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      Inter-sectoral carbon ties and final demand in a high climate risk country: The case of Pakistan

      Journal of Cleaner Production

      Elsevier BV

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          Most cited references 71

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          Quantitative Input and Output Relations in the Economic Systems of the United States

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            BUILDING EORA: A GLOBAL MULTI-REGION INPUT–OUTPUT DATABASE AT HIGH COUNTRY AND SECTOR RESOLUTION

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              Mapping the structure of the world economy.

              We have developed a new series of environmentally extended multi-region input-output (MRIO) tables with applications in carbon, water, and ecological footprinting, and Life-Cycle Assessment, as well as trend and key driver analyses. Such applications have recently been at the forefront of global policy debates, such as about assigning responsibility for emissions embodied in internationally traded products. The new time series was constructed using advanced parallelized supercomputing resources, and significantly advances the previous state of art because of four innovations. First, it is available as a continuous 20-year time series of MRIO tables. Second, it distinguishes 187 individual countries comprising more than 15,000 industry sectors, and hence offers unsurpassed detail. Third, it provides information just 1-3 years delayed therefore significantly improving timeliness. Fourth, it presents MRIO elements with accompanying standard deviations in order to allow users to understand the reliability of data. These advances will lead to material improvements in the capability of applications that rely on input-output tables. The timeliness of information means that analyses are more relevant to current policy questions. The continuity of the time series enables the robust identification of key trends and drivers of global environmental change. The high country and sector detail drastically improves the resolution of Life-Cycle Assessments. Finally, the availability of information on uncertainty allows policy-makers to quantitatively judge the level of confidence that can be placed in the results of analyses.
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                Author and article information

                Journal
                Journal of Cleaner Production
                Journal of Cleaner Production
                Elsevier BV
                09596526
                October 2020
                October 2020
                : 269
                : 122254
                Article
                10.1016/j.jclepro.2020.122254
                © 2020

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