This paper extends the PEP-1-1 model (a static computable general equilibrium, or CGE, model for small open economies) to incorporate variable capital utilization. It argues that CGE models with fixed sectoral capital may underestimate the impact of shocks in the short run by ignoring industries’ adjustment of their capital utilization rate (or intensity of use) in response to changes in their economic environment. The model is calibrated to a 2014 Mongolian social accounting matrix. An increase in the export price of coal is considered as a shock for demonstration purposes. Compared to the standard PEP-1-1 model the impact of the shock is larger in the expanded model. In addition, the results of the PEP-1-1 model are derived as a special case of the model involving capital utilization.
{"title":"Endogenous Capital Utilization in CGE Models: A Mongolian Application with the PEP-1-1 Model","authors":"Ragchaasuren Galindev, B. Decaluwé","doi":"10.21642/jgea.070103af","DOIUrl":"https://doi.org/10.21642/jgea.070103af","url":null,"abstract":"This paper extends the PEP-1-1 model (a static computable general equilibrium, or CGE, model for small open economies) to incorporate variable capital utilization. It argues that CGE models with fixed sectoral capital may underestimate the impact of shocks in the short run by ignoring industries’ adjustment of their capital utilization rate (or intensity of use) in response to changes in their economic environment. The model is calibrated to a 2014 Mongolian social accounting matrix. An increase in the export price of coal is considered as a shock for demonstration purposes. Compared to the standard PEP-1-1 model the impact of the shock is larger in the expanded model. In addition, the results of the PEP-1-1 model are derived as a special case of the model involving capital utilization.","PeriodicalId":44607,"journal":{"name":"Journal of Global Economic Analysis","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49645222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We discuss the construction of the GTAP-AGROFOOD database for CGE modelling which is consistent with the standard GTAP Data Base Version 10a (Aguiar et al. 2019). GTAP-AGROFOOD departs from the full detail of 141 regions, 65 products and 8 factors in GTAP, by introducing 51 additional agro-food products and sectors in lieu of 11 original GTAP agro-food products, thereby yielding a database with 105 products. Such additional detail can improve and enrich, for instance, analysis of trade, bio-economy or climate change mitigation or adaptation issues. It also eases linkage to other, more detailed data sets, such as for nutrition accounting or irrigation water use. The main data sources used are the FABIO Multi-Regional Input-Output Database (Bruckner et al. 2019) which reports on production, land, seed, feed, and food use, mainly for primary agricultural products, and market balances for dairy products from FAOSTAT. They are combined with TASTE V10a (Pelikan et al. 2020) which provides bilateral trade and tariffs revenues at the level of tariff lines. The balancing methodology which ensures consistency with the GTAP Data Base is based on the linear loss based split utility of CGEBox (Britz 2021).
我们讨论了GTAP- agrofood数据库的构建，该数据库与标准GTAP database Version 10a (Aguiar et al. 2019)一致。GTAP- agrofood从GTAP中141个地区、65种产品和8个因素的全部细节出发，通过引入51个额外的农产品和部门来代替原来的11个GTAP农产品，从而产生一个包含105种产品的数据库。这些额外的细节可以改善和丰富对贸易、生物经济或减缓或适应气候变化问题的分析。它还简化了与其他更详细的数据集的联系，例如营养核算或灌溉用水。使用的主要数据来源是FABIO多区域投入产出数据库(Bruckner等人，2019年)，该数据库报告了主要用于初级农产品的生产、土地、种子、饲料和粮食使用情况，以及来自粮农组织统计数据库的乳制品市场平衡情况。它们与TASTE V10a (Pelikan et al. 2020)相结合，提供关税细目水平的双边贸易和关税收入。确保与GTAP数据库一致性的平衡方法是基于CGEBox (Britz 2021)的基于线性损失的分割实用程序。
{"title":"Disaggregating Agro-Food Sectors in the GTAP Data Base","authors":"W. Britz","doi":"10.21642/jgea.070102af","DOIUrl":"https://doi.org/10.21642/jgea.070102af","url":null,"abstract":"We discuss the construction of the GTAP-AGROFOOD database for CGE modelling which is consistent with the standard GTAP Data Base Version 10a (Aguiar et al. 2019). GTAP-AGROFOOD departs from the full detail of 141 regions, 65 products and 8 factors in GTAP, by introducing 51 additional agro-food products and sectors in lieu of 11 original GTAP agro-food products, thereby yielding a database with 105 products. Such additional detail can improve and enrich, for instance, analysis of trade, bio-economy or climate change mitigation or adaptation issues. It also eases linkage to other, more detailed data sets, such as for nutrition accounting or irrigation water use. The main data sources used are the FABIO Multi-Regional Input-Output Database (Bruckner et al. 2019) which reports on production, land, seed, feed, and food use, mainly for primary agricultural products, and market balances for dairy products from FAOSTAT. They are combined with TASTE V10a (Pelikan et al. 2020) which provides bilateral trade and tariffs revenues at the level of tariff lines. The balancing methodology which ensures consistency with the GTAP Data Base is based on the linear loss based split utility of CGEBox (Britz 2021).","PeriodicalId":44607,"journal":{"name":"Journal of Global Economic Analysis","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45893203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With a wide range of implications for welfare, food security, land use, trade and the environment, nutrition-related policies pose complex questions that should be assessed using an approach that properly accounts for all the involved interactions. Widely used partial equilibrium models fail to properly account for the post-farmgate food value chains. At the same time, most of the available integrated assessment and computable general equilibrium models have some major limitations in terms of the consistent representation of nutritional data flows. In this paper, we address some of the limitations identified in the literature and develop an approach for incorporating nutritional accounts into the Global Trade Analysis Project (GTAP) Data Base, tracing quantities of food, calories, fats, proteins and carbohydrates along the value chains. We further showcase how the developed nutritional database can be linked to the standard GTAP model. A sample application is developed in the paper to provide an assessment of the impact of import tariff elimination on nutritional flows.
{"title":"Incorporating Nutritional Accounts to the GTAP Data Base","authors":"M. Chepeliev","doi":"10.21642/jgea.070101af","DOIUrl":"https://doi.org/10.21642/jgea.070101af","url":null,"abstract":"With a wide range of implications for welfare, food security, land use, trade and the environment, nutrition-related policies pose complex questions that should be assessed using an approach that properly accounts for all the involved interactions. Widely used partial equilibrium models fail to properly account for the post-farmgate food value chains. At the same time, most of the available integrated assessment and computable general equilibrium models have some major limitations in terms of the consistent representation of nutritional data flows. In this paper, we address some of the limitations identified in the literature and develop an approach for incorporating nutritional accounts into the Global Trade Analysis Project (GTAP) Data Base, tracing quantities of food, calories, fats, proteins and carbohydrates along the value chains. We further showcase how the developed nutritional database can be linked to the standard GTAP model. A sample application is developed in the paper to provide an assessment of the impact of import tariff elimination on nutritional flows.","PeriodicalId":44607,"journal":{"name":"Journal of Global Economic Analysis","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43421530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Chepeliev, A. Golub, T. Hertel, Wajiha Saeed, Jayson Beckman
Computable general equilibrium (CGE) models provide valuable insights into economy-wide and aggregate sectoral impacts of trade policies. However, when it comes to the assessment of specific interventions, the level of aggregation in these models is often deemed too coarse to inform negotiations. For example, in the Global Trade Analysis Project (GTAP) Data Base, all vegetables, fruits and nuts – over hundred individual commodities – are represented under one sector. Analysis at the tariff line level is typically provided by partial equilibrium (PE) models, which cannot, however, capture economy-wide effects. In this paper, we contribute to the development of the GTAP-HS framework, which comprises disaggregated values of output, trade flows and domestic absorption with supporting model components nested within the standard GTAP GE model. We construct the GTAP-HS database with GTAP vegetables, fruits and nuts sector disaggregated into 79 commodities. We apply this modelling framework to the assessment of the ongoing trade frictions between the United States and its trading partners. We find that there are significant advantages to using this nested approach to trade policy analysis, including possibilities of the trade policies assessment at the tariff line, representation of the commodity-specific substitution and avoidance of the ‘false competition’ critique.
{"title":"Disaggregating the Vegetables, Fruits and Nuts Sector to the Tariff Line in the GTAP-HS Framework","authors":"M. Chepeliev, A. Golub, T. Hertel, Wajiha Saeed, Jayson Beckman","doi":"10.21642/JGEA.060103AF","DOIUrl":"https://doi.org/10.21642/JGEA.060103AF","url":null,"abstract":"Computable general equilibrium (CGE) models provide valuable insights into economy-wide and aggregate sectoral impacts of trade policies. However, when it comes to the assessment of specific interventions, the level of aggregation in these models is often deemed too coarse to inform negotiations. For example, in the Global Trade Analysis Project (GTAP) Data Base, all vegetables, fruits and nuts – over hundred individual commodities – are represented under one sector. Analysis at the tariff line level is typically provided by partial equilibrium (PE) models, which cannot, however, capture economy-wide effects. In this paper, we contribute to the development of the GTAP-HS framework, which comprises disaggregated values of output, trade flows and domestic absorption with supporting model components nested within the standard GTAP GE model. We construct the GTAP-HS database with GTAP vegetables, fruits and nuts sector disaggregated into 79 commodities. We apply this modelling framework to the assessment of the ongoing trade frictions between the United States and its trading partners. We find that there are significant advantages to using this nested approach to trade policy analysis, including possibilities of the trade policies assessment at the tariff line, representation of the commodity-specific substitution and avoidance of the ‘false competition’ critique.","PeriodicalId":44607,"journal":{"name":"Journal of Global Economic Analysis","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2021-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46550507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Constructing a balanced and sufficiently detailed Social Accounting Matrix (SAM) is a necessary step for any work with Computable General Equilibrium (CGE) models. Even when starting with a given SAM, researchers might wish to develop their own, more detailed variants for a specific study by dis-aggregating sectors and products, a process termed splitting the SAM. We review three approaches for balancing and splitting a SAM: Cross-Entropy (CE), a Highest Posterior Density (HPD) estimator resulting in a quadratic loss penalty function, and a linear loss penalty function. The exercise considers upper and lower bounds on the (new) SAM entries, different weights for penalizing deviations from a priori information, and unknown row or column totals, to give the user flexibility in controlling outcomes. The approaches are assessed first by a systematic Monte-Carlo experiment. It re-balances smaller SAMs, after errors with known distributions are added. Here we find quite limited numerical differences between the CE and quadratic loss approaches. The CE approach was however considerably slower than the other candidates. Second, we tested the three approaches for dis-aggregating the Global Trade Analysis Project (GTAP) data base to provide, as an example, further agri-food detail. In such empirical applications, the distribution of the errors of the new SAM entries is typically not known. As in the SAM balancing exercise, we use CONOPT4 as a multi-purpose (non)linear solver which can be also be employed to solve the CGE model itself. For comparison, we add the specialized Linear and Quadratic Programming (QP) solvers CPLEDX and GUROBI. As in the Monte-Carlo experiment, the differences in results between the three approaches were moderate. The specialized solvers require very little time to solve the linear and quadratic loss problems. However, they did not achieve the same, very high accuracy as CONOPT4 for the quadratic loss problem. The CE problem could take longer by a factor of 100 or more, compared to a linear or quadratic loss approach solved with the specialized solvers. We conclude that using linear or quadratic loss approaches, especially combined with a specialized solver, are the most suitable candidates for larger SAM splitting / balancing problems. Additionally, we present a fast and accurate data processing chain to yield a benchmark data set for a CGE model from the GTAP Data Base which involves filtering out small cost, expenditure and revenue shares, and allows users to introduce further product and sectoral detail based on user provided information.
{"title":"Comparing Penalty Functions in Balancing and Dis-aggregating Social Accounting Matrices","authors":"W. Britz","doi":"10.21642/JGEA.060102AF","DOIUrl":"https://doi.org/10.21642/JGEA.060102AF","url":null,"abstract":"Constructing a balanced and sufficiently detailed Social Accounting Matrix (SAM) is a necessary step for any work with Computable General Equilibrium (CGE) models. Even when starting with a given SAM, researchers might wish to develop their own, more detailed variants for a specific study by dis-aggregating sectors and products, a process termed splitting the SAM. We review three approaches for balancing and splitting a SAM: Cross-Entropy (CE), a Highest Posterior Density (HPD) estimator resulting in a quadratic loss penalty function, and a linear loss penalty function. The exercise considers upper and lower bounds on the (new) SAM entries, different weights for penalizing deviations from a priori information, and unknown row or column totals, to give the user flexibility in controlling outcomes. The approaches are assessed first by a systematic Monte-Carlo experiment. It re-balances smaller SAMs, after errors with known distributions are added. Here we find quite limited numerical differences between the CE and quadratic loss approaches. The CE approach was however considerably slower than the other candidates. Second, we tested the three approaches for dis-aggregating the Global Trade Analysis Project (GTAP) data base to provide, as an example, further agri-food detail. In such empirical applications, the distribution of the errors of the new SAM entries is typically not known. As in the SAM balancing exercise, we use CONOPT4 as a multi-purpose (non)linear solver which can be also be employed to solve the CGE model itself. For comparison, we add the specialized Linear and Quadratic Programming (QP) solvers CPLEDX and GUROBI. As in the Monte-Carlo experiment, the differences in results between the three approaches were moderate. The specialized solvers require very little time to solve the linear and quadratic loss problems. However, they did not achieve the same, very high accuracy as CONOPT4 for the quadratic loss problem. The CE problem could take longer by a factor of 100 or more, compared to a linear or quadratic loss approach solved with the specialized solvers. We conclude that using linear or quadratic loss approaches, especially combined with a specialized solver, are the most suitable candidates for larger SAM splitting / balancing problems. Additionally, we present a fast and accurate data processing chain to yield a benchmark data set for a CGE model from the GTAP Data Base which involves filtering out small cost, expenditure and revenue shares, and allows users to introduce further product and sectoral detail based on user provided information.","PeriodicalId":44607,"journal":{"name":"Journal of Global Economic Analysis","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2021-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44914928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Non-tariff measures (NTMs) are a prominent feature of many recent free trade agreement (FTA) negotiations. The implementation of NTMs within computable general equilibrium (CGE) models has been relatively simple to date, with modelers generally incorporating NTMs as tariff equivalents via export or import taxes or as import-augmenting technological (iceberg) change. Our study compares and contrasts two new methods with the traditional mechanisms used. The first new method is the willingness to pay method developed by Walmsley and Minor (2020); and the second, introduced here, provides a new mechanism for adjusting the exporters’ production costs directly, referred to as the export cost method. We find that the choice of mechanism can have important consequences for the estimated impact of changes in NTMs, with mechanisms that raise productivity leading to larger changes in real GDP than those that treat NTMs as associated with economic rents or demand shocks. We emphasize the importance of careful consideration being given to the nature of the NTMs being investigated, the econometric estimates of the associated trade costs, and the CGE model mechanisms being used to assess the impacts of changes in NTMs.
{"title":"A Comparison of Approaches to Modelling Non-Tariff Measures","authors":"T. Walmsley, Anna Strutt","doi":"10.21642/jgea.060101af","DOIUrl":"https://doi.org/10.21642/jgea.060101af","url":null,"abstract":"Non-tariff measures (NTMs) are a prominent feature of many recent free trade agreement (FTA) negotiations. The implementation of NTMs within computable general equilibrium (CGE) models has been relatively simple to date, with modelers generally incorporating NTMs as tariff equivalents via export or import taxes or as import-augmenting technological (iceberg) change. Our study compares and contrasts two new methods with the traditional mechanisms used. The first new method is the willingness to pay method developed by Walmsley and Minor (2020); and the second, introduced here, provides a new mechanism for adjusting the exporters’ production costs directly, referred to as the export cost method. We find that the choice of mechanism can have important consequences for the estimated impact of changes in NTMs, with mechanisms that raise productivity leading to larger changes in real GDP than those that treat NTMs as associated with economic rents or demand shocks. We emphasize the importance of careful consideration being given to the nature of the NTMs being investigated, the econometric estimates of the associated trade costs, and the CGE model mechanisms being used to assess the impacts of changes in NTMs.","PeriodicalId":44607,"journal":{"name":"Journal of Global Economic Analysis","volume":"1 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2021-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43109493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper explores the key role of importer's love of variety in applied general equilibrium models featuring product differentiation. The paper compares the Armington-, Krugman-, and Melitz-type trade specifications. Experimental simulations with the model reveal that as love of variety weakens, based on the empirical evidence revealed by Ardelean (2006), the models with homogeneous firms may generate larger welfare gains than the Melitz-type heterogeneous firm model. This stands in marked contrast to the findings of Melitz and Redding (2013), based on the assumption of maximum valuation on increasing variety.
{"title":"Love of variety in trade models with product differentiation","authors":"Kazuhiko Oyamada","doi":"10.21642/jgea.050201af","DOIUrl":"https://doi.org/10.21642/jgea.050201af","url":null,"abstract":"This paper explores the key role of importer's love of variety in applied general equilibrium models featuring product differentiation. The paper compares the Armington-, Krugman-, and Melitz-type trade specifications. Experimental simulations with the model reveal that as love of variety weakens, based on the empirical evidence revealed by Ardelean (2006), the models with homogeneous firms may generate larger welfare gains than the Melitz-type heterogeneous firm model. This stands in marked contrast to the findings of Melitz and Redding (2013), based on the assumption of maximum valuation on increasing variety.","PeriodicalId":44607,"journal":{"name":"Journal of Global Economic Analysis","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49459948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-11DOI: 10.21642/jgea.050202sm1f
F. Taheripour, Xin Zhao, Mark Horridge, Farid Farrokhi, W. Tyner
Constant Elasticity of Transformation (CET) functions are widely used to allocate land across uses in Computable General Equilibrium (CGE) models. These models fail to maintain the physical area of land in balance. This paper examines this issue. It shows that heterogeneity in land prices (rents) is the main source of imbalance in land area, not the curvature of the CET function. It also shows that the available approaches that restore balance to physical area either introduce ad hoc adjustments in land allocation or undermine the conventional welfare assessments of the CET results. An alternative approach involves implementing stochastic productivity distribution functions (e.g. Frechet) to allocate land among uses maintain area of land in balance, thereby respecting conventional welfare assessments. A particular feature of these models is that the aggregate production functions of the land using sectors exhibit decreasing returns to scale even if land is the only factor of production. This approach also requires equalization of land rents across uses. This is not consistent with empirical observation. Both the CET and stochastic methods consider the implicit opportunity costs of moving land across uses but fail to take into account preparation costs associated with land use conversion.
{"title":"Land use in computable general equilibrium models","authors":"F. Taheripour, Xin Zhao, Mark Horridge, Farid Farrokhi, W. Tyner","doi":"10.21642/jgea.050202sm1f","DOIUrl":"https://doi.org/10.21642/jgea.050202sm1f","url":null,"abstract":"Constant Elasticity of Transformation (CET) functions are widely used to allocate land across uses in Computable General Equilibrium (CGE) models. These models fail to maintain the physical area of land in balance. This paper examines this issue. It shows that heterogeneity in land prices (rents) is the main source of imbalance in land area, not the curvature of the CET function. It also shows that the available approaches that restore balance to physical area either introduce ad hoc adjustments in land allocation or undermine the conventional welfare assessments of the CET results. An alternative approach involves implementing stochastic productivity distribution functions (e.g. Frechet) to allocate land among uses maintain area of land in balance, thereby respecting conventional welfare assessments. A particular feature of these models is that the aggregate production functions of the land using sectors exhibit decreasing returns to scale even if land is the only factor of production. This approach also requires equalization of land rents across uses. This is not consistent with empirical observation. Both the CET and stochastic methods consider the implicit opportunity costs of moving land across uses but fail to take into account preparation costs associated with land use conversion.","PeriodicalId":44607,"journal":{"name":"Journal of Global Economic Analysis","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46565968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper documents changes introduced to version 10 of the Global Trade Analysis Project (GTAP) Power (GTAP-Power 10) Data Base construction process relative to the GTAP-Power build stream developed in Peters (2016). First, in Peters (2016) output of the electricity and heat generation sector was split into different technologies using electricity generation data only. We use heat and electricity generation volumes to provide a more representative sectoral split and achieve a better concordance with GTAP 10 Data Base sectoral definitions. Second, we introduce data on country and year-specific shares of transmission and distribution costs in electricity price for 80 countries. In the GTAP-Power 9 Data Base this cost share was assumed to be uniform across all countries and regions. Finally, for every reference year, we update the levelized cost of electricity generation. We first compare GTAP-Power 9 Data Base construction results with and without corresponding changes. We then construct the GTAP-Power 10 Data Base and showcase how it can be used to estimate carbon dioxide emissions embodied in final consumption of electricity generated by different technologies.
本文记录了全球贸易分析项目（GTAP）Power（GTAP Power 10）数据库构建过程第10版相对于Peters（2016）开发的GTAP Power构建流的更改。首先，在Peters（2016）中，仅使用发电数据将发电和供热部门的产出划分为不同的技术。我们使用热量和发电量来提供更具代表性的部门划分，并实现与GTAP 10数据库部门定义的更好一致性。其次，我们介绍了80个国家输电和配电成本在电价中的具体国家和年份份额的数据。在GTAP Power 9数据库中，假设所有国家和地区的成本份额是一致的。最后，对于每个参考年，我们都会更新发电的平准成本。我们首先比较了GTAP Power 9数据库的构建结果（有相应的更改和没有相应的更改）。然后，我们构建了GTAP Power 10数据库，并展示了如何使用它来估计不同技术产生的最终电力消耗中的二氧化碳排放量。
{"title":"GTAP- Power Database: Version 10","authors":"M. Chepeliev","doi":"10.21642/jgea.050203af","DOIUrl":"https://doi.org/10.21642/jgea.050203af","url":null,"abstract":"This paper documents changes introduced to version 10 of the Global Trade Analysis Project (GTAP) Power (GTAP-Power 10) Data Base construction process relative to the GTAP-Power build stream developed in Peters (2016). First, in Peters (2016) output of the electricity and heat generation sector was split into different technologies using electricity generation data only. We use heat and electricity generation volumes to provide a more representative sectoral split and achieve a better concordance with GTAP 10 Data Base sectoral definitions. Second, we introduce data on country and year-specific shares of transmission and distribution costs in electricity price for 80 countries. In the GTAP-Power 9 Data Base this cost share was assumed to be uniform across all countries and regions. Finally, for every reference year, we update the levelized cost of electricity generation. We first compare GTAP-Power 9 Data Base construction results with and without corresponding changes. We then construct the GTAP-Power 10 Data Base and showcase how it can be used to estimate carbon dioxide emissions embodied in final consumption of electricity generated by different technologies.","PeriodicalId":44607,"journal":{"name":"Journal of Global Economic Analysis","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48369503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-11DOI: 10.21642/jgea.050204sm1f
É. Blanc
This article and companion code is an update to Blanc (2017b) which provided a tool to use statistical emulators of global gridded crop models described in Blanc and Sultan (2015) and Blanc (2017) and aggregated the projections at the regional level. This new version includes, in addition to rainfed yields of maize, rice, soybean and wheat, irrigated crops yields as well as associated irrigation water requirements as estimated in Blanc (2020).
{"title":"Aggregation of gridded emulated projections at the national or regional level: rainfed and irrigated crop yields and irrigation water requirements","authors":"É. Blanc","doi":"10.21642/jgea.050204sm1f","DOIUrl":"https://doi.org/10.21642/jgea.050204sm1f","url":null,"abstract":"This article and companion code is an update to Blanc (2017b) which provided a tool to use statistical emulators of global gridded crop models described in Blanc and Sultan (2015) and Blanc (2017) and aggregated the projections at the regional level. This new version includes, in addition to rainfed yields of maize, rice, soybean and wheat, irrigated crops yields as well as associated irrigation water requirements as estimated in Blanc (2020).","PeriodicalId":44607,"journal":{"name":"Journal of Global Economic Analysis","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42516396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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