Suchergebnisse

At head of title: "Quelles sont au point de vue juridique et au point de vue philosophique." ; "Ouvrage qui a obtenu une première mention honorable à l'Académie des sciences morales et politiques de Paris." ; Mode of access: Internet.

MANY FACTORS POINT TOWARD THE EMERGENCE OF A REGIONAL "BLOC" IN EAST ASIA, DESPITE THE ABSENCE OF ANY FORMAL AGREEMENT OF THAT KIND BETWEEN THE NATIONS CONCERNED. DENSITY OF TRADE AND TECHNOLOGICAL AND FINANCIAL FLOWS ALL CONTRIBUTE TO THE INCREASING ECONOMIC INTEGRATION OF EAST ASIA. THE ECONOMIC INTEGRATION OF A REGION ACCOUNTING FOR ONE-THIRD OF THE WORLD'S GROSS NATIONAL PRODUCT COULD HAVE ENORMOUS IMPLICATIONS FOR EUROPE, WHICH HAS KEPT A VERY LOW POLITICAL PROFILE IN ASIA SINCE DECOLONIZATION. THE EUROPEAN ECONOMIC PRESENCE ALSO REMAINS QUITE MODEST DESPITE THE SPECTACULAR EXPANSION OF THE ASIAN-PACIFIC MARKETS SINCE THE 1980'S. HOWEVER, THERE ARE MANY OLD AND NEW ARGUMENTS IN FAVOR OF CLOSER EURO-ASIAN COOPERATION TO PRESERVE WORLD PEACE AND STABILITY.

Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27 + UK). We integrate recent emission inventory data, ecosystem process-based model results and inverse modeling estimates over the period 1990-2017. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported to the UN climate convention UNFCCC secretariat in 2019. For uncertainties, we used for NGHGIs the standard deviation obtained by varying parameters of inventory calculations, reported by the member states (MSs) following the recommendations of the IPCC Guidelines. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model-specific uncertainties when reported. In comparing NGHGIs with other approaches, a key source of bias is the activities included, e.g., anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011-2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr-1 (EDGAR v5.0) and 19.0 Tg CH4 yr-1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr-1. The estimates of TD total inversions give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher-resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr-1. Coarser-resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4 yr-1) and surface network (24.4 Tg CH4 yr-1). The magnitude of natural peatland emissions from the JSBACH-HIMMELI model, natural rivers and lakes emissions, and geological sources together account for the gap between NGHGIs and inversions and account for 5.2 Tg CH4 yr-1. For N2O emissions, over the 2011-2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr-1, respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr-1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr-1, respectively. The TD and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at the EU+UK scale and at the national scale. The referenced datasets related to figures are visualized at. (Petrescu et al., 2020b).

Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27 + UK). We integrate recent emission inventory data, ecosystem process-based model results and inverse modeling estimates over the period 1990-2017. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported to the UN climate convention UNFCCC secretariat in 2019. For uncertainties, we used for NGHGIs the standard deviation obtained by varying parameters of inventory calculations, reported by the member states (MSs) following the recommendations of the IPCC Guidelines. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model-specific uncertainties when reported. In comparing NGHGIs with other approaches, a key source of bias is the activities included, e.g., anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011-2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr-1 (EDGAR v5.0) and 19.0 Tg CH4 yr-1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr-1. The estimates of TD total inversions give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher-resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr-1. Coarser-resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4 yr-1) and surface network (24.4 Tg CH4 yr-1). The magnitude of natural peatland emissions from the JSBACH-HIMMELI model, natural rivers and lakes emissions, and geological sources together account for the gap between NGHGIs and inversions and account for 5.2 Tg CH4 yr-1. For N2O emissions, over the 2011-2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr-1, respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr-1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr-1, respectively. The TD and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at the EU+UK scale and at the national scale. The referenced datasets related to figures are visualized at. (Petrescu et al., 2020b).

Journal Article ; Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates, consistency within and among components, alongside methodology and data limitations. CO2 emissions from fossil-fuel combustion and cement production (EFF) are based on energy statistics, while emissions from land-use change (ELUC), mainly deforestation, are based on combined evidence from land-cover change data, fire activity associated with deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (G ATM) is computed from the annual changes in concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated for the first time in this budget with data products based on surveys of ocean CO2 measurements. The global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models forced by observed climate, CO2 and land cover change (some including nitrogen-carbon interactions). All uncertainties are reported as ±1σ , reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2003-2012), EFF was 8.6±0.4 GtC yr-1, ELUC 0.9±0.5 GtC yr-1, GATM 4.3±0.1 GtC yr-1, SOCEAN 2.5±0.5 GtC yr -1, and SLAND 2.8±0.8 GtC yr-1. For year 2012 alone, EFF grew to 9.7±0.5 GtC yr-1, 2.2% above 2011, reflecting a continued growing trend in these emissions, G ATM was 5.1±0.2 GtC yr-1, SOCEAN was 2.9±0.5 GtC yr-1, and assuming an ELUC of 1.0±0.5 GtC yr-1 (based on the 2001-2010 average), S LAND was 2.7±0.9 GtC yr-1. GATM was high in 2012 compared to the 2003-2012 average, almost entirely reflecting the high EFF. The global atmospheric CO2 concentration reached 392.52±0.10 ppm averaged over 2012. We estimate that EFF will increase by 2.1% (1.1- 3.1 %) to 9.9±0.5 GtC in 2013, 61% above emissions in 1990, based on projections of world gross domestic product and recent changes in the carbon intensity of the economy.With this projection, cumulative emissions ofCO2 will reach about 535±55 GtC for 1870-2013, about 70% from EFF (390±20 GtC) and 30% from ELUC (145±50 GtC). This paper also documents any changes in the methods and data sets used in this new carbon budget from previous budgets (Le Quéré et al., 2013). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/GCP-2013-V2.3). © 2014 Author(s) CC Attribution 3.0 License. ; International Ocean Carbon Coordination Project (IOCCP) ; Surface Ocean Lower Atmosphere Study (SOLAS) ; Integrated Marine Biogeochemistry and Ecosystem Research program (IMBER) ; NERC ; International Opportunities Fund ; US Department of Energy, Office of Science, Biological and Environmental Research (BER) ; Norwegian Research Council ; EU FP7 for funding through projects GEOCarbon, COMBINE, CARBOCHANGE, EMBRACE, and LUC4C ; US National Science Foundation ; NASA LCLUC program ; Swiss National Science Foundation ; Joint UK DECC/Defra Met Office Hadley Centre Climate Programme ; Environment Research and Technology Development Fund (S-10) of the Ministry of Environment of Japan ; Australian Climate Change Science Program ; Leverhulme Research Fellowship