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      Exploring spatial characteristics of city-level CO2 emissions in China and their influencing factors from global and local perspectives

      , , , ,
      Science of The Total Environment
      Elsevier BV

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          Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity

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            Is Open Access

            China CO 2 emission accounts 1997–2015

            China is the world’s top energy consumer and CO2 emitter, accounting for 30% of global emissions. Compiling an accurate accounting of China’s CO2 emissions is the first step in implementing reduction policies. However, no annual, officially published emissions data exist for China. The current emissions estimated by academic institutes and scholars exhibit great discrepancies. The gap between the different emissions estimates is approximately equal to the total emissions of the Russian Federation (the 4th highest emitter globally) in 2011. In this study, we constructed the time-series of CO2 emission inventories for China and its 30 provinces. We followed the Intergovernmental Panel on Climate Change (IPCC) emissions accounting method with a territorial administrative scope. The inventories include energy-related emissions (17 fossil fuels in 47 sectors) and process-related emissions (cement production). The first version of our dataset presents emission inventories from 1997 to 2015. We will update the dataset annually. The uniformly formatted emission inventories provide data support for further emission-related research as well as emissions reduction policy-making in China.
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              Footprints on the Earth: The Environmental Consequences of Modernity

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                Author and article information

                Contributors
                Journal
                Science of The Total Environment
                Science of The Total Environment
                Elsevier BV
                00489697
                February 2021
                February 2021
                : 754
                : 142206
                Article
                10.1016/j.scitotenv.2020.142206
                32920414
                ecb7d910-7186-46a5-97d8-9f5624aec861
                © 2021

                https://www.elsevier.com/tdm/userlicense/1.0/

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