investigated the technical possibilities of bioethanol production from coffee pulp in a sustainable manner. Coffee pulp is a largely underutilized waste stream and has the potential to become a bioethanol feedstock if processing is economically viable. This study aimed to characterize the chemical composition of coffee pulp with relevance to bioethanol production and to compare results to other bioethanol feedstocks. Based on the total sugar yield, we investigated bioethanol production using AspenPlus simulation software. In the sugar characterization part, total carbohydrates were measured after complete acid hydrolysis of dry pulp, while water-soluble carbohydrates were measured after acid hydrolysis of soxhlet extracted solutions. Moisture, lignin, and ash contents were measured gravimetrically after appropriate heating treatments. The results showed sugar contents, expressed as percentages of dry mass, as follows: 5.8, 5.2, 20.2, 4.2, and 4.7 % for arabinose, galactose, glucose, xylose, and mannose, respectively. Arabinose, galactose, and glucose were the only water-extracted simple sugars, at 1.0, 1.4, and 2.6 % of dry mass, respectively. AspenPlus simulation was based on processing 10,000 ton/day of coffee pulp. The results demonstrated sugar and ethanol yield of 2100 and 1050 ton/day, respectively. This would make annually profit of $0.13 million. The simulation estimated the capital investment cost was about $2 million. In order to satisfy the process economy, the process operation cost must be operated at minimum of $1.87 million annually. The life-cycle analysis showed the net value of energy of the whole process is an economical as well as the balance of CO emission/reduction was on the environmental favor. [ABSTRACT FROM AUTHOR]

Parties to the Paris Agreement (PA, 2015) outline their planned contributions towards achieving the PA temperature goal to hold […] the increase in the global average temperature to well below 2 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C (Article 2.1.a, PA) in their Nationally Determined Contributions (NDCs). Most NDCs include targets to mitigate national greenhouse gas (GHG) emissions, which need quantifications to assess, i.a., whether the current NDCs collectively put us on track to reach the PA temperature goals or the gap in ambition to do so. We implemented the new open-source tool NDCmitiQ to quantify GHG mitigation targets defined in the NDCs for all countries with quantifiable targets on a disaggregated level, and to create corresponding national and global emissions pathways. In light of the five-year update cycle of NDCs and the global stocktake, the quantification of NDCs is an ongoing task for which NDCmitiQ can be used, as calculations can easily be updated upon submission of new NDCs. In this paper, we describe the methodologies behind NDCmitiQ and quantification challenges we encountered by addressing a wide range of aspects, including: target types and the input data from within NDCs; external time series of national emissions, population, and GDP; uniform approach vs. country specifics; share of national emissions covered by NDCs; how to deal with the Land Use, Land-Use Change and Forestry (LULUCF) component and the conditionality of pledges; establishing pathways from single year targets. For use in NDCmitiQ, we furthermore construct an emissions data set from the baseline emissions provided in the NDCs. Example use cases show how the tool can help to analyse targets on a national, regional, or global scale, and to quantify uncertainties caused by a lack of clarity in the NDCs. Results confirm that the conditionality of targets and assumptions on economic growth dominate uncertainty in mitigated emissions on a global scale, which are estimated as 49.2-55.7 Gt CO2 eq AR4 for 2030 (10th/90th percentiles, median: 52.4 Gt CO2 eq AR4; excl. LULUCF and bunker fuels). We estimate that 77 % of global 2017 emissions were emitted from sectors and gases covered by current NDCs (excl. the USA). [ABSTRACT FROM AUTHOR]

China has set out an ambitious target of emission abatement; that is, a 60–65% reduction in CO2 emission intensity by 2030 compared with the 2005 baseline level and emission peak realisation. This paper aimed to forecast whether China can fulfil the reduction target of CO2 emission intensity and peak by 2030 based on the historical time series data from 1990 to 2018. Four different forecasting techniques were used to improve the accuracy of the forecasting results: the autoregressive integrated moving average (ARIMA) model and three grey system-based models, including the traditional grey model (1,1), the discrete grey model (DGM) and the rolling DGM. The behaviours of these techniques were compared and validated in the forecasting comparisons. The forecasting performance of the four forecasting models was good considering the minimum mean absolute percentage error (MAPE), demonstrating MAPE values lower than 2%. ARIMA showed the best forecasting performance over the historical period with a MAPE value of 0.60%. The forecasting results of ARIMA indicate that China would not achieve sufficient reductions despite its projected emission peak of 96.3 hundred million tons by 2021. That is, the CO2 emission intensity of China will be reduced by 57.65% in 2030 compared with the 2005 levels. This reduction is lower than the government goal of 60–65%. This paper presents pragmatic recommendations for effective emission intensity reduction to ensure the achievements of the claimed policy goals. [ABSTRACT FROM AUTHOR]

Cloud computing offers attractive features for both service providers and customers. Users benefit from the pay-as-you-go model by saving expenditures and service providers are deploying their services to cloud data centers to reduce their maintenance efforts. However, due to the fast growth of cloud data centers, the energy consumed by the data centers can lead to a huge amount of carbon emission with environmental impacts, and the carbon intensity of different locations are varied among different power plants according to the sources of energy. Thus, in this paper, to address the carbon emission problem of data centers, we consider shifting the workloads among multi-cloud located in different time zones. We also formulate the energy usage and carbon emission of data centers and model the solar power corresponding to the locations. This helps to reduce the usage of brown energy and maximize the utilization of renewable energy at different locations. We propose an approach for managing carbon footprint and renewable energy for multiple data centers at California, Virginia, and Dublin, which are in different time zones. The results show that our proposed approaches that apply workload shifting can reduce around 40% carbon emission in comparison to the baseline while ensuring the average response time of user requests. • Model considering power consumption, renewable energy and carbon footprint. • Algorithm for managing the renewable energy and carbon footprint for data centers. • Evaluations on the impacts of requests executed in different time zones. [ABSTRACT FROM AUTHOR]

• Presenting a set of mathematical models for green flowshop scheduling problems. • Considering common carbon reduction policies and their corresponding scenarios. • Providing analysis of models and insights to design a framework for green flowshops. • Industries can use our cost-effective strategies to meet limits mandated by government. • Government can explore our integrated framework to gauge impacts of carbon policies. In this paper we consider, from an environmental policy-maker perspective, how carbon reduction policies impact the economic competitiveness of the manufacturing sector. Specifically, we focus on flowshop scheduling – which typically aims to minimize makespan for purely economic objectives – and consider how three common carbon reduction policies – namely, taxes on emissions, baselines on emissions, and emissions trading schemes – can create competitive green flowshops that balance minimization of makespan and carbon emissions. The goal is to enable policy-makers to understand how to set policies and control parameters to achieve environmental objectives while ensuring global economic competitiveness of industry. We initially present a set of mixed-integer linear programming (MILP) models for flowshop scheduling problems operating in a regulated environment in which each carbon reduction policy is adopted. We then introduce a bi-objective scheduling framework for the corresponding problem to obtain alternative solutions under each policy. These models and their computational results however, are not the main focus of the study, but are presented as a means to demonstrate how green policies co-exist with economic objectives, with policy-makers in control of the balance. To this end, based on financial data from Australia's carbon emissions profile, we provide a policy-oriented analysis of the models, and some managerial insights into the effect of scheduling strategies on carbon emissions under different reduction policies. These insights offer support to both environmental policy-makers and corporate production and sustainability managers to determine whether it is technically feasible and profitable to replace traditional scheduling strategies with environmentally friendly scheduling strategies. [ABSTRACT FROM AUTHOR]

Natural climate solutions (NCS) are recognized as an important tool for governments to reduce greenhouse gas emissions and remove atmospheric carbon dioxide. Using California as a globally relevant reference, we evaluate the magnitude of biological climate mitigation potential from NCS starting in 2020 under four climate change scenarios. By mid-century NCS implementation leads to a large increase in net carbon stored, flipping the state from a net source to a net sink in two scenarios. Forest and conservation land management strategies make up 85% of all NCS emissions reductions by 2050, with agricultural strategies accounting for the remaining 15%. The most severe climate change impacts on ecosystem carbon materialize in the latter half of the century with three scenarios resulting in California ecosystems becoming a net source of carbon emissions under a baseline trajectory. However, NCS provide a strong attenuating effect, reducing land carbon emissions 41–54% by 2100 with total costs of deployment of 752–777 million USD annually through 2050. Rapid implementation of a portfolio of NCS interventions provides long-term investment in protecting ecosystem carbon in the face of climate change driven disturbances. This open-source, spatially-explicit framework can help evaluate risks to NCS carbon storage stability, implementation costs, and overall mitigation potential for NCS at jurisdictional scales. [ABSTRACT FROM AUTHOR]

Highlight • A model updating method for building structures is presented using modal responses. • A representative value of modal responses, MPR, is proposed and used in the method. • A baseline model is obtained by minimizing MPRs between measurement and model. • Based on the damage scenario for the baseline model, damages are detected. Abstract This paper presents a model updating method for the damage detection of building structures on the basis of modal participation without the need for system identification to extract the modal parameters. The presented method uses the modal participation ratio (MPR), which is defined by a representative value of modal responses extracted from vibration responses measured by sensors mounted on a building structure, as an indicator of the extent of modal contribution. Such MPR extraction assumes that the structure is under ambient excitation; thus, no loading on the structure is required to apply the presented method. In the model updating method, the differences between MPRs extracted from the sensors and MPRs estimated from a model are established as objective functions that are generated corresponding to the number of modes considered. These functions are minimized by a multi-objective optimization algorithm while searching for the optimal properties of a dynamic system, and the finally derived optimal solution is regarded as a baseline model that reflects the actual behaviors of a structure. Through analyses of the variations of the MPRs based on the damage scenarios of the derived baseline model, specific rules for damage localization are derived. In addition, to identify the damage severity of a structure, a damage severity function that describes the relationship between damage severity and MPR variation based on the baseline model is proposed. The proposed method is employed to construct a baseline model and detect the location and severity of the damage of a multi-story structure under ambient excitation. [ABSTRACT FROM AUTHOR]

Territorial energetic and environmental planning provides operational solidity to the concept of sustainable development, in particular in energy-related issues, where recent attention to and social awareness of climate change are driving actions and policies at local and international levels. The goals of the United Nations Agenda 2030 can be reached through the strategy of glocalization, giving more responsibility to local administrations like municipalities. In this work, a scientific methodology is developed and validated to revise Sustainable Energy Action Plans (SEAP) and the monitoring phase of municipalities. The methodology starts from measured data in the territory considered and makes use of specific statistical models in order to estimate the needed data. The methodology considers the energy consumption of the main sectors: residential, transportation, tertiary, and commercial, with a particular focus on municipal competences (public lighting, urban transport, municipal fleet, etc.). Renewable energy is also considered due to its importance in local energy production. In order to go deeper into SEAPs, in this paper, the authors describe the quantitative analysis of the Baseline Emission Inventory, the quantification of the SEAP planning actions, and the definition of the Monitoring Emission Inventory, which is the final step of the planning process. This step was done for nine municipalities of the Abruzzo region with different characteristics (size, population, climate, geographical position, economy, etc.) in order to widen the results of the analysis and test the robustness of the methodology. Indeed, it gave a quantitative dimension to the primary energy consumption and CO2 emissions for 2018, compared with the 2005 baseline values, and the final results are related to the reduction commitments planned for 2020. All the municipalities were considered to have achieved this goal, surpassing the 20% emissions reduction. This validated methodology is also the basis for the development of the Sustainable Energy and Climate Action Plans (SECAPs), which integrate adaptation actions and mitigation ones. [ABSTRACT FROM AUTHOR]

In this paper, we provide a comprehensive overview of the state-of-the-art in hybrid PV-T collectors and the wider systems within which they can be implemented, and assess the worldwide energy and carbon mitigation potential of these systems. We cover both experimental and computational studies, identify opportunities for performance enhancement, pathways for collector innovation, and implications of their wider deployment at the solar-generation system level. First, we classify and review the main types of PV-T collectors, including air-based, liquid-based, dual air–water, heat-pipe, building integrated and concentrated PV-T collectors. This is followed by a presentation of performance enhancement opportunities and pathways for collector innovation. Here, we address state-of-the-art design modifications, next-generation PV cell technologies, selective coatings, spectral splitting and nanofluids. Beyond this, we address wider PV-T systems and their applications, comprising a thorough review of solar combined heat and power (S–CHP), solar cooling, solar combined cooling, heat and power (S–CCHP), solar desalination, solar drying and solar for hydrogen production systems. This includes a specific review of potential performance and cost improvements and opportunities at the solar-generation system level in thermal energy storage, control and demand-side management. Subsequently, a set of the most promising PV-T systems is assessed to analyse their carbon mitigation potential and how this technology might fit within pathways for global decarbonization. It is estimated that the REmap baseline emission curve can be reduced by more than 16% in 2030 if the uptake of solar PV-T technologies can be promoted. Finally, the review turns to a critical examination of key challenges for the adoption of PV-T technology and recommendations. • Scientific and engineering challenges of hybrid photovoltaic-thermal (PV-T) collectors. • Research gaps and various pathways for innovation of PV-T collectors and systems. • Design modifications, selective coatings, nanofluids and spectral splitting. • Carbon mitigation potential and pathways for global decarbonization with PV-T collectors. • Key challenges for the adoption of PV-T technology and recommendations. [ABSTRACT FROM AUTHOR]

The need for safer and cleaner environments for all humankind remains a topical issue that cannot be overemphasized. To provide an updated perspective, this study analyzes the carbon potential of construction waste resource management based on carbon trading policy. In this study, the system dynamics principle was used to establish a carbon potential model of construction and demolition waste (C&DW) resource treatment by taking the regeneration project of Xiancun Village as an example. The results showed that the use of construction waste for recycling and resource treatment can generate enormous opportunities to reduce carbon emission. The implementation of the carbon trading policy can create significant benefits in terms of reducing carbon emission, while the total reduction of carbon emission in the baseline scenario can reach 100.66% when compared to the scenario without a carbon trading policy. Moreover, the findings shows that the combination of the carbon trading policy of "carbon price + free allowance ratio" can improve the return on investment of resource utilization companies and the carbon reduction benefits of the combined policy are greater than those of the single policy, but only if the level of the carbon price or free allowance ratio in the combined policy is accepted by the carbon trading subjects. The results of this research contribute to the theory of construction waste resourceization management, provide the theoretical basis for government departments to introduce carbon reduction policies for construction waste resourceization, and provide guidance for the management of companies' carbon reduction. [ABSTRACT FROM AUTHOR]

There is high confidence that global warming intensifies all components of the global water cycle. Our goal is to investigate the possible effects of the global warming on river flows worldwide in the coming decades. We conducted 18 global hydrological simulations to assess how the river flows are expected to change in the near future (2015–2050) compared to the recent past (1950–2014). The simulations are forced by runoff from HighResMIP-CMIP6 GCMs, which assume a high-emission scenario for the projections. The assessment includes estimating the signal-to-noise (S/N) ratio and the time of emergence (ToE) of all the rivers in the world, with further evaluation of those presenting significant departures from their historic mean flow. Consistent with the water cycle intensification, the hydrological simulations project a clear positive global river discharge trend from ~2000, that emerges beyond the levels of natural variability and becomes 'unfamiliar' by 2017 and 'unusual' by 2033. This climate change signal is dominated by strong increases in flows of rivers originating in central Africa, east Russia, Alaska and Greenland. African rivers project most future annual cycles above the climatological annual cycle, with the largest differences occurring during peak flows. Recent unprecedent floods in the Republic of Congo, D.R.C., Nigeria, and Chad highlight the potential catastrophic consequences of these changes in metropolitan areas. However, the positive trend of Lake Chad tributaries may aid its recovery from the strong reduction observed since the 1970s. Lastly, the projected Nile streamflow rise reinforces the need for collaboration in dam management. The simulated and observed extra release of freshwater into the Arctic Ocean produces a freshening of the ocean, potentially impacting the global ocean overturning circulation. It is concerning that several important rivers are projected to exceed their natural variability. However, the hydrological predictions assume a very high baseline emission scenario and should be interpreted as an upper limit for decision-making. [ABSTRACT FROM AUTHOR]

This paper describes an efficient loosely coupled fluid-solid heat transfer method for steady state heat transfer analysis. The method is based on the improvement of a loosely coupled Dirichlet-Robin approach. The analysis of the loosely coupled process using a 1D model shows that the convergence rate is influenced by both, the physical and virtual Biot numbers. The impact of using incompletely converged CFD solutions has also been further investigated. The proposed method has been first validated against the analytical solution of a 2D coupled flat plate, and further applied to real turbomachinery configurations. The results have shown that the computational time has been substantially reduced compared to the baseline method, and it only spends around 35% more than a steady state CFD simulation, which is attractive for industrial applications. [ABSTRACT FROM AUTHOR]