scholarly journals What would dense atmospheric observation networks bring to the quantification of city CO<sub>2</sub> emissions?

2016 ◽  
Vol 16 (12) ◽  
pp. 7743-7771 ◽  
Author(s):  
Lin Wu ◽  
Grégoire Broquet ◽  
Philippe Ciais ◽  
Valentin Bellassen ◽  
Felix Vogel ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Metropolitan Area ◽  
Land Surface ◽  
Co2 Emission ◽  
Power Plants ◽  
Lower Cost ◽  
Residential Buildings ◽  
Inversion Method ◽  
Monitoring Tool ◽  
Model Framework

Abstract. Cities currently covering only a very small portion ( <  3 %) of the world's land surface directly release to the atmosphere about 44 % of global energy-related CO2, but they are associated with 71–76 % of CO2 emissions from global final energy use. Although many cities have set voluntary climate plans, their CO2 emissions are not evaluated by the monitoring, reporting, and verification (MRV) procedures that play a key role for market- or policy-based mitigation actions. Here we analyze the potential of a monitoring tool that could support the development of such procedures at the city scale. It is based on an atmospheric inversion method that exploits inventory data and continuous atmospheric CO2 concentration measurements from a network of stations within and around cities to estimate city CO2 emissions. This monitoring tool is configured for the quantification of the total and sectoral CO2 emissions in the Paris metropolitan area (∼  12 million inhabitants and 11.4 TgC emitted in 2010) during the month of January 2011. Its performances are evaluated in terms of uncertainty reduction based on observing system simulation experiments (OSSEs). They are analyzed as a function of the number of sampling sites (measuring at 25 m a.g.l.) and as a function of the network design. The instruments presently used to measure CO2 concentrations at research stations are expensive (typically ∼  EUR 50 k per sensor), which has limited the few current pilot city networks to around 10 sites. Larger theoretical networks are studied here to assess the potential benefit of hypothetical operational lower-cost sensors. The setup of our inversion system is based on a number of diagnostics and assumptions from previous city-scale inversion experiences with real data. We find that, given our assumptions underlying the configuration of the OSSEs, with 10 stations only the uncertainty for the total city CO2 emission during 1 month is significantly reduced by the inversion by ∼  42 %. It can be further reduced by extending the network, e.g., from 10 to 70 stations, which is promising for MRV applications in the Paris metropolitan area. With 70 stations, the uncertainties in the inverted emissions are reduced significantly over those obtained using 10 stations: by 32 % for commercial and residential buildings, by 33 % for road transport, by 18 % for the production of energy by power plants, and by 31 % for total emissions. These results indicate that such a high number of stations would be likely required for the monitoring of sectoral emissions in Paris using this observation–model framework. They demonstrate some high potential that atmospheric inversions can contribute to the monitoring and/or the verification of city CO2 emissions (baseline) and CO2 emission reductions (commitments) and the advantage that could be brought by the current developments of lower-cost medium precision (LCMP) sensors.

scholarly journals Atmospheric inversion for cost effective quantification of city CO<sub>2</sub> emissions

2015 ◽  
Vol 15 (21) ◽  
pp. 30693-30756 ◽  
Author(s):  
L. Wu ◽  
G. Broquet ◽  
P. Ciais ◽  
V. Bellassen ◽  
F. Vogel ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Land Surface ◽  
Power Plants ◽  
Energy Use ◽  
Residential Buildings ◽  
Co2 Concentration ◽  
Road Transport ◽  
Inversion Method ◽  
Atmospheric Measurements ◽  
Atmospheric Inversion

Abstract. Cities, currently covering only a very small portion (< 3 %) of the world's land surface, directly release to the atmosphere about 44 % of global energy-related CO2, and are associated with 71–76 % of CO2 emissions from global final energy use. Although many cities have set voluntary climate plans, their CO2 emissions are not evaluated by Monitoring, Reporting and Verification (MRV) procedures that play a key role for market- or policy-based mitigation actions. Here we propose a monitoring tool that could support the development of such procedures at the city scale. It is based on an atmospheric inversion method that exploits inventory data and continuous atmospheric CO2 concentration measurements from a network of stations within and around cities to estimate city CO2 emissions. We examine the cost-effectiveness and the performance of such a tool. The instruments presently used to measure CO2 concentrations at research stations are expensive. However, cheaper sensors are currently developed and should be useable for the monitoring of CO2 emissions from a megacity in the near-term. Our assessment of the inversion method is thus based on the use of several types of hypothetical networks, with a range of numbers of sensors sampling at 25 m a.g.l. The study case for this assessment is the monitoring of the emissions of the Paris metropolitan area (~ 12 million inhabitants and 11.4 Tg C emitted in 2010) during the month of January 2011. The performance of the inversion is evaluated in terms of uncertainties in the estimates of total and sectoral CO2 emissions. These uncertainties are compared to a notional ambitious target to diagnose annual total city emissions with an uncertainty of 5 % (2-sigma). We find that, with 10 stations only, which is the typical size of current pilot networks that are deployed in some cities, the uncertainty for the 1-month total city CO2 emissions is significantly reduced by the inversion by ~ 42 % but still corresponds to an annual uncertainty that is two times larger than the target of 5 %. By extending the network from 10 to 70 stations, the inversion can meet this requirement. As for major sectoral CO2 emissions, the uncertainties in the inverted emissions using 70 stations are reduced significantly over that obtained using 10 stations by 32 % for commercial and residential buildings, by 33 % for road transport and by 18 % for the production of energy by power plants, respectively. With 70 stations, the uncertainties from the inversion become of 15 % 2-sigma annual uncertainty for dispersed building emissions, and 18 % for emissions from road transport and energy production. The inversion performance could be further improved by optimal design of station locations and/or by assimilating additional atmospheric measurements of species that are co-emitted with CO2 by fossil fuel combustion processes with a specific signature from each sector, such as carbon monoxide (CO). Atmospheric inversions based on continuous CO2 measurements from a large number of cheap sensors can thus deliver a valuable quantification tool for the monitoring and/or the verification of city CO2 emissions (baseline) and CO2 emission reductions (commitments).

scholarly journals A global map of emission clumps for future monitoring of fossil fuel CO<sub>2</sub> emissions from space

2019 ◽  
Vol 11 (2) ◽  
pp. 687-703 ◽  
Author(s):  
Yilong Wang ◽  
Philippe Ciais ◽  
Grégoire Broquet ◽  
François-Marie Bréon ◽  
Tomohiro Oda ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Land Surface ◽  
Urban Areas ◽  
Power Plants ◽  
Fossil Fuel ◽  
Large Fraction ◽  
Point Sources ◽  
Bottom Up ◽  
Large Power ◽  
Data Product

Abstract. A large fraction of fossil fuel CO2 emissions emanate from “hotspots”, such as cities (where direct CO2 emissions related to fossil fuel combustion in transport, residential, commercial sectors, etc., excluding emissions from electricity-producing power plants, occur), isolated power plants, and manufacturing facilities, which cover a small fraction of the land surface. The coverage of all high-emitting cities and point sources across the globe by bottom-up inventories is far from complete, and for most of those covered, the uncertainties in CO2 emission estimates in bottom-up inventories are too large to allow continuous and rigorous assessment of emission changes (Gurney et al., 2019). Space-borne imagery of atmospheric CO2 has the potential to provide independent estimates of CO2 emissions from hotspots. But first, what a hotspot is needs to be defined for the purpose of satellite observations. The proposed space-borne imagers with global coverage planned for the coming decade have a pixel size on the order of a few square kilometers and a XCO2 accuracy and precision of <1 ppm for individual measurements of vertically integrated columns of dry-air mole fractions of CO2 (XCO2). This resolution and precision is insufficient to provide a cartography of emissions for each individual pixel. Rather, the integrated emission of diffuse emitting areas and intense point sources is sought. In this study, we characterize area and point fossil fuel CO2 emitting sources which generate coherent XCO2 plumes that may be observed from space. We characterize these emitting sources around the globe and they are referred to as “emission clumps” hereafter. An algorithm is proposed to identify emission clumps worldwide, based on the ODIAC global high-resolution 1 km fossil fuel emission data product. The clump algorithm selects the major urban areas from a GIS (geographic information system) file and two emission thresholds. The selected urban areas and a high emission threshold are used to identify clump cores such as inner city areas or large power plants. A low threshold and a random walker (RW) scheme are then used to aggregate all grid cells contiguous to cores in order to define a single clump. With our definition of the thresholds, which are appropriate for a space imagery with 0.5 ppm precision for a single XCO2 measurement, a total of 11 314 individual clumps, with 5088 area clumps, and 6226 point-source clumps (power plants) are identified. These clumps contribute 72 % of the global fossil fuel CO2 emissions according to the ODIAC inventory. The emission clumps is a new tool for comparing fossil fuel CO2 emissions from different inventories and objectively identifying emitting areas that have a potential to be detected by future global satellite imagery of XCO2. The emission clump data product is distributed from https://doi.org/10.6084/m9.figshare.7217726.v1.

scholarly journals A methodology to constrain carbon dioxide emissions from coal-fired power plants using satellite observations of co-emitted nitrogen dioxide

2020 ◽  
Vol 20 (1) ◽  
pp. 99-116 ◽  
Author(s):  
Fei Liu ◽  
Bryan N. Duncan ◽  
Nickolay A. Krotkov ◽  
Lok N. Lamsal ◽  
Steffen Beirle ◽  
...  
Keyword(s):  
Nitrogen Dioxide ◽  
Co2 Emissions ◽  
Co2 Emission ◽  
Power Plants ◽  
Operating Conditions ◽  
Satellite Observations ◽  
Nox Emissions ◽  
Bottom Up ◽  
Monitoring Instrument ◽  
The Us

Abstract. We present a method to infer CO2 emissions from individual power plants based on satellite observations of co-emitted nitrogen dioxide (NO2), which could serve as complementary verification of bottom-up inventories or be used to supplement these inventories. We demonstrate its utility on eight large and isolated US power plants, where accurate stack emission estimates of both gases are available for comparison. In the first step of our methodology, we infer nitrogen oxides (NOx) emissions from US power plants using Ozone Monitoring Instrument (OMI) NO2 tropospheric vertical column densities (VCDs) averaged over the ozone season (May–September) and a “top-down” approach that we previously developed. Second, we determine the relationship between NOx and CO2 emissions based on the direct stack emissions measurements reported by continuous emissions monitoring system (CEMS) programs, accounting for coal quality, boiler firing technology, NOx emission control device type, and any change in operating conditions. Third, we estimate CO2 emissions for power plants using the OMI-estimated NOx emissions and the CEMS NOx∕CO2 emission ratio. We find that the CO2 emissions estimated by our satellite-based method during 2005–2017 are in reasonable agreement with the US CEMS measurements, with a relative difference of 8 %±41 % (mean ± standard deviation). The broader implication of our methodology is that it has the potential to provide an additional constraint on CO2 emissions from power plants in regions of the world without reliable emissions accounting. We explore the feasibility by comparing the derived NOx∕CO2 emission ratios for the US with those from a bottom-up emission inventory for other countries and applying our methodology to a power plant in South Africa, where the satellite-based emission estimates show reasonable consistency with other independent estimates. Though our analysis is limited to a few power plants, we expect to be able to apply our method to more US (and world) power plants when multi-year data records become available from new OMI-like sensors with improved capabilities, such as the TROPOspheric Monitoring Instrument (TROPOMI), and upcoming geostationary satellites, such as the Tropospheric Emissions: Monitoring Pollution (TEMPO) instrument.

scholarly journals Towards space-borne monitoring of localized CO<sub>2</sub> emissions: an instrument concept and first performance assessment

2020 ◽  
Author(s):  
Johan Strandgren ◽  
David Krutz ◽  
Jonas Wilzewski ◽  
Carsten Paproth ◽  
Ilse Sebastian ◽  
...  
Keyword(s):  
Power Plant ◽  
Performance Assessment ◽  
Co2 Emissions ◽  
Co2 Emission ◽  
Power Plants ◽  
Point Sources ◽  
Source Strength ◽  
Spectral Window ◽  
Instrument Noise ◽  
Noise Error

Abstract. The UNFCCC (United Nations Framework Convention on Climate Change) requires the nations of the world to report their carbon dioxide (CO2) emissions. Independent verification of these reported emissions is a corner stone for advancing towards emission accounting and reduction measures agreed upon in the Paris agreement. In this paper, we present the concept and first performance assessment of a compact space-borne imaging spectrometer that could support the task of "monitoring, verification, reporting" (MVR) of CO2 emissions worldwide. With a single spectral window in the short-wave infrared spectral region and a spatial resolution of 50 x 50 m2, the goal is to reliably estimate the CO2 emissions from localized sources down to a source strength of approx. 1 MtCO2 yr-1, hence complementing other planned CO2 monitoring missions, like the planned European Carbon Constellation (CO2M). Resolving CO2 plumes also from medium-sized power plants (1–10 MtCO2 yr-1) is of key importance for independent quantification of CO2 emissions from the coal-fired power plant sector. Through radiative transfer simulations, including a realistic instrument noise model and a global trial ensemble covering various geophysical scenarios, it is shown that an instrument noise error of 1.1 ppm (1σ) can be achieved for the retrieval of the column-averaged dry-air mole fraction of CO2 (XCO2). Despite limited amount of information from a single spectral window and a relatively coarse spectral resolution, scattering by atmospheric aerosol and cirrus can be partly accounted for in the XCO2 retrieval, with deviations of at most 4.0 ppm from the true abundance for 68 % of the scenes in the global trial ensemble. We further simulate the ability of the proposed instrument concept to observe CO2 plumes from single power plants in an urban area using high-resolution CO2 emission and surface albedo data for the city of Indianapolis. Given the preliminary instrument design and the corresponding instrument noise error, emission plumes from point sources with an emission rate down to the order of 0.3 MtCO2 yr-1 can be resolved, i.e. well below the target source strength of 1 MtCO2 yr-1. Hence, the proposed instrument concept could be able to resolve and quantify the CO2 plumes from localized point sources responsible for approx. 90 % of the power plant CO2 emission budget, assuming global coverage through a fleet of sensors and favorable conditions with respect to illumination and particle scattering.

scholarly journals A remote sensing technique for global monitoring of power plant CO<sub>2</sub> emissions from space and related applications

2010 ◽  
Vol 3 (1) ◽  
pp. 55-110 ◽  
Author(s):  
H. Bovensmann ◽  
M. Buchwitz ◽  
J. P. Burrows ◽  
M. Reuter ◽  
T. Krings ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Power Plant ◽  
Greenhouse Gas ◽  
Co2 Emissions ◽  
Kyoto Protocol ◽  
Inverse Modeling ◽  
Co2 Emission ◽  
Power Plants ◽  
Independent Verification ◽  
Anthropogenic Co2

Abstract. Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas causing global warming. The atmospheric CO2 concentration increased by more than 30% since pre-industrial times – primarily due to burning of fossil fuels – and still continues to increase. Reporting of CO2 emissions is required by the Kyoto protocol. Independent verification of reported emissions, which are typially not directly measured, by methods such as inverse modeling of measured atmospheric CO2 concentrations is currently not possible globally due to lack of appropriate observations. Existing greenhouse gas observing satellites such as SCIAMACHY and GOSAT focus on advancing our understanding of natural CO2 sources and sinks. The obvious next step for future generation satellites is to also measure anthropogenic CO2 emissions. Here we present a promising satellite remote sensing technology based on spectroscopic measurements of reflected solar radiation in the short-wave infrared (SWIR) and near-infrared (NIR) spectral regions and show, using power plants as an example, that strong localized CO2 point sources can be detected and their emissions quantified. This requires mapping the CO2 column distribution at a spatial resolution of 2×2 km2 or better with a precision of about 0.5% (2 ppm) or better of the background column. We indicate that this can be achieved with existing technology. For a single satellite in sun-synchronous orbit with an across-track swath width of 500 km each power plant is overflown every 6 days or faster. Based on clear sky statistics we conservatively estimate that about one useful measurement per 1–2 months for a given power plant can typically be achieved. We found that the uncertainty of the retrieved power plant CO2 emission during a single satellite overpass is in the range 0.5–5 MtCO2/year – depending on observation conditions – which is about 2–20% of the CO2 emission of large power plants (25 Mt CO2/year). The investigated instrument aims at fulfilling all requirements for global regional-scale CO2 and CH4 surface flux inverse modeling. Using a significantly less demanding instrument concept based on a single SWIR channel we indicate that this also enables the monitoring of power plant CO2 emissions in addition to high-quality methane retrievals. The latter has already been demonstrated by SCIAMACHY. The discussed technology has the potential to significantly contribute to an independent verification of reported anthropogenic CO2 emissions and therefore could be an important component of a future global anthropogenic CO2 emission monitoring system. This is of relevance in the context of Kyoto protocol follow-on agreements but also allows to detect and monitor strong natural CO2 and CH4 emitters such as (mud) volcanoes.

scholarly journals Actual Measurement and Evaluation of the Balance between Electricity Supply and Demand in Waste-Treatment Facilities and Development of Adjustment Methods

2021 ◽  
Vol 11 (22) ◽  
pp. 10747
Author(s):  
Daiki Yoshidome ◽  
Ryo Kikuchi ◽  
Yuki Okanoya ◽  
Andante Hadi Pandyaswargo ◽  
Hiroshi Onoda
Keyword(s):  
Power Generation ◽  
Renewable Energy ◽  
Co2 Emissions ◽  
Co2 Emission ◽  
Power Plants ◽  
Solar Power ◽  
Supply And Demand ◽  
Actual Measurement ◽  
Power Sources ◽  
Power Load

In Japan, breakthroughs to improve the share of renewable energy in the energy mix have become an urgent issue. However, the problem could not be solved by simply adding more power plants for various technical reasons, such as the unsuitability of using renewable energy as baseloads due to its intermittency. Furthermore, establishing the required cooperative systems for regionally distributed power adjustment is also tricky. Based on these backgrounds, this paper constructs an operation plan that minimizes CO2 emissions by correcting the generation and load patterns of the renewable energy of solar power, utilizing power generation from waste as a substitute for baseload power, and estimating the power demand of each facility. The result shows that by adjusting the operation plans, the model can reduce CO2 emission by 20.95 and 8.30% in weeks with high and low solar power generation surpluses, respectively. Furthermore, these results show that it is possible to reduce CO2 emissions in regions that have power sources with low CO2 emission coefficients by forecasting the amount of power generation and power load in the region and appropriately planning the operation in advance.

scholarly journals Structural Decomposition Analysis of CO2 Emission Reduction due to Energy Tax in Power Sector Planning.

2019 ◽  
Vol 1 (2) ◽  
pp. 39-44 ◽  
Author(s):  
Charles O. P. Marpaung ◽  
Ram M. Shresta
Keyword(s):  
Co2 Emissions ◽  
Co2 Emission ◽  
Power Plants ◽  
Decomposition Analysis ◽  
Final Demand ◽  
Tax Rates ◽  
Power Sector ◽  
Environmental Implications ◽  
Co2 Emission Reduction ◽  
Energy Tax

This study analyses the CO2 emission implications of considering energy tax in power sector planning for the case of Indonesia. There are four energy tax rates considered in this study i.e. US$0.5/MBtu, US$1.0/MBtu, US$2.0/MBtu and US$5/MBtu. Furthermore, this study also analyses the decomposition of the economy-wide CO2 emission changes due to the carbon tax rates by using an input-output model. The implications of energy tax on utility planning would bring the sytem more efficient because more energy efficient technology power plants, such as CCGT, would be selected, while in the case of environmental implications, CO2 emissions would be reduced. The results show that there is a significant change in the annual CO2 emissions if energy tax rate of US$5/MBtu is introduced. There are three major components that affect the total economy-wide change in CO2 emissions, i.e., fuel mix-, structural-, and final demand- effects. The results show that the fuel mix effect is found to be most influential in reducing the CO2 emission during the planning horizon under all of the energy tax rates considered and is followed by the final demand- and structural-effects.

scholarly journals A global map of emission clumps for future monitoring of fossil fuel CO<sub>2</sub> emissions from space

2018 ◽  
Author(s):  
Yilong Wang ◽  
Philippe Ciais ◽  
Grégoire Broquet ◽  
François-Marie Bréon ◽  
Tomohiro Oda ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Land Surface ◽  
Power Plants ◽  
Emission Inventory ◽  
Fossil Fuel ◽  
Large Fraction ◽  
Satellite Observations ◽  
Pixel Size ◽  
Accuracy And Precision ◽  
Global Coverage

Abstract. A large fraction of fossil fuel CO2 emissions occur within “hotspots”, such as cities and power plants, which cover a very small fraction of the land surface. Although some of these emission hotspots are monitored closely, there is no detailed emission inventory for most of them. Space-borne imagery of atmospheric CO2 has the potential to provide independent estimates of CO2 emissions from hotspots. But first, what is a hotspot needs to be defined for the purpose of satellite observations. The proposed space-borne imagers with global coverage planned for the coming decade have a pixel size on the order of a few square kilometers, and a XCO2 accuracy and precision of

scholarly journals ISLAMIC FINANCIAL DEVELOPMENT, ECONOMIC GROWTH AND CO2 EMISSION IN INDONESIA

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Azwar Iskandar ◽  
Bayu Taufiq Possumah ◽  
Khaerul Aqbar
Keyword(s):  
Economic Growth ◽  
Co2 Emissions ◽  
Co2 Emission ◽  
Residential Buildings ◽  
Development Economic ◽  
Islamic Finance ◽  
Dynamic Relationship ◽  
Long Run ◽  
Finance Development ◽  
Short Run

This study aims to investigate the dynamic relationship between Islamic financial development, economic growth, and CO2 emissions with Environmental Kuznets Curve (EKC) approach in Indonesia over the 2000-2018 period.  This study employs the Auto Regressive Distributed Lag (ARDL) bound testing approach and the Error Correction Mechanism (ECM) to examine the existence of long-run and short-run relationship between variables. From the results of the model, we do not find any support for the existence of the EKC for Indonesia. Moreover, the results present that there is no dynamic relationship in the short run among growth, Islamic finance development and CO2 emission. Long-run findings suggest that CO2 emission from transport; other sectors, excluding residential buildings and commercial and public services; and residential buildings and commercial and public services sector are significantly associated to the Islamic finance development in Indonesia. The findings of this study  shows that Islamic finance development can help the country to adjust its CO2 emissions and play its role in protecting the environment by encouraging environmental-friendly and energy-efficient projects. A strong and efficient financial sector would be helpful in facilitating the investment process by advancing loans for business in condition with curbing CO2 emissions.

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