The global spatiotemporal distribution of the mid-tropospheric CO2 concentration and analysis of the controlling factors

The atmospheric infrared sounder (AIRS) provides a robust and accurate data source to investigate the variability of mid-tropospheric CO2 globally. In this paper, we use the AIRS CO2 product and other auxiliary data to survey the spatiotemporal distribution characteristics of mid-tropospheric CO2 and the controlling factors using linear regression, empirical orthogonal functions (EOFs), geostatistical analysis, and correlation analysis. The results show that areas with low mid-tropospheric CO2 concentrations (20 degrees S-5 degrees N) (384.2 ppm) are formed as a result of subsidence in the atmosphere, the presence of the Amazon rainforest, and the lack of high CO2 emission areas. The areas with high mid-tropospheric CO2 concentrations (30 degrees N-70 degrees N) (382.1 ppm) are formed due to high CO2 emissions. The global mid-tropospheric CO2 concentrations increased gradually (the annual average rate of increase in CO2 concentration is 2.11 ppm/a), with the highest concentration occurring in spring (384.0 ppm) and the lowest value in winter (382.5 ppm). The amplitude of the seasonal variation retrieved from AIRS (average: 1.38 ppm) is consistent with that of comprehensive observation network for trace gases (CONTRAIL), but smaller than the surface ground stations, which is related to altitude and coverage. These results contribute to a comprehensive understanding of the spatiotemporal distribution of mid-tropospheric CO2 and related mechanisms

Trade, Technique and Composition Effects: What is Behind the Fall in World-Wide SO2 Emissions 1990-2000?

Combining unique data bases on emissions with sectoral output and employment data, we study the sources of the fall in world-wide SO2 emissions and estimate the impact of trade on emissions. Contrarily to concerns raised by environmentalists, an emission-decomposition exercise shows that scale effects are dominated by technique effects working towards a reduction in emissions. A second exercise comparing the actual trade situation with an autarky benchmark estimates that trade, by allowing clean countries to become net importers of emissions, leads to a 10% increase in world emissions with respect to autarky in 1990, a figure that shrinks to 3.5% in 2000. Additionally, back-of-the-envelope calculations suggest that emissions related to transport are of the same magnitude. In a third exercise, we use linear programming to simulate extreme situations where world emissions are either maximal or minimal. It turns out that effective emissions correspond to a 90% reduction with respect to the worst case, but that another 80% reduction could be reached if emissions were minimal.Trade, Growth, Environment, Decomposition, Embodied Emissions in Trade, Transport

Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression

Conventional methods are less robust in terms of accurately forecasting non-stationary and nonlineary carbon prices. In this study, we propose an empirical mode decomposition-based evolutionary least squares support vector regression multiscale ensemble forecasting model for carbon price forecasting. Firstly, each carbon price is disassembled into several simple modes with high stability and high regularity via empirical mode decomposition. Secondly, particle swarm optimization-based evolutionary least squares support vector regression is used to forecast each mode. Thirdly, the forecasted values of all the modes are composed into the ones of the original carbon price. Finally, using four different-matured carbon futures prices under the European Union Emissions Trading Scheme as samples, the empirical results show that the proposed model is more robust than the other popular forecasting methods in terms of statistical measures and trading performances

Review of Analytical Tools for Assessing Trade and Climaite Change Linkages

In this paper, the authors briefly refer to the essential elements underlying the theoretical linkages between trade, economic development, and climate change and review the analytical tools which are used to describe these linkages.Climate Changes, Trade Linkages, CGE

Summary of Carbon Emission Statistics and Measurement of Transportation Industry

Transportation is a critical area for China to achieve its goal of carbon neutrality. A successful national climate plan and the overall development of a strong transportation nation are tied to achieving the carbon peak and carbon neutrality in transportation. This study first sorts out the statistical boundaries and methods of carbon emissions. Secondly, it introduces the measurement methods of carbon emissions in the transportation industry, including the emission factor method and the full life cycle method. Thirdly, the research on influencing factors of carbon emissions in the transportation industry is reviewed from four aspects: economy, transportation industry itself, technology, and others, and relevant research on carbon emission prediction methods in the transportation industry is summarized. Finally, the limitations of existing research are analyzed, and future research on carbon emissions statistics and measurement in the transportation industry is prospected

Mathematical Models in Input-Output Economics

This paper describes the mathematical basis for input-output economics, the major types of models, and the underlying economic theory. The features of these models that make them especially well suited for understanding the connections between the economy and the environment are emphasized throughout. These include the dual physical and price representations and the representation of resource inputs as factors of production, whether they are priced or not. The basic static physical and price models are described, along with their major properties and associated databases. The most important approaches to analysis involve multipliers, decomposition, and scenario analysis. Going beyond the basic static framework requires the progressive closure of the model by making exogenous variables endogenous while maintaining simplicity, transparency, and the distinctive feature of an input-output model: the simultaneous determination of solutions at the sectoral level and the economy-wide level. Closures for household activities and for investment are described by way of example. The major extensions of the basic model accommodate the representation of pollutant emissions and policies for constraining them, dynamic models, and multi-regional models, the latter including a new version of a world model that solves for bilateral trade flows and region-specific prices based on comparative advantage with factor constraints. The concluding section describes the challenges currently being addressed within the field. An annotated bibliography provides references for further reading and includes both classic articles and a representation of recent research.

Urban energy consumption and CO2 emissions in Beijing: current and future

This paper calculates the energy consumption and CO2 emissions of Beijing over 2005–2011 in light of the Beijing’s energy balance table and the carbon emission coefficients of IPCC. Furthermore, based on a series of energy conservation planning program issued in Beijing, the Long-range Energy Alternatives Planning System (LEAP)-BJ model is developed to study the energy consumption and CO2 emissions of Beijing’s six end-use sectors and the energy conversion sector over 2012–2030 under the BAU scenario and POL scenario. Some results are found in this research: (1) During 2005–2011, the energy consumption kept increasing, while the total CO2 emissions fluctuated obviously in 2008 and 2011. The energy structure and the industrial structure have been optimized to a certain extent. (2) If the policies are completely implemented, the POL scenario is projected to save 21.36 and 35.37 % of the total energy consumption and CO2 emissions than the BAU scenario during 2012 and 2030. (3) The POL scenario presents a more optimized energy structure compared with the BAU scenario, with the decrease of coal consumption and the increase of natural gas consumption. (4) The commerce and service sector and the energy conversion sector will become the largest contributor to energy consumption and CO2 emissions, respectively. The transport sector and the industrial sector are the two most potential sectors in energy savings and carbon reduction. In terms of subscenarios, the energy conservation in transport (TEC) is the most effective one. (5) The macroparameters, such as the GDP growth rate and the industrial structure, have great influence on the urban energy consumption and carbon emissions