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This report addresses carbon labeling schemes, a high-profile issue and one that has important economic implications for developing countries. Carbon accounting and labeling instruments are designed to present information on greenhouse gas emissions (GHG) from supply chains. These instruments have become an important awareness-raising channel for governments, producers, retailers and consumers to bring about the reduction of GHGs. At the same time, they have emerged as a crucial element of supply chain management, trade logistics and, potentially, trade regulations between countries. But the underlying science of GHG emissions is only partially developed. Many of these schemes are based on rudimentary knowledge of GHG emissions and have mainly been designed by industrialized countries. There is a concern that these systems do not accurately reflect production processes in developing countries, and that they may even shift consumer preferences away from developing country exports. The report includes an analysis of current and emerging carbon labeling schemes and an assessment of available data, emissions factors and knowledge gaps of carbon footprinting methodologies. The report also analyzes carbon accounting methodologies for sugar and pineapple products from Zambia and Mauritius according to PAS 2050 guidelines, to illustrate whether these schemes accurately represent the production systems in developing countries. The report concludes with a series of recommendations on how carbon footprint labeling can be made more development-friendly

The objective of the Indianapolis Flux Experiment (INFLUX) is to develop, evaluate and improve methods for measuring greenhouse gas (GHG) emissions from cities. INFLUX’s scientific objectives are to quantify CO2 and CH4 emission rates at 1 km2 resolution with a 10% or better accuracy and precision, to determine whole-city emissions with similar skill, and to achieve high (weekly or finer) temporal resolution at both spatial resolutions. The experiment employs atmospheric GHG measurements from both towers and aircraft, atmospheric transport observations and models, and activity-based inventory products to quantify urban GHG emissions. Multiple, independent methods for estimating urban emissions are a central facet of our experimental design. INFLUX was initiated in 2010 and measurements and analyses are ongoing. To date we have quantified urban atmospheric GHG enhancements using aircraft and towers with measurements collected over multiple years, and have estimated whole-city CO2 and CH4 emissions using aircraft and tower GHG measurements, and inventory methods. Significant differences exist across methods; these differences have not yet been resolved; research to reduce uncertainties and reconcile these differences is underway. Sectorally- and spatially-resolved flux estimates, and detection of changes of fluxes over time, are also active research topics. Major challenges include developing methods for distinguishing anthropogenic from biogenic CO2 fluxes, improving our ability to interpret atmospheric GHG measurements close to urban GHG sources and across a broader range of atmospheric stability conditions, and quantifying uncertainties in inventory data products. INFLUX data and tools are intended to serve as an open resource and test bed for future investigations. Well-documented, public archival of data and methods is under development in support of this objective.

This mini-review addresses the critical problem of significant greenhouse gas (GHG) emissions from the global construction industry, which accounts for 37% of energy-related carbon emissions. With global building areas expected to double by 2060, this paper aims to analyze carbon emission characteristics and control strategies throughout the buildings' entire life cycle, emphasizing the urgent need for effective life cycle carbon management. We introduce and contextualize life cycle assessment (LCA) methods, focusing particularly on Scope 1, 2, and 3 emissions across different life cycle stages of buildings—from design through demolition. Our key findings highlight the potential of intelligent grid energy management systems (EMS) to optimize carbon efficiency in real-time, a pioneering approach that has yet to be widely implemented. The review synthesizes global advancements in green building practices, particularly in regions like Europe, America, and China, and discusses the varied success of these regions in integrating comprehensive carbon management strategies throughout the building life cycle. We conclude with strategic recommendations for future research directions, policy-making, and international cooperation to enhance the sustainability of the construction industry. This study ultimately aims to contribute robust evidence supporting the adoption of advanced LCA methodologies and intelligent EMS in reducing the construction sector's carbon footprint.HighlightsIdentifies significant carbon footprints in both the construction and operational phases of buildings.Highlights advancements in life cycle assessment (LCA) techniques to optimize carbon management.Emphasizes the role of technological innovation in reducing greenhouse gas emissions effectively.

Urban areas are the major sources of greenhouse gas emissions but also leaders in emission reduction efforts. Appropriate techniques to quantify emissions and any potential reductions over time are necessary to effectively inform these mitigation efforts. The aircraft mass balance experiment (MBE) is an established technique used for such a purpose. In this work, we use a series of 55 MBEs downwind of power plants to assess the technique’s bias and precision. In addition, we investigate what factors drive the absolute error, determined as the absolute difference between observed and reported emission rates, in individual experiments using multilinear regressions. Power plants are required to monitor their carbon dioxide emissions with an hourly resolution, and these publicly available reported emissions can be directly compared to the mass balance estimates as a pseudo-known release. To quantify the bias we calculated the mean error, which was 10 ± 240 Mg·h−1 (1σ), regressed mass balance emission rates against reported emission rates to yield a slope of 0.967 ± 0.062, and compared the sum across all mass balance emission rates, 31,000 ± 1,000 Mg·h−1, to the sum across all reported emissions, 30,660 ± 740 Mg·h−1. All three of these approaches suggest no systematic bias. Then to quantify the precision for individual determinations we calculated the slope of a regression between the standard deviation across repeated MBEs and the corresponding average emission rate, which is 30.7% ± 6.7%. The main drivers of the absolute error were sparse sampling of the plume, poor horizontal and vertical mixing of the plume, and smaller signal-to-noise ratios. Quantifying the capabilities of this technique provides context for previous analyses and allows stakeholders and researchers to make informed decisions when choosing quantification methods. Identifying the factors that drive the absolute error also allows us to adjust flight design to minimize it and potentially improve uncertainty estimates.

This study aims to present the atmospheric CO2 and CH4 levels and analyze their source characteristics at an observation station in a northeastern highland area of Korea for the 2012–2014 period. We summarized the measured CO2 and CH4 concentrations for the 2012–2014 period. In addition, we characterized the major source of the rise of CO2 and CH4 in Ganseong (GS) by employing bivariate polar plots (BPP) and the concentration weighted trajectory (CWT) method together with currently available information on emission sources. For the three years, CO2 was generally high in the order of winter, spring, autumn and summer and CH4 high in the order of winter, autumn, spring and summer. The observed positive correlations between the hourly CO2 and CH4 in every season suggested the possibility of shared common emission sources, but there is a necessity for elucidation on this in the future. The BPP analysis indicated the local sources that are likely to be associated with the rise of greenhouse gases (GHGs) observed at GS (combustion in the village, plant respirations nearby GS, and mobile emissions on the nearby road for CO2 and leakages from the gas stations along the road and agricultural activities for CH4). Synthesizing the CWT results together with emission source information from national and global emission inventories, we identified likely major source areas and characterized major emission sources. For example, the identified major sources for the winter CO2 are coal combustion, coal washing and industrial activities in Inner Mongolia, northern and the northeastern China, fuel burning for the energy for the infrastructure of a northwestern city in South Korea, and the manufacturing industry and fuel combustion in the northern parts of North Korea. Hopefully, these kinds of results will aid environmental researchers and decision-makers in performing more in-depth studies for GHG sources in order to derive effective mitigation strategies.

To effectively address the unprecedented acceleration of climate change, cities across the United States are leading efforts to reduce greenhouse gas emissions. Coherent, aggressive, and lasting mitigation policies in controlling carbon emissions are beginning to be adopted to help strengthen climate resilience across different sectors. However, evaluating the effectiveness of current climate legislation requires careful monitoring of emissions through measurable and verifiable means to inform policy decisions. As a part of this effort, we developed a new method to spatially allocate aircraft-based mass balance carbon dioxide (CO2) emissions. In this work, we conducted 7 aircraft flights, performed downwind of New York City (NYC) to quantify CO2 emissions during the nongrowing seasons between 2018 and 2020. We used an ensemble of emission inventories and transport models to calculate the fraction of enhancements (Φ) produced by sources within the policy-relevant boundaries of the 5 NYC boroughs and then applied that to the bulk emissions calculated using the mass balance approach. We derived a campaign-averaged source-apportioned mass balance CO2 emission rate of (57 ± 24) (1σ) kmol/s for NYC. We evaluated the performance of this approach against other top-down methods for NYC including inventory scaling and inverse modeling, with our mean emissions estimate resulting in a 6.5% difference from the average emission rate reported by the 2 complementary approaches. By combining mass balance and transport model approaches, we improve upon traditional mass balance experiment methods to enable quantification of emissions in complex emission environments. We conducted an assessment using an ensemble of emission inventories and transport models to determine the sources of variability in the final calculated emission rates. Our findings indicate that the choice of inventory accounted for 2.0% of the variability in the emission estimates and that the atmospheric transport model contributed 3.9% at the campaign level. Additionally, on average, at the daily scale, the transport model contributed 7.6% and the inventory accounted for 14.1%. The daily flight-to-flight variability contributed a significant portion, at 42.1%. This approach provides a solution to the difficulty of interpreting aircraft-based mass balance results in complex emission environments.

Using the Purdue University Airborne Laboratory for Atmospheric Research, we measured concentrations of methane and ethane emanating from seven U.S. cities (New York, NY, Philadelphia, PA, Washington, D.C./Baltimore, MD, Boston, MA, Chicago, IL, Richmond, VA, and Indianapolis, IN), in order to determine (with a median 95% CI of roughly 7%) the fraction of methane emissions attributable to natural gas (Thermogenic Methane Emission Ratio [TMER]), for both summer and winter months. New methodology is introduced to compute inflow concentrations and to accurately define the spatial domain of the sampling region, using upwind measurements coupled with Lagrangian trajectory modeling. We show discrepancies in inventory-estimated TMER from cities when the sample domain is defined using political boundaries versus urban centers encircled by the flight track and highlight this as a potential source of error common to top-down studies. We found that methane emissions of natural gas were greater than winter biogenic emissions for all cities except Richmond, where multiple landfills dominate. Biogenic emissions increased in summer, but natural gas remained important or dominant (20%–80%). National inventories should be updated to reflect the dominance of natural gas emissions for urban environments and to account for seasonal increases in biogenic methane in summer.

Appropriate techniques to quantify greenhouse gas emission reductions in cities over time are necessary to monitor the progress of these efforts and effectively inform continuing mitigation. We introduce a scaling factor (SF) method that combines aircraft measurements and dispersion modeling to estimate urban emissions and apply it to 9 nongrowing season research aircraft flights around New York City (NYC) in 2018–2020. This SF approach uses a weighting function to focus on an area of interest while still accounting for upwind emissions. We estimate carbon dioxide (CO2) emissions from NYC and the Greater New York Area (GNA) and compare to nested inversion analyses of the same data. The average calculated CO2 emission rates for NYC and the GNA, representative of daytime emissions for the flights, were (49 ± 16) kmol/s and (144 ± 44) kmol/s, respectively (uncertainties reported as ±1σ variability across the 9 flights). These emissions are within ∼15% of an inversion analysis and agree well with inventory estimates. By using an ensemble, we also investigate the variability introduced by several sources and find that day-to-day variability dominates the overall variability. This work investigates and demonstrates the capability of an SF method to quantify emissions specific to particular areas of interest.

This study was conducted from December 2021 to July 2022, except May 2022, and aimed to evaluate and validate CO2, CH4, and O3 GHGs in 13 different locations over Sulaimani city, Kurdistan Region-Iraq by means of remote sensing techniques from Sentinel 5 Precursor (S5P)/ TROPOMI and Orbiting Carbon Observatory-2 (OCO-2) satellites against ground-based measurements by using a  portable gases analyzer via three types of sensor heads, GSS  for the nominated gases of CO2, CH4, and O3. The Inverse Distance Weight (IDW) interpolation methods were used to map the CO2, CH4, and O3. The results of ground measurements showed high variability in some greenhouse gas concentration values and ranged between 285-508 ppm, 0-17000 ppb, and 0.25-64 ppb for CO2, CH4, and O3, respectively, in different locations and months. Satellite-predicted values for CO2, CH4, and O3 ranged between 416 - 418 ppm, 1858.99 - 1908.26, and 15.13 - 16.96 ppb, respectively, among the studied locations during the study periods. The RMSE ranged between 0.5 - 92.75 ppm, 99.11 – 2593.05 ppb, and 0.08 – 48.87 ppb for CO2, CH4, and O3, respectively.