The Experts below are selected from a list of 75 Experts worldwide ranked by ideXlab platform
Gaodi Xie - One of the best experts on this subject based on the ideXlab platform.
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the calculation of Productivity Factor for ecological footprints in china a methodological note
Ecological Indicators, 2014Co-Authors: Moucheng Liu, Dan Zhang, Qingwen Min, Gaodi XieAbstract:Abstract The ecological footprint (EF), a physical indicator to measure the extent of humanity's use of natural resources, has gained much attention since it was first used by Wackernagel and Rees in 1996. In order to account for differences between countries in Productivity of a given land type (i.e., arable land, pasture, forest and water/fishery), Productivity Factor was introduced to relate the regional primary bio-productivities of the 4 types of land to the integrated average primary bio-Productivity of the corresponding land types. Hence, the Productivity Factor is an important parameter in the EF model and it directly affects the reliability of all results. Thus, this article calculates Productivity Factor on the national and provincial level in China based on Net Primary Production (NPP) from MODIS 1 km data in 2008. Firstly, based on the Light Utility Efficiency and CASA model, the NPP of different biologically productive lands of China and of different provinces was calculated. Secondly, China's Productivity Factor for a given land type was calculated as the ratio of national average NPP of that land type and world-average NPP of that land type. Finally, Productivity Factors of each province in China for a given land type was calculated. The NPP of each ecosystem type varies along with the Productivity Factor in different provinces. However, the ranking of the Productivity Factors remain the same, with that of arable land being the largest, and the water/fishery being the smallest.
Moucheng Liu - One of the best experts on this subject based on the ideXlab platform.
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the calculation of Productivity Factor for ecological footprints in china a methodological note
Ecological Indicators, 2014Co-Authors: Moucheng Liu, Dan Zhang, Qingwen Min, Gaodi XieAbstract:Abstract The ecological footprint (EF), a physical indicator to measure the extent of humanity's use of natural resources, has gained much attention since it was first used by Wackernagel and Rees in 1996. In order to account for differences between countries in Productivity of a given land type (i.e., arable land, pasture, forest and water/fishery), Productivity Factor was introduced to relate the regional primary bio-productivities of the 4 types of land to the integrated average primary bio-Productivity of the corresponding land types. Hence, the Productivity Factor is an important parameter in the EF model and it directly affects the reliability of all results. Thus, this article calculates Productivity Factor on the national and provincial level in China based on Net Primary Production (NPP) from MODIS 1 km data in 2008. Firstly, based on the Light Utility Efficiency and CASA model, the NPP of different biologically productive lands of China and of different provinces was calculated. Secondly, China's Productivity Factor for a given land type was calculated as the ratio of national average NPP of that land type and world-average NPP of that land type. Finally, Productivity Factors of each province in China for a given land type was calculated. The NPP of each ecosystem type varies along with the Productivity Factor in different provinces. However, the ranking of the Productivity Factors remain the same, with that of arable land being the largest, and the water/fishery being the smallest.
Davin Chor - One of the best experts on this subject based on the ideXlab platform.
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unpacking sources of comparative advantage a quantitative approach
Journal of International Economics, 2010Co-Authors: Davin ChorAbstract:Abstract This paper develops an approach for quantifying the importance of different sources of comparative advantage, by extending the Eaton and Kortum (2002) model to predict industry trade flows. In this framework, comparative advantage is determined by the interaction of country and industry characteristics, with countries specializing in industries whose production needs they can best meet with their Factor endowments and institutional strengths. I estimate the model parameters using: (i) OLS; and (ii) a simulated method of moments procedure that accounts for the prevalence of zeros in the bilateral trade data. I apply the model to explore various quantitative questions, such as how much distance, Ricardian Productivity, Factor endowments, and institutions each matter for country welfare in the global trade equilibrium.
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unpacking sources of comparative advantage a quantitative approach
2009Co-Authors: Davin ChorAbstract:This paper develops an approach for quantifying the importance of different sources of comparative advantage for country welfare, based on the Eaton and Kortum (2002) model extended to predict industry trade flows. In this framework, comparative advantage is determined by the interaction of country and industry characteristics, with countries specializing in industries whose production needs they can best meet with their Factor endowments and institutional strengths. I estimate the model parameters using a simulated method of moments procedure to account for the prevalence of zeroes in the bilateral trade data. I apply the model to explore various quantitative questions, such as how much distance, Ricardian Productivity, Factor endowments, and institutions each matter for country welfare in the global trade equilibrium.
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unpacking sources of comparative advantage a quantitative approach
2008Co-Authors: Davin ChorAbstract:This paper develops an approach for quantifying the importance of different sources of comparative advantage for country welfare. To explain patterns of specialization, I present a multi-country trade model that extends Eaton and Kortum (2002) to predict industry trade ows. In this framework, comparative advantage is determined by the interaction of country and industry characteristics, with countries specializing in industries whose specific production needs they are best able to meet with their Factor endowments and institutional strengths. I estimate the model parameters on a large dataset of bilateral trade ows, presenting results from both a baseline OLS approach, as well as a simulated method of moments (SMM) procedure to account for the prevalence of zero trade ows in the data. I apply the model to explore various quantitative questions, in particular how much distance, Ricardian Productivity, Factor endowments, and institutional conditions each matter for country welfare in the global trade equilibrium. I also illustrate the shift in industry composition and the accompanying welfare gains in policy experiments where a country raises its Factor endowments or improves the quality of its institutions.
Dan Zhang - One of the best experts on this subject based on the ideXlab platform.
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the calculation of Productivity Factor for ecological footprints in china a methodological note
Ecological Indicators, 2014Co-Authors: Moucheng Liu, Dan Zhang, Qingwen Min, Gaodi XieAbstract:Abstract The ecological footprint (EF), a physical indicator to measure the extent of humanity's use of natural resources, has gained much attention since it was first used by Wackernagel and Rees in 1996. In order to account for differences between countries in Productivity of a given land type (i.e., arable land, pasture, forest and water/fishery), Productivity Factor was introduced to relate the regional primary bio-productivities of the 4 types of land to the integrated average primary bio-Productivity of the corresponding land types. Hence, the Productivity Factor is an important parameter in the EF model and it directly affects the reliability of all results. Thus, this article calculates Productivity Factor on the national and provincial level in China based on Net Primary Production (NPP) from MODIS 1 km data in 2008. Firstly, based on the Light Utility Efficiency and CASA model, the NPP of different biologically productive lands of China and of different provinces was calculated. Secondly, China's Productivity Factor for a given land type was calculated as the ratio of national average NPP of that land type and world-average NPP of that land type. Finally, Productivity Factors of each province in China for a given land type was calculated. The NPP of each ecosystem type varies along with the Productivity Factor in different provinces. However, the ranking of the Productivity Factors remain the same, with that of arable land being the largest, and the water/fishery being the smallest.
Qingwen Min - One of the best experts on this subject based on the ideXlab platform.
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the calculation of Productivity Factor for ecological footprints in china a methodological note
Ecological Indicators, 2014Co-Authors: Moucheng Liu, Dan Zhang, Qingwen Min, Gaodi XieAbstract:Abstract The ecological footprint (EF), a physical indicator to measure the extent of humanity's use of natural resources, has gained much attention since it was first used by Wackernagel and Rees in 1996. In order to account for differences between countries in Productivity of a given land type (i.e., arable land, pasture, forest and water/fishery), Productivity Factor was introduced to relate the regional primary bio-productivities of the 4 types of land to the integrated average primary bio-Productivity of the corresponding land types. Hence, the Productivity Factor is an important parameter in the EF model and it directly affects the reliability of all results. Thus, this article calculates Productivity Factor on the national and provincial level in China based on Net Primary Production (NPP) from MODIS 1 km data in 2008. Firstly, based on the Light Utility Efficiency and CASA model, the NPP of different biologically productive lands of China and of different provinces was calculated. Secondly, China's Productivity Factor for a given land type was calculated as the ratio of national average NPP of that land type and world-average NPP of that land type. Finally, Productivity Factors of each province in China for a given land type was calculated. The NPP of each ecosystem type varies along with the Productivity Factor in different provinces. However, the ranking of the Productivity Factors remain the same, with that of arable land being the largest, and the water/fishery being the smallest.
